Generalized multiple time windows model based parallel machine scheduling for TDRSS

The scheduling efficiency of the tracking and data relay satellite system（TDRSS）is strictly limited by the scheduling degrees of freedom（DoF）,including time DoF defined by jobs＇ flexible time windows and spatial DoF brought by multiple servable tracking and data relay satellites（TDRSs）.In this paper,ageneralized multiple time windows（GMTW）model is proposed to fully exploit the time and spatial DoF.Then,the improvements of service capability and job-completion probability based on the GMTW are theoretically proved.Further,an asymmetric path-relinking（APR）based heuristic job scheduling framework is presented to maximize the usage of DoF provided by the GMTW.Simulation results show that by using our proposal 11%improvement of average jobcompletion probability can be obtained.Meanwhile,the computing time of the time-to-target can be shorten to 1/9 of the GRASP.

Integrated Power Generation Scheduling Compilation Method for Large-scale Small Hydropower Station Group

针对小水电无序管理，弃水、窝电现象严重，水资源浪费问题突出的现状，设计省地一体化小水电发电计划编制方法。该方法规范了小水电计划编制流程，对发电计划实行分级制作与管理，增加了输电断面安全校核环节，并给出了超限断面计划调整方法，通过建议计划上报一输电断面校核一计划协调发布的闭环方式有效提高了发电计划的准确性和可操作性。该方法目前已应用于云南电网小水电管理系统，方法的科学性与有效性得到了实际工程的验证。

An Optimal Dynamic Generation Scheduling for a Wind-Thermal Power System

In this paper, a dynamic generation scheduling model is formulated, aiming at minimizing the costs of power generation and taking into account the constraints of thermal power units and spinning reserve in wind power integrated systems. A dynamic solving method blended with particle swarm optimization algorithm is proposed. In this method, the solution space of the states of unit commitment is created and will be updated when the status of unit commitment changes in a period to meet the spinning reserve demand. The thermal unit operation constrains are inspected in adjacent time intervals to ensure all the states in the solution space effective. The particle swarm algorithm is applied in the procedure to optimize the load distribution of each unit commitment state. A case study in a simulation system is finally given to verify the feasibility and effectiveness of this dynamic optimization algorithm.

Short-term power generation scheduling rules for cascade hydropower stations based on hybrid algorithm

Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm （POA） in combination with the genetic algorithm （GA）. After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching.

Joint Dynamic Clustering and User Scheduling for Downlink Cloud Radio Access Network with Limited Feedback

In limited feedback-based CloudRAN(C-RAN) systems,the inter-cluster and intra-cluster interference together with the quantification error can seriously deteriorates the system spectral efficiency.We,in this paper,propose an efficient three-phase framework and corresponding algorithms for dealing with this problem.Firstly,a greedy scheduling algorithm based on the lower bound of the ergodic rate is performed for generating an elementary cluster in the first phase.And then the elementary cluster is divided into many small clusters according to the following proposed algorithms based on the short term instantaneous information in the second phase.In the end,based on the limited feedback two zero-forcing(ZF) precoding strategies are adopted for reducing the intra-cluster interference in the third phase.The provided Monte Carlo simulations show the effectiveness of our proposed algorithms in the respect of system spectral efficiency and average user rate.

Performance Evaluation of Scheduling Algorithms for 4G (LTE)

Long Term Evolution (LTE) is designed to revolutionize mobile broadband technology with key considerations of higher data rate, improved power efficiency, low latency and better quality of service. This work analyzes the impact of resource scheduling algorithms on the performance of LTE (4G) and WCDMA (3G) networks. In this paper, a full illustration of LTE system is given together with different scheduling algorithms. Thereafter, 3G WCDMA and 4G LTE networks were simulated using Simulink simulator embedded in MATLAB and performance evaluations were carried out. The performance metrics used for the evaluations are average system throughput, packet delay, latency and allocation of fairness using Round Robin, Best CQI and Proportional fair Packet Scheduling Algorithms. The results of the evaluations on both networks were analysed. The results showed that 4G LTE network performs better than 3G WCDMA network in all the three scheduling algorithms used.

Deep Cutting Plane Inequalities for Stochastic Non-Preemptive Single Machine Scheduling Problem

We study the classical single machine scheduling problem but with uncertainty. A robust optimization model is presented, and an effective deep cut is derived. Numerical experiments show effectiveness of the derived cut.

A Gain Scheduled Method for Speed Control of Wind Driven Doubly Fed Induction Generator

This paper proposes a gain scheduled control method for a doubly fed induction generator driven by a wind turbine. The purpose is to design a variable speed control system so as to extract the maximum power in the region below the rated wind speed. Gain scheduled control approach is applied in order to achieve high performance over a wide range of wind speed. A double loop configuration is adopted. In the inner loop, the rotor speed is used as the scheduling parameter, while a function of wind and rotor speed is used as the scheduling parameter in the outer loop. It is verified in simulations that a high tracking performance has been achieved.

