Electricity Market Trading Research Articles

Strategic behaviors of renewable energy generation companies participating in the electricity and carbon coupled markets based on non-cooperative game theory

Renewable energy (RE) can participate in electricity and carbon market trading in China, which can help stimulate the investment of RE. Thus, we propose a bi-level model for non-cooperative game interactions among power generation companies (GenCos), considering the electricity and carbon coupled markets. The upper-level model considers strategic behaviors driven by maximizing profits for GenCos. The lower-level model includes a two-stage electricity market: the day-ahead market and the real-time market; it implements joint clearing of energy and reserves, and continuous trading with competitive auctions in the carbon market based on the clearing results of the electricity market. Simulations are performed based on the actual data of the power system in a certain region of Guangdong Province in China. The results show that electricity and carbon coupled markets can effectively reduce market costs and carbon emissions, improve the society welfare and RE benefits. The uncertain increase in RE output and higher installed capacity reduce the total revenue of the RE in the coupled markets. The initial carbon quota decomposition optimization decision of the coal-fired GenCos and gradual reduction in the initial carbon quota will increase the revenue of the RE GenCos.

Optimization of multi-energy complementary power generation system configuration based on particle swarm optimization

The multi-energy complementary power generation system, incorporating wind, solar, thermal, and storage energy sources, plays a crucial role in facilitating the coexistence and mutual reinforcement of conventional thermal power and renewable energy. Against the backdrop of evolving power systems and the increasing integration of wind, solar, thermal, and storage technologies, scientifically optimizing the configuration of multi-energy complementary power generation systems has become an essential prerequisite for their sustainable development. This study introduces a dual-layer optimization model for configuring multi-energy complementary power generation systems based on the particle swarm optimization algorithm. The outer layer optimization aims to maximize the net revenue from wind, solar, and storage power generation while the inner layer optimization focuses on minimizing carbon emissions from thermal power units. The proposed approach comprehensively considers both environmental benefits and economic gains of multi-energy complementary power generation systems. Simulation results demonstrate that this method effectively enhances annual net income while significantly reducing carbon emissions by 1.1058 million tons. It establishes a rational capacity configuration scheme that not only improves economic performance but also promotes efficient utilization of clean energy resources. Furthermore, it highlights the advantages of coordinated carbon electricity market trading over traditional modes.

Economic optimization scheduling of virtual power plants considering an incentive based tiered carbon price

Against the backdrop of China's carbon peak and carbon neutrality goals, the use of virtual power plants for electricity economic dispatch has gradually become a research hotspot. The virtual power plant includes both supply and demand sides. If the inherent conflicts of interest between both parties cannot be effectively addressed, there are significant hidden dangers in the sustainable operation of the virtual power plant. On the basis of existing research, this article constructs a regional virtual power plant. With the goal of maximizing supply and demand utility, a two-stage Stackelberg dynamic game model was established between virtual power plant operators and users in the electricity market and carbon trading market mechanisms, and an incentive based hierarchical carbon price constraint was designed in the optimization model. In the model constructed in this article, the operator of a virtual power plant will consider environmental benefits based on expected returns, adjust the output of controllable units to change the electricity supply strategy, and formulate a pricing strategy. At the same time, users will adjust their electricity consumption strategy based on their own electricity consumption characteristics, and both will achieve a game equilibrium. Based on the optimization results, this article conducted a sensitivity analysis on the incentive coefficient of graded carbon prices and obtained the optimal incentive coefficient under the case conditions. The research results indicate that the model can coordinate and meet the interests and needs of both the power generation and consumption sides, and the implementation of incentive based hierarchical carbon pricing strategies has certain advantages compared to traditional carbon pricing strategies in different user types scenarios. The research conclusion of this article has good reference value for the sustainable green operation of virtual power plants.

