基于时序计算的电力系统风电并网接纳能力研究

为了准确评估区域电力系统风电并网接纳能力．针对目前风电规划问题,给出了一种基于时序计算的分步优化方法。根据序列相等性原则,将系统负荷与系统机组出力视作供需双方,对系统机组出力进行序列化分析,在时序上达到供需匹配平衡。将系统消纳风电过程分解为两级过程,以分步求取系统最优运行方式。利用时序计算方法,定性分析系统风电消纳空间,结合风资源信息及系统负荷信息定量计算系统可接纳的风电电量。结合实际区域电网负荷数据及机组发电数据进行了仿真计算,仿真结果表明,该规划方法能准确评估区域电力系统风电接纳能力,从而可为风电产业规划提供技术参考指标。     

A Parametric Genetic Algorithm Approach to Assess Complementary Options of Large Scale Wind-solar Coupling

The transitional path towards a highly renewable power system based on wind and solar energy sources is investigated considering their intermittent and spatially distributed characteristics. Using an extensive weather-driven simulation of hourly power mismatches between generation and load, we explore the interplay between geographical resource complementarity and energy storage strategies. Solar and wind resources are considered at variable spatial scales across Europe and related to the Swiss load curve, which serve as a typical demand side reference. The optimal spatial distribution of renewable units is further assessed through a parameterized optimization method based on a genetic algorithm. It allows us to explore systematically the effective potential of combined integration strategies depending on the sizing of the system, with a focus on how overall performance is affected by the definition of network boundaries. Upper bounds on integration schemes are provided considering both renewable penetration and needed reserve power capacity. The quantitative trade-off between grid extension, storage and optimal wind-solar mix is highlighted. This paper also brings insights on how optimal geographical distribution of renewable units evolves as a function of renewable penetration and grid extent.      

Optimal power flow solution using improved harmony search method

This paper describes the improved harmony search method (IHS) to solve optimal power flow (OPF) problems. The harmony search is one of meta-heuristic search methods inspired by the improvisation of musicians developed by Geem (2001) [23]. The proposed algorithm was tested with five standard IEEE test systems (6-bus, 14-bus, 30-bus, 57-bus and 118-bus test systems). The tests were divided into smooth and non-smooth fuel-cost cases. The comparisons among solutions obtained by sequential quadratic programming (SQP), genetic algorithms (GA) and IHS were conducted. As revealed from the simulated results, the effectiveness of the IHS for solving OPF problems was confirmed.     

Congestion management by determining optimal location of series FACTS devices using hybrid bacterial foraging and Nelder–Mead algorithm

In this paper, the Thyristor-Controlled Series-Compensated (TCSC) devices are located for congestion management in the power system by considering the non-smooth fuel cost function and penalty cost of emission. For this purpose, it is considered that the objective function of the proposed optimal power flow (OPF) problem is minimizing fuel and emission penalty cost of generators. A hybrid method that is the combination of the bacterial foraging (BF) algorithm with Nelder–Mead (NM) method (BF-NM) is employed to solve the OPF problems. The optimal location of the TCSC devices are then determined for congestion management. The size of the TCSC is obtained by using of the BF-NM algorithm to minimize the cost of generation, cost of emission, and cost of TCSC. The simulation results on IEEE 30-bus, modified IEEE 30-bus and IEEE 118-bus test system confirm the efficiency of the proposed method for finding the optimal location of the TCSC with non-smooth non-convex cost function and emission for congestion management in the power system. In addition, the results clearly show that a better solution can be achieved by using the proposed OPF problem in comparison with other intelligence methods.     

基于量子遗传算法的冷藏集装箱功率平衡调度方法

现有的集装箱船对各冷藏集装箱的控制相互独立,且单个冷藏集装箱的电力需求是随机的,造成总电力需求峰谷差较大,进而影响船舶电站的功率配置.为解决上述问题,需在保证温度安全的前提下对冷藏集装箱集群进行统一调度,本文提出一种基于量子遗传算法的功率平衡调度方法寻找冷藏集装箱集群的最优调度策略.首先,对冷藏集装箱优化调度问题建立数学模型,确定其约束条件及优化目标;然后,分别采用遗传算法（GA）及量子遗传算法（QGA）对优化目标求解,并比较经两类算法调度前后的冷藏集装箱实际功率变化情况及各项指标,评价两类算法的优化调度能力.实验结果表明：GA及QGA均能实现冷藏集装箱的优化调度,减小总电力需求的峰谷差,使负载功率趋于平衡,但QGA的寻优速度比GA快,平衡电力需求的能力及优化电站配置能力更强.     

