Polish Power System Research Articles

Development of the Polish Power Sector towards Energy Neutrality—The Scenario Approach

This article discusses three scenarios for the transformation of the Polish power system leading to the achievement of climate neutrality through investment in renewable energy sources. The scenarios differ in terms of the role played by the selected energy technologies. The scenarios were developed using the balancing tool, the Simulator Polish Power System, which analyzed the development of the national power system in terms of optimal costs of achieving climate neutrality and the lowest possible price of electricity generation. The research shows that under Polish climatic conditions, 100% RES energy can be achieved in the balance, but ensuring the continuity of energy supply, which in future will be generated mainly from this type of source, requires the participation of controllable sources such as gas-fired power plants powered by biomethane and energy storage facilities. The scenarios assume balancing RES sources with 15–25 GW of available gas sources and 40 GWh in electricity storage. The article also discusses factors limiting the suggested scenarios, such as the availability of sustainable biomethane in Poland or limitations associated with the possibility of building onshore and offshore wind turbines.

Analysis of the selected business models for energy storage in the Polish Power System

Analysis of the selected business models for energy storage in the Polish Power System

Suitable law-based location selection of high-power electric vehicles charging stations on the TEN-T core network for sustainability: a case of Poland

With the upcoming implementation of the amendment to Regulation (EU) 2019/631 of the European Parliament and of the Council, from 2035 there will be a ban on the registration of new vehicles with internal combustion engines (ICE) in the Member States of the European Union (EU). Consequently, changes in the transportation sector, resulting from the increasing use of electric vehicles, appear to be inevitable. According to the adopted legal acts, the European Union Member States will be obliged to develop, among others, a charging infrastructure and access to public charging stations for electric vehicles. As a result, there will be a need to ensure a significant increase in the power and the number of charging stations and to determine their appropriate location. The article presents the challenges faced by charging station operators and difficulties related to the further development of electric vehicle charging infrastructure in Poland. The still poorly developed public charging infrastructure for electric vehicles, especially in service areas located along the main communication routes, remains the main obstacle to the development of electromobility. In the context of legal, financial, technological, and organizational challenges, the problem of the proper distribution of electric vehicle charging stations along the main communication routes is therefore of particular importance. The aim of the article is to present a new, proprietary method for determining the location of electric vehicle charging stations in Poland within the Trans-European Transport Network (TEN-T), which considers objective location factors: adherence to AFIR requirements, the specificity of the Polish power system and existing parking infrastructure. As a result of using the developed method, a list of 188 recommended locations for the construction of electric vehicle charging stations in Poland along the Trans-European Transport Network (TEN-T) was created. It has been shown in this way that the use of the presented method enables the suitable determination of the location of electric vehicle charging stations along transport routes, considering legal, financial, and technological requirements, which will significantly facilitate the operation of zero-emission transport.

Model and Data Driven Machine Learning Approach for Analyzing the Vulnerability to Cascading Outages With Random Initial States in Power Systems

In this paper, a hybrid machine learning model is applied to evaluate the relationship between random initial states and the power system’s vulnerability to cascading outages. A cascading outage simulator (CS), which uses off-line AC power flows, is proposed for generating training data. The initial states are randomly selected and the CS model is deployed for each initial state, where power system generation and loads are adjusted dynamically and power flows are redistributed to quantify the vulnerability metric. Furthermore, the proposed hybrid machine learning model deploys a combined Support Vector Machine (SVM) classification and Gradient Boosting Regression (GBR) to improve the learning precision. The classification model is trained by SVM, which divides the data into two categories with and without load shedding. Then, GBR is adopted only for the data with load shedding to determine the relationship between input power outage states and the vulnerability metric. The proposed vulnerability analysis approach is applied to several test systems and the results are analyzed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The power system vulnerability can be quantified by cascading outage simulations. However, there are two challenges: i) there are a huge number of possible initial states and we cannot enumerate all these initial states for the cascading outage simulation. Neither can we precisely quantify the bus vulnerability. ii) The cascading outage simulation may be time-consuming for large-scale power systems, which is challenging for the online application. To address the above challenges, we expect to design a machine learning technique to predict the power system vulnerability, which can train the model in an offline way and then use it for the online application. Firstly, since there is not enough operation data from practical power systems, we develop a cascading outage simulator, using off-line AC power flows, for generating synthetic training data. Secondly, we observe that the training precision by directly applying the regression model may be very poor because the output of the machine learning model may take on an uneven distribution concerning input parameters. Thus, we propose a hybrid machine learning model with a combined classification and regression method, where the classification model is employed to remove the data without the load shedding, and the regression model then determines the relationship between input power outage states and the vulnerability metric. The proposed model and method have been tested on several systems including a practical large-scale Polish power system to show the effectiveness.

