Coordinated Planning Transmission Tasks in Heterogeneous Space Networks:A Semi-Distributed Approach

This paper studies the coordinated planning of transmission tasks in the heterogeneous space networks to enable efficient sharing of ground stations cross satellite systems.Specifically,we first formulate the coordinated planning problem into a mixed integer liner programming(MILP)problem based on time expanded graph.Then,the problem is transferred and reformulated into a consensus optimization framework which can be solved by satellite systems parallelly.With alternating direction method of multipliers(ADMM),a semi-distributed coordinated transmission task planning algorithm is proposed,in which each satellite system plans its own tasks based on local information and limited communication with the coordination center.Simulation results demonstrate that compared with the centralized and fully-distributed methods,the proposed semi-distributed coordinated method can strike a better balance among task complete rate,complexity,and the amount of information required to be exchanged.

A hybrid particle swarm optimization approach with neural network and set pair analysis for transmission network planning

Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, network reliability and the network loss are the main objective of transmission network planning. Combined with set pair analysis (SPA), particle swarm optimization (PSO), neural network (NN), a hybrid particle swarm optimization model was established with neural network and set pair analysis for transmission network planning (HPNS). Firstly, the contact degree of set pair analysis was introduced, the traditional goal set was converted into the collection of the three indicators including the identity degree, difference agree and contrary degree. On this bases, using shi(H), the three objective optimization problem was converted into single objective optimization problem. Secondly, using the fast and efficient search capabilities of PSO, the transmission network planning model based on set pair analysis was optimized. In the process of optimization, by improving the BP neural network constantly training so that the value of the fitness function of PSO becomes smaller in order to obtain the optimization program fitting the three objectives better. Finally, compared HPNS with PSO algorithm and the classic genetic algorithm, HPNS increased about 23% efficiency than THA, raised about 3.7% than PSO and improved about 2.96% than GA.

Minimum Load-curtailment in Transmission Network Planning Considering Integrated Wind Farms

提出应用鲁棒线性优化理论来研究电网规划中含多个风电场的最小切负荷量计算问题,为含多个风电场的系统安全性研究提供了一条新的思路。根据Seng-Cheol Kang提出的鲁棒线性优化理论,建立电网规划中考虑风电场影响的最小切负荷量模型。该模型以出力上下限和出力期望值来描述风电场的出力,最终转化为一确定性的线性规划问题并进行求解。在计及或不计及发电机调整的情况下,该模型均能够给出最安全的切负荷方案,除此以外还能给出更多介于最可靠与最经济之间的切负荷方案,实现灵活决策;在计及发电机出力可调的情况下,该模型能够给出相应的发电机出力方案;该模型能够初步给出各种切负荷方案下电网规划方案的可靠程度。基于修正的Garver’s 6节点系统和修正的巴西南部46节点系统算例测试结果验证了该方法的有效性。

Determination of Weak Points Based on Risk Theory and Application to Transmission Network Planning

In the traditional power transmission network planning,deterministic analysis methods are widely used.In such methods,all contingencies are deemed to have the same occurrence probability,which is not reasonable.In this paper,risk assessment is introduced to the process of transmission network planning considering the probabilistic characteristics of contingencies.Risk indices are given to determine the weak points of the transmission network based on local information,such as bus risk,line overload risk,contingency severity.The indices are calculated by the optimal cost control method based on risk theory,which can help planners to quickly determine weak points in the planning and find solution to them.For simplification,only line overload violation is considered.Finally,the proposed method is validated by an IEEE-RTS test system and a real power system in China from two aspects.In the first case,the original system is evaluated by the proposed method to find the weak points,and then four planning schemes are established,among which the best scheme is selected.In the second case,four initial planning schemes are established by combining the experiences of planners,and after the evaluation by using the proposed method,the best planning scheme is improved based on the information of weak points in the initial schemes,and the risk of improved scheme is reduced from 42 531.86 MW·h per year to 4 431.26 MW·h per year.

Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified by the 18-node typical system.

Particle Swarm Optimization and Its Application in Transmission Network Expansion Planning

The author introduced particle swarm optimization as a new method for power transmission network expansion planning. A new discrete method for particle swarm optimization, was developed, which is suitable for power transmission network expansion planning, and requires less computer s memory. The optimization fitness function construction, parameter selection, convergence judgement, and their characters were analyzed. Numerical simulation demonstrated the effectiveness and correctness of the method, This paper provides an academic and practical basis of particle swarm optimization in application of transmission network expansion planning for further investigation.

PLANNING DESIGN OF COMPUTER REGION NETWORK

In this paper, authors describe the characters and the present situation of computer region network, Moreover, authors put forward the method to plan and de sign ration ally the computer regional network system depending on the actual situa tion, in order to share the resources of software and hardware resources of the net work in best way and get higher efficiency of information management.

