Control area performance improvement by extended securityconstrained economic dispatch

An algorithm for solving the extended security constrained economic dispatch (ESCED) problem with real-time economic dispatch grade speed and reliability is presented. The ESCED problem is formulated by adding a regulating margin and ramp rate constraints to the network security constrained economic dispatch problem previously solved by the CEDC algorithm. Starting with Newton's method to optimize the Lagrangian, the ESCED is developed by superimposing on Newton's method eight major components called tracking start initialization, hessian pre-elimination, implicit dual variable calculations, regulating margin sensitivity coefficient calculations, traumatic event evaluation, constraint relaxation, implicit ramp rate constraint implementation, and relaxed incremental cost calculations. Test results are also presented     

Daily generation scheduling optimization with transmissionconstraints: a new class of algorithms

The authors describe a novel class of algorithm dealing with the daily generation scheduling (DGS) problem. These algorithms have been designed by adding artificial constraints to the original optimization problem; handling these artificial constraints by using a dual approach; using an augmented Lagrangian technique rather than a standard Lagrangian relaxation technique; and applying the auxiliary problem principle which can cope with the nonseparable terms introduced by the augmented Lagrangian. To deal with the DGS optimization problem these algorithms are shown to be more effective than classical ones. They are well suited to solve this DGS problem taking into account transmission constraints     

Numerical experiments with an optimal power flow algorithm based onparametric techniques

This paper presents the results of numerical experiments with a new optimal power flow (OPF) algorithm based on a parametric technique. The approach consists of relaxing the original OPF problem by incorporating parametric terms to the objective function, the equality and inequality constraints. Such relaxation assures that any arbitrary initial solution, feasible or unfeasible, be the optimal solution of the OPF problem. As the scalar parameter changes, a family of OPF problems is created, whose necessary conditions are solved by Newton's method. An efficient strategy is proposed for updating the parameter and the optimal set of active inequality constraints of each intermediate problem. Two applications of the methodology are reported: the economic dispatch problem and the minimum transmission loss problem. These problems were solved for an 810-bus and a 2256-bus equivalent network of the South/Southeast interconnected Brazilian power system. The results show that the parametric approach is robust and efficient when applied to large-scale OPF problems     

考虑交流潮流约束的机组组合并行解法

针对传统机组组合模型的种种不足,该文提出了一种考虑交流潮流约束及静态安全约束的机组组合模型,并给出了一种完整的并行化解法。该法借助于扩展拉格朗日松弛法和变量复制技术,将原问题转换为其对偶问题,并利用附加问题原理将对偶问题分解为动态规划和最优潮流(OPF)子问题。对于OPF子问题,采用鲁棒性好、收敛速度快的预测校正内点法求解,同时在求解过程中,采用并行处理技术。IEEE118节点及IEEE300节点仿真结果表明,该方法收敛性好,非常适合并行处理。     

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.     

Ramp rate constrained unit commitment by improved priority list and augmented Lagrange Hopfield network

This paper proposes an improved priority list (IPL) and augmented Hopfield Lagrange neural network (ALH) for solving ramp rate constrained unit commitment (RUC) problem. The proposed IPL-ALH minimizes the total production cost subject to the power balance, 15 min spinning reserve response time constraint, generation ramp limit constraints, and minimum up and down time constraints. The IPL is a priority list enhanced by a heuristic search algorithm based on the average production cost of units, and the ALH is a continuous Hopfield network whose energy function is based on augmented Lagrangian relaxation. The IPL is used to solve unit scheduling problem satisfying spinning reserve, minimum up and down time constraints, and the ALH is used to solve ramp rate constrained economic dispatch (RED) problem by minimizing the operation cost subject to the power balance and new generator operating frame limits. For hours with insufficient power due to ramp rate or 15 min spinning reserve response time constraints, repairing strategy based on heuristic search is used to satisfy the constraints. The proposed IPL-ALH is tested on the 26-unit IEEE reliability test system, 38-unit and 45-unit practical systems and compared to combined artificial neural network with heuristics and dynamic programming (ANN-DP), improved adaptive Lagrangian relaxation (ILR), constraint logic programming (CLP), fuzzy optimization (FO), matrix real coded genetic algorithm (MRCGA), absolutely stochastic simulated annealing (ASSA), and hybrid parallel repair genetic algorithm (HPRGA). The test results indicate that the IPL-ALH obtain less total costs and faster computational times than some other methods.     

