Energy Trading Research Articles

Comparative Analysis of Market Clearing Mechanisms for Peer-to-Peer Energy Market Based on Double Auction

This paper aims to develop an optimisation-based price bid generation mechanism for the sellers and buyers in a double-auction-aided peer-to-peer (P2P) energy trading market. With consumers being prosumers through the continuous adoption of distributed energy resources, P2P energy trading models offer a paradigm shift in energy market operation. Thus, it is essential to develop market models and mechanisms that can maximise the incentives for participation in the P2P energy market. In this sense, the proposed approach focuses on maximising profit at the sellers, as well as maximising cost savings at the buyers. The bids generated from the proposed approach are integrated with three different market clearing mechanisms, and the corresponding market clearing prices are compared. A numerical analysis is performed on a real-life dataset from Ausgrid to demonstrate the bids generated from sellers/buyers, as well as the associated market clearing prices throughout different months of the year. It can be observed that the market clearing prices are lower when the solar generation is higher. The statistical analysis demonstrates that all three market clearing mechanisms can achieve a consistent market clearing price within a range of 5 cents/kWh for 50% of the time when trading takes place.

Dynamic Modeling and Optimization of Energy Storage in Peer-to-Peer Energy Trading Systems

The power system landscape has evolved from isolated end-users to interactive communities due to advances in information and communication technologies. This paper explores peer-to-peer energy (P2PE) trading and sharing within a community, where customer incentives for energy exchange enhance collective profits. A two-stage optimization (TSO) framework is proposed: the first stage determines customer participation in P2PE, balancing individual and collective benefits, while the second stage optimizes economic aspects of P2P trading using a payment bargaining model. A case study demonstrates significant cost reductions and improved renewable energy utilization, with notable profit increments for participants. The study highlights the effectiveness of Nash bargaining theory and privacy-preserving algorithms in optimizing social welfare and economic interactions. Limitations include a focus on wind energy and simplified assumptions about energy storage. Future research should incorporate diverse renewable sources, dynamic modeling, and multi-community interactions.

A Framework for Distributed Orchestration of Cyber-Physical Systems: An Energy Trading Case Study

The increasing number of active energy consumers, also known as energy prosumers, is dramatically changing the electricity system. New products and services that adopt the concept of dynamic pricing are available to the market, where demand and price forecasting are applied to determine schedule loads and prices. Throughout this manuscript, a novel framework for energy trading among prosumers is introduced. Rather than solving the problem in a centralized manner, the proposed orchestrator relies on a distributed game theory to determine optimal bids. Experimental results validate the efficiency of proposed solution, since it achieves average energy cost reduction of 2×, as compared to the associated cost from the main grid. Additionally, the hardware implementation of the introduced framework onto a low-cost embedded device achieves near real-time operation with comparable performance to state-of-the-art computational intensive solvers.

Assessing the environmental impact of institutional quality at aggregate and disaggregate levels: The role of renewable and non-renewable energy consumption and trade in MENA countries

Assessing the environmental impact of institutional quality at aggregate and disaggregate levels: The role of renewable and non-renewable energy consumption and trade in MENA countries

Advanced System for Optimizing Electricity Trading and Flow Redirection in Internet of Vehicles Networks Using Flow-DNET and Taylor Social Optimization

The transportation system has a big impact on daily lifestyle and it is essential to energy transition and decarbonization initiatives. Stabilizing the grid and incorporating sustainable energy sources require technologies like the Internet of Energy (IoE) and Internet of Vehicles (IoV). Electric vehicles (EVs) are essential for cutting emissions and reliance on fossil fuels. According to research on flexible charging methods, allowing EVs to trade electricity can maximize travel distances and efficiently reduce traffic. In order to improve grid efficiency and vehicle coordination, this study suggests an ideal method for energy trading in the Internet of Vehicles (IoV) in which EVs bid for electricity and Road Side Units (RSUs) act as buyers. The Taylor Social Optimization Algorithm (TSOA) is employed for this auction process, focusing on energy and pricing to select the best Charging Station (CS). The TSOA integrates the Taylor series and Social Optimization Algorithm (SOA) to facilitate flow redirection post-trading, evaluating each RSU’s redirection factor to identify overloaded or underloaded CSs. The Flow-DNET model determines redirection policies for overloaded CSs. The TSOA + Flow-DNET approach achieved a pricing improvement of 0.816% and a redirection success rate of 0.918, demonstrating its effectiveness in optimizing electricity trading and flow management within the IoV framework.

