Xu Yang, Houyu He, Jin Zhu, Hongming Yang, Yu Zheng, Yu Lei, Zhuo Long, Yan Xu. A Discrete-continuous Two-layer Optimization Methodology for Distribution Networks Considering Power Converter Embedded Hybrid On-load Regulator Transformers[J]. Chinese Journal of Electrical Engineering, 2025, 11(1): 105-118. DOI: 10.23919/CJEE.2025.000105
Citation: Xu Yang, Houyu He, Jin Zhu, Hongming Yang, Yu Zheng, Yu Lei, Zhuo Long, Yan Xu. A Discrete-continuous Two-layer Optimization Methodology for Distribution Networks Considering Power Converter Embedded Hybrid On-load Regulator Transformers[J]. Chinese Journal of Electrical Engineering, 2025, 11(1): 105-118. DOI: 10.23919/CJEE.2025.000105

A Discrete-continuous Two-layer Optimization Methodology for Distribution Networks Considering Power Converter Embedded Hybrid On-load Regulator Transformers

  • In addressing voltage overruns and line losses in distribution networks with a high percentage of distributed photovoltaic (PV) connections, traditional on-load regulator transformers can achieve only fixed-step voltage regulation and have a limited switching lifespan. Consequently, a discrete-continuous two-layer optimization methodology for distribution networks, which accounts for power-converter-embedded hybrid on-load regulator transformers, has been proposed to adapt to rapid stochastic fluctuations associated with distribution networks having a high percentage of PV access. In the discrete layer, the mechanical ratio is employed as the decision variable at each moment. In the continuous layer, the power electronic converter ratio, STATCOM compensation capacity, and energy storage charging and discharging power are utilized as decision variables at each moment. A composite optimal allocation model is established with an integrated objective function comprising the PV consumption rate, operating costs, and line losses, while simultaneously ensuring that the voltage at each node remains within the prescribed limits. Based on this model, an improved particle swarm algorithm is employed to determine the optimal configuration. Finally, the efficacy of the proposed method is validated through enhancements of the IEEE 33 node system example.
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