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## Optimal coordination of vehicle-to-grid batteries and renewable generators in a distribution system

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The increasing penetration of electric vehicles (EVs) and renewable generators (RGs) in the power grid is an inevitable trend to combat air pollution and reduce the usage of fossil fuels. This will challenge distribution networks, which have constrained capacity. However, appropriate dispatch of electric vehicles via vehicle-to-grid (V2G) operation in coordination with the distributed renewable generators can provide support for the grid, reduce the reliance on traditional fossil-fuel generators and benefit EV users. This paper develops a novel agent-based coordinated dispatch strategy for EVs and distributed renewable generators, taking into account both grid's and EV users' concerns and priorities. This optimal dispatch problem is formulated as a distributed multi-objective constraint optimisation problem utilizing the Analytic Hierarchy Process and is solved using a dynamic-programming-based algorithm. The proposed strategy is tested on a modified UK Generic Distribution System (UKGDS). The electricity network model is simplified using a virtual sub-node concept to alleviate the computation burden of a node's agent. Simulation results demonstrate the feasibility and stability of this dispatch strategy.

Chapter Contents:

• 15.1 Introduction
• 15.2 General description of the electricity network and agents
• 15.3 Formulation of DMOCOP
• 15.3.1 Objectives
• 15.3.1.1 Objective 1 (RE): Reduce wasted RE
• 15.3.1.2 Objective 2 Sufficient EV battery SOC
• 15.3.1.3 Objective 3 (CC): Save charging cost to EV users
• 15.3.1.4 Objective 4 (LL): Load levelling in the distribution network
• 15.3.2 The AHP
• 15.3.3 Constraints
• 15.4 A* optimal dispatch procedure
• 15.5 The application of A* search to optimal decentralized coordination ofEVs and RGs in a distribution network
• 15.5.1 Stochastic modelling of uncertainties
• 15.5.1.1 Copula
• 15.5.1.2 Modelling of EV travel patterns and on-road energy consumption
• 15.5.1.3 Wind power modelling
• 15.5.2 Simulation results
• 15.6 Complexity discussion
• 15.7 Conclusion
• References

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