为充分发挥主动配电网提高电力系统灵活性和消纳可再生能源的潜力,该文提出一种计及综合能源系统(integrated energy system,IES)动态特性的主动配电网与输电网协同机组组合模型。一方面引入电-气-热综合能源系统实现多能耦合,使主动配...为充分发挥主动配电网提高电力系统灵活性和消纳可再生能源的潜力,该文提出一种计及综合能源系统(integrated energy system,IES)动态特性的主动配电网与输电网协同机组组合模型。一方面引入电-气-热综合能源系统实现多能耦合,使主动配电网对多能互补的支持融入到输电网的调度优化中;另一方面,为提高调度决策的灵活性,将天然气网与热网的动态特性纳入到输配协同机组组合模型中。基于此模型,根据电-气-热IES多能耦合特性和输-配物理互联特征构建协同优化框架。以联络线交换功率作为耦合变量,将其等效为虚拟能源站,采用目标级联分析法对所提模型进行解耦,从而得到一个独立的输电网优化问题和多个主动配电网局部优化问题。为提高计算效率,采用增量分段方法处理天然气Weymouth方程的非凸性,将该文模型转换为混合整数线性规划问题,保证迭代过程的收敛性,进一步降低计算负担。以T6D2系统和T118D10系统为例,验证所提模型和方法的有效性。展开更多
A coordinated scheduling model based on two-stage distributionally robust optimization(TSDRO)is proposed for integrated energy systems(IESs)with electricity-hydrogen hybrid energy storage.The scheduling problem of the...A coordinated scheduling model based on two-stage distributionally robust optimization(TSDRO)is proposed for integrated energy systems(IESs)with electricity-hydrogen hybrid energy storage.The scheduling problem of the IES is divided into two stages in the TSDRO-based coordinated scheduling model.The first stage addresses the day-ahead optimal scheduling problem of the IES under deterministic forecasting information,while the sec-ond stage uses a distributionally robust optimization method to determine the intraday rescheduling problem under high-order uncertainties,building upon the results of the first stage.The scheduling model also considers col-laboration among the electricity,thermal,and gas networks,focusing on economic operation and carbon emissions.The flexibility of these networks and the energy gradient utilization of hydrogen units during operation are also incor-porated into the model.To improve computational efficiency,the nonlinear formulations in the TSDRO-based coordinated scheduling model are properly linearized to obtain a Mixed-Integer Linear Programming model.The Column-Constraint Generation(C&CG)algorithm is then employed to decompose the scheduling model into a mas-ter problem and subproblems.Through the iterative solution of the master problem and subproblems,an efficient analysis of the coordinated scheduling model is achieved.Finally,the effectiveness of the proposed TSDRO-based coordinated scheduling model is verified through case studies.The simulation results demonstrate that the proposed TSDRO-based coordinated scheduling model can effectively accomplish the optimal scheduling task while consider-ing the uncertainty and flexibility of the system.Compared with traditional methods,the proposed TSDRO-based coordinated scheduling model can better balance conservativeness and robustness.展开更多
综合能源系统(integrated energy system,IES)的效益分析不仅取决于能源供给侧的调度方案,也受到需求侧用能方式的影响。基于此,在IES的需求侧引入柔性负荷响应,以平滑负荷曲线,进一步提升IES的风电消纳能力和经济效益;同时为尽量减小...综合能源系统(integrated energy system,IES)的效益分析不仅取决于能源供给侧的调度方案,也受到需求侧用能方式的影响。基于此,在IES的需求侧引入柔性负荷响应,以平滑负荷曲线,进一步提升IES的风电消纳能力和经济效益;同时为尽量减小供能侧风电出力不确定性的影响、实现调度方案鲁棒性与经济性的均衡,构建了考虑柔性电负荷和柔性热负荷的IES两阶段分布鲁棒优化调度模型:预调度阶段以IES的日前综合调度成本最低为目标;再调度阶段以风电历史数据为基础,寻找最恶劣风电出力概率分布下的最优机组调节方案,并使用列约束生成算法进行求解。最后,采用算例验证了该模型的有效性。展开更多
