基于投资的可持续能源税收政策决策模型

为提高能源资源利用效率,科学确定最优节能减排目标及为实现目标所采取的能源税收政策方案,建立了中国可持续能源税收政策决策模型.模型以系统动力学方法为基础,考虑能源价格、利率、能源税率、能源削减边际成本等因素对节能项目投资愿意的影响,着重研究国家能源财经税收政策对能源战略目标的支持作用.研究表明:以中国交通用能为对象,在确定2005-2020年间5%年度最优节能指标后,相应能源税率从2005年的28%提高到2011年的40%,并逐渐回落至2020年的33%;交通崩能消费总量,相应CO2排放量和含税原油价格分别从2005年的120Mt、364.57Mt和611.78USD/t提高到2020年的321.8Mt、977.78Mt和1533.99USD/t.结论对于设计未来可持续的能源财经与税收政策具有指导意义.     

Cost-Based Filtering Techniques for Stochastic Inventory Control Under Service Level Constraints

This paper¹ considers a single product and a single stocking location production/inventory control problem given a non-stationary stochastic demand. Under a widely-used control policy for this type of inventory system, the objective is to find the optimal number of replenishments, their timings and their respective order-up-to-levels that meet customer demands to a required service level. We extend a known CP approach for this problem using three cost-based filtering methods. Our approach can solve to optimality instances of realistic size much more efficiently than previous approaches, often with no search effort at all. This work was supported by Science Foundation Ireland under Grant No. 03/CE3/I405 as part of the Centre for Telecommunications Value-Chain-Driven Research (CTVR) and Grant No. 00/PI.1/C075. ¹This paper is an extended version of [19].     

Dynamic file grouping for load balancing in streaming media clustered server systems

A dynamic file grouping strategy is presented to address the load balancing problem in streaming media clustered server systems. This strategy increases the server cluster availability by balancing the workloads among the servers within a cluster. Additionally, it improves the access hit ratio of cached files in delivery servers to alleviate the limitation of I/O bandwidth of storage node. First, the load balancing problem is formulated as a two layers semi-Markov switching state-space control process. This analytic model captures the behaviors of streaming media clustered server systems accurately, and is with constructional flexibility and scalability. Then, a policy iteration based reinforcement learning algorithm is proposed to optimize the file grouping policy online. By utilizing the features of the event-driven policy, the proposed optimization algorithm is adaptive and with less computational cost. Simulation results demonstrate the effectiveness of the proposed approach. Recommended by Editor Hyun Seok Yang. This work was supported by the National Natural Science Foundation of China under grant Nos. 60774038, 60574065, National 863 HI-TECH Research & Development Plan of China under grant Nos. 2006AA01Z114, 2008AA01A317, Natural Science Foundation of Anhui Province under grant No. 070412063, Graduate Student Innovation Foundation of USTC under grant No. KD2006036, and Science Research Development Foundation of HFUT under grant No. GDBJ2008-045. Qi Jiang received the B.S. degree in Industrial Electrical Automation from Southeast University in 1989 and the Ph.D. degree in Control Science and Engineering from University of Science and Technology of China in 2008. He is currently a Post-doc in USTC. His research interests include optimization and control of stochastic dynamic systems, and performance analysis and optimization of network communication systems. Hong-Sheng Xi received the M.S. degree in Applied Mathematics from University of Science and Technology of China in 1977. He is currently a Professor in Department of Automation, USTC. His research interests include discrete event dynamic systems, performance analysis and optimization of network communication systems, robust control, and network security. Bao-Qun Yin received the B.S. degree in Mathematics from Sichuan University in 1985, the M.S. degree in Applied Mathematics and the Ph.D. degree in Pattern Recognition and Intelligent Systems from University of Science and Technology of China in 1993 and 1998, respectively. He is currently a Professor in Department of Automation, USTC. His research interests include discrete event dynamic systems, and Markov decision processes.     

基于策略和代理的无线传感器网络数据管理架构

为方便用户对无线传感器网络中的数据进行有效管理,并进一步减少网络中的数据流量,对无线传感器网络数据管理的现状进行了分析和研究,提出了一种基于策略技术和代理的数据管理体系结构。该架构结合了策略技术和代理技术,进行了优势互补,用移动代理把用户的策略信息传到各个节点。策略技术的使用可以减少移动代理的代码的大小,在每个节点上都由策略代理来执行策略信息。     

基于价值分解深度强化学习的分布式光伏主动电压控制方法

针对主动电压控制问题,深度强化学习能够有效地解决数学优化方法在精确性和实时性方面的不足。但传统多智能体深度强化学习方法存在信用分配、过度泛化等问题,难以学习到全局最优的协调策略,控制效果较差。为此,提出了一种基于价值分解深度强化学习的分布式光伏主动电压控制方法。将主动电压控制问题建模为分布式部分可观测马尔可夫决策过程,然后基于中心化训练和去中心化执行框架,提出分解式价值网络、集中式策略梯度2项改进措施：将全局价值网络分解为个体价值网络和混合网络,并采用所有智能体的当前策略进行集中参数更新。改进的IEEE 33节点配电网系统的算例结果表明,所提方法表现出了优越的稳压减损控制性能,且在训练速度、场景鲁棒性等方面具备一定的优势。     

