Trading Mechanism Research Articles

Low-Carbon Optimization Scheduling of Integrated Energy Systems Based on Bilateral Demand Response and Two-Level Stackelberg Game

In the context of low-carbon energy transformation, fully utilizing the integrated demand response (IDR) resources on the load side can improve the operational flexibility and economy of the integrated energy system (IES). However, establishing a reasonable trading mechanism to enhance users’ participation in IDR has become a key issue that IES urgently needs to solve. To this end, this paper first establishes an IES model that includes electricity, heat, and gas. To reduce carbon emissions, a ladder-type carbon trading mechanism is introduced while adding low-carbon technologies such as carbon capture devices and power-to-gas conversion. Secondly, a bilateral IDR mechanism centered on the load aggregator (LA) is proposed, and a multi-agent operation model including IES, LA, and users is established. The IDR subsidy price is dynamically determined through a two-level Stackelberg game model involving IES, LA, and users. Then, through KKT conditions and the Big M method, the two-level game model is turned into an IES-LA game model, which is solved using a combination of the White Shark Optimization method and the Gurobi solver. The final simulation results show that the scheduling model can fully reflect the time value of IDR resources, and the IES cost is decreased by USD 152.22, while LA and user benefits are increased by USD 54.61 and USD 31.85. Meanwhile, the ladder-type carbon trading mechanism and low-carbon technology have effectively achieved low-carbon operation of the system.

Profit-driven distributed trading mechanism for IoT data

Data trading is a crucial means of unlocking the value of Internet of Things (IoT) data. However, IoT data differs from traditional material goods due to its intangible and replicable nature. This difference leads to ambiguous data rights, confusing pricing, and challenges in matching. Additionally, centralized IoT data trading platforms pose risks such as privacy leakage. To address these issues, we propose a profit-driven distributed trading mechanism for IoT data. First, a blockchain-based trading architecture for IoT data, leveraging the transparent and tamper-proof features of blockchain technology, is proposed to establish trust between data owners and data requesters. Second, an IoT data registration method that encompasses both rights confirmation and pricing is designed. The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data. For pricing, we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network. Finally, an IoT data matching method is designed based on the Stackelberg game. This establishes a Stackelberg game model involving multiple data owners and requesters, employing a hierarchical optimization method to determine the optimal purchase strategy. The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated. Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.

Enhancing Carbon Trading Mechanisms through Innovative Collaboration: Case Studies from Developing Nations

Against a backdrop of global climate change mitigation efforts, carbon trading has emerged as a critical mechanism, yet developing countries often lack the necessary infrastructure and collaborative frameworks. The research investigates how intermediaries facilitate stakeholders and employ innovative practices to foster effective carbon trading markets. Methodologically, it employs a qualitative approach, conducting in-depth case studies of four intermediaries through 32 semi-structured interviews and archival document. Findings underscore the pivotal role of digital platforms in enabling real-time trading, rigorous standardization processes to ensure market credibility, and multi-stakeholder engagement strategies that promote inclusive participation. This research contributes to the theoretical understanding of carbon trading by illustrating how digital platforms and technological integration enhance resource acquisition and allocation. It emphasizes the significance of standardization and verification processes in building trust among stakeholders, crucial for effective supply chain collaboration. Practically, the study highlights the benefits of these approaches in enhancing market efficiency and transparency. It also underscores the importance of multi-stakeholder engagement strategies and strategic alliances for creating resilient and inclusive carbon trading markets, offering actionable insights for stakeholders and policymakers alike.

