Congestion management with ex ante pricing for decentralized electricity markets

In this paper, the authors present a method and a model for managing transmission congestion based on ex ante congestion prices. Their method is influenced by the yield management approach widely used for airline reservation systems, and their model is built based on the relations between transmission congestion prices and electricity commodity prices that exist for an optimal solution. They formulate the congestion pricing problem as a master problem and the electricity commodity (energy and reserve) pricing as subproblems. Examples are presented to illustrate how a system operator can use this approach to compute ex ante congestion prices and how market operators can determine clearing prices and schedules of forward electric energy and reserve markets.     

Enhanced techniques on sequential unit commitment with interchangetransactions

This paper presents two enhanced techniques for improving the solution optimality and computation performance of the sequential unit commitment (SC) method with interchange transactions. The conventional SC method, although often presenting superior performance over other methods, can lead to nonnear-optimal solutions in some circumstances due to the use of a local decision scheme to identify the best unit to be committed at each stage. The proposed technique, instead, uses a global-like decision scheme. It defines a small set of locally advantageous units which are individually examined globally by generating tentative commitment schedules to identify the globally best unit to commit at each stage. Studies have shown that the global-like decision scheme can effectively improve the solution optimality. Meanwhile, while an interchange transaction is incorporated with the unit commitment study, the constant transaction price often causes solution oscillation during iterations. A varying-λ technique is proposed in this paper. This technique properly models the impact of the interchange transactions on the power system hourly energy prices and, hence, successfully overcomes the oscillation problem such that the loading level of a transaction can be optimally determined similarly as for a generating unit     

发电机组启停机的智能优化经济调度研究

遗传算法用于解决启停机优化调度问题中,常出现群体早熟和有时收敛于局部最优解等问题.而模拟退火算法在接受新解时却显示出较好的特性.在遗传算法的评价函数中引入模拟退火算法以及在选择操作中采用模拟退火算法的接受准则,将两者进行混合可有效地缓解其选择压力,增强算法的全局收敛性.采用十进制编码,无需解码,可减少计算误差和时间.算例分析表明该算法可以在满足安全可靠的多种约束条件下,较好地改善机组启停计划的经济性.     

A new method for unit commitment with ramping constraints

Unit commitment with ramping constraints is a very difficult problem with significant economic impact. A new method is developed in this paper for scheduling units with ramping constraints within Lagrangian relaxation framework based on a novel formulation of the discrete states and the integrated applications of standard dynamic programming for determining the optimal discrete states across hours, and constructive dynamic programming for determining optimal generation levels. A section of consecutive running or idle hours is considered as a commitment state. A constructive dynamic programming (CDP) method is modified to determine the optimal generation levels of a commitment state without discretizing generation levels. The cost-to-go functions, required only for a few corner points with a few continuous state transitions at a particular hour, are constructed in the backward sweep. The optimal generation levels can be obtained in the forward sweep. The optimal commitment states across the scheduling horizon can then be obtained by standard dynamic programming. Numerical testing results show that this method is efficient and the optimal commitment and generation levels are obtained in a systematic way without discretizing or relaxing generation levels.     

机组组合问题的罚函数法

机组组合问题是一个大规模的非线性、0、1变量混合整数规划问题,是一个难问题.以罚函数方法解决0、1变量整数规划问题是一个新的尝试.文中考虑包括发电机爬坡约束和时间约束等动态约束在内的多种约束条件,对机组组合问题的0、1变量进行松弛,并在目标函数中增加一个惩罚函数项,将问题变换成一个非线性连续变量的规划问题,以SQP法求解.本算法经过一个简单的算例检验,说明是行之有效的.     

Hybrid Taguchi-Immune Algorithm for the thermal unit commitment

In this paper, a Hybrid Taguchi-Immune Algorithm (HTIA) is presented to deal with the unit commitment problem. HTIA integrates the Taguchi method and the Traditional Immune Algorithm (TIA), providing a powerful global exploration capability. The Taguchi method (TM) is incorporated in the crossover operations in order to select the better gene for achieving crossover consequently, enhancing the TIA. It has been widely used in experimental designs for problems with multiple parameters. The effectiveness and efficiency of HTIA are demonstrated by presenting several cases, and the results are compared with previous publications. Our results show that the proposed method is feasible, robust, and more effective than many other previously developed computation algorithms.     

基于SRMC的区域电力市场输电定价方案

从我国区域电力市场的运行实际出发,提出一种基于SRMC的输电定价方案,该方案不仅可以向发电厂和用户提供有效的经济信号,引导短期市场效率,而且可以保证电网公司的年收支平衡,并为电网扩建积累专项资金,具有一定的合理性和实用性.     

