30:T5fd,

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:T1400,{ 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(',.0f')(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('$,.2r')(Math.abs(m[1])/1000000) + 'm' } else { return '' } }), false ) svg.append('g') .selectAll("rect") .data(sapp_filtered) .join("rect") .attr("x", d => projection.centroid(d.geometry)[0] + node_dict[d.properties.ref].offset[0]-rW/2) .attr("y", d => projection.centroid(d.geometry)[1] + node_dict[d.properties.ref].offset[1]-rH/2) .attr('width', rW) .attr('height', rH) .attr('stroke', 'steelblue') .attr('stroke-width', '1.5') .attr('fill', 'white') .attr('fill-opacity', '0.3') svg.append('g') .selectAll("polyline") .data(sapp_filtered.filter(d=>node_dict[d.properties.ref].leader)) .join("polyline") .attr("points",d=>`${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 and their total net supply (green) or consumption (brown) in GWh through the DAM market from January 2016 to September 2023.') div.prepend(svg.node()) return div }32:T88e,{ let data=[] let selected_nodes=nodes.filter(v=>v.default_show).map(v=>v.ref) let share='mwh' function get_val(p) { if (share=='mwh') { return p.flow_sum/1000 } else { return p.value/1000 } } let flow_nodes= d3.rollup(flow, v=>d3.sum(v,p=>get_val(p)), f=>f.node, f=>f.date, f=>f.flow_sum<0?'supply':'consume') //let flow_total= d3.rollup(flow_filtered, v=>d3.sum(v,p=>get_val(p)), f=>f.date, f=>f.flow_sum<0?'supply':'consume') function series(node_ref) { if (flow_nodes.get(node_ref)) { const nde=Array.from(flow_nodes.get(node_ref)) function series_side (side) { data.push({ y: nde.map((v,i)=>v[1].get(side)), x: nde.map(h=> h[0]), mode: 'lines', marker: {opacity: 0.8,shape:'spline', color: node_dict[node_ref].col, width: 2}, type: 'bar', hoverinfo: 'x,y', showlegend:side=='consume', 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) } }) var layout = { height: 350, width: width-30, barmode: 'relative', font: font, showlegend: true, legend: {xanchor: 'left', x:1, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { range:[ monthly[0].lux_date.minus({month:0.5}).toJSDate(), monthly[monthly.length-1].lux_date.plus({month:0.5}).toJSDate() ], showgrid: true, zeroline: false, ticks:'outside' }, yaxis: { title:share=='mwh'?'MWh':'$000s', showgrid: true, zeroline: true, tickformat: '.0f', ticks:'outside' }, annotations: [{ xref: 'paper', yref: 'paper', showarrow: false, x: 0.07, y: 0.35, font: {color: "#555",size: 17}, text: 'Sellers', }, { xref: 'paper', yref: 'paper', showarrow: false, x: 0.07, y: 0.65, font: {color: "#555",size: 17}, text: 'Buyers', }] } const div=container('Supply and consumption participation in the market to September 2023.') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`${div}` }33:Tc22,{ let yrly=yearly//.slice(0,yearly.length-1) function vol() { var data = [{ y: yrly.map(m=>(m[1].volume/1000)), x: yrly.map(m=>m[0]), text: yrly.map((m,i)=>d3.format(',.3r')(m[1].volume/1000) + ' GWh'), textposition:'auto', marker: {color: '#2980b9', width: 2}, type: 'bar', hoverinfo: 'none', showlegend:false }] var layout = { height: 240, font: font, width:width*.33, 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:2}, yaxis: { range:[300,2300], title: 'GWh', showgrid: false, zeroline: true, tickformat: ',.0f', ticks:'outside' }}; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return div } function val() { var data = [{ y: yrly.map(m=>(m[1].value/1000000)), x: yrly.map(m=>m[0]), text: yrly.map((m,i)=>d3.format('$,.3r')(m[1].value/1000000) +'m' ), textposition:'auto', marker: {color: '#d68910', width: 2}, type: 'bar', hoverinfo: 'none', showlegend:false }] var layout = { height: 240, width: width*.3, font: font, showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 65,r: 10,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside', dtick:2}, yaxis: { range:[0,140], title: '$m', showgrid: false, zeroline: true, tickformat: ',.0f', ticks:'outside' }}; const div=DOM.element('div') div.setAttribute('style',`background-color:'red'`) plt.newPlot(div, data, layout,{displayModeBar: false}); return div } function rte() { var data = [{ y: yrly.map(m=>(m[1].rate)), x: yrly.map(m=>m[0]), text: yrly.map((m,i)=>d3.format('$,.3r')(m[1].price_mean) +'/MWh' ), textposition:'auto', marker: {shape:'spline',color: '#16a085'}, type: 'bar', hoverinfo: 'none', showlegend:false }] var layout = { height: 240, width: (width-30)*.33, font: font, showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 65,r: 10,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside', dtick:2}, yaxis: { range:[0,130], title: '$/MWh', showgrid: false, zeroline: true, tickformat: ',.0f', ticks:'outside' }}; const div=DOM.element('div') div.setAttribute('style',`background-color:'red'`) plt.newPlot(div, data, layout,{displayModeBar: false}); return div } const div=container('Mean annual price, total annual value and total annual volume traded on the SAPP DAM market.') const inner= html`

