31:T8e9,Zambia has significant potential for new renewable energy generation particularly from Solar PV and Wind plants. Both wind and solar plants are characterised as variable, intermittent energy sources meaning their output varies over all timescales from minute-to-minute all the way up to annual, seasonal variability.

Intermittency in wind and solar is caused by unpredictable, rapid - over seconds, minutes and hours - changes in the underlying energy resource - ie the wind speed and the solar irradiation due to cloud cover. Intermittency can be smoothed (although never eliminated) using relatively small capacity energy storage systems including chemical batteries or by using short-term capacity reserves from other generators on the grid.

Variability reffers to cyclic patterns in the underlying energy resource at various timescales from annual, seasonal patterns through to daily, diurnal variability. Solar generation has very obvious diurnal variability with zero generation throughout the night rising gradualy to peak generation at midday. However, wind generation in Zambia also has a strong diurnal variability with higher generation at night. Wind and, to a lesser extent, solar also show annual, seasonal variability.

Diurnal variability in solar can be balanced using larger energy storage systems including chemical batteries but this has a significant cost. Diurnal and seasonal variability in wind and solar can more efficienty be balanced using other grid connected power generators, in particular in Zambia storage hydro. However, the diurnal variability of solar PV and wind in Zambia are complimentary with most of the wind resource falling at night when there is no solar output. An optimum ratio of Solar PV to Wind capacity of 0.34MW:1.00MW solar:wind gives the lowest average diurnal variation in output over the year.

Variability and intermittency in wind and solar resources are not independent of each other. Solar irradiation, for example, is higher in the summer months but in Zambia this also corresponds with the annual rainy season which leads to much higher intermittency due to cloud cover. The overall impact is very similar average monthly production but much higher variation in hourly and daily output. 32:T491,{ let cap='Modelled annual output of Zambian wind and solar plants over a 20 year period based on measured historic weather conditions.' var data = [ { y: solarYearly.filter((v,i)=>i<20).map(v=>v.normalisedYearlySpecificYield), type: 'bar', showlegend:false, name: 'P90', name: '', mode:'lines', marker: {color: makeTrans(colors.solar[1],0.7)} },{ y: windYearly.filter((v,i)=>i<20).map(v=>v.normalisedYearlySpecificYield), type: 'bar', showlegend:false, name: 'P90', name: '', mode:'lines', marker: {color: makeTrans(colors.wind[1],0.7)} }] var layout = { height: 300, width: width>800?770:width-30, showlegend: true, legend: {xanchor: 'left', x:0, y: -0.2, orientation:'h'}, margin: {l: 70,r: 5,b: 45,t: 10}, font:font, xaxis: { title:'Year',showgrid: false,zeroline: false,ticks:'outside', }, yaxis: { title: '% of median', range:[0.8,1.02], showgrid: true, zeroline: false, tickformat: '.0%', ticks:'outside', } } const div=container(cap, width>800?790:width-10) plt.newPlot(div, data, layout,{displayModeBar: false}) return div }33:T57b,At ${d3.format(',.1f')(windMedianAnualSpecificYield/1000) + ' GWh/MW/year'}, wind provides ${d3.format(',.0%')((windMedianAnualSpecificYield-solarMedianAnualSpecificYield)/solarMedianAnualSpecificYield)} more energy on an annual basis than the same capacity of solar generation at ${d3.format(',.1f')(solarMedianAnualSpecificYield/1000) + ' GWh/MW/year'}.

However, wind capacity has a higer capital cost - approx. ${'$' + d3.format(',.1f')(specificCapitalCost.wind) + ' million/MW'} - than solar at approx. ${'$' + d3.format(',.1f')(specificCapitalCost.solar) + ' million/MW'}. Adjusting for the difference in capital cost of the two types of generation shows that wind capacity is ${d3.format(',.0%')((specificCapitalCostOfAnnualEnergy.wind-specificCapitalCostOfAnnualEnergy.solar)/specificCapitalCostOfAnnualEnergy.solar)} more expensive than annual solar generation at ${ '$' + d3.format(',.0f')(specificCapitalCostOfAnnualEnergy.solar) + 'capital/GWh/year'} for solar compared to ${ '$' + d3.format(',.0f')(specificCapitalCostOfAnnualEnergy.wind) + 'capital/GWh/year'} for wind generation.

