Energy Trading Research Articles

A Survey of Multi-Agent Systems for Smartgrids

This paper provides a survey of the literature on the application of Multi-agent Systems (MAS) technology for Smartgrids. Smartgrids represent the next generation electric network, as communities are developing self-sufficient and environmentally friendly energy production. As a cyber-physical system, the development of the vision of Smartgrids requires the resolution of major technical problems; this has fed over a decade of research. Due to the stochastic, intermittent nature of renewable energy resources and the heterogeneity of the agents involved in a Smartgrid, demand and supply management, energy trade and control of grid elements constitute great challenges for stable operation. In addition, in order to offer resilience against faults and attacks, Smartgrids should also have restoration, self-recovery and security capabilities. Multi-agent systems (MAS) technology has been a popular approach to deal with these challenges in Smartgrids, due to their ability to support reasoning in a distributed context. This survey reviews the literature concerning the use of MAS models in each of the relevant research areas related to Smartgrids. The survey explores how researchers have utilized agent-based tools and methods to solve the main problems of Smartgrids. The survey also discusses the challenges in the advancement of Smartgrid technology and identifies the open problems for research from the view of multi-agent systems.

Enhancing sustainability in electric mobility: Exploring blockchain applications for secure EV charging and energy management

The global shift towards reducing carbon emissions has spurred a growing interest in electric vehicles (EVs) over traditional fossil fuel-based internal combustion engines. Concurrently, blockchain technology has emerged as a transformative solution across various sectors, characterized by its decentralized database and distributed ledger. Its notable attributes, including transparency, immutability, traceability, security, auditability, and authenticity, have led major companies to recognize its potential integration into the EV sector. As a result, there has been a steady increase in case studies and research initiatives exploring blockchain applications in EVs. This article provides a detailed overview of the use of blockchain in the EV domain, focusing on the challenges and applications in four key areas: EV charging infrastructure, energy trading, supply chain management, and energy management. Initially, the basic concepts of blockchain technology are briefly explained, followed by its applications in different fields. The study presents the opportunities and crucial requirements for implementing blockchain technology for secure energy trading, charging, energy management, and supply chain operations. While blockchain can improve security and efficiency in the EV domain, it remains vulnerable to various attacks. This article explores these attacks and other potential challenges in integrating blockchain technology in the EV sector. The main findings of this rEView provide insights into the importance of blockchain in the EV sector and underscore the need to overcome existing challenges for the widespread adoption of this technology in the near future.

Incentivizing energy and carbon rights transactions among network-constrained energy hubs: Cooperative game with externalities

Joint energy and carbon rights transactions for multiple energy hubs could boost the utilization of renewable distributed energy resources and reduce carbon emissions in energy sectors. The joint transaction mechanism based on the cooperative game is proposed, in which cooperative energy hubs form a grand coalition to share diverse forms of energy and carbon rights in order to maximize their total benefit. Furthermore, the grand coalition-based transaction mechanism is augmented with network constraints to account for the influence of network constraints on the trading behaviors of energy hubs, which is mathematically represented as a cooperative game with externalities. In addition, mathematical proof is provided to demonstrate the stability of the formed network-constrained grand coalition. To determine the trading behaviors and allocate payoff among energy hubs while satisfying both network constraints and coalition stability, a scalable two-stage optimization structure is provided, with the first stage ensuring coalition stability and the second preventing network constraint violations. A solution technique combined with Karush-Kuhn-Tucker conditions and Benders decomposition is proposed to solve the two-stage optimization problem. The effectiveness and scalability of the proposed joint transaction mechanism and the solution technique are supported by numerical results for the 4-EH and 33-EH systems. The introduction of the carbon market leads to a 20.6 % reduction in carbon emissions and a 10.5 % increase in total payoff by facilitating peer-to-peer trading of both energy and carbon rights.