Scenario Analysis of the Development of the Polish Power System towards Achieving Climate Neutrality in 2050

The Polish power system has been reducing its impact on the environment for a long time, mainly through modernization and investments in new generation capacities, including renewable energy technologies. However, its starting point is still more difficult compared to other less coal-dependent power systems in the European Union. The study was conducted in the direction of optimizing the Polish energy mix of electricity generation from the perspective of 2050. Two energy scenarios for the possible transformation of the Polish power system towards achieving at least a 95% reduction in CO2 emissions by 2050 with an increasing share of renewables were analyzed. The study was carried out with the use of the TIMES-PL model, which minimizes the total system cost over the analyzed time horizon. The model was calibrated according to data from 2018. The two scenarios show relatively similar pathways for CO2 emission reductions by 2050. In the case of no investment in nuclear power plants, power plants equipped with CO2 capture and storage systems are an alternative solution for achieving climate neutrality and increasing national energy security.

Revisiting Market Power in the Polish Power System

The consequences of the liberalisation of electricity markets have been widely discussed in the literature emphasising the successes or failures of privatisation and deregulation. While most developed power systems have undergone a form of economic transformation, they still require to be monitored and analysed to assess market power. The Polish power system is an example wherein the potential of market power examined fifteen years ago was summarised as significant. Since then, the transformation process and changes in the ownership structure have taken place. This study focuses on the assessment of the potential of market power in the Polish electricity market. For this purpose, statistics on power companies were collected and processed. Then, structural and behavioural measures were applied, including concentration ratios, the entropy coefficient, the Gini coefficient, the Herfindahl–Hirschman Index (HHI), the Residual Supply Index (RSI), and the Lerner Index. The results reveal that, despite a dynamic increase in renewable capacity, market concentration has increased in recent years, achieving an HHI of 2020.9 in 2021. An increase in the Lerner Index of lignite and hard coal-fired units is also observed, indicating high mark-ups by the key market players. Based on quantitative analysis, policy recommendations are outlined to reduce the negative impact of market power on consumers.

Energy Productivity Potential of Offshore Wind in Poland and Cooperation with Onshore Wind Farm

Wind power is the leader in electricity production among the standing RES technologies, both in Poland and in Europe/World. In Poland, so far there are only onshore wind turbines. Their dynamic increase in installed capacity has been observed, especially between 2011 and 2017. This study analyzed the impact of offshore wind energy on the ability of the Polish power system to meet power demands. For this purpose, methods of statistical analysis (of existing onshore and planned offshore technologies) for the determination of wind turbine productivity based on wind speed components data from the ERA5 service were used. For onshore wind turbines, the value of the capacity factor CF(P) in Poland was 25.5% in 2021 and 30.1% in 2022. As a result of the simulation, it was calculated that for the planned offshore wind farms, the capacity factor CF(B) would be 55.6% under 2022 wind speed conditions. The 2022 peak load demands in the Polish system were also analyzed. The quantitative impact of installing 6 GW of offshore wind turbine capacity on the national power system was also identified.

Analysis of changes in power demand in the Polish Power System

The last decade has seen significant changes in the Polish economy and industry, which resulted in substantial changes in the power demand. This paper investigates and analyses the trends of changes in power demand in the Polish Power System (PPS). Based on the historical data of the PPS, the following aspects were determined and subjected to comprehensive analysis: minimum and maximum demand values, annual average daily demand profiles, annual average monthly demand profiles, and annual values of energy demand. The correlation of power demand with wind generation, a dominant share among RES in Poland, was also examined. The analyses were also extended to consider the forecasted value of the power demand on the horizon until 2030. The obtained results can be a part of the model used to determine Poland's energy mix in the horizon until 2030.

Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development

The main objective of this study was to conduct multi-stage and multi-variant prognostic research to assess the impact of e-mobility development on the Polish power system for the period 2022–2027. The research steps were as follows: forecast the number of electric vehicles (using seven methods), forecast annual power demand arising solely out of the operation of the forecast number of electric vehicles, forecast annual power demand with and without the impact of e-mobility growth (using six methods), forecast daily profiles of typical days with and without the impact of e-mobility growth (using three methods). For the purpose of this research, we developed a unique Growth Dynamics Model to forecast the number of electric vehicles in Poland. The application of Multi-Layer Perceptron (MLP) to the extrapolation of non-linear functions (to the forecast number of electric vehicles and forecast annual power demand without the impact of e-mobility growth) is our original, unique proposal to use the Artificial Neural Network (ANN). Another unique, innovative proposal is to include Artificial Neural Networks (Multi-Layer Perceptron and Long short-term memory (LSTM)) in an Ensemble Model for simultaneous extrapolation of 24 non-linear functions to forecast daily profiles of typical days without taking e-mobility into account. This research determined the impact of e-mobility development on the Polish power system, both in terms of annual growth of demand for power and within particular days (hourly distribution) for two typical days (summer and winter). Under the (most likely) balanced growth variant of annual demand for power, due to e-mobility, such demand would grow by more than 4%, and almost 7% under the optimistic variant. Percentage growth of power demand in terms of variation according to time of day was determined. For instance, for the balanced variant, the largest percentage share of e-mobility was in the evening “peak” time (about 6%), and the smallest percentage was in the night “valley” (about 2%).

Electromagnetic Transients in Multi-Voltage Transmission Lines during Non-Simultaneous Faults

It is vitally important that a transmission network provides a continuity of electricity to end users and that the transmission system operator prioritizes its safety and reliability. This article presents the possibilities of using multi-track overhead lines with varying levels of rated voltage, run on a common support structure. This solution would ensure safe operation of the system, reduce the area used for the construction of new overhead lines, and, at the same time increase the transmission network capacity. This article focuses on the method of modeling overhead lines for the analysis of electromagnetic transient states that occur during non-simultaneous disturbance phenomena. This article presents the idea of multi-circuit, multi-voltage lines and their validity of use in power systems. An analysis of electromagnetic transient states for non-simultaneous disturbances in multi-path, multi-voltage lines is presented. This analysis has been made on the basis of a network model, using ATP-EMTP (Alternative Transients Program-Electromagnetic Transients Program) software for solutions, for a Polish power system. Particular attention was paid to the most unfavorable phenomena appearing in the modeled system. The impact of the emerging electromagetic transients in multi-path, multi-voltage lines was evaluated. It was shown that the specificity and rapid changeability of the emerging electromagnetic transients and lack of a universal model of the overhead power line make the analyses of electromagnetic transients highly labor-intensive and time-consuming. Each change is connected with the necessity to create new line models for the study of electromagnetic transient states dependent on many factors.

Analysis of Changes in Power Demand in the Polish Power System

Analysis of Changes in Power Demand in the Polish Power System

ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise

The paper addresses the problem of insufficient knowledge on the impact of noise on the auto-regressive integrated moving average (ARIMA) model identification. The work offers a simulation-based solution to the analysis of the tolerance to noise of ARIMA models in electrical load forecasting. In the study, an idealized ARIMA model obtained from real load data of the Polish power system was disturbed by noise of different levels. The model was then re-identified, its parameters were estimated, and new forecasts were calculated. The experiment allowed us to evaluate the robustness of ARIMA models to noise in their ability to predict electrical load time series. It could be concluded that the reaction of the ARIMA model to random disturbances of the modeled time series was relatively weak. The limiting noise level at which the forecasting ability of the model collapsed was determined. The results highlight the key role of the data preprocessing stage in data mining and learning. They contribute to more accurate decision making in an uncertain environment, help to shape energy policy, and have implications for the sustainability and reliability of power systems.

