Electricity Trading Research Articles

A privacy-preserving trading strategy for blockchain-based P2P electricity transactions

With the rising demand for energy in distribution networks and the growing development of communication technology, peer-to-peer (P2P) electricity trading realized by blockchain technology not only promotes the participation of distributed generation resources in the energy supply but also enhances the fairness of P2P electricity trading through the decentralized and transparent characteristics of the blockchain. However, owing to the openness and transparency of the on-chain data in the blockchain, the order information submitted by P2P market participants to the blockchain may encounter threats of information disclosure. To address this challenge, we have designed a novel three-layer architecture for P2P electricity trading, whose primary objective is to protect the private information submitted to the blockchain through a new privacy-preserving trading strategy. The proposed three-layer architecture is composed of a physical layer, information layer and market layer. An on-chain data protection method with homomorphic encryption and secure multi-party computing (SMPC) is used to sort and clear encrypted orders in the information layer, and the trading checking and safe settlement process of encrypted orders in continuous double auction (CDA) market are realized through grid security-constrained market mechanism in the market layer. In addition, the effectiveness of the proposed method is verified by case studies running on the IBM Hyperledger Fabric.

Assessing the Co-movements Between Electricity Use and Carbon Emissions in the GCC Area: Evidence from a Wavelet Coherence Method

This paper investigates the association between CO2 emissions and a range of factors, including electricity consumption, economic growth, urbanization, and trade openness for six Gulf Cooperation Council (GCC) countries using data covering the 1965–2019 period. Namely, Oman, Saudi Arabia, the UAE, Kuwait, Bahrain, and Qatar. Contrasting with the standard literature, our empirical strategy uses the wavelet coherence approach on the frequency domain, thought to complement the time series econometric procedures reported earlier on this topic. Supplied at the country level, associated evidence presents far-reaching policy recommendations whose applications may directly benefit environmental planning and bring high information value for the sake of sustainable energies in the Gulf region.

Financial and Economic Stability of Energy Sector Enterprises as a Condition for Poland’s Energy Security—Legal and Economic Aspects

The energy security of each country is one of the main factors of its proper functioning. Currently, in the era of problems related to energy security resulting from, among other things, the war in Ukraine, this topic is particularly important. This article presents issues related to Poland’s energy security, understood as the financial and economic stability of enterprises operating in the energy industry. This stability is considered in two aspects: macroeconomic, where the focus is mainly on the aspect of state intervention in market processes; and microeconomic, where factors determining the financial security of energy enterprises were identified, including internal and external factors affecting the functioning of these entities. In order to achieve the assumed research goals, the analysis of the indicated problems was based on non-reactive research, consisting in the assessment of the available information. It included studies of normative acts, official statistical data, industry reports and analyses, as well as data obtained in the form of a public information request. Two basic research methods were used in the work—dogmatic–legal and comparative analyses. The identification of factors affecting the security of companies in the sector was carried out on the basis of data on the entire energy sector in Poland for the years 2015–2021 on a semi-annual basis. Vector-autoregressive models were used for the analysis. As a result of the analyses, it was established that market failures and public safety are the premises justifying the public financing of enterprises in the electricity generation, transmission, distribution and trade sectors. At the same time, the conducted research showed that the level of financial security of energy enterprises in Poland was affected by the ratio of the value of goods and materials sold to net sales revenue, as well as the level of EBIT (earnings before deducting interest and taxes) margin, and among external factors, the level of GDP (gross domestic product), CPI (consumer price index) and Crude Oil were important.

Water-saving co-benefits of CO2 reduction in China’s electricity sector

Electricity sector is the largest CO2 emitter and water user in China's industrial sectors. The low-carbon transition of China's electricity sector reduces its cooling water consumption. Here we firstly quantify CO2 emission and virtual water embodied in electricity trade with Quasi-Input-Output model. Then, we analyze the impacts of energy substitution, efficiency improvement, and electricity trade on water-saving co-benefits of CO2 reduction with the differences between the baseline scenario and counterfactual scenario. Results show that the low-carbon transition contributes to water-saving in China's electricity sector. Virtual water and embodied CO2 have relatively decoupled from electricity trade since 2012. Water-saving (+10.4% yr-1) outweighed CO2 reduction (+8.4% yr-1) through energy substitution and efficiency improvement in the 'new normal' stage. Our work emphasizes the need to integrate water-saving co-benefits of CO2 reduction into electricity system planning and highlights the challenges to facilitate coordinated development of the electricity-water nexus in China.

