An efficient optimization framework for distribution network planning by simultaneous allocation of photovoltaic distributed generations and transformers

Abstract Among the distribution network planning problems, allocation of transformers is one of the most important and challenging ones. On the other hand, owing to the widespread growth of distributed generations (DGs), their inclusion in the planning problems is vital. This paper proposes an efficient optimization framework for simultaneous allocation of photovoltaic (PV) systems and service transformers in the distribution network. For this aim, to improve the network performance, location and size of the transformers and PV units are optimally determined. Due to the difficulty of the planning problem, four variants of crow search algorithm (CSA) including original CSA, differential CSA (CSAd), directed differential CSA (CSAdd) and chaotic directed differential CSA (CSAcdd) are introduced and applied to the planning problem. To evaluate the impact of PV cost on the results, the planning problem is solved considering different PV system costs. Simulation results show that at the price of $1.25/W, installation of PV systems is cost‐effective, so that over the case study, 400 kW PV system was installed. By decreasing the PV price to$1.2/W, the maximum capacity considered for a PV system is installed. Moreover, on average, CSAd produces more accurate and robust results than the other studied algorithms

Capacitor Allocation in Distribution Networks Based on a Two-Stage Analytical-Heuristic Approach

In distribution networks, reduction of search space can be an efficient way to conquer the complexity of capacitor placement. In this paper, based on search space reduction, a two-stage procedure is proposed to efficiently solve capacitor placement problem. For this aim, as the first stage, analytical indices are introduced to determine the importance of network buses for compensation. The indices prioritize buses based on the network losses sensitivity to change of voltage and/or reactive power. As a result of the first stage, a number of most important buses are considered as the candidates for compensation. Then, the second stage is conducted using an optimization method to solve the capacitor placement problem with respect to the candidate buses. To do the second stage, an enhanced metaheuristic method, named enhanced crow search algorithm (ECSA), is developed. Compared to popular methods of search space reduction, loss sensitivity factor (LSF) and loss sensitivity indices (LSI1 and LSI2), simulation results confirm the superiority of the proposed two-stage procedure. On 33-bus network with respect to peak condition, when 3 buses are regarded as the candidate buses (around 10% of the total network buses), the optimal solution is found. In this case, compared to LSF, LSI1 and LSI2, objective function value decreases 2.9, 11.2 and 6.6%, respectively. On 69-bus network, when 6 buses are selected as the candidate buses (around 10% of the total network buses), the optimal solution is obtained. In this case, compared to LSF, LSI1 and LSI2, objective function value decreases around 0.7%.</p