A new approach for coupling the short- and long-term planning models to design a pathway to carbon neutrality in a coal-based power system

Transformation towards climate-neutral energy systems is becoming a challenge worldwide. Most often, strategic planning for the mid- and long-term is tackled with the use of generation expansion optimization models, which have limited capabilities to track in detail power system operation. On the other hand, the unit commitment and economic dispatch models consider many technical constraints but are usually used for short-term planning. The methodological approach presented in this paper aims to improve the planning capabilities by the combination of generation expansion and short-term planning models into one modelling system. Two models were coupled, namely TIMES-PL and MEDUSA, to design a pathway to carbon neutrality in the Polish power system. Three energy scenarios elaborated in this work show that a 95 % reduction in carbon dioxide emissions from the public electricity and district heat production sectors is possible by 2050. The proposed approach improved the robustness of elaborated energy scenarios as in their first versions two of them turned out to be technically infeasible. Detail simulations of the system operation showed that load balancing is possible once 15–20 GW of electrical capacity is installed in dispatchable power generation technologies by 2050 in addition to energy storage and combined heat and power plants.

The Impact of Fleet Electrification on Carbon Emissions: A Case Study from Poland

Fleet electrification is one of the measures proposed for achieving climate neutrality in the coming years. The replacement of internal combustion engine vehicles with electric vehicles has a positive impact on carbon emission reduction in some countries. However, in countries highly dependent on fossil fuels, such a possibility requires examination with respect to the means of electricity generation and fuel mix used in their power systems. One such country is Poland, selected as an example of an economy strongly dependent on fossil fuels. The main objective of this paper is to investigate the impact of fleet electrification of an individual company located in Poland on the reduction of carbon emissions. The concept and calculations are based on historical data on the single-year mileage and fuel consumption of 619 cars used by this company. Even though the Polish power system is based on fossil fuels, fleet electrification could contribute to a reduction in carbon emissions of 24%. The decrease in operational costs by EUR 370 thousand/year is also significant. Apart from environmental and economic impacts, this paper provides valuable findings on the difference between catalogue and real-driving data application in the various analyses. With respect to Polish fuel mix in 2019, the application of data published by car producers shows that fleet electrification would increase carbon emissions by 14% in this company. This means that depending on the initial assumptions, different conclusions can be drawn by policymakers, regulatory bodies, academics, or other groups of interest.

Can Decarbonisation and Capacity Market Go Together? The Case Study of Poland

In recent years, numerous countries have introduced or considered capacity markets as remuneration mechanisms for long-term capacity adequacy. Since adequacy is frequently linked with thermal power generation, there is an ongoing debate as to whether this instrument could impact decarbonisation. In this context, the paper presents a quantitative assessment of the consequences of introducing a capacity market on decarbonisation pathways. The Polish power system is taken as an example due to its heavy dependence on fossil fuels. To this end, a computable model of the Polish power system is developed and applied to the study of two research scenarios. The first scenario presents the power system without introducing a capacity market, while the latter considers the system with a capacity market in place. The analysis shows that the introduction of a capacity market delays the decarbonisation of the power system and has a negative impact on carbon neutrality. Even though coal-fired units are phased out, they are mainly replaced by natural gas. The method and model developed within this study can be applied to countries where a capacity market is being discussed, and fossil fuels continue to play a dominant role.

OVERVOLTAGE PROTECTION OF PV MICROINSTALLATIONS – REGULATORY REQUIREMENTS AND SIMULATION MODEL

In low-voltage power networks with a large share of distributed energy sources, the phenomenon of overvoltage is increasingly observed. Although it may be desirable to raise the voltage value downstream of the network, in some cases the upper allowable voltage limit is exceeded. The method of eliminating voltage rises commonly used in the Polish power system is the installation of overvoltage protections, disconnecting the source from the grid. Such action reduces the profitability of prosumer installations, discouraging future potential investors. It turns out, however, that this is not the only disadvantage of such a solution. Sudden and uncoordinated disconnections and reconnections of more energy sources cause abrupt voltage changes that negatively affect the voltage conditions in the network. The aim of the paper is to present the operating algorithms of a standard overvoltage relay used in inverters of photovoltaic microinstallations. These algorithms – described in standards and national regulations – were tested in a typical inverter used in public low-voltage networks and implemented in the created simulation model of the relay. The described tests will be used for further work to demonstrate the need to coordinate the operation of overvoltage protections or replace them with other measures to improve voltage conditions in the grid with high share of photovoltaic sources.

