Distributed Energy Management for Networked Microgrids Embedded Modern Distribution System Using ADMM Algorithm

This paper proposed an distributed energy management for modern distribution systems with various actively interfaced participants, such as distributed energy resources (DERs), flexible loads, and different kinds of microgrids. Given different ownerships, objectives, requirements on autonomy and data privacy, a multi-objective optimization model considering various network operational objectives and constraints was formulated to coordinated the operation of all active participants for better economics and/or system operator preferred performances. The formulated optimization problem was iteratively solved in a distributed way using the alternating direction method of multipliers (ADMM) algorithm. For each iteration, all participants could adjust their schedules based on their own objectives and the price and nodal unbalance signals received. Then, the distribution management system (DMS) updates the nodal price signals according to the generation-load unbalance at the corresponding bus calculated using the previously updated schedules of all participants. The updated price and nodal unbalance signals are distributed to participants on corresponding buses for another iteration until the calculated generation-load unbalance of all buses are small enough, i.e., power balance is achieved at each bus under updated price signals. In addition, the analytical hierarchy process (AHP) was employed to determine the corresponding weighting coefficients of different objectives. The validity of proposed method is verified by simulations on a modern distribution systems with several actively interfaced microgrids, DERs and flexible loads. The effects of weighting coefficients on the obtained solution as well as convergence of ADMM are also investigated.

Automated summary

This paper proposes a distributed energy management method for modern distribution systems, which can coordinate the operation of various active participants to achieve better economics and/or preferred performances. Through iterative solving of a multi-objective optimization model using ADMM algorithm, the method adjust participants' schedules and update price signals in a distributed way, ultimately achieving power balance.