However, despite being marginally more capital intensive on an annual energy basis than solar generation, wind power has better yield characteristics at shorter timeframes, most obviously on a daily, diurnal basis where wind generation is highest at night when solar generation is zero.34:T89c,{ let cap = `Average flow on the Zambezi River at Victoria Falls compared to average monthly energy yield for a Zambian wind plant.` var data = [ { y: vicfalls_calmonthly.map(month=>month.flow), text: vicfalls_calmonthly.map(month=>d3.format(',.0f')(month.flow) + `m\u00b3/s`), x: vicfalls_calmonthly.map(month=>month.month), yaxis: 'y2', hoverinfo: 'x+text', mode: 'lines+markers', name:'Mean Flow at Victoria Falls', marker: {color: makeTrans(colors.combined[4],0.5)}, type: 'bar' }, { x: windCalmonthly.map(v=>v.month), y: windCalmonthly.map(v=>v.p90MonthlySpecificYield), type: 'scatter', showlegend:false, name: 'P90', name: '', mode:'lines', line: {shape: 'spline',width:0, color: '#BBB'} },{ x: windCalmonthly.map(v=>v.month), y: windCalmonthly.map(v=>v.p10MonthlySpecificYield), type: 'scatter', showlegend:false,fill:'tonexty', name: 'P90-P10 Range', mode:'lines', line: {shape: 'spline',width:0, color: colors.wind[1]} },{ x: windCalmonthly.map(v=>v.month), y: windCalmonthly.map(v=>v.medianMonthlySpecificYield), type: 'scatter', showlegend:true, name: 'Wind', mode:'lines', line: {shape: 'spline',width:1.5, color: colors.wind[1], dash:''} }] var layout = { width: width>700?670:width-30, height: 270, 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:['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'], }, yaxis: { title: 'Monthly Energy MWh/MW', 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 }35:T7ac,{ var data =[ { y:ratios[10].combined_cfs[5], x:[...Array(24).keys()], mode: 'lines', line: {shape:'spline',color: colors.combined[1],width:2}, fill:'', type: 'scatter', hoverinfo: 'name', showlegend:true, name: `${d3.format('.2f')(ratios[10].ratio)}MW solar per MW wind` },{ y:optimum_ratio.combined_cfs[5], x:[...Array(24).keys()], mode: 'lines', line: {shape:'spline',color: colors.combined[4],width:4}, fill:'', type: 'scatter', hoverinfo: 'name', showlegend:true, name: `${d3.format('.2f')(optimum_ratio.ratio)}MW solar per MW wind` },{ y:ratios[90].combined_cfs[5], x:[...Array(24).keys()], mode: 'lines', line: {shape:'spline',color: colors.combined[10],width:2}, fill:'', type: 'scatter', hoverinfo: 'name', showlegend:true, name: `${d3.format('.2f')(ratios[90].ratio)}MW solar per MW wind` }] var layout = { height: 270, width: width>1000?970:width-30, font: font, hovermode:'closest', showlegend: true, legend: {orientation: 'h',xanchor: 'left', x:0, y: 1.1}, margin: {l: 50,r: 5,b: 45,t: 0}, xaxis: { title: 'Hour', showgrid: true, zeroline: false, ticks:'outside', dtick:1, }, yaxis: { title: 'Output', showgrid: true, zeroline: false, tickformat: '.0%', ticks:'outside', range:[0,1] } }; const div=container(`Average output in June for a combined plant with various proportions of wind and solar capacity.`,width>1000?990:width-10) plt.newPlot(div, data, layout,{displayModeBar: false}); div.on('plotly_hover', d => { var curveNumber = d.points[0].curveNumber var vals = div.data.map((_, i) => i === curveNumber ? 1 : 0.2) plt.restyle(div, 'opacity', vals) }) div.on('plotly_unhover', d => { var curveNumber = d.points[0].curveNumber var vals = div.data.map((_, i) => i === curveNumber ? 1 : 1) plt.restyle(div, 'opacity', vals) }) return div }36:T584,{ var dat = ratios[Math.round(ratio*99)].combined_cfs var data =dat.map((month,month_no) => { return { y:month, x:[...Array(24).keys()], mode: 'lines', line: {shape:'spline',color: colors.combined[month_no],width:2}, fill:'', type: 'scatter', hoverinfo: 'name', showlegend:true, name: months[month_no] } }) var layout = { height: 270, width: width>1000?970:width-30, font: font, hovermode:'closest', showlegend: true, legend: {orientation: 'h',xanchor: 'left', x:0, y: 1.1}, margin: {l: 50,r: 5,b: 45,t: 0}, xaxis: { title: 'Hour', showgrid: true, zeroline: false, ticks:'outside', dtick:1, }, yaxis: { title: 'Output', showgrid: true, zeroline: false, tickformat: '.0%', ticks:'outside', range:[0,1] } } const div=container(`Average diurnal profiles by month for a combined plant with ${ratio}MW solar per MW wind.`,width>1000?990:width-10,) plt.newPlot(div, data, layout,{displayModeBar: false}) div.on('plotly_hover', d => { var curveNumber = d.points[0].curveNumber var vals = div.data.map((_, i) => i === curveNumber ? 1 : 0.2) plt.restyle(div, 'opacity', vals) }) div.on('plotly_unhover', d => { var curveNumber = d.points[0].curveNumber var vals = div.data.map((_, i) => i === curveNumber ? 1 : 1) plt.restyle(div, 'opacity', vals) }) return div }37:T995,{ var data =[...Array(12).keys()].map((month_no) => { return { y:ratios.map(v=>v.monthly_store[month_no]), x:ratios.map(v=>v.ratio), mode: 'lines', line: {shape:'spline',color: colors.storage[month_no],width:2}, fill:'', type: 'scatter', hoverinfo: 'name', showlegend:false, name: months[month_no] } }) data=data.concat([{ y:ratios.map(v=>v.store_max), x:ratios.map(v=>v.ratio), text:ratios.map(v=>d3.format('.2f')(v.store_max)),name:'Storage requirement', mode:'lines',type:'scatter', hoverinfo:'x+text', line:{shape:'spline', width:2.5, color:'red'} }]) data=data.concat([{ y:[optimum_ratio.store_max], x:[optimum_ratio.ratio], text: [d3.format('.2f')(optimum_ratio.ratio) + ','+ d3.format('.1f')(optimum_ratio.store_max)], name:'Optimum ratio',textposition:'top-center',showlegend:false, mode:'markers+text',type:'scatter', hoverinfo:'none', marker:{size:15, color:'black'} }]) data=data.concat([{ x:[ratios[Math.round(ratio*99)].ratio,ratios[Math.round(ratio*99)].ratio], y:[0,2.2], showlegend:false, mode:'lines',line:{width:1,color:'#555'} }]) var layout = { height: 270, width: width>1000?970:width-30, font: font, hovermode:'closest', showlegend: true, legend: {orientation: 'h',xanchor: 'left', x:0, y: 1.1}, margin: {l: 50,r: 5,b: 50,t: 0}, xaxis: { title:'Ratio solar : wind capacity', showgrid: true, zeroline: false,tickformat: '.1f', ticks:'outside', }, yaxis: { title: 'Hours', showgrid: true, zeroline: false, tickformat: '.1f', ticks:'outside' } }; const div=container(`Daily storage requirement by month and ratio of solar to wind capacity to flatten the diurnal for a combined plant. Maximum storage requirement over the year in red. Optimum ratio is ${d3.format('.2f')(optimum_ratio.ratio)}MW solar per MW wind with ${d3.format('.1f')(optimum_ratio.store_max)} hours of storage.