${rte()}

${val()}

${vol()}

` div.prepend(inner) return div }34:T6a9,viewof dam_range = { var data = [ { y: daily.map(d=>d.price_max).concat(daily.map(d=>d.price_min).reverse()), x: daily.map(d=>d.date).concat(daily.map(d=>d.date).reverse()), mode: 'lines', line: { width: 0}, fill:'toself', fillcolor:'rgba(100, 149, 237,0.3)', type: 'scatter', hoverinfo: 'none', showlegend:false,name: '', } , { y: daily.map(d=>d.price_weight_av), x: daily.map(d=>d.date), mode: 'lines', line: {shape: 'spline', color:'grey', width: 1.5}, type: 'scatter', hoverinfo: 'none', showlegend:false,name: '', }, { y: movingAverage(daily.map(d=>d.price_weight_av),14), x: daily.map(d=>d.date), mode: 'lines', line: {shape: 'spline', color:d3.schemeCategory10[3], width: 1.5}, type: 'scatter', hoverinfo: 'x,y', showlegend:false,name: '', }, ] var layout = { height: 300, width: width-30, font: font, showlegend: true, legend: {xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 53,b: 10,t: 0}, xaxis: { rangeslider: {}, range:years_range, showgrid: true, zeroline: false, ticks:'outside', }, yaxis: { title: '$/MWh', range: [0,320], showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container() const form = html`` plt.newPlot(div, data, layout,{displayModeBar: false}); div.on('plotly_relayout', d => { form.dispatchEvent(new CustomEvent("input", { detail: d['xaxis.range'],bubbles: true })); }) form.oninput = (evt) => { form.value = evt.detail; }; form.oninput({detail: [daily[0].date, daily[daily.length-1].date]}) return form; }35:T60f,{ var data = [ { y: daily.map(d=>d.volume/1000), x: daily.map(d=>d.date), mode: 'lines', line: {shape: 'spline', color:'grey', width: 1.5}, type: 'scatter', hoverinfo: 'none', showlegend:false,name: '', }, { y: daily.map(d=>d.volume/24), x: daily.map(d=>d.date), mode: 'lines', line: {shape: 'spline', color:'grey', width: 0.5}, yaxis: 'y2', type: 'scatter', hoverinfo: 'none', showlegend:false,name: '', }, { y: movingAverage(daily.map(d=>d.volume/1000),14), x: daily.map(d=>d.date), mode: 'lines', line: {shape: 'spline', color:d3.schemeCategory10[3], width: 1.5}, type: 'scatter', hoverinfo: 'x,y', showlegend:false,name: '', } ] var layout = { height: 200, width: width-30, font: font, showlegend: true, legend: {xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 50,b: 30,t: 0}, xaxis: { range: dam_range, showgrid: true, zeroline: false, ticks:'outside', }, yaxis: { title: 'GWh/day', range:[0,16], showgrid: false, zeroline: false, tickformat: '.0f', ticks:'outside' }, yaxis2: { title:'MW', range:[0,16000/24], overlaying: 'y', side: 'right' } }; const div=container('SAPP DAM weighted average daily price (top) and daily volume and equivalent power (bottom). 14 day moving average in red. Blue area is the daily maximum and minimum prices.') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`${div}` }36:T4c1,{ 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:5}, type: 'scatter', hoverinfo: 'x+y', showlegend:false, name:'Average price' }) 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 div }37:T6f6,{ function yr(ref) { const annual_price=d3.rollups(monthly.filter(m=>m.year<2023),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])), text: annual_price.map(m=>Math.round(m[1])), x: annual_price.map(m=>m[0].toJSDate()), type: 'scatter', showlegend:false, mode: 'text+lines+markers', textposition:'top-left', line: {color: make_trans(catCol(cats[ref].index),0.4),width:2}, marker: {color: make_trans(catCol(cats[ref].index),0.8),size: 10}, hoverinfo: '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: {shape:'',color: catCol(cats[ref].index), width: 1.5}, marker:{size:3}, type: 'scatter', hoverinfo: 'none', name: cats[ref].name, showlegend:true, connectgaps: false } } var data = [] Object.values(cats).forEach(c=>{ if (Object.fromEntries(selected_cats2.map(c=>[c,c]))[c.name]) { data.push(mon(c.ref)) } }) 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:years_range, showgrid: true, zeroline: false, ticks:'outside', }, yaxis: { title: '$/MWh', range:[0,410], showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Monthly average prices for each time-of-day category and overall market volume weighted average price.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }38:T6a3,{ function priceChart() { let ymax=d3.max(selected_hours,h=>h.price)*1.1 var data = [] days.forEach(d=>{ if (d.weekday==6) { data.push({ x:[d.toJSDate(),d.endOf('day').toJSDate()],showlegend:false, y:[ymax,ymax], fillcolor:make_trans('#f4a4a4',0.3), line: {width:0},mode:'lines', fill:'tozeroy', hoverinfo:'none' }) } if (d.weekday==7) { data.push({ x:[d.toJSDate(),d.endOf('day').toJSDate()],showlegend:false, y:[ymax,ymax], fillcolor:make_trans('#f4a4a4',0.5), line: {width:0},mode:'lines', fill:'tozeroy', hoverinfo:'none' }) } }) data.push({ y: selected_hours.map(h=>h.price), x: selected_hours.map(h=>h.date), mode: 'lines', line: {color:'#555', width: 1.4}, type: 'scatter', hoverinfo: 'x,y', showlegend:false,name: '', }) var layout = { height: 270, width: width-30, font: font, showlegend: true, legend: {orientation: 'h',xanchor: 'left', x:0, y: 1.1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { range: [selected_hours[0].date,selected_hours[selected_hours.length-1].date], showgrid: true, zeroline: false, ticks:'outside' }, yaxis: { range:[0,ymax], title: '$/MWh', showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return div } const div=container('A representative hourly price pattern during the first half of July 2018. Weekends are highlighted.') div.prepend(priceChart()) return div }39:T5d4,{ function priceChart() { var data = [] data.push({ y: selected_hours.map(h=>h.price/d3.max(daily.filter(d=>d.year==month.year && d.month==month.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==month.year && d.month==month.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: 270, 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=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return div } const div=container('Hourly price and volume relative to the monthly max during the first half of July 2018.') div.prepend(priceChart()) return div }3a:T695,{ let margin = ({top: 20, right: 65, bottom: 0, left: 73}) let height=300 let ys=timeofday.map(d => d.dayofweek == 1 ? month_names[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(${margin.left},0)`) .call(d3.axisLeft(y)) .call(g => g.select(".domain").remove()) .call(g => g.selectAll(".tick text") .attr("font-size", 12)) svg.append("g") .attr('stroke', 'white') .attr('stroke-width', '1') .attr('fill-opacity', '0.5') .selectAll("rect") .data(timeofday_flat) .join("rect") .attr('y', d=>y(d.dayofweek == 1 ? month_names[d.month-1] : d.dayofweek == 6 ? 