Transactive energy framework in fuel cell based multi-carrier energy hubs based on conditional value-at-risk

The prevalence of peer-to-peer operation among industrial energy hubs is considered a promoting method to enhance local energy autonomy, efficiency, and flexibility. By conducting stochastic programming, this paper aims to minimize the operating cost of multiple industrial energy hubs that trade power and heat energies with each other and utility. Concerning the important role of hydrogen systems, fuel cell-based combined heat and power (CHP) generators are employed within the hubs to increase sustainability. The presence of electric vehicles (EVs) and shiftable loads enhances the flexibility during peer-to-peer operating mode. The trading price for power and heat energies are derived at first by solving a deterministic problem and considering average values for demands and generation. However, to address the uncertainties in renewable generation, electric and heat demands, and electricity prices under operation, scenario-based stochastic programming tries to optimize the expected objective function. Furthermore, the Conditional Value-at-Risk (CVaR) approach is proposed to provide various risk-averse scheduling tactics and measure the risk of each scenario. The results show that the proposed risk-constrained approach reduces the risk and makes cautious decisions about energy resources and trades. In this regard, the expected profit has been reduced by 75%, i.e., from 1636.78 NOK under a risk-neutral case to 414.97 NOK under a risk-averse case to reduce the expected risk in operating cost.

Robust optimization for integrated energy systems based on multi-energy trading

Amid rising energy demands and environmental concerns, integrated energy systems (IESs) face conflicting interests. Conventional strategies of interest distribution face issues such as irrational resource allocation. Accordingly, establishing energy trading strategies with multiple stakeholders becomes essential. This paper proposes a robust optimization (RO) for IESs based on multi-energy trading to reduce energy trading cost. In this regard, a single-leader-multi-follower Stackelberg game is first modeled where IES acts as the leader, and the users and the electric vehicles (EVs) act as the followers. Secondly, this model is transformed into a single-layer linear model and integrated into the multi-stage RO. With this, the nonanticipativity in the two-stage RO can be effectively handled. Besides, by constructing multi-interval uncertainty sets for renewable energy, load, and electricity prices, the conservatism of the model is reduced, making the optimization results closer to actual condition. Noteworthy that the Nash bargaining method ensures a fair distribution of benefits among IESs and encourages them to participate in energy trading. Finally, the multi-energy trading model is solved using the prediction-correction-based alternating direction method with multipliers (PCB-ADMM) algorithm. The PCB-ADMM not only protects each IES’s privacy but also has less execution time than the ADMM algorithm. The effectiveness of the proposed strategy is validated through simulation using Matlab.

Game theory-based electricity pricing decisions incorporating prosumer energy preferences and renewable portfolio standard

The implementation of renewable energy-based power generation substantially enhances the greenness of the energy market. Concerns about pricing policies and widespread renewable energy (RE) industries have resulted in ongoing economic conflicts among stakeholders, including power companies (PCs) and different prosumer groups. This study proposes a game theory framework embedded in a pricing model to examine the influences of three-part tariff (3 PT) and renewable portfolio standard (RPS) on electricity pricing. Employing a Stackelberg game, the objective is to maximize stakeholders' profits through a sequential decision-making relationship between PCs (as the leader), and residential and industrial prosumers (as the followers). The findings reveal that the PC can maximize profit by coordinating the 3 PT and RPS, regardless of environmental awareness. Furthermore, given the prosumers’ preference for peer-to-peer (P2P) energy trading, the PC should employ a comprehensive approach encompassing 3 PT, P2P, and buy-back to maximize the total profit. On the energy trading platform, the relationship between residential and industrial prosumers is mutually beneficial, with the P2P scheme allowing residents to sell electricity while the industry approaches a lower electricity price from peer channels. Overall, understanding the energy preferences of prosumers should provide flexible and reliable power supply options for different groups of prosumers.

