Optimal Energy Scheduling and Transaction Mechanism for Multiple Microgrids

Abstract

Abstract:In this paper, we propose a framework for optimal energy scheduling combined with a transaction mechanism to enable multiple microgrids to exchange their energy surplus/deficit with others while the distributed networks of microgrids remain secure. Our framework is based on two layers: a distributed network layer and a market layer. In the distributed network layer, we first solve optimal power flow (OPF) using a predictor corrector proximal multiplier algorithm to optimally dispatch diesel generation considering renewable energy and power loss within a microgrid. Then, in the market layer, the agent of microgrid behaves either as a load agent or generator agent so that the auctioneer sets a reasonable transaction price for both agents by using the naive auction-inspired algorithm. Finally, energy surplus/deficit is traded among microgrids at a determined transaction price while the main grid balances the transaction. We implement the proposed mechanism in MATLAB (Matlab Release 15, The MathWorks Inc., Natick, MA, USA) using an optimization solver, CVX. In the case studies, we compare four scenarios depending on whether OPF and/or energy transaction is performed or not. Our results show that the joint consideration of OPF and energy transaction achieves as minimal a cost as the ideal case where all microgrids are combined into a single microgrid (or called grand-microgrid) and OPF is performed. We confirm that, even though microgrids are operated by private owners who are not collaborated, a transaction-based mechanism can mimic the optimal operation of a grand-microgrid in a scalable way.

Multi-period flexibility forecast for low voltage prosumers

“…Optimizing the operation of these small-scale distribution grids is being seen as crucial to endow DSOs of means of accommodate current levels of DRES and allow for a deeper penetration of micro-generation in LV distribution grids in the short-term future. Controlling, modeling, optimizing schedules of microgrid flexible assets has been topic of research for the last years [8,9] with some focus given to energy storage [10,11].…”

“…Accordingly, for each time step of the operation horizon considered, the SoC variable is updated by summing up the PV generation surplus, P V sur , which consequently increases the SoC, and subtracting the EWH possible power in those time steps. The combination between the PV generation surplus to be accommodated, the decision variable regarding the charging (or not) of the battery, and the decision variable representing the EWH operating status must respect the maximum charging power, by verifying (9).…”

“…As the developed EPSO algorithm considers a two-dimensional formulation, this state variable assessment procedure includes two independent functions. The trajectory feasibility relies on the fulfillment of ( 6), (9), and (7). For each time step where ( 6), (9), and (7) are not respected, a penalty term is added in the penalty verification function of the particle being evaluated (18).…”

“…The trajectory feasibility relies on the fulfillment of ( 6), (9), and (7). For each time step where ( 6), (9), and (7) are not respected, a penalty term is added in the penalty verification function of the particle being evaluated (18).…”

Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids

“…In order to improve the profits via energy trading, it is necessary to predict and utilize information about each MG. In order to predict energy surplus or deficit, the ETS can be modeled as a resilience system [13]. For maximizing the benefits to consumers, optimal transacted quantities have been determined by considering uncertainties and coordinating day-ahead optimal scheduling [14].…”