Optimal Bidding and Operation of a Power Plant with Solvent-BasedCarbon Capture under a CO2 Allowance Market： A Solution with aReinforcement Learning-Based Sarsa Temporal-Difference Algorithm

In this paper, a reinforcement learning （RL）-based Sarsa temporal-difference （TD） algorithm is applied tosearch for a unified bidding and operation strategy for a coal-fired power plant with monoethanolamine（MEA）-based post-combustion carbon capture under different carbon dioxide （CO2） allowance market con-ditions. The objective of the decision maker for the power plant is to maximize the discounted cumulativeprofit during the power plant lifetime. Two constraints are considered for the objective formulation. Firstly,the tradeoff between the energy-intensive carbon capture and the electricity generation should be made un-der presumed fixed fuel consumption. Secondly, the CO2 allowances purchased from the CO2 allowance mar-ket should be approximately equal to the quantity of COs emission from power generation. Three case stud-ies are demonstrated thereafter. In the first case, we show the convergence of the Sarsa TD algorithm andfind a deterministic optimal bidding and operation strategy. In the second case, compared with the inde-pendently designed operation and bidding strategies discussed in most of the relevant literature, the SarsaTD-based unified bidding and operation strategy with time-varying flexible market-oriented CO2 capturelevels is demonstrated to help the power plant decision maker gain a higher discounted cumulative profit.In the third case, a competitor operating another power plant identical to the preceding plant is consideredunder the same CO2 allowance market. The competitor also has carbon capture facilities but applies a differ-ent strategy to earn profits. The discounted cumulative profits of the two power plants are then compared,thus exhibiting the competitiveness of the power plant that is using the unified bidding and operation strat-egy explored by the Sarsa TD algorithm.

Bidding Strategy in Deregulated Power Market Using Differential Evolution Algorithm

The primary objective of this research article is to introduce Differential Evolution (DE) algorithm for solving bidding strategy in deregulated power market. Suppliers (GENCOs) and consumers (DISCOs) participate in the bidding process in order to maximize the profit of suppliers and benefits of the consumers. Each supplier bids strategically by choosing the bidding coefficients to counter the competitors bidding strategy. Electricity or electric power is traded through bidding in the power exchange. GENCOs sell energy to power exchange and in turn ancillary services to Independent System Operator (ISO). In this paper, Differential Evolution algorithm is proposed for solving bidding strategy problem in operation of power system under deregulated environment. An IEEE 30 bus system with six generators and two large consumers is employed to demonstrate the proposed technique. The results show the adaptability of the proposed method compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Monte Carlo simulation in terms of Market Clearing Price (MCP).

Market Power of Coordinated Hydro-wind Joint Bidding:Croatian Power System Case Study

The paper analyses the coordinated hydro-wind power generation considering joint bidding in the electricity market.The impact of mutual bidding strategies on market prices,traded volumes,and revenues has been quantified.The coordination assumes that hydro power generation is scheduled mainly to compensate the differences between actual and planned wind power outputs.The potential of this coordination in achieving and utilizing of market power is explored.The market equilibrium of asymmetric generation companies is analyzed using a game theory approach.The assumed market situation is imperfect competition and non-cooperative game.A nu-merical approximation of the asymmetric supply function equilibrium is used to model this game.An introduced novelty is the application of an asymmetric supply function equilibrium approximation for coordinated hydro-wind power generation.The model is tested using real input data from the Croatian power system.

A Reinforcement-learning-based Bidding Strategy for Power Suppliers with Limited Information

The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors.Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy.However,this assumption may not be true in reality,particularly when a power market is newly launched.To help power suppliers bid with the limited information,a modified continuous action reinforcement learning automata algorithm is proposed.This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game.Simulation results verify the effectiveness of the proposed learning algorithm.

Optimal Price-maker Trading Strategy of Wind Power Producer Using Virtual Bidding

This paper proposes a stochastic optimization model for generating the optimal price-maker trading strategy for a wind power producer using virtual bidding,which is a kind of financial tool available in most electricity markets of the United States.In the proposed model,virtual bidding is used to improve the wind power producer’s market power in the dayahead(DA)market by trading at multiple buses,which are not limited to the locations of the wind units.The optimal joint wind power and virtual trading strategy is generated by solving a bi-level nonlinear stochastic optimization model.The upperlevel problem maximizes the total expected profit of the wind power and virtual bidding while using the conditional value at risk(CVa R)for risk management.The lower-level problem represents the clearing process of the DA market.By using the Karush-Kuhn-Tucker(KKT)conditions,duality theory,and big-M method,the bi-level nonlinear stochastic model is firstly transferred into an equivalent single-level stochastic mathematical program with the equilibrium constraints(MPEC)model and then a mixed-integer linear programming(MILP)model,which can be solved by existing commercial solvers.To reduce the computational cost of solving the proposed stochastic optimization model for large systems,a method of reducing the number of buses considered for virtual bidding is proposed to simplify the stochastic MPEC model by reducing its decision variables and constraints related to virtual bidding.Case studies are performed to show the effectiveness of the proposed model and the method of reducing the number of buses considered for virtual bidding.The impacts of the transmission limits,wind unit location,risk aversion parameters,wind power volatility,and wind and virtual capacities on the price-maker trading strategy are also studied through case studies.

