考虑分时电价的水电站调度图优化研究

本文以狮子滩水电站为例,将分时电价理论加入到水电站调度图理论进行研究,建立了考虑分时电价的水电站优化调度模型,应用改进的遗传算法进行优化模型计算。结果表明:考虑分时电价的水电站调度总发电量减少,峰电增加,可减少电力系统负担,增加发电效益。     

基于改进粒子群算法梯级水电站短期优化调度研究

应用改进粒子群算法求解梯级水电站短期优化调度问题.考虑发电效益和存水效益结合峰谷电价建立综合效益最大模型,针对粒子群算法存在的后期收敛速度慢和易陷入局部最优等缺点,引入收缩因子和杂交算子对其进行改进.应用改进粒子群算法对"水布垭水电站一隔河岩水电站一高坝洲水电站"进行短期优化调度,分别采用传统优化模型和峰谷优化模型进行实例计算,结果表明实施峰谷电价,对于提高梯级水电站综合效益,同时缓解高峰期用电紧张局面有更高的应用价值;应用改进粒子群算法求解梯级水电站短期优化调度问题在求解时间、求解精度上都得到了较满意的效果.     

分时电价下水电站水库优化调度的探讨

随着电力市场的开放和分时电价制度的实施,传统以发电量最大作为准则的情况已不能适应水电站优化调度的要求,文章提出了分时电价条件下水电站水库优化调度的数学模型,并以实例结果来说明模型的可行性。     

宝珠寺水电站水库优化调度浅析

随着四川发电电力市场的开放和分时电价制度的实施，以发电量最大作为目标已不能适应水电站优化调度的要求，文章提出了丰枯分时电价条件下水电站水库长期优化调度的数学模型，并以宝珠寺电站为例，研究结果说明了模型的合理性及可行性。     

电力市场交易背景下水电站优化调度研究

电力市场交易是影响我国未来电力体制改革走向的重要落地措施之一。通过对电力市场交易背景下水电站优化调度特点分析,构建了分时电价下水电站优化调度模型。在粒子群算法基础上,提出了改进惯性因子、加速因子以及迭代速度的改进策略,并将其运用于模型求解,以广东省梅州市青溪水电站为实例进行了研究,验证了模型和算法的有效性和适用性,该研究成果为电力市场背景下的水电站优化调度提供了新思路。     

粒子群算法在水电站日优化调度中的应用

针对传统的动态规划方法求解水库优化调度问题存在的“维数灾”问题,给出一种全局随机优化算法[1]——粒子群优化算法并应用于水库日优化调度问题中。相对于动态规划,该算法原理简单,易编程,占用计算机内存少,能以较快的速度收敛到全局最优解,从而为分时电价环境下的水电站日优化调度问题提供了一种有效的解决办法。     

冶勒水库年末消落水位研究

提出了冶勒水库在考虑丰枯分时上网电价条件下。确定年末消落水位的方法。研究表明，分时上网电价攻策下年末消落水位对冶勒水库的运用调度产生很大影响。研究中提出的考虑丰枯分时电价水库长期优化调度模型。可以确定较优的年末消落水位，使水电站可靠出力提高．发电收益提高。该方法对分时上网电价攻策下，多年调节水库的调度运用具有推广价值。     

SAPSO算法在梯级水电站日优化调度中的应用

针对传统的动态规划方法求解水库优化调度阋题存在的"维数灾"问题,提出一种全局随机优化算法[1]--SAPSO算法及其在水库日优化调度问题中的应用.相对于动态规划,该算法原理简单,易编程实现,占用计算机内存少,能以较快的速度收敛到全局最优解,从而为分时电价环境下的水电站日优化调度问题提供了一种有效的解决办法.     

基于用户价格响应和满意度的峰谷分时电价决策模型

峰谷分时电价是电力需求侧管理(DSM)的重要手段之一，合理地制定峰谷电价水平是其实施的关键。在了解用户实行的峰谷电价历史数据的基础上，提出建立用户的电价响应矩阵以反映用户对峰谷分时电价的响应;并建立用户满意度模型，综合考虑用户用电方式的满意度和电费支出的满意度。给出了基于用户价格响应和满意度的峰谷分时电价优化决策模型。最后给出一大工业用户数据验证了文中方法的有效性。     

基于自适应混沌蜂群算法的梯级电站优化调度

在峰谷分时电价背景下建立梯级水电站短期发电效益最大模型,通过自适应混沌蜂群算法(ACABC)对模型进行求解。为兼顾算法全局搜索能力及局部求解精度,引入搜索步长和蜜源选择概率自适应修正策略。考虑到模型求解过程中涉及的多维复杂约束问题,提出一种基于双向廊道的约束处理技术,用于规范解寻优空间。以峰谷分时电价下清江梯级短期优化调度为背景进行仿真建模,将计算结果与普通人工蜂群算法(ABC)和差分进化算法(DE)所得结果进行比较。实例研究表明,ACABC处理灵活且具有更强的全局搜索能力,工程实用价值较高。     

