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The global energy crisis, which began in 2021 due to the extraordinary economic recovery after the pandemic and intensified after Russia's invasion of Ukraine in February 2022, has changed the conditions of energy management, paying more attention to energy efficiency. Natural gas prices have reached record levels and, consequently, so have electricity prices in some markets. Oil prices have reached their highest level since 2008. Higher energy prices have contributed to sharply increased inflation. Households are again becoming interested in buying coal as a source of heat. High energy and gas prices have pushed many families into poverty and forced some factories to cut production or even close. They have also slowed economic growth to the point where some countries are heading for a serious recession. Paradoxically, the negative effects of the energy crisis may accelerate the introduction of cleaner, sustainable, renewable energy such as wind and solar energy. The energy crisis is comparable to the oil crisis of the 1970s, when it contributed to significant advances in energy efficiency. The current crisis has highlighted the importance of investments in renewable energy resources and initiated the process of integrating regional markets, developing energy efficiency and promoting renewable energies. The aim of this article is to comprehensively explore the complex relationship between energy awareness, consumption patterns, and energy efficiency, with a focus on both individual consumers and industries, during the global energy crisis. This paper is based on a literature review, overarching policy documents, energy reports, and other secondary documents. The primary research method was the systematic literature review method, based on which the impact of the global energy crisis on energy efficiency was evaluated. This study emphasizes the diverse influences on energy awareness, ranging from economic factors to consumer preferences and environmental consciousness. The findings of the paper underscore the significant responsibility of industries in contributing to energy-saving efforts and the active role of consumers in the energy market. The responsibility of industries in contributing to energy efficiency is highlighted, with a call for a comprehensive approach that integrates energy-saving criteria into product development and corporate social responsibility. [ABSTRACT FROM AUTHOR]

Blockchain technology and, in particular, smart contracts based on it, offers a new, decentralized mechanism for entering into and fulfilling contracts in diverse markets. Energy markets are no exception, and indeed, the decentralized nature of the blockchain may be particularly important for them as the penetration of residential prosumers offering microgeneration to the grid grows. At this time, however, the literature on smart contracts in energy markets—and particularly their interaction with the technical infrastructure of the smart grid—is limited and scattered. There is a need to consolidate these studies into a comprehensive understanding of the state-of-the-art in smart contract design for the smart grid. However, no existing reviews focus on smart contracts in energy systems. The scope of our study is the role of smart contracts in energy systems and what limitations they encounter. We conduct a systematic review of this topic, focusing on systems that have been implemented as prototypes. These studies provide key evidence on the scalability of smart contracts for energy systems and their interaction with the technical elements of the smart grid. We selected a pool of 76 papers meeting our criteria, with three others excluded for misinterpreting fundamental aspects of blockchains and smart contracts. After reviewing each paper, we found that this literature falls into four categories: market operations, ancillary services, auditing and monitoring, and cybersecurity. We then identify and examine the cross-cutting concerns of data storage in and interoperability between blockchains. We finally discuss the implications of our findings for future research. In particular, there is likely to be a complex interplay between the data generated and stored via the blockchain versus the data required to meet energy system reliability targets and market obligations for participants. [ABSTRACT FROM AUTHOR]

The energy crisis is affecting a number of countries, but particularly those that are heavily dependent on the traditional energy generation formula (e.g., Poland), as well as those neighbouring the territory of the ongoing war in Ukraine, i.e., Poland, Lithuania, Latvia and Estonia. With this in mind, the authors of this study decided to explore the issue of water energy as a source of green (cheap and environmentally safe) energy in these countries. The main objective of the paper is to review the available literature, which is needed to identify the role hydropower plays in energy security and energy transition in these energy markets. This paper is based on a review and critical appraisal of the available literature and studies together with an inference. The structure of the paper consists of an introduction, the main part of the review and conclusions. The geopolitical location and socio-economic conditions of the adopted set of countries, form the basis of the review of the existing literature on the theme explored and the substantive inference. The main findings of this review indicate that the literature is most strongly focused on the overall assessment of the energy transition of the adopted countries for review, where the hydropower thread is most often taken up as one source of renewable energy supply. Hydropower, due to its relatively low share in the energy systems of the adopted set of countries, is, according to the authors, insufficiently explored. The most significant gap relates to the aspect of the potential for hydropower development in these areas, considering both the construction of new hydropower plants and opportunities signalled in the literature for the modernisation or restoration of existing ones. In this respect, the need for analyses (studies and simulations) of hydropower development, considering the economic benefits associated with their development juxtaposed with the safety dimension of this course of action for the environment (analysis and assessment of environmental costs) is indicated, which is the main recommendation of the review. [ABSTRACT FROM AUTHOR]

Virtual power plants (VPPs) are an effective platform for attracting private investment and customer engagement to speed up the integration of green renewable resources. In this paper, a robust bidding strategy to participate in both energy and ancillary service markets in the wholesale electricity market is proposed for a realistic VPP in Western Australia. The strategy is accurate and fast, so the VPP can bid in a very short time period. To engage customers in the demand management schemes of the VPP, the gamified approach is utilized to make the exercise enjoyable while not compromising their comfort levels. The modelling of revenue, expenses, and profit for the load-following ancillary service (LFAS) is provided, and the effective bidding strategy is developed. The simulation results show a significant improvement in the financial indicators of the VPP when participating in both the LFAS and energy markets. The payback period can be improved by 3 years to the payback period of 6 years and the internal rate of return (IRR) by 7.5% to the IRR of 18% by participating in both markets. The accuracy and speed of the proposed bidding strategy method is evident when compared with a mathematical method. [ABSTRACT FROM AUTHOR]

