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.     

Optimal self-scheduling of a dominant power company in electricity markets

This paper addresses the problem of the self-scheduling of a power company with a dominant role in both the production and retail sectors of an electricity market. An integrated 0/1 mixed integer linear programming (MILP) formulation is provided, which combines both thermal and hydro subsystems in a single portfolio for a dominant power company through a detailed modeling of the operating constraints of thermal units and hydroplants. Residual demand curves for energy and reserves are used to model the effect of the power company’s interactions with its competitors. Test results on a medium-scale real test system address the effect that the power company’s forward commitments and the market rules have on its daily self-scheduling and profits as well as on the resulting energy and reserve market clearing prices.     

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.     

Stochastic Multiobjective Market Clearing of Joint Energy and Reserves Auctions Ensuring Power System Security

In this paper, a stochastic multiobjective framework is proposed for day-ahead joint market clearing. The proposed multiobjective framework can concurrently optimize competing objective functions including augmented generation offer cost and security indices (overload index, voltage drop index, and voltage stability margin). Besides, system uncertainties including generating units and branches contingencies and load uncertainty are explicitly considered in the stochastic market clearing scheme. The solution methodology consists of two stages, which firstly, employs roulette wheel mechanism and Monte Carlo simulation (MCS) for random adaptive 24-h scenario generation wherein the stochastic multiobjective market clearing procedure is converted into its respective deterministic equivalents (scenarios). In the second stage, for each deterministic scenario, a multiobjective mathematical programming (MMP) formulation based on the epsiv -constrained approach is implemented for provision of spinning reserve (SR) and nonspinning reserve (NSR) as well as energy. The MMP formulation of the market clearing process is optimized while meeting AC power flow constraints and expected interruption cost (EIC). The IEEE 24-bus Reliability Test System (RTS 24-bus) is used to demonstrate the performance of the proposed method.     

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.     

On the self-scheduling of a power producer in uncertain trading environments

This work presents a scenario-based approach to the self-scheduling problem of a price taker power producer in a DA market. It concentrates on three categories of uncertainty including price, forced outage and generation reallocation and analyses their effects on the producer revenue. To tackle the uncertainties a set of price scenarios are so generated that their means and covariance matrix are the same as the base-case scenario. Forced outage and generation reallocation of generator for each price scenario are appropriately modeled through a probabilistic methodology. In this work Downside Risk (DR) is employed as the risk measure which quantifies the downside violations from a specified target. A risk-constrained self-scheduling problem is therefore formulated and solved as a mixed integer linear programming problem. Numerical results for a case study are discussed.     

碳市场背景下发电商竞价策略及电力市场均衡分析

碳市场交易提高了传统化石能源机组的发电成本,进而影响其在电力市场的竞价行为,电力市场均衡也随之发生改变。文章针对碳市场背景下的电力市场均衡问题建立了双层均衡模型,上层模型为考虑碳成本的发电商最优竞价模型,下层模型为考虑碳配额总量约束的电力市场出清模型,通过最优性条件理论以及二进制展开法、大M法等线性化手段将双层模型转化为混合整数线性规划,基于PJM 5节点系统,求解了多市场环境下有无碳市场的电力市场均衡,分析了碳市场对电力市场均衡状态的影响;同时,基于碳配额总量对电力市场均衡的灵敏度分析发现碳配额总量需适度收紧才能最大程度实现电力低碳减排作用。     

Multiobjective optimal thermal power dispatch

A multiobjective thermal power dispatch problem is formulated using noncommensurable objectives such as operating costs and minimal emission. A sensitivity measure is chosen whereby the effects of variations in the nominal conditions describing a given multiobjective problem can be measured and incorporated as a performance index to be minimized. A nonlinear programming problem provides the framework for examining the objective constraint level in an ε-constant form of the multiobjective optimization problem. The dispersion index is chosen as the sensitivity measure for the investigation of the effects of random variations in the model parameters of the optimal solution. A sensitivity trade-off is exploited for the multiobjective problem that represents the trade-off between sensitivity and objective level. Validity of the method has been demonstrated by analysing a three-generator sample system.     