Hydro-wind Optimal Operation for Joint Bidding in Day-ahead Market: Storage Efficiency and Impact of Wind Forecasting Uncertainty

Wind power production is uncertain.The imbalance between committed and delivered energy in pool markets leads to the increase of system costs,which must be incurred by defaulting producers,thereby decreasing their revenues.To avoid this situation,wind producers can submit their bids together with hydro resources.Then the mismatches between the predicted and supplied wind power can be used by hydro producers,turbining or pumping such differences when convenient.This study formulates the problem of hydro-wind production optimization in operation contexts of pool market.The problem is solved for a simple three-reservoir cascade case to discuss optimization results.The results show a depreciation in optimal revenues from hydro power when wind forecasting is uncertain.The depreciation is caused by an asymmetry in optimal revenues from positive and negative wind power mismatches.The problem of neutralizing the effect of forecasting uncertainty is subsequently formulated and solved for the three-reservoir case.The results are discussed to conclude the impacts of uncertainty on joint bidding in pool market contexts.

风氢耦合系统参与现货市场的竞标策略及优化调度方法

为解决高比例风电市场竞标能力弱、市场消纳困难的问题,提出高比例风电耦合氢储能系统联合参与现货市场竞价的双层Stackelberg博弈模型。首先,研究风氢耦合系统内部能量转换关系,揭示风氢耦合系统的能量“时空平移”机理,在综合考虑风氢耦合系统收益和成本的基础上,建立风电投标和氢储能充放电决策模型;其次,兼顾风氢耦合系统和独立系统运营商的利益诉求,基于Stackelberg博弈建立以火电机组、风氢耦合系统为主体的市场出清模型;最后,针对双层模型求解困难的问题,运用KKT条件和强对偶理论将双层模型转换为易于求解的单层混合整数规划模型。仿真结果表明,所提策略对高比例风电的市场消纳起到了积极作用。

考虑风电出力和电价不确定性的水风联合现货市场竞价策略

以风电为代表的新能源参与现货市场已是大势所趋,但出力不确定性导致其市场竞争力较弱,而电价的不确定性又进一步增大了其承担的收益风险。联合具有灵活调节能力的水电是有效的解决途径,而制定符合决策者风险意愿的竞价策略则是其中亟须解决的关键问题。为此,从价格接受者角度提出了水电联合风电参与现货市场的竞价模型。模型中采用笛卡尔积组合后的典型场景集描述风电出力、市场电价等多种不确定性,采用条件风险价值衡量不确定性导致的市场风险,并以风险因子作为决策者风险偏好纳入决策过程。通过引入0/1整数变量,将模型中的非线性函数转换为混合整数线性规划模型予以求解。以中国西南某省梯级水电站和风电场为背景,验证了模型的有效性,并进一步分析了风险偏好对竞价决策的影响。

考虑不确定性的火电发电商现货-深度调峰市场优化决策

针对电力现货市场与深度调峰市场联合运行背景下发电商电能量利润空间压缩、深度调峰功能亟待拓展等问题,提出考虑不确定性的发电商现货-深度调峰市场优化策略。基于Stackelberg博弈,构建兼顾联合市场利润和风险的发电商日前报价策略双层优化模型。在上层模型中,以发电商联合市场利润最大化为目标,对发电商分段报价进行优化;在下层模型中,考虑竞争对手报价场景的不确定性,提出电力现货-深度调峰市场联合出清模型。针对净负荷不确定性,引入波动不确定度参数,将双层模型转换为基于信息间隙决策理论的发电商现货-深度调峰市场决策模型。基于安徽省实际市场数据进行仿真验证,得到适应不同负荷场景的发电商联合市场日前报价策略,结果表明考虑不确定性的联合优化决策能够有效规避负荷不确定性带来的利润波动风险。

