In the competitive energy market,energy retailers are facing the uncertainties of both energy price and demand,which requires them to formulate reasonable energy purchasing and selling strategies for improving their c...In the competitive energy market,energy retailers are facing the uncertainties of both energy price and demand,which requires them to formulate reasonable energy purchasing and selling strategies for improving their competitiveness in this market.Particularly,the attractive multi-energy retail packages are the key for retailers to increase their benefit.Therefore,combined with incentive means and price signals,five types of multi-energy retail packages such as peak-valley time-of-use(TOU)price package and day-night bundled price package are designed in this paper for retailers.The iterative interactions between retailers and end-users are modeled using a bi-level model of stochastic optimization based on multi-leader multi-follower(MLMF)Stackelberg game,in which retailers are leaders and end-users are followers.Retailers make decisions to maximize the profit considering the conditional value at risk(CVaR)while end-users optimize the satisfaction of both energy comfort and economy.Besides,a distributed algorithm is proposed to obtain the Nash equilibrium of above MLMF Stackelberg game model while the particle swarm optimization(PSO)algorithm and CPLEX solver are applied to solve the optimization model for each participant(retailer or end-user).Numeral results show that the designed retail packages can increase the overall profit of retailers,and the overall satisfaction of industrial users is the highest while that of residential users is the lowest after game interaction.展开更多
Advanced adiabatic compressed air energy storage(AA-CAES)has the advantages of large capacity,long service time,combined heat and power generation(CHP),and does not consume fossil fuels,making it a promising storage t...Advanced adiabatic compressed air energy storage(AA-CAES)has the advantages of large capacity,long service time,combined heat and power generation(CHP),and does not consume fossil fuels,making it a promising storage technology in a low-carbon society.An appropriate self-scheduling model can guarantee AA-CAES’s profit and attract investments.However,very few studies refer to the cogeneration ability of AA-CAES,which enables the possibility to trade in the electricity and heat markets at the same time.In this paper,we propose a multimarket self-scheduling model to make full use of heat produced in compressors.The volatile market price is modeled by a set of inexact distributions based on historical data through-divergence.Then,the self-scheduling model is cast as a robust risk constrained program by introducing Stackelberg game theory,and equivalently reformulated as a mixed-integer linear program(MILP).The numerical simulation results validate the proposed method and demonstrate that participating in multienergy markets increases overall profits.The impact of uncertainty parameters is also discussed in the sensibility analysis.展开更多
Uncertainties in wind power forecasting,dayahead and imbalance prices for the next day possess a great deal of risks for the profit of generation companies participating in a day-ahead electricity market.Generation co...Uncertainties in wind power forecasting,dayahead and imbalance prices for the next day possess a great deal of risks for the profit of generation companies participating in a day-ahead electricity market.Generation companies are exposed to imbalance penalties in the balancing market for unordered mismatches between associated day-ahead power schedule and real-time generation.Coordination of wind and thermal power plants alleviates the risks raised from wind uncertainties.This paper proposes a novel optimal coordination strategy by balancing wind power forecasting deviations with thermal units in the Turkish day-ahead electricity market.The main focus of this study is to provide an optimal trade-off between the expected profit and the risk under wind uncertainty through conditional value at risk(CVa R)methodology.Coordination problem is formulated as a two-stage mixed-integer stochastic programming problem,where scenario-based wind power approach is used to handle the stochasticity of the wind power.Dynamic programming approach is utilized to attain the commitment status of thermal units.Profitability of the coordination with different day-ahead bidding strategies and trade-off between expected profit and CVa R are examined with comparative scenario studies.展开更多
储能设备能够提高清洁能源利用率、减少系统运行成本,但传统储能由于价格昂贵限制了其本身的发展。为了充分发挥储能在综合能源系统中的优势,将需求响应、热惯性、电储和热储整合为广义储能(Generalized Energy Storage,GES)资源进行统...储能设备能够提高清洁能源利用率、减少系统运行成本,但传统储能由于价格昂贵限制了其本身的发展。为了充分发挥储能在综合能源系统中的优势,将需求响应、热惯性、电储和热储整合为广义储能(Generalized Energy Storage,GES)资源进行统一协调调度。另外,由于风电具有很强的不确定性,通过k-means方法聚类得到风电的典型出力场景。在充分考虑各种设备运行约束的基础上,采用条件风险价值(Conditional Value at Risk,CVaR)量化风电不确定性带来的收益风险,构建了计及CVaR的综合能源系统经济调度模型。最后,在Matlab环境下调用Cplex求解器验证了所提模型的有效性。展开更多
针对高比例可再生分布式能源的接入造成主动配电网运行电压越限和支路过载问题,提出了计及网络重构基于条件风险价值理论(conditional value-at-risk,CVaR)的智能软开关(soft open point,SOP)三层规划模型。首先,为了模拟分布电源出力...针对高比例可再生分布式能源的接入造成主动配电网运行电压越限和支路过载问题,提出了计及网络重构基于条件风险价值理论(conditional value-at-risk,CVaR)的智能软开关(soft open point,SOP)三层规划模型。首先,为了模拟分布电源出力不确定性,建立了基于Wasserstein距离的最优场景。其次,上层模型兼顾了综合成本最小化、安全风险最小化两个目标以确定SOP位置与容量;中层模型以每个场景运行成本最小化为目标进行网络重构;下层运行优化模型中考虑了有载调压变压器、投切电容器组、需求响应以及SOP功率传输多种主动调节措施。为了降低模型求解复杂度,采用基于灰靶决策技术的LDBAS算法和二阶锥优化的混合方法进行求解。最后,以修改的IEEE 33节点配电系统为例,对提出的规划模型进行了验证和分析。展开更多
基金supported by the National Natural Science Foundation of China(No.52077146)the Sichuan Science and Technology Program(No.2023YFSY0032).
