A Novel Bi-Level VSC-DC Transmission Expansion Planning Method of VSC-DC for Power System Flexibility and Stability Enhancement

Investigating flexibility and stability boosting transmission expansion planning(TEP)methods can increase the renewable energy(RE)consumption of the power systems.In this study,we propose a bi-level TEP method for voltage-source-converter-based direct current(VSC-DC),focusing on flexibility and stability enhancement.First,we established the TEP framework of VSC-DC,by introducing the evaluation indices to quantify the power system flexibility and stability.Subsequently,we propose a bi-level VSC-DC TEP model:the upper-level model acquires the optimal VSC-DC planning scheme by using the improved moth flame optimization(IMFO)algorithm,and the lower-level model evaluates the flexibility through time-series production simulation.Finally,we applied the proposedVSC-DC TEPmethod to the modified IEEE-24 and IEEE-39 test systems,and obtained the optimalVSCDC planning schemes.The results verified that the proposed method can achieve excellent RE curtailment with high flexibility and stability.Furthermore,the well-designed IMFO algorithm outperformed the traditional particle swarm optimization(PSO)and moth flame optimization(MFO)algorithms,confirming the effectiveness of the proposed approach.

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.

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.

Robust transmission expansion planning model considering multiple uncertainties and active load

Uncertainty must be well addressed in transmission expansion planning(TEP)problem,and it significantly affects the reliability and cost-effectiveness of power systems.Owing to the complex operating environment of power systems,it is crucial to consider different types of uncertainties during the planning stage.In this paper,a robust TEP model is proposed by considering multiple uncertainties and active load.Specifically,in this model,the uncertainties of wind power output and contingency probability are considered simultaneously.The uncertainties are described by scenario and interval,and the Benders decomposition technique is applied to solve the model.The feasibility and effectiveness of the proposed model are illustrated using the IEEE RTS and IEEE 118-node systems.

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.

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。

Multi-objective Transmission Expansion Planning Considering Life Cycle Cost

为克服目前全寿命周期成本（life cycle cost,LCC）技术的应用局限于设备运行或维护阶段的不足,针对输电网整体建立一个3维LCC层级模型,包括时间维度、元件维度和费用维度。费用维度进一步分解为设备级、系统级、外部环境成本。研究了以可靠性为中心的维护手段的应用对维护成本的影响,利用电量不足期望值计算故障成本。在此基础上,对风电等4种不确定因素进行建模,建立以LCC成本最小和切负荷量最小为多目标的输电网机会约束规划模型。采用正态边界交点算法联合改进小生境遗传算法,对模型有效求解。最后,分别对18节点系统和77节点系统进行算例分析。研究结果给出了帕累托解集及推荐的最优规划方案;此外,LCC费用分解图表明了各费用的比重和影响,有利于指导未来资产管理。

Market-Based Transmission Expansion Planning

The paper presents the main features of transmission expansion problem (TEP). In accord with review the aims and influencing factors are defined. The competitive behaviors of market participants, transmission losses, discrete investment costs, various operating conditions are considered in the model. The model is a mixed-integer linear programming formulation for a static TEP in the competitive environment. The presented methodology is applied to six-node system. In order to point out efficiency of the model the results obtained are compared with traditional problem solution.

A New Model for Transmission Network Expansion and Reactive Power Planning in a Deregulated Environment

Transmission network expansion planning (TNEP) is a challenging issue especially in new restructured electricity mar-kets environment. TNEP can be incorporated with reactive power planning in which the operating conditions will be satisfied. In this paper a combinatorial mathematical model has been presented to solve transmission expansion and reactive power planning problem (TEPRPP) simultaneously. The proposed model is a non-convex problem having a mixed integer nonlinear nature where the number of candidate solutions to be evaluated increases exponentially according to the system size. The objective function of TEPRPP comprises the new circuits’ investment and production costs as well as load curtailment penalty payments. A real genetic algorithm (RGA) aimed to obtaining a significant quality solution to handle such a complicated problem has been employed. An interior point method (IPM) is applied to solve the proposed concurrent optimization problem in the solution steps of TEPRPP model. This paper proposes a new methodology for the best location as well as the capacity of VAr sources;it is tested on two well-known systems;the Garver and IEEE 24-bus systems. The obtained results show the capability and the viability of the proposed TEPRPP model incorporating operating conditions.

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 analyzod.Numerical simulation demonstrated the effectiveness and correctness or the method. This paper provides an academic and practical basis of particle swarm optimization in application of transmission network expansion planning for further investigation.

The Concept of MDSA （Macro Demand Spatial Approach） on Spatial Demand Forecasting for Main Development Area in Transmission Planning

MDSA （macro demand spatial approach） is an approach introduced in long time electricity demand forecasting considering location. It will be used at transmission planning and policy decision on electricity infrastructure development in a region. In the model, MDSA combined with PCA （principal component analysis） and QA （qualitative analysis） to determine main development area in region and the variables that affecting electricity demand in there. Main development area is an area with industrial domination as a driver of economic growth. The electricity demand driver variables are different for type of electricity consumer. However, they will be equal for main development areas. The variables which have no significant effect can be reduced by using PCA. The generated models tested to assess whether it still at the range of confidence level of electricity demand forecasting. At the case study, generated model for main development areas at South Sumatra Subsystem as a part of Sumatra Interconnection System is still in the range of confidence level. Thus, MDSA can be proposed as alternative approach in transmission planning that considering location.

