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Electricity balanced model and agent for community energy optimisation

Electricity balanced model and agent for community energy optimisation
Electricity balanced model and agent for community energy optimisation
The excess or shortage of electricity because of massive penetration of renewable energy generators in the local network needs to be handled. Adopting this perspective, community energy using installed renewable generators should maintain the electricity balance and optimise the use of electricity generations to fulfil the demand (load) as well as reducing cost and generating income.
Besides utilising batteries and retailer settlement, grid-connected community energy can join a local market to trade electricity among communities. Using an agent, community energy seeks to achieve an optimised solution to maintain the electricity balance while maximising benefit. Therefore, an optimisation model is proposed.

To demonstrate the optimisation model, specifically in the market settlement, single sealed bid double auction format is used. By adding some assumptions related to the market response, simulations are run to predict the best price to bid (offer/ask) into the electricity market to achieve maximum payoff. Some experiments were performed to choose the best optimisation strategy, specifically in terms of market response and finding the equilibrium prices for all internal traders.

It showed that using an optimisation agent, community energy can achieve an optimum solution to create a balance profile as well as achieving optimum profits for customers, suppliers and battery owners. By using binary search algorithm, suitable internal selling and buying prices as equilibrium prices for all internal traders can be established after the optimum payoff is calculated.

Extended simulation is run using 2018 community energy data. It can be concluded that our optimisations and market response assumptions are capable to achieve optimum profit for community energy, which can be shown using the optimum payoff; local electricity market and battery have a positive impact to all community members although there are several battery limitations. Our community energy management system ensures positive outcome for all members as well as giving easiness for them in terms of financial settlement because, although our optimisation is running every day, price settlement to all community energy members can be done on a monthly basis. It, thus, becomes an easier approach to all members since they do not have to deal with financial settlement on an hourly or daily basis. In terms of internal selling and buying prices, results approximate to the competitive equilibrium price and, therefore, a very significant impact can be obtained compared to the export tariff and retail price.
University of Southampton
Wiyono, Didiek Sri
9c5b2cee-5068-40e2-8762-cdf8797f4ce7
Wiyono, Didiek Sri
9c5b2cee-5068-40e2-8762-cdf8797f4ce7
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362

Wiyono, Didiek Sri (2019) Electricity balanced model and agent for community energy optimisation. University of Southampton, Doctoral Thesis, 135pp.

Record type: Thesis (Doctoral)

Abstract

The excess or shortage of electricity because of massive penetration of renewable energy generators in the local network needs to be handled. Adopting this perspective, community energy using installed renewable generators should maintain the electricity balance and optimise the use of electricity generations to fulfil the demand (load) as well as reducing cost and generating income.
Besides utilising batteries and retailer settlement, grid-connected community energy can join a local market to trade electricity among communities. Using an agent, community energy seeks to achieve an optimised solution to maintain the electricity balance while maximising benefit. Therefore, an optimisation model is proposed.

To demonstrate the optimisation model, specifically in the market settlement, single sealed bid double auction format is used. By adding some assumptions related to the market response, simulations are run to predict the best price to bid (offer/ask) into the electricity market to achieve maximum payoff. Some experiments were performed to choose the best optimisation strategy, specifically in terms of market response and finding the equilibrium prices for all internal traders.

It showed that using an optimisation agent, community energy can achieve an optimum solution to create a balance profile as well as achieving optimum profits for customers, suppliers and battery owners. By using binary search algorithm, suitable internal selling and buying prices as equilibrium prices for all internal traders can be established after the optimum payoff is calculated.

Extended simulation is run using 2018 community energy data. It can be concluded that our optimisations and market response assumptions are capable to achieve optimum profit for community energy, which can be shown using the optimum payoff; local electricity market and battery have a positive impact to all community members although there are several battery limitations. Our community energy management system ensures positive outcome for all members as well as giving easiness for them in terms of financial settlement because, although our optimisation is running every day, price settlement to all community energy members can be done on a monthly basis. It, thus, becomes an easier approach to all members since they do not have to deal with financial settlement on an hourly or daily basis. In terms of internal selling and buying prices, results approximate to the competitive equilibrium price and, therefore, a very significant impact can be obtained compared to the export tariff and retail price.

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Electricity Balanced Model and Agent for Community Energy Optimisation - Version of Record
Available under License University of Southampton Thesis Licence.
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Published date: November 2019

Identifiers

Local EPrints ID: 438642
URI: http://eprints.soton.ac.uk/id/eprint/438642
PURE UUID: e3c2da90-b5ed-4f6f-b21b-7505e2d6037f
ORCID for Enrico Gerding: ORCID iD orcid.org/0000-0001-7200-552X

Catalogue record

Date deposited: 19 Mar 2020 17:36
Last modified: 20 Mar 2020 01:29

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Contributors

Author: Didiek Sri Wiyono
Thesis advisor: Enrico Gerding ORCID iD

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