Risk-constrained self-scheduling of a fuel and emission constrained power producer using rolling window procedure

This work addresses a relevant methodology for self-scheduling of a price-taker fuel and emission constrained power producer in day-ahead correlated energy, spinning reserve and fuel markets to achieve a trade-off between the expected profit and the risk versus different risk levels based on Markowitz’s seminal work in the area of portfolio selection. Here, a set of uncertainties including price forecasting errors and available fuel uncertainty are considered. The latter uncertainty arises because of uncertainties in being called for reserve deployment in the spinning reserve market and availability of power plant. To tackle the price forecasting errors, variances of energy, spinning reserve and fuel prices along with their covariances which are due to markets correlation are taken into account using relevant historical data. In order to tackle available fuel uncertainty, a framework for self-scheduling referred to as rolling window is proposed. This risk-constrained self-scheduling framework is therefore formulated and solved as a mixed-integer non-linear programming problem. Furthermore, numerical results for a case study are discussed.     

Stochastic multiobjective self-scheduling of a power producer in joint energy and reserves markets

This paper presents a stochastic multiobjective model for self-scheduling of a power producer which participates in the day-ahead joint energy and reserves markets. The objective of a power producer is to compromise the conflicting objectives of payoff maximization and gaseous emissions minimization when committing its generation of thermal units. The proposed schedule will be used by the power producers to decide on emission quota arbitrage opportunities and for strategic bidding to the energy and reserves market. The paper analyzes a scenario-based multiobjective model in which random distributions, such as price forecasting inaccuracies as well as forced outage of generating units are modeled as scenarios tree using a combined fuzzy c-mean/Monte-Carlo simulation (FCM/MCS) method. With the above procedure the stochastic multiobjective self-scheduling problem is converted into corresponding deterministic problems. Then a multiobjective mathematical programming (MMP) approach based on ?-constraint method is implemented for each deterministic scenario. Piecewise linearized fuel and emission cost functions are applied for computational efficiency and the model is formulated as a mixed-integer programming (MIP) problem. Numerical simulations for a power producer with 21 thermal units are discussed to demonstrate the performance of the proposed approach in increasing expected payoffs by adjusting the emission quota arbitrage opportunities.     

A hybrid approach based on IGDT–MPSO method for optimal bidding strategy of price-taker generation station in day-ahead electricity market

This paper considers a price-taker generation station producer that participates in a day-ahead market. The producer behaves as a price-taker participant in the day-ahead electricity market. In electricity market, the price-taker producer could develop bidding strategies to maximize own profits. While making optimal bidding strategy, the market price uncertainty needs to be considered as they have direct impact on the expected profit and bidding curves. In this paper, a hybrid approach based on information gap decision theory (IGDT) and modified particle swarm optimization (MPSO) is used to develop the optimal bidding strategy. Information gap decision theory is used to model the optimal bidding strategy problem. It assesses the robustness/opportunity of optimal bidding strategy in the face of the market price uncertainty while price-taker producer considers whether a decision risk-averse or risk-taking. The optimization problems to delivering IGDT approach are solved using MPSO. It is shown that risk-averse or risk-taking decisions might affect the expected profit and bidding curve to day-ahead electricity market. The IGDT–MPSO method is illustrated through a case study and compared to IGDT–MINLP method.     

Hydrothermal self-scheduling problem in a day-ahead electricity market

This paper addresses the self-scheduling problem of determining the unit commitment status for power generation companies before submitting the hourly bids in a day-ahead market. The hydrothermal model is formulated as a deterministic optimization problem where expected profit is maximized using the 0/1 mixed-integer linear programming technique. This approach allows precise modelling of non-convex variable cost functions and non-linear start-up cost functions of thermal units, non-concave power-discharge characteristics of hydro units, ramp rate limits of thermal units and minimum up and down time constraints for both hydro and thermal units. Model incorporates long-term bilateral contracts with contracted power and price patterns, as well as forecasted market hourly prices for day-ahead auction. Solution is achieved using the homogeneous interior point method for linear programming as state of the art technique, with a branch and bound optimizer for integer programming. The effectiveness of the proposed model in optimizing the generation schedule is demonstrated through the case studies and their analysis.     