A Novel Particle Swarm Optimization for Optimal Scheduling of Hydrothermal System

A fuzzy adaptive particle swarm optimization (FAPSO) is presented to determine the optimal operation of hydrothermal power system. In order to solve the shortcoming premature and easily local optimum of the standard particle swarm optimization (PSO), the fuzzy adaptive criterion is applied for inertia weight based on the evolution speed factor and square deviation of fitness for the swarm, in each iteration process, the inertia weight is dynamically changed using the fuzzy rules to adapt to nonlinear optimization process. The performance of FAPSO is demonstrated on hydrothermal system comprising 1 thermal unit and 4 hydro plants, the comparison is drawn in PSO, FAPSO and genetic algorithms (GA) in terms of the solution quality and computational efficiency. The experiment showed that the proposed approach has higher quality solutions and strong ability in global search.

Non-dominated sorting culture differential evolution algorithm for multi-objective optimal operation of Wind-Solar-Hydro complementary power generation system

Due to the intermittency and instability of Wind-Solar energy and easy compensation of hydropower, this study proposes a Wind-Solar-Hydro power optimal scheduling model. This model is aimed at maximizing the total system power generation and the minimum ten-day joint output. To effectively optimize the multi-objective model, a new algorithm named non-dominated sorting culture differential evolution algorithm(NSCDE) is proposed. The feasibility of NSCDE was verified through several well-known benchmark problems. It was then applied to the Jinping Wind-Solar-Hydro complementary power generation system. The results demonstrate that NSCDE can provide decision makers a series of optimized scheduling schemes.

New Maintenance Strategies for Generating Companies in Power Market

A new approach to maintenance scheduling of generating units(MSU)in competitive electricity markets was presented,which was formulated as a noncooperative game with complete information.The payoff of each generating company(Genco)was defined as the profit from the energy auction market minus maintenance cost and risk loss.The compensation fee of interruptible load was a part of the maintenance cost when the permitted maintenance capacity in the system was insufficient.Hourly energy auction was incorporated in the computation of both revenues from energy market and risk loss of maintenance strategy as a nested game.A new heuristic search algorithm for the calculation of the game equilibrium of MSU was presented,which coordinates the solutions of non-equilibrium,unique equilibrium and multiple equilibria.Numerical results for a two-Genco system and a realistic system were used to demonstrate the basic ideas and the applicability of the proposed method,as well as its computational efficiency.

广义确定性标识网络

随着智能制造、智能交通等重大国家战略实施,确定性成为信息网络尤其是行业专网的新焦点.现有确定性网络技术始终关注网络传输要素(带宽、时隙等)来保障数据流的确定性传输.然而,仅靠保障传输要素无法支撑新兴行业应用的多样化需求.例如,在算网融合场景,智算任务要求同时保障传输与计算要素的确定性来实现高性能通信;在绿色通信场景,需要考虑节点能量要素的确定性以维持网络稳定运行.针对上述需求,本文基于前期提出的标识网络技术,研究面向传输、计算、存储、能量等多要素的广义确定性网络.首先提出广义确定性标识网络架构,包括差异化服务层、异构融合网络层和智慧化适配层.差异化服务层和异构融合网络层,分别实现差异化确定性应用需求和异构化确定性网络要素的统一标识和描述,并通过标识解析映射实现确定性信息向智慧化适配层的统一封装和传递;智慧化适配层完成差异化确定性应用需求和异构化确定性网络要素的适配.现有确定性资源适配方法,即使仅考虑单一网络内的基本确定性要素,仍面临计算时间长、求解复杂性高、灵活度低等问题,为了支持更加复杂的多确定性要素、多种异构网络的协同适配,设计了基于深度强化学习的端到端的确定性调度(End-to-end Deterministic resource scheduling,E2eDet)算法,该算法可统一化、端到端地为混合数据流协同分配多种确定性网络资源,满足不同应用的差异化确定性需求.实验表明,E2eDet比DeepCQF和Random算法分别提升了28.4%和6.38倍数据流调度数量,同时E2eDet可以较好地权衡计算时间和调度能力.

Schedule-Aware Power Management for Energy-Efficiency Improvement in 802.11u WLAN

Mobile stations supporting the 802.11u standard can access WLAN automatically when they are within the coverage of the network service provided by this WLAN. To achieve this goal, the stations need to keep “on” states includingidleandactiveall the time. However, studies have noted that the idleness of stations often lead to considerable power consumption. Although the conventional power saving mode (PSM) can provide energy saving effect to some extent, its own disadvantage leads to lower energy efficiency when the number of stations accessing the target WLAN. In this paper, we propose a Schedule-Aware PSM (S-PSM), which can improve the energy efficiency in 802.11u WLAN. Particularly, we use the Generic advertisement service (GAS) defined in 802.11u standard to broadcast the transmission schedule information and all stations switch off their radios based on this information accordingly. We introduce the Respond Contention Window to reduce the collision probability of competition channel. When there is no packet in the access point (AP), AP broadcasts the GAS frame and actives the Idle Timer. All stations will turn into sleep and AP will not send GAS frame until Idle Timer expires. Simulations have shown that our proposed scheme can significantly reduce power consumption compared with the conventional PSM.

