Our Research

battery_250Our current research focuses on methods of distributed agent-based coordination of active elements in the electricity network, including collaborative battery networks; energy storage control algorithms for reducing peak demand in distribution network feeders based on forecasted demand; and managing energy consumption in retail buildings to reduce their carbon footprint and avoid triad charges.

 

Research Areas

Agents in the Grid

Integration of active elements such as distributed generation (DG), energy storage and responsive demand, in to low voltage network has a potential to benefit distribution network operators in improving network security and reliability and a potential to positively impacting network users. To achieve these benefits existing distribution network with high penetration of DG and other active elements must be effectively managed and active elements must be coordinated to positively contribute to network operation.

An individual active element can considered to have positive effect on the network. For example an overnight storage heater following a schedule to store heat during demand troughs would help to reduce strain in the network during evening peak times or a small-scale solar photovoltaic installation would help to offset some of the power being bought from the network.

Integration of active elements such as distributed generation (DG), energy storage and responsive demand, in to low voltage network has a potential to benefit distribution network operators in improving network security and reliability and a potential to positively impacting network users. To achieve these benefits existing distribution network with high penetration of DG and other active elements must be effectively managed and active elements must be coordinated to positively contribute to network operation.

An individual active element can considered to have positive effect on the network. For example an overnight storage heater following a schedule to store heat during demand troughs would help to reduce strain in the network during evening peak times or a small-scale solar photovoltaic installation would help to offset some of the power being bought from the network. However a collection of these active elements could introduce new peaks at different time or cause voltage problems respectively.

To accommodate the increasing numbers of active elements and handle growing demand, the low voltage distribution networks require costly reinforcements. However, these reinforcements can be postponed through introducing active network management and coordinating the behaviour of active elements in the distribution network.

To address complications caused by high penetration of active elements in the low voltage distribution networks our Energy Research Lab is working on active network management solution: distributed agent-based energy management system.

In the distributed agent-based energy management system all software agents are of the same type and have modular structure. This allows an agent to present an object (e.g. household, building or a substation) with more than one active entity. The objective of the agents is to achieve supply and demand balance and minimise financial costs with carbon emissions through optimally coordinating active elements by trading energy with the neighbouring agents. Such arrangement also provide benefit for the DNO as the substation is also represented as an agent is capable of initiating change of the network by propagating request though neighbouring agents.

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Energy Storage in the LV Distribution Network

The Distribution Network Operators (DNOs) role is becoming more difficult as electric vehicles and electric heating penetrate the network, increasing the demand. As a result it becomes harder for the distribution networks infrastructure to remain within its operating constraints. Energy storage is a potential alternative to conventional network reinforcement such as upgrading cables and transformers.

Research has shown that as the price of storage drops, Low Voltage (LV) connected storage projects with specific applications become cost effective. Our research studies LV storage owned by the DNO. If it were possible to predict the demand on the LV network to the same level of accuracy as typically possible on larger aggregations of demand, then existing methods could be used to effectively reduce demand using energy storage. However, small aggregations of loads, typical of a single-phase of a LV feeder, are difficult to predict, making the control of LV storage devices complex and difficult. Our research uses forecasts of individual customers demand to improve the performance of the storage devices.

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