多间隙平面机械系统动态性能分析

在考虑多间隙的条件下,对机械系统的动态性能进行了分析。首先,建立了转动副考虑间隙时的运动学模型,其次,选取了Flores接触力模型建立了间隙关节元素之间的法向接触力,并利用修正的"Coulomb"摩擦力模型计算了摩擦力。最后,以平面曲柄滑块机构为例,在机械系统中分别考虑单个间隙和多间隙进行了对比分析,并对不同尺寸的间隙关节进行了分别仿真,数值结果证明机械系统中引入间隙关节越多且间隙尺寸越大其动力学性能越恶化。     

Energy-saving generation dispatch toward a sustainable electric power industry in China

Energy shortages, climate change and environmental pollution are critical issues that the entire world is faced with currently. To tackle the challenge and realize sustainable development, the Chinese government launched the Energy-Saving Generation Dispatch (ESGD) in 2007. In the ESGD scheme, generating units are dispatched based on fuel consumption rates and pollutant emission intensities from low to high. However, annual generation quotas still widely exist. With the mandatory shutdown of small-capacity and low-efficiency thermal generating units in 2006–2010, most of the currently running thermal generating units are large-capacity and highly efficient units. The additional improvement of the overall energy efficiency under this situation is a key problem for the Chinese electric power industry. To this end, a new type of ESGD framework is designed in this paper. Sequential coordination among yearly, monthly, day-ahead and real-time generation schedules is proposed. Based on the framework, the corresponding models are formulated. Empirical analysis is conducted using the realistic data obtained from the Guangdong Power Grid Corporation. Four generation dispatch modes are compared. The results indicate that the proposed ESGD mode can further reduce energy consumption and pollutant emissions. Hopefully, this paper can provide a valuable reference for policy making in the Chinese power sector.     

Constrained ALS-based tensor blind receivers for multi-user MIMO systems

Tensor factorizations has shown to be an efficient approach for symbols and/or channel estimation in multi-input multi-output (MIMO) systems, where the factor matrices of tensor that correspond to symbols, channel, code/diversity of signals, are often estimated by using alternating least squares (ALS) algorithm. Although the performance of tensor approaches strongly depend on the initializations of the factor matrices. However, due to the absence of a priori on channels, these initializations are done randomly in traditional ALS algorithm. This generally implies a slow convergence. Further, ALS does not take into account the potential orthogonal structure in the factor matrices, which can be exploited to improve the accuracy of factor matrices recovery. To address these insures, this paper proposes constrained ALS tensor blind receivers for multi-user MIMO systems. We show that the multi-user MIMO signals can be expressed as a third-order tensor model, where the matrices of users symbols, direction-of-arrival (DOA) and delay can be viewed as three factor matrices of the tensor model. Two constrained ALS blind algorithms that take into account the potential orthogonal and Vandermonde structures in the factor matrices, are proposed to learn the tensor model, where the users symbols, DOA and delay are joint estimated as three factor matrices. Besides provide the estimations for the factor matrices, the orthogonal and Vandermonde structures also give a better uniqueness results for the use of tensor model. Interestingly, these structures are the nature properties of the factor matrices in our system. This results in an efficient blind approach that has better performance and lower complexity compare with the traditional ALS.     

异构分布式系统中一种新型主副版本调度算法

针对异构分布式系统中处理器数量相对较少时优先级约束条件带来的副版本调度易失败问题，提出一种新型高可靠性主副版本调度算法（HRPB）。任务模型以有向无环图（DAG）表示，该算法共计调度主、副两个版本的任务。在任务优先级排序阶段，根据任务执行时间及截止时限来制定新指标平均最晚开始时间（ALST）进行排序；在任务处理器分配阶段，采取多一重备份策略以解决处理器数量相对较少时优先级约束条件带来的副版本调度易失败问题，并且改进了副版本调度时的可靠性指标计算方法。通过随机生成DAG图进行算法仿真测试，实验结果表明，HRPB比eFRD具有更优的副版本调度成功率、更高的系统可靠性。     

求解约束优化问题的自适应人工蜂群算法

针对约束优化问题，提出一种自适应人工蜂群算法。算法采用反学习初始化方法使初始种群均匀分布于搜索空间。为了平衡搜索过程中可行个体和不可行个体的数量，算法使用自适应选择策略。在跟随蜂阶段，采用最优引导搜索方程来增强算法的开采能力。通过对13个标准测试问题进行实验并与其他算法比较，发现自适应人工蜂群算法具有较强的寻优能力和较好的稳定性。     

Lift-and-project MVEE based convex hull for robust SCED with wind power integration using historical data-driven modeling approach

This paper presents an adjustable robust security constrained economic dispatch (SCED) model with wind power uncertainties. First, the scenario based adjustable robust SCED model is presented. It considers multiple scenarios from historical data as well as the spatial correlation among wind farms. Then, the proposed SCED model becomes an optimization problem with a large amount of constraints which is skillfully solved using a lift-and-project minimum volume enclosing ellipsoid (MVEE) based convex hull. Furthermore, the proposed model is transformed into a second order cone programming (SOCP) model by the use of participation factors to generate adjustable generation outputs and thus guarantee the energy balance. In order to further reduce the computational complexity, the inactive constraints reduction strategy is proposed to quickly eliminate inactive SOC security constraints before solving the model. Numerical results of IEEE 14-bus and 118-bus test systems as well as the practical Polish power systems with several wind farms show that the proposed model can achieve better economies. Moreover, more than 82% of security constraints are identified as inactive in various cases of the simulation, and the proposed inactive constraints reduction strategy is promising for improving the computational performance.     

