共查询到18条相似文献,搜索用时 140 毫秒
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采用模拟进化优化算法———蚁群优化算法来求解机组最优启停问题。引入了状态、决策、路径等概念,把机组最优启停问题设计成蚁群算法模式,通过附加惩罚项来处理各种约束,用tabu表限制不满足约束的状态,使得蚂蚁的搜索总在可行域内进行,对算法的搜索进程起到了有效的引导作用。仿真证明利用蚁群优化算法求解机组最优启停问题是可行的、有效的。 相似文献
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在电力市场环境下,发电商需要在不确定信息下,考虑机组的最小开、停机时间,确定各自机组期望的最优运行状态,并进而优化各自的报价.为了使电力市场模拟中的发电商决策模型更合理,作者基于报价中标概率函数,建立了考虑机组开、停机时间约束的报价决策模型,对机组自组合状态优化和报价决策行为进行研究和模拟.为了求解该不确定性混合整数问题,将其转化成为Markov过程,并提出相应算法.算例表明:考虑机组最小开、停机时间约束后,发电商会为了增加连续开机的可能性而降低在谷荷时段的报价,而这种报价策略会进一步加大市场中的峰谷电价差.该研究为电力市场模拟中发电商的决策提供了新的思路. 相似文献
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为优化电动汽车(electric vehicle,EV)充电方案,降低充电成本,在机组组合模型(unit commitment,UC)的基础上,提出了电动汽车最优充电模型。针对该模型,提出基于最小边际成本的近似求解算法,根据UC结果计算各调度时段的边际发电成本,优先调度EV在平均边际发电成本最小的区间充电。若现有开机机组不能满足EV的充电需求,则按照新增发电成本最低的原则优先开启满负荷状态下平均耗费最低的机组。以上过程迭代进行,直至所有EV充电完毕。仿真结果验证了该模型及求解算法的有效性,此外,算例分析表明,EV平均充电成本随EV渗透率的提高而增加,且快充模式对应的充电成本最低。 相似文献
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基于改进离散粒子群算法的电力系统机组组合问题 总被引:2,自引:0,他引:2
提出一种新的离散粒子群算法。结合改进的自学习策略优化粒子群算法适用于求解电力系统中的机组组合(unit commitment,UC)问题。算法将UC问题分解为具有整型变量和连续变量的2个优化子问题,采用离散粒子群优化和原对偶内点法相结合的双层嵌套方法对外层机组启、停状态变量和内层机组功率经济分配子问题进行交替迭代优化求... 相似文献
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针对传统的模式搜索法(general pattern search filter algorithm,GPS—Filter)效率低的问题,提出一种改进的广义模式搜索一过滤器算法(improved general pattem search filter algorithm,IGPS—Filter)来求解机组组合(unit commitment problems,uo问题,该算法能在求解过程中直接处理离散变量,有效地求解0.1混合变量的规划问题。首先使IGPS—Filter算法融合UC问题的特点,预先确定大部分机组的开停状态,只对少量机组进行“1-邻域”搜索;其次,结合线搜索和域搜索对连续域变量进行求解,充分利用线搜索的快速性及域搜索处理病态问题的有效性,既提高运算效率又提高解的质量。最后,采用10-100机组24时段和IEEE-118节点54机24时段系统进行仿真,验证了方法的有效性。 相似文献
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机组组合优化问题是一个大规模、多约束、非线性的混合整数规划问题,因此求解非常困难。粒子群优化(PSO)算法是一类随机全局优化技术,它通过粒子间的相互作用发现复杂搜索空间中的最优区域。PSO算法的优势在于操作简单,可调参数少易于实现而又功能强大。该文采用二进制粒子群优化方法解决机组状态组合问题,用遗传算法结合启发式技术解决经济分配问题,并对最小开停机时间及启停费用进行了处理,使得运算速度大大加快。方法的可行性在10台机组系统中检验。模拟结果表明文章所提出的算法具有收敛速度快及解的质量高等优点。 相似文献
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《中国电机工程学报》2010,(25)
基于混合整数二阶锥规划(mixed integer second-order cone programming,MI-SOCP)提出一种求解电力系统计及爬坡约束机组组合问题(unit commitment,UC)的新方法。利用UC问题的混合整数二次规划(mixed integer quadratic programming,MI-QP)模型和一个简单混合整数集合的凸包表示,产生UC问题一个更紧的MI-SOCP模型。将最小覆盖不等式作为割平面,应用内点割平面法求解MI-SOCP以获得不计爬坡约束UC问题的机组启停状态。为满足爬坡约束,提出一种简单易行的机组启停状态修正方法。100机组96时段等多个系统的仿真结果表明,利用内点割平面法求解2种模型时,MI-SOCP能比MI-QP获得质量更好的次优解,所提方法能有效处理爬坡约束,适用于大规模的UC问题。 相似文献
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Unit commitment (UC) is a NP-hard nonlinear mixed-integer optimization problem. This paper proposes ELRPSO, an algorithm to solve the UC problem using Lagrangian relaxation (LR) and particle swarm optimization (PSO). ELRPSO employs a state-of-the-art powerful PSO variant called comprehensive learning PSO to find a feasible near-optimal UC schedule. Each particle represents Lagrangian multipliers. The PSO uses a low level LR procedure, a reserve repairing heuristic, a unit decommitment heuristic, and an economic dispatch heuristic to obtain a feasible UC schedule for each particle. The reserve repairing heuristic addresses the spinning reserve and minimum up/down time constraints simultaneously. Moreover, the reserve repairing and unit decommitment heuristics consider committing/decommitting a unit for a consecutive period of hours at a time in order to reduce the total startup cost. Each particle is initialized using the Lagrangian multipliers obtained from a LR that iteratively updates the multipliers through an adaptive subgradient heuristic, because the multipliers obtained from the LR tend to be close to the optimal multipliers and have a high potential to lead to a feasible near-optimal UC schedule. Numerical results on test thermal power systems of 10, 20, 40, 60, 80, and 100 units demonstrate that ELRPSO is able to find a low-cost UC schedule in a short time and is robust in performance. 相似文献