'Sat' : 'Sun')) .attr('x', d=>x(d.hour)) .attr('width', x(1)-x(0)) .attr('height', y.bandwidth()) .attr('fill', d=>catCol(cats[d.cat].index) ) const div=container('Average pricing patterns by time-of-day and month and for Saturdays and Sundays.') div.prepend(svg.node()) return div }3b:T70e,{ function yr(ref) { const annual_price=d3.rollups(monthly.filter(m=>m.year<2025),v=>d3.mean(v, d=>d[ref + '_price_mean']), v=>v.lux_date.startOf('year').plus({month:6})) return { y: annual_price.map(m=>Math.round(m[1])), text: annual_price.map(m=>Math.round(m[1])), x: annual_price.map(m=>m[0].toJSDate()), type: 'scatter', showlegend:false, mode: 'text+lines+markers', textposition:'top-left', line: {color: make_trans(catCol(cats[ref].index),0.4),width:2}, marker: {color: make_trans(catCol(cats[ref].index),0.8),size: 10}, hoverinfo: 'y', name: cats[ref].name } } function mon(ref) { return { y: monthly.map(m=>m[ref + '_price_mean']), x: monthly.map(m=>m['date']), mode: 'lines+markers', line: {shape:'',color: catCol(cats[ref].index), width: 1.5}, marker:{size:3}, type: 'scatter', hoverinfo: 'none', name: cats[ref].name, showlegend:true, connectgaps: false } } var data = [] Object.values(cats).forEach(c=>{ if (Object.fromEntries(selected_cats2.map(c=>[c,c]))[c.name]) { data.push(mon(c.ref)) data.push(yr(c.ref)) } }) var layout = { height: 300, width: width-30, font: font, showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { range:years_range, showgrid: true, zeroline: false, ticks:'outside', }, yaxis: { title: '$/MWh', range:[0,410], showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Monthly average prices for each time-of-day category and overall market volume weighted average price.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }3c:T619,{ function series(ref) { return [{ y: monthly.map(m=>m[ref + '_price_mean'] + m[ref + '_price_stdev']).concat(monthly.map(m=>m[ref + '_price_mean'] - m[ref + '_price_stdev']).reverse()), x: monthly.map(m=>m['date']).concat(monthly.filter(m=>m[ref + '_price_max']).map(m=>m['date']).reverse()), mode: 'lines', line: {shape:'spline',color: catCol(cats[ref].index), width: 1}, fill:'toself', type: 'scatter', hoverinfo: 'none', name: cats[ref].name, }, { y: monthly.map(m=>m[ref + '_price_mean']), x: monthly.map(m=>m['date']), mode: 'lines', line: {shape:'spline',color: catCol(cats[ref].index), width: 1}, fill:'', type: 'scatter', hoverinfo: 'x,y', name: cats[ref].name, showlegend:false, }, ] } var data = [] Object.values(cats).forEach(c=>{ if (Object.fromEntries(selected_cats2.map(c=>[c,c]))[c.name]) { data=data.concat(series(c.ref)) } }) var layout = { height: 270, width: width-30, font: font, showlegend: true, legend: {xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { range:years_range, showgrid: true, zeroline: false, ticks:'outside', }, yaxis: { range:[0,410], title: '$/MWh', showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Monthly average prices and Bollinger bands (+ and - 1 standard deviation) by time-of-day categories.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }3d:T61a,{ function series(ref ,cl, nme) { return [{ y: monthly.map(m=>m[ref + 'price_stdev'] / m[ref + 'price_mean']), x: monthly.map(m=>m['date']), mode: 'lines', line: {shape:'spline',color: cl, width: nme=='Average'?4:2}, fill:'none', type: 'scatter', hoverinfo: 'none', name: nme, }] } var data = [] Object.values(cats).forEach(c=>{ if (Object.fromEntries(selected_cats2.map(c=>[c,c]))[c.name]) { data=data.concat(series(c.ref + '_', catCol(cats[c.ref].index), cats[c.ref].name)) } }) let av = monthly.map(m=>{ let sm=0 Object.values(cats).map(c => sm+=(m[c.ref + '_price_stdev']/m[c.ref + '_price_mean'])) return sm/4 }) data=data.concat([{ y: av, x: monthly.map(m=>m['date']), mode: 'lines', line: {shape:'spline',color: '#666', width:4}, fill:'none', type: 'scatter', hoverinfo: 'none', name: 'Average', }]) var layout = { height: 260, 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:years_range, showgrid: true, zeroline: false, ticks:'outside', }, yaxis: { range:[0,0.72], title: '', showgrid: true, zeroline: false, tickformat: '%.0f', ticks:'outside' } }; const div=container('Monthly coefficient of variation (standard deviation / mean) for each time of day category and the overall market.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }3e:T5ac,{ 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}, fill:'', type: 'scatter', hoverinfo: 'x,y', name: cats[ref].name, } } function series2(ref) { return { y: calmonthly.map(v=>v[ref + '_price_median']), 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}, fill:'', type: 'scatter', hoverinfo: 'x,y', name: cats[ref].long_name, showlegend: false, } } var data = [ series('off'), series('morning'), series('standard'), series('evening'), ] 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', range: [-0.1,11.1] }, yaxis: { range:[0,320], title: `$/MWh`, showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Average price by calendar month for each time-of-day category from 2020 to date.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }3f:T471,{ function series(ref) { return { y: calmonthly.map(v=>v[ref + '_price_mean']/(d3.mean(calmonthly.map(v=>v[ref + '_price_mean'])))-1), 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}, fill:'', type: 'scatter', hoverinfo: 'x,y', name: cats[ref].name, } } var data = [ series('off'), series('morning'), series('evening'), series('standard'), ] 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', range: [-0.1,11.1] }, yaxis: { range:[-0.52,1.02], title: ``, showgrid: true, zeroline: true, tickformat: '.0%', ticks:'outside' } }; const div=container('Variation in price around their mean annual price for each time-of-day category for 2020 to date.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }40:T4a7,{ function chart(ref) { var data = [{ x: daily.map(d => d[ref + '_price_mean']), marker: {color: catCol(cats[ref].index), opacity:0.8}, type: 'histogram', hoverinfo: 'none', showlegend:false }] var layout = { height: 200, font: font, width:(width-40)*.25, showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 55,r: 5,b: 50,t: 0}, xaxis: {range:[0,350], showgrid: true, zeroline: false, ticks:'outside', title: '$/MWh'}, yaxis: { title: 'no', showgrid: false, zeroline: true, tickformat: ',.0f', ticks:'outside' }}; const div=DOM.element('div') plt.newPlot(div, data, layout,{displayModeBar: false}); return div } const div=container('Histograms of daily prices for each time-of-day category.') let inner = html`