Research on Changes in International Energy Trade Pattern under the Russia-Ukraine Conflict

The impact of the conflict between Russia and Ukraine on world energy markets has received great attention. Some researchers have found that the Russia-Ukraine conflict has caused an energy crisis to a certain extent. However, there is a lack of overview of the changes in the trade pattern and the scope of its impact. Therefore, through a comprehensive analysis of changes in policies and economic data, this study analyzes the different response measures taken by countries in different regions such as Europe and Asia to deal with the reduction in Russia's exports of major energy products and the fluctuations in international energy prices. This study also analyzes how the mutual sanctions between Russia and Western countries have contributed to the shift of Russia's energy export focus to the Asia-Pacific region and the diversification of Western countries' energy supply. Research results show that energy trade is at risk of being affected by geopolitical factors such as regional conflicts and mutual sanctions. The Russia-Ukraine conflict has strengthened the attention of countries on energy security issues. The research also highlights the implications for future policies and actions, providing an understanding of emerging trends in the future. Economically, this study, based on past data, emphasizes the inevitability of the Russia-Ukraine conflict affecting energy fluctuations and affirms that the Russia-Ukraine conflict is conducive to global new energy transformation and sustainable development in the long term.

Evaluation of a Peer-To-Peer Smart Grid Using Digital Twins: A Case Study of a Remote European Island

Decarbonization of the built environment by electrifying energy systems and decarbonizing the electrical grid coupled with the digitization of these systems is a central strategy implemented by the European Commission (EC) to meet carbon reduction policies. The proliferation of technologies such as renewable energy sources (RES) and demand-side management (DSM) systems can be improved by using digital twins to predict and optimize their integration with existing systems. Digital twins in the built environment have been used for multiple purposes, such as predicting the performance of a system before its inception or optimizing its operation during use. To this end, a novel application of a combination of these technologies towards optimized DSM is peer-to-peer (P2P) energy trading, which can improve the local use of RES in the built environment. This paper investigates the potential of P2P energy trading in optimizing local RES of a remote island, Inishmore, Republic of Ireland, using a combination of data-driven and predictive digital twins towards the island’s journey to net zero. Data-driven digital twins are used to evaluate the current energy use at the pilot site. Predictive digital twins are applied to estimate the impact of applying P2P in the future and its influence on RES consumption at the pilot site. The findings show that in scenarios with limited RES coverage, P2P can significantly increase the local consumption of excess RES energy, reducing the risk of transmission or curtailment losses. However, P2P is limited in scenarios with widespread RES installation without storage or behavioral change to shift energy loads.

Cooperative energy and reserve trading strategies for multiple integrated energy systems based on asymmetric nash bargaining theory

To tackle the issues of cooperative energy and reserve trading as well as fair cooperative benefit allocation among multiple integrated energy systems (IESs), this paper proposes a two-stage cooperative energy and reserve trading model for multiple integrated energy systems (MIESs). Specifically, at day-ahead stage, MIESs aim to maximize their overall profit through cooperative electricity and heat trading. At real-time stage, MIESs trade demand response (DR) reserve to minimize the overall wind power deviation compensation cost. To reduce the complexity in model solution, we transform the model into two sub-problems. In sub-problem 1, we determine the energy and DR reserve trading volumes. Here, distributionally robust optimization (DRO) is utilized to manage the severe uncertainties in wind power distribution. In sub-problem 2, based on the outcomes from sub-problem 1, we settle the energy and DR reserve trading prices. To ensure the fairness of benefit allocation, asymmetric Nash bargaining theory is applied to assess each IES's contributions in trading volumes and profit growth. Interval adaptive alternating direction method of multipliers (IA-ADMM) is used to preserve each IES's privacy. Finally, simulation results demonstrate that, compared with independent operation, cooperative trading among MIESs increases profits for all IESs, thereby motivating their participation in cooperative trading.

