Electricity Trading Research Articles

Study on China’s Position on Its Strategic Location, Planning, and Future Outline

This comprehensive analysis highlights the potential of China, the world’s largest energy producer. The research paper provides direction to China in the energy sector. Although currently relying on coal for power generation, in the long term, the country will become the largest exporter of electricity. The transition to sustainable energy production is critical, considering global commitments to reduce greenhouse gas emissions under the Paris Agreement. China’s ability to shift to clean energy is strengthened by its vast labor force, financial strength, and the Chinese government’s commitment to national rejuvenation. By establishing an “Electro-Yuan” system for trading electricity, China could challenge the global dominance of the US dollar. The One Belt, One Road (OBOR) initiative further facilitates a clean electricity trade network with strategic partners worldwide. However, a successful transition hinges on shifting from coal to renewable energy sources, enhancing existing grid infrastructure for cross-border transmission, and effectively implementing the Electro-Yuan system.

Биржи в структуре энергетического рынка: новый взгляд

The paper offers a systematized and updated view of exchange-traded energy products. The increase in natural gas and electricity trade volumes at commodity exchanges has dramatically changed the wholesale market structure in Europe. Nowadays commodity exchanges have played an essential role in energy trading. The complexity of exchange technologies and the reasons for their demand are often hidden under everyday exchange routine. Academic analysis of exchange activity has traditionally been feebly developed, because, firstly, classical economics assumes that finding of equilibrium between supply and demand is an instantaneous and cost-free process. Markets were mainly described as simple market exchanges where faceless buyers and sellers meet for an instant to exchange standardized goods at equilibrium prices. Secondly, energy exchanges are relatively new phenomenon. The justification of exchange trading became possible as a result of new institutional economics (NIE) and transaction cost theory (TCT) findings. Under conditions of positive transaction costs, incomplete foresight and bounded rationality – the conditions of the New Institutional Economics (NIE) – the institutional framework not only of firms but also of markets matters. Also, within this institutional framework the concept of “governance structure” was introduced. A high-performance system shall align transactions with governance structures in relation to their adaptive needs. Governance structures are properly regarded as part of the optimization problem. A shift from one structure to another may permit a simultaneous reduction in both the expense of writing a complex contract and the expense of executing it effectively in an adaptive, sequential way. The founders of NIE and TCT, having been investigating the stock and commodity exchanges, approached them as a measure of transaction costs for other-the-counter trades. However, they did not include exchange trading into classification of governance structures. As far as is known, it has not been described in this way in terms of NIE and TCT to date. Natural gas and electricity markets is a good examples of network industries, which are an appropriate case of institutional flexibility regarding the alignment between transactions and governance structures. “Liberalised” network industries needed and still need an “intense” design and re-design to be able to host any kind of markets and to interact with. Organized markets, energy exchanges have developed a required bridge between leaving long-term contracts and price promulgation. The hypothesis of energy exchange classification as a special type of governance structure, in the meaning of NIE and TCT, for network industries is discussed in this article.

Research on the Reasonability of Electricity Trading Data Based on Electricity Data Analysis

The rationality of power transaction data based on power data analysis can effectively promote the diversified development of China's power industry and meet the diverse electricity needs of the people in production and life. Professional technical personnel in the power field will integrate new technological means into the rationality of transaction data work, which will provide guidance for its sustainable development. Based on this, this article conducts a more targeted analysis from three levels: the advantages of electricity trading data rationality based on electricity data analysis, the classification of electricity trading data rationality based on electricity data analysis, and the exploration of electricity trading data rationality methods based on electricity data analysis, in order to provide reference for the effective promotion of Gini in related project engineering.

