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HomeA: Technical - Energy

(Current and future ‘local power generation’ levels and intermittency caused)
(Power, heat and gas flows in city and known patterns of change)
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== Power, heat and gas flows in city and known patterns of change ==
 
== Power, heat and gas flows in city and known patterns of change ==
  
The three maps below show the spatial distribution of consumption of three different fuels the boundaries used are MSOAs.  
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The three maps below show the spatial distribution of consumption of three different fuels the boundaries used are MSOAs. The data used to derive these maps is from DECC's sub-national electricity and gas consumption data.<ref>DECC, Sub-national electricity consumption data, https://www.gov.uk/government/collections/sub-national-electricity-consumption-data</ref> <ref>DECC, Sub-national gasconsumption data, https://www.gov.uk/government/collections/sub-national-gas-consumption-data</ref>
  
 
<data-map name="msoa_elec" height="600px"/>
 
<data-map name="msoa_elec" height="600px"/>
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<data-map name="msoa_gas" height="600px"/>
 
<data-map name="msoa_gas" height="600px"/>
  
The map below shows the distribution of heat demand across Bristol from the National Heat Map. It may be possible to use the same methodology to create an address based map of electricity demand.
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The map below shows the distribution of heat demand across Bristol from the National Heat Map<ref>DECC, National Heat Map, http://tools.decc.gov.uk/nationalheatmap/</ref>. It may be possible to use the same methodology to create an address based map of electricity demand.
  
 
One of the key opportunities of a smart energy system is the understand both spatial and temporal variations in demand. Enhanced understanding of this will allow for smarter management of the energy system which could lead to cost savings and reduced peak demand. The above maps give information about the spatial nature of the energy demand in Bristol but there is a lack of temporal information. To get an idea of energy use over time typical load profiles could be applied to buildings based on build type and likely occupancy.
 
One of the key opportunities of a smart energy system is the understand both spatial and temporal variations in demand. Enhanced understanding of this will allow for smarter management of the energy system which could lead to cost savings and reduced peak demand. The above maps give information about the spatial nature of the energy demand in Bristol but there is a lack of temporal information. To get an idea of energy use over time typical load profiles could be applied to buildings based on build type and likely occupancy.

Revision as of 14:42, 28 August 2015

Power, heat and gas flows in city and known patterns of change

The three maps below show the spatial distribution of consumption of three different fuels the boundaries used are MSOAs. The data used to derive these maps is from DECC's sub-national electricity and gas consumption data.[1] [2]

The map below shows the distribution of heat demand across Bristol from the National Heat Map[3]. It may be possible to use the same methodology to create an address based map of electricity demand.

One of the key opportunities of a smart energy system is the understand both spatial and temporal variations in demand. Enhanced understanding of this will allow for smarter management of the energy system which could lead to cost savings and reduced peak demand. The above maps give information about the spatial nature of the energy demand in Bristol but there is a lack of temporal information. To get an idea of energy use over time typical load profiles could be applied to buildings based on build type and likely occupancy.

Current and future ‘local power generation’ levels and intermittency caused

The three tables below show the local generation capacity in Bristol for electricity, heat and CHP. The data has been obtained from the Ofgem Renewables and CHP Register.[4] These are only installations which are claiming available government grants, it is possible therefore that these figures underestimate the installed capacity.

Renewable Obligation Certificates are awarded to installations greater than 5MW in generating capacity. The table below lists all sites which are within Bristol City Council boundaries or which have no closer urban area.

Stations in italics also have a heat output, need to quantify.

Generating Station Capacity (kW) Technology
Avonmouth Energy Facility 12,936 Biomass
Avonmouth RSU 958 Biomass
All Biomass 13,894
Shortwood Landfill Gas 3,408 Landfill Gas
Yanly 1,015 Landfill Gas
Yanley Phase II 764 Landfill Gas
Berwick Farm Power Plant 625 Landfill Gas
All Landfill Gas 5,812
Grange Farm Conergy 17,160 Photovoltaic
Moorhouse 1,390 Photovoltaic
Kite Field 444.4 Photovoltaic
P & S Mitchell Ltd 225.64 Photovoltaic
Castle Court Sainsbury's Supermarkets Ltd 200 Photovoltaic
Harry Yearsley Ltd 165 Photovoltaic
Bristol PV 149.91 Photovoltaic
Solarner Park 95.2 Photovoltaic
406 Central Park 68 Photovoltaic
Oakham Farm 68 Photovoltaic
Molsons 64 Photovoltaic
Aldi Bristol Church Road 50 Photovoltaic
Red House Farm PV 50 Photovoltaic
All Photovoltaic 20,130
Avonmouth STW CHP Generation 5,550 Sewage gas
All Sewage Gas 5,550
Triodos Renewables (Severn) limited 8,120 Wind
Bristol Port Wind Park Ltd 6,000 Wind
Bristol City Council's Avonmouth Wind Farm 4,750 Wind
BristolWind 330 Wind
All Wind 19,200
Total 64,586

The feed in tariff is paid to generation with a generation capacity less than 5MW. The table below shows the generation capacity within Bristol City Council boundaries.