Electricity consumption and economic growth: Evidence from the East African community

This study investigates the dynamic causal relationship between electricity consumption and economic growth in the East African Community (1990–2021). It seeks to interrogate the nature of relationship between electricity consumption and economic growth. The hypothesis used in this study is four folded including growth, conservation, feedback and neutral hypothesis.It uses panel data estimation techniques, particularly the panel dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and non-linear autoregressive distributed lag (NARDL). The panel augmented Dickey Fuller (ADF)-fisher and Levin, Lin & Chu (LLC), 2003, was used to test the unit root process. Dumitreschu-Hurlin (2012) and pairwise Granger tests were used to test for the direction of causality.The findings indicate growth hypothesis with a unidirectional relationship running from electricity consumption to economic growth. Regional governments must increase their investments in electricity market trading to boost economic growth. Greater benefits from regional cooperation can be realized with increased investment in electricity consumption.This is a novel study of the dynamic causal relationship between electricity consumption and economic growth in the East African Community. It is a ground-breaking inquiry into the possibility of integrating electricity markets and their role in promoting economic growth.

Design of distributed trading mechanism for prosumers considering the psychological gap effect in community electricity markets

With the large-scale customer-side development of distributed energy and continuous advancements in power market reform, the market-oriented trading of distributed power generation with the participation of prosumers has gradually become an important solution to promote the consumption of distributed energy. In addition, prosumers exhibit obvious bounded rationality characteristics; therefore, the design of efficient and reasonable trading mechanisms determines the efficiency of the market. From this perspective, a distributed electricity market trading mechanism based on the Stackelberg game is proposed for the community electricity trading market, with multiple prosumers participating in the distribution side. Firstly, the trading architecture of the community electricity market was established under the distribution network level. Secondly, the pricing strategy of the distribution network operator and the quantitative strategy of community prosumers were analysed; on this basis, the characteristics of the bounded rationality of prosumers were considered, and the impact of the psychological gap effect of prosumers on their energy utility was introduced into the objective function of prosumers. This facilitated the construction of a Stackelberg game model in which distribution network operators lead and community prosumers follow. Thirdly, the Stackelberg equilibrium was proven to exist and be unique, which was then solved by the distributed algorithm – combining the evolutionary algorithm and quadratic programming – which improves the solution efficiency and effectively protects the privacy of prosumers. Finally, the effectiveness and feasibility of the proposed trading mechanism were verified through numerical examples.

Optimal Scheduling of Regional Integrated Energy System Considering the Integration of Electric Vehicles and the Life Cycle Assessment Method

With the increasing scale of renewable energy and electric vehicles (EVs), the optimization scheduling of the regional integrated energy system (RIES) is facing new challenges due to the integration of EVs and new energy into the system. To realize the low-carbon economic operation of the RIES, a bi-level optimization scheduling strategy for low-carbon economic operation of the RIES is proposed, which considers EVs' participation and lifecycle carbon emissions, and integrates the electricity market and carbon trading market. Firstly, there are many factors affecting the carbon emissions of EVs and the difficulties in prediction. From the indirect carbon emissions during the use of EVs, the relationship between the carbon emissions of EVs and the charging mode and carbon emission sources is comprehensively analyzed. A carbon emission model of EVs based on the Life Cycle Assessment (LCA) is established in this paper. Secondly, the charging model of EVs based on demand response is combined with the electricity market; The carbon emission model of EVs based on the LCA is combined with the carbon trading market, and a bi-level scheduling strategy based on the flexible interaction between carbon and electricity is proposed. Then the strategy is solved to minimize system economic costs and carbon transaction costs. Finally, simulation example analysis is conducted to demonstrate that the proposed strategy can effectively reduce the total charging costs of electric vehicle users, improve the utilization rate of renewable energy, and reduce carbon dioxide emissions, with good economic and environmental benefits.

