Optimal Home Energy Demand Management Based Multi-Criteria Decision Making Methods

Abstract

From the growth of residential energy demands has emerged new approaches for load scheduling to realize better energy consumption by shifting the required demand in response to cost changes or incentive offers. In this paper, a hybrid method is proposed to optimize the load scheduling problem for cost and energy saving. The method comprises a multi-objective optimization differential evolution (MODE) algorithm to obtain a set of optimal solutions by minimizing the cost and peak of a load simultaneously, as a multi-objective function. Next, an integration of the analytic hierarchy process (AHP) and a technique for order preferences by similarity to ideal solution (TOPSIS) methods are used as multi-criteria decision making (MCDM) methods for sorting the optimal solutions’ set from the best to the worst, to enable the customer to choose the appropriate schedule time. The solutions are sorted based on the load peak and energy cost as multi-criteria. Data are for ten appliances of a household used for 24 h with a one-minute time slot. The results of the proposed method demonstrate both energy and cost savings of around 47% and 46%, respectively. Furthermore, the results are compared with other recent methods in the literature to show the superiority of the proposed method.

Rating the Participation in Demand Response Programs for a More Accurate Aggregated Schedule of Consumers after Enrolment Period

“…The related literature presents distinct approaches to solve the scheduling problem when dealing with the introduction of demand-side in the energy market and how the load diagram can influence network reliability. Muhsen et al [15] proposed a multi-objective optimization differential evolution method to solve the load scheduling problem in terms of cost and energy saving, creating a set of optimal solutions. Ilo et al [16] considered a holistic power system architecture that gathers the relevant components in a single structure focusing on the decarbonization of the sector cost-effectively while guaranteeing data privacy and safety against external threats.…”

A New Pricing Scheme for Intra-Microgrid and Inter-Microgrid Local Energy Trading

“…The constraint Equation (8) limits the stored energy between S min and S max . To ensure that the storages are charged and discharged at the corresponding rates, constraints Equations (10) and (9) are applied.…”

“…Such linear function is widely adopted in the literature when considering energy trading applications [36]. Substituting the function into the optimization problem defined by Equations (1)- (10), the original problem can be re-written as: (15) constraints Equations (2), (11), (12)…”

“…) × P util ∀u ∈ U 1 (18) constraints Equations (8)- (10). The optimization problem in Equation (15) can be represented as a quadratic programming problem with quadratic constraints and can be solved using existing solver packages like BMIBNB [37] employing branch and bound method for non-convex problems.…”

“…Multi-criteria decision making algorithms have been used to schedule operation times for customer loads in Reference [10]. Very recently, the concept of peer-to-peer or local energy trading has emerged as an innovative strategy for DR process [11].…”