Research on the Model of Long Term Generation Planning in Power Market Reform

With the deepening of China’s power market, bilateral transactions will continue to grow in large scale. The release of bilateral transactions locked more regulatory resources of the power grid, will directly affect the operation mode of the unit and the implementation of planned electricity. In the paper, considering the large-scale bilateral trade effect on the peak regulation of power grid, energy saving and emission reduction, power system security and other factors, and then putting forward the method of long term generation planning and annual planning model to adapt to the safe operation of power grid in China. In the model, the target is minimizing the monthly load rate deviation and the annual electric quantity deviation rate, the latter includes the capacity factor. In addition, the constraints include the monthly quantity of electricity, adjustable utilization rate deviation, load rate, reserve and key sections, etc. Through an example to verify the correctness of the model, the planning and power transaction results can satisfy the peak regulation of load, energy saving and emission reduction and safety operation of the power grid requirements.

基于主动尾流控制的风电机群协同优化调度

针对机组间尾流效应严重影响风电机组发电效率的问题,提出了风电机组安全偏航约束计算方法、尾流特性混合半机理建模方法以及风电机群多目标协同优化调度方法。基于FAST.FARM平台完善了多自由度可控机组与尾流的动态交互集成仿真环境,对比分析了2台机组串列式排布以及华东地区某海上风电场7台机组实际排布下的协同运行优化性能。结果表明:所建立的集成仿真模型能够合理表征风电机群与空气流场的多领域动态交互特性,所提方法能够有效提升风电机群发电效能,促进经济效益、资源利用和成本控制的均衡优化。

基于GAMS的水库群调度及风险补偿研究

为平衡流域水库群成员的风险与收益,调动水库参与联合调度的积极性,保障水库群稳定运行,基于GAMS软件建立了水库群发电优化调度模型,进而确定补偿效益。采用熵权法基于水库特征参数确定水库重要程度,结合聚合降维思路改进了合作博弈理论的Shapley值法,建立了风险指标体系评价水库兴利调度风险,提出了基于调度风险修正效益分摊方案的水库群风险补偿方法,进而实现不同运行调度方案、不同发电破坏情形下水库群风险效益平衡。长江上游6条干支流上12座控制性水库实例应用结果表明,该风险补偿方法不仅考虑了水库个体特征和调度效益贡献,还兼顾了水库调度风险,可实现水库群补偿效益的合理化分摊。

基于IGDT的含广义储能的独立直流微网日前优化调度

微电网的能量管理与优化调度作为构建新型电力系统的重要环节,提高其可再生能源的消纳水平、降低源荷不确定性风险以及优化系统运行成本具有重要意义。因此,文中提出一种基于信息间隙决策理论(information gap decision theory,IGDT)的含广义储能的独立直流微电网日前优化调度模型。首先,构建含超级电容的混合储能系统,以降低蓄电池运行成本,将具备虚拟储能特性的柔性负荷与混合储能相结合,形成广义储能,充分发挥微电网系统内灵活性资源特性;其次,考虑系统风光荷不确定性,引入IGDT模型,在确定性模型基础上建立风险规避策略下的鲁棒模型和风险投机策略下的机会模型,从2种决策角度追求降低风险与最大化收益;最后,基于算例仿真分析,证明该调度策略在降低微电网运行成本的基础上可量化不确定性因素对系统调度决策的影响,验证了模型的有效性和可参考性。

考虑太阳能辅助碳捕集技术的综合能源生产单元随机低碳调度策略

为解决综合能源生产单元(integrated energy production unit,IEPU)中燃煤机组碳捕集过程的高能耗问题,同时应对新能源不确定性对运行调度带来的挑战,该文提出一种考虑太阳能辅助碳捕集技术的IEPU随机低碳调度策略,旨在实现IEPU的多能协同与低碳运行。首先,对含太阳能辅助碳捕集热电联产单元(combined heat and power based on solar-assisted carbon capture,CHP-SACC)的能量流动与运行机理进行分析,并构建其运行模型;其次,考虑风电不确定性带来的影响,提出一种基于条件最小二乘生成对抗网络(conditional-least squares generative adversarial networks,C-LSGANs)的可再生能源场景生成方法来提高场景的生成质量;然后,考虑异质能流耦合约束、多元设备运行约束以及能量平衡约束等,以最大化系统运行收益期望为目标构建IEPU随机低碳调度模型;最后,在算例仿真中设置不同的运行策略验证所提低碳转型方案的有效性,并分析了能源价格、设备容量等因素对系统运行收益的影响。