30:T5d9,

Until 2009 the total installed generation capacity at Kariba of 1,266MW almost exactly matched the long term average inflow of water into the reservoir providing a firm baseload capacity.
The nearly 1,000MW of additional capacity installed since 2009 is peaking capacity that can only be used to change the timing of energy generation and not to increase the total average generation above 9TWh per year provided by the average inflow.
The Capacity Factor of the combined Kariba power stations since 2015 was 50.3%.
Total average inflow to the reservoir in recent years has been very nearly equal to the long-term average – 52km³ per year. There has been no observable impact on inflow due to climate change, climate variability or increased abstraction in recent years.
The low reservoir level reached at the end of 2022 was not due to a reduction in flows into the reservoir. Rather, this was because the additional peaking capacity installed since 2009 being used as baseload capacity particularly in the period March 2021 to November 2022.
The peaking capacity at Kariba is ideally suited to provide balancing services to new variable, intermittent renewables such as wind and solar. Kariba could be used to balance up to 1,700MW of new solar PV or 2,400MW of new wind power plant delivering constant, baseload power from these sources.

31:T807,{ const div=DOM.element('div') div.setAttribute("class","") div.setAttribute("style",`width:${width-20}px`) div.append(html`

Kariba: The largest battery in the world for balancing and smoothing new solar and wind?

When it is full, the Kariba reservoir stores enough water to generate ${d3.format(',.2r')(kariba.storage * kariba.yield)}GWh of electricity (similar to the entire annual consumption of Zambia) at a rate of more than 2,000MW.

The largest lithium-ion, grid connected battery storage system in the world currently is the Crimson Storage Facility in California. This has a capacity of 1.4GWh at a rate of 350MW. Even assuming that this battery has two duty cycles per day (charge-discharge cycles) then its total storage per year is 1,000GWh, 15 times less than Kariba.

Lithium-ion battery systems currently cost around $250 million per GWh of storage so a 20GWh system which could deliver a similar level of storage as Kariba per year would cost around $5 billion.

Modern power grids rely on storage systems - whether hydro storage reservoirs, pumped-storage hydro or batteries - to enable the use of renewable energy sources, particularly solar and wind power. These variable, intermittent renewables vary their output from minute to minute depending on wind conditions and cloud cover and need to be backed up by storage systems providing smooting and balancing power.

Kariba and other large, storage hydro power plants provide ready-to-go smoothing and balancing capacity to enable the addition of significant amounts of new variable, intermittent renewable power plants, particularly wind and solar. See: ${headlink(7)}

`) return div }32:T75b, { let recent = 2009 var data = [{ x:[recent-0.5,2022.9,2022.9,recent-0.5,recent-0.5], y:[0,0,2500,2500,0], showlegend:false, hoverinfo:'none', line: {dash:'dash',width:2, color: '#B26666'}, type: 'scatter', mode:'lines' },{ y: capacities.map(yr=>yr.north), x: capacities.map(yr=>yr.year), showlegend:true, mode:'lines', shape:'hvh', name: 'North bank - Zambia', hoverinfo:'x', line: {width:0}, fillcolor: colr(1,0.5), type: 'scatter', stackgroup:'one' },{ y: capacities.map(yr=>yr.south), x: capacities.map(yr=>yr.year), showlegend:true, mode:'lines', shape:'hvh', name: 'South bank - Zimbabwe', hoverinfo:'x', line: {width:0}, fillcolor: colr(5,0.2), type: 'scatter', stackgroup:'one' },{ y: capacities.map(yr=>yr.capacity), x: capacities.map(yr=>yr.year), text: capacities.map(yr=>yr.label), showlegend:false, mode:'lines+markers+text', shape:'hvh', name: '', hoverinfo:'x+text', textposition:['topright','topright','top','top','top','top','top','top','top','top'], marker: {color: capColor}, type: 'scatter' }]; var layout = { width: width>800?770:width-30, height:240, font: font, showlegend: true, margin: {t:5,b:70,l:70,r:10}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.14}, xaxis: { showgrid: false, zeroline: false, ticks:'outside',showticklabels: true, range:[1959, 2023]}, yaxis: { title: 'Capacity', tickformat: ',.0f', zeroline: false, range: [0,2500], showline: false} }; const div=container('Fig 2. Installed power generation capacity at the Kariba dam. Installation of peaking capacity starting in 2009 highlighted.' ,width>800?790:width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }33:T590,html` The capacity of 1,266MW, which was not increased for 32 years, almost exactly matches the average yearly flow into the dam (see: ${headlink(4)}) and is, therefore, a baseload capacity which can be constantly maintained without negatively impacting on the dam storage level over multiple years.

The combined installed capacity increased slightly to 1,470MW in 2009 through a refurbishment program. The Kariba north bank extension was completed in 2013 which added an additional 360MW of capacity in Zambia and in 2018 the south bank extension added 300MW in Zimbabwe to bring the total installed capacity to 2,130MW.

As these expansion programs, totalling 864MW, increased the generating capacity above the 1,266MW baseload capacity of the dam supported by the average annual inflows of water, they are considered "peaking" capacity. This peaking capacity can only be used to change the timing of when maximum electricity is produced and cannot increase the total production. Overuse of this peaking capacity will lead to an unsustainable drawdown in the reservoir levels.

The Kariba dam provides Zambia around 1/3 of its total installed electricity generation capacity of 3,400MW and Zimbabwe nearly 1/2 of its capacity of 2,300MW. Assuming a 50% share of Kariba's average annual production then the dam can supply around 25% of Zambia's total electricity generation of 17.6TWh in 2021. `34:Ta59,{ const div=container('Fig 5. Upper catchment runoff. Total volume of water passing Victoria Falls.',width-10) div.prepend(annualChart(vicfalls_yearly, 'volume','Volume','km\u00B3',[null,null], colr_b)) return div function annualChart (series, parm, label, unit, yRange, cols) { let recent = 2009 let per1 = series.filter(yr=>yr.water_year<1960) let per2 = series.filter(yr=>yr.water_year>1959) let mean = d3.mean(series.map(yr=>yr[parm])) let longterm_mean = d3.mean(series.map(yr=>yr[parm])) let recent_mean = d3.mean(series.filter(v=>v.water_year>recent).map(yr=>yr[parm])) let movingAv = movingAverage(series.map(yr=>yr[parm]), 15) var data = [ { x:[recent-0.5,2021.4,2021.4,recent-0.5,recent-0.5], y:[0,0,80,80,0], showlegend:false, hoverinfo:'none', line: {dash:'dash',width:2, color: '#B26666'}, type: 'scatter', mode:'lines' },{ y: per1.map(yr=>yr[parm]), x: per1.map(yr=>yr.water_year), text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:false, name: '', hoverinfo:'x+text', marker: {color: '#c9d7e8'}, type: 'bar' },{ y: per2.map(yr=>yr[parm]), x: per2.map(yr=>yr.water_year), text: per2.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:false, name: '', hoverinfo:'x+text', marker: {color: cols(1,0.3)}, type: 'bar' },{ y: movingAv, x: series.map(yr=>yr.water_year), name:'15yr Moving Average', mode: 'lines', hoverinfo:'none', line: {width:2,color: cols(4,0.7)}, type: 'scatter' },{ x: [1924, recent-0.5], y: [longterm_mean, longterm_mean], mode: 'lines', line: {color: 'rgba(0,0,0,0.8)', width:1, dash:'dashdot'}, type: 'scatter', hoverinfo:'none', name: '1924-2017 Mean ' + d3.format(',.0f')(longterm_mean) + unit },{ x: [recent-0.5, 2022], y: [recent_mean, recent_mean], mode: 'lines', line: {color: 'rgba(0,0,0,0.8)', width:2, dash:'dashdot'}, type: 'scatter', hoverinfo:'none', name: '2017-2022 Mean ' + d3.format(',.0f')(recent_mean) + unit }]; var layout = { width: width-50, height:300, font: font, showlegend: true, margin: {t:5,b:40,l:70,r:5}, legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', range:[1923,2022]}, yaxis: { title: label + ' ' + unit,range: yRange, zeroline: false, tickformat: ',.0f', showline: false}, }; const div=DOM.element('div') plt.newPlot(div, data, layout, {displayModeBar: false}); return div } }35:T66e,{ let sum = d3.sum(kariba_calmonthly, v=>v.vol_inflow) let cap = `Fig 6. Mean monthly inflow into the Kariba reservoir (bars) and the mean cumulative inflow over the hydrological year (line).` let cumulative = kariba_calmonthly.map((m,i)=>{ return d3.sum(kariba_calmonthly.slice(0,i),v=>v.vol_inflow)/sum }) var data = [{ y: kariba_calmonthly.map(yr=>yr.vol_inflow), x: kariba_calmonthly.map(yr=>yr.water_month), showlegend: true, name: 'Mean inflow', hoverinfo:'x+y', marker: {color: colr_b(1,0.3)}, type: 'bar' },{ y:cumulative, x: kariba_calmonthly.map(yr=>yr.water_month), showlegend: true, name: 'Cumulative inflow', hoverinfo:'x+y', line: {color: colr_b(5,0.7)}, type: 'scatter', yaxis:'y2' }]; var layout = { width: width>700?670:width-30, height:250, font: font, showlegend: true, margin: {t:5,b:50,l:50,r:40}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.15}, xaxis: { range: [0.5, 12.5], tickvals:[1,2,3,4,5,6,7,8,9,10,11,12], ticktext:['Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep'], showgrid: false, zeroline: false, ticks:'outside'}, yaxis: { title: cubickm, zeroline: false, tickformat: ',.1f', showline: false}, yaxis2: { title:'', overlaying: 'y', showgrid: false, side: 'right',zeroline: false, tickformat: ',.0%', showline: false, } }; const div=container(cap ,width>700?690:width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }36:Tbd6,{ let cap = `Fig 7. Annual total inflow into the Kariba reservoir.` let recent = 2009 let movingAv = movingAverage(inflow_longterm.map(yr=>yr.vol_inflow), 15) // let recent_mean = d3.mean(kariba_yearly.filter(v=>v.water_year>recent-1), v=>v.vol_inflow) var data = [{ x:[recent-0.5,2021.4,2021.4,recent-0.5,recent-0.5], y:[0,0,110,110,0], showlegend:false, hoverinfo:'none', line: {dash:'dash',width:2, color: '#B26666'}, type: 'scatter', mode:'lines' } ,{ y: [inflow_stats.recent_mean,inflow_stats.recent_mean], x: [recent-0.5,2021.5], showlegend:true, name: 'Recent Mean Inflow - ' + format_km3(inflow_stats.recent_mean), hoverinfo:'none', mode: 'lines', line: {color: 'rgba(0,0,0,0.8)', width:2, dash:'dashdot'}, type: 'scatter' },{ y: [inflow_stats.mean,inflow_stats.mean], x: [1924,recent-0.5], showlegend:true, name: 'Longterm Mean Inflow - ' + format_km3(inflow_stats.mean), hoverinfo:'none', mode: 'lines', line: {color: 'rgba(0,0,0,0.8)', width:1, dash:'dashdot'}, type: 'scatter' },{ y: movingAv, x: inflow_longterm.filter(m=>m.water_year<2022).map(yr=>yr.water_year), showlegend:true, name: '15 year moving average', hoverinfo:'none', line: {shape: 'spline',color: colr_b(5,0.8)}, type: 'scatter' },{ y: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.vol_vicfalls), x: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.water_year), text: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>format_km3(yr.vol_vicfalls)), showlegend:true, name: 'Inflow from above Victoria Falls', hoverinfo:'none', marker: {color: colr_b(1,0.3)}, type: 'bar' },{ y: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.vol_lowercatchment), x: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.water_year), text: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>format_km3(yr.vol_inflow)), showlegend:true, name: 'Inflow from Lower Catchment ', hoverinfo:'text', marker: {color: colr_b(1,0.5)}, type: 'bar' },{ y: inflow_longterm.filter(v=>v.estimate==true).map(yr=>yr.vol_inflow), x: inflow_longterm.filter(v=>v.estimate==true).map(yr=>yr.water_year), text: inflow_longterm.filter(v=>v.estimate==true).map(yr=>format_km3(yr.vol_inflow)), showlegend: true, name: 'Estimated Total Inflow', hoverinfo:'text', marker: {color: '#c9d7e8'}, type: 'bar' }]; var layout = { width: width-50, height:300, font: font, showlegend: true, margin: {t:5,b:40,l:70,r:10}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.14}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', }, yaxis: { title: cubickm, tickformat: ',.0f', zeroline: false, showline: false}, }; const div=container(cap ,width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }37:T42e,html ` Average annual inflow to the reservoir is ${format_km3(inflow_stats.mean)} with a range of ${format_km3(inflow_stats.min)} to ${format_km3(inflow_stats.max)} (Fig 7). On average, more than 50% of the inflow is within four months between February and May. As would be expected, annual inflow to Kariba show the same long term cyclic trend as the flows at Victoria Falls with higher inflows from the 1950s to the 1970s and lower inflows during the 1990s.

The average annual inflows during the recent period - where electricity generating capacity at the dam has increased by 864MW from 1,266MW to 2,130MW - are only very marginally below the long term average - ${format_km3(inflow_stats.recent_mean)} per year compared to ${format_km3(inflow_stats.mean)} per year.

As recent inflows are very close to the long term average, recent low water levels in the dam can not be due to lower inflow of water but rather must be due to increased outflow due to increased electricity generation using the new 864MW of peaking capacity installed since 2009. `38:Tb8a,{ let cap =html`Fig 8. Energy and equivalent power* potential from annual inflow into the Kariba reservoir for each year since construction (bars). Average power and energy potential from annual inflow (dashed line) and total installed capacity (solid line).

