Hydrothermal self-scheduling problem in a day-ahead electricity market

This paper addresses the self-scheduling problem of determining the unit commitment status for power generation companies before submitting the hourly bids in a day-ahead market. The hydrothermal model is formulated as a deterministic optimization problem where expected profit is maximized using the 0/1 mixed-integer linear programming technique. This approach allows precise modelling of non-convex variable cost functions and non-linear start-up cost functions of thermal units, non-concave power-discharge characteristics of hydro units, ramp rate limits of thermal units and minimum up and down time constraints for both hydro and thermal units. Model incorporates long-term bilateral contracts with contracted power and price patterns, as well as forecasted market hourly prices for day-ahead auction. Solution is achieved using the homogeneous interior point method for linear programming as state of the art technique, with a branch and bound optimizer for integer programming. The effectiveness of the proposed model in optimizing the generation schedule is demonstrated through the case studies and their analysis.     

考虑时间一致性的电力系统风险规避多阶段随机规划建模与求解

针对中长期电力系统扩展规划中,电需求和能源价格不确定性导致成本波动风险的问题,首先基于多阶段场景树描述不确定信息,引入具有"时间一致性"的多阶段风险测度,进而建立电力系统风险规避多阶段随机规划模型.通过调节各阶段置信水平,可改变模型的风险规避程度.利用逐步对冲算法对模型进行重构求解,可得到全局最优解,并为每个场景求得一...     

Mathematical representation of radiality constraint in distribution system reconfiguration problem

Distribution systems are most commonly operated in a radial configuration for a number of reasons. In order to impose radiality constraint in the optimal network reconfiguration problem, an efficient algorithm is introduced in this paper based on graph theory. The paper shows that the normally followed methods of imposing radiality constraint within a mixed-integer programming formulation of the reconfiguration problem may not be sufficient. The minimum-loss network reconfiguration problem is formulated using different ways to impose radiality constraint. It is shown, through simulations, that the formulated problem using the proposed method for representing radiality constraint can be solved more efficiently, as opposed to the previously proposed formulations. This results in up to 30% reduction in CPU time for the test systems used in this study.     

Global optimization for power dispatch problems based on theory of moments

Non-convex of an optimal power dispatch problem makes it difficult to guarantee the global optimum. This paper presents a convex relaxation approach, called the Moment Semidefinite Programming (MSDP) method, to facilitate the search for deterministic global optimal solutions. The method employs a sequence of moments, which can linearize polynomial functions and construct positive semidefinite moment matrices, to form an SDP convex relaxation for power dispatch problems. In particular, the rank of the moment matrix is used as a sufficient condition to ensure the global optimality. The same condition can also be leveraged to estimate the number of global optimal solution(s). This method is effectively applied to {0,1}-economic dispatch (ED) problems and optimal power flow (OPF) problems. Simulation results showed that the MSDP method is capable of solving {0,1}-ED problems with integer values directly, and is able to identify if more than one global optimal solutions exist. In additional, the method can obtain rank-1 moment matrices for OPF’s counterexamples of existing SDP method, this ensures the global solution and overcomes the problem that existing SDP method cannot meet the rank-1 condition sometimes.     

Real‐time calculation of power system bus voltage using a hybrid approach combining the Newton–Raphson method and dynamic programming

The calculation of the magnitudes and phase angles of the bus voltage is a challenging task in real‐time applications for power systems. Voltage profile, which denotes the present conditions of a power system, is determined by executing the traditional AC power flow program or by searching the supervisory control and data acquisition system. The AC power flow program is not suitable for several real‐time applications, such as contingency analysis and security control calculations, because of its complexity and convergence problems. Fast computation is the major concern in such applications. In this paper, a new method based on sensitivity factors, referred to as Jacobian‐based distribution factors (JBDFs), is proposed for calculating the magnitudes and phase angles of bus voltages. This method requires setting up JBDFs and deriving optimal solution paths of bus voltage for non‐swing buses through dynamic programming under base‐case loading conditions. Under real‐time conditions, the proposed method initially calculates real and reactive power line flows via JBDFs, and then computes the voltage magnitudes and phase angles of non‐swing buses through the derived optimal solution paths. The excellence of the proposed hybrid calculation method is verified by IEEE test systems. Simulation results demonstrate that the proposed method exhibits fast computation and high accuracy. Thus, the method is suitable for real‐time applications. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