文摘In the competitive energy market,energy retailers are facing the uncertainties of both energy price and demand,which requires them to formulate reasonable energy purchasing and selling strategies for improving their competitiveness in this market.Particularly,the attractive multi-energy retail packages are the key for retailers to increase their benefit.Therefore,combined with incentive means and price signals,five types of multi-energy retail packages such as peak-valley time-of-use(TOU)price package and day-night bundled price package are designed in this paper for retailers.The iterative interactions between retailers and end-users are modeled using a bi-level model of stochastic optimization based on multi-leader multi-follower(MLMF)Stackelberg game,in which retailers are leaders and end-users are followers.Retailers make decisions to maximize the profit considering the conditional value at risk(CVaR)while end-users optimize the satisfaction of both energy comfort and economy.Besides,a distributed algorithm is proposed to obtain the Nash equilibrium of above MLMF Stackelberg game model while the particle swarm optimization(PSO)algorithm and CPLEX solver are applied to solve the optimization model for each participant(retailer or end-user).Numeral results show that the designed retail packages can increase the overall profit of retailers,and the overall satisfaction of industrial users is the highest while that of residential users is the lowest after game interaction.
基金supported in part by National Key R&D Program of China(2020YFD1100500)National Natural Science Foundation of China(under Grant 51621065 and 51807101)in part by State Grid Anhui Electric Power Co.,Ltd.Science and Technology Project“Research on grid-connected operation and market mechanism of compressed air energy storage”under Grant 521205180021.
文摘Advanced adiabatic compressed air energy storage(AA-CAES)has the advantages of large capacity,long service time,combined heat and power generation(CHP),and does not consume fossil fuels,making it a promising storage technology in a low-carbon society.An appropriate self-scheduling model can guarantee AA-CAES’s profit and attract investments.However,very few studies refer to the cogeneration ability of AA-CAES,which enables the possibility to trade in the electricity and heat markets at the same time.In this paper,we propose a multimarket self-scheduling model to make full use of heat produced in compressors.The volatile market price is modeled by a set of inexact distributions based on historical data through-divergence.Then,the self-scheduling model is cast as a robust risk constrained program by introducing Stackelberg game theory,and equivalently reformulated as a mixed-integer linear program(MILP).The numerical simulation results validate the proposed method and demonstrate that participating in multienergy markets increases overall profits.The impact of uncertainty parameters is also discussed in the sensibility analysis.
基金the project"Intelligent system for trading on wholesale electricity market"(SMARTRADE),co-financed by the European Regional Development Fund(ERDF),through the Competitiveness Operational Programme(COP)2014-2020,priority axis 1–Research,technological development and innovation(RD&I)to support economic competitiveness and business development,Action 1.1.4–Attracting high-level personnel from abroad in order to enhance the RD capacity,contract ID P_37_418,no.62/05.09.2016,beneficiary The Bucharest University of Economic Studies.
文摘Uncertainties in wind power forecasting,dayahead and imbalance prices for the next day possess a great deal of risks for the profit of generation companies participating in a day-ahead electricity market.Generation companies are exposed to imbalance penalties in the balancing market for unordered mismatches between associated day-ahead power schedule and real-time generation.Coordination of wind and thermal power plants alleviates the risks raised from wind uncertainties.This paper proposes a novel optimal coordination strategy by balancing wind power forecasting deviations with thermal units in the Turkish day-ahead electricity market.The main focus of this study is to provide an optimal trade-off between the expected profit and the risk under wind uncertainty through conditional value at risk(CVa R)methodology.Coordination problem is formulated as a two-stage mixed-integer stochastic programming problem,where scenario-based wind power approach is used to handle the stochasticity of the wind power.Dynamic programming approach is utilized to attain the commitment status of thermal units.Profitability of the coordination with different day-ahead bidding strategies and trade-off between expected profit and CVa R are examined with comparative scenario studies.
文摘储能设备能够提高清洁能源利用率、减少系统运行成本,但传统储能由于价格昂贵限制了其本身的发展。为了充分发挥储能在综合能源系统中的优势,将需求响应、热惯性、电储和热储整合为广义储能(Generalized Energy Storage,GES)资源进行统一协调调度。另外,由于风电具有很强的不确定性,通过k-means方法聚类得到风电的典型出力场景。在充分考虑各种设备运行约束的基础上,采用条件风险价值(Conditional Value at Risk,CVaR)量化风电不确定性带来的收益风险,构建了计及CVaR的综合能源系统经济调度模型。最后,在Matlab环境下调用Cplex求解器验证了所提模型的有效性。
文摘针对高比例可再生分布式能源的接入造成主动配电网运行电压越限和支路过载问题,提出了计及网络重构基于条件风险价值理论(conditional value-at-risk,CVaR)的智能软开关(soft open point,SOP)三层规划模型。首先,为了模拟分布电源出力不确定性,建立了基于Wasserstein距离的最优场景。其次,上层模型兼顾了综合成本最小化、安全风险最小化两个目标以确定SOP位置与容量;中层模型以每个场景运行成本最小化为目标进行网络重构;下层运行优化模型中考虑了有载调压变压器、投切电容器组、需求响应以及SOP功率传输多种主动调节措施。为了降低模型求解复杂度,采用基于灰靶决策技术的LDBAS算法和二阶锥优化的混合方法进行求解。最后,以修改的IEEE 33节点配电系统为例,对提出的规划模型进行了验证和分析。