Low-carbon optimal dispatch of park integrated energy system considering coordinated ammonia production from multiple hydrogen energy

The utilization of ammonia energy has garnered considerable global attention as a viable approach to alleviate pressure on energy supply within park integrated energy systems (PIES) and enhance energy efficiency. However, the introduction of ammonia production requires the system to increase large amounts of hydrogen, diversifying the sources of hydrogen and challenging the coordinated operation of PIES. In this paper, the inertia characteristics of the gas and thermal energy delivery process are utilized to increase the flexibility of the PIES operation. A technological framework for coordinated ammonia production from multi-hydrogen energy in PIES under consideration of having inertia characteristics and a low-carbon operation strategy for ammonia-doped combustion in thermal generation is constructed. Limiting carbon emissions through a stepped carbon trading mechanism in this strategy. In addition, while addressing the ammonia-doped requirement for thermal generation combustion, and by flexibly setting the ammonia doping ratio of thermal generation combustion, the operation of PIES under different ammonia-doped ratios has been analyzed in detail. The results demonstrate that the proposed model enhances the utilization of renewable energy, leading to reduction in total economic costs by 4.52 % and total carbon emissions by 24.81 %. This makes the operation of PIES more economical, low-carbon, and flexible.

A Blockchain and Zero Knowledge Proof Based Data Security Transaction Method in Distributed Computing

In distributed computing, data trading mechanisms are essential for ensuring the sharing of data across multiple computing nodes. Nevertheless, they currently encounter considerable obstacles, including low accuracy in matching trading parties, ensuring fairness in transactions, and safeguarding data privacy throughout the trading process. In order to address these issues, we put forward a data trading security scheme based on zero-knowledge proofs and smart contracts. In the phase of preparing the security parameters, the objective is to reduce the complexity of generating non-interactive zero-knowledge proofs and to enhance the efficiency of data trading. In the pre-trading phase, we devise attribute atomic matching smart contracts based on precise data property alignment, with the objective of achieving fine-grained matching of data attributes between trading parties. In the trading execution phase, lightweight cryptographic algorithms based on elliptic curve cryptography (ECC) and non-interactive zero-knowledge proofs are employed for the dual encryption of trading data and the generation of attribute proof contracts, thus ensuring the security and privacy of the data. The results of experiments conducted on the Ethereum platform in an industrial IoT scenario demonstrate that our scheme maintains stable and low-cost consumption while ensuring accuracy in matching and privacy protection.

A low-carbon supply chain pricing mechanism considering CSR under carbon cap-and-trade policy.

In the context of environmental deterioration and people's growing environmental protection awareness, governments or regions have put forward corresponding carbon emission reduction policies. Among them, the carbon trading mechanism, as an effective means to promote enterprises to implement emission reduction measures, plays a crucial role in regulating enterprise behavior and promoting social sustainable development. Since various industries and sectors support each other in social and economic development, it is more reasonable to study the carbon emission reduction optimization decisions of society and enterprises from the perspective of the supply chain. To achieve the carbon reduction target of the supply chain system, manufacturing enterprises usually need to incur additional costs to invest in emission reduction technologies, and retail enterprises also need to conduct low-carbon publicity to increase product market share. On one hand, considering the impact of the government's emission reduction constraints and consumers' low-carbon preferences, manufacturers will take corporate social responsibility (CSR) into consideration to enhance product competitiveness. On the other hand, smaller retailers are more concerned about being treated fairly than about their own profits due to the extra cost of low-carbon advertising. In this paper, considering the background of carbon trading, the manufacturer's CSR and retailer's fairness concern behavior are introduced into the decision-making process of the low-carbon supply chain (LCSC), and the relevant emission reduction decision-making model is constructed by using Stackelberg game theory and backward derivation method. Through comparative analysis of relevant parameters, members' profits and utilities, this paper focuses on the influence of CSR and fairness concerns on system decision-making. The results show that the optimal way for LCSC decision-making is to cooperate with fair-concerned retailers and manufacturers with CSR. When manufacturers consider social responsibility within a certain range and retailers bear part of the cost of social responsibility as followers, it can not only effectively improve the emission reduction level of the supply chain and the profits of each entity, but also help to increase the enthusiasm of each entity for carbon emission reduction and the overall social welfare.

