A genetic algorithm approach to short‐term scheduling of crude oil operations in refinery

As a type of process plant, a refinery is characterized by the interaction of discrete events and continuous processes. To schedule crude oil operations in a refinery, it is necessary to define and schedule the jobs simultaneously such that heuristics and meta‐heuristics cannot be directly applied. It is very challenging to schedule crude oil operations. To solve this problem, it is decomposed into two subproblems hierarchically. At the upper level, a refining schedule is found, while at the lower level a detailed schedule is obtained to realize the refining schedule. Given a refining schedule at the upper level, this paper studies the detailed scheduling problem at the lower level. Based on a control‐theoretic perspective, the problem is innovatively transformed to a problem of assigning charging tanks to distillers such that meta‐heuristic methods can be applied. Then, a genetic algorithm (GA) approach is developed to solve the problem. In realizing the proposed GA, based on a set of existence conditions of a feasible schedule, methods are presented to guarantee that each chromosome corresponds to a feasible schedule. An industrial case study is used to show the application of the proposed method. It shows that the method works well and is applicable to real‐life problems. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于分布式最优潮流算法的跨区输电阻塞管理研究

使用辅助问题原理(APP)和序列2次规划(SQP)的分布式最优潮流算法来解决跨区域的输电阻塞问题。研究大系统互联电网的最优潮流优化(OPF)策略，分析电网分区的分解协调模型，使用APP来解决区域耦合约束，将一个大的电网互联系统分解成多个区域子问题。跨区域阻塞管理问题可描述为调整成本最小的优化问题，通过区域分解把跨区域阻塞管理问题分解为多个区域的SQP问题，这些问题可在分布并行的方式下求解。对3个区域互联的IEEE RTS-96算例进行分析，结果表明该文算法是一种有效的跨区域输电阻塞消除算法，在电力市场中有良好的应用前景。     

计及风电不确定性的多场景多时段安全约束机组组合解耦求解方法

为了加速求解计及风电不确定性的安全约束机组组合问题,提出计及风电不确定性的多场景多时段安全约束机组组合解耦求解方法。将原问题解耦为多个场景的安全约束机组组合问题;通过将各场景的调度时段分为多个子时段对各场景安全约束机组组合问题进行解耦,形成多个并行的子问题;为了确保多场景解耦和多时段解耦解的可行性,利用一致性约束耦合不同的子问题,并在目标函数中添加惩罚项。通过算例分析验证了所提方法的有效性。结果表明,在可接受的精度下,所提方法比传统集中式方法显著缩短了多场景安全约束机组组合问题的求解时间。     

Hydrothermal scheduling of a multireservoir power system withstochastic inflows

A suboptimal method for solving the annual hydrothermal scheduling problem for a multireservoir hydrothermal system is presented. Hydrothermal scheduling is performed in order to find the optimum allocation of hydro energy so that the annual operating cost of a mixed hydrothermal system is minimized. In the present work, the hydrothermal scheduling problem is formulated as a constrained optimization problem which is decomposed into three subproblems; maintenance scheduling of thermal units, dispatch of thermal units, and dispatch of hydroplants. The algorithm takes into consideration the stochastic nature of the water inflows to the reservoirs and the forced outage rates of the thermal units. Results obtained by the application of the algorithm to a simplified version of the Hellenic Hydrothermal Power System, showing the effect of the hydroplants, are reported     

Power system operations scheduling using separable programming

The problem of electric power operations scheduling comprises maintenance scheduling, intermediate- and short-term hydro-thermal scheduling, unit commitment and production scheduling. Recognizing the inter-relationship among these subproblems, the effect on one another is analyzed. Computationally efficient formulations and algorithms for maintenance scheduling and intermediate- and short-term hydro-thermal scheduling are presented.A variation of the separable programming technique is presented. This results in a linear programming formulation of the nonlinear scheduling problem. This technique is applied to a hypothetical system containing nuclear, fossil, hydro-electric and pumped-storage units. The large problem of hydro-thermal scheduling due to the inclusion of a nuclear unit is decomposed into two stages. In the first stage, the relatively stable nuclear generation is optimized with respect to the generation from large fossil-steam units. Hourly generation levels for all units in the system are then determined in the second stage.     