The development of smart grids has paved the way for sustainable energy infrastructure to transition towards decentralized energy trading. As intelligent agents, energy sources engage in energy trading based on their energy surplus or deficit. Buyers and sellers (participants) should achieve maximum payoffs in which buyers cut costs and sellers improve their utilities, and the security of sensitive information of smart agents must be guaranteed. This paper provides a blockchain-based energy trading network where intelligent agents can exchange energy in a secure manner, without the intervention of third parties. We model energy trading as a Stackelberg game, ensuring that the platform maximizes social welfare while participants increase their payoffs. Using the inherited characteristics of blockchain technology, a novel decentralized swarm intelligence technique is applied to solve the game while ensuring the privacy of the smart agents' sensitive information. The numerical analysis demonstrates that the suggested method outperforms the present methods (Constant Utility Optimization, average method...) for optimizing the objectives of each agent by maximizing the sellers' utilities and reducing the buyers' costs. In addition, the experimental results demonstrate that it significantly reduces carbon footprint (15%) by enhancing energy exchange between intelligent agents. [ABSTRACT FROM AUTHOR]

Rooftop solar power generation is becoming more widespread in residential microgrids. As well as new concepts of electricity markets, such as peer-to-peer (P2P) markets, where consumers and prosumers can directly exchange locally generated energy with each other without any intermediary third party for sustainable development. Data security is a big concern with energy trading; therefore, blockchain technology is being used more and more in energy markets. It has the potential to simplify P2P energy trading. In this paper, blockchain is designed to fit into the decentralized nature of the P2P market, securing the payment mechanism and transaction data store. The blockchain-enabled platform is developed using the Proof-of-Work (PoW) consensus algorithm, and is verified with the help of the Postman application programming interface (API). All transactions involving the buying and selling of energy are handled by a miner without the help of any third parties. The study of a five-user residential community, whether the strategy is recommended or not, is validated through simulation findings. An overview of the results revealed that all users benefited from the developed, secure P2P platform. [ABSTRACT FROM AUTHOR]

Due to the prospect of climate change and the challenges posed by the European Union to the modern power grid, a decentralized system based on distributed energy sources is being created from a centralized system based on utility power. It also involves new ideas on the operation and management of power grids, especially at the level of low-voltage distribution networks, where prosumers play a special role. In addition to the transformation of sources to renewables, the aim is to increase the flexibility of power grids by exploiting the regulatory potential of flexible grid components. The issue of grid flexibility assumes particular importance in the case of microgrids and local grids covered by the energy communities. Many posts describe the realization of the task of flexibility through energy storage, e.g., storing storage resources in electric vehicles or the use of energy transformation through conversion to heat, air compression air, or process cooling. However, there seems to be a lack of exploration of the topic, where the photovoltaic inverter could provide a flexible energy source while maintaining the rigor of power quality. This article presents current developments in low-voltage grids and the prospect of using prosumer installations to provide grid flexibility and stability. [ABSTRACT FROM AUTHOR]

In recent years, the energy market has seen an increase in small and medium enterprises (SMEs) participating in the sector and providing relevant services to customers. The energy sector SMEs need to acknowledge whether reengineering their marketing strategy by modeling customers' website behavior could enhance their digital marketing efficiency. Web Analytics refers to the extracted data of customers' behavior from firms' websites, a subclass of big data (big masses of uncategorized data information). This study aims to provide insights regarding the impact that energy SMEs' web analytics has on their digital marketing efficiency as a marketing reengineering process. The paper's methodology begins with the retrieval of behavioral website data from SMEs in the energy sector, followed by regression and correlation analyses and the development of simulation models with Fuzzy Cognitive Mapping (FCM). Research results showed that customer behavioral data originating from SMEs' websites can effectively impact key digital marketing performance indicators, such as increasing new visits and reducing organic costs and bounce rate (digital marketing analytics). SMEs in the energy sector can potentially increase their website visibility and customer base by re-engineering their marketing strategy and utilizing customers' behavioral analytic data. [ABSTRACT FROM AUTHOR]

Forecasting return and profit is a primary challenge for financial practitioners and an even more critical issue when it comes to forecasting energy market returns. This research attempts to propose an effective method to predict the Brent Crude Oil return, which results in remarkable performance compared with the well-known models in the return prediction. The proposed hybrid model is based on long short-term memory (LSTM) and convolutional neural network (CNN) networks where the autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroscedasticity (GARCH) outputs are used as features, along with return lags, price, and macroeconomic variables to train the models, resulting in significant improvement in the model's performance. According to the obtained results, our proposed model performs better than other models, including artificial neural network (ANN), principal component analysis (PCA)-ANN, LSTM, and CNN. We show the efficiency of our proposed model by testing it with a simple trading strategy, indicating that the cumulative profit obtained from trading with the prediction results of the proposed 2D CNN-LSTM model is higher than those of the other models presented in this research. In the second part of this study, we consider the spread of COVID-19 and its impact on the financial markets to present a precise LSTM model that can reflect the impact of this disease on the Brent Crude Oil return. This paper uses the significance test and correlation measures to show the similarity between the series of Brent Crude Oil during the SARS and the COVID-19 pandemics, after which the data during the SARS period are used along with the data during COVID-19 to train the LSTM. The results demonstrate that the proposed LSTM model, tuned by the SARS data, can better predict the Brent Crude Oil return during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Electricity Price Forecasting (EPF) influences the sale conditions in the energy sector. Proper models of electricity price prognosis can be decisive for choice between energy sources as a start point of transformation toward renewable energy sources. This article aims to present and compare various EPF models scientific publications. Adopted in this study procedure, the EPF publications models are compared into two main categories: the most popular and the most accurate. The adopted method is a bibliometric study as a variation of Systematic Literature Review (SLR) with specified automated queries supported by the VOSviewer bibliometric maps exploration. The subject of this research is the exploration of EPF models in two databases, Web of Science and Scopus, and their content comparison. As a result, the SLR research queries were classified into two groups, the most cited and most accurate models. Queries characteristics were explained, along with the graphical presentation of the results. Future promising research avenues can be dedicated to the most accurate EPF model formulation proved by statistical testing of its significance and accuracy. [ABSTRACT FROM AUTHOR]

