Trading Mechanism Research Articles

Enhanced Management of Unified Energy Systems Using Hydrogen Fuel Cell Combined Heat and Power with a Carbon Trading Scheme Incentivizing Emissions Reduction

In the quest to achieve “double carbon” goals, the urgency to develop an efficient Integrated Energy System (IES) is paramount. This study introduces a novel approach to IES by refining the conventional Power-to-Gas (P2G) system. The inability of current P2G systems to operate independently has led to the incorporation of hydrogen fuel cells and the detailed investigation of P2G’s dual-phase operation, enhancing the integration of renewable energy sources. Additionally, this paper introduces a carbon trading mechanism with a refined penalty–reward scale and a detailed pricing tier for carbon emissions, compelling energy suppliers to reduce their carbon footprint, thereby accelerating the reduction in system-wide emissions. Furthermore, this research proposes a flexible adjustment mechanism for the heat-to-power ratio in cogeneration, significantly enhancing energy utilization efficiency and further promoting conservation and emission reductions. The proposed optimization model in this study focuses on minimizing the total costs, including those associated with carbon trading and renewable energy integration, within the combined P2G-Hydrogen Fuel Cell (HFC) cogeneration system. Employing a bacterial foraging optimization algorithm tailored to this model’s characteristics, the study establishes six operational modes for comparative analysis and validation. The results demonstrate a 19.1% reduction in total operating costs and a 22.2% decrease in carbon emissions, confirming the system’s efficacy, low carbon footprint, and economic viability.

Operation Optimization of Regional Integrated Energy Systems with Hydrogen by Considering Demand Response and Green Certificate–Carbon Emission Trading Mechanisms

Amidst the growing imperative to address carbon emissions, aiming to improve energy utilization efficiency, optimize equipment operation flexibility, and further reduce costs and carbon emissions of regional integrated energy systems (RIESs), this paper proposes a low-carbon economic operation strategy for RIESs. Firstly, on the energy supply side, energy conversion devices are utilized to enhance multi-energy complementary capabilities. Then, an integrated demand response model is established on the demand side to smooth the load curve. Finally, consideration is given to the RIES’s participation in the green certificate–carbon trading market to reduce system carbon emissions. With the objective of minimizing the sum of system operating costs and green certificate–carbon trading costs, an integrated energy system optimization model that considers electricity, gas, heat, and cold coupling is established, and the CPLEX solver toolbox is used for model solving. The results show that the coordinated optimization of supply and demand sides of regional integrated energy systems while considering multi-energy coupling and complementarity effectively reduces carbon emissions while further enhancing the economic efficiency of system operations.

Impact analysis of digital trade on carbon emissions from the perspectives of supply and demand

Amidst the escalating challenge of global climate change, it is imperative to further explore whether digital trade, as an emerging element in the global development landscape, can reduce carbon emissions and achieve sustainable development. This study draws upon panel data encompassing 30 provinces and municipalities in China spanning the years 2013 to 2021. By establishing an index system to gauge regional digital trade development levels, the article examines the impact mechanism and spillover effects of digital trade on carbon reduction from both the supply (enterprises) and demand (residents) perspectives. The research results show that: (1) Digital trade can effectively promote regional carbon reduction, with a more pronounced effect in China's central and western regions and lower carbon emissions regions. (2) Digital trade can incentivize green innovation by enterprises and improve residents' consumption behavior, thereby reducing carbon emissions. (3) Digital trade has spillover effect on carbon emissions, and this “neighborhood effect” is greater than the “local effect”. Digital trade provides strong support for carbon reduction and sustainable development and also provides a strategic direction for government policy formulation.