`,width>1000?990:width-10) plt.newPlot(div, data, layout,{displayModeBar: false}); div.on('plotly_hover', d => { var curveNumber = d.points[0].curveNumber var vals = div.data.map((_, i) => i === curveNumber ? 1 : 0.2) plt.restyle(div, 'opacity', vals) }) div.on('plotly_unhover', d => { var curveNumber = d.points[0].curveNumber var vals = div.data.map((_, i) => i === curveNumber ? 1 : 1) plt.restyle(div, 'opacity', vals) }) return div }38:T79b,function hourlyDailyChart(from, to, cap) { var solHours = solarHourly.filter(v=> v.date > from && v.date < to) var solDays = solarDaily.filter(v=> v.date > from.minus({day:1}) && v.date < to ) var windHours = windHourly.filter(v=> v.date > from && v.date < to) var windDays = windDaily.filter(v=> v.date > from.minus({day:1}) && v.date < to ) var data = [{ x: solHours.map(v=>v.date.toJSDate()), y: solHours.map(v=>v.capFactor), type: 'scatter', showlegend:true, hoverinfo:'none',name:'Hourly output', fill:'', mode:'lines', line: {shape: 'spline', width:2, color: colors.solar[1]} },{ x: windHours.map(v=>v.date.toJSDate()), y: windHours.map(v=>v.capFactor), type: 'scatter', showlegend:true, hoverinfo:'none',name:'Hourly output', fill:'', mode:'lines', line: {shape: 'spline', width:2, color: colors.wind[1]} },{ x: solDays.map(v=>v.date.plus({hours:12}).toJSDate()), y: solDays.map(v=>v.relativeDailyCapFactor), type: 'bar', showlegend:true, hoverinfo:'y',name:'Daily yield, % of maximum for month', marker: {color: makeTrans(colors.solar[1],0.3)} },{ x: windDays.map(v=>v.date.plus({hours:12}).toJSDate()), y: windDays.map(v=>v.dailyCapFactor), type: 'bar', showlegend:true, hoverinfo:'y',name:'Daily yield, % of maximum for month', marker: {color: makeTrans(colors.wind[1],0.3)} }] var layout = { height: 350, width: width>800?770:width-30, showlegend: true, legend: {xanchor: 'left', x:0, y: -0.2, orientation:'h'}, margin: {l: 70,r: 5,b: 30,t: 10}, font:font,barmode:'', xaxis: { showgrid: false, zeroline: false, ticks:'outside', }, yaxis: { title: 'Output', showgrid: true, zeroline: false, tickformat: '.0%', ticks:'outside', } } const div=container(cap, width>800?790:width-10) plt.newPlot(div, data, layout,{displayModeBar: false}) return div }39:T95f,function monthlyEnergyPerCalMonthChart() { let cap='Median monthly specific yield (solid line) and P90 to P10 specific yield (filled area) by calendar month for a Zambian wind and solar plants.' var data = [ { x: solarCalmonthly.map(v=>v.month), y: solarCalmonthly.map(v=>v.p90MonthlySpecificYield), type: 'scatter', showlegend:false, name: 'P90', name: '', mode:'lines', line: {shape: 'spline',width:0, color: '#BBB'} },{ x: solarCalmonthly.map(v=>v.month), y: solarCalmonthly.map(v=>v.p10MonthlySpecificYield), type: 'scatter', showlegend:false,fill:'tonexty', name: 'P90-P10 Range', mode:'lines', line: {shape: 'spline',width:0, color: colors.solar[1]} },{ x: solarCalmonthly.map(v=>v.month), y: solarCalmonthly.map(v=>v.medianMonthlySpecificYield), type: 'scatter', showlegend:true, name: 'Solar', mode:'lines', line: {shape: 'spline',width:1.5, color: colors.solar[1], dash:''} }, { x: windCalmonthly.map(v=>v.month), y: windCalmonthly.map(v=>v.p90MonthlySpecificYield), type: 'scatter', showlegend:false, name: 'P90', name: '', mode:'lines', line: {shape: 'spline',width:0, color: '#BBB'} },{ x: windCalmonthly.map(v=>v.month), y: windCalmonthly.map(v=>v.p10MonthlySpecificYield), type: 'scatter', showlegend:false,fill:'tonexty', name: 'P90-P10 Range', mode:'lines', line: {shape: 'spline',width:0, color: colors.wind[1]} },{ x: windCalmonthly.map(v=>v.month), y: windCalmonthly.map(v=>v.medianMonthlySpecificYield), type: 'scatter', showlegend:true, name: 'Wind', mode:'lines', line: {shape: 'spline',width:1.5, color: colors.wind[1], dash:''} }] var layout = { height: 370, width: width>1100?(1040/2):(width-60/2), showlegend: true, legend: {xanchor: 'left', x:0, y: -0.2, orientation:'h'}, margin: {l: 70,r: 5,b: 30,t: 10}, font:font, xaxis: { showgrid: false, zeroline: false, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext: months, range:[0.5,12.5] }, yaxis: { title: 'Monthly Energy MWh/MW', showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside', } } const div=container(cap, width>1100?(1070/2):(width-30/2)) plt.newPlot(div, data, layout,{displayModeBar: false}) return div }3a:T954,function dailyEnergyPerCalMonthChart() { let cap='Median monthly specific yield (solid line) and P90 to P10 specific yield (filled area) by calendar month for a Zambian wind and solar plants.' var data = [ { x: solarCalmonthly.map(v=>v.month), y: solarCalmonthly.map(v=>v.p90DailySpecificYield), type: 'scatter', showlegend:false, name: 'P90', name: '', mode:'lines', line: {shape: 'spline',width:0, color: '#BBB'} },{ x: solarCalmonthly.map(v=>v.month), y: solarCalmonthly.map(v=>v.p10DailySpecificYield), type: 'scatter', showlegend:false,fill:'tonexty', name: 'P90-P10 Range', mode:'lines', line: {shape: 'spline',width:0, color: colors.solar[1]} },{ x: solarCalmonthly.map(v=>v.month), y: solarCalmonthly.map(v=>v.medianDailySpecificYield), type: 'scatter', showlegend:true, name: 'Solar', mode:'lines', line: {shape: 'spline',width:1.5, color: colors.solar[1], dash:''} }, { x: windCalmonthly.map(v=>v.month), y: windCalmonthly.map(v=>v.p90DailySpecificYield), type: 'scatter', showlegend:false, name: 'P90', name: '', mode:'lines', line: {shape: 'spline',width:0, color: colors.wind[1]} },{ x: windCalmonthly.map(v=>v.month), y: windCalmonthly.map(v=>v.p10DailySpecificYield), type: 'scatter', showlegend:false,fill:'tonexty', name: 'P90-P10 Range', mode:'lines', line: {shape: 'spline',width:0, color: colors.wind[1]} },{ x: windCalmonthly.map(v=>v.month), y: windCalmonthly.map(v=>v.medianDailySpecificYield), type: 'scatter', showlegend:true, name: 'Wind', mode:'lines', line: {shape: 'spline',width:1.5, color: colors.wind[1], dash:''} }] var layout = { height: 370, width: width>1100?