基于边际成本比较型竞价模式的电力现货市场机制设计

在建设新型电力系统、极端天气频发、一次能源价格飙涨、保供电常态化等背景下,文中提出了基于边际成本比较型竞价模式的电力现货市场机制,为统筹多市场主体利益、协调省间与省内交易提供了可行思路。首先,深度解析了现有电力现货市场机制在统筹多市场主体利益上面临的挑战,在采用边际成本比较型竞价模式的基础上,深入考虑不同类型发电机组的成本特性,提出了一种两阶段分层边际定价的电力现货市场机制,可有效统筹发电侧成本的可疏导性、用电侧电费的可负担性以及新能源机组的激励性。然后,提出了基于边际成本比较型竞价模式的省间-省内两级现货市场机制,并对其应用价值进行了探讨。最后,基于电力现货市场仿真平台开展算例分析,对所提市场机制的有效性和可行性进行了验证。

电力市场中基于报价策略画像的发电成本估计

针对发电商申报的成本信息可能失真的问题,提出一种基于报价策略画像的发电成本估计方法。首先采用画像技术,基于历史报价,对发电商报价策略偏好进行分析,找出按“成本加成”策略报价的发电商集;随后提出一种“分层拟合法”,实施各单机容量机组发电成本函数及其合理波动范围分析;最后对我国北方某省的案例进行分析。结果表明:所提方法能获得准确度可信的发电成本波动范围,为市场电力监测提供依据。

考虑电价不确定性的发电企业电能量-辅助服务市场报价策略

为适应国内电力市场发展,灵活应对电价不确定性,文中以国内电力市场建设现状为基础,重点围绕发电企业在日前市场下的交易展开研究。综合考虑由于日前出清电价存在不确定性而造成的影响以及碳排放权交易成本,构建了在参与日前电能量市场与辅助服务市场时发电企业的联合报价模型。日前电价的不确定性通过获取典型电价场景并基于Kantorovich距离的向前选择方法进行场景削减。基于条件风险价值(conditional value at risk,CVaR)将发电企业选择竞价策略时的风险偏好进行量化并建立模型,再对所提模型进行计算求解得到相应的报价策略。算例分析结果表明,所提模型可以为发电企业提供有效的报价策略,灵活应对电价波动。相较于不参加二次调频辅助服务市场时发电企业收益得到提升,且企业可根据自己的风险偏好更改模型中的风险因子,以得到理想报价策略。

含风电的综合能源供应商参与投标竞争的多能源市场博弈分析

综合能源系统的多能互补特性有助于间歇性可再生能源的消纳。为了体现综合能源系统的市场价值并鼓励其投资和发展,综合能源供应商参与多能源市场竞争将是一种趋势。该文研究拥有风电机组的综合能源供应商同时参与电力批发市场与天然气市场的竞争问题,考虑电转气设备和燃气轮机等电气转换装置,并在天然气价格普遍比电力价格低的背景下引入实时天然气市场弥补综合能源供应商投标偏差,建立一个包含风电及电气转换装置的综合能源供应商参与投标竞争的多能源市场联合均衡模型,并采用非线性互补方法求解该均衡模型。算例分析验证了模型的合理性和有效性,并表明综合能源供应商增加风电比例有助于降低日前电价、实时气价和日前气价,并会增加综合能源供应商的利润和社会福利。

考虑需求响应的虚拟电厂两阶段市场竞标模型

虚拟电厂为大规模新能源电力涌入电网提供框架和技术支撑的同时,也为其参与电力市场提供了保障,因此为虚拟电厂建立经济可靠的竞标模型是当前研究的热点。在双结算模式与偏差考核的背景之下,提出了考虑激励型需求响应参与的虚拟电厂竞标模型。考虑在日前与实时市场、当前实时与后续实时市场的双重耦合下,得到虚拟电厂的日前与实时竞标策略;并提出“双因素激励因子”的概念,调动实时需求响应的积极性,减小虚拟电厂的偏差惩罚的同时也可以利用价差获利。最后,以38节点的RBTS配电系统为例进行仿真,仿真结果表明该策略在充分挖掘用户需求响应潜力的同时,提高了虚拟电厂的经济效益,为虚拟电厂参与电力现货市场提供了模型参考。

基于非凸报价的高比例新能源现货市场机制

构建高比例新能源的新型电力系统指明了双碳背景下我国电力转型发展的方向。然而,由于新能源成本和出力特性与传统电源有显著差别,基于火电为主体的电力系统设计的现货市场机制不再适应新型电力系统的发展要求。为此,提出了新能源报价新机制,新能源每个时段申报一条先降后升的U型功率量价曲线,能够充分体现新能源边际成本为0、平均成本递减的成本特性和波动的出力特性,并相应提出了适应新型电力系统的现货市场和火电容量市场耦合机制。然而,这一报价模式将使得市场出清模型变为非凸函数,为此,提出了基于非凸报价的现货市场出清模型,引入一系列0-1变量,并通过不同报价段之间的耦合关系,将其转化为一个可求解的混合整数规划模型。算例验证了所提机制和模型的有效性。

