Bi-Level Carbon Trading Model on Demand Side for Integrated Electricity-Gas System

Zhi-chao, Yan; Li, Chunyan; Yao, Yiming; Lai, Weibin; Tang, Jiayu; Shao, Changzheng; Zhang, Qian

doi:10.1109/tsg.2022.3229278

Cited by 19 publications

(6 citation statements)

References 17 publications

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“…Tianrui and Chongqing 19 defines carbon emissions on the demand side based on node carbon emission intensity, and realizes carbon emission reduction by differentiated management on the demand side. In addition, Yan et al 20 validated the feasibility of load aggregator (LA) participating in the carbon trading market, clarified the carbon emission responsibility on the demand side, which can more economically and effectively promote the consumption of low‐carbon energy. However, the above references lack a more comprehensive IES carbon emission flow model, which considers various aspects, such as networks, loads, energy conversion devices, and energy storage devices.…”

Section: Introductionmentioning

confidence: 99%

Research on bi‐layer low carbon scheduling strategy for source‐load collaborative optimization based on node carbon emission intensity

Liu

2024

Energy Science & Engineering

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To accurately calculate the carbon emission of integrated energy system (IES), and fully explore the potential for load side on carbon emission reduction, this paper proposes a method of guiding load to participate in demand response based on node carbon emission intensity, and constructs a bi‐layer low‐carbon scheduling model for source‐load collaborative optimization. First, the carbon flow calculation model of IES in the total process is established, such as source, network, load, and storage. It can depict the carbon emission characteristics of energy conversion equipment and energy storage devices. The principle of proportional sharing is used to track carbon emission flows, the changes in carbon emission intensity at each node is perceived from a spatiotemporal perspective. Second, carbon flow analysis is incorporated into the load demand response mechanism, and a carbon emission model for load aggregator (LA) after demand response is established based on the node carbon emission intensity. Third, a bi‐layer low‐carbon scheduling model is constructed, which considers the source‐load collaborative optimization. The upper layer is the optimal economic dispatch of IES operators, while the lower layer is the optimal economic dispatch of LAs. Finally, the effectiveness of the proposed method is verified using the system as an example, such as improved 14‐node power grid, 6‐node heating network, and 6‐node nature gas network.

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Section: Introductionmentioning

confidence: 99%

Research on bi‐layer low carbon scheduling strategy for source‐load collaborative optimization based on node carbon emission intensity

Liu

2024

Energy Science & Engineering

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“…Huo et al introduce a spatio-temporal flexibility requirement envelope for the low-carbon power system operation with uncertainties [15]. Yan et al consider a bi-level carbon trading scheme to realize the maximization of consumer surplus and incentive the lowcarbon operation [16]. Despite that low-carbon operation has been studied both on generation side and demand side through the carbon emission flow, researchers mainly focus on the specific low-carbon operation model and have developed some methods to address the nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

“…(2) Contrary to [4], [14], [16] that dealing with the nonconvexities of the bi-level operation framework through single level reformulation or decomposition, the developed distributed demand management model is reformulated into a networked multiagent constrained Markov Decision Process which only needs partial information exchange, decouples intractable power-carbon flow constraints and adapts to variant states.…”

Section: Introductionmentioning

confidence: 99%

Finite-Sample Analysis for Decentralized Batch Multiagent Reinforcement Learning With Networked Agents

Zhang

Yang

Liu

et al. 2021

IEEE Trans. Automat. Contr.

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This paper proposes a multiagent based bi-level operation framework for the low-carbon demand management in distribution networks considering the carbon emission allowance on the demand side. In the upper level, the aggregate load agents optimize the control signals for various types of loads to maximize the profits; in the lower level, the distribution network operator makes optimal dispatching decisions to minimize the operational costs and calculates the distribution locational marginal price and carbon intensity. The distributed flexible load agent has only incomplete information of the distribution network and cooperates with other agents using networked communication. Finally, the problem is formulated into a networked multi-agent constrained Markov decision process, which is solved using a safe reinforcement learning algorithm called consensus multi-agent constrained policy optimization considering the carbon emission allowance for each agent. Case studies with the IEEE 33-bus and 123-bus distribution network systems demonstrate the effectiveness of the proposed approach, in terms of satisfying the carbon emission constraint on demand side, ensuring the safe operation of the distribution network and preserving privacy of both sides.

