A noise annealing neural network for hydroelectric generationscheduling with pumped-storage units

A new approach based on neural network is proposed for the hydroelectric generation scheduling with pumped-storage units at Taiwan power system. The purpose of hydroelectric generation scheduling is to determine the optimal amounts of generated powers for the hydro units in the system. To achieve an economical dispatching schedule for the hydro units including two large pumped-storage plants, a neural network is employed to reach a schedule in which total fuel cost of the thermal units over the study period is minimized. The neural network model presented can solve nonlinear constrained optimization problems with continuous decision variables. Incorporating the noise annealing concepts, the model is able to produce such a solution which is the global optimum of the original problem with probability close to 1. The proposed approach is applied to hydroelectric generation scheduling of Taiwan power system. It is concluded from the results that the proposed approach is very effective in reaching proper hydro generation schedules     

A decoupled solution of hydro-thermal optimal power flow problem bymeans of interior point method and network programming

This paper presents a new decoupled model together with a very efficient coordination algorithm to solve a hydrothermal optimal power flow (HTOPF) problem over a certain time horizon. Based on the Lagrange relaxation at the level of the KKT (Karush-Kuhn-Tucker) conditions of the primal problem, the HTOPF is decomposed into thermal plant subproblems formulated as OPF and hydroplant subproblems. To solve efficiently the thermal OPF subproblems, the warm-starting scheme has been incorporated into interior point quadratic programming (IPQP). As to the hydroplant subproblems, a united network flow model is presented in which a fixed head plant is treated as a special case of a variable head plant. The hydroplant subproblem can be formulated as a minimum-cost maximum-flow problem for which unit cost functions of hydroplants are defined exactly. A proposed variant of the partitioning shortest path algorithm has brought about a great speed up in the computation of the subproblems. The validity of the proposed method has been examined by solving the IEEE test systems and a Chinese power system consisting of 13 thermal plants and 12 hydro power plants; the last system is a large size problem such that it has 107712 primal and dual variables. Simulation results obtained are quite convincing     

Optimum short-term hydrothermal scheduling with spinning reservethrough network flows

Optimizing the thermal production of electricity in the short term in an integrated power system when a thermal unit commitment has been decided means coordinating hydro and thermal generation in order to obtain the minimum thermal generation costs over the time period under study. Fundamental constraints to be satisfied are the covering of each hourly load and satisfaction of spinning reserve requirements and transmission capacity limits. A nonlinear network flow model with linear side constraints with no decomposition into hydro and thermal subproblems was used to solve the hydrothermal scheduling. Hydrogeneration is linearized with respect to network variables and a novel thermal generation and transmission network is introduced. Computational results are reported     

Hydrothermal scheduling of a multireservoir power system withstochastic inflows

A suboptimal method for solving the annual hydrothermal scheduling problem for a multireservoir hydrothermal system is presented. Hydrothermal scheduling is performed in order to find the optimum allocation of hydro energy so that the annual operating cost of a mixed hydrothermal system is minimized. In the present work, the hydrothermal scheduling problem is formulated as a constrained optimization problem which is decomposed into three subproblems; maintenance scheduling of thermal units, dispatch of thermal units, and dispatch of hydroplants. The algorithm takes into consideration the stochastic nature of the water inflows to the reservoirs and the forced outage rates of the thermal units. Results obtained by the application of the algorithm to a simplified version of the Hellenic Hydrothermal Power System, showing the effect of the hydroplants, are reported     

大型水火电力系统短期最优机组启停调度

给出了描述大型水火电力系统短期机组启停调度的数学模型,应用大系统分散控制理论将这一复杂问题分解为3个简单的子问题,分别用一维动态规划和网络规划法术解,最后,通过拉格朗日乘子协调控制得出最优可行解。该算法也可同时考虑火电的最优启停机计划和水电的优化调度。     

Optimal generation scheduling with ramping costs

In this paper, a decomposition method is proposed which relates the unit ramping process to the cost of fatigue effect in the generation scheduling of thermal systems. The objective of this optimization problem is to minimize the system operation cost, which includes the fuel cost for generating the required electrical energy and starting up decommitted units, as well as the rotor depreciation during ramping processes, such as starting up, shutting down, loading, and unloading. According to the unit fatigue index curves provided by generator manufacturers, fixed unit ramping-rate limits, which have been used by previous studies, do not reflect the physical changes of generator rotors during the ramping processes due to the fatigue effect. By introducing ramping costs, the unit on/offstates can be determined more economically by the proposed method. The Lagrangian relaxation method is proposed for unit commitment and economic dispatch, in which the original problem is decomposed into several subproblems corresponding to the optimization process of individual units. The network model is employed to represent the dynamic process of searching for the optimal commitment and generation schedules of a unit over the entire study time span. The experimental results for a practical system demonstrate the effectiveness of the proposed approach in optimizing the power system generation schedule     

