31:T6ed,html `

Introduction

The current design and operating principals of the Ngonye Falls hydroelectic project are for a true run-of-river operation with zero water storage and constant power output dependent only on the flow of water in the river.

However, there may be opportunites to sell power to offtakers or through markets that provide time-of-day tariffs. In particular the Southern African Power Pool (SAPP) markets have trades segmented into time periods and trade prices have differentiated based on those time segments.

To take advantage of time-of-day tariffs, the mode of operation of the power station would be altered so that power output is curtailed during off-peak, low tariff periods with water stored in the heapdond behind the headworks weir. During peak, high tariff periods the headpond level would be drawn down and the additional water used to increase power output in these periods.

Overall energy production would be identical under both modes of operation but under 'peaking' operation a proportion of the daily energy production would be transferred from the off-peak to the peak periods. In theory, the maximum amount of energy transfer would be achieved by having zero generation flow during off-peak periods. Whatever proportion of the generation flows are held back from off-peak periods to be used during peak periods, the project's environmental flow requirements will have to be met. `32:T52c,html`

Environmental and Social Impact

The primary constraints on the operation of the project with intra-day storage are environmental and social. In particular, the peaking operation will have an impact on water levels in the headpond (upstream of the headworks) and downstream of the tailrace.

In the river downstream of the tailrace flows (and water elevation) will be raised during peak periods and fall during the off-peaks. The change in flow rates (and water elevation) between the peak and off-peak periods will be relatively rapid.

Conversely at the headpond, the water elevation will rise gradually during off-peak periods as water is stored and then gradually fall during peak periods as flow is released for generation. Because of the area of the heapond compared to the area of the downstream gorge, the change in water elevation will be considerably less upstream compared to below the tailrace.

The changes in water elevation due to peaking operations will decrease with distance upstream (due to the effect of the backwater curve) and downstream (due to attenuation within the river course). Detailed hydrological modelling will be required to determine how far these effects stretch. `33:T6ec,{ let data = selected_months.map((m, i)=>{ const ser = monthProfiles[monthDict[m].index] return { y: [...Array(24).keys()].map(h=>ser.get(h)), mode: 'lines', line: {shape: 'spline', color: d3.schemeDark2[monthDict[m].group], width:2}, fill:'', type: 'scatter', hoverinfo: 'x,y', name: m, } }) var layout = { height: 260, width: width, font: font, showlegend: true, legend: {xanchor: 'left', x:1,y: 1}, margin: {l: 90,r: 90,b: 30,t: 0}, xaxis: { title: '', showgrid: true, zeroline: false, dtick: 1, ticks:'outside', }, yaxis: { range: [20,170], title: `$/MWh`, showgrid: false, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html` ${div}

Mean SAPP DAM tariff by time-of-day for selected months. Nighttime, off-peak prices are consistently low between 22h and 6h. A morning peak from 6h to 9h is considerably more pronounced in the cold winter months, June July and August. Daytime, standard, prices are consistent across the year. The timing, average price and length of the evening peak varies through the year. In the winter months, June July and August, the peak has higher average pricing and lasts three hours from 17h to 20h. In the summer months, January, February and March, the peak is shorter and later, from 19h to 21h. In other months the peak is two hours on average from 18h to 20h. There is a one or two hour 'shoulder' of standard prices after the evening peak and before the off-peak. Prices during the evening peak are on average higher than during the morning peak.

` }34:T808,{ let margin = ({top: 20, right: 65, bottom: 0, left: 73}) let height=300 let ys=timeofday.map(d => d.dayofweek == 1 ? monthNames[d.month-1] : d.dayofweek == 6 ? 'Sat' : 'Sun') ys.splice(12,0,'') let y = d3.scaleBand() .domain(ys) .range([margin.top, height - margin.bottom]) .padding(0.1) .round(true) let x = d3.scaleLinear() .domain([0, 24]) .range([margin.left, width - margin.right]) .interpolate(d3.interpolateRound) const svg = d3 .create("svg") .attr("viewBox", [0, 0, width, height]) .style("max-width", `${width}px`) .style("height", "auto") svg.append("g") .attr('transform', `translate(0,${margin.top})`) .call(d3.axisTop(x).ticks(23)) .call(g => g.select(".domain").remove()) .call(g => g.selectAll(".tick text") .attr("font-size", 12)) svg.append("g") .attr('transform', `translate(${margin.left},0)`) .call(d3.axisLeft(y)) .call(g => g.select(".domain").remove()) .call(g => g.selectAll(".tick text") .attr("font-size", 12)) svg.append("g") .attr('stroke', 'white') .attr('stroke-width', '1') .attr('fill-opacity', '0.5') .selectAll("rect") .data(timeofday_flat) .join("rect") .attr('y', d=>y(d.dayofweek == 1 ? monthNames[d.month-1] : d.dayofweek == 6 ? 'Sat' : 'Sun')) .attr('x', d=>x(d.hour)) .attr('width', x(1)-x(0)) .attr('height', y.bandwidth()) .attr('fill', d=>catCol(cats[d.cat].index) ) return html` ${svg.node()}

Time of day pricing patterns on the SAPP DAM market by month and for Saturdays and Sundays. Nighttime, off-peak: Blue. Moring peak: Orange. Daytime, standard: Green. Evening peak: Red. The timing of the morning peak is consistent across the year but the evening peak varies in timing and duration by season. Intra-day storage would be during the night, off-peak and daytime standard periods with additional flow released during the morning and evening peaks.

` }35:T7dd,{ const energy_mean=d3.mean(pe_calmonths, mnth=>mnth.Energy_median) const energies=pe_calmonths.sort((a,b)=>b.Month-a.Month) function series(ref) { return { y: sapp_calmonths.map(v=>v[ref + '_price_mean']), x: sapp_calmonths.month, mode: 'lines', line: {shape: 'spline', color: d3.schemeCategory10[cats[ref].index], width:2}, fill:'', type: 'scatter', hoverinfo: 'x,y', name: cats[ref].name, } } var data = [ series('evening'), series('morning'), series('standard'), series('off'), { x:energies.map(m=>m.Month-1),//['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'], y:energies.map(mnth=>mnth.Energy_median/1000), hoverinfo:'none', text: energies.map(mnth=>d3.format('.0f')(mnth.Energy_median/1000)), type: 'bar',mode: 'markers+text', yaxis:'y2', textposition:'auto', showlegend:false,marker: {color:colr(1,0.6)} }, ] var layout = { height: 260, width: width, font: font, showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 90,r: 90,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside', dtick:1, tickvals:[...Array(12).keys()], ticktext:['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'], }, yaxis: { title: `$/MWh`, range:[0,d3.max(sapp_calmonths.map(v=>v.evening_price_mean))*1.1], showgrid: false, zeroline: false, tickformat: '.0f', ticks:'outside' }, yaxis2: { range: [0,160], overlaying: 'y',side: 'right', title: `GWh`, showgrid: false, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html` ${div}

Average time-of-day tariffs on the SAPP DAM market - lines and average Ngonye Falls station energy produciton - bars by calendar month.

` }36:T683,{ var data = [{ y: catsMeans.map(m=>m[1]), x: catsMeans.map(m=>cats[m[0]].name), text: catsMeans.map((m,i)=>d3.format('$,.2r')(m[1]) +'/MWh' ), textposition:'auto', marker: {color: catsMeans.map(m=>catCol(cats[m[0]].index))}, type: 'bar', hoverinfo: 'none', showlegend:false },{ y: [catsMeans[1][1] - catsMeans[0][1],catsMeans[3][1] - catsMeans[2][1]], x: ['Off-peak','Standard'], text: [ d3.format('$,.2r')(catsMeans[1][1] - catsMeans[0][1]), d3.format('$,.2r')(catsMeans[3][1] - catsMeans[2][1]) ], textposition:'auto', marker: {color: [makeTrans(catCol(1),0.4),makeTrans(catCol(3),0.4)]}, type: 'bar', hoverinfo: 'none', showlegend:false }] var layout = { height: 180, width: width*.5, font: font, barmode:'stack', showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 65,r: 10,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside', dtick:1}, yaxis: { range:[0,280], title: '$/MWh', showgrid: false, zeroline: true, tickformat: ',.0f', ticks:'outside' }}; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html` ${div}

Overall avarage prices on the SAPP DAM market by time of day category and difference between off-peak and morning and standard and evening categories. The differences between tariffs according to the time of day gives value to storage.

