Integration of Distributed Generations (DGs) in electric power grids present a wide range of advantages that encompass economic, environmental, and technical benefits, besides encourage consumers and distribution companies actively to increase using of generation [1-3]. The minimizing of transmission and distribution costs, electricity prices and fuel savings are deemed as the economic and environmental advantages [2-5]. The technical benefits and advantages of DGs are concluded in voltage profile improvement, power loss reduction, reliability protection, stability and increased power quality [4, 6]. An increasing in electricity demand, high quality, reliable electrical power, and increasing number of loads may cause raise the awareness of power quality both by utilities by customers [7-8]. In the distribution grids, the power losses are high compared to the transmission systems due to high R/X ratio and radial nature [4]. Besides, the distribution networks are suffering from power quality disturbances, because they are deemed as the final connection between the consumers the bulk of power system [7-8]. Nowadays, many researches are concentrated on using Photovoltaic (PV) system and wind turbine (WT) as DGs that produce a cleaner power production [1-8]. Hence it is indispensable to install DGs optimally with an appropriate capacity in the distribution grids. In the other hand, the maximum consuming of active power by customers due to the development in distribution grids lead to the shortage in realizing the kVAr of reactive load requirements [9]. In addition to the flow of the reactive power in distribution networks always leads to high system power loss, high voltage drop, and low power factor. These disturbances and effects can be minimized by locating SCBs as proper source of reactive power compensation [10]. Therefore, the optimal sitting and size of SCBs have an effective role in power system planning to confirm the minimum system power losses and improving voltage profile [11].
The installing of DGs and SCBs simultaneously can increase the economic and technical benefits greatly than working each one independently. In this regard, this article produces optimal location and size of DGs and SCBs simultaneously in distribution network.
In last years, there are many Artificial Intelligent-Mechanisms (AI-M) like hybrid Particle Swarm Optimization algorithm with Quasi Newton (PSO-QN), Flower Pollination Algorithm (FPA), Fuzzy-Genetic Algorithm (FGA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Tabu Search (TS), Harmony Search Algorithm (HSA), ant colony search, immune-based optimization technique, integrated DE-PS, Bacterial Foraging Optimization (BFO), big bang-big crunch optimization, Artificial Bee Colony (ABC)-based algorithm and Plant Growth Simulation Algorithm (PGSA) employed to overcome the optimal location and capacity of SCBs [8,12]. The AI-M like modified Firefly Algorithm (FA), Bat Algorithm (BA), Backtracking Search Optimization Algorithm (BSOA), Simulated Annealing (SA), GA, PSO, ABC, Modified Teaching-Learning Based Optimization (MTLBO), and HAS have been employed to solve the problem of optimal sitting and size of DGs in distribution networks [2, 13, 14, and 15]. Nevertheless, little researchers have combined DGs and SCBs simultaneously based on their optimal allocations to illustrate their effect on the performance of distribution grids as the following techniques. M. Dixit et al. [16] employed Index Vector Method (IVM) and Power Loss Index (PLI) to evaluate the most suitable position of DGs and SCBs based on Gbest-guided Artificial Bee Colony (GABC) optimization algorithm. In [9, 17], the authors used Particle Swarm Optimization algorithm (PSO) to detect the optimum simultaneously DGs and SCBs allocation on the basis only minimization of power losses. Muthukumar [18] used the Loss Sensitivity Factors (LSFs) to find the most appropriate location for DGs and SCBs, and then the Harmony Search Algorithm is incorporated with Particle Artificial Bee Colony algorithm (HAS-PABC) to determine the optimum size for DGs and SCBs and proper placement from the selected buses. Naik [19] proposed analytical approach for optimum location and capacity of DGs and SCBs in distribution networks. Reddy [20] used Fuzzy-Genetic Algorithm (FGA) to find the optimal allocation of DGs and optimal sitting with proper capacity of SCBs are deduced by Genetic Algorithm (GA). Imran [21] used LSFs for deducing the search space of the optimization algorithm to find the appropriate location of DGs and SCBs, and then Bacterial Foraging Optimization Algorithm (BFOA) is constructed to evaluate their optimal capacity and location from the elected buses. Khodabakshian [22] constructed a new Intersect Mutation Differential Evolution optimization algorithm (IMDE) for solving the problems of both DGs and SCBs allocation. Kansal [23] implemented PSO to find the optimal level/capacity of DGS and SCBs together in distribution networks. In [24], fuzzy Multi-Objective Particle Swarm Optimization (MOPSO) with Pareto solutions is proposed to solve the allocation problem of DGs and SCBs in distribution network. The MOPSO estimates Pareto front optimal solutions, after that fuzzy election process nominates the best solution from the Pareto front optimal solutions. The results of this optimization algorithm are compared with Strength Pareto Evolutionary Algorithm (SPEA), Non-dominated Sorting Genetic Algorithm (NSGA), Multi-Objective Differential Evolution (MODE), and Imperialist Competitive Algorithm and Genetic Algorithm (ICA/GA).
Notwithstanding, the reported algorithms and techniques that are combined DGs and SCBs simultaneously may suffered from some drawbacks and difficult to reach the optimal solutions for minimizing the power losses and the total operating cost due to following reasons. In some researches, the objective function is limited to minimize the system active power losses without taking into account other important factors such as voltage level/index, costs of installation, operating and maintenance of DGs and SCBs simultaneously [9, 17, 18, 19, 21, and 23]. Some works were limited to small systems scale [22, 20]. In the previous works, the integrated capacity of DGs and SCBs in the distribution networks may be not reached to the most optimum values [9] and [17-24]. Besides, there is not any of these researches take in their considerations the operating and maintenance costs of SCBs. As a result of complexity and change of the distribution networks, new methods are asked to enhancement the power quality.
This article provides a novel MFO to solve the problems of optimal allocations of DGs and SCBs in the distribution grids. Also, LSFs are employed to estimate the most appropriate buses to install DGs and SCBs to reduce the search space of the proposed approach. Furthermore, a Loss-Voltage-Cost Index approach is implemented with the proposed optimization scheme as an effective objective function to improve the voltage profile, minimize the system active power losses and the total annual operating cost. The Backward/Forward sweep (BFS) algorithm is implemented for power flow estimations [25]. The proposed technique is tested on 33-bus and 69-bus IEEE Radial Distribution Systems (RDN), as well practical case study of Moscow Region 111-bus RDN under different load conditions. To guarantee the suggested methodology capability and performance, the numerical results are compared with other various optimization techniques.