机组组合问题的罚函数法

机组组合问题是一个大规模的非线性、0、1变量混合整数规划问题 ,是一个难问题。以罚函数方法解决0、1变量整数规划问题是一个新的尝试。文中考虑包括发电机爬坡约束和时间约束等动态约束在内的多种约束条件 ,对机组组合问题的 0、1变量进行松弛 ,并在目标函数中增加一个惩罚函数项 ,将问题变换成一个非线性连续变量的规划问题 ,以SQP法求解。本算法经过一个简单的算例检验 ,说明是行之有效的。     

解除管制的电力零售市场实时电价研究

零售侧打破垄断引入竞争是电力市场发展的趋势,制定合理的零售电价是市场开放的关键.根据电力零售市场的特点,基于负荷价格响应的定义以及边际成本定价理论,推导出了考虑负荷价格响应的有功实时电价的表达式.以社会成本最大化为目标函数,建立了考虑负荷价格响应特性的最优潮流模型,从而求解出未来引入竞争的电力零售市场中电力库交易模式下的实时电价.采用32节点配电系统对所提方法进行仿真,结果证明为促进零售市场竞争、引导用户合理用电提供了合理有效的定价方法.     

应用MAS解决基于利润的机组组合问题研究

电力系统引入放松管制的市场运行机制之后,形成一种基于利润的机组组合问题:①优化目标从费用最小转为利润最大;②各发电公司从自身利益出发,可以不完全满足中心调度的要求。针对以上特点,提出一种基于多Agent系统的解决方法。仿真结果表明,该方法能够适应解决现代电力系统机组组合问题的新需要,能够获得更大的经济效益。     

应用MAS解决基于利润的机组组合问题研究

电力系统引入放松管制的市场运行机制之后,形成一种基于利润的机组组合问题:①优化目标从费用最小转为利润最大;②各发电公司从自身利益出发,可以不完全满足中心调度的要求.针对以上特点,提出一种基于多Agent系统的解决方法.仿真结果表明,该方法能够适应解决现代电力系统机组组合问题的新需要,能够获得更大的经济效益.     

Analysis of the value for unit commitment of improved loadforecasts

Load forecast errors can yield suboptimal unit commitment decisions. The economic cost of inaccurate forecasts is assessed by a combination of forecast simulation, unit commitment optimization, and economic dispatch modeling for several different generation/load systems. The forecast simulation preserves the error distributions and correlations actually experienced by users of a neural net-based forecasting system. Underforecasts result in purchases of expensive peaking or spot market power; overforecasts inflate start-up and fixed costs because too much capacity is committed. The value of improved accuracy is found to depend on load and generator characteristics; for the systems considered here, a reduction of 1% in mean absolute percentage error (MAPE) decreases variable generation costs by approximately 0.1%-0.3% when MAPE is in the range of 3%-5%. These values are broadly consistent with the results of a survey of 19 utilities, using estimates obtained by simpler methods. A conservative estimate is that a 1% reduction in forecasting error for a 10,000 MW utility can save up to $1.6 million annually     

基于PSO-GA算法的电力系统机组组合研究

机组组合优化问题是一个大规模、多约束、非线性的混合整数规划问题,因此求解非常困难.粒子群优化(PSO)算法是一类随机全局优化技术,它通过粒子间的相互作用发现复杂搜索空间中的最优区域.PSO算法的优势在于操作简单,可调参数少易于实现而又功能强大.该文采用二进制粒子群优化方法解决机组状态组合问题,用遗传算法结合启发式技术解决经济分配问题,并对最小开停机时间及启停费用进行了处理,使得运算速度大大加快.方法的可行性在10台机组系统中检验.模拟结果表明文章所提出的算法具有收敛速度快及解的质量高等优点.     

Studies on stochastic unit commitment formulation with flexible generating units

In recent years, restructured power system has emerged and renewable energy generation technology has developed. More and more different unit characteristics and stochastic factors make the unit commitment (UC) more difficult than before. A novel stochastic UC formulation which covered the usual thermal units, flexible generating units and wind generation units is proposed to meet the need of energy-savings and environment protection. By introducing a UC risk constraint (UCRC), many stochastic factors such as demand fluctuations, unit force outages, variety of energy price, even the stochastic characteristics of wind generation can be dealt with. Based on the theory of chance constrained programming (CCP), the UCRC, a probabilistic constraint is changed into a determinate constraint, and then the presented formulation can be solved by usual optimization algorithms. Numerical simulations on 4 test systems with different scales show that different UC schedules can be determined according to different stochastic factors and its calculation time is acceptable in the view of practical engineer.     

