The large-scale integration of distributed renewable energy blurs the traditional boundaries among generation, grid, load, and storage, imposing new operational challenges on power grids, such as reverse power flow and frequent load fluctuations. To address the increased voltage regulation demands and associated economic costs of traditional Static Var Compensators (SVC), this paper proposes a coordinated control strategy integrating source, grid, load, and storage components. Leveraging the four-quadrant operational capability of energy storage systems, the proposed method simultaneously optimizes active power dispatch and reactive power compensation by enhancing the conventional state-of-energy model to explicitly account for reactive power. The particle swarm optimization algorithm is employed to minimize both voltage fluctuations and economic costs. Simulation results validate that the proposed integrated strategy effectively maintains voltage deviations within ±3.3%, satisfying the ±5% operational standard, while achieving monthly economic savings of approximately 1100 CNY compared to conventional SVC-based approaches.

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A Source-Grid-Load-Storage Integrated Coordinated Control Strategy to Meet the Needs of Power Grid Services

  • Qing Zhu,
  • Weiwei Zhu,
  • Chunlei Shao,
  • Wenchao Zhao,
  • Wensen Gao

摘要

The large-scale integration of distributed renewable energy blurs the traditional boundaries among generation, grid, load, and storage, imposing new operational challenges on power grids, such as reverse power flow and frequent load fluctuations. To address the increased voltage regulation demands and associated economic costs of traditional Static Var Compensators (SVC), this paper proposes a coordinated control strategy integrating source, grid, load, and storage components. Leveraging the four-quadrant operational capability of energy storage systems, the proposed method simultaneously optimizes active power dispatch and reactive power compensation by enhancing the conventional state-of-energy model to explicitly account for reactive power. The particle swarm optimization algorithm is employed to minimize both voltage fluctuations and economic costs. Simulation results validate that the proposed integrated strategy effectively maintains voltage deviations within ±3.3%, satisfying the ±5% operational standard, while achieving monthly economic savings of approximately 1100 CNY compared to conventional SVC-based approaches.