*equivalent power calculation assumes a standard industrial plant availability factor of 8,000 hours per year or ${d3.format(',.0%')(availability)}. This is not to be confused with the capacity factor (CF) for the plant which is dependent on available 'fuel' resources, in this case water.` var data = [ { y: capacities.map(yr=>yr.capacity), x: capacities.map(yr=>yr.year), text: capacities.map(yr=>yr.label), showlegend:true, mode:'lines+markers+text', shape:'hvh', name: 'Installed Capacity', hoverinfo:'text', textposition:['topright','topright','top','top','top','top','top','top','top','top'], line: {color: capColor, dash:'', width:2}, type: 'scatter',yaxis:'y2', }, { y: [inflow_potential_stats.mean, inflow_potential_stats.mean], x: [1960,2021], text: [format_twh(inflow_potential_stats.mean) + ', ' + d3.format(',.0f')(inflow_potential_power) + 'MW'], showlegend:true, name: 'Mean Inflow Potential ', hoverinfo:'text',mode:'lines+text',textposition:'topright', line: {color: capColor, width:1.5, dash:'dashdot'}, type: 'scatter' }/*,{ y: kariba_yearly.map(yr=>yr.energy_gwh/1000), x: kariba_yearly.map(yr=>yr.water_year), text: kariba_yearly.map(yr=>d3.format(',.1f')(yr.energy_gwh/1000) + 'TWh'), showlegend: true, name: 'Actual Production', hoverinfo:'x+text', line: {color: colr(5,0.8), shape:'hvh', width:1.5}, type: 'scatter' }*/,{ y: inflow_longterm.filter(v=>v.estimate==false).map(yr=>yr.inflow_energy_potential), x: inflow_longterm.filter(v=>v.estimate==false).map(yr=>yr.water_year), text: inflow_longterm.filter(v=>v.estimate==false).map(yr=>d3.format(',.1f')(yr.inflow_energy_potential) + 'TWh'), showlegend: true, name: 'Inflow Potential', hoverinfo:'x+text', marker: {color: colr(1,0.4)}, type: 'bar' }]; var layout = { width: width-50, height:300, font: font, showlegend: true, margin: {t:5,b:40,l:70,r:70}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.14}, xaxis: { showgrid: false, zeroline: false, range:[1959,2021.4], ticks:'outside'}, yaxis: { title: 'TWh', zeroline: false, tickformat: ',.0f', showline: false, range: [0,23], showgrid:false, }, yaxis2: { title: 'MW', zeroline: false,range: [0,23*1000000/(24*365)/availability], showline: false, showgrid:true, overlaying: 'y', separatethousands:true, side: 'right' } }; const div=container(cap ,width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }39:T678,{ let cap = `Fig 9. Histogram of annual energy potential (after evaporation) from inflows to the Kariba reservoir.` var data = [{ x: kariba_yearly.map(v=>v.inflow_energy_potential), marker: {color: colr(1,0.4)}, type: 'histogram',histnorm: 'probability' },{ y:[0,0.15], x: [inflow_potential_stats.p90,inflow_potential_stats.p90], text:['','P90, ' + d3.format(',.2r')(inflow_potential_stats.p90) + 'TWh'], mode:'lines+text', textposition:'top', line: {color: colr(4,0.8)}, type: 'scatter', },{ y:[0,0.15], x: [inflow_potential_stats.p10,inflow_potential_stats.p10], text:['','P10, ' + d3.format(',.2r')(inflow_potential_stats.p10) + 'TWh'], mode:'lines+text', textposition:'top', line: {color: colr(4,0.8)}, type: 'scatter', },{ y:[0,0.2], x: [inflow_potential_stats.median,inflow_potential_stats.median], text:['','P50, ' + d3.format(',.2r')(inflow_potential_stats.median) + 'TWh'], mode:'lines+text', textposition:'top', line: {color: colr(4,0.8)}, type: 'scatter', }]; var layout = { width: width>700?670:width-30, height: 230, font: font, showlegend: false, margin: {t:5,b:50,l:50,r:60}, legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.17}, xaxis: { showgrid: false, zeroline: false, title:'Annual Energy TWh', ticks:'outside', }, yaxis: { title: '', zeroline: false, showline: false,range:[0,0.22], showgrid:true,tickformat:',.0%', } }; const div=container(cap, width>700?690:width-10) plt.newPlot(div, data, layout,{displayModeBar: false}); return div }3a:T56d,html` Each year a different volume of water flows into the Kariba reservoir and this gives rise to a different electrical energy generation potential (Fig 8). The mean energy available from inflow is ${format_twh(inflow_potential_stats.mean)} equivalent to ${d3.format(',.0f')(inflow_potential_stats.mean*1000000/(24*365))}MW operating constantly throughout the year or ${d3.format(',.0f')(inflow_potential_stats.mean*1000000/(24*365)/availability)}MW at a standard capacity factor of ${d3.format(',.0%')(availability)}.

This is very close to the 1,266MW that was operating at the two Kariba generating stations between 1977 and 2009 and is the firm, baseload capacity that the Zambezi can support. The additional capacity installed after 2009 is, therefore, peaking capacity.

However, as the live storage volume of the reservoir is only ${d3.format(',.1f')(kariba.storage/inflow_stats.mean)} x Mean Annual Runoff (MAR), there is very little additional storage to carry water from flood years over to drought years. For this reason, the additional peaking capacity installed after 2009 can only be used for periods of less than one year, either for seasonal peaking - for example during the low flow months when run-of-river hydro plants are producing least power - or for intra-day peaking, to produce more power during the times of day with the highest consumption. `3b:Ta50,{ let cap='Fig 10. Energy production and equivalent power by year from the Kariba power stations. Highlighted years are where where annual energy exceeded the mean annual inflow potential.' var data = [ { y: [inflow_potential_stats.mean, inflow_potential_stats.mean], x: [1960,2021], text: [format_twh(inflow_potential_stats.mean) + ', ' + d3.format(',.0f')(inflow_potential_power) + 'MW'], showlegend:false, name: 'Mean Inflow Potential ', hoverinfo:'text',mode:'lines+text',textposition:'topright', line: {color: capColor, width:1.5, dash:'dashdot'}, type: 'scatter' },{ y: capacities.map(yr=>yr.capacity), x: capacities.map(yr=>yr.year), text: capacities.map(yr=>yr.label), showlegend:false, mode:'lines+markers+text', shape:'hvh', name: '', hoverinfo:'none', textposition:'topright', line: {color: capColor}, yaxis: 'y2', type: 'scatter' },{ y: kariba_yearly.filter(m=>m.water_year<2022).filter(v=>v.energy_gwh/1000>inflow_potential_stats.mean).map(yr=>yr.energy_gwh/1000), x: kariba_yearly.filter(m=>m.water_year<2022).filter(v=>v.energy_gwh/1000>inflow_potential_stats.mean).map(yr=>yr.water_year), //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:false, name: '', hoverinfo:'x+y', marker: {color: colr(5,0.6)}, type: 'bar' },{ y: kariba_yearly.filter(m=>m.water_year<2022).filter(v=>v.energy_gwh/1000yr.energy_gwh/1000), x: kariba_yearly.filter(m=>m.water_year<2022).filter(v=>v.energy_gwh/1000yr.water_year), //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:false, name: '', hoverinfo:'x+y', marker: {color: colr(1,0.4)}, type: 'bar' }]; var layout = { width: width-50, height:250, font: font, showlegend: true, margin: {t:5,b:40,l:70,r:70}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside',showticklabels: true, range:[1959, 2023]}, yaxis: { title: 'Energy TWh', tickformat: ',.0f', zeroline: false, range: [0,23],showgrid:false, showline: false}, yaxis2: { title: 'MW', zeroline: false,range: [0,23*1000000/(24*365)/availability], showline: false, showgrid:true, overlaying: 'y', separatethousands:true, side: 'right' } }; const div = container(cap ,width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }3c:T62d,{ let cap='Fig 11. Surplus (positive) and deficit (negative) of energy from water inflow for the Kariba power stations. In deficit years the reservoir is drawn down to provide additional energy.' let net = kariba_yearly.filter(m=>m.water_year<2022).map(yr=>{ return { water_year: yr.water_year, net: yr.inflow_energy_potential - (yr.energy_gwh/1000) } }) var data = [ { y: net.filter(v=>v.net>0).map(v=>v.net), x: net.filter(v=>v.net>0).map(v=>v.water_year), text: net.filter(v=>v.net>0).map(yr=>d3.format('.1f')(yr.net) + ' TWh'), showlegend:false, name: '', hoverinfo:'x+text', marker: {color: colr(1,0.4)}, type: 'bar' },{ y: net.filter(v=>v.net<0).map(v=>v.net), x: net.filter(v=>v.net<0).map(v=>v.water_year), text: net.filter(v=>v.net<0).map(yr=>d3.format('.1f')(yr.net) + ' TWh'), showlegend:false, name: '', hoverinfo:'x+text', marker: {color: colr(5,0.6)}, type: 'bar' }]; var layout = { width: width-50, height:220, font: font, showlegend: true, margin: {t:5,b:40,l:70,r:70}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside',showticklabels: true, range:[1959, 2023]}, yaxis: { title: 'Energy TWh', tickformat: ',.0f', zeroline: true,showgrid:true,range:[-11,11], showline: false}, }; const div = container(cap ,width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }3d:T40d,The total energy produced by the Kariba power stations (Fig 10.) depends both on the water available for generation (Fig 7). Until the mid-1980s a lack of demand meant that generation was often curtailed and the reservoir was able to fill and often spilled. The late 1980s and 1990s were periods of lower than average inflows to the reservoir. From the early-2000s higher demand and higher inflows meant that the station began to produce close to the mean inflow potential of ${format_twh(inflow_potential_stats.mean)} every year.

With new peaking capacity added in 2009, the power stations produced more than the mean inflow potential in 2009, 2010 and each year from 2012 to 2014 (Fig 10). Each year from 2011 to 2015 water use for generation also exceeded the annual inflow (Fig 11.) leading to a net drawdown in the reservoir. A combination of reservoir drawdown and exceeding the mean annual inflow potential puts increasing stress on the reservoir level which is unlikely to be compensated by higher flows in flood years.3e:T4e8,{ let cap= `Fig 12. Storage level of the Kariba reservoir since construction.` var data = [{ y: kariba_monthly.map(yr=>yr.store_end_pct), x: kariba_monthly.map(yr=>yr.lux_date.toJSDate()), showlegend:true, fill:'tozeroy', fillcolor: colr_b(1,0.3), name: 'Store level',hoverinfo:'x+y', line: {color: colr_b(1,0.5)}, type: 'scatter' },{ y: kariba_monthly.map(yr=>yr.vol_spill>0.05?yr.store_end_pct:undefined), x: kariba_monthly.map(yr=>yr.vol_spill>0.05?yr.lux_date.toJSDate():undefined), showlegend: true, name: 'Spill periods', hoverinfo:'none', line: {width: 3, color: 'rgba(0.8,0.2,0.2,0.7)'}, type: 'scatter', mode: 'limnes' }]; var layout = { width: width-50, height:250, font: font, showlegend: true, margin: {t:5,b:40,l:70,r:70}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.13}, xaxis: { showgrid: false, zeroline: false,range:['1959-10-01', '2022-02-01'], ticks:'outside'}, yaxis: { title: '', range:[0,1], zeroline: false, tickformat: ',.0%', showline: false} }; const div=container(cap ,width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }3f:Tc18,{ let cap = `Fig 13. Recent reservoir storage level (area), water inflow rate (line) and electricity generation (bars).` let kariba_monthly_recent = kariba_monthly.filter(v=>v.water_year>2014) let from3=new Date('2015-10-10') let to3 = new Date('2018-8-25') let from1=new Date('2018-9-01') let to1 = new Date('2019-10-01') let from2=new Date('2021-3-01') let to2 = new Date('2022-11-30') var data = [{ y: [0, 0.9,0.9,0,0], x: [from1, from1,to1,to1,from1], //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:false, name: '', hoverinfo:'x', fill:'', line: {dash:'dash',width:2, color: '#FF8C00'}, fillcolor:'', type: 'scatter', mode:'lines' },{ y: [0, 0.9,0.9,0,0], x: [from2, from2,to2,to2,from2], //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:false, name: '', hoverinfo:'x', fill:'', line: {dash:'dash',width:2, color: 'rgba(0.8,0.2,0.2,0.9)'}, fillcolor:'', type: 'scatter', mode:'lines' },{ y: [0, 0.9,0.9,0,0], x: [from3, from3,to3,to3,from3], //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:false, name: '', hoverinfo:'x', fill:'', line: {dash:'dash',width:2, color: 'rgba(0.2,0.8,0.2,0.9)'}, fillcolor:'', type: 'scatter', mode:'lines' },{ y: kariba_monthly_recent.map(yr=>yr.capacity_factor), x: kariba_monthly_recent.map(yr=>yr.lux_date.toJSDate()), text: kariba_monthly_recent.map(yr=>d3.format(',.0f')(yr.power_mw) + 'MW'), showlegend:true, name: 'Capacity Factor %', hoverinfo:'x+text', marker: {color: colr(1,0.3)}, type: 'bar' },{ y: kariba_monthly_recent.map(yr=>yr.store_end_pct), x: kariba_monthly_recent.map(yr=>yr.lux_date.toJSDate()), //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:true, name: 'Available Storage %', hoverinfo:'x+y', fill:'tozeroy', fillcolor:colr_b(3,0.2), line: {color: colr_b(1,0.1)}, type: 'scatter' },{ y: kariba_monthly_balance.map(yr=>yr.vol_inflow), x: kariba_monthly_balance.map(yr=>yr.lux_date.toJSDate()), //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:true, yaxis:'y2', name: 'Reservoir Inflow Rate', hoverinfo:'x', line: {shape:'spline',width:3, color: colr_b(2,0.5)}, type: 'scatter' }]; var layout = { width: width-50, height:300, font: font, showlegend: true, margin: {t:5,b:40,l:70,r:70}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.1}, xaxis: { showgrid: false, zeroline: false, range:['2015-10-01', '2022-12-30'], ticks:'outside'}, yaxis: { title: '', zeroline: false, tickformat: ',.0%', showline: false}, yaxis2: { title:'Inflow ' + cubickm + '/month', overlaying: 'y', showgrid: false, side: 'right',zeroline: false, tickformat: ',.0f', showline: false, } }; const div=container(cap ,width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }40:T49f,Despite the reservoir reaching one of the lowest levels of storage ever by the end of 2015, prudent curtailment of generation through 2016, 2017 and 2018 coupled with moderate inflows resulted in recovery of the reservoir to over 85% full by mid 2018 (green highlight in Fig 13).

