•  

HomeKey Stats Bristol

(Recent trends)
(Energy stats)
Line 178: Line 178:
 
   ggtitle("Average non-domestic gas consumption\nper customer in Bristol")
 
   ggtitle("Average non-domestic gas consumption\nper customer in Bristol")
 
</r>
 
</r>
 +
 +
===Customer types===
 +
{| class="wikitable"
 +
! Profile class
 +
! Description
 +
! Count
 +
|-
 +
| 1||Domestic Unrestricted Customer||170,308
 +
|-
 +
| 2||Domestic Economy 7 Customers||23,144
 +
|-
 +
| 3||Non-Domestic Unrestricted Customers||14,367
 +
|-
 +
| 4||Non-Domestic Economy 7 Customers||2,750
 +
|-
 +
| 5||Non-Domestic Maximum Demand (MD) Customers with a Peak Load Factor (LF) of less than 20%||367
 +
|-
 +
| 6||Non-Domestic Maximum Demand Customers with a Peak Load Factor between 20% and 30%||630
 +
|-
 +
| 7||Non-Domestic Maximum Demand Customers with a Peak Load Factor between 30% and 40%||354
 +
|-
 +
| 8||Non-Domestic Maximum Demand Customers with a Peak Load Factor over 40%||457
 +
|-
 +
| 0||Non-Domestic Half Hourly Metered||11,126
 +
|-
 +
|}
  
 
===Local electricity infrastructure===
 
===Local electricity infrastructure===

Revision as of 14:49, 16 September 2015

Demographic stats

2011 census Mid 2013 estimated
Population 428,100 437,500
Households 182,747 186,760

Fuel poverty

For the Low Income High Costs definition of fuel poverty the latest figures (2013) estimate that there are 25,379 households in Bristol in fuel poverty. This is equivalent to 13.2% of households which is in the top 10% in England. For the old 10% definition the most recent data available is from 2012 and estimates that 28,661 households are in fuel poverty, this corresponds to 15.2%.

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 (MWe) Heat Capacity (MWt)
Renewable Electricity 75.6 -
Renewable Heat - 3.6
CHP 3.4 4.9
Total 79.0 8.5

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

Recent trends

The plots below show the change in electricity and gas consumption for domestic and no-domestic sectors over the last five years. In general there has been a slow and steady decline in consumption, with the exception of non-domestic electricity which has fluctuated but remained roughly constant.

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")

Customer types

Profile class Description Count
1 Domestic Unrestricted Customer 170,308
2 Domestic Economy 7 Customers 23,144
3 Non-Domestic Unrestricted Customers 14,367
4 Non-Domestic Economy 7 Customers 2,750
5 Non-Domestic Maximum Demand (MD) Customers with a Peak Load Factor (LF) of less than 20% 367
6 Non-Domestic Maximum Demand Customers with a Peak Load Factor between 20% and 30% 630
7 Non-Domestic Maximum Demand Customers with a Peak Load Factor between 30% and 40% 354
8 Non-Domestic Maximum Demand Customers with a Peak Load Factor over 40% 457
0 Non-Domestic Half Hourly Metered 11,126

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") 
 
OFFICIAL SUPPLIER