考虑事故后频率约束的两阶段经济调度

为了保证事故后电力系统仍能正常运行,基于多阶段决策的思想提出了考虑事故后频率约束的两阶段随机经济调度模型。该模型中同时考虑事故前(第一阶段)和事故后(第二阶段)系统的运行成本,分别称为"当前成本"和"未来成本"。第一阶段的优化目标函数考虑在满足供电可靠性的条件下使常规机组和风电机组的发电成本最小;为了保证系统频率在事故后较短时间内恢复正常,第二阶段考虑了对低频减载的约束,目标函数为常规机组和风电机组的发电成本,风能高估低估的惩罚成本以及减负荷的惩罚成本最小。针对模型的非光滑性,采用光滑化方法对其进行处理,并以IEEE30节点系统进行仿真,仿真结果验证了新模型和算法的有效性。     

Sparse LMS/F algorithms with application to adaptive system identification

Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advantages of both least mean square (LMS) and least mean fourth (LMF). The advantage of LMS is fast convergence speed while its shortcoming is suboptimal solution in low signal‐to‐noise ratio (SNR) environment. On the contrary, the advantage of LMF algorithm is robust in low SNR while its drawback is slow convergence speed in high SNR case. Many finite impulse response systems are modeled as sparse rather than traditionally dense. To take advantage of system sparsity, different sparse LMS algorithms with l_p‐LMS and l₀‐LMS have been proposed to improve adaptive identification performance. However, sparse LMS algorithms have the same drawback as standard LMS. Different from LMS filter, standard LMS/F filter can achieve better performance. Hence, the aim of this paper is to introduce sparse penalties to the LMS/F algorithm so that it can further improve identification performance. We propose two sparse LMS/F algorithms using two sparse constraints to improve adaptive identification performance. Two experiments are performed to show the effectiveness of the proposed algorithms by computer simulation. In the first experiment, the number of nonzero coefficients is changing, and the proposed algorithms can achieve better mean square deviation performance than sparse LMS algorithms. In the second experiment, the number of nonzero coefficient is fixed, and mean square deviation performance of sparse LMS/F algorithms is still better than that of sparse LMS algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

Smooth penalty function method for rigorous optimization of distillation processes

Integer decisions on stage numbers and feed locations, and global optimality are still challenging for rigorous optimization of distillation processes. In the present article, we propose a smooth penalty function method to address both these problems. The proposed method is based on the relaxation of the integer decision problem into continuous nonlinear programming (NLP) problem by adopting the bypass efficiency model developed by Dowling and Biegler. A smooth penalty term (SPT) is proposed and added to the total annual cost (TAC) function to form a new objective function, namely, the smooth penalty function. Using the new objective function, the problem is initially solved with negative weight coefficients for the SPTs regarding each column section to get an optimum near the global optimum of the SPT. Then, starting from this solution, the problem is solved again iteratively by increasing the values of the weight coefficients until all the stage numbers become integers. The performance of the method is validated by an illustrating problem and in three case studies, including a reactive distillation optimization problem.     

Fast total variation deconvolution for blurred image contaminated by Poisson noise

In this paper, we present a fast non-blind deconvolution method for restoring blurred images contaminated by Poisson noise. The problem is formulated by finding the minimizer of the negative logarithmic Poisson log-likelihood combined with the total variation (TV). To attack the challenging task, we adopt the well-known variable splitting and penalty technique to convert the problem into two easier sub-problems: one is a modified TV regularized deconvolution and the other is a simple convex optimization problem. Experimental results show that the proposed method runs very fast and the quality of the restored image is comparable with that of some state of the art methods.     

Carbon capture and storage: Fundamental thermodynamics and current technology

Carbon capture and storage (CCS) is considered a leading technology for reducing CO₂ emissions from fossil-fuelled electricity generation plants and could permit the continued use of coal and gas whilst meeting greenhouse gas targets. However considerable energy is required for the capture, compression, transport and storage steps involved. In this paper, energy penalty information in the literature is reviewed, and thermodynamically ideal and “real world” energy penalty values are calculated. For a sub-critical pulverized coal (PC) plant, the energy penalty values for 100% capture are 48.6% and 43.5% for liquefied CO₂, and for CO₂ compressed to 11 MPa, respectively. When assumptions for supercritical plants were incorporated, results were in broad agreement with published values arising from process modelling. However, we show that energy use in existing capture operations is considerably greater than indicated by most projections. Full CCS demonstration plants are now required to verify modelled energy penalty values. However, it appears unlikely that CCS will deliver significant CO₂ reductions in a timely fashion. In addition, many uncertainties remain over the permanence of CO₂ storage, either in geological formations, or beneath the ocean. We conclude that further investment in CCS should be seriously questioned by policy makers.     

Prospect options of CO2 capture technology suitable for China

Reviewing the progress of CO₂ capture and storage (CCS) technology, the main obstacles and the potentials of greenhouse gas control in China are identified. An important point can be drawn is that the innovative energy systems, besides simple implementation of existing technology, are needed for CO₂ control in China. On the basis of integration principle of energy utilization and CO₂ separation, several innovative energy systems, including chemical-looping combustion with CO₂ capture, a partial gasification with O₂/CO₂ cycle, and a polygeneration system with CO₂ capture, are introduced. With synergetic integrating CO₂ into chemical energy conversion and utilization processes, these systems may make breakthrough in CO₂ capture with less or even zero energy penalty. Finally, according to the specific issue of China, a new scenario of Energy Network, which composed of energy source, transportation chain, and terminal user, is recommended for sustainable development in China.     

集中入流滞时演进型流域地貌汇流模型

在Ｓｔｒａｈｌｅｒ河流分级法的基础上，提出了单元级别的确定原则，并进一步定义了概念性流域地貌汇流模型．为了将河道洪水演算方法移置到流域汇流模型中，还定义了“滞时演进河段”这一新的概念性元素．推导了三级流域瞬时单位线，选用内罚函数法估计模型参数，实例检验，模型拟合精度较高．     

三维恒定磁场罚函数有限元法的离散格式

对于三雏线性恒定磁场的罚画数有限元法，本文给出了关于四面体元、直三棱柱元和长方体元这三种常用单元的离散格式。     

软件定义无线局域网的切换接入机制

软件定义无线局域网（WLAN）是管理无线网络的一个趋势。针对无线访问接入点（AP）部署密集环境中频繁切换、接入失败等问题，提出了基于惩罚因子的全局关联记忆保留的接入控制的方法。首先，扩展OpenFlow协议；其次，利用扩展数据报文通过AP向控制器报告网络的质量、负载、吞吐量以及利用率等指标；接着，引入变异系数法确定网络指标的系数，然后利用这些指标参数构建AP接入权值；最后，引入全局惩罚因子记录网络中发生频繁来回切换的情况，根据全局记忆保留的惩罚因子修正AP接入权值和发射功率。通过与信号最强接入法和负载均衡接入法的实验对比表明，终端站点（STA）在复杂网络环境中移动时，该方法能有效地降低切换的“乒乓效应”，减少网络的切换次数和切换时延并提高网络切换成功率。与传统信号最强接入法相比，所提方法的切换请求次数减少了21.7%，增强了网络接入性能的稳定性。