A Stackelberg game-based approach to load aggregator bidding strategies in electricity spot markets

Load aggregators (LA) can aggregate the users with adjustable load capacity such as shared energy storage into a scaled resource in a package, and solve the problem of insufficient demand-side management by participating in the competition of electricity spot market. It is gradually becoming an emerging way to mobilize adjustable loads and improve load demand response capability. But it takes a set of economical and reliable bidding strategies to guide LA's participation in the spot market. This paper first describes the framework of the LA and the functions of the stakeholders in the system, as well as the trading mechanisms for their participation in the spot market, proposing a two-stage determination model of pricing and quantification; Secondly, a Stackelberg game model is developed, combining the operational constraints of different players, with the trading center as the leader and the LA and generators as the followers; Finally, four scenarios are designed to verify the validity of the model, and the results show that the proposed LA trading strategy reduces the purchasing cost by 16.53 % and increases the revenue by 12.66 %. The introduction of the Stackelberg game model ensures the sustainability of the strategy and provides an innovative idea for the trading strategy of customer-side in the spot market.

Optimal decision-making and coordination of the shipping logistics service supply chain cooperation mode under the carbon quota and trading mechanism

Carbon cap-and-trade policy based on market mechanism becomes an important means to restrain shipping logistics enterprises' carbon emission reduction. This paper studies the new requirements of shipping enterprises under the pressure of carbon emission reduction. Considering the logistics service supply chain composed of two competing providers and one integrator under the constraint of carbon cap-and-trade policy, this paper explores the optimal pricing and revenue decision of shipping logistics service supply chain through vertical cooperation, horizontal cooperation between providers and revenue sharing cooperation among integrators. This paper obtains feedback strategies under different cooperation modes by constructing a game model. In the vertical game, the shipping logistics supplier is the Stackelberg leader, and the shipping logistics integrator is the follower; there is a Nash equilibrium in the emission reduction decisions among suppliers in the horizontal game. On this basis, the results of different cooperation modes are compared, and the influence of competitive strategies on the optimal results is discussed. We found that the vertical cooperation of the shipping logistics service supply chain has a higher carbon emission reduction rate and a lower sales price, and the horizontal cooperation of shipping logistics suppliers can improve their own profits but reduce the income of the shipping logistics integrators and consumer welfare. When the revenue sharing contract provides the shipping logistics integrator with a share proportion in a certain range, the revenue sharing contract can generate higher supply chain profits than the integration of two chains, which can urge shipping logistics suppliers to forfeit the horizontal cooperation and help the shipping logistics service supply chain achieve win-win results.

Safe reinforcement learning based optimal low-carbon scheduling strategy for multi-energy system

Multi-energy system with distributed energy resources has become the inevitable trend in recent years due to their potential for creating the efficient and sustainable energy infrastructure, with a strong ability on carbon emission reduction. To accommodate the uncertainties of renewable energy generation and energy demand, model-free deep reinforcement learning methods are emerging for energy management in multi-energy system. However, traditional reinforcement learning methods still have operation safety issue of violating the physical constraints of multi-energy system. To address the challenges, a low-carbon scheduling strategy based on safe soft actor-critic algorithm is proposed in this paper. Firstly, an electricity-thermal-carbon joint scheduling framework is constructed, where carbon trading mechanism is incorporated to further motivate carbon emission reductions. Secondly, the energy cost and carbon trading cost are simultaneously integrated in the objective function, and the dynamic optimization problem of multi-energy system is modeled as a constrained Markov decision process by taking into account the diverse uncertainties. Then, a novel safe soft actor-critic method is proposed to achieve the benefits of economic and carbon emissions, where the security networks and Lagrangian relaxation are introduced to deal with operation constraints. The case study validates that the proposed scheduling strategy can reduce the energy cost and carbon trading cost by up to 26.24% and 33.73% within constraints, compared with existing methods.

Hierarchical distributed optimal scheduling decision-making method for district integrated energy system based on analysis target cascade

The district integrated energy system has the characteristics of multi-layer and multi-subjects. Aiming at the problems of data privacy, conflict of interest, and mismatch of interaction power among these multi-subjects, a hierarchical distributed optimal scheduling decision-making method for district integrated energy system based on analysis target cascade is proposed. Firstly, the operational communication architecture of the district integrated energy system based on the cloud-pipe-edge-end is constructed to solve the problems of data transmission, data computation, and data privacy. Secondly, a two-layer distributed scheduling model of district integrated energy system considering waste heat recovery is established based on a laddered carbon trading mechanism. At the upper level, an optimal scheduling model targeting low-carbon and economic performance is developed by considering the participation of district integrated energy system in demand response and the purchase and sale of electricity. At the lower level, an optimal scheduling model considering waste heat recovery for subsystem energy-coupled equipment is developed. Then, the analytical target cascade is used to realize the decoupling of the two-layer model, and the efficient solution is achieved in the process of multiple iterations. Finally, by analyzing the arithmetic examples, it is verified that the proposed method can improve the economy of system operation by 4.38% and the absorption capacity of wind power by 9.89%, while protecting the privacy and interest claims of multiple parties.