Blockchain technology is used as a distributed ledger to store and secure data and perform transactions between entities in smart grids. This paper proposes a platform based on blockchain technology and the multi-agent system paradigm to allow for the creation of an automated peer-to-peer electricity market in micro-grids. The use of a permissioned blockchain network has multiple benefits as it reduces transaction costs and enables micro-transactions. Moreover, an improvement in security is obtained, eliminating the single point of failure in the control and management of the platform along with creating the possibility to trace back the actions of the participants and a mechanism of identification. Furthermore, it provides the opportunity to create a decentralized and democratic energy market while complying with the current legislation and regulations on user privacy and data protection by incorporating Zero-Knowledge Proof protocols and ring signatures. [ABSTRACT FROM AUTHOR]

In the energy market, we observe a dynamic development of innovative business models that take into account various aspects related to the direction of zero-emission economic growth. Companies are intensifying their efforts in utilizing renewable energy sources, implementing significant photovoltaic projects, and advancing technologies related to wind and hydrodynamic energy. Within this trend, microgrids become a crucial element, enabling efficient management of local energy sources. Contemporary energy companies also focus on innovative digital technologies, harnessing the potential of the Internet of Things (IoT) and artificial intelligence (AI). These tools allow for precise monitoring and optimization of energy consumption, resulting in increased operational efficiency. The expansion of subscription-based energy services encompasses not only traditional energy deliveries but also new aspects, such as intelligent management of home energy installations or the provision of advisory services on energy conservation. This approach emphasizes the customer as a partner in sustainable energy usage. Hybrid energy models, integrating diverse energy sources, constitute a key element in the transformation of the sector. The combination of photovoltaic, wind, and traditional power plants allows for flexible adaptation to changing conditions and maintains stability in the energy supply. In the face of a changing energy landscape, companies consistently strive for sustainable practices, implementing strategies that not only reduce their carbon footprint but also contribute to improving efficiency, ecology, and the decentralization of the energy system. Adapting to these dynamic changes becomes not only a challenge but also an opportunity to create a more sustainable energy future. The objective of this research is to analyze key business models in the energy market and identify their impact on operational efficiency and market competitiveness. The main results indicate significant improvements in energy management and sustainability through the adoption of these models. [ABSTRACT FROM AUTHOR]

In this paper, a new virtual framework for load aggregation in the context of the liberalized energy market is proposed. Since aggregation is managed automatically through a dedicated platform, the purchase of energy can be carried out without intermediation as it happens in peer-to-peer energy transaction models. Differently from what was done before, in this new framework, individual customers can join a load aggregation program through the proposed aggregation platform. Through the platform, their features are evaluated and they are clustered according to their reliability and to the width of range of regulation allowed. The simulations show the deployment of an effective clustering and the possibility to meet the target power demand at a given hour according to each customer's availability. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic represents not just a global health crisis, but may signal the beginning of a new era of economic activity, the potential consequences of which we currently do not fully understand. In this context, the mid-to-long-range impacts of the pandemic on the energy sector have been studied extensively in the last few months. Despite these efforts, the pandemic still raises many open questions concerning the long-term operation and planning of power systems. For instance, how will the pandemic affect the integration of renewable energy sources? Should current power system expansion plans change in light of the COVID-19 pandemic? What new tools should be provided to support system operators during global health crises? It is the purpose of this paper to better understand the many aspects of these open questions by reviewing the relevant recent literature and by analyzing measured data. We point out the main challenges that the pandemic introduced by presenting patterns of electricity generation and demand, frequency deviations, and load forecasting. Moreover, we suggest directions for future research that may assist in coping with the mentioned challenges. We hope that this paper will trigger fruitful discussions and encourage further research on these important emerging topics. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of policy changes on the Romanian renewable energy producers. Attracted by a generous subsidy scheme, foreign and domestic investors flocked to the market. Consequently, the sector witnessed remarkable progress, especially in the wind power category. Romania fast approached the national target set by the European Union concerning the share of the country's energy consumption from renewable sources. However, frequent changes in the support scheme and in the regulations issued by public authorities led to chaos. The aim of the paper was to emphasize the evolution of renewable energy policy in Romania, to investigate the incentives and their effects, and to critically assess the impact of the changes on renewable energy producers. It highlights, by means of an exploratory study and several interviews with executives of renewable energy companies, the challenges and shortcomings of policymaking. The main finding was that the revision of the subsidy scheme and the changes in energy policy that followed are the major determinants for the declining financial performance of renewable energy producers. Subsequently, some recommendations for improved policymaking are suggested, so as to re-establish the trust of investors and to promote the sustainable development of the sector. [ABSTRACT FROM AUTHOR]

In the light of Germany's chosen path towards the energy transition, the regulatory framework has changed considerably. New players have succeeded in entering the market, and renewable energies have become increasingly competitive. Greater electrification of the transport and heating sectors will be needed in the future to achieve national climate targets. Against this background, Germany's big energy companies need to be sure that their sales will increase. However, they were unable to anticipate this development, and made strategic mistakes in the past. The development of sustainable business models in line with the energy transition failed to materialize. Now it is becoming increasingly clear that companies must create new business models to survive in the long term. These business models have to keep with the tradition, whilst meeting the needs of low-carbon power supplies. In this paper, we will examine the past and future challenges of the four energy companies and develop a proposal for evaluating sustainable business models. For this purpose, we use the multi-level perspective to categorize developments in the electricity market over the last 50 years, and then apply a multi-criteria analysis to derive five suitable business models from the results. [ABSTRACT FROM AUTHOR]

