Optimal flexible alternative current transmission system device allocation under system fluctuations due to demand and renewable generation

Galloway, Stuart; Elders, Ian; Burt, Graeme; Sookananta, Bongkoj

doi:10.1049/iet-gtd.2009.0221

Cited by 38 publications

(31 citation statements)

References 7 publications

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“…Also, it is assumed that the loads on nodes 13,14,15,16,19,20,22,27,32, and 37 are random variables that follow normal distribution [34]. It is supposed that the expected values of these variables in various time intervals are equal to their values obtained from (28) and (29).…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Robust optimal operation of smart distribution grids with renewable basedgenerators

Sehsalar¹,

Galvani²,

Farsadi³

2020

Turk J Elec Eng & Comp Sci

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Modern distribution systems are equipped with various distributed energy resources (DERs) because of the importance of local generation. These distribution systems encounter more and more uncertainties because of the ever-increasing use of renewable energies. Other sources of uncertainty, such as load variation and system components' failure, will intensify the unpredictable nature of modern distribution systems. Integrating energy storage systems into distribution grids can play a role as a flexible bidirectional source to accommodate issues from constantly varying loads and renewable resources. The overall functionality of these modern distribution systems is enhanced using communication and computational abilities in smart grid frameworks. Robust operation of these systems is effectively taken into consideration to manage the uncertainty, which offers an explicit way to control the desired conservativeness. This paper presents an optimal operating program for smart grids equipped with wind generators, controllable distributed generators, energy storage systems, and reactive power compensators. In order to make the studies more practical, uncertainty about wind generators and grid loads is taken into account. Furthermore, the presented operating program is robust in various conditions, i.e. there is no need to change the operating program in a wide range of probable states. The point estimation method and fuzzy clustering method are used for probabilistic assessment of the distribution system in the presence of uncertainties. The IEEE 37-node standard test system, which is a highly unbalanced system, is selected for the case study and the results are discussed comprehensively.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Robust optimal operation of smart distribution grids with renewable basedgenerators

Sehsalar¹,

Galvani²,

Farsadi³

2020

Turk J Elec Eng & Comp Sci

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“…The main challenge in resolving the optimization is related to nonlinearity of the Power Flow relations (10), (11) and also to nonlinearity of the line thermal limits (17). Available nonlinear solvers, such as IPOPT, are not effective in resolving the nonlinearities for large systems efficiently.…”

Section: Optimization Modelmentioning

confidence: 99%

“…However, it also became clear from the studies that this sustainable upgrade needs to be creative -types, locations and capacities of the newly introduced FACTS devices must be chosen carefully in order to exploit their benefits. Several objectives, including decreasing operational [7], [10], [11] and investment costs [5], [9], reducing transmission losses [6], [10], increasing power system loadability and managing system congestion [10], [12]- [14], reducing load curtailment [15] and improving voltage profile [9] and voltage stability index [8], [14], have been considered. Formally, these problems were stated as mixed Integer Nonlinear Programming (MINLP) optimizations which exact solutions are limited to only very small (typically not exceeding tens of nodes) power system models.…”

Section: Introductionmentioning

confidence: 99%

Operations- and Uncertainty-Aware Installation of FACTS Devices in a Large Transmission System

Frolov

Thakurta

Backhaus

et al. 2019

IEEE Trans. Control Netw. Syst.

