Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer

Abstract

Minimizing energy costs while maintaining consumer satisfaction is a very challenging task in a smart home. The contradictory nature of these two objective functions (cost of energy and satisfaction level) requires a multi-objective problem formulation that can offer several trade-off solutions to the consumer. Previous works have individually considered the cost and satisfaction, but there is a lack of research that considers both these objectives simultaneously. Our work proposes an optimum home appliance scheduling method to obtain an optimum satisfaction level with a minimum cost of energy. To achieve this goal, first, an energy management system (EMS) is developed using a rule-based algorithm to reduce the cost of energy by efficient utilization of renewable energy resources and an energy storage system. The second part involves the development of an optimization algorithm for optimal appliance scheduling based on consumer satisfaction level, involving their time and device-based preferences. For that purpose, a multi-objective grey wolf accretive satisfaction algorithm (MGWASA) is developed, with the aim to provide trade-off solutions for optimal load patterns based on cost per unit satisfaction index (Cs_index) and percentage satisfaction (%S). The MGWASA is evaluated for a grid-connected smart home model with EMS. To ensure the accuracy of the numerical simulations, actual climatological data and consumer preferences are considered. The Cs_index is derived for six different cases by simulating (a) optimal load, (b) ideal load, and (c) base (random) load, with and without EMS. The results of MGWASA are benchmarked against other state-of-the-art optimization algorithms, namely, binary non-dominated sorting genetic algorithm-2 (NSGAII), multi-objective binary particle swarm optimization algorithm (MOBPSO), Multi-objective artificial bee colony (MOABC), and multi-objective evolutionary algorithm (MOEA). With the proposed appliance scheduling technique, a % reduction in annual energy cost is achieved. MGWASA yields Cs_index at 0.049$ with %S of 97%, in comparison to NSGAII, MOBPSO, MOABC, and MOEA, which yield %S of 95%, 90%, 92%, and 94% at 0.052$, 0.048$, 0.0485$, and 0.050$, respectively. Moreover, various related aspects, including energy balance, PV utilization, energy cost, net present cost, and cash payback period, are also analyzed. Lastly, sensitivity analysis is carried out to demonstrate the impact of any future uncertainties on the system inputs.

Enhanced Multi-Objective Grey Wolf Optimizer with L関y Flight and Mutation Operators for Feature Selection

“…Recently, GWO has been extensively used to solve feature selection problems in diverse areas, including benchmarks problems as in [5,40,48], diagnosis of Parkinson's disease [49,50], facial emotion recognition facial [51], face recognition [52], EMG signals classification [53], diagnosis of Paraquat disease [54], Coronary Artery disease classification [23], medical diagnosis [55] and intrusion detection [56,57]. A comprehensive review that includes publications from 2015 up to 2019 on feature selectionbased GWO is presented in a published book chapter [6], where interested readers on GWO feature selection can find a detailed state-of-the-art.…”

“…GWO has lower computationally cost than other metaheuristic algorithms such as PSO and GA and can converge faster. As a result, GWO has been utilized as an efficient algorithm in several domains, including feature selection [5,23,24].…”

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

“…Grey wolf optimizer (GWO) has gained significant attention in recent years due to its flexibility, scalability, and few parameters [ 61 ]. It is applied in various applications such as gait analysis [ 62 ], structural strain reconstruction [ 63 ], engines [ 64 ], renewable energy systems [ 65 ], robotics [ 66 ], deep learning [ 67 ], wireless sensor networks [ 68 ], smart grid [ 69 ], medical [ 70 ], and energy management [ 71 ]. Even though GWO has been utilized in different applications, due to the complexity of real-world optimization problems, various improvements have been made in GWO in terms of updating mechanisms, hybridization, encoding schemes, multi-objective, and new operators.…”

A Hybrid Discrete Artificial Bee Colony Algorithm for Imaging Satellite Mission Planning

“…As a very effective means to solve optimization problems, intelligent optimization algorithm has been widely concerned by researchers. At present, many advanced intelligent optimization algorithms have been produced, and scholars have tried to apply them to their own research fields and made some beneficial explorations, such as: brain storm optimization [21], differential evolution algorithm [22], biogeography algorithm [23], multi-verse optimizer [24], grey wolf optimizer [25] and Seagull optimization algorithm [26], etc. At the same time, it should be noted that most of the current intelligent algorithms were first proposed for continuous optimization problems, which need to be discretized before they can be applied to discrete optimization problems such as satellite mission planning.…”