A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems(RIESs)with multiple stakeholders.A two-level Stackelberg game model is ...A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems(RIESs)with multiple stakeholders.A two-level Stackelberg game model is established,with the RIES operator as the leader and the users as the followers.It considers the interests of the RIES operator and demand response users in energy trading.The leader optimizes time-of-use(TOU)energy prices to minimize costs while users formulate response plans based on prices.A two-stage distributionally robust game model with comprehensive norm constraints,which encompasses the two-level Stackelberg game model in the day-ahead scheduling stage,is constructed to manage wind power uncertainty.Karush-Kuhn-Tucker(KKT)conditions transform the two-level Stackelberg game model into a single-level robust optimization model,which is then solved using column and constraint generation(C&CG).Numerical results demonstrate the effectiveness of the proposed strategy in balancing stakeholders'interests and mitigating wind power risks.展开更多
风光氢微电网对推动能源转型及提高能源利用效率具有重要的作用。文章对风电、光伏、氢储能微网系统的容量配置问题进行了研究,构建了风光氢微电网系统模型,考虑到风电、光伏出力不确定性,基于历史数据,通过改进K-means聚类算法选取典...风光氢微电网对推动能源转型及提高能源利用效率具有重要的作用。文章对风电、光伏、氢储能微网系统的容量配置问题进行了研究,构建了风光氢微电网系统模型,考虑到风电、光伏出力不确定性,基于历史数据,通过改进K-means聚类算法选取典型日场景,并采用综合范数1-范数和∞-范数共同约束不确定性概率分布置信集合。文章建立了风光氢微网容量配置两阶段分布鲁棒模型,第一阶段确定各设备容量,以投资成本最低为目标,第二阶段以运行成本最小为目标,该模型通过列与约束生成(Column and Constraint Generation,C&CG)算法实现求解。结果表明,该模型能取得合理的容量配置,且能提高风光氢微电网的能源利用率和经济性。展开更多
To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads,a two-stage distributionally robust optimization model is proposed for the ...To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads,a two-stage distributionally robust optimization model is proposed for the active distribution network(ADN)optimization problem considering the uncertainties of the source and load in this paper.By establishing an ambiguity set to capture the uncertainties of the photovoltaic(PV)power,wind power and load,the piecewise-linear function and auxiliary parameters are introduced to help characterize the probability distribution of uncertain variables.The optimization goal of the model is to minimize the total expected cost under the worst-case distribution in the ambiguity set.The first-stage expected cost is obtained based on the predicted value of the uncertainty variable.The second-stage expected cost is based on the actual value of the uncertainty variable to solve the first-stage decision.The generalized linear decision rule approximates the two-stage optimization model,and the affine function is introduced to provide a closer approximation to the second-stage optimization model.Finally,the improved IEEE 33-node and IEEE 118-node systems are simulated and analyzed with deterministic methods,stochastic programming,and robust optimization methods to verify the feasibility and superiority of the proposed model and algorithm.展开更多
This paper proposes an adjustable and distributionally robust chance-constrained(ADRCC) optimal power flow(OPF) model for economic dispatch considering wind power forecasting uncertainty. The proposed ADRCC-OPF model ...This paper proposes an adjustable and distributionally robust chance-constrained(ADRCC) optimal power flow(OPF) model for economic dispatch considering wind power forecasting uncertainty. The proposed ADRCC-OPF model is distributionally robust because the uncertainties of the wind power forecasting are represented only by their first-and second-order moments instead