Honesty Verification Approach to Address Dishonest Bidding Behavior in Blockchain-Based P2P Electricity Trading

Honesty Verification Approach to Address Dishonest Bidding Behavior in Blockchain-Based P2P Electricity Trading

Least‐Cost for High Renewable Energy Share in the Distributed Energy System through Integrated Supply and Demand Side Management

This study analyzes energy supply and demand‐side management strategies to increase the renewable energy share in a distributed energy system (DES), which includes demand response (DR) options and different renewable energy sources (RES) and energy storage systems (ESS). To analyze the least costs for increasing the renewable energy share by integrating the DR, RES, and ESS, four scenarios of the DES were generated. Then, a new optimization model was proposed to identify the optimal design and operation strategies, which includes maximizing the profit as an objective function subject to various constraints, such as technical capacity and electricity price. To illustrate the capability of the proposed model, this study presented a case study of the DES for the residential sector of Jeju Island, Korea. The results showed that the optimal scenario, involving the integration of the DR, RES, and ESS, shows 0.115 $/kW hr of the levelized cost of electricity and 10% of renewable energy share, which correspond to 32.7% and 31.5% improvement, respectively, compared to the baseline. Additionally, the practical strategies to meet the Korean target of the high‐share RES were discussed through a sensitivity analysis of major external factors, such as carbon tax, incentives, and the electricity trading price in electricity markets.

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.

Power flow traceable P2P electricity market segmentation and cost allocation

This study explores peer-to-peer (P2P) electricity trading, emphasizing not just the export and consumption, but also the feasible physical supply of electricity and the use of distribution network assets. Building on a transaction-oriented dynamic power flow tracing model, a novel P2P market architecture is proposed. This architecture integrates the electricity market with the power network, considering technical constraints, network losses, and asset usage. The network is segmented into potential markets using second-order cone programming (SOCP), with an optimization problem introduced for loss-allocation. This problem merges network physical analysis and variable outputs from distributed energy resources (DERs). A graph-based P2P electricity trading model is designed to determine optimal transaction cost allocation and maximize benefits for both DERs and consumers. A case study on a modified IEEE 33-node test feeder substantiates the benefits of this market structure, demonstrating increased revenues for DERs and reduced bills for consumers compared to traditional feed-in-tariffs.

Understanding public preferences towards Ireland's offshore wind sector: A study on renewable energy trade, public involvement, and setback distance

We examine the preferences for offshore wind farms involving different levels of participation, distances to shore and energy export using a choice experiment in Ireland. Respondents are willing to make monetary trade-offs to permit offshore wind development involving trade in renewable electricity. However, they prefer developments that retain wind energy in Ireland rather than export and ventures which involve high levels of participation located further from the shoreline. We find significant preference heterogeneity with respect to participation and trade. Women have stronger preferences for a higher level of engagement in the development process than men, are less supportive of exports, are more likely to indicate their uncertainty in the face of potential environmental impacts posed by offshore wind but are equally knowledgeable about the industry.

CARBON FOOTPRINT AND ECONOMIC GROWTH IN NIGERIA AND GHANA

The study investigates the relationship between carbon footprint and economic growth in Nigeria and Ghana over the period 1990 to 2020 (31 years). The carbon footprint related variables used in the study include greenhouse gas emissions, renewable energy consumption, electricity consumption and trade openness; these were regressed against GDP per capita (a proxy for economic growth). The fully modified least square and panel dynamic least square were employed for the main analysis of the study. The findings revealed that greenhouse gas emissions and renewable energy consumption have significant negative effect on economic growth in Nigeria and Ghana; while electricity consumption and trade openness have insignificant positive and negative relationship with economic growth respectively. The study recommends among others that, the governments should initiate carbon pricing law which should be implemented through a tax policy specifically on the emissions from burning of biomass which consist of methane (CH4) and nitrous oxide (N2O) from the combustion of biomass in forest areas and carbon dioxide gas from the combustion of organic soils. High taxes will scare and deter indiscriminate bush burning among others resulting in serious environmental pollution and degradations. This measure will help reduce adverse GHG emissions and positively impact economic growth.