Scheduling hydro power systems with restricted operating zones anddischarge ramping constraints

An optimization-based algorithm is presented for scheduling hydro power systems with restricted operating zones and discharge ramping constraints. Hydro watershed scheduling problems are difficult to solve because many constraints, continuous and discrete, including hydraulic coupling of cascaded reservoirs have to be considered. Restricted or forbidden operating zones as well as minimum generation limits of hydro units result in discontinuous preferred operating regions, and hinder direct applications of efficient continuous optimization methods such as network flow algorithms. Discharge ramping constraints due to navigational, environmental and recreational requirements in a hydro system add another dimension of difficulty since they couple generation or water discharge across time horizon. Integrated consideration of the above constraints is very challenging. The key idea of this paper is to use additional sets of multipliers to relax discontinuous operating region and discharge ramping constraints on individual hydro units so that a two-level optimization structure is formed. The low level consists of a continuous discharge scheduling subproblem determining the generation levels of all units in the entire watershed, and a number of pure integer scheduling subproblems determining the hydro operating states, one for each unit. The discharge subproblem is solved by a network flow algorithm, and the integer scheduling problems are solved by dynamic programming with a small number of states and well-structured transitions     

A FAST ALGORITHM FOR OPTIMAL HYDROTHERMAL SCHEDULING WITH CASCADED AND INDEPENDENT HYDRO PLANTS

ABSTRACT

This paper presents a fast algorithm for solving the short-term hydrothermal scheduling problem in a power system consisting of cascaded plants with time delay and independent hydro plants. The operational planning of such problem is concerned with the determination of scheduling for hydro as well as thermal plants to meet the daily system demand with the objective of minimizing the total fuel cost of the thermal plants over the day subject to the relevant operating constraints associated with the thermal and hydro plants.

The algorithm employs a fast and simple alternating solution approach for hydrothermal scheduling in which the hydro subproblem is solved using the method of local variation while the associated thermal subproblem is solved through a judicious combination of Successive Linear Programming (SLP) method and Participation Factor method. Many computational features are incorporated in the solution algorithm exploiting the inherent characteristic of the complex hydrothermal scheduling problem.     

A genetic algorithm solution to the optimal short-term hydrothermal scheduling

This paper presents an algorithm for solving the hydrothermal scheduling through the application of genetic algorithm (GA). The hydro subproblem is solved using GA and the thermal subproblem is solved using lambda iteration technique. Hydro and thermal subproblems are solved alternatively. GA based optimal power flow (OPF) including line losses and line flow constraints are applied for the best hydrothermal schedule obtained from GA. A 9-bus system with four thermal plants and three hydro plants and a 66-bus system with 12 thermal plants and 11 hydro plants are taken for investigation. This proposed GA reduces the complexity, computation time and also gives near global optimum solution.     

Short term hydro–wind–thermal scheduling based on particle swarm optimization technique

Hydro–wind–thermal scheduling is one of the most important optimization problems in power system. An aim of the short term hydrothermal scheduling of power systems is to determine the optimal hydro, wind and thermal generations in order to meet the load demands over a scheduled horizon of time while satisfying the various constraints on the hydraulic, wind and thermal power system network. In this paper we present optimal hourly schedule of power generation in a hydro–wind–thermal power system applying PSO technique. The simulation results inform that the proposed PSO approach appears to be the powerful to minimize fuel cost and it has better solution quality and good convergence characteristics than other techniques.     