` }37:T969,{ let data = [{ x: [0.27,0,0,0.27], y: [200,200,0,0], fill: 'toself', mode:'none', hoverinfo:'none', fillcolor: 'rgba(220,120,120,0.3)', showlegend:false, type: 'scatter' },{ name: 'No storage', x: modelledFdcMarch[0].map(v => v.exceedance), y: movingAverage(modelledFdcMarch[0].map(v => v.power),10), text: movingAverage(modelledFdcMarch[0].map(v => v.flow),10).map(v=> { return `${d3.format('.0f')(v)}`}), type: 'scatter', mode: 'lines', textposition:'outside', hoverinfo: 'x+y+text', showlegend:false, line: {color: 'rgb(85, 107, 47)',shape: 'spline', width:3} }] data = data.concat(hydrology_calmonths.map((m,i)=> { let pos = i==2?'top-left':i==4?'top-right':'top' return { x: [flowExceedance(m.Flow_median), flowExceedance(m.Flow_median)], y: [0,200], text: ['',monthNames[i]], textposition:pos, hoverinfo:'none', type: 'scatter', name: monthNames[i], mode: 'lines+text', line: {color:'grey', width:1}, showlegend: false } })) var layout = { width: width*0.7, height:300, font: font, annotations: [ { x: 0.13, y: 50, text: 'No storage
Head curtailed output', showarrow: false, }], margin: {l: 65,r: 10,b: 45,t: 10}, showlegend: true, legend: {x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', range: [1,0], tickformat: '%.0f', title: 'Flow exceedance' }, yaxis: { title: 'Power (MW)', range: [0,230], zeroline: false, showline: false, separatethousands:true, tickformat: '.0f' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`

${div}

Power station output for full range of flows. Flows are plotted on a time exceedance basis with low flows on the left and highest flows on the right. At high flows the plant output is reduced due to low head and at low flows the output is curailed by low flow. The median flow for each calendar month is also shown. At flows above 1,257 m\u00B3/s, within the shaded area, no storage is available because the plant is operating at its maximum flow capcity of of 1,100 m\u00B3/s throughout the day. Median (P50) flow for each claendar month is also shown.

` }38:T446,html ` At flows below around ${formatFlow(1250)} (greater than P27) the daily energy produciton of the plant is lower than the maximum as there is insufficuent flow to maintain maximum power throughout the day.

To provide intra-day storage at Ngonye the operation of the plant would be varied to reduce generation during the nightime off-peak and daytime standard periods, with excess water being held in the headpond and then released during the morning and evening peak periods to boost power output during these periods. The overall energy produced during the day would not change compared to the no-storage scenario.

At flows above around ${formatFlow(1250)} (less than P27) there is sufficient flow in the river for the plant to operate at maximum capacity (${formatFlow(1100)} canal flow) throughout the day. Under these higher flow conditions therefore there is no opportunity for intra-day storage as the plant is working at maximum power output throughout the day. Considering median (P50) river flows then storage will not be available during March, April and May. `39:T474,{ var data = [{ x: scenarios.map(v => v.name), y: scenarios.map(v => v.flowMin), text: scenarios.map(v => formatFlow(v.flowMin)), type: 'bar', hoverinfo: 'y', marker:{color: [colr(1),colr(3),colr(5),colr(7)], opacity:0.8}, showlegend:false, textposition:'outside' }] var layout = { width: width*0.5, height:200, font: font, bargap:0.05, margin: {l: 85,r: 10,b: 45,t: 20}, showlegend: true, legend: {orientation: 'h', x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', dtick:1, tickfont: {size: 12} }, yaxis: { zeroline: true, showline: false, range: [0, 290], separatethousands:true, tickformat: ',.0f', title: 'Minimum
off-peak flow' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`

${div}

Four storage scenarios modelled. In each case the volume of water stored is limited by setting the minimum off-peak flow ranging from ${formatFlow(250)} to ${formatFlow(0)}.

` }3a:T8b4,{ function trace(name, set, series, col, dash) { return { name: name, x: set.filter(v=>v[series]).map(v => v.exceedance), y: movingAverage(set.filter(v=>v[series]).map(v => v[series]),10), type: 'scatter', mode: 'lines', hoverinfo: 'none', showlegend:true, line: {color: col,shape: 'spline', dash: dash, width:1.5} } } var data = [ trace('Night off-peak', modelledFdcMarch[0], 'power_Off', colr(1, 0.5), 'dashdot'), trace('Morning peak', modelledFdcMarch[0], 'power_Morning', colr(1, 0.5), ''), trace('Standard', modelledFdcMarch[0], 'power_Standard', colr(1), 'dashdot'), trace('Evening peak', modelledFdcMarch[0], 'power_Evening', colr(1), ''), ] data.push({ name: 'No storage', x: modelledFdcMarch[0].map(v => v.exceedance), y: movingAverage(modelledFdcMarch[0].map(v => v.power),10), text: movingAverage(modelledFdcMarch[0].map(v => v.flow),10).map(v=>d3.format('.0f')(v)), type: 'scatter', mode: 'lines', textposition:'outside', hoverinfo: 'x+y+text', showlegend:true, line: {color: 'rgb(85, 107, 47)',shape: 'spline', width:3} }) var layout = { width: width*0.9, height:350, font: font, margin: {l: 65,r: 10,b: 45,t: 40}, showlegend: true, legend: {x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', range: [1,0], tickformat: '%.0f', title: 'Flow exceedance' }, yaxis: { title: 'Power (MW)', range: [0,210], zeroline: false, showline: false, separatethousands:true, tickformat: '.0f' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`

${div}

Power station output across the full range of flows without storage - dark green - and at different times of day for a storage scenario - orange. The storage scenario is for off-peak generation set to one turbine rated flow of ${formatFlow(250)}. Assumes Jan - March SAPP DAM tariff category hours - 8 off-peak night, 3 morning peak, 10 standard and 2 evening peak hours (plus 1 standard hour after the evening peak not used for storage).

` }3b:Tab6,{ function trace(name, set, series, col, dash, legend) { return { name: name, x: set.filter(v=>v[series]).map(v => v.exceedance), y: movingAverage(set.filter(v=>v[series]).map(v => v[series]),10), type: 'scatter', mode: 'lines', hoverinfo: 'y', showlegend: legend, line: {color: col,shape: 'spline', dash: dash, width:1.5} } } let mon=6 var data = [ trace('', modelledFdcJune[3], 'power_Standard', colr(7), 'dashdot', false), trace(scenarios[3].name, modelledFdcJune[3], 'power_Evening', colr(7), '', true), trace('', modelledFdcJune[2], 'power_Standard', colr(5), 'dashdot', false), trace(scenarios[2].name, modelledFdcJune[2], 'power_Evening', colr(5), '', true), trace('', modelledFdcJune[1], 'power_Standard', colr(3), 'dashdot', false), trace(scenarios[1].name, modelledFdcJune[1], 'power_Evening', colr(3), '', true), trace('', modelledFdcJune[0], 'power_Standard', colr(1), 'dashdot', false), trace(scenarios[0].name, modelledFdcJune[0], 'power_Evening', colr(1), '', true), ] data.push({ name: 'No storage', x: modelledFdcJune[0].map(v => v.exceedance), y: movingAverage(modelledFdcJune[0].map(v => v.power),10), text: movingAverage(modelledFdcJune[0].map(v => v.flow),10).map(v=>d3.format('.0f')(v)), type: 'scatter', mode: 'lines', textposition:'outside', hoverinfo: 'x+y+text', showlegend:false, line: {color: 'rgb(85, 107, 47)',shape: 'spline', width:3} }) data = data.concat(hydrology_calmonths.map((m,i)=> { let pos = i==2?'bottom-left':i==4?'bottom-right':'bottom' return { x: [flowExceedance(m.Flow_median), flowExceedance(m.Flow_median)], y: [0,200], text: [monthNames[i], ''], textposition:pos, hoverinfo:'none', type: 'scatter', name: monthNames[i], mode: 'lines+text', line: {color:'grey', width:1}, showlegend: false } })) var layout = { width: width*0.9, height:450, font: font, margin: {l: 65,r: 10,b: 45,t: 40}, showlegend: true, legend: {orientation: 'h', x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', range: [1,0], tickformat: '%.0f', title: 'Flow exceedance' }, yaxis: { title: 'Power (MW)', range: [-10,230], zeroline: false, showline: false, separatethousands:true, tickformat: '.0f' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`

${div}

Power station output across the full range of flows for all four storage scenarios and the no storage case. Assumes June - August SAPP DAM tariff category hours.

