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Microgrids (MG) can be considered as a reduced power system network wherein the generation, transmission, and distribution of power take place within a limited geographical area. They are employed to utilize the maximum penetration of renewable energy sources (RES). Microgrid also has advantages such as reduction of transmission losses and costs pertaining to such losses. In this research, economic dispatch, emission dispatch, combined economic emission dispatch (CEED) based on fractional programming (FP), and environment-constrained economic dispatch (ECED) were assessed. An MG system for three distinct scenarios is studied. A novel, robust, and powerful swarm-intelligence hybrid algorithm, created by amalgamating the features of the conventional gray-wolf optimizer (GWO), sine-cosine algorithm (SCA), and crow search algorithm (CSA), is implemented as the optimization tool for the study. A significant reduction in generation costs of about 4.50-9.75% was achieved throughout the study when the time-of-usage (TOU) electricity market pricing strategy was used instead of the fixed pricing strategy. Furthermore, the paper also dealt with the Demand Side Management (DSM) to facilitate the generation cost by managing the controllable load demand. The generation cost decreased by a notable amount when ECED was evaluated with a 15% and 20% DSM-based revised load curve for the MG system. [ABSTRACT FROM AUTHOR]

Demand side management (DSM) is one of the trending economic strategies which shifts the elastic demand to the off-peak hours from the peak hours so as to reduce the overall generation cost of the system. The work done in this paper can be categorized in three phases. In the first phase, various wind speed to power conversion mathematical models available in literature are analysed to find out the one with maximum level of wind penetration. For second phase, an economic DSM strategy is implemented to restructure the forecasted load demand model for various participation levels. In the final phase the cost-effective optimization of two microgrid distribution systems are percolated. As an optimization tool, novel hybrid CSAJAYA has been used to carry on the study. Different types of grid participating and pricing strategies along with valve point loading effect and wind energy uncertainty are considered to amplify the complexity and practicality of the study. The generation costs reduced from 3 to 5% when the forecasted demand was reformed with 20% DSM participation for both the test systems. A detailed comparison with the results from various optimization tools studied confirms the effectiveness of the proposed hybrid approach. The hybrid optimization tool presented in this paper performs better in terms of central tendencies, nonparametric statistical analysis, and algorithm execution time. • Five different wind speed-to-power mathematical modelling have been studied. • Effects of DSM in reducing the microgrid generation cost was analysed. • Grid participation strategies and their impact on microgrid generation cost was analysed. • Grid pricing strategies and their impact on microgrid generation cost was analysed. [ABSTRACT FROM AUTHOR]

Microgrids can be considered a reduced power system network wherein the generation, transmission, and distribution of power occurs within a limited geographical area. They are employed to use the maximum penetration of renewable energy sources (RES). Microgrid also has advantages like reduction of transmission losses and costs pertaining to such losses. The objectives of economic dispatch, emission dispatch, penalty‐factor (PPF) based combined economic emission dispatch (CEED) and environmental constrained economic dispatch (ECED) are evaluated in this paper. A low‐voltage microgrid system for three different scenarios is studied. A novel, robust and powerful hybrid swarm‐intelligence optimization algorithm is used, which is based on amalgamating the features of conventional gray‐wolf optimizer, sine‐cosine and crow search algorithm. Due to the use of RES, the active power production cost and carbon emission of the microgrid system were reduced to approximately 3% for all the objectives. More than 15% reductions in generation costs were obtained throughout the study when the time‐of‐usage electricity market pricing strategy was used instead of the fixed pricing strategy. Also, results show that the ECED methods of economic emission dispatch provided better‐compromised solutions than the PPF‐based CEED method. Finally, non‐parametric statistical analysis is used to analyze and validate the quality of the projected novel technique, which is measured against the outcomes found with algorithms used in the state‐of‐art literature. [ABSTRACT FROM AUTHOR]

Microgrids are a type of restricted power distribution systems in which electricity is generated, transmitted, and distributed within a small geographic region. They are used to ensure that renewable energy sources are used to their full potential. Microgrids provide further benefits, such as lowering transmission losses and the expenses associated with them. This research compares and contrasts the aims of economic dispatch, emission dispatch, fractional programing based combined economic emission dispatch, and environmental restricted economic dispatch (ECED). A low‐voltage microgrid system is investigated for three different scenarios. As a study optimization tool, an innovative, resilient, and strong hybrid swarm‐intelligence optimization algorithm is utilised, which is based on combining the properties of the traditional grey‐wolf optimiser, sine‐cosine algorithm, and crow search algorithm. The employment of a time‐of‐use energy market pricing approach instead of a fixed pricing plan resulted in a 15% decrease in generation costs throughout the course of the research. When ECED was assessed with a 15%–20% demand side management based restructured load demand model for the microgrid system, the generation costs were reduced even further. [ABSTRACT FROM AUTHOR]

