Poziom sprzedaży danego dobra uzależniony jest w dużej mierze od sieci dystrybucji. Przestrzenna analiza dystrybucji umożliwia racjonalizację sieci sprzedaży, co podnosi efektywność i wydajność sprzedaży przedsiębiorstwa z bezpośrednim przełożeniem na wzrost zysków. Z pomocą przychodzą tu tak zwane analizy przestrzenne. W artykule przedstawiono analizę sieci autoryzowanych sprzedawców Polskiej Grupy Górniczej dla województwa opolskiego. Analiza została wykonana z wykorzystaniem narzędzi GIS (SIP). Celem przeprowadzonej analizy było zaprezentowanie możliwych do zastosowania narzędzi weryfikacji już istniejącej sieci dystrybucji, jej racjonalizacji, bądź też tworzenia nowych punktów sprzedaży. Przedstawione narzędzia należą do operacji GIS stosowanych do przetwarzania danych przechowywanych w zasobach Systemów Informacji Przestrzennej. Są to tak zwane narzędzia geoprocessingu, czyli geoprzetwarzania. W artykule zaprezentowano kilka analiz przestrzennych, których rezultatem jest wybór najlepszej lokalizacji punktu dystrybucji pod względem określonych kryteriów. Stosowane narzędzia to między innymi zapytanie przestrzenne intersect (iloczyn), suma. Posłużono się także geokodowaniem, utworzono tak zwany kartodiagram. Przedstawiona przykładowa analiza może zostać wykonana dla sieci autoryzowanych sprzedawców zarówno w skali jednego województwa, miasta, jak też obszaru całego kraju. Użyte narzędzia dają możliwość sprecyzowania grupy docelowych odbiorców, obszarów na jakich się oni znajdują, obszarów koncentracji potencjalnych odbiorców. Pozwalają tym samym na ulokowanie punktów sprzedaży na obszarach charakteryzujących się wysokim prawdopodobieństwem znalezienia nowych klientów, umożliwiają wybór lokalizacji, np. zapewniającej dostęp do dróg, transportu kolejowego, lokalizacji o odpowiedniej powierzchni, sąsiedztwie.
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.
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 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.
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.