(1040/2):(width-60/2), showlegend: true, legend: {xanchor: 'left', x:0, y: -0.2, orientation:'h'}, margin: {l: 70,r: 5,b: 30,t: 10}, font:font, xaxis: { showgrid: false, zeroline: false, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext: months, range:[0.5,12.5] }, yaxis: { title: 'Daily Energy MWh/MW', showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside', } } const div=container(cap, width>1100?(1070/2):(width-30/2)) plt.newPlot(div, data, layout,{displayModeBar: false}) return div }3b:T772,function annualYield() { let cap='Annual specific yield exceedance probability .' var data = [ { x: solarAnnualExceedance.map(v => v.exceedance), y: solarAnnualExceedance.map(v => v.yearlySpecificYield/1000), type: 'scatter', showlegend:true, hoverinfo:'x+y',name: 'Solar', mode:'lines', line: {shape: '',width:3.5, color: colors.solar[1]} },{ text: [d3.format(',.2f')(solarMedianAnualSpecificYield/1000) + ' GWh/MW/year'], x: [0.5], y: [solarMedianAnualSpecificYield/1000], type: 'scatter', showlegend:false,hoverinfo:'y',textposition:'bottomright', mode:'markers+text', marker:{size:10, color:'#666'} },{ x: windAnnualExceedance.map(v => v.exceedance), y: windAnnualExceedance.map(v => v.yearlySpecificYield/1000), type: 'scatter', showlegend:true, hoverinfo:'x+y', name: 'Wind', mode:'lines', line: {shape: '',width:3.5, color: colors.wind[1]} },{ text: [d3.format(',.2f')(windMedianAnualSpecificYield/1000) + ' GWh/MW/year'], x: [0.5], y: [windMedianAnualSpecificYield/1000], type: 'scatter', showlegend:false,hoverinfo:'y',textposition:'bottomright', mode:'markers+text', marker:{size:10, color:'#666'} }] var layout = { height: 350, width: width>1200?(1140/2):(width-60)/2, showlegend: true, legend: {xanchor: 'left', x:0, y: 1, orientation:'h'}, margin: {l: 70,r: 10,b: 50,t: 10}, font:font, xaxis: { showgrid: false, dtick:0.1, zeroline: false, tickformat:'.0%', range: [1,-0.02], ticks:'outside', title: 'Exceedance Probability' }, yaxis: { showgrid: true,zeroline: false, showline: false,title: 'Annual Energy GWh/MW/year', tickformat: ',.2f', } } const div=container(cap, width>1200?(1170/2):(width-30)/2) plt.newPlot(div, data, layout,{displayModeBar: false}) return div }3c:T789,function annualProb() { let cap='Annual specific yield exceedance probability .' var data = [ { x: solarAnnualExceedance.map(v => v.exceedance), y: solarAnnualExceedance.map(v => v.normalisedYearlySpecificYield), type: 'scatter', showlegend:true, hoverinfo:'x+y',name:'Solar', mode:'lines', line: {shape: '',width:3.5, color: colors.solar[1]} }, { x: windAnnualExceedance.map(v => v.exceedance), y: windAnnualExceedance.map(v => v.normalisedYearlySpecificYield), type: 'scatter', showlegend:true, hoverinfo:'x+y',name:'Wind', mode:'lines', line: {shape: '',width:3.5, color: colors.wind[1]} },{ x: [1,0], y: [1,1], type: 'scatter', showlegend:false, hoverinfo:'none', mode:'lines', line: {width:1, color: '#000'} }] var layout = { height: 350, width: width>1200?(1140/2):(width-60)/2, showlegend: true, legend: {xanchor: 'left', x:0, y: 1, orientation:'h'}, margin: {l: 70,r: 10,b: 50,t: 10}, font:font, xaxis: { showgrid: false, dtick:0.1, zeroline: false, tickformat:'.0%', range: [1,-0.02], ticks:'outside', title: 'Exceedance Probability' }, yaxis: { title: '% difference from median', dtick:0.02, showgrid: true, zeroline: false, tickformat: ',.0%', ticks:'outside', range:[d3.min(windAnnualExceedance,v=>v.normalisedYearlySpecificYield),d3.max(windAnnualExceedance,v=>v.normalisedYearlySpecificYield)], }, yaxis2: { showgrid: false,zeroline: false, showline: false,title: 'Specific Yield GWh/MW/year', tickformat: ',.2f', range:[d3.min(windAnnualExceedance,v=>v.yearlySpecificYield/1000),d3.max(windAnnualExceedance,v=>v.yearlySpecificYield/1000)], overlaying: 'y',side: 'right' } } const div=container(cap, width>1200?(1170/2):(width-30)/2) plt.newPlot(div, data, layout,{displayModeBar: false}) return div }3d:T8b8,function monthChart(indx) { var data = [{ y:solarCalmonthlyHours[indx].map(v=>v.p90HourlyCapFactor), x:[...Array(24).keys()], mode: 'lines', line: {shape:'spline',color: makeTrans(colors.solar[indx],0.8),width:0.5}, fill:'', type: 'scatter', hoverinfo: 'none', showlegend:false, },{ y:solarCalmonthlyHours[indx].map(v=>v.p10HourlyCapFactor), x:[...Array(24).keys()], mode: 'lines', line: {shape:'spline',color: makeTrans(colors.solar[indx],0.8),width:0.5}, fill:'tonexty', type: 'scatter', hoverinfo: 'none', showlegend:false, },{ y:solarCalmonthlyHours[indx].map(v=>v.meanHourlyCapFactor), x:[...Array(24).keys()], mode: 'lines', line: {shape:'spline',color: makeTrans(colors.solar[indx],0.9),width:1.5}, fill:'', type: 'scatter', hoverinfo: 'none', showlegend:false, },{ y:windCalmonthlyHours[indx].map(v=>v.p90HourlyCapFactor), x:[...Array(24).keys()], mode: 'lines', line: {shape:'spline',color: makeTrans(colors.wind[indx],0.8),width:0.5}, fill:'', type: 'scatter', hoverinfo: 'none', showlegend:false, },{ y:windCalmonthlyHours[indx].map(v=>v.p10HourlyCapFactor), x:[...Array(24).keys()], mode: 'lines', line: {shape:'spline',color: makeTrans(colors.wind[indx],0.8),width:0.5}, fill:'tonexty', type: 'scatter', hoverinfo: 'none', showlegend:false, },{ y:windCalmonthlyHours[indx].map(v=>v.meanHourlyCapFactor), x:[...Array(24).keys()], mode: 'lines', line: {shape:'spline',color: makeTrans(colors.wind[indx],0.9),width:1.5}, fill:'', type: 'scatter', hoverinfo: 'none', showlegend:false, }] var layout = { height: 150, width: 350, font: font, hovermode:'closest', title:{xref:'paper',x: 0.05, yref:'paper', y:0.85, font:{size: 16}, text:months[indx]}, showlegend: false, margin: {l: 40,r: 10,b: 40,t: 0}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', dtick:4, range:[0,23], visible:indx==0 }, yaxis: { showgrid: true, zeroline: false, tickformat: '.0%', ticks:'none', showticklabels:indx==0,showticks:false, range: [-0.01,1.04] } } const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}) return div } 3e:T666,function hourlyByMonth(series, hours, cols, cap) { function trace(data, hourNo, color, dash) { let mons = data.map(m => m[hourNo]) return [{ x: mons.map((v,i)=>i+1), y: mons.map(v=>v.meanHourlyCapFactor), type: 'scatter', showlegend:true, mode:'lines', line: {shape: 'spline',width:2.5, color: color, dash: dash}, name: d3.format('02')(hourNo) + ':00', hoverinfo:'name+y' },{ x: mons.map((v,i)=>i+1), y: mons.map(v=>v.p90HourlyCapFactor), type: 'scatter', showlegend:false, mode:'lines', line: {shape: 'spline',width:0, color: color}, hoverinfo:'none' },{ x: mons.map((v,i)=>i+1), y: mons.map(v=>v.p10HourlyCapFactor), type: 'scatter', showlegend:false, mode:'lines', line: {shape: 'spline',width:0, color: color}, fill:'tonexty', hoverinfo:'none' }] } var data = [] hours.forEach(h=>{ data=data.concat(trace(series, h, cols[Math.floor((h/2))],'')) }) var layout = { height: 350, width: width>1100 ? 