Decentralized AIoT based intelligence for sustainable energy prosumption in local energy communities: A citizen-centric prosumer approach

The role of prosumers who are consumers who produce, store, and consume energy is vital to the uptake of renewable energies in Local Energy Communities (LEC). However, the integration of prosumers in the smart grid to facilitate bidirectional flows of energy and information depends on intelligent operations of energy systems and flexible structures of the existing energy markets. But existing energy trading mechanisms are faced with issues of trust, privacy, security, and energy pricing determination. Also, there are fewer studies based on a citizen-centric prosumer approach. Thus, there is need to provide reliable solutions that addresses the aforementioned challenges faced by prosumers in LEC. Advancements in disruptive technologies, such as Distributed Ledger Technologies (DLT), Artificial Intelligence (AI), and the Internet of Things (IoT) have transformed a broad spectrum of intelligent systems in smart cities. Therefore, this study examines the integration of AI and IoT as AIoT and DLT towards a citizen-centric prosumer approach for decentralized energy markets trading. Additionally, this article develops an architectural model for energy prosumption in LEC using design science approach based on a user-centred design method that shows a possible implementation concept to support energy sharing and trading in LEC. The architectural model supports trust, data privacy, security, and energy pricing determination using AI and smart contracts to provides real-time energy trading monitoring, easy access, control, and immutable logs to unearth underlying energy demand and supply patterns thereby supporting citizen-centric prosumer approach. Finally, a use case scenario of DLT and AIoT for prosumption operations is presented.

Blockchain-enabled framework for transactive home energy management with cloud energy storage under uncertainties

In modern paradigm of smart grid, prosumers can trade energy peer-to-peer in a transactive energy market to reduce energy costs and improve energy efficiency. In addition, cloud energy storage (CES) is a type of shared energy storage systems with high economic efficiency that can provide energy storage services for prosumers and become an active player in energy trading. However, transactive energy implementation in power systems has several challenges such as data privacy and security. In existing researches, energy trading mechanisms rely on central authorities, where security and privacy cannot be maintained. Therefore, this paper presents a decentralized stochastic optimization model of transactive energy management based on blockchain in the presence of CES. In this framework, the smart contract based on Alternating Direction Method of Multipliers technique is deployed on the Ganache and Ethereum blockchains, and the YALMIP toolbox along with CPLEX solver handle decentralized optimization in the MATLAB software. The numerical results demonstrate that not only the capacity of distributed generations is fully utilized, but also total costs of smart homes are reduced by more than 27%, and the total grid’s delivery power to smart homes is decreased by more than 24% compared to the inflexible scheduling.

Research on the Quality Evaluation and Improvement of Carbon Emission Right Accounting Information Disclosure in the Context of Low-Carbon Economy

This paper explores the accounting treatment of carbon emission rights in the petrochemical industry in China, focusing on the challenges and opportunities presented by carbon trading mechanisms. The study analyzes existing norms in the EU's carbon trading market and evaluates SINOPEC's approach to carbon accounting and emission reduction. Through a comprehensive review of financial reports, sustainability disclosures, and industry trends, the paper offers insights into improving the accounting treatment of carbon emission rights and promoting low-carbon transformation in the petrochemical sector.

