研究目的
Proposing a dynamic tariff-subsidy (DTS) method for congestion management in distribution networks with high penetration of PVs, HPs, and V2G-enabled EVs, extending the dynamic tariff method to handle feed-in power flows and enable negative regulation prices (subsidies).
研究成果
The DTS method effectively manages congestion in distribution networks with high DER penetration by allowing negative regulation prices (subsidies) to handle feed-in power flows from PVs and V2G. It achieves optimal social welfare through decentralized aggregator optimizations, with case studies on RBTS demonstrating successful congestion resolution in both power flow directions. Future work could address uncertainties and real-time implementation challenges.
研究不足
The study uses DC power flow approximations which may introduce errors; uncertainties in DER parameters and prices are not fully addressed; the method assumes accurate forecasts and may require additional measures for unsolvable congestion; computational and communication times in real applications need optimization.
1：Experimental Design and Method Selection:
The study employs a convex quadratic programming model and dual decomposition for DTS calculation, comparing a single optimization method with an iterative dual decomposition method. Theoretical models include power flow and voltage constraints using DC power flow approximations.
2：Sample Selection and Data Sources:
The Roy Billinton Test System (RBTS) is used as the case study network, with parameters for load points, lines, and DERs (EVs, HPs, PVs) based on realistic data from sources like driving patterns and Danish TSO data.
3：List of Experimental Equipment and Materials:
Software tools include GAMS for optimization and MATLAB for iteration control and DTS calculation; no physical equipment is specified as it is a simulation-based study.
4：Experimental Procedures and Operational Workflow:
For the dual decomposition method, the DSO initializes DTS to zero, aggregators optimize schedules, report aggregated demands, DSO updates DTS using subgradient method, and iterations continue until convergence to resolve congestion.
5：Data Analysis Methods:
Analysis involves comparing line loading and voltage profiles before and after DTS application, observing convergence of key variables, and evaluating the efficacy in managing congestion.
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