Economic and environmental factors based multi-objective approach for optimizing energy management in a microgrid

Multi-objective energy management using a smart charging technique of a microgrid with the charging impact of plug-in hybrid electric vehicles

Developing Hybrid Demand Response Technique for Energy Management in Microgrid Based on Pelican Optimization Algorithm

Today, the use of renewable energy is increasing rapidly due to the reduction of pollution and the optimal use of available energy. Microgrids (MGs) are used as systems to control and exploit various resources and loads. Due to the connection of different resources in MGs, control and operation of this system are complex and important. In this paper, the optimal utilization of MGs is presented by considering various parameters including production sources, different loads and energy storage. In this paper for energy management in systems with various MGs, we aim to apply the multi-objective optimization algorithm to obtain the optimal energy management in MG, while simultaneously satisfying pollution and economic objectives. The multi-objective particle swarm optimization (MOPSO) method and non-dominated sorting genetic algorithm (NSGA-II) are used to solve the problem and their results are compared. Various factors are considered in the cost function including operation cost, losses, pollution and other operation characteristics. Using multi-objective optimization functions considering cost functions and various constraints ensures optimal operation of the MG. Simulation results verify various models and effectiveness of the proposed methods. Simulation results show that using NSGA-II for scheduling DG resources gives more optimal results in terms of reducing operation cost and losses such that considering operation cost and pollution cost using NSGA-II reduces operation cost and losses by 10% and 4%, respectively.

Optimal design of stand-alone hybrid PV/wind/biomass/battery energy storage system in Abu-Monqar, Egypt

Multi-Energy Microgrids (ME-MGs) represent an integrated and advanced energy system, playing a vital role in delivering optimal and sustainable energy solutions in modern societies. These systems combine various energy sources, such as electricity, heat, and storage systems, to ensure efficient resource management and operation. One of the primary challenges in managing ME-MGs is reducing operational costs and emissions while addressing uncertainties. This study investigates the optimization and energy management (EM) in ME-MGs through the application of the Multi-Objective Walrus Optimization Algorithm (MOWaOA) combined with fuzzy decision-making techniques. The main objective of the research is to minimize operational costs and emissions in the face of uncertain conditions. To achieve this goal, multiple scenarios were analyzed, including EM without considering demand response and electric vehicles, EM with the inclusion of these factors, and EM under uncertain conditions. The results demonstrated that integrating electric vehicles and demand response into microgrid EM led to a 15.6% reduction in operational costs and a 12.8% decrease in emissions compared to scenarios where these factors were excluded. Furthermore, when uncertainties were accounted for, operational costs increased by 2.1% and emissions rose by 1.2%. This increase emphasizes the significance of employing more precise management techniques and advanced strategies to effectively address uncertainties in ME-MGs.

Towards the integration of interconnected microgrids to deregulated electricity markets

Due to technical advancements and the proliferation of mobile applications, facial analysis (FA) of humans has recently become an important area for computer vision research. FA investigates a variety of difficulties, including gender recognition, facial expression recognition, age and race recognition, with the goal of automatically comprehending social interactions. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This article presents a gender recognition system based on scAOA, that is a modified version of the Archimedes optimization algorithm (AOA). The latest variant (scAOA) enhances the exploitation stage by using trigonometric operators inspired by the sine cosine algorithm (SCA) in order to prevent local optima and to accelerate the convergence. The main purpose of this paper is to apply scAOA to select the relevant deep features provided by two pretrained models of CNN (AlexNet & ResNet) to recognize the gender of a human person categorized into two classes (men and women). Two datasets are used to evaluate the proposed approach (scAOA): the Brazilian FEI dataset and the Georgia Tech Face dataset (GT). In terms of accuracy, Fscore and statistical test, the comparison analysis demonstrates that scAOA outperforms other modern and competitive optimizers such as AOA, SCA, Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Grey wolf optimizer (GWO), Simple genetic algorithm (SGA), Grasshopper optimization algorithm (GOA) and Particle swarm optimizer (PSO).

