The main objective of this paper is to show an overview analysis of market power issues.Market power reflects the scarcity of power supply.It is the ability of a particular seller or group of sellers to maintain price...The main objective of this paper is to show an overview analysis of market power issues.Market power reflects the scarcity of power supply.It is the ability of a particular seller or group of sellers to maintain prices profitably above competitive levels for a significant period of time.Because the electric power system has its own characteristics that are different to other economic systems,both physical factors and economic factors of power system are key elements on this definition.We study some cases here,including different line limit levels,load levels and bid strategy through a market model based on OPF (optimal power flow) with a decommitment algorithm.展开更多
Electricity markets need to continuously evolve to address the growing complexity of a predominantly renewable energy-driven,highly interconnected,and sector-integrated energy system.Simulation models allow testing ma...Electricity markets need to continuously evolve to address the growing complexity of a predominantly renewable energy-driven,highly interconnected,and sector-integrated energy system.Simulation models allow testing market designs before implementation,which offers advantages for market robustness and efficiency.This work presents a novel approach to simulate the electricity market by using multi-agent deep reinforcement learning for representing revenue-maximizing market participants.The learning capability makes the agents highly adaptive,thereby facilitating a rigorous performance evaluation of market mechanisms under challenging yet practical conditions.Through distinct test cases that vary the number and size of learning agents in an energy-only market,we demonstrate the ability of the proposed method to diagnose market manipulation and reflect market liquidity.Our method is highly scalable,as demonstrated by a case study of the German wholesale energy market with 145 learning agents.This makes the model well-suited for analyzing large and complex electricity markets.The capability of the presented simulation approach facilitates market design analysis,thereby contributing to the establishment future-proof electricity markets to support the energy transition.展开更多
This paper deals with an extension of the one-period model in non-life insurance markets (cf. [1]) by using a transition probability matrix depending on some economic factors. We introduce a multi-period model and in ...This paper deals with an extension of the one-period model in non-life insurance markets (cf. [1]) by using a transition probability matrix depending on some economic factors. We introduce a multi-period model and in each period the solvency constraints will be updated. Moreover, the model has the inactive state including some uninsured population. Similar results on the existence of premium equilibrium and sensitivity analysis for this model are presented and illustrated by numerical results.展开更多
In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper intr...In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.展开更多
传统电动汽车充电负荷建模通常采用对电动汽车个体进行抽样模拟的方式,未能从分析机理的角度描述电动汽车群体相互作用形成的宏观运行状态。为此,提出一种基于半动态交通均衡模型和组合荷电状态(combined states of the charge,CSOC)概...传统电动汽车充电负荷建模通常采用对电动汽车个体进行抽样模拟的方式,未能从分析机理的角度描述电动汽车群体相互作用形成的宏观运行状态。为此,提出一种基于半动态交通均衡模型和组合荷电状态(combined states of the charge,CSOC)概率计算的电动汽车充电负荷概率分布计算方法。首先,分析电动汽车的交通特性和充电特性,并提出一种可行路径集构建方法;然后,引入交通均衡理论进行电动汽车空间分布建模,建立考虑随机效用的半动态交通均衡模型,实现宏观交通流均衡分配。进一步地,从理论层面分析电动汽车群的荷电状态变化,建立基于CSOC的充电负荷概率分布计算模型。最后,分别在13节点路网和实际大路网中验证所提方法的有效性,并分析了电动汽车渗透率和路网结构对充电负荷概率分布的影响。展开更多
基金This paper supported by National Natural Science Foundation of China (50079006).
文摘The main objective of this paper is to show an overview analysis of market power issues.Market power reflects the scarcity of power supply.It is the ability of a particular seller or group of sellers to maintain prices profitably above competitive levels for a significant period of time.Because the electric power system has its own characteristics that are different to other economic systems,both physical factors and economic factors of power system are key elements on this definition.We study some cases here,including different line limit levels,load levels and bid strategy through a market model based on OPF (optimal power flow) with a decommitment algorithm.
文摘Electricity markets need to continuously evolve to address the growing complexity of a predominantly renewable energy-driven,highly interconnected,and sector-integrated energy system.Simulation models allow testing market designs before implementation,which offers advantages for market robustness and efficiency.This work presents a novel approach to simulate the electricity market by using multi-agent deep reinforcement learning for representing revenue-maximizing market participants.The learning capability makes the agents highly adaptive,thereby facilitating a rigorous performance evaluation of market mechanisms under challenging yet practical conditions.Through distinct test cases that vary the number and size of learning agents in an energy-only market,we demonstrate the ability of the proposed method to diagnose market manipulation and reflect market liquidity.Our method is highly scalable,as demonstrated by a case study of the German wholesale energy market with 145 learning agents.This makes the model well-suited for analyzing large and complex electricity markets.The capability of the presented simulation approach facilitates market design analysis,thereby contributing to the establishment future-proof electricity markets to support the energy transition.
文摘This paper deals with an extension of the one-period model in non-life insurance markets (cf. [1]) by using a transition probability matrix depending on some economic factors. We introduce a multi-period model and in each period the solvency constraints will be updated. Moreover, the model has the inactive state including some uninsured population. Similar results on the existence of premium equilibrium and sensitivity analysis for this model are presented and illustrated by numerical results.
文摘In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.
文摘传统电动汽车充电负荷建模通常采用对电动汽车个体进行抽样模拟的方式,未能从分析机理的角度描述电动汽车群体相互作用形成的宏观运行状态。为此,提出一种基于半动态交通均衡模型和组合荷电状态(combined states of the charge,CSOC)概率计算的电动汽车充电负荷概率分布计算方法。首先,分析电动汽车的交通特性和充电特性,并提出一种可行路径集构建方法;然后,引入交通均衡理论进行电动汽车空间分布建模,建立考虑随机效用的半动态交通均衡模型,实现宏观交通流均衡分配。进一步地,从理论层面分析电动汽车群的荷电状态变化,建立基于CSOC的充电负荷概率分布计算模型。最后,分别在13节点路网和实际大路网中验证所提方法的有效性,并分析了电动汽车渗透率和路网结构对充电负荷概率分布的影响。
