Market Trading Mechanism Research Articles

Analysis of Problems and Countermeasures in Chinese Stock Market

Chinas stock is one of the most popular trading platforms with high global attention, it plays an important role in Chinese economy and global economy. With the rapid development of Chinese economy, Chinas stock market has also achieved significant achievements. It not only provides a vital platform to finance, but also optimize resource allocation. Whats more, it decreases the investors risk of the investment, and makes it is possible for government to carry out macro-economic policies through market stock. it boosts the development of Chinas financial system, and increases the ability of the Chinas financial system to anti-risks. However, there are also some problems existing in Chinese stock market, for example, the less reasonable structure of investors, serious disadvantages in the small and medium-sized GEM market governance, and frequent problems occur in the stock market trading mechanism and exchange system. This paper will as these three problems basis of starting the topic, and put forward countermeasures and strategies, which will have significant role in solving the existing problems efficiently in Chinese stock market.

Harnessing the potential: exploring development opportunities and charting strategic pathways for China’s enhanced engagement in international blue carbon cooperation

Abstract In light of the urgent need to combat climate change and attain carbon neutrality, the development of blue carbon has emerged as a pivotal strategy for the global community. This approach offers significant potential for both mitigating and adapting to the impacts of climate change. As a result, international collaboration on blue carbon initiatives has become a focal point in the worldwide response to the climate crisis. China, blessed with extensive blue carbon resources, recognizes the development of these ecosystems as a critical component in its efforts to make a substantial contribution to the global endeavor to curb climate change and achieve sustainable environmental goals. This article offers a comprehensive analysis of China’s opportunities and challenges in international blue carbon collaboration. Despite its vast potential, China faces significant hurdles, including the degradation of blue carbon ecosystems, a delayed start in implementing blue carbon practices, and inconsistencies in the blue carbon standard system. To enhance its participation in global blue carbon initiatives, China should pursue several key strategies, including preserving and restoring its blue carbon ecosystems, strengthening domestic protection and application mechanisms, promoting the construction of an international blue carbon system, establishing a scientific cooperation platform to facilitate capacity building and technology transfer and advocating for an international blue carbon market trading mechanism. By prioritizing these key areas, China can simultaneously foster domestic blue carbon conservation and development while solidifying its role as a pivotal contributor to global climate change mitigation efforts.

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.

The research of river basin ecological compensation based on water emissions trading mechanism.

By integrating the successful case of the European Union emissions trading system, this study proposes a water emissions trading system, a novel method of reducing water pollution. Assuming that upstream governments allocate initial quotas to upstream businesses as the compensation standard, this approach defines the foundational principles of market trading mechanisms and establishes a robust watershed ecological compensation model to address challenges in water pollution prevention. To be specific, the government establishes a reasonable initial quota for upstream enterprises, which can be used to limit the emissions of upstream pollution. When enterprises exceed their allocated emissions quota, they face financial penalties. Conversely, these emissions rights can be transformed into profitable assets by participating in the trading market as a form of ecological compensation. Numerical simulations demonstrate that various pollutant emissions from upstream businesses will have various effects on the profits of other businesses. Businesses in the upstream region received reimbursement from the assigned emission rights through the market mechanism, demonstrating that ecological compensation for the watershed can be achieved through the market mechanism. This novel market trading system aims at controlling emissions management from the perspectives of individual enterprises and ultimately optimizing the aquatic environment.

Operational decisions of photovoltaic closed-loop supply chains with industrial distributed photovoltaic subsidy policy in China

Photovoltaic (PV) generation, as a clean and renewable energy technology, aligns with the global needs for energy transition and sustainable development. Due to its industrial distributed PV subsidy support policy, China has witnessed rapid growth in distributed PV industrial projects, leading to the formation of a mature market trading mechanism. This paper, focusing on China’s industrial distributed PV and its user, aims to explore how the PV system manufacturer (PSM) and PV system service provider (PSSP) can optimize the combination of “recycling mode & power structure” to simultaneously achieve multiple performance improvements, including those in economy, society, and environment (ESS) in the photovoltaic closed-loop supply chain (PV-CLSC), in consideration of the government’s PV subsidy policy. Therefore, by using three game-theoretical approaches including Nash game existed between the PSM and the PSSP under equal power and the Stackelberg game respectively led by PSM and PSSP, the optimal decisions of the PSM and PSSP, and the effective government subsidies under different combinations are obtained. Conclusions show that: To achieve individual optimal economic performance, both PSM and PSSP, as recyclers, invariably prefer a dominant position and choose to conduct recycling activities independently. For social welfare and subsidy efficiency, the government consistently tends to choose the PSM recycling mode in which the PSM and PSSP are expected to be in an equal power structure. Both in the PSM recycling and PSSP recycling modes, the government subsidy is smaller when two members are under the equal position than that respective under the dominant position, and in the latter scenario, the one with the advantage can mainly obtain the difference in the increased government subsidy.

