Industrial Demand-Side Management by Means of Differential Evolution Considering Energy Price and Labour Cost

Abstract

In the context of the high dependency on fossil fuels, the strong efforts aiming to shift towards a more sustainable world are having significant economic and political impacts. The electricity market is now encouraging prosumers to consume their own production, and thus reduce grid exchanges. Self-consumption can be increased using storage systems or rescheduling the loads. This effort involves not only residential prosumers but also industrial ones. The rescheduling process is an optimisation problem that can be effectively solved with evolutionary algorithms (EAs). In this paper, a specific procedure for bridging demand-side management from the theoretical application to a practical industrial scenario was introduced. In particular, the toroidal correction was used in the differential evolution with the aim of preventing the local minima worsening the effectiveness of the proposed method. Moreover, to achieve reasonable solutions, two different cost contributions have been considered: the energy cost and the labour cost. The method was tested on real data from a historical textile factory, Ratti S.p.A. Due to the nature of the loads, the design variables were the starting time of the 30 shiftable loads. The application of this procedure achieves a reduction in the total cost of approximately 99,500 EUR/year.

A Review on the Applications of PSO-Based Algorithm in Demand Side Management: Challenges and Opportunities

“…For instance, ACO based method has been proposed to optimize the residential load profile with DSM techniques while analyzing the impact of DR tariff on the user's electricity bill [40], [41]. The DE with toroidal correction has been utilized to achieve a good convergence of the algorithm and reduce the possibility of stagnation in local optima in solving the industrial load optimization considering the energy and labour costs [42]. The GA is adopted in [43] to minimize the cost and maximize the load factor simultaneously for DR residential.…”

Preface to “Swarm and Evolutionary Computation—Bridging Theory and Practice”

“…In the work of Niccolai et al [12], the authors introduce a specific procedure to bridge demand-side management from the theoretical application scenario to the practical industrial scenario. In particular, toroidal correction was used in the differential evolution to prevent the local optima from worsening the effectiveness of their method.…”