Optimal Placement and Sizing of TCSC for Improving the Voltage and Economic Indices of System with Stochastic Load Model

2020 ◽  
Vol 29 (13) ◽  
pp. 2050217 ◽  
Author(s):  
Saman Ghaedi ◽  
Behrouz Tousi ◽  
Maysam Abbasi ◽  
Masoud Alilou
Keyword(s):  
Optimal Allocation ◽  
Estimation Method ◽  
Point Estimation ◽  
Optimal Placement ◽  
Load Model ◽  
Multi Objective ◽  
Voltage Deviation ◽  
Stochastic Load ◽  
Economic Indices ◽  
Point Estimation Method

In this paper, an efficient method is proposed for optimal allocation and sizing of Thyristor Controlled Series Compensator (TCSC) to improve the technical and economic indices of a power network with deterministic and stochastic load models. First, the compensator allocation is done in the transmission system with the deterministic load model. After calculating the technical and economic indices of the network in the presence of a deterministic load model, the proposed method is applied to the system with a stochastic load model. The two-point estimation method is used for simulating the stochastic conditions. The indices of voltage deviation and economics of the system are optimized for selecting the optimal location and size of TCSCs. The economic index comprises loss cost, cost of the produced active power of generators and also the costs of installation, operation and maintenance of TCSCs. The multi-objective particle swarm optimization (MOPSO) is utilized to optimize the objective functions. After the multi-objective optimization, the fuzzy decision method is employed to extract one of the Pareto-optimal solutions as the best compromise one. For evaluating the proposed method, comprehensive simulations have been performed on the IEEE 39-bus network by using MATLAB/Matpower software. The simulation results clearly prove the remarkable performance of the proposed method in improving the technical and economic indices of the system.

Multi-Objective Optimization Model for Non-Suspending Runway Reconstruction Project Scheduling in Busy Airports

2014 ◽  
Vol 587-589 ◽  
pp. 1884-1887
Author(s):  
Yu Lin Yang ◽  
Zheng Hao Ma
Keyword(s):  
Critical Path ◽  
Estimation Method ◽  
Pso Algorithm ◽  
Point Estimation ◽  
Process Time ◽  
Multi Objective ◽  
Objective Model ◽  
Scheduling Plan ◽  
Point Estimation Method ◽  
Reconstruction Time

As a result of the urgency of runway reconstruction and the inevitability of taking non-suspending reconstruction, time, cost and quality are taken as three basic objectives of the optimization with the complex environment of the runway, and the special related constraints are taken into consideration as well. Combining the other two objectives with the time objective and quantifying three objectives in the same way help to present the multi-objective model that is based on the multi-attribute utility function theory. Establishing the project network of asphalt repaving project and using the critical path method contribute to dealing with the uncertainty and randomness based on the distribution of process time by the three-point estimation method. Particle swarm optimization (PSO) algorithm helps solve the model and offers a scheduling plan of the critical path. In the end, one non-suspending the construction project of the runway is taken as an example and it proves the validity of the model compared with the related researches and the actual applied schedule.

scholarly journals Optimal Micro-PMU Placement Using Mutual Information Theory in Distribution Networks

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1917 ◽  
Author(s):  
Zhi Wu ◽  
Xiao Du ◽  
Wei Gu ◽  
Ping Ling ◽  
Jinsong Liu ◽  
...  
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Estimation Method ◽  
Distribution Networks ◽  
Point Estimation ◽  
Optimal Placement ◽  
Measurement Unit ◽  
Selection Strategy ◽  
Smart Meters ◽  
Point Estimation Method

Micro-phasor measurement unit (μPMU) is under fast development and becoming more and more important for application in future distribution networks. It is unrealistic and unaffordable to place all buses with μPMUs because of the high costs, leading to the necessity of determining optimal placement with minimal numbers of μPMUs in the distribution system. An optimal μPMU placement (OPP) based on the information entropy evaluation and node selection strategy (IENS) using greedy algorithm is presented in this paper. The uncertainties of distributed generations (DGs) and pseudo measurements are taken into consideration, and the two-point estimation method (2PEM) is utilized for solving stochastic state estimation problems. The set of buses selected by improved IENS, which can minimize the uncertainties of network and obtain system observability is considered as the optimal deployment of μPMUs. The proposed method utilizes the measurements of smart meters and pseudo measurements of load powers in the distribution systems to reduce the number of μPMUs and enhance the observability of the network. The results of the simulations prove the effectiveness of the proposed algorithm with the comparison of traditional topological methods for the OPP problem. The improved IENS method can obtain the optimal complete and incomplete μPMU placement in the distribution systems.

scholarly journals Allocation of EV Public Charging Station in Renewable based Distribution Network using HHO Considering Uncertainties and Traffic Congestion

2021 ◽  
Author(s):  
Arnab Pal ◽  
Aniruddha Bhattacharya ◽  
Ajoy Kumar Chakraborty
Keyword(s):  
Distribution System ◽  
Traffic Congestion ◽  
Distribution Network ◽  
Estimation Method ◽  
Point Estimation ◽  
Rank Test ◽  
Voltage Profile ◽  
Charging Station ◽  
Soft Computing Techniques ◽  
Point Estimation Method