Carbon-Aware Peer-to-Peer Joint Energy and Reserve Trading Market for Prosumers in Distribution Networks

Carbon-Aware Peer-to-Peer Joint Energy and Reserve Trading Market for Prosumers in Distribution Networks

Trustful double auction design for Peer-to-Peer energy trading between interconnected micro-grids with supply–demand imbalance

Peer-to-Peer (P2P) energy trading between Interconnected Micro-Grids (IMGs) presents a promising approach for enhancing the economic advantages for prosumers while mitigating supply–demand imbalances within individual Micro-Grids (MGs). This paper proposes a trustful double auction mechanism for P2P energy trading among prosumers in IMGs, structured into two distinct stages. Initially, concurrent auctions are conducted within each MG to facilitate intra-grid trading, followed by subsequent auctions facilitating inter-grid trading across IMGs. We figure out the allocation and pricing rules that satisfy the required properties in mechanism design even when the auction consists of two stages. Given that energy transactions across IMGs entail non-negligible power losses, equitable allocation of these losses among prosumers is paramount. To address this, we integrate a fair cost distribution methodology into our auction mechanism, implemented by an iterative algorithm. Rigorous analysis substantiates our proposed auction mechanism’s incentive compatibility, individual rationality, and budget balance, thereby fostering truthful and voluntary prosumer participation while averting potential market deficits. Numerical analysis underscores the efficacy of our approach, showcasing a significant improvement in P2P transactions and supply–demand balance enhancement compared to trading solely within individual MGs.

Analysis of the Competitiveness, Complementarity, and Trade Combination of Kazakhstan and China in the Oil and Gas Trade

The oil and gas trade is one of the main ways to promote regional economic development by improving the effectiveness of resource allocation. While regional energy cooperation could lead to growth in the energy trade, blind investment will reduce effective yields. Kazakhstan and China maintain a stable oil and gas trade, but resource exports to China are not growing as expected. The aim of this research is to analyze the competitiveness and complementarity of Kazakhstan and China in the oil and gas trade, as well as the main factors affecting the oil and gas trade between Kazakhstan and China. By creating a linear regression equation to analyze the gravity model of the oil and gas trade between Kazakhstan and China, it was revealed that a 1% growth of the gross domestic product in both countries would lead to a 1.471% increase in the oil and gas trade. However, an increase in oil and gas production in Kazakhstan will not contribute to the expansion of the oil and gas trade with China. Kazakhstan and China could improve their oil and gas trade by strengthening financial cooperation, improving energy efficiency, increasing investment in infrastructure such as oil refineries and pipelines, and developing new oil and gas fields in Kazakhstan.

Assessment of PV excess generation savings in Ghana considering peer-to-peer energy trading

ABSTRACT Solar is one of the Renewable (RE) sources with a high electricity generation potential. However, the challenge with using individual stand-alone Photovoltaic (PV) systems is the mismatch between load demands and electricity generated such that surplus and insufficient energy are experienced by prosumers. This study used the Net Present Value (NPV), and levelized cost of energy (LCOE) economic models to assess the impact of modern (peer-to-peer, P2P) trading and applicable PV system cost reduction strategy in Ghana, considering a cluster of stand-alone systems. The energy demand and generations of a total of 7 prosumer and consumer buildings in the Don Bosco Technical Institute, Ashaiman, were used. The results, considering a mini-grid connection between the buildings, showed an LCOE of GH₵ 1.358 over 10 years and NPV of about GH₵ 5 million for 25 years when excess generation is traded as compared to GH₵ 2.423 for LCOE and about GH₵ 180,000 NPV when excess generation is disregarded. A proposed shared investment community-owned system design showed further LCOE reduction to GH₵ 1.122 because the capital cost decreased. This shows that P2P energy trading can make PV generation attractive amidst other benefits like job creation, revenue generation and emissions reduction if RE is used. Lastly, a suitable energy trading market mechanism was proposed for implementing P2P in the context of Ghana. Though the research revealed that more benefits can be obtained from PV systems in Ghana, future technical analysis regarding transmission losses, the Internet of Things (IoTs), and blockchain need to be done.

