As the largest source of carbon emissions in China,the thermal power industry is the only emission-controlled industry in the first national carbon market compliance cycle.Its conversion to clean-energy generation tec...As the largest source of carbon emissions in China,the thermal power industry is the only emission-controlled industry in the first national carbon market compliance cycle.Its conversion to clean-energy generation technologies is also an important means of reducing CO_(2)emissions and achieving the carbon peak and carbon neutral commitments.This study used fractional Brownian motion to describe the energy-switching cost and constructed a stochastic optimization model on carbon allowance(CA)trading volume and emission-reduction strategy during compliance period with the Hurst exponent and volatility coefficient in the model estimated.We defined the optimal compliance cost of thermal power enterprises as the form of the unique solution of the Hamilton–Jacobi–Bellman equation by combining the dynamic optimization principle and the fractional It?’s formula.In this manner,we obtained the models for optimal emission reduction and equilibrium CA price.Our numerical analysis revealed that,within a compliance period of 2021–2030,the optimal reductions and desired equilibrium prices of CAs changed concurrently,with an increasing trend annually in different peak-year scenarios.Furthermore,sensitivity analysis revealed that the energy price indirectly affected the equilibrium CA price by influencing the Hurst exponent,the depreciation rate positively impacted the CA price,and increasing the initial CA reduced the optimal reduction and the CA price.Our findings can be used to develop optimal emission-reduction strategies for thermal power enterprises and carbon pricing in the carbon market.展开更多
With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this prob...With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this problem while considering the potential of the large-scale charge load of electric vehicles(EVs), an aggregator-based demand response(DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study. In this aggregator-based DR mechanism, the profits for the power grid’s operation and the participation willingness of the EV owners are considered. Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling, a rolling unit commitment model with the DR is established to maximize the social welfare. In addition, to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator(ISO) and the aggregators, the clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.展开更多
为了探索双碳目标下虚拟电厂(virtual power plants,VPP)低碳、经济的竞标策略,提出了一种基于分时碳价和分布鲁棒优化思想的VPP参与日前多类市场的竞标策略。从减小电力市场运营成本的角度,提出了一种与电价负相关的分时碳价,采用基准...为了探索双碳目标下虚拟电厂(virtual power plants,VPP)低碳、经济的竞标策略,提出了一种基于分时碳价和分布鲁棒优化思想的VPP参与日前多类市场的竞标策略。从减小电力市场运营成本的角度,提出了一种与电价负相关的分时碳价,采用基准线法为VPP无偿分配碳配额,计算新能源发电的国家核证减排量;综合考虑碳交易市场、能量市场和调峰市场的情况,搭建了VPP参与多类市场的竞标模型;针对风电的不确定性,构建了基于数据驱动的分布鲁棒两阶段竞标模型,并采用列与约束生成算法求解。算例分析表明,分时碳价能够有效降低电力市场运营商的运行成本,提高VPP运营商的收益,所提的VPP竞标模型能给出不同市场的最优投标方案,而分布鲁棒能够兼顾经济性与鲁棒性。展开更多
Electric vehicle(EV)is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future.However,a large number of EVs will be concentrated on charging during the valley hours ...Electric vehicle(EV)is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future.However,a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff.Therefore,this paper proposes a dynamic time-of-use tariff mechanism,which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean(FCM)clustering algorithm,and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period.Based on the proposed tariff mechanism,an EV charging optimization model with the lowest cost to the users and the lowest variance of the grid-side load as the objective function is established.Then,a weight selection principle with an equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem.Finally,the EV charging load optimization model under three tariff strategies is set up and solved with the mathematical solver GROUBI.The results show that the EV charging load optimization strategy based on the dynamic time-of-use tariff can better balance the benefits between charging stations and users under different numbers and proportions of EVs connected to the grid,and can effectively reduce the grid load variance and improve the grid load curve.展开更多
基金like to thank Major Program of National Philosophy and Social Science Foundation of China(Grant No.21ZDA086)National Natural Science Foundation of China(Grant No.71974188),and Jiangsu Soft Science Fund(Grant No.BR2022007).
