Energy management of community energy storage in grid-connected microgrid under uncertain real-time prices

Although several energy management schemes are developed in the literature, they are not thoroughly examined in order to reduce the impact of uncertain power generation, demand and electricity prices to minimise the operating cost of a small-scale power system. This paper investigates energy management schemes under uncertain environments and proposes a scheduling scheme to minimise the operating cost of a grid-connected microgrid. Optimisation problems are formulated as real-time scheduling approaches, and are solved by developing modified particle swarm optimisation (MPSO) algorithms. The modification in the MPSO algorithm is performed through structural change in incorporating a mechanism that regulates the selection procedure for decision variables. The effectiveness of the proposed algorithm is justified by comparing the results with other recent algorithms. All the algorithms are separately tuned using the Taguchi technique to demonstrate a fair comparison in solving the scheduling problem. The scheduling program demonstrates superior performance in all cases, including when there is uncertainty in prediction, as compared to other energy management approaches, although solutions have significant deviations due to prediction errors. It is also shown that the proposed MPSO algorithm for the scheduling program can save 16.80 per cent operational cost as compared to the PSO algorithm.

Automated summary

This paper investigates energy management schemes under uncertain environments and proposes a scheduling scheme to minimize the operating cost of a grid-connected microgrid. The study demonstrates that the proposed modified particle swarm optimization (MPSO) algorithm outperforms other recent algorithms in solving real-time scheduling problems. The results indicate that the MPSO algorithm can save 16.80% operational cost compared to the PSO algorithm, showing significant improvements in cost reduction.