Multi-objective optimization of hydrothermal energy system considering economic and environmental aspects

Along with continuous global warming, the environmental problems, besides the economic objective, are expected to play more and more important role in the operation of hydrothermal power system. In this paper, the short-term multi-objective economic environmental hydrothermal scheduling (MEEHS) model is developed to analyze the operating approach of MEEHS problem, which simultaneously optimize energy cost as well as the pollutant emission effects. Meanwhile, transmission line losses among generation units, valve-point loading effects of thermal units and water transport delay between hydraulic connected reservoirs are taken into consideration in the problem formulation. In order to solve MEEHS problem, a new multi-objective cultural algorithm based on particle swarm optimization (MOCA-PSO) is presented in way of combining the cultural algorithm framework with particle swarm optimization (PSO) to carry though the evolution of population space. Furthermore, an effective constrain handling method is proposed to handle the operational constraints of MEEHS problem. The proposed method is applied to a hydrothermal power system consisting of four hydro plants and three thermal units for the case studies. Compared with several previous methods, the simulation solutions of MOCA-PSO with smaller fuel cost and lower emission effects proves that it can be an alternative method to deal with MEEHS problems. The obtained results demonstrate that the change of optimization objective leads to the shift of optimal operation schedules. Finally, the scheduling results of MEEHS problem offer enough choices to the decision makers. Thus, the operation with better performance of environment is achieved by more energy system cost.     

Short-term hydrothermal generation scheduling model using a genetic algorithm

A new model to deal with the short-term generation scheduling problem for hydrothermal systems is proposed. Using genetic algorithms (GAs), the model handles simultaneously the subproblems of short-term hydrothermal coordination, unit commitment, and economic load dispatch. Considering a scheduling horizon period of a week, hourly generation schedules are obtained for each of both hydro and thermal units. Future cost curves of hydro generation, obtained from long and mid-term models, have been used to optimize the amount of hydro energy to be used during the week. In the genetic algorithm (GA) implementation, a new technique to represent candidate solutions is introduced, and a set of expert operators has been incorporated to improve the behavior of the algorithm. Results for a real system are presented and discussed.     

A new long-term hydro production scheduling method for maximizingthe profit of hydroelectric systems

This paper presents a new long-term hydrothermal production scheduling method. The proposed method maximizes the profit of hydroelectric plants, based on the monthly energy requirement of the system, instead of minimizing the production cost of thermal units. It is shown that different forms of composite thermal marginal costs will lead to the same hydro production schedule. Thus a linear marginal cost, the simplest form, is sufficient for long-term hydrothermal scheduling. A linear hydro marginal profit is also sufficient for this purpose. An immediate conclusion is that an actual composite thermal cost function, which is complicated by thermal unit availability, may not be needed for the long-term optimal hydrothermal scheduling. Due to this simplification, traditional long and mid-term hydrothermal scheduling, a complicated problem, becomes easier to solve. The method can be used by the owners of independent hydro plants in a region for long-term hydroelectric scheduling under both deregulation and competition. A case study shows that the model allocates successfully and efficiently the hydroelectric resources to peak demand periods with negligible computation time     

Daily integrated generation scheduling for thermal,pumped‐storage,and cascaded hydro units and purchasing power considering network constraints

We have developed an innovative power generation scheduling method using quadratic programming (QP). The advantage of using our method is that it simultaneously solves unit commitment and economic load dispatch. We relax the binary variables of the unit state into continuous variables to apply QP to this problem. We also add a penalty term to converge the value of those variables to 0 or 1 to the objective function: the sum of the fuel costs and the start‐up costs. This penalty term depends on the per‐unit fuel cost. The possibility of its variable converging to zero increases as the cost increases. This method was applied to a test system of daily generation scheduling that consisted of 29 thermal units, two pumped‐storage units, four cascaded hydro units, and one transmission. The schedule satisfied all constraints, that is, load‐power balance, operation reserve, power flow, minimum up/down‐times, and fuel consumption. This result shows that the proposed method is effective. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 175(1): 25–34, 2011; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.21014     

考虑多因素协调的梯级水电站群调度策略研究

本文提出了一种计及多种因素间协调的梯级水电站群调度策略,以兼顾梯级水电站群的经济效益和社会效益。首先引入发电弃水量、抗旱放水量、以及泄洪水量等参数,用以完善梯级水电站数学模型;提出采用虚拟水库来表征流域分布泄洪点及抗旱灌溉点,并建立相应的数学描述;考虑梯级水电站水力调度,火电站的发电机组出力、能耗成本及污染排放三者之间,梯级电站的出力、发电流量、发电弃水量、防洪水量、抗旱水量与其它水力耦合参数之间,以及水火电机组联合调度之间综合效益基础上,建立了多目标综合节能调度模型;算例验证了该算法的可行性,并具有较为明显的节能效果和社会效益。     