Econometric modeling of carbon price impacts on the bidding mechanism between different energy sources in the electricity market

Abstract Virtual power plants (VPPs) can aggregate different energy sources into a whole to participate in the electricity market and carbon trading market, and as the scale of VPPs continues to expand, they will also be transformed from the original price takers to price makers. The paper considers VPP as a price setter and suggests a two-tier bidding strategy for the VPP day-ahead electricity market that incorporates the impact of the carbon price. Among them, the upper-layer model aims to maximize the expected revenue of VPP, while the lower-layer model aims to minimize the system clearing cost. The dynamic carbon emission factor is used in the strategy to create a market incentive factor that corrects the offer price of each unit before clearing it again. Finally, the optimal bidding strategy, as well as the operation plan of the VPP, are given through an arithmetic example, and the impact of the uncertainty in the wind farm output forecast in the VPP on the expected return of the VPP is analyzed. The impact of carbon prices on power prices, output, and rates of energy demand change are further explored to provide a basis for the compensation mechanism for ecological protection of carbon values. The arithmetic example shows that VPP can influence the market price through strategic bidding decisions, and after considering the carbon price, the power market clearing price is 4.46 yuan/MWh, and the maximum gain is obtained as 225.54. The consumption rate of new energy units such as WT and PV is increased, the carbon emissions are reduced, and the total gain of VPP is increased. The increase in carbon prices and the offer of traditional generators increase the amount of electricity cleared by renewable energy units, while the increase in carbon quota ratio and the offer of new energy generators increase the average clearing price.

Distributed Online Incentive Scheme for Energy Trading in Multi-Microgrid Systems

Microgrids (MGs) are essential components of a smart grid, and they play an important role in assisting smart grid operation. Furthermore, multi-MG systems, which can further improve smart grid operation, raise significant challenges in terms of management and interoperation because of their increased complexity. To address this, we propose a multi-bid online incentive scheme for energy trading in multi-MG systems, in which MGs act as buyers and sellers to trade surplus energy. In the proposed system, buyer MGs can submit multiple bids to different sellers, and the distributed MG system operator matches buyer and seller MGs in a way that maximizes the utility of buyers. Theoretical analysis demonstrates that the proposed method meets the properties of incentive compatibility and individual rationality. Moreover, through detailed performance evaluation, the proposed scheme was capable of achieving better performance in comparison with existing schemes regarding utility, energy purchasing cost, and buyer MG’s satisfaction ratio. Our experimental results also demonstrated that the proposed scheme could shift the system peak-load, improving the robustness of the multi-MG system. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this article, we addressed the energy trading issues in multi-microgrid (MG) system, and proposed a distributed incentive mechanism to achieve more flexible and efficient energy allocation. Most existing works on energy trading use centralized methods, which means all electrical users will submit only one bid to the platform, and finally the platform decides to match the trading parties. This will lead to users unable to actively choose trading partners, thus reducing the volume of the whole market. To this end, in this paper, we proposed a distributed incentive mechanism for the multiple microgrid system, in which the buyer microgrids are able to submit multiple bids to different sellers. Moreover, each seller microgrids act as a distributed energy market, and the distributed MG system operator matches the buyers and sellers which aims to maximize the utility of buyers. The proposed scheme is helpful in managing the energy trading among the multi-MG system and can be readily implemented in the real-world large-scale electricity trading market.

Design and optimization of electricity market trading mechanism based on blockchain technology

Design and optimization of electricity market trading mechanism based on blockchain technology

How to promote the trading in China's green electricity market? Based on environmental perceptions, renewable portfolio standard and subsidies

The behavior evolution of market players is crucial to promoting green electricity trading. This paper develops an evolutionary game model of China's green electricity market to analyze the strategic interactions among players. The model incorporates key variables such as renewable energy subsidies, renewable energy quotas, and environmental perceptions. Then, a system dynamics simulation model is constructed, and simulation experiments are designed to reveal the influence of key factors on the evolutionary path. The results indicate that: (1) Players exhibit varying sensitivities to green electricity prices under different trading modes. (2) Renewable energy projects with high subsidy intensity have no willingness to enter the market if the benefits of participating in the green electricity market cannot surpass subsidies. (3) The evolutionary stabilization trend depends on users' environmental perceptions, and the environmental perception effect exhibits a diminishing marginal trend. (4) The implementation of renewable portfolio standard is an effective way to promote green electricity trading, especially when there is a high green electricity premium. (5) There is a trade-off between low incentive participation and high incentive coefficients in the system. These findings provide valuable policy recommendations for effectively promoting green electricity trading in China.