However, the 2018 water year (October 2018 to September 2019, highlighted in orange in Fig 13.) was a significant drought. Despite poor inflows, generation was high at over 110% of the ${format_twh(inflow_potential_stats.mean)} supported by mean annual inflow and the third highest annual generation ever recorded. 85% of the water used for generation in 2018 came from storage and only 15% from inflows, the highest proportion ever (Fig 11). By early 2020 the reservoir level had fallen to a new all-time low of 9%.

2019 and 2021 were average years for inflows and 2020 was a relatively high flood year. However, starting in March 2021 power output was raised to around 1,500MW and maintained at this level until November 2022. At this level of generation there was no opportunity for the the storage level to recover properly from the 2018 water year drought (red outline Fig 13). 41:Ta91,{ let cap = `Fig 14. Simulation of reservoir levels from March 2021 to December 2022 operating the Kariba power stations constantly at their baseload capacity of 1,024MW.` let kariba_monthly_recent = kariba_monthly.filter(v=>v.water_year>2014) let from2=new Date('2021-3-01') let to2 = new Date('2022-11-30') var data = [{ y: [0, 0.9,0.9,0,0], x: [from2, from2,to2,to2,from2], //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:false, name: '', hoverinfo:'x', fill:'', line: {dash:'dash',width:2, color: 'rgba(0.8,0.2,0.2,0.9)'}, fillcolor:'', type: 'scatter', mode:'lines' },{ y: kariba_monthly_recent.map(yr=>yr.capacity_factor), x: kariba_monthly_recent.map(yr=>yr.lux_date.toJSDate()), text: kariba_monthly_recent.map(yr=>d3.format(',.0f')(yr.power_mw) + 'MW'), showlegend:true, name: 'Actual Capacity Factor %', hoverinfo:'x+text', marker: {color: colr(1,0.2)}, type: 'bar' },{ y: recent_model.map(yr=>yr.capacity_factor), x: recent_model.map(yr=>yr.lux_date.toJSDate()), text: recent_model.map(yr=>d3.format(',.0f')(yr.power_mw) + 'MW'), showlegend:true, name: 'Baseload Capacity Factor %', hoverinfo:'x+text', marker: {color: colr(3,0.6)}, type: 'bar' },{ y: kariba_monthly_recent.map(yr=>yr.store_end_pct), x: kariba_monthly_recent.map(yr=>yr.lux_date.toJSDate()), //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:true, name: 'Actual Storage %', hoverinfo:'x+y', fill:'tozeroy', fillcolor:colr_b(3,0.2), line: {color: colr_b(1,0.3)}, type: 'scatter' },{ y: recent_model.map(yr=>yr.store_end_pct_new), x: recent_model.map(yr=>yr.lux_date.toJSDate()), //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:true, name: 'Simulated Storage %', hoverinfo:'x+y', fill:'tozeroy', fillcolor:colr_b(3,0.2), line: {color: colr_b(4,0.8)}, type: 'scatter' }]; var layout = { width: width-50, height:300, font: font, showlegend: true, margin: {t:5,b:40,l:70,r:70}, legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.1}, xaxis: { showgrid: false, zeroline: false, range:['2020-10-01', '2022-12-30'], ticks:'outside'}, yaxis: { title: '', zeroline: false, tickformat: ',.0%', showline: false}, yaxis2: { title:'Inflow ' + cubickm + '/month', overlaying: 'y', showgrid: false, side: 'right',zeroline: false, tickformat: ',.0f', showline: false, } }; const div=container(cap ,width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }42:T421,{ let cap= `Storage level of the Kariba reservoir to the end of 2022. The lowest ever level of 0.8% available storage was reached on 30th December 2022.` var data = [{ y: kariba_daily.filter(d=>d.lux_date.year>2020).map(yr=>yr.store_pct), x: kariba_daily.filter(d=>d.lux_date.year>2020).map(yr=>yr.lux_date.toJSDate()), showlegend:false, fill:'tozeroy', fillcolor: colr_b(1,0.3), name: 'Store level',hoverinfo:'x+y', line: {color: colr_b(1,0.5)}, type: 'scatter' }]; var layout = { width: width>700?670:width-30, height:200, font: font, showlegend: true, margin: {t:5,b:40,l:70,r:70}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.13}, xaxis: { showgrid: false, zeroline: false, ticks:'outside'}, yaxis: { title: '', range:[0,0.6], zeroline: false, tickformat: ',.1%', showline: false} }; const div=container(cap ,width>700?690:width-10,) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }43:T760,{ let cap = `Fig 15. Capacity factor of annual generation of the Kariba power stations.` let recent_av=d3.mean(kariba_yearly.filter(m=>m.water_year<2022 && m.water_year>2014),yr=>yr.capacity_factor) var data = [{ y: [recent_av,recent_av], x: [2015,2022], text: [d3.format(',.1%')(recent_av),''], showlegend:false, mode:'lines+text', textposition:'top', name: '', hoverinfo:'none', line: {color: 'rgba(0,0,0,0.8)', width:2, dash:'dashdot'}, type: 'scatter' },{ y: capacities.map(yr=>yr.capacity), x: capacities.map(yr=>yr.year), text: capacities.map(yr=>yr.label), showlegend:false, mode:'lines+markers+text', shape:'hvh', name: '', hoverinfo:'none', textposition:'topright', line: {color: capColor}, yaxis: 'y2', type: 'scatter' },{ y: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.capacity_factor), x: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.water_year), //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:false, name: '', hoverinfo:'x+y', marker: {color: colr(1,0.4)}, type: 'bar' }]; var layout = { width: width-50, height:250, font: font, showlegend: true, margin: {t:5,b:40,l:70,r:70}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside',showticklabels: true, range:[1959, 2023]}, yaxis: { title: 'Capacity Factor', tickformat: ',.0%', zeroline: false, range:[.3,1], showline: false}, yaxis2: { title:'', overlaying: 'y', showgrid: false, showticklabels: false, side: 'right',zeroline: false, range:[0,2500], showline: false, } }; const div = container(cap ,width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }44:T871,{ const div=container('Fig 16b. Zambezi upper catchment runoff. Total volume of water passing Victoria Falls.',width-10) div.prepend(annualChart(vicfalls_yearly, 'volume','Volume','km\u00B3',[null,null], colr_b)) return div function annualChart (series, parm, label, unit, yRange, cols) { let recent = 2009 let per1 = series.filter(yr=>yr.water_year<1960) let per2 = series.filter(yr=>yr.water_year>1959) let mean = d3.mean(series.map(yr=>yr[parm])) let longterm_mean = d3.mean(series.map(yr=>yr[parm])) let recent_mean = d3.mean(series.filter(v=>v.water_year>recent).map(yr=>yr[parm])) let movingAv = movingAverage(series.map(yr=>yr[parm]), 15) var data = [{ y: per1.map(yr=>yr[parm]), x: per1.map(yr=>yr.water_year), text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:false, name: '', hoverinfo:'x+text', marker: {color: '#c9d7e8'}, type: 'bar' },{ y: per2.map(yr=>yr[parm]), x: per2.map(yr=>yr.water_year), text: per2.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:false, name: '', hoverinfo:'x+text', marker: {color: cols(1,0.3)}, type: 'bar' },{ y: movingAv, x: series.map(yr=>yr.water_year), name:'15yr Moving Average', mode: 'lines', hoverinfo:'none', line: {width:2,color: cols(4,0.7)}, type: 'scatter' },{ x: [1924, 2022], y: [longterm_mean, longterm_mean], mode: 'lines', line: {color: 'rgba(0,0,0,0.8)', width:1, dash:'dashdot'}, type: 'scatter', hoverinfo:'none', name: '1924-2017 Mean ' + d3.format(',.0f')(longterm_mean) + unit }]; var layout = { width: width-50, height:300, font: font, showlegend: true, margin: {t:5,b:40,l:70,r:5}, legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', range:[1923,2022]}, yaxis: { title: label + ' ' + unit,range: yRange, zeroline: false, tickformat: ',.0f', showline: false}, }; const div=DOM.element('div') plt.newPlot(div, data, layout, {displayModeBar: false}); return div } }45:T581,{ let cap = `Fig 17. Proportion of total inflow provided by the Lower Catchment.` let movingAv = movingAverage(kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.lowercatchment_prop), 5) var data = [{ y: movingAv, x: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.water_year), //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:true, name: '10 year moving average', hoverinfo:'x+y', line: {color: colr_b(5,0.8)}, type: 'scatter' },{ y: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.lowercatchment_prop), x: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.water_year), //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:false, name: '% of inflow provided by lower catchment', hoverinfo:'x+y', marker: {color: colr_b(1,0.3)}, type: 'bar' }]; var layout = { width: width-50, height:230, font: font, showlegend: true, margin: {t:5,b:40,l:70,r:10}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.14}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', range:[1959, 2022]}, yaxis: { title: '', tickformat: ',.0%', zeroline: false, showline: false}, }; const div=container(cap ,width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }46:T408,It is possible that the impact of climate change is already being felt at Kariba. There appears to have been a sustained fall in inflow from the lower catchment* since the mid-1990s (Fig 17). However, this could also be partially or entirely due to climate variability cycles which are clear from the long term record of the upper catchment (Fig 16).

Because of the complex nature of continental and global-scale climate systems, it is extremely difficult to accurately predict the extent to which climate change (and combinations of climate change and climate variability) will impact on rainfall, runoff and reservoir inflow. Howerver, it is clear that any increase in mean annual temperature will necessarily lead to an increase in evaporation losses from the reservoir.

* Due to the way the water balance is calculated for Kariba (see Appendix C) this may actually be entirely or partially due to an increase in evaporation rather than a decrease in lower catchment inflow.

47:T409,There is a global shift underway to install modern renewable electricity generation sources, particularly wind, solar and hydro power to reduce carbon dioxide emissions from fossil fuels and combat climate change. Both wind and solar power are characterised as variable (diurnal) with intermittency. Grid connected run-of-river hydro on larger rivers is generally not intermittent but is often variable (seasonal) due to variations in river flow.

For wind and solar plants, intermittency means the tendency for power output to change over short periods of time - seconds, minutes and hours - due to changes in light levels and wind speed caused by weather patterns. Intermittency is by nature chaotic and random and cannot be predicted in advance (Fig 18).

With enough intermittent renewables power plants installed on the power grid, especially where these are geographically disbursed, the impact of intermittency is reduced as cloudy or still conditions are less likely to impact all these plants at once.48:T4c3,{ let cap = `Fig 18. Ten second interval output from an operating solar PV plant in Spain over three days in July 2016. Each day has a cyclic variability due to the day-night cycle. Day 1 and particularly Day 3 show intermittency due to cloud and Day 2 is curtailed (consistently reduced output) also due to cloud cover.` var data = [{ y: solar_example.map(yr=>yr.Output/100), //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:true, name: '', hoverinfo:'y', line: {width:2, color: d3.quantize(d3.interpolateHcl("#d66000", "#a9a9b4"), 12)[5]}, type: 'scatter' }]; var layout = { width: width>900?870:width-30, height:200, font: font, showlegend: false, margin: {t:5,b:20,l:70,r:70}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.1}, xaxis: { showgrid: false, showticklabels: false, zeroline: false, range:['2015-10-01', '2022-12-30'], ticks:'outside'}, yaxis: { title: 'Output', zeroline: false, tickformat: ',.0f', showline: false} }; const div=container(cap ,width>900?890:width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }49:T544,{ let cap='Fig 19. Average relative power output through the day of a solar PV plant in Zambia. Power required to balance or firm the power to produce constant output is shaded.' function plot(ser, leg) { var data = [{ y: solar_profile, x: [...Array(24).keys()], mode: 'lines', line: {shape:'spline', width: 4, color: d3.quantize(d3.interpolateHcl("#d66000", "#a9a9b4"), 12)[1]}, type: 'scatter', hoverinfo: 'name', showlegend:true, name: 'Mean' },{ y:[1,1], x:[0,23], type: 'scatter', hoverinfo: 'none', mode: 'lines', line: {width: 1, color: '#CCC'}, fill:'tonexty' }] var layout = { height: 200, width: width>900?870:width-30, font: font, hovermode:'closest', showlegend: leg, legend: {orientation: 'h', xanchor: 'left', x:0, y: 1}, margin: {l: 70,r: 70,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside', dtick:2, }, yaxis: { title: 'Output', range: [0,1.19], showgrid: false, zeroline: false, tickformat: '%.0f', ticks:'outside' } } const pdiv = DOM.element('div') plt.newPlot(pdiv, data, layout, { displayModeBar: false }) return pdiv } const div = container(cap, width>900?890:width-10,) div.prepend(plot(relative_disp, false)) return div }4a:T545,{ let cap='Fig 20. Average relative power output through the day of a wind plant in Zambia. Power required to balance or firm the power to produce constant output is shaded.' function plot(ser, leg) { var data = [{ y: wind_profile, x: [...Array(24).keys()], mode: 'lines', line: {shape:'spline', width: 4, color: d3.quantize(d3.interpolateHcl("#d66000", "#a9a9b4"), 12)[11]}, type: 'scatter', hoverinfo: 'name', showlegend:true, name: 'Mean' },{ y:[1,1], x:[0,23], type: 'scatter', hoverinfo: 'none', mode: 'lines', line: {width: 1, color: '#CCC'}, fill:'tonexty' }] var layout = { height: 200, width: width>900?870:width-30, font: font, hovermode:'closest', showlegend: leg, legend: {orientation: 'h', xanchor: 'left', x:0, y: 1}, margin: {l: 70,r: 70,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside', dtick:2, }, yaxis: { title: 'Output', range: [0,1.19], showgrid: false, zeroline: false, tickformat: '%.0f', ticks:'outside' } } const pdiv = DOM.element('div') plt.newPlot(pdiv, data, layout, { displayModeBar: false }) return pdiv } const div = container(cap, width>900?890:width-10,) div.prepend(plot(relative_disp_wind, false)) return div }4b:T61c,{ let cap = 'Fig 21. Average power output from a planned run-of-river hydroelectric power station on the upper Zambezi River in Zambia. Power required to balance or firm the power to produce constant output is shaded.' var sum; function trace(name, field, colour, trans) { return } var data = [{ name: name, x:calmonths.map(mnth=>mnth.waterMonth), y:calmonths.map(mnth=>(mnth['plantPowerMedian'])), text:calmonths.map(mnth=>(d3.format('.0f')(mnth['plantPowerMedian']))), type: 'scatter', hoverinfo: 'y', mode: 'lines', line: {shape:'spline', width: 4, color: d3.quantize(d3.interpolateHcl("#d66000", "#a9a9b4"), 12)[7]} },{ y:[165,165], x:[1,12], type: 'scatter', hoverinfo: 'none', mode: 'lines', line: {width: 1, color: '#CCC'}, fill:'tonexty' }] var layout = { width: width>900?870:width-30, height:220, font: font, barmode:'overlay', margin: {l: 70,r: 70,b: 45,t: 0}, showlegend: false, legend: {x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext:['Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep'], }, yaxis: { title: 'MW', range:[0,180], zeroline: false, showline: false, separatethousands:true, tickformat: '.0f' } }; const div=container(cap ,width>900?890:width-10,) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }4c:T834,To integrate significant new renewable energy with the power network reserve capacity is required, both at short notice for smoothing the gaps due to intermittency and, on a scheduled basis, to balance the daily variable output from solar and wind plants and the seasonally variable output from run-of-river hydro plants to make their power firm.