` }3c:T93b,{ function trace(name, set, col, dash) { let max = set[d3.maxIndex(set, v => (v.energyTransfer_Morning+v.energyTransfer_Evening))] console.log(max) return [{ x: [max.exceedance], y: [max.energyTransfer_Morning+max.energyTransfer_Evening], text: [d3.format('.2r')(max.energyTransfer_Morning+max.energyTransfer_Evening) + 'MWh'], type: 'scatter', mode: 'markers+text', textposition: 'top-right',showlegend: false, marker: {size:15, color:'grey'}, hoverinfo: 'none', },{ name: name, x: set.filter(v=>v.energyTransfer_Morning).map(v => v.exceedance), y: movingAverage(set.filter(v=>v.energyTransfer_Morning).map(v => (v.energyTransfer_Morning+v.energyTransfer_Evening)),5), type: 'scatter', mode: 'lines', hoverinfo: 'x+y', showlegend:true, line: {color: col, shape: '', dash: dash, width:1.5} }] } let mon=6 var data = [] data=data.concat(trace(scenarios[0].name, modelledFdcJune[0], colr(1, 1), '')) data=data.concat(trace(scenarios[1].name, modelledFdcJune[1], colr(3, 1), '')) data=data.concat(trace(scenarios[2].name, modelledFdcJune[2], colr(5, 1), '')) data=data.concat(trace(scenarios[3].name, modelledFdcJune[3], colr(7, 1), '')) data=data.concat([{ x: [0.27,0,0,0.27], y: [1000,1000,0,0], fill: 'toself', mode:'none', fillcolor: 'rgba(220,120,120,0.3)', showlegend:false, type: 'scatter' }]) var layout = { width: width*0.9, height:320, font: font, margin: {l: 65,r: 10,b: 45,t: 20}, annotations: [ { x: 0.15, y: 200, text: 'No storage', showarrow: false, }], showlegend: true, legend: {orientation: 'h', x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', range: [1,0], tickformat: '%.0f', title: 'Flow exceedance' }, yaxis: { title: 'MWh / day', range: [0, 1100], zeroline: true, showline: false, separatethousands:true, tickformat: '.0f' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`

${div}

Total daily energy storage and transfer achievable depending on river flow for four storage scenarios. Assumes June - August SAPP DAM tariff category hours.

` }3d:T43e,{ function trace(scen , no) { let xs = Object.entries(monthNames).map(v=>v[1]) let ys = scen.map((m,i) => (m.energyTransferTotal/1000)) return { name: scenarios[no].name, x: xs, y: ys, text: scen.map((m,i) => (d3.format(',.2r')(m.energyTransferTotal/1000))), textposition:'inside', type: 'bar', hoverinfo: 'y', marker:{color: colr((no*2)+1), opacity:0.8}, showlegend:true } } var data = modelledMonths.map((c, i) => trace(c, i)) var layout = { width: width, height:200, font: font, bargap:0.05, margin: {l: 65,r: 10,b: 5,t: 20}, showlegend: true, legend: {orientation: 'h', x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', dtick:1,showticklabels:false, }, yaxis: { zeroline: true, showline: false, title:'GWh', separatethousands:true, tickformat: '.0f', } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`

${div} ` }3e:T69b,{ function trace(scen , no) { let xs = Object.entries(monthNames).map(v=>v[1]) let ys = scen.map((m,i) => ((m.valueTotal - modelledMonthNoStore[i].valueTotal))).map(v=>(v/1000)) return { name: scenarios[no].name, x: xs, y: ys, text: scen.map((m,i) => (d3.format('.0%')((m.valueTotal - modelledMonthNoStore[i].valueTotal) / modelledMonthNoStore[i].valueTotal))), textposition:'inside', type: 'bar', hoverinfo: 'y', marker:{color: colr((no*2)+1), opacity:0.8}, showlegend:false } } var data = modelledMonths.map((c, i) => trace(c, i)) var layout = { width: width, height:200, font: font, bargap:0.05, margin: {l: 65,r: 10,b: 30,t: 0}, annotations: [ { x: 'Apr', y: 1000, text: 'High flow months.
No storage available.', showarrow: false, }, { x: 'Oct', y: 1200, text: 'Low flow months.
.', showarrow: false, }], showlegend: true, legend: {orientation: 'h', x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', dtick:1, }, yaxis: { zeroline: true, showline: false, title: `$ 000's`, separatethousands:true, tickformat: ',.0f', range: [0,1900] } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`

${div}

Transfer of energy from off-peak to peak periods (top) and additional energy value due to storage (bottom) by calendar month for each of four energy storage scenarios each with progressively lower off-peak flow and generation.

` }3f:T78e,{ function trace(name, set, col, dash) { let max = set[d3.maxIndex(set, v => (v.tailwaterChange_Off))] return [{ x: [max.exceedance], y: [max.tailwaterChange_Off*100], text: [d3.format('.2r')(max.tailwaterChange_Off*100) + 'cm'], type: 'scatter', mode: 'markers+text', textposition: 'top-right',showlegend: false, marker: {size:15, color:'grey'}, hoverinfo: 'none', },{ name: name, x: set.filter(v=>v.tailwaterChange_Off).map(v => v.exceedance), y: movingAverage(set.filter(v=>v.tailwaterChange_Off).map(v => (v.tailwaterChange_Off*100)),5), type: 'scatter', mode: 'lines', hoverinfo: 'x+y', showlegend:true, line: {color: col, shape: '', dash: dash, width:1.5} }] } let mon=6 var data = [] data=data.concat(trace(scenarios[0].name, modelledFdcJune[0], colr(1, 1), '')) data=data.concat(trace(scenarios[1].name, modelledFdcJune[1], colr(3, 1), '')) data=data.concat(trace(scenarios[2].name, modelledFdcJune[2], colr(5, 1), '')) data=data.concat(trace(scenarios[3].name, modelledFdcJune[3], colr(7, 1), '')) var layout = { width: width*0.9, height:320, font: font, margin: {l: 65,r: 10,b: 45,t: 20}, showlegend: true, legend: {orientation: 'h', x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', range: [1,0], tickformat: '%.0f', title: 'Flow exceedance' }, yaxis: { title: 'cm', zeroline: true, showline: false, range: [0,450], separatethousands:true, tickformat: '.0f' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`

${div}

Twice-daily change in tailwater level dowstream of the powerstation depending on river flow for four storage scenarios. Assumes June - August SAPP DAM tariff category hours.