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We develop new optimization methodology for planning installation of Flexible Alternating Current Transmission System (FACTS) devices of the parallel and shunt types into large power transmission systems, which allows to delay or avoid installations of generally much more expensive power lines. Our methodology takes as an input projected economic development, expressed through a paced growth of the system loads, as well as uncertainties, expressed through multiple scenarios of the growth. We price new devices according to their capacities. Installation cost contributes to the optimization objective in combination with the cost of operations integrated over time and averaged over the scenarios. The multi-stage (-time-frame) optimization aims to achieve a gradual distribution of new resources in space and time. Constraints on the investment budget, or equivalently constraint on building capacity, is introduced at each time frame. Our approach adjusts operationally not only newly installed FACTS devices but also other already existing flexible degrees of freedom. This complex optimization problem is stated using the most general AC Power Flows. Non-linear, non-convex, multiple-scenario and multi-time-frame optimization is resolved via efficient heuristics, consisting of a sequence of alternating Linear Programmings or Quadratic Programmings (depending on the operational cost dependence on the power injected by the generators) and AC-PF solution steps designed to maintain operational feasibility for all scenarios. Computational scalability and other benefits of the newly developed approach are illustrated on the example of the 2736-nodes large Polish system. One most important advantage of the framework is that the optimal capacity of FACTS is build up gradually at each time frame in a limited number of locations, thus allowing to prepare the system better for possible congestion due to future economic and other uncertainties. NOMENCLATURE Parameters: N l Number of power lines in operation N b Number of buses in the system M Number of loading scenarios representing given time frame N Number of scenarios representing planning horizon T Number of time frames representing horizon t = 1..T Index of a decision point a = 1..M Index of a scenario at time frame t P r t,aOccurrence probability of a scenario a at time frame t x 0 ∈ R N l Vector of initial line inductancesVector of line apparent power limitsVector of maximum (minimum) allowed voltages C SC ∈ R Cost per Ohm of a series FACTS device C SV C ∈ R Cost per MVAr of a shunt FACTS device N years ∈ R Planning horizon Optimization variables (operational, scenario dependent): V ∈ R N b Vector of bus voltage magnitudes θ ∈ R N b Vector of bus voltage angles P G ∈ R N b Vector of generator active power injections Q G ∈ R N b Vector of generator reactive power injections x ∈ R N l Vector of line inductances modified by SC devices ∆x ∈ R N l Vector of series FACTS settings ∆Q ∈ R N b Vector of shunt FACTS settings Optimization variables (investment, scenario independent): ∆x t...

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“…Some criteria have been proposed for the proper placement of FACTS devices in recent research. In such research, transmission capability, load flow equations, different points' voltages [7,15], voltage stabilities [13], transient stabilities and market optimization [3], consumer load variations, and variable amounts of renewable energy generation [14] are the most important factors considered in allocation of FACTS controllers. To achieve the optimum locations for FACTS devices, some evolutionary algorithms (e.g., genetic algorithm (GA), particle swarm optimization (PSO), and imperialistic competition algorithm (ICA)) have been used in different research projects.…”

Section: Introductionmentioning

confidence: 99%

A new method based on sensitivity analysis to optimize the placement of SSSCs

Hashemi‐Dezaki¹,

Mohammadalizadeh-Shabestary²,

Abyaneh³

et al. 2013

Turk J Elec Eng & Comp Sci

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Abstract:The flexible altering current transmission system (FACTS) controllers such as static synchronous series compensators (SSSCs) and unified power flow controllers can strongly improve the different parameters of power systems.They can be used to improve the transient stabilities, voltage profiles, lines transmission capabilities, etc. Therefore, the optimized allocation of FACTS devices is an important issue in recent research. The optimal placements of FACTS devices can have a determinant effect on the performance of a system, too. In this paper, the proper allocation of SSSCs is proposed. Sensitivity analysis is used in order to model SSSCs' effects on the power system. The advantage of this modeling based on sensitivity analysis is to accelerate the optimization process. By using SA for modeling the SSSC, optimization needs only one load flow in the initial state. According to this method, there is no need for exact modeling of SSSCs. Due to its high speed, another advantage of this method is its ability to cover all possible input values and all candidate places for SSSCs. Additionally, the number of SSSCs is not limited and it can vary during optimization.This approach is based on the objective function including transient stability and active power transmission capacity indices. The proposed method is applied on a typical 6-bus network and the 57-bus IEEE test system. The results show the performance of this method.

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Optimal flexible alternative current transmission system device allocation under system fluctuations due to demand and renewable generation

Cited by 38 publications

References 7 publications

Robust optimal operation of smart distribution grids with renewable basedgenerators

Robust optimal operation of smart distribution grids with renewable basedgenerators

Operations- and Uncertainty-Aware Installation of FACTS Devices in a Large Transmission System

A new method based on sensitivity analysis to optimize the placement of SSSCs

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