Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids

Li, Zhengmao; Wu, Lei; Xu, Yan; Wang, Luhao; Yang, Nan

doi:10.1016/j.apenergy.2022.120282

Cited by 83 publications

(22 citation statements)

References 37 publications

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“…Finally, we also want to model multiple IESs connected to the grid. Based on a comprehensive consideration of the cooperative and competitive relationships between different systems and different energy suppliers (Li et al, 2023), we will analyze the optimal scheduling results of each system. Nomenclature Variables t, j the time scale of 1 day, from 1 to 24 M(t, j) the elasticity coefficient of the load at time t with respect to the electricity price at time j ΔL t e the load change at time t after demand response L t e,0 the initial load at time t Δc j the electricity price change at time j after demand response c j,0 the initial electricity price at time j ΔL Total the equivalent output power of CHP and GB at time t a 1 /b 1 /c 1 the actual carbon emission calculation parameters of coalfired units of purchasing power e,buy the purchasing power from the superior grid at time t P t g,buy the purchased natural gas volume at time t π t e,price the purchased power price at time t π t g,price the purchased gas price per unit of natural gas at time t δ i the O&M coefficient of the device i. i takes 1, 2, .…”

Section: Discussionmentioning

confidence: 99%

Optimal dispatch of integrated energy systems considering integrated demand response and stepped carbon trading

et al. 2023

Front.Electron.

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The integrated energy system is an effective way to achieve carbon neutrality. To further exploit the carbon reduction potentials of IESs, an optimal dispatch strategy that considers integrated demand response and stepped carbon trading is proposed. First, an integrated demand response (IDR) pricing approach is proposed based on the characteristics of different load types. Classify multi-energy loads into curtailable and substitutable loads, and incentivize both loads through a price elasticity matrix and low-price energy in the same period. Then, to better incentivize IESs to reduce carbon emissions, a stepped pricing mechanism was introduced in the carbon price. Finally, an optimal dispatch model is developed with an objective function that minimizes the sum of energy purchase cost, carbon trading cost, and operation and maintenance (O&M) cost. Considering the high-dimensional and non-linear characteristics of the model, an improved differential evolution (DE) algorithm is introduced in this paper. In addition, this paper also analyzes the effects of the stepped carbon trading parameters on the optimal dispatching results of the system in terms of carbon trading base price, carbon emission interval length, and carbon price growth rate. Compared to the case of adopting a single IDR model or a single stepped carbon trading, carbon emissions from the IESs decreased by 6.28% and 3.24%, respectively, while total operating costs decreased by 1.24% and 0.92%, The results show that the model proposed in this paper has good environmental and economic benefits, and the reasonable setting of stepped carbon trading parameters can effectively promote the low-carbon development of IESs.

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Section: Discussionmentioning

confidence: 99%

Optimal dispatch of integrated energy systems considering integrated demand response and stepped carbon trading

et al. 2023

Front.Electron.

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“…At present, methods for the uncertainty problems mainly contain the stochastic optimization and robust optimization. The stochastic optimization (Liu et al, 2022;Li et al, 2023) formulates the source-load uncertainty with discrete scenarios. The method samples multiple scenarios according to the probability distribution of the source-load uncertainty and optimizes the expected operation cost formulated with these sampled scenarios (Li and Xu, 2019).…”

Section: Introductionmentioning

confidence: 99%

Real-time low-carbon scheduling for the wind–thermal–hydro-storage resilient power system using linear stochastic robust optimization

Qiu

Zhang³

et al. 2023

Front. Energy Res.

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With the large-scale wind power integration, power systems have to address not only the conventional power demand fluctuations but also the wind uncertainty. To improve the economical effectiveness, resilience, and environmental protection of power systems in the source-load uncertainty, a real-time low-carbon scheduling for the wind–thermal–hydro-storage integrated system is proposed. The power imbalance caused by the uncertainty is neutralized by the synergetic linear decision of multiple resources. To address the source-load uncertainty, a stochastic robust optimization is introduced, which establishes the system constraints by robust optimization for the resilience operation, while optimizing the expected operation cost in the empirical uncertainty distribution for economic efficiency. Moreover, a multi-point estimation is applied to formulate the expected operation cost precisely and quickly. By using the dual theory, the proposed real-time power scheduling is derived as a mixed integer bilinear constrained programming. A multi-step sequential convexified solution is developed to solve the complex scheduling problem, which linearizes the bilinear constraints with alternate optimization and relaxes the state variables of energy storages with an “estimation–correction” strategy. Finally, case studies show the superiority of the proposed scheduling method and convexified solution.

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“…In terms of operating strategies, an operation coordination model of the multiple multi-energy microgrids is proposed by Xuanyue et al (Xuanyue et al, 2022), which took active and passive demand responses and CVaR into consideration. Based on CVaR, Li et al (Li et al, 2023) explored energy trading methods of grid-tied multi-energy microgrids participating in the market, which provides a novel view in dealing with uncertainties. In addition, CVaR is also quite vital in the configuration process.…”

Section: Introductionmentioning

confidence: 99%

A multi-energy microgrid configuration method in remote rural areas considering the condition value at risk

Yang²,

Liang³

et al. 2023

Front. Energy Res.

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The uncertainty caused by the growing use of renewable energy sources, such as wind and solar energy, makes it difficult to forecast the operation costs of micro-energy systems, particularly those in remote rural areas. Motivated by this point, this paper analyzes the possible operational risks and then introduces Condition Value at Risk (CVaR) to quantify the cost of the operational risk. On this basis, stochastic programming based on a multi-energy microgrid planning model that minimizes the investment cost, the operating cost, and the cost of operational risk, while considering the physical limitations of the multi-energy microgrid, is presented. Especially, scenarios of wind and solar energy output are generated using the Latin hypercube sampling method and reduced using the crowding measure-based scenario reduction method. After piecewise linearization and second-order cone relaxation, the model proposed in this paper is processed as a mixed integer linear model and solved by CPLEX. According to the achieved typical scenarios processed by the reduction method, the simulation shows that the presented configuration model can balance the investment cost and the cost of the operational risk, which effectively enhances the system’s ability to cope with uncertainties and fluctuations. Moreover, by adjusting the risk preference coefficient, the conservativeness of the planning scheme can be correspondingly adjusted.

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Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids

Cited by 83 publications

References 37 publications

Optimal dispatch of integrated energy systems considering integrated demand response and stepped carbon trading

Optimal dispatch of integrated energy systems considering integrated demand response and stepped carbon trading

Real-time low-carbon scheduling for the wind–thermal–hydro-storage resilient power system using linear stochastic robust optimization

A multi-energy microgrid configuration method in remote rural areas considering the condition value at risk

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