30:T5f4,

The Southern African Power Pool (SAPP) was created in August 1995. SAPP has seventeen members from twelve member countries including national utilities, transmission companies and independent generators. SAPP established the Short-Term Energy Market in April 2001. From January 2004, SAPP started the development of a competitive electricity market for the SADC region.

The Day-ahead Market (DAM) matches buyers and sellers in hourly intervals for settlement the following day. The DAM has historically been the largest SAPP market accounting for at least 50% of total turnover.

For more information on the Day-ahead market:

31:T106f,{ let prev_year=monthly[selected_month-12] let cal_month=calmonthly_dict[month.month] //let cal_month_solar=calmonthly_solar[month.month-1] //let prev_year_solar=monthly_solar[selected_month-12] function diff(a,b) { return html` (${d3.format('+,.0%')((a-b)/b)})` } function diff2(a,b, cont) { return html`${cont}` } function spark(ser, fld) { return Plot.plot({ height: 90, width: 220, marginLeft:27,marginTop:5,marginRight:5,marginBottom:20, y: {grid: true, ticks: 3}, marks: [ Plot.line(ser.slice(-18), {x: "date", y: fld}), Plot.dot([[ser[selected_month].date,ser[selected_month][fld]]],{stroke: 'red', fill:'red'}) ] }) } return html`

${diff2(month.price_mean,prev_month.price_mean, 'Price')}	${diff2(month.price_mean,prev_month.price_mean, d3.format('$,.3r')(month.price_mean) + '/MWh')}
${prev_month.lux_date.toFormat('LLL yyyy')}	${d3.format('$,.2r')(prev_month.price_mean)}/MWh ${diff(month.price_mean,prev_month.price_mean)}
${prev_year ? prev_year.lux_date.toFormat('LLL yyyy') : '-'}	${prev_year ? d3.format('$,.2r')(prev_year.price_mean) : '-'}/MWh ${prev_year ? diff(month.price_mean,prev_year.price_mean) : '-'}
${month.lux_date.toFormat('LLL')} Average	${d3.format('$,.2r')(cal_month.price_mean)}/MWh ${diff(month.price_mean,cal_month.price_mean)}
${spark(monthly,"price_mean")}

${diff2(month.value,prev_month.value,'Value')}	${diff2(month.value,prev_month.value,d3.format('$,.2r')(month.value/1000000) + 'm')}
${prev_month.lux_date.toFormat('LLL yyyy')}	${d3.format('$,.2r')(prev_month.value/1000000)}m ${diff(month.value,prev_month.value)}
${prev_year ? prev_year.lux_date.toFormat('LLL yyyy') : '-'}	${prev_year ? d3.format('$,.2r')(prev_year.value/1000000) :'-'}m ${prev_year ? diff(month.value,prev_year.value) : '-'}
${month.lux_date.toFormat('LLL')} Average	${d3.format('$,.2r')(cal_month.value/1000000)}m ${diff(month.value,cal_month.value)}
${spark(monthly,"value_m")}

${diff2(month.volume,prev_month.volume, "Volume")}	${diff2(month.volume,prev_month.volume,d3.format('.0f')(month.volume/1000) + 'GWh')}
${prev_month.lux_date.toFormat('LLL yyyy')}	${d3.format('.0f')(prev_month.volume/1000)} GWh ${diff(month.volume,prev_month.volume)}
${prev_year ? prev_year.lux_date.toFormat('LLL yyyy') :'-'}	${prev_year ? d3.format('.0f')(prev_year.volume/1000): '-'} GWh ${prev_year ? diff(month.volume,prev_year.volume) : '-'}
${month.lux_date.toFormat('LLL')} Average	${d3.format('.0f')(cal_month.volume/1000)} GWh ${diff(month.volume,cal_month.volume)}
${spark(monthly,"volume_gwh")}

