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.     

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.     

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.     

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.     

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 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.     

基于改进PSO算法的短期发电计划研究

介绍了粒子群优化算法PSO(Panicle Swarm Optimization),并针对短期发电计划中的优化问题提出了一种改进PSO算法,将表示机组开停机状态的离散变量转换为0～1范围内的连续变量．与机组出力一起进行PSO优化搜索,然后再利用就近取整函数“mund”将其转换成整数变量。详细描述了应用改进PSO算法求解机组优化启停问题的具体步骤。将该方法应用于10机系统,实验结果表明该改进PSO算法用于短期发电计划是可行的。     

Short-term hydrothermal generation scheduling by a modified dynamic neighborhood learning based particle swarm optimization

The main objective of the short-term hydrothermal generation scheduling (SHGS) problem is to determine the optimal strategy for hydro and thermal generation in order to minimize the fuel cost of thermal plants while satisfying various operational and physical constraints. Usually, SHGS is assumed for a 1 day or a 1 week planing time horizon. It is viewed as a complex non-linear, non-convex and non-smooth optimization problem considering valve point loading (VPL) effect related to the thermal power plants, transmission loss and other constraints. In this paper, a modified dynamic neighborhood learning based particle swarm optimization (MDNLPSO) is proposed to solve the SHGS problem. In the proposed approach, the particles in swarm are grouped in a number of neighborhoods and every particle learns from any particle which exists in current neighborhood. The neighborhood memberships are changed with a refreshing operation which occurs at refreshing periods. It causes the information exchange to be made with all particles in the swarm. It is found that mentioned improvement increases both of the exploration and exploitation abilities in comparison with the conventional PSO. The presented approach is applied to three different multi-reservoir cascaded hydrothermal test systems. The results are compared with other recently proposed methods. Simulation results clearly show that the MDNLPSO method is capable of obtaining a better solution.     

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.     

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。     

Short term generation scheduling of cascaded hydro electric system using novel self adaptive inertia weight PSO

Hydro power plants are multipurpose projects, which are not only generating power but also responsible for the fulfillment of irrigation requirements of nearby zone. To harness the potential energy of released water Canal Head Power Houses (CHPHs) are also located at irrigation canals of the multipurpose projects. The present paper proposed a Novel Self Adaptive Inertia Weight Particle Swarm Optimization (NSAIW_PSO) approach to determine the optimal generation schedule of real operated cascaded hydroelectric system located at Narmada river in Madhya Pradesh, India. Here generation scheduling problem has been formulated in two cases. Case one considers natural inflows, evaporation losses and irrigation requirements assuming no generation through CHPHs. Whereas second case considers all above factors along with generation through CHPHs. Results show that in case two the amount of water discharge through all hydro power plants is less in comparison to case one to fulfill the same load demand, which shows the importance of CHPHs.     

Long term scheduling for optimal allocation and sizing of DG unit considering load variations and DG type

This paper proposes a new long term scheduling for optimal allocation and sizing of different types of Distributed Generation (DG) units in the distribution networks in order to minimize power losses. The optimization process is implemented by continuously changing the load of the system in the planning time horizon. In order to make the analysis more practical, the loads are linearly changed in small steps of 1% from 50% to 150% of the actual value. In each load step, the optimal size and location for different types of DG units are evaluated. The proposed approach will help the distribution network operators (DNOs) to have a long term planning for the optimal management of DG units and reach the maximum efficiency. On the other hand, since the optimization process is implemented for the entire time period, the short term scheduling is also possible. The proposed method is applied to IEEE 33-bus test system using both the analytical approach and particle swarm optimization (PSO) algorithm. The simulation results show the effectiveness and acceptable performance of the proposed method.     

云计算多目标任务调度的优化粒子群算法研究

针对传统粒子群算法求解云计算多目标任务调度的收敛速度慢、精度低的缺陷,提出一种优化多目标任务调度粒子群算法(MOTS-PSO)。首先,引入非线性自适应惯性权重,改变粒子的寻优能力,避免算法陷入局部最优;其次引入花朵授粉算法概率更新机制,平衡粒子的全局搜索和局部寻优,并对粒子的全局搜索位置更新公式进行改进;最后引入萤火虫算法,产生"精英解"对局部搜索位置更新公式进行改进;同时利用"精英解"对粒子的位置进行扰动,跳出局部最优状态。实验表明,MOTS-PSO算法在收敛速度和收敛精度上,比PSO算法提高了27.1%、19.9%,比FA算法提高了22.09%、5.2%。进一步实验表明,MOTS-PSO算法在解决不同规模数量的任务调度时,比PSO、FA算法效果更优。     

