On the problem of controlling shiftable prosumer devices with price signals

Price-based control approaches can be derived from the Lagrangian relaxation of the unit commitment problem and provide a scalable framework for solving practical sized optimization problems. The framework postulates an iterative gradient-based pricing mechanism, in which the price signal is used to negotiate the energy exchange between individual (small sized) market participants. Within this framework shiftable loads, renewables and storage devices induce discontinuous utility functions, which can cause divergence of the pricing mechanism resulting suboptimal solutions.The authors propose a new simple but efficient method to deal with this problem in large scale market setups by introducing the concept of randomized price offsets.     

The conditions for obtaining feasible solutions to security-constrained unit commitment problems

The core of solving security-constrained unit commitment (SCUC) problems within the Lagrangian relaxation framework is how to obtain feasible solutions. However, due to the existence of the transmission constraints, it is very difficult to determine if feasible solutions to SCUC problems can be obtained by adjusting generation levels with the commitment states obtained in the dual solution of Lagrangian relaxation. The analytical and computational necessary and sufficient conditions are presented in this paper to determine the feasible unit commitment states with grid security constraints. The analytical conditions are proved rigorously based on the feasibility theorem of the Benders decomposition. These conditions are very crucial for developing an efficient method for obtaining feasible solutions to SCUC problems. Numerical testing results show that these conditions are effective.     

Cooperative coevolutionary algorithm for unit commitment

This paper presents a new cooperative coevolutionary algorithm (CCA) for power system unit commitment. CCA is an extension of the traditional genetic algorithm (GA) which appears to have considerable potential for formulating and solving more complex problems by explicitly modeling the coevolution of cooperating species. This method combines the basic ideas of Lagrangian relaxation technique (LR) and GA to form a two-level approach. The first level uses a subgradient-based stochastic optimization method to optimize Lagrangian multipliers. The second level uses GA to solve the individual unit commitment sub-problems. CCA can manage more complicated time-dependent constraints than conventional LR. Simulation results show that CCA has a good convergent property and a significant speedup over traditional GAs and can obtain high quality solutions. The "curse of dimensionality" is surmounted, and the computational burden is almost linear with the problem scale     

Thermal power generation scheduling by simulated competition

This paper presents a new method for solving the unit commitment problem by simulation of a competitive market where power is traded through a power exchange (PX). Procedures for bidding and market clearing are described. The market clearing process handles the spinning reserve requirements and power balance simultaneously. The method is used on a standard unit commitment problem with minimum up/down times, start-up costs and spinning reserve requirement taken into account. Comparisons with solutions provided by Lagrangian relaxation, genetic algorithms and Chao-an Li's unit decommitment procedure demonstrate the potential benefits of this new method. The motivation for this work was to design a competitive electricity market suitable for thermal generation scheduling. However, performance in simulations of the proposed market has been so good that it is presented here as a solving technique for the unit commitment problem     

机组优化组合的人工神经网络拉格朗日混合方法

本文提出了一种求解电力系统组合优化问题的混合神经网络－拉格朗日方法，至今，拉格朗日枪驰法－直被记是机组优化组合近解的实用方法，这样，基于神经网络的监督学习和自适应识别概念，我们用神经网络来推测负荷需求与拉格朗日乘子的非线性关系，并且采用了优化的学习速率和势态项来加速网络的收敛，数值计算的结果表明本文的方法是可行的。     

A solution for unit commitment using Lagrangian relaxation combined with evolutionary programming

This paper proposes an approach which combines Lagrangian relaxation principle and evolutionary programming for short-term thermal unit commitment. Unit commitment is a complex combinatorial optimization problem which is difficult to be solved for large-scale power systems. Up to now, the Lagrangian relaxation is considered the best to deal with large-scale unit commitment although it cannot guarantee the optimal solution. In this paper, an evolutionary programming algorithm is used to improve a solution obtained by the Lagrangian relaxation method: Lagrangian relaxation gives the starting point for a evolutionary programming procedure. The proposed algorithm takes the advantages of both methods and therefore it can search a better solution within short computation time. Numerical simulations have been carried out on two test systems of 30 and 90 thermal units power systems over a 24-hour periods.     

A new unit commitment method

This paper introduces a new unit commitment method based on a decommitment procedure for solving the power system resource scheduling problem. From an initial schedule of all available units committed over the study period, a `one-at-a-time' unit decommitment is accomplished by dynamic programming according to some specified economic criteria. The decommitment process continues until no further reduction in total cost is possible, or the unit schedules of two consecutive iterations over the time period remain unchanged without any violation of the spinning reserve constraint. Two criteria for decommiting a unit are introduced and described in detail. Comparisons of the proposed unit commitment method with the Lagrangian relaxation (LR) approach and Fred Lee's sequential unit commitment method (SUC) demonstrate the potential benefits of the proposed approach for power system operations planning     

Augmented Lagrange Hopfield network based Lagrangian relaxation for unit commitment