Large storage hydro plants with peaking capacity such as Kariba are ideally placed to provide smoothing and particularly balancing services.

From Fig 19, the total current generation capacity at Kariba could be used to produce firm, continuous output of 2,130MW if combined with the same capacity of solar PV - whose output goes to 0MW at night. However, a high yield solar PV plant in Zambia operates at a capacity factor of only ${d3.format(',.0%')(solar_mwh_per_mw_day/24)}ac or ${d3.format(',.1f')(solar_mwh_per_mw_day)}MWh of electricity per MW of capacity per day. This means that smoothing or firming 2,130MW of solar PV would require Kariba to operate at a capacity factor of ${d3.format(',.0%')(1-(solar_mwh_per_mw_day/24))}, equivalent to ${d3.format(',.0f')(2130*(1-(solar_mwh_per_mw_day/24)))}MW continuous power which is greater than the ${d3.format(',.0f')(inflow_potential_power)}MW supported by mean annual inflow of water (Fig 10).

Therefore, using only mean annual inflow, Kariba could balance around ${d3.format(',.2r')(solar_mw_install)}MW of solar PV with the combined plant (Kariba + solar) producing ${d3.format(',.2r')(solar_mw)}MW of continuous, firm power without exceeding the mean annual inflow of the reservoir.

As wind plants have a higher capacity factors than solar plants, Kariba could balance around ${d3.format(',.2r')(wind_mw_install)}MW of wind power with the combined plant producing ${d3.format(',.2r')(wind_mw)}MW of firm power without exceeding the mean annual inflow of the reservoir. This is only slightly less than the total installed capacity of 2,130MW at Kariba and therefore represents a very efficient use of the installed capacity.4d:T460,{ let cap='Fig 22. Relative power output for a combined wind and solar plant with 1MW of solar PV capacity for every 3MW of wind capacity by month and time of day.' var dat = combined_cfs var data =dat.map((month,month_no) => { return { y:month, x:[...Array(24).keys()], mode: 'lines', line: {shape:'spline',color: d3.quantize(d3.interpolateHcl("#922b21", "#797d7f"), 12)[month_no],width:3}, fill:'', type: 'scatter', hoverinfo: 'name', showlegend:true, name: month_names[month_no] } }) var layout = { height: 270, width: width>900?870:width-30, font: font, hovermode:'closest', showlegend: true, legend: {orientation: 'h',xanchor: 'left', x:0, y: 1.1}, margin: {l: 50,r: 50,b: 45,t: 0}, xaxis: { title: '', showgrid: false, zeroline: false, ticks:'outside', dtick:1, }, yaxis: { title: 'Output', showgrid: true, zeroline: false, tickformat: ',.0%', ticks:'outside', range:[0,1] } }; const div=container(cap, width>900?890:width-10) plt.newPlot(div, data, layout,{displayModeBar: false}); return div }4e:T4c8, A high yield (bifacial, single axis tracker) solar PV power station in Zambia will operate with an overall capacity factor of around ${d3.format(',.0%')(solar_cf)}ac. This gives an annual energy production of ${format_gwh(solar_cf*24*365/1000)} per MWac of capacity installed. For Kariba, this energy can be considered as water that can be kept in the dam and used during periods of peak demand or lowest generation from other sources - e.g. August to December when river flows are low.

Per 100MW	Annual Energy	Equivalent Water Volume	Equivalent Dam Level
Solar PV	${format_gwh(solar_cf24365/10)}	${format_km3((solar_cf24365/10)/kariba.yield)}	${d3.format(',.2r')((solar_cf24365/10)/kariba.yieldkariba.drawdown_rate100)} cm
Wind	${format_gwh(wind_cf24365/10)}	${format_km3((wind_cf24365/10)/kariba.yield)}	${d3.format(',.2r')((wind_cf24365/10)/kariba.yieldkariba.drawdown_rate100)} cm

4f:Tae2,

Availability Factor and Capacity Factor are two related but distinct concepts applied to power stations. Both are usually expressed as a percentage.

Availability Factor is the proportion of time that a power station is available to operate at its full capacity irrespective of whether the fuel (coal, water, wind or sunshine) is supplied to operate it. The standard 'rule-of-thumb' availability factor for industrial plant is ${d3.format(',.0%')(availability)} which assumes the plant is available for 8,000 hours each year. The availability factor for a plant will depend on a combination of the type of technology being used, the quality of the equipment and the quality and quantity of the operations & maintenance (O&M) undertaken on the equipment.

Well maintained solar power plants should have an Availability Factor close to 100%. Wind power plants will also have a very high Availability Factor. Hydro plants should have Availability Factors of over 95%. Thermal plants including coal, gas and nuclear will have the lowest Availability Factors for power plants of around 91% due to the maintenance requirements of numerous complex systems including fuel handling, boilers, steam cycles and turbogenerators.

The Capacity Factor for a power station is simply the ratio between the quantity of energy actually produced over a period of time and the quantity of energy that would have been produced if the plant was operating at 100% of its capacity over that period. For example, a 100MW plant operating at 100% for one year could produce 100 x 24 x 365 = 876GWh of energy. If this plant actually produced 438GWh then its Capacity Factor for that year would be 50%.

The maximum Capacity Factor for any power station is the same as its Availability Factor but will be less than this for any power station that has a constrained fuel supply. A solar power station for example has no fuel resource (sunshine) at night so its Capacity Factor must be less than 50%. Wind and hydropower stations may not have enough wind or river water respectively to run at 100% of their capacity at all times. Fossil fuel and other thermal plants (coal, gas, biomass, nuclear) should have Capacity Factors very close or equal to their Availability Factors.

For renewable energy power stations with seasonal fuel supply such as wind and hydro, higher Capacity Factors are not necessarily better and will often produce more expensive electricity. A techno-economic optimisation must be completed for these plants to calculate the optimum installed capacity and Capacity Factor.