Modeling the consumer benefit in the optimal power flow

The addition of a model of the consumer into the traditional optimal power flow (OPF) algorithm that minimizes supplier costs is investigated. The development of such a model is based on the solution of the OPF using an objective function for maximization of social welfare. A traditional OPF algorithm can be modified to solve the social welfare maximization problem by including price-dependent load models. This modification to the traditional OPF is intuitive and very simple. This modified OPF formulation facilitates simulation of spot markets for both real and reactive power. The algorithm is effective on systems of hundreds of buses, but small examples to compare the results to the traditional OPF are also insightful. The impact of price-dependent loads on systems with transmission congestion, increased fuel costs, and voltage problems can be studied.     

基于ADWOA算法的风光水互补系统优化调度研究

为了有效应对大规模风能和光伏并网带来的波动问题,保障电网的安全稳定运行,本研究通过深入分析风力发电、光伏发电、水利发电以及抽水蓄能之间的互补特性,构建了一个综合的互补发电系统。在此基础上,针对系统中存在的多种功率约束,本文提出了两种优化调度模型：一种旨在最小化互补系统的波动,另一种则是最小化火电机组等效负荷波动。为求解这两个优化模型,采用了改进的自适应权重优化算法（Adaptive Weight Optimization Algorithm, ADWOA）。通过仿真实验验证了该算法在两种模型中的有效性,结果表明这两种最优调度模型均能有效追踪优化目标,并显著减小由于风力和光伏发电并网引起的影响,从而增强电网的运行稳定性。     

考虑源荷随机性的跨区互联电网直流联络线调度学习优化

在跨区互联电网中,充分利用直流联络线调度能力可以有效地平衡电力资源的配置,促进新能源的消纳.本文针对源荷不确定性的跨区互联电网直流联络线调度问题,首先用连续马尔科夫过程模型描述互联电网中风电出力与负荷需求随机动态特性;然后在功率平衡及联络线日交易电量约束等实际运行要求前提下,将直流联络线调度优化问题建立成离散马尔科夫决策过程模型.在该模型下,调度机构根据互联电网系统各时段源荷的功率情况,动态调整联络线输电计划和配套的柔性负荷调节方案,以达到提升系统运行效益的优化目标;最后引入强化学习方法对调度策略进行优化求解.通过学习优化,系统平均日运行代价显著下降且最终收敛.实验结果表明考虑源荷随机性的直流联络线动态调整方法可有效地提高互联电网发输电系统的运行效益.     

Minimum energy storage for power system with high wind power penetration using p-efficient point theory

Minimum energy storage （ES） and spinning reserve （SR） for day-ahead power system scheduling with high wind power penetration is significant for system operations. A chance-constrained energy storage optimization model based on unit commitment and considering the stochastic nature of both the wind power and load demand is proposed. To solve this proposed chance-constrained model, it is first converted into a deterministic-constrained model using p-efficient point theory. A single stochastic net load variable is developed to represent the stochastic characteristics of both the wind power and load demand for convenient use with the p-efficient point theory. A probability distribution function for netload forecast error is obtained via the Kernel estimation method. The proposed model is applied to a wind-thermal-storage combined power system. A set of extreme scenarios is chosen to validate the effectiveness of the proposed model and method. The results indicate that the scheduled energy storage can effectively compensate for the net load forecast error, and the increasing wind power penetration does not necessarily require a linear increase in energy storage.     

Enhanced goal attainment method for solving multi-objective security-constrained optimal power flow considering dynamic thermal rating of lines

Security-constrained optimal power flow (SCOPF) is an important problem in power system operation. Dynamic thermal rating (DTR), as an effective method to increase transmission capacity of power systems, has been recently considered in some optimal power flow (OPF) and SCOPF models. Additionally, in today power systems, OPF problem involves various objectives leading to multi-objective OPF models. In this paper, a new multi-objective SCOPF model considering DTR of transmission lines is presented. In addition, a new multi-objective solution method is proposed to solve the multi-objective SCOPF problem. The proposed method is an enhanced version of goal attainment technique in which the search capability of this technique to cover borders of the Pareto frontier is enhanced. The proposed multi-objective DTR-included SCOPF model as well as the proposed multi-objective solution method are tested on the IEEE 118-bus test system and the obtained results are compared with the results of other alternatives.     

