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The whole world is now widely using green energy compared to fossil because of the depletion of fossil fuels, the rising temperature of the earth, and changing weather conditions, all these things are becoming a big threat to the life of the earth. This study proposed a stand-alone hybrid renewable energy system using different types of batteries. This model includes photovoltaic arrays, wind turbines, diesel generators, converters, and batteries. Lead-acid and lithium-ion batteries have been compared for the selection of optimal battery based on hybrid renewable energy system and sustainable development requirements. The purpose of this study is to find the optimal configuration, and techno-economic characteristics, using the hybrid optimization of multiple energy resources technique. The results of Lithium-ion and Lead Acid have been compared and it is found that the best configuration is photovoltaic arrays/wind turbines/ diesel generators /Battery/converter with lithium-ion Batteries. The net present cost and cost of energy are found to be 1.64M and 0.144$ respectively, for the selected study location. The carbon dioxide emission for configuration with LI batteries is 107314 kg/year as against the LA batteries which have 351288 kg/year. The results show LI batteries are technically as well as economically better than the LA batteries. [ABSTRACT FROM AUTHOR]

Presently, several communities are employing renewable integrated combined heat-power (CHP) microgrids to optimally supply connected heat-power loads. Whilst microturbines are often employed in CHP microgrids, their operational flexibility as a CHP technology remains underexamined. The proposed work studies this perspective with acceptable penetrations of renewable energy sources (RES). Dynamic scheduling of combined heat-power islanded microgrid with RES and energy storage is presented for optimizing cost, emission, losses, and heat output considering RES and load uncertainties. RES uncertainties are modeled using Weibull distribution while load uncertainties are generated stochastically. To obtain the best solution for the contradictory multiple objectives and to reduce dependency on any specific tuning parameter, a fuzzy attainment module is integrated into the modified differential evolution algorithm with dynamic mutation rates. First, the operational flexibility of cogeneration units with RES and RES uncertainties is studied without storage. Secondly, with storage, and finally with storage under different load uncertainty scenarios. With both electrical and thermal storage, total operating costs are found to be reduced by 2.6%, emission by 1.2%, waste heat by 12.1%, and power losses by 25.4% per day. Random load scenarios in ± 25% uncertainty range were found to produce variations in cost in the range of − 2.49% to + 5.93% and emission from − 1.13 to 2.22% showing the capability of the proposed approach under practical conditions. This study demonstrates a cost-effective combined heat-power dispatch to do away with unwarranted auxiliary units with environmental, and climatic benefits. [ABSTRACT FROM AUTHOR]

The integration of renewable energy resources (RESs) into the existing power grid is an effective approach to reducing harmful emission content. Environmental economic dispatch is one of the complex constrained optimization problems of power systems. These problems have become more complex as a result of integrating RESs, as the availability of solar and wind power is stochastic in nature. To obtain the solution of such types of complex constrained optimization problems, a robust optimization method is required. Literature shows that chaotic maps help to boost the search capability through improvisation in the exploration and exploitation phases of an algorithm; hence, they are able to provide superior solutions during optimization. Therefore, in this study, a new optimization technique was developed based on the Jaya algorithm called the chaotic Jaya algorithm. Here the main aim was to investigate the impact of RES integration into conventional thermal systems on total power generation cost and emissions released to the environment. The proposed approach was tested for two standard cases: (i) scheduling of a committed generating unit for a specific time and (ii) scheduling of a committed generating unit for a time period of 24 hours with 24 intervals of 1 hour each. The simulation results show that a tent map is the best-performing map for a sample problem under consideration, as it provides better results. Hence, it has been considered for detailed analysis. [ABSTRACT FROM AUTHOR]

In this paper, the economically self-sufficient microgrid is planned to provide electric power and heat demand. The combined heat and power-based microgrid needs strategic placement of distributed generators concerning optimal size, location, and type. As fossil fuel cost and emission depend mainly on the types of distributed generator units used in the microgrid, economic emission dispatch is performed for an hour with a static load demand and multiple load demands over 24 h of a day. The TOPSIS ranking approach is used as a tool to obtain the best compromise solution. Harris Hawks Optimization (HHO) is used to solve the problem. For validation, the obtained results in terms of cost, emission, and heat are compared with the reported results by DE and PSO, which shows the superiority of HHO over them. The impact of renewable integration in terms of cost and emission is also investigated. With renewable energy integration, fuel cost is reduced by 18% and emission is reduced by 3.4% for analysis under static load demand, whereas for the multiple load demands over 24 h, fuel cost is reduced by 14.95% and emission is reduced by 5.58%. [ABSTRACT FROM AUTHOR]

Summary: With the rapid depletion of coal reserves and rising concern to reduce the harmful fossil fuel emissions, the inclusion of renewable energy sources like wind and solar is gaining acceptance worldwide. In this paper, a multi‐objective optimization (MOO) model of a wind‐solar integrated microgrid is solved using a mixed integer fuzzy membership based differential evolution algorithm with dynamically varying mutation rates. The idea is to determine the best compromise solution for minimizing total cost, emission, and power losses, together. Wind uncertainty is modeled by an additional probabilistic cost term to include the cost of storage/reserve and penalty imposed for curtailment of renewable power. The proposed model reduces the impact of the control parameters and maximizes the attainment level of all three objectives. The results obtained using the proposed approach are compared and validated with the traditional weighted sum price penalty method and some recently published algorithms. The solutions attained by all these methods are found to be non‐dominating. However, the best compromise solution obtained for the MOO problem by the proposed method is found to have a higher fuzzy rank than the solutions obtained by the other methods. Moreover, there are no tuning parameters in the proposed approach. [ABSTRACT FROM AUTHOR]

Due to increasing concern over global warming, the penetration of renewable energy in power systems is increasing day by day. Gencos that traditionally focused only on maximizing their profit in the competitive market are now also focusing on operation with the minimum pollution level. The paper proposes a multiobjective model capable of finding a set of trade-off solutions for the joint optimization problem, considering the cost of reserve and curtailment of power from renewable sources. Managing a hybrid power system is a challenging task due to its stochastic nature mixed with the objective function and complex practical constraints associated with it. A novel metaheuristic Equilibrium Optimizer (EO) algorithm incepted in the year 2020 utilizes the concept of control volume and mass balance for finding equilibrium state is proposed here for computing the optimal schedule and impact of renewable energy integration on profit and emission for different optimization objectives. In this paper, EO has shown dominant performance over well-established metaheuristic algorithms such as particle swarm optimizer (PSO) and artificial bee colony (ABC). In addition, EO produces well-distributed Pareto-optimal solutions and the fuzzy min-ranking is used as a decision maker to acquire the best compromise solution. [ABSTRACT FROM AUTHOR]