An Efficient Scenario-Based Stochastic Model for Dynamic Operational Scheduling of Community Microgrids with High Penetration Renewables

TL;DR

This paper introduces a scenario-based stochastic model for optimizing the operation of community microgrids with high renewable energy penetration, focusing on cost, emissions, and reliability.

Contribution

It presents a novel stochastic programming approach combined with an improved meta-heuristic algorithm for dynamic microgrid scheduling considering renewable uncertainties.

Findings

Effective handling of renewable variability and uncertainty.

Improved solution quality with the Cuckoo Optimization Algorithm.

Validated efficiency and feasibility in typical microgrid scenarios.

Abstract

The supply of electrical energy is being increasingly sourced from renewable generation resources. The variability and uncertainty of renewable generation, compared to a dispatch-able plant, is a significant dissimilarity of concern to the traditionally reliable and robust distribution systems. In order to reach the optimal operation of community Micro-grids including various Distributed Energy Resource, the stochastic nature of renewable generation should be considered in the decision-making process. To this end, this paper proposes a stochastic scenario based model for optimal dynamic energy management of Micro-grids with the goal of cost and emission minimization as well as reliability maximization. In the proposed model, the uncertainties of load consumption and also, the available output power of wind and photo-voltaic units are modeled by a scenario-based stochastic programming. Through this method, the inherent stochastic nature of the proposed problem is released and the problem is decomposed into a deterministic problem. Finally, an improved meta-heuristic algorithm based on Cuckoo Optimization Algorithm (COA) is implemented to yield the best global optimal solution. The proposed framework is applied in the typical grid-connected Micro-grids in order to verify its efficiency and feasibility.