This paper presents the static voltage stability analysis of RDS. Initially the performance of RDS is evaluated using backward/forward load flow considering voltage-dependent load modeling. Later, the load flow solution is used for determining the static voltage stability of the system. The analysis is performed for different type of loads such as constant power, constant current, constant impedance, residential, industrial, commercial, agricultural and electric vehicle loads. The simulations are performed for standard and optimal reconfigured topology of standard IEEE 33-bus test system. The comparative study reveals the importance of load type and topology while assessing the static stability analysis of radial distribution systems.

In this paper, a new nature-inspire meta-heuristic algorithm called future search algorithm (FSA) is proposed for the first time to solve the simultaneous optimal allocation of distribution generation (DG) and electric vehicle (EV) fleets considering techno-environmental aspects in the operation and control of radial distribution networks (RDN). By imitating the human behavior in getting fruitful life, the FSA starts arbitrary search, discovers neighborhood best people in different nations and looks at worldwide best individuals to arrive at an ideal solution. A techno-environmental multi-objective function is formulated using real power loss, voltage stability index. The active and reactive power compensation limits and different operational constraints of RDN are considered while minimizing the proposed objective function. Post optimization, the impact of DGs on conventional energy sources is analyzed by evaluating their greenhouse gas emission. The effectiveness of the proposed methodology is presented using different case studies on Indian practical 106-bus agriculture feeder for DGs and 36-bus rural residential feeder for simultaneous allocation of DGs and EV fleets. Also, the superiority of FSA in terms of global optima, convergence characteristics is compared with various other recent heuristic algorithms.

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Currently, electrical distribution networks (EDNs) have used modern technologies to operate and serve many types of consumers such as renewable energy, energy storage systems, electric vehicles, and demand response programs. Due to the variability and unpredictability of these technologies, all these technologies have brought various challenges to the operation and control of EDNs. In this case, in order to operate effectively, it is inevitable that effective power redistribution is required in the entire network. In this paper, a multi-objective based dynamic optimal network reconfiguration (DONR) problem is formulated using power loss and voltage deviation index considering the hourly variation of load, photovoltaic (PV) power, and electric vehicle (EV) fleet load in the network. This paper introduces recently introduced meta-heuristic butterfly optimization algorithm (BOA) and it’s improve variant of self-adaptive method (SABOA) for solving the DONR problem. The simulation study of IEEE 33-bus EDN under different conditions has proved the effectiveness of DONR, and its adoptability for real-time applications. In addition, by comparing different performance indicators (such as mean, standard deviation, variance, and average calculation time) of 50 independently run simulations, the efficiency of SABOA can be evaluated compared with other heuristic methods (HMs). Comparative studies show that SABOA is better than PSO, TLBO, CSA and FPA in the frequent occurrence of global optimal values.

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