` }40:T79c,{ function trace(name, set, col, dash) { let max = set[d3.maxIndex(set, v => (v.headpondLevelChange_Off))] return [{ x: [max.exceedance], y: [max.headpondLevelChange_Off*100], text: [d3.format('.2r')(max.headpondLevelChange_Off*100) + 'cm'], type: 'scatter', mode: 'markers+text', textposition: 'top-right',showlegend: false, marker: {size:15, color:'grey'}, hoverinfo: 'none', },{ name: name, x: set.filter(v=>v.headpondLevelChange_Off).map(v => v.exceedance), y: movingAverage(set.filter(v=>v.headpondLevelChange_Off).map(v => (v.headpondLevelChange_Off*100)),5), type: 'scatter', mode: 'lines', hoverinfo: 'x+y', showlegend:true, line: {color: col, shape: '', dash: dash, width:1.5} }] } let mon=6 var data = [] data=data.concat(trace(scenarios[0].name, modelledFdcJune[0], colr(1, 1), '')) data=data.concat(trace(scenarios[1].name, modelledFdcJune[1], colr(3, 1), '')) data=data.concat(trace(scenarios[2].name, modelledFdcJune[2], colr(5, 1), '')) data=data.concat(trace(scenarios[3].name, modelledFdcJune[3], colr(7, 1), '')) var layout = { width: width*0.9, height:320, font: font, margin: {l: 65,r: 10,b: 45,t: 20}, showlegend: true, legend: {orientation: 'h', x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', range: [1,0], tickformat: '%.0f', title: 'Flow exceedance' }, yaxis: { title: 'cm', zeroline: true, showline: false, range: [0,90], separatethousands:true, tickformat: '.0f' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`

${div}

Twice-daily change in headpond level upstream of the headworks depending on river flow for four storage scenarios. Assumes June - August SAPP DAM tariff category hours.

` }41:T5cc,{ function trace(name, set, col, dash) { return [{ name: name, x: [...Array(24).keys()], y: [...Array(24).keys()].map(v => set[134].profilePower(v)), type: 'scatter', mode: 'lines', hoverinfo: 'y', fill: 'tonexty', showlegend:true, line: {shape:'hv', color: col, dash: dash, width:1.5} }] } let mon=3 var data = [] data=data.concat(trace(scenarios[0].name, modelledFdcJune[0], colr(1, 1), '')) data=data.concat(trace(scenarios[1].name, modelledFdcJune[1], colr(3, 1), '')) data=data.concat(trace(scenarios[2].name, modelledFdcJune[2], colr(5, 1), '')) data=data.concat(trace(scenarios[3].name, modelledFdcJune[3], colr(7, 1), '')) var layout = { width: width*0.9, height:220, font: font, margin: {l: 65,r: 10,b: 45,t: 20}, showlegend: true, legend: {orientation: 'h', x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', tickformat: '.0f', dtick: 2 }, yaxis: { title: 'MW', range: [0,250], zeroline: true, showline: false, separatethousands:true, tickformat: '.0f' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`

${div}

Daily profile of power output for four storage scenarios at P${fdc[134].ExceedanceRnd*100}, ${formatFlow(fdc[134].Flow)}. Assumes June - August SAPP DAM tariff category hours.

` }42:T5cb,{ function trace(name, set, col, dash) { return [{ name: name, x: [...Array(24).keys()], y: [...Array(24).keys()].map(v => set[134].profileHeadpond(v)*100), type: 'scatter', mode: 'lines', hoverinfo: 'y', fill: 'tonexty', showlegend:true, line: {shape:'', color: col, dash: dash, width:1.5} }] } let mon=3 var data = [] data=data.concat(trace(scenarios[0].name, modelledFdcJune[0], colr(1, 1), '')) data=data.concat(trace(scenarios[1].name, modelledFdcJune[1], colr(3, 1), '')) data=data.concat(trace(scenarios[2].name, modelledFdcJune[2], colr(5, 1), '')) data=data.concat(trace(scenarios[3].name, modelledFdcJune[3], colr(7, 1), '')) var layout = { width: width*0.9, height:220, font: font, margin: {l: 65,r: 10,b: 45,t: 20}, showlegend: true, legend: {orientation: 'h', x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', tickformat: '.0f', dtick:2 }, yaxis: { title: 'cm', range: [0,100], zeroline: true, showline: false, separatethousands:true, tickformat: '.0f' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`

${div}

Change in headpond level for four storage scenarios at P${fdc[134].ExceedanceRnd*100}, ${formatFlow(fdc[134].Flow)}. Assumes June - August SAPP DAM tariff category hours.

` }43:T5c4,{ function trace(name, set, col, dash) { return [{ name: name, x: [...Array(24).keys()], y: [...Array(24).keys()].map(v => set[134].profileTailwater(v)), type: 'scatter', mode: 'lines', hoverinfo: 'y', fill: 'tonexty', showlegend:true, line: {shape:'hv', color: col, dash: dash, width:1.5} }] } let mon=3 var data = [] data=data.concat(trace(scenarios[0].name, modelledFdcJune[0], colr(1, 1), '')) data=data.concat(trace(scenarios[1].name, modelledFdcJune[1], colr(3, 1), '')) data=data.concat(trace(scenarios[2].name, modelledFdcJune[2], colr(5, 1), '')) data=data.concat(trace(scenarios[3].name, modelledFdcJune[3], colr(7, 1), '')) var layout = { width: width*0.9, height:220, font: font, margin: {l: 65,r: 10,b: 45,t: 20}, showlegend: true, legend: {orientation: 'h', x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', tickformat: '.0f', dtick:2 }, yaxis: { title: 'mASL', range:[964,970], zeroline: true, showline: false, separatethousands:true, tickformat: '.0f' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`

${div}

Tailwater level for four storage scenarios at P${fdc[134].ExceedanceRnd*100}, ${formatFlow(fdc[134].Flow)}. Assumes June - August SAPP DAM tariff category hours.

` }44:Tc51,function summaryBars() { function ch1() { let xs = ['No storage'] xs = xs.concat(scenarios.map(v => v.name)) let ys = [modelledYearNoStore.valueTotal/1000000] ys = ys.concat(modelledYears.map(v=>(v.valueTotal/1000000))) var data = [{ x: xs, y: ys, text: ys.map((v,i)=> { let ret = '' let div = 0 if (i>0) { div = modelledYears[i-1].energyTotal } else { div = modelledYearNoStore.energyTotal } ret+='$' + d3.format(',.0f')(v/div*1000000) + '/MWh' return ret }), type: 'bar', hoverinfo: 'y', marker:{color: ['grey', colr(1),colr(3),colr(5),colr(7)], opacity:0.8}, showlegend:false, textposition:'inside' }] var layout = { width: width*0.55, height:250, font: font, bargap:0.05, margin: {l: 40,r: 0,b: 45,t: 20}, showlegend: true, legend: {orientation: 'h', x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', dtick:1, tickfont: {size: 11} }, yaxis: { zeroline: true, showline: false, separatethousands:true, tickformat: ',.0f', range: [55,d3.max(ys)],title:'$m' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return div } function ch2() { const ys2 = modelledYears.map(v=>(v.valueTotal - modelledYearNoStore.valueTotal)/modelledYearNoStore.valueTotal) var data = [{ x: scenarios.map(v => v.name), y: ys2, text: modelledYears.map(v=>'$' + d3.format(',.2r')((v.valueTotal - modelledYearNoStore.valueTotal)/1000) + 'k
$' + d3.format(',.2r')((v.tariff - modelledYearNoStore.tariff)) + '/MWh'), type: 'bar', hoverinfo: 'y', marker:{color: [colr(1),colr(3),colr(5),colr(7)], opacity:0.8}, showlegend:false, textposition:'inside' }] var layout = { width: width*0.45, height:250, font: font, bargap:0.05, margin: {l: 40,r: 0,b: 45,t: 20}, showlegend: true, legend: {orientation: 'h', x: 0,xanchor: 'left',y: 1}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', dtick:1, tickfont: {size: 11} }, yaxis: { zeroline: true, showline: false, separatethousands:true, tickformat: '%.0f', range: [0,d3.max(ys2)] } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}) return div } return html`

${ch1()}

Average annual revenue and tariff achieved for average pricing on the SAPP DAM market for median annual energy under four storage scenarios.