This paper presents a stochastic optimization framework for microgrid (MG) energy management, integrating electric bicycle (E-Bike) and electric vehicle (EV) charging stations with a green certificate market (GCM) to enhance sustainability and economic efficiency. Uncertainties in renewable energy generation (solar and wind) and load demand are modeled using the Two-Point Estimation Method (TPEM), enabling robust handling of variability. A novel metaheuristic algorithm, Mountaineering Team-Based Optimization (MTBO), is developed to solve a three-objective optimization problem: minimizing operational costs, minimizing emissions, and maximizing GCM revenue. MTBO is benchmarked against particle swarm optimization, achieving a 21.6% reduction in operational costs and a 13.12% reduction in emissions in deterministic scenarios. Three cases are analyzed: (I) no mobile storage, (II) deterministic mobile storage, and (III) stochastic management. In case II, integrating mobile storage (EVs and E-Bikes) with V2G capabilities reduces operational costs by 18.6% and emissions by 10.9% compared to case I. Case III, incorporating stochastic management, further lowers costs by 1.5% and by 19.8% relative to case I, but increases demand response costs by 7.5% and reduces GCM revenue by 38.9% due to renewable fluctuations. These results highlight the practical benefits of combining E-Bike and EV mobile storage with GCM trading in microgrids, demonstrating MTBO's superior exploration and exploitation capabilities for high-dimensional, uncertainty-aware energy scheduling.

Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)

Centroid mutation-based Search and Rescue optimization algorithm for feature selection and classification

This article introduces a relatively current method named grasshopper optimization algorithm (GOA) for explaining power system based dynamic economic load dispatch (DELD) problem. However, like other optimization approaches, GOA suffers from premature convergence and a slow convergence rate. Thus, to boost the convergence mobility of GOA, oppositional based learning (OBL) is merged with the GOA approach. Moreover, another improvization, namely, chaotic concept, is also integrated with the GOA to improve its solution quality. The suggested oppositional based chaotic grasshopper optimization algorithm (OCGOA) method is applied to handle the DELD problem in the most efficient manner. Among the renewable energies, intermittent wind energy (WE) is the most sustainable one and remains technically and economically advantageous for electrical energy generation. The thermal and wind based power systems are hybridized, so that power generation is distributed among the operating units in an equivalent manner such that overall cost and loss part are optimized until all practical constraints are fulfilled. The production cost of the unpredictable wind generation power is further incorporated in the operational cost by employing a probability density function (PDF) formula. The precision and performance of the suggested GOA and OCGOA approaches are validated on 6-unit and 10-unit DELD systems for conventional and wind based energy systems. The efficacy and performance of the suggested OCGOA is judged by comparing it with conventional GOA and other presently developed meta-heuristic optimization techniques found in the literature.

An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection

Even with the advantages of the sine cosine algorithm (SCA) in solving multimodal problems, there are some shortcomings for this method. We observe that the random patterns utilized in SCA cause an increasing attraction toward local optima. This study developed a rationalized version of this technique to deal with several representative benchmark cases with different dimensions. The improved algorithm combines the chaotic local search mechanism and Lévy flight operator with the core trends of SCA. The new variant is named as CLSCA. The Lévy flight with long jumps is adopted to boost the exploratory tendencies of the algorithm, while the chaotic local search mechanism is used as a local search for the destination point, which helps to further enhance the exploitation capability of SCA. Therefore, a suitable equilibrium between the exploration and exploitation can be kept in the CLSCA by two embedded patterns. To investigate the effectiveness and strength of the developed method, the CLSCA was tested on many benchmark functions, including different types of tasks such as single modal, multi-modal, hybrid, and composition functions. We compare the CLSCA with well-known optimizers, like particle swarm optimization (PSO) algorithm, grey wolf optimizer (GWO), SCA with differential evolution (SCADE), opposition-based SCA (OBSCA), fuzzy self-tuning PSO (FST_PSO), chaotic salp swarm algorithm (CSSA), and Chaotic whale optimizer (CWOA). Numerical experimental results demonstrate that the exploratory and exploitative properties of the classical SCA are clearly improved. The experimental results also show that our improved CLSCA is a better technique for different kinds of optimization tasks.

Optimized droop controller based energy management for stand-alone micro-grid using hybrid monarch butterfly and sine-cosine algorithm

Energy management and operational planning of renewable energy resources-based microgrid with energy saving