` }32:T1552,{ const wdth = 1050 const height = wdth * 0.75 let scle = 1 const rW=140 const rH=100 const m=6 const f=16 if (width>650) { scle = 650/width } let sapp_filtered=sapp.features.filter(s=>flows_by_node.get(s.properties.ref)) let totalExtent=[d3.min(Array.from(flows_by_node),f=>f[1][2]), d3.max(Array.from(flows_by_node),f=>f[1][2])] let min=totalExtent[0] let max=totalExtent[1] const svg = d3 .create("svg") .attr("viewBox", [0, 0, wdth, height]) //.style('background-color','#fbfbfb') .style("width", d3.format('.0%')(scle)) .style("height", "auto"); var cSupply = d3.scaleSequential().domain([min,0]) .interpolator(d3.interpolateLab(sideCol(2), "#CCC")) var cConsume = d3.scaleSequential().domain([0,max]) .interpolator(d3.interpolateLab("#CCC", sideCol(5))) /* svg.append('rect') .attr('x', 0) .attr('y', 0) .attr('width', wdth) .attr('height', height) .attr('stroke', 'black') .attr('fill', 'none'); */ svg.append("g") .selectAll("path") .data(sapp.features) .join("path") .attr('stroke', '#AAA') .attr('stroke-width', '1.5') .attr('fill-opacity', '0.5') .attr("fill", d=>{ let m=flows_by_node.get(d.properties.ref) if (m) { return m[2]<0 ? cSupply(m[2]) : cConsume(m[2]) } else { return '#CCC' } }) .attr("d", projection); function make_text(parent,fX, fY, fT,bold) { parent.append("g") .attr("font-family", "sans-serif") .attr("font-size", f) .attr("fill", "#555") .attr("font-weight", bold ? 'bold' : '') .attr("stroke-linecap", "round") .attr("stroke-linejoin", "round") .attr("text-anchor", "left") .selectAll("text") .data(sapp_filtered) .join("text") .text(fT) .attr("x", fX) .attr("y", fY) } function fnX() { return (d => projection.centroid(d.geometry)[0] + node_dict[d.properties.ref].offset[0]-rW/2+m) } function fnY(line, off) { return (d => projection.centroid(d.geometry)[1] + node_dict[d.properties.ref].offset[1]-rH/2+m+f/2+3 + (f*line) + off) } make_text( svg, fnX(), fnY(0,0), (d => node_dict[d.properties.ref].member), true ) make_text( svg, fnX(), fnY(1,0), (d => node_dict[d.properties.ref].country), false ) make_text( svg, fnX(), fnY(2,2), (d=>{ let m=flows_by_node.get(d.properties.ref) if (m) { return d3.format(',.1f')(Math.abs(m[2])/1000) + ' GWh' } else { return '' } }), false ) make_text( svg, fnX(), fnY(3,2), (d=>{ let m=flows_by_node.get(d.properties.ref) if (m) { return d3.format('$,.2r')(Math.abs(m[1])/1000000) + 'm' } else { return '' } }), false ) make_text( svg, fnX(), fnY(4,2), (d=>{ let m=flows_by_node.get(d.properties.ref) if (m) { return d3.format('$,.0f')(Math.abs(m[1])/Math.abs(m[2])) + '/GWh' } else { return '' } }), false ) svg.append('g') .selectAll("rect") .data(sapp_filtered) .join("rect") .attr("x", d => projection.centroid(d.geometry)[0] + node_dict[d.properties.ref].offset[0]-rW/2) .attr("y", d => projection.centroid(d.geometry)[1] + node_dict[d.properties.ref].offset[1]-rH/2) .attr('width', rW) .attr('height', rH) .attr('stroke', 'steelblue') .attr('stroke-width', '1.5') .attr('fill', 'white') .attr('fill-opacity', '0.3') svg.append('g') .selectAll("polyline") .data(sapp_filtered.filter(d=>node_dict[d.properties.ref].leader)) .join("polyline") .attr("points",d=>`${projection.centroid(d.geometry).join(',')} ${ projection.centroid(d.geometry)[0] + node_dict[d.properties.ref].offset[0] + ( (rW/2) * (node_dict[d.properties.ref].offset[0]<0?1:-1) ) },${ projection.centroid(d.geometry)[1] + node_dict[d.properties.ref].offset[1] }`) .attr('stroke', 'steelblue') .attr('stroke-width', '1.5') addPieChartsToMap(svg,1,rW,rH,m,f) addPieChartsToMap(svg,2,rW,rH,m,f) svg.append("g") .attr("font-family", "sans-serif") .attr("font-size", f - 2) .attr("fill", "grey") .attr("text-anchor", "left") .selectAll("text") .data(sapp_filtered) .join("text") .text('MWh') .attr("x", d=>projection.centroid(d.geometry)[0] + node_dict[d.properties.ref].offset[0]+rW/2 - (f * 2.2)) .attr("y", d => projection.centroid(d.geometry)[1] + node_dict[d.properties.ref].offset[1]+rH/2-3) svg.append("g") .attr("font-family", "sans-serif") .attr("font-size", f - 2) .attr("fill", "grey") .attr("text-anchor", "left") .selectAll("text") .data(sapp_filtered) .join("text") .text('$') .attr("x", d=>projection.centroid(d.geometry)[0] + node_dict[d.properties.ref].offset[0]+rW/2-(f * 0.8)) .attr("y", d => projection.centroid(d.geometry)[1] + node_dict[d.properties.ref].offset[1]+rH/2-85) const div=container('SAPP members participating in the market for the month. Net suppliers in green and net consumers in brown.') div.prepend(svg.node()) if (flow_filtered().length>0) { return div } else { return html`` } }33:T5c0,{ var data = [] data.push({ y: monthly.map(m=>m['price_mean']), x: monthly.map(m=>m['date']), mode: 'lines+markers', line: {color: 'black', width: 2}, marker:{size:4}, type: 'scatter', hoverinfo: 'x+y', showlegend:true, name: 'Market mean' }) data.push({ y: [0,220], x: [monthly[selected_month].date, monthly[selected_month].date], mode: 'lines+markers', line: {color: 'grey', width: 2}, marker:{size:10}, type: 'scatter', hoverinfo: 'none', showlegend:false }) data.push({ y: yearly.map(m=>m[1].price_mean), text: yearly.map(m=>'$' + d3.format('.0f')(m[1].price_mean) + '/MWh'), textposition:'top-left', x: yearly.map(m=>DateTime.fromObject({year: m[0], month: 6, day: 15}).toJSDate()), mode: 'markers+text', line: {color: 'red', width: 2}, marker:{size:10}, type: 'scatter', hoverinfo: 'text', showlegend:false }) var layout = { height: 270, width: width-30, font: font, showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside',showspikes: false, range:years_range }, yaxis: {title: '$/MWh',showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside'} }; const div=container('Monthly and annual average market price.') plt.newPlot(div, data, layout,{displayModeBar: false}) return html`${div}` }34:T707,{ function yr(ref) { const annual_price=d3.rollups(monthly,v=>d3.mean(v, d=>d[ref + '_price_mean']), v=>v.lux_date.startOf('year').plus({month:6})) return { y: annual_price.map(m=>Math.round(m[1])), x: annual_price.map(m=>m[0].toJSDate()), type: 'scatter', showlegend:false, mode: 'lines+markers', line: {color: make_trans(catCol(cats[ref].index),0.3),width:2}, marker: {color: make_trans(catCol(cats[ref].index),0.7),size: 10}, hoverinfo: 'x+y', name: cats[ref].name } } function mon(ref) { return { y: monthly.map(m=>m[ref + '_price_mean']), x: monthly.map(m=>m['date']), mode: 'lines+markers', line: {color: catCol(cats[ref].index), width: 1.5}, marker:{size:3}, type: 'scatter', hoverinfo: 'x,y', name: cats[ref].name, showlegend:true, connectgaps: false } } var data = [] Object.values(cats).forEach(c=>{ data.push(mon(c.ref)) //data.push(yr(c.ref)) }) data.push({ y: [0,370], x: [monthly[selected_month].date, monthly[selected_month].date], mode: 'lines+markers', line: {color: 'grey', width: 2}, marker:{size:10}, type: 'scatter', hoverinfo: 'none', showlegend:false }) var layout = { height: 270, width: width-30, font: font, showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside',showspikes: false, range:years_range }, yaxis: {title: '$/MWh',showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside'} }; const div=container('Monthly and annual average prices by time-of-day categories.') plt.newPlot(div, data, layout,{displayModeBar: false}) return html`${div}` }35:T5e8,{ function series(ref) { return { y: monthly.map(v=>v[ref + '_volume']/1000), x: monthly.map(h=> h.date), mode: 'lines', marker: {color: make_trans(catCol(cats[ref].index),0.7), width:1}, fill:'tonexty', type: 'bar', hoverinfo: 'x,y', name: cats[ref].name, stackgroup: 'one', } } var data = [ series('evening'), series('morning'), series('standard'), series('off'), { y: monthly.map(v=>v['volume']/1000), x: monthly.map(h=> h.date), mode: 'lines', line: {shape: 'spline', color: 'grey', width:2}, type: 'scatter', hoverinfo: 'x,y', name: 'Total', showlegend:false, } ] data.push({ y: [0,300], x: [monthly[selected_month].date, monthly[selected_month].date], mode: 'lines+markers', line: {color: 'grey', width: 2}, marker:{size:10}, type: 'scatter', hoverinfo: 'none', showlegend:false }) var layout = { height: 300, width: width-30, font: font, barmode: 'stack', showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside', range: years_range }, yaxis: { range:[0,315], title: `GWh`, showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Monthly volume by time-of-day.') