Distributed Shadow Dynamic Consensus Optimization Framework for Power Market with Parameterization Bidding Strategy of GENCOs

This paper presents a distributed shadow dynamic consensus (DSDC) framework to address the generation company (GENCO) strategic bidding problem in the independent system operator (ISO) operation. First, each sub-group is defined according to the communication principle. The global resource allocation can be solved by introducing the local shadow power demand. Then, for the general market clearing optimization problem, a distributed dynamic consensus algorithm is presented to estimate the general equality constraint. The optimal locational marginal price (LMP) and power allocation in each sub-group are obtained in this step. Finally, the optimal solution is corrected when considering the transmission constraint. A theoretical analysis of the DSDC framework is provided. The algorithm and model are tested by the IEEE 30-bus test system to support the proposed DSDC framework.

Automated summary

This paper presents a distributed shadow dynamic consensus (DSDC) framework to address the generation company (GENCO) strategic bidding problem in the independent system operator (ISO) operation. The framework uses a distributed dynamic consensus algorithm to estimate the general equality constraint and obtain optimal locational marginal prices (LMP) and power allocation. The proposed framework is tested using the IEEE 30-bus test system.