Multi-objective market clearing of electrical energy,spinning reserves and emission for wind-thermal power system

This paper proposes energy and spinning reserve market clearing mechanism for wind-thermal power system, including uncertainties in wind power generation and load demand forecasts. The impact of wind power and load demand volatility on the energy and spinning reserve market is taken into account. This paper considers reserve offers from the conventional thermal generators. The stochastic behavior of wind speed, and wind power is represented by Weibull probability density function (PDF), and the load demand uncertainty is represented by Normal PDF. This paper considers two objectives: energy and spinning reserves cost minimization, and emission minimization. The energy and spinning reserves cost minimization objective includes cost of energy provided by conventional thermal generators and wind generators, cost of reserves provided by conventional thermal generators. It also includes costs due to over-estimation and under-estimation of available wind power, and load demand. The proposed market clearing model provides a compromise solution by optimizing both the objectives simultaneously using multi-objective Strength Pareto Evolutionary Algorithm 2+ (SPEA 2+). The effectiveness of the proposed approach is established from the results on IEEE 30 bus system.     

抽水蓄能-火电调峰优化对污染物处理成本影响分析

受经济下行影响,火电平均负荷率不断刷低,调峰能耗随之陡增。对抽水蓄能与火电机组联合调峰对污染物处理成本影响定性分析,建立了考虑污染物处理成本的抽水蓄能与火电机组联合调峰优化模型,依据混合整数规划原理,对仿真计算模型进行了求解。结果表明：一个日调峰周期内,抽水蓄能与火电机组两班制联合调峰运行可有效降低脱硝、脱硫、烟尘处理成本以及碳排放成本和发电总成本,与低负荷调峰方式相比,可有效解决因脱硝系统入口温度过低而退出运行的问题,故对其成本影响更加明显,证明了在充分考虑污染物处理成本的前提下,抽水蓄能与火电两班制联合调峰方式可有效解决当前火电机组调峰能耗较高的问题,验证了所建模型及方法的合理性和实用性以及区域系统调峰期污染物超低排放的可达性。     

考虑自适应传输备用的含风电电力系统双层随机调度方法

大规模风电接入增加了电网发生输电阻塞的概率.为克服确定性阻塞管理方法在应对系统随机扰动上的不足,基于考虑阻塞风险的机会约束规划条件,提出一种计及风电不确定性的传输备用自适应量化方法,构建了包含自适应传输备用的双层随机调度模型.该模型将线路传输备用纳入上层的机组组合模型中,针对下层的经济再调度问题,采用改进点估计法求得线路实时潮流的统计分布特性,并将结果返回上层模型以动态调整备用需求大小.同时,基于下层模型的Karush-Kuhn-Tucher最优条件,将双层优化结构合并为单层,并最终转化为便于求解的混合整数规划问题.最后,通过IEEE RTS-24节点测试算例验证了所提模型和方法能够有效缓解传输阻塞,提高风电消纳和备用可用性.     

Two-stage stochastic-robust model for the self-scheduling problem of an aggregator participating in energy and reserve markets

This paper addresses a two-stage stochastic-robust model for the day-ahead self-scheduling problem of an aggregator considering uncertainties. The aggregator, which integrates power and capacity of small-scale prosumers and fexible community-owned devices, trades electric energy in the day-ahead (DAM) and real-time energy markets (RTM), and trades reserve capacity and deployment in the reserve capacity (RCM) and reserve deployment markets (RDM). The ability of the aggregator providing reserve service is constrained by the regulations of reserve market rules, including minimum ofer/bid size and minimum delivery duration. A combination approach of stochastic programming (SP) and robust optimization (RO) is used to model diferent kinds of uncertainties, including those of market price, power/demand and reserve deployment. The risk management of the aggregator is considered through conditional value at risk (CVaR) and fuctuation intervals of the uncertain parameters. Case studies numerically show the economic revenue and the energy-reserve schedule of the aggregator with participation in diferent markets, reserve regulations, and risk preferences.     