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`${div}` }36:T641,{ let value_total= Array.from(d3.rollup(flow.filter(f=>f.side=='consume'), v=>d3.sum(v,p=>p.value), f=>f.date)) function series(ref) { return { y: monthly.map(v=>v[ref + '_value']/1000000), x: monthly.map(h=> h.date), mode: 'lines', marker: {shape: 'spline', color: make_trans(catCol(cats[ref].index),0.7), width:1}, fill:'tonexty', type: 'bar', hoverinfo: 'x,y', name: cats[ref].name, stackgroup: 'one', } } var data = [ series('evening'), series('morning'), series('standard'), series('off'), { y: monthly.map(v=>v['value']/1000000), x: monthly.map(h=> h.date), mode: 'lines', line: {shape: 'spline', color: 'grey', width:2}, type: 'scatter', hoverinfo: 'x,y', name: 'Total', showlegend:false, }, ] data.push({ y: [0,20], x: [monthly[selected_month].date, monthly[selected_month].date], mode: 'lines+markers', line: {color: 'grey', width: 2}, marker:{size:10}, type: 'scatter', hoverinfo: 'none', showlegend:false }) var layout = { height: 300, width: width - 30, font: font, barmode:'stack', showlegend: false, legend: {xanchor: 'right', x:1, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { range:years_range, showgrid: true, zeroline: false, ticks:'outside', }, yaxis: { title: `$m`, showgrid: true, zeroline: false, tickformat: '.1f', ticks:'outside' } }; const div=container('Monthly value by time-of-day.') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`${div}` }37:T57e,{ var data = [] data.push({ y: monthly.map(m=>m['power_mean']), x: monthly.map(m=>m['date']), mode: 'lines+markers', line: {color: 'black', width: 2}, marker: { size: 6 }, type: 'scatter', hoverinfo: 'x+y', showlegend: false }) data.push({ y: monthly.map(m=>m['power_min']).concat(monthly.map(m=>m['power_max']).reverse()), x: monthly.map(m=>m['date']).concat(monthly.map(m=>m['date']).reverse()), mode: 'none', line: {color: 'black', width: 2}, fill:'toself', fillcolor:make_trans('grey',0.2), type: 'scatter', hoverinfo: 'x+y', showlegend:false }) data.push({ y: [0,600], x: [monthly[selected_month].date, monthly[selected_month].date], mode: 'lines+markers', line: {color: 'grey', width: 2}, marker:{size:10}, type: 'scatter', hoverinfo: 'none', showlegend:false }) var layout = { height: 300, width: width - 30, font: font, showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside',showspikes: false, range:years_range }, yaxis: {title: 'MW',showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside'} }; const div=container('Monthly power, mean and range.') plt.newPlot(div, data, layout,{displayModeBar: false}) return html`${div}` }38:T800,{ function priceChart() { let ymax=d3.max(selected_hours,h=>h.price)*1.1 function series(ref) { return [{ y: selected_days.map(d=>d[ref + '_price_mean']), x: selected_days.map(d=>d.lux_date.plus({hour:ref=='evening'?18:9}).toJSDate()), mode: 'lines', line: { shape:'spline',color: make_trans(catCol(cats[ref].index),0.6), width: 3}, type: 'scatter', hoverinfo: 'none', showlegend:true,name: cats[ref].name, fill:'' }] } var data = [] days.forEach(d=>{ if (d.weekday==6) { data.push({ x:[d.toJSDate(),d.endOf('day').toJSDate()],showlegend:false, y:[ymax,ymax], fillcolor:make_trans('#f4a4a4',0.3), line: {width:0},mode:'lines', fill:'tozeroy', hoverinfo:'none' }) } if (d.weekday==7) { data.push({ x:[d.toJSDate(),d.endOf('day').toJSDate()],showlegend:false, y:[ymax,ymax], fillcolor:make_trans('#f4a4a4',0.5), line: {width:0},mode:'lines', fill:'tozeroy', hoverinfo:'none' }) } }) Object.values(cats).forEach(c=>{data=data.concat(series(c.ref))}) data.push({ y: selected_hours.map(h=>h.price), x: selected_hours.map(h=>h.date), mode: 'lines', line: {color:'#555', width: 1.4}, type: 'scatter', hoverinfo: 'x,y', showlegend:false,name: '', }) var layout = { height: 300, width: width-30, font: font, showlegend: true, legend: {orientation: 'h',xanchor: 'left', x:0, y: 1.1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { range: [selected_hours[0].date,selected_hours[selected_hours.length-1].date], showgrid: true, zeroline: false, ticks:'outside' }, yaxis: { range:[0,ymax], title: '$/MWh', showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Hourly market value.') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`${div}` } return html`${priceChart()}` }39:T4ca,{ function ser(no, col) { return yearly_month_profiles.map((y,i)=>{ const ser = y.profile[no] return { y: ser, mode: 'lines', line: {shape: 'spline', color: make_trans(col,(i+2)/10), width:2}, fill:'', type: 'scatter', hoverinfo: 'x,y', name: y.year, } }) } let data = [] data=data.concat(ser(month.month-1,'black')) data.push({ y: dailyprofile, mode: 'lines+markers', line: {shape:'spline',color: '#a33', width: 4}, marker: { size: 6 }, type: 'scatter', hoverinfo: 'x+y', showlegend: false }) var layout = { height: 260, width: width-30, font: font, showlegend: true, legend: {xanchor: 'left', x:1,y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { title: '', showgrid: true, zeroline: false, dtick: 1, ticks:'outside', range:[0,23] }, yaxis: { range: [0,410], title: `$/MWh`, showgrid: false, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Mean price by time-of-day for this ' + month_names[month.month-1] + ' (red) and each ' + month_names[month.month-1] + ' since 2016.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }3a:T723,{ function powerChart() { var data = [] let ymax=d3.max(selected_hours,h=>h.volume)*1.1 days.forEach(d=>{ if (d.weekday==6) { data.push({ x:[d.toJSDate(),d.endOf('day').toJSDate()],showlegend:false, hoverinfo:'none', y:[ymax,ymax], fillcolor:make_trans('#f4a4a4',0.3), line: {width:0},mode:'lines', fill:'tozeroy' }) } if (d.weekday==7) { data.push({ x:[d.toJSDate(),d.endOf('day').toJSDate()],showlegend:false, hoverinfo:'none', y:[ymax,ymax], fillcolor:make_trans('#f4a4a4',0.5), line: {width:0},mode:'lines', fill:'tozeroy' }) } }) data.push({ y: selected_hours.map(h=>h.volume), x: selected_hours.map(h=>h.date), mode: 'lines', line: {color:'#555', width: 1.4}, type: 'scatter', hoverinfo: 'x,y', showlegend:false,name: '', }) data.push({ y: movingAverage(selected_hours.map(h=>h.volume),24), x: selected_hours.map(h=>h.date), mode: 'lines', line: {color:make_trans(d3.schemeDark2[1],0.6), width: 3}, type: 'scatter', hoverinfo: 'x,y', showlegend:false,name: '', }) var layout = { height: 300, width: width-30, font: font, showlegend: true, legend: {orientation: 'h',xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { range: [selected_hours[0].date,selected_hours[selected_hours.length-1].date], showgrid: true, zeroline: false, ticks:'outside' }, yaxis: { title: 'MW', showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Hourly market volume.') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`${div}` } return html`${powerChart()}` }3b:T5b3,{ function priceChart() { var data = [] data.push({ y: selected_hours.map(h=>h.price/d3.max(daily.filter(d=>d.year==selected_date.year && d.month==selected_date.month),v=>v.price_max)), x: selected_hours.map(h=>h.date), mode: 'lines', line: {color:'#555', width: 1.5}, type: 'scatter', hoverinfo: 'x', showlegend:true,name: 'Price % of Monthly Max', }) data.push({ y: selected_hours.map(h=>h.volume/d3.max(daily.filter(d=>d.year==selected_date.year && d.month==selected_date.month),v=>v.power_max)), x: selected_hours.map(h=>h.date), marker: {color:make_trans(d3.schemeDark2[1],0.6)}, line: {width: 0}, type: 'bar', hoverinfo: 'x', showlegend:true,name: 'Volume % of Monthly Max', }) var layout = { height: 300, width: width-30, font: font, showlegend: true, legend: {orientation: 'h',xanchor: 'left', x:0, y: 1.2}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { range: [selected_hours[0].date,selected_hours[selected_hours.length-1].date], showgrid: true, zeroline: false, ticks:'outside' }, yaxis: { title: '', showgrid: true, zeroline: false, tickformat: '%.0f', ticks:'outside' } }; const div=container('Hourly price and volume relative to the monthly maximum.') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`${div}` } return html`${priceChart()}` }3c:T455,{ function priceChart() { var data = [] let wdth = 0 if (width>530) { wdth = 500 } else {wdth = width-30} data.push({ y: selected_hours.map(h=>h.price), x: selected_hours.map(h=>h.volume), mode: 'markers', marker: { size:7, color: make_trans(d3.schemeDark2[1],0.6) }, type: 'scatter', hoverinfo: 'x,y', showlegend:false,name: 'Price % of Monthly Max', }) var layout = { height: wdth-150, width: wdth, font: font, showlegend: true, legend: {orientation: 'h',xanchor: 'left', x:0, y: 1.2}, margin: {l: 90,r: 90,b: 50,t: 10}, xaxis: { title: 'Volume MWh', showgrid: true, zeroline: false, ticks:'outside',tickformat: '.0f' }, yaxis: { title: 'Price $/MWh', showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Correlation between hourly price and volume highlighting time-of-day price bands.') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`${div}` } return html`${priceChart()}` }3d:T6a2,{ function series(ref) { return { y: calmonthly.map(v=>v[ref + '_price_mean']), x: ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'], mode: 'lines', line: {shape: 'spline', color: catCol(cats[ref].index), width:3}, type: 'scatter', hoverinfo: 'none', name: cats[ref].name, } } function series2(ref, data, is_curr) { return { y: [data[ref + '_price_mean']], x: [month_names[data.lux_date.month-1]], text: ['' + d3.format('$.0f')(data[ref + '_price_mean']) + ''], textposition:'middle-right', mode: 'markers' + (is_curr? '+text':''), marker: {color: catCol(cats[ref].index), size:10}, type: 'scatter', hoverinfo: is_curr? 'none' : 'y', showlegend: false } } var data = Object.keys(cats).map(c=>series(c)) data = data.concat(Object.keys(cats).map(c=>series2(c, month, true))) data = data.concat(Object.keys(cats).map(c=>series2(c, prev_month, false))) const max = d3.max(Object.keys(cats).map(c=>month[c + '_price_mean']).concat(130)) var layout = { height: 270, width: width-30, font: font, showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 20,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside' }, yaxis: { range:[0, max*1.3], title: `US$ / MWh`, showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Current and previous month mean market prices by time-of-day category overlaid on long term average mean market prices.') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`${div}` }3e:Tb08,{ function series(ref) { return { y: calmonthly.map(v=>v[ref + '_volume']/1000), x: [0,1,2,3,4,5,6,7,8,9,10,11], mode: 'lines', line: {shape: 'spline', color: catCol(cats[ref].index), width:1}, fill:'tonexty', type: 'scatter', hoverinfo: 'none', name: cats[ref].name, stackgroup: 'two', fillcolor: make_trans(catCol(cats[ref].index),0.3) } } function series2(ref, mnth) { return { y: [mnth[ref+'_volume']/1000], x: [mnth.lux_date.month-1], marker: {line: {color:'black', width:1}, color: catCol(cats[ref].index)}, fill:'tonexty', showlegend:false, type: 'bar', hoverinfo: 'x,y', name: cats[ref].name, stackgroup: 'one', width:1 } } var data = [ series('evening'), series('morning'), series('standard'), series('off'), { y: calmonthly.map(v=>v['volume']/1000), x: [0,1,2,3,4,5,6,7,8,9,10,11], mode: 'lines', line: {shape: 'spline', color: 'grey', width:2}, type: 'scatter', hoverinfo: 'x,y', name: 'Average', showlegend:false, }, { y: [month.volume/1000], x: [month.lux_date.month-1], text: [d3.format('.0f')(month.volume/1000)+'Gwh'],textposition:'top right', mode: 'markers+text', marker: { color: 'black', size:10}, type: 'scatter', hoverinfo: 'none', name: 'Total', showlegend:false, }, { y: [prev_month.volume/1000], x: [prev_month.lux_date.month-1], text: [d3.format('.0f')(prev_month.volume/1000)+'Gwh'],textposition:'top right', mode: 'markers+text', marker: { color: 'black', size:10}, type: 'scatter', hoverinfo: 'none', name: 'Total', showlegend:false, }, series2('evening', month), series2('morning', month), series2('standard', month), series2('off', month), series2('evening', prev_month), series2('morning', prev_month), series2('standard', prev_month), series2('off', prev_month), ] var layout = { height: 260, width: width-30, font: font, barmode:'stack', showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showticklabels:true, showgrid: true, zeroline: false, ticks:'outside', range:[-1,12], tickmode: 'array', tickvals: [-1,0,1,2,3,4,5,6,7,8,9,10,11,12], ticktext: ['','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec',''] }, yaxis: { range:[0,260], title: `GWh`, showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' }, }; const div=container('Current and previous month volumes by time-of-day category overlaid on the long-term average volumes by month.') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`${div}` }3f:Tb0b,{ function series(ref) { return { y: calmonthly.map(v=>v[ref + '_value']/1000000), x: [0,1,2,3,4,5,6,7,8,9,10,11], mode: 'lines', line: {shape: 'spline', color: catCol(cats[ref].index), width:1}, fill:'tonexty', type: 'scatter', hoverinfo: 'none', name: cats[ref].name, stackgroup: 'two', fillcolor: make_trans(catCol(cats[ref].index),0.3) } } function series2(ref, mnth) { return { y: [mnth[ref+'_value']/1000000], x: [mnth.lux_date.month-1], width:1, marker: {line: {color:'black', width:1}, color: catCol(cats[ref].index)}, fill:'tonexty', showlegend:false, type: 'bar', hoverinfo: 'x,y', name: cats[ref].name, stackgroup: 'one', } } var data = [ series('evening'), series('morning'), series('standard'), series('off'), { y: calmonthly.map(v=>v['value']/1000000), x: [0,1,2,3,4,5,6,7,8,9,10,11], mode: 'lines', line: {shape: 'spline', color: 'grey', width:2}, type: 'scatter', hoverinfo: 'x,y', name: 'Average', showlegend:false, }, { y: [month.value/1000000], x: [month.lux_date.month-1], text: [d3.format('$.1f')(month.value/1000000)+'m'],textposition:'top right', mode: 'markers+text', marker: { color: 'black', size:10}, type: 'scatter', hoverinfo: 'none', name: 'Total', showlegend:false, }, { y: [prev_month.value/1000000], x: [prev_month.lux_date.month-1], text: [d3.format('$.1f')(prev_month.value/1000000)+'m'],textposition:'top right', mode: 'markers+text', marker: { color: 'black', size:10}, type: 'scatter', hoverinfo: 'none', name: 'Total', showlegend:false, }, series2('evening', month), series2('morning', month), series2('standard', month), series2('off', month), series2('evening', prev_month), series2('morning', prev_month), series2('standard', prev_month), series2('off', prev_month), ] var layout = { height: 240, width: width-30, font: font, barmode:'stack', showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showticklabels:true, showgrid: true, zeroline: false, ticks:'outside',range:[-1,12], tickmode: 'array', tickvals: [-1,0,1,2,3,4,5,6,7,8,9,10,11,12], ticktext: ['','Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec',''] }, yaxis: { title: `$m`, showgrid: true, zeroline: false, tickformat: '.1f', ticks:'outside' }, }; const div=container('Current and previous month market turnover by time-of-day category overlaid on the long-term average turnover by month.') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`${div}` }40:T7a5,{ let data=[] let dt=d3.rollups( flow_filtered(), v=>d3.rollup(v,t=>{ return { flow_sum:d3.sum(t, m=>m.flow_sum), value:d3.sum(t, m=>m.value), rate:d3.sum(t, m=>m.value)/d3.sum(t, m=>m.flow_sum) } },t=>t.side), v=>node_dict[v.node].member ).sort() function ys(side) { return dt.map(f=>f[1].get(side) ? f[1].get(side).rate * (side=='supply'?-1:1) : null) } function series(side) { data.push( { y: ys(side), x: dt.map(f=>f[0]), textposition: 'auto', text: dt.map(f=>txt(f, side)), marker: {color: make_trans(sideCol(side=='consume'?5:2),0.7), width:1}, fill:'tonexty', type: 'bar', hoverinfo: 'text', name: '', showlegend: false } ) } function txt(dat, side) { let d = dat[1].get(side) if (d) { return '' + d3.format('$,.0f')(d.rate) + '/MWh
