Currently, we are living through the implementation of the electrical smart grid, a network that can intelligently integrate the actions of all users connected to it in order to efficiently deliver the sustainable, economical and secure supply of electricity [...]

The aim of the article is to analyse the impact of selected conditions on the use of municipal solid waste in energy production processes. The authors in this publication indicated that the factors determining the effective use of solid municipal waste may be, in particular, formal and legal conditions, methods used in the processes of thermal waste transformation, recovery logistics or emergency situations such as pandemics or armed conflicts. Their knowledge can be very useful in the processes of the effective use of municipal waste for the production of thermal energy, especially in the current period of the energy crisis faced by most EU countries. This paper also emphasizes the importance of RDF (refuse-derived fuel) for the thermal energy sector, which can be used both in large combustion plants and in less powerful facilities, including local heating and combined heat and power plants as an alternative to traditional fossil fuels. The article was prepared by means of the systema... [more]

The energy sector has been a pioneer in the use of information and communication technologies for many years, and has undergone enormous changes in recent years as a result of the transition resulting from the fourth industrial revolution. In the paper, we examine and analyse relevant studies and their findings in order to show the current status of research on three selected aspects of digitalisation in energy sector enterprises. The paper sheds light on the diverse drivers influencing the digitalisation processes of energy sector companies. It also provides an overview of business models that are or will soon be implemented in the energy sector thanks to opportunities offered by digitalisation in response to observed trends in the energy market. Finally, it illustrates open research challenges and future dilemmas related to various aspects of energy sector digitalisation. The paper was prepared using the critical literature review method. It covers a large volume of the most recent a... [more]

Offshore wind is poised for huge growth in the coming years, the UK government has set targets of 40 GW of offshore wind, including 1 GW of floating wind, to be installed in the UK by 2030. Many proposed wind development sites are in deeper waters, farther from shore and will therefore need to be developed as floating wind sites. Developing sites in deeper waters provides access to higher, more consistent wind speeds, however this also means increased wave heights and tougher operating conditions. This makes the challenge of site accessibility critical to the goal of lowering the costs of offshore wind. Accessibility is the amount of time that vessels can safely access a given site. The objective of this study was to make a comparison of the accessibility of potential future floating wind sites to existing fixed bottom wind farm sites. Accessibility was calculated by developing Matlab code using established techniques and definitions found in the literature. A case study was then compl... [more]

For attack detection in the smart grid, transfer learning is a promising solution to tackle data distribution divergence and maintain performance when facing system and attack variations. However, there are still two challenges when introducing transfer learning into intrusion detection: when to apply transfer learning and how to extract effective features during transfer learning. To address these two challenges, this paper proposes a transferability analysis and domain-adversarial training (TADA) framework. The framework first leverages various data distribution divergence metrics to predict the accuracy drop of a trained model and decides whether one should trigger transfer learning to retain performance. Then, a domain-adversarial training model with CNN and LSTM is developed to extract the spatiotemporal domain-invariant features to reduce distribution divergence and improve detection performance. The TADA framework is evaluated in extensive experiments where false data injection... [more]

The transmission of electricity from the source to the load is mainly carried out using a three-phase line [...]

In 2017, the 19th CPC National Congress proposed to “establish a sound economic system of green, low-carbon and circular development”, which indicates the direction of high-quality economic development in the new era of China. Clean power investment is a powerful way to promote high-quality economic development by adopting non-fossil-energy utilization and low-emission technologies, as well as creating new jobs. Meanwhile, large-scale investment and a long investment return period result in negative effects on local economies. To better understand the effect of clean power investment, this paper selects panel data of thirty provinces in China from 2010 to 2019 to establish a spatial Durbin model to explore the impact of clean power investment on regional high-quality economic development. The results show that inter-regional high-quality economic development shows significant spatial auto-correlation characteristics. Clean power investment has not only a positive direct effect on high-... [more]

The aim of the article was to assess the implementation of the management accounting tools in managing an intelligent energy sector enterprise. The energy industry, characterized by high price fluctuation, rising costs, and the development of modern technologies, will in the future look for diversified sources of income and changes in business models. Energy entities will move towards intelligent management using management accounting tools, providing access to complete and up-to-date information from internal and external sources and extended management capabilities. In order to obtain answers to the questions, we conducted standardized interviews and an in-depth interview (IDI—individual in-depth interview) with large enterprises, with particular emphasis on the case of Hager Polo Ltd.