Exploring the participation willingness and potential carbon emission reduction of Chinese residential green electricity market

China officially established a green electricity trading market in 2021. This study investigated the intention of Chinese households to participate in the green electricity tariff and surveyed their acceptable maximum premium; afterwards, the corresponding potential carbon emission reduction is evaluated. The survey shows that 82% of Chinese households support the additional willingness-to-pay (WTP) for green electricity. The mean willingness-to-pay values without protest-zeros are evaluated as RMB14.6 with a gender differential that men prefer to pay more than women. Relative impact factors analysis shows that household' intention of using green-e significantly impacts their participation in extra WTP, but households' income does not show a significant effect. On the other hand, the model for extra expenditure shows the opposite effect of these two factors. Last, the study conducted a scenario analysis to evaluate the potential emission reduction and related welfare transformation efficiency. The scenario analysis reveals that households’ green electricity trading potentially contributes to 30%–54% of the carbon reduction and a reasonable trading mechanism could achieve emission reductions goal with less welfare loss. Finally, some policy implications are proposed, providing theoretical support for policy-makers.

CROATIA AND ELECTRICITY TRADING IN CENTRAL EUROPE

This article will discuss electricity trading in Central Europe, with particular emphasis on Croatia. Croatia is a terminal and also a transit hub: a unique combination on the Central European electricity market. It will be explained how cross-border capacity auctions influence prices on the Central European electricity market, followed by a discussion about the most important factors that are significant for Croatia’s electricity trading strategy. Finally, the article will describe the current HEP electricity tendering process and make some recommendations for possible improvements in the future.

A Cloud-Edge-Based Framework for Electric Vehicle Emergency Energy Trading

The number of electric vehicles (EVs) is increasingly growing day by day and the charging infrastructure for covering this growing number of EVs should be developed. The construction of charging stations is one of the main solutions for supporting EVs while it costs huge investments for installation. Thus, this is not financially logical to invest in charging stations in remote areas with lower demands. An alternative way of constructing charging stations is to provide a peer-to-peer (P2P) energy exchange system in order to support out-of-charge EVs. In this paper, a private cloud-edge emergency energy trading framework is proposed to facilitate energy exchange among consumers and providers. Furthermore, a bidding system is suggested to encourage EVs with extra charges to exchange their energy. Moreover, a matching strategy for pairing consumers and providers is suggested in this paper that considers the benefit of both consumers and providers. In the proposed matching system, a measurement strategy is also suggested for considering the effect of the reliability and punctuality of the providers. To develop the accuracy and efficiency of the proposed framework, employing deep learning methods is also suggested in different layers of the framework. The performance of the proposed framework is evaluated on several case studies in the presence of EVs with realistic features to prove its efficiency, feasibility, and scalability.

CE-SDT: A new blockchain-based distributed community energy trading mechanism

With the spread of distributed renewable energy, residents are shifting from being mere consumers to being energy producers and consumers. This role shift poses challenges to the electricity trading mechanism that connects distributed renewable energy sources to the grid. In this paper, a new efficient and secure blockchain-based distributed community energy trading mechanism is proposed, called CE-SDT. Our system is proved to be stable and scalable. It can also help shift loads and power peaks and reduce customer costs by 60%. As a result, our proposed blockchain-based trading mechanism, as compared to the centralized trading mechanism, is applied to microgrids formed by distributed renewable energy sources, not only obtaining greater economic benefits but also reducing the carbon footprint of residents, and furthermore, it promotes low or zero-carbon configurations of the power system, thereby achieving certain environmental benefits.