Technology Capacity (kW)
Micro CHP 0.99
Photovoltaic 10,997.41
Total 10,998.4

The domestic Renewable Heat Incentive (RHI) is paid to domestic generators of renewable heat. The table below shows the generation capacity within Bristol City Council boundaries. No data is available for the sizes of individual installations. For heat pumps and biomass, the national median capacity of installations in Great Britain are used to estimate the capacity. For solar thermal an average size of 4m2 and average thermal capacity density of 0.7 kWt/m[5] are used to estimate capacity.

Technology Bristol Domestic Average Capacity (kw) Total Capacity (kW)
ASHP 39 9 330
GSHP 14 12 162
Biomass 20 26 530
Solar Thermal 20 2.8 57
Total 93 1,080

The non-domestic Renewable Heat Incentive (RHI) is paid to non-domestic generators of renewable heat. The table below shows the generation capacity within Bristol City Council boundaries. Only the total number of installations and capacity are known. National trends in installation by type have been used to estimate the number of each type of installation in Bristol. The average capacity of each measure across GB was scaled by the know total capacity to give the values shown.

Technology Bristol Non Domestic Average Capacity (kW) Total Capacity (kW)
Biomass 16 164 2,448
Heat Pumps 1 86 80
Total 17 159 2,528

The table below shows the non-renewable Combined Heat and Power (CHP) electrical and thermal generation capacity within Bristol City Council boundaries.

Site Fuel Capacity (kWe) Capacity (kWt)
University Hospitals Bristol Gas 979 1,400*
University of Bristol 1 Gas 501 720*
University of Bristol 2 Gas 1,160 1,660*
University of Bristol 3 Gas 380 540*
Bristol Royal Marriott Hotel Gas 210 300*
Bristol City Marriott Hotel Gas 210 300*
Total 3,440 4,920

*heat capacity is estimated based on a 0.7 electricity to heat ratio.[6]

The main generation in Bristol comes from Seabank Power Station. This is around two orders of magnitude greater capacity than any other installation. The electricity from this station feeds into the national grid and produces around five times the electricity demand of Bristol.

The intermittency caused over the course of a year can be estimated by using typical capacity factors for the various technologies. For some technologies such as wind turbines historical data of the actual capacity factor are available. This data may also be used to model seasonal variation in generation by renewable technologies. It will however be insufficient to understand typical daily fluctuations. For wind and solar technologies output varies on a minutely basis and the variation will not be consistent across days. This makes accurately projecting output difficult. Detailed data from actual installations may aid in approximating typical fluctuations.

Data may be available from BCC from their wind turbine (and also Triodos Renewables), they or another partner may have data on solar PV.

Electricity distribution system operational issues and expectations in ‘smarter’ system

The spatial units of the distribution network are substations and feeders. Below is a map showing the ratio of energy consumption to number of substations per LSOA.

This map may give an indication of the substations which have the highest demand and are therefore under the most stress. There are some potential issues however:

  • The LSOA boundaries are different to the area covered by substations, this will cause some error.
  • The main issue affecting substations is not the total daily consumption but peak consumption. These may be but are not necessarily related.
  • This could be completely invalid if different substations have different levels of reinforcement.

This analysis could be improved by:

  • Incorporating information from WPDs distributed generation map
  • Using data from the potential electricity demand map
  • Incorporating load profiles to find modelled peak values

It seems likely that DNOs would seek to use smart energy network to reduce peak demand. This could be done by either; remotely or automatically controlling demand side devices or by signalling peak times to consumers. Some method of incentive would probably have to be offered to consumers for them to reduce demand; this could be delivered using the smart network.

The SoLa Bristol project has linked PV and battery storage to investigate the potential benefit this could bring to the distribution network. Most recent published report focuses on methodology and installs, is more up to data information available?

Increased levels of distributed generation (most probably domestic solar PV) could lead to issues for the DNO. The transmission and distribution network is designed to work with centralised generation at high voltage powering customers at low voltage. They are not designed to accommodate low voltage networks exporting electricity. By using the smart grid to match demand to supply such issues could be avoided. This would make use of smart appliances that could be either be controlled remotely or detect optimum times of use, energy storage could also be employed.