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An effective method is proposed to schedule spinning reserve optimally. The method considers the transmission constraint in the whole scheduling process. To get the feasible solution faster, transmission line limits are first relaxed using the Lagrangian Relaxation technique. In the economic dispatch, after unit generation and spinning reserve are allocated among the committed units to satisfy the system andunit constraints, the schedule is then modified by a linear programming algorithm to avoid line overloads. The schedule is then updated by a probabilistic reserve assessment to meet a given risk index. The optimal value of the risk index is selected via a cost/benefit analysis based on the tradeoff between the total Unit Commitment (UC) schedule cost and the expected cost of energy not served. Finally, a unit decommitment technique is incorporated to solve the problem of reserve over-commitment in the Lagrangian Relaxation–based UC. The results of reserve scheduling with the transmission constraint are shown by the simulation runs performed on the IEEE reliability test system. 相似文献
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《Power Systems, IEEE Transactions on》2007,22(4):1612-1621
This paper presents an evolutionary iteration particle swarm optimization (EIPSO) algorithm to solve the nonlinear optimal scheduling problem. A new index called iteration best is incorporated into particle swarm optimization (PSO) to improve the solution quality. The new PSO, named iteration PSO (IPSO), is embedded into evolutionary programming (EP) to further improve the computational efficiency. The EIPSO is then applied to solve the optimal spinning reserve for a wind-thermal power system (OSRWT). Results are used to evaluate the effects of wind generation on the spinning reserve selection of a power system. The OSRWT program considers the outage cost as well as the total operation cost of thermal units to evaluate the level of spinning reserve. The up spinning reserve (USR) and down spinning reserve (DSR) are also introduced into the OSRWT problem. The optimal scheduling of spinning reserve was reached while minimizing the sum of total operation cost and outage cost. Two practical power systems are used as numerical examples to test the new algorithm. The feasibility of the new algorithm is demonstrated by the numerical example, and EIPSO solution quality and computational efficiency are compared to those of other algorithms. 相似文献
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首先介绍了美国新英格兰电力市场备用市场的设计,然后提出一种在联营型电力市场中能量和备用调度的联合优化新模型,该模型是一个不可微优化模型,能够模拟备用机组的机会成本,因此比文献中报道的模型更加全面,完整,文中将联合优化模型转化为等价的混合整数优化模型,并建议运用分支定界法求解,针对不同的市场方案推导了相应的能量和备用的边际成本,讨论了联合优化问题的解的求解方法和最优解的多值性,此外,文中还讨论了一种常见的联合优化计算方法,并指出其启发性质,研究表明一个包含机会费用的市场设计从结构上讲非常复杂,从计算上讲也非常有挑战性,这些研究结果为实现美国联邦能源管制委员会所倡导的主辅市场联合优化设计奠定了理论基础。 相似文献
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本文从一个新的角度探讨了电力系统机组日运行调度问题。以系统等运行风险度和机组投运前导时间为约束,旋转备用为目标函数,建立了求解机组日运行计划的动态规划数学模型并提出了相应的算法。该方法可与常规的机组最优投入方法结合,进一步研究大型发电系统的可靠、安全、经济运行。 相似文献