Demand Side Management using a multi-criteria ϵ-constraint based exact approach

• Published in: Expert systems with applications 2018-06, Vol.99, p.180-192
• Main Authors: Batista, André Costa, Batista, Lucas S.
• Format: Article
• Language: English
• Subjects: Demand Side Management; Multi-criteria decision-making; Multi-objective optimization; Quadratic Programming; Smart-Grids
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2018.01.040

•Statement of an exact (efficient) multi-objective formulation for the problem.•Robustness analysis of the candidate solutions under uncertainties.•Decision-making approach to aid the definition of a practical solution.•A trade-off scheduling can be used as an incentive-based strategy to users.•The results show promising (and robust) solutions regarding the criteria. Demand Side Management (DSM) has been a subject of much research due to the demand of strategies which enable the development of power generation, transmission and distribution in Smart Grids (SG). Throughout the years, many authors have applied mathematical programming and, or, metaheuristic methods for obtaining a satisfactory solution for load management, mainly considering only one performance function in their respective optimization approaches. However, in addition to maximum load peak minimization (an often used performance function), it may be interesting to deal with some other merit functions, such as energy production cost, costumer’s preferences and constraints, among others. Due to this multi-criteria nature of this problem, in this paper it is proposed an exact multi-objective methodology to the optimal Demand Side Management. Essentially, the DSM problem is addressed by using an ϵ-constraint approach, in which one of the objectives is handled with Quadratic Programming whilst the others are considered as linear constrains. This paper also discusses the behavior of the solutions under the presence of uncertainties on the problem parameters due to possible unpredictable variations of the consumption pattern, energy price and number of appliances. The results, regarding the case studies analyzed, have shown promising solutions with interesting trade-off relations between power consumption and energy production cost. Moreover, the robustness analysis has indicated not sensitive solutions even when some parameters of the problem are varied, such as the energy price and the number of types of appliances, which can be regarded as a useful information for the utility.