Gradient Tracking for Coalitional Aggregation of Wind Power
Stefanny Ramírez, Dario Bauso · 2022 IEEE 61st Conference on Decision and Control (CDC) · 2022
In this work we study coalition formation for a set of independent wind power producers. The wind power producers bid a contract in a day-ahead market, and they wish to determine the optimal contract that maximizes their expected profit. To cope with the volatility of the wind, the producers can form coalitions and aggregate their power production. We consider a communication topology and we assume that each wind power producer gets information about the wind powers realisation in the network through the contracts bidden by its neighbours. To determine the optimal contract for each coalition, we use a data learning approach based on gradient tracking. We prove that, for each coalition, the producers converge to the optimal contract for such a coalition. From the optimal contract we obtain the profit of each coalition which represents the coalitions’ values of the resulting coalitional game. Then, we design a stabilizing allocation mechanism based on the Shapley value.