In recent years, with the intensification of global warming and the greenhouse effect, the global consensus has focused on efficient, clean, low-carbon, and green development as a means of achieving new economic growth. China, as a major carbon emitter, has been at the forefront of efforts to reduce carbon emissions. The establishment of the carbon emissions trading market, commonly known as the "carbon market", provides an economic solution for reducing carbon emissions in both the carbon and energy markets. As China's carbon market continues to grow rapidly, fluctuations in the energy or carbon markets caused by information shocks can easily spread between the two markets, leading to increased interconnectedness. Moreover, the spillover effect of the volatility between China's carbon market and energy market is not constant, and the intensity and direction of this effect vary depending on different market volatility levels and periods. Therefore, it is crucial to conduct a comprehensive study on the characteristics of the volatility spillover effect between China's carbon market and energy market and to fully understand the mechanism of energy regulation on carbon prices. This research will have significant practical implications for promoting the establishment of a well-functioning internal price transmission mechanism between China's carbon market and energy market. This study took the risk spillover between the carbon market and energy market as the research object and systematically combed through its pricing mechanism and spillover impact. Through constructing the DY overflow index model based on a VAR model and generalized variance decomposition method, this study explored the linkage between China's carbon and energy markets, i.e., the linkage of price fluctuations between China's energy and carbon markets, as well as the time-varying nature of inter-market spillovers, and provides suggestions on the risk control of price fluctuations between the carbon and energy markets. [ABSTRACT FROM AUTHOR]

The rapid increase of electric vehicles (EVs) would lead to a rise in load demand on power grids but create different potential benefits as well. Those benefits comprise EVs serving as a mobile energy storage system to participate in adjusting the load on the power grids and helping manage renewable energy resources. This paper evaluates the effect of dynamic electricity prices and home photovoltaic (PV) system incentives on users' EVs charging behavior and potential impacts on grid load and household economy. This has been done by establishing and assessing three different optimized charging configurations and comparing them to an uncontrolled charging strategy. In this study, the charging incentives are applied to a representative sample of 100 households with EVs and PV systems in a metropolitan area. The results show that an optimized charging strategy based on the dynamic electricity tariff can reduce charging costs by 18.5%, while a PV-based optimized strategy can reduce the costs by 33.7%. Moreover, the PV-integrated optimization strategies significantly increase the utilization of PV energy by almost 46% on average, compared to uncontrolled charging. In addition, the simulations of this research have depicted the capability of using home PV systems' incentives to smoothen the charging profiles and hence significantly reduce the maximum grid load. However, the electricity price optimization strategy increases the aggregated charging peaks, which can only be slightly reduced by peak shaving. Therefore, an identical price signal for all households might be critical. Further analyses have shown that direct charging occurs simultaneously with household electricity assigned to a specific low-voltage grid while PV and price incentive charging configurations shift the charging peaks away from household load peaks. [ABSTRACT FROM AUTHOR]

The global warming phenomenon is commonly associated with the emission of greenhouse gases. However, there may be other factors related to industry and global energy production which cause climate change—for example, heat emission caused by the production of any useful form of energy. This paper discussed the importance of heat emission—the final result of various forms of energy produced by our civilization. Does the emission also influence the climate warming process, i.e., the well-known greenhouse effect? To answer this question, the global heat production was compared to total solar energy, which reaches the Earth. The paper also analyzed the current global energy market. It shows how much energy is produced and consumed, as well as the directions for further development of the energy market. These analyses made it possible to verify the assumed hypothesis. [ABSTRACT FROM AUTHOR]

The need for new business models to replace existing ones, soon obsolete, is a subject often discussed among researchers in the area. It is essential to find a practical solution that includes the concepts of demand response and distributed generation in the energy markets, these being the future of the electricity grid. It is believed that these resources can bring advantages to the operation of the system, namely increasing technical efficiency. However, one of the problems is the aggregation of small resources as a result of the associated uncertainties. The authors propose a business model with three main phases used in planning: optimal scheduling, aggregation, and remuneration. In this paper, a new phase was added, the classification, with the main purpose of assisting the aggregator of these small resources in operating situations. The focus is on the fair remuneration of participants in the management of the market, in addition to minimizing operating costs. After testing four different remuneration methods, it was proved that the method proposed by the authors obtained better results, proving the viability of the proposed model. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the expectations of the inhabitants of south-eastern Poland in relation to the main trends emerging in the energy market, in a specific crisis situation resulting from the response to COVID-19. The time range of the research covered the period from March to December 2022, i.e., the time just after the official easing of the most drastic restrictions related to COVID-19 in Poland. The CAWI method was used in the study. Access to the survey form was granted to several dozen people, who invited other respondents to participate in the survey. A total of 732 questionnaires were collected, of which 637 were verified, meeting the research assumptions regarding the place of residence of the respondents. The study was correlational in nature, as it looked for relationships between the examined features, without the possibility of influencing the level of individual variables. As the study used non-probability sampling, the inferences apply only to the study group. Two research questions were formulated: (1) Did the COVID-19 crisis shape the expectations of the inhabitants of south-eastern Poland regarding the energy market, and were these expectations consistent with the policies of the state and the European Union? (2) Were perceptions of the energy market and social phenomena differentiated in terms of sex, place of residence, and type of heating in the respondent's apartments? Statistical analyses of the collected material were performed using Statistica13.3 and MS Excel 19. The greatest support among respondents was recorded for the optimal use of the country's own energy resources, and the development of energy networks, in conjunction with the expansion of its own energy potential, and improvement of energy efficiency. Respondents confirmed the need to increase the use of renewable energy sources. However, the EU trend of abandoning electricity production in nuclear power plants was not confirmed. This is also evidenced by Poland's energy policy, which emphasizes the development of the nuclear energy market. Men were more positive than women about the implementation of nuclear energy in Poland. Women rated the development of RES use higher than men. Both women and men had a more positive attitude toward renewable energy than toward nuclear energy. Respondents living in rural areas assessed the development of RES higher and showed greater interest in increasing energy efficiency. The limitations of this study are that all the variables were measured simultaneously, so the study is cross-sectional, and additional attention may need to be paid to other causes of the studied phenomena. [ABSTRACT FROM AUTHOR]

With the introduction of the renewable energy communities in the current electrical market environment, it becomes possible to aggregate small generation resources and users' loads to exchange power within the aggregation and at the same time provide services to the electrical system. The renewable energy community of users equipped with nanogrid technology allows performing an adequate level of flexibility. It may be the solution to coordinate in the best possible way the energy resources in order to increase the community self-consumption and to provide ancillary services to the grid. In this paper, a model for the interaction between the Distribution System Operator (DSO)—Transmission System Operator (TSO) and the energy community based on nanogrids is proposed and an operational example is presented. [ABSTRACT FROM AUTHOR]

