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HomeKey Stats Bristol

(Energy stats)
(Local electricity infrastructure)
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===Local electricity infrastructure===
 
===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.
 
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.
 +
 +
{| class="wikitable"
 +
|
 +
! Bristol City
 +
! Bristol Urban Area
 +
|-
 +
| 132kV substations||5||7
 +
|-
 +
| 33kV Substations||24||35
 +
|-
 +
| 11kV Substations||1858||2616
 +
|-
 +
|}
  
 
== Housing stock stats ==
 
== Housing stock stats ==

Revision as of 12:48, 26 August 2015

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.

Breakdown of typical contributions to peak electricity demand across all sectors.

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 Heat Capacity
Seabank 1,145 -
Renewable Electricity 53.9 -
Renewable Heat - 19.9
CHP 9.2 11.9
Total 1,208 32

Electricity and gas consumption by sector

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 1858 2616

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") 

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
 
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