A blockchain‐based smart contract model for secured energy trading management in smart microgrids

AbstractThe extension of emerging renewable energy sources such as wind and water turbines, solar panels, and the increasing usage of electric vehicles requires the supply and distribution of energy in a small device on local scale and it has created new methods of supplying and selling electricity. Middle buyers and end users can obtain the local energy with the peer‐to‐peer trading method in this large and hierarchical market. This method enables market to manage and exchange the electricity between major suppliers and medium and local levels. Blockchain technology is developing in peer‐to‐peer exchange of electricity and acts as a reliable, efficient, and safe technology in the electricity trading market. In this method, while preserving the privacy of electricity users, by using smart contracts and by removing intermediaries in the energy supply and demand market, direct commercial interactions between energy suppliers and consumers are done. The blockchain technology, while creating trust between the parties in the energy market, reduces the cost of electricity trading and increases its scalability with using the intermediate energy aggregators. In this research, the blockchain‐based model, is presented for distribution and peer‐to‐peer transactions in the energy market. The suggested model provides the possibility of registration low‐cost instant transactions at the power grid in any specific period of time. The above method, unlike periodic payments, provides immediate access to bills and small payments. Since the transactions outside the blockchain chain are not recorded, this system guarantees its honest and independent operation without fraud and failure. The smart contract method based on blockchain, reduces the transaction fees and speeds up electricity trading. Also, the experimental investigation in 20 nodes shows the time required to determine the exchange contract in the blockchain method. The average is improved by 49.7% in this method. Also, the negotiation convergence time has become 47% faster.

A Wasserstein‐distance‐based distributionally robust chance constrained bidding model for virtual power plant considering electricity‐carbon trading

AbstractVirtual power plant (VPP) can aggregate distributed energy resources (DER) and controllable loads to participate in the bidding of electricity market and carbon trading market. However, the uncertainty of renewable power production brings high transaction risks to VPP. Given this background, this paper proposes a novel Wasserstein‐distance based two‐stage distributionally robust chance constrained (DRCC) bidding model for VPP participating in the electricity‐carbon coupled market. The uncertainties are modelled as an ambiguity set based on Wasserstein distance, in which the two‐sided chance constraints are guaranteed satisfied. In the first stage, VPP's revenue is maximized according to the forecast information. In the second stage, the re‐dispatch measures are determined to hedge against the perturbation of uncertainties under the worst‐case distribution within the ambiguity set. Finally, a reformulation approach based on strong duality theory and conditional value‐at‐risk (CVaR) approximation is proposed to transform the DRCC problem into a tractable mixed‐integer linear programming (MILP) framework. Case studies are carried out on the IEEE 30‐bus system to verify the effectiveness and efficiency of the proposed approach.

A multi-criteria decision-making framework for distributed generation projects investment considering the risk of electricity market trading

Under the background of distributed generation (DG) participating in market trade, the scale of DG has developed rapidly. The investment decision of DG is actually a multi-criteria decision-making (MCDM) problem, nevertheless the current framework rarely considers the risk of market trading. Thus a framework containing scheme attributes and market risks needs to be established. Firstly, an index system including economic benefit, environmental and social benefit and market risk is established. Secondly, subjective and objective weight are combined by using game theory in weighting process. Among them, the subjective weight of each index is determined based on the plus-minus decision-making trial and evaluation laboratory model (plus-minus DEMATEL); the objective weight is determined by the combination of triangular intuitionistic fuzzy number (TIFN) and integrated determination of objective criteria weights (IDOCRIW) model. Finally, the TODIM model is used to rank the alternatives. Taking Shanghai as a case study, the robustness of the proposed framework is verified by sensitivity analysis and comparative analysis. The results show that the weights of index C34 is 0.12, indicating that market participants have a greater influence on market risk. The research will provide an effective tool for investment decision of DG in market trading.

