The influence of an artificial neural network structure on the quality of short-term electric energy consumption forecasts is described. The problem of short-term electric energy consumption forecasting is very important in the daily activity of electricity distribution companies and other industrial enterprises. Accurate electric energy consumption forecast enables optimisation of the costs of electric energy purchase and planning of technological processes. There are many different forecasting methods on the market. The most accurate methods are those based on an artificial neural network technique. An attempt to investigate the influence of a one-directional ANN structure (number of hidden layers and number of neurons in layers) on the short-term forecast quality is presented. The quality of an ANN learning process is also investigated. Negative phenomena observed during the learning process, especially the phenomena of an artificial neural network overlearning, are shown. Computational experiments have been executed on the test problems. Proper data from electricity distribution companies have been taken into account. Results obtained show that the ANN structure has an essential influence on the quality of electric energy consumption forecasts.

In recent years, the increased distributed generation (DG) capacity in electric distribution systems has been observed. Therefore, it is necessary to research existing structures of distribution networks as well as to develop new (future) system structures. There are many works on the reliability of distribution systems with installed DG sources. This paper deals with a reliability analysis for both present and future medium voltage (MV) electric distribution system structures. The impact of DG technology used and energy source location on the power supply reliability has been analyzed. The reliability models of electrical power devices, conventional and renewable energy sources as well as information and communications technology (ICT) components have been proposed. Main contribution of this paper are the results of performed calculations, which have been analyzed for specific system structures (two typical present network structures and two future network structures), using detailed information on DG types, their locations and power capacities, as well as distribution system automation applied (automatic stand-by switching on—ASS and automatic power restoration—APR). The reliability of the smart grid consisting of the distribution network and the coupled communications network was simulated and assessed. The observations and conclusions based on calculation results have been made. More detailed modeling and consideration of system automation of distribution grids with DG units coupled with the communication systems allows the design and application of more reliable MV network structures.

This paper concerns very-short-term (5-Minute) forecasting of photovoltaic power generation. Developing the methods useful for this type of forecast is the main aim of this study. We prepared a comprehensive study based on fragmentary time series, including 4 full days, of 5 min power generation. This problem is particularly important to microgrids’ operation control, i.e., for the proper operation of small energy micro-systems. The forecasting of power generation by renewable energy sources on a very-short-term horizon, including PV systems, is very important, especially in the island mode of microgrids’ operation. Inaccurate forecasts can lead to the improper operation of microgrids or increasing costs/decreasing profits for microgrid operators. This paper presents a short description of the performance of photovoltaic systems, particularly the main environmental parameters, and a very detailed statistical analysis of data collected from four sample time series of power generation in an existing PV system, which was located on the roof of a building. Different forecasting methods, which can be employed for this type of forecast, and the choice of proper input data in these methods were the subject of special attention in this paper. Ten various prognostic methods (including hybrid and team methods) were tested. A new, proprietary forecasting method—a hybrid method using three independent MLP-type neural networks—was a unique technique devised by the authors of this paper. The forecasts achieved with the use of various methods are presented and discussed in detail. Additionally, a qualitative analysis of the forecasts, achieved using different measures of quality, was performed. Some of the presented prognostic models are, in our opinion, promising tools for practical use, e.g., for operation control in low-voltage microgrids. The most favorable forecasting methods for various sets of input variables were indicated, and practical conclusions regarding the problem under study were formulated. Thanks to the analysis of the utility of different forecasting methods for four analyzed, separate time series, the reliability of conclusions related to the recommended methods was significantly increased.

There has been a growing demand in electric power in many urban agglomerations. To address this demand, 110 kV power networks are regularly expanded inside and around cities in Poland. These power networks are often constructed as underground cable lines. The 110 kV cable lines are usually designed as three single-core cables laid in a common route. In case of long 110 kV cable lines, the cross-bonding method is applied to the connection of metal sheaths of these cables. Additionally, the one or more earth continuity conductors are laid along the main cable line and earthed at both their ends. These earth continuity conductors can be uninsulated and can cause electric shock hazard (danger step voltages). The problem defined in this paper consists in an attempt to calculate the expected step voltages for different considered earth continuity conductor arrangements in 110 kV cable lines and to compare the obtained results to admissible values of the voltages found in an appropriate standard. Protection against electric shock, calculation of earthing resistance and step voltages, as well as simulation studies in case of sample 110 kV cable lines have been described in the paper. The results show the analyzed layouts of 110 kV cable lines have significant impact on the values of voltages occurring in and around earth continuity conductors. The maximum values of earth electrode voltage (and in this way expected touch voltages) as well as step voltages occur in the layout of single cable line and single earth continuity conductor arranged in parallel.

This paper addresses very short-term (10 s) forecasting of power demand of highly variable loads. The main purpose of this study is to develop methods useful for this type of forecast. We have completed a comprehensive study using two different time series, which are very difficult to access in practice, of 10 s power demand characterized by big dynamics of load changes. This is an emerging and promising forecasting research topic, yet to be more widely recognized in the forecasting research community. This problem is particularly important in microgrids, i.e., small energy micro-systems. Power demand forecasting, like forecasting of renewable power generation, is of key importance, especially in island mode operation of microgrids. This is due to the necessity of ensuring reliable power supplies to consumers. Inaccurate very short-term forecasts can cause improper operation of microgrids or increase costs/decrease profits in the electricity market. This paper presents a detailed statistical analysis of data for two sample low voltage loads characterized by large variability, which are located in a sewage treatment plant. The experience of the authors of this paper is that very short-term forecasting is very difficult for such loads. Special attention has been paid to different forecasting methods, which can be applied to this type of forecast, and to the selection of explanatory variables in these methods. Some of the ensemble models (eight selected models belonging to the following classes of methods: random forest regression, gradient boosted trees, weighted averaging ensemble, machine learning) proposed in the scope of choice of methods sets constituting the models set are unique models developed by the authors of this study. The obtained forecasts are presented and analyzed in detail. Moreover, qualitative analysis of the forecasts obtained has been carried out. We analyze various measures of forecasts quality. We think that some of the presented forecasting methods are promising for practical applications, i.e., for microgrid operation control, because of their accuracy and stability. The analysis of usefulness of various forecasting methods for two independent time series is an essential, very valuable element of the study carried out. Thanks to this, reliability of conclusions concerning the preferred methods has considerably increased.

The paper raises the issue of controlling rural low voltage microgrids in an optimal manner. The impact of different criterion functions, related to the amount of energy exchanged with the distribution system operator network, the level of active power losses, the amount of energy generated by different energy sources and the value of financial performance measures regarding the microgrid operation, on the choice of operating points for devices suggested by the optimization algorithm has been analyzed. Both island and synchronous microgrid operation modes are being considered. We propose two variants of the optimization procedure: the first one is based on the particle swarm optimization algorithm and centralized control logic, and the second one takes advantage of the decentralized approach and Monte Carlo methods. A comparison of the simulation results for two sample rural microgrids, obtained for different objective functions, microgrid operation modes and optimization procedure variants, with the use of prepared algorithm implementations, has been provided. The results show that the proper choice of an objective function can have a crucial impact on the optimization algorithm’s behavior, the choice of operating points and, as a consequence, on microgrid behavior as well. The choice of the proper form of the objective function is the responsibility of the person in charge of both the microgrid itself and its operation. This paper can contribute towards making correct decisions in this area. Generally, slightly better results have been achieved for the centralized control mode of operation. Nevertheless, the results also suggest that in many cases the approach based on distributed logic can return results that are better or sufficiently close to the ones provided by the centralized and more sophisticated approach.