Investigating the impact of the future carbon market on the profitability of carbon capture, utilization, and storage (CCUS) projects; the case of oil fields in southern Iraq

Carbon capture utilization and storage for enhanced oil recovery (CCUS-EOR) is considered a promising solution to meet future global energy needs while significantly reducing carbon dioxide emissions. However, the future growth of CCUS-EOR is uncertain due to the global economic recession and low oil prices. This research analyzes the impact of uncertain oil and carbon market prices on the economic value of a CCUS-EOR project which contributes to appropriate investment and future policy decisions. To explore the potential trade-offs between available carbon dioxide (CO2) quantities for trading or injection for enhanced oil recovery, the market's trading mechanism is analyzed by predicting future oil and carbon prices. The forecasted market prices are then used in an optimization model to estimate the optimal cumulative cash flow from implementing a proposed CCUS-EOR project to capture CO2 from the Hartha power plant located in the southern Basra Province of Iraq, considering three different oil fields of Rumaila, Nassriya, and Noor. The results revealed that the profitability of the proposed CCUS-EOR project is affected by both oil and carbon market prices, and a higher carbon price is needed to compensate for the financial setbacks, resulting from reduced oil prices. The research also features the strategic decision-making of oil fields across different sizes in response to various price scenarios. The medium-sized fields consistently prioritize carbon injection for enhanced oil recovery (EOR) over market trading. In contrast, small and giant fields adapt their approach based on the prevailing market price dynamics.

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

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

Research on the transaction mechanism of electricity retail market that satisfies the full liberalization of industrial and commercial users

This paper proposes an electricity retail market transaction mechanism that satisfies the full liberalization of industrial and commercial users in the future environment of full liberalization of industrial and commercial users. The method and path of the whole process construction of the electricity retail market trading mechanism are analyzed. Aiming at the situation that a large number of industrial and commercial users all enter the electricity retail market, the electricity retail package system mechanism, diversified development and effective management and control paths have been constructed. Analyzed the countermeasures and mechanism suggestions for market transaction organizers to standardize the operation behavior of electricity sales companies and effectively prevent market risks, put forward relevant suggestions to guide electricity sales companies to diversify the development of retail services, and promote business model innovation, and analyzed the effect of case applications.

Impact mechanism of ecosystem services on resident well-being under sustainable development goals: A case study of the Shanghai metropolitan area

Understanding the intricate connections between ecosystem services (ESs) and resident well-being (RWB) is critical to managing ESs and achieving sustainable development goals (SDGs). However, it is difficult to quantify the impact mechanism between different ESs and RWB. We constructed a theoretical framework to analyze the impact mechanism of ESs on RWB under SDGs. We sought to quantify the spatiotemporal characteristics of ESs and RWB in Shanghai at the district scale and then explored the impact mechanism of ESs on RWB using a structural equation model (SEM). Results show that habitat quality, carbon storage capacity, food availability, and cultural services declined from 2000 to 2020. The level of RWB in the central urban area was higher than that in the suburbs. Material satisfaction grew in most districts but declined somewhat in Huangpu, Chongming. Spiritual fulfillment declined in most districts while it increased in Changning, Hongkou, and Pudong. Health and social relations improved in most districts, but the safety in suburbs has decreased. Supporting services directly promoted regulating services and cultural services but inhibited provisioning services. Cultural services had a negative impact on regulating services and provisioning services. Regulating services promoted provisioning services and suppressed cultural services while provisioning services promoted cultural services greatly and suppressed regulating services. Regulating and cultural services directly promoted the RWB, and provisioning services had an inhibitory effect on it. Supporting services indirectly promoted the RWB by promoting regulating and cultural services, while it suppressed the provisioning services to reduce the negative impact on RWB, it is crucial for improving RWB. Accordingly, we proposed several spatially targeted ecological measures. Community ecological parks should be constructed to promote the variety, stability, and sustainability of ecosystems in downtown areas. Supporting services should be improved by targeting habitat quality capacity to manage and restore the ecological environment. The cultural and tourism infrastructure of country parks in the suburbs should be improved to provide more employment opportunities and high-quality cultural services. The ecological compensation mechanism and market trading mechanism should be optimized to shorten the transition period of the ecological economy.

