HomeKey Stats Bristol
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===Electricity and gas consumption by sector=== | ===Electricity and gas consumption by sector=== | ||
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+ | The total energy bills for Bristol broken down by sector are: | ||
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+ | {| class="wikitable" | ||
+ | ! | ||
+ | ! | ||
+ | !Annual Consumption (GWh) | ||
+ | !Meters | ||
+ | !Average Annual Consumption (kWh) | ||
+ | !Average Unit Price (£) | ||
+ | !Average Annual Bill (£) | ||
+ | !Total Spend (£000,000's) | ||
+ | |- | ||
+ | | Domestic||Standard Electricity||593||171,945||3,450||0.15||518||89 | ||
+ | |- | ||
+ | | ||Economy 7 Electricity||116||22,138||5,246||0.17||910||20 | ||
+ | |- | ||
+ | | ||Gas||2003||167,876||11,934||0.05||586||98 | ||
+ | |- | ||
+ | | Non-Domestic||Electricity||1152||17,590||65,509||0.10||6,616||116 | ||
+ | |- | ||
+ | | ||Gas||735||1,966||373,871||0.03||10,917||21 | ||
+ | |- | ||
+ | |} | ||
+ | |||
<R output="display" iframe="width:400px;height:400px"> | <R output="display" iframe="width:400px;height:400px"> | ||
pdf(rpdf, width=5, height=5) | pdf(rpdf, width=5, height=5) | ||
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xlab("Tenure") + ylab("Number of homes") | xlab("Tenure") + ylab("Number of homes") | ||
</r> | </r> | ||
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Revision as of 11:47, 16 September 2015
Contents
Demographic stats
2011 census | Mid 2013 estimated | |
Population | 428,100 | 437,500 |
Households | 182,747 | 186,760 |
Energy stats
Bristol has an annual electricity consumption of 1862 GWh (data from 2013) and a daily peak power demand of around 307 MW during winter months. The daily peak demand is calculated by applying the peak to annual consumption ratio of the UK to Bristol.
Bristol's annual gas consumption is 2738 GWh (data from 2013). There is insufficient data available to calculate the peak gas consumption. Because of the inherent storage capacity in the gas network there are fewer issues surrounding the peak.
Local Generation
Type | Electrical Capacity (MWe) | Heat Capacity (MWt) |
---|---|---|
Renewable Electricity | 75.6 | - |
Renewable Heat | - | 3.6 |
CHP | 3.4 | 4.9 |
Total | 79.0 | 8.5 |
Electricity and gas consumption by sector
The total energy bills for Bristol broken down by sector are:
Annual Consumption (GWh) | Meters | Average Annual Consumption (kWh) | Average Unit Price (£) | Average Annual Bill (£) | Total Spend (£000,000's) | ||
---|---|---|---|---|---|---|---|
Domestic | Standard Electricity | 593 | 171,945 | 3,450 | 0.15 | 518 | 89 |
Economy 7 Electricity | 116 | 22,138 | 5,246 | 0.17 | 910 | 20 | |
Gas | 2003 | 167,876 | 11,934 | 0.05 | 586 | 98 | |
Non-Domestic | Electricity | 1152 | 17,590 | 65,509 | 0.10 | 6,616 | 116 |
Gas | 735 | 1,966 | 373,871 | 0.03 | 10,917 | 21 |
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) elec.consumption <- data.frame(Year = c(2009,2010,2011,2012,2013),
DomesticConsumption = c(718754856.9, 718735925.8, 718586755.1, 715382012, 709352993.9), DomesticMPANs = c(189391, 191066, 192449, 193322, 194083), NonDomesticConsumption = c(1176217057, 1202799481, 1185903139, 1135575674, 1152307023), NonDomesticMPANs = c(17500, 17444, 17350, 17552, 17590))elec.consumption$Domestic <- elec.consumption$DomesticConsumption/elec.consumption$DomesticMPANs elec.consumption$NonDomestic <- elec.consumption$NonDomesticConsumption/elec.consumption$NonDomesticMPANs elec.consumption <- elec.consumption[,c(1,6,7)] ggplot(data=elec.consumption, aes(x=Year, y=Domestic)) +
geom_bar(stat="identity", fill="#4D4D4D") + theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + theme(plot.title = element_text(vjust = 2)) + scale_y_continuous(expand = c(0,0)) + xlab("Year") + ylab("Average consumption (kWh)")+ ggtitle("Average domestic electricity consumption\nper customer in Bristol")
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) elec.consumption <- data.frame(Year = c(2009,2010,2011,2012,2013),
DomesticConsumption = c(718754856.9, 718735925.8, 718586755.1, 715382012, 709352993.9), DomesticMPANs = c(189391, 191066, 192449, 193322, 194083), NonDomesticConsumption = c(1176217057, 1202799481, 1185903139, 1135575674, 1152307023), NonDomesticMPANs = c(17500, 17444, 17350, 17552, 17590))elec.consumption$Domestic <- elec.consumption$DomesticConsumption/elec.consumption$DomesticMPANs elec.consumption$NonDomestic <- elec.consumption$NonDomesticConsumption/elec.consumption$NonDomesticMPANs elec.consumption <- elec.consumption[,c(1,6,7)] ggplot(data=elec.consumption, aes(x=Year, y=NonDomestic)) +
geom_bar(stat="identity", fill="#4D4D4D") + theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + theme(plot.title = element_text(vjust = 2)) + scale_y_continuous(expand = c(0,0)) + xlab("Year") + ylab("Average consumption (kWh)")+ ggtitle("Average non-domestic electricity consumption\nper customer in Bristol")
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) gas.consumption <- data.frame(Year = c(2009,2010,2011,2012,2013),
