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     

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.     

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.     

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.     

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     

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.     

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     

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。     

A study of a unit commitment problem with transmission constraints

Unit commitment problem is an optimization problem to determine the start‐up and shut‐down schedule of thermal units while satisfying various constraints, for example, generation‐demand balance, unit minimum up/down time, system reserve, and so on. Since this problem involves a large number of 0–1 type variables that represent up/down status of the unit and continuous variables expressing generation output, it is a difficult combinatorial optimization problem to solve. The study at present concerns the method for requiring the suboptimum solution efficiently. Unit commitment method widely used solves the problem without consideration of voltage, reactive power, and transmission constraints. In this paper, we will propose a solution of unit commitment with voltage and transmission constraints, based on the unit decommitment procedure (UDP) method, heuristic method, and optimal power flow (OPF). In this method, initial unit status will be determined from random numbers and the feasibility will be checked for minimum start‐up/shut‐down time and demand‐generation balance. If the solution is infeasible, the initial solution will be regenerated until a feasible solution can be found. Next, OPF is applied for each time period with the temporary unit status. Then, the units that have less contribution to the cost are detected and will be shut down based on the unit decommitment rules. This process will be repeated until suboptimal solution is obtained. The proposed method has been applied to the IEEE 118‐bus test system with 36 generating units with successful result. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 144(3): 36–45, 2003; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10187     

Effects of ramp-rate limits on unit commitment and economicdispatch

The authors propose an algorithm to consider the ramp characteristics in starting up and shutting down the generating units as well as increasing and decreasing power generation. They consider the inclusion of ramping constraints in both unit commitment and economic dispatch. Since implementing ramp-rate constraints is a dynamic process, dynamic programming (DP) is a proper tool to treat this problem. To overcome the computational expense which is the main drawback of DP, this study initially employs artificial intelligence techniques to produce a unit commitment schedule which satisfies all system and unit operation constraints except unit ramp-rate limits. Then, a dynamic procedure is used to consider the ramp properties as units are started up and shut down. According to this adjustment, maximum generating capabilities of units will change the unit operation status instead of following a step function. Finally, a dynamic dispatch procedure is adopted to obtain a suitable power allocation which incorporates the unit generating capability information given by unit commitment and unit ramping constraints, as well as the economical considerations. Two examples are presented to demonstrate the efficiency of the method     

Monte Carlo based unit commitment procedures for the deregulated market environment

The unit commitment problem, originally conceived in the framework of short term operation of vertically integrated utilities, needs a thorough re-examination in the light of the ongoing transition towards the open electricity market environment. In this work the problem is re-formulated to adapt unit commitment to the viewpoint of a generation company (GENCO) which is no longer bound to satisfy its load, but is willing to maximize its profits. Moreover, with reference to the present day situation in many countries, the presence of a GENCO (the former monopolist) which is in the position of exerting the market power, requires a careful analysis to be carried out considering the different perspectives of a price taker and of the price maker GENCO. Unit commitment is thus shown to lead to a couple of distinct, yet slightly different problems. The unavoidable uncertainties in load profile and price behaviour over the time period of interest are also taken into account by means of a Monte Carlo simulation. Both the forecasted loads and prices are handled as random variables with a normal multivariate distribution. The correlation between the random input variables corresponding to successive hours of the day was considered by carrying out a statistical analysis of actual load and price data. The whole procedure was tested making use of reasonable approximations of the actual data of the thermal generation units available to come actual GENCOs operating in Italy.     

大规模机组组合问题计及网络约束的线性化求解方法

为了提高求解机组组合问题计算效率,给出线性化方法,将目标函数分段线性化,将启机费用作为约束并将其线性化,同时将网络安全约束通过直流潮流模型进行线性化,从而建立较完备的混合整数线性规划的机组组合模型.采用世界上广为流行的CPLEX优化求解器求解,在对偶间隙设定为较合理的情况下,求解速度快.不同测试算例表明,该方法速度快,精度较高,能够求解较大规模的机组组合问题.     

基于自适应粒子群优化算法的机组组合

机组组合是一个大规模、非线性混合整数优化问题,求解比较困难,为了提高粒子群算法的全局和局部搜索能力,提出一种惯性权值自适应调整的粒子群算法.该算法按照适应度的大小将粒子群分成两个子群,然后根据适应度的进化速度和进化停滞系数动态调整惯性权值.通过对典型函数的测试以及10台机组24小时的优化调度,计算结果表明该方法收敛精度较高.     

Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem

This paper presents a new algorithm based on integrating genetic algorithms, tabu search and simulated annealing methods to solve the unit commitment problem. The core of the proposed algorithm is based on genetic algorithms. Tabu search is used to generate new population members in the reproduction phase of the genetic algorithm. A simulated annealing method is used to accelerate the convergence of the genetic algorithm by applying the simulated annealing test for all the population members. A new implementation of the genetic algorithm is introduced. The genetic algorithm solution is coded as a mix between binary and decimal representation. The fitness function is constructed from the total operating cost of the generating units without penalty terms. In the tabu search part of the proposed algorithm, a simple short-term memory procedure is used to counter the danger of entrapment at a local optimum, and the premature convergence of the genetic algorithm. A simple cooling schedule has been implemented to apply the simulated annealing test in the algorithm. Numerical results showed the superiority of the solutions obtained compared to genetic algorithms, tabu search and simulated annealing methods, and to two exact algorithms     

Closure on "Extended analysis of ground impedance measurement using the fall-of-potential method"

The authors reply to the comments by J. Fortin (see ibid., vol.18, no.2, p.645, 2003) on their paper on "Extended analysis of ground impedance measurement using the fall-of-potential method" (see ibid., vo.17, p.881-5, 2002).     

Closure on "Adaptive noncommunication protection of double circuit line systems"

As closure to the discussion on "Adaptive noncommunication protection of double circuit line systems" (see ibid., vol.17, p.43-49, 2003), the authors thank the discusser, M. Sanaye-Pasand (see ibid., vol.18, no.2, p.657, 2003) for his interest in the paper and valuable comments.     

Fast unit commitment based on optimal linear approximation to nonlinear fuel cost: Error analysis and applications

Mixed-integer linear programming (MILP) based techniques are among the most widely applied methods for unit commitment (UC) problems. The fuel cost functions are often replaced by their piecewise linear approximations whereas it is more or less disturbing to use piecewise linear approximations without knowing the exact effect on solution deviation from the optima. Therefore, error analysis is important since the optimal solutions are different when different objective functions are adopted. Another important problem is balancing between solution quality and computation efficiency since better solution quality relies on finer discretization with exponentially increased computational efforts. A detailed error analysis is presented in this paper. It is found that the approximation error is inverse proportional to the square of the number of piecewise segments. Lower bounds on the minimum necessary number of discretization segments are also derived. A 2-Stage Procedure is then established to achieve a better balance between solution quality and computation efficiency. Numerical testing to 2 groups of UC problems is exciting. It is found that the operating cost increases no more than 0.6% in all cases while the CPU time is greatly reduced regarding other MILP approaches. The results are still valid in electric power market clearing computation.     

Machine learning approaches to the unit commitment problem: Current trends,emerging challenges,and new strategies

Traditional power system operation and control decision-making processes, such as the unit commitment (UC) problem, primarily rely on the physical models and numerical calculations. With the growing scale and complexity of modern power grids, it becomes more complicated to accurately formulate the physical power system and more difficult to efficiently solve the corresponding UC problems. As a matter of fact, plenty of historical power system operation records as well as real-time data could provide useful information and insights of the underlying power grid. To this end, machine learning methods could be valuable to help understand the relationship of UC performance to power system parameters, reveal the rationality behind such relationship, and finally address UC problems in a more efficient and accurate way. This article discusses the current practices of using machine learning approaches to solve the mixed-integer linear programming based UC problems. The associated challenges are analyzed, and several promising strategies for adopting machine learning approaches to effectively solve UC problems are discussed in this article. In addition, we will also explore machine learning approaches to promptly solve steady-state nonlinear AC power flow and dynamics differential equations, so that they can be integrated into the UC problems to guarantee AC power flow security and dynamic stability of system operations, as compared to the current DC power flow constrained UC practice. Our studies show that machine learning, as model-free methods, is a valuable alternative or addition to the existing model-based methods. As a result, the effective combination of machine learning based approaches and physical model based methods are expected to derive more efficient UC solutions that can improve the secure and economic operation of power systems.