Dispatch strategies for large-scale heat pump based district heating under high renewable share and risk-aversion: A multistage stochastic optimization approach

Heat pumps play an essential role in decarbonizing the heating sector, and their adoption is projected to rise significantly. The high share of large-scale heat pumps in district heating exposes heating utilities to uncertainty in electricity markets. This challenge is further exacerbated by 1) future high share of renewable energy resulting in increased uncertainty of electricity prices, and 2) introduction of voluntary energy bids for balancing energy markets (or regulation market). Despite the importance of this issue, little research has been conducted to address it. Therefore, this paper investigates the optimal dispatch of large-scale heat pump based district heating by adopting multi-stage stochastic optimization approach to minimize the total expected heat generation cost, taking sequential electricity markets prices as stochastic. We also evaluate the impact of high renewable energy penetration on this optimal dispatch strategy. Furthermore, we include the risk preference of district heating operator by using conditional value at risk in the objective function. We conclude that heat pump should be coupled with flexible technologies like electric boilers as more renewable energy and balancing market trading increases flexible units' dispatch. Also, most of the heat produced (50%) in our case study is used flexibly via thermal storage. We find more renewable could lead to negative expected costs and voluntary bids in balancing market further reduces the expected cost by 28–59%. Furthermore, trading risk increases significantly with renewable energy penetration, which can be mitigated to a limited extent by bidding in up and down balancing market. Risk aversion shifts trading from intraday to day-ahead market and promotes heat pump dispatch.

Causality of Environmental Degradation and Economic Growth in Indonesia

Nowdays, the issue of global warming is being widely discussed in all countries, both developed and developing countries, because of its negative impact on the environment. This study aims to develop an integrated analytical framework of causality between energy consumption, carbon emissions, financial development, trade openness, and economic growth in Indonesia, 1990-2021. This study provides research benefits in the form of a set of empirical evidence to assist in developing a series of key strategies that can improve environmental quality through sustainable development. With granger causality analysis and vector autoregression model, this study provides results that high economic growth and energy consumption can cause environmental degradation through increased carbon gas emissions.

A resilience‐oriented restoration framework for multi‐area active distribution network following a disaster

AbstractA disaster disrupts energy trading between the distribution network (DN) and autonomous areas in the multi‐area active distribution network (MA‐ADN). Since these areas are self‐interested agents, the energy transaction cost between them should be considered when determining the grid restoration scheme. In this regard, a new three‐level resilience‐oriented restoration (TLROR) framework is proposed to optimally schedule available tie‐lines, and distributed energy resources (DERs) in the MA‐ADN, considering the autonomy and privacy of their ownership. In the first level of the proposed TLROR, an electrical price vector (EPV) is created with the contribution of different areas. In the second level, each area should calculate the amount of imported active and reactive power from the DN through its tie branches and save it in the transactive power list (TPL). Finally, at the third level, the transactive energy market is cleared by the DN operator considering economic issues and operating limits. The proposed method provides energy transactions between independent agents to enhance MA‐ADN resiliency, with the least information sharing, and privacy preserving. The effectiveness of the proposed method is verified using the modified IEEE 33‐bus MA‐ADN and a part of the DN in the Pol‐e Dokhtar in the Lorestan province of Iran.

Lightweight adaptive Byzantine fault tolerant consensus algorithm for distributed energy trading

With the rapid development of smart grid, constructing distributed energy trading market (DETM) based on blockchain to coordinate distributed energy resources (DER) has become a future direction. However, existing consensus algorithms of blockchain face many challenges in large-scale energy trading scenarios, such as high resource overhead, slow transaction procedure. To solve the above crucial problems for wide deployment of distributed energy trading, this study proposes a novel consensus algorithm named Lightweight adaptive Byzantine fault tolerant consensus (LA-BFT), and a reputation calculation method based on behavioral characteristics for selection of consensus nodes. The LA-BFT consists of two parts: (i) weak consensus for normal cases. By introducing threshold signature mechanism, weak consensus simplifies the consensus process to achieve linear communication complexity O(n). (ii) byzantine node detection scheme is enable for malicious cases. With consensus committee, the detection scheme can detect the potential byzantine nodes by cross-validation, which ensures transaction safety. The reputation calculation method is presented to cooperate with LA-BFT to elect leaders and candidates for consensus procedure. Once a round of consensus is completed, the reputation of each node needs to be updated, only nodes with high reputation are eligible to become leaders or committee nodes in the next round. With the reputation calculation method, honest nodes and byzantine nodes can be effectively identified, ensuring the security of the consensus process. Numerical results indicate that LA-BFT exhibits superior performance on communication overhead and bandwidth occupancy in large-scale concurrent energy trading scenarios. When the number of nodes is 50, under normal scenarios, LA-BFT’s communication overhead is notably lower, constituting a mere 6.02% of PBFT and 24.26% of SHBFT, while bandwidth occupancy amounts to merely 5.55% of PBFT and 9.76% of SHBFT.

