Civilized swarm optimization for multiobjective short-term hydrothermal scheduling

This paper presents a solution technique for multiobjective short-term hydrothermal scheduling (MSTHTS) through civilized swarm optimization (CSO) which is the hybrid of society–civilization algorithm (SCA) and particle swarm optimization (PSO). The intra and inter society communication mechanisms of SCA have been embedded into the food-searching strategy of PSO to form CSO. The MSTHTS problem is formulated by considering economic and emission objectives. A new ideal guide method has been proposed to find out the Pareto-optimal front. Multi-reservoir cascaded hydro power plants having nonlinear generation characteristics and thermal power plants with non-smooth cost and emission curves are considered for analysis. Other aspects such as, water transport delay, water availability, storage conformity, power loss and operating limits are fully accounted in the problem formulation. The performance of the proposed CSO is demonstrated through two MSTHTS problems and the results are compared with those presented in the literature. CSO along with the new ideal guide method outperforms all the previous approaches by providing quality Pareto-optimal fronts.     

An improved PSO technique for short-term optimal hydrothermal scheduling

This paper presents a new approach to the solution of optimal power generation to short-term hydrothermal scheduling problem, using improved particle swarm optimization (IPSO) technique. The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydraulic network, water transport delay and scheduling time linkage that make the problem of finding global optimum difficult using standard optimization methods. In this paper an improved PSO technique is suggested that deals with an inequality constraint treatment mechanism called as dynamic search-space squeezing strategy to accelerate the optimization process and simultaneously, the inherent basics of conventional PSO algorithm is preserved. To show its efficiency and robustness, the proposed IPSO is applied on a multi-reservoir cascaded hydro-electric system having prohibited operating zones and a thermal unit with valve point loading. Numerical results are compared with those obtained by dynamic programming (DP), nonlinear programming (NLP), evolutionary programming (EP) and differential evolution (DE) approaches. The simulation results reveal that the proposed IPSO appears to be the best in terms of convergence speed, solution time and minimum cost when compared with established methods like EP and DE.     

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.     

Differential evolution technique-based short-term economic generation scheduling of hydrothermal systems

This paper presents differential evolution (DE)-based optimization technique for solving short-term economic generation scheduling of hydrothermal systems. A multi-reservoir cascaded hydrothermal system with non-linear relationship between water discharge rate, power generation and net head is considered here. The water transport delay between the connected reservoirs is also taken into account. Several equality and non-equality constraints on thermal units as well as hydro units and the effect of valve-point loading are also included in the problem formulation. The effectiveness of the proposed method is demonstrated on two test systems comprising of hydro and thermal units. Convergence characteristic of the proposed technique has been found to be quite satisfactory. The results obtained by the proposed technique are compared with other evolutionary methods. It is seen that the proposed technique is capable of producing encouraging solutions.     

A second order network flow algorithm for hydrothermal scheduling

This paper presents a second-order network flow algorithm specially designed for hydrothermal scheduling problems. The algorithm is based on the truncated Newton method and takes advantage of the particular layout of the hydro scheduling network. The three-diagonal structure of the Hessian matrix is also exploited. Heuristic strategies for variable partition into basic-superbasic-nonbasic sets are suggested to improve the algorithm's efficiency. Tests with systems of dimensions up to 27 hydro plants in cascade have been performed in order to evaluate the algorithm's performance and compare some variable partition strategies. Results have demonstrated the high efficiency of the code     

A modified chaotic differential evolution algorithm for short-term optimal hydrothermal scheduling

The short-term optimal hydrothermal scheduling (STOHS) plays one of the most important roles in power systems operation. The STOHS problem involves the solution of difficult constrained optimization problems that require good computational techniques. This paper proposes a modified chaotic differential evolution (MCDE) approach for the solution of this difficult optimization problem. A repair strategy and a novel selection operation are simultaneously introduced into the MCDE approach for handling constraints of the problem. The repair strategy preserves the feasibility of solutions generated and avoids the use of penalty factors as much as possible. The introduced selection operation makes a not clearly distinction between feasible solutions and infeasible ones at early stage of the algorithm and makes a clearly distinction at the later stage. Additionally, an adaptive regeneration operation is proposed to enhance population diversity and to avoid local optimums. Moreover, a chaotic local search technique is introduced also to accelerate the searching process of the algorithm. The proposed MCDE approach is applied to three well-known hydrothermal test systems in order to verify its feasibility and efficiency. The obtained results are compared with those obtained by other population-based heuristic approaches reported in literature. It is observed from the comparisons that the proposed MCDE approach performs effectively and can yield competitive solutions.     

