The level of sales of a given good depends largely on the distribution network. An analysis of the distribution network allows companies to optimize business activity, which improves the efficiency and profitability of a company’s sales with an immediate effect on profit growth. The so-called spatial analysis is highly useful in this regard. The paper presents an analysis of the network of authorized dealers of the Polish Mining Group for the Opolskie Province. The analysis was done using GIS (SIP) tools. The purpose of the analysis was to present tools that could be used to verify an existing distribution network, to optimize it, or to create a new sales outlet. The prresented tools belong to GIS operations used to process data stored in Spatial Information System resources. These are so-called geoprocessing tools. The article contains several spatial analyses, which results in choosing the optimum location of the distribution point in terms of the defined criteria. The used tools include a spatial intersection and sum. Geocoding and the so-called cartodiagram were also used. The presented analysis can be performed for both the network of authorized retailers within a region, a city or an entire country. The presented tools provide the opportunity to specify the target consumers, areas where they are located and areas of potential consumer concentration. This allows the points of sale in areas with a high probability of finding new customers to be located, which enables the optimal location to be chosen, for example, in terms of access to roads, rail transport, locations of the right area and neighborhood. Spatial analysis tools will also enable the coal company to verify its already existing distribution network.

The paper addresses the problem of placement of sectionalizing switches in medium voltage distribution networks. Proper placement of sectionalizing switches is one of the elements leading to higher power networks reliability. The methods of optimal allocation of such switches in a MV distribution network are presented in the paper. SAIDI was used as a criterion for the sectionalizing switches placement. For selecting optimum placements, three methods were used: brute force method, evolutionary algorithm and heuristic algorithm. The calculations were performed for a real MV network.

In order to solve the problem of harmonic waves caused by battery energy storage (BES) and distributed generation (DG) inverters in an active distribution network, an intelligent optimal dispatching method based on a modified flower pollination algorithm (MFPA) is proposed. Firstly, the active distribution network dispatching model considering the power quality (PQ) problem caused by BES and DG is proposed. In this model, the objective function considers the additional network loss caused by a harmonic wave, as well as the constraints of the harmonic wave and voltage unbalance. Then, the MFPA is an improvement of a flower pollination algorithm (FPA). Because the MFPA has the characteristics of higher solution accuracy and better convergence than the FPA and it is not easy to fall into local optimal, the MFPA is used to solve the proposed model. Finally, simulation experiments are carried out on IEEE 37 bus and IEEE 123 bus systems, respectively. The experimental results show that this method can achieve satisfactory power quality while optimizing the total active power loss of the branch. The comparative experimental results show that the developed algorithm has better convergence than the FPA.

The loss of power and voltage can affect distribution networks that have a significant number of distributed power resources and electric vehicles. The present study focuses on a hybrid method to model multi-objective coordination optimisation problems for dis- tributed power generation and charging and discharging of electric vehicles in a distribution system. An improved simulated annealing based particle swarm optimisation (SAPSO) algorithm is employed to solve the proposed multi-objective optimisation problem with two objective functions including the minimal power loss index and minimal voltage deviation index. The proposed method is simulated on IEEE 33-node distribution systems and IEEE-118 nodes large scale distribution systems to demonstrate the performance and effectiveness of the technique. The simulation results indicate that the power loss and node voltage deviation are significantly reduced via the coordination optimisation of the power of distributed generations and charging and discharging power of electric vehicles.With the methodology supposed in this paper, thousands of EVs can be accessed to the distribution network in a slow charging mode.

Currently, overhead lines dominate in the Polish medium and low voltage distribution networks. Maintaining their high reliability constitutes a very important challenge, especially under the severely changing climate conditions. An overhead power line exposed to high ice and rime loads has been considered. Using the finite element method (FEM), mechanical reliability of the distribution infrastructure was examined under various atmospheric conditions. Loads under the stressful conditions of rime, ice and wind were determined for the weakest section of the 30 kV overhead line, which consisted of concrete poles and ACSR conductors. SAIDI and SAIFI reliability indices and costs were determined for several variants of object reconstruction. The results allowed for determination of a solution relying on relocating the cables of all lateral branches and main line ice protection, through a system based on a weather-coordinated increase of the electrical load. To verify the solution proposed, a field experiment was conducted. The experiment confirmed the effectiveness of the solution proposed that appears to be universal. The paper is a result of synergic cooperation of two academic teams, i.e. a mechanical and electrical power engineering one, and the distribution system operator (DSO).

In this paper, a control strategy for real-time operation of a master-slave controlled microgrid is developed. The basic idea of this control strategy is to schedule all dispatchable energy sources available into a microgrid to minimize its operational costs. Control actions are centrally evaluated by solving a two-stage optimization problem formulated to take place on two different time-scales: in the day-ahead and in the real-time. The first one provides a 24-hour plan in advance. It mainly draws up the active power levels that Distributed Energy Resources (DERs) should provide for each quarter hour of the next day by taking into account energy prices of the day-ahead energy market, the forecasted energy production of non-dispatchable renewables and loads. The real-time optimization problem updates the active power set-points of DERs in order to minimize as much as possible the real-time deviations between the actual power exchanged with the utility grid and its scheduled value. The effectiveness of the proposed methodology has been experimentally tested on an actual microgrid.

The smart grid concept is predicated upon the pervasive With the construction and development of distribution automation, distributed power supply needs to be comprehensively considered in reactive power optimization as a supplement to reactive power. The traditional reactive power optimization of a distribution network cannot meet the requirements of an active distribution network (ADN), so the Improved Grey Wolf Optimizer (IGWO) is proposed to solve the reactive power optimization problem of the ADN, which can improve the convergence speed of the conventional GWO by changing the level of exploration and development. In addition, a weighted distance strategy is employed in the proposed IGWO to overcome the shortcomings of the conventional GWO. Aiming at the problem that reactive power optimization of an ADN is non-linear and non-convex optimization, a convex model of reactive power optimization of the ADN is proposed, and tested on IEEE33 nodes and IEEE69 nodes, which verifies the effectiveness of the proposed model. Finally, the experimental results verify that the proposed IGWO runs faster and converges more accurately than the GWO.