Energy Management in Storage-Augmented, Grid-Connected Prosumer Buildings and Neighbourhoods Using a Modified Simulated Annealing Optimization
This addresses energy efficiency and cost optimization for prosumer buildings and neighbourhoods, but it is incremental as it modifies an existing optimization method.
The paper tackled energy management in grid-connected prosumer buildings with storage and photovoltaics by introducing a modified simulated annealing optimizer, which found significantly better solutions than gradient descent and was close to the global optimum with less computational effort than total state space search.
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.