A parallel genetic algorithm approach to solving the unitcommitment problem: implementation on the transputer networks

Through a constraint handling technique, this paper proposes a parallel genetic algorithm (GA) approach to solving the thermal unit commitment (UC) problem. The developed algorithm is implemented on an eight-processor transputer network, processors of which are arranged in master-slave and dual-direction ring structures, respectively. The proposed approach has been tested on a 38-unit thermal power system over a 24-hour period. Speed-up and efficiency for each topology with different number of processor are compared to those of the sequential GA approach. The proposed topology of dual-direction ring is shown to be well amenable to parallel implementation of the GA for the UC problem     

Solving the unit commitment problem with a genetic algorithm through a constraint satisfaction technique

This paper proposes a genetic algorithm (GA) in conjunction with constraint handling techniques to solve the thermal unit commitment problem. To deal effectively with the constraints of the problem and prune the search space of the GA in advance, the difficult minimum up- and down-time constraints are embedded in the binary strings that are coded to represent the on-off states of the generating units. The other constraints are handled by integrating penalty factors into the cost function within an enhanced economic dispatch program. The proposed GA approach has been tested on a practical Taiwan Power (Taipower) thermal system over a 24-hour period for different utility factors and GA control parameters. Test results reveal that the features of easy implementation, fast convergence, and a highly near-optimal solution in solving the UC problem can be achieved by the proposed GA approach.     

基于内点半定规划的机组组合问题

提出一种基于内点半定规划(semidefinite program- ming, SDP)直接求解机组组合(unit commitment, UC)问题的新方法。通过引入辅助变量,该方法将原整数变量约束转化为凸二次约束,进而将UC问题转化为半定规划问题,并用现代内点法进行求解。针对计算结果中整数变量存在微小偏差的问题,采用启发式技术进行修正。100机24时段等6个系统的仿真结果表明,所提方法能有效处理机组爬坡约束,具有较快的计算时间,适合于求解大规模的UC问题,是一种有应用前景的方法。     

基于混沌遗传混合优化算法的短期负荷环境和经济调度

环境和经济短期负荷调度主要由在调度周期内的最优机组组合和负荷分配组成,该文将优先次序法、遗传算法与混沌优化相结合,以应用到电站机组环境／经济运行优化问题中,在混沌遗传算法中采用递阶基因结构,将控制基因用于机组组合全局粗寻优,参数基因用于负荷分配局部优化, 基因修正与罚函数相结合解决约束问题,采用混沌扰动避免遗传算法早熟,运用基于线性搜索的混沌局部优化方法,加快算法的收敛速度和降低计算时间,优化计算结果可以同时得到最优机组组合及负荷最优分配,为实际调度系统提供了一个良好的方法。     

Unit commitment by an enhanced simulated annealing algorithm

A new simulated annealing (SA) algorithm combined with a dynamic economic dispatch method has been developed for solving the short-term unit commitment (UC) problem. SA is used for the scheduling of the generating units, while a dynamic economic dispatch method is applied incorporating the ramp rate constraints in the solution of the UC problem. New rules concerning the tuning of the control parameters of the SA algorithm are proposed. Three alternative mechanisms for generating feasible trial solutions in the neighborhood of the current one, contributing to the reduction of the required CPU time, are also presented. The ramp rates are taken into account by performing either a backward or a forward sequence of conventional economic dispatches with modified limits on the generating units. The proposed algorithm is considerably fast and provides feasible near-optimal solutions. Numerical simulations have proved the effectiveness of the proposed algorithm in solving large UC problems within a reasonable execution time.     

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 solution methodology using genetic algorithm

Solution methodology of unit commitment (UC) using genetic algorithms (GA) is presented. Problem formulation of the unit commitment takes into consideration the minimum up and down time constraints, start up cost and spinning reserve, which is defined as minimization of the total objective function while satisfying the associated constraints. Problem specific operators are proposed for the satisfaction of time dependent constraints. Problem formulation, representation and the simulation results for a 10 generator-scheduling problem are presented     

基于矩阵实数编码遗传算法求解大规模机组组合问题

该文提出了一种采用矩阵实数编码遗传算法(MRCGA)进行机组组合优化的新方法：采用矩阵实数编码方式对整体发电计划进行编码后，可直接运用遗传操作求解机组组合问题，避免将其分解成机组启停安排和经济负荷分配的两层优化问题进行求解；采用多窗口变异技术，增强了算法的搜索能力。此方法提出了一种新的个体调整方法，可以处理各项约束条件，保证了结果的可行性。文中通过2个算例及与其它算法的对比分析，验证了所提出的方法在大规模机组组合问题求解时具有很强的适应性和全局搜索能力。     

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.     