${ch2()}

Increase in revenue and tariff achieved for median annual energy under four storage scenarios.

` }45:T634,monthChart=function(mnth, showlegend) { function trace(cat) { let xs = ['No store'].concat(scenarios.map(s => s.name)) let ys = [modelledMonthNoStore[mnth].valueProp[cat]] ys = ys.concat(modelledMonths.map(s => ((s[mnth].valueProp[cat] * s[mnth].valueTotal) / modelledMonthNoStore[mnth].valueTotal) )) return { name: cats[cat].name, x: xs, y: ys, type: 'bar', hoverinfo: 'y', marker:{color: catCol(cats[cat].index)}, showlegend:showlegend } } let mon=6 var data = catslist.map(c => trace(c)) let xs = ['No store'].concat(scenarios.map(s => s.name)) let ys = [1] ys = ys.concat(modelledMonths.map(s => (s[mnth].valueTotal / modelledMonthNoStore[mnth].valueTotal) )) data = data.concat({ name: '', x: xs, y: ys, text: ys.map(y => d3.format('.0%')(y)), type: 'scatter', hoverinfo: 'none', mode: 'text', textposition: 'top-center', showlegend: false }) var layout = { width: width*0.5, height:220, font: font, barmode: 'stack', title: {text: monthNames[mnth], font: {size: 14}, y:0.9, x:0.95}, margin: {l: 65,r: 10,b: 45,t: 40}, showlegend: true, legend: {orientation: 'h', x: 0,xanchor: 'left',y: 1.2}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', }, yaxis: { zeroline: true, showline: false, separatethousands:true, tickformat: '%.0f', range: [0, 1.8] } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`

${div} ` }46:T7b5,modelMonth = function(monthNo, scenario, flow) { let ret = { weekday: modelDay(monthNo, scenario, flow, false), saturday: modelDay(1, scenario, flow, true) } ret.month = monthNo ret.energy_median = pe_calmonths[monthNo-1].Energy_median ret.saturdays = monthWeekends(monthNo) ret.sundays = monthWeekends(monthNo) ret.weekdays = monthDays(monthNo) - ret.saturdays - ret.sundays ret.energy = { off: (ret.weekday.energy_Off * ret.weekdays) + ((ret.saturday.energy_Off + ret.saturday.energy_Standard) * ret.saturdays) + (ret.weekday.energyTotal * ret.sundays), morning: (ret.weekday.energy_Morning * ret.weekdays), standard: ((ret.weekday.energy_Standard + ret.weekday.energy_StandardShoulder) * ret.weekdays) + ((ret.saturday.energy_Morning + ret.saturday.energy_Evening) * ret.saturdays), evening: (ret.weekday.energy_Evening * ret.weekdays), } ret.energyTransfer = { morning: (ret.weekday.energyTransfer_Morning * ret.weekdays) + (ret.saturday.energyTransfer_Morning * ret.saturdays), evening: (ret.weekday.energyTransfer_Evening * ret.weekdays) + (ret.saturday.energyTransfer_Evening * ret.saturdays), } ret.energyTransferTotal = ret.energyTransfer.morning + ret.energyTransfer.evening ret.value = { off: ret.energy.off * sapp_calmonths[monthNo-1].off_price_mean, morning: ret.energy.morning * sapp_calmonths[monthNo-1].morning_price_mean, standard: ret.energy.standard * sapp_calmonths[monthNo-1].standard_price_mean, evening: ret.energy.evening * sapp_calmonths[monthNo-1].evening_price_mean, } ret.valueTotal = ret.value.off + ret.value.morning + ret.value.standard + ret.value.evening ret.valueProp = { off: ret.value.off / ret.valueTotal, morning: ret.value.morning / ret.valueTotal, standard: ret.value.standard / ret.valueTotal, evening: ret.value.evening / ret.valueTotal, } return ret }47:T1f65,// Prepare storage and energy profiles for a day modelDay = function(monthNo, scenario, flow, saturday) { let model = {} model.hours = hourCounts(monthNo, saturday) let ngo = fdc.filter(v=>v.Flow<=flow)[0] // Copy over model.flow = ngo.Flow model.flowEWR = ngo.EWRTotal model.flowSpill = ngo.FlowSpill model.levelTailwater = ngo.LevelTailwater model.levelHeadpond = ngo.LevelHeadpond model.nethead = ngo.HeadTurbine1 model.energy = ngo.Energy model.power = ngo.Power model.exceedance = ngo.Exceedance // Total daily generation volume Mm3 model.generationVolume = ngo.FlowCanal * (24 * 60 * 60) / 1e6 // Rough calculation of the maximum flow during peak time - flow that produced maximum output at current head and high efficiency model.maxGenerationFlow = params.maxPlantPower / ngo.HeadTurbine1 / 9.81 / params.effectiveEfficiency * 1000 // Limit maximum flow to canal capacity model.maxGenerationFlow = d3.min([model.maxGenerationFlow, params.canalCapacity]) // Maximum flow that could be used during the peak periods from storage m3/s model.maxFlowFromStore = model.maxGenerationFlow - ngo.FlowCanal // Max volume that could be used by the peaks Mm3 if (scenario.ref==='nostore') { model.maxVolFromStore_Morning = 0 model.maxVolFromStore_Evening = 0 } else { model.maxVolFromStore_Morning = model.maxFlowFromStore * (model.hours.morning * 60 * 60) / 1e6 model.maxVolFromStore_Evening = model.maxFlowFromStore * (model.hours.evening * 60 * 60) / 1e6 } if (scenario.ref==='nostore') { model.maxVolToStore_Off = 0 model.maxVolToStore_Standard = 0 } else { // Max volume that could be taken from the night period for storage Mm3 model.maxVolToStore_Off = d3.max([((ngo.FlowAvailableForGeneration - scenario.flowMin) * (model.hours.off * 60 * 60) / 1e6),0]) // Max volume that could be taken from the standard period for storage Mm3 model.maxVolToStore_Standard = d3.max([((ngo.FlowAvailableForGeneration - scenario.flowMin) * (model.hours.standard * 60 * 60) / 1e6),0]) } // Volume per night that will be stored (min of available or needed) model.storeVol_Off = d3.min([model.maxVolFromStore_Morning, model.maxVolToStore_Off]) // Not enough head for generation so no point storing if (ngo.LowHeadShutoff === 1) { model.storeVol_Off = 0 } // Volume per standard that will be stored (min of available or needed) model.storeVol_Standard = d3.min([model.maxVolFromStore_Evening, model.maxVolToStore_Standard]) // Not enough head for generation so no point storing if (ngo.LowHeadShutoff === 1) { model.storeVol_Standard = 0 } //Flows model.extraFlow_Off = model.storeVol_Off / (60 * 60 * model.hours.off / 1e6) model.extraFlow_Morning = model.storeVol_Off/ (60 * 60 * model.hours.morning / 1e6) model.extraFlow_Standard = model.storeVol_Standard / (60 * 60 * model.hours.standard / 1e6) model.extraFlow_Evening = model.storeVol_Standard / (60 * 60 * model.hours.evening / 1e6) model.flowCanal_Off = ngo.FlowCanal - model.extraFlow_Off model.flowCanal_Standard = ngo.FlowCanal - model.extraFlow_Standard model.flowCanal_StandardShoulder = ngo.FlowCanal model.flowCanal_Morning = ngo.FlowCanal + model.extraFlow_Morning model.flowCanal_Evening = ngo.FlowCanal + model.extraFlow_Evening //Total flow model.flowTotal_Off = model.flowCanal_Off + ngo.FlowSpill + ngo.EWRTotal model.flowTotal_Morning = model.flowCanal_Morning + ngo.FlowSpill + ngo.EWRTotal model.flowTotal_Standard = model.flowCanal_Standard + ngo.FlowSpill + ngo.EWRTotal model.flowTotal_StandardShoulder = model.flowCanal_StandardShoulder + ngo.FlowSpill + ngo.EWRTotal model.flowTotal_Evening = model.flowCanal_Evening + ngo.FlowSpill + ngo.EWRTotal // Total volume model.volume_Off = model.flowCanal_Off * model.hours.off * 60 * 60 / 1e6 model.volume_Morning = model.flowCanal_Morning * model.hours.morning * 60 * 60 / 1e6 model.volume_Standard = model.flowCanal_Standard * model.hours.standard * 60 * 60 / 1e6 model.volume_StandardShoulder = model.flowCanal_StandardShoulder * model.hours.standardShoulder * 60 * 60 / 1e6 model.volume_Evening = model.flowCanal_Evening * model.hours.evening * 60 * 60 / 1e6 model.volumeTotal = model.volume_Off + model.volume_Morning + model.volume_Standard + model.volume_Evening + + model.volume_StandardShoulder // Energy model.energy_Off = ngo.Energy / model.generationVolume * model.volume_Off model.energy_Morning = ngo.Energy / model.generationVolume * model.volume_Morning model.energy_Standard = ngo.Energy / model.generationVolume * model.volume_Standard model.energy_StandardShoulder = ngo.Energy / model.generationVolume * model.volume_StandardShoulder model.energy_Evening = ngo.Energy / model.generationVolume * model.volume_Evening model.energyTotal = model.energy_Off + model.energy_Morning + model.energy_Standard + model.energy_Evening + + model.energy_StandardShoulder model.energyTransfer_Morning = ngo.Energy / model.generationVolume * model.storeVol_Off model.energyTransfer_Evening = ngo.Energy / model.generationVolume * model.storeVol_Standard // Power model.power_Off = model.energy_Off / model.hours.off model.power_Morning = model.energy_Morning / model.hours.morning model.power_Standard = model.energy_Standard / model.hours.standard model.power_StandardShoulder = model.energy_StandardShoulder / model.hours.standardShoulder model.power_Evening = model.energy_Evening / model.hours.evening //Headpond levels model.headpondLevelChange_Off = model.storeVol_Off / params.storageCurve model.headpondLevelChange_Standard = model.storeVol_Standard / params.storageCurve //Tailwater levels model.tailwaterLevel_Off = tailwaterLevel(model.flowTotal_Off) model.tailwaterLevel_Morning = tailwaterLevel(model.flowTotal_Morning) model.tailwaterLevel_Standard = tailwaterLevel(model.flowTotal_Standard) model.tailwaterLevel_StandardShoulder = tailwaterLevel(model.flowTotal_StandardShoulder) model.tailwaterLevel_Evening = tailwaterLevel(model.flowTotal_Evening) model.tailwaterChange_Off = model.tailwaterLevel_Morning - model.tailwaterLevel_Off model.tailwaterChange_Standard = model.tailwaterLevel_Evening - model.tailwaterLevel_Standard if (!saturday) { model.profilePower = function(hour) { let hourcat = hourCategory(monthNo, hour) if (hourcat === 'off') { return model.power_Off } else if (hourcat === 'morning') { return model.power_Morning } else if (hourcat === 'standard') { return model.power_Standard } else if (hourcat === 'evening') { return model.power_Evening } else { return model.power_StandardShoulder } } model.profileTailwater = function(hour) { let hourcat = hourCategory(monthNo, hour) if (hourcat === 'off') { return model.tailwaterLevel_Off } else if (hourcat === 'morning') { return model.tailwaterLevel_Morning } else if (hourcat === 'standard') { return model.tailwaterLevel_Standard } else if (hourcat === 'evening') { return model.tailwaterLevel_Evening } else { return model.tailwaterLevel_StandardShoulder } } model.profileHeadpond = function(hour) { let hourcat = hourCategory(monthNo, hour) let hourpos = hourPosition(monthNo, hour) if (hourcat === 'off') { return model.headpondLevelChange_Off * hourpos / model.hours.off } else if (hourcat === 'morning') { return model.headpondLevelChange_Off - (model.headpondLevelChange_Off * hourpos / model.hours.morning) } else if (hourcat === 'standard') { return model.headpondLevelChange_Standard * hourpos / model.hours.standard } else if (hourcat === 'evening') { return model.headpondLevelChange_Standard - (model.headpondLevelChange_Standard * hourpos / model.hours.evening) } else { return 0 } } } return model }48:T41b,hourPosition = function(monthNo, hour) { let hrs = timeofday_flat.filter(v => (v.month===monthNo && v.dayofweek===1)) if (hour >= d3.min(hrs.filter(v => (v.cat === 'off' && v.hour>19)), m => m.hour)) { return hour - d3.min(hrs.filter(v => (v.cat === 'off' && v.hour>19)), m => m.hour) } else if (hour >= d3.min(hrs.filter(v => (v.cat === 'standard' && v.hour>19)), m => m.hour)) { return hour - d3.min(hrs.filter(v => (v.cat === 'standard' && v.hour>19)), m => m.hour) } else if (hour >= d3.min(hrs.filter(v => v.cat === 'evening'), m => m.hour)) { return hour - d3.min(hrs.filter(v => v.cat === 'evening'), m => m.hour) } else if (hour >= d3.min(hrs.filter(v => v.cat === 'standard'), m => m.hour)) { return hour - d3.min(hrs.filter(v => v.cat === 'standard'), m => m.hour) } else if (hour >= d3.min(hrs.filter(v => v.cat === 'morning'), m => m.hour)) { return hour - d3.min(hrs.filter(v => v.cat === 'morning'), m => m.hour) } else { return hour + hrs.filter(v => (v.cat === 'off' && v.hour>19)).length } }49:[] 2f:{"title":"Intra-day Storage and the SAPP DAM Market","cells":[{"id":0,"value":"# Intra-day Storage and the SAPP DAM Market","pinned":false,"hidden":false,"mode":"md"},{"id":1652,"value":"html`\n

