研究目的
Investigating the coordinated control of active and reactive power in distribution networks with distributed photovoltaic (PV) based on scene analysis to address the randomness and volatility of distributed generation (DG) output and load demand.
研究成果
The proposed method based on scene analysis for coordinated control of active and reactive power in distribution networks effectively addresses the uncertainty of PV output and load demand. It ensures the economy and security of system operation by regulating distributed PV, SVC, and ESS. The effectiveness of the method is confirmed by simulation results on the improved IEEE 33-node system. Future studies could include other uncertainties like wind power generation to make the scenes more specific.
研究不足
The study focuses on the uncertainty of PV output and load demand but does not consider other uncertainties such as wind power generation. The simulation is based on the improved IEEE 33-node system, which may not fully represent all distribution networks.
1：Experimental Design and Method Selection:
The study uses Latin hypercube sampling (LHS) to generate multiple scene samples based on the mathematical distribution model of variables, then reduces the scene samples by K-means clustering to obtain typical scenes and their probabilities. A multi-objective optimization model for coordinated control of active and reactive power is established, considering the adjustable capabilities of distributed PV and energy storage system (ESS), and solved using second-order cone relaxation (SOCR) technique.
2：Sample Selection and Data Sources:
The improved IEEE 33-node system is used for simulation analysis.
3：List of Experimental Equipment and Materials:
Distributed PVs, ESS, and SVC are considered in the simulation.
4：Experimental Procedures and Operational Workflow:
The simulation involves generating typical scenes of PV output and load demand, optimizing the system for minimum network loss and node voltage deviation, and analyzing the results.
5：Data Analysis Methods:
The optimization model is transformed into a second-order cone model and solved using the MOSEK algorithm package.
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