三角旋回算法及其在短期水火协调优化中的应用

提出一种新的全局优化方法--三角旋回算法(Triangle Gyration Algorithm,简称TGA),认为该算法具有结构简单、鲁棒性强和快速收敛的特点.算法的寻优过程采用历史最优目标函数值进行指导,利用三角变换进行迭代使其能够快速收敛到全局最优.用一个典型的算例对三角旋回算法进行了性能分析,且将该算法应用在水火电力系统短期(日前现货市场)协调优化调度中,得出的结论是该算法综合性能优越,在优化方法领域具有广阔的应用前景.     

水火电力系统短期发电计划优化方法综述

水火电力系统的短期发电计划问题在电力系统的安全可靠和经济运行中发挥着越来越重要的作用，由于其本身的复杂性，很难从理论上找到全局最优解。深入探讨各种优化算法，并加以分类，详细综述各种优化方法在水火电力系统短期发电计划问题中所取得的研究成果和存在的不足之处。     

A method to eliminate solution trapping in applying progressiveoptimality principle to short-term hydrothermal scheduling

A method is described to eliminate solution trapping for the POP (progressive-optimality-principle) based short-term hydrothermal scheduling algorithm. In a POP-based scheduling algorithm, the trapping phenomenon can severely hinder solution optimality and thus limits the algorithm's applicability. In order to ensure path continuity, the proposed method identifies and bypasses the potential weak links, so that global optimality can be achieved progressively by successively solving a sequence of two-hour subproblems. Examples are given to illustrate the trapping phenomenon and the proposed method     

Optimization-based sale transactions and hydrothermal scheduling

Selling and purchasing power are important activities for electric utilities because of potential savings. When a selling utility presents an offer including prices, power levels and durations, a purchasing utility selects power levels and durations within the offered range subject to relevant constraints. The decision making process is complicated because transactions are coupled with system demand and reserve, therefore decisions have to be made in conjunction with the commitment and dispatching of units. Furthermore, transaction decisions have to be made in almost real time in view of the competitiveness of the power market caused by deregulation. In this paper, transactions are analyzed from a selling utility's viewpoint for a system consisting of thermal, hydro and pumped-storage units. To effectively solve the problem, linear sale revenues are approximated by nonlinear functions, and nonprofitable options are identified and eliminated from consideration. The multipliers are then updated at the high level by using a modified subgradient method to obtain near optimal solutions quickly. Testing results show that the algorithm produces good sale offers efficiently     

Teaching learning based optimization for short-term hydrothermal scheduling problem considering valve point effect and prohibited discharge constraint

This article presents a novel teaching learning based optimization (TLBO) to solve short-term hydrothermal scheduling (HTS) problem considering nonlinearities like valve point loading effects of the thermal unit and prohibited discharge zone of water reservoir of the hydro plants. TLBO is a recently developed evolutionary algorithm based on two basic concept of education namely teaching phase and learning phase. In first phase, learners improve their knowledge or ability through the teaching methodology of teacher and in second part learners increase their knowledge by interactions among themselves. The algorithm does not require any algorithm-specific parameters which makes the algorithm robust. Numerical results for two sample test systems are presented to demonstrate the capabilities of the proposed TLBO approach to generate optimal solutions of HTS problem. To test the effectiveness, three different cases namely, quadratic cost without prohibited discharge zones; quadratic cost with prohibited discharge zones and valve point loading with prohibited discharge zones are considered. The comparison with other well established techniques demonstrates the superiority of the proposed algorithm.     