ARX-GARCH Probabilistic Price Forecasts for Diversification of Trade in Electricity Markets—Variance Stabilizing Transformation and Financial Risk-Minimizing Portfolio Allocation

In this paper, we propose dynamic, short-term, financial risk management strategies for small electricity producers and buyers that trade in the wholesale electricity markets. Since electricity is mostly nonstorable, financial risk coming from extremely volatile electricity prices cannot be reduced by using standard finance-based approaches. Instead, a short-term operational planing and a proper trade diversification might be used. In this paper, we analyze the price risk in terms of the Markowitz mean–variance portfolio theory. Hence, it is crucial to forecast properly the variance of electricity prices. To this end, we jointly model day-ahead and intraday or balancing prices from Germany and Poland using ARX-GARCH type models. We show that using heteroscedastic volatility significantly improves probabilistic price forecasts according to the pinball score, especially if variance stabilizing transformation is applied prior to a model estimation. The price forecasts are then used for construction of dynamic diversification strategies that are based on volatility-type risk measures. We consider different objectives as well as a buyer’s and a seller’s perspective. The proposed strategies are applied for the diversification of trade among different markets in Germany and Poland. We show that the objective of the strategy can be achieved using the proposed approach, but the risk minimization is usually related to lower profits. We find that risk minimization is especially important for a seller in both markets, while for a buyer a profit maximization objective leads to a more optimal risk–return trade-off.

Insightful Electric Vehicle Utility Grid Aggregator Methodology Based on the G2V and V2G Technologies in Egypt

Due to the exponential expansion of the global fleet of electric vehicles (EVs) in the utility grid, the vehicle-to-grid paradigm is gaining more attention to alleviate the pressure on the grid. Therefore, an EV aggregator acts as a resilient load to enhance the power deficiency in the electrical grid. This paper proposes the vital development of a central aggregator to optimize the hierarchical bi-directional technique throughout the vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technologies. This study was implemented using three different types of EVs that are assumed to penetrate the utility grid throughout the day in an organized pattern. The aggregator determines the number of EVs that would participate in the electric power trade during the day and sets the charging/discharging capacity level for each EV. In addition, the proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner using the V2G technology and ensuring a sufficient grid peak load demand shaving based on the genetic algorithm (GA). Three case studies were investigated based on the parking interval time where the battery degradation cost was minimized to reach approx. 82.04%. However, the revenue of the EV owner increased when the battery degradation cost was ignored. In addition, the load demand decreased by 26.5%. The implemented methodology ensured an effective grid stabilization service by shaving the load demand to identify the average required power throughout the day. The efficiency of the proposed methodology is ensured since our output findings were in good agreement with the literature survey.

ВПЛИВ ФІНАНСОВИХ, СОЦІАЛЬНИХ, ЕКОНОМІЧНИХ ДЕТЕРМІНАНТ ТА ФАКТОРІВ ГРОМАДСЬКОГО ЗДОРОВ'Я НА ШВИДКІСТЬ ВІДНОВЛЕННЯ МАКРОЕКОНОМІЧНОЇ СТАБІЛЬНОСТІ ЄВРОПЕЙСЬКИХ КРАЇН

This article aims to assess the impact of financial, social, and economic determinants and public health factors on the speed of restoration of macroeconomic stability in European countries. The study was conducted on the example of fifteen European countries (Albania, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Moldova, Poland, Romania, Serbia, Slovakia, Slovenia and Ukraine) based on fourteen relevant indicators during 2000–2022. Six indicators were selected using the principal components method to build a panel multivariate regression model. Using the Hausman test, it was determined that a panel regression model with fixed effects should be built for this set of data. Based on the results of regression modeling, it was found that the rate of restoration of macroeconomic stability has a positive, statistically significant effect on the level of electricity consumption and trade activity of the state and a negative, statistically significant impact on gross capital accumulation, state spending on final consumption, and the current account balance. It was found that with a unit increase in the level of electricity consumption and trade activity of the state, the rate of recovery of macroeconomic stability increases by 0.0001 and 0.004 units, respectively. In contrast, with the increase in gross capital formation, government final consumption expenditure, and current account balance per unit, the rate of recovery of macroeconomic stability decreases by 0.006, 0.014, and 0.01, respectively. No statistically significant relationship with current state spending on health care was found. In general, understanding the impact of financial, social, and economic determinants and public health factors on macroeconomic stability is vital for developing scientific theories and practical strategies and measures to ensure the stability of economies and protect citizens in crises. The relevance of this topic goes beyond purely theoretical considerations and directly affects the well-being and life of citizens, as well as the stability of states and the global economic system.