1040/2 : (width-60)/2, showlegend: true, legend: {xanchor: 'left', x:0, y: -0.2, orientation:'h'}, margin: {l: 70,r: 5,b: 30,t: 10}, font:font, xaxis: { showgrid: false, zeroline: false, ticks:'outside', tickvals: [ 1,2,3,4,5,6,7,8,9,10,11,12 ], ticktext: months, range: [0.5, 12.5] }, yaxis: { title: 'Hourly Yield MWh/MW', showgrid: true, zeroline: false, tickformat: '.2f', ticks:'outside' } } const div=container(cap, width>1100?1070/2:(width-30)/2) plt.newPlot(div, data, layout,{displayModeBar: false}) return div }3f:T53d,windCalmonthly = d3.rollups(windDaily, d => { let ret = { date: d[0].date, month: d[0].month, p90DailyCapFactor: d3.quantile(d, 0.9, v=>v.dailyCapFactor), p10DailyCapFactor: d3.quantile(d, 0.1, v=>v.dailyCapFactor), maxDailyCapFactor: d3.max(d, v=>v.dailyCapFactor), meanDailyCapFactor: d3.mean(d, v=>v.dailyCapFactor), maxDailySpecificYield: d3.max(d, v=>v.dailySpecificYield), p90DailySpecificYield: d3.quantile(d, 0.9, v=>v.dailySpecificYield), p10DailySpecificYield: d3.quantile(d, 0.1, v=>v.dailySpecificYield), meanDailySpecificYield: d3.mean(d, v=>v.dailySpecificYield), medianDailySpecificYield: d3.quantile(d, 0.5, v=>v.dailySpecificYield), } ret.maxMonthlySpecificYield = ret.maxDailySpecificYield * monthDays[ret.month-1]//MWh/MW taking account of seasonality ret.meanMonthlySpecificYield = d3.mean(windMonthly.filter(v=>v.month==d[0].month),v=>v.monthlySpecificYield) ret.medianMonthlySpecificYield = d3.quantile(windMonthly.filter(v=>v.month==d[0].month),0.5,v=>v.monthlySpecificYield) ret.p90MonthlySpecificYield = d3.quantile(windMonthly.filter(v=>v.month==d[0].month),0.9,v=>v.monthlySpecificYield) ret.p10MonthlySpecificYield = d3.quantile(windMonthly.filter(v=>v.month==d[0].month),0.1,v=>v.monthlySpecificYield) return ret }, d => d.month).map(v=>v[1])40:T544,solarCalmonthly = d3.rollups(solarDaily, d => { let ret = { date: d[0].date, month: d[0].month, p90DailyCapFactor: d3.quantile(d, 0.9, v=>v.dailyCapFactor), p10DailyCapFactor: d3.quantile(d, 0.1, v=>v.dailyCapFactor), maxDailyCapFactor: d3.max(d, v=>v.dailyCapFactor), meanDailyCapFactor: d3.mean(d, v=>v.dailyCapFactor), maxDailySpecificYield: d3.max(d, v=>v.dailySpecificYield), p90DailySpecificYield: d3.quantile(d, 0.9, v=>v.dailySpecificYield), p10DailySpecificYield: d3.quantile(d, 0.1, v=>v.dailySpecificYield), meanDailySpecificYield: d3.mean(d, v=>v.dailySpecificYield), medianDailySpecificYield: d3.quantile(d, 0.5, v=>v.dailySpecificYield), } ret.maxMonthlySpecificYield = ret.maxDailySpecificYield * monthDays[ret.month-1]//MWh/MW taking account of seasonality ret.meanMonthlySpecificYield = d3.mean(solarMonthly.filter(v=>v.month==d[0].month),v=>v.monthlySpecificYield) ret.medianMonthlySpecificYield = d3.quantile(solarMonthly.filter(v=>v.month==d[0].month),0.5,v=>v.monthlySpecificYield) ret.p90MonthlySpecificYield = d3.quantile(solarMonthly.filter(v=>v.month==d[0].month),0.9,v=>v.monthlySpecificYield) ret.p10MonthlySpecificYield = d3.quantile(solarMonthly.filter(v=>v.month==d[0].month),0.1,v=>v.monthlySpecificYield) return ret }, d => d.month).map(v=>v[1]) 41:T61e,function normalinv(p, mean, std) { return -1.41421356237309505 * std * erfcinv(2 * p) + mean; function erfcinv(p) { var j = 0; var x, err, t, pp; if (p >= 2) return -100; if (p <= 0) return 100; pp = (p < 1) ? p : 2 - p; t = Math.sqrt(-2 * Math.log(pp / 2)); x = -0.70711 * ((2.30753 + t * 0.27061) / (1 + t * (0.99229 + t * 0.04481)) - t); for (; j < 2; j++) { err = erfc(x) - pp; x += err / (1.12837916709551257 * Math.exp(-x * x) - x * err); } return (p < 1) ? x : -x; } function erfc(x) { return 1 - erf(x); } function erf(x) { var cof = [-1.3026537197817094, 6.4196979235649026e-1, 1.9476473204185836e-2, -9.561514786808631e-3, -9.46595344482036e-4, 3.66839497852761e-4, 4.2523324806907e-5, -2.0278578112534e-5, -1.624290004647e-6, 1.303655835580e-6, 1.5626441722e-8, -8.5238095915e-8, 6.529054439e-9, 5.059343495e-9, -9.91364156e-10, -2.27365122e-10, 9.6467911e-11, 2.394038e-12, -6.886027e-12, 8.94487e-13, 3.13092e-13, -1.12708e-13, 3.81e-16, 7.106e-15, -1.523e-15, -9.4e-17, 1.21e-16, -2.8e-17 ]; var j = cof.length - 1; var isneg = false; var d = 0; var dd = 0; var t, ty, tmp, res; if (x < 0) { x = -x; isneg = true; } t = 2 / (2 + x); ty = 4 * t - 2; for (; j > 0; j--) { tmp = d; d = ty * d - dd + cof[j]; dd = tmp; } res = t * Math.exp(-x * x + 0.5 * (cof[0] + ty * d) - dd); return isneg ? res - 1 : 1 - res; } }42:[{"id":"9cdefb6ee10601279e0b13ae34726d70e946a71dead450f7345e25179d1b720dbd12c5cf24cdc688bd854f3683b9715e952debc7849d218095f9ac42b652ab76","href":"https://static.observableusercontent.com/files/9cdefb6ee10601279e0b13ae34726d70e946a71dead450f7345e25179d1b720dbd12c5cf24cdc688bd854f3683b9715e952debc7849d218095f9ac42b652ab76","name":"ilute_c@1.csv","type":"text/csv","lastModified":1729577974639.772,"size":9508732},{"id":"8a7c711b57ea4b32b051ee38f2dbb6f304fc2c2bdfef14c00abea0e1795b8f27a8aaf2ae252bfb649b6c8eb332a5f191f0e33a3b07d271cf88d255115679fa79","href":"https://static.observableusercontent.com/files/8a7c711b57ea4b32b051ee38f2dbb6f304fc2c2bdfef14c00abea0e1795b8f27a8aaf2ae252bfb649b6c8eb332a5f191f0e33a3b07d271cf88d255115679fa79","name":"unika_180mw_yield.csv","type":"text/csv","lastModified":1729578112490.134,"size":6638066}] 2f:{"title":"Comparison of Zambian wind and solar yield characteristics","cells":[{"id":0,"value":"# Comparison of Zambian wind and solar yield characteristics","pinned":false,"hidden":false,"mode":"md"},{"id":1288,"value":"$31","pinned":false,"hidden":false,"mode":"html"},{"id":1542,"value":"

