• Energy Research
• 7. Clean energy close
• FI close
• Osuva (University of Vaasa) close

The smart grid is a fully automatic delivery grid for electricity power with a two-way reliable flow of electricity and information among different equipment on the grid. Smart meters and sensors monitoring the system provide a huge amount of data in various part of smart grid. To logically manage this trouble, a new lossy data compression approach for big data compression is proposed. The optimal singular value decomposition (SVD) is applied to a matrix that achieves the optimal number of singular values to the sending process, and the other ones will be neglected. This goal is done due to the quality of retrieved data and the compression ratio. In the presented scheme, to implement the optimization framework, various intelligent optimization methods are used to determine the number of optimal values in the elimination stage. The efficiency and capabilities of the proposed method are examined using a wide range of data types, from electricity market data to image processing benchmarks. The comparisons show that the compression level obtained by the proposed method can dominate the points given by the existing SVD rank reduction methods. Also, as the other finding of this article, the performance of the rank reduction methods depends on the application and data types. It means that a rank reduction method can reveal a good performance in one application and performs unacceptably for another purpose. So, the optimized rank reduction can pave the way toward a robust and reliable performance.

AbstractElectricity theft is a primary concern for utility providers, as it leads to substantial financial losses. To address the issue, a novel extreme gradient boosting (XGBoost)‐based model utilizing the consumers’ electricity consumption patterns for analysis is proposed for electricity theft detection (ETD). To remove the imbalance in the real‐world electricity consumption dataset and ensure an even distribution of theft and non‐theft data instances, six different artificially created theft attacks were used. Moreover, the utilization of the XGBoost algorithm for classification, especially to identify malicious instances of electricity theft, yielded commendable accuracy rates and a minimal occurrence of false positives. The proposed model identifies electricity theft specific to the regions, utilizing electricity consumption parameters, and other variables as input features. The authors’ model outperformed existing benchmarks like k‐neural networks, light gradient boost, random forest, support vector machine, decision tree, and AdaBoost. The simulation results using the false attacks for balancing the dataset have improved the proposed model's performance, achieving a precision, recall, and F1‐score of 96%, 95%, and 95%, respectively. The results of the detection rate and the false positive rate (FPR) of the proposed XGBoost‐based detection model have achieved 96% and 3%, respectively.

Abstract One of the most significant current topics in the energy market is the decentralized operation of the power system wherein the entire discipline is constructed based on the consensus concept. In such a framework, an agreement among all effective participants in the market should be brought without the presence of an independent arbitrator. This paper proposes a secured energy market architecture based on peer-to-peer (P2P) idea to provide an appropriate platform based on the decentralized network and key aspects of energy market, including network architecture, interface communication, and network security. It is apparent that communication interfaces connecting the market participants is a fundamental property for achieving this purpose and that must be secured against the malicious attacks. In this regard, reaching the secured consensus is guaranteed by providing a new blockchain platform coincided by P2P energy market. The energy market is conducted by an effective Relaxed Consensus-Innovation (RCI) based algorithm with the aim of bringing the power/price exchanged among connecting participants in the form of P2P structure. In the proposed model, a microgrid and a smart grid are considered as the market participants, who tend to negotiate with each other in a way that follow their own benefits in the secured environment. The microgrid includes the wind turbine (WT), photovoltaic (PV), tidal turbine and storage unit to satisfy its demand and the smart grid is composed of distributed generations (DGs) and lines in the form of the IEEE 24-bus test system. In order to handle the uncertainty effects in the problem, a stochastic framework based on unscented transform (UT) is proposed in the P2P energy market. In order to assess and verify the fault-tolerant system ability against the cyber-attack, the fault data injection attack (FDIA) is modeled and applied to the P2P energy market in a blockchain platform. The simulation results approve the appropriate performance and applicable nature of the proposed concepts in this paper.

