A hybrid artificial neural network—differential dynamic programming approach for short-term hydro scheduling

In this paper, a hybrid artificial neural network-differential dynamic programming (ANN-DDP) method for the scheduling of short-term hydro generation is developed. The purpose of short-term hydro scheduling is to find the optimal amounts of generated powers for the hydro units in the system for the next N (N= 24 in this work) hours in the future. In the proposed method, the DDP procedures are performed offline on historical load data. The results are compiled and valuable information is obtained by using ANN algorithms. The DDP algorithm is then performed online according to the obtained information to give the hydro generation schedule for the forecasted load. Two types of ANN algorithm, the supervised learning neural network by Rumelhart et al. and the unsupervised learning neural network by Kohonen, are employed and compared in this paper. The effectiveness of the proposed approach is demonstrated by the short-term hydro scheduling of Taiwan power system which consists of ten hydro plants. It is concluded from the results that the proposed approach can significantly reduce the execution time of the conventional differential dynamic programming algorithm which is required to reach proper hydro generation schedules.     

梯级水电站联合优化发电调度

梯级水电站联合发电调度的优化模型的确定在整个电网经济、安全运行中起着非常重要的作用。文中提出一种新的梯级水电站群联合发电优化调度的调度准则——以单位水体发电电价最高优先发电,在此基础上建立梯级水电站群联合发电优化调度模型及其评价方法。首先建立基于四层水体的水库能的水电站发电模型,在此基础上提出单位水体发电电价的概念。建立优化调度模型时,将电力系统中的负荷变化和在电力市场机制下分时上网电价的影响因素考虑在内。该模型能较为客观地反映梯级水电站运行情况,能给系统调度员做出最佳调度决策提供一定的依据。优化仿真计算结果证明该调度准则具有可行性和适用性。     

A linear programming method for the scheduling of pumped-storageunits with oscillatory stability constraints

Scheduling of pumped-storage units at the Taiwan Power Company (TPC) is investigated in this work. In addition to economic considerations, oscillatory stability of the power system as affected by the operation of pumped-storage units is of major concern. To achieve a secure as well as economical dispatching schedule for the pumped-storage units, differential dynamic programming is first employed to reach a preliminary schedule in which total fuel cost of the thermal units over the study period is minimized. Oscillatory stability analysis of the power system under the preliminary schedule is then performed by computing the eigenvalues and eigenvectors for all electromechanical modes. Sensitivities of the eigenvalues for the poorly-damped inter-area mode with respect to individual generator outputs are evaluated. Then a systematic approach based on linear programming is presented to refine the preliminary schedule in order to improve the damping for the inter-area mode. The effectiveness of the proposed approach is demonstrated by short term operational planning of the Taiwan power system     

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     

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.     

A solution method using neural networks for the generator commitment problem

This paper studies the feasibility of applying the Hopfield-type neural network to unit commitment problems in a large power system. The unit commitment problem is to determine an optimal schedule of what thermal generation units must be started or shut off to meet the anticipated demand; it can be formulated as a complicated mixed integer programming problem with a number of equality and inequality constraints. In our approach, the neural network gives the on/off states of thermal units at each period and then the output power of each unit is adjusted to meet the total demand. Another feature of our approach is that an ad hoc neural network is installed to satisfy inequality constraints which take into account standby reserve constraints and minimum up/down time constraints. The proposed neural network approach has been applied to solve a generator scheduling problem involving 30 units and 24 time periods; results obtained were close to those obtained using the Lagrange relaxation method.     

Effect of a pumped storage plant on the generation reliability of the Taiwan power system

A novel approach to the reliability analysis of a power system with both hydro and pumped storage plants is presented in this paper. The proposed method is based on the concept of peak shaving for the hydro and pumped storage units. Different values of the stored energy of the pumped storage units are employed to demonstrate their effect on the generation reliability of a power system. The proposed scheme has been applied successfully to the reliability analysis of Taiwan power system.     