50:T43c,{ let cap = `Fig B1. Box plots of annual rainfall for selected towns in the Kariba dam catchment. Central, horizontal, line is the average (median) annual rainfall and the extent of the whisker lines shows the range in total annual rainfall.` function buildTrace(station,i){ return { y: rainYearly.map(y=>y[station]===null?null:Math.round(y[station])), type: 'box', hoverinfo:'y', name: station + " " + Math.round(Math.abs(rainStationDict[station].Lat)) + "\u00B0S", marker: {color: colr_b(i)}, } } var data = [ buildTrace('Livingstone',1), buildTrace('Mongu',2), buildTrace('Kaoma',3), buildTrace('Zambezi',4), buildTrace('Luena',5),buildTrace('Mwinilunga',6) ]; var layout = { showlegend: false, width:width>900?870:width-30, height: 280, font:font, margin:{t:10,r:10,b:20,l:70}, yaxis: { title: "Annual Rainfall (mm)", separatethousands:true, range:[250,2000] } }; const div=container(cap,width>900?890:width-10) plt.newPlot(div, data, layout,{displayModeBar: false}); return div }51:T617,{ function chart(){ rainCalMonthly.sort((e1, e2) => e1.RainMonth - e2.RainMonth); var data_monthly = [ { y: rainCalMonthly.map(month => month['Livingstone_mean']), text: rainCalMonthly.map(month => d3.format(',.0f')(month['Livingstone_mean']) + 'mm'), hoverinfo:'x+text', x: [ 'Jul', 'Aug','Sep', 'Oct', 'Nov', 'Dec','Jan','Feb','Mar','Apr','May', 'Jun' ], name: 'Livingstone', marker: { color: colr_b(1,0.5) }, type: 'bar' }, { y: rainCalMonthly.map(month => month['Mwinilunga_mean']), text: rainCalMonthly.map(month => d3.format(',.0f')(month['Mwinilunga_mean']) + 'mm'), hoverinfo:'x+text', x: [ 'Jul', 'Aug','Sep', 'Oct', 'Nov', 'Dec','Jan','Feb','Mar','Apr','May', 'Jun' ], name: 'Mwinilunga', marker: { color: colr_b(6,0.5) }, type: 'bar' } ]; var layout_monthly = { width: width>700?670:width-30, height: 250, font: font, showlegend: true, margin: {l:70, b:40, r:10, t:10}, legend: {x: 0.04,xanchor: 'left',y: 1}, xaxis: {showgrid: false,zeroline: false,ticks: 'outside'}, yaxis: {title: 'Rainfall (mm)',zeroline: false,showline: false } }; const dv = DOM.element('div'); plt.newPlot(dv, data_monthly, layout_monthly,{displayModeBar: false}); return dv } const div=container('Fig B2. Mean monthly rainfall for Livingstone towards the south of the Kariba catchment and Mwinilunga in the north of the catchment.' ,width>700?690:width-10) div.prepend(chart('Livingstone', colr_b(1))) return div }52:T70e,{ let cap = `Fig B4. Mean monthly rainfall at Mwinilunga in the north of the catchment and mean monthly river flow at Victoria Falls in the southern part of the catchment.` var data = [{ y: rainCalMonthly.map(month=>month.Mwinilunga_mean), text: rainCalMonthly.map(month=>d3.format(',.0f')(month.Mwinilunga_mean) + 'mm'), x: rainCalMonthly.map(month=>month.WaterMonth), name:'Mean Rainfall at Mwinilunga', hoverinfo: 'x+text', marker: {color: colr_b(1,0.3)}, type: 'bar' }, { y: vicfalls_calmonthly.map(month=>month.flow), text: vicfalls_calmonthly.map(month=>d3.format(',.0f')(month.flow) + cumec), x: vicfalls_calmonthly.map(month=>month.water_month), yaxis: 'y2', hoverinfo: 'x+text', mode: 'lines+markers', name:'Mean Flow at Victoria Falls', line: {shape: 'spline'}, marker: {color: colr_b(4)}, type: 'scatter' }]; var layout = { width: width>700?670:width-30, height: 250, font: font, showlegend: true, margin: {t:5,b:50,l:50,r:60}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.15}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext:['Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep'], }, yaxis: { title: 'Rainfall (mm)', zeroline: false, showline: false, showgrid:false, }, yaxis2: { title: 'Flow (m\u00B3/s)', zeroline: false, showline: false, showgrid:false, overlaying: 'y', separatethousands:true, side: 'right' } }; const div=container(cap, width>700?690:width-10) plt.newPlot(div, data, layout,{displayModeBar: false}); return div }53:T52f,{ let cap = `Fig C2. World Bank, 1993 - Average monthly open-pan evaporation estimates for the Kariba reservoir.` var data = [{ y: kariba.evaporation_raw, x: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], text: kariba.evaporation_raw.map(month=>d3.format(',.0f')(month) + 'mm'), name:'World Bank, 1993', hoverinfo: 'x+text', marker: {color: colr_b(1,0.4)}, type: 'bar' },{ y: kariba.evaporation, x: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], text: kariba.evaporation.map(month=>d3.format(',.0f')(month) + 'mm'), name:'Scaled for this Study', hoverinfo: 'x+text', marker: {color: colr_b(4,0.4)}, type: 'bar' }]; var layout = { width: width>700?670:width-30, height: 200, font: font, showlegend: true, margin: {t:5,b:50,l:50,r:60}, legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.17}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext:['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'], }, yaxis: { title: 'Evaporation mm', zeroline: false, showline: false, showgrid:true, } }; const div=container(cap, width>700?690:width-10) plt.newPlot(div, data, layout,{displayModeBar: false}); return div }54:T826,{ let cap = `Fig C3. Annual open-pan evaporation estimates for the Kariba reservoir (lines) and total annual flow at Victoria Falls (bars).` var data = [ { y: vicfalls_yearly.filter(m=>m.water_year<2022).map(yr=>yr.volume), x: vicfalls_yearly.filter(m=>m.water_year<2022).map(yr=>yr.water_year), showlegend:true, name: 'Volume at Vic Falls', hoverinfo:'none', marker: {color: colr_b(1,0.4)}, yaxis: 'y2', type: 'bar' }, { y: evaporation_murisa.map(yr=>yr.evaporation/1000), x: evaporation_murisa.map(yr=>yr.year), showlegend:true, name: 'Evaporation: Ndebele-Murisa et al. 2011', hoverinfo:'none', marker: {color: colr(1,0.8)}, type: 'scatter' },{ y: kariba_yearly.map(yr=>yr.vol_evaporation/kariba.area*1000), x: kariba_yearly.map(yr=>yr.water_year), showlegend:true, mode:'lines', name: 'Evaporation: Assumed for model', hoverinfo:'none', line: {width: 3, shape: 'spline', color: colr(5,0.8)}, type: 'scatter' },{ y: [kariba.evaporation_annual_base/1000, kariba.evaporation_annual_base/1000], x: [1962,2021], showlegend:true, mode:'lines', name: 'Evaporation: World Bank 1993', hoverinfo:'none', line: {width: 3, shape: 'spline', color: 'grey'}, type: 'scatter' }]; var layout = { width: width>900?870:width-30, height:300, font: font, showlegend: true, margin: {t:5,b:50,l:70,r:70}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.16}, xaxis: { showgrid: false, range: [1962,2021], zeroline: false, ticks:'outside'}, yaxis: { title:'Evaporation
m per year',tickformat: ',.1f', zeroline: false,showgrid:'false', range:[0,3.9], showline: false}, yaxis2: { title:'Volume ' + cubickm, overlaying: 'y', showgrid: false, side: 'right',zeroline: false, tickformat: ',.0f', showline: false, } }; const div=container(cap ,width>900?890:width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }55:T50b,{ let cap = `Fig C4. Total evaporation per year from the Kariba reservoir used in the water balance.` var data = [{ y: [long_mean_evap,long_mean_evap], x: [1959,2021.5], showlegend:true, name: 'Longterm Mean ' + format_km3(long_mean_evap), hoverinfo:'none', mode: 'lines', line: {color: 'rgba(0,0,0,0.8)', width:1, dash:'dashdot'}, type: 'scatter' },{ y: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.vol_evaporation), x: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.water_year), text: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>format_km3(yr.vol_evaporation)), showlegend:true, name: 'Evaporation ', hoverinfo:'text', marker: {color: colr_b(1,0.3)}, type: 'bar' }]; var layout = { width: width-50, height:250, font: font, showlegend: true, margin: {t:5,b:40,l:70,r:10}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.14}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', }, yaxis: { title: cubickm, tickformat: ',.0f', range:[5,20], zeroline: false, showline: false}, }; const div=container(cap ,width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }56:T420,{ let cap = `Fig C5. Mean monthly open-pan evaporation estimate used for the Kariba reservoir.` var data = [{ y: kariba.evaporation, x: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], text: kariba.evaporation.map(month=>d3.format(',.0f')(month) + 'mm'), name:'Mean monthly evaporation', hoverinfo: 'x+text', marker: {color: colr_b(1,0.3)}, type: 'bar' }]; var layout = { width: width>700?670:width-30, height: 200, font: font, showlegend: true, margin: {t:5,b:50,l:50,r:60}, legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.17}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext:['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'], }, yaxis: { title: 'Evaporation mm', zeroline: false, showline: false, showgrid:true, } }; const div=container(cap, width>700?690:width-10) plt.newPlot(div, data, layout,{displayModeBar: false}); return div }57:T581,{ let cap = `Fig C6. Proportion of total inflow provided by the Lower Catchment.` let movingAv = movingAverage(kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.lowercatchment_prop), 5) var data = [{ y: movingAv, x: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.water_year), //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:true, name: '10 year moving average', hoverinfo:'x+y', line: {color: colr_b(5,0.8)}, type: 'scatter' },{ y: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.lowercatchment_prop), x: kariba_yearly.filter(m=>m.water_year<2022).map(yr=>yr.water_year), //text: per1.map(yr=>d3.format('.0f')(yr[parm]) + unit), showlegend:false, name: '% of inflow provided by lower catchment', hoverinfo:'x+y', marker: {color: colr_b(1,0.3)}, type: 'bar' }]; var layout = { width: width-50, height:230, font: font, showlegend: true, margin: {t:5,b:40,l:70,r:10}, barmode:'stack', legend: {orientation: 'h', x: 0, xanchor: 'left', y: -0.14}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', range:[1959, 2022]}, yaxis: { title: '', tickformat: ',.0%', zeroline: false, showline: false}, }; const div=container(cap ,width-10) plt.newPlot(div, data, layout, {displayModeBar: false}); return div }58:T602,kariba_yearly_initial = d3.rollups(kariba_monthly, v => { let ret = { lux_date:v[0].lux_date, start_date:v[0].start_date, water_year:v[0].water_year, water_year_name:v[0].water_year_name, level_min: d3.min(v,l=>l.level_min), level_max: d3.max(v,l=>l.level_max), level_start: v[0].level_start, level_end: v[v.length-1].level_end, store_level_start: v[0].store_level_start, store_level_end: v[v.length-1].store_level_end, store_start_pct: v[0].store_start_pct, store_end_pct: v[v.length-1].store_end_pct, store_min_pct: d3.min(v, l=>l.store_min_pct), store_max_pct: d3.max(v, l=>l.store_min_pct), vol_vicfalls: d3.sum(v,l => l.vol_vicfalls), vol_generation: d3.sum(v,l => l.vol_generation), vol_spill: d3.sum(v,l => l.vol_spill), vol_outflow: d3.sum(v,l => l.vol_outflow), vol_store_start: v[0].vol_store_start, vol_store_end: v[v.length-1].vol_store_end, capacity_mw: v[0].capacity_mw, energy_gwh: d3.sum(v,l => l.energy_gwh), energy_spill_gwh: d3.sum(v,l => l.energy_spill_gwh), } ret.store_level_change=ret.store_level_end - ret.store_level_start ret.store_pct_change=ret.store_end_pct - ret.store_start_pct ret.vol_store_change = ret.vol_store_end - ret.vol_store_start ret.capacity_factor = (ret.energy_gwh * 1000) / (ret.capacity_mw * 24 * 365) ret.vol_spill_pct = ret.vol_spill / kariba.storage return ret }, v=>v.water_year).map(v=>v[1])59:T720,kariba_monthly = kariba_monthly_raw.map(v => { v.lux_date=DateTime.fromObject({year: v.year, month: v.month, day: 15}) // VOLUMES // Upper catchment volume v.vol_vicfalls = vicfalls_monthly.filter(m=>m.year==v.year && m.month==v.month)[0].volume/1000 // volume at vicfalls km3 // Generation volume v.vol_generation = v.outflow_generation * 60 * 60 * 24 * month_days(v.month) / 1000000000 // Spill volume v.vol_spill = v.outflow_spill * 60 * 60 * 24 * month_days(v.month) / 1000000000 // Total outflow sum of generation and spill v.vol_outflow = v.vol_spill + v.vol_generation //LEVELS // Start of month v.store_level_start = v.level_start-kariba.mol //m v.vol_store_start = v.store_level_start / kariba.drawdown_rate //km3 v.store_start_pct = v.store_level_start / (kariba.fsl-kariba.mol) // End of month v.store_level_end = v.level_end-kariba.mol v.vol_store_end = v.store_level_end / kariba.drawdown_rate //km3 v.store_end_pct = v.store_level_end / (kariba.fsl-kariba.mol) // Change in level v.store_level_change = v.store_level_end - v.store_level_start //m v.vol_store_change = v.vol_store_end - v.vol_store_start //km3 //Max & Min v.store_level_min = v.level_min - kariba.mol v.store_level_max = v.level_max - kariba.mol v.store_min_pct = v.store_level_min / (kariba.fsl-kariba.mol) v.store_max_pct = v.store_level_max / (kariba.fsl-kariba.mol) // POWER AND ENERGY v.energy_gwh = v.vol_generation * kariba.yield // GWh v.energy_spill_gwh = v.vol_spill * kariba.yield // GWh v.power_mw = v.energy_gwh * 1000 / (24 * month_days(v.month)) //MW v.capacity_mw = kariba.capacities.filter(c=> (v.year >= c.from && v.year <= c.to))[0].total // MW v.capacity_factor = (v.energy_gwh * 1000 ) / (v.capacity_mw * 24 * month_days(v.month) ) return v 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2e:{"title":"Kariba Dam - Hydrology and Power Generation","cells":[{"id":4527,"value":"

Kariba Dam - Hydrology and Power Generation

","pinned":false,"hidden":false,"mode":"html"},{"id":4827,"value":"

${i+1}. ${h}

${i+1+8}. ${h}

","pinned":false,"hidden":false,"mode":"html"},{"id":1154,"value":"html`\n

Summary

\nSince its completion in 1959 the Kariba dam on the Zambezi River between Zambia and Zimbabwe has created the world's largest reservoir by volume, 280 km long and 32 km at its widest point covering ${d3.format(',.0f')(kariba.area)} km\\u00b2 and with a total volume of water when full of 185 ${cubickm} and live storage of 65${cubickm}.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4975,"value":"$30","pinned":false,"hidden":false,"mode":"html"},{"id":1509,"value":"{\nconst div = container('Fig 1. The Kariba reservoir on the Zambezi river between Zambia and Zimbabwe.',width>700?690:width-10,)\ndiv.prepend(html`500?670:width-30}\" src=\"${await FileAttachment(\"kariba@1.jpg\").url()}\">`)\nreturn div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4553,"value":"head(1)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1257,"value":"html`\n\nThe available storage volume in the Kariba reservoir is ${d3.format(',.0f')(kariba.storage) + cubickm} over a reservoir level range of ${kariba.fsl-kariba.mol}m and with a mean generating head of around ${kariba.head}m produces an energy yield of approximately ${d3.format(',.2r')(kariba.yield)} GWh/${cubickm} of water.\n

\nThe average inflow to the dam each year is ${format_km3(inflow_stats.mean)} and after evaporation from the reservoir equivalent to ${format_km3(long_mean_evap)} the annual energy potential from inflow is ${d3.format(',.0f')(inflow_potential_stats.mean)}TWh of electricity. The reservoir has a live storage capacity of ${d3.format(',.1f')(kariba.storage/inflow_stats.mean)} x Mean Annual Runoff (MAR).\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1479,"value":"html`The Kariba dam has a catchment area of around ${d3.format(',.0f')(700000)} km\\u00b2 stretching from 11\\u00B0S to 20\\u00B0S and 19\\u00B0E to 31\\u00B0E and covering large areas in Angola, Zambia and Zimbabwe and smaller parts of Namibia and Botswana. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":3250,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1503,"value":"head(2)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3186,"value":"html`\nKariba initially had an installed electricity generation capacity of 666MW on the south bank of the river - now Zimbabwe. In 1977 with increased demand for electricity, a further 600MW was commissioned on the north bank in what is now Zambia brining the total capacity to 1,266MW.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1133,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1186,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":4569,"value":"head(3)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1047,"value":"{\n const div=container(`Fig 3. The catchment of the Kariba dam on the Zambezi River around ${d3.format(',.0f')(d3.sum(catchments.children, v=>v.area))} km\\u00b2.`,width-10)\n const frame=html`\n `\n\n div.prepend(frame)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1551,"value":"html`\nZambia makes up the largest proportion of the catchment by land area at 37% compared to 35% for Angola and 23% for Zimbabwe.\n

\nThe catchment is often considered in two parts, above and below the gauging station at Victoria Falls (Livingstone in Zambia). The larger, Upper catchment, is 525,000km\\u00b2 in extent (74%) and the Lower catchment 182,000km\\u00b2 (26%). The Upper catchment is predominantly in Angola and Zambia while the majority of the Lower catchment is in Zimbabwe. \n

\nBecause total annual rainfall increases in the north of the catchment, a higher proportion of the rainfall runoff is due to the Upper catchment and Zambia and Angola than the Lower catchment and Zimbabwe (Fig 4). See: ${headlink(10)}.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1575,"value":"{\n const div = container('Fig 4. Breakdowns by sub-catchment and country of the catchment land area and rainfall runoff for the Kariba dam catchment.',width-10)\n const fig = html`\n

Sub-catchments Area

\n ${build_tree(catchments_main, 'area','area_pct','km\\u00B2')}\n

Countries Area

\n ${build_tree(catchments, 'area','area_pct','km\\u00B2')}\n

Sub-catchments Rainfall

\n ${build_tree(catchments_main, 'runoff','runoff_pct','Mm\\u00B3')}\n

Countries Rainfall

\n ${build_tree(catchments, 'runoff','runoff_pct','Mm\\u00B3')}\n

\n `;\n div.prepend(fig)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3351,"value":"html`There has been a gauge at Victoria Falls (Livingstone in Zambia) measuring flow in the Zambezi River from the upper catchment since 1924 (Fig 5). The longterm mean annual volume of water passing Victoria Falls is ${format_km3(vol_vicfalls_mean)} per year (${d3.format(',.0f')(vol_vicfalls_mean)} billion cubic meters).`","pinned":false,"hidden":false,"mode":"ojs"},{"id":71,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":2284,"value":"The annual runoff from the upper catchment (Fig 5.) shows a cyclic trend with a long period of at least 50 years. A period of above average flows from the mid 1940s to around 1980 was followed by a period of below average flows up to the early 2000s. To date there is no evidence of any systematic change in flows due to climate change. Climate change may well have already begun to have some impact but it is masked by the natural cyclic variability evident in the long term record (see below). \n