' + d3.format('$,.2r')(Math.abs(d.value/1000)) + '
' + d3.format(',.2r')(Math.abs(d.flow_sum/1000)) + 'GWh' } else { return null } } series('consume') series('supply') var layout = { height: 400, width: width - 30, font: font, barmode: 'relative', bargap:0.2, showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1.2}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside', range: years_range }, yaxis: { range:[d3.min(ys('supply'))*1.1,d3.max([90,d3.max(ys('consume'))*1.1])], title: `$/MWh`, showgrid: true, zeroline: true, tickformat: '.0f', ticks:'outside' } }; const div=container(monthly[selected_month].lux_date.toFormat('LLL yyyy') + ' - Buy (brown) and sell (green) tariff achieved by market participants.') plt.newPlot(div, data, layout,{displayModeBar: false}) if (flow_filtered().length>0) { return html`${div}` } else { return html`` } }41:T7ac,{ let data=[] let flws=Array.from( d3.rollup(flow_filtered(),v=>d3.sum(v,d=>d.value)/1000,v=>node_dict[v.node].member,v=>v.side) ).sort() let flows_net=Array.from(d3.rollup(flow_filtered(),v=>d3.sum(v,d=>d.value)/1000,v=>node_dict[v.node].member)) data.push({ y: flws.map(f=>f[1].get('consume')), x: flws.map(f=>f[0]), textposition: 'none', offsetgroup :'a', text: flws.map(f=>d3.format('$,.2r')(f[1].get('consume'))), marker: {color: make_trans(sideCol(5),0.5), width:1}, type: 'bar', hoverinfo: 'text', name: '', showlegend: false }) data.push( { y: flws.map(f=>f[1].get('supply')), x: flws.map(f=>f[0]), textposition: 'none', offsetgroup :'a', text: flws.map(f=>d3.format('$,.2r')(f[1].get('supply'))), marker: {color: make_trans(sideCol(2),0.5), width:1}, type: 'bar', hoverinfo: 'text', name: '', showlegend: false }) data.push( { y: flows_net.map(f=>f[1]), x: flows_net.map(f=>f[0]), textposition: 'auto', offsetgroup :'b', text: flows_net.map(f=>d3.format('$,.2r')(f[1])), marker: {color: make_trans(sideCol(3),0.8), width:1}, type: 'bar', hoverinfo: 'text', name: '', showlegend: false }) var layout = { height: 360, width: width - 30, font: font, barmode: '', bargap:0.4, showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1.2}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside', range: years_range, }, yaxis: { range:[d3.max(flws,f=>f[1])*1.2,d3.min(flws,f=>f[1])*1.2], title: `$ (000s)`, showgrid: true, zeroline: true, tickformat: ',.0f', ticks:'outside' } }; const div=container('Buy, sell and net (in red) trade value by market participant.') plt.newPlot(div, data, layout,{displayModeBar: false}); if (flow_filtered().length>0) { return html`${div}` } else { return html`` } }42:T83e,{ let data=[] let sers=d3.rollup(flow_filtered(),f=>{ return d3.rollups( flow_filtered(), v=>Object.fromEntries(v.filter(m=>m.cat==f[0].cat).map(t=>{return {side:t.side, flow_sum:t.flow_sum, value:t.value, hour_count: t.hour_count}}).map(p=>[p.side, {flow_sum: p.flow_sum, value: p.value, hour_count: p.hour_count}])), v=>node_dict[v.node].member) .sort() },f=>f.cat) function series(cat_ref, side) { let flows=sers.get(cat_ref) if (flows) { data.push( { y: flows.map(f=>f[1][side] ? (f[1][side].flow_sum / f[1][side].hour_count) : null), x: flows.map(f=>f[0]), mode: 'lines', marker: {color: make_trans(catCol(cats[cat_ref].index),side=='consume'?0.7:1), width:1}, fill:'tonexty', type: 'bar', hoverinfo: 'x,y', name: cats[cat_ref].name, stackgroup: 'one', showlegend: side=='consume' } ) } } Object.keys(cats).forEach(n=>series(n,'consume')) Object.keys(cats).forEach(n=>series(n,'supply')) var layout = { height: 400, width: width-30, font: font, showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1.2}, margin: {l: 50,r: 5,b: 30,t: 0}, bargap: 0.5,bargroupgap: 0, xaxis: { ticktext:d3.groups(flow_filtered(),f=>f.node,f=>f.node).map(d=>node_dict[d[0]].member).sort(), showgrid: true, zeroline: false, ticks:'outside', range: years_range }, yaxis: { title: `MW`, showgrid: true, zeroline: true, tickformat: '.0f', ticks:'outside' }, annotations: [{ xref: 'paper', yref: 'paper', showarrow: false, x: 0.07, y: 0.10, font: {color: "#777",size: 15}, text: 'Supply', }, { xref: 'paper', yref: 'paper', showarrow: false, x: 0.07, y: 0.90, font: {color: "#777",size: 15}, text: 'Consumption', }] }; const div=container('Net trade power by time-of-day category and market participant.') plt.newPlot(div, data, layout,{displayModeBar: false}); if (flow_filtered().length>0) { return html`${div}` } else { return html`` } }43:T83a,{ let data=[] let sers=d3.rollup(flow_filtered(),f=>{ return d3.rollups( flow_filtered(), v=>Object.fromEntries(v.filter(m=>m.cat==f[0].cat).map(t=>{return {side:t.side, flow_sum:t.flow_sum, value:t.value, hour_count: t.hour_count}}).map(p=>[p.side, {flow_sum: p.flow_sum, value: p.value, hour_count: p.hour_count}])), v=>node_dict[v.node].member) .sort() },f=>f.cat) function series(cat_ref, side) { let flows=sers.get(cat_ref) if (flows) { data.push( { y: flows.map(f=>f[1][side] ? (f[1][side].flow_sum / 1000) : null), x: flows.map(f=>f[0]), mode: 'lines', marker: {color: make_trans(catCol(cats[cat_ref].index),side=='consume'?0.7:1), width:1}, fill:'tonexty', type: 'bar', hoverinfo: 'x,y', name: cats[cat_ref].name, stackgroup: 'one', showlegend: side=='consume' } ) } } Object.keys(cats).forEach(n=>series(n,'consume')) Object.keys(cats).forEach(n=>series(n,'supply')) var layout = { height: 400, width: width - 30, font: font, bargap: 0.5,bargroupgap: 0, showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1.2}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { ticktext:d3.groups(flow_filtered(),f=>f.node,f=>f.node).map(d=>node_dict[d[0]].member).sort(), showgrid: true, zeroline: false, ticks:'outside', range: years_range }, yaxis: { title: `GWh`, showgrid: true, zeroline: true, tickformat: '.1f', ticks:'outside' }, annotations: [{ xref: 'paper', yref: 'paper', showarrow: false, x: 0.07, y: 0.10, font: {color: "#777",size: 15}, text: 'Supply', }, { xref: 'paper', yref: 'paper', showarrow: false, x: 0.07, y: 0.90, font: {color: "#777",size: 15}, text: 'Consumption', }] }; const div=container('Net trade energy by time-of-day category and market participant.') plt.newPlot(div, data, layout,{displayModeBar: false}); if (flow_filtered().length>0) { return html`${div}` } else { return html`` } }44:T707,{ var data = [ { y: monthly_solar_price.map(d=>d[1].price), x: monthly_solar_price.map(d=>d[1].start_date), mode: 'lines', line: {shape: '', color: col_sol, width: 1.5}, type: 'scatter', showlegend:true, name:'APP Solar Benchmark', hovertemplate: "%{x}
" + "%{y:,.0f} %{yaxis.title.text}" + "", },{ y: monthly_wind_price.map(d=>d[1].price), x: monthly_wind_price.map(d=>d[1].start_date), mode: 'lines', line: {shape: '', color: col_wind, width: 1.5}, type: 'scatter', showlegend:true, name:'APP Wind Benchmark', hovertemplate: "%{x}
" + "%{y:,.0f} %{yaxis.title.text}" + "", },{ y: monthly.map(d=>d.price_mean), x: monthly.map(d=>d.date), mode: 'lines+markers', line: {shape: '', color: '#AAA', width: 2}, marker:{size:2}, type: 'scatter', showlegend:true, fill:'', name:'SAPP DAM Average Price', hovertemplate: "%{x}
" + "%{y:,.0f} %{yaxis.title.text}" + "", } ] var layout = { height: 220, width: width-30, font: font, showlegend: true, legend: {orientation: 'h',xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks: 'outside', range:[d3.min(monthly_solar_price, d=>d[1].start_date),d3.max(monthly_solar_price, d=>d[1].start_date)] }, yaxis: { range: [20,230], title: `$/MWh`, showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Monthly average APP Solar and Wind Benchmark prices from 2016 to date.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }45:T59f,{ var data = [ { y: monthly_solar_variance.map(d=>d.variance), x: monthly_solar_variance.map(d=>d.start_date), marker: {color: make_trans(col_sol,0.7)}, type: 'bar', showlegend:true, fill:'tozeroy', name:'APP Solar Benchmark', hovertemplate: "Solar