A novel differential evolution application to short-term electrical power generation scheduling

This paper proposes a new way of applying a differential evolution algorithm to short-term electrical power generation scheduling. Traditionally, the problem is divided into two subproblems. An evolutionary algorithm, which works with binary decision variables, is applied to the first subproblem to find a low cost scheduling of power generators, satisfying some operational constraints. Then, the lambda-iteration method, is used to calculate the power generated by the online generators. In this study, the problem is treated as a whole for the first time in literature and an application of a real-valued differential evolution algorithm is proposed. This approach eliminates the use of an iterative local search technique such as lambda-iteration in all solution evaluations. Through comparisons with results from literature, it is shown that the proposed method achieves a similar solution quality to existing methods, without needing the time consuming lambda-iteration step. Finally, the new approach is applied to real-world data from the Turkish interconnected power network.     

基于稀疏表示的配电网故障区段定位研究

提出一种基于稀疏表示的故障定位算法,仅需要少量的故障前后的电压幅值信息,通过故障节点电压方程,重构故障电流向量,根据该向量非零元素的位置来确定网络中发生故障的具体位置。提出了一种重构稀疏向量的求解算法,在定位算法中施加了两类约束条件,缩小了求解空间,为避免约束条件中出现的非线性约束条件造成的求解困难,将原问题分解成多个子问题,达到求解目的。IEEE33节点配电系中仿真验证表明:本方法仅需要6个测点便可有效定位故障,并且此方法基本不受故障类型、过渡电阻的影响,还具有强抗噪性。     

考虑模糊分时能源价格需求响应的综合能源分布式优化调度方法

为充分挖掘综合能源系统的可调度潜力,提出了一种考虑模糊分时能源价格需求响应的综合能源分布式优化调度方法。首先,为增强价格信号对需求响应的激励效应,在分时价格的基础上结合模糊数学理论提出了模糊分时能源价格,进而建立了价格型综合需求响应模型;其次,综合考虑电–热–气系统的耦合约束以及天然气网络的管道动态特性,构建了综合能源系统的日前调度模型;最后,针对电–热–气系统的复杂性以及信息的不透明特性,提出了基于正则化交替方向乘子法的调度方法,将优化调度模型分解为电–热–气3个子问题,通过多个协调变量进行信息传递,并在迭代求解各子问题的同时通过添加含协调变量的正则项以加快收敛速度。结合具体算例验证了所提模型和方法的有效性。     

Optimization-based scheduling of hydrothermal power systems withpumped-storage units

This paper presents an optimization-based method for scheduling hydrothermal systems based on the Lagrangian relaxation technique. After system-wide constraints are relaxed by Lagrange multipliers, the problem is converted into the scheduling of individual units. This paper concentrates on the solution methodology for pumped-storage units. There are, many constraints limiting the operation of a pumped-storage unit, such as pond level dynamics and constraints, and discontinuous generation and pumping regions. The most challenging issue in solving pumped-storage subproblems within the Lagrangian relaxation framework is the integrated consideration of these constraints. The basic idea of the method is to relax the pond level dynamics and constraints by using another set of multipliers. The subproblem is then converted into the optimization of generation or pumping; levels for each operating state at individual hours, and the optimization of operating states across hours. The optimal generation or pumping level for a particular operating state at each hour can be obtained by optimizing a single variable function without discretizing pond levels. Dynamic programming is then used to optimize operating states across hours with only a few number of states and transitions. A subgradient algorithm is used to update the pond level Lagrangian multipliers. This method provides an efficient way to solve a class of subproblems involving continuous dynamics and constraints, discontinuous operating regions, and discrete operating states     

Two-stage distributionally robust optimization-based coordinated scheduling of integrated energy system with electricity-hydrogen hybrid energy storage

