1.Introduction In the era of the new century,driven by the development of the intelligent society,the integration of the field of electronics and information with various technical fields and industries has accelerate...1.Introduction In the era of the new century,driven by the development of the intelligent society,the integration of the field of electronics and information with various technical fields and industries has accelerated and become the major driving force for a new round of technological revolution and industrial transformation.This has advanced the profound adjustment of global technology,industry,and division of labor as well as reshaping the innovation and competitiveness of countries around the world.Electronics information has received the most concentrated research and development investment worldwide and has been actively advancing and playing a leading role in dissemination.Naturally,it has become an important strategic area in which the world’s scientific and technological powers seek economic advances and competitive advantages.展开更多
The offshore-onshore integrated energy system (OOIES) comprises offshore gas production platforms,wind farms,and onshore gas-fired combined heat and power plants,facilitating the integrated operation of multiple energ...The offshore-onshore integrated energy system (OOIES) comprises offshore gas production platforms,wind farms,and onshore gas-fired combined heat and power plants,facilitating the integrated operation of multiple energy sources.To address the challenge of optimally configuring the device capacities in carbon capture and power to gas (CC-P2G) amid stochastic fluctuations in offshore gas and wind power outputs,this study proposes a multi-objective approximate dynamic programming algorithm.This algorithm solves the multi-objective stochastic optimal configuration for the device capacities in CC-P2G in OOIES by simultaneously optimizing investment and operation costs,wind power curtailment,and carbon emissions.By leveraging value function matrices for multiple objectives to solve the extended Bellman equation,the multi-objective multi-period model is decomposed into a series of multi-objective single-period optimization problems,which are solved recursively.Additionally,a weighted Chebyshev function is introduced to obtain the compromise optimal solution for multi-objective optimization model during each period.A case study of an OOIES confirms the effectiveness and efficiency of the proposed algorithm.展开更多
Demand response(DR)is considered to be an effective way to bring significant economic benefit to the commercial campus integrated energy system(CCIES)due to the large amount of flexible cooling and electric vehicle(EV...Demand response(DR)is considered to be an effective way to bring significant economic benefit to the commercial campus integrated energy system(CCIES)due to the large amount of flexible cooling and electric vehicle(EV)charging loads.To maximize DR’s benefits,this paper proposes an integrated DR framework that includes direct load control for cooling loads and time-of-use for EV charging station load in the CCIES.Moreover,multiple uncertainties threaten the secure and economic operation of the CCIES.To deal with these challenges,this paper establishes an interval optimization(IO)based economic dispatch(ED)model,considering the uncertain parameters,including ambient temperature,DR parameters,pipeline parameters,and maximum available PV power output.To improve the solution efficiency,the nonlinear constraints are linearized by applying multi-layer perceptron and affine arithmetic.The order relation and the possibility degrees of intervals are used to transform the interval ED model into a bi-level optimization model.The extreme value theorem of linear interval functions is used to obtain the analytical expressions of the optimal solutions of inner-level models,and the ED model is finally transformed into a solvable mix-integer linear programming model.Test results from actual CCIES demonstrate that the DR can improve the economy and reduce the uncertain fluctuation range of both the objective function and state variables.The ED result can maintain an economical and secure operation under multiple uncertain fluctuations.展开更多
Due to the uncertain fluctuations of renewable energy and load power, the state variables such as bus voltages and pipeline mass flows in the combined cooling, heating, and power campus microgrid(CCHP-CMG) may exceed ...Due to the uncertain fluctuations of renewable energy and load power, the state variables such as bus voltages and pipeline mass flows in the combined cooling, heating, and power campus microgrid(CCHP-CMG) may exceed the secure operation limits. In this paper, an optimal energy flow(OEF) model for a CCHP-CMG using parameterized probability boxes(p-boxes) is proposed to describe the higher-order uncertainty of renewables and loads. In the model, chance constraints are used to describe the secure operation limits of the state variable p-boxes, and variance constraints are introduced to reduce their random fluctuation ranges. To solve this model, the chance and variance constraints are transformed into the constraints of interval cumulants(ICs) of state variables based on the p-efficient point theory and interval Cornish-Fisher expansion. With the relationship between the ICs of state variables and node power, and using the affine interval arithmetic method, the original optimization model is finally transformed into a deterministic nonlinear programming model. It can be solved by the CONOPT solver in GAMS software to obtain the optimal operation point of a CCHP-CMG that satisfies the secure operation requirements considering the higher-order uncertainty of renewables and loads. Case study on a CCHP-CMG demonstrates the correctness and effectiveness of the proposed OEF model.展开更多
基金This work has been supported by the Strategic Research on Disruptive Technologies for Engineering Science and Technology(2019-ZD-27-04).