\nThe most recent five years - corresponding to the period with the highest installed generating capacity of 2,130MW - has seen above average total flow at Victoria Falls. \n","pinned":false,"hidden":false,"mode":"html"},{"id":2255,"value":"head(4)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2759,"value":"html`\nThe amount of electricity that can be generated at Kariba each year depends almost entirely on the volume of water entering the dam in the previous year. (See ${headlink(11)}). Despite the very large absolute reservoir live storage volume of ${format_km3(kariba.storage)}, with a mean annual inflow of ${format_km3(inflow_stats.mean)} the storage ratio of ${d3.format(',.1f')(kariba.storage/inflow_stats.mean)}xMAR is relatively low. This means there is relatively little storage capacity available to capture extra flows in flood years to be carried over to drought years.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":864,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1731,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":2566,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":937,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":2943,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":3418,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":3433,"value":"head(5)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3439,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":3563,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":3554,"value":"$3d","pinned":false,"hidden":false,"mode":"html"},{"id":1013,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":3627,"value":"Energy production greater than that supported by mean annual inflow between 2009 and 2014 and greater than the annual water inflow from 2011 to 2015 resulted in the reservoir level reaching one of the lowest ever levels of 13% by the end of 2015 (Fig 12 and 13). ","pinned":false,"hidden":false,"mode":"html"},{"id":523,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"id":3649,"value":"$40","pinned":false,"hidden":false,"mode":"html"},{"id":3700,"value":"$41","pinned":false,"hidden":false,"mode":"ojs"},{"id":4970,"value":"By December 2022 the reservoir storage level had fallen to 1% and it was necessary to all but shut off generation. Had power output been maintained at the baseload level of around 1,020MW from 2019 through to 2021 then the reservoir level would have ended 2022 at 32% full and there would have been no need for an emergency shutdown and consequent load-shedding in Zambia and Zimbabwe may have been avoided (Fig 14).","pinned":false,"hidden":false,"mode":"html"},{"id":4956,"value":"$42","pinned":false,"hidden":false,"mode":"ojs"},{"id":3731,"value":"Operating the reservoir in this way from 2019 would have reduced the power generation from Kariba by a total of ${format_gwh(recent_model_end.energy_avoided_cum)} over the 22 month period.\n

\nThis lost energy could have been provided by ${d3.format(',.2r')(recent_avoid_mw/solar_cf)}MW of solar PV, ${d3.format(',.2r')(recent_avoid_mw/wind_cf)}MW of wind power or, most efficiently, a combination of ${d3.format(',.2r')(recent_avoid_mw/combined_cf/4)}MW of solar pv and ${d3.format(',.2r')(recent_avoid_mw/combined_cf/4*3)}MW of wind power (see: ${headlink(7)}).","pinned":false,"hidden":false,"mode":"html"},{"id":395,"value":"$43","pinned":false,"hidden":false,"mode":"ojs"},{"id":3741,"value":"The installation of additional peaking capacity at the Kariba power stations has resulted in the overall Capacity Factor for the plants falling from an average of 70% to 50% over the last 7 years (Fig 15).","pinned":false,"hidden":false,"mode":"html"},{"id":3425,"value":"head(6)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4511,"value":"For a fuller analysis of climate change and climate variability impacts on the upper Zambezi see this analysis for the proposed Ngonye Falls power station.","pinned":false,"hidden":false,"mode":"html"},{"id":4417,"value":"Like all hydroelectric schemes, the Kariba power stations are susceptible to the impact of climate change and climate variability. For schemes with storage like Kariba there are two interrelated but separate mechanisms for this impact; changes in the volume of water entering the reservoir (inflow) and changes to the rate of evaporation.\n","pinned":false,"hidden":false,"mode":"html"},{"id":4515,"value":"{\nconst div = container('Fig 16a. Relative flow volume from 1945 for 9 African rivers.',width>600?590:width-10,)\ndiv.prepend(html`600?570:width-30}\" src=\"${await FileAttachment(\"africarivers.jpg\").url()}\">`)\nreturn div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4522,"value":"Climate variability refers to natural, long-term cycles in regional climate that impact river flow and reservoir water balance. The Zambezi river, along with many major rivers in Africa (Fig 16a), shows such a long term cycle in annual flow. These cycles with periods of above and below average annual flow (Fig 16b.) have a variable period of between 50 and 70 years. Their cause is unknown but may be driven by long term solar cycles.","pinned":false,"hidden":false,"mode":"html"},{"id":4411,"value":"$44","pinned":false,"hidden":false,"mode":"ojs"},{"id":4429,"value":"Climate change is the long-term change in regional and global climate variables causing changes in regional and local weather patterns. There is scientific concensus that human-induced (anthropogenic) global warming due to increasing greenhouse gas concentrations in the atmosphere is causing global climate change.\n

\nAll hydropower stations will be impacted by anthropogenic climate change in the coming decades to some extent. Storage schemes like Kariba are likely to see the greatest impact as increases in temperature lead to higher rates of evaporation from large reservoirs. Historically, mean average evaporation from Kariba has averaged ${format_km3(long_mean_evap)} per year from a mean annual inflow of ${format_km3(inflow_stats.mean)} - ${d3.format(',.0%')(long_mean_evap/inflow_stats.mean)}. This water, lost to evaporation, could generate around ${format_twh(long_mean_evap*kariba.yield/1000)} of electricty per year.","pinned":false,"hidden":false,"mode":"html"},{"id":4408,"value":"$45","pinned":false,"hidden":false,"mode":"ojs"},{"id":4443,"value":"$46","pinned":false,"hidden":false,"mode":"html"},{"id":3816,"value":"head(7)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3877,"value":"$47","pinned":false,"hidden":false,"mode":"html"},{"id":3910,"value":"$48","pinned":false,"hidden":false,"mode":"ojs"},{"id":3945,"value":"Output from wind, solar and run-of-river power plants is also variable and cyclic. For wind and solar plants their power output varies significantly in daily (diurnal) cycles and, less significantly, seasonal cycles. Solar plants can only produce power during the day (Fig 19.) and onshore wind in Zambia and Zimbabwe also has a daily cyclic pattern, although not as pronounced, with higher output on average at night (Fig 20).","pinned":false,"hidden":false,"mode":"html"},{"id":3836,"value":"$49","pinned":false,"hidden":false,"mode":"ojs"},{"id":4103,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs"},{"id":3996,"value":"Power output from run-of-river hydroelectric plants (with no storage) in Zambia and Zimbabwe is seasonally cyclic following the seasonal pattern of rainfall and river flows (Fig 21).","pinned":false,"hidden":false,"mode":"html"},{"id":3961,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs"},{"id":3957,"value":"$4c","pinned":false,"hidden":false,"mode":"html"},{"id":4204,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs"},{"id":4231,"value":"Analysis has shown that combining wind power and solar power plants in Zambia in the ratio 3 to 1 by MW of installed capacity gives the most constant (firmest) daily combined power output (Fig 22). Combining wind and solar in this way would allow Kariba (and other storage hydro power plants) to deliver many 1,000s of MWs of reliable, firm power.\n

\nUsing Kariba to provide smoothing and balancing services for variable, intermittent renewables such as wind and solar power does not mean that these other power stations need to actually be located at Kariba. These plants can and should be located wherever there is the best solar resources or strongest wind speeds as well as the best grid connections. Balancing and smoothing 'services' can travel over the grid from Kariba to where they are needed.","pinned":false,"hidden":false,"mode":"html"},{"id":4589,"value":"head(8)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4249,"value":"$4e","pinned":false,"hidden":false,"mode":"html"},{"id":4593,"value":"head(9)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3616,"value":"$4f","pinned":false,"hidden":false,"mode":"html"},{"id":3210,"value":"head(10)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3310,"value":"html`\nMean annual rainfall increases with decreasing latitude from less than 600mm per year in Bulawayo in the south of the catchment and 700mm in Livingstone (Victoria Falls) to more than 1,350mm in Mwinilunga in northern Zambia (Fig B1).\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1105,"value":"$50","pinned":false,"hidden":false,"mode":"ojs"},{"id":1301,"value":"$51","pinned":false,"hidden":false,"mode":"ojs"},{"id":1369,"value":"html`\nThe annual rainfall pattern is similar across the catchment with a 'rainy season' starting in October - November, peaking in December - January and ending in March - April. In the higher rainfall areas of the northern catchment the rainy season starts earlier and lasts longer than in the drier, southern, catchment (Fig B2).\n

\nIn the north of the catchment there is a distinct double-peak in the rainfall pattern with a first peak in December and a second in March. This pattern is also recorded in river flows in the north of the catchment - upstream of the attenuation provided by the Barotse Floodplain.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3231,"value":"{\n const div = container('Fig B3. Breakdowns by sub-catchment and country of mean annual rainfall volume for the Kariba dam catchment.')\n const fig = html`\n

Sub-catchments Rainfall

\n ${build_tree(catchments_main, 'runoff','runoff_pct','Mm\\u00B3')}\n

Countries Rainfall

\n ${build_tree(catchments, 'runoff','runoff_pct','Mm\\u00B3')}\n

\n `;\n div.prepend(fig)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1407,"value":"html`\nThe breakdown of mean annual rainfall on the Upper and Lower catchments and on the catchments of the countries comprising the basin are similar to the breakdown in land areas. However, because mean annual rainfall increases travelling north up the catchment, the Upper and Angolan catchments have a moderately higher proportion of the rainfall compared to their land areas.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1377,"value":"$52","pinned":false,"hidden":false,"mode":"ojs"},{"id":1439,"value":"html`\nThe seasonal pattern of rainfall across the basin leads to a similar annual pattern of flood and drought flow in the river.\n\nHowever, the attenuating effect of the Barotse Floodplain, the vast area of seasonal wetlands which extends more than 400km across Zambia's Western Province north from the Ngonye Falls to the border with Angola, means that the flood peak of the river is 3 months or more later than the peak rainfall (Fig B4).\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4803,"value":"head(11)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2497,"value":"The water balance model relies on identifying all of the inputs and possible outputs of water from the reservoir over a given period and balancing these with changes in the measured lake level and, therefore, water storage (Fig C1).","pinned":false,"hidden":false,"mode":"html"},{"id":1704,"value":"{\nconst div = container('Fig C1. Reservoir water balance. Inflow is balanced by the change is storage volume, evaporation and outflow. Outflow is the sum of power generation flows and spill flows. Bank flow has the impact of increasing the effective size of the reservoir.',width>800?790:width-10,)\ndiv.prepend(html`800?770:width-30}\" src=\"${await FileAttachment(\"Balance@2.png\").url()}\">`)\nreturn div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2117,"value":"Not all the sources of inflow for the Kariba reservoir are gauged to allow direct measurement of their flow and even the Zambezi itself has no gauge downstream of Livingstone (Victoria Falls) which is more than 150km upstream of the lake. To calculate the inflow into the the reservoir a water balance equation is used to balance inflow, outflow and lake level (a measure of the volume of water stored in the reservoir).","pinned":false,"hidden":false,"mode":"html"},{"id":2119,"value":"md`\n ${tex` \n Change In Stored Volume = Inflow - ( Evaporation + Generation Flow + Spill Flow\\,{\\displaystyle \\pm }\\,Bank Flow )\n `}\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2131,"value":"The water balance can be completed for any time period where corresponding level and flow data are available. Bank flow, which is flow of water into and out of sediment and rocks surrounding the reservoir, changes the effective storage volume of the reservoir and can be a significant factor over shorter time periods but will tend to cancel itself out over longer time periods.

\nThe water balance allows calculation of the flow from the lower catchment (below Victoria Falls) - and therefore the total reservoir inflow - using records of the reservoir level (for storage volume), generation and spill flows, flow at Victoria Falls and evaporation. ","pinned":false,"hidden":false,"mode":"html"},{"id":2134,"value":"md`\n ${tex` \n LowerCatchmentFlow = ChangeInStoredVolume + Evaporation + Generation Flow + Spill Flow - VictoriaFallsFlow\n `}\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2157,"value":"The water balance, and calculation of inflow, requires an estimate for evaporation. Due to the balance equation, underestimating evaporation for a given period will tend to underestimate the flow coming from the lower catchment. Calculating the balance over monthly time periods is also susceptible to errors caused by differences in the timing of higher and lower rates of evaporation and bank flow.","pinned":false,"hidden":false,"mode":"html"},{"id":1646,"value":"$53","pinned":false,"hidden":false,"mode":"ojs"},{"id":2208,"value":"html`The World Bank (1993) estimated average annual evaporation from the reservoir as 2,000mm (Fig C2.) Ndebele-Murisa et al. (2011), in their study of commercial fishing yields, presented a chart of annual evaporation measured between 1963 and 1998 at the Kariba meteorology station in Zimbabwe (Fig C3).\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1850,"value":"$54","pinned":false,"hidden":false,"mode":"ojs"},{"id":2364,"value":"html`\nFor the current model an average annual evaporation rate of ${d3.format(',.0f')(kariba.evaporation_annual)}mm has been used with the same annual distribution as that given in the World Bank study.