Applying trade mechanisms to quantify dynamic GHG emissions of electricity consumption in an open economy - The case of Switzerland

Quantifying greenhouse gas (GHG) emissions from electricity consumption presents a significant challenge due to the variable nature of the generation mix throughout the year and the complex dynamics of cross-border electricity trade. In this paper, we introduce an innovative methodology designed to accurately capture the hourly GHG emissions linked to electricity imports, and thus electricity consumption. Contrary to conventional approaches, our methodology identifies the relevant generation technologies in exporting countries which are activated for export. This selection is based on (i) the classification of the technologies according to their dispatch cost (merit order), and (ii) considers trade mechanisms from economic theory.We apply this methodology to Switzerland, a country characterized by a low-carbon electricity generation mix, which however heavily relies on imports from neighbouring countries, particularly during the winter months. Over the period from 2017 to 2021, we find that 88 % of Switzerland's domestic electricity demand is met by its own generation—primarily hydro and nuclear—while the remaining 12 % is imported, mainly from fossil-fuel-based sources. Consequently, the average annual GHG emission factor for electricity consumption in Switzerland is 98 g-CO2eq/kWh, with 29 % attributed to domestic generation and 71 % to imports. This figure is 40 % higher than the 70 g-CO2eq/kWh estimated using a conventional approach, which typically underestimates the specific impacts of electricity imports.The disparity between these approaches becomes even more pronounced when considering the seasonal and hourly variations in the electricity mix and the resulting emission factors. These insights are particularly crucial in the context of emerging electricity demands, such as heat pumps and electric vehicles, where accurate GHG accounting can significantly influence policy and investment decisions.

Optimising load control of multi-type air conditioning in demand-side management

ABSTRACT With the continuous expansion of renewable energy capacity and the increasing demand for thermostatically controlled loads in China, new challenges have arisen for maintaining power supply stability. This paper establishes a control model for the aggregation of different types of air conditioning loads. To improve unsatisfactory demand response outcomes, a novel incentive mechanism based on tracking the target load curve is proposed. Additionally, a cost model for air conditioning load aggregators and the regional grid operator, along with an implementation scheme for trading mechanisms, is also developed. Case simulations validate the effectiveness and rationality of the proposed incentive mechanism.

Ambiguity and informativeness of (non-)trading

We study a sequential trading mechanism with ambiguity-averse agents modeled by multiple prior preferences. Informed traders generally mix between trading and non-trading, and their trading probability decreases with ambiguity. If agents are sufficiently ambiguous, informed traders do not trade, and only noise traders place orders; trading becomes uninformative. When signal accuracy is ambiguous, trading can make public beliefs more ambiguous over time, which leads to social learning failures in the long run. Moreover, since informed traders may not trade, no-trade can also be informative when signal accuracy is asymmetric. Even with continuous action spaces, sufficiently high ambiguity stops social learning.

Game theoretic operation optimization of photovoltaic storage charging station considering uncertainty and carbon trading

With the advancement of energy conservation and emission reduction efforts, the orderly charging of electric vehicles and the operation of photovoltaic-storage-charging stations associated with electric vehicles have become increasingly important topics. This study constructs an optimization model for the operation of stations under the synergy of electricity and carbon markets from a game theory perspective. Firstly, Latin hypercube sampling and Monte Carlo sampling are employed to handle the uncertainties in photovoltaic output and the stochastic nature of electric vehicles charging. Secondly, a ladder-type carbon trading mechanism is introduced, and a charging optimization model based on Stackelberg game theory is developed to describe the benefit interaction between charging stations and electric vehicles users. In this model, the upper level represents the charging station operator aiming to maximize joint electricity and carbon revenue while minimizing load fluctuations, whereas the lower level represents electric vehicles users aiming to maximize consumer surplus. Finally, a genetic algorithm nested with mixed-integer linear programming is used to solve the optimization model. The simulation results validate the model's effectiveness and superiority. The results indicate that, compared to centralized optimization methods, the Stackelberg game mechanism can increase consumer surplus by 119.40 %, reduce carbon emissions by 217.92 %, and achieve a win-win situation for both parties. Compared to a fixed carbon trading value, the ladder-type carbon trading mechanism can reduce carbon emissions by 23.84 % and smooth load fluctuations.