考虑网络动态重构的分布式电源选址定容优化方法

以投资周期经济收益最高为目标,基于二阶锥规划提出了一种考虑网络动态重构的分布式电源选址定容优化方法。首先,针对闭环设计的配电网结构,提出计及联络线和分段开关状态的拓展DistFlow潮流模型,并基于此建立了考虑网络动态重构的分布式电源配置优化模型。引入"虚拟支路电压"的概念对模型进行线性化处理,提出了基于"有功流"的辐射形拓扑线性约束方法,同时配合电流、电压变量替换和二阶锥松弛,建立了统一优化模型的二阶锥形式。采用添加辅助电压约束的方法,解决了对含电压上限约束时二阶锥模型松弛不紧致的问题。在IEEE标准算例中测试了算法的有效性,结果表明,考虑网络动态重构可以提高电网对分布式电源的消纳能力,同时提升分布式电源投资周期内的总体经济效益。     

基于机会约束混合整数规划的风火协调滚动调度

为降低大规模风电随机性和波动性对电力系统调度的影响,保障电力系统运行的安全性和经济性,提出了考虑风电功率特性的基于机会约束混合整数规划的滚动调度模型。首先,研究了风电功率特性及风电预测误差和风电爬坡事件对系统调度的影响。然后,建立了考虑风电功率特性的机会约束混合整数规划滚动调度模型。通过滚动调度策略,有效减少系统备用容量,降低系统运行成本,提高系统的经济性,模型考虑风电爬坡约束,能有效降低风电爬坡事件的危害,提高系统的安全性。最后,通过算例对所提模型和调度策略的有效性进行了验证,结果表明,模型和调度策略能结合风电功率特性,有效兼顾系统的安全性和经济性。     

考虑运行策略和投资主体利益的电转气容量双层优化配置

电转气(P2G)技术的日益成熟,促进了电网和天然气网间的耦合,使两者间实现大规模互联成为可能。文中利用条件风险价值(CVaR)理论,对风电不确定性给电—气互联系统带来的运行风险及其成本进行了分析。在计及风力发电企业和电—气互联系统两个利益主体后,构建了P2G设备容量配置双层规划模型,以风力发电企业净利润作为上层目标,电—气互联系统运行成本为下层目标。并通过基于灾变遗传算法和内点法的混合求解算法进行仿真求解。利用IEEE 39节点电网和修改的比利时20节点天然气网组成的仿真系统,验证了配置P2G设备来提高风电消纳率和降低系统弃风风险的可行性。并进一步对比分析了置信度和弃风风险成本系数对P2G配置策略及系统运行的影响。     

Design of energy‐efficient train speed profiles considering fixed‐block signaling system

In dense traffic railway networks, trains may often slow down or stop between stations owing to previous train delays. If preceding train trajectory can be predicted, energy‐efficient driving can be achieved by suppressing unnecessary speed changes. In this paper, we propose an algorithm to find energy‐efficient driving considering fixed‐block signaling (FBS) system by using dynamic programming (DP). DP is suitable for use because it can optimize the control inputs with discrete and state constraints. In this paper, we discuss energy‐efficient driving by considering a FBS system using some case studies of simulation. In the simulation, we examine a technique to drive an express train in an energy‐efficient way when the preceding local train is running toward the station with passing loops. The results show that the proposed method can derive complex speed profiles for energy‐efficient driving and the train can be operated with a maximum reduced energy consumption of 8.3%.     

基于两层规划的电力系统N-K故障分析方法

传统的电力系统N-K故障分析一般采用组合遍历的方式,存在故障组合数量大、计算效率低的缺点。文中建立了此类问题的两层规划模型,通过寻优方式确定造成电网损失最大的故障组合。上层问题在故障重数给定的条件下,寻求能够引起电网切负荷量最大的故障组合。下层问题则针对上层问题决策的故障组合,模拟电网事故后的运行情况,在满足电网各类运行约束的条件下,通过调整发电机组输出功率最小化电网切负荷量,进一步给出了模型的遗传算法求解流程。最后,通过IEEE-14节点和IEEE-118节点算例系统,对模型有效性进行了验证。