Fault diagnosis for wind turbines based on LSTM and feature optimization strategies

High penetration of renewable energy is the development trend of the future power system. As one of the clean energy sources, wind power generation has an increasing share in the energy market. However, due to the harsh working environment, the high fault rate and poor accessibility of the wind farms, resulting in the difficult maintenance process and high cost. This article proposes a fault diagnosis (FD) method based on long short-term memory (LSTM) and feature optimization strategies for wind turbines (WTs), thus reducing the operation and maintenance costs of WTs. First, Pearson correlation coefficient analysis is performed on the collected data features to remove redundant features, and wavelet transform is adopted to remove the redundant data, so as to optimize the fault features and fault data. Then the selected features samples are used to train LSTM-based FD model. Finally, the actual production data is adopted to verify the proposed method. The proposed method can effectively locate the faults, and provide data support for wind farms, thus improving the reliability, safety, and economic benefits of wind farms.     

Adaptive Grey Wolf Optimization for Weightage-based Combined Economic Emission Dispatch in Hybrid Renewable Energy Systems

Nowadays, the electric power networks comprise diverse renewable energy resources, with the rapid development of technologies. In this scenario, the optimal Economic Dispatch is required by the power system due to the increment of power generation cost and ever growing demand of electrical energy. Thus, the reduction of power generation cost in terms of fuel cost and emission cost has become one of the main challenges in the power system. Accordingly, this article proposes the Grey Wolf Optimization-Extended Searching (GWO-ES) algorithm to provide the excellent solution for the problems regarding Combined Economic and Emission Dispatch (CEED). It validates the robustness of the proposed algorithm in seven Hybrid Renewable Energy Systems (HRES) test bus systems, which combines the wind turbine along with the thermal power plant. Furthermore, it compares the performance of the proposed GWO-ES algorithm with conventional algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and GWO. Next, the article emulates a valuable convergence analysis and justification for the quality of CEED through the GWO-ES algorithm. Finally, the result was compared to four other conventional algorithms to assure the efficiency of the proposed algorithm in terms of fuel cost and emission cost reduction.     

Optimal placement of UPFC in power systems using immune algorithm

This paper presents the application of immune algorithm (IA) to find optimal location of unified power flow controller (UPFC) to achieve optimal power flow (OPF) and congestion management. Objective function in the OPF, that is to be minimized, is the overall cost functions, which includes the total active and reactive production cost function of the generators and installation cost of UPFCs. The OPF constraints are generators, transmission lines and UPFCs limits. In power system, it may not always be possible to dispatch all of the contracted power transactions due to congestion of the transmission corridors. We propose IA method to minimize the objective function under all equality and inequality constraints. Simulations are performed on 4-bus, IEEE 14-bus and IEEE 30-bus test systems for optimal location of UPFC and the results obtained are encouraging and will be useful in electrical restructuring.     

A Game Theory‐Based Coordination and Optimization Control Methodology for a Wind Power‐Generation Hybrid Energy Storage System

The installation of an energy storage system to smooth the fluctuations of wind power output at a certain wind farm can improve the electric quality of wind power connected to the grid. In order to reduce the capacity of the energy storage system and the loss of the battery and make full use of the advantages of the super‐capacitor, a game theory‐based coordination and optimization control methodology for a wind power‐generation and storage system (WPGSS) is presented in this paper. Aiming to maximize the WPGSS's overall profit, the methodology, taking the smoothing effect of the active power, the cost of the hybrid energy storage system (HESS), and the earnings of wind power connected to grid into consideration, builds a coordination and optimization control model based on the ensemble empirical mode decomposition (EEMD) algorithm combined with game theory. In the model, the low‐pass filtering signal obtained by the EEMD is used to smooth the fluctuations of wind power output, and the band‐pass filtering signal and high‐pass filtering signal obtained by the EEMD are used to achieve energy distribution among the HESS. Cooperative game theory is introduced to determine the filter order of the EEMD according to the state of charge (SOC) of the HESS and to achieve the coordination and optimization control of the WPGSS taking the maximization of the WPGSS's overall profit as the game's goal constraint conditions. The genetic algorithm (GA) and particle swarm optimization (PSO) are adopted to solve the model's optimal solution, and the simulation tests were realized to verify the effectiveness of the proposed method, which can provide a theoretical basis for the coordination and optimization control of the WPGSS.     