The magnitude of the impact of the pandemic on key variables, such as electricity demand, mobility of people and number of COVID-19 hospitalization cases, has been unprecedented. Existing economic models have not estimated the impact of sucokh events. This paper fills this gap, investigating the nexus among electricity demand elasticity, shifting behaviors of mobility and COVID-19 contagion with econometric estimation techniques. Firstly, using the single bids to purchase recorded in the Italian day-ahead wholesale electricity market in 2020, we estimate hourly electricity demand and price elasticity directly from short-run consumer behavior. Then, we analyze the effects of the main aspects of the pandemic, the health situation and the mobility contraction at the national level, on the estimated price elasticities. The period of heavy lockdown between 10 March and 3 June recorded a reduction in the price elasticity of electricity demand. However, when the pandemic broke out again at th... [more]

In the context of power quality problems, the voltage imbalance index is relevant, given its harmful impacts on the networks and loads. Thus, reliable and viable methodologies for practical use are necessary to determine agents’ contributions. This article presents a noninvasive method for sharing responsibility for imbalances based on the principle of superposing the individual voltage imbalance produced by the parties. A procedure based on the Complex Independent Component Analysis (CICA) technique is proposed to meet the parameters required by the superposition method. Based on the measurements of voltages and currents carried out on the point of common coupling (PCC), the negative sequence impedances are determined using the CICA method under the terms needed by the superposition principle. The methodology’s effectiveness is evaluated through performance comparisons carried out over the process, in light of the response from the methods for sharing responsibilities currently publis... [more]

Determining the demand for heat power for domestic hot water preparation is necessary to perform a building energy assessment. For this, we need to predict domestic hot water consumption. Considering the number of factors influencing domestic hot water consumption, it is difficult to develop a highly accurate methodology. The aim of the study was to compare the real domestic hot water consumption and heat power for its preparation with the values calculated based on the available prediction methods in multi-family buildings. The analysis was carried out based on annual monitoring (2021 year) of domestic hot water consumption and the actual demand for heat power in eight multi-family buildings located in Grudziądz, in Central Poland. The results of these measurements were compared with the values determined based on the available methodologies for forecasting the demand for heat power and domestic hot water consumption: Sander’s, Recknagel’s, the standard method and the method according... [more]

The development of emerging technologies not only has recently affected current industrial production but also has generated promising manufacturing opportunities that impact significantly on social and economic factors. Exploring upcoming renovation tendencies of technologies prematurely is essential for governments, research and development institutes, and industrial companies in managing strategies to achieve dominant advantages in business competitiveness. Additionally, the prospective changes, the scientific research directions, and the focus of technologies are crucial factors in predicting promising technologies. On the other hand, Industry 4.0 revolutionizes standards and models by accompanying significant technology developments in numerous sectors, including the sector of Smart energy. Moreover, asset performance management is always a prominent topic that has attained prevalence over the last decade because numerous challenges force all industrial companies to optimize their... [more]

Energy collection in a capacitor, which was charged by four connection structures of Macro Fiber Composite (MFC) patches, was the subject of laboratory research. The first structure was the delta circuit created by three MFC patches and connected with a three-phase rectifier; the second structure was the delta circuit created by three MFC patches and connected with a three-phase rectifier; the third structure was the parallel connection of three circuits, each of which consisted of an MFC patch and a full bridge rectifier; and the fourth structure the series connection of three circuits, each of which consisted of an MFC patch and a full bridge rectifier. Laboratory experiments were carried out on a laboratory stand which consisted of a rotating shaft, three MFC patches powering an energy storage system, and a data acquisition system. The star connection generated the highest values of voltage across a capacitor in the long time period. The delta connection produced the highest capacit... [more]

Climate neutrality is one of the greatest challenges of our century, and a decarbonised energy system is a key step towards this goal. To this end, the electricity system is expected to become more interconnected, digitalised, and flexible by engaging consumers both through microgeneration and through demand side flexibility. A successful use of these flexibility tools depends widely on the evaluation of their effects, hence the definition of methods to assess and evaluate them is essential for their implementation. In order to enable a reliable assessment of the benefits from participating in demand response, it is necessary to define a reference value (“baseline”) to allow for a fair comparison. Different methodologies have been investigated, developed, and adopted for estimating the customer baseline load. The article presents a structured overview of methods for the estimating the customer baseline load, based on a review of academic literature, existing standardisation efforts, an... [more]

This paper investigates the effects of coronavirus disease 2019 (COVID-19) on the performance of arbitrage trading in the energy market using daily data covering the period between 1 January 2015 and 5 December 2021. The investigation was achieved by utilizing a parametric pairs-trading model, where pairs of energy-related securities, including futures, stocks and ETFs traded in the United States, are formed. The empirical results suggest that the out-of-sample performances of pair trading declined sharply in the face of COVID-19. Dividing the whole data sample into two sub-samples, we found that the strategy performed well before COVID-19 but yielded poor results in the pandemic era. The analysis presented in this paper could serve as a benchmark for arbitrage-based trading models in the energy market during the pandemic.

Accurate short-term solar forecasting is challenging due to weather uncertainties associated with cloud movements. Typically, a solar station comprises a single prediction model irrespective of time and cloud condition, which often results in suboptimal performance. In the proposed model, different categories of cloud movement are discovered using K-medoid clustering. To ensure broader variation in cloud movements, neighboring stations were also used that were selected using a dynamic time warping (DTW)-based similarity score. Next, cluster-specific models were constructed. At the prediction time, the current weather condition is first matched with the different weather groups found through clustering, and a cluster-specific model is subsequently chosen. As a result, multiple models are dynamically used for a particular day and solar station, which improves performance over a single site-specific model. The proposed model achieved 19.74% and 59% less normalized root mean square error (... [more]