Grid‐connected mode of microgrid (MG) operation is the most economical and reliable mode of service because the grid participates in the buying and selling of power with the MG, which reduces the generation cost of the MG system. Various types of electricity market pricing strategies are available in the literature with which the grid buys and sells power to the MG system. These strategies are implemented in turns on two different low voltage (LV) MG systems, and the generation cost of the systems are recorded to distinguish the cheapest strategy among them. A novel hybrid of recently developed superior optimization algorithms viz. grey wolf optimizer (GWO), sine cosine algorithm (SCA), and crow search algorithm (CSA) is used to minimize the overall generation cost of the MG system. It was evident from the results that generation cost was minimum when electricity market pricing was done using Strategy 2. Also, active participation of the grid incurred 15% savings in the system generation cost for the same strategy compared to the case when the grid was operating passively. Furthermore, statistical analysis corroborates to the superiority of the proposed hybrid MGWO‐SCA‐CSA over other algorithms used. [ABSTRACT FROM AUTHOR]

This paper aims to percolate energy management of microgrid systems by minimizing the generation cost of the same. Energy management of microgrid refers to the optimal sizing and scheduling of the distributed energy resources to reduce the generation cost and pollutant emission. A recently developed crow search algorithm (CSA) is implemented to execute the optimization. The proposed CSA imitates the crows' memory and tactics of hiding and chasing their food. Six renewable integrated microgrid test systems and a total of eighteen different cases are considered for this study. Various practical complexities such as valve point loading effect, combined economic–emission dispatch using price penalty factor method, modeling of the renewable energy sources and energy storage systems are taken into consideration for energy management of the microgrid systems. Results obtained are then compared to a number of different soft computing techniques such as genetic algorithm and particle swarm optimization and the likes to justify the effectiveness of the proposed algorithm. A statistical analysis, viz. Wilcoxon signed-rank test, is performed to prove the superiority of the proposed approach over the various other optimization techniques used in the paper. [ABSTRACT FROM AUTHOR]

Generation of electricity comes with the emission of toxic gases into the atmosphere by the fossil fueled generators. Along with the promotion in the utilization of renewable energy sources (RES), it is also the duty of the power engineers to arrive at a compromised solution such that less emission of toxic gases occurs with economic generation of electricity. This paper proposes a balanced trade-off method for solving environment constrained economic dispatch (ECED) problems. A novel comparative analysis is performed among proposed ECED method with existing price-penalty-factor (PPF) and fractional programming (FP) methods for solving combined economic emission dispatch (CEED) problems on a 3-unit dynamic test system to sort out the method, which yields a better trade off solution between generation cost and pollutants emitted. An algorithm, following the hunting strategy of wolves, is improvised by incorporating strategies from population-based sine-cosine algorithm along with position updating methods of crows to form a robust hybrid algorithm, which was used as the optimization tool for the study. Involvement of RES diminished the generation cost to 5.5% for both economic dispatch and PPF based CEED, and 6.5% decrease in emission of pollutants was observed due to the same. The generation cost and amount of emitted pollutants, evaluated using proposed ECED approach, were much closer to the economic dispatch and emission dispatch values respectively compared to PPF based and FP based CEED solution. Furthermore, statistical analysis endorses the superiority of the proposed hybrid optimizer over other algorithms presented in the state-of-art literature. • Three different types of CEED methods are studied. • Subject test system is a 3-units RES integrated microgrid system. • Comparative analysis among the generation costs and pollutants emitted is performed. • A novel HMGWO algorithm was proposed as the optimization tool for the study. [ABSTRACT FROM AUTHOR]

Summary: Microgrids have lately proved to be the most efficient type of power system delivering uninterrupted power to a local area incurring negligible amount of transmission loss. Inclusion of the available renewable energy sources among the various distributed generation (DG) sources in a microgrid have decreased the emission of harmful toxic gases to a great extent. Dynamic economic dispatch indicates the economic dispatch of a microgrid allocating the timely optimal output of the distributed generation sources. However, the setup of a microgrid involves a huge amount of capital out of which installation cost, operation and maintenance cost, and depreciation cost instigated through the passing of time are a few. This paper uses three soft computing techniques viz. particle swarm optimization, differential evolution, and differential evolution with local global neighborhoods to perform a novel dynamic cost analysis of a microgrid and minimize its overall cost which includes fuel, emission, operation and maintenance cost, installation, and depreciation costs. Four different case studies are pulled off on a microgrid, and a comparative analysis is done to identify the best case. Numerical results and figures also point out the best optimization technique which was able to give the most efficient and minimal cost for the microgrid. [ABSTRACT FROM AUTHOR]