Low carbon scheduling method of mine integrated energy system based on two-stage P2G technology

Coal mines derive a large amount of various associated resources that can be used for production and living needs during the mining process. In order to realize the recycling of coal associated resources, a typical architecture of coal mine comprehensive energy system including the recycling of coal derived resources is proposed. Firstly, according to the coal mine resource endowment and its geological characteristics, the corresponding mine integrated energy system is constructed. Then, the refined two-stage P2G technology is combined with the mine integrated energy system to realize the complete consumption of wind and light and the efficient utilization of energy. Finally, the stepped carbon trading mechanism is used to further constrain the carbon emissions and realize the further reduction of carbon emissions in the mining area. The simulation model of the integrated mine energy system is established in MATLAB and the GUROBI solver is called to solve the results. The simulation results show that the method can effectively reduce carbon emissions, realize the complete consumption of wind and light and the efficient use of energy.

Spontaneous Formation of Evolutionary Game Strategies for Long-Term Carbon Emission Reduction Based on Low-Carbon Trading Mechanism

In the context of increasing global efforts to mitigate climate change, effective carbon emission reduction is a pressing issue. Governments and power companies are key stakeholders in implementing low-carbon strategies, but their interactions require careful management to ensure optimal outcomes for both economic development and environmental protection. This paper addresses this real-world challenge by utilizing evolutionary game theory (EGT) to model the strategic interactions between these stakeholders under a low-carbon trading mechanism. Unlike classical game theory, which assumes complete rationality and perfect information, EGT allows for bounded rationality and learning over time, making it particularly suitable for modeling long-term interactions in complex systems like carbon markets. This study builds an evolutionary game model between the government and power companies to explore how different strategies in carbon emission reduction evolve over time. Using payoff matrices and replicator dynamics equations, we determine the evolutionarily stable equilibrium (ESE) points and analyze their stability through dynamic simulations. The findings show that in the absence of a third-party regulator, neither party achieves an ideal ESE. To address this, a third-party regulatory body is introduced into the model, leading to the formulation of a tripartite evolutionary game. The results highlight the importance of regulatory oversight in achieving stable and optimal low-carbon strategies. This paper offers practical policy recommendations based on the simulation outcomes, providing a robust theoretical framework for government intervention in carbon markets and guiding enterprises towards sustainable practices.

The synergetic competition of cross-regional integrated energy systems with low-carbon technology heterogeneity under carbon emission trading mechanism to achieve carbon reduction target

The integration of renewable and fossil energy in a Region Integrated Energy System (RIES) is recognized as an effective measure for carbon emission reduction. The emergence of carbon emission trading (CET) markets worldwide has raised the need to combine RIES optimal planning with CET to achieve carbon reduction targets. This study focuses on the synergetic competition of a cross-RIES system with low-carbon technology heterogeneity under the CET mechanism. A cross-RIES model is constructed, incorporating fossil Carbon Capture System (fossil-CCS), renewable Combined Cooling Heating and Power (renewable-CCHP), and cleaner production paradigms. An improved version of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to propose a bi-level multi-objective optimal planning framework for the cross-RIES model, employing a grandfather permit allocation method. Comparative analysis is conducted between cross-RIES optimal planning with and without considering the CET mechanism, with a focus on environmental indexes such as carbon emission, carbon intensity, and marginal abatement cost. Sensitivity studies are also performed to explore the impact of key CET design parameters on the system's environmental performance. The simulation results demonstrate that the CET mechanism effectively reduces carbon emission, primary energy consumption, and carbon intensity by promoting synergetic competition among the low-carbon technology heterogeneity of RIESs. Furthermore, the environmental performance varies among the three types of RIES. In terms of CET parameter design, lowering the cap constraint, reducing the allowance allocation ratio of fossil-CCS RIES, and increasing the trading price contribute significantly to reducing carbon emission and carbon intensity, albeit at the expense of higher marginal abatement cost.