\nIt is also apparent from the Ndebele-Murisa et al. data that the total annual evaporation is inversely proportional to total volume of water passing Victoria Falls (Fig C3). This can be explained by generally wetter and therefore cooler and cloudier years having lower rates of evaporation. For this model therefore, evaporation for each year has been scaled from a mean of ${d3.format(',.0f')(kariba.evaporation_annual)}mm to have 50% of the relative variation from the mean of the volume at Victoria Falls (Fig C4). \n

\nIt would be preferable to use actual measured annual evaporation if this was available. \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2680,"value":"$55","pinned":false,"hidden":false,"mode":"ojs"},{"id":2448,"value":"$56","pinned":false,"hidden":false,"mode":"ojs"},{"id":2470,"value":"html `\nFig C5. shows the mean monthly evaporation from the Kariba reservoir used in this model. Mean total annual evaporation in the model is ${d3.format(',.0f')(kariba.evaporation_annual)}mm and ranges from ${d3.format(',.0f')(evaporation_range.depth_min)}mm to ${d3.format(',.0f')(evaporation_range.depth_max)}mm. Over the reservoir area of ${d3.format(',.0f')(kariba.area)}km\\u00b2 this equates to between ${format_km3(evaporation_range.vol_min)} and ${format_km3(evaporation_range.vol_max)} of water per year (${d3.format(',.0%')(evaporation_range.vol_min/inflow_stats.mean)} to ${d3.format(',.0%')(evaporation_range.vol_max/inflow_stats.mean)} of mean annual inflow).\n\nEvaporation equates to between ${format_gwh(evaporation_range.energy_min)} and ${format_gwh(evaporation_range.energy_max)} of lost energy potential each year.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":279,"value":"$57","pinned":false,"hidden":false,"mode":"ojs"},{"id":4808,"value":"head(12)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2160,"value":"

References

\nNdebele-Murisa et al. 2011: Mzime Regina Ndebele-Murisa, Emmanuel Mashonjowa & Trevor Hill (2011) The implications of a\nchanging climate on the Kapenta fish stocks of Lake Kariba, Zimbabwe, Transactions of the Royal Society of South Africa,\n66:2, 105-119, DOI: 10.1080/0035919X.2011.600352\n

\nWorld Bank 1993: Joint UNDP/World Bank Energy Sector Management Assistance Programme (ESMAP). Regional Generation and Transmission Capacities including Interregional Pricing Policies. Phase II. Volume 4 Hydrology Report. 1993.\n

Sources

\nAll of the data for this study were kindly provided by the Zambezi River Authority (ZRA).","pinned":false,"hidden":false,"mode":"html"},{"id":49,"value":"

\n

Data & Models

","pinned":false,"hidden":false,"mode":"html"},{"id":5034,"value":"

Headings

","pinned":false,"hidden":false,"mode":"html"},{"id":4824,"value":"{\n const isLocalLink = a => a instanceof HTMLAnchorElement && a.getAttribute('href').match(/^#/);\n const scrollTo = e => {\n let l = e.target, t;\n console.log(l)\n if(isLocalLink(l) && (t = document.querySelector(`[name=\"${l.hash.slice(1)}\"]`))) {\n e.preventDefault();\n t.scrollIntoView();\n }\n };\n document.addEventListener('click', scrollTo);\n invalidation.then(() => document.removeEventListener('click', scrollTo));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4859,"value":"headlink = function (no) {\n return html`${no}. ${heads[no-1]}`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4556,"value":"head = function (no) {\n return html`

${no}. ${heads[no-1]}`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4549,"value":"heads = [\n 'Introduction',\n 'Generation Capacity',\n 'The Catchment',\n 'Inflow and Energy Potential',\n 'Inflow, Generation and Reservoir Levels',\n 'Climate Change and Climate Variability',\n 'Supporting Variable, Intermittent Renewables',\n 'Storage Value of Variable, Intermittent Renewables',\n 'Appendix A - Availability Factor and Capacity Factor',\n 'Appendix B - Catchment Rainfall and Runoff',\n 'Appendix C - Water Balance and Evaporation Estimate',\n 'References and Sources',\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":5038,"value":"

Kariba

","pinned":false,"hidden":false,"mode":"html"},{"id":3030,"value":"model=kariba_monthly.filter(v=>v.water_year < 2022 && v.water_year > 2008).map(m=>{\n \n return m\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":4899,"value":"recent_avoid_mw=recent_model_end.energy_avoided_cum/22*12*1000/(24*365)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4888,"value":"recent_model_end = recent_model[recent_model.length-1]","pinned":false,"hidden":false,"mode":"ojs"},{"id":3666,"value":"recent_model = {\n\n let out = []\n let vol_cum = 0\n let energy_cum = 0\n let curtail = 1024\n \n kariba_monthly.filter(m=>m.lux_date.toJSDate()>new Date('2021-03-01')).forEach(m=>{\n let ret = {\n lux_date: m.lux_date,\n year: m.year,\n month: m.month,\n power_mw: curtail,\n power_avoided: d3.max([0,m.power_mw - curtail]),\n }\n ret.energy_avoided = ret.power_avoided * 24 * month_days(m.month) / 1000\n ret.vol_avoided = ret.energy_avoided / kariba.yield\n vol_cum += ret.vol_avoided\n energy_cum += ret.energy_avoided\n ret.vol_avoided_cum = vol_cum\n ret.energy_avoided_cum = energy_cum\n ret.vol_store_end_old = m.vol_store_end\n ret.vol_store_end_new = d3.min([kariba.storage,m.vol_store_end + vol_cum])\n ret.store_end_pct_new = ret.vol_store_end_new / kariba.storage\n ret.store_end_pct_old = m.store_end_pct\n ret.capacity_factor = curtail / 2130\n out.push(ret)\n})\n return out\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2971,"value":"capColor='#CD5C5C'","pinned":false,"hidden":false,"mode":"ojs"},{"id":2960,"value":"availability = (8000/(24*365))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3881,"value":"long_mean_evap = d3.mean(kariba_yearly, v=>v.vol_evaporation)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2292,"value":"evaporation_murisa_mean = d3.mean(evaporation_murisa, v=>v.evaporation)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1843,"value":"evaporation_murisa = evaporation_murisa_raw.map(v => { return {\n year: v[0],\n evaporation: v[1]*365\n} })","pinned":false,"hidden":false,"mode":"ojs"},{"id":2514,"value":"evaporation_range = {\n return {\n vol_max: d3.max(kariba_yearly,v=>v.vol_evaporation),\n vol_min: d3.min(kariba_yearly,v=>v.vol_evaporation),\n depth_min: d3.min(kariba_yearly,v=>v.vol_evaporation)/kariba.area*1000000,\n depth_max: d3.max(kariba_yearly,v=>v.vol_evaporation)/kariba.area*1000000,\n energy_max: d3.max(kariba_yearly,v=>v.vol_evaporation)*kariba.yield,\n energy_min: d3.min(kariba_yearly,v=>v.vol_evaporation)*kariba.yield, \n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1837,"value":"evaporation_murisa_raw=[\n[1963,\t6.4],\n[1964,\t5.6],\n[1965,\t6.7],\n[1966,\t6.9],\n[1967,\t6.6],\n[1968,\t7.3],\n[1969,\t5.9],\n[1970,\t6.9],\n[1971,\t6.6],\n[1972,\t5],\n[1973,\t6.8],\n[1974,\t5.8],\n[1975,\t6.8],\n[1976,\t5.5],\n[1977,\t6.5],\n[1978,\t5.5],\n[1979,\t5.3],\n[1980,\t5.8],\n[1981,\t5.7],\n[1982,\t6.5],\n[1983,\t7.7],\n[1984,\t7.6],\n[1985,\t6.7],\n[1986,\t6.5],\n[1987,\t8.4],\n[1988,\t7.5],\n[1989,\t7.1],\n[1990,\t7.3],\n[1991,\t7.5],\n[1992,\t8.4],\n[1993,\t7.4],\n[1994,\t8.1],\n[1995,\t8.8],\n[1996,\t7.1],\n[1997,\t6.7],\n[1998,\t6.4]]","pinned":false,"hidden":false,"mode":"ojs"},{"id":843,"value":"kariba_calmonthly = d3.rollups(kariba_monthly_balance,v=>{\n return {\n month: v[0].month,\n water_month: v[0].water_month,\n vol_inflow: d3.mean(v, m=>m.vol_inflow)\n }\n},v=>v.month).map(v=>v[1]).sort((a,b)=>a.water_month-b.water_month)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2370,"value":"kariba_monthly_balance = kariba_monthly.map(m => {\n const ret = {\n lux_date: m.lux_date,\n year: m.year,\n month: m.month,\n water_year: m.water_year,\n water_month: m.water_month\n }\n ret.vol_evaporation_base = kariba.evaporation[m.month-1]*evaporation_monthly_prop[m.month-1]*kariba.area/1000000\n ret.vol_vicfalls_prop = kariba_yearly.filter(m=>m.water_year===m.water_year)[0].vol_vicfalls_prop\n ret.vol_evaporation = ret.vol_evaporation_base - (ret.vol_evaporation_base * ret.vol_vicfalls_prop)\n \n ret.vol_inflow = (m.vol_outflow + m.vol_store_change + ret.vol_evaporation)\n return ret\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2588,"value":"inflow_stats = {\n return {\n min: d3.min(kariba_yearly, v=>v.vol_inflow),\n max: d3.max(kariba_yearly, v=>v.vol_inflow),\n mean: d3.mean(kariba_yearly, v=>v.vol_inflow),\n recent_mean: d3.mean(kariba_yearly.filter(v=>v.water_year>2009-1), v=>v.vol_inflow),\n median: d3.median(kariba_yearly, v=>v.vol_inflow),\n sd: d3.deviation(kariba_yearly, v=>v.vol_inflow),\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4076,"value":"inflow_potential_power = inflow_potential_stats.mean*1000000/(24*365)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2927,"value":"inflow_potential_stats = {\n let yrs=kariba_yearly.filter(v=>v.water_year<2022)\n return {\n min: d3.min(yrs, v=>v.inflow_energy_potential),\n max: d3.max(kariba_yearly, v=>v.inflow_energy_potential),\n mean: d3.mean(kariba_yearly, v=>v.inflow_energy_potential),\n median: d3.median(kariba_yearly, v=>v.inflow_energy_potential),\n sd: d3.deviation(kariba_yearly, v=>v.inflow_energy_potential),\n p90: d3.quantile(kariba_yearly.map(v=>v.inflow_energy_potential),0.1),\n p10: d3.quantile(kariba_yearly.map(v=>v.inflow_energy_potential),0.9),\n }\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":2314,"value":"vol_vicfalls_mean = d3.mean(vicfalls_yearly,v=>v.volume)","pinned":false,"hidden":false,"mode":"ojs"},{"id":927,"value":"lowercatchment_prop = d3.mean(kariba_yearly, v=>v.lowercatchment_prop)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2367,"value":"evaporation_monthly_prop=kariba.evaporation.map(v=>v/evaporation_murisa_mean)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2038,"value":"inflow_longterm = vicfalls_yearly.map(v => {\n let ret = {}\n ret.water_year = v.water_year\n let kar = kariba_yearly.filter(m => m.water_year == v.water_year)[0]\n if (kar) {\n ret.estimate=false\n ret.vol_inflow = kar.vol_inflow\n ret.vol_lowercatchment = kar.vol_lowercatchment\n ret.vol_vicfalls = kar.vol_vicfalls_volume\n } else {\n ret.estimate=true\n ret.vol_vicfalls = v.volume\n ret.vol_inflow = v.volume / (1 - lowercatchment_prop)\n ret.vol_lowercatchment = ret.vol_inflow-ret.vol_vicfalls\n }\n let evap = kariba.area*kariba.evaporation_annual/1000000 //km3\n ret.inflow_energy_potential = (ret.vol_inflow - evap) * kariba.yield / 1000\n return ret\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2307,"value":"kariba_yearly = kariba_yearly_initial.map(v => {\n\n // BALANCE\n v.vol_vicfalls_prop = (v.vol_vicfalls-vol_vicfalls_mean)/vol_vicfalls_mean*0.5\n v.vol_evaporation = (kariba.evaporation_annual - (kariba.evaporation_annual * v.vol_vicfalls_prop))*kariba.area/1000000\n v.vol_inflow = (v.vol_outflow + v.vol_store_change + v.vol_evaporation)\n v.vol_lowercatchment = v.vol_inflow-v.vol_vicfalls\n v.lowercatchment_prop = (v.vol_inflow-v.vol_vicfalls) / v.vol_inflow \n \n v.inflow_energy_gwh = d3.max([0,d3.min([ v.energy_gwh, (v.vol_inflow - v.vol_evaporation) * kariba.yield ])])\n v.drawdown_energy_gwh = v.energy_gwh - v.inflow_energy_gwh\n v.inflow_energy_pct = v.inflow_energy_gwh / v.energy_gwh\n v.drawdown_energy_pct = v.drawdown_energy_gwh / v.energy_gwh\n v.inflow_energy_potential = (v.vol_inflow - v.vol_evaporation) * kariba.yield / 1000\n \n return v\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2006,"value":"$58","pinned":false,"hidden":false,"mode":"ojs"},{"id":124,"value":"$59","pinned":false,"hidden":false,"mode":"ojs"},{"id":739,"value":"kariba_daily=kariba_daily_raw.map(v=>{\n \n let drawdown_rate = (kariba.fsl-kariba.mol)/kariba.storage // m per km3\n \n v.store_level=v.level-kariba.mol\n v.store_pct = v.store_level / (kariba.fsl-kariba.mol)\n v.lux_date = DateTime.fromFormat(v.date,'dd/MM/yyyy')\n return v\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1382,"value":"vicfalls_calmonthly = d3.rollups(vicfalls_monthly,v=>{\n return {\n month: v[0].month,\n water_month: v[0].water_month,\n flow: d3.mean(v, m=>m.flow)\n }\n},v=>v.month).map(v=>v[1]).sort((a,b)=>a.water_month-b.water_month)","pinned":false,"hidden":false,"mode":"ojs"},{"id":56,"value":"vicfalls_yearly = d3.rollups(vicfalls_monthly, v => {\n return {\n water_year: v[0].water_year,\n volume: d3.sum(v, l=>l.volume)/1000\n }\n}, v=>v.water_year).map(v=>v[1]).filter(v=>v.water_year<2022)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1759,"value":"water_years = d3.rollups(kariba_monthly_raw,v=>v[0].water_year,v=>v.water_year).map(v=>v[0])","pinned":false,"hidden":false,"mode":"ojs"},{"id":335,"value":"capacities = {\n let ret = [{\n year: 1960,\n north: 0,\n south: 0,\n capacity: 0,\n label: ''\n }]\n\n kariba.capacities.map(c=>{\n ret.push({\n year: c.from,\n north: c.north,\n south: c.south,\n capacity: c.total,\n label: d3.format(',.0f')(c.total) + ' MW'\n }) \n ret.push({\n year: c.to+1,\n north: c.north,\n south: c.south,\n capacity: c.total\n }) \n })\n \n return ret.map(v=>{\n v.energy_potential = v.capacity * 24 * 365 / 1000000\n return v\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":776,"value":"kariba = {\n kariba_raw.power_yield=kariba_raw.head*kariba_raw.system_efficiency*9.81/1000 //MW per cumec\n kariba_raw.yield = kariba_raw.power_yield/60/60*1000000 //GWh/km3\n kariba_raw.drawdown_rate = (kariba_raw.fsl - kariba_raw.mol) / kariba_raw.storage // m per km3\n kariba_raw.capacities.map(c=>{\n c.consumption = c.total / kariba_raw.power_yield /1000000000 * 60 *60 * 24 //km3 per day\n c.store_days = kariba_raw.storage / c.consumption //days\n })\n kariba_raw.evaporation_annual_base = d3.sum(kariba_raw.evaporation_raw) //mm\n kariba_raw.evaporation = kariba_raw.evaporation_raw.map(v=>v*(evaporation_murisa_mean/kariba_raw.evaporation_annual_base)) //mm\n kariba_raw.evaporation_annual = d3.sum(kariba_raw.evaporation) //mm\n return kariba_raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":85,"value":"kariba_raw = { return {\narea: 5400, //km2\nstorage: 65, //Bm3 or km3\nfsl: 488.5, //m\nmol: 475.5, //m\nhead:95, //m\nsystem_efficiency:0.9, //%\n//yield: 246, //GWh/km3\n//power_yield: 0.886, //MW per cumec\nevaporation_raw: [\n 188,\n 161,\n 184,\n 159,\n 130,\n 105,\n 112,\n 146,\n 189,\n 235,\n 204,\n 187\n], //mm per month\ncapacities: [\n {\n from: 1960,\n to: 1976,\n north: 0, //MW\n south: 666, //MW\n total: 666\n },\n {\n from: 1977,\n to: 2008,\n north: 600,\n south: 666,\n total: 1266\n },\n {\n from: 2009,\n to: 2013,\n north: 720,\n south: 750,\n total: 1470\n },\n {\n from: 2014,\n to: 2017,\n north: 1080,\n south: 750,\n total: 1830\n },\n {\n from: 2018,\n to: 2022,\n north: 1080,\n south: 1050,\n total: 2130\n }\n ]\n} }","pinned":false,"hidden":false,"mode":"ojs"},{"id":1109,"value":"\nrainStationDict = rainStations.reduce(function(map, obj) {\n map[obj.Station] = obj;\n return map;\n}, {});","pinned":false,"hidden":false,"mode":"ojs"},{"id":5074,"value":"