Risk-averse stochastic capacity planning and P2P trading collaborative optimization for multi-energy microgrids considering carbon emission limitations: An asymmetric Nash bargaining approach

The increasing penetration of renewable energy with stochastic characteristics and continuous refinement of carbon emission policies put forward higher requirements for the construction of new power systems. This paper proposes a risk-averse stochastic capacity planning and peer-to-peer (P2P) trading collaborative optimization method for multi-energy microgrids (MEMGs) considering carbon emission limitations. First, a cooperative operation model for MEMGs considering the capacity planning of distributed generation units, uncertainty from renewable energy generations, carbon emission limitations and P2P electricity trading among MEMGs is formulated. Second, a risk-averse stochastic programming method is applied to avoid the potential risk losses caused by randomness and intermittence of renewable energy. Third, an asymmetric Nash bargaining approach is adopted to ensure the fair allocation of benefits and maintain the willingness of individual MEMGs to participate in cooperation. Then, to protect the privacy of individual MEMGs belonging to different stakeholders, the alternating direction method of multipliers is used to solve the two subproblems in a distributed manner. Meanwhile, to further alleviate the computation burdens, a diagonal quadratic approximation method is applied to linearize the quadratic penalty term in the augmented Lagrangian function and realize the parallel solution of all optimization subproblems. Moreover, the influence of different carbon emission targets on the optimal resource combination strategy of the system is investigated by introducing carbon emission factors. Simulations on different models, strategies, and distributed algorithms are conducted to verify the effectiveness and superiority of the proposed method.

Modelling of Prosumer Behaviour in Peer-to-Peer Energy Trading Environment Through Markov Chain Analysis

The emergence of distributed generation has become significant over the last few years, bringing several challenges to traditional grid-connected electricity distribution systems. Technical limitations of integrating these new renewable distributed generation to the existing electricity grid made new avenues to change the way of energy consumption, which converted traditional consumers to prosumers. Peer-to-peer energy trading platforms enable these prosumers to trade electricity with their neighbours in microgrids where new electricity markets emerge with new technologies. This research introduces a concept of peer-to-peer energy trading for grid-connected electricity distribution networks. In its initial phase, the study models prosumer load sharing for households equipped with solar PV and battery energy storage. This modelling employs a Markov chain, where energy share states are determined by parameters in a Markov transition probability matrix. These parameters are varied during different time intervals of the day, reflecting varying cost components under a Time-of-Use (TOU) tariff structure. This approach enables prosumers to meet their load demand cost-effectively, fostering engagement in peer-to-peer energy exchanges. The proposed Markov model is simulated with 15-minute spot pricing intervals, demonstrating effective management of prosumer load demands in the peer-to-peer market environment, showcasing the potential benefits of this innovative energy trading approach.

Global transcontinental power pools for low-carbon electricity

The transition to low-carbon electricity is crucial for meeting global climate goals. However, given the uneven spatial distribution and temporal variability of renewable resources, balancing the supply and demand of electricity will be challenging when relying on close to 100% shares of renewable energy. Here, we use an electricity planning model with hourly supply-demand projections and high-resolution renewable resource maps, to examine whether transcontinental power pools reliably meet the growing global demand for renewable electricity and reduce the system cost. If all suitable sites for renewable energy are available for development, transcontinental trade in electricity reduces the annual system cost of electricity in 2050 by 5–52% across six transcontinental power pools compared to no electricity trade. Under land constraints, if only the global top 10% of suitable renewable energy sites are available, then without international trade, renewables are unable to meet 12% of global demand in 2050. Introducing transcontinental power pools with the same land constraints, however, enables renewables to meet 100% of future electricity demand, while also reducing costs by up to 23% across power pools. Our results highlight the benefits of expanding regional transmission networks in highly decarbonized but land-constrained future electricity systems.