High Precision LSTM Model for Short-Time Load Forecasting in Power Systems

The paper presents the application of recurrent LSTM neural networks for short-time load forecasting in the Polish Power System (PPS) and a small region of a power system in Central Poland. The objective of the present work was to develop an efficient and accurate method of forecasting the 24-h pattern of power load with a 1-h and 24-h horizon. LSTM showed effectiveness in predicting the irregular trends in time series. The final forecast is estimated using an ensemble consisted of five independent predictions. Numerical experiments proved the superiority of the ensemble above single predictor resulting in a reduction of the MAPE the RMSE error by more than 6% in both forecasting tasks.

Day-Ahead Wind Power Forecasting in Poland Based on Numerical Weather Prediction

The role of renewable energy sources in the Polish power system is growing. The highest share of installed capacity goes to wind and solar energy. Both sources are characterized by high variability of their power output and very low dispatchability. Taking into account the nature of the power system, it is, therefore, imperative to predict their future energy generation to economically schedule the use of conventional generators. Considering the above, this paper examines the possibility to predict day-ahead wind power based on different machine learning methods not for a specific wind farm but at national level. A numerical weather prediction model used operationally in the Institute of Meteorology and Water Management–National Research Institute in Poland and hourly data of recorded wind power generation in Poland were used for forecasting models creation and testing. With the best method, the Extreme Gradient Boosting, and two years of training (2018–2019), the day-ahead, hourly wind power generation in Poland in 2020 was predicted with 26.7% mean absolute percentage error and 4.5% root mean square error accuracy. Seasonal and daily differences in predicted error were found, showing high mean absolute percentage error in summer and during daytime.

Fixed Transmission Charges Based on the Degree of Network Utilization

The core objective of transmission tariffs is the recovery of costs related to the transport of electricity. A usual component of a tariff is a fixed charge that covers the costs of the network infrastructure. As many customers use the power grid, the rate of this charge should reflect, as closely as possible, the actual costs of supplying energy to the individual consumers. These costs result from which network elements have been used in delivering the electricity, and to what extent these elements have been used. Therefore, the fixed transmission rates should depend on the degree of network utilization. This article investigates definitions of the degree of network utilization based on the active power flow. To calculate the degree of network utilization, the flow of electricity on a branch must be decomposed into the streams flowing to individual customers. For this decomposition, two methods are examined: a power flow tracing method, based on the proportional sharing principle, and an incremental power flow method, based on the superposition principle. The analyzed methodology is applied to a small test system for conceptual discussions, as well as to the transmission network of the Polish power system, as an example of practical application. The results of this study were then compared with the commonly used “postage stamp” method. Finally, several practical aspects related to the potential implementation of the presented methodology are discussed.

Analysis of Various Options for Balancing Power Systems’ Peak Load

The balancing of the power of the Polish Power System (KSE) is a key element in ensuring the safety of electric energy supplies to end users. This article presents an analysis of the power demand in power systems (PS), with emphasis on the typical power variability both in subsequent hours of the day and on particular days and in particular months each year. The methodology for calculating the costs of electric energy undelivered to the end users and the amount of these costs for KSE is presented. Different possibilities have been analyzed for balancing power systems’ peak load and assumptions have been formulated for calculating the amount of the related costs. On this basis, a comparative analysis has been made of the possibility to balance peak load using operators’ system services, trans-border connections, and various energy storage solutions. On the basis of the obtained results, optimal tools have been proposed for market-based influence from transmission and distribution system operators on energy market participants’ behaviors in order to ensure the power systems’ operating safety and continuous energy deliveries to end users.