${chart('off')}

${chart('morning')}

${chart('standard')}

${chart('evening')}

` div.prepend(inner) return div }41:T6fa,{ function series(ref) { let p90 = pdcs[ref][Math.floor(pdcs[ref].length*0.1)] let p50 = pdcs[ref][Math.floor(pdcs[ref].length*0.5)] return [{ y: pdcs[ref].map(v=>v.price), x: pdcs[ref].map(v=>v.exceedance), mode: 'lines', line: {shape: 'spline', color: catCol(cats[ref].index), width:3}, fill:'', type: 'scatter', hoverinfo: 'x,y', name: cats[ref].name, }, { y: [p90.price], x: [p90.exceedance], text: [d3.format('$.0f')(p90.price)], mode: 'markers+text', marker: {color: catCol(cats[ref].index), size:10}, textposition:'top-right', type: 'scatter', hoverinfo: 'none', name: cats[ref].name, showlegend:false }, { y: [p50.price], x: [p50.exceedance], text: [d3.format('$.0f')(p50.price)], mode: 'markers+text', marker: {color: catCol(cats[ref].index), size:10}, textposition:'top-right', type: 'scatter', hoverinfo: 'none', name: cats[ref].name, showlegend:false } ] } var data = [] data=data.concat(series('off')) data=data.concat(series('morning')) data=data.concat(series('standard')) data=data.concat(series('evening')) var layout = { height: 270, width: width-30, font: font, showlegend: true, legend: {orientation:'h',xanchor: 'right', x:1, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside', tickformat: '%.0f' }, yaxis: { title: `$/MWh`, showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Price exceedance curves by time-of-day category. Median (P50) and P90 prices highlighted.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }42:T66e,{ let flow_total= Array.from(d3.rollup(flow.filter(f=>f.side=='consume'), v=>d3.sum(v,p=>p.flow_sum), f=>f.date)) 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, }, { y: flow_total.map(v=>v[1]/1000), x: monthly.map(h=> h.date), mode: 'lines', line: {shape: 'spline', color: 'red', width:1.5, dash:'dashdot'}, type: 'scatter', hoverinfo: 'x,y', name: 'Traded', showlegend:true, }, ] var layout = { height: 270, width: width-30, font: font, barmode: 'stack', showlegend: true, legend: {orientation:'h',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: { range:[0,315], title: `GWh`, showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Mean monthlhy matched and traded volumes, total and by time of day.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }43:T53d,{ function series(ref) { return [ { y: monthly.filter(m=>m[ref + '_power']).map(m=>m[ref + '_power']), x: monthly.filter(m=>m[ref + '_power']).map(m=>m['date']), mode: 'lines', line: {shape:'spline',color: catCol(cats[ref].index), width: 2}, fill:'', type: 'scatter', hoverinfo: 'x,y', name: cats[ref].name, showlegend:true, }, ] } var data = [] Object.values(cats).forEach(c=>{ data=data.concat(series(c.ref)) }) data.push( { y: monthly.map(m=>m.volume/24/31), x: monthly.map(m=>m.date), mode: 'lines', line: {shape:'spline',color: 'black', width: 3}, fill:'', type: 'scatter', hoverinfo: 'x,y', name: '', 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: {range:years_range, showgrid: true, zeroline: false, ticks:'outside', }, yaxis: { range: [0,570], title: 'MW', showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Power traded on the market for each time-of-day category and the overall market average.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }44:T580,{ 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, }, ] var layout = { height: 270, width: width-30, font: font, barmode:'stack', showlegend: true, legend: {xanchor: 'left', x:0, 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: '.0f', ticks:'outside' } }; const div=container('Value of energy traded on the market for each time-of-day category and the overall total value.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }45:T41a,{ function series(ref) { return { y: calmonthly.map(v=>v[ref + '_power']), 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}, fill:'', type: 'scatter', hoverinfo: 'x,y', name: cats[ref].long_name, } } var data = [ series('off'), series('morning'), series('standard'), series('evening'), ] 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', range: [-0.1,11.1] }, yaxis: { title: `MW`, showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside', range:[0,230] } }; const div=container('Mean power by calendar month and time-of-day category for 2020 to date.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }46:T43b,{ function series(ref) { return { y: calmonthly.map(v=>v[ref + '_power']/(d3.mean(calmonthly.map(v=>v[ref + '_power'])))-1), 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}, fill:'', type: 'scatter', hoverinfo: 'x,y', name: cats[ref].long_name, } } var data = [ series('off'), series('morning'), series('evening'), series('standard'), ] var layout = { height: 270, width: width-30, font: font, showlegend: true, legend: {xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside', range: [-0.1,11.1] }, yaxis: { title: ``, showgrid: true, zeroline: true, tickformat: '.0%', ticks:'outside' } }; const div=container('Variation in power relative to the mean annual power for each time-of-day category.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }47:T548,{ function series(ref) { return { y: calmonthly.map(v=>v[ref + '_value']/1000000), x: ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'], marker: {color: make_trans(catCol(cats[ref].index),0.8), width:1}, type: 'bar', hoverinfo: 'x,y', name: cats[ref].name, } } var data = [ series('evening'), series('morning'), series('standard'), series('off'), { y: calmonthly.map(v=>v['value']/1000000), x: ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'], mode: 'lines', line: {shape: 'spline', color: 'grey', width:2}, type: 'scatter', hoverinfo: 'x,y', name: 'Total', showlegend:false, } ] var layout = { height: 270, 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', }, yaxis: { range:[0,11], title: `$m`, showgrid: true, zeroline: false, tickformat: ',.0f', ticks:'outside' }, }; const div=container('Mean market value by calendar month for each time-of-day category for 2020 to date.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }48:T495,{ var data = [] data.push({ y: monthly.map(m=>m['price_mean']), x: monthly.map(m=>m['date']), mode: 'lines+markers', line: {color: 'grey', width: 1.5}, marker:{size:3}, type: 'scatter', hoverinfo: 'x+y', showlegend:true, name:'SAPP DAM average price' }, { y: monthly_solar_price.map(d=>d[1].price), x: monthly_solar_price.map(d=>d[1].start_date), mode: 'lines+markers', line: {color: solcol, width: 3}, marker:{size:7}, type: 'scatter', hoverinfo: 'x+y', showlegend:true, name: 'APP Solar Benchmark price' } ) var layout = { height: 270, width: width-30, font: font, showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside',showspikes: false, range:years_range }, yaxis: {title: '$/MWh',showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside'} }; const div=container('Monthly and annual average market price and monthly average APP Solar Benchmark price.') plt.newPlot(div, data, layout,{displayModeBar: false}); return html`${div}` }49: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)) } }); }4a:[{"id":"057ef0b5dd0e520a9be2a77bad99a70d2f85fdd133ca99dc821899e6ac74130d84d5eb7a818867487c356c8b893dbf6d256953734722a765b4719da9e513de10","href":"https://static.observableusercontent.com/files/057ef0b5dd0e520a9be2a77bad99a70d2f85fdd133ca99dc821899e6ac74130d84d5eb7a818867487c356c8b893dbf6d256953734722a765b4719da9e513de10","name":"sapp.json","type":"application/json","lastModified":1621408382098.258,"size":55475},{"id":"f97fdeb7341d3f752a7271f84a46608de7c558c00d96c577adeee0965b8603a8c97870615569a9fa16d0c6fa12d7e7b4892657bad77501f966f08b8e13e8e6b9","href":"https://static.observableusercontent.com/files/f97fdeb7341d3f752a7271f84a46608de7c558c00d96c577adeee0965b8603a8c97870615569a9fa16d0c6fa12d7e7b4892657bad77501f966f08b8e13e8e6b9","name":"APP logo.png","type":"image/png","lastModified":1660163253753.053,"size":92844}] 2e:{"title":"Day-ahead Market (DAM) - Historic Analysis","cells":[{"id":0,"value":"html`