Energy communities (EC) are among the new actors in the energy market, playing an important role in the uptake of photovoltaics (PV) in European markets. This paper estimates their aggregate contribution to the low-carbon energy transition in terms of installed capacities for PV and evaluates their economic performance comparing with market prices. We compiled a database of PV facilities with 3672 entries for Germany and 64 entries for Italy. Our statistical analysis does not support an economic under-performance of EC. The aggregate contribution of EC currently amounts to 600–838 MWp installed capacity in Germany and 10.6 MWp installed capacity in Italy, which makes 1.2–1.7% and 0.07% of all PV installations in Germany and Italy, respectively. [ABSTRACT FROM AUTHOR]

Participants in deregulated electricity markets face risks from price volatility due to various factors, including fuel prices, renewable energy production, electricity demand, and crises such as COVID-19 and energy-related issues. Price forecasting is used to mitigate risk in markets trading goods which have high price volatility. Forecasting in electricity markets is difficult and challenging as volatility is attributed to many unpredictable factors. This work studies and reports the performance both in terms of forecasting error and of computational time of forecasting algorithms that are based on Extreme Learning Machine, Artificial Neural Network, XGBoost and random forest. All these machine learning techniques are combined with the Bootstrap technique of creating new samples from the available ones in order to improve the forecasting errors. In order to assess the performance of these methodologies, the Day-Ahead market prices are divided into three classes, namely normal, extremely high and negative, and these algorithms are subsequently used to provide forecasts for the whole year 2020 of the German and Finnish Day-Ahead markets. The average yearly forecasting errors along with the computation time required by each methodology are reported. The findings indicate that the random forest algorithm performs best for the normal and extremely high price categories, while XGBoost demonstrates better results for the negative price category. The methodology based on Extreme Learning Machine requires the least computational time and achieves forecasting errors that are comparable to the best-performing methods. [ABSTRACT FROM AUTHOR]

The main purpose of the paper was to present the energy balance in the EU countries. The specific objectives were to show the concentration and directions of changes in the demand, production, import, and export of energy in the EU countries, to determine the degree of variability (or stability) of these energy balance parameters, and to establish the correlation between the energy balance parameters and economic parameters. All members of the European Union were determinedly selected for research on 31 December 2018 (28 countries). The research period embraced the years 2004–2018. The sources of materials were the literature on the topic and data from Eurostat. Descriptive, tabular, and graphical methods, Gini coefficient, Lorenz curve, coefficient of variation, Pearson's linear correlation coefficient, and constant-based dynamics indicators were used in the analysis and presentation of materials. It was determined that only the demand for energy and its import in EU countries were nearly related to the economic situation. In turn, exports and production were medium and weakly correlated. In these parameters, economic factors had a smaller impact than other factors, such as political development or the level of energy development in the country. It was also found that the EU countries' energy imports were characterized by lower volatility than its exports. As a rule, the most significant stabilization in the given parameters occurred in countries with a stable economy, the so-called developed economies, while the most significant volatility was in developing countries. Energy security is of great importance in all EU countries. [ABSTRACT FROM AUTHOR]

Recently, the natural gas (NG) global market attracted much attention as it is cleaner than oil and, simultaneously in most regions, is cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological development, emerging unconventional resources, energy security challenges, and shipment are some of the forces made the NG market more dynamic and complex. From a policy-making perspective, it is vital to uncover demand-side future trends. This paper proposed an intelligent forecasting model to forecast NG global demand, however investigating a multi-dimensional purified input vector. The model starts with a data mining (DM) step to purify input features, identify the best time lags, and pre-processing selected input vector. Then a hybrid artificial neural network (ANN) which is equipped with genetic optimizer is applied to set up ANN's characteristics. Among 13 available input features, six features (e.g., Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption) were selected as the most relevant feature via the DM step. Then, the hybrid learning prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different five error based evaluation statistics consist of R2, MAE, MAPE, MBE, and RMSE. In addition, as the model proposed the best input feature set, results compared to the model which used the raw input set, with no DM purification process. The comparison showed that DmGNn overcame dramatically a simple GNn. Also, a looser prediction model, such as a generalized neural network with purified input features obtained a larger R2 indicator (=0.9864) than the GNn (=0.9679). [ABSTRACT FROM AUTHOR]

This paper aims to analyze what happens with renewable energy power plants, such as onshore wind, photovoltaics and biomass, when the public policy support based on the Renewable Energy Law expires. With its expiration, the first renewable energy (and especially onshore wind) power plants will have to be scrutinized as to whether they can economically continue operation, whether they have to be repowered, or whether they need to be decommissioned. The relative merits of these three alternatives are evaluated by applying real options analysis. In contrast to traditional project evaluation techniques, the real options approach takes advantage of the use of uncertain parameters included in the model, such as the development of the electricity price or electricity output. The results obtained suggest that parameters such as the level of future operation and maintenance costs, the expected development of the electricity price at the spot market, and the interrelations between these, as well as the development of the electricity output from renewables can significantly affect the profitability of these power plants and thus impact the decision about their further optimal operation. [ABSTRACT FROM AUTHOR]

By coordinating the operation of regionally interconnected virtual power plants (VPPs), the growing penetration problem of renewable energy sources (RESs) into the power system can be addressed. This study presents an interactive trading cooperation model of regionally interconnected VPPs using bilateral contracts. The proposed model maximizes overall electricity market social welfare (SW) (i.e., maximization of consumer benefits while minimizing energy costs). The focus of the proposed approach is to design and develop a parallel energy exchange cooperation model of interconnected VPPs, ensuring the operational efficiency and reliability of interconnected power systems over the planning horizon. Given that adjacent VPPs may have differences in their energy generation and usage patterns, a scenario tree method is used to model the uncertainties associated with solar irradiation and load demand. A case study of two interconnected VPPs is used, the operational scenario is designed, and the corresponding computational model is developed. The results highlight that the proposed approach gives VPPs the option to utilize their internal network's maximum capacity. As a result, there will be less reliance on the main grid for interconnected VPPs, and an improvement in key performance indicators, including the cost of the VPPs systems and renewable power variations. [ABSTRACT FROM AUTHOR]