Carbon Market Trading Mechanisms Considering Multi-Layer Reactive Power Compensation

In the context of achieving carbon peaking and carbon neutrality goals, focusing on coordinated efficiency in loss and carbon reduction, and promoting comprehensive green transformation of economic and social development are critical strategies. Line loss is an economic and technical indicator for measuring losses in a power system, and loss reduction is one of the important ways to achieve the carbon peaking and carbon neutrality goals in the power system. However, with the continuous increase in the power grid scale and the increasingly complex operation mode of the system, it is difficult to clearly quantify the carbon reduction benefits brought by system loss reduction. In order to synergize grid loss reduction and system carbon reduction, and generate economic and environmental benefits at the same time, this paper proposes a carbon market trading model that considers multi-layer reactive power compensation strategies. Based on the carbon emission flow model, a node carbon cost pricing is formed, and multi-layer reactive power compensation measures are set in the distribution network nodes to obtain an optimal loss reduction strategy, with the carbon market trading cost minimization as the objective. The effectiveness of the model is verified by simulating and analyzing four scenarios. Compared with the original system that does not consider carbon trading and reactive compensation, the model proposed in this paper can reduce losses by 20% and reduce carbon emissions by 5.7%. This paper is of great value for reactive power loss reduction management in distribution networks of a low-carbon background.

Study on the electricity spot market trading mechanism considering the proportion of renewable energy consumption quota

The challenge of harmonizing the integration of renewable energy in market-driven transactions and assured accommodations presents a predicament in the development of China's electricity spot market. Moreover, as renewable energy penetration escalates, the issue of reserve undeliverability due to transmission congestion diminishes the power system's capacity to utilize renewable energy resources. To address this concern, this study introduces a secondary clearing mechanism for the electricity spot market, taking into account the proportion of renewable energy consumption quotas. Based on the first clearing, when renewable curtailment occurs, the bid pricing of abandoned power units undergoes flexible adjustment through the optimization of the price correction coefficient, followed by the execution of a secondary clearing utilizing the revised bidding information to fulfill the stipulations of the renewable energy consumption quota ratio. Drawing on the outcomes of the two-stage clearing, an incentive-compatible settlement compensation mechanism is proposed to preserve the impartiality of the market operator. The spot market clearing model accounts for the transmission safety margin, effectively mitigating the likelihood of transmission congestion, reserve inaccessibility, and renewable energy curtailment issues in real-time dispatching. Finally, a modified IEEE 30-bus system serves to substantiate the efficacy of the proposed market mechanism.

Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer

Accurate forecasting of power plant loads is critical for maintaining a stable power supply, minimizing grid fluctuations, and enhancing power market trading mechanisms. However, the data on power plant generation load (hereinafter abbreviated as load) are non-stationary. The focus of existing load forecasting methods has been on continuously improving the ability to capture the dependent coupling between outputs and inputs, while research on external factors, which are the causes of non-stationary load data, has been neglected. The identification of the causal relationship between external variables and load is a significant factor in accurately predicting load. In the present study, the causal effects of various external variables on load were identified and then quantitatively calculated using various methods. Based on the improved Informer model, a long-time series forecasting model, a hybrid forecasting method was proposed called causal inference-improved Informer (hereinafter abbreviated as Causal–Informer). In the present study, the mutual information method was used to remove insignificant external variables. Subsequently, external factors such as GDP, holidays, ambient temperature, wind speed, power plant maintenance status, and rainfall were selected as input features of the proposed forecasting model. Finally, the proposed Causal–Informer method was evaluated using the historical load of a power plant in East China. Compared with four popular forecasting models, measurements on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for the proposed method were reduced by 89.8 million kwh–672.3 million kwh, 56.8 million kwh–637.9 million kwh, and 5.1–25.4%. The proposed method achieved the most accurate and stable results. The MAPE reached 10.4% and 24.8% in 30 time steps ahead and 90 time steps ahead of forecasts, respectively.