This analysis uses hourly yield models for a planned grid connected solar PV plant in Western Zambia and a planned grid connected wind farm in the Eastern Province of Zambia. In both cases the yield models use measured weather conditions (temperature, cloud cover, wind speed) over 20 years derived from remote, satellite measurements. For the Eastern Province wind farm site the data is validated using measurements from instruments on a met-mast operated at the project site since 2019.

","pinned":false,"hidden":false,"mode":"html"},{"id":1156,"value":"

Annual Energy

","pinned":false,"hidden":false,"mode":"html"},{"id":1377,"value":"Both solar PV and wind plants provide stable, consistent output on an annual basis as shown by the very flat annual energy exceedance probability charts for the two types of generation. \n

\n","pinned":false,"hidden":false,"mode":"html"},{"id":1188,"value":"

${annualProb()}

${annualYield()}

","pinned":false,"hidden":false,"mode":"html"},{"id":1437,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1430,"value":"$33","pinned":false,"hidden":false,"mode":"html"},{"id":1215,"value":"

Monthly and Daily Yield - Seasonality

","pinned":false,"hidden":false,"mode":"html"},{"id":1477,"value":"Zambian wind and solar plants show very different seasonal patterns of production driven by differing aspects of seasonal climatic conditions. \n

\nSolar seasonality in monthly yield is relatively small varying from an average of 227 MWh/MW of capacity during the rainy season in February to 280 MWh/MW in August. By contrast, wind yield is strongly seasonal with average monthly yield of 81 MWh/MW in rainy season January rising to 467 MWh/MW in dry season July.\n

\nA similar pattern is also evident in the average daily yield over the seasons. However, as would be expected, the relative variation in daily output is significantly higher than the relative variation in monthly output and also shows a significant seasonality. This is particularly true for the yield from wind generation. The variation in daily output for solar plants is highest during the summer, rainy season, from November to March where cloudy conditions can significantly curtail daily output.","pinned":false,"hidden":false,"mode":"html"},{"id":1482,"value":"

${monthlyEnergyPerCalMonthChart()}

${dailyEnergyPerCalMonthChart()}

","pinned":false,"hidden":false,"mode":"html"},{"id":1499,"value":"Seasonality in average monthly yield from Zambian wind plants provide a useful hedge for the seasonal variation in river flow and consequent seasonal pressure on hydroelectric plants - which remain the dominant form of electricity generaiton in Zambia. Average wind yield reaches a peak from May through to September which corresponds to reducing river flows and reservoir inflows. This period also corresponds to most of the peak period of agricultural irrigation in Zambia.","pinned":false,"hidden":false,"mode":"html"},{"id":1264,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1229,"value":"

Diurnal (hourly) Yield

","pinned":false,"hidden":false,"mode":"html"},{"id":1558,"value":"Comparison of the diurnal yield profile for each month for wind and solar plants show how the differing seasonaility in the underlying resources impact on the diurnal yields.\n

\nAverage hourly output for solar PV is relatively constant throughout the year, however, from November through to March solar PV has much more variability in hourly output driven by the annual rainy season, increased cloud cover and increased intermittency.\n

\n","pinned":false,"hidden":false,"mode":"html"},{"id":993,"value":"

\n ${monthChart(0)}\n

\n ${monthChart(1)}\n

\n ${monthChart(2)}\n

\n ${monthChart(3)}\n

\n ${monthChart(4)}\n

\n ${monthChart(5)}\n

\n ${monthChart(6)}\n

\n ${monthChart(7)}\n

\n ${monthChart(8)}\n

\n ${monthChart(9)}\n

\n ${monthChart(10)}\n

\n ${monthChart(11)}\n

Average (line) and P90-P10 range (area) diurnal capacity factor by calebdar month for a Zambian solar and wind plants.