Reservoir Operation by a New Evolutionary Algorithm: Kidney Algorithm

This article shows an application of a new algorithm, called kidney algorithm, for reservoir operation which employs three different operators, namely filtration, secretion, and excretion that lead to faster convergence and more accurate solutions. The kidney algorithm (KA) was used for generating the optimal operation of a reservoir namely; Aydoghmoush dam in eastern Azerbaijan province in Iran whose purpose was to decrease irrigation deficit downstream of the dam. Results from the algorithm were compared with those by other evolutionary algorithms, including bat (BA), genetic (GA), particle swarm (PSO), shark (SA), and weed algorithms (WA). The results showed that the kidney algorithm provided the best performance against the other evolutionary algorithms. For example, the computational time for the KA was 3 s, 2 s, 4 s, 6 s and 3 s less than BA, SA, GA PSA and WA, respectively. Also, the objective function for the optimization problem was the minimization of the irrigation deficits and its value for the KA was 55%, 28%, 52%, 44 and 54% less than GA, SA, WA, BA and PSA, respectively. Also, the different performance indexes showed the superiority of the KA compared to the other algorithms. For example, the root mean square error for the KA was 74%, 61%, 68%, 33 and 54% less than GA, SA, WA, BA and PSA, respectively. Different multi criteria decision models were used to select the best models. The results showed that the KA achieved the first rank for the optimization problem and thus, it shows a high potential to be applied for different problems in the field of water resources management.     

基于新型蝙蝠算法-投影寻踪模型的文山州水量分配方法

基于公平和效率原则,构建文山州水量分配指标体系和水量分配投影寻踪(PP)模型。针对PP模型最佳投影方向难以确定的不足,利用新型蝙蝠算法(NBA)搜寻PP模型最佳投影方向,构建NBA-PP水量分配模型对文山州8县(市)水量进行分配。通过5个典型测试函数对NBA算法进行仿真验证,仿真结果与基本蝙蝠算法(BA)、人工蜂群算法(ABC)、布谷鸟搜索(CS)算法和差分进化算法(DE)进行对比。结果表明:通过引入生境选择策略及自适应补偿回声多普勒效应机制的NBA算法能有效平衡全局搜索和局部开发能力,寻优效果优于DE、CS、ABC和BA算法,具有较快的收敛速度、较高的寻优精度和较好的收敛稳定性与收敛可靠性;NBA-PP模型水量分配结果较目前分类权重法分配结果更科学、客观。     

蜜蜂算法及其在水电站优化调度中的应用

详细阐述了蜜蜂算法的优化原理及实现方法,并对算法的求解速度和全局寻优能力进行详细讨论。在此基础上,以国内广泛开展的复杂高维、非线性、多约束水库中长期优化调度问题为例,同时应用蜜蜂、基本粒子群、退火粒子群和交叉粒子群四种现代群智能优化算法进行分别求解。应用情况的对比分析表明,蜜蜂算法具有比上述其它几种算法更快的求解速度和全局寻优能力。此外,论文的研究成果也为水电站中长期发电优化调度问题提供了一种新的求解途径。     

NBA-MEPP模型在区域水资源安全评价中的应用

利用新型蝙蝠算法(NBA)优化最大熵投影寻踪(MEPP)模型的最佳投影方向,提出NBA-MEPP水资源安全评价模型,并构建了蝙蝠算法(BA)-MEPP、人工蜂群(ABC)算法-MEPP和粒子群优化(PSO)算法-MEPP作为对比模型;从水资源禀赋、水量安全、效率安全以及水质安全4个方面提出了水资源安全评价指标体系和分级标准,并以云南省16个州市的水资源安全评价为例进行实例研究。研究结果表明,NBA算法寻优MEPP目标函数获得的最优值、最劣值、平均值和标准差均要优于BA、ABC和PSO算法,具有较好的全局极值寻优能力。NBA-MEPP模型对德宏州的水资源安全评价为"安全",对丽江市的评价为"临界安全",对其他14个州市的评价为"较安全"。NBA-MEPP模型对16个州市的水资源安全评价结果与BA-MEPP模型的结果相同,但是在排序上存在着差异;与ABC-MEPP、PSO-MEPP模型在评价结果及排序上均存在差异。提出的模型及评价方法具有通用性,可为相关评价研究提供参考。     

Reservoir Optimization for Energy Production Using a New Evolutionary Algorithm Based on Multi-Criteria Decision-Making Models

