Improved artificial bee colony algorithm considering harvest season for computing economic dispatch on power system

This paper proposes the harvest season artificial bee colony (HSABC) algorithm, a novel improvement of the artificial bee colony (ABC) algorithm, for computing an economic dispatch solution of a power system based on fuel consumption and the produced emissions. A standard model of the power system, the IEEE‐62 bus system, is used to show the performance of HSABC using equality and inequality constraints to determine the optimal solution for the economic operation of the power system. Simulations involving the proposed algorithm show that HSABC has better ability to determine the minimum values for the operating cost problem with faster convergence and shorter running time when compared to the traditional ABC algorithm. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Optimal power flow for distribution networks using gravitational search algorithm

This paper presents a gravitational search algorithm (GSA)-based approach to solve the optimal power flow (OPF) problem in a distribution network with distributed generation (DG) units. The OPF problem is formulated as a nonlinear optimization problem with equality and inequality constraints, where optimal control settings in case of fuel cost minimization of DG units, power loss minimization in the distribution network, and finally simultaneous minimization of the fuel cost and power loss are obtained. The proposed approach is tested on an 11-node test system and on a modified IEEE 34-node test system. Simulation results obtained from the proposed GSA approach are compared with that obtained using a genetic algorithm approach. The results show the effectiveness and robustness of the proposed GSA approach.     

Thermal unit commitment using binary/real coded artificial bee colony algorithm

This paper presents a binary/real coded artificial bee colony (BRABC) algorithm to solve the thermal unit commitment problem (UCP). A novel binary coded ABC with repair strategies is used to obtain a feasible commitment schedule for each generating unit, satisfying spinning reserve and minimum up/down time constraints. Economic dispatch is carried out using real coded ABC for the feasible commitment obtained in each interval. In addition, non-linearities like valve-point effect, prohibited operating zones and multiple fuel options are included in the fuel cost functions. The effectiveness of the proposed algorithm has been tested on a standard ten-unit system, on IEEE 118-bus test system and IEEE RTS 24 bus system. Results obtained show that the proposed binary ABC is efficient in generating feasible schedules.     

An adaptive chaotic artificial bee colony algorithm for short-term hydrothermal generation scheduling

Short-term hydrothermal scheduling (SHS) is a complicated nonlinear optimization problem with a set of constraints, which plays an important role in power system operations. In this paper, we propose to use an adaptive chaotic artificial bee colony (ACABC) algorithm to solve the SHS problem. In the proposed method, chaotic search is applied to help the artificial bee colony (ABC) algorithm to escape from a local optimum effectively. Furthermore, an adaptive coordinating mechanism of modification rate in employed bee phase is introduced to increase the ability of the algorithm to avoid premature convergence. Moreover, a new constraint handling method is combined with the ABC algorithm in order to solve the equality coupling constraints. We used a hydrothermal test system to demonstrate the effectiveness of the proposed method. The numerical results obtained by ACABC are compared with those obtained by the adaptive ABC algorithm (AABC), the chaotic ABC algorithm (CABC) and other methods mentioned in literature. The simulation results indicate that the proposed method outperforms those established optimization algorithms.     

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.     

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.     

Solution to scalarized environmental economic power dispatch problem by using genetic algorithm

Nowadays, the widespread use of fossil based fuels in power generation units requires the consideration of the environmental pollution. Therefore, in this study, the solution of scalarized environmental economic power dispatch problem in which the environmental pollution has been taken into consideration has been analyzed by using genetic algorithm (GA). In order to turn the environmental economic power dispatch problem into the single objective optimization problem, the conic scalarization method (CSM) has been used. Also, weighted sum method (WSM) has been utilized in the scalarization of the same problem for comparison with CSM. The solution algorithm is tested for the electric power system of thermal units which has been solved by different methods in the literature. The best solution values that give minimum total fuel cost and minimum total emission values have been obtained (Pareto optimal values) for different weight values under electric constraints via CSM and WSM. The obtained Pareto optimal values for different scalarization methods have been compared with each other.     

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.     

