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Due to the stochastic characteristics of wind power generation and following varying demands for load consumption over a planning period, the optimal reconfiguration (OR) of the radial distribution network (RDN) represents a complex problem of a combinatorial nature. This paper evaluates two types of optimal reconfiguration searching for an optimal solution and considering time-varying changes. The first one is a static reconfiguration of RDN (SRRDN) made at a fixed load consumption point and during constant generated renewable power integration. The second one is a dynamic reconfiguration of RDN (DRRDN) made following a stochastic integration of wind energy (WTDG) and/or variation in load demand characteristics. In total, five scenarios are investigated in order to evaluate optimal reconfiguration of RDN (ORRDN) with the aim of reducing total active power losses (TAPL), improving the voltage profile (VP), and minimizing switches’ change costs (SCC). To deal with this, a hybrid optimization technique (SAMPSO) combining the simulated annealing algorithm (SA) with a modified particle swarm optimization (MPSO) is undertaken. This hybrid method coupled with the MATPOWER toolbox is tested on the standard IEEE 69-bus RDN through both SRRDN and DRRDN. The results show the effectiveness of this improved reconfiguration procedure for enhancing the test system performance. A comparison between the proposed optimization method and previous findings’ methods is undertaken in this work. The obtained results proved the superiority and effectiveness of the SAMPSO method in solving the SRRDN and DRRDN problems.

The penetration of distributed generation (DG) in the distribution network has become a necessity and a significant solution to improve power grid quality, and solve power losses issue. To reach these targets, integrating these DGs in an optimal placement with an optimal sizing should be investigated and taken into consideration. This paper focuses on obtaining the optimal allocation and size of a photovoltaic (PV) distributed generation (PVDG) in order to reduce the total power losses and enhance voltage and frequency profiles of a modified IEEE 14 node distribution network (Hooshmand in J Appl Sci 8(16):2788–2800, 2008, https://doi.org/10.3923/jas.2008.2788.2800 ). An objective function is used in this paper aims to reduce grid power losses, and two optimization algorithms are applied to solve this function which are the particle swarm optimization (PSO) and the genetic algorithm (GA). Added to that, two scenarios are discussed in this paper in order to analyze the effects of variable PV penetration level, hourly load consumption profile variation and atmospheric condition change on the sizing optimization resolution. The obtained simulation results prove that the PSO algorithm has better performance compared to the GA in terms of speed convergence, power loss reduction and grid quality improvement (voltage and frequency profiles). Then, it shows that variable load consumption curve and weather condition change can affect not only the determination of the PVDG optimal position and capacity but also grid security.

Advances in PV technology have given rise to the increasing integration of PV-based distributed generation (PVDG) systems into distribution systems to mitigate the dependence on one power source and alleviate the global warming caused by traditional power plants. However, high power output coming from intermittent PVDG can create reverse power flow, which can cause an increase in system power losses and a distortion in the voltage profile. Therefore, the appropriate placement and sizing of a PVDG coupled with an energy storage system (ESS) to stock power during off-peak hours and to inject it during peak hours are necessary. Within this context, a new methodology based on an optimal power flow management strategy for the optimal allocation and sizing of PVDG systems coupled with battery energy storage (PVDG-BES) systems is proposed in this paper. To do this, this problem is formulated as an optimization problem where total real power losses are considered as the objective function. Thereafter, a new optimization technique combining a genetic algorithm with various chaotic maps is used to find the optimal PVDG-BES placement and size. To test the robustness and applicability of the proposed methodology, various benchmark functions and the IEEE 14-bus distribution network under fixed and intermittent load profiles are used. The simulation results prove that obtaining the optimal size and placement of the PVDG-BES system based on an optimal energy management strategy (EMS) presents better performance in terms of power losses reduction and voltage profile amelioration. In fact, the total system losses are reduced by 20.14% when EMS is applied compared with the case before integrating PVDG-BES.

As the load characteristic of the distribution system varies over time, the network topology also changes from one point of consumption to another. The methodology addressed in this study consists of alternating distribution network switches for different load points on the daily consumption curve, two scenarios are investigated, with a major goal of obtaining the most optimal configuration of the test network while reducing the total losses of active power and also improving the voltage profile. To deal with, an improved PSO methodology coupled with MATPOWER toolbox was applied on the IEEE 33-bus radial distribution network and the results found demonstrate the effectiveness of reconfiguration procedure for enhancing the test system performance.