Abstract
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.
Highlights
The exhausting fuel sources for conventional energy sources (CES), non-expanding transmission and distribution networks and ever increasing demand for electricity have made the power system operation and control very complex
Defining new global solution (GS) and local solution (LS) at each iteration and again using them for updating the optimal solution within the same iteration is the key feature of future search algorithm (FSA) for outperformance than the other harmony search algorithm (HSA)
Case 3: Simultaneous allocation of capacitor banks (CBs) and distribution generation (DG) operating at unity power factor
Summary
The exhausting fuel sources for conventional energy sources (CES), non-expanding transmission and distribution networks and ever increasing demand for electricity have made the power system operation and control very complex. In [19], a shuffled frog leaping algorithm (SFLA) is applied for minimizing real power loss and economic efficiency of distribution system operation is evaluated via allocating the multiple DGs. In [20], a multi-objective whale optimization algorithm (MOWOA) is applied for identifying the locations and sizes of solar PV and wind turbine systems considering different types of load models. In [27], a multi-objective PSO (MOPSO) is applied for solving optimal allocation of renewable DGs and CBs. Recently, the optimal allocation of DGs problem is addressed using quasi-oppositional chaotic symbiotic organisms search (QOCSOS) algorithm by aiming for reduction in real power loss, improvement in voltage profile and enhancement in voltage stability [28, 29].
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