Stochastic-based Optimal Daily Energy Management of Microgrids in Distribution Systems

TL;DR

This paper introduces a stochastic-based optimization method for daily energy management of microgrids, aiming to minimize costs and emissions while considering uncertainties, validated on a standard distribution system.

Contribution

It proposes a novel stochastic optimization framework using scenario reduction and cuckoo optimization algorithm for microgrid energy management.

Findings

Reduces overall operating and emission costs.

Effectively manages uncertainties with scenario reduction.

Validated on PG&E 69-bus system showing efficiency.

Abstract

Microgrid (MG) with different technologies in distributed generations (DG) and different control facilities require proper management and scheduling strategies. In these strategies, in order to reach the optimal management, the stochastic nature of some decision variables should be considered. Therefore, we proposes a new stochastic-based method for optimal daily energy management (SDEM) of MGs considering economic and reliability aspects. The optimization aim is to minimize overall operating cost, power losses cost, pollutants emission cost and cost of energy not supply (ENS). The network is assumed to be supplied by renewable and dispatch able generators and energy storage systems (ESS). The system uncertainties are considered using a set of scenarios and a scenario reduction method is applied to enhance a trade-off between the accuracy of the solution and the computational burden. Cuckoo optimization algorithm (COA) is applied to minimize the objective function as an optimization algorithm. The effectiveness and efficiency of the proposed method are validated through extensive numerical tests on PG&E 69-bus test distribution system. The results show that the proposed framework can be considered as an efficient tool in optimal daily energy management of smart distribution networks.