A coordinated scheduling model based on two-stage distributionally robust optimization (TSDRO) is proposed for integrated energy systems (IESs) with electricity-hydrogen hybrid energy storage. The scheduling problem of the IES is divided into two stages in the TSDRO-based coordinated scheduling model. The frst stage addresses the day-ahead optimal scheduling problem of the IES under deterministic forecasting information, while the second stage uses a distributionally robust optimization method to determine the intraday rescheduling problem under high-order uncertainties, building upon the results of the frst stage. The scheduling model also considers collaboration among the electricity, thermal, and gas networks, focusing on economic operation and carbon emissions. The fexibility of these networks and the energy gradient utilization of hydrogen units during operation are also incorporated into the model. To improve computational efciency, the nonlinear formulations in the TSDRO-based coordinated scheduling model are properly linearized to obtain a Mixed-Integer Linear Programming model. The Column-Constraint Generation (C&CG) algorithm is then employed to decompose the scheduling model into a master problem and subproblems. Through the iterative solution of the master problem and subproblems, an efcient analysis of the coordinated scheduling model is achieved. Finally, the efectiveness of the proposed TSDRO-based coordinated scheduling model is verifed through case studies. The simulation results demonstrate that the proposed TSDRO-based coordinated scheduling model can efectively accomplish the optimal scheduling task while considering the uncertainty and fexibility of the system. Compared with traditional methods, the proposed TSDRO-based coordinated scheduling model can better balance conservativeness and robustness.     

A decoupled solution of hydro-thermal optimal power flow problem bymeans of interior point method and network programming

This paper presents a new decoupled model together with a very efficient coordination algorithm to solve a hydrothermal optimal power flow (HTOPF) problem over a certain time horizon. Based on the Lagrange relaxation at the level of the KKT (Karush-Kuhn-Tucker) conditions of the primal problem, the HTOPF is decomposed into thermal plant subproblems formulated as OPF and hydroplant subproblems. To solve efficiently the thermal OPF subproblems, the warm-starting scheme has been incorporated into interior point quadratic programming (IPQP). As to the hydroplant subproblems, a united network flow model is presented in which a fixed head plant is treated as a special case of a variable head plant. The hydroplant subproblem can be formulated as a minimum-cost maximum-flow problem for which unit cost functions of hydroplants are defined exactly. A proposed variant of the partitioning shortest path algorithm has brought about a great speed up in the computation of the subproblems. The validity of the proposed method has been examined by solving the IEEE test systems and a Chinese power system consisting of 13 thermal plants and 12 hydro power plants; the last system is a large size problem such that it has 107712 primal and dual variables. Simulation results obtained are quite convincing     

基于机组综合状态评价策略的大型水电站?精细化日发电计划编制方法

为减小水电站日发电计划与实际运行的偏差,提出一种基于机组综合状态评价策略的大型水电站精细化日发电计划编制方法.依据机组综合运行状态评价策略,确定机组优先开停次序;考虑水量、水库库容、机组运行限制等多重安全生产约束条件,以发电量最大为目标建立大型水电站日发电计划精细化模型,将其分解为机组组合子问题和开机机组最优流量分配子问题;采用原始量子进化算法和实数差分量子进化算法循环嵌套求解,获得水电站精细化日发电计划最优解.将所提算法应用于葛洲坝水电站并与其他求解方法对比,结果表明所提精细化日发电计划编制方法求解精度高,优化效果好     

Power generation scheduling for multi-area hydro-thermal systemswith tie line constraints, cascaded reservoirs and uncertain data

The authors propose an approach to the short-term generation scheduling of hydro-thermal power systems (GSHT). The objective of GSHT is to minimize the total operation cost of thermal units over the scheduling time horizon. To solve the problem within a reasonable time, the problem is decomposed into thermal and hydro subproblems. The coordinator between these subproblems is the system Lagrange multiplier. For the thermal subproblem, in a multi-area power pool, it is necessary to coordinate the area generations for reducing the operation cost without violating tie limits. A probabilistic method is employed in considering load forecasting errors and forced outages of generating units to satisfy system reliability requirements. For the hydro subsystem, network flow concepts are adopted to coordinate water use over the entire study time span and the reduced gradient method is used to overcome the linear characteristic of the network flow method to obtain the optimal solution. Three case studies for the proposed method are presented     

