Decentralized optimal multi-energy flow of large-scale integrated energy systems in a carbon trading market

Qu, Kaiping; Yu, Tao; Huang, Linni; Yang, Bo; Zhang, Xiaoshun

doi:10.1016/j.energy.2018.02.083

Cited by 87 publications

(43 citation statements)

References 30 publications

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“…In [17], multiobjective optimization of a multi-energy network is proposed to jointly optimize the total operating cost and the total emissions generated by the network. In order to maximize the use of the economic and environmental advantages of the integrated energy system, a large-scale integrated energy system (IES) optimal energy flow model that considers the carbon trading market is proposed in [18], and three decentralized algorithms are used to deal with limited information exchange. However, in [15][16][17][18], most of the environmental benefits are calculated by using a penalty function or environmental cost.…”

Section: Introductionmentioning

confidence: 99%

Capacity Planning of Micro Energy Grid Using Double-Level Game Model of Environment-Economic Considering Dynamic Energy Pricing Strategy

et al. 2020

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Multi-energy unified planning is difficult because of the complex conflicting relationship between the coupling and complementary interaction of multiple forms of energy in micro energy grids (MEGs). Conflicting relationships between the economy and the environment as well as the impact of uncertain energy prices must be considered during MEG planning. To address these problems, this paper proposes a two-level game with an environment-economic planning model that considers dynamic energy pricing strategies. This model consists of an upper environment-economic planning level based on a multistrategy evolution game considering players' bounded rationality and a lower dynamic energy pricing level, including the MEG operator-user leader-follower Stackelberg game. Simultaneously, based on the energy hub theory, a multi energy coupling matrix is established for a MEG and includes electricity, gas, heat, and cooling. The evolutionary stability strategy (ESS) of the planning results is analyzed using the replicator dynamic equation of the evolutionary game, and the existence of the Nash equilibrium is proven for the dynamic energy pricing of Stackelberg games. Finally, the effectiveness of the proposed environmenteconomic planning two-level game model considering dynamic energy pricing strategies is verified using simulations. Because dynamic energy pricing and the environment-economic planning are considered, the number of energy equipment required during peak hours is reasonably reduced, thereby reducing the total planning cost and improving the energy utilization efficiency. Simultaneously, greenhouse gas (CO2) and air pollutant (NOx) emissions are reduced to decrease environmental impact.

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Section: Introductionmentioning

confidence: 99%

Capacity Planning of Micro Energy Grid Using Double-Level Game Model of Environment-Economic Considering Dynamic Energy Pricing Strategy

et al. 2020

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“…Reference [13] studied the optimization of low-carbon operation of EGHS. The above studies all focus on the optimization of the singlezone integrated energy system inside the region, but the research on the collaborative optimization of the interconnected integrated energy system, especially the multi-region EGHS collaborative optimization, is still in scarcity, and many studies have not considered the topology of the thermal network, but only involve the type of thermal load [14]. As the heating area continues to expand and the heat demand continues to increase, in china, the National Energy Administration vigorously promotes the construction of ''combined heat and power'' projects, so it is necessary to accurately model the heat network in the integrated energy system [15].…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimal Dispatching of Interconnected Electricity-Gas-Heating System

Zhu

Chen

et al. 2020

IEEE Access

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In the researching background of integrated energy system, a single electricity-gas-heating system (EGHS) can be regarded as an active producer. In order to solve the joint optimization of integrated energy systems under the condition of incomplete information, this paper proposes a distributed optimal scheduling framework of EGHS. First, establish a coupling model of the interconnected EGHS, and perform strict second-order cone convexity of the complex natural gas flow model. Next, use the bus splitting method to realize the decoupling between different regions of the interconnected system, and employ the alternating direction multiplier method (ADMM) to solve the model. Then, construct two-region energy system (78-node grid + 40-node gas grid + 40-node heat grid) and three-region energy system (117-node grid + 60 node gas grid + 60-node heat grid) as simulation examples to verify the effectiveness of the distributed optimization framework. In the end, the algorithm solution process, the effectiveness of scheduling results, and the comparison of optimization results under different interconnection methods are analyzed in detail. INDEX TERMS Electricity-gas-heating system, alternating direction method of multiplies, distributed optimal scheduling, second-order cone programming.

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“…In [3], a regional integrated energy system including wind turbines (WT), photovoltaics (PV), gas turbines and battery energy storage was introduced to minimize the operating costs of the system and determine the optimal coordination between the various energy sources. In order to fully exploit the economic and environmental advantages of the system, considering the difficulties of information collection from subareas, a novel decentralized optimal multi-energy flow (OMEF) for large-scale IESs in a carbon trading market was proposed in [4]. The improved differential evolution algorithm [5] was designed to obtain the minimum operation cost of the IES, while considering battery lifetime loss.…”

Section: Introductionmentioning

confidence: 99%

Multi-Searcher Optimization for the Optimal Energy Dispatch of Combined Heat and Power-Thermal-Wind-Photovoltaic Systems

Tang

Zhang

et al. 2019

Applied Sciences

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This paper proposes a novel multi-searcher optimization (MSO) algorithm for the optimal energy dispatch (OED) of combined heat and power-thermal-wind-photovoltaic systems. The available power of wind turbine (WT) units and photovoltaic (PV) units is approximated with the probability density functions of wind speed and solar irradiance, respectively. The chaos theory is used to implement a wide global search, which can effectively avoid a low-quality local optimum for OED. Besides, a double-layer searcher is designed to guarantee fast convergence to a high-quality optimal solution. Finally, three benchmark functions and an energy system with 27 units are used for testing the performance of the MSO compared with nine other frequently used heuristic algorithms. The simulation results demonstrate that the proposed technique not only can solve the highly nonlinear, non-smooth, and non-convex OED problem of an energy system, but can also achieve a superior performance for the convergence speed and the optimum quality.

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Decentralized optimal multi-energy flow of large-scale integrated energy systems in a carbon trading market

Cited by 87 publications

References 30 publications

Capacity Planning of Micro Energy Grid Using Double-Level Game Model of Environment-Economic Considering Dynamic Energy Pricing Strategy

Capacity Planning of Micro Energy Grid Using Double-Level Game Model of Environment-Economic Considering Dynamic Energy Pricing Strategy

Distributed Optimal Dispatching of Interconnected Electricity-Gas-Heating System

Multi-Searcher Optimization for the Optimal Energy Dispatch of Combined Heat and Power-Thermal-Wind-Photovoltaic Systems

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