Day-ahead Market (DAM) - Historic Analysis

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4173,"value":"$30","pinned":false,"hidden":false,"mode":"html"},{"id":5424,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":5456,"value":"Mozambique (EDM and HCB) and Eskom dominate the supply and NamPower, BPC and CEC dominate the consumption.","pinned":false,"hidden":false,"mode":"html"},{"id":5482,"value":"{\n const form = html`\n `\n \n const div=container()\n div.prepend(form)\n return div \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5474,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":3571,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":4224,"value":"html`\n

Price and Volume

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1924,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":2427,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":5523,"value":"Since 2016 weighted average daily prices have ranged between a minimum of $${d3.format(',.3r')(d3.min(daily.map(d=>d.price_weight_av)))}/MWh and a maximum of $${d3.format(',.3r')(d3.max(daily.map(d=>d.price_weight_av)))}/MWh. Prices have been higher on average since 2019 and the daily maximum and range has also increased.\n

\nConversely volumes have generally been lower since early 2020. Before this time the volume traded was often in excess of 200MW equivalent whearas it has rarely exceeded 200MW since that time except for a period around the end of 2023 and early 2024.","pinned":false,"hidden":false,"mode":"html"},{"id":4994,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":5934,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":4232,"value":"html`\n

Intra-day Price Patterns

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4312,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":5616,"value":"Prices on the SAPP DAM are determined hourly by matching bids and offers provided by market participants. Supply and demand factors mean that two periods of peak prices occur on most non-holiday weekdays, one in the morning and one in the evening. Between these two peaks a period of relatively higher prices occurs (referred to as 'standard'). The lowest prices occur through the night.\n

\nA different pattern typically occurs at weekends and during holidays with lower morning and evening peak prices on Saturdays and little or no price differentiation on Sundays. ","pinned":false,"hidden":false,"mode":"html"},{"id":4344,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":5627,"value":"In general there is an inverse relationship between hourly price and volume on the market and the daily pattern of peak and off-peak prices is also apparent in the hourly volumes traded with higher prices producing to lower volumes and lower prices leading to higher volumes. ","pinned":false,"hidden":false,"mode":"html"},{"id":1854,"value":"viewof selected_months = {\n const form = html`\n `\n\n form.oninput = () => {\n form.value = Array.prototype.filter\n .call(form.input, i => i.checked)\n .map(i => i.value)\n }\n form.oninput()\n return form\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1064,"value":"{ \n let data = selected_months.map((m, i)=>{\n const ser = month_profiles[month_dict[m].index]\n return {\n y: [...Array(24).keys()].map(h=>ser.get(h)),\n mode: 'lines', line: {shape: 'spline', color: d3.schemeDark2[month_dict[m].group], width:3}, fill:'',\n type: 'scatter', hoverinfo: 'x,y', name: m,\n }\n })\n \n var layout = {\n height: 260, width: width-30,\n font: font, \n showlegend: true, legend: {xanchor: 'left', x:1,y: 1},\n margin: {l: 50,r: 5,b: 30,t: 0},\n xaxis: {\n title: '',\n showgrid: true, zeroline: false, dtick: 1, ticks:'outside',\n },\n yaxis: {\n range: [20,310],\n title: `$/MWh`,\n showgrid: false, zeroline: false, tickformat: '.0f', ticks:'outside'\n }\n };\n\n const div=container('Average price by time-of-day for selected months 2020 to date.')\n plt.newPlot(div, data, layout,{displayModeBar: false});\n return div\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5660,"value":"{ \n function ser(no, col) {\n return yearly_month_profiles.map((y,i)=>{\n const ser = y.profile[no]\n return {\n y: ser,\n mode: 'lines', line: {shape: 'spline', color: colr_c(i,0.7), width:3}, fill:'',\n type: 'scatter', hoverinfo: 'x,y', name: y.year,\n }\n })\n }\n let data = []\n data=data.concat(ser(7))\n \n var layout = {\n height: 260, width: width-30,\n font: font, \n showlegend: true, legend: {xanchor: 'left', x:1,y: 1},\n margin: {l: 50,r: 5,b: 30,t: 0},\n xaxis: {\n title: '',\n showgrid: true, zeroline: false, dtick: 1, ticks:'outside',\n },\n yaxis: {\n range: [20,330],\n title: `$/MWh`,\n showgrid: false, zeroline: false, tickformat: '.0f', ticks:'outside'\n }\n };\n\n const div=container('Mean price by time-of-day for each August since 2020.')\n plt.newPlot(div, data, layout,{displayModeBar: false});\n return div\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5634,"value":"Nighttime, off-peak prices are consistently low between 22h and 5h. A morning peak from 6h to 9h is considerably more pronounced in the cold winter months, June July and August.\nDaytime, standard, prices are consistent across the year.