A New Genetic Algorithm Applied to Multi-Objectives Optimal of Upgrading Infrastructure in NGWN

A problem of upgrading to the Next Generation Wireless Network (NGWN) is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and installing new wireless network infrastructure elements that can accommodate both voice and data demand. In this paper, we propose a new genetic algorithm has double population to solve Multi-Objectives Optimal of Upgrading Infrastructure (MOOUI) problem in NGWN. We modeling network topology for MOOUI problem has two levels in which mobile users are sources and both base stations and base station controllers are concentrators. Our objective function is the sources to concentrators connectivity cost as well as the cost of the installation, connection, replacement, and capacity upgrade of infrastructure equipment. We generate two populations satisfy constraints and combine them to build solutions and evaluate the performance of my algorithm with data randomly generated. Numerical results show that our algorithm is a promising approach to solve this problem.

Transmission Network Expansion Planning Considering Uncertainties in Loads and Renewable Energy Resources

This paper proposes a novel method for transmission network expansion planning(TNEP)that take into account uncertainties in loads and renewable energy resources.The goal of TNEP is to minimize the expansion cost of candidate lines without any load curtailment.A robust linear optimization algorithm is adopted to minimize the load curtailment with uncertainties considered under feasible expansion costs.Hence,the optimal planning scheme obtained through an iterative process would be to serve loads and provide a sufficient margin for renewable energy integration.In this paper,two uncertainty budget parameters are introduced in the optimization process to limit the considered variation ranges for both the load and the renewable generation.Simulation results obtained from two test systems indicate that the uncertainty budget parameters used to describe uncertainties are essential to arrive at a compromise for the robustness and optimality,and hence,offer a range of preferences to power system planners and decision makers.

A Scenario-Based Robust Transmission Network Expansion Planning Method for Consideration of Wind Power Uncertainties

This paper uses a novel scenario generation method for tackling the uncertainties of wind power in the transmission network expansion planning(TNEP)problem.A heuristic moment matching(HMM)method is first applied to generate the typical scenarios for capturing the stochastic features of wind power,including expectation,standard deviation,skewness,kurtosis,and correlation of multiple wind farms.Then,based on the typical scenarios,a robust TNEP problem is presented and formulated.The solution of the problem is robust against all the scenarios that represent the stochastic features of wind power.Three test systems are used to verify the HMM method and is compared against Taguchi’s Orthogonal Array(OA)method.The simulation results show that the HMM method has better performance than the OA method in terms of the trade-off between robustness and economy.Additionally,the main factors influencing the planning scheme are studied,including the number of scenarios,wind farm capacity,and penalty factors,which provide a reference for system operators choosing parameters.

Transmission network expansion planning with embedded constraints of short circuit currents and N-1 security

An approach of transmission network expan-sion planning with embedded constraints of short circuit currents and N-1 security is proposed in this paper.The problem brought on by the strong nonlinearity property of short circuit currents is solved with a linearization method based on the DC power flow.The model can be converted to a mixed-integer linear programming problem,realizing the optimization of planning model that considers the constraints of linearized short circuit currents and N-1 security.To compensate the error caused by the assump-tions of DC power flow,the compensation factor is pro-posed.With this factor,an iterative algorithm that can compensate the linearization error is then presented.The case study based on the IEEE 118-bus system shows that the proposed model and approach can be utilized to:opti-mize the construction strategy of transmission lines;ensure the N-1 security of the network;and effectively limit the short circuit currents of the system.

Distributionally Robust Co-optimization of Transmission Network Expansion Planning and Penetration Level of Renewable Generation

Transmission network expansion can significantly improve the penetration level of renewable generation.However,existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation.This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation.The proposed model includes distributionally robust joint chance constraints,which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set.The proposed formulation yields a twostage robust optimization model with variable bounds of the uncertain sets,which is hard to solve.By applying the affine decision rule,second-order conic reformulation,and duality,we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers.Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method.

Transmission Network Expansion Planning Considering the Generators’Contribution to Uncertainty Accommodation

This paper presents an optimization for transmission network expansion planning(TNEP)under uncertainty circumstances.This TNEP model introduces the approach of parameter sets to describe the range that all possible realizations of uncertainties in load and renewable generation can reach.While optimizing the TNEP solution,the output of each generator is modeled as an uncertain variable to linearly respond to changes caused by uncertainties,which is constrained by the extent to which uncertain parameters may change the operational range of generators,and network topology.This paper demonstrates that the robust optimization approach is effective to make the problem with uncertainties tractable by converting it into a deterministic optimization,and with the genetic algorithm,the optimal TNEP solution is derived iteratively.Compared with other robust TNEP results tested on IEEE 24-bus systems,the proposed method produces a least-cost expansion plan without losing robustness.In addition,the contribution that each generator can make to accommodate with every uncertainty is optimally quantified.Effects imposed by different uncertainty levels are analyzed to provide a compromise of the conservativeness of TNEP solutions.