A Multi-agent Model for Cross-border Trading in the Continuous Intraday Electricity Market

The increasing importance of cross-border trade has brought the topic of allocating cross-border transmission capacity under the limelight. In this paper, we focus on two different transmission capacity calculation methods, namely, Available Transfer Capacity and Flow-based Capacity allocation, in the continuous intraday electricity market. The effect of these two methods is studied on the continuous intraday electricity market through an agent-based model comprising various types of trading agents. It also models market operator agent and transmission system operator agent inspired by the Single Intraday Coupling project in Europe. The price–volume decisions for the orders posted by the market participants are determined by two different strategies where one of them adapts to changing market conditions and new information while the other is naive. The model accounts for ramping constraints which enables the analysis of the trading behavior and interaction of agents across multiple delivery products simultaneously. A switch parameter is availed in the trading timeline for the thermal and storage agents to change from a less conservative approach of ignoring the ramping and charging/discharging rate constraints respectively to considering them. An interplay between different switch parameters and cross-border transmission capacity calculation methods is studied.

Leveraging hybrid probabilistic multi-objective evolutionary algorithm for dynamic tariff design

Dynamic tariffs play an important role in demand response, contributing to smoothing power consumption and reducing generation capacity requirement and carbon emission. However, in the existing works, tariffs are usually designed without comprehensive consideration, such as potential user responses to tariffs. Thus, assuming an electricity trading market contains a utility company and multiple residential users, a dynamic tariff design method is proposed in this paper, considering user responses to tariff changes. Leveraging the non-intrusive load monitoring technique, rated power and user preference features for each appliance are acquired by the utility company to quantify user comfort (discomfort) based on derived user appliance usage habits. Then, a bi-level Stackelberg game model is built on the supply side for designing optimal dynamic tariffs and imitating the influence of tariff changes on DR plans for users. The upper level represents the utility company, trying to maximize utility profit, social welfare and carbon emission reduction. While the lower level represents users, aiming to minimize electricity bills and user discomfort. By solving such an optimization problem with multiple objectives, a novel hybrid probabilistic multi-objective evolutionary algorithm balancing evolutionary efficiency and stability is applied where random forest is adopted to boost performance. The proposed model is benchmarked with two state-of-the-art pricing methods and validated on a publicly accessible REFIT dataset, where low-rate power measurements are collected from real houses in the UK. The experimental results show the proposed model generally outperforms benchmarks on dynamic tariff design in achieving peak-shaving and low carbon emission while preserving user satisfaction. Furthermore, a case study is implemented, which verifies the necessity of various objectives employed in the proposed method.

Configuration Optimization of Hydrogen-Based Multi-Microgrid Systems under Electricity Market Trading and Different Hydrogen Production Strategies

Hydrogen-based multi-microgrid systems (HBMMSs) are beneficial for energy saving and emission reductions. However, the optimal sizing of HBMMSs lacks a practical configuration optimization model and a reasonable solution method. To address these problems, we designed a novel structure of HBMMSs that combines conventional energy, renewable energy, and a hydrogen energy subsystem. Then, we established a bi-level multi-objective capacity optimization model while considering electricity market trading and different hydrogen production strategies. The objective of the inner model, which is the minimum annual operation cost, and the three objectives of the outer model, which are the minimum total annual cost (TAC); the annual carbon emission (ACE); and the maximum self-sufficiency rate (SSR), are researched simultaneously. To solve the above optimization model, a two-stage solution method, which considers the conflicts between objectives and the objectivity of objective weights, is proposed. Finally, a case study is performed. The results show that when green hydrogen production strategies are adopted, the three objectives of the best configuration optimization scheme are USD 404.987 million, 1.106 million tons, and 0.486, respectively.