Summary

\nIntra-day storage models combined with direct sale on the SAPP DAM regional market for the Ngonye Falls hydroelectric project show that average tariff and revenue achieved can be increased by 12%, $7.7/MWh or $6.6m pa using a reasonable storage scenario which leaves the power station operating at a minimum level during the off-peak hours. \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1666,"value":"summaryBars()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1014,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1017,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1020,"value":"html`\n

SAPP DAM and Time of Day Tariffs

\n\nThe Southern African Power Pool (SAPP) Day Ahead Market (DAM) provides a possible market-based time-of-day offtaker. Trades on the DAM are have naturally segmented into Peak (Morning and Evening) and Off-peak (Nighttime and Daytime Standard) periods and, due to supply and demand differences and, during these periods different tariffs tend to result.\n

\nDetail of the historic pricing and volume trends of the SAPP DAM market can be found here.\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1028,"value":"viewof selected_months = {\n const form = html`\n `\n\n form.oninput = () => {\n form.value = Array.prototype.filter\n .call(form.input, i => i.checked)\n .map(i => i.value)\n }\n form.oninput()\n return form\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1032,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1302,"value":"html`\nThe average price realised on the SAPP DAM market varies both with the time of day and through the year. Peak, morning and evening prices vary more through the year than nighttime, off-peak and daytime, standard prices. The timing and duration of the evening peak also varies according to the season.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1053,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1121,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":1119,"value":"html `\nThe value of storage is a product of the amount of energy which can be moved from the off-peak to peak tariff periods and the difference in tariffs between peak and off-peak.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1312,"value":"html`\n

Plant Operation & Storage Scenarios

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1358,"value":"html`\nThe current design and operating principals of the Ngonye Falls hydroelectic project are for a true run-of-river operation with zero water storage and constant power output dependent only on the flow of water in the river. \n

