Stochastic optimal TCSC placement in power system considering high wind power penetration

Xu, Xu; Zhao, Jian; Xu, Zhao; Chai, Songjian; Li, Jiayong; Yu, Yi

doi:10.1049/iet-gtd.2017.1320

Cited by 13 publications

(14 citation statements)

References 34 publications

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“…Here C TCSC represents the operation cost coefficient of each TCSC unit in $/MVAR and S TCSC is The installed capacity of the TCSC in MVAR which can be calculated using (10) where Il max is nominal current of the line in which TCSC is integrated.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Among the FACTS devices, TCSC is characterized by its fast response at the lowest costs [3]. In the literature, TCSC is one of most useful FACTS devices, which can be installed to improve the system loadability, increase the power transmission capacity, improve the transient stability, reduce transmission loss, and suppress the network low-frequency oscillation [4]- [10]. To achieve the previous merits, the TCSC devices need to be optimally installed in the appropriate network routes at the fine-tuned parameters.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Multi Objective Parallel Seeker Optimization Algorithm for Incorporating TCSC Devices into Optimal Power Flow Framework

et al. 2019

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Usually, the optimal integration of thyristor-controlled series compensators (TCSCs) aims at enhancing power system performance like all of flexible AC transmission systems (FACTS) devices. The insertion of TCSC unites targets to minimize active/reactive power losses, increase transmission-lines flow reserve beyond the thermal limit, and improve the voltage profile while maintaining the total generation cost of the system slightly affected compared to its single objective base case. In this paper, the optimal power flow (OPF) framework is considered to find the best site and size of the TCSCs devices considering techno-economic issues for reducing the costs of installed TCSCs devices as well as for generation costs. An adaptive parallel seeker optimization algorithm (APSOA) is investigated to employ this techno-economic study. The proposed APSOA is used to solve the multi-objective OPF problem while LSR reduces the search space. The proposed algorithm is tested over three IEEE standards with 9−, 30− and 57-bus test systems at normal and contingency operating conditions. Also, a large system of IEEE 118-bus is used also in order for the proposed technique to be adopted by industry, sound solutions for practical and realistic test systems are needed besides proof of concept on small IEEE test systems. Four-study cases considered to demonstrate the capabilities and gains of the proposed method from the point of view of reducing losses and total voltage deviation to lower levels as compared to those on literature for better energy utilization efficiency. INDEX TERMS Energy utilization efficiency, optimal power flow problem, compensation, power loss minimization, FACTS devices.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Multi Objective Parallel Seeker Optimization Algorithm for Incorporating TCSC Devices into Optimal Power Flow Framework

et al. 2019

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“…Notice that operation constraints in each scenario remain the same as constraints to 8 and and 16. On the basis of the historical data used in proposed model, the scenario‐generation steps and scenario‐reduction technology in the literature are adopted. As a result, 2000 scenarios considering temporal correlation are generated, and 10 representative scenarios are finally selected via backward‐reduction algorithm for the scenario set.…”

Section: Case Studiesmentioning

confidence: 99%

Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration

Wen

et al. 2019

Int Trans Electr Energ Syst

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To counterbalance the significant challenges imposed by renewable distributed generations penetration, this paper discusses the need of distributed energy storage system investment in distribution networks and proposes a robust optimization based storage investment model. The operational constraints of distribution network (e.g., voltage profile and substation capacity limitation) and storage device (e.g., state of energy and charging/discharging limit) are considered to guarantee the technical operation requirements. The proposed model is mathematically formulated as a two‐stage robust optimization with uncertainty of renewable distributed generator that is quantified by a polyhedral uncertainty set. The investment‐decision variables are optimized in the first stage, and the feasibility in the real‐time worst‐case scenario is checked in the second stage. A column‐and‐constraint generation (C&CG) algorithm and the big‐M linearization method are employed to solve the associated optimization problem. Numerical experiments on IEEE‐37‐node and IEEE‐123‐node distribution networks demonstrate the effectiveness of the proposed model.

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“…Non-conventional computationally intelligent algorithms [20,21] are popularly used for obtaining optimised line flows of a network incorporating the UPFC instead of deterministic approaches. Different versions of particle swarm optimisation (PSO) [22,23], brainstorm optimisation algorithm [24], pseudodynamic tabu search-based optimisation [25], sparse optimisation [26] and other techniques [27][28][29] are applied to various problem models of FACTS device usage in the modern networks. Further complicated models, like co-ordination of multiple types of FACTS devices in the presence of reactive sources are solved using gravitational search algorithm in [30], and the efficiency of the chaotic krill herd algorithm is established in [31] to solve the OPFbased DC-link placement problem.…”

Section: Literature Surveymentioning

confidence: 99%

“…The pressure function of air parcels is formulated according to the fitness function values of the problem at hand. Once the assignment is done, the pressure function values are ranked according to their fitness values and velocity is updated for each of the air parcels by (29). Based on their velocity, the positions of the parcels are updated by (30).…”

Section: Flowchart and Implementation Of The Proposed Awdomentioning

confidence: 99%

Optimal line flows based on voltage profile, power loss, cost and conductor temperature using coordinated controlled UPFC

Sen

Acharjee

2019

IET Generation, Transmission & Distribution

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Stochastic optimal TCSC placement in power system considering high wind power penetration

Cited by 13 publications

References 34 publications

Adaptive Multi Objective Parallel Seeker Optimization Algorithm for Incorporating TCSC Devices into Optimal Power Flow Framework

Adaptive Multi Objective Parallel Seeker Optimization Algorithm for Incorporating TCSC Devices into Optimal Power Flow Framework

Optimal distributed energy storage investment scheme for distribution network accommodating high renewable penetration

Optimal line flows based on voltage profile, power loss, cost and conductor temperature using coordinated controlled UPFC

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