Peer-to-Peer Joint Electricity and Carbon Trading Based on Carbon-Aware Distribution Locational Marginal Pricing

This paper proposes a novel Peer-to-Peer (P2P) joint electricity and carbon (E&C) trading model to co-optimize the energy and carbon emissions permit transactions considering the trading preferences in the distribution network. To realize the secure and low-carbon network operation, a decomposable carbon-aware distribution locational marginal pricing (CDLMP)-based operation service pricing scheme of the distribution system operator (DSO) is proposed to guide the P2P transactions among prosumers. In practice, the coordination between the P2P market and the DSO is cast as a bi-level optimization. In the upper level, the network operation optimization of the DSO is modeled as a carbon-aware optimal power flow (COPF) problem based on a carbon emission flow model to ensure the security of network operation and derive the CDLMP. In the lower level, a decentralized P2P joint E&C trading is modeled to simultaneously clear energy and carbon permit. Later, this paper develops an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> (p)-Box ADMM method to relax the binary variables originating from the peer-matching process of the carbon trading. Finally, a novel demurrage protocol is designed to facilitate the convergence of the bi-level interaction with an incentive for trading adjustments. Case studies illustrate the effectiveness of the proposed method in motivating “grid-friendly” and “low-carbon” P2P trading.

Collaborative Complementary Electricity Trading Mechanism Between new Energy and Coal-Fired Power Generation Enterprises in Power Grid Under the Dual Carbon Targets

To promote the implementation of the national strategy of “carbon peaking and carbon neutrality” (the dual carbon targets), China is constructing a new power grid with renewable energy as its main component. This power grid presents new challenges regarding the reliability, security, and stability of power supply and economic operations. At the same time, there are also a series of key issues in the collaborative and complementary electricity trading between renewable energy and coal-fired power generation companies, especially how to strengthen the optimal allocation of resources between provinces. This article firstly identifies the changes in the new power system under the dual carbon targets. Then, it analyzes the key issues of the collaborative and complementary trading mechanism between renewable energy and coal-fired power generation companies from two perspectives: inter provincial and intra provincial market collaboration and the diversified transaction demand of multiple market entities. Finally, suggestions are put forward for the construction of this electricity trading mechanism.

Construction of an evaluation index system for power market considering economic-environmental multi-objectives

At present, China's electricity market has been initially built, direct trading of electricity continues to increase, and the mode of "multiple purchase and multiple sale" has emerged in the market, which enables the electricity market to compete effectively and make the optimal allocation of resources. Therefore, it is necessary to evaluate the operation of the electricity market. The article firstly studied the indicators of power market operation from three aspects: economic value, green value and security value; secondly, established a three-level evaluation index system of power system; on this basis, proposed a comprehensive evaluation model of power market based on fuzzy comprehensive evaluation. It provides a reference basis for the evaluation of China's power grid operation and is of great significance to promote the healthy development of China's power market.