碳中和愿景下考虑电氢耦合的风光场站氢储能优化配置

由于新能源出力具有波动性和间歇性,其直接接入电网会影响电力系统的安全稳定。为促进可再生能源消纳,可通过发展电氢耦合的氢储能系统解决此问题。为此,针对发电侧新能源风光场站的氢储能容量优化配置问题,以氢储能投资成本最小、系统累计跟踪计划误差最小和二氧化碳减排量增量最大为目标,以场站弃电率和实际场地面积为约束,构建了氢储能多目标优化配置模型。采用带精英策略的非支配排序的遗传算法和熵权法相结合的综合算法求解模型的最优折衷解。最后,以中国甘肃某风光场站为例,验证了所提模型与算法的有效性。结果显示:面向200 MW光伏和400 MW风电的可再生能源基地,200 kW电解槽、6 kg储氢罐及200 kW燃料电池的最佳配置数量分别为268、291和222个,所提氢储能系统积极响应了调度指令并大幅度降低了弃电率。     

基于互补机制的水火电力系统短期节能发电调度

为充分提高水火电力系统联合运行的经济性,将减少非可再生能源的使用量及降低火电成本为主要目标的水火电力系统短期发电调度问题,转化为水力发电量最大、耗水量最小和火力发电燃料总耗量最小且具有时序的3个优化子问题。该优化模型不仅可确定水电的最佳放水策略和火电的最佳出力,还可描述水电和火电的互补作用,充分体现节能和效益的理念。针对水电系统具有强非线性的特点,采用改电磁学算法进行求解,对火电子系统则采用内点法进行求解。算例结果验证了该方法的有效性。     

基于总体协调满意度的水火电力系统多目标短期优化调度

水火电力系统多目标优化调度符合国家节能减排的政策导向,在考虑整个系统梯级水电站发电效益最大化、火电机组发电成本最小化和梯级水电站发电用水最小化的目标同时,考虑了火电机组二氧化碳排放最小化的优化目标。针对水火电力系统短期优化运行调度中目标函数的权重处理困难的因素,采用一种基于目标函数总体协调满意度的处理方法,通过采用自然选择机制策略和异步学习因子策略提高算法的性能。以5个梯级水电站和4台火电机组组成的水火电力系统为实例进行计算．优化仿真计算证明了所提出的优化调度模型和求解算法具有可行性和适用性。     

Optimal load shedding study of naval-ship power system using the Everett optimization technique

This paper reports an application of the Everett optimization technique for value-based load shedding in naval-ship power system with network constraints and application of reliability constraints. A new congestion analysis index—expected contingency margin (ECM), which considers the probability of disturbance, and reliability indices loss of load probability (LOLP) and expected unserved energy (EUE) are presented to filter the critical outage and congestion cases. The optimal load shedding scheme carried out using the Everett optimization technique uses a utility function, which addresses the cost of allocating pay-off while satisfying the network and reliability constraints and generation capacity limits. The algorithm search for the optimal λ to form a fair pricing mechanism, which produces maximum pay-off and minimum load shedding while, satisfying system constraints. The proposed approach is examined on naval-ship power system and detailed results are shown.     

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.     

Artificial bee colony algorithm for solving multi-objective optimal power flow problem

This paper presents a new and efficient method for solving optimal power flow (OPF) problem in electric power systems. In the proposed approach, artificial bee colony (ABC) algorithm is employed as the main optimizer for optimal adjustments of the power system control variables of the OPF problem. The control variables involve both continuous and discrete variables. Different objective functions such as convex and non-convex fuel costs, total active power loss, voltage profile improvement, voltage stability enhancement and total emission cost are chosen for this highly constrained nonlinear non-convex optimization problem. The validity and effectiveness of the proposed method is tested with the IEEE 9-bus system, IEEE 30-bus system and IEEE 57-bus system, and the test results are compared with the results found by other heuristic methods reported in the literature recently. The simulation results obtained show that the proposed ABC algorithm provides accurate solutions for any type of the objective functions.     

Effects of load modeling on minimum loss,minimum emission,and multiple-objective optimal hydrothermal power flow

The effects of incorporating load models in three formulations of hydrothermal optimal power flow are considered in this paper. The formulations are transmission loss minimization, NO_x minimization, and the multiple objectives of minimum NO_x emission and minimum cost. The conventional algorithms and those incorporating load models were tested using a 14-bus and a 30-bus test system. Solutions are obtained using the MINOS optimization package. The computation time requirements of the conventional algorithm are lower than those incorporating load models. In all cases, there are measurable differences in the optimal voltage magnitudes for the systems tested. Incorporating load models yields lower active power generations and transmission losses, lower thermal fuel costs and reduced environmental impact than the conventional formulations.     