Two-stage robust optimization for optimal operation of hybrid hydrogen–electricity storage system considering ladder-type carbon trading

The prominent problems of renewable energy curtailment and its uncertainty have become a hot topic. To the end, with consideration of environmental friendliness, energy utilization efficiency and operation cost, this paper proposes a hybrid hydrogen–electricity storage system (HHES) operation framework comprising assorted types of coupling devices and carbon capture system (CCS) under ladder-type carbon trading (LCT) mechanism. By considering energy balance constraints, equipments’ operation constraints and erratic rates of RESs and multiple loads, a scheduling model with source-load uncertainties is developed with the goal of minimizing the system’s operation cost and carbon emission. Then, a two-stage tri-level robust model is built to find the optimal scheduling plan under multiple uncertainties given the various sources and loads. The first level is to make an optimal dispatch plan and the second level is to find the worst scenario under the given uncertainty set. The third level aims at enhancing renewable energy accommodation rate and reducing load shedding rate in the worst case. Since the model is formed as a tri-level mixed integer optimization problem, the Column and Constraint Generation (CCG) method is adopted to achieve the optimal result. Finally, numerical analyses are performed. The results show that the proposed approach realizes a flexible dispatch of multiple energy and a high utilization level of multiple storages. Furthermore, this paper studies the influence of uncertainty and uncertainty budget parameters with the comparison of the deterministic model and robust models with different uncertainties. The results show that the proposed HHES has strong robustness and can provide a good reference for energy dispatch.

An optimal scheduling strategy for electricity-thermal synergy and complementarity among multi-microgrid based on cooperative games

Multi-microgrid (MMG) systems provide an effective way to convert renewable energy into other forms of energy for low-carbon utilization. However, the coupling of multi-energy and renewable energy brings a challenge to the stable operation and dispatch of the system. Therefore, an electricity-thermal-gas-hydrogen MMG complementary optimization model based on peer-to-peer (P2P) electricity-thermal synergy is proposed. The model focuses on the coupling and conversion of electricity, hydrogen, and thermal through a hydrogen energy storage system (HESS) combined with renewable energy, and incorporates integrated demand response (IDR) and ladder-type carbon trading mechanism (LCTM). Subsequently, based on the cooperative game and the alternating direction multiplier method (ADMM), a multi-energy synergistic and complementary optimal scheduling strategy is proposed, which increases the flexibility of multi-energy sharing. In addition, a revenue allocation optimization strategy that takes into account the fairness and renewable energy consumption rate is proposed to achieve the stability of the alliance system operation. Finally, the simulation results show the effectiveness of the proposed model and strategy, realizing the complementary sharing of regional multi-energy, enabling the expansion of the boundaries of optimal energy allocation, further improving the low-carbon economic operation capability of MMG, and reducing the dependence of the system on the energy market.

Evolution of the financial market: Concepts and mechanisms

Subject. The article deals with the transformation of basic trading procedures, mechanisms, and conceptual justifications of the financial market. Objectives. Our aim is to substantiate the genesis of the basic concepts of financial markets and trading practices. Methods. The study employs the evolutionary analysis of both the financial market and the main approaches to its explanation. We assess changes in market processes through structural analysis and identification of drivers, and the logic of ongoing transformations. Results. We underpinned the possibilities and limitations of using traditional financial structures to analyze modern processes associated with the development of algorithmic market procedures. Determining the logic of transitions of conceptual justifications of the financial market, trading practices and technological changes as a single process made it possible to open new research space to study algorithmic-computational transformations of the financial market and the performative function of finance. Conclusions. Analysis of the genesis of the main financial hypotheses and the influence of financial models and structures on the market and its organization enables to conclude about strengthening the performative properties of finance.