This article reviews the issue of the use of cryptocurrencies (crypto-assets, in general) for an electricity settlement system. The development of digital techniques, including blockchain-based mechanisms, has meant that an increased interest in blockchain-based solutions is to be expected. Blockchain and similar approaches are characterised by decentralisation, so they are concurrent with the trends of the transforming power sector. Decentralised energy generation based on a high proportion of prosumer installations requires the implementation of a new settlement system for grid activities related to electricity use. The first projects of such systems based on a dedicated cryptocurrency have emerged. Based on these, the general concept of such a system with its own cryptocurrency called CCE is presented, including variants implementing net-metering and net-billing. Furthermore, issues requiring interdisciplinary research work and discussion before implementing such systems were identified. A settlement system in which a cryptocurrency is linked to a unit of energy used could be a first step towards introducing a new universal means of value exchange, linked to energy as the primary measure of the value of goods. [ABSTRACT FROM AUTHOR]

This paper aims to establish whether the Romanian energy market has an influence on the good running of the associated capital market. In order to achieve this objective, we approached a series of econometric techniques that allowed us to study the cointegration between variables, the presence of short-term or long-term causality relationships, and the application of impulse-response functions to analyze how the BET index responds to the shocks applied. The empirical findings from the Johansen cointegration test, ARDL model, and VAR/VECM models confirmed both the presence of a long-term and short-term relationship between the energy market and capital market. From all energy market indicators, only hard coal presented a causal relationship with the BET index. We also noticed a unidirectional relationship from the WTI crude oil to the Romanian capital market. Our findings should be of interest to researchers, regulators, and market participants. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) output power inherently exhibits an intermittent property depending on the variation of weather conditions. Since PV power producers may be charged to large penalties in forthcoming energy markets due to the uncertainty of PV power generation, they need a more accurate PV power prediction scheme in energy market operation. In this paper, we characterize the effect of PV power prediction errors on energy storage system (ESS)-based PV power trading in energy markets. First, we analyze the prediction accuracy of two machine learning (ML) schemes for the PV output power and estimate their error distributions. We propose an efficient ESS management scheme for charging and discharging operation of ESS in order to reduce the deviations between the day-ahead (DA) and real-time (RT) dispatch in energy markets. In addition, we estimate the capacity of ESSs, which can absorb the prediction errors and then compare the PV power producer's profit according to ML-based prediction schemes with/without ESS. In case of ML-based prediction schemes with ESS, the ANN and SVM schemes yield a decrease in the deviation penalty by up to 87% and 74%, respectively, compared with the profit of those schemes without ESS. [ABSTRACT FROM AUTHOR]

The last three years have been a period of many challenges related to the dynamically changing conditions of the economic environment. Among these many changes, some of the most important for the further functioning of private and public entities are those related to the instability of the energy market. Rapidly rising energy prices increase the costs of implementing public tasks. They also greatly increase the search for innovative, energy-saving and environmentally friendly ways of performing municipal tasks. The main aim of the article is to present the concept of a smart village as an instrument for the implementation of public tasks in rural areas. The theoretical basis of the smart village concept is the basic point of reference. The implementation of the assumptions of the smart village concept in Poland gives municipalities the possibility of an innovative approach to the implementation of local public services. In addition, examples of good practices implemented by rural local communities that can act as models for other groups of residents are also included. It was essential, from the point of view of measurable effects, to identify potential limitations and hazards in the implementation of the smart village concept, which may be identified in the outermost regions. Analysis and critical literature review were used to achieve the article's goals. These methods are characteristic for review publications. At the beginning, we presented the theoretical foundations of the smart village concept. Secondly, we indicated how the smart village concept contributes to the improvement in public service delivery in rural areas. The authors demonstrated that there is no universal model for each unit. A smart village will implement solutions tailored to economic, social, cultural, and natural conditions. [ABSTRACT FROM AUTHOR]

This study examines the roles of resource consumption accounting and competitive prices in attaining sustainable profitability. The objectives were (1) to determine whether the adoption of resource consumption accounting practices yields significant improvements in competitive strategies in a highly competitive situation where activity-based costing has proved to be insignificant, and (2) to ascertain if the positive relationship between competitive pricing and sustainable profitability is increased by the extent to which resource consumption accounting exerts pressure for sustainability profitability. A PLS-SEM procedure was applied in analysing 129 of the top 30 performing companies' structured questionnaire responses drawn from five industries in Kurdistan from 2021. The empirical results demonstrated that competitive pricing models involving resource consumption accounting systems provide superior price forecasting, error reduction and profit maximisation capabilities than existing energy models. The study's outcomes highlight that the extent to which resource consumption accounting exerts pressure on sustainability profitability significantly increases the positive relationship between competitive pricing and sustainable profitability. The results of this study advance construct and item development involving competitive pricing and resource consumption accounting while testing relationships to uncover the moderating role of resource consumption accounting in profit maximisation. Thus, energy and non-energy industrial companies must rely on resource consumption accounting to set competitive prices and enhance and sustain their profitability by considering the overlooked energy pricing stochastic parameters and errors amid rising energy shortages and costs. [ABSTRACT FROM AUTHOR]