of a specific distribution assumption. The proposed model is adjustable because it is formulated as a second-order cone programming(SOCP) model with an adjustable coefficient.This coefficient can control the robustness of the chance constraints, which may be set up for the Gaussian distribution, symmetrically distributional robustness, or distributionally robust cases considering wind forecasting uncertainty. The conservativeness of the ADRCC-OPF model is analyzed and compared with the actual distribution data of wind forecasting error. The system operators can choose an appropriate adjustable coefficient to tradeoff between the economics and system security.展开更多
文摘为充分发挥主动配电网提高电力系统灵活性和消纳可再生能源的潜力,该文提出一种计及综合能源系统(integrated energy system,IES)动态特性的主动配电网与输电网协同机组组合模型。一方面引入电-气-热综合能源系统实现多能耦合,使主动配电网对多能互补的支持融入到输电网的调度优化中;另一方面,为提高调度决策的灵活性,将天然气网与热网的动态特性纳入到输配协同机组组合模型中。基于此模型,根据电-气-热IES多能耦合特性和输-配物理互联特征构建协同优化框架。以联络线交换功率作为耦合变量,将其等效为虚拟能源站,采用目标级联分析法对所提模型进行解耦,从而得到一个独立的输电网优化问题和多个主动配电网局部优化问题。为提高计算效率,采用增量分段方法处理天然气Weymouth方程的非凸性,将该文模型转换为混合整数线性规划问题,保证迭代过程的收敛性,进一步降低计算负担。以T6D2系统和T118D10系统为例,验证所提模型和方法的有效性。
基金supported in part by the National Natural Science Foundation(51977181,52077180)Natural Science Foundation of Sichuan Province(2022NSFSC0027)+2 种基金Fok Ying-Tong Education Foundation of China(171104)14th Five-year Major Science and Technology Research Project of CRRC(2021CXZ021-2)Key research and development project of China National Railway Group Co.,Ltd(N2022J016-B).
文摘A coordinated scheduling model based on two-stage distributionally robust optimization(TSDRO)is proposed for integrated energy systems(IESs)with electricity-hydrogen hybrid energy storage.The scheduling problem of the IES is divided into two stages in the TSDRO-based coordinated scheduling model.The first stage addresses the day-ahead optimal scheduling problem of the IES under deterministic forecasting information,while the sec-ond stage uses a distributionally robust optimization method to determine the intraday rescheduling problem under high-order uncertainties,building upon the results of the first stage.The scheduling model also considers col-laboration among the electricity,thermal,and gas networks,focusing on economic operation and carbon emissions.The flexibility of these networks and the energy gradient utilization of hydrogen units during operation are also incor-porated into the model.To improve computational efficiency,the nonlinear formulations in the TSDRO-based coordinated scheduling model are properly linearized to obtain a Mixed-Integer Linear Programming model.The Column-Constraint Generation(C&CG)algorithm is then employed to decompose the scheduling model into a mas-ter problem and subproblems.Through the iterative solution of the master problem and subproblems,an efficient analysis of the coordinated scheduling model is achieved.Finally,the effectiveness of the proposed TSDRO-based coordinated scheduling model is verified through case studies.The simulation results demonstrate that the proposed TSDRO-based coordinated scheduling model can effectively accomplish the optimal scheduling task while consider-ing the uncertainty and flexibility of the system.Compared with traditional methods,the proposed TSDRO-based coordinated scheduling model can better balance conservativeness and robustness.