Peer-to-Peer Transactive Computation–Electricity Trading for Interconnected Virtual Power Plant Buildings

Advancements of the virtual power plant (VPP) concept have aggregated buildings as their power plants and/or service providers. This paper proposes a peer-to-peer transactive computation–electricity trading framework for multiple-building virtual power plants (BVPPs). In this framework, the interconnected BVPPs can proactively trade their available computation–electricity with each other. Multiple BVPP trading is an intractable optimization problem due to its strong computation–electricity decision-making couplings. Thus, the original problem is described as a game theoretic problem and resolved into the sequential subproblems of social computation–electricity allocation and payoff allocation. By considering the local decision-making of heterogeneous BVPPs, a fully distributed algorithm is further designed to optimize the trading problem by sharing only limited trading information. Finally, a three-BVPP system is used to verify the merits of system resource utilization and operational economy.

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 bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy

Under the policy of renewable portfolio standard (RPS), many microgrid (MG) users with high electricity consumption are installed with renewable energy (RE) generation devices to complete their RE consumption responsibility and reduce carbon emissions. In view of the conflict between the goals of microgrid operators (MGO) and MG users, it is a challenge to achieve economic optimization for both MGO and MG users at the same time when they participate in system dispatch and complete RE consumption responsibility. Therefore, this paper proposes a bi-level noncooperative dispatch strategy that takes into account user satisfaction and RE consumption willingness. In this bi-level model, the upper level is led by an MGO who decides electricity retail prices, while the lower level is followed by MG users who participate in demand response (DR) and change electricity purchase strategies. First, a novel comprehensive evaluation model is formulated to evaluate users' willingness to consume green electricity. Based on this, users' consumption behavior of green and thermal electricity is modeled independently not only in electricity trading but also in power dispatching. Second, optimization objectives are established with the goal of maximizing MG users' welfare, including user satisfaction improvement and energy purchase cost reduction, as well as maximizing the revenue of the MGO. Then, a Stackelberg game model is constructed, and a differential evolution algorithm nested CPLEX is used to solve the Stackelberg game problem of balancing the interests of both parties. Finally, our simulation analysis shows that under the dispatch strategy in this paper, load peak-valley difference is reduced. In addition, when the green electricity generated within the system is sufficient, based on a high level of users' willingness to consume green electricity, the total consumption of green electricity is increased by 29.5% and the overall welfare of users is increased by 0.75%.

Universal Heater Industries: managing conflict with a distributor

Learning outcomes This case study exposes students to conflicts with distributors, escalated scenarios of a trade association and the possible repercussions of such a scenario. Upon completion of this case study, the students will be able to understand the critical success factors for a distribution setup and alignment of channels for driving growth; understand and manage the power dynamics with a stakeholder, like trade associations, distribution reach, fallacies in managing the distributors and identifying the gaps; critically evaluate negotiation opportunities when a trade association is not directly related to the principal organization. Case overview/synopsis This case study showcased a conflict between the distributor and Universal Heater Industries (UHI), a leading player in the water heater business in India. In 2015, the global leadership of UHI identified India as an emerging market and undertook a complete management overhaul to implement a new growth plan. Several measures were put in place that leveraged the global product portfolio and new people were appointed to push the agenda. Manish Singhal, the national sales head of UHI, selected Kerala as the pilot state to implement the new plan. However, the projects failed, as the distributor escalated the treatment meted out by UHI to the Electrical Trade Association (ETA). Trade associations have had a history of playing truant with players like UHI, and because of this, business came to a complete halt. The UHI and ETA teams met once; however, the suggested closure by ETA needed to be aligned with UHI’s interests. Singhal’s dilemma deepened, and they had to decide the next steps. Complexity academic level This case study is suitable for a postgraduate marketing course in a segment on managing channels, intermediaries, distribution management and channel conflicts. The uniqueness of this case is in the dimension of the trade association and managing the stakeholders. Supplementary material Teaching notes are available for educators only. Subject code CSS8: Marketing.