This paper proposes an augmented Lagrange Hopfield network based Lagrangian relaxation (ALHN-LR) for solving unit commitment (UC) problem with ramp rate constraints. ALHN-LR is a combination of improved Lagrangian relaxation (ILR) and augmented Lagrange Hopfield network (ALHN) enhanced by heuristic search. The proposed ALHN-LR method solves the UC problem in three stages. In the first stage, ILR is used to solve unit scheduling satisfying load demand and spinning reserve constraints neglecting minimum up and down time constraints. In the second stage, heuristic search is applied to refine the obtained unit schedule including primary unit de-commitment, unit substitution, minimum up and down time repairing, and de-commitment of excessive units. In the last stage, ALHN which is a continuous Hopfield network with its energy function based on augmented Lagrangian relaxation is applied to solve constrained economic dispatch (ED) problem and a repairing strategy for ramp rate constraint violations is used if a feasible solution is not found. The proposed ALHN-LR is tested on various systems ranging from 17 to 110 units and obtained results are compared to those from many other methods. Test results indicate that the total production costs obtained by the ALHN-LR method are much less than those from other methods in the literature with a faster manner. Therefore, the proposed ALHN-LR is favorable for large-scale UC implementation.     

考虑网络安全约束的机组组合新算法

市场机制驱使电网运行于安全极限的边缘,考虑网络安全约束的机组组合问题变得尤为重要,基于对偶原理的拉格朗日松弛法是解决这一问题的有效途径。文章提出了一种解决网络安全约束下的机组组合问题的新算法,在拉格朗日对偶分解的基础上结合变量复制技术,通过引入附加人工约束将网络约束嵌入单机子问题中,实现在机组组合中考虑网络安全约束。该算法摆脱了现有各种处理手段在解决网络安全约束的机组组合问题时将网络安全约束与机组启停相分离的不足,揭示了安全经济调度和安全约束下的机组组合在概念上的区别和联系。     

A Method for Solving the Fuel Constrained Unit Commitment Problem

The unit commitment problem involves finding the hourly commitment schedule for the thermal units of an electrical system, and their associated generation, over a period of up to a week. For some utilities, contractual or other factors limit the amount of fuel available to certain of the units or plants. This paper describes a new method which solves the unit commitment problem in the presence of fuel constraints. The method uses a Lagrangian decomposition and successive approximation technique for solving the unit commitment problem where the generation, reserve and fuel constraints are adjoined onto the cost function using Lagrange multipliers. All important operating constraints have been incorporated including minimum up and down times, standby operation, ramping limits, time-dependent start-up cost, spinning and supplemental reserve. The method is being applied to a production-grade program suitable for Energy Management Systems applications.     

求解机组组合问题的改进混合整数二次规划算法

混合整数二次规划(MIQP)算法求解机组组合问题具有全局优化能力,但是针对大规模优化问题,其计算速度和计算精度将受影响.文中提出了求解机组组合问题的改进MIQP算法.该算法的核心思想是引入了松弛和解耦2种改进策略.通过求解松弛整数变量的二次规划模型,首先获得机组组合的下界空间,然后再通过拉格朗日解耦算法获得机组组合的上界空间,进而在上下界确定的寻优空间内采用MIQP算法进行再优化.不同测试算例表明,改进的MIQP算法快速且有效,可以降低优化问题的复杂度,显著减少计算时间.     

Using immune genetic algorithm based hybrid techniques for short-term unit commitment problem

This paper presents a hybrid chaos search (CS), immune algorithm (IA)/genetic algorithm (GA), and fuzzy system (FS) method (CIGAFS) for solving short-term thermal generating unit commitment (UC) problems. The UC problem involves determining the start-up and shut-down schedules for generating units to meet the forecasted demand at the minimum cost. The commitment schedule must satisfy other constraints such as the generating limits per unit, reserve, and individual units. First, we combined the IA and GA, then we added the CS and the FS approach. This hybrid system was then used to solve the UC problems. Numerical simulations were carried out using three cases: 10, 20, and 30 thermal unit power systems over a 24 h period. The produced schedule was compared with several other methods, such as dynamic programming (DP), Lagrangian relaxation (LR), standard genetic algorithm (SGA), traditional simulated annealing (TSA), and traditional Tabu search (TTS). A comparison with an immune genetic algorithm (IGA) combined with the CS and FS was carried out. The results show that the CS and FS all make substantial contributions to the IGA. The result demonstrated the accuracy of the proposed CIGAFS approach.     

兼顾经济和环保效益的机组组合

在节能减排背景下,提出了同时兼顾经济和环保效益的多目标机组组合模型,基于拉格朗日松弛法的思想,将该问题分解为单机子问题,在子问题中利用并列选择遗传算法对经济和环保效益进行双目标优化。分别以发电费用最小、排放最小和综合最小为目标建立模型,对6机30节点算例进行了计算和比较,验证了算法和模型的有效性,实现了综合考虑经济和环保因素的机组组合问题的求解。     

A combination of the genetic algorithm and lagrangian relaxation decomposition techniques for the generation unit commitment problem

Unit commitment involves the scheduling of generators in a power system in order to meet the requirements of a given load profile. An analysis of the basis for combining the genetic algorithm (GA) and Lagrangian relaxation (LR) methods for the unit commitment problem is presented. It is shown that a robust unit commitment algorithm can be obtained by combining the global search property of the genetic algorithm with the ability of the Lagrangian decomposition technique to handle all kinds of constraints such as pollution, unit ramping and transmission security.     

