Energy Management in Storage-Augmented, Grid-Connected Prosumer Buildings and Neighbourhoods Using a Modified Simulated Annealing Optimization

TL;DR

This paper presents a modified simulated annealing method for optimizing energy management in grid-connected, storage-enabled prosumer buildings and neighborhoods, outperforming gradient descent and approaching global optima efficiently.

Contribution

It introduces a novel modified simulated annealing algorithm tailored for energy management, demonstrating superior performance over traditional methods in specific scenarios.

Findings

Outperforms gradient descent in solution quality

Achieves near-global optimal solutions

Requires less computational effort than exhaustive search

Abstract

This article introduces a modified simulated annealing optimization approach for automatically determining optimal energy management strategies in grid-connected, storage-augmented, photovoltaics-supplied prosumer buildings and neighbourhoods based on user-specific goals. For evaluating the modified simulated annealing optimizer, a number of test scenarios in the field of energy self-consumption maximization are defined and results are compared to a gradient descent and a total state space search approach. The benchmarking against these two reference methods demonstrates that the modified simulated annealing approach is able to find significantly better solutions than the gradient descent algorithm - being equal or very close to the global optimum - with significantly less computational effort and processing time than the total state space search approach.