" + "%{y:%.0f} %{yaxis.title.text}" + "", }, { y: monthly_wind_variance.map(d=>d.variance), x: monthly_wind_variance.map(d=>d.start_date), marker: {color: make_trans(col_wind,0.7)}, type: 'bar', showlegend:true, fill:'tozeroy', name:'APP Wind Benchmark', hovertemplate: "Wind
" + "%{y:%.0f} %{yaxis.title.text}" + "", } ] var layout = { height: 200, width: width-30, font: font, showlegend: true, legend: {orientation: 'h',xanchor: 'left', x:0, y: 0.2}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks: 'outside', range:[d3.min(monthly_solar_price, d=>d[1].start_date),d3.max(monthly_solar_price, d=>d[1].start_date)+20] }, yaxis: { title: ``, range:[-0.32,0.32], showgrid: true, zeroline: false, tickformat: '%.0f', ticks:'outside' } }; const div=container('Relative APP Solar and Wind Benchmark prices compared to the average SAPP DAM Price.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }46:T64d,function build_tree(dat, param,pct_param,name_param, col_param, unit) { const root = treemap2(dat, param); const svg = d3.create("svg") .attr("width", 300) .attr("viewBox", [0, 0, 300, 300]) .style("font", "10px sans-serif"); const leaf = svg.selectAll("g") .data(root.leaves()) .join("g") .attr("transform", d => `translate(${d.x0},${d.y0})`); leaf.append("title") .text(d => `${d.ancestors().reverse().slice(1,).map(d => d.data[name_param]).join(" - ")}\n${d3.format(",.3r")(d.value)} ${unit}`); leaf.append("rect") .attr("id", d => (d.leafUid = DOM.uid("leaf")).id) .attr("fill", d => { var e=d; while (e.depth > 1) e = e.parent; return d.data[col_param]; }) .attr("width", d => d.x1 - d.x0) .attr("height", d => d.y1 - d.y0); leaf.append("clipPath") .attr("id", d => (d.clipUid = DOM.uid("clip")).id) .append("use") .attr("xlink:href", d => d.leafUid.href); leaf.append("text") .attr("clip-path", d => d.clipUid) .selectAll("tspan") .data(d => [ d.data[name_param], d3.format(".0%")(d.data[pct_param]/100), d3.format('.2r')(d.data[param]*(d.data[param]<0?-1:1)) + unit, d3.format('$.0f')(d.data.price) + '/MWh' ]) .join("tspan") .attr("x", 3) .attr("y", (d, i, nodes) => `${(i === nodes.length - 1) * 0.3 + 1.1 + i * 0.9}em`) .attr("fill-opacity", (d, i, nodes) => i > 0 ? 0.7 : null) .text(d => d); if (flow_filtered().length>0) { return svg.node() } else { return '' } }47:T92c,function bars(share_type) { let data=[] let selected_nodes = nodes.map(v=>v.ref) // let share_type='mwh' function get_val(p) { if (share_type=='mwh') { return p.flow_sum } else { return p.value } } let flow_nodes= d3.rollup(flows_filtered(), v=>d3.sum(v,p=>Math.abs(get_val(p))), f=>f.node, f=>f.date, f=>f.flow_sum<0?'supply':'consume') let flow_total= d3.rollup(flows_filtered(), v=>d3.sum(v,p=>get_val(p)), f=>f.date, f=>f.flow_sum<0?'supply':'consume') function series(node_ref, selected_dt) { if (flow_nodes.get(node_ref)) { const nde=Array.from(flow_nodes.get(node_ref)).filter(v => (selected_dt && +selected_date.toJSDate() === +v[0]) || (!selected_dt && +selected_date.toJSDate() != +v[0])) function series_side (side) { data.push({ y: nde.map((v,i)=>v[1].get(side) * (side==='consume'?-1:1)/(share_type==='mwh'?1000:1000000)), x: nde.map(h=> h[0]), mode: 'lines', marker: {opacity: (selected_dt?1:0.5),shape:'spline', color: d3.schemeTableau10[node_dict[node_ref].col], width: 2}, type: 'bar', hoverinfo: 'x,y', showlegend:side==='consume' && selected_dt, name: node_dict[node_ref].member }) } series_side('consume') series_side('supply') } } nodes.forEach(n=>{ if (Object.fromEntries(selected_nodes.map(c=>[c,c]))[n.ref]) { series(n.ref, false) series(n.ref, true) } }) var layout = { height: 300, width: 320, barmode: 'relative', font: font, showlegend: false, legend: {xanchor: 'left', x:1, y: 1}, margin: {l: 30,r: 5,b: 30,t: 0}, bargap:0.01, xaxis: { range:[ '2019-12-01', monthly[monthly.length-1].lux_date.plus({month:0.5}).toJSDate() ], showgrid: true, zeroline: false, ticks:'outside' }, yaxis: { showgrid: true, zeroline: true, tickformat: '.2r', ticks:'outside' }, annotations: [{ xref: 'paper', yref: 'paper', showarrow: false, x: 0.07, y: 0.35, font: {color: "#222",size: 14}, text: 'Buy', }, { xref: 'paper', yref: 'paper', showarrow: false, x: 0.07, y: 0.65, font: {color: "#222",size: 14}, text: 'Sell', }] } const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}) return html` ${div} ` }48:T450,addPieChartsToMap = function(map,index, rW, rH, m, f) { // Create segments for each region sapp.features.forEach(feat => { let flw=flows_by_node.get(feat.properties.ref) if (flw) { const rad=f function fnX(d) { return projection.centroid(d.geometry)[0] + node_dict[d.properties.ref].offset[0]+rW/2-rad-m } function fnY(d) { return projection.centroid(d.geometry)[1] + node_dict[d.properties.ref].offset[1]+rH/2-rad-m - (index==1?(rad*2 + m):0) - (f/2)-1 } let x = fnX(feat) let y = fnY(feat) let arcs = pie([Math.abs(flw[index]),flows_total.get('consume')[index]-Math.abs(flw[index])]) map .append('g') .attr("transform", "translate(" + x + "," + y + ")") .attr('stroke', 'grey') .selectAll('path') .data(arcs) .join('path') .attr('fill', (d,i)=> [flw[index]>0 ? sideCol(5):sideCol(2),'white'][i]) .attr('fill-opacity', (d,i)=> [1,0][i]) .attr("d", d3 .arc() .innerRadius(0) .outerRadius(rad)) } }); }49:T590,nodes=[ {ref: 'BOT', country: 'Botswana', member:'BPC', col: 1, offset:[-300,170], leader:true}, {ref: 'MOZ', country: 'Mozambique', member:'EDM',col: 2, offset:[180, 20], leader:true}, {ref: 'HCB', country: 'Mozambique', member:'HCB',col: 0, offset:[280,-90], leader:true}, {ref: 'NAM', country: 'Namibia', member:'NamPower',col: 3, offset:[-200,20], leader:true}, {ref: 'RSA', country: 'South Africa', member:'Eskom',col: 4, offset:[-30,30]}, {ref: 'ZAMC', country: 'Zambia', member:'CEC',col: 5, offset:[-415,-100], leader:true}, {ref: 'ZAMZ', country: 'Zambia', member:'ZESCO',col: 7, offset:[-460,50], leader:true}, {ref: 'ZAMN', country: 'Zambia', member:'NECL',col: 2, offset:[-290,-100], leader:true}, {ref: 'ZAML', country: 'Zambia', member:'LHPC',col: 6, offset:[-320,50], leader:true}, {ref: 'ZIM', country: 'Zimbabwe', member:'ZESA',col: 9, offset:[250,120], leader:true}, {ref: 'LES', country: 'Lesotho', member:'LEC',col: 8, offset:[160,130], leader:true}, {ref: 'ESW', country: 'Swaziland', member:'EEC',col: 9, offset:[140,80], leader:true}, {ref: 'DRC', country: 'DRC', member:'SNEL',col: 3, offset:[20,30]}, {ref: 'TNZ', country: 'Tanzania', member:'TENESCO',col: 2, offset:[170,-25], leader:true}, {ref: 'MAL', country: 'Malawi', member:'ESCOM',col: 6, offset:[170,-50], leader:true}, {ref: 'ANG', country: 'Angola', member:'RNT',col: 2, offset:[-150,-0], leader:true}, ]4a:[{"id":"057ef0b5dd0e520a9be2a77bad99a70d2f85fdd133ca99dc821899e6ac74130d84d5eb7a818867487c356c8b893dbf6d256953734722a765b4719da9e513de10","href":"https://static.observableusercontent.com/files/057ef0b5dd0e520a9be2a77bad99a70d2f85fdd133ca99dc821899e6ac74130d84d5eb7a818867487c356c8b893dbf6d256953734722a765b4719da9e513de10","name":"sapp.json","type":"application/json","lastModified":1621409748809.175,"size":55475},{"id":"f97fdeb7341d3f752a7271f84a46608de7c558c00d96c577adeee0965b8603a8c97870615569a9fa16d0c6fa12d7e7b4892657bad77501f966f08b8e13e8e6b9","href":"https://static.observableusercontent.com/files/f97fdeb7341d3f752a7271f84a46608de7c558c00d96c577adeee0965b8603a8c97870615569a9fa16d0c6fa12d7e7b4892657bad77501f966f08b8e13e8e6b9","name":"APP logo.png","type":"image/png","lastModified":1661101536369.224,"size":92844}] 2e:{"title":"Day-ahead Market (DAM) - Monthly Bulletin","cells":[{"id":0,"value":"html`