","pinned":false,"hidden":false,"mode":"html"},{"id":1575,"value":"Examples of simulated performance of wind and solar PV plants for two 10 day periods in 2019 show the intermittency that both types of generation can experience. Intermittency in solar PV is simply caused by cloud cover - which in Zambia is almost entirely restricted to the rainy season between November and March.\n

\nDry season output, for example in June, is far more consistent for both solar PV and wind.","pinned":false,"hidden":false,"mode":"html"},{"id":1353,"value":"hourlyDailyChart( DateTime.fromObject({year:2019,month:2,day:9}), DateTime.fromObject({year:2019,month:2,day:20}),'Simulated hourly and daily output over 10 days in February 2019 for Zambian solar and wind plants.')","pinned":false,"hidden":false,"mode":"ojs"},{"id":1368,"value":"hourlyDailyChart( DateTime.fromObject({year:2019,month:6,day:9}), DateTime.fromObject({year:2019,month:6,day:20}),'Simulated hourly and daily output over 10 days in June 2019 for Zambian solar and wind plants.')","pinned":false,"hidden":false,"mode":"ojs"},{"id":1580,"value":"

Firm diurnal by combining wind and solar

","pinned":false,"hidden":false,"mode":"html"},{"id":1595,"value":"Because Zambian solar PV and wind plants have complimentary daily, diurnal output profiles mixing generation from the two sources can produce a firmer, more constant diurnal production profile.\n

\nIt can be shown that the optimum ratio of solar PV to wind capacity for the firmest, most constant diurnal throughout the year is 0.34MW:1.00MW solar:wind. See here for the analysis.","pinned":false,"hidden":false,"mode":"html"},{"id":1583,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1600,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":1617,"value":"It can also be shown that the minimum capacity of storage required to completely flatten the average diurnal output of a compbined wind and solar plant with a ratio of solar to wind capacity of 0.34MW/MW is 0.9 hours of storage or 0.9MWh / MW installed. Higher quantities of storage are required where other ratios of solar to wind are constructed.\n

\nIt is important to realise that this storage is only designed to flatten the average diurnal output of the combined plants and not to smooth any intermittency due to cloud or still conditions. Considering the cloudy, still conditions can go on for many days, significantly more storage would be needed to smooth the intermittency.\n

\nAlso, in Zambia there is very significant existing storage potential (battery capacity) in the hydroelectric reservoirs particularly at Kariba and to a lesser extent Itezi-tezi. New chemical batteries (eg Lithium polymer) will be considerably more expensive than this legacy hydroelectric capacity.","pinned":false,"hidden":false,"mode":"html"},{"id":1611,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":1531,"value":"

\n

Setup

","pinned":false,"hidden":false,"mode":"html"},{"id":545,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":1239,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":174,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":1168,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":786,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":995,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":273,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":1399,"value":"specificCapitalCostOfAnnualEnergy = {\n return {\n solar: (specificCapitalCost.solar*1000000) / (solarMedianAnualSpecificYield), //$/GWh/year\n wind: (specificCapitalCost.wind*1000000) / (windMedianAnualSpecificYield), //$/GWh/year\n } \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1203,"value":"specificCapitalCost = { // $m per MW\n return {\n solar: 1.5,\n wind: 2,\n hydro: 3.5\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1266,"value":"import {vicfalls_calmonthly} from \"@westernpower/kariba\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1603,"value":"ratio=0.34","pinned":true,"hidden":false,"mode":"ojs"},{"id":1587,"value":"import {optimum_ratio} from \"@andyflem/wind-solar-and-storage-combinations\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1585,"value":"import {ratios} from \"@andyflem/wind-solar-and-storage-combinations\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1110,"value":"

Wind

","pinned":false,"hidden":false,"mode":"html"},{"id":1152,"value":"windDurationVariability={\n let durations = [5, 10, 15, 18, 20, 25]\n\n let stDev = d3.deviation(windYearly,v=>v.yearlySpecificYield)\n let mean = d3.mean(windYearly,v=>v.yearlySpecificYield)\n\n return durations.map(d=>{\n return {\n duration_years: d,\n stDev: stDev/Math.sqrt(d),\n p50: mean,\n p75: normalinv(0.25, mean, stDev/Math.sqrt(d)),\n p90: normalinv(0.10, mean, stDev/Math.sqrt(d)),\n p99: normalinv(0.01, mean, stDev/Math.sqrt(d))\n }\n })\n\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1148,"value":"windAnnualExceedance=windYearly.toSorted((a,b) => a.yearlySpecificYield-b.yearlySpecificYield).map((v,i) => {\n v.exceedance = 1-(i/windYearly.length)\n v.normalisedYearlySpecificYield = v.yearlySpecificYield/windMedianAnualSpecificYield\n return v\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1143,"value":"windMedianAnualSpecificYield=d3.median(windYearly,v=>v.yearlySpecificYield)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1133,"value":"windCalmonthlyHours={\n let mons = Array.from(new Array(12), (x,i) => i+1)\n\n return mons.map(m=>{\n return d3.rollups(windHourly.filter(d=>d.month==m), v=>{\n return {\n hour: v[0].hour,\n meanHourlyCapFactor: d3.mean(v,l=>l.capFactor),\n p90HourlyCapFactor: d3.quantile(v, 0.9, l=>l.capFactor),\n p10HourlyCapFactor: d3.quantile(v, 0.1, l=>l.capFactor)\n }\n },v=>v.hour).map(v=>v[1])\n })\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1130,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"id":1145,"value":"windYearly = d3.rollups(windMonthly, d => {\n return {\n year: d[0].year,\n yearlySpecificYield: d3.sum(d,v=>v.monthlySpecificYield)\n }\n}, d => d.year).map(v=>v[1])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1127,"value":"windMonthly = d3.rollups(windDaily, d => {\n return {\n date: d[0].date.startOf('month'),\n year: d[0].date.year,\n month: d[0].date.month,\n meanDailyCapFactor: d3.mean(d, v=>v.dailyCapFactor),\n monthlySpecificYield: d3.sum(d, v => v.dailyCapFactor * 24) //MWh/MW\n }\n}, d => d.date.startOf('month')).map(v=>v[1])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1123,"value":"windDaily = d3.rollups(windHourly,v=>{\n let ret = {\n date: v[0].date,\n year: v[0].year,\n month: v[0].month,\n day: v[0].day,\n dailyCapFactor: (d3.sum(v,h=>h.energyMWh))/ (windCapacityMW*24)\n }\n ret.dailySpecificYield = ret.dailyCapFactor * 24 \n \n return ret\n}, v=>v.date.startOf('day')).map(v=>v[1])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1116,"value":"windHourly=unikaYield_raw.map(v=> {\n v.date=DateTime.fromObject({year:v.year,month:v.month,day:v.day, hour: v.hour})\n //v.ghi=parseFloat(v.ghi)\n v.energyMWh=parseFloat(v.mwh)/180*windCapacityMW\n v.capFactor = parseFloat(v.mwh)/180\n v.year=parseInt(v.year)\n v.month=parseInt(v.month)\n v.day=parseInt(v.day)\n v.hour=parseInt(v.hour)\n return v\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1106,"value":"unikaYield_raw = FileAttachment(\"unika_180mw_yield.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1118,"value":"windCapacityMW=180","pinned":false,"hidden":false,"mode":"ojs"},{"id":1108,"value":"

