access icon free Distributed generation and energy storage system planning for a distribution system operator

© The Institution of Engineering and Technology
Received 08/08/2017, Accepted 28/06/2018, Revised 16/04/2018, Published 20/07/2018

The smart distribution system architecture provides value-based control techniques that facilitate bi-directional power flows and energy transactions. Although consensus and understanding continue to develop around peer-to-peer transactions, a distribution system operator aims to promote and enable interoperability among entities, particularly those who own distributed energy resources such as energy storage system (ESS) and distributed generation (DG). In this study, the authors address the optimal allocation of ESS and DG in the smart distribution system architecture, in order to help the integration of wind energy. The formulated objective is to minimise the sum of the annualised investment cost, the expected profit and the imbalance cost in the two-stage of power scheduling. The proposed model is verified on the modified IEEE 15-bus distribution radial system. The simulation results have verified the proposed planning approach. Also, results show that a more risk-seeking operation strategy is recommended if wind power penetration increases.

Inspec keywords: power generation control; minimisation; open systems; power generation economics; power generation planning; wind power; power distribution planning; power distribution control; power generation scheduling; power distribution economics; energy storage; distributed power generation

Other keywords: imbalance cost; bidirectional power flows; wind power penetration; annualised investment cost; distributed generation planning; smart distribution system architecture; energy transactions; distribution system operator; energy storage system planning; peer-to-peer transactions; distributed energy resources; risk-seeking operation strategy; modified IEEE 15-bus distribution radial system; wind energy integration; power scheduling; value-based control techniques; interoperability; optimal DG allocation; optimal ESS allocation

Subjects: Distributed systems software; Distributed power generation; Control engineering computing; Distribution networks; Optimisation techniques; Power system control; Power system planning and layout; Energy resources; Control of electric power systems; Power system management, operation and economics; Optimisation techniques; Power engineering computing

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