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“…The emergence of carbon capture technology has provided a solution for cleaner coal-fired power generation. Reference [4] has proposed transforming traditional thermal power plants into carbon-capture power plants using post-combustion capture (PCC) technology. Simultaneously, they consider carbon trading mechanisms, which effectively minimize system carbon emissions.…”

Section: Introductionmentioning

confidence: 99%

Optimal Capacity Configuration of a Low-Carbon Energy System Considering Carbon Capture Technology and Hydrogen-Diversified Utilization Under Multiple Operational Scenarios

Liu,

Wang,

Wang

et al. 2024

IEEE Access

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To enhance the utilization of concentrated solar power (CSP), and reduce the wind abandonment rate of wind power (WP) and the carbon emissions from the traditional coal-fired unit (CFU), this study introduces oxygen-enriched combustion capture technology to retrofit the conventional CFU, and configures CSP with heat recovery unit (HRU) for participation in heating, along with coupling methanation reactor (MR), high-temperature solid oxide electrolysis cell (SOEC), hydrogen-blended gas cogeneration system, and energy storage units to create a low-carbon energy system (LCES) that includes electricity-heat-gas integration and hydrogen diversified utilization. The system's optimal capacity configuration methodology is also established. Firstly, considering the uncertainties of WP and direct normal irradiance (DNI), as well as their temporal correlations with electrical load (EL), a multi-operational scenarios extraction model is established based on a multi-stage data processing algorithm. Secondly, building upon the probability-based multi-operational scenarios, the conditional value at risk (CVaR) theory is employed to quantify the risks arising from uncertainties. With the objective of minimizing total cost, an LCES optimal capacity configuration model is formulated. Finally, through case study validation, the results indicate that the system, under the scenario of meeting the load demand, can reduce annual carbon emissions to 249,573.31 t, decrease the wind abandonment rate to 1.32%, enhance the CSP utilization rate to over 84%, and provide the basis of quantification for decision-makers with varying risk preferences when addressing capacity configuration issues.INDEX TERMS Multi-operational scenarios, oxygen-enriched combustion capture technology, low-carbon energy system (LCES), conditional value at risk (CVaR), capacity configuration. NOMENCLATUREA. ABBREVIATIONS CSP Concentrated solar power. WP Wind power. CFU Coal-fired unit. HRU Heat recovery unit. MR Methanation reactor. SOEC Solid oxide electrolysis cell. LCES Low-carbon energy system. DNI Direct normal irradiance. EL Electric load. CVaR Conditional value at risk. PCC Post-combustion capture. ASU Air separation unit. CPU Compression purification unit. CSP-HRU Concentrated solar power with heat recovery unit. CFPS Coal-fired power system. HST Hydrogen storage tank. HBGT Hydrogen-blended gas turbine. HBGB Hydrogen-blended gas boiler. HL Heat load.

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Bi-Level Carbon Trading Model on Demand Side for Integrated Electricity-Gas System

Cited by 19 publications

References 17 publications

Research on bi‐layer low carbon scheduling strategy for source‐load collaborative optimization based on node carbon emission intensity

Research on bi‐layer low carbon scheduling strategy for source‐load collaborative optimization based on node carbon emission intensity

Finite-Sample Analysis for Decentralized Batch Multiagent Reinforcement Learning With Networked Agents

Optimal Capacity Configuration of a Low-Carbon Energy System Considering Carbon Capture Technology and Hydrogen-Diversified Utilization Under Multiple Operational Scenarios

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