Solar

","pinned":false,"hidden":false,"mode":"html"},{"id":831,"value":"solarMedianAnualSpecificYield=d3.median(solarYearly,v=>v.yearlySpecificYield)","pinned":false,"hidden":false,"mode":"ojs"},{"id":882,"value":"solarDurationVariability={\n let durations = [5, 10, 15, 18, 20, 25]\n\n let stDev = d3.deviation(solarYearly,v=>v.yearlySpecificYield)\n let mean = d3.mean(solarYearly,v=>v.yearlySpecificYield)\n\n return durations.map(d=>{\n return {\n duration_years: d,\n stDev: stDev/Math.sqrt(d),\n p50: mean,\n p75: normalinv(0.25, mean, stDev/Math.sqrt(d)),\n p90: normalinv(0.10, mean, stDev/Math.sqrt(d)),\n p99: normalinv(0.01, mean, stDev/Math.sqrt(d))\n }\n })\n\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":793,"value":"solarAnnualExceedance=solarYearly.toSorted((a,b) => a.yearlySpecificYield-b.yearlySpecificYield).map((v,i) => {\n v.exceedance = 1-(i/solarYearly.length)\n v.normalisedYearlySpecificYield = v.yearlySpecificYield/solarMedianAnualSpecificYield\n return v\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":96,"value":"solarCalmonthlyHours={\n let mons = Array.from(new Array(12), (x,i) => i+1)\n\n return mons.map(m=>{\n return d3.rollups(solarHourly.filter(d=>d.month==m), v=>{\n return {\n hour: v[0].hour,\n meanHourlyCapFactor: d3.mean(v,l=>l.capFactor),\n p90HourlyCapFactor: d3.quantile(v, 0.9, l=>l.capFactor),\n p10HourlyCapFactor: d3.quantile(v, 0.1, l=>l.capFactor)\n }\n },v=>v.hour).map(v=>v[1])\n })\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":89,"value":"$40","pinned":false,"hidden":false,"mode":"ojs"},{"id":93,"value":"solarYearly = d3.rollups(solarMonthly, d => {\n return {\n year: d[0].year,\n yearlySpecificYield: d3.sum(d,v=>v.monthlySpecificYield)\n }\n}, d => d.year).map(v=>v[1])","pinned":false,"hidden":false,"mode":"ojs"},{"id":70,"value":"solarMonthly = d3.rollups(solarDaily, d => {\n return {\n date: d[0].date.startOf('month'),\n year: d[0].date.year,\n month: d[0].date.month,\n meanDailyCapFactor: d3.mean(d, v=>v.dailyCapFactor),\n monthlySpecificYield: d3.sum(d, v => v.dailyCapFactor * 24) //MWh/MW\n }\n}, d => d.date.startOf('month')).map(v=>v[1])","pinned":false,"hidden":false,"mode":"ojs"},{"id":688,"value":"solarYears = d3.groups(solarDaily, v=>v.date.year).map(v=>v[1])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1634,"value":"solarDaily[solarDaily.length-1]","pinned":true,"hidden":false,"mode":"ojs"},{"id":11,"value":"solarDaily = d3.rollups(solarHourly,v=>{\n let ret = {\n date: v[0].date,\n year: v[0].year,\n month: v[0].month,\n day: v[0].day,\n dailyCapFactor: (d3.sum(v,h=>h.energyMWh))/ (solarCapacityMW*24)\n }\n ret.dailySpecificYield = ret.dailyCapFactor * 24 \n \n return ret\n}, v=>v.date.startOf('day')).map(v=>v[1])","pinned":false,"hidden":false,"mode":"ojs"},{"id":24,"value":"solarHourly=iluteYield_raw.map(v=> {\n v.date=DateTime.fromObject({year:v.year,month:v.month,day:v.day, hour: v.hour})\n //v.ghi=parseFloat(v.ghi)\n v.energyMWh=parseFloat(v.kw)/25*solarCapacityMW/1000\n v.capFactor = parseFloat(v.kw)/(25*1000)\n v.year=parseInt(v.year)\n v.month=parseInt(v.month)\n v.day=parseInt(v.day)\n v.hour=parseInt(v.hour)\n return v\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":138,"value":"solarCapacityMW = 11","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"iluteYield_raw = FileAttachment(\"ilute_c@1.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1163,"value":"

Setup

","pinned":false,"hidden":false,"mode":"html"},{"id":1160,"value":"colors={\n return {\n wind: d3.quantize(d3.interpolateHcl(\"#2874a6\", \"#666b74\"), 12),\n solar: d3.quantize(d3.interpolateHcl(\"#d68910\", \"#747266\"), 12),\n combined: d3.quantize(d3.interpolateHcl(\"#922b21\", \"#797d7f\"), 12),\n storage: d3.quantize(d3.interpolateHcl(\"#2874a6\", \"#aed6f1\"), 12), \n demand: d3.quantize(d3.interpolateHcl(\"#1e8449\", \"#45b39d\"), 12), \n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":259,"value":"months=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']","pinned":false,"hidden":false,"mode":"ojs"},{"id":686,"value":"monthDays=[31,28,31,30,31,30,31,31,30,31,30,31]","pinned":false,"hidden":false,"mode":"ojs"},{"id":51,"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":884,"value":"$41","pinned":false,"hidden":false,"mode":"ojs"},{"id":636,"value":"makeTrans=function(col,opacity) {\n let c=d3.color(col)\n c.opacity=opacity\n return c.formatRgb()\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":910,"value":"d3hsv = require(\"d3-hsv@0.1\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":908,"value":"colr=d3.quantize(d3.interpolateHcl(\"#d68910\", \"#747266\"), 12)","pinned":false,"hidden":false,"mode":"ojs"},{"id":190,"value":"style=html`css `","pinned":false,"hidden":false,"mode":"ojs"},{"id":53,"value":"font = ({\n family: 'Courier New, monospace',\n size: 13,\n color: '#6f6f6f'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":16,"value":"luxon = require(\"luxon@1/build/amd/luxon.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":18,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":14,"value":"DateTime = luxon.DateTime","pinned":false,"hidden":false,"mode":"ojs"},{"id":43,"value":"plt = require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W42","stdlib":"1","titling":"auto"}