Unit commitment problem in deregulated environment

This paper addresses the self-scheduling problem of generation companies owning thermal power units considering bilateral contracts and day-ahead market. This approach allows precise modelling of variable costs, start-up costs and comprehensive system of constraints. The self-scheduling model is formulated as deterministic optimization problem in which expected profit is maximized by 0/1 mixed-integer linear programming technique. Solution is achieved using the homogeneous and self-dual interior point method for linear programming, with a branch and bound optimizer for integer programming. The effectiveness of the proposed model for optimizing the thermal generation schedule is demonstrated through the case study with detailed discussion.     

联网分布式发电系统规划运行研究

分布式发电（DG）与输电网联合供电成为电力系统发展趋势。输电系统与DG联合供电的规划、运行分析对满足可靠性条件下的经济供电尤为重要。文中针对远离发电中心、与输电系统弱连接的配电系统,为了满足负荷增长要求,以DG投资费用和向输电网购电费用最小为优化目标,分别对输电网正常运行且价格固定、考虑停运状态和实时电价3种典型输电网情况下联网DG系统进行规划。通过应用启发式算法,得到了各种情况下系统最优供电方案。根据不同情况下系统运行性能分析,定量讨论了输电网停运、实时电价以及可再生能源随机发电对分布式电源类型、容量等规划运行结果的影响,优化结果体现了联网DG系统联合供电优势。     

Price-taker bidding strategy under price uncertainty

This paper provides a framework to obtain the optimal bidding strategy of a price-taker producer. An appropriate forecasting tool is used to estimate the probability density functions of next-day hourly market-clearing prices. This probabilistic information is used to formulate a self-scheduling profit maximization problem that is solved taking advantage of its particular structure. The solution of this problem allows deriving a simple yet informed bidding rule. Results from a realistic case study are discussed in detail.     

An efficient stochastic self-scheduling technique for power producers in the deregulated power market

Generation scheduling and dispatch are determined by individual power producers’ bids in a deregulated power market. The benefits obtained by a power producer will depend largely on how effectively it can incorporate the variation of the market price in its generation scheduling. A stochastic scheduling technique is presented in this paper for maximizing a producer’s benefit considering the stochastic nature of power price. Formulation of the problem has been presented in detail. Two approaches to the solution, namely two-stage and multi-stage stochastic methods, are presented which accommodate the features of the day-ahead and hour-ahead power markets, respectively.Implementation of the technique is illustrated by applying the technique to scheduling by a power producer with 11 generators in two different seasons. The price data from a real system has been used for the price forecasting and the formation of price scenario tree. It is shown through illustrative case studies that the proposed stochastic methods can enhance benefits if proper values are assigned for the probabilities of the price scenarios and if the selection of the probabilities of price scenarios are directly related to the variation of the actual power prices. The proposed technique is particularly effective when the uncertainty in the price is high and the price forecast is not very accurate.     

发输电一体化检修计划安全评估与辅助决策方法

检修计划编制过程中需要对设定检修方式下的系统运行状态进行安全评估,而目前检修计划编制缺乏必要的安全分析手段,过度依赖经验,难以适应大电网运行控制需要。提出基于母线负荷供应充裕度和等效发电容量损失指标的检修计划评估与安全辅助方法。利用网络拓扑灵敏度分析技术计算满足静态安全约束的母线负荷供应充裕度和等效发电容量损失,并根据该指标评估检修计划的安全性。该方法已在某区域电网检修计划优化管理系统中得到应用,结果表明该方法能够为计划编制人员提供指导性意见。     

