This research paper proposes an efficient methodology for the allocation of multiple photovoltaic (PV)-based distributed generation (DG) units in the radial distribution network (RDN), while considering the loading capacity of the network. The proposed method is structured using a two-stage approach. In the first stage, the additional active power loading capacity of the network and each individual bus is determined using an iterative approach. This analysis quantifies the network's additional active loadability limits and identifies buses with high active power loading capacity, which are considered candidate nodes for the placement of DG units. Subsequently, in the second stage, the optimal locations and sizes of DG units are determined using the Monte Carlo method, with the objectives of minimizing voltage deviation and reducing active power losses in the network. The methodology is validated on the standard IEEE 33-bus RDN to determine the optimal locations and sizes of DG units. The results demonstrate that the optimal allocation of one, two, and three DG units, achieved from proposed method, reduces network active power losses by 50.37%, 58.62%, and 65.16%, respectively, and also significantly enhances the voltage profile across all buses. When the obtained results are compared with the results of several existing studies, it is found that the proposed method allows for larger DG capacities and maintains better voltage profiles throughout the RDN.
T HE increasing penetration of renewable energy sources (RES) into power distribution networks is a critical step toward achieving sustainable and low-carbon energy systems. However, the integration of RES poses significant technical challenges due to the intermittent nature and the limited hosting capacity of existing distribution infrastructure. Traditionally, distribution networks were structured to transmit power unidirectionally from central generating stations to end-users. Accommodating distributed RES requires careful planning to maintain voltage stability, minimize power losses, and enhance network reliability [1], [2].
In recent years, there has been notable focus on developing efficient techniques for optimal DG allocation in distribution networks. These approaches are mainly intended to improve voltage profiles, minimize losses in the network, and strengthen the reliability and resilience of power system [3]. Numerous optimization strategies have focused on determining optimal allocation of DG units. Broadly, these strategies are divided into three main categories. The first group includes metaheuristic algorithms, such as evolutionary computation and simulated annealing, which are widely adopted for their ability to escape local optima and handle nonlinear search spaces [4], [5]. The second group involves mathematical programming approaches, where formulations such as linear, nonlinear, and mixed-integer programming are employed depending on the problem structure [6], [7], [8], [9]. A third category comprises search-based techniques, for example, Tabu search and group search optimization, which rely on systematic exploration of feasible solutions [10], [11]. Different load flow models are incorporated to evaluate candidate solutions, including backward/forward sweep, probabilistic power flow, and the distFlow formulation [12], [13], [14]. Moreover, advanced optimization frameworks like sequential quadratic programming (SQP) and AC optimal power flow (ACOPF) are often used for enhancing solution accuracy and reliability [15], [16].
The author of [17] focuses on determining the additional loading capacity to ensure that the system can handle increased demand or integrate distributed generation without compromising network performance. The system’s capability to accommodate increased load demand through the integration of wind and solar DG units without violating network constraints is assessed in [18].
The particle swarm optimization (PSO) algorithm in [19] used for optimal allocation of PV-DG units, aiming to minimize losses, improve voltage deviation, and enhance costeffectiveness. The joint optimization of RDN reconfiguration with DG allocation has been formulated as MILP problem to overcome the limitations of metaheuristic methods. By linearizing the nonlinear problem, the proposed approach ensures global optimality and convergence [20], [21], [22].
A nonlinear programming framework has also been proposed for determining the optimal allocation for renewable DGs, with objective of reducing network losses through localized generation [23]. The approach incorporates ACOPF, while accounting for operational limits and uncertainties in demand and renewable output. Another contribution in this area combines probabilistic nonlinear optimization with sensitivitybased analysis to reduce losses while simultaneously determine DG allocation and transformer tap positions [24]. These methods offered a more effective and reliable alternative compared to traditional planning techniques.
Multi-objective optimization approaches have been adopted to enhance both the loadability limits and reduce losses in distribution networks. One such technique frames the problem of optimal DG allocation as an MINLP model and solves it using a two-stage (bi-level) strategy. The first stage, referred to as the siting planning model (SPM) [25], utilizes indexbased methods to identify promising bus locations. In the second stage, the capacity planning model (CPM) [26], optimization techniques as SQP and BAB [27] are applied to determine optimal DG sizes. The use of heuristic index-based methods in the siting phase may overlook critical operational constraints, the computational burden of mixed-integer nonlinear programming-based sizing strategies can hinder practical deployment, especially in larger or real-time applications.
It can be observed from the existing literature that only few studies consider a composite objective function as the minimization of voltage deviation and network active power losses when solving the optimal allocation problem. Most studies focus solely on single optimization criterion, such as reducing power losses or improving the voltage profile. Furthermore, in many existing approaches, vital operational constraints of the distribution system such as line flow limits, equipment ratings, and permissible bus voltage limits are not considered. As a result, the actual capacity of the network to accommo
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