Effects of asymmetric policies to achieve emissions reduction on energy trade: A North American perspective

Effects of asymmetric policies to achieve emissions reduction on energy trade: A North American perspective

Optimization of a Bayesian Game for Peer-to-Peer Trading among Prosumers under Incomplete Information via a CNN-LSTM-ATT

In modern low-carbon industrial parks, various distributed renewable energy resources are employed to fulfill production needs. Despite the growing capacity of renewable energy generation, a significant portion of the power produced by these renewable resources remains unconsumed, resulting in a waste of resources. Within an industrial park, microgrids that both generate and consume energy resources act as energy prosumers. Peer-to-peer (P2P) trading provides an efficient means of utilizing renewable energy among these energy prosumers, who possess both power generation and consumption capabilities. However, within the current market mechanism, each prosumer retains private information that is not disclosed on the network. To address the issue of incomplete information among multiple prosumers during the decision-making process, we develop a Bayesian game model based on the CNN-LSTM-ATT prediction method for P2P electricity transactions among multiple prosumers. The energy prosumers in each industrial park aim to minimize their energy consumption costs by adjusting strategies that include P2P energy trading and managing thermal loads. Prosumers make decisions on the basis of their own characteristics and estimates of other prosumer characteristics, which are obtained from the joint probability distribution predicted by the CNN-LSTM-ATT method. These decisions are aimed at minimizing each prosumer's electricity costs. The simulation results demonstrate the effectiveness of the Bayesian game model proposed in this study.

Systemic risk spillovers among global energy firms: Does geopolitical risk matter?

In recent years, the worsening global geopolitical conditions have led investors and policy makers to become increasingly concerned about systemic risk in the global energy market. However, the existing literature has little empirical evidence on this problem from a geopolitical perspective. Based on an Elastic-Net-VAR model for depicting high-dimensional left-tail interdependence, this paper measures time-varying systemic risk spillovers among 212 energy firms in 36 countries. Then, a panel data model is established to examine the impact of geopolitical risk (GPR) on risk spillovers. Our results indicate that the GPR of a country directly intensifies the risk spillovers of the country's energy firms, as well as spreads to other countries based on geographic proximity and energy trade flows and indirectly intensifies the risk spillovers of the energy firms in these countries. Specifically, the indirect effects of GPR are more pronounced than the direct effects for fossil fuel firms, in oil-consuming countries, and during stable periods in global geopolitics. Moreover, the potential firm-level mechanisms through which GPR intensifies risk spillovers are declines in asset turnover, deceleration in corporate investment, and negative market forecasts. These findings can help to mitigate the adverse effects of GPR on energy firm performance, national energy security, and stability in the global energy market.

Bilevel Peer-to-Peer Energy Trading to Maintain Network Quality and Benefit Distribution

Bilevel Peer-to-Peer Energy Trading to Maintain Network Quality and Benefit Distribution

Multi-Agent Deep Reinforcement Learning for Blockchain-Based Energy Trading in Decentralized Electric Vehicle Charger-Sharing Networks

With The integration of renewable energy sources into smart grids and electric vehicle (EV) charger-sharing networks is essential for achieving the goal of environmental sustainability. However, the uneven distribution of distributed energy trading among EVs, fixed charging stations (FCSs), and mobile charging stations (MCSs) introduces challenges such as inadequate supply at FCSs and prolonged latencies at MCSs. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based auction algorithm for energy trading that effectively balances charger supply with energy demand in distributed EV charging markets, while also reducing total charging latency. Specifically, this involves a MADRL-based hierarchical auction that dynamically adapts to real-time conditions, optimizing the balance of supply and demand. During energy trading, each EV, acting as a learning agent, can refine its bidding strategy to participate in various local energy trading markets, thus enhancing both individual utility and global social welfare. Furthermore, we design a cross-chain scheme to securely record and verify transaction results of energy trading in decentralized EV charger-sharing networks to ensure integrity and transparency. Finally, experimental results show that the proposed algorithm significantly outperforms both the second-price and double auctions in increasing global social welfare and reducing total charging latency.