Day-ahead Market (DAM) - Monthly Bulletin

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2334,"value":"$30","pinned":false,"hidden":false,"mode":"html"},{"id":70,"value":"viewof selected_month = {\n const form = html``;\n form.oninput = () => {\n form.querySelector(\"[id=month_disp]\").textContent = monthly[form.month_index.valueAsNumber].lux_date.toFormat('LLL yyyy')\n form.value = form.month_index.valueAsNumber;\n };\n form.oninput();\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":950,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":636,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1210,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":89,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":205,"value":"viewof cat = {\n const form = html`\n `\n \n form.oninput = () => {form.value = form.period_select.value}\n form.oninput()\n return form\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1524,"value":"{\n const content = html`\n

\n ${bars('mwh')}\n

Buyers

\n ${build_tree(consume, 'flow_sum','flow_pct', 'member','col', 'GWh')}\n

Sellers

\n ${build_tree(supply, 'flow_sum','flow_pct', 'member','col', 'GWh')}\n

`;\n \n const div=container(monthly[selected_month].lux_date.toFormat('LLL yyyy') + ' buyers and sellers of energy by market share (GWh).')\n div.prepend(content)\n return html`${div}`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1629,"value":"{\n const content = html`\n

\n ${bars('$')}\n

Buyers

\n ${build_tree(consume, 'value','value_pct', 'member', 'col','$m')}\n

Sellers

\n ${build_tree(supply, 'value','value_pct', 'member', 'col', '$m')}\n

`\n \n const div=container(monthly[selected_month].lux_date.toFormat('LLL yyyy') + ' buyers and sellers of energy by market value share ($m).')\n div.prepend(content)\n return html`${div}`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":173,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1020,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":2087,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":23,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":2515,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":59,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":2486,"value":"{ \n var data = []\n data.push({\n y: daily_power_profile,\n mode: 'lines+markers', line: {shape:'spline',color: '#088', width: 2}, marker: { size: 6 },\n type: 'scatter', hoverinfo: 'x+y', showlegend: false, fill:'tozeroy'\n })\n\n var layout = {\n height: 180, width: width - 30, font: font,\n showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1},\n margin: {l: 50,r: 5,b: 30,t: 0},\n xaxis: {\n showgrid: true, zeroline: false, ticks:'outside',showspikes: false,dtick:1, range:[0,23]\n },\n yaxis: {title: 'MW',showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside'}\n };\n \n const div=container(monthly[selected_month].lux_date.toFormat('LLL yyyy') + ' average daily power profile.')\n plt.newPlot(div, data, layout,{displayModeBar: false})\n return html`${div}`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":25,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":1132,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":409,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":1119,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"id":532,"value":"$40","pinned":false,"hidden":false,"mode":"ojs"},{"id":570,"value":"$41","pinned":false,"hidden":false,"mode":"ojs"},{"id":322,"value":"$42","pinned":false,"hidden":false,"mode":"ojs"},{"id":1197,"value":"$43","pinned":false,"hidden":false,"mode":"ojs"},{"id":2351,"value":"

Solar and Wind Benchmarks

","pinned":false,"hidden":false,"mode":"html"},{"id":2404,"value":"$44","pinned":false,"hidden":false,"mode":"ojs"},{"id":2446,"value":"$45","pinned":false,"hidden":false,"mode":"ojs"},{"id":693,"value":"html`\n