REVIEWING LIMITED SUPPLY CRYPTO PROJECTS: ULTIMA, COREDAOVIP

Ultima (also referred to as PLC Ultima) is a cryptocurrency and blockchain-based ecosystem aimed at providing a range of financial services. It offers tools like a cryptocurrency debit card, minting and farming solutions, cashback programs, and cryptocurrency wallets. The primary goal of Ultima is to facilitate global financial inclusion by giving users access to fintech services that traditional financial systems may not offer. On other hand, COREDAOVIP is a blockchain-based platform designed to enhance decentralized finance (DeFi) and NFT ecosystems through innovative liquidity pooling and automated trading mechanisms. Built on the foundation of Core DAO’s decentralized principles, COREDAOVIP aims to provide users with secure, transparent, and efficient financial solutions. It integrates smart contracts and cross-chain compatibility, enabling seamless interaction between different blockchain networks. Additionally, COREDAOVIP facilitates decentralized identity systems, offering users control over their data and assets while supporting NFT projects like 9NFTMania, which merges digital currency with NFT art. As DeFi and blockchain adoption grow, COREDAOVIP is positioned to play a key role in transforming financial ecosystems through enhanced security, liquidity, and decentralized governance. This platform’s innovative approach to token integration and decentralized trading enhances its potential in the rapidly evolving blockchain landscape.

Research on the Impact and Path of Digital Trade Driving the Enhancement of Export Competitiveness of Chinese Enterprises

With the rapid development of the global digital economy, digital trade, as a new trade model, has a profound impact on the export competitiveness of Chinese enterprises. Starting from the connotation and characteristics of digital trade, this article analyzes the impact mechanism of digital trade on the export competitiveness of enterprises, explores the path of digital trade promoting the improvement of Chinese enterprises' export competitiveness, and provides theoretical basis for the transformation and upgrading of Chinese enterprises and the development of foreign trade.

Dynamic operating envelopes embedded peer-to-peer-to-grid energy trading

A novel decentralized peer-to-peer-to-grid (P2P2G) trading mechanism considering distribution network integrity is proposed. In order to direct prosumers’ peer-to-peer (P2P) trading behavior to be grid-friendly, the proposed method incorporates Dynamic Operating Envelopes (DOEs) into the existing P2P2G trading. Moreover, DOEs are determined through negotiations between the distribution system operator (DSO) and prosumers alongside the process of P2P trading, avoiding compromising prosumers’ privacy and network parameters leakage. To reduce communication costs during P2P trading, a variant of the alternating direction method of multipliers (ADMM), i.e., communication-censored ADMM (COCA) is used to solve the P2P2G trading problem. Finally, the DOE price is shown to be comprised of several economically interpretable components. Simulations validate the effectiveness of the proposed mechanism.

Fuzzy credibility chance-constrained multi-objective optimization for multiple transactions of electricity–gas–carbon under uncertainty

This paper proposes a fuzzy credibility chance-constrained multi-objective optimization model to optimize market transactions in the electricity–gas–carbon sectors under uncertainty. The model aims to maximize the profit of an integrated energy service provider by incorporating a ladder-type carbon trading mechanism, which adjusts carbon prices based on emission levels, and detailed multi-energy flow constraints. To effectively manage uncertainties in electricity, gas, and carbon markets, we derive credibility distributions for uncertain variables and introduce fuzzy credibility chance constraints—tools that assess the likelihood of meeting multiple transactions under uncertainty. The proposed model hedges the risks associated with multiple uncertainties while maximizing the credibility of expected costs, effectively balancing risk and cost. Through simulation analysis on an IEEE 33-node power network and a 32-node heat network, the proposed model achieved a 9.8% reduction in total system cost and an 8.5% reduction in total carbon emissions. Additionally, the model effectively determined credibility levels under different risk preferences, demonstrating its robustness in enhancing electricity–gas–carbon trading and promoting a low-carbon economy. This research provides a novel planning method for formulating trading strategies in multi-energy markets, with significant real-world implications for energy management and environmental sustainability.