Two-stage robust operation of electricity-gas-heat integrated multi-energy microgrids considering heterogeneous uncertainties

With the widespread adoption of combined heat and power and power-to-heat technologies, multi-energy microgrids (MEMGs) have been garnering significant research attention from both industry and academia. However, dealing with uncertainties from renewable energy and load and coordinating multiple energy carriers are the main challenges for MEMG operation. In this regard, a two-stage robust operation method of electricity-gas-heat integrated MEMGs considering heterogeneous uncertainties is proposed in this paper. First, network models for an electricity-gas-heat-based distribution-level MEMG are formulated considering the dynamic characteristics of gas and heat networks. Then, the power-to‑hydrogen-and-heat unit and ladder-type carbon trading mechanism are introduced to reduce the curtailment of wind power and carbon emissions. Further, a two-stage robust optimization (TSRO) method is applied to tackle uncertainties of wind power and load under extreme scenarios in the MEMG operation by iteratively solving the operation problem with the column and constraint generation (C&CG) algorithm. Finally, case studies are conducted to verify our proposed method, demonstrating that it can reduce the multi-energy supply cost while the stepped carbon trading mechanism can also significantly reduce carbon emissions.

ANALISIS PERLINDUNGAN HUKUM ISLAM TERHADAP PEMBERIAN MAHAR PERNIKAHAN BERUPA SAHAM

Dowry is an essential aspect of marriage. Scholars agree that while there is no fixed amount for a dowry, it must be beneficial and valuable, reflecting sincerity and affection. This study is normative research using descriptive-analytical data analysis. It explains that dowry in the form of shares is permissible in Islam as long as it does not harm anyone, is agreed upon by both parties and benefits the wife. The shares given must have clear contracts and be within the realm of Sharia-compliant business, even though they cannot be physically displayed, and there must be original proof of ownership of the shares. Legal protection regarding shares is based on the DSN MUI fatwa No. 40 of 2003 article 2, which states: Article 2 (a) The capital market and all its mechanisms, particularly concerning issuers, types of securities traded, and trading mechanisms, are considered compliant with Sharia principles if they fulfil Sharia principles. (b) All securities comply with Sharia principles if they have obtained a Sharia compliance statement.

Retraction Note: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

Retraction Note: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

Economic Optimal Scheduling of Integrated Energy System Considering Wind–Solar Uncertainty and Power to Gas and Carbon Capture and Storage

With the shortage of fossil energy and the increasingly serious environmental problems, renewable energy based on wind and solar power generation has been gradually developed. For the problem of wind power uncertainty and the low-carbon economic optimization problem of an integrated energy system with power to gas (P2G) and carbon capture and storage (CCS), this paper proposes an economic optimization scheduling strategy of an integrated energy system considering wind power uncertainty and P2G-CCS technology. Firstly, the mathematical model of the park integrated energy system with P2G-CCS technology is established. Secondly, to address the wind power uncertainty problem, Latin hypercube sampling (LHS) is used to generate a large number of wind power scenarios, and the fast antecedent elimination technique is used to reduce the scenarios. Then, to establish a mixed integer linear programming model, the branch and bound algorithm is employed to develop an economic optimal scheduling model with the lowest operating cost of the system as the optimization objective, taking into account the ladder-type carbon trading mechanism, and the sensitivity of the scale parameters of P2G-CCS construction is analyzed. Finally, the scheduling scheme is introduced into a typical industrial park model for simulation. The simulation result shows that the consideration of the wind uncertainty problem can further reduce the system’s operating cost, and the introduction of P2G-CCS can effectively help the park’s integrated energy system to reduce carbon emissions and solve the problem of wind and solar power consumption. Moreover, it can more effectively reduce the system’s operating costs and improve the economic benefits of the park.