Evaluation of the potential power generation resources in SAARC region for sustainable energy trade

AbstractThis article presents a comprehensive review on the contemporary condition of electrical energy in the SAARC countries with particular focus on conventional as well as renewable resources. The region lies at the center of the Asia with a dense population where demand and supply gap has always been a challenging factor for the governments. In addition, the region has observed enormous industrial growth during past two and half decades whose sustainability is totally dependent upon the continuous supply of energy. A bulk of valuable literature has been published in recent years that addresses the challenges, infrastructure improvements margins and several schemes to mitigate the energy deficiency in the region but most of it focuses upon either generation or transmission. This article covers this gap and presents to‐the‐point data that can be used for future forecasting, cross‐border trade possibilities and indigenous energy generation for remote and islanded regions within and across the SAARC mainland. A detailed review on electrical power generation potential by exploiting the renewable energy resources (RERs), generation capacity, installed capacity, energy short fall and transmission and distribution (T&D) losses is presented for each SAARC country and the results are presented in forms of tables and graphs. It can be inferred from the data presented in this paper that the energy crisis of the SAARC region can be overcome by mutual trade among the land‐connected countries by transporting the electrical energy, generated through RERs, across the borders. The paper concludes that the region has enormous potential for renewable energy available in form of hydal, solar, wind and biomass that, if maturely harnessed, can only not fulfil the local demands of the region but also be exported to establish the framework of cross‐border energy trade in future for sustainability of industrial and domestic utilization.

A Study on the Evolution of Oil Trade Relations Between Japan and Russia from Geopolitical Perspective

The research on the relationship between international trade and geopolitics has garnered extensive attention. However, there are still numerous aspects worth discussing in the study of the impact of East Asian geopolitics on its economic and trade exchanges. This paper analyzes the changes in the oil trade policy of Japan and Russia under the influence of international relations between ecology and geopolitics. This article also examines the distinct dilemmas of Japan and Russia and the trend of international relations. That is, Russia is facing the blockade of its energy economy and trade by Western countries, and Japan is facing the problem of oil trade simplification, as well as the impact and influence of the international environment and the policies of the two countries. Based on this, this paper puts forward the following suggestions. Japan should seek a trade balance among big countries flexibly and pragmatically. As a big country, Russia should assume a leadership role in global trade while pursuing mutually beneficial and win-win results with other countries.

Dynamic impacts of globalization and environmental performance on renewable energy

This study investigates the dynamic relationships between renewable energy consumption, globalization, trade openness, carbon emissions, and arable land in Somalia from 1990 to 2020 using annual time series data from various sources. Employing econometric models such as the ARDL, FMOLS, and DOLS, the analysis reveals significant long-run relationships. A 1% increase in globalization (LnGI) and trade openness (LnTO) boosts renewable energy consumption (LnRE) by 0.123% and 0.020%, respectively, while a 1% rise in carbon emissions (LnCO2) decreases LnRE by 0.088%. Short-run dynamics show similar trends. These findings, validated by FMOLS and DOLS techniques, emphasize the impact of globalization, trade openness, and carbon emissions on renewable energy adoption. Policymakers should promote global integration and trade openness while enforcing strict carbon reduction measures to support renewable energy use and achieve sustainable development goals.

Fair Energy Trading in Blockchain-Inspired Smart Grid: Technological Barriers and Future Trends in the Age of Electric Vehicles

