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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 proposes a novel hybrid technique called enhanced grey wolf optimization‐sine cosine algorithm‐cuckoo search (EGWO‐SCA‐CS) algorithm to improve the electrical power system stability. The proposed method comprises of a popular grey wolf optimization (GWO) in an enhanced and hybrid form. It embraces the well‐balanced exploration and exploitation using the cuckoo search (CS) algorithm and enhanced search capability through the sine cosine algorithm (SCA) to elude the stuck to the local optima. The proposed technique is validated with the 23 benchmark functions and compared with state‐of‐the‐art methods. The benchmark functions consist of unimodal, multimodal function from which the best suitability of the proposed technique can be identified. The robustness analysis also presented with the proposed method through boxplot, and a detailed statistical analysis is performed for a set of 30 individual runs. From the inferences gathered from the benchmark functions, the proposed technique is applied to the stability problem of a power system, which is heavily stressed with the nonlinear variation of the load and thereby operating conditions. The dynamics of power system components have been considered for the mathematical model of a multimachine system, and multiobjective function has been framed in tuning the optimal controller parameters. The effectiveness of the proposed algorithm has been assessed by considering two case studies, namely, (i) the optimal controller parameter tuning, and (ii) the coordination of oscillation damping devices in the power system stability enhancement. In the first case study, the power system stabilizer (PSS) is considered as a controller, and a self‐clearing three‐phase fault is considered as the system uncertainty. In contrast, static synchronous compensator (STATCOM) and PSS are considered as controllers to be coordinated, and perturbation in the system states as uncertainty in the second case 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]