Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm

ABSTRACT

Wind speed forecasting plays an important role in power grid dispatching management. This article proposes a short-term wind speed forecasting method based on random forest model combining ensemble empirical modal decomposition and improved harmony search algorithm. First, the initial wind speed data set is decomposed into several ensemble empirical mode functions by EEMD, then feature extraction of each sub-modal IMF is performed using fast Fourier transform to solve the cycle of each sub-modal IMF. Next, combining the high-performance parameter optimization ability of the improved harmony search algorithm, two optimal parameters of random forest model, number of decision trees, and number of split features are determined. Finally, the random forest model is used to forecast the processing results of each submodal IMF. The proposed model is applied to the simulation analysis of historical wind data of Chaoyang District, Liaoning Province from April 27, 2015 to May 22, 2015. To illustrate the suitability and superiority of the EEMD-RF-IHS model, three types of models are used for comparison: single models including ANN, SVM, RF; EMD combination models including EMD-ANN, EMD-SVM, EMD-RF; EEMD combination models including EEMD-ANN, EEMD-SVM, EEMD-RF. The analysis results of evaluation indicators show that the proposed model can effectively forecast short-term wind data with high stability and precision, providing a reference for forecasting application in other industry fields.     

Multi-objective-based optimal transmission switching and demand response for managing congestion in hybrid power systems

ABSTRACT

This paper proposes a novel congestion management (CM) approach by using the optimal transmission switching (OTS) and demand response (DR) for a system with conventional thermal generators and renewable energy sources (RESs). In this paper, wind and solar PV units are considered as the RESs. The stochastic behavior of wind and solar PV powers are modeled by using the appropriate probability density functions (PDFs). The proposed CM methodology simultaneously optimizes the generation dispatch, demand response, and also the network topology of the power system. The OTS identifies the branches that should be taken out of service by significantly reducing the operating cost of the system while respecting the system security. Here, the total operating cost minimization/social welfare maximization and system losses minimization are considered as the objectives to be optimized. The proposed CM problem is solved using the multi-objective Jaya algorithm and it is used to determine a set of Pareto-optimal solutions. The Jaya algorithm is simple and it does not have any algorithmic-specific parameters to be tuned. This aspect reduces the designer’s effort in tuning the parameters to arrive at the optimum objective function value. A fuzzy logic-based approach is used to identify the best compromise solution. The effectiveness of the proposed CM approach is examined on modified IEEE 30 and practical Indian 75 bus test systems. The obtained simulation results are analyzed and they show the effectiveness of the proposed approach.     

Energy consumption estimation model for dual-motor electric vehicles based on multiple linear regression

ABSTRACT

The drive range of electric vehicle (EV) is one of the major limitations that impedes its universalism. A great deal of research has been devoted to drive range improvement of EV, an accurate and efficiency energy consumption estimation plays a crucial role in these researches. However, the majority of EV’s energy consumption estimation models are based on single motor EV, these models are not suitable for dual-motor EVs, which are composed of more complex transmission mechanisms and multiple operating modes. Thus, an energy consumption estimation model for dual-motor EV is proposed to estimate battery power. This article focuses on studying the operating modes and system efficiency in each operating mode. The limitation of working area of each mode ensures the vehicle dynamic performance, then PSO algorithm is adopted to optimize the torque (speed) distribution between two motors to improve the system efficiency in the coupled driving mode. Finally, the energy consumption estimation model is established by multiple linear regression (MLR). The result shows that the proposed model has a high precision in energy consumption estimation of dual-motor EV.     

Risk-based optimal operation of hybrid power system using multiobjective optimization

ABSTRACT

This paper solves an optimal generation scheduling problem of hybrid power system considering the risk factor due to uncertain/intermittent nature of renewable energy resources (RERs) and electric vehicles (EVs). The hybrid power system considered in this work includes thermal generating units, RERs such as wind and solar photovoltaic (PV) units, battery energy storage systems (BESSs) and electric vehicles (EVs). Here, the two objective functions are formulated, i.e., minimization of operating cost and system risk, to develop an optimum scheduling strategy of hybrid power system. The objective of proposed approach is to minimize operating cost and system risk levels simultaneously. The operating cost minimization objective consists of costs due to thermal generators, wind farms, solar PV units, EVs, BESSs, and adjustment cost due to uncertainties in RERs and EVs. In this work, Conditional Value at Risk (CVaR) is considered as the risk index, and it is used to quantify the risk due to intermittent nature of RERs and EVs. The main contribution of this paper lies in its ability to determine the optimal generation schedules by optimizing operating cost and risk. These two objectives are solved by using a multiobjective-based nondominated sorting genetic algorithm-II (NSGA-II) algorithm, and it is used to develop a Pareto optimal front. A best-compromised solution is obtained by using fuzzy min-max approach. The proposed approach has been implemented on modified IEEE 30 bus and practical Indian 75 bus test systems. The obtained results show the best-compromised solution between operating cost and system risk level, and the suitability of CVaR for the management of risk associated with the uncertainties due to RERs and EVs.     