USE OF PUMPED-HYDRO AS PEAK-LOAD MANAGEMENT PLANT IN OPTIMAL SCHEDULING OF POWER SYSTEMS

ABSTRACT

The system demand, during some intervals of a day, may exceed the available system generation (excluding the pumped-hydro generation). A pumped-hydro plant may then, be used as a peak-load management unit to safeguard the power system by minimizing the power blackout in order to avoid large deviation in system frequency. In this paper, an algorithm is presented to obtain the optimal schedule for hydro, thermal plants including the pumped-hydro unit by proper selection of initial feasible trajectory for the pumped-hydro plant. An additional constraint is introduced to ensure the power balance in each time interval during the perturbation of water storage trajectory. The proposed method decomposes the problem into hydro and thermal subproblems and solves them alternately. The hydro subproblem is solved using a search procedure namely, the local variation method and the thermal subproblem is solved using a judicious combination of participation factors/linear programming method. Optimal scheduling was conducted on a 9-bus system and a 66-bus Utility system. The results obtained for the above two systems demonstrate the effectiveness of the algorithm proposed.     

Hybrid Chemical Reaction Optimization Approach for Combined Economic Emission Short-term Hydrothermal Scheduling

—This article presents the hybridization of a newly developed, novel, and efficient chemical reaction optimization technique and differential evolution for solving a short-term hydrothermal scheduling problem. The main objective of the short-term scheduling is to schedule the hydro and thermal plants generation in such a way that minimizes the generation cost. However, due to strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained hydrothermal scheduling formulation is to estimate the optimal generation schedule of hydro and thermal generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. In this context, this article proposes a hybrid chemical reaction optimization and differential evolution approach for solving the multi-objective short-term combined economic emission scheduling problem. The effectiveness of the proposed hybrid chemical reaction optimization and differential evolution method is validated by carrying out extensive tests on two hydrothermal scheduling problems with incremental fuel-cost functions taking into account the valve-point loading effects. The result shows that the proposed algorithm improves the solution accuracy and reliability compared to other techniques.     

含流域梯级水电的水火风互补发电系统联合运行优化

大规模水电和风电并网后,来水和风速的随机性降低了发电的可控性,如何科学地在多元电源系统联合运行中确定旋转备用容量,更好地协调系统经济性与可靠性,是市场化背景下亟需解决的理论和现实问题。在分析水电和风电的自然与技术互补特性的基础上,建立考虑水流滞时效应的梯级水电水量平衡关系。通过结合传统梯级水电联合调度模型与火电、风电的经济调度模型,引入水力发电的水资源费用成本,综合考虑梯级水电能量转换、水量耦合、库容限制、水流滞时和风电穿透率等多类复杂约束,在分析电能市场与备用市场顺序决策经济意义的基础上,构建以运行成本最小为目标的考虑梯级水电的水火风互补发电系统短期优化运行模型。采用外点罚函数法将模型转化为无约束优化问题,基于加惯性权重的粒子群优化算法,运用MATLAB软件编程实现优化功能。4个梯级水电、3个常规火电、2个大型风电场构成的算例系统验证了所提模型的有效性和适用性。     

大规模风电接入下的火电机组灵活性改造规划

风力发电具有极强的随机波动性,大规模风电的消纳需要火电提供辅助服务。然而,中国的火电机组普遍存在总量富余但灵活性不足的问题,严重限制了风电的消纳。文中构建了考虑火电机组灵活性改造的电力系统长期调度模型。考虑到该模型是具有多时间维度耦合特征的大规模优化模型,利用Benders分解算法,将原问题分解成关于灵活性改造的投资主问题与不同改造方案下的各调度时段的运行子问题,从而实现了对具有多时间维度的大规模优化问题的高效求解。算例分析显示,所构建的模型能够在提升电力系统运行经济性的同时促进高比例风电消纳。     

Scheduling of hydrothermal power systems

The authors present a method for scheduling hydrothermal power systems based on the Lagrangian relaxation technique. By using Lagrange multipliers to relax system-wide demand and reserve requirements, the problem is decomposed and converted into a two-level optimization problem. Given the sets of Lagrange multipliers, a hydro unit subproblem is solved by a merit order allocation method, and a thermal unit subproblem is solved by using dynamic programming without discretizing generation levels. A subgradient algorithm is used to update the Lagrange multipliers. Numerical results based on Northeast Utilities data show that this algorithm is efficient, and near-optimal solutions are obtained. Compared with previous work where thermal units were scheduled by using the Lagrangian relaxation technique and hydro units by heuristics, the new coordinated hydro and thermal scheduling generates lower total costs and requires less computation time