Optimal Scheduling of an Isolated Microgrid with Battery Storage Considering Load and Renewable Generation Uncertainties

TL;DR

This paper introduces a chance-constrained programming approach for optimal microgrid scheduling that accounts for uncertainties in load and renewable generation, improving cost efficiency and reliability.

Contribution

It develops a novel MILP model with a discretized step transformation for better solvability and demonstrates superior performance over existing algorithms in microgrid management.

Findings

Significantly reduces calculation times.

Achieves a better trade-off between reliability and economy.

Outperforms hybrid intelligent algorithms in stability and results.

Abstract

By modeling the uncertainty of spinning reserves provided by energy storage with probabilistic constraints, a new optimal scheduling mode is proposed for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming. The model is transformed into a readily solvable mixed integer linear programming (MILP) formulation in GAMS via a proposed discretized step transformation (DST) approach and finally solved by applying the CPLEX solver. By properly setting the confidence levels of the spinning reserve probability constraints, the MG operation can be achieved a trade-off between reliability and economy. The test results on the modified ORNL DECC lab MG test system reveal that the proposal significantly exceeds the commonly used hybrid intelligent algorithm with much better and more stable optimization results and significantly reduced calculation times.