计及多种需求响应资源的虚拟电厂运行机制及控制策略优化

需求响应是虚拟电厂应对电力市场交易中由不确定性因素带来交易风险的重要资源。本文提出一种计及多种需求响应资源的虚拟电厂运行机制以及控制策略。首先,通过v型支持向量回归机对虚拟电厂可再生能源出力、负荷出力、电力市场价格等不确定量进行日前预测,并利用自回归移动平均对预测误差进行典型场景生成。其次,考虑来自虚拟电厂内部直接可控负荷以及外部需求响应交换市场所购得的需求响应资源,基于多阶段随机规划优化框架,以预期利润最大化以及可再生能源未消纳率最小化为目标,构建了不考虑风险控制和考虑风险控制两种条件下的虚拟电厂竞价策略优化模型。最后,通过算例分析不确定性和风险偏好对虚拟电厂的预期收益及可再生能源消纳率的影响,验证了所提运行策略的有效性。

虚拟电厂参与电-碳联合市场运行的竞价策略研究

电力系统脱碳是全社会零碳发展的关键。近年来分布式小微主体发展迅速,成为未来我国能源结构的重要组成部分。首先,从虚拟电厂(virtual power plant,VPP)出发,在分析VPP参与我国现行碳交易市场方式的基础上,提出VPP参与电-碳联合市场的运行机制。其次,构建了联合市场运行机制下多主体互动博弈的两阶段双层竞价策略模型。第一阶段为VPP内部分布式小微主体预调度模型;第二阶段内层为VPP与多市场主体间非合作博弈竞价模型,以VPP参与日前现货市场和碳交易市场的总收益最大为目标,采用多场景描述竞争对手报价的不确定性;外层是以全社会福利最大化为目标函数的多主体互动决策出清模型。最后,采用Q-learning算法和路径跟踪内点法进行模型求解,算例验证了所提模型的可行性和有效性。

基于密度峰值聚类的水电现货报价单元组建方法

随着电力现货市场的不断推进与完善,发电侧主体多元化使得发电主体的竞价环境更加激烈,报价单元的组建有利于提高发电企业抵御市场竞价风险的能力,分析了不同的流域水文情势、多元的开发主体以及电站的规模属性等因素对发电企业竞价的影响,以流域、发电企业以及上网点3个主要因素为指标,提出基于密度峰值聚类的报价单元组建方法,并将该方法运用于西南地区主要流域的67个电站,结果表明,报价单元的组建对提高水电竞价能力具有实际意义,单元方案与电网规定的统调单元较为一致,验证了该模型合理可行,可为发电企业参与电力现货市场竞价提供一种新思路。

基于多智能体强化学习的发电商竞价策略模型

随着越来越多的新能源发电商加入电力现货市场的竞价行列,各发电商都面临着如何调整报价策略来使自身利益最大化的问题。为了解决各发电商的报价策略问题,提出了基于多智能体强化学习的MARL-SCCP模型来模拟各发电商的竞价行为以学习报价策略。上述模型首先将每一个发电商建模为一个智能体。其次在建模过程中使用随机机会约束规划来解决风力发电商的不确定性。最后,将神经网络引入WoLF-PHC算法来更好应对智能体较大的状态空间并大幅提高求解速度。实验表明,使用多智能体强化学习来模拟各发电商的竞价过程是可行的,并且能够在较少的迭代次数后学习到较优的策略。在此策略下,各发电商均能实现利益最大化,且新能源发电商能够减少由外界不确定性带来的影响。

考虑储能共享的风光储集群联合优化运行及竞标策略

在电力市场和低碳经济背景下,为增强新能源电站间的时空互补能力,提高自配储能利用率,建立了考虑储能共享的风光储集群联合优化运行及竞标策略模型。首先,基于新能源电站的互补特性,设计了考虑储能共享的风光储集群联合运行机制,并分析了风光储协同运行机理;其次,考虑以市场手段促进新能源消纳,建立风光电站参与日前能量市场,自配储能及共享储能为其提供平衡波动备用容量的市场收益模型;在此基础上,考虑电价及风光出力的不确定性,提出风光储集群联合竞标策略,以实现风光储集群利益最大化;最后,利用改进的粒子群优化算法求解联合竞标及协同运行模型。仿真结果表明,所提策略能够有效提高自配储能利用率,增加风光储集群收益,对新能源的市场消纳起到积极作用。