Photovoltaic supply chain and government subsidy decision-making based on China's industrial distributed photovoltaic policy: A power perspective

As a clean energy source, photovoltaic (PV) power generation best meets the current demand for energy transformation. In particular, industrial distributed PV projects in China have developed rapidly, forming a mature market trading mechanism, and the Chinese government's subsidy policy has strongly supported their development. However, lucrative government subsidies often lead to PV enterprises not paying attention to technological innovation and blind production. Therefore, to improve the efficiency of government subsidies, enhance the overall performance of the PV supply chain, and achieve the healthy and long-term development of the PV industry. In this paper, we consider the actual demand preference characteristics of users, and construct game models of the PV supply chain under different power structures. We explore the optimal decisions of the PV supply chain enterprises and the formulation of optimal government subsidies under different power structures. The results show that (1) reasonable government subsidies have an incentive effect, which can increase manufacturers' profits, service providers' profits and social welfare level, and improve the demand for PV products. (2) Different power structures significantly affect the government's optimal subsidy, PV system manufacturer (PSM)'s and PV system service provider (PSSP)'s optimal decision-makings, while it does not affect social welfare. (3) Government subsidies are much larger in the Stackelberg case than in the Nash equilibrium case. Government subsidies are positively proportional to PV product market size, user quality preference factor (QPF), user product coverage preference factor (PCPF), industrial unit electricity price, photovoltaic conversion efficiency, and effective generation hours, and inversely proportional to PV product discount sensitivity, production cost, quality innovation cost factor (QICF), and effort performance cost factor (EPCF).

Optimal decision analysis of PV supply chain considering power structure

As a clean energy source, photovoltaic power generation is most in line with the current demand for energy transformation. Therefore, China's PV industry has developed rapidly, among which, industrial distributed PV projects have developed particularly well and formed a mature market trading mechanism. However, the high-speed development has caused behavioral decision dysfunctions among PV companies and overall performance losses in the PV supply chain. Therefore, this paper integrates the actual demand preference characteristics of users and constructs a PV supply chain game model consisting of PV system manufacturers and service providers, and then, this paper studies the optimal decision analysis of PV supply chain firms in different situations from the perspective of different power structures.

Research on Electricity Market Trading Mechanism Considering Peak‐to‐Valley Ratio and Multitime Coupling

With the development of the economy and society, electricity demand is increasing. However, there are some problems on the power grid side, such as the excess capacity of power installed, abandoned power of renewable energy, low overall utilization rate, and high pressure of stable operation. The problem stems from the inconsistency between the peak of electricity production and consumption and the lack of obvious power price signals. In this article, the market development objectives are combined with the time‐divided transaction, and two objectives of market development are proposed: (1) Reduce the peak‐valley difference of power supply and demand; (2) Expand the peak‐valley price difference in the market. Combined with the physical properties of the power market, a multiperiod coupling trading mechanism is designed. The results show that the speculative and profit‐seeking behavior of power producers will be stopped under the proposed trading mode, the potential of users for peak‐valley filling will be effectively developed, the peak‐valley gap of the power system can be controlled within a reasonable range, and the peak‐valley gap of power trading can be further amplified.

A novel community‐based local electricity market for multiple communities with joint energy trading considering the risk of participation

AbstractGrowing penetration of distributed energy resources (DER) leads to emerging changes and issues in future power systems. A system oprator aims to maximise demnad side energy trading while minimising energy improts from upstream grid. This paper proposes a reliable local energy market (LEM) trading mechanism in which consumers and prosumers can trade self‐produced energy in a multiple community‐based market (MCBM) framework to optimally provide the balance. The proposed framework intends to maximise social welfare by fair allocation of resources, increasing the provided reserve for the main grid, and shaving the peak power resulting in enhancement of network flexibility. The overall model is formulated in the style of a bi‐level problem in which the lower‐level clears the market and the upper‐level is designated to redistribute the costs and benefits among market participants. Furthemore, the conditional value at risk (CVaR) approach is employed to evaluate how risk affects social welfare. Moreover, the impact of multiple communities and joint energy trading on the outcomes of the market is examined. The proposed model has been evaluated by analysing IEEE 33‐bus standard test system, and numerical studies verify the effectiveness of the proposed approach.

Toward efficient waste electric vehicle battery recycling via auction-based market trading mechanisms

This paper proposes an auction-based market trade mechanism for the electric vehicle battery recycling (EVBR) problem, which aims to realise the optimal resource allocation and pricing of EVBR. The main motivation of this paper is to attempt to explore an approach to achieving efficient battery recycling. We first consider an EVBR market with m buyers and n sellers, and develop the multi-unit trade reduction (MTR) mechanism in the EVBR market. According to the supply and demand relationship in the EVBR market, we consider three market scenarios of supply and demand balance, oversupply, and overdemand, and formulated corresponding auction allocation rules. Numerical study results show that the proposed MTR mechanism can achieve efficient resource allocation. We also observed that not all results increased with the number of sellers/buyers. Second, considering the distance between sellers and buyers, we developed a stochastic multiple MTR (SM-MTR) mechanism to enable sellers and buyers within the region to conduct transactions. Finally, we propose an integrated MTR, SM-MTR and one-sided Vickrey–Clarke–Groves auction mechanism that is feasible in both one-sided and bilateral environments. Furthermore, our work can provide novel managerial implications for EVBR market stakeholders in terms of practical application.