Opportunities for demand side response and reduction in the city (power and heat)

Peak demand in Bristol is around 307 MW during the winter and 199 MW in the summer. This peak occurs between 16:00 - 19:00 on weekdays.

Morning peak (MW) Evening peak (MW)
Winter 116 105
Summer 76 58

The figures quoted above are an estimated technical potential and are unlikely to be achieved in reality. Most estimates of the actual level of shiftable load are between 5 and 15 percent.

The benefits from demand side response will be shared between four main market actors; National Grid, energy suppliers, DNOs and aggregators. It is unclear how much of this value could be captured within Bristol. Potential methods could be the establishment of a Bristol aggregating service and the matching of demand to the growing Bristol based renewable energy generation.

Anticipated uses of smart energy data in future energy system management - what gets smart?

Smart meters are currently being rolled out this is to be completed by 2020. There is an expected increase in the amount of electric vehicles, these could offer some potential energy storage and demand flexibility which could be utilised in a smart system. There is unlikely to be take up of smart appliances on a meaningful scale for some time to come.

Inventory of large discretionary loads in the city

Information about large loads can be found from Display Energy Certificates. However, this may not cover all loads and the data is from 2010. Given the age of the data the figures are likely unreliable but may give a rough idea of which loads are largest. The top users are shown below, records from the same users on adjacent sites are excluded.

  • Senate House, University of Bristol
  • MOD, AbbyWood
  • Bristol Royal Infirmary
  • Southmead Hospital
  • Frenchay Hospital
  • University of the West of England, Frenchay
  • National Blood Service, Filton
  • Bristol Royal Hospital For Children
  • Bristol Mail Centre, Gloucester Road North
  • HM Prison, Cambridge Road

Storage technology development trends and potential opportunities

Energy storage presents two major opportunities, to store locally generated energy when generation exceeds demand and to level peak demand by storing energy at off peak times to use later. Storage could be a distributed network of small scale systems in homes and business or fewer larger centralised systems.

Domestic storage is still in its infancy and with current electricity pricing would not appear to be economically viable. This could change if time varying tariffs were introduced which passed on the savings to suppliers, the grid and DNOs to consumers.

EVs could offer some storage option by charging and discharging to match lulls and peaks in demand. However the potential benefits could be offset by the additional load they would bring. There could also be a shift in spatial electricity demand within Bristol as the cars create a geographically shiftable load.

Summary

What we know What we could know
Power, heat and gas flows in city and known patterns of change
  • MSOA level electricity and gas consumption
  • A bottom up address level model of heat demand
  • Bottom up address level model of electricity
  • Temporal model of electricity demand based on load profiles
  • Information on upcoming developments in Bristol with large projected load
Current and future ‘local power generation’ levels and intermittency caused
  • Good estimate of current energy generation capacity within Bristol
  • More information about intermittency of renewable generation
Electricity distribution system operational issues and expectations in ‘smarter’ system
  • Map of demand per substation per LSOA
  • Available capacity in distribution network for distributed generation
  • More about the effects of distributed generation on the distribution network
  • Outcome of SoLa Bristol project
Opportunities for demand side response and reduction in the city (power and heat)
  • Technical maximum demand shift at different times of day and year
Anticipated uses of smart energy data in future energy system management - what gets smart?
  • There are several potential smart devices that could be adopted
  • Expected levels of uptake of smart devices within the home
Inventory of large discretionary loads in the city
Storage technology development trends and potential opportunities
  • The different existing storage technologies
  • The options that could be used within a city like Bristol do not appear to be economically viable at present
  • More about EVs being used as a storage medium
  • Future feasibility of storage

Potential partner contributions

WPD could:

  • provide information about the issues arising from distributed generation
  • provide data from distributed generation map

BCC could:

  • provide data from their wind turbines

References

  1. DECC, Sub-national electricity consumption data, https://www.gov.uk/government/collections/sub-national-electricity-consumption-data
  2. DECC, Sub-national gasconsumption data, https://www.gov.uk/government/collections/sub-national-gas-consumption-data
  3. DECC, National Heat Map, http://tools.decc.gov.uk/nationalheatmap/
  4. Ofgem, Ofgem Renewables and CHP Register, https://www.renewablesandchp.ofgem.gov.uk/
  5. Wardell Armstrong, Solar Thermal Systems, https://www.iema.net/system/files/solar_thermal_systems.pdf
  6. International Energy Agency, Combined Heat and Power, https://www.iea.org/publications/freepublications/publication/chp_report.pdf
 
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