Coordinated Design of Power System Stabilizers and Static Phase Shifters Using Genetic Algorithm

Coordinated design of a power system stabilizer (PSS) and a static phase shifter (SPS) using genetic algorithm (GA) is investigated in this paper. The design problem of PSS and SPS controller is formulated as an optimization problem. An eigenvalue-based objective function to increase the system damping is proposed. Then, GA is employed to search for optimal controller parameters. Different control schemes have been proposed and tested on a weakly connected power system with different disturbances, loading conditions, and parameter variations. It was observed that although the PSS enhances the power system stability, the SPS controller provides most of the damping and improves the voltage profile of the system. The nonlinear simulation results show the effectiveness and robustness of the proposed control schemes over a wide range of loading conditions and system parameter variations.     

Generator maintenance scheduling in power systems using metaheuristic-based hybrid approaches

The effective maintenance scheduling of power system generators is very important for the economical and reliable operation of a power system. This represents a tough scheduling problem which continues to present a challenge for efficient optimization solution techniques. This paper presents the application of metaheuristic approaches, such as a genetic algorithm (GA), simulated annealing (SA) and their hybrid for generator maintenance scheduling (GMS) in power systems using an integer representation. This paper mainly focuses on the application of GA/SA and GA/SA/heuristic hybrid approaches. GA/SA hybrid uses the probabilistic acceptance criterion of SA within the GA framework. GA/SA/heuristic hybrid combines heuristic approaches within the GA/SA hybrid to seed the initial population. A case study is formulated in this paper as an integer programming problem using a reliability-based objective function and typical problem constraints. The implementation and performance of the metaheuristic approaches and their hybrid for the test case study are discussed. The results obtained are promising and show that the hybrid approaches are less sensitive to the variations of technique parameters and offer an effective alternative for solving the generator maintenance scheduling problem.     

An application of reactive tabu search for service restoration in distribution systems and its comparison with the genetic algorithm and parallel simulated annealing

Service restoration in distribution systems can be formulated as a combinatorial optimization problem. It is the problem to determine power sources for each load considering various operational constraints in distribution systems. Up to now, the problem has been dealt with using conventional methods such as the branch and bound method, expert systems, neural networks, and fuzzy reasoning. Recently, modern heuristic methods such as genetic algorithms (GA), simulated annealing (SA), and tabu search (TS) have been attracting notice as efficient methods for solving large combinatorial optimization problems. Moreover, reactive tabu search (RTS) can solve the parameter tuning problem, which is recognized as the essential problem of the TS. Therefore, RTS, GA, and SA can be efficient search methods for service restoration in distribution systems. This paper develops an RTS for service restoration and compares RTS, GA, and PSA (parallel SA) for the problem. The feasibility of the proposed methods is shown and compared on a typical distribution system model with promising results. © 2000 Scripta Technica, Electr Eng Jpn, 133(3): 71–82, 2000     

A dynamic programming based fast computation Hopfield neural network for unit commitment and economic dispatch

This paper develops a new dynamic programming based direct computation Hopfield method for solving short term unit commitment (UC) problems of thermal generators. The proposed two step process uses a direct computation Hopfield neural network to generate economic dispatch (ED). Then using dynamic programming (DP) the generator schedule is produced. The method employs a linear input–output model for neurons. Formulations for solving the UC problems are explored. Through the application of these formulations, direct computation instead of iterations for solving the problems becomes possible. However, it has been found that the UC problem cannot be tackled accurately within the framework of the conventional Hopfield network. Unlike the usual Hopfield methods which select the weighting factors of the energy function by trials, the proposed method determines the corresponding factor using formulation calculation. Hence, it is relatively easy to apply the proposed method. The Neyveli Thermal Power Station (NTPS) unit II in India with three units having prohibited operating zone has been considered as a case study and extensive study has also been performed for power system consisting of 10 generating units.     

用遗传算法解算机组组合的研究

用遗传算法解决电力系统机组组合及机组间的负荷分配问题。在机组数目增加时，二进制编码的遗传算法的计算量及存储量会增加很多，并且经典的遗传算法不具有渐近收敛性。针对这些问题，作者采用二进制与浮点数混合的编码方案，并根据这一特点设计了遗传算子；对经典的遗传算法在计算中出现的随机性问题，则采用压缩映射遗传算法使计算过程渐近收敛。计算表明，该算法的具有渐近收敛性，与二进制编码的算法相比，计算所需时间及内存少，而且更易引入问题的相关信息。     