The problem of rising energy prices stems from the weakening of economies operating in the realities of the COVID-19 coronavirus pandemic, as well as from the rising cost of CO2 emission allowances in connection with the European Union's environmental policy. The outbreak of war in Ukraine has deepened the energy crisis in European countries, which, to a significant extent, benefit from hydrocarbon resources imported from Russia. This problem is particularly acute in countries that are heavily dependent on conventional forms of energy production (e.g., Poland). In light of the problems observed, the need to develop a market for renewable energy has become more urgent than ever. Although this problem affects a number of countries, the authors of the study decided that it is particularly important in the region of the countries neighbouring the line of the ongoing armed conflict in Ukraine, i.e., Poland and the Baltic States (Lithuania, Latvia and Estonia). The choice of the research area was determined by direct proximity to the examined countries, as well as the generally comparable social and economic conditions of Poland, Lithuania, Latvia and Estonia, which creates the basis for factual comparisons and conclusions. The main objective of the study was to assess the directions of the development of the geothermal energy market in Poland against the background of the Baltic States in the current economic conditions, together with an attempt to determine the most justified direction of geothermal energy development in Poland—with the strongest impact on increasing the share of RES in the energy system and limiting the increase in electricity prices in this country. Identification of solutions practiced in the geothermal energy market in Lithuania, Latvia and Estonia for comparative purposes (Poland region) was helpful in this respect. The subject of this study was oriented towards the diagnosis of the state of the geothermal energy market in the surveyed countries to determine the general directions of its development. Particular attention was paid to the study of needs and preferences in this field on the Polish market concerning individual users, which constitutes the basis of the research. The instruments required for carrying out the research work in the outlined scope were basically drawn from the area of economic analysis and evaluation of phenomena (multifaceted analysis including critical analysis of the literature, comparative analysis of research results, situational analysis of geothermal market), in comparison with the results of our own research (questionnaire survey) carried out on the Polish energy market (individual customers). The research revealed the strong position of Poland in the field of the development of deep and shallow geothermal energy in the area of the countries accepted for the study. The determinant of the above is the abundance in this country of easily accessible heat deposits of the Earth. In general, the actions taken by the surveyed countries in the field of geothermal development are of a similar nature (shallow geothermal heat pumps are the most developed), while, in the field of deep geothermal energy, an interesting observation is the orientation towards solutions based on the energy of crystalline rocks in Lithuania and Estonia, determined by the availability of such deposits in this region. Surveys indicate the growing awareness of the validity of efforts to extract energy from the earth. However, this problem still represents a major challenge (e.g., deep geothermal energy in Estonia), so indicating the role of geothermal energy in the energy market—especially in the area of the countries accepted for the survey—is an important and relevant topic. [ABSTRACT FROM AUTHOR]

The economic crisis caused by the COVID-19 pandemic reinforces the problem of rising electricity prices, which mainly affects countries that are forced to pay ever-higher CO₂ emission allowance fees (e.g., Poland). In the light of signals confirming the need for intensive development of the wind energy market in the Baltic Sea region, the authors consider the need to examine this issue concerning Poland and the Baltic States (i.e., Lithuania, Latvia and Estonia) as extremely important and demanding. The development of the RES market is currently an absolute necessity. The immediate neighbourhood and similar general social and economic conditions of Poland and the Baltic States enable factual comparisons, reinforcing the rationale for choosing the adopted research area. The main objective of the study was to assess the development of the wind energy market in Poland in the background of the Baltic Sea bordering countries in the era of the COVID-19 pandemic, in order to try to answer the question: what direction of wind energy development in Poland in the realities of the COVID-19 pandemic is justified and may have an impact on limiting the increase in electricity prices in this country? In this context, it turned out to be particularly interesting to identify solutions practised in the wind energy market in the Baltic States with their potential to be applied in Poland. The research instruments were drawn from an economic analysis and evaluation of phenomena and supported by the results of our own research (questionnaire) conducted on the Polish energy market, to substantiate the findings. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) techniques and algorithms are increasingly being utilized in energy and renewable research to tackle various engineering problems. However, a majority of the AI studies in the energy domain have been focusing on solving specific technical issues. There is limited discussion on how AI can be utilized to enhance the energy system operations, particularly the electricity market, with a holistic view. The purpose of the study is to introduce the platform architectural logic that encompasses both technical and economic perspectives to the development of AI-enabled energy platforms for the future electricity market with massive and distributed renewables. A constructive and inductive approach for theory building is employed for the concept proposition of the AI energy platform by using the aggregated data from a European Union (EU) Horizon 2020 project and a Finnish national innovation project. Our results are presented as a systemic framework and high-level representation of the AI-enabled energy platform design with four integrative layers that could enable not only value provisioning but also value utilization for a distributed energy system and electricity market as the new knowledge and contribution to the extant research. Finally, the study discusses the potential use cases of the AI-enabled energy platform. [ABSTRACT FROM AUTHOR]

The household, industrial, and service sectors in Poland and the Baltic States have been facing ever-higher bills for their electricity consumption at a time when a number of them have been hit hard financially by the pandemic. Rising inflation, the border crisis—with its set of restrictions, or the spread of the fourth wave of the COVID-19 coronavirus pandemic, is causing strong concerns in the social and economic sphere, with significant increases in electricity prices. Many countries are implementing measures to reduce the adverse effects of rising electricity prices in response to this complex situation. The main orientation is towards obtaining energy from renewable sources, such as the sun. The current situation in the energy market determines the price per 1 KW. Among the countries under study, the price of electricity has increased the most in Poland. On the other hand, the development of the photovoltaic segment in Poland is undergoing a strong, upward trend. The above inspired the authors to explore the energy market situation in Poland and the Baltic States in the current economic conditions, along with an analysis of its development potential in light of the coronavirus pandemic. The main research problem of this study is an attempt to answer the question of what should be changed in the development of the renewable energy market in Poland, with particular emphasis on photovoltaics, to accelerate the process of reducing CO2 emissions, leading to a reduction in dramatically rising electricity prices. Which solutions implemented in the Baltic countries can inspire strengthening Poland's energy market development? [ABSTRACT FROM AUTHOR]

The subject of this publication is an analysis of the sentiment of stock exchange investors in terms of making investment decisions in the energy sector of the Polish stock exchange. The investment mood is considered in the context of the possible impact of weather factors on investment decisions. Possible effects are verified in relation to the rates of return and the volume of trading of energy sector entities. The analysis is carried out both in terms of co-integration analyses as well as in econometric terms, in the cross-section of classic OLS models or causality analysis using VAR vector autoregression models. The main purpose of the issues discussed is the problem of indicating (illustrating) the presence or absence of mutual relations between weather factors and the stock market in terms of the methods considered. [ABSTRACT FROM AUTHOR]