Electric cooperatives and the political economy of rural electrification in Africa: Insights from Uganda

Rural electrification is a crucial process of enhancing the modern energy access of rural communities which is an important global development goal. Electric cooperatives have played an important role as drivers of rural electrification worldwide, both historically and contemporaneously. The emerging role of cooperatives in rural electrification is yet to be examined in the context of East Africa, a region that experiences serious infrastructure deficits alongside widespread rural poverty. This study adopts a political economy perspective to explore the roles and challenges of cooperatives in rural electrification in Uganda. Through a high-level case study of two electric cooperatives, we show that while the cooperatives can deliver rural electrification, they face a series of political and economic challenges in fulfilling their mission. To enhance rural electrification through cooperatives, the challenges should be addressed by policy solutions: (1) providing more support to rural households that cannot afford electricity; (2) providing further support to cooperatives to avoid overburdening the community; (3) enhancing investment to upgrade the grid and extend its geographical coverage; and (4) exploring new financial and technological options for rural electrification. We expect the findings of this study to be useful to policymakers, implementers, cooperatives, and international donors currently working in rural electrification in Africa and beyond.     

Jointly improving energy efficiency and smoothing power oscillations of integrated offshore wind and photovoltaic power: a deep reinforcement learning approach

This paper proposes a novel deep reinforcement learning (DRL) control strategy for an integrated ofshore wind and photovoltaic (PV) power system for improving power generation efciency while simultaneously damping oscillations. A variable-speed ofshore wind turbine (OWT) with electrical torque control is used in the integrated ofshore power system whose dynamic models are detailed. By considering the control system as a partially-observable Markov decision process, an actor-critic architecture model-free DRL algorithm, namely, deep deterministic policy gradient, is adopted and implemented to explore and learn the optimal multi-objective control policy. The potential and efectiveness of the integrated power system are evaluated. The results imply that an OWT can respond quickly to sudden changes of the infow wind conditions to maximize total power generation. Signifcant oscillations in the overall power output can also be well suppressed by regulating the generator torque, which further indicates that complementary operation of ofshore wind and PV power can be achieved.     

Neural reactive path planning with Riemannian motion policies for robotic silicone sealing

Due to its excellent chemical and mechanical properties, silicone sealing has been widely used in many industries. Currently, the majority of these sealing tasks are performed by human workers. Hence, they are susceptible to labor shortage problems. The use of vision-guided robotic systems is a feasible alternative to automate these types of repetitive and tedious manipulation tasks. In this paper, we present the development of a new method to automate silicone sealing with robotic manipulators. To this end, we propose a novel neural path planning framework that leverages fractional-order differentiation for robust seam detection with vision and a Riemannian motion policy for effectively learning the manipulation of a sealing gun. Optimal control commands can be computed analytically by designing a deep neural network that predicts the acceleration and associated Riemannian metric of the sealing gun from feedback signals. The performance of our new methodology is experimentally validated with a robotic platform conducting multiple silicone sealing tasks in unstructured situations. The reported results demonstrate that compared with directly predicting the control commands, our neural path planner achieves a more generalizable performance on unseen workpieces and is more robust to human/environment disturbances.     

APPCorp: a corpus for Android privacy policy document structure analysis

With the increasing popularity of mobile devices and the wide adoption of mobile Apps, an increasing concern of privacy issues is raised. Privacy policy is identified as a proper medium to indicate the legal terms, such as the general data protection regulation (GDPR), and to bind legal agreement between service providers and users. However, privacy policies are usually long and vague for end users to read and understand. It is thus important to be able to automatically analyze the document structures of privacy policies to assist user understanding. In this work we create a manually labelled corpus containing 231 privacy policies (of more than 566,000 words and 7,748 annotated paragraphs). We benchmark our data corpus with 3 document classification models and achieve more than 82% on F1-score.     

Power supply to electric vehicle charging stations in India:Justification of a framework for a dynamic and adaptive electricity tariff policy

Given the steady growth in the use of electric vehicles in India, and the need to locate charging stations in various places to fulfill the corresponding demand, this paper focuses on an applicable Electricity Tariff Policy Framework in the context of power supply to Electric Vehicle Charging Stations(EVCSs). The framework is conceptualized by a theoretical foundation, an operational/functional foundation, and a transactional foundation. The theoretical foundation incorporates justice risk across the Energy Lifecycle stages by classifying the relevant international, regional, and national issues within the legal layers of Energy Law while examining the core ideas of the 'Energy Trilemma'. The paper identifies two basic dimensions, (a) the target sets of end-users, and (b) EVCSs settings and the applicable tariff schemes, as the foundation of the framework. The operational/functional foundation consists of techno-economic and social criteria, and the transactional foundation involves the consideration of different target sets of end-users. The proposed framework will benefit policymakers in general and those, especially in India. Adaptation and use of the framework in other countries should consider their basic economic, geographic and political structures.