A New Method of Transmission Network Flexible Planning Using the Connection Number

Because connection number can express and process synthetic uncertainties caused by various uncertainties in the transmission network planning, a connection number model (CNM) was presented to compare the values of connection number logically. This paper proposed a novel model for transmission network flexible planning with uncertainty. In the proposed planning model both certainty and uncertainty information were included, and the cost-benefit analysis method was used to evaluate the candidate schemes in the objective function. Its good adaptability and flexibility were illustrated through two examples.

高比例新能源接入的输电网外送通道与储能分布鲁棒优化协同规划方法

针对具有强随机性、集中式、大容量的风电与光伏并网给输电网的安全运行及新能源消纳带来的巨大挑战,考虑风电与光伏出力的不确定性和时序相关性,提出高比例新能源接入的输电网外送通道与储能的分布鲁棒优化协同规划方法。首先,以外送通道与储能的规划共同作为决策变量,将富余的新能源资源通过规划的外送通道送出;其次,利用储能缓解和抑制新能源发电的间歇性和随机波动性,促进实现新能源的全额消纳;然后,应用二阶锥凸松弛、泰勒级数展开等技术,将原混合整数非凸非线性规划模型转化成混合整数凸规划模型,以实现高效求解。最后,以改进IEEE 39节点输电系统为算例进行仿真计算,验证了所提模型和算法的正确性和有效性。

面向新型电力系统的输电网与分布式变速抽水蓄能联合规划

针对高比例新能源场景下分布式变速抽水蓄能的容量配置及其在电网中的位置接入问题,提出了输电网和分布式变速抽水蓄能的联合规划方案。首先,考虑了各类抽水蓄能的运行和库容约束、火电机组的出力和爬坡约束以及输电网的传输能力,以分布式新能源的经济消纳为优化目标,建立了分布式变速抽水蓄能和输电网间的联合规划模型。然后,通过凸松弛方法将该模型转化为混合整数线性规划模型,以两阶段优化方法求解得到了分布式变速抽水蓄能与输电网的联合规划方案。最后,采用两个标准测试系统对比验证了所提含分布式变速抽水蓄能的联合规划相较于定速抽水蓄能的规划方案,能更好地与其他灵活性资源相协调以消纳风光可再生能源,总体上可降低约20%的成本。

基于Benders分解的交直流混合输电网随机扩展规划方法

采用随机场景模拟法描述机组和输电线路随机停电事故、负荷预测误差,提出了一种基于Benders分解的交直流混合输电网随机扩展规划方法。首先建立以交直流输电线路投资成本、失负荷成本、系统运行成本等为综合成本目标,采用Benders分解,将交直流混合输电网混合整数线性规划模型分解为规划主问题和可靠性校验和经济调度的子问题迭代求解。在6节点系统和改进的IEEE-118节点系统算例仿真结果表明:与交流输电系统相比,扩建直流输电线路可以节省更多的成本,在选择交流/直流输电线路时,直流线路输送潮流的可控性以及经济性都可以减少失负荷。

基于生产模拟的新型输电网规划方案效率效益评价方法

常规基于典型日的输电网规划方案评价方法不仅缺乏综合的效率效益评价指标体系,而且对新能源随机性与波动性的考虑不足。针对新型输电网规划方案效率效益评价需求,从多个时间尺度提出了综合效率效益评价指标体系,建立了考虑了风电、光伏出力随机波动性的新型输电网时序生产模拟模型,实现对规划方案全年8760 h的生产模拟,得到一系列基于生产模拟结果的评价指标值。在此基础上,提出了一种基于主客观赋权与模糊综合评价结合的输电网规划方案效率效益综合评价方法,避免了评价过程中的主观与客观方面的不足,为新型输电网规划方案多时间尺度的效率效益评价提供了有效的方法。基于西南某实际电网规划方案的效率效益评价算例,证明了所提方法的有效性。

基于路网分级规划的输电线路机械化施工技术研究

为提升输电线路工程的施工效率、降低施工成本、缩短施工周期,需要采用全过程机械化施工技术,但输电线路常规机械化施工技术未考虑线路沿线路网的规划条件,未考虑地形地貌的整体布局,导致所规划的机械化施工工序在局部上可行,但整体不成体系,可能自相矛盾,可持续施工性能差,达不到降低施工成本、缩短施工周期的目的,更达不到绿色安全的效果。结合输电线路地形地貌特点,在平丘地区,结合线路沿线已建路网条件,展开了输电线路施工临时施工道路的路网分级规划分析,建立了输电线路机械化施工的路网体系,基于路网体系开展输电线路基础施工、铁塔组立施工架线施工和水保措施等方面的机械化技术研究,最终得到输电线路基于路网分级规划的全过程机械化施工技术,提升了输电线路施工的本质安全,推动了输电线路全过程机械化施工技术的发展。