文摘As the largest source of carbon emissions in China,the thermal power industry is the only emission-controlled industry in the first national carbon market compliance cycle.Its conversion to clean-energy generation technologies is also an important means of reducing CO_(2)emissions and achieving the carbon peak and carbon neutral commitments.This study used fractional Brownian motion to describe the energy-switching cost and constructed a stochastic optimization model on carbon allowance(CA)trading volume and emission-reduction strategy during compliance period with the Hurst exponent and volatility coefficient in the model estimated.We defined the optimal compliance cost of thermal power enterprises as the form of the unique solution of the Hamilton–Jacobi–Bellman equation by combining the dynamic optimization principle and the fractional It?’s formula.In this manner,we obtained the models for optimal emission reduction and equilibrium CA price.Our numerical analysis revealed that,within a compliance period of 2021–2030,the optimal reductions and desired equilibrium prices of CAs changed concurrently,with an increasing trend annually in different peak-year scenarios.Furthermore,sensitivity analysis revealed that the energy price indirectly affected the equilibrium CA price by influencing the Hurst exponent,the depreciation rate positively impacted the CA price,and increasing the initial CA reduced the optimal reduction and the CA price.Our findings can be used to develop optimal emission-reduction strategies for thermal power enterprises and carbon pricing in the carbon market.
基金supported by the Science and Technology Project from the State Grid Shanghai Municipal Electric Power Company of China (52094019006U)the Shanghai Rising-Star Program (18QB1400200)。
文摘With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this problem while considering the potential of the large-scale charge load of electric vehicles(EVs), an aggregator-based demand response(DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study. In this aggregator-based DR mechanism, the profits for the power grid’s operation and the participation willingness of the EV owners are considered. Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling, a rolling unit commitment model with the DR is established to maximize the social welfare. In addition, to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator(ISO) and the aggregators, the clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.
文摘为了探索双碳目标下虚拟电厂(virtual power plants,VPP)低碳、经济的竞标策略,提出了一种基于分时碳价和分布鲁棒优化思想的VPP参与日前多类市场的竞标策略。从减小电力市场运营成本的角度,提出了一种与电价负相关的分时碳价,采用基准线法为VPP无偿分配碳配额,计算新能源发电的国家核证减排量;综合考虑碳交易市场、能量市场和调峰市场的情况,搭建了VPP参与多类市场的竞标模型;针对风电的不确定性,构建了基于数据驱动的分布鲁棒两阶段竞标模型,并采用列与约束生成算法求解。算例分析表明,分时碳价能够有效降低电力市场运营商的运行成本,提高VPP运营商的收益,所提的VPP竞标模型能给出不同市场的最优投标方案,而分布鲁棒能够兼顾经济性与鲁棒性。
基金Key R&D Program of Tianjin,China(No.20YFYSGX00060).
文摘Electric vehicle(EV)is an ideal solution to resolve the carbon emission issue and the fossil fuels scarcity problem in the future.However,a large number of EVs will be concentrated on charging during the valley hours leading to new load peaks under the guidance of static time-of-use tariff.Therefore,this paper proposes a dynamic time-of-use tariff mechanism,which redefines the peak and valley time periods according to the predicted loads using the fuzzy C-mean(FCM)clustering algorithm,and then dynamically adjusts the peak and valley tariffs according to the actual load of each time period.Based on the proposed tariff mechanism,an EV charging optimization model with the lowest cost to the users and the lowest variance of the grid-side load as the objective function is established.Then,a weight selection principle with an equal loss rate of the two objectives is proposed to transform the multi-objective optimization problem into a single-objective optimization problem.Finally,the EV charging load optimization model under three tariff strategies is set up and solved with the mathematical solver GROUBI.The results show that the EV charging load optimization strategy based on the dynamic time-of-use tariff can better balance the benefits between charging stations and users under different numbers and proportions of EVs connected to the grid,and can effectively reduce the grid load variance and improve the grid load curve.