The use of energy sharing models in smart grids has been widely addressed in the literature. However, feasible technical solutions that can deploy these models into reality, as well as the correct use of energy forecasts are not properly addressed. This paper proposes a simple, yet viable and feasible, solution to deploy energy management systems on the end-user-side in order to enable not only energy forecasting but also a distributed discriminatory-price auction peer-to-peer energy transaction market. This work also analyses the impact of four energy forecasting models on energy transactions: a mathematical model, a support-vector machine model, an eXtreme Gradient Boosting model, and a TabNet model. To test the proposed solution and models, the system was deployed in five small offices and three residential households, achieving a maximum of energy costs reduction of 10.89% within the community, ranging from 0.24% to 57.43% for each individual agent. The results demonstrated the pot... [more]

Accurately forecasting the output of grid connected wind and solar systems is critical to increasing the overall penetration of renewables on the electrical network. This is especially the case in Australia, where there has been a massive increase in solar and wind farms in the last 15 years, as well as in roof top solar, both domestic and commercial. For example, in 2020, 27% of the electricity in Australia was from renewable sources, and in South Australia almost 60% was from wind and solar. In the literature, there has been extensive research reported on solar and wind resource, entailing both point and interval forecasts, but there has been much less focus on the forecasting of output from wind and solar systems. In this review, we canvass both what has been reported and also what gaps remain. In the case of the latter topic, there are numerous aspects that are not well dealt with in the literature. We have added discussion on the value of forecasts, rather than just focusing on fo... [more]

We estimate fundamental pricing relationships in selected European day-ahead electricity markets. Using a fractionally integrated panel data model with unobserved common effects, we quantify the responsiveness of hourly electricity prices to two fundamental leading indicators of day-ahead markets: the predicted load and renewable generation. The application of fractional cointegration analysis techniques gives further insight into the pricing mechanism of power delivery contracts, enabling us to measure the persistence of fundamental shocks.

Forecasting the electricity consumption is an essential activity to keep the grid stable and avoid problems in the devices connected to the grid. Equaling consumption to electricity production is crucial in the electricity market. The grids worldwide use different methodologies to predict the demand, in order to keep the grid stable, but is there any difference between making a short time prediction of active power and reactive power into the grid? The current paper analyzes the most usual forecasting algorithms used in the electrical grids: ‘X of Y’, weighted average, comparable day, and regression. The subjects of the study were 36 different buildings in Terni, Italy. The data supplied for Terni buildings was split into active and reactive power demand to the grid. The presented approach gives the possibility to apply the forecasting algorithm in order to predict the active and reactive power and then compare the discrepancy (error) associated with forecasting methodologies. In this... [more]

COVID-19 disease shocked global economic activity and affected the electricity markets due to lockdown and work-from-home policies. Therefore, this study proposes an empirical analysis to identify the electricity spot price response during the preventive and mandatory insulation in Colombia, where the economic contraction caused the largest decrease in the electricity demand, especially in the industrial sector. The methodology applied was quantile regression to quantify the non-linear effect on the spot price returns, and two sample periods were selected to contrast the results: 2018 and 2019. The main findings showed that regulated demand variation caused the highest variability on the spot price dynamic during the strict quarantine. However, the price could not fully capture the effects of the demand change due to the short duration of the shock and, also, the price variability in 2019 was higher than 2020 by an El Niño shock.

The energy efficiency gap is known as the difference between optimal level of energy efficiency and the actual level of achieved energy efficiency. Energy management has proven to further close the energy efficiency gap. Energy management may differ depending on whether it concerns a large, energy-intensive company or small and medium-sized enterprises (SMEs). SMEs are of high interest since they form a large share of the economy today. For SMEs, a lighter form of energy management, in the form of energy efficiency network participation, has proven to deliver sound energy efficiency impact, while for larger, energy-intensive firms, a certified energy management system may be more suitable. However, various barriers inhibit adoption of energy efficiency measures. While there is an array of research on barriers to and driving forces for energy efficiency in general, research on barriers to, and driving forces for, energy management is rare, one exception being a study of energy-intensive... [more]

It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at each level were then combined using the median approach. The comparisons were carried out using daily peak electricity demand data ranging from January 1997 to May 2014. Proper scoring rules were used to compare the three models, and the model with the smallest score was preferred. The results could be useful to system operators including decision-makers in power utility companies by giving insights... [more]

The European Commission emphasised that a bioeconomy is an economy that uses renewable biological resources from the land and sea (e [...]

The world is transitioning from the conventional grid to the smart grid at a rapid pace. Innovation always comes with some flaws; such is the case with a smart grid. One of the major challenges in the smart grid is to protect it from potential cyberattacks. There are millions of sensors continuously sending and receiving data packets over the network, so managing such a gigantic network is the biggest challenge. Any cyberattack can damage the key elements, confidentiality, integrity, and availability of the smart grid. The overall smart grid network is comprised of customers accessing the network, communication network of the smart devices and sensors, and the people managing the network (decision makers); all three of these levels are vulnerable to cyberattacks. In this survey, we explore various threats and vulnerabilities that can affect the key elements of cybersecurity in the smart grid network and then present the security measures to avert those threats and vulnerabilities at th... [more]