Mean Field Game based Energy Flow Management for Grid Integration of Large-scale Electric Vehicles*

To integrate the distributed energy of electric vehicles (EVs) into power grid, handling the collective energy demand of the EVs is a key issue due to the uncertainties in mobility behavior of the individual vehicle. This paper addresses this problem by using mean-field limit approach to challenge this issue. The system targeted in this paper consists of an electric energy supplier and a parking lot that hosts a electric vehicle (EV) fleet with sufficiently large population. The energy integration between the system and the power grid is formulated as two problems: a day-ahead electric trading planning problem of the supplier, where the benefit maximization is considered under the constraint of charging/discharging demand of the parking lot, and a decentralized charging control problem is solved for the individual EV with consideration of the collective behavior of the whole EV fleet. To decouple the two problems, the mean-field limit approach is introduced to predict the charging/discharging demand of the EV fleet, and this also enables one to design the decentralized control strategy by solving a mean-field game problem.

Machine Learning-Based Day-Ahead Prediction of Price-Setting Scheduled Energy in the Korean Electricity Trading Mechanism

Power generation companies, which participate in the electricity trading mechanism, need to determine optimal capacity bidding strategy to maximize their operational profit in Korea. Since the Price-setting Power Generation Schedule determines the profit of power generators, it is important to predict Price-setting Scheduled Energy during the trading day before the bidding phase. We propose a methodology for predicting the Pricing-setting Scheduled Energy from the power exchange without optimizing it. Instead of simulating the planning process, machine learning algorithms are applied and compared in the process of predicting the Pricing-setting Scheduled Energy. The input variables consist of seasonal and price information including calendar, fuel cost, and system marginal price. Three categories of machine learning (ML) algorithms including single, bagging and boosting approaches are implemented and tested to compare their performances. The computational experiments show that ML algorithms with price variable are shown to be better in terms of the considered measures. In addition, boosting approach is more effective than single and bagging approaches.

Agent simulation research of the impact of free market electricity trading on the power supply mix

Agent simulation research of the impact of free market electricity trading on the power supply mix

The Cost of Carbon Leakage: Britain’s Carbon Price Support and Cross-border Electricity Trade

Carbon taxes create global benefits unless offset by increased emissions elsewhere. An additional carbon tax in one country may cause leakage through imports and will also increase costs by creating a wedge between economic marginal costs in different markets, causing an offsetting deadweight loss. We estimate the global benefit, carbon leakage and deadweight cost of the British Carbon Price Support (CPS) on GB’s cross-border electricity trade with France and The Netherlands. Over 2015–2020 the unilateral CPS created €72넠 m/yr deadweight loss, about 31% of the initial economic value created by the interconnector, or 2.5% of the global emissions benefit of the CPS at €2.9±0.1 bn/yr. About 16.3±3.5% of the CO2 emissions reduction is undone by France and The Netherlands, the monetary loss of which is about €584±127 m/yr.

Blockchain Technology in Modern Power Systems: A Systematic Review

Blockchain is a distributed decentralized peer-to-peer network, which is used for sharing data across a large number of entities in a trusted and secure way. Blockchain utilizes different mechanisms, such as hash functions, consensus algorithms, etc., for data verification and validation. In modern power systems, blockchain technology is used for balancing supply and demand, contributing to the demand-side management programs, and mainly, transitioning consumers to prosumers to trade electricity and reduce operational costs. This article aims at providing an in-depth discussion on energy transition and digitalization in power systems and investigating the role of blockchain technology in modern power systems. In addition, opportunities, challenges, and limitations of blockchain technology in modern power systems are discussed.

Economic Prospects of Cross-Border Electricity Trade Between Nepal and India

This study focuses on exploring the economic opportunities and potential challenges of cross-border electricity trade between Nepal and India. The main focus is on the transmission infrastructure, economic prospects, regulatory frameworks, and challenges involved. The study was conducted through secondary data analysis and descriptive research methods, to explore the strategic importance of electricity trade for Nepal, considering its abundant hydropower potential and geographical advantages. The existing transmission lines, infrastructure, and the role of private sector involvement in facilitating trade were examined. Moreover, the study discussed the seasonal demand variations between the two countries and the regulatory hurdles that need to be addressed for successful trade implementation. The finding highlights the potential economic benefits of electricity trade and emphasizes the need for clear guidelines, infrastructure development, and effective cooperation to overcome challenges and maximize trade’s positive impacts on economic growth and trade balance between India and Nepal.