- 1 Power, heat and gas flows in city and known patterns of change
- 2 Current and future ‘local power generation’ levels and intermittency caused
- 3 Electricity distribution system operational issues and expectations in ‘smarter’ system
- 4 Opportunities for demand side response and reduction in the city (power and heat)
- 5 Anticipated uses of smart energy data in future energy system management - what gets smart?
- 6 Inventory of large discretionary loads in the city
- 7 Storage technology development trends and potential opportunities
- 8 Summary
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 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.
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. 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.
|Generating Station||Capacity (kW)||Technology|
|Avonmouth Energy Facility||12,936||Biomass|
|Shortwood Landfill Gas||3,408||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|
|P & S Mitchell Ltd||225.64||Photovoltaic|
|Castle Court Sainsbury's Supermarkets Ltd||200||Photovoltaic|
|Harry Yearsley Ltd||165||Photovoltaic|
|406 Central Park||68||Photovoltaic|
|Aldi Bristol Church Road||50||Photovoltaic|
|Red House Farm PV||50||Photovoltaic|
|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|
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.
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.
|Technology||Bristol Domestic||Average Capacity (kw)||Total Capacity (kW)|
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.
|Technology||Bristol Non Domestic||Average Capacity (kW)||Total Capacity (kW)|
The table below shows the Combined Heat and Power 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||30*|
|Bristol City Marriott Hotel||Gas||210||30*|
*heat capacity is estimated based on a 0.7 electricity to heat ratio.
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)|
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.
|What we know||What we could know|
|Power, heat and gas flows in city and known patterns of change||
|Current and future ‘local power generation’ levels and intermittency caused||
|Electricity distribution system operational issues and expectations in ‘smarter’ system||
|Opportunities for demand side response and reduction in the city (power and heat)||
|Anticipated uses of smart energy data in future energy system management - what gets smart?||
|Inventory of large discretionary loads in the city|
|Storage technology development trends and potential opportunities||
Potential partner contributions
- provide information about the issues arising from distributed generation
- provide data from distributed generation map
- provide data from their wind turbines