The study applies kidney algorithm for the optimization of reservoir operation for hydropower generation. The objective function defined for optimization is to minimize the hydroelectric power deficiency. Results of kidney algorithm are compared with those of bat algorithm (BA), water cycle algorithm (WCA), biogeography-based optimization algorithm (BBO), genetic algorithm (GA), particle swarm optimization algorithm (PSOA), and scatter matters search algorithm (SMSA). All algorithms are evaluated by Complex proportional assessment (COPRAS), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), modified TOPSIS, and Weighted Aggregated Sum Product Assessment (WASPAS), as well as Borda count social choice theory. Then, vulnerability, time and volumetric reliability, as well as resiliency indices are used for comparison and multi-criteria decision-making indicators for selecting the best algorithm. It is found that no algorithm is ranked uniformly the best. Results indicate that kidney and particle swarm algorithms are ranked higher than other algorithms by most indices. Results of 10 random implementations of the objective function indicate that KA has a lower coefficient of variation and is computationally moe efficient. Further, most of the multi-criteria decision making models allocate the first rank to KA.     

Robust Water Supply Chain Network Design under Uncertainty in Capacity

This paper focuses on the capacity uncertainty in water supply chains that occurs when facilities face disruption. A combination of scenario-based two-stage stochastic programming with the min-max robust optimization approach is proposed to optimize the water supply chain network design problem. In the first stage, the decisions are made on locations and capacities of reservoirs and water-treatment plants while recourse decisions including amount of water extraction, amount of water refinement, and consequently amount of water held in reservoirs are made at the second stage. The proposed robust two-stage stochastic programming model can help decision makers consider the impacts of uncertainties and analyze trade-offs between system cost and stability. The literature reveals that most exact methods are not able to tackle the computational complexity of mixed integer non-linear two-stage stochastic problems at large scale. Another contribution of this study is to propose two metaheuristics - a particle swarm optimization (PSO) and a bat algorithm (BA) - to solve the proposed model in large-scale networks efficiently in a reasonable time. The developed model is applied to several hypothetical cases of water resources management systems to evaluate the effectiveness of the model formulation and solution algorithms. Sensitivity analyses are also carried out to analyze the behavior of the model and the robustness approach under parameters variations.

     

改进差分进化算法在水库优化调度中的应用研究

差分进化算法在求解水库优化调度时,进化后期种群多样性急剧下降,导致算法无法跳出局部最优解而出现“早熟”收敛。针对该问题,该文对算法的贪婪选择策略进行改进,使其以一定的概率动态接受稍差解作为子代个体,从而提高算法的种群多样性;同时,提出种群基因重生策略,进一步改善种群进化的基因信息结构。将改进的差分进化算法应用于清江梯级发电调度问题,并与差分进化算法、模拟退火算法求解结果进行对比。模拟结果表明,改进算法具有更强的全局搜索能力,求解梯级水库优化调度问题更具有优势。     

AN OPERATOR-SPLITTING ALGORITHM FOR THREE-DIMEN-SIONAL CONVECTION-DIFFUSION PROBLEMS

An operator-splitting algorithm for the three-dimensional convection-diffusion equa-tion is presented.The flow region is discretized into tetrahedronal elements which are fixed in time.The transport equation is split into two successive initial value problems:a pure convection problemand a pure diffusion problem.For the pure convection problem,solutions are found by the method ofcharacteristiCS.The solution algorithm involves tracing the characteristic lines backwards in time froma vertex of an element to an interior point.A cubic polynomial is used to interpolate the concentrationand its derivatives within each element.For the diffusion problem,an explicit finite element algorithmis employed.Numerical examples are given which agree well with the analytical solutions.     

河流水质多参数识别反问题的演化算法

给出了利用演化计算方法求解河流水质多参数反问题的一种新方法。该方法把参数识别反问题转化为优化问题用演化方法求解。它的特点在于：从多个初始点开始寻优，并借助交叉和变异算子来获得水质参数的全局最优解。数据模拟结果表明，该方法具有很高的精度，收敛速度快，且编程简单，易于计算机实现，值得在实际工作中采用。     

飞蛾火焰优化算法在承压含水层参数反演中的应用

通过10个典型低维函数对一种新型群体智能仿生算法——飞蛾火焰优化(MFO)算法进行仿真验证,并与粒子群优化(PSO)算法的寻优结果进行对比。以无界井流问题及直线隔水边界附近井流问题的解析解为基础,将MFO算法应用于分析抽水试验数据,进行反演承压含水层参数,并以2个实例对MFO算法进行验证。结果表明:MFO算法在低维函数极值寻优问题上具有较好的收敛精度和全局寻优能力,寻优精度较PSO算法提高了7个数量级以上。MFO算法对2个实例的反演精度较文献改进SA算法等提高了56.5%以上,具有较好的稳健性能、收敛速度和全局寻优能力。利用MFO算法对承压含水层参数进行反演,可获得比相关文献更高的反演精度,不但为精确估计承压含水层参数提供了有效方法,而且拓展了MFO算法在地下水模型参数反演中的应用,具有良好的应用价值和前景。