U-shaped energy loss curves utilization for distributed generation optimization in distribution networks

We propose novel techniques to find the optimal achieve the maximum loss reduction for distribution networks location, size, and power factor of distributed generation （DG） to Determining the optimal DG location and size is achieved simultaneously using the energy loss curves technique for a pre-selected power factor that gives the best DG operation. Based on the network＇s total load demand, four DG sizes are selected. They are used to form energy loss curves for each bus and then for determining the optimal DG options. The study shows that by defining the energy loss minimization as the objective function, the time-varying load demand significantly affects the sizing of DG resources in distribution networks, whereas consideration of power loss as the objective function leads to inconsistent interpretation of loss reduction and other calculations. The devised technique was tested on two test distribution systems of varying size and complexity and validated by comparison with the exhaustive iterative method （EIM） and recently published results. Results showed that the proposed technique can provide an optimal solution with less computation.     

风力发电与电动汽车充放电协同优化调度策略

大规模电动汽车无序充电以及风力发电在电网中的渗透率不断提高,给电力系统带来安全经济运行问题。在考虑电动汽车电池容量约束、充放电功率约束以及24 h的电动汽车运行行为特性基础上,建立了风力发电及电动汽车负荷平抑、降低电动汽车充放电费用和负荷峰谷差率的多目标协调优化调度模型;采用传统遗传算法和自适应非线性遗传算法对所建模型进行求解。仿真结果验证了模型的合理性以及算法的正确性。     

Optimal power flow of HVDC system using teaching–learning-based optimization algorithm

In this paper, a solution to the optimal power flow (OPF) problem in electrical power networks is presented considering high voltage direct current (HVDC) link. Furthermore, the effect of HVDC link converters on the active and reactive power is evaluated. An objective function is developed for minimizing power loss and improving voltage profile. Gradient-based optimization techniques are not viable due to high number of OPF equations, their complexity and equality and inequality constraints. Hence, an efficient global optimization method is used based on teaching–learning-based optimization (TLBO) algorithm. The performance of the suggested method is evaluated on a 5-bus PJM network and compared with other algorithms such as particle swarm optimization, shuffled frog-leaping algorithm and nonlinear programming. The results are promising and show the effectiveness and robustness of TLBO method.

     

计及光伏及风电并网的电力系统短期负荷预测

为提升光伏、风电等分布式能源大量接入电网后短期电力负荷的预测精度,促进电网消纳能力提升,本文对光伏出力及短期用电负荷采用小波——径向基函数(RBF)神经网络预测方法;对风力发电首先利用总体平均经验模态分解(EEMD)方法对其功率数据分解,再采用BP神经网络、RBF神经网络、小波神经网络、ELMAN神经网络四种神经网络预测方法进行预测,并用粒子群算法(PSO)和灰色关联度(GRA)修正。最后,利用等效负荷的概念,分析光伏、风力发电并网对于短期电力负荷预测的影响,并将三种模型有效结合,得到了考虑光伏及风力发电并网的电力系统短期负荷预测的等效负荷预测模型。实例分析表明,本文所提方法相较于其他方法在该预测项目上具有相对更高的预测精度。     

小型风电系统变步长扰动MPPT控制仿真研究

最大功率跟踪(MPPT)策略是提高风电系统功率转换效率的重要方法.文中提出了变步长扰动MPPT策略,并在MATLAB仿真环境中,开发了带有此策略的小型风电系统仿真模型.该模型包括风力机模型、永磁发电机(PMSG)模型、DC/DC斩波器模型、负载功率采样模型和MPPT控制模型等.利用该模型进行计算机仿真,给出了一定风速下负载功率和发电机输出功率的仿真结果.结果表明:实现了变步长扰动MPPT策略控制的仿真,与传统固定步长MPPT策略相比,提高了最大功率跟踪的快速性和准确性,进而提高了风能转换效率.     

风光互补发电系统的分布式模型预测控制

风光互补发电系统中,风力和光伏独立发电且两者在地理上相隔较远,彼此没有通讯交流.对此问题,提出用分布式模型预测控制的方法去解决.首先,在风光互补发电系统中存在大量的非线性环节,运用神经网络线性逼近,训练得出各个子系统的神经网络线性化模型.然后,在此基础上,基于风力优先发电、光伏配合、蓄电池必要时输出的原则,设计出满足相...