\nA feature of the Ngonye plant is that the head available for generation varies inversely with the flow of water in the river as high flows tend to flood the downstream gorge reducing the elevation difference. For this reason, power output of the the station falls at higher flows - above ${formatFlow(1250)} or less than P27.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1148,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":1315,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":1363,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":1378,"value":"html`\nFour storage scenarios have been modelled and compared to the no-storage case. These scenarios have prgoressively lower off-peak minimum flows and higher proportion of water held back in the headpond and therefore daily energy shifted from SAPP DAM off-perak to peak tariff periods. ${formatFlow(50)} is the lowest flow required to drive a single turbine and in this scenario off-peak power output is the minimum possible before plant shutdown. \n

\nWith ${formatFlow(0)} the plant will be shut down during off-peak periods. There may be additional technical considerations to this scenario including synchronisation to grid which make it unpracticable.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":197,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":1145,"value":"html`\nPower output for all flow conditions across the flow duration curve under the storage scenario with minimum off-peak flow equal to the rated flow for a single turbine (${formatFlow(250)}) compared with the no stograge scenario is shown above.\n

\nAt flows above around ${formatFlow(1250)} (less than P27) no storage is practicable as the plant is already operating at full flow capacity throughout the day.\n

\nAt flows below around ${formatFlow(370)} (greater than P68) there is insuficient flow to meet the minimum off-peak flow requirement of ${formatFlow(250)} as well as storing water and so no storage is possible.\n

\nThe power outputs shown above assume the peak and off-peak hours applicable for March where there are 10 standard hours available for storage to supply 2 evening peak hours compared with 8 nighttime off-peak hours to supply 3 morning peak hours. For this reaosns, maximum power can be achieved during the evening peak at lower flows compared to the morning peak.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":347,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":1398,"value":"html `\nThe figure above shows that storage scenarios with progressively lower off-peak minimum flow and power produce maximum power output at peak periods for progressively lower river flows. These models assume June through August SAPP DAM tariff category hours with equal off-peak nighttime and daytime standard storage hours and equal morning and evening peak hours. \n

\nWith median August flows of around ${formatFlow(360)} no storage power output would be a constant 50MW. With the ${formatFlow(50)} minimum off-peak storage scenario peak period power would be around 160MW and off-peak around 11MW. \n

\nAt P95 flows of around ${formatFlow(200)} no storage power output would be a constant 27MW. With the ${formatFlow(50)} minimum off-peak storage scenario peak period power would be around 70MW and off-peak around 11MW. \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":363,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":1454,"value":"html`\n

Revenue & Tariff Implications

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1662,"value":"summaryBars()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1616,"value":"html `\nThe figures above detail the revenue and average tariff achieved for median monthly flows assuming all energy is sold on the SAPP DAM market at average historic tariffs (and neglecting any transaction costs) for the no storage case and four storage scenarios.\n

\nThe ${formatFlow(50)} storage scenario leads to an increase in revenue of $6.6m per year and increase in average tariff achieved of $7.7/MWh or 12%.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1080,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":1060,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":1629,"value":"html`\nThe additional revenue value due to storage on a monthly basis is a product of time of day tariff of the SAPP DAM market as well as the availability of water for storage at lower flows and the avialability of capacity at higher flows.\n

\nSAPP DAM prices have historicaly been higher June through September. However, median flows from February through June are generally too high to allow for storage as the plant is constantly running at maximum capacity.\n

\nWith median flows and market prices the greatest value from intra-day storage is achieved in July and August.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":791,"value":"html`

${monthChart(6,true)}

${monthChart(7,false)}

SAPP DAM value split by time of day category as a percentage of the no storage case for the four energy storage scenarios. July and August.

\n \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1474,"value":"html`\n

Headpond, Tailwater and Power Profiles

\nThe impact of intra-day storage is twice daily drawdown of the headpond level and change in the tailwater level. These changes will have an impact on the ecology of these areas and the communities that rely on them. \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":468,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"id":449,"value":"$40","pinned":false,"hidden":false,"mode":"ojs"},{"id":490,"value":"$41","pinned":false,"hidden":false,"mode":"ojs"},{"id":533,"value":"$42","pinned":false,"hidden":false,"mode":"ojs"},{"id":572,"value":"$43","pinned":false,"hidden":false,"mode":"ojs"},{"id":1509,"value":"$44","pinned":false,"hidden":false,"mode":"ojs"},{"id":711,"value":"$45","pinned":false,"hidden":false,"mode":"ojs"},{"id":49,"value":"html`\n

Model

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":767,"value":"modelledMonthNoStore=hydrology_calmonths.map((m, i) => {\n return modelMonth(i+1, {ref:'nostore'}, m.Flow_median)\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":884,"value":"modelledYearNoStore = rollupYear(modelledMonthNoStore)","pinned":false,"hidden":false,"mode":"ojs"},{"id":879,"value":"rollupYear = function(scen) {\n let vals = catslist.map(cat => [cat, d3.sum(scen, v => v.value[cat])]) \n let ens = catslist.map(cat => [cat, d3.sum(scen, v => v.energy[cat])])\n let ret = {\n value: Object.fromEntries(vals)\n }\n ret.energy = Object.fromEntries(ens)\n \n ret.valueTotal = d3.sum(vals, v=>v[1])\n ret.energyTotal = d3.sum(ens, v=>v[1])\n ret.tariff = ret.valueTotal / ret.energyTotal\n ret.valueProp = {\n off: ret.value.off / ret.valueTotal,\n morning: ret.value.morning / ret.valueTotal,\n standard: ret.value.standard / ret.valueTotal,\n evening: ret.value.evening / ret.valueTotal,\n } \n return ret\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":845,"value":"modelledYears = modelledMonths.map(scen => {\n return rollupYear(scen)\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":190,"value":"modelledMonths = {\n let ret = []\n ret=ret.concat(scenarios.map(s => {\n return hydrology_calmonths.map((m, i) => {\n return modelMonth(i+1, s, m.Flow_median)\n })\n }))\n return ret\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":623,"value":"modelledFdcMarch = scenarios.map(s => {\n return fdc.map(v => {\n return modelDay(3, s, v.Flow, false)\n })\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1438,"value":"modelledFdcJune = scenarios.map(s => {\n return fdc.map(v => {\n return modelDay(6, s, v.Flow, false)\n })\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":591,"value":"$46","pinned":false,"hidden":false,"mode":"ojs"},{"id":51,"value":"$47","pinned":false,"hidden":false,"mode":"ojs"},{"id":536,"value":"hourCategory = function(monthNo, hour) {\n let hrs = timeofday_flat.filter(v => (v.month===monthNo && v.dayofweek===1))\n \n if (hour < d3.min(hrs.filter(v => v.cat === 'morning'), m => m.hour)) {\n return 'off'\n } else if (hour < d3.min(hrs.filter(v => v.cat === 'standard'), m => m.hour)) {\n return 'morning'\n } else if (hour < d3.min(hrs.filter(v => v.cat === 'evening'), m => m.hour)) {\n return 'standard'\n } else if (hour < d3.min(hrs.filter(v => (v.cat === 'standard' && v.hour>19)), m => m.hour)) {\n return 'evening'\n } else if (hour < d3.min(hrs.filter(v => (v.cat === 'off' && v.hour>19)), m => m.hour)) {\n return 'standardshoulder'\n } else {\n return 'off'\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":546,"value":"$48","pinned":false,"hidden":false,"mode":"ojs"},{"id":166,"value":"tailwaterLevel = function(flow) {\n let logFlow = Math.log10(flow)\n if (logFlow >= 3.8) {\n return (-21.429 * logFlow**2) + (182.03 * logFlow) + 597.15\n }\n else {\n if (logFlow >= 2.3) {\n return (6.5299 * logFlow**2) - (30.155 * logFlow) + 999.52\n } else {\n return (-2.6828 * logFlow**2) + (11.265 * logFlow) + 952.68\n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":74,"value":"hourCounts = function(month, saturday) {\n if (!saturday) {\n let hrs = timeofday_flat.filter(v => (v.month===month && v.dayofweek===1))\n let cnts = Object.fromEntries(catslist.map(c => {\n let cnt = hrs.filter(n=>n.cat==c).length\n if (c === 'standard') {\n cnt -= hrs.filter(n => (n.cat === 'standard' && n.hour > 19)).length\n }\n return [c, cnt]\n }))\n cnts.standardShoulder = hrs.filter(n => (n.cat === 'standard' && n.hour > 19)).length\n return cnts\n } else {\n let hrs = timeofday_flat.filter(v => (v.month === month && v.dayofweek === 6))\n let cnts = {\n morning: hrs.filter(n => (n.cat === 'standard' && n.hour < 15)).length,\n evening: hrs.filter(n => (n.cat === 'standard' && n.hour > 15)).length,\n standard: hrs.filter(n => (n.cat === 'off' && n.hour > 9 && n.hour < 19)).length\n }\n cnts.off = 24 - (cnts.morning + cnts.evening + cnts.standard)\n cnts.standardShoulder = 0\n return cnts\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":55,"value":"html`\n

