With the increasing proportion of renewable energy in the power market,the demands on government financial subsidies are gradually increasing.Thus,a joint green certificate-carbon emission right-electricity multi-mark...With the increasing proportion of renewable energy in the power market,the demands on government financial subsidies are gradually increasing.Thus,a joint green certificate-carbon emission right-electricity multi-market trading process is proposed to study the market-based strategy for renewable energy.Considering the commodity characteristics of green certificates and carbon emission rights,the dynamic cost models of green certificates and carbon rights are constructed based on the Rubinstein game and ladder pricing models.Furthermore,considering the irrational bidding behavior of energy suppliers in the actual electricity market,an evolutionary game based multi-market bidding optimization model is presented.Subsequently,it is solved using a composite differential evolutionary algorithm.Finally,the case study results reveal that the proposed model can increase profits and the consumption rate of renewable energy and reduce carbon emission.展开更多
As an important part of demand side,residential users have the characteristics of imperfect rationality and strong randomness,which are rarely considered in the existing study.Moreover,to effectively improve the energ...As an important part of demand side,residential users have the characteristics of imperfect rationality and strong randomness,which are rarely considered in the existing study.Moreover,to effectively improve the energy efficiency,integrated demand response(IDR)is proposed as an effective measure to reduce the local energy supply pressure.This paper focuses on a scenario for IDR programs,in which the intelligent building aggregator(IBA)wants to encourage residential users to participate in IDR according to a proper contract price policy.To analyze how the participation degree tendency evolves over time,an evolutionary game approach is proposed considering residential users’bounded-rationality.A symmetric evolutionary game model and an asymmetric evolutionary game model are established,and the stability of equilibrium points in the above models is proven.Simulation results show that different contract price policies will obviously influence residential users’strategy,and affect the stable equilibrium points of the evolutionary game.The simulation results provide an effective reference for IBA to set proper and effective price incentives.展开更多
With the development of smart grids, a renewable energy generation system has been introduced into a smart house. The generation system usually supplies a storage system with the capability to store the produced energ...With the development of smart grids, a renewable energy generation system has been introduced into a smart house. The generation system usually supplies a storage system with the capability to store the produced energy for satisfying a user's future demand. In this paper,the main objective is to determine the best strategies of energy consumption and optimal storage capacities for residential users, which are both closely related to the energy cost of the users. Energy management with storage capacity optimization is studied by considering the cost of renewable energy generation, depreciation cost of storage and bidirectional energy trading. To minimize the cost to residential users, the non-cooperative game-theoretic method is employed to formulate the model that combines energy consumption and storage capacity optimization.The distributed algorithm is presented to understand the Nash equilibrium which can guarantee Pareto optimality in terms of minimizing the energy cost. Simulation results show that the proposed game approach can significantly benefit residential users. Furthermore, it also contributes toreducing the peak-to-average ratio(PAR) of overall energy demand.展开更多
The operation of integrated energy systems(IESs)is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms.Scenario generation is one of ordinary met...The operation of integrated energy systems(IESs)is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms.Scenario generation is one of ordinary methods to alleviate the system uncertainties by extracting several typical scenarios to represent the original high-dimensional data.This paper proposes a novel representative scenario generation method based on the feature extraction of panel data.The original high-dimensional data are represented by an aggregated indicator matrix using principal component analysis to preserve temporal variation.Then,the aggregated indicator matrix is clustered by an algorithm combining density canopy and K-medoids.Together with the proposed scenario generation method,an optimal operation model of IES is established,where the objective is to minimize the annual operation costs considering carbon trading cost.Finally,case studies based on the data of Aachen,Germany in 2019 are performed.The results indicate that the adjusted rand index(ARI)and silhouette coefficient(SC)of the proposed method are 0.6153 and 0.6770,respectively,both higher than the traditional methods,namely K-medoids,K-means++,and density-based spatial clustering of applications with noise(DBSCAN),which means the proposed method has better accuracy.The error between optimal operation results of the IES obtained by the proposed method and all-year time series benchmark value is 0.1%,while the calculation time is reduced from 11029 s to 188 s,which verifies that the proposed method can be used to optimize operation strategy of IES with high efficiency without loss of accuracy.展开更多
基金supported by the National Key R&D Program of China(2017YFB0902200).