Connections to world markets facilitate local markets developments to support more efficient capital allocation and greater investment and growth opportunities. Under the framework of cross-market rebalancing theory, in this study, we aim to systematically examine the market connections among world financial, energy, renewable energy and European carbon markets by measuring the return spillovers from 2008 to 2021. We find that the renewable energy market is more closely connected to the world financial and energy markets in the sense of the return transmission, while the carbon market is less connected to them. However, due to improved market regulations and determinations related to fighting climate change, the connections between the carbon market and other markets have gradually intensified. Plotting the return spillover indexes, we observe that strong return spillovers from the renewable energy market to other markets occurred when large investment plans were announced. Regarding the carbon market, regulation changes introduced by the EU Commission to improve and stabilize market environment induced intensified return transmission from carbon market to other markets. Another interesting finding is that the highly intensified return transmission among markets due to the COVID-19 crisis started to loosen when COVAX published the first interim distribution forecast on 3 February 2021. [ABSTRACT FROM AUTHOR]

It is because of competencies that there is a possibility of ensuring the strategic safety of each country when it comes to energy security. With the vast development of IT and teamwork, there are various competencies needed in the whole energy sector. The aim of this study is to assess the needs of competencies in the Polish energy labor market as well as the trends among hard ad soft skills also in the context of renewable energy sources. Within an exploratory approach, 245 job advertisements were analyzed using various tools, including general descriptive statistics, Shapiro-Wilk, Kruskal–Wallis H and Mann–Whitney U tests, as well as Spearman's Rho. The research confirmed that teamwork and MS Office are crucial demands of employers. It is also important that the market is diverse when it comes to competence demands, but soft skills are needed in every position, even purely technical ones. [ABSTRACT FROM AUTHOR]

Power suppliers in a dynamic power market can achieve full benefit by introducing a bidding strategy mechanism. In the power sector, renewable resources have significant gradual usage and their effect on the production of detailed bidding approaches is becoming further complicated in the industry. Due to the irregular nature of these renewable resources and because they are subject to several fluctuations, there is an inherent issue with generating electricity. Taking these considerations into account, attempts have been made to create a model of bidding strategy to optimize the benefit of the electricity producers using the oppositional gravitational search algorithm. The Weibull and Beta distribution functions are utilized to describe the stochastic characteristics of the wind-speed profile and solar-irradiation, respectively. For the IEEE-30 and IEEE-57 frameworks, the suggested method is being checked and explained. In comparison to other optimization approaches, the results of this approach were taken into account, and it was discovered that it outperformed other techniques in addressing bid difficulties. In addition, it is worth noting that the impact of renewable energy on the bidding strategy lowered market clearing and thermal power generating costs, and encouraged renewable influenced producers to put forward the excess electricity into the real-time market. [ABSTRACT FROM AUTHOR]

Renewable energy technologies (RETs) are crucial for solving the world's energy dilemma. However, the diffusion rate of RETs is still dissatisfactory. One critical reason is that conventional energy technologies (CETs) are dominating energy markets. Emergent technologies that have inferior initial performance but eventually become new dominators of markets are frequently observed in various industries, which can be explained with the disruptive innovation theory (DIT). DIT suggests that instead of competing with incumbent technologies in the dominated dimension, redefining the competition on a two-dimensional basis is wise. Aiming at applying DIT to RET diffusion, this research builds an agent-based model (ABM) considering the order of entering the market, price, preference changing and RET improvement rate to simulate the competition dynamics between RETs and CETs. The findings include that the order of entering the market is crucial for a technology's success; disruptive innovation is an effective approach to cope with the disadvantage of RETs as latecomers; generally, lower price, higher consistency with consumers' preferences and higher improvement rate in the conventional dimension are beneficial to RET diffusion; counter-intuitively, increasing RET's improvement rate in the conventional dimension is beneficial to RET diffusion when the network is sparse; while it is harmful when the network is densified. [ABSTRACT FROM AUTHOR]

Oil is an important energy commodity. The difficulties of forecasting oil prices stem from the nonlinearity and non-stationarity of their dynamics. However, the oil prices are closely correlated with global financial markets and economic conditions, which provides us with sufficient information to predict them. Traditional models are linear and parametric, and are not very effective in predicting oil prices. To address these problems, this study developed a new strategy. Deep (or hierarchical) multiple kernel learning (DMKL) was used to predict the oil price time series. Traditional methods from statistics and machine learning usually involve shallow models; however, they are unable to fully represent complex, compositional, and hierarchical data features. This explains why traditional methods fail to track oil price dynamics. This study aimed to solve this problem by combining deep learning and multiple kernel machines using information from oil, gold, and currency markets. DMKL is good at exploiting multiple information sources. It can effectively identify the relevant information and simultaneously select an apposite data representation. The kernels of DMKL were embedded in a directed acyclic graph (DAG), which is a deep model and efficient at representing complex and compositional data features. This provided a solid foundation for extracting the key features of oil price dynamics. By using real data for empirical testing, our new system robustly outperformed traditional models and significantly reduced the forecasting errors. [ABSTRACT FROM AUTHOR]

Energy markets are based on energy transactions with a central control entity, where the players are companies. In this research work, we propose an IoT (Internet of Things) system for the accounting of energy flows, as well as a blockchain approach to overcome the need for a central control entity. This allows for the creation of local energy markets to handle distributed energy transactions without needing central control. In parallel, the system aggregates users into communities with target goals and creates new markets for players. These two approaches (blockchain and IoT) are brought together using a gamification approach, allowing for the creation and maintenance of a community for electricity market participation based on pre-defined goals. This community approach increases the number of market players and creates the possibility of traditional end users earning money through small coordinated efforts. We apply this approach to the aggregation of batteries from electrical vehicles so that they become a player in the spinning reserve market. It is also possible to apply this approach to local demand flexibility, associated with the demand response (DR) concept. DR is aggregated to allow greater flexibility in the regulation market based on an OpenADR approach that allows the turning on and off of predefined equipment to handle local microgeneration. [ABSTRACT FROM AUTHOR]