A solution to security constrained non-convex economic dispatch problem by modified subgradient algorithm based on feasible values

A security constrained power dispatch problem with non-convex total cost rate function for a lossy electric power system is formulated. Then, an iterative solution method proposed by us and based on modified subgradient algorithm operating on feasible values (F-MSG) is used to solve it.Since all equality and inequality constraints in our nonlinear optimization model are functions of bus voltage magnitudes and phase angles, off-nominal tap settings and susceptance values of svar systems, they are taken as independent variables. Load flow equations are added to the model as equality constraints. The unit generation constraints, transmission line capacity constraints, bus voltage magnitude constraints, off-nominal tap setting constraints and svar system susceptance value constraints are added into the optimization problem as inequality constraints. Since F-MSG algorithm requires that all inequality constraints should be expressed in equality constraint form, all inequality constraints are converted into equality constraints by the method, which does not add any extra independent variable into the model and reducing the solution time because of it, before application of it to the optimization model.The proposed technique is tested on IEEE 30-bus and IEEE 57 bus test systems. The minimum total cost rates and the solution times obtained from F-MSG algorithm and from the other techniques are compared, and the outperformance of the F-MSG algorithm with respect to the other methods in each test system is demonstrated.     

多区域输电阻塞管理的拉格朗日松驰分解算法

提出一种新的基于增广拉格朗日松驰的区域分解最优潮流算法,将一个大的最优潮流问题分解成多个区域子问题,并用此算法求解多区域电力市场输电阻塞管理问题,与现有的其他方法相比,该算法的主要优点在于无需在原始网络模型的基础上增加任何虚拟发电机或负荷,通过将该算法与电力市场实时平衡机制相结合,多区域有力阻塞管理问题可以分解为单区域二次规划子问题。这些子问题可以顺序求解也可以并行求解。采用这一方法,所有的区域市场独立调度员在得到不到其他区域网络信息的情况下仍然可以相互协作消除网络阻塞,在这一过程中,惟一需要进行区域间交换的信息是与区域间“耦合”约束相对应的拉格朗日乘子,最后,通过分析3区域的IEEE RTS－96标准测试系统说明了该方法的有效性。     

Loss minimization by the predictor–corrector modified barrier approach

This paper presents a new approach, predictor–corrector modified barrier approach (PCMBA), to minimize the active losses in power system planning studies. In the PCMBA, the inequality constraints are transformed into equalities by introducing positive auxiliary variables, which are perturbed by the barrier parameter, and treated by the modified barrier method. The first-order necessary conditions of the Lagrangian function are solved by predictor–corrector Newton's method. The perturbation of the auxiliary variables results in an expansion of the feasible set of the original problem, reaching the limits of the inequality constraints. The feasibility of the proposed approach is demonstrated using various IEEE test systems and a realistic power system of 2256-bus corresponding to the Brazilian South-Southeastern interconnected system. The results show that the utilization of the predictor–corrector method with the pure modified barrier approach accelerates the convergence of the problem in terms of the number of iterations and computational time.     

基于非线性互补问题函数的半光滑牛顿最优潮流算法

提出了一种新的基于非线性互补问题(NCP)函数的半光滑牛顿方法，以用于求解最优潮流(OPF)问题。通过引入NCP函数，将OPF模型KKT条件的互补松弛约束转化为等式约束，并采用非光滑牛顿法求解。算法的突出优势在于能够有效地处理OPF模型中的不等式约束，从而完全避免了OPF计算中起作用的不等式约束的识别问题。同时，文中利用电力系统的弱耦合特性，构造了牛顿分解算法。IEEE多个算例的数值试验表明：提出的算法具有很好的收敛特性和计算效果，有很好的实际应用前景。     

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。     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

Optimization-based scheduling of hydrothermal power systems withpumped-storage units

This paper presents an optimization-based method for scheduling hydrothermal systems based on the Lagrangian relaxation technique. After system-wide constraints are relaxed by Lagrange multipliers, the problem is converted into the scheduling of individual units. This paper concentrates on the solution methodology for pumped-storage units. There are, many constraints limiting the operation of a pumped-storage unit, such as pond level dynamics and constraints, and discontinuous generation and pumping regions. The most challenging issue in solving pumped-storage subproblems within the Lagrangian relaxation framework is the integrated consideration of these constraints. The basic idea of the method is to relax the pond level dynamics and constraints by using another set of multipliers. The subproblem is then converted into the optimization of generation or pumping; levels for each operating state at individual hours, and the optimization of operating states across hours. The optimal generation or pumping level for a particular operating state at each hour can be obtained by optimizing a single variable function without discretizing pond levels. Dynamic programming is then used to optimize operating states across hours with only a few number of states and transitions. A subgradient algorithm is used to update the pond level Lagrangian multipliers. This method provides an efficient way to solve a class of subproblems involving continuous dynamics and constraints, discontinuous operating regions, and discrete operating states     

基于实际报价结算的电力市场下考虑消除阻塞费用最小的竞价算法

提出了在电力市场下按实际报价结算时以购电费用最小来消除阻塞的竞价算法.该算法以购电费用最小为目标,以过载支路的过载量和相应的发电机灵敏度与修正量的乘积为等式约束以及发电机组功率的上、下限等为不等式约束.根据计算出的灵敏度的不同分成三种情况,采用拉格朗日函数求极值的方法,只需解一个线性方程组就可得出各发电机的修正量,计算简单快捷.算例证明了所提算法的正确有效.     