An intelligent θ-Modified Bat Algorithm to solve the non-convex economic dispatch problem considering practical constraints

This paper proposes a practical formulation for the non-convex economic dispatch problem to consider multi-fuel options, ramp rate limits, valve loading effect, prohibited operating zones and spinning reserve. A new optimization algorithm based on the θ-bat algorithm (θ-BA) is suggested to solve the problem. The θ-BA converts the Cartesian search space into the polar coordinates such that more search ability would be achieved. According to the complex, nonlinear, and constrained nature of the problem, a new self-adaptive modification method is proposed. The proposed modified θ-BA (θ-MBA) is constructed based on the roulette wheel mechanism to effectively increase the convergence of the algorithm. The high ability and satisfying performance of the proposed optimization method is examined on IEEE 15-unit, 40-unit and 100-unit test systems.     

Assessing plant design with regard to MPC performance

Model Predictive Control is ubiquitous in the chemical industry and offers great advantages over traditional controllers. Notwithstanding, new plants are being projected without taking into account how design choices affect the MPC’s ability to deliver better control and optimization. Thus a methodology to determine if a certain design option favours or hinders MPC performance would be desirable. This paper presents the economic MPC optimization index whose intended use is to provide a procedure to compare different designs for a given process, assessing how well they can be controlled and optimised by a zone constrained MPC. The index quantifies the economic benefits available and how well the plant performs under MPC control given the plant’s controllability properties, requirements and restrictions. The index provides a monetization measure of expected control performance.This approach assumes the availability of a linear state-space model valid within the control zone defined by the upper and lower bounds of each controlled and manipulated variable. We have used a model derived from simulation step tests as a practical way to use the method. The impact of model uncertainty on the methodology is discussed. An analysis of the effects of disturbances on the index illustrates how they may reduce profitability by restricting the ability of a MPC to reach dynamic equilibrium near process restrictions, which in turn increases product quality giveaway and costs. A case of study consisting of four alternative designs for a realistically sized crude oil atmospheric distillation plant is provided in order to demonstrate the applicability of the index.     

Hybrid optimization implemented for distributed generation parameters in a power system network

This paper presents a novel hybrid optimization algorithm for optimizing the Distributed Generation (DG) parameters in deregulated power system which improves the stability, reduces the losses and also increases the cost of generation. This Hybrid algorithm which includes Fuzzy-Genetic Algorithm (FGA) is used to optimize the various DG parameters simultaneously. The various parameters taken into consideration are their type, location and size of the DG devices. The simulation was performed on a distribution system and modeled for steady state studies. The optimization results are compared to the solution given by another search method like Genetic Algorithm (GA) and Micro Genetic Algorithm (MGA). The results reveal the benefits of the proposed method, for solving simultaneous combinorial problems of DG devices in a power system network.     

Design optimization of grid-connected PV-Hydrogen for energy prosumers considering sector-coupling paradigm: Case study of a university building in Algeria

Integrating sector coupling technologies into Hydrogen (H₂) based hybrid renewable energy systems (HRES) is becoming a promising way to create energy prosumers, despite the very little research work being done in this largely unexplored field. In this paper, a sector coupling strategy (building and transportation) is developed and applied to a grid-connected PV/battery/H₂ HRES, to maximise self-sufficiency for a University campus and to produce power and H₂ for driving electric tram in Ouargla, Algeria. A multi-objective size optimization problem is solved as a single objective problem using the ε-constraint method, in which the cost of energy (COE) is defined as the main objective function to be minimized, while both loss of power supply probability (LPSP) and non-renewable usage (NRU) are defined as constraints. Particle swarm optimization and HOMER software are then employed for simulation and optimization purposes. Prior to the two scenarios investigated, a sensitivity study is performed to determine the effects of H₂ demand by tram and NRU on the techno-economic feasibility of the proposed system, followed by a new reliability factor introduced in the optimization, namely loss of H₂ supply probability (LHSP). The results of the first scenario show that by setting NRU^max = 100%, the system without H₂ provides the best solution with COE of 0.016 $/kWh that reaches grid parity and has 13% NRU. However, by setting NRU^max = 1% in the second scenario, an optimized configuration consisting of grid/PV/Electrolyzer/Fuel cell/Storage tank is obtained, which has 0% NRU and COE of 0.1 $/kWh. In the second scenario, it is also observed that an increased number of trams (i.e. increased H₂ demands) causes a significant reduction in LHSP, COE, NRU and CO₂ emissions. It is thus concluded that the grid/PV combination is the optimal choice for the studied system when considering economic aspects. However, taking into account the growing requirements of future energy systems, grid-connected PV with H₂ will be the best solution.