Load

","pinned":false,"hidden":false,"mode":"html"},{"id":54,"value":"kariba_monthly_raw = (await d3.csv(karibaFolder + \"kariba.csv?\" + Math.floor(Math.random() * 1300),d3.autoType))","pinned":false,"hidden":false,"mode":"ojs"},{"id":736,"value":"kariba_daily_raw = (await d3.csv(karibaFolder + \"kariba_daily.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))","pinned":false,"hidden":false,"mode":"ojs"},{"id":51,"value":"vicfalls_monthly = (await d3.csv(karibaFolder + \"vicfalls_monthly.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1303,"value":"rainCalMonthly= (await d3.csv(rainfallFolder + \"rainfall_calmonthly.csv\",d3.autoType)).map(mo=>{\n mo['WaterMonth']=mo['Month']>=10 ? mo['Month']-9 : mo['Month']+3\n return mo\n}).sort((m1,m2)=>{return m1.WaterMonth-m2.WaterMonth})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1107,"value":"rainYearly= (await d3.csv(rainfallFolder + \"rainfall_yearly.csv\",d3.autoType))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1111,"value":"rainStations= (await d3.csv(rainfallFolder + \"rainfall_stations.csv\",d3.autoType))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1541,"value":"catchments_main = (await d3.json(karibaFolder + \"catchments_main.json?\" + Math.floor(Math.random() * 2000),d3.autoType))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1074,"value":"catchments = (await d3.json(karibaFolder + \"catchments.json?\" + Math.floor(Math.random() * 1000),d3.autoType))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3965,"value":"calmonths=(await d3.csv(peFolder + peModelName + \"_pe_calmonthly.csv?\" + Math.floor(Math.random() * 1000), d3.autoType))","pinned":false,"hidden":false,"mode":"ojs"},{"id":5103,"value":"karibaFolder=server + '/other/kariba/'","pinned":false,"hidden":false,"mode":"ojs"},{"id":5117,"value":"rainfallFolder = server + '/other/rainfall/'","pinned":false,"hidden":false,"mode":"ojs"},{"id":5093,"value":"peFolder = `${server}/ngonyePlantModels/models/${peModelName}/`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5091,"value":"hydrologySet='2024'","pinned":false,"hidden":false,"mode":"ojs"},{"id":5089,"value":"peModelName='sh_2024_7.8mhead'","pinned":false,"hidden":false,"mode":"ojs"},{"id":5087,"value":"server='https://data.westernpower.org'","pinned":false,"hidden":false,"mode":"ojs"},{"id":3832,"value":"

Solar Wind Hydro

","pinned":false,"hidden":false,"mode":"html"},{"id":4918,"value":"combined_cf = d3.mean(combined_cfs.map(m=>d3.mean(m)))","pinned":false,"hidden":false,"mode":"ojs"},{"id":4202,"value":"$5a","pinned":false,"hidden":false,"mode":"ojs"},{"id":3907,"value":"solar_example=FileAttachment(\"pvexample@2.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3845,"value":"monthly_hours_p50_raw_wind = FileAttachment(\"unika_p50-1.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3843,"value":"monthly_hours_p50_wind=[...Array(12).keys()].map(month_no=> {\n return Object.fromEntries(monthly_hours_p50_raw_wind.map((hour,hour_no)=>[hour_no,parseFloat(hour[month_no+1])*1000]))\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3840,"value":"relative_disp_wind = monthly_hours_p50_wind.map((m,i) => Object.values(m).slice(0,24).map(h=>h/month_days(i+1)/200))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3823,"value":"relative_disp = monthly_hours_p50.map((m,i) => Object.values(m).slice(0,24).map(h=>h/month_days(i+1)/25))","pinned":false,"hidden":false,"mode":"ojs"},{"id":4223,"value":"wind_mw_install = wind_mw / d3.max(solar_profile)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4192,"value":"wind_mw = 1024/wind_balance_cf","pinned":false,"hidden":false,"mode":"ojs"},{"id":4188,"value":"wind_balance_cf = 1-wind_cf","pinned":false,"hidden":false,"mode":"ojs"},{"id":4186,"value":"wind_cf = wind_mwh_per_mw_day/24","pinned":false,"hidden":false,"mode":"ojs"},{"id":4133,"value":"wind_mwh_per_mw_day=d3.sum(wind_profile)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4221,"value":"solar_mw_install = solar_mw / d3.max(solar_profile)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4174,"value":"solar_mw = 1024/solar_balance_cf","pinned":false,"hidden":false,"mode":"ojs"},{"id":4148,"value":"solar_balance_cf = 1-solar_cf","pinned":false,"hidden":false,"mode":"ojs"},{"id":4145,"value":"solar_cf = solar_mwh_per_mw_day/24","pinned":false,"hidden":false,"mode":"ojs"},{"id":4061,"value":"solar_mwh_per_mw_day=d3.sum(solar_profile)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4107,"value":"wind_profile=[...Array(24).keys()].map(h=>{\n return d3.mean(relative_disp_wind.map(m=>m[h])) \n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":4009,"value":"solar_profile=[...Array(24).keys()].map(h=>{\n return d3.mean(relative_disp.map(m=>m[h])) \n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":5058,"value":"monthly_hours_p50=d3.groups(monthly_hours_p50_raw,v=>v.month).map(v=>v[1].map(l=>l.medianHourlyEnergyMWh*30))","pinned":false,"hidden":false,"mode":"ojs"},{"id":5055,"value":"monthly_hours_p50_raw = {\n const raw = (await d3.csv('https://data.africanpowerprojects.com/sapp/output/' + \"/pv/iluteCalmonthlyHours.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3773,"value":"

SAPP

","pinned":false,"hidden":false,"mode":"html"},{"id":4354,"value":"peak_power = (inflow_potential_power * 24)/15","pinned":false,"hidden":false,"mode":"ojs"},{"id":4330,"value":"dam_offpeak_price = d3.mean(dam_average.slice(0,7).concat(dam_average.slice(23)))","pinned":false,"hidden":false,"mode":"ojs"},{"id":4324,"value":"dam_peak_price=d3.mean(dam_average.slice(6,22))","pinned":false,"hidden":false,"mode":"ojs"},{"id":4320,"value":"dam_average_day = d3.sum(dam_average)*inflow_potential_power","pinned":false,"hidden":false,"mode":"ojs"},{"id":4334,"value":"dam_average_price = d3.mean(dam_average)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3797,"value":"dam_average = [...Array(24).keys()].map(h=>d3.mean(month_profiles.map(m=>m.get(h))))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3758,"value":"calmonthly_hours = {\n const raw = (await d3.csv(\"https://data.africanpowerprojects.com/sapp/output/\" + \"dam_calmonthly_hours.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n \n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3762,"value":"selected_months=['Mar','Jun','Aug','Dec']","pinned":false,"hidden":false,"mode":"ojs"},{"id":3754,"value":"month_profiles=[...Array(12).keys()].map(m=>month_profile_price(m+1))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3756,"value":"function month_profile_price(month_no) {\n return d3.rollup(calmonthly_hours.filter(m=>m.month==month_no),v=>d3.mean(v,m=>m.price),t=>t.hour)\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":33,"value":"

Libraries

","pinned":false,"hidden":false,"mode":"html"},{"id":1061,"value":"$5b","pinned":false,"hidden":false,"mode":"ojs"},{"id":1063,"value":"blues=d3.scaleSequential(d3.interpolateBlues);","pinned":false,"hidden":false,"mode":"ojs"},{"id":1067,"value":"oranges=d3.scaleSequential(d3.interpolateOranges);","pinned":false,"hidden":false,"mode":"ojs"},{"id":1065,"value":"treemap2 = (data,property_name) => d3.treemap()\n .tile(d3.treemapBinary)\n .size([300, 300])\n .padding(1)\n .round(true)\n (d3.hierarchy(data)\n .sum(d => d[property_name])\n .sort((a, b) => b[property_name] - a[property_name]))","pinned":false,"hidden":false,"mode":"ojs"},{"id":128,"value":"function month_days(i) {\n if(i === 4 || i === 6 || i === 9 || i === 11) {\n\t\treturn 30\n\t} else if(i === 2) {\n\t\treturn 28\n\t} else {\n\t\treturn 31\n\t}\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":130,"value":"month_names={\n return {\n 0:'Jan',\n 1:'Feb',\n 2:'Mar',\n 3:'Apr',\n 4:'May',\n 5:'Jun',\n 6:'Jul',\n 7:'Aug',\n 8:'Sep',\n 9:'Oct',\n 10:'Nov',\n 11:'Dec',\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":133,"value":"month_dict={\n return {\n 'Jan':{index: 0, group: 1},\n 'Feb':{index: 1, group: 1},\n 'Mar':{index: 2, group: 6},\n 'Apr':{index: 3, group: 6},\n 'May':{index: 4, group: 2},\n 'Jun':{index: 5, group: 2},\n 'Jul':{index: 6, group: 3},\n 'Aug':{index: 7, group: 3},\n 'Sep':{index: 8, group: 4},\n 'Oct':{index: 9, group: 4},\n 'Nov':{index: 10, group: 4},\n 'Dec':{index: 11, group: 1},\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":67,"value":"function movingAverage(values, N) {\n let a = new Array(Math.floor((N-1)/2)).fill(NaN)\n let b = new Array(N-Math.round((N-1)/2)).fill(NaN)\n return a.concat(windows(values,N).map(v=>d3.mean(v))).concat(b);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":69,"value":"function windows(inputArray, size) {\n return Array.from(\n {length: inputArray.length - (size - 1)}, //get the appropriate length\n (_, index) => inputArray.slice(index, index+size) //create the windows\n )\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"expanding_mean = function(elems) {\n return elems.map((e,i) => {\n return d3.mean(elems.slice(-(elems.length-(i))))\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":25,"value":"import {margin} from \"@andyflem/commonlibrary\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":23,"value":"import {font} from \"@andyflem/commonlibrary\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":29,"value":"function colr_b(i = 1, a = 1) {\n return d3hsv.hsv(215, 1, ((6 - i + 1.5) * 15)/120, a).toString();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":21,"value":"function colr(i = 1, a = 1) {\n return d3hsv.hsv(26, 0.85, ((6 - i + 1.5) * 15)/100, a).toString();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"import {format_flow} from \"@westernpower/commonlibrary\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2523,"value":"function format_km3(val) {\n return d3.format(',.2r')(val)+cubickm\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2538,"value":"function format_gwh(val) {\n return d3.format(',.2r')(val)+'GWh'\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3517,"value":"function format_twh(val) {\n return d3.format(',.2r')(val)+'TWh'\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"cumec=`m\\u00b3/s`","pinned":false,"hidden":false,"mode":"ojs"},{"id":679,"value":"cubickm=`km\\u00b3`","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"container = function(captionText, width_over) {\n const div=DOM.element('div')\n div.setAttribute(\"class\",\"border m-2 p-2\")\n div.setAttribute(\"style\",`width: ${width_over?width_over:(width>800?800:width)}px`)\n if (captionText) {\n const caption=DOM.element('figcaption')\n caption.setAttribute(\"style\",\"max-width:100%\")\n caption.append(captionText)\n div.append(caption)\n }\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":13,"value":"html`\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":11,"value":"style=html`css `","pinned":false,"hidden":false,"mode":"ojs"},{"id":31,"value":"d3hsv = require(\"d3-hsv@0.1\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"d3 = require(\"d3@7\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":7,"value":"plt = require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":527,"value":"DateTime = luxon.DateTime","pinned":false,"hidden":false,"mode":"ojs"},{"id":529,"value":"luxon = require(\"luxon@1/build/amd/luxon.js\")","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W5c","stdlib":"1","titling":"auto"}