AbstractWith the exemption of Canada, the G‐7 countries have largely flourished at the detriment of their ecological sustainability bearing in mind that these countries' have remained ecologically deficit for several decades. Given the potential effect of environmental degradation associated with the trend of ecological deficit of these countries, this study attempts to understand the contribution of renewable energy dimensions through the measure of renewable energy efficiency and renewable energy use alongside evaluating the role of the four main aspects of economic freedom. By using empirical tools, the findings revealed that renewable energy aspects contribute to environmental sustainability among the countries through a significant mitigation of their ecological footprint. Importantly, the aspects of economic freedom, that is, government size, legal system and property rights, freedom to trade internationally, and regulation hampers environmental sustainability by increasing the countries ecological footprint. The elasticity of impact of this dimension of economic freedom is in the range of 0.19–0.21 at 1% statistically significant level. However, population of these countries does not show a detrimental effect, rather the finding revealed that population improves environmental quality by a statistically significant degree. Given these revelations, there are deducible policy take home from this study.

The uncertainty of wind energy makes wind power producers (WPPs) incur profit/loss due to balancing costs in electricity markets, a phenomenon that restricts their participation in markets. This paper proposes a stochastic bidding strategy based on virtual power plants (VPPs) to increase the profit of WPPs in short-term electricity markets in coordination with energy storage systems and demand response. To implement the stochastic solution strategy, the Kantorovich method is used for scenario generation and reduction. The optimization problem is formulated as a Mixed-Integer Linear Programming problem. From testing the proposed method for a Spanish WPP, it is inferred that the proposed method enhances the profit of the VPP compared to previous models.

AbstractGlobal warming and environmental degradation caused essentially by changes in climate have attracted enormous surveillance considering the menace of its reverberation on the health of humans during the past two decades. Utilization of energy and financial development (FD) are among the key drivers of climatic change. Thus, using second‐generation panel cointegration (the Westerlund, 2007 error‐correction model), pooled mean group autoregressive distributive lag model (PMG‐ARDL), and the panel dynamic ordinary least square (PDOLS) estimation techniques, the paper scrutinized the nexus between financial development, clean energy usage, economic growth, and environmental quality (proxied by CO2 emissions) of BRICS countries starting from 1980 to 2018. The findings from the study reveal that economic growth and labor force participation, in the long run, deteriorate the environmental quality by increasing the effusion of carbon. Contrarily, financial development, industrialization, trade openness, and renewable energy usage enhance the environmental quality of BRICS countries in the long run. In the short run, financial development was found to have a significant positive impact on the environmental quality of Brazil, China, and Russia, while it is negative for South Africa and India. The outcome of the PVECM Granger causality test reveals a two‐way Granger causality that runs from renewable energy to carbon emissions in the short run. The policy implication of this study is that the government of BRICS countries needs to concentrate on improving their clean energy sources and also work on their industries. The BRICS nations' governments should formulate financial and trade policies that promote a sustainable environment and economic development.

Abstract Investigating the performance and productivity level of different energy consuming sectors in all countries is an inevitable action. This procedure will be conducted by comparing the energy input and the output of the system which is vital to ensure that the system is used properly. The proper utilization of systems will lead to more efficiency in the energy consumption section. One of the most important tasks in this type of study is the analysis of uncertainty indicators. The analysis and evaluation of uncertainty indices in energy consumption system is a tool that prioritizes the indicators in terms of importance and impact on each of the consumption targets. These consumption goals include energy, environmental, technical, economical, and social objectives. Ultimately, the output data of the uncertainty analysis will be very helpful for making the system more reliable and usable. In this study, we first introduced different sectors of the energy consumption system in Finland and examined each of these sectors in terms of physical and environmental goals. Then the uncertainty indexes in different sectors are extracted, evaluated qualitatively and quantified using the fuzzy logic method. Finally, indicators are prioritized based on the level of effectiveness and uncertainty. According to the results of this research, among 44 considered indices, the security of energy supply, carbon emission, equivalent annual cost, reliability, and political acceptability are respectively the most important indices for energy, environmental, economic, technical and social goals.