文摘With the increasing proportion of renewable energy in the power market,the demands on government financial subsidies are gradually increasing.Thus,a joint green certificate-carbon emission right-electricity multi-market trading process is proposed to study the market-based strategy for renewable energy.Considering the commodity characteristics of green certificates and carbon emission rights,the dynamic cost models of green certificates and carbon rights are constructed based on the Rubinstein game and ladder pricing models.Furthermore,considering the irrational bidding behavior of energy suppliers in the actual electricity market,an evolutionary game based multi-market bidding optimization model is presented.Subsequently,it is solved using a composite differential evolutionary algorithm.Finally,the case study results reveal that the proposed model can increase profits and the consumption rate of renewable energy and reduce carbon emission.
基金supported by Science and Technology Project of State Grid Corporation of China(No.5230HQ19000J)。
文摘As an important part of demand side,residential users have the characteristics of imperfect rationality and strong randomness,which are rarely considered in the existing study.Moreover,to effectively improve the energy efficiency,integrated demand response(IDR)is proposed as an effective measure to reduce the local energy supply pressure.This paper focuses on a scenario for IDR programs,in which the intelligent building aggregator(IBA)wants to encourage residential users to participate in IDR according to a proper contract price policy.To analyze how the participation degree tendency evolves over time,an evolutionary game approach is proposed considering residential users’bounded-rationality.A symmetric evolutionary game model and an asymmetric evolutionary game model are established,and the stability of equilibrium points in the above models is proven.Simulation results show that different contract price policies will obviously influence residential users’strategy,and affect the stable equilibrium points of the evolutionary game.The simulation results provide an effective reference for IBA to set proper and effective price incentives.
基金supported by the National Natural Science Foundation of China (No. 51577030)the Excellent YoungTeachers Program of Southeast University (No. 2242015R30024)Six Talent Peaks Project of Jiangsu Province (No. 2014-ZBZZ001)
文摘With the development of smart grids, a renewable energy generation system has been introduced into a smart house. The generation system usually supplies a storage system with the capability to store the produced energy for satisfying a user's future demand. In this paper,the main objective is to determine the best strategies of energy consumption and optimal storage capacities for residential users, which are both closely related to the energy cost of the users. Energy management with storage capacity optimization is studied by considering the cost of renewable energy generation, depreciation cost of storage and bidirectional energy trading. To minimize the cost to residential users, the non-cooperative game-theoretic method is employed to formulate the model that combines energy consumption and storage capacity optimization.The distributed algorithm is presented to understand the Nash equilibrium which can guarantee Pareto optimality in terms of minimizing the energy cost. Simulation results show that the proposed game approach can significantly benefit residential users. Furthermore, it also contributes toreducing the peak-to-average ratio(PAR) of overall energy demand.
基金supported by the State Grid Corporation of China“Research and Demonstration on Key Technologies of Distributed Energy Supply System with Complementary Renewable Energy”(No.5230HQ19000J).
文摘The operation of integrated energy systems(IESs)is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms.Scenario generation is one of ordinary methods to alleviate the system uncertainties by extracting several typical scenarios to represent the original high-dimensional data.This paper proposes a novel representative scenario generation method based on the feature extraction of panel data.The original high-dimensional data are represented by an aggregated indicator matrix using principal component analysis to preserve temporal variation.Then,the aggregated indicator matrix is clustered by an algorithm combining density canopy and K-medoids.Together with the proposed scenario generation method,an optimal operation model of IES is established,where the objective is to minimize the annual operation costs considering carbon trading cost.Finally,case studies based on the data of Aachen,Germany in 2019 are performed.The results indicate that the adjusted rand index(ARI)and silhouette coefficient(SC)of the proposed method are 0.6153 and 0.6770,respectively,both higher than the traditional methods,namely K-medoids,K-means++,and density-based spatial clustering of applications with noise(DBSCAN),which means the proposed method has better accuracy.The error between optimal operation results of the IES obtained by the proposed method and all-year time series benchmark value is 0.1%,while the calculation time is reduced from 11029 s to 188 s,which verifies that the proposed method can be used to optimize operation strategy of IES with high efficiency without loss of accuracy.