Hydrothermal scheduling by augmented Lagrangian: consideration oftransmission constraints and pumped-storage units

This paper presents an augmented Lagrangian (AL) approach to scheduling a generation mix of thermal and hydro resources. AL presents a remedy to duality gap encountered with the ordinary Lagrangian for nonconvex problems. It shapes the Lagrangian function as a hyperparaboloid associating penalty in the direction of the coupling constraints. This work accounts further for the transmission constraints. We use a hydrothermal resource model with pumped-storage units. An IEEE 24-bus test system is used for AL performance illustration. Computational models are all coded in C. The results of the test case show that the AL approach can provide better scheduling results as it can detect optimal on/off schedules of units over a planning horizon at a minimal cost with no constraint violation. It requires no iteration with economic dispatch algorithms. The approach proves accurate and practical for systems with generation diversity and limited transmission capacity     

多元电力系统中抽水蓄能的经济性问题研究

提出了从多元电力系统的角度对抽水蓄能进行经济性分析的方法。基于混合整数规划构建了含火电、CCGT、水电、风电、核电、抽水蓄能等各类电源的多元电力系统机组组合模型。采用该模型计算抽水蓄能单位容量的收益现值,并与单位容量的投资成本比较以分析其经济性。算例测试的结果证明了模型和方法的可行性。在此基础上,进一步研究了负荷侧的峰谷差率、电源侧的风电容量、水火电比例、核电容量和火电调节范围变化对抽水蓄能经济性的影响。算例结果显示,水火电比例、风电容量以及核电容量的变化对抽水蓄能的经济性的影响较大。     

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.     

Experiences with mixed integer linear programming based approacheson short-term hydro scheduling

This paper describes experiences with mixed integer linear programming (MILP) based approaches on the short-term hydro scheduling (STHS) function. The STHS is used to determine the optimal or near-optimal schedules for the dispatchable hydro units in a hydro-dominant system for a user-definable study period at each time step while respecting all system and hydraulic constraints. The problem can be modeled in detail for a hydro system that contains both conventional and pumped-storage units. Discrete and dynamic constraints such as unit startup/shutdown and minimum-up/minimum-down time limits are also included in the model for hydro unit commitment (HUC). The STHS problem is solved with a state-of-the-art package which includes an algebraic modeling language and a MILP solver. The usefulness of the proposed solution algorithm is illustrated by testing the problem with actual hydraulic system data. Numerical experiences show that the solution technique is computationally efficient, simple, and suitable for decision support of short-term hydro operations planning. In addition, the proposed approaches can be easily extended for scheduling applications in a deregulated environment     

Optimal dispatch of generating units of the Itaipu hydroelectricplant

This paper is concerned with the optimal dispatch of generating units of Itaipu, the world's largest hydroelectric plant in operation. Itaipu is a 12.6 GW hydro plant, located on the Parana river, in South America, composed of 18 identical 700 MW generating units. A dynamic programming model has been developed to optimize the number of generating units in operation at each hour of the day in order to attain the total generation scheduling of the plant in the most economic way. The model highlights the tradeoff between start-up/shut-down of generating units and hydro power efficiency, taking into account variations in tailrace elevation, penstock head losses and turbine-generator efficiencies. The methodology has been tested for a typical generation scheduling, and the results show that the number of the turbine-generator sets dispatched has a major influence on the overall hydroplant efficiency, and therefore it is a key aspect to be considered in the dispatch of hydro generating units. In the case of Itaipu, the economic benefits, in terms of greater power efficiency with respect to actual operation, are in the range of millions of dollars per year     

Parameterisation effect on the behaviour of a head-dependent hydro chain using a nonlinear model

This paper is on the problem of short-term hydro scheduling (STHS), particularly concerning a head-dependent hydro chain. We use a method based on nonlinear programming (NLP), namely quadratic programming, to consider hydroelectric power generation a function of water discharge and of the head. The method has been applied successfully to solve a test case based on a realistic cascaded hydro system with a negligible computational time requirement and is also applied to show that the role played by reservoirs in the hydro chain do not depend only on their relative position. As a new contribution to earlier studies, which presented reservoir operation rules mainly for medium and long-term planning procedures, we show that the physical data defining hydro chain parameters used in the nonlinear model have an effect on the STHS, implying different optimal storage trajectories for the reservoirs accordingly not only with their position in the hydro chain but also with the new parameterisation defining the data for the hydro system. Moreover, considering head dependency in the hydroelectric power generation, usually neglected for hydro plants with a large storage capacity, provides a better short-term management of the conversion of the potential energy available in the reservoirs into electric energy, which represents a major advantage for the hydroelectric utilities in a competitive electricity market.     

