Research on Output Controllability of Photovoltaic Grid Connected Generation System with Hybrid Energy Storage

Xijun, Li; Ren, Jiayu; Yang, Ling; Han, Wei

doi:10.1109/icsgea.2017.44

Cited by 6 publications

(1 citation statement)

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“…Solar energy resources as a clean renewable energy has now been widely used in distributed photovoltaic power generation systems [1]. However, the random and intermittent nature of photovoltaic power generation leads to large fluctuations in output power, bringing serious challenges to the safe and stable operation of the power grid [2]. At the same time, with the increase of peak-tovalley difference of demand-side load, the problem of power supply at peak load moments becomes more prominent.…”

Section: Introductionmentioning

confidence: 99%

A Method for Optimal Allocation of Distributed PV and Energy Storage Based on High Reliability of Distribution Network

Shao

Zhou

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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In the context of the current widespread application of distributed PV, the energy storage system has bi-directional power characteristics and flexible regulation capability, and the reasonable joint planning of the two can enhance the safe and reliable operation of the system. In order to maximize long-term economic benefits, this paper proposes an optimal allocation method of distributed PV and energy storage based on high reliability of distribution network. Considering both normal operation and disaster scenarios for distributed photovoltaic storage planning, firstly, the investment and operation cost of distributed energy storage system is the target, and the storage planning model under normal operation scenario is established by considering the storage access location, configuration capacity, load state and grid operation state as constraints; then, the distributed photovoltaic storage optimal configuration model under disaster scenario is established by taking grid economy as the objective function. In summary, a two-layer planning model is established, with the upper layer using genetic algorithm to optimize the location, power and capacity of energy storage configuration, and the lower layer using particle swarm algorithm combined with tide calculation tool to optimize the intra-day operation strategy of energy storage; finally, the feasibility and effectiveness of the optimal configuration method are verified.

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Section: Introductionmentioning

confidence: 99%