文摘综合能源系统(integrated energy system,IES)的效益分析不仅取决于能源供给侧的调度方案,也受到需求侧用能方式的影响。基于此,在IES的需求侧引入柔性负荷响应,以平滑负荷曲线,进一步提升IES的风电消纳能力和经济效益;同时为尽量减小供能侧风电出力不确定性的影响、实现调度方案鲁棒性与经济性的均衡,构建了考虑柔性电负荷和柔性热负荷的IES两阶段分布鲁棒优化调度模型:预调度阶段以IES的日前综合调度成本最低为目标;再调度阶段以风电历史数据为基础,寻找最恶劣风电出力概率分布下的最优机组调节方案,并使用列约束生成算法进行求解。最后,采用算例验证了该模型的有效性。
基金supported by National Natural Science Foundation of China(No.52207133)Science and Technology Project of State Grid Corporation of China(No.5400-202112571A-0-5-SF)。
文摘A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems(RIESs)with multiple stakeholders.A two-level Stackelberg game model is established,with the RIES operator as the leader and the users as the followers.It considers the interests of the RIES operator and demand response users in energy trading.The leader optimizes time-of-use(TOU)energy prices to minimize costs while users formulate response plans based on prices.A two-stage distributionally robust game model with comprehensive norm constraints,which encompasses the two-level Stackelberg game model in the day-ahead scheduling stage,is constructed to manage wind power uncertainty.Karush-Kuhn-Tucker(KKT)conditions transform the two-level Stackelberg game model into a single-level robust optimization model,which is then solved using column and constraint generation(C&CG).Numerical results demonstrate the effectiveness of the proposed strategy in balancing stakeholders'interests and mitigating wind power risks.
文摘风光氢微电网对推动能源转型及提高能源利用效率具有重要的作用。文章对风电、光伏、氢储能微网系统的容量配置问题进行了研究,构建了风光氢微电网系统模型,考虑到风电、光伏出力不确定性,基于历史数据,通过改进K-means聚类算法选取典型日场景,并采用综合范数1-范数和∞-范数共同约束不确定性概率分布置信集合。文章建立了风光氢微网容量配置两阶段分布鲁棒模型,第一阶段确定各设备容量,以投资成本最低为目标,第二阶段以运行成本最小为目标,该模型通过列与约束生成(Column and Constraint Generation,C&CG)算法实现求解。结果表明,该模型能取得合理的容量配置,且能提高风光氢微电网的能源利用率和经济性。
基金supported by Natural Science Foundation of Beijing Municipality(No.3161002)National Key R&D Program(No.2017YFB0903300).
文摘To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads,a two-stage distributionally robust optimization model is proposed for the active distribution network(ADN)optimization problem considering the uncertainties of the source and load in this paper.By establishing an ambiguity set to capture the uncertainties of the photovoltaic(PV)power,wind power and load,the piecewise-linear function and auxiliary parameters are introduced to help characterize the probability distribution of uncertain variables.The optimization goal of the model is to minimize the total expected cost under the worst-case distribution in the ambiguity set.The first-stage expected cost is obtained based on the predicted value of the uncertainty variable.The second-stage expected cost is based on the actual value of the uncertainty variable to solve the first-stage decision.The generalized linear decision rule approximates the two-stage optimization model,and the affine function is introduced to provide a closer approximation to the second-stage optimization model.Finally,the improved IEEE 33-node and IEEE 118-node systems are simulated and analyzed with deterministic methods,stochastic programming,and robust optimization methods to verify the feasibility and superiority of the proposed model and algorithm.
基金co-authored by Alliance for Sustainable Energy, LLC, the manager and operator of the National Renewable Energy Laboratory for the U.S. Department of Energy (DOE) (No. DE-AC36-08GO28308)provided by U.S. DOE Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office
文摘This paper proposes an adjustable and distributionally robust chance-constrained(ADRCC) optimal power flow(OPF) model for economic dispatch considering wind power forecasting uncertainty. The proposed ADRCC-OPF model is distributionally robust because the uncertainties of the wind power forecasting are represented only by their first-and second-order moments instead of a specific distribution assumption. The proposed model is adjustable because it is formulated as a second-order cone programming(SOCP) model with an adjustable coefficient.This coefficient can control the robustness of the chance constraints, which may be set up for the Gaussian distribution, symmetrically distributional robustness, or distributionally robust cases considering wind forecasting uncertainty. The conservativeness of the ADRCC-OPF model is analyzed and compared with the actual distribution data of wind forecasting error. The system operators can choose an appropriate adjustable coefficient to tradeoff between the economics and system security.