面向业务优先级的宽带卫星ATDM前向链路资源调度算法

研究了宽带卫星通信系统中异步时分复用(ATDM)前向链路资源受限条件下的资源调度问题。以优先传输实时业务、兼顾用户优先级以及系统吞吐量为优化目标，以调制编码模式、复帧数量和优先级等为约束，建立了资源调度目标函数。提出了以初始解集构造、增强全局搜索为核心的改进蚁群优化算法(ACO),求解该资源调度问题，避免了传统的蚁群优化算法存在初始阶段信息素匮乏导致的搜索速度过慢、局部搜索能力较弱以及易陷入局部最优等缺点，提高了算法在实时性、高效性需求较强的卫星调度过程中的应用。仿真实验表明，所提算法可以准确求得最优解，准确率高达99.8%,其收敛速度较传统算法提高了55.6%,与传统算法相比，所提算法已调度业务的目标函数f、综合权重y、系统吞吐量分别提高了8.4%、6.6%、12.1%,在资源调度方面具有良好的准确性、敛散性和优化性能，且与同类型优化算法相比具有更高的调度效率，适用于ATDM卫星通信系统资源调度。     

基于自适应混合差分进化算法的水火电力系统短期发电计划优化

针对水火电力系统发电计划优化问题,引入差分进化 (differential evolution,DE)算法,提出了一种自适应混合差分进化算法(adaptive hybrid differential evolution,AHDE)。该算法在计算过程中自适应调整交叉算子,保持了种群的多样性,提高了算法的全局搜索能力,克服了DE在寻优过程中容易早熟收敛的缺点。同时针对AHDE难以处理实际工程中复杂约束条件问题,提出了一种适合AHDE的约束处理方法。将AHDE应用于求解某水火电力系统的发电计划优化问题,与其他求解方法的对比表明,AHDE求解精度高、计算速度快。     

相同机组调度与竞标问题研究

本文在对利用拉格朗日松驰法解决大型电力市场综合资源的调度与竞标时所碰到的相同机组问题进行了讨论,认为改进电力市场竞标模式并不是解决相同机组调度与竞标问题的根本方法。     

用多种群并行自适应遗传算法求解多机多阶段Flowshop提前/拖期调度问题

多机多阶段流水车间(Flowshop)提前/拖期调度问题的目标是使工件的提前/拖期惩罚成本最小,这是一个NP完全问题,很难用一般的方法解决.本文首先给出了问题的数学模型,然后构造并采用多种群并行自适应遗传算法求解该问题.仿真结果表明此算法不仅具有较强的全局收敛性,而且有更快的寻优速度,是求解复杂调度问题的有效算法.     

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.     

Short-term hydro-thermal dispatch detailed model and solutions

An efficient method is described for the solution of the short-term hydro-thermal dispatch problem including optimal power flow (OPF) as the mathematical model of the thermal subsystem. This approach has the capability of taking into account the following effects: coupling of cascaded multichannel reservoirs, water time delays, reservoir head variations, load flow, and other constraints due to security and environmental considerations. The problem is decomposed into hydro and thermal subproblems which are then solved iteratively. An effective adjustment has been proposed to take into account the nonlinear relation between the two subproblems to speed up the convergence of the iterative process. In this adjustment, as well as in solving the thermal subproblem, equations of coordination and OPF are combined for better computational efficiency. On the basis of the proposed approach, four different methods, which differ in the degree of details in modeling the thermal system, have been tested and investigated. Numerical examples are included to demonstrate the advantages of the approach     

面向电力通信网现场可穿戴运维作业工单调度优化方法

电力通信网现场可穿戴运维对于电力通信网以及智能电网的稳定和有效运营至关重要,为了保证电力通信网现场可穿戴运维作业质量和提高运维作业效率,本文提出了一种电力通信网现场可穿戴运维作业工单调度优化方法,促进现场可穿戴运维作业的高效实施。针对目前电力通信网现场工单调度业务特征,结合运维人员技能、作业资源等特点,描述并建立多资源约束下的现场可穿戴运维作业技能最大化与时间最优工单调度模型,然后使用改进病毒遗传算法进行求解。本文提供了对提出算法的实验评估,并通过数值算例验证了该算法在多资源约束下的现场可穿戴运维作业工单调度中的有效性和可行性。