31:T4fa,md`# Napa Microgrid Performance In California, extreme weather events, planned utility shut-offs, and inherently fragile “poles and wires” gird infrastructure pose significant threats to power reliability. To increase energy security at the [property in Napa], Integral Group has proposed a microgrid system composed of ~200 kW of solar, 3 MWh of battery capacity, and a microgrid controller that can optimize system operations under variable conditions. This microsite addresses two questions: * Probabilistically, how many days can the system be expected to provide power during an outage, assuming the building retains its existing mechanical equipment? * What is the impact of upgrading the chiller efficiency, transitioning to all-electric mechanical systems, or only providing power to critical loads, in terms of the number of days the system is likely to provide power? Integral’s analysis reveals that the proposed system can provide, with 90% confidence, at least 4.5 days of uninterrupted power using the building’s existing mechanical equipment. Upgrading the chiller provides at least 5 days, an all-electric system provides at least 3.5 days, and providing power to critical loads provides at least 8 days, each with 90% confidence.

`32:Teda,beeswarm = { const svg = d3.create("svg") .attr("viewBox", [0, 0, 960, height]); const pointData = dodge(grouped.current, {radius: radius * 2 + padding, x: d => x(d.duration)}); const tooltip = d3.select("body").append("div").call(createTooltip) const tipContent = d => { const html = ` Weather Year: ${d.data.year}
Outage Day: ${d3.timeFormat("%B %d")(dates[Math.floor(d.data.hour/24)])}
Backup Duration: ${d3.format(".2f")(d.data.duration)} Days
` return html; } const checkMonthColor = d => { let date = dates[Math.floor(d.data.hour/24)].getMonth(); if ([8, 9].includes(date)) { return "#89cda8"; } else { return color; } } const textLabel = d => { return `90th Percentile: ${d3.format(".2f")(d)} Days`; } let lineData = [[pointData.percentile90, 35], [pointData.percentile90, height - 35]]; let percentileLine = svg.append("path") .datum(lineData) .attr("fill", "none") .attr("stroke", "#c1c1c1") .attr("stroke-width", 1.5) .attr("stroke-dasharray", "5,5") .attr("d", line); let style = "font-family: Open Sans; color: #c1c1c1; font-size: 12px;" let label = svg.append("foreignObject") .datum(pointData.percentile90) .attr("transform", d => `translate(${x(d) + 10},27)`) .attr("width", 200) .attr("height", 100) .append("xhtml:div") .html(d => `

${textLabel(d)}

`); let point = svg.append("g") .selectAll("circle") .data(pointData, (d, i) => i) .join("circle") .attr("cx", d => d.x) .attr("cy", d => height / 2 + d.y) .attr("r", radius) .attr("fill", d => checkMonthColor(d)); point.on("mouseover", function(event, d) { tooltip.style("display", null); tooltip.html(tipContent(d)) .style("left", (event.pageX + 15) + "px") .style("top", (event.pageY - 20) + "px"); let c = checkMonthColor(d); d3.select(this).attr("fill", chroma(c).darken(2)) }) .on("mousemove", function(event, d) { tooltip .style("left", (event.pageX + 15) + "px") .style("top", (event.pageY - 20) + "px"); }) .on("mouseout", function(event, d) { tooltip.style("display", "none"); d3.select(this).attr("fill", d => checkMonthColor(d)) }); const g = svg.append("g") .call(xAxis); const leg = svg.append("g") .call(legend); return Object.assign(svg.node(), { update(data) { let dat = dodge(data, {radius: radius * 2 + padding, x: d => x(d.duration)}); const t = svg.transition().ease(d3.easeCubicOut).duration(750); // gx.transition(t) // .call(xAxis, x.domain([0, d3.max(agedata, d => d.value)])); // gy.transition(t) // .call(yAxis, y.domain(agedata.map(d => d.name))); point = point .data(dat, (d, i) => i) .attr("fill", d => checkMonthColor(d)) .call(point => point.transition(t) .attr("cx", d => d.x) .attr("cy", d => height / 2 + d.y)); lineData = [[dat.percentile90, 35], [dat.percentile90, height - 35]]; percentileLine = percentileLine .datum(lineData) .call(percentileLine => percentileLine.transition(t) .attr("d", line)); label = label .datum(dat.percentile90) .html(d => `