基于多场景随机规划的电力系统生产模拟方法

针对电力系统生产模拟中新能源不确定性问题,引入多场景随机规划理论,研究了含新能源电力系统的生产模拟方法。在时间尺度为周的生产模拟中,由多场景方式刻画新能源不确定性,建立随机规划数学模型模拟系统运行,并采用基于Benders分解的算法求解该模型。通过实际系统算例,计算了新能源年弃电率、火电盈余装机容量等指标,比较了不同求解方法下生产模拟的结果,研究了旋转备用率对新能源接纳能力的影响,结果表明所提出的生产模拟方法能够计算系统运行相关指标,所采用的算法相对于直接调用数学软件求解有一定的优势。另外,给出了旋转备用率与新能源弃电率、接纳电量之间的关系。     

基于粒子群算法求解电力市场发电商最优供给函数模型

发电商最优供给函数是一个两层优化问题,其中上层是发电商生产效益最大化问题,下层是基于最优潮流的市场最优化调度问题。启发式算法由于简单易行,最优解具有全局性,与初始点选择无关。因此是解决这类问题的一个良好选择。文中运用启发式粒子群优化算法（PSO）求解发电商生产效益两层优化问题,以获得发电商最优供给函数,与确定性方法的计算结果进行了比较,并对最优解的全局性和初始点选择进行了讨论。IEEE30节点6机系统验证了所提方法的有效性。     

可再生能源配额制及绿色证书交易机制设计及仿真

近期,可再生能源消纳问题已经成为能源低碳转型的主要瓶颈,为解决这一问题,有针对性地在发电侧设计了一种尽量保证绿色证书供需平衡的可再生能源配额制以及其配套的绿色证书交易机制,其中将上期可再生能源发电情况作为设定本期可再生能源单位发电量所获得的绿色证书数的依据之一;将上期化石能源配额完成程度作为设定本期化石能源发电商配额目标的依据之一。根据电力批发市场和绿色证书交易市场的寡头竞争特点,建立多寡头非合作博弈模型,对设计的机制进行模拟仿真。结果表明,设计的机制对促进可再生能源消纳具有显著作用。     

基于电力节能减排双目标调度优化模型及方法的研究

节能发电调度的实施意味着高能耗高污染的发电机组能够获得的发电小时数将越来越少,客观上有利于低效小火电机组加快关停,优化电力工业的结构和布局.以节能环保、经济、公平为目标,建立兼顾购电费用与能源节约的模糊双目标节能发电调度优化模型,并利用模糊多目标规划理论将该模型转化为单目标规划问题并对其进行求解.算例结果表明,该模型符合节能配置原则.     

基于能耗和排放绩效的火电厂发电调度模式研究

基于火力发电机组能耗和污染物排放绩效,运用层次分析法(AHP)建立了节能环保发电调度模式。指出该模式将能耗最低和排污最小的多目标问题转化为单目标问题,能够实现节能、环保多目标的发电调度优化,可作为火力发电机组发电调度排序的参考。     

含大型风电场的环境经济调度模型与解法

大规模风电并网给电力系统发电调度带来了新的不确定性因素。基于多目标机会约束规划,构建含风电场环境经济调度的随机优化模型,将燃煤机组的发电成本最小和污染气体排放量最小同时作为目标函数,并计及系统正、负旋转备用容量约束。利用风电出力的分布函数将随机模型转化为确定性模型。设计一种包含多目标优化和辅助决策的2阶段解法,该方法在求出模型的Pareto最优集后,能为运行人员提供决策指导。以含40台燃煤机组和1个大型并网风电场的电力系统为例进行环境经济发电调度,结果表明,所提模型和解法是可行、有效的。     

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。     

Possibilistic programming approach for mid-term electric power planning in deregulated markets

The mid-term electric power planning problem under uncertainty for a generation company (GenCo) in a deregulated market having some thermal, hydro and wind power plants is addressed. We propose a novel possibilistic price-based mixed integer linear programming approach to solve the considered problem. Because the uncertainties especially those are related to market could not usually be based upon probabilities, the possibilistic distribution functions are used to simulate some key imprecise/ambiguous parameters: GenCo’s electricity market share; electricity, fuel and emission market prices; possible wind electric power; availability of generating units; available water reservoir for hydro units. To solve the proposed model, we develop an interactive possibilistic solution method based upon two recent solution approaches. Finally, we use a real-world case study to show the ability and appropriateness of our model and report computational results. Notably, the proposed approach can be easily applied for a regulated environment.