When regional energy cooperation fails: learning from the struggles of Northeast Asia’s joint oil import mechanism

The question of why some regions reject energy cooperation initiatives that aim to improve their overall security of supply puzzles scholars and policymakers advocating for deeper regional integration through energy trade. This was the case in Northeast Asia, where efforts to create a joint oil import mechanism to improve China, South Korea and Japan’s collective bargaining power against Middle East suppliers repeatedly failed and turned instead into competing bilateral contracts at higher prices for all three countries. This article offers two explanations for this puzzle. First, the unique technical challenges and costly logistics of establishing reliable oil and gas supply routes make bilateral agreements more credible than multilateral guarantees in regions characterised by geographical remoteness and high differential vulnerability to disruptions among its members. Second, the tendency of policymakers to securitise their country’s energy supply pushes them to overemphasise their relative vulnerabilities and fear any loss of autonomy over their supplies.

Measuring the Costs of Renewable Resources and Their Role in Reducing the Costs of Available Resources in The Iraqi Economic Units

As a result of the developments witnessed by the world and technological progress in all fields and its trend towards preserving the environment and its resources and reducing pollution in it, which required the economic units to search for resources to help them reduce costs and protect the environment from pollution while preserving its non-renewable resources, and in order to address this problem, the researcher determined the costs of available resources (non-renewable) for the production of electrical energy, which constitutes the largest proportion of indirect costs, Therefore, the research was launched to reduce these costs by replacing non-renewable resources with renewable resources in order to reduce costs on the one hand, protect the environment on the other hand, and help preserve non-renewable resources for future generations. To achieve this goal, the researcher carried out the field pension for Al-Mamoon factory in order to determine energy costs Electric using non-renewable resources, as well as field visits to obtain information from the Free Lights Company for Solar Energy and General Trading in order to determine the costs of renewable resources represented by solar energy. One of the most important results of the research was the is that the best resource of the six resources for generating electrical energy is the solar energy resource, it is safe and available in abundance and does not pose a threat to humans and the environment, Also, the findings of the current research revealed that investing in solar cells leads to an increase in investment costs, but at the same time leads to reducing the costs of use over its life of 30 years as a result leads to reducing the costs of producing electrical energy.

Research on Energy Trading Mechanism Based on Individual Level Carbon Quota

High economic growth is accompanied by substantial consumption of fossil energy and significant negative externalities on the ecological environment. The global warming effect resulting from environmental pollution caused by energy has brought energy carbon emissions into the forefront of social attention. Establishing a carbon trading market is an essential measure to achieve the “double carbon” goal, with individual and household carbon emissions accounting for 70% of China’s total emissions. Constructing an individual-level carbon trading market will facilitate the efficient realization of this goal. However, addressing the challenge of handling vast amounts of data and network congestion in relation to frequent but small-scale individual carbon trading has become an urgent issue that needs to be resolved. In light of this, the present study designs a digital technology-based framework for the carbon market trading system and proposes an individual carbon asset price-based model for carbon market trading, aiming to establish a research framework for the carbon quota market. Furthermore, blockchain technology is employed as the underlying technology in the proposed carbon trading market model to cater to individual-level carbon trading services and achieve optimal matching between carbon quota suppliers, thereby enhancing profitability of the carbon trading platform. The numerical results obtained from the model demonstrate that in absence of government subsidy mechanisms, individual-level carbon trading can effectively reduce total consumer emissions. The present study successfully overcomes the carbon lock-in effect of consumer groups and achieves the generation and trading of individual carbon assets despite capital constraints. This study facilitates accumulation and trade of individual carbon resources, reduces overall consumer emissions, enhances environmental benefits at societal level, and provides a foundation for governmental decision-making.