The global electricity demand from electric vehicles (EVs) increased by 3631% over the last decade, from 2600 gigawatt hours (GWh) in 2013 to 97,000 GWh in 2023. The global electricity demand from EVs will rise to 710,000 GWh by 2030. These EVs will depend on smart grids (SGs) for their charging requirements. Like EVs, SGs are a booming market. In 2021, SG technologies were valued at USD 43.1 billion and are projected to reach USD 103.4 billion by 2026. As EVs become more prevalent, they introduce additional complexity to the SG landscape, with EVs not only consuming energy, but also potentially supplying it back to the grid through vehicle-to-grid (V2G) technologies. The entry of numerous independent sellers and buyers, including EV owners, into the market will lead to intense competition, resulting in rapid fluctuations in electricity prices and constant energy transactions to maximize profit for both buyers and sellers. Blockchain technology will play a crucial role in securing data publishing and transactions in this evolving scenario, ensuring transparent and efficient interactions between EVs and the grid. This survey paper explores key research challenges from an engineering design perspective of SG operation, such as the potential for voltage instability due to the integration of numerous EVs and distributed microgrids with fluctuating generation capacities and load demands. This paper also delves into the need for a synergistic balance to optimize the energy supply and demand equation. Additionally, it discusses policies and incentives that may be enforced by national electricity carriers to maintain grid reliability and manage the influx of EVs. Furthermore, this paper addresses emerging issues of SG technology providing primary charging infrastructure for EVs, such as incentivizing green energy, the technical difficulties in integrating diverse hetero-microgrids based on HVAC and HVDC technologies, challenges related to the speed of energy transaction processing during fluctuating prices, and vulnerabilities concerning cyber-attacks on blockchain-based SG architectures. Finally, future trends are discussed, including the impact of increased EV penetration on SGs, advancements in V2G technologies, load-shaping techniques, dynamic pricing mechanisms, and AI-based stability enhancement measures in the context of widespread SG adoption.

Trend Analysis of Cross-Border Electricity Trading in Pan-European Network

The long-term global target of facilitating energy security and optimizing resource utilization while supporting the transition to sustainable energy systems has led the pathway towards regional cooperation in the context of energy infrastructures and trading. Recent trends highlight the development of transnational energy grids, pipelines, and renewable energy projects, facilitating efficient distribution of electricity, gas, and other energy forms. These advancements offer benefits such as increased reliability of energy supply, cost savings through shared resources, and strengthened relationships between countries. However, challenges persist, including regulatory differences, geopolitical tensions, alignment of market rules as well as substantial investment requirements in infrastructure and technology. Addressing these challenges necessitates harmonized policies, robust legal frameworks, and cooperative international governance, which are crucial for effective cross-border energy trading. The aim of the work is to present the current landscape as well as analyze the latest trends and developments in the dynamic field of electricity interconnectors, providing insights into its trajectory and implications for the European internal energy market.

The Causal Nexus Among Energy Dependency, Human Capital, and Renewable Energy: An Empirical Analysis for EU Members

Countries have turned to developing renewable energy production, avoiding the risks posed by the disruptions in global energy trade, the high volatility in energy prices, and the remarkable environmental impairment. Numerous economic, environmental, institutional, and social factors have been put forward as driving factors toward renewable energy. The goal of this research article is to study the causal nexus among energy dependency, human capital, real GDP per capita, CO2 emissions, and renewable energy of the 27 EU members between 2000 and 2020 through Emirmahmutoglu and Kose causality test. The results of the panel-level causality tests demonstrate feedback interplay among energy dependency, human capital, real GDP per capita, CO2 emissions, and renewable energy use. However, the results of the country-level causality analysis unveil that the interplay among renewable energy utilization, energy dependency, real GDP per capita, CO2 emissions, and human capital remarkably varies among EU members. The results of this study suggest that renewable energy investments are significant instruments to make progress in energy security, human capital, real GDP per capita, and CO2 emissions. Furthermore, energy security, human capital, real GDP per capita, and CO2 emissions are significant drivers of renewable energy development.

Maximizing profitability in integrated energy markets: A novel framework for multi-faceted energy trading

The emergence of Integrated Energy Systems (IESs) has shifted energy management from traditional electricity paradigms to a more diverse approach involving electricity, thermal energy, and natural gas. This study introduces a framework for an Integrated Energy Network (IEN) that enables simultaneous trading across these interconnected markets. The objective is to maximize the profitability of integrated energy companies by optimizing energy trading strategies. The proposed business strategy incorporates integrated load response, addressing constraints within electric distribution systems, heating networks, and natural gas pipelines. A three-stage process is used: information sharing in the unified energy market, energy conversion in hubs, and flexible load planning in the retail market. Flexible loads are categorized as displaceable, reducible, or transformable. A Conditional Value-at-Risk (CVaR) mean-variance model addresses market price volatility. Simulations on a test system validate the model, demonstrating its effectiveness in improving profitability and operational efficiency for integrated energy companies.