基于坏场景集的含风电机组组合模型

针对风电出力的不确定性和波动性,引入坏场景集和鲁棒优化方法来解决含风电的机组组合优化问题。在分析场景集和鲁棒优化在电力系统不确定性调度中的应用基础上,构建评价坏场景恶化程度的保守度指标,在优化目标中加入方差来抑制个别坏场景导致整体优化结果的恶化,并结合储能系统对平抑风电波动的固有特点,建立了基于坏场景集的鲁棒机组组合模型及其算法。最后,通过算例进行了成本、储能装置容量和鲁棒度量分析,验证了该模型的有效性和实用性。     

基于PSO-GA算法的电力系统机组组合研究

机组组合优化问题是一个大规模、多约束、非线性的混合整数规划问题,因此求解非常困难。粒子群优化(PSO)算法是一类随机全局优化技术,它通过粒子间的相互作用发现复杂搜索空间中的最优区域。PSO算法的优势在于操作简单,可调参数少易于实现而又功能强大。该文采用二进制粒子群优化方法解决机组状态组合问题,用遗传算法结合启发式技术解决经济分配问题,并对最小开停机时间及启停费用进行了处理,使得运算速度大大加快。方法的可行性在10台机组系统中检验。模拟结果表明文章所提出的算法具有收敛速度快及解的质量高等优点。     

A method for unit commitment with transmission losses and flow constraint

Researches on the unit commitment with transmission network have been reported recently. However, most of these researches mainly discussed the security constrained unit commitment, while the relationship between unit commitment and transmission losses was not considered. However, from the standpoint of operating reserve for ensuring power supply reliability, a unit commitment considering transmission losses is required. Further, under the deregulation and liberalization of the electric power industry, not only the line's security but also transmission losses are expected to play an important role in calculating the network access charge, and unit commitment taking into account transmission losses is also desired from this viewpoint. In this paper, a unit commitment approach with both transmission losses and line flow constraint is presented. Based on a heuristic iterative optimization method, first, an initial schedule is created by using a successively decommitting unit approach that is proposed in this paper. Then, we determine constraints included in the unit commitment schedule by a heuristic iterative optimization approach, in which an algorithm able to get rid of line overload by DC optimal power flow is developed. Through numerical simulations on two test power systems, the effectiveness of the proposed method is shown. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 142(4): 9–19, 2003; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10116     

Economic analysis of unit commitment with distributed energy resources

Classic unit commitment (UC) is an important and exciting task of distributing generated power among the committed units subject to several constraints over a scheduled time horizon to obtain the minimum generation cost. Large integration of distributed energy resources (DERs) in modern power system makes generation planning more complex. This paper presents the individual and collective impact of three distributed energy resources (DERs), namely, wind power generator as a renewable energy source, plug-in electric vehicles (PEVs) and emergency demand response program (EDRP) on unit commitment. In this paper, an inconsistent nature of wind speed and wind power is characterized by the Weibull probability distribution function considering overestimation and underestimation cost model of the stochastic wind power. The extensive economic analysis of UC with DERs is carried out to attain the least total cost of the entire system. To obtain the optimum solution, Teaching–learning based optimization (TLBO) algorithm is employed to solve the unit commitment problem considering IEEE standard 10 unit test system in this study. It is found that the combined effect of wind power generator, plug-in electric vehicles and emergency demand response program on UC significantly lessen the total cost of the system.     

基于机组组合的风电消纳能力研究

电力系统消纳风电能力的准确掌握,对于风电场建设规划以及辅助实际运行的调度决策意义重大。风电消纳的效率分析给电网高效利用风电提供了理论支持,风电消纳效率主要是用来衡量扣除电网消纳风力发电的成本后,风电对于电网净减排的贡献率。本文以目前我国的电源结构作为切入点,采用机组组合的生产模拟方法,从成本上分析了电网消纳风电的费用构成。基于二次规划模型,从理论上推导出了电网消纳风电效率与系统运行方式、机组参数和电网消纳风能比例等因素有关。最后通过实验仿真验证了理论的有效性和实用性。     

基于KL散度的含储能机组组合分布鲁棒优化方法

为应对风电场出力的波动性和随机性给机组组合带来的问题,提出了基于Kullback-Leibler(KL)散度的含储能机组组合的两阶段分布鲁棒优化模型。在电池储能的运行模型的基础,将电池储能模型嵌入到传统的火电机组组合模型中,建立了含储能的机组组合两阶段优化模型;基于KL散度构建了风电场出力的模糊集,形成了含储能机组组合的两阶段分布鲁棒优化模型,通过对偶变换和广义Benders分解将其转化成易于求解的混合整数凸优化模型进行求解。通过IEEE RTS 24节点系统仿真结果表明,所提出的分布鲁棒优化方法保守性优于鲁棒优化方法,经济性接近随机优化方法,且随着KL散度增大,机组组合成本缓慢增加。