Incentivizing sustainable practices: Game-theoretic approach to peer-to-peer energy trading in the green transition era

The urgent global concern regarding climate change has highlighted the necessity for transitioning to power generation with zero carbon emissions to promote a sustainable and environmentally conscious society. A crucial element in this transformation is reducing our dependence on the primary grid, which is predominantly powered by fossil fuels, natural gas, and coal. An innovative strategy for achieving this essential transition is through peer-to-peer energy trading (P2PET). However, the effectiveness of P2PET relies on successfully aligning the energy-related objectives of its participants. Identifying and effectively addressing these goals is a significant challenge. In response, this paper introduces a game-theoretic framework designed to encourage subscribers to engage in P2PET, both in islanded microgrids and interconnected grid configurations. Our methodology begins by introducing a model that captures the core energy-related objectives of both energy producers and consumers. This model is supported by a layered architectural framework tailored for peer-to-peer (P2P) marketplaces, enhancing the identification and classification of existing technologies in this domain. Following this, we delve into the formulation of an extended-form game rooted in non-cooperative game theory. We systematically evaluate the presence of strict Nash equilibria within this game-theoretic structure. To promote active engagement and trading in the peer-to-peer energy market (P2PEM), we introduce an innovative energy allocation policy. This policy is strategically devised to ensure the inclusion of every subscriber in the market, irrespective of fluctuations in supply and demand dynamics. Our proposed P2PET scheme is tested on a representative system, specifically a 14-bus IEEE network, incorporating 8 energy producers and 11 consumers as active participants in the market. By conducting an extensive series of tests, we accurately evaluate the design's performance. The results, compared to previous studies, show a significant reduction in consumer energy bills, ranging from 33 % to 7 %. This convincing result underscores the effectiveness and robustness of our proposed energy trading framework. In a world grappling with the imperative to transition to sustainable energy practices, our game-theoretic approach to incentivizing participants in P2PET emerges as a pivotal contribution. It demonstrates tangible benefits, promotes green energy production, and encourages responsible energy consumption.

Liner shipping connectivity: A dynamic link between energy trade, green exchange and inclusive growth using advanced econometric modelling

The ocean economy is crucial in facilitating green exchanges and promoting inclusive growth. Despite its significance, this domain needs more investigation, leading to diverse findings in existing literature. This study empirically examines the links between energy trade, green exchanges, and inclusive growth within the context of the ocean economy in the Belt and Road Initiative (BRI) region. Using the Liner Shipping Connectivity Index as a proxy for ocean economy and debt-for-nature swaps as a proxy for green exchanges, we employ an advanced Two-Step System of GMM econometric modelling. Data from 23 BRI member countries, sourced from WDI (World Bank), OECD Statistics, and UNCTAD Statistics, covering the period from 2004 to 2022, form the basis of our analysis. Our findings confirm a long-run dynamic relationship between the ocean economy, energy trade, green exchanges, and inclusive growth. The results indicate that the ocean economy and debt-for-nature swap financing contribute to economic growth. However, results show a weak marine structural development because the results are not highly significant. To address this, we propose comprehensive policy measures to strengthen structural development to enhance inclusive economic growth. Our study highlights the need for strategic policies that balance economic benefits with sustainability goals, leveraging technological advancements and promoting equitable growth.

Retraction Note: The asymmetric effects of renewable energy consumption and trade openness on carbon emissions in Sweden: new evidence from quantile-on-quantile regression approach.

Retraction Note: The asymmetric effects of renewable energy consumption and trade openness on carbon emissions in Sweden: new evidence from quantile-on-quantile regression approach.