Optimum battery energy storage system using PSO considering dynamic demand response for microgrids

This article proposes a novel optimum sizing of battery energy storage system (BESS) using particle swarm optimization (PSO) incorporating dynamic demand response (DR) to improve a fast, smooth and secure system stability and performance, avoiding a microgrid from instability and system collapse during an emergency situation. An optimum size of BESS integrating DR can play an important role in frequency control of the microgrid in order to rapidly improve the system stability, restore the power equilibrium and prevent system collapse in the microgrid. The optimum size of BESS is evaluated by PSO incorporating DR based on frequency control of the microgrid. The results show that the optimum size of BESS-based PSO with DR can improve a fast, smooth and safe system performance and dynamic stability compared with the optimum size of BESS-based simulated annealing (SA) with DR and the conventional size. Nevertheless, the proposed sizing methods also determined the impact of BESS specified costs between modern and conventional BESS technologies. The capital cost, operating and maintenance cost of BESS were then investigated and compared in terms of economical performance for microgrid operations.     

智能电网下用户用电系统多目标调度策略研究

用户用电系统是智能电网在居民侧的延伸,是智能电网领域的研究热点之一.通过对居民主要家电的用电起始时间、用电时长、用电时段数的设计,依据动态电价信息,结合分布式电源、电动汽车向电网反馈电能的能力,对用电负载、光伏电池板以及电动汽车建立数学模型,利用粒子群算法进行寻优求解,对用户用电系统多目标运行进行调度安排,在不影响用户用电舒适度的情况下,给出一种以用电费用最小为目标的用电安排策略.最后经过实例分析,通过仿真结果比较发现,用电安排策略有效的降低了34.6％的用电费用,验证了用电安排策略的可行性和经济性,为用户合理用电提供了理论指导.     

考虑源网荷储协调的主动配电网优化调度方法研究

针对源网荷储的一体化运行目标,提出一种考虑源网荷储的主动配电网协调运行调度方法.基于多时间尺度的思想,以最小化可调节量和最大程度地增加新能源消纳为目标,建立一个具有两个互补时间尺度的主动配电网日前-日内优化调度模型,将差分进化算法和帝国竞争算法相结合求解最优调度模型.仿真验证了所提出的多源协同调度方法的优越性.仿真结果...     

含多微网的主动配电网分层调度策略

近年来,可再生能源高渗透率接入电网面临着明显的弃风弃光现象。微网为可再生能源的充分利用提供了良好的平台,主动配电网下多微网间的协调调度将进一步提高可再生能源的利用率。将分层控制理论与多微网并列运行的配电网架构结合,提出一种以配网协调能力最强为目标的先配网后微网的两步动态分层调度策略。重点研究配网能量管理中心和微网EMS的信息交互过程,旨在最大化利用可再生能源,减少主动配电网与主网的交互功率,同时保证微网运行的经济性。最后,以含多微网的主动配电网为例分析,验证所提出策略的正确性。与其他策略的对比体现了所提策略在充分利用可再生能源、减少主动配电网与主网的交互功率、缩短优化用时方面的优势。     

含电动汽车充电站商业型虚拟电厂的日前调度优化策略研究

研究商业型虚拟电厂运行机制能为新能源电源入网提供一定的技术支撑。将风力发电系统、光伏发电系统、储能系统、电动汽车充电站整合为一个虚拟发电厂,可显著降低因新能源单独并网时的出力不确定性及电动汽车无序充电对电网造成的不良影响,减轻电网压力,并可有效促进新能源消纳。以虚拟电厂经济效益最优为目标,在满足各约束条件的前提下,对其进行调度优化策略研究。通过线性惯性权重粒子群算法及非线性惯性权重粒子群算法对所提模型进行求解,结果表明采用非线性惯性权重粒子群算法不仅能避免过早收敛陷入局部最优而且得到的效益更高。通过算例验证了该模型的合理性及求解方法的有效性。     

An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time

This paper presents an improved maximum power point tracking (MPPT) strategy for photovoltaic (PV) systems based on particle swarm optimization (PSO). The capability of the PSO algorithm to cope with partially shaded conditions (PSCs) is the primary motivation of this research. Unlike conventional PSO-based MPPT systems, a variable sampling time strategy (VSTS) based on the investigation of the dynamic behavior of converter current is deployed to increase system tracking time. The performance of the proposed system is evaluated using MATLAB simulation and experimentation, in which a digital signal controller is used to implement the proposed algorithm on a real boost converter connected to a PV simulator. The main advantage of the proposed algorithm is fast and accurate performance under different conditions, including PSCs.