An electricity and carbon trading mechanism integrated with TSO-DSO-prosumer coordination

Due to the proliferation of distributed renewable energy, bidirectional electricity and carbon interaction appears in the coordinated transmission and distribution network and increases complexity in entities' decision-making. To address the issues, this paper develops a hierarchical market framework integrated with electricity and carbon trading. Specifically, an electricity market which combines locational marginal price (LMP) and peer-to-peer (P2P) trading is established considering transmission and distribution coordination. Meanwhile, based on carbon emission flow and P2P trading, a hierarchical coordinated carbon market is also designed to precisely measure and price carbon responsibility and contribution from prosumers. In addition, a hierarchical distributed algorithm is developed to clear the proposed market with privacy protection. Numerical results authenticate the necessity for transmission and distribution coordination, benefits of distribution electricity P2P trading as well as superiority in carbon measurement and pricing method.

Bi-level stackelberg game-based distribution system expansion planning model considering long-term renewable energy contracts

With the deregulation of electricity market in distribution systems, renewable distributed generations (RDG) are being invested in by third-party social capital, such as distributed generations operators (DGOs) and load aggregators (LAs). However, their arbitrary RDG investment and electricity trading behavior can bring great challenges to distribution system planning. In this paper, to reduce distribution system investment, a distribution system expansion planning model based on a bi-level Stackelberg game is proposed for the distribution system operator (DSO) to guide this social capital to make suitable RDG investment. In the proposed model, DSO is the leader, while DGOs and LAs are the followers. In the upper level, the DSO determines the expansion planning scheme including investments in substations and lines, and optimizes the variables provided for followers, such as RDG locations and contract prices. In the lower level, DGOs determine the RDG capacity and electricity trading strategy based on the RDG locations and contract prices, while LAs determine the RDG capacity, demand response and electricity trading strategy based on contract prices. The capacity information of the DRG is sent to the DSO for decision-making on expansion planning. To reduce the cost and risk of multiple agents, two long-term renewable energy contracts are introduced for the electricity trading. Conditional value-at-risk method is used to quantify the RDG investment risk of DGOs and LAs with different risk preferences. The effectiveness of the proposed model and method is verified by studies using the Portugal 54-bus system.

Research on power sharing optimization strategy based on hybrid multi-microgrid

To alleviate the increasing distributed energy penetration problem in the distribution network under the “double carbon”, a P2P multi-microgrid power sharing and trading method based on the alternating direction method of multipliers (ADMM) are proposed in the hybrid multi-microgrid. The paper constructs the optimization model of each sub-microgrid and a multi-microgrid model for P2P power sharing transactions based on the ADMM algorithm. Finally, the proposed model is simulated and verified using a multi-microgrid composed of three microgrids. The results show that the electric energy trading tariff between microgrids obtained from the model in this paper is reasonable, which can improve the utilization rate of new energy and reduce the penetration rate of distributed energy in the distribution network.

Multi-Timescale Trading Strategy for Renewable Power to Ammonia Virtual Power Plant in the Electricity, Hydrogen, and Ammonia Markets

Renewable power to ammonia (RePtA) is a prominent zero-carbon pathway for decarbonization. Due to the imbalance between renewables and production energy demand, the RePtA system relies on electricity exchange with the power grid. Participating in the electricity market as a virtual power plant (VPP) may help to reduce energy costs. However, the power profile of local photovoltaics and wind turbines is similar to those in the market, resulting in rising energy costs under the conventional strategy. Hence, we develop a multi-timescale trading strategy for the RePtA VPP in the electricity, hydrogen, and ammonia markets. By utilizing the hydrogen and ammonia buffer systems, the RePtA VPP can optimally coordinate production planning. Moreover, we find it possible to describe the trading of electricity, ammonia, and hydrogen in a unified timeframe. The two-stage robust optimization model of the electricity market is extended to multiple markets and solved by the column and constraint generation (CC&G) algorithm. The case is derived from an actual project in the Inner Mongolia Autonomous Region. Sensitivity analysis demonstrates the economic advantages of the RePtA VPP joining multiple markets over the conventional strategy and reveals the necessity of the hydrogen and ammonia buffer and reactor flexibility.