基于拉格朗日函数鞍距分配的广义内点法

不等式约束的处理是电力系统优化分析中比较困难的问题。文中根据拉格朗日函数的鞍点理论,将优化问题的等式约束进行松弛,形成计及等式约束的原始问题以及相应的对偶问题。通过定义原始和对偶问题之间的鞍距,并将鞍距在不等式约束之间进行分配,从而形成不同的针对不等式约束拉格朗日乘子的修正方程,进一步形成不同的优化算法。推导表明,内点罚函数法只是拉格朗日鞍点理论应用的一个特例。所提出的基于拉格朗日函数鞍距分配的广义内点法可以在电力系统优化分析中进行应用,将其应用于大规模间歇式电源接入情况下的电力系统最大传输能力问题中时,IEEE 30节点系统的计算结果及IEEE 14节点系统中不同算法的比较结果表明,此算法能够有效处理潮流问题不等式约束。     

Reliability and reserve in competitive electricity marketscheduling

Power systems are typically scheduled at least cost subject to operational and security constraints. Generally, no account is taken of generator reliability when scheduling units. Also, the security criteria, which include reserve, are usually deterministic in nature. This paper proposes a method to consider generator reliability explicitly in the scheduling problem. A competitive structure is proposed which includes a market for reserve. This is formulated as an augmented Lagrangian dual function and is solved using a new recurrent neural network. The price for reserve is used, along with the unit reliability, to find a balance between the cost of reserve and the risk of not providing it     

Emission,reserve and economic load dispatch problem with non-smooth and non-convex cost functions using epsilon-multi-objective genetic algorithm variable

This paper addresses a novel method for the multi-objective economic load dispatch (ELD) problem. Power generation, spinning reserve costs and emission are considered in the objective function of the frequency ELD problem. The frequency deviation, minimum frequency limits and other practical constraints are also taken into account in this problem. It is a highly constrained multi-objective optimization problem that involves conflicting objectives with both equality and inequality constraints. In this paper, an elitist evolutionary multi-objective optimization algorithm based on the concept of ε-dominance, called ε-multi-objective genetic algorithm variable (εv-MOGA), is proposed to solve the frequency ELD problem. In this study, the performance of the proposed εv-MOGA algorithm is compared with the performance of other classic and intelligent algorithms. The proposed method is tested on 6, 10, 13 and 40 generating units, and the simulation results of four power systems demonstrate the advantages of the proposed method for reducing the cost function.     

电力系统最优潮流的分布式并行算法

在构造电力系统分解协调模型的基础上，采用辅助问题原理进行分布式并行最优潮流运算，并用通用增广拉格朗日法加快收敛速度，对分布式并行优化算法做了详细推导。仿真结果表明，该算法可显著加快大系统的优化速度，提高控制的实时性和可靠性，符合电力市场发展的需求。     

一种求解多目标最优潮流的模糊优化算法

将模糊集理论和非线性原－对偶路径跟踪内点法应用于求解具有可伸缩约束的多目标最优潮流问题,选择合适的加速因子以改善算法的收敛性,并与单目标非线性最优潮流问题的计算结果进行了比较。对几个试验系统的计算表明,该算法具有稳定收敛性能,优化结果精确,灵活方便,处理变量不等工约束和函数不等式约束的能力很强,适合于求解大规模电力系统的多目标优化问题。     

考虑暂态稳定约束的可用传输能力计算

提出了一种考虑暂态稳定约束的可用输电能力(ATC)的计算方法。建立了在传统的静态ATC模型中加入暂态稳定约束的有效方法,并在此基础上给出了动态ATC问题的优化计算模型。同时提出了求解动态ATC问题的内点非线性规划算法。该算法不仅具有强大的处理等式约束和不等式约束的能力,而且具有良好的收敛性,能够有效求解动态ATC问题。所提出的模型与算法已在若干系统得到了验证,文中以IEEJ-WESTlO和IEEJ-WEST30系统的计算结果为例说明了该方法的有效性。