近断层强震速度脉冲效应及连续梁桥减隔震特性分析

应用非线性时程分析的方法 ,对于减、隔震连续桥梁在近场地震作用下的有效性和响应特性 ,作了深入的研究。通过有代表性的脉冲型近场强震记录时程分析 ,提出了用速度脉冲峰值及其周期之积来表示速度脉冲能量的方法 ,计算表明近场地震速度脉冲波能近似表征地震动破坏能力的大小 ,它和桥梁结构承受的地震响应大体成正比例关系。铅芯橡胶隔震支座在近场地震作用下仍然适用 ,但对于个别脉冲型强震记录效果不够明显 ,支座参数有必要进行优化。     

浅析我国城市土地的储备问题

通过对城市土地储备制度的运作过程与运转机理的简要阐述，形成了一个较为完整的理论框架，侧重分析了我国现行的城市土地储备制度尚存的一些问题，提出了相应解决对策，以求进一步完善我国城市土地储备制度。     

我国民航灾害应急联动机制优化研究

针对我国现行的民航灾害应急联动救援体系所存在着多头指挥、组织形式不明确、责任关系不协调、信息沟通不顺畅、资源调度不合理等问题,运用文献检索、理论分析与实地调研相结合的方法,笔者提出优化思路:民航灾害应急联动机制应以各级民航管理当局的飞行事故应急指挥部为主要指挥决策者,采用虚拟的网络式组织形式,理顺联动单位的责任与关系,将应急联动的信息网络与各地突发公共危机事件的信息平台接轨,成立全国统一的、具有双重功能的民航灾害应急资源调度中心。其中,对民航灾害应急联动虚拟网络组织形式的设计,较好地改进了现行组织的效率,体现了快速反应和协调行动的要求。     

矿井通风网络解算可视化软件研究

采用面向对象编程语言Visual Basic6.0为开发工具,以Access为后台数据库,开发了具有风机优选功能的矿井通风网络解算软件。该软件承袭了Windows系统应用程序界面直观、交互性好的特点,集矿井通风网络解算、自然风压计算、风机自动选型以及经济断面优化等功能于一体,并辅以强大的数据管理系统,为矿山通风设计和管理工作提供了便利工具。     

人员位置固定的公共场所分区疏散研究

通过将分区疏散转化为分配问题(Allocation Problem),并采用启发式的A*优化算法,对人员位置固定的公共场所分区疏散进行研究。进一步利用基于元胞自动机模型的大型公共场所人员疏散行为模拟仿真系统,在充分考虑公共场所中每个人员的状态、人员之间以及人员与周围环境的相互作用的前提下,对疏散分区的效果进行模拟;通过与未分区的模拟结果相比较,可以认为,分区疏散有助于人员快速疏散,大大缩短整体避难时间,而且基于移动路径搜索的分区更加切合实际。     

Water Network Synthesis Using Mutation-Enhanced Particle Swarm Optimization

Different techniques for the synthesis of industrial water reuse/recycle networks have been developed in recent process integration research. These tools range from graphical pinch analysis approaches to mathematical programming models. The latter have the advantage of being flexible enough to incorporate various water network constraints, but in many cases these are often non-linear, thus making the identification of global optima difficult. Recent work has demonstrated the effectiveness of metaheuristic algorithms such as particle swarm optimization (PSO), for finding good solutions these problems. This work describes the use of a modified PSO for solving mixed integer non-linear programming (MINLP) models for water network synthesis. By incorporating a mutation operator for the binary variables in the model, the algorithm is able to escape sub-optimal network topologies and proceed towards better solutions than can be found with ordinary PSO. Two case studies involving water recycle/reuse are used to demonstrate the new design methodology.