文摘1.Introduction In the era of the new century,driven by the development of the intelligent society,the integration of the field of electronics and information with various technical fields and industries has accelerated and become the major driving force for a new round of technological revolution and industrial transformation.This has advanced the profound adjustment of global technology,industry,and division of labor as well as reshaping the innovation and competitiveness of countries around the world.Electronics information has received the most concentrated research and development investment worldwide and has been actively advancing and playing a leading role in dissemination.Naturally,it has become an important strategic area in which the world’s scientific and technological powers seek economic advances and competitive advantages.
基金supported by Guangdong Basic and Applied Basic Research Foundation(No.2023A1515240075)Smart Grid-National Science and Technology Major Project(No.2024ZD0802200).
文摘The offshore-onshore integrated energy system (OOIES) comprises offshore gas production platforms,wind farms,and onshore gas-fired combined heat and power plants,facilitating the integrated operation of multiple energy sources.To address the challenge of optimally configuring the device capacities in carbon capture and power to gas (CC-P2G) amid stochastic fluctuations in offshore gas and wind power outputs,this study proposes a multi-objective approximate dynamic programming algorithm.This algorithm solves the multi-objective stochastic optimal configuration for the device capacities in CC-P2G in OOIES by simultaneously optimizing investment and operation costs,wind power curtailment,and carbon emissions.By leveraging value function matrices for multiple objectives to solve the extended Bellman equation,the multi-objective multi-period model is decomposed into a series of multi-objective single-period optimization problems,which are solved recursively.Additionally,a weighted Chebyshev function is introduced to obtain the compromise optimal solution for multi-objective optimization model during each period.A case study of an OOIES confirms the effectiveness and efficiency of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(51977080)Natural Science Foundation of Guangdong Province(2022A1515010332,2023A1515240075)。
文摘Demand response(DR)is considered to be an effective way to bring significant economic benefit to the commercial campus integrated energy system(CCIES)due to the large amount of flexible cooling and electric vehicle(EV)charging loads.To maximize DR’s benefits,this paper proposes an integrated DR framework that includes direct load control for cooling loads and time-of-use for EV charging station load in the CCIES.Moreover,multiple uncertainties threaten the secure and economic operation of the CCIES.To deal with these challenges,this paper establishes an interval optimization(IO)based economic dispatch(ED)model,considering the uncertain parameters,including ambient temperature,DR parameters,pipeline parameters,and maximum available PV power output.To improve the solution efficiency,the nonlinear constraints are linearized by applying multi-layer perceptron and affine arithmetic.The order relation and the possibility degrees of intervals are used to transform the interval ED model into a bi-level optimization model.The extreme value theorem of linear interval functions is used to obtain the analytical expressions of the optimal solutions of inner-level models,and the ED model is finally transformed into a solvable mix-integer linear programming model.Test results from actual CCIES demonstrate that the DR can improve the economy and reduce the uncertain fluctuation range of both the objective function and state variables.The ED result can maintain an economical and secure operation under multiple uncertain fluctuations.
基金supported by the National Natural Science Foundation of China (No. 51977080)the Natural Science Foundation of Guangdong Province (No. 2022A1515010332)。
文摘Due to the uncertain fluctuations of renewable energy and load power, the state variables such as bus voltages and pipeline mass flows in the combined cooling, heating, and power campus microgrid(CCHP-CMG) may exceed the secure operation limits. In this paper, an optimal energy flow(OEF) model for a CCHP-CMG using parameterized probability boxes(p-boxes) is proposed to describe the higher-order uncertainty of renewables and loads. In the model, chance constraints are used to describe the secure operation limits of the state variable p-boxes, and variance constraints are introduced to reduce their random fluctuation ranges. To solve this model, the chance and variance constraints are transformed into the constraints of interval cumulants(ICs) of state variables based on the p-efficient point theory and interval Cornish-Fisher expansion. With the relationship between the ICs of state variables and node power, and using the affine interval arithmetic method, the original optimization model is finally transformed into a deterministic nonlinear programming model. It can be solved by the CONOPT solver in GAMS software to obtain the optimal operation point of a CCHP-CMG that satisfies the secure operation requirements considering the higher-order uncertainty of renewables and loads. Case study on a CCHP-CMG demonstrates the correctness and effectiveness of the proposed OEF model.
