Multi-Objective Optimal Scheduling of a Microgrid Using Oppositional Gradient-Based Grey Wolf Optimizer

Abstract

Optimal energy management has become a challenging task to accomplish in today’s advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids under grid-connected, as well as isolated modes. The authors presented a new optimization algorithm, i.e., Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) in the current study to elucidate the optimal operation in microgrids that is loaded with sustainable, as well as unsustainable energy sources. With the integration of non-Renewable Energy Sources (RES) with microgrids, environmental pollution is reduced. The current study proposes this hybrid algorithm to avoid stagnation and achieve premature convergence. Having been strategized as a bi-objective optimization problem, the ultimate aim of this model’s optimal operation is to cut the costs incurred upon operations and reduce the emission of pollutants in a 24-h scheduling period. In the current study, the authors considered a Micro Turbine (MT) followed by a Wind Turbine (WT), a battery unit and a Fuel Cell (FC) as storage devices. The microgrid was assumed under the grid-connected mode. The authors validated the proposed algorithm upon three different scenarios to establish the former’s efficiency and efficacy. In addition to these, the optimization results attained from the proposed technique were also compared with that of the results from techniques implemented earlier. According to the outcomes, it can be inferred that the presented OGGWO approach outperformed other methods in terms of cost mitigation and pollution reduction.

A Comprehensive Review of Existing and Pending University Campus Microgrids

“…Numerous different optimization techniques have been proposed to analyze the campus microgrid performance systems. Among these techniques, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Tuna Swarm Optimization (TSO), Cuckoo Search (CS), Grey Wolf Optimizer (GWO), and Gradient-based Grey Wolf Optimizer (GGWO), the Hybrid Optimization of Multiple Electric Renewables (HOMER), Firefly Algorithm (FA), LabVIEW Simulation Model (LSM), Mixed Integer Linear Programming (MILP) [101], nonlinear programming [90], High-Reliability Distribution System (HRDS), YALMIP toolbox of MATLAB, Mixed Integer Conic Programming (MICP), and Quantum Teaching Learning-Based Optimization (QTLBO), NSGA-II, and EDNSGA-II [102][103][104][105][106][107]. Sardou et al [108] proposed a robust algorithm that integrates the PSO algorithm with the primal-dual interior point (PDIP) method for the efficient management of microgrid energy.…”

“…Li et al [106] introduced a novel approach that combines incremental conductance (INC) and Improved Tuna Swarm Optimization Hybrid INC (ITSO-INC) to accurately track the maximum power point. Moreover, Rajagopalan et al [107] enhanced the Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) algorithm to clarify the microgrids' optimal operation.…”

Framework of Transactive Energy Market Strategies for Lucrative Peer-to-Peer Energy Transactions

“…The market-clearing price for every exporter during interval t shall be obtained using the computed importer price (λ t I ) as expressed in (12).…”

“…Numerous studies have recently been undertaken in this field of P2P energy market [10][11][12][13]. In [14], the authors developed a computational transactive market architecture for energy transaction between the prosumers and consumers in wholesale electricity markets.…”

A Multi-Stage Constraint-Handling Multi-Objective Optimization Method for Resilient Microgrid Energy Management

“…Li et al [21] addressed a wide spectrum of objectives, including power quality, security, and financial considerations, in their quest to optimize energy scheduling within microgrids. Rajagopalan et al [22] considered a variant of nature-inspired metaheuristics for multi-objective power generation scheduling.…”