\n

Appendix

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2436,"value":"col_sol='#d68910'","pinned":false,"hidden":false,"mode":"ojs"},{"id":2438,"value":"col_wind='#2980b9'","pinned":false,"hidden":false,"mode":"ojs"},{"id":2422,"value":"import {monthly_wind_variance} from \"@andyflem/app-wind-benchmark\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2513,"value":"monthly_solar_variance","pinned":true,"hidden":false,"mode":"ojs"},{"id":2424,"value":"import {monthly_solar_variance} from \"@andyflem/app-solar-benchmark\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2420,"value":"import {monthly_wind_price} from \"@andyflem/app-wind-benchmark\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2387,"value":"import {monthly_solar_price} from \"@andyflem/app-solar-benchmark\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1962,"value":"container = function(captionText) {\n const div=DOM.element('div')\n div.setAttribute(\"class\",\"border m-2 p-2\")\n const caption=DOM.element('figcaption')\n caption.append(captionText)\n div.append(caption)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1526,"value":"$46","pinned":false,"hidden":false,"mode":"ojs"},{"id":1532,"value":"treemap2 = (data,property_name) => d3.treemap()\n .tile(d3.treemapBinary)\n .size([280, 280])\n .padding(1)\n .round(true)\n (d3.hierarchy(data)\n .sum(d => d[property_name])\n .sort((a, b) => b[property_name] - a[property_name]))","pinned":false,"hidden":false,"mode":"ojs"},{"id":2119,"value":"$47","pinned":false,"hidden":false,"mode":"ojs"},{"id":1452,"value":"#### Map","pinned":false,"hidden":false,"mode":"md"},{"id":663,"value":"pie = d3\n .pie()\n .sort(null)\n .value(d => d)","pinned":false,"hidden":false,"mode":"ojs"},{"id":638,"value":"$48","pinned":false,"hidden":false,"mode":"ojs"},{"id":890,"value":" projection=d3.geoPath(d3.geoEquirectangular().center([25,-14]).scale(1300))","pinned":false,"hidden":false,"mode":"ojs"},{"id":202,"value":"$49","pinned":false,"hidden":false,"mode":"ojs"},{"id":881,"value":"sapptopo=FileAttachment(\"sapp.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":642,"value":"sapp=topojson.feature(sapptopo, sapptopo.objects.sapp)","pinned":false,"hidden":false,"mode":"ojs"},{"id":884,"value":"topojson = require('topojson')","pinned":false,"hidden":false,"mode":"ojs"},{"id":1456,"value":"#### Data","pinned":false,"hidden":false,"mode":"md"},{"id":2519,"value":"function yearly_month_profile_price(year , month_no) {\n let dat = yearly_calmonthly_hours.filter(v=>v.year==year)\n return d3.rollups(dat.filter(m=>m.month==month_no),v=>d3.mean(v,m=>m.price),t=>t.hour)\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2517,"value":"yearly_month_profiles=yearly.map(y=>{ \n return { \n year: y[0],\n profile: [...Array(12).keys()].map(m=>yearly_month_profile_price(y[0], m+1)).map(v=>v.map(l=>l[1]))\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2521,"value":"yearly_calmonthly_hours = {\n const raw = (await d3.csv(base_url + market_ref + \"_yearly_calmonthly_hours.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1585,"value":"supply = {\n \n const ret = d3.rollups(flow_filtered().filter(f=>f.side=='supply'), v=> { return { \n node: v[0].node, \n flow_sum: d3.sum(v,p=>p.flow_sum)/1000, \n value: d3.sum(v,p=>p.value)/1000000, \n price: d3.sum(v,p=>p.value)/d3.sum(v,p=>p.flow_sum),\n member: node_dict[v[0].node].member,\n col: d3.schemeTableau10[node_dict[v[0].node].col]\n }}, f=>f.node).map(v => v[1])\n const fl_sum = d3.sum(ret, v=>v.flow_sum)\n const vl_sum = d3.sum(ret, v=>v.value)\n const ret2 = ret.map(v=> {\n v.flow_pct = v.flow_sum / fl_sum * 100\n v.value_pct = v.value / vl_sum * 100\n return v\n })\n return {name: 'Supply', children: ret2}\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1534,"value":"consume = {\n \n const ret = d3.rollups(flow_filtered().filter(f=>f.side=='consume'), v=> { return { \n node: v[0].node, \n flow_sum: d3.sum(v,p=>p.flow_sum)/1000,\n value: d3.sum(v,p=>p.value)/1000000,\n price: d3.sum(v,p=>p.value)/d3.sum(v,p=>p.flow_sum),\n member: node_dict[v[0].node].member,\n col: d3.schemeTableau10[node_dict[v[0].node].col]\n }}, f=>f.node).map(v => v[1])\n const fl_sum = d3.sum(ret, v=>v.flow_sum)\n const vl_sum = d3.sum(ret, v=>v.value)\n \n const ret2 = ret.map(v=> {\n v.flow_pct = v.flow_sum / fl_sum * 100\n v.value_pct = v.value / vl_sum * 100\n return v\n })\n return {name: 'Consume', children: ret2}\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":647,"value":"flows_total=d3.rollup(flow_filtered(),v=>[\n d3.sum(v,d=>d.value)/d3.sum(v,d=>d.flow_sum),\n d3.sum(v,d=>d.value),\n d3.sum(v,d=>d.flow_sum)\n ],v=>v.side)","pinned":false,"hidden":false,"mode":"ojs"},{"id":644,"value":"flows_by_node=d3.rollup(flow_filtered(),v=>[\n d3.sum(v,d=>d.value)/d3.sum(v,d=>d.flow_sum),\n d3.sum(v,d=>d.value),\n d3.sum(v,d=>d.flow_sum)\n ],v=>v.node)","pinned":false,"hidden":false,"mode":"ojs"},{"id":210,"value":"node_dict=Object.fromEntries(nodes.map(n=>[n.ref,n]))","pinned":false,"hidden":false,"mode":"ojs"},{"id":185,"value":"years_range= [monthly[0].lux_date.plus({month:-0.5}).toJSDate(),monthly[monthly.length-1].lux_date.plus({month:0.5}).toJSDate()]","pinned":false,"hidden":false,"mode":"ojs"},{"id":2131,"value":"function flows_filtered() {\n let ret = null\n if (cat=='all') {\n ret = flow\n } else {\n ret = flow.filter(f=>f.cat==cat)\n }\n return ret.filter(v => v.lux_date.year>2019).filter(v=>v.flow_sum>1000 || v.flow_sum<-1000)\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2549,"value":"flow","pinned":false,"hidden":false,"mode":"ojs"},{"id":2551,"value":"flow_filtered()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2553,"value":"selected_date.month","pinned":false,"hidden":false,"mode":"ojs"},{"id":217,"value":"function flow_filtered() {\n if (cat=='all') {\n return flow.filter(f=>f.year==selected_date.year && f.month==selected_date.month)\n } else {\n return flow.filter(f=>f.year==selected_date.year && f.month==selected_date.month && f.cat==cat)\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":966,"value":"calmonthly_dict=Object.fromEntries(calmonthly.map(n=>[n.month,n]))","pinned":false,"hidden":false,"mode":"ojs"},{"id":166,"value":"selected_date=monthly[selected_month].lux_date","pinned":false,"hidden":false,"mode":"ojs"},{"id":2478,"value":"daily_power_profile=d3.rollups(selected_hours, v=>d3.mean(v,l=>l.volume), v=>v.hour).map(v=>v[1])","pinned":false,"hidden":false,"mode":"ojs"},{"id":2462,"value":"dailyprofile=d3.rollups(selected_hours, v=>d3.mean(v,l=>l.price), v=>v.hour).map(v=>v[1])","pinned":false,"hidden":false,"mode":"ojs"},{"id":2575,"value":"monthFile(monthly[selected_month].lux_date)","pinned":true,"hidden":false,"mode":"ojs"},{"id":2579,"value":"getHourly_old(monthFile_old(monthly[selected_month].lux_date))","pinned":true,"hidden":false,"mode":"ojs"},{"id":29,"value":"selected_hours = getHourly(monthFile(monthly[selected_month].lux_date))","pinned":true,"hidden":false,"mode":"ojs"},{"id":31,"value":"selected_days=daily.filter(d=>d.year == selected_date.year && d.month == selected_date.month)","pinned":false,"hidden":false,"mode":"ojs"},{"id":112,"value":"yearly=d3.rollups(monthly,m=>{ return {\n standard_price_mean: d3.mean(m,m=>m.standard_price_mean),\n off_price_mean: d3.mean(m,m=>m.off_price_mean),\n morning_price_mean: d3.mean(m,m=>m.morning_price_mean),\n evening_price_mean: d3.mean(m,m=>m.evening_price_mean),\n price_mean: d3.mean(m,m=>m.price_mean),\n \n}},v=>v.year)","pinned":false,"hidden":false,"mode":"ojs"},{"id":419,"value":"month_names={\n return {\n 0:'Jan',\n 1:'Feb',\n 2:'Mar',\n 3:'Apr',\n 4:'May',\n 5:'Jun',\n 6:'Jul',\n 7:'Aug',\n 8:'Sep',\n 9:'Oct',\n 10:'Nov',\n 11:'Dec',\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":416,"value":"month=monthly[selected_month]","pinned":false,"hidden":false,"mode":"ojs"},{"id":481,"value":"prev_month=monthly[selected_month-1]","pinned":false,"hidden":false,"mode":"ojs"},{"id":484,"value":"days=[...Array(month.lux_date.daysInMonth).keys()].map(i=>month.lux_date.plus({day:i}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":20,"value":"month_dict={\n return {\n 'Jan':{index: 0, group: 1},\n 'Feb':{index: 1, group: 1},\n 'Mar':{index: 2, group: 6},\n 'Apr':{index: 3, group: 6},\n 'May':{index: 4, group: 2},\n 'Jun':{index: 5, group: 2},\n 'Jul':{index: 6, group: 3},\n 'Aug':{index: 7, group: 3},\n 'Sep':{index: 8, group: 4},\n 'Oct':{index: 9, group: 4},\n 'Nov':{index: 10, group: 4},\n 'Dec':{index: 11, group: 1},\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":47,"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":1276,"value":"### Solar","pinned":false,"hidden":false,"mode":"md"},{"id":1052,"value":"md `\n### Data Load\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2572,"value":"async function getHourly (filename) {\n const url=`https://data.africanpowerprojects.com/sapp/input/monthly/${market_ref}/${filename}?${Math.floor(Math.random() * 1000)}`\n \n const rw = (await d3.csv(url,d3.autoType))\n let hours=[]\n rw.forEach(h=> {\n const lux = DateTime.fromJSDate(h.date).plus({hours:h.hour-2})\n //if (lux.isValid) {\n hours.push({\n raw: h,\n lux_date: lux,\n hour: h.hour,\n price: parseFloat(h.price),\n volume: parseInt(h.volume)\n })\n //}\n })\n hours=hours.map(h=>{\n h.date=h.lux_date.toJSDate()\n return h\n })\n return hours\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":37,"value":"async function getHourly_old (filename) {\n const url=`https://data.africanpowerprojects.com/sapp/input/monthly/${market_ref}/${filename}?${Math.floor(Math.random() * 1000)}`\n \n const rw = (await d3.json(url,d3.autoType))\n rw.shift()\n \n let hours=[]\n rw.forEach(h=> {\n const lux = DateTime.fromFormat(h[0].slice(1,15),'yyyy/LL/dd) HH')\n if (lux.isValid) {\n hours.push({\n raw: h,\n lux_date: lux,\n hour: parseInt(h[0].slice(13,15)),\n price: parseFloat(h[1]),\n volume: parseInt(h[2])\n })\n }\n })\n hours=hours.map(h=>{\n h.date=h.lux_date.toJSDate()\n return h\n })\n return hours\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2582,"value":"monthFile_old= function (dte) {\n return `dam_${dte.toFormat('yyyy')}_${dte.toFormat('LL')}.json`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":35,"value":"monthFile= function (dte) {\n return `dam_${dte.toFormat('yyyy')}_${dte.toFormat('LL')}.csv`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":411,"value":"calmonthly = {\n const raw = (await d3.csv(base_url + market_ref + \"_calmonthly.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n \n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":215,"value":"flow = {\n const raw = (await d3.csv(base_url + market_ref + \"_flow_monthly_cat.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n raw.forEach(r=>{\n r.lux_date=DateTime.fromObject({year: r.year, month: r.month, day: 1, hour: 0, zone: 0})\n r.date = r.lux_date.toJSDate()\n })\n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":33,"value":"daily = {\n const raw = (await d3.csv(base_url + market_ref + \"_daily.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n raw.forEach(r=>{\n r.lux_date=DateTime.fromObject({year: r.year, month: r.month, day: r.day})\n r.date=r.lux_date.toJSDate()\n }) \n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":72,"value":"monthly = {\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, hour: 0, zone: 0})\n r.date=r.lux_date.toJSDate()\n r.volume_gwh = r.volume/1000\n r.value_m = r.value / 1000000\n })\n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1050,"value":"market_ref='dam'","pinned":false,"hidden":false,"mode":"ojs"},{"id":1048,"value":"base_url='https://data.africanpowerprojects.com/sapp/output/'","pinned":false,"hidden":false,"mode":"ojs"},{"id":1057,"value":"md `\n### Imports\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1098,"value":"function sideCol(index) {\n return d3.schemeCategory10[index]\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1100,"value":"function catCol(index) {\n return d3.schemeCategory10[index]\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":91,"value":"make_trans=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":16,"value":"font = ({\n family: 'Courier New, monospace',\n size: 13,\n color: '#6f6f6f'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":12,"value":"plt = require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":14,"value":"d3 = require(\"d3@5\",\"d3-array@^2.10\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":10,"value":"html`\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":946,"value":"style=html`css `","pinned":false,"hidden":false,"mode":"ojs"},{"id":294,"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":8,"value":"luxon = require(\"luxon@1/build/amd/luxon.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":6,"value":"DateTime = luxon.DateTime","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W4a","stdlib":"1","titling":"auto"}

${diff2(month.price_mean,prev_month.price_mean, 'Price')}

${diff2(month.price_mean,prev_month.price_mean, d3.format('$,.3r')(month.price_mean) + '/MWh')}

${diff2(month.value,prev_month.value,'Value')}

${diff2(month.value,prev_month.value,d3.format('$,.2r')(month.value/1000000) + 'm')}

${diff2(month.volume,prev_month.volume, "Volume")}

${diff2(month.volume,prev_month.volume,d3.format('.0f')(month.volume/1000) + 'GWh')}

Day-ahead Market (DAM) - Monthly Bulletin

Solar and Wind Benchmarks

Appendix