Abstract Electric vehicle (EV) is the growing vehicular technology for sustainable development to reduce carbon emission and to save fossil fuel. The charging station (CS) is necessary at appropriate locations to facilitate the EV owners to charge their vehicle as well as to keep the distribution system parameters within permissible limits. Besides that, the selection of a charging station is also a significant task for the EV user to reduce battery energy wastage while reaching the EV charging station. This paper presents a realistic solution for the allocation of public fast-charging stations (PFCS) along with solar distributed generation (SDG). A 33 node radial distribution network is superimposed with the corresponding traffic network to allocate PFCSs and SDGs. Two interconnected stages of optimization are used in this work. The first part deals with the optimization of PFCS’s locations and SDG’s locations with sizes, to minimize the energy loss and to improve voltage profile using harris hawk optimization (HHO) and few other soft computing techniques. The second part handles the proper assignment of EVs to the PFCSs with less consumption of the EV’s energy considering the road distances with traffic congestion using linear programming (LP), where the shortest paths are decided by Dijkstra's algorithm. The 2m point estimation method (2m PEM) is employed to handle the uncertainties associated with EVs and SDGs. The robustness of solutions are tested using wilcoxon signed rank test and quade test.

scholarly journals Two-Stage Optimal Scheduling of Large-Scale Renewable Energy System Considering the Uncertainty of Generation and Load

2020 ◽  
Vol 10 (3) ◽  
pp. 971 ◽  
Author(s):  
Xiangyu Kong ◽  
Shuping Quan ◽  
Fangyuan Sun ◽  
Zhengguang Chen ◽  
Xingguo Wang ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Demand Response ◽  
Large Scale ◽  
Thermal Power ◽  
Estimation Method ◽  
Point Estimation ◽  
Low Carbon ◽  
System Operation ◽  
Two Stage ◽  
Point Estimation Method

With the development of smart grid and low-carbon electricity, a high proportion of renewable energy is connected to the grid. In addition, the peak-valley difference of system load increases, which makes the traditional grid scheduling method no longer suitable. Therefore, this paper proposes a two-stage low-carbon economic scheduling model considering the characteristics of wind, light, thermal power units, and demand response at different time scales. This model not only concerns the deep peak state of thermal power units under the condition of large-scale renewable energy, but also sets the uncertain models of PDR (Price-based Demand Response) virtual units and IDR (Incentive Demand Response) virtual units. Taking the system operation cost and carbon treatment cost as the target, the improved bat algorithm and 2PM (Two-point Estimation Method) are used to solve the problem. The introduction of climbing costs and low load operating costs can more truly reflect the increased cost of thermal power units. Meanwhile, the source-load interaction can weigh renewable energy limited costs and the increased costs of balancing volatility. The proposed method can be applied to optimal dispatch and safe operation analysis of the power grid with a high proportion of renewable energy. Compared with traditional methods, the total scheduling cost of the system can be reduced, and the rights and obligations of contributors to system operation can be guaranteed to the greatest extent.

An IPEM for optimal control of uncertain beam-moving mass systems with saturation nonlinearity

2017 ◽  
Vol 24 (13) ◽  
pp. 2760-2781
Author(s):  
Xiao-Xiao Liu ◽  
Xing-Min Ren
Keyword(s):  
Nonlinear Control ◽  
Vibration Suppression ◽  
Estimation Method ◽  
Feedback System ◽  
Random Excitation ◽  
Point Estimation ◽  
Maximum Principles ◽  
Moving Mass ◽  
Nonlinear Feedback ◽  
Point Estimation Method

This paper addresses the vibration control of single-span beams subjected to a moving mass by coupling the saturated nonlinear control and an improved point estimation method (IPEM). An optimal nonlinear feedback control law, for a kind of uncertain linear system with actuator nonlinearities, is derived using the combination of Pontryagin's maximum principles and the improved point estimation method. The stability of the feedback system is guaranteed using a Lyapunov function. In order to obtain the instantaneously probabilistic information of output responses, a novel moment approach is presented by combining the improved point estimation method, the maximum entropy methodology and the probability density evolution theory. In addition to the consideration of stochastic system parameters, the external loadings are considered as a nonstationary random excitation and a moving sprung mass, respectively. The proposed strategy is then used to perform vibration suppression analysis and parametric sensitivity analysis of the given beam. From numerical simulation results, it is deduced that the improved point estimation method is a priority approach to the optimal saturated nonlinear control of stochastic beam systems. This observation has widespread applications and prospects in vehicle–bridge interaction and missile–gun systems.

Resonance Analysis of Train–Track–Bridge Interaction Systems with Correlated Uncertainties

2019 ◽  
Vol 20 (01) ◽  
pp. 2050008 ◽  
Author(s):  
Lifeng Xin ◽  
Xiaozhen Li ◽  
Jiaxin Zhang ◽  
Yan Zhu ◽  
Lin Xiao
Keyword(s):  
Probability Distributions ◽  
Three Dimensional ◽  
Estimation Method ◽  
Interaction Model ◽  
Point Estimation ◽  
Statistical Characteristics ◽  
Resonance Analysis ◽  
Track Bridge ◽  
Point Estimation Method ◽  
Nataf Transformation

Over the last decades, the resonance-related dynamics for bridge systems subjected to a moving train has been researched and discussed from mechanics, physics and mathematics. In the current work, new perspectives of train-induced resonance analysis are investigated through introducing random propagation process into the train–bridge dynamic interactions. Besides, the Nataf-transformation-based point estimation method is applied to generate pseudorandom variables following arbitrarily correlated probability distributions. A three-dimensional (3D) nonlinear train-ballasted track–bridge interaction model founded on fundamental physical and mechanical principles is employed to convey and depict train–bridge interactions with random properties considered. After that, extensive applications are illustrated in detail for revealing the statistical characteristics of the so-called “random resonance”. Numerical results show that the critical train speeds associated with resonance and cancelation are random in essence owing to the variability of system parameters; the correlation between parameters exerts obvious influences on system dynamic behaviors; the last vehicle of a train will be in more violent vibrations compared to the front vehicles; the influences of track irregularities on the wheel–rail interactions are significantly greater than those of resonance.

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