考虑多种约束条件的机组组合新算法

提出了考虑系统降出力备用约束、机组出力变化速率、线路潮流约束和断面传输功率约束的机组组合新算法。算法没有引入任何乘子,计算单调收敛,速度快,并且不需要初始可行解。用ＩＥＥＥ ２４母线系统对算法进行了验证,结果表明,算法对各种约束条件的处理正确,解的质量好。     

具有爬升约束机组组合的充分必要条件

在Lagrangian松弛框架下，很难确定机组组合问题的一个可行解是否可通过调整对偶机组组合而获得。对于具有爬升约束的机组组合调度问题来说，由于机组出力在连续的2个开机区间的耦合性，求解可行解就更困难。在Lagrangian松弛框架下，开发1个机组组合新方法的核心是如何获得1个可行的机组组合。文中采用Benders分解可行性条件严格证明了在给定时段，机组组合可行的充分必要条件：即在该时段一个相应于系统负载平衡约束和旋转各用约束的不等式组成立。该条件不需要求解经济分配问题，就可以判定机组组合的可行性。有了此条件，可在发电功率经济分配前知道机组组合是否可行，若不可行，则可通过调整机组组合状态而获得可行的组合。该条件对于构造一个求解机组组合问题的系统方法是重要且有效的。数值测试表明该条件是判定机组组合可行性的有效方法。     

Unit commitment with ramp multipliers

This paper presents a new decomposition method, based on the Lagrangian relaxation technique, for solving the unit commitment problem with ramp rate constraints. By introducing an additional vector of multipliers to represent the cost of “system ramping demand”, this method can handle the coupling constraints between time periods while still keeping the simplicity of the original decomposition method. A new algorithm for updating multipliers is also proposed. Similar to the bundle algorithm, this algorithm maintains the previous iteration history to approximate the dual envelope. Unlike the bundle algorithm, this new algorithm generates an update step along the subgradient direction without any quadratic programming (QP) code. The new algorithm combines the bundle algorithm's smooth approach to the dual optimum with the sub-gradient method's fast update     

Application embedded chaos search immune genetic algorithm for short-term unit commitment

This paper presents a Hybrid Chaos Search (CS) immune algorithm (IA)/genetic algorithm (GA) and Fuzzy System (FS) method (CIGAFS) for solving short-term thermal generating unit commitment (UC) problems. The UC problem involves determining the start-up and shutdown schedules for generating units to meet the forecasted demand at the minimum cost. The commitment schedule must satisfy other constraints such as the generating limits per unit, reserve and individual units. First, we combined the IA and GA, then we added the chaos search and the fuzzy system approach. This hybrid system was then used to solve the UC problems. Numerical simulations were carried out using three cases: 10, 20 and 30 thermal unit power systems over a 24 h period. The produced schedule was compared with several other methods, such as dynamic programming (DP), Lagrangian relaxation (LR), Standard genetic algorithm (SGA), traditional simulated annealing (TSA), and Traditional Tabu Search (TTS). A comparison with an IGA combined with the Chaos Search and FS was carried out. The results show that the Chaos Search and FS all make substantial contributions to the IGA. The result demonstrated the accuracy of the proposed CIGAFS approach.     

Unit commitment using a hybrid model between Lagrangian relaxation and genetic algorithm in competitive electricity markets

This paper presents a hybrid model between Lagrangian relaxation (LR) and genetic algorithm (GA) to solve the unit commitment problem. GA is used to update the Lagrangian multipliers. The optimal bidding curves as a function of generation schedule are also derived. An IEEE 118-bus system is used to demonstrate the effectiveness of the proposed hybrid model. Simulation results are compared with those obtained from traditional unit commitment.     

Unit commitment scheduling by Lagrange relaxation method taking into account transmission losses

Since the application of the Lagrange relaxation method to the unit commitment scheduling by Muckstadt in 1979, many papers using this method have been published. The greatest advantage of applying the Lagrange relaxation method for the unit commitment problem is that it can relax (ignore) each generator's output dependency caused by the demand–supply balance constraint so that a unit commitment of each generator is determined independently by dynamic programming. However, when we introduce the transmission loss into the demand–supply balance constraint, we cannot decompose the problem into the partial problems in which each generator's unit commitment is determined independently and have to take some measures to obtain an optimal schedule by the Lagrange relaxation method directly. In this paper, we present an algorithm for the unit commitment schedule using the Lagrange relaxation method for the case of taking into account transmission losses. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 152(4): 27–33, 2005; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20119