${textLabel(d)}

`); label.select(function() { return this.parentNode; }) .transition(t).attr("transform", d => `translate(${x(dat.percentile90) + 10},27)`); // label.transition(t).attr("transform", d => `translate(${x(dat.percentile) + 10},${margin.top -8})`)); } }); }33:T4ca,md`## Why Stochastic Analysis Two key factors influence the number of days that the proposed microgrid system can provide backup power: energy supply (i.e. solar production potential) and energy demand (driven by the building’s mechanical systems, meteorological conditions, occupancy, and the degree to which specific loads are served during an outage). Traditional microgrid performance analyses often assume worst- or best-case scenarios (ex. consistently cloudy or sunny conditions). At best, this over-simplified approach can undervalue the potential performance of a system when conditions are optimal and at worst, grossly oversell its capabilities under low-risk, high-impact events (i.e., “black swans”). To understand the range of days that the microgrid can be expected to provide backup power, Integral conducted stochastic analyses that account for variability in both energy supply and demand. These analyses were based on 22 years of historical weather data and a conservative assumption that grid outages are more likely to occur during peak fire season (September and October). Further, it was assumed that the battery will be fully charged (or near fully charged) when an outage occurs.

`34:T697,md`## Results Integral conducted stochastic analyses to understand the range of days for which the proposed microgrid system could provide backup power - under four (4) scenarios in which the building is occupied: Current operations – assumes building retains its existing mechanical systems. Chiller upgrade – assumes the chiller is upgraded to an air-cooled chiller (efficiency increases by 10%), while all other mechanical systems remain unchanged. All-electric – assumes electrification of all propane-dependent heating loads and the installation of a heat pump. Critical loads – assumes electrification of all propane-dependent heating loads, but during the outage event, roughly 50% of loads would be reduced or modulated (e.g., set point for cooling could rise to 75°). Each dot in the graphs above represent one simulation, which uses randomized inputs to assess the probability distribution of various results. Hovering over a dot displays the total duration that the system can provide power, and other key data points such as the year of the historical weather file and the day of the outage. Integral’s analysis reveals that the proposed system can provide, with 90% confidence, at least 4.5 days of uninterrupted power using the building’s existing mechanical equipment; the median is over one week. If the outage seemed like it might extend for a particularly long period of time, the building’s power could be modulated to reduce consumption by half (to “critical loads”). n this scenario, the microgrid provides backup power for at least 8 days with 90% certainty; the average outcome is nearly three weeks of backup power.

`35:T61c,function dodge(data, {radius = 1, x = d => d} = {}) { const radius2 = radius ** 2; const circles = data.map((d, i) => ({x: +x(d, i, data), data: d})).sort((a, b) => a.x - b.x); const epsilon = 1e-3; let head = null, tail = null; // Returns true if circle ⟨x,y⟩ intersects with any circle in the queue. function intersects(x, y) { let a = head; while (a) { if (radius2 - epsilon > (a.x - x) ** 2 + (a.y - y) ** 2) { return true; } a = a.next; } return false; } // Place each circle sequentially. for (const b of circles) { // Remove circles from the queue that can’t intersect the new circle b. while (head && head.x < b.x - radius2) head = head.next; // Choose the minimum non-intersecting tangent. if (intersects(b.x, b.y = 0)) { let a = head; b.y = Infinity; do { let y1 = a.y + Math.sqrt(radius2 - (a.x - b.x) ** 2); let y2 = a.y - Math.sqrt(radius2 - (a.x - b.x) ** 2); if (Math.abs(y1) < Math.abs(b.y) && !intersects(b.x, y1)) b.y = y1; if (Math.abs(y2) < Math.abs(b.y) && !intersects(b.x, y2)) b.y = y2; a = a.next; } while (a); } // Add b to the queue. b.next = null; if (head === null) head = tail = b; else tail = tail.next = b; } const percentile = (d, p) => { let dat = d.map(dd => dd.duration).sort((a, b) => a - b) return d3.quantile(dat, p); } circles.percentile90 = percentile(data, .1); circles.percentile50 = percentile(data, .5); return circles; } 36:[{"id":"614b9b1ad02b05bacfcde48b46108949b5a88576ea3667165d9ba3a28af3ec5544b5c6b335faa3345bc8313c32415bbbfae935bf8b79c5a44af4eeaa33100d09","href":"https://static.observableusercontent.com/files/614b9b1ad02b05bacfcde48b46108949b5a88576ea3667165d9ba3a28af3ec5544b5c6b335faa3345bc8313c32415bbbfae935bf8b79c5a44af4eeaa33100d09","name":"Aggregate Results_210326.csv","type":"text/csv","lastModified":1616795234177.999,"size":122991}] 2f:{"title":"Napa Microgrid Performance","cells":[{"id":0,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":4,"value":"data = {\n \n let data = await d3.csv(\"https://www.integraladvancedenergy.com/projects/napa/data/aggregate_uniform_cleaned.csv\", d3.autoType);\n // let l = data.filter(d => d.duration < 40);\n // l = l.map(d => {\n // let duration = d.duration;\n // duration = duration < 3 ? 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