A Multi-objective Optimization Framework for Acceptance Capacity in Distribution Networks Under Coordinated Operation of Photovoltaic-Storage-Charging Systems
DOI:
https://doi.org/10.13052/spee1048-5236.4514Keywords:
Distribution network acceptance capacity, coordinated photovoltaic-storage-charging system, multi-objective optimization, second-order cone programming, investment cost optimizationAbstract
The increasing penetration of distributed photovoltaic (PV) generation and the rapid growth in electric vehicle (EV) adoption have substantially increased operational, safety, and economic pressures on distribution networks. Intermittent renewable output and stochastic charging demand have pushed existing security margins and the network’s new-energy acceptance capacity to unprecedented limits. To better exploit the consumption potential of coordinated PV-storage-charging resources and to raise the distribution network’s capacity to accommodate new energy and diverse loads, this study examined the multi-objective acceptance-capacity optimization problem for a coordinated PV-storage-charging system. We proposed an innovative bi-level optimization framework. The upper level introduced a multi-objective optimization algorithm to balance conflicting goals – maximizing PV acceptance capacity, minimizing voltage deviations at key nodes, and minimizing total investment and operating costs – and to identify optimal interconnection locations and capacity allocations for PV-storage-charging systems. On the basis of the upper-level decisions, the lower level utilized a second-order cone programming (SOCP) relaxation technology to transform the distribution network’s nonlinear power flow constraints into a convex optimization model that can be efficiently solved. This yielded a solvable convex model for detailed, cost-effective operational scheduling of the PV-storage-charging system within the planning horizon. The proposed optimization framework was validated on the IEEE 33-node standard distribution network model through numerical simulations. The experimental results showed that the proposed optimization framework significantly enhanced the overall operational performance of the distribution network. Compared to a conventional single-objective capacity configuration scheme, the proposed framework increased PV acceptance capacity by 32.7% on a representative operating day, reduced maximum voltage deviations at key nodes by 8.2%, and raised the voltage qualification rate to 99.3%. Coordination among PV modules, energy units, and charging piles also lowered investment and operating cost per unit capacity by approximately 26.3 yuan/kW. The results demonstrated that the proposed optimization framework effectively promoted on-site consumption and efficient utilization of renewable energy, mitigated voltage violations, and contributed to peak load shaving and valley load filling. The research results provide both theoretical insight and practical solutions for addressing technical challenges associated with high-penetration renewable energy sources and diverse loads in distribution networks.
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