\nThe 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.","pinned":false,"hidden":false,"mode":"html"},{"id":5690,"value":"The timing and magnitude of variation in time-of-day pricing changes from year to year. The chart of all August time-of-day prices shows that some years the morning peak is hardly present and the evening peak also has a significant variation in magnitude. ","pinned":false,"hidden":false,"mode":"html"},{"id":3628,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":5698,"value":"By observing the patterns in time-of-day pricing for each month a map of price categories for weekdays for each month can be produced. A separate pattern for Saturdays and Sundays can be prepared in the same way.","pinned":false,"hidden":false,"mode":"html"},{"id":4247,"value":"html`\n

Time-of-day prices

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3001,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":5715,"value":"differentials_chart","pinned":false,"hidden":false,"mode":"ojs"},{"id":5723,"value":"During 2021 the average difference between standard daytime and evening prices was $136/MWh and the difference between nighttime offpeak and morning prices was $113/MWh. These differences may provide an opportunity for battery backed price arbitrage.","pinned":false,"hidden":false,"mode":"html"},{"id":4740,"value":"html`\n

Volatility

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4730,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":5733,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":5790,"value":"Overall price volatility has remained relatively constant over time although with some periods of higher volatility paerticularly in evening peak prices.","pinned":false,"hidden":false,"mode":"html"},{"id":4253,"value":"html`\n

Price seasonality

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2262,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":1348,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"id":5793,"value":"Prices are higher during the winter months, June, July and August for all time-of-day categories but particularly for the morning and evening peak periods. The highest relative increase in price during the winter months is for the morning peak. There is a smaller rise in peak prices during November however, this may be due to an outlier in the data rather than a fundamental feature of the market.","pinned":false,"hidden":false,"mode":"html"},{"id":4861,"value":"html`\n

Price distribution

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4808,"value":"$40","pinned":false,"hidden":false,"mode":"ojs"},{"id":4758,"value":"$41","pinned":false,"hidden":false,"mode":"ojs"},{"id":4260,"value":"html`\n

Market volume and traded power

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":280,"value":"$42","pinned":false,"hidden":false,"mode":"ojs"},{"id":5829,"value":"Trade volumes have been generally lower and more flat since early 2020. This may be due to the effect of the COVID-19 pandemic.","pinned":false,"hidden":false,"mode":"html"},{"id":2049,"value":"{ \n\n let flow_total= Array.from(d3.rollup(flow.filter(f=>f.side=='consume'), v=>d3.sum(v,p=>p.flow_sum), f=>f.date))\n\n var data = [\n {\n y: flow_total.map((_,i)=> ((flow_total[i][1]/1000)/(monthly[i]['volume']/1000)) ),\n x: monthly.map(h=> h.date),\n mode: 'lines', line: {shape: 'spline', color: 'grey', width:2}, fill:'tozeroy',\n type: 'scatter', hoverinfo: 'x,y', name: 'Total', showlegend:false,\n } ]\n \n var layout = {\n height: 170, width: width-30,\n font: font, \n showlegend: true, legend: {xanchor: 'right', x:1, y: 1},\n margin: {l: 50,r: 5,b: 30,t: 0},\n xaxis: {range:years_range,\n showgrid: true, zeroline: false, ticks:'outside',\n },\n yaxis: {\n title: '',\n showgrid: true, zeroline: false, tickformat: '.0%', ticks:'outside'\n }\n };\n\n const div=container('Effect of transmission constraints: proportion of traded versus matched volume.')\n plt.newPlot(div, data, layout,{displayModeBar: false});\n return div\n\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5820,"value":"Traded volumes are lower than matched volumes where transmission constraints interfere with wheeling. Transmission constraints were more common until 2020.","pinned":false,"hidden":false,"mode":"html"},{"id":789,"value":"{ \n\n function series(ref) {\n\n return {\n y: monthly.map((v,i)=>v[ref + '_volume']/v['volume']),\n x: monthly.map(h=> h.date), \n mode: 'lines', line: {shape: 'spline',color:catCol(cats[ref].index), width:1}, fill:'tonexty',\n type: 'scatter', hoverinfo: 'x,y', name: cats[ref].name, stackgroup: 'one',\n } \n }\n \n var data = [\n series('evening'),\n series('morning'), \n series('standard'),\n series('off'),\n ]\n \n var layout = {\n height: 230, width: width-30,\n font: font, \n showlegend: false, legend: {xanchor: 'left', x:0, y: 1},\n margin: {l: 50,r: 5,b: 30,t: 0},\n xaxis: {\n range:years_range,\n showgrid: true, zeroline: false, ticks:'outside',\n },\n yaxis: {\n title: 'GWh %',\n showgrid: true, zeroline: false, tickformat: '.0%', ticks:'outside'\n }\n };\n\n const div=container('Proportion of traded volume by time-of-day categories.')\n plt.newPlot(div, data, layout,{displayModeBar: false});\n return div\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5832,"value":"Volumes traded on the off-peak periods are often greater than 90% of the total volume.","pinned":false,"hidden":false,"mode":"html"},{"id":2227,"value":"$43","pinned":false,"hidden":false,"mode":"ojs"},{"id":5844,"value":"Since early 2020 the market has generally traded at between 100 and 150MW of average power.","pinned":false,"hidden":false,"mode":"html"},{"id":4281,"value":"html`\n

Market value

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3454,"value":"$44","pinned":false,"hidden":false,"mode":"ojs"},{"id":5851,"value":"{ \n\n function series(ref) {\n\n return {\n y: monthly.map((v,i)=>v[ref + '_value']/v['value']),\n x: monthly.map(h=> h.date), \n mode: 'lines', line: {shape: 'spline',color:catCol(cats[ref].index), width:1}, fill:'tonexty',\n type: 'scatter', hoverinfo: 'x,y', name: cats[ref].name, stackgroup: 'one',\n } \n }\n \n var data = [\n series('evening'),\n series('morning'), \n series('standard'),\n series('off'),\n ]\n \n var layout = {\n height: 230, width: width-30,\n font: font, \n showlegend: false, legend: {xanchor: 'left', x:0, y: 1},\n margin: {l: 50,r: 5,b: 30,t: 0},\n xaxis: {\n range:years_range,\n showgrid: true, zeroline: false, ticks:'outside',\n },\n yaxis: {\n title: '',\n showgrid: true, zeroline: false, tickformat: '.0%', ticks:'outside'\n }\n };\n\n const div=container('Proportion of traded value by time-of-day categories.')\n plt.newPlot(div, data, layout,{displayModeBar: false});\n return div\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4256,"value":"html`\n

Volume, power and value seasonality

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4647,"value":"$45","pinned":false,"hidden":false,"mode":"ojs"},{"id":4656,"value":"$46","pinned":false,"hidden":false,"mode":"ojs"},{"id":5872,"value":"Power and traded volumes are consistently higher during August, September, October and November and lower from January to June.","pinned":false,"hidden":false,"mode":"html"},{"id":2818,"value":"{ \n \n let data = selected_months3.map((m, i)=>{\n const ser = month_profiles_volume[month_dict[m].index]\n return {\n y: [...Array(24).keys()].map(h=>ser.get(h)),\n mode: 'lines', line: {shape: 'spline', color: d3.schemeCategory10[month_dict[m].group], width:3}, fill:'',\n type: 'scatter', hoverinfo: 'x,y', name: m,\n }\n })\n \n var layout = {\n height: 270, width: width-30,\n font: font, \n showlegend: true, legend: {xanchor: 'left', x:1,y: 1},\n margin: {l: 50,r: 5,b: 30,t: 0},\n xaxis: {\n title: '',\n showgrid: true, zeroline: false, dtick: 1, ticks:'outside',\n },\n yaxis: {\n range:[0,210],\n title: `MW`,\n showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside'\n }\n }\n\n const div=container('Daily average power profiles for selected months for 2020 to date.')\n plt.newPlot(div, data, layout, {displayModeBar: false});\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3480,"value":"$47","pinned":false,"hidden":false,"mode":"ojs"},{"id":5596,"value":"html`\n

Solar Prices

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5600,"value":"$48","pinned":false,"hidden":false,"mode":"ojs"},{"id":5587,"value":"The APP Solar Benchmark shows the price that a nominal solar PV plant located in Western Zambia would receive if it sold all its production on the SAPP DAM market.","pinned":false,"hidden":false,"mode":"html"},{"id":5412,"value":"

\n

Setup

","pinned":false,"hidden":false,"mode":"html"},{"id":5882,"value":"selected_months3=[\"Mar\", \"Jun\", \"Oct\", \"Dec\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":5713,"value":"import {differentials_chart} from \"@andyflem/sapp-dam-time-of-day-arbitrage-analysis\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":5705,"value":"selected_cats2=[\"Evening\", \"Morning\", \"Standard\", \"Off-peak\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":5568,"value":"solcol='#d68910'","pinned":false,"hidden":false,"mode":"ojs"},{"id":5438,"value":"pie = d3\n .pie()\n .sort(null)\n .value(d => d)","pinned":false,"hidden":false,"mode":"ojs"},{"id":5445,"value":" projection=d3.geoPath(d3.geoEquirectangular().center([25,-14]).scale(1300))","pinned":false,"hidden":false,"mode":"ojs"},{"id":5443,"value":"$49","pinned":false,"hidden":false,"mode":"ojs"},{"id":5415,"value":"container = function(captionText) {\n const div=DOM.element('div')\n div.setAttribute(\"class\",\"border m-2 p-2\")\n if (captionText) {\n const caption=DOM.element('figcaption')\n caption.append(captionText)\n div.append(caption)\n }\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3972,"value":"countries={\n return {\n 'Botswana': {ref: 'BOT', country: 'Botswana', col: 1},\n 'Mozambique': {ref: 'MOZ', country: 'Mozambique', col: 2},\n 'Namibia': {ref: 'NAM', country: 'Namibia', col: 3},\n 'South Africa': {ref: 'RSA', country: 'South Africa',col:7 },\n 'Zambia': {ref: 'ZAM', country: 'Zambia', col: 5},\n 'Zimbabwe': {ref: 'ZIM', country: 'Zimbabwe', col: 4},\n 'Lesotho': {ref: 'LES', country: 'Lesotho', col: 8},\n 'Swaziland': {ref: 'ESW', country: 'Swaziland', col: 9},\n 'DRC': {ref: 'DRC', country: 'DRC', col: 9},\n 'Tanzania': {ref: 'TNZ', country: 'Tanzania', col: 3},\n 'Malawi': {ref: 'MAL', country: 'Malawi', col: 6},\n 'Angola': {ref: 'ANG', country: 'Angola', col: 2},\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5466,"value":"import {nodes} from \"@andyflem/day-ahead-market-dam-market-participants\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3927,"value":"sapp=topojson.feature(sapptopo, sapptopo.objects.sapp)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3798,"value":"sapptopo=FileAttachment(\"sapp.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3929,"value":"topojson = require('topojson')","pinned":false,"hidden":false,"mode":"ojs"},{"id":3027,"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":3491,"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":4377,"value":"cats_means={\n return [\n ['off', d3.mean(monthly, d=>d.off_price_mean)],\n ['morning', d3.mean(monthly, d=>d.morning_price_mean)], \n ['standard', d3.mean(monthly, d=>d.standard_price_mean)],\n ['evening', d3.mean(monthly, d=>d.evening_price_mean)], \n ]\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3512,"value":"yearly=d3.rollups(monthly,m=>{\n return {\n rate: d3.sum(m,d=>d.value)/d3.sum(m,d=>d.volume),\n value:d3.sum(m,d=>d.value),\n volume:d3.sum(m,d=>d.volume),\n price_mean: d3.mean(m,d=>d.price_mean),\n }\n},m=>m.year)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1914,"value":"md `\n### Daily\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4910,"value":"monthly_volatility=d3.rollups(daily_price_1st_difference, v=> { return {\n off: d3.mean(v, d => Math.abs(d.off)),\n morning: d3.mean(v, d => Math.abs(d.morning)),\n standard: d3.mean(v, d => Math.abs(d.standard)),\n evening: d3.mean(v, d => Math.abs(d.evening)),\n} }, v=>v.date)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4800,"value":"daily_price_1st_difference=\n{\n let last = {\n off: 0,\n standard: 0,\n morning: 0,\n evening: 0\n }\n \n let dif = daily.map((day, i) => {\n let cur = {}\n if (i>0) {\n cur = {\n year: day.year,\n month: day.month,\n day: day.day,\n date: DateTime.fromObject({year: day.year, month:day.month}),\n off: day.off_price_mean-last.off, \n standard: day.standard_price_mean ? day.standard_price_mean-last.standard : null, \n morning: day.morning_price_mean ? day.morning_price_mean-last.morning : null, \n evening: day.evening_price_mean ? day.evening_price_mean-last.evening : null\n }\n }\n last.off=day.off_price_mean\n if (day.standard_price_mean) { last.standard = day.standard_price_mean }\n if (day.morning_price_mean) { last.morning = day.morning_price_mean }\n if (day.evening_price_mean) { last.evening = day.evening_price_mean }\n return cur\n })\n dif.shift()\n return dif\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5315,"value":"pdcs={\n let out={}\n Object.keys(cats).forEach(c=>{\n let prices=daily.filter(a=>a[c + '_price_mean']).sort((a,b)=>a[c + '_price_mean']-b[c + '_price_mean'])\n out[c]=prices.map((d,i)=>{\n return {\n exceedance: 1-(i/prices.length),\n price: d[c + '_price_mean']?d[c + '_price_mean']:0\n }\n })\n }) \n return out\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4761,"value":"pdcs_2122={\n let out={}\n Object.keys(cats).forEach(c=>{\n let prices=daily.filter(a=>a.year>2020).filter(a=>a[c + '_price_mean']).sort((a,b)=>a[c + '_price_mean']-b[c + '_price_mean'])\n out[c]=prices.map((d,i)=>{\n return {\n exceedance: 1-(i/prices.length),\n price: d[c + '_price_mean']?d[c + '_price_mean']:0\n }\n })\n }) \n return out\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":4314,"value":"days=[...Array(month.lux_date.daysInMonth).keys()].map(i=>month.lux_date.plus({day:i}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":4316,"value":"month=monthly[selected_month]","pinned":false,"hidden":false,"mode":"ojs"},{"id":4318,"value":"selected_month=29","pinned":false,"hidden":false,"mode":"ojs"},{"id":1471,"value":"md `\n### Flows\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5447,"value":"flows_total=d3.rollup(flow,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":5435,"value":"flows_by_node=d3.rollup(flow,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":1521,"value":"node_dict=Object.fromEntries(nodes.map(n=>[n.ref,n]))","pinned":false,"hidden":false,"mode":"ojs"},{"id":5232,"value":"md `\n### Solar\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5250,"value":"hourly = {\n const raw = (await d3.csv(base_url + market_ref + \"_hourly_slim.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n raw.forEach(r=>{\n r.lux_date=DateTime.fromJSDate(r.date)\n }) \n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5246,"value":"monthly_hours_p50 = {\n const raw = (await d3.csv(\"https://data.africanpowerprojects.com/sapp/output/pv/25mw_p50.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5244,"value":"monthly_yield_totals = {\n return monthly_hours_p50.map(m => {\n let obs=Object.entries(m)\n obs.pop()\n return d3.sum(obs, v => v[1])\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5228,"value":"relative_yield_profiles = {\n return monthly_hours_p50.map((m, i) => {\n let tmp = Object.keys(m).map(h=>{\n return m[h]/monthly_yield_totals[i]\n })\n tmp.pop()\n return tmp\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5226,"value":"hourly_weighted_price={\n return hourly.map(h => {\n return {\n date: h.date,\n hour: h.hour,\n weighted_price: h.price * relative_yield_profiles[h.lux_date.month-1][h.hour],\n }\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5224,"value":"daily_solar_price = d3.rollups(hourly_weighted_price, v => { \n return {\n date: d3.max(v, v=>v.date),\n lux_date: d3.max(v, v=>DateTime.fromJSDate(v.date)),\n price: d3.sum(v, d => d.weighted_price)\n }\n}, v => v.date)","pinned":false,"hidden":false,"mode":"ojs"},{"id":5220,"value":"monthly_solar_price = {\n \n let ret = d3.rollups(\n daily_solar_price.map(v => v[1]),\n v => {\n return {\n start_date: d3.min(v, d => d.date),\n weekday_price: d3.mean(v.filter(p => p.lux_date.weekday < 6), d => d.price),\n saturday_price: d3.mean(v.filter(p => p.lux_date.weekday == 6), d => d.price),\n sunday_price: d3.mean(v.filter(p => p.lux_date.weekday == 7), d => d.price),\n price: d3.mean(v, d => d.price),\n }\n },\n v => v.lux_date.year + '_' + v.lux_date.month\n )\n return ret\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":982,"value":"md `\n### Months\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1077,"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":1094,"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":972,"value":"md `\n### Calendar Months\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5647,"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":1039,"value":"month_profiles=[...Array(12).keys()].map(m=>month_profile_price(m+1))","pinned":false,"hidden":false,"mode":"ojs"},{"id":2816,"value":"month_profiles_volume=[...Array(12).keys()].map(m=>month_profile_volume(m+1))","pinned":false,"hidden":false,"mode":"ojs"},{"id":2814,"value":"function month_profile_volume(month_no) {\n return d3.rollup(calmonthly_hours.filter(m=>m.month==month_no),v=>d3.mean(v,m=>m.volume),t=>t.hour)\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5639,"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":1036,"value":"function month_profile_price(month_no) {\n return d3.rollup(calmonthly_hours.filter(m=>m.month==month_no),v=>d3.mean(v,m=>m.price),t=>t.hour)\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":967,"value":"md `\n### Hours and Categories\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3680,"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":2125,"value":"cats = {\n return {\n evening: { index: 3, name: 'Evening', ref: 'evening', long_name: 'Evening Peak'}, \n morning: { index: 1, name: 'Morning', ref: 'morning', long_name: 'Morning Peak'}, \n standard: {index: 2, name: 'Standard', ref: 'standard', long_name: 'Daytime Standard'},\n off: {index: 0, name: 'Off-peak', ref: 'off', long_name: 'Night Off-peak'}, \n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4499,"value":"md `\n### Data Load\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5637,"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 \n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3625,"value":"timeofday = {\n const raw = (await d3.csv(base_url + market_ref + \"_timeofday.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n \n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2118,"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 })\n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2260,"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":2289,"value":"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":1918,"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":4321,"value":"selected_hours = {\n const url=`https://data.africanpowerprojects.com/sapp/input/monthly/dam/dam_2018_06.json?${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.filter(d=>d.lux_date.day<14)\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2645,"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,minute:0, zone:0})\n r.date = r.lux_date.toJSDate()\n })\n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4509,"value":"base_url='https://data.africanpowerprojects.com/sapp/output/'","pinned":false,"hidden":false,"mode":"ojs"},{"id":4512,"value":"market_ref='dam'","pinned":false,"hidden":false,"mode":"ojs"},{"id":964,"value":"md `\n### Imports\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5991,"value":"function colr_c(i = 1, a = 1) {\n return d3hsv.hsv(160, 0.85, ((6 - i + 1.5) * 15)/100, a).toString();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4692,"value":"function sideCol(index) {\n return d3.schemeCategory10[index]\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4702,"value":"function catCol(index) {\n return d3.schemeCategory10[index]\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4690,"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":14,"value":"font = ({\n family: 'Courier New, monospace',\n size: 13,\n color: '#6f6f6f'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":16,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":18,"value":"plt = require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1201,"value":"ss = require('simple-statistics')","pinned":false,"hidden":false,"mode":"ojs"},{"id":1703,"value":"html`\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5993,"value":"d3hsv = require(\"d3-hsv@0.1\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":4407,"value":"style=html`css `","pinned":false,"hidden":false,"mode":"ojs"},{"id":2137,"value":"luxon = require(\"luxon@1/build/amd/luxon.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2140,"value":"DateTime = luxon.DateTime","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W4a","stdlib":"1","titling":"auto"}