Contribution of FACTS devices in power systems security using MILP‐based OPF

Nikoobakht, Ahmad; Aghaei, Jamshid; Parvania, Masood; Sