A Collaborative Demand-Controlled Operation Strategy for a Multi-Energy System

Mao, Yunshou; Jiekang, Wu; Wang, Ruidong; Zhihong, Cai; Zhang, Ran; Chen, Lingmin; Zhang, Wenjie

doi:10.1109/access.2021.3083922

Cited by 15 publications

(9 citation statements)

References 25 publications

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“…Since many of the optimization techniques used in district energy systems are in the operation phase [15][16][17], the energy consumption, cost, environmental impacts, and optimization framework have only been determined for the operation stage. However, the entire life cycle is rarely considered.…”

Section: Literature Reviewmentioning

confidence: 99%

Regional Renewable Energy Optimization Based on Economic Benefits and Carbon Emissions

Wei¹,

Zhao²,

Cong³

et al. 2023

Energy Engineering

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With increasing renewable energy utilization, the industry needs an accurate tool to select and size renewable energy equipment and evaluate the corresponding renewable energy plans. This study aims to bring new insights into sustainable and energy-efficient urban planning by developing a practical method for optimizing the production of renewable energy and carbon emission in urban areas. First, we provide a detailed formulation to calculate the renewable energy demand based on total energy demand. Second, we construct a dual-objective optimization model that represents the life cycle cost and carbon emission of renewable energy systems, after which we apply the differential evolution algorithm to solve the optimization result. Finally, we conduct a case study in Qingdao, China, to demonstrate the effectiveness of this optimization model. Compared to the baseline design, the proposed model reduced annual costs and annual carbon emissions by 14.39% and 72.65%, respectively. These results revealed that dual-objective optimization is an effective method to optimize economic benefits and reduce carbon emissions. Overall, this study will assist energy planners in evaluating the impacts of urban renewable energy projects on the economy and carbon emissions during the planning stage.

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Section: Literature Reviewmentioning

confidence: 99%

Regional Renewable Energy Optimization Based on Economic Benefits and Carbon Emissions

Wei¹,

Zhao²,

Cong³

et al. 2023

Energy Engineering

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“…The coupling coordination degree is the horizontal embodiment of mutual promotion and benign interaction between systems. In recent years, the theory of coupling coordination has been widely used in many fields of transportation, such as control operation strategy [24], planning strategy [25], predictive control and so on [26]. In the composite system composed of the logistics industry and manufacturing industry, the coordinated development of two industries refers to the evolution of two subsystems and their components in a harmonious and consistent direction, and the coupling of two industries refers to the mutual influence and interaction between two subsystems and their components.…”

Section: A Coupling Coordination Model 1) Coupling Coordination Model...mentioning

confidence: 99%

Research on the Influence of the Coupling Coordination of the Logistics Industry and Manufacturing Industry on the Upgrading of the Manufacturing Industry

Wang

2022

IEEE Access

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One of the objectives of coupling coordination between the logistics industry and manufacturing industry (hereinafter referred to as "two industries coordination") is to improve the competitiveness of the manufacturing industry.Using the slacks-based method (SBM) and coupling coordination degree model, the coupling coordination scheduling between the logistics industry and manufacturing industry is measured, and the coupling coordination measurement system of the logistics industry and manufacturing industry under low-carbon constraints is constructed. The DEA Malmquist model is used to measure the level of manufacturing upgrading, and a threshold regression model is used to study the mechanism of the two industries' coordination on manufacturing upgrading. The empirical study found that during 2009-2019, the average coordination of the two industries in the Yangtze River Delta was 0.64, which was basically in the stage of good coordination, and the coupling level showed a gradual upward trend. From 2009 to 2019, the average total factor productivity index of the manufacturing industry was 1.23, showing an upward trend. The coordination of the two industries can promote the upgrading of the manufacturing industry, and there is an obvious inverted U-shaped relationship. There is a double threshold for industrial scale and technological innovation. When the scale of the manufacturing industry is between 79.1 billion yuan and 97.4 billion yuan, the impact of industrial coupling coordination on the production efficiency of the manufacturing industry is greater.When the R&D expenditure is less than 9.89 billion yuan, the impact of industrial coupling coordination on the production efficiency of the manufacturing industry is greater. The study found that the larger the industrial scale of the manufacturing industry is, the more conducive it is to the coordination of the two industries, and the coupling coordination does not increase with the increase in R&D investment. The manufacturing industry is mainly light industry, so the proportion of optimal assets is relatively low under low-carbon constraints. The upper limit of optimal science and technology expenditure shows that there is the problem of excessive investment of optimal science and technology funds under low-carbon constraints.

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“…The objective function of the operation cost of the CCHP plant agent includes the cost of purchasing electricity from the grid, the cost of purchasing natural gas, the O&M cost, the cost of purchasing electricity from the WT plant and the cost of purchasing electricity from PV farm agent. The detailed operation model of each device in the CCHP plant can be found in the works [7][8][9]. The model for minimizing the operating cost of the CCHP plant agent is shown below:…”

Section: Cchp Plant Agent Operation Modelmentioning

confidence: 99%

“…Case studies verified the validity and feasibility of the graph-based matrix model for multi-energy systems with different structures and the effectiveness of the demand response strategy in terms of economics. Flexible electric and thermal load control programs are also incorporated into the operation strategy to obtain the optimal operating profit [9]. For the operation scheduling problem of multi-time scale and multi-spatial scale integrated energy systems, the coordination of multiple operation measures considering integrated energy demand response is proposed to achieve an optimal operation mode in different scheduling stages on the system [10].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Operation Framework for a Wind-Solar-CCHP Multi-Energy System Based on Nash Bargaining Solution

et al. 2021

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A multi-energy system can supply both electric and thermal energy simultaneously to the end-users to achieve a high energy use-efficiency. However conventional operational strategies for multienergy systems, in the existing works, do not consider renewable energy generation plants and CCHP plants as belonging to different interests. To cope with this problem, this article studies a cooperative operation framework model for a Wind-Solar-CCHP Multi-energy system. Instead of the conventional noncooperative solution-based method, the optimal operation scheduling problem of a multi-energy system is described as an optimization problem of Nash Bargaining. Then the optimization problem of Nash Bargaining for the multi-energy system decomposed into two subproblems: social welfare maximization problem (P1) and energy trading payment problem (P2). The optimal energy transaction profiles can be obtained by solving the P1 problem and the problem of payment of energy trading between PV, WT plant agents and the CCHP plant agent can be obtained by solving the P2 problem. Both problems P1 and P2 can be formulated into mixed-integer linear programming problems and solved by the alternating directional multiplier method. Numerical studies demonstrate the correctness and effectiveness of the proposed cooperative operation framework and distributed algorithm for solving the two subproblems. Moreover, the bargaining-based cooperative operation method can further induce the multi-energy system to improve the operating profit with the reduced feed-in tariff of distributed generation.

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A Collaborative Demand-Controlled Operation Strategy for a Multi-Energy System

Cited by 15 publications

References 25 publications

Regional Renewable Energy Optimization Based on Economic Benefits and Carbon Emissions

Regional Renewable Energy Optimization Based on Economic Benefits and Carbon Emissions

Research on the Influence of the Coupling Coordination of the Logistics Industry and Manufacturing Industry on the Upgrading of the Manufacturing Industry

Cooperative Operation Framework for a Wind-Solar-CCHP Multi-Energy System Based on Nash Bargaining Solution

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