A novel carbon price forecasting method based on model matching, adaptive decomposition, and reinforcement learning ensemble strategy.

Carbon price is closely related to energy conservation and emission reduction cost, and carbon price forecasting is conducive to improving and stabilizing market trading mechanisms. Considering the matching relationship between the prediction model and the carbon price market, a decomposition ensemble carbon price forecasting method based on reinforcement learning model fusion is designed. Firstly, considering the problem of disharmony between different base models and data patterns, a model matching strategy is developed to match the appropriate prediction base model for each carbon trading market. Secondly, the empirical wavelet transform (EWT) algorithm is used to decompose the carbon price into several subseries, which reduces the complexity of the original data and improves the prediction effect of each base model. Finally, for each carbon price market, the Q-learning algorithm of reinforcement learning is used to integrate the prediction results of the selected multiple base models into the final price forecasting of the corresponding market. The model is verified in the seven carbon trading markets, and experiments show that the model has superior and stable prediction performance compared with other methods. The mean absolute percentage errors of the Hubei, Beijing, Shenzhen, Chongqing, Tianjin, Shanghai, and EU markets are only 0.3515%, 1.2335%, 1.3388%, 1.2128%, 0.3229%, 0.2418%, and 0.4094%, respectively. It shows that the EWT method and reinforcement learning ensemble strategy do improve the prediction performance, and the proposed model can be used as a feasible tool for price assessment and management in carbon price markets.

Deep reinforcement learning for energy trading and load scheduling in residential peer-to-peer energy trading market

The popularization of solar generation enables residential households to supply their loads and trade the surplus energy through residential peer-to-peer (P2P) energy trading market. In a residential community with multi-households, households are able to determine demand-side management (DSM) and energy trading strategies to reduce electricity cost. However, residential users often decide market participation biddings according to their experience and guesses on other households’ biddings, and it is difficult to know how far these biddings are from the best possible market biddings achievable by the P2P market trading mechanism. Targeting the maximum cost savings achievable from this P2P market, this paper focuses on how to obtain such a maximum saving under optimal energy trading and DSM strategies using a multi-agent deep reinforcement learning approach to obtain the optimal solution. In this regard, the energy trading and DSM problem for residential households is formulated as a partially observable Markov decision process (POMDP). Then, a reinforcement learning algorithm with decentralized training and execution approach is adopted in the proposed POMDP problem to fit the policy function and value function, which are trained against each other to learn the optimal strategies by trial-and-error. The simulation result validates that by using the proposed multi-agent reinforcement learning method to optimize the P2P energy trading and DSM strategies, the average reward for households can be significantly improved by 8.52% and 14.03%, respectively, compared with other two state-of-the-art deep reinforcement learning methods.

Research on the Electricity Market Clearing Model for Renewable Energy

The development of renewable energy in China has made remarkable achievements, but the problem of renewable energy consumption has become increasingly prominent. This paper establishes a power market trading system for renewable energy, with the aim of promoting large-scale renewable energy consumption and increasing the enthusiasm of renewable energy producers and users to participate in market transactions. First, according to the power generation cost, the backup cost of renewable energy power plants and the possible quotation strategies of other renewable energy producers, a quotation model of renewable energy producers is established. In the clearing of the spot market by renewable energy producers, the independent market operator conducts the first-stage clearing of the electricity market with the goal of maximizing social welfare. After the announcement of the clearing results, the renewable energy producers that did not win the bid will revise their quotations and carry out the second stage clearing to realize the consumption of renewable energy. In this paper, the particle swarm algorithm combined with the CPLEX solver is used to solve the problem, and finally, different scenarios are analyzed through example analysis. The results show that, compared with the conventional power market trading mechanism, the energy abandonment rate of the power market trading mechanism for renewable energy proposed in this paper drops from 8.2% to 2.1%, and the profit margin of renewable energy producers increase by 6.6%. It is demonstrated that the proposed electricity market mechanism can effectively promote the consumption of renewable energy and increase the income of renewable energy producers.