Power generation scheduling for multi-area hydro-thermal systemswith tie line constraints, cascaded reservoirs and uncertain data

The authors propose an approach to the short-term generation scheduling of hydro-thermal power systems (GSHT). The objective of GSHT is to minimize the total operation cost of thermal units over the scheduling time horizon. To solve the problem within a reasonable time, the problem is decomposed into thermal and hydro subproblems. The coordinator between these subproblems is the system Lagrange multiplier. For the thermal subproblem, in a multi-area power pool, it is necessary to coordinate the area generations for reducing the operation cost without violating tie limits. A probabilistic method is employed in considering load forecasting errors and forced outages of generating units to satisfy system reliability requirements. For the hydro subsystem, network flow concepts are adopted to coordinate water use over the entire study time span and the reduced gradient method is used to overcome the linear characteristic of the network flow method to obtain the optimal solution. Three case studies for the proposed method are presented     

精细化日发电计划模型与方法

电网调度精细化管理水平的提高客观上要求日发电计划的精细化管理。针对传统日发电计划功能单一且难以实现准确的安全校核与网损管理的问题,提出了精细化日发电计划模型及优化算法。该模型以电网状态估计的网络拓扑和参数构造约束条件,决策目标兼顾电网经济性与安全裕度价值,具有应对电网运行不确定性的能力。采用交直流混合迭代优化算法,提出了交流潮流分析与有功优化的一体化决策方法,工程实用性强,有助于实现网损管理和安全校核工作的精细化管理。最后采用IEEE 30节点算例验证了算法的合理性。     

基于人工神经网络的梯级水电厂日优化运行

提出了一种利用人工神经网络(ANN)进行梯级水电厂日优化运行研究的方法。既可用于制订 梯级日最优发电计划,又可用于梯级实时发电控制。为了加快神经网络的收敛速度,采用分 解网络技术,将一个复杂的网络分解为多个简单的网络。仿真结果表明,将神经网络应用于 这一领域,取得了较为满意的结果。     

A Joint Smart Generation Scheduling Approach for Wind Thermal Pumped Storage Systems

Abstract

Energy storage plays a crucial role in the development of smart grids with high wind power penetration. Pumped storage is an effective solution for smoothing wind power fluctuation and reducing the operating cost for a wind thermal power system. The joint generation scheduling of power systems with mixed wind power, pumped storage, and thermal power is a challenging problem. This article proposes a novel two-stage generation scheduling approach for this problem in the contexts of smart grids. Through optimization, a day-ahead thermal unit commitment and pumped storage schedule are provided; then, in real time, the pumped storage schedule is updated to mitigate the wind power forecasting error and hence avoid the curtailment of wind power generation. The proposed model aims to reduce the total operating cost, accommodate uncertain wind power as much as possible, and smooth the output fluctuation faced by thermal units, while making the system operate in a relatively reliable way. A binary particle swarm optimization algorithm for solving the proposed model and the pumped storage schedule update algorithm are also presented. The model and algorithm are tested on a ten-generator test system.     

提高广东电网自动发电控制应用水平的探讨

通过对广东电网自动发电控制(AGC)运行情况的分析,指出了影响AGC运行的主要原因是目前的控制方式为滞后的控制方式,适用于水电机组占较大比例的电网,而对火电机组占多数的广东电网,目前的控制方式并不能满足要求。为此,针对负荷预测和发电计划的应用,从管理和技术角度提出了水、火电协调的AGC控制方式。采用该方式可提高广东电网AGC运行水平,但具体的调节效果还有待于实际系统运行的验证。     

含机组组合的短期水火储联合最优经济调度

为研究抽水蓄能给水火联合调度系统带来的影响,提出了新的含机组组合的水火储联合最优经济调度模型,采用GAMS中含分支定界法的求解器对模型进行求解,仿真验证了所述模型的有效性和可行性。结果表明:所建模型可充分利用水力资源发电,缓解火电的调峰压力;并根据抽水储能的抽水发电特性实现了对火电机组的削峰填谷的作用,在改善火电运行条件的同时,平滑火电出力,减少污染气体的排放,提高整个电力系统运行的稳定性、经济性和环保性。同时,为进一步研究储能对电力系统的影响具有一定的借鉴作用。     

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