Static security in power system operation with fuzzy real loadconditions

This paper presents a mathematical formulation for optimal power flow (OPF) taking into account the fuzzy modeling of static power system security constraints due to the uncertainty in bus loads. Uncertainties in MW loads and generations are translated into possibility distribution functions. The fuzzy OPF problem is decomposed, via Dantzig-Wolfe decomposition, into subproblems corresponding to the possibility distributions of loads. The effects of phase shifters are modeled as equivalent real power injections at corresponding system buses, which preserves the Y-bus symmetry and maintains minimum memory requirements. Contingency constraints are added to the fuzzy OPF problem. Fuzzy sets are utilized to exercise a tighter control on least cost real power generation with minimum emission dispatch solution. The final solution is a compromise among cost, static security and emission considerations. Numerical results for the application of the proposed approach to test systems are discussed     

Optimal Power Flow Using a Hybrid Optimization Algorithm of Particle Swarm Optimization and Gravitational Search Algorithm

Abstract—This article presents a hybrid algorithm based on the particle swarm optimization and gravitational search algorithms for solving optimal power flow in power systems. The proposed optimization technique takes advantages of both particle swarm optimization and gravitational search algorithms by combining the ability for social thinking in particle swarm optimization with the local search capability of the gravitational search algorithm. Performance of this approach for the optimal power flow problem is studied and evaluated on standard IEEE 30-bus and IEEE 118-bus test systems with different objectives that reflect fuel cost minimization, voltage profile improvement, voltage stability enhancement, power loss reduction, and fuel cost minimization with consideration of the valve point effect of generation units. Simulation results show that the hybrid particle swarm optimization–gravitational search algorithm provides an effective and robust high-quality solution of the optimal power flow problem.     

综合环境保护及峰谷电价的水火电短期优化调度

为了使电力市场环境中的发电侧能够实现节能环保且高收益的发电目标,对机组出力变化与分时电价波动之间的关系进行了研究,构建了一种新的水火电短期优化调度模型,该模型以实现电力市场条件下最大发电收益为目标,同时综合考虑了峰谷分时电价和环境保护成本对发电侧经济效益的影响,还考虑了梯级水电站群的蓄水量、下泄流量、机组出力等约束条件,由此得出机组的优化调度方案。针对传统优化算法难以处理高维梯级水电站优化调度多约束条件的缺陷,利用微分进化算法对此优化模型进行求解,仿真计算结果证明了该模型的合理性和算法的有效性。     

基于ORACLE的入炉配煤优化问题的实现

以太原第一发电厂燃料成本分析管理为背景,以降低燃料成本为目的,提出了入炉煤配比的最优化问题,该问题是衡量燃煤电厂经济技术指标的重要基础。该问题以单纯形算法为基础原理,采用ORACLE中的PL/SQL程序设计语言编写算法,计算出了入炉配煤比及耗煤的最低成本,较准确地反映了每台锅炉的用煤量,大大提高了计算效率。入炉煤配比的最优化问题对于电力系统燃料成本分析,给予了较好的决策支持,决策者可以根据计算出的配煤比,来做出相应的决策,这样使得系统决策可以更准确、更方便地进行,并且对于系统的成本节约起到了较好的作用,所以入炉煤配比的最优化问题对于电厂具有重要的现实和经济意义。     

A new model for optimal generation scheduling of power system considering generation units availability

The objective of the short-term combined economic–environmental dispatch is to obtain the optimal power outputs of all generating units in the system given simultaneous minimization of the corresponding fuel cost and the release of gaseous pollutants in the environment. In this paper the classical model of the dynamic combined economic–environmental power dispatch is upgraded considering the availability of the generating units present in the system. The unavailability of power generation is defined as risk index and is considered to be a function of the generating units power level. An efficient multi-objective based genetic algorithm is applied for optimization purposes. Combined hydro-thermal power system is used as a case study system. The results show increase of the availability of power generation followed by small increase of the fuel cost and the gaseous emission.