电力市场中考虑机组启停约束的购电策略

在电力市场环境下,电网以最小化购电费用为目标,而发电公司以最大化售电收益为目标,如何寻找二者之间的市场成交点是一项复杂而重要的工作。制定发电计划时,机组的启停是必须考虑的问题。基于此,文中对电力市场中考虑机组启停约束的购电策略进行了研究,并建立了相应的数学模型,提出了机组报价对电网总购电费用灵敏度的概念,以及考虑机组启停约束的购电算法,得出的结论对制定发电计划有一定的指导意义。     

电力市场条件下的发电资产评估

研究电力市场条件下电源投资评估问题。建立电源投资评估模型,提出电价的随机微分模型。模型中考虑电价的多重周期性、价格的随机漂移、发电机的强迫停运及机组的服役期等特性。依据随机微分方程理论,证明提出的价格模型满足该文描述电价的物理特征。采用期望效用法,推导电力市场条件下电源投资评估的解析公式并进行灵敏度分析。算例仿真验证结论的正确性。     

Price maker self-scheduling in a pool-based electricity market: a mixed-integer LP approach

This paper addresses the self-scheduling problem faced by a price-maker to achieve maximum profit in a pool-based electricity market. An exact and computationally efficient mixed-integer linear programming (MILP) formulation of this problem is presented. This formulation models precisely the price-maker capability of altering market-clearing prices to its own benefits, through price quota curves. No assumptions are made on the characteristics of the pool and its agents. A realistic case study is presented and the results obtained are analyzed in detail.     

GENCO's Risk-Based Maintenance Outage Scheduling

This paper presents a stochastic model for the optimal risk-based generation maintenance outage scheduling based on hourly price-based unit commitment in a generation company (GENCO). Such maintenance outage schedules will be submitted by GENCOs to the ISO for approval before implementation. The objective of a GENCO is to consider financial risks when scheduling its midterm maintenance outages. The GENCO also coordinates its proposed outage scheduling with short-term unit commitment for maximizing payoffs. The proposed model is a stochastic mixed integer linear program in which random hourly prices of energy, ancillary services, and fuel are modeled as scenarios in the Monte Carlo method. Financial risks associated with price uncertainty are considered by applying expected downside risks which are incorporated explicitly as constraints. This paper shows that GENCOs could decrease financial risks by adjusting expected payoffs. Illustrative examples show the calculation of GENCO's midterm generation maintenance schedule, risk level, hourly unit commitment, and hourly dispatch for bidding into energy and ancillary services markets.     

基于运行优化的含储能电力系统可靠性评估方法

针对传统可靠性评估方法容易导致停电损失的问题,提出基于运行优化的含储能的电力系统可靠性评估方法。首先,根据分布式电源出力建立概率模型,并在峰谷电价机制下,建立受利益驱动影响的储能概率模型;然后,根据故障类型细化故障隔离范围,对非故障隔离区域进行孤岛划分和功率匹配,减少停电损失;最后,提出了基于序贯蒙特卡罗的可靠性评估方法,通过仿真验证了该方法的有效性。     

Reconciling social welfare, agent profits, and consumer payments in electricity pools

Under a common condition called profit suboptimality, market equilibrium cannot be reached in electricity pools; in other words, no system marginal price exists for which the profit-driven independent generators would self-schedule to levels that exactly meet the demand. On the other hand, although a centrally imposed generation schedule satisfies power balance, it may force some agents to operate at a profit below what they could achieve under self-scheduling. This paper examines these incompatible goals and proposes a conflict resolution scheme based on the notion of generalized uplift functions. These functions are defined such that they: (i) change the offered generation cost characteristics so as to increase the system marginal price, thus forcing the consumers to compensate the generators for part of their combined loss of profit; (ii) execute an equitable transfer of revenues among the generators so that these also participate in any loss of profit compensation; (iii) ensure market equilibrium at the centralized minimum cost solution; and (iv) ensure that the net sum of the uplifts adds up to zero.     

