Optimal Allocation of Series FACTS Devices in Large Scale Systems

Series FACTS devices, such as the variable series reactor (VSR), have the ability to continuously regulate the transmission line reactance so as to control power flow. This paper presents a new approach to optimally locating such devices in the transmission network considering multiple operating states and contingencies. To investigate optimal investment, a single target year planning with three different load patterns is considered. The transmission contingencies may occur under any of the three load conditions and the coupling constraints between base case and contingencies are included. A reformulation technique transforms the original mixed integer nonlinear programming (MINLP) model into mixed integer linear programing (MILP) model. To further relieve the computational burden and enable the planning model to be directly applied to practical large scale systems, a two phase decomposition algorithm is introduced. Detailed numerical simulation results on IEEE 118-bus system and the Polish 2383-bus system illustrate the efficient performance of the proposed algorithm.Comment: Accepted by IET Generation, Transmission & Distributio

Electric Power System Operations with a Variable Series Reactor

Series FACTS devices, such as a Variable Series Reactor (VSR), have the ability to continuously regulate the transmission line reactance so as to control power ow. This research work evaluates the benefits brought by VSRs in different aspects of power system and develops efficient planning models and algorithms to provide optimal investment plan for the VSRs. First, an optimization approach capable of finding both optimal locations and settings of VSRs under a specific operating condition is developed. The tool implements a full ac model as well as detailed models for different power system components. Second, an optimization tool which can optimally allocate VSRs to improve the load margin in a transmission network considering a multi-scenario framework including base case and some critical contingencies is proposed. Starting from a mixed integer nonlinear programming (MINLP) model, a reformulation technique is leveraged to transform the MINLP model into a mixed integer linear programming (MILP) model so that it is computationally tractable for large scale power systems. Detailed numerical simulations on the practical Northwest US power network demonstrate the proposed technique and the capability of VSRs. Third, the VSR is introduced in the Transmission Expansion Planning (TEP) problem. A security constrained multi-stage TEP with the VSR is formulated as an MILP model. To reduce the computational burden for a practical large scale system, a decomposition approach is proposed. Simulation results demonstrate the effectiveness of the proposed approach and show that the appropriately allocated VSRs allow reduced planning costs. Fourth, in order to investigate the economic benefits brought by VSR in contingencies, a planning model to allocate VSR considering different operating conditions and the N - 1 contingencies is formulated. We consider a single target year planning. Three distinct load patterns which represent peak, normal and low load level are selected to accommodate the yearly load profile. The transmission contingencies can occur in any of the three load conditions. A two phase Benders decomposition is proposed to solved the large scale MILP model. Simulation results on the IEEE-118 bus system and the practical Polish system establish the efficient performance of the proposed algorithm

Bi-objective optimization model for optimal placement of thyristor-controlled series compensator devices

Exposure to extreme weather conditions increases power systems’ vulnerability in front of high impact, low probability contingency occurrence. In the post-restructuring years, due to the increasing demand for energy, competition between electricity market players and increasing penetration of renewable resources, the provision of effective resiliency-based approaches has received more attention. In this paper, as the major contribution to current literature, a novel approach is proposed for resiliency improvement in a way that enables power system planners to manage several resilience metrics efficiently in a bi-objective optimization planning model simultaneously. For demonstration purposes, the proposed method is applied for optimal placement of the thyristor controlled series compensator (TCSC). Improvement of all considered resilience metrics regardless of their amount in a multi-criteria decision-making framework is novel in comparison to the other previous TCSC placement approaches. Without loss of generality, the developed resiliency improvement approach is applicable in any power system planning and operation problem. The simulation results on IEEE 30-bus and 118-bus test systems confirm the practicality and effectiveness of the developed approach. Simulation results show that by considering resilience metrics, the performance index, importance of curtailed consumers, congestion management cost, number of curtailed consumers, and amount of load loss are improved by 0.63%, 43.52%, 65.19%, 85.93%, and 85.94%, respectively

FACTS allocation considering loads uncertainty, steady state operation constraints, and dynamic operation constraints

This study proposes an algorithm to allocate different types of flexible AC transmission system (FACTS) in power systems. The main objective of this study is to maximize profit by minimizing the system’s operating cost including FACTS devices (FDs) installation cost. Dynamic and steady state operating restrictions with loads uncertainty are included in the problem formulation. The overall problem is solved using both teaching learning based optimization (TLBO) technique for attaining the optimal allocation of the FDs as main-optimization problem and matpower interior point solver (MIPS) for optimal power flow (OPF) as the sub-optimization problem. The validation of the proposed approach is verified by applying it to test system of 59-bus; Simplified 14-Generator model of the South East Australian power system

Optimized placement of multiple FACTS devices using PSO and CSA algorithms

This paper is an attempt to develop a multi-facts device placementin deregulated power system using optimization algorithms. The deregulated power system is the recent need in the power distribution as it has many independent sellers and buyers of electricity. The problem of deregulation is the quality of the power distribution as many sellers are involved. The placement of FACTS devices provides the solution for the above problem. There are researches available for multiple FACTS devices. The optimization algorithms like Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) are implemented to place the multiple FACTS devices in a power system. MATLAB based implementation is carried out for applying Optimal Power Flow (OPF) with variation in the bus power and the line reactance parameters. The cost function is used as the objective function. The cost reduction of FACTS as well as generation by placement of different compensators like, Static Var Compensator (SVC), Thyristor Controlled Series Compensator (TCSC) and Unified Power Flow Controller (UPFC). The cost calculation is done on the 3-seller scenario. The IEEE 14 bus is taken here as 3-seller system

Optimal placement model of TCSC in power system network considering the budget available

Abstract: This paper presents an optimal placement of TCSC which is a FACTS (Flexible Alternative Current transmission Systems) controller in order to increase the loadbility of the system. The optimization problem is solved using the genetic algorithm. In this study the availablity of the budget is taken in consideration. The result show that the increase in loadability can be restricted by the availability of budget and also that beyond a certain budget there will not be any further increase in loadability. Also beyond a certain number of TCSC there will be no further increase in system loadability