Optimal Allocation Scheme of Renewable Energy Consumption Responsibility Weight under Renewable Portfolio Standards: An Integrated Evolutionary Game and Stochastic Optimization Approach

Developing renewable energy has become a major strategy for China to accelerate the energy transition and combat climate change. Accordingly, a guarantee mechanism for renewable energy consumption with renewable portfolio standards (RPS) has been set in China. However, currently, the top-down allocation of regional renewable energy consumption targets often has issues of unfairness and inefficiency. It is necessary to investigate the issue of how to stimulate the renewable energy consumption potential on the demand side and reasonably formulate the consumption responsibility weights of various market entities. This paper aimed to develop a new methodology for the weight allocation of renewable energy consumption responsibilities. In doing so, an integrated model of an evolutionary game and stochastic optimization was constructed between market entities and governments. Then, the equilibrium strategies of market entities and governments were obtained through the evolutionary game. Furthermore, based on the equilibrium strategies, this paper optimized the renewable energy consumption weight of each market entity, which constitutes the optimal allocation scheme of renewable energy consumption responsibility weights. Finally, using the data of 7069 market entities in Hubei Province in 2021, this study simulated the model to verify its effectiveness and practicability. The results indicate that the willingness of market entities to assume more consumption responsibility is positively correlated with the government’s incentives and the maturity of the green electricity trading market. This study provides important implications for optimizing government regulations and promoting renewable energy consumption.

Peer-to-Peer Energy Trading Under Network Constraints Based on Generalized Fast Dual Ascent

The wide deployment of renewable energy resources, combined with a more proactive demand-side management, is inducing a new paradigm in both power system operation and electricity market trading, which especially boosts the emergence of the peer-to-peer (P2P) market. A more flexible local market mechanism is highly desirable in response to fast changes in renewable power generation at the distribution network level. Moreover, large-scale implementation of P2P energy trading inevitably affects the secure and economic operation of the distribution network. This paper presents a new P2P electricity trading framework with distribution network security constraints considered using the generalized fast dual ascent method. First, an event-driven local P2P market framework is presented to facilitate short-term or immediate local energy transactions. Then, the sensitivity analysis of nodal voltage and network loss with respect to nodal power injections is used to evaluate the impacts of P2P transactions on the distribution network, which ensures the secure operation of the distribution system. Thereby, the external operational constraints are internalized, and the cost of P2P energy trading can be appropriately allocated in an endogenous way. Moreover, a generalized fast dual ascent method is employed to implement distributed market-clearing efficiently. Finally, numerical results indicate that the proposed model could guarantee secure operation of the distribution system with P2P energy trading, and the solution method enjoys good convergence performance.

Multi-Agent Deep Reinforcement Learning for Coordinated Energy Trading and Flexibility Services Provision in Local Electricity Markets

Local electricity markets (LEM) have recently attracted great interest as an effective solution to the challenging problem of distributed energy resources’ (DER) management. However, LEM designs combining the market functions of local energy trading and flexibility services (FS) provision to wider system operators have not attracted sufficient attention. In the context of addressing this research gap, this paper firstly provides a new model-based system-centric formulation for the coordination of such a LEM, which provides a theoretical optimality benchmark. Compared to previous formulations, it considers the time-coupling operating characteristics of flexible DERs, and optimizes the two market functions simultaneously. Furthermore, this paper explores for the very first time a model-free prosumer-centric coordination approach for such a LEM, in order to address the practical limitations of model-based system-centric approaches. This is achieved through a new multi-agent deep reinforcement learning method which combines the beneficial properties of the multi-actor-attention-critic and the prioritized experience replay approaches. Case studies on a real-world, large-scale setting validate that the proposed LEM design successfully encapsulates the economic benefits of both local energy trading and FS provision functions, and demonstrate that the proposed learning method outperforms previous methods.

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.