Scenarios

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":57,"value":"scenarios = [ \n {\n ref: 'unitrated',\n name: 'Unit Rated Flow',\n flowMin: params.unitRatedFlow\n },\n {\n ref: 'halfrated',\n name: '50% Unit Rated',\n flowMin: params.unitRatedFlow * 0.5\n },\n {\n ref: 'fiftycumec',\n name: formatFlow(50),\n flowMin: 50\n },\n {\n ref: 'zero',\n name: 'Zero Off-peak',\n flowMin: 0\n }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":60,"value":"params = { return {\n canalCapacity: 1100,\n unitRatedFlow: 250,\n maxPlantPower: 185,\n storageCurve: 10, // Mm3/m headpond rise - From Feasibility Study Rev D Appendix H - Mott MacDonald\n effectiveEfficiency: 0.9212, // P80 efficiency. Used to determine maximum storage flows\n fromYear: 2020\n}}","pinned":false,"hidden":false,"mode":"ojs"},{"id":24,"value":"html`\n

SAPP

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1126,"value":"catsMeans={\n return [\n ['off', d3.mean(sapp_monthly, d=>d.off_price_mean)],\n ['morning', d3.mean(sapp_monthly, d=>d.morning_price_mean)], \n ['standard', d3.mean(sapp_monthly, d=>d.standard_price_mean)],\n ['evening', d3.mean(sapp_monthly, d=>d.evening_price_mean)], \n ]\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":694,"value":"sapp_calmonths_old = {\n const raw = (await d3.csv(base_url + market_ref + \"_calmonthly.csv?\" + Math.floor(Math.random() * 1000), d3.autoType))\n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1714,"value":"sapp_calmonths = d3.rollups(sapp_monthly, v=> { return {\n month: v[0].lux_date.month,\n off_price_mean: d3.mean(v, w => w.off_price_mean),\n standard_price_mean: d3.mean(v, w => w.standard_price_mean),\n evening_price_mean: d3.mean(v, w => w.evening_price_mean),\n morning_price_mean: d3.mean(v, w => w.morning_price_mean) \n} }, v => v.lux_date.month).map(v=>v[1])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1129,"value":"sapp_monthly = sapp_monthly_raw.filter(v=>v.lux_date.year>=params.fromYear)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1708,"value":"sapp_monthly_raw = {\n const raw = (await d3.csv(base_url + market_ref + \"_monthly.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n raw.map(r=>{\n r.lux_date=DateTime.fromObject({year: r.year, month: r.month, day: 1})\n r.date=r.lux_date.toJSDate()\n })\n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1692,"value":"

Time of day price chart

","pinned":false,"hidden":false,"mode":"html"},{"id":1039,"value":"monthProfiles=[...Array(12).keys()].map(m=>monthProfilePrice(m+1))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1041,"value":"function monthProfilePrice(month_no) {\n return d3.rollup(sapp_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":1044,"value":"sapp_calmonthly_hours = {\n const raw = (await d3.csv(base_url + market_ref + \"_calmonthly_hours.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n \n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":37,"value":"timeofday = {\n const raw = (await d3.csv(base_url + market_ref + \"_timeofday.csv?\" + Math.floor(Math.random() * 1000),d3.autoType)) \n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":39,"value":"timeofday_flat={\n let o=[]\n timeofday.forEach(t=>{\n [...Array(24).keys()].forEach((h,i)=>{\n o.push({month: t.month, hour: i, dayofweek: t.dayofweek, cat: t[i]}) \n })\n })\n return o\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1696,"value":"

General

","pinned":false,"hidden":false,"mode":"html"},{"id":78,"value":"catslist = Object.keys(cats)","pinned":false,"hidden":false,"mode":"ojs"},{"id":29,"value":"cats = {\n return {\n evening: { index: 3, name: 'Evening', ref: 'evening'}, \n morning: { index: 1, name: 'Morning', ref: 'morning' }, \n standard: {index: 2, name: 'Standard', ref: 'standard'},\n off: {index: 0, name: 'Off-peak', ref: 'off'}, \n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":35,"value":"function catCol(index) {\n return d3.schemeCategory10[index]\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":33,"value":"market_ref='dam'","pinned":false,"hidden":false,"mode":"ojs"},{"id":31,"value":"base_url='https://gis.westernpower.org/data/sapp/output/'","pinned":false,"hidden":false,"mode":"ojs"},{"id":99,"value":"html`\n

Ngonye

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1191,"value":"flowExceedance = function(flow) {\n return fdc.filter(v=>v.Flow<=flow)[0].Exceedance\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":97,"value":"pe_calmonths=(await d3.csv(\"https://gis.westernpower.org/data/output_data/base_pe_calmonthly.csv?\" + Math.floor(Math.random() * 1000), d3.autoType)).sort((a, b) => a.Month-b.Month)","pinned":false,"hidden":false,"mode":"ojs"},{"id":620,"value":"hydrology_calmonths = (await d3.csv(\"https://gis.westernpower.org/data/output_data/ngonye_flow_calmonthly.csv\",d3.autoType)).sort((a, b) => a.Month-b.Month)","pinned":false,"hidden":false,"mode":"ojs"},{"id":110,"value":"fdc=(await d3.csv(\"https://gis.westernpower.org/data/output_data/base_pe_fdc.csv?\" + Math.floor(Math.random() * 1000), d3.autoType))","pinned":false,"hidden":false,"mode":"ojs"},{"id":43,"value":"html`\n

Common

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1292,"value":"function formatFlow(flow){\n return d3.format(',.0f')(flow) + ' m\\u00b3/s'\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1135,"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":587,"value":"function monthWeekends(i) {\n if(i === 4 || i === 6 || i === 9 || i === 11) {\n\t\treturn 4.3\n\t} else if(i === 2) {\n\t\treturn 4.0\n\t} else {\n\t\treturn 4.4\n\t}\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":585,"value":"function monthDays(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":41,"value":"monthNames={\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":1034,"value":"monthDict={\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":251,"value":"cols1=d3.quantize(d3.interpolateHcl(\"#d66000\", \"#a9a9b4\"), 12)","pinned":false,"hidden":false,"mode":"ojs"},{"id":18,"value":"html`\n

Imports

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":327,"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":224,"value":"function movingAverage(values, N) {\n let i = 0;\n let sum = 0;\n const means = new Float64Array(values.length).fill(NaN);\n for (let n = Math.min(N - 1, values.length); i < n; ++i) {\n sum += values[i];\n }\n for (let n = values.length; i < n; ++i) {\n sum += values[i];\n means[i - Math.floor(N / 2)] = sum / N;\n sum -= values[i - N + 1];\n }\n return means;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":215,"value":"font = ({\n family: 'Courier New, monospace',\n size: 13,\n color: '#6f6f6f'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":11,"value":"luxon = require(\"luxon@1/build/amd/luxon.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"DateTime = luxon.DateTime","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":329,"value":"d3hsv = require(\"d3-hsv@0.1\")","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"}],"files":"$W49","stdlib":"1","titling":"auto"}