Suggestion on 9th Five year and 2010 plan for transmission and substation equipment

China’s power transmission equipment industry has formed, through technical innovation and development in past several decades especially in the period from the "6th Five Year Plan" to "8th Five Year Plan", a capability in furnishing equipments for 15,000 MW yearly key power transmission, power networks and substations project with voltage up to 500 kV. These achievements

Power generation expansion planning approach considering carbon emission constraints

Decarbonization of the power sector in China is an essential aspect of the energy transition process to achieve carbon neutrality.The power sector accounts for approximately 40%of China’s total CO_(2) emissions.Accordingly,collaborative optimization in power generation expansion planning(GEP)simultaneously considering economic,environmental,and technological concerns as carbon emissions is necessary.This paper proposes a collaborative mixedinteger linear programming optimization approach for GEP.This minimizes the power system’s operating cost to resolve emission concerns considering energy development strategies,flexible generation,and resource limitations constraints.This research further analyzes the advantages and disadvantages of current GEP techniques.Results show that the main determinants of new investment decisions are carbon emissions,reserve margins,resource availability,fuel consumption,and fuel price.The proposed optimization method is simulated and validated based on China’s power system data.Finally,this study provides policy recommendations on the flexible management of traditional power sources,the market-oriented mechanism of new energy sources,and the integration of new technology to support the attainment of carbon-neutral targets in the current energy transition process.

A production-cost-simulation-based method for optimal planning of the grid interconnection between countries with rich hydro energy

Cross-border grid interconnection is a critical means to achieve wide-area share of hydro and other clean energy.Economic benefit assessment of cross-border grid interconnection projects should be carefully performed during early stage.In this paper,a method based on cost-benefit economic assessment for optimal planning of cross-border grid interconnection is proposed.An economic index for comprehensively assessing the cost of a transmission project and its resulting benefits of more usage of hydro energy is designed first.A chronological production cost simulation model considering hydro energy spillage due to transmission congestion and thermal operational limitation is then proposed to calculate the economic index.A case study is performed using the proposed method to determine the optimal capacity of a potential transmission link between Brazil and Argentina,which have rich and complementary hydro energy resources.

Resilience-oriented Transmission Expansion Planning with Optimal Transmission Switching Under Typhoon Weather

This paper presents resilience-oriented transmission expansion planning(RTEP)with optimal transmission switching(OTS)model under typhoon weather.The proposed model carefully considers the uncertainty of component vulnerability by constructing a typhoon-related box uncertainty set where component failure rate varies within a range closely related with typhoon intensity.Accordingly,a min-max-min model is developed to enhance transmission network resilience,where the upper level minimizes transmission lines investment,the middle level searches for the probability distribution of failure status leading to max worst-case expected load-shedding(WCEL)under typhoon,and the lower level optimizes WCEL by economic dispatch(ED)and OTS.A nested decomposition algorithm based on benders decomposition is developed to solve the model.Case studies of modified IEEE 30-bus and 261-bus system of a Chinese region illustrate that:a)the proposed RTEP method can enhance resilience of transmission network with less investment than widely used RTEP method based on attacker and defender(DAD)model,b)the influence of OTS on RTEP is closely related with contingency severity and system scale and c)the RTEP model can be efficiently solved even in a large-scale system.

AUV Local Path Planning Based on Acoustic Image Processing

The forward-looking image sonar is a necessary vision device for Autonomous Underwater Vehicles （AUV）. Based on the acoustic image received from forward-looking image sonar, AUV local path is planned. When the environment model is made to adapt to local path planning, an iterative algorithm of binary conversion is used for image segmentation. Raw data of the acoustic image, which were received from serial port, are processed. By the use of ＂Mathematic Morphology＂ to filter noise, a mathematic model of environment for local path planning is established after coordinate transformation. The optimal path is searched by the distant transmission （Dt） algorithm. Simulation is conducted for the analysis of the algorithm. Experiment on the sea proves it reliable.

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.

Westinghouse Electric Corp.plans long-term investment in China

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Insulin Age-Dependently Modulates Synaptic Transmission and AMPA Receptor Trafficking in Region CA1 of the Rat Hippocampus

Insulin induces long-term depression (insulin-LTD) in the CA1 region of the rat juvenile hippocampus. This insulin-LTD may be due in part to internalization of the GluA2 subunit of the AMPA receptor (AMPAR) events that haven’t been studied in the mature rat hippocampus. In our studies, we used hippocampal preparations from juvenile (14 - 25 days) and mature (60 - 90 days) rats to assess insulin modulation of CA1 synaptic transmission and AMPAR trafficking and phosphorylation. Using field potential electrophysiology, we observed that insulin induced LTD in the juvenile hippocampus (as previously reported) in the presence and absence of phosphoinositide 3-kinase (PI3K) activity, but produced no significant long-term changes in the mature hippocampus in the presence of PI3K activity. Interestingly, during PI3K inhibition, insulin did produce LTD in the mature hippocampus. Additionally, insulin induced a long-term decrease in plasma membrane expression of the GluA2 and GluA1 subunits of the AMPAR in the juvenile, but not mature hippocampus. Furthermore, there was a long-term decrease in GluA1 phosphorylation at Serine 845 in the juvenile, but not mature hippocampus. These data reveal that insulin modulation of synaptic plasticity and AMPAR modulation within the hippocampus is age-dependent, suggesting that insulin-regulated behaviors may also show age-dependence. These findings are important largely due to the increased use of insulin as a therapeutic throughout the lifespan. Our data suggest that additional work should be done to determine how this use of insulin throughout different stages of life might affect synaptic function and development.