基于集对分析理论的发电投资决策模型

在市场环境下,发电投资面临的诸多不确定因素将导致发电投资收益的不确定性,这需要发电投资决策评价方法来辅助发电商进行投资决策。为此首先分析了集对分析(set pair analysis,SPA)理论和发电投资的成本–效益问题;然后采用蒙特卡罗法对上网电价、煤炭价格和上网电量等不确定性因素进行模拟,得出发电投资的不确定性损失序列,并利用SPA对损失序列进行同、异、反特性描述,给出求解联系度系数的方法;在此基础上构建了基于SPA的发电投资决策模型,探讨了不确定数i的确定方法;最后通过算例证明了所构建模型的可行性,为发电商提供投资决策依据。     

基于运行优化的含储能电力系统可靠性评估方法

针对传统可靠性评估方法容易导致停电损失的问题,提出基于运行优化的含储能的电力系统可靠性评估方法。首先,根据分布式电源出力建立概率模型,并在峰谷电价机制下,建立受利益驱动影响的储能概率模型;然后,根据故障类型细化故障隔离范围,对非故障隔离区域进行孤岛划分和功率匹配,减少停电损失;最后,提出了基于序贯蒙特卡罗的可靠性评估方法,通过仿真验证了该方法的有效性。     

考虑综合需求响应混合不确定性的综合能源系统优化调度策略

综合需求响应(integrated demand response,IDR)是挖掘综合能源系统(integrated energy system,IES)负荷侧调节潜力的主要手段。然而,IDR用户的用能行为具有随机性、模糊性相耦合的混合不确定性特征,由此产生的IDR不确定性不仅会给调度策略的制定带来挑战,而且会对系统的可靠性产生影响,甚至可能引起负荷断供。针对上述问题,首先提出了基于改进PMV-PPD(predicted mean vote-predicted percentage of dissatisfied)指标的用户参与意愿评估模型;其次,基于云模型理论,建立了考虑混合不确定性的价格型IDR模型;在此基础上,综合考虑各类能源响应量边界和价格弹性系数等因素的不确定性,提出了IES优化调度策略。算例结果表明所提的IES优化调度策略可以更好地应对IDR不确定性带来的负荷波动,有效提升系统可靠性。     

NSGA-II algorithm for multi-objective generation expansion planning problem

This paper presents an application of Elitist Non-dominated Sorting Genetic Algorithm version II (NSGA-II), to multi-objective generation expansion planning (GEP) problem. The GEP problem is considered as a two-objective problem. The first objective is the minimization of investment cost and the second objective is the minimization of outage cost (or maximization of reliability). To improve the performance of NSGA-II, two modifications are proposed. One modification is incorporation of Virtual Mapping Procedure (VMP), and the other is introduction of controlled elitism in NSGA-II. A synthetic test system having 5 types of candidate units is considered here for GEP for a 6-year planning horizon. The effectiveness of the proposed modifications is illustrated in detail.     

计及风险的发电厂商机组检修规划模型

根据发电厂商的风险偏好,提出了基于效用函数的机组检修规划模型,用以规避市场电价预测不确定性及机组突然故障带来的风险。首先,分析了影响发电厂商经济效益的相关不确定性因素,采用MonteCado法及SBR（Simuhaneous Backward Reduction）降阶技术模拟可能存在的场景以评估其影响水平。然后,基于日前电能市场交易模式及发电厂商面临的风险．分析了规划期内的经济效益,主要包括各时段的售电利润、更新费用及检修费用,并根据发电厂商的自身风险偏好,构建了基于期望一风险的检修效用函数,在其经济效益期望与风险之间取得平衡。基于此,以规划期内发电厂商的效用函数最大为目标,考虑相关约束后,利用遗传算法和线性规划方法确定各机组检修时段,并用简单算例进行了定量验证。与常规模型相比,本文充分考虑了不确定性因素及发电厂商的风险偏好对检修规划、经济效益及机组出力的影响,规避了相关风险损失。