基于改进遗传算法的发电机调速器侧模糊PSS优化设计

基于模糊控制理论设计了发电机组调速器侧模糊电力系统稳定器,采用遗传算法优化其参数以克服模糊控制器参数设计的主观性。为了提高多参数控制器参数优化的速度和精度,对传统单点交叉遗传算法进行改进,提出了一种首尾轮换交叉遗传算法。该方法交替采用尾部和头部单点交叉,保证了各参数参与优化的概率基本均等。采用该算法对某水轮发电机组调速器侧模糊电力系统稳定器的参数进行优化计算,表明其优化速度和精度较试探法和传统单点交叉遗传算法有显著提高。     

一种求解大规模机组组合问题的混合智能遗传算法

针对传统的采用二进制编码的遗传算法在求解大规模机组组合问题时收敛速度慢、易早熟等问题,作者结合机组组合问题的特点,提出了一种混合智能遗传算法.该算法以机组状态作为个体编码,结合启发式方法的自适应智能变异算子求解目标函数,显著缩小了求解问题的规模,保证了群体多样性,提高了算法的搜索效率,改善了算法的收敛性.仿真计算结果表明了该算法的有效性和实用性.     

基于遗传算法的机组组合研究

针对遗传算法应用于机组组合问题的具体实现技术进行了深入的研究，实现了采用不同采样空间，不同选择策略，不同适值函数和不同交叉率／变异率的遗传算法和机组组合计算程序，并对10机系统和110机系统的仿真计算进行了分析和比较。结果表明各种不同实现技术的遗传算法应用到机组组合问题具有不同程度的有效性，另外，遗传算法的不同实现技术对收敛时间，收敛代数和收敛值也有较大的影响。文中在计算研究的基础上提出了适用于机组组合问题的遗传算法的具体实现技术，为遗传算法应用到机组组合问题的实用化研究奠定了坚实的基础。     

A new hybrid approach for the solution of nonconvex economic dispatch problem with valve-point effects

Economic dispatch (ED) generally formulated as convex problem using optimization techniques by approximating generator input/output characteristic curves of monotonically increasing nature results in an inaccurate dispatch. The genetic algorithm has previously been used for the solution of problem for economic dispatch but takes longer time to converge to near optimal results. The hybrid approach is one of the methodologies used to fine tune the near optimal results produced by GA. This paper proposes new hybrid approach to solve the ED problem by using the valve-point effect. The approach we propose combines the genetic algorithm (GA) with active power optimization (APO) based on the Newton's second order approach (NSO). The genetic algorithm acts as a global optimizer giving near optimal generation schedule, which becomes the input for generation buses in APO algorithm. This algorithm acting as local search technique dispatching the generated active power of units for minimization of cost and gives optimum generation schedule. Three machines 6-bus, IEEE 5-machines 14-bus, and IEEE 6-mchines 30-bus systems have been tested for validation of our approach. Results of the proposed scheme compared with results obtained from GA alone give significant improvements in the generation cost showing the promise of the proposed approach.     

A combinatorial artificial intelligence real-time solution to the unit commitment problem incorporating V2G

Plug-in hybrid electric vehicles (PHEVs) have been the center of attention in recent years as they can be utilized to set up a bidirectional connection to a power grid for ancillary services procurement. By incorporating Vehicle to Grid (V2G), this paper proposes a real-time solution to a non-convex constrained unit commitment (UC) optimization problem considering V2G parking lots as dispersed generation units. V2G parking lots can be considered as virtual power plants that my decrease dependency to small expensive units in a UC problem. In this paper, firstly a probabilistic attendance model of PHEVs in a parking lot is investigated, while expected number of PHEVs as well as the equivalent generation capacity of the parking lot is obtained using a radial basis neural network. Secondly, a particular UC problem considering V2G parking lot is solved using GA-ANN as a hybrid heuristic method. A real-time estimation of PHEVs number in the V2G parking lot and real-time solution to UC–V2G problem associated with load variation makes this work distinguished, while the proposed method is applied to a standard IEEE 10-unit test system with promising results.     

ACO based speed control of SRM fed by photovoltaic system

This paper proposes a speed control of Switched Reluctance Motor (SRM) supplied by Photovoltaic (PV) system. The proposed design of the speed controller is formulated as an optimization problem. Ant Colony Optimization (ACO) algorithm is employed to search for the optimal Proportional Integral (PI) parameters of the proposed controller by minimizing the time domain objective function. The behavior of the proposed ACO has been estimated with the behavior of Genetic Algorithm (GA) in order to prove the superior efficiency of the proposed ACO in tuning PI controller over GA. Also, the behavior of the proposed controller has been estimated with respect to the change of load torque, variable reference speed, ambient temperature, and radiation. Simulation results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on GA over a wide range of operating conditions.