DomesticConsumption = c(2188753981, 2158524075, 2014008753, 2045449904, 2003430860), DomesticMPANs = c(162126, 163573, 164780, 168961, 167876), NonDomesticConsumption = c(881539141, 832499075, 783226304, 783928929, 735031042), NonDomesticMPANs = c(2013, 1975, 1915, 1974, 1966))gas.consumption$Domestic <- gas.consumption$DomesticConsumption/gas.consumption$DomesticMPANs gas.consumption$NonDomestic <- gas.consumption$NonDomesticConsumption/gas.consumption$NonDomesticMPANs gas.consumption <- gas.consumption[,c(1,6,7)] ggplot(data=gas.consumption, aes(x=Year, y=Domestic)) +
geom_bar(stat="identity", fill="#4D4D4D") + theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + theme(plot.title = element_text(vjust = 2)) + scale_y_continuous(expand = c(0,0)) + xlab("Year") + ylab("Average consumption (kWh)")+ ggtitle("Average domestic gas consumption\nper customer in Bristol")
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) gas.consumption <- data.frame(Year = c(2009,2010,2011,2012,2013),
DomesticConsumption = c(2188753981, 2158524075, 2014008753, 2045449904, 2003430860), DomesticMPANs = c(162126, 163573, 164780, 168961, 167876), NonDomesticConsumption = c(881539141, 832499075, 783226304, 783928929, 735031042), NonDomesticMPANs = c(2013, 1975, 1915, 1974, 1966))gas.consumption$Domestic <- gas.consumption$DomesticConsumption/gas.consumption$DomesticMPANs gas.consumption$NonDomestic <- gas.consumption$NonDomesticConsumption/gas.consumption$NonDomesticMPANs gas.consumption <- gas.consumption[,c(1,6,7)] ggplot(data=gas.consumption, aes(x=Year, y=NonDomestic)) +
geom_bar(stat="identity", fill="#4D4D4D") + theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + theme(plot.title = element_text(vjust = 2)) + scale_y_continuous(expand = c(0,0)) + xlab("Year") + ylab("Average consumption (kWh)")+ ggtitle("Average non-domestic gas consumption\nper customer in Bristol")
Local electricity infrastructure
There are two Grid Supply Points (GSPs) that directly supply Bristol. These are located at Seabank and Iron Acton and shown on the map below along with the outline of the Bristol urban area.
The table below gives the number of substations at different voltage levels in both the Bristol City limits and the Bristol Urban Area.
Bristol City | Bristol Urban Area | |
---|---|---|
132kV substations | 5 | 7 |
33kV Substations | 24 | 35 |
11kV Substations | 1,858 | 2,616 |
Meters | 211,673 |
Local gas infrastructure
Housing stock stats
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) library(scales) library(grid) build_year <- data.frame (
build.year= c("pre-1870", "1871-1919", "1920-1945", "1946-1954", "1955-1979", "post-1980") , number=c(9847,37379,60307,25823,29126,18297))order <- c("pre-1870", "1871-1919", "1920-1945", "1946-1954", "1955-1979", "post-1980") ggplot(data=build_year, aes(x=build.year, y=number)) + geom_bar(stat="identity", fill="#4D4D4D", colour="black") +
theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + scale_y_continuous(expand = c(0,0), labels=comma) + scale_x_discrete(limits = order) + ggtitle('Build year of Bristol homes') + theme(plot.title = element_text(vjust=2)) + theme(axis.title.y = element_text(vjust = 1)) + xlab("Build year") + ylab("Number of homes")
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) library(scales) library(grid) built_form <- data.frame (
built.form= c("Detached", "Semi-detached", "Bungalow", "Terraced", "Flat") , number=c(8387,48887,2567,75569,45369))ggplot(data=built_form, aes(x=built.form, y=number)) + geom_bar(stat="identity", fill="#4D4D4D", colour="black") +
theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + scale_y_continuous(expand = c(0,0), labels=comma) + ggtitle('Built form of Bristol homes') + theme(plot.title = element_text(vjust=2)) + theme(axis.title.y = element_text(vjust = 1)) + xlab("Built form") + ylab("Number of homes")
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) library(scales) library(grid) bedrooms <- data.frame (
bedrooms= c("1 bedroom", "2 bedroom", "3 bedroom", "4 bedroom", "5 or more") , number=c(23952,40834,92492,11340,12161))ggplot(data=bedrooms, aes(x=bedrooms, y=number)) + geom_bar(stat="identity", fill="#4D4D4D", colour="black") +
theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + scale_y_continuous(expand = c(0,0), labels=comma) + ggtitle('Number of bedrooms in Bristol homes') + theme(plot.title = element_text(vjust=2)) + theme(axis.title.y = element_text(vjust = 1)) + xlab("Number of bedrooms") + ylab("Number of homes")
REngine.php:in
pdf(rpdf, width=5, height=5) library(ggplot2) library(scales) library(grid) tenure <- data.frame (
tenure= c("Owner occupied", "Privately rented", "Council/housing association") , number=c(97622,29360,53797))ggplot(data=tenure, aes(x=tenure, y=number)) + geom_bar(stat="identity", fill="#4D4D4D", colour="black") +
theme_bw() + theme(panel.border = element_blank(), axis.line = element_line()) + scale_y_continuous(expand = c(0,0), labels=comma) + ggtitle('Tenure of Bristol homes') + theme(plot.title = element_text(vjust=2)) + theme(axis.title.y = element_text(vjust = 1)) + xlab("Tenure") + ylab("Number of homes")