Short-term hydrothermal scheduling using clonal selection algorithm

An efficient optimization procedure based on the clonal selection algorithm (CSA) is proposed for the solution of short-term hydrothermal scheduling problem. CSA, a new algorithm from the family of evolutionary computation, is simple, fast and a robust optimization tool for real complex hydrothermal scheduling problems. Hydrothermal scheduling involves the optimization of non-linear objective function with set of operational and physical constraints. The cascading nature of hydro-plants, water transport delay and scheduling time linkage, power balance constraints, variable hourly water discharge limits, reservoir storage limits, operation limits of thermal and hydro units, hydraulic continuity constraint and initial and final reservoir storage limits are fully taken into account. The results of the proposed approach are compared with those of gradient search (GS), simulated annealing (SA), evolutionary programming (EP), dynamic programming (DP), non-linear programming (NLP), genetic algorithm (GA), improved fast EP (IFEP), differential evolution (DE) and improved particle swarm optimization (IPSO) approaches. From the numerical results, it is found that the CSA-based approach is able to provide better solution at lesser computational effort.     

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     

Non-uniform composite representation hydroelectric systems forlong-term hydrothermal scheduling

This paper presents a nonuniform composite representation of hydroelectric power systems for use in long-term hydrothermal generation scheduling. This representation was developed from reservoir operational rules based on optimal reservoir trajectories obtained with a deterministic hydrothermal scheduling algorithm. A test system consisting of 7 large hydroelectric power plants of the Southeast Brazilian power system with 12572 MW of installed power capacity was selected for a case study. Operational cost comparisons with the classical uniform composite representation reveal significant savings     

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     

A short term hydrothermal scheduling approach for dominantly hydrosystems

A short-term hydrothermal scheduling approach is presented for predominantly hydroelectric systems. The model takes into account both the operating hydroelectric system and the electric transmission network constraints. The model consists of a simulation of the hydraulic system with the discharge decisions given by an optimal DC power flow algorithm. The release targets of the reservoirs, established by long-term operational planning, are enforced by a dual Lagrangean approach that fixes a penalty for the use of water in the reservoirs. Two illustrative examples have been solved in order to evaluate the efficiency of the approach     

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.     

Optimum scheduling procedure for cogenerating small power producingfacilities

A comprehensive model of a small power-producing facility (SPPF) is discussed to determine its optimum operational schedule under utility energy-spot-pricing policies. The model is particularly well suited for SPPFs with both topping and bottoming cogeneration cycles in that the various thermal energy flows are explicitly modeled along with the electrical energy flows. The optimum scheduling algorithm is based on a linear programming method that provides for the explicit inclusion of the various thermal and electrical operating constraints. A realistic performance index, based on the true SPPF revenues and costs, is used in the linear programming procedure     

Optimal short-term scheduling for a large-scale cascaded hydrosystem

This paper describes a short term hydro generation optimization program that has been developed by the Hydro Electric Commission (HEC) to determine optimal generation schedules and to investigate export and import capabilities of the Tasmanian system under a proposed DC interconnection with mainland Australia. The optimal hydro scheduling problem is formulated as a large scale linear programming algorithm and is solved using a commercially-available linear programming package. The selected objective function requires minimization of the value of energy used by turbines and spilled during the study period. Alternative formulations of the objective function are also discussed. The system model incorporates the following elements: hydro station (turbine efficiency, turbine flow limits, penstock head losses, tailrace elevation and generator losses), hydro system (reservoirs and hydro network: active volume, spillway flow, flow between reservoirs and travel time), and other models including thermal plant and DC link. A valuable by-product of the linear programming solution is system and unit incremental costs which may be used for interchange scheduling and short-term generation dispatch     

A short-term scheduling for a hydropower plant chain

An optimal control of a hydropower plant chain is introduced. The aim is to meet a predefined power demand and in the same time provide sufficient control capabilities. At first a state-space model of the river chain is presented. Then an optimal feedback control method is developed by introducing Hamiltonian for the system. The constraints are considered using Lagrangian multipliers. As an example a river bed with eight hydro plants is studied. The results show the suitability of the method to production planning and to analysis of the hydro system behaviour. It can also be used for on-line scheduling.