Low-Carbon Economic Dispatch Model of Integrated Energy System Accounting for Concentrating Solar Power and Hydrogen-Doped Combustion

Against the background of carbon peak and carbon neutralization, in order to solve the problem of poor flexibility of integrated energy systems and wind power consumption while improving the potential of hydrogen energy emission reduction, this study proposes an integrated energy system that takes into account the coupling of concentrating solar power (CSP), hydrogen-doped combustion, and power-to-gas (P2G) conversion. Firstly, a mathematical model of a CSP-CHP unit is established by introducing a CSP power station, aiming at the defect of the “heat to power” mode in the CHP system. Secondly, the energy consumption of P2G hydrogen energy production is satisfied by surplus wind power. The utilization stage of hydrogen energy is divided into supply CHP combustion and CO2 methanation, forming a CSP-P2G-HCHP collaborative framework and establishing an IES low-carbon economic dispatch model with CSP-P2G-HCHP. At the same time, the carbon trading mechanism is introduced to constrain the carbon emissions of the system. Finally, an optimization strategy with the minimum sum of the operation and maintenance cost, the energy purchase cost, the wind curtailment cost, and the carbon emission cost as the objective function is proposed, and the CPLEX solver is used to solve and carry out multi-case analysis. The simulation results show that the carbon emissions are reduced by 6.34%, the wind curtailment cost is reduced by 52.2%, and the total cost is reduced by 1.67%. The model takes into account the carbon reduction effect and operating efficiency and effectively improves the new energy consumption capacity.

Low-carbon scheduling research of integrated energy system based on Stackelberg game under sharing mode

To achieve a win-win situation among multiple stakeholders within the integrated energy system, this paper proposes a low-carbon scheduling research of integrated energy system based on Stackelberg game under sharing mode. Firstly, an operation framework of integrated energy system based on leader-follower game was established, and a model of integrated energy system operator and user coalition was established considering carbon trading mechanism. Secondly, bi-directional long short-term memory is used to predict the travel data of electric vehicles, and a schedulable capacity model for electric vehicle cluster as shared energy storage was established. Then, an energy trading optimization model based on leader-follower game was constructed for integrated energy system operator and user coalition, and an improved Shapley value is adopted to realize cost allocation within user coalition. Finally, based on the demonstration project of Sino-Singapore Tianjin Eco-City is selected as the research object to make example analysis. The calculation results show this paper can achieve a reasonable benefit allocation among multiple participants, reduce carbon emissions of integrated energy system, and increase renewable energy accommodation capacity. The relevant achievements are a preliminary exploration for an effective mode of sharing economy in integrated energy system.

A Study on Mechanism of Online Trading with Reference to India Bulls

A Study on Mechanism of Online Trading with Reference to India Bulls

Optimization conversion of integrated energy system under stepped carbon trading mechanism considering source-load uncertainty

Abstract This paper develops an optimization strategy for integrated energy systems under a stepwise carbon trading mechanism, considering source-load uncertainty. It aims to enhance operational efficiency and reduce carbon emissions within economic and environmental constraints. The strategy examines the impact of carbon pricing on energy management and leverages uncertainty for better energy efficiency and lower emissions. A case study confirms its effectiveness in improving energy utilization and reducing carbon footprint.

Optimization of multi-integrated energy microgrid scheduling under carbon trading mechanism

Abstract Realizing the power transaction and benefit distribution among multi-integrated energy microgrids with different interests in the system, a multi-microgrid cooperative operation optimization scheduling method under a carbon-trading mechanism is proposed. Considering the microgrid’s electric energy interaction, a model of multi-microgrid cooperation is established. The multi-microgrid cooperative game model is equivalently transformed into two sub-problems, multi-microgrid cooperative operation cost minimization and internal power transaction bargaining, and to solve the two sub-problems successively. The simulation results show that the method is valid.

Robust optimization scheduling of microgrid considering a tiered carbon trading mechanism and flexible loads

Abstract Considering the comprehensive demand response, stepwise carbon trading, and uncertainty of wind power output for cogeneration microgrids, relevant optimization scheduling research will be carried out. The electric heating load is divided into non-dispatchable electric heating load and dispatchable electric heating load. By introducing electric heating demand and tiered carbon trading, the economic and low-carbon operation of microgrids can be achieved. The fluctuation of the output of new energy units is reflected using probability scenario sets in statistics, and a robust optimization model with related distributions is constructed using the C&CG algorithm to calculate and solve the established model. The simulation and comparison of numerical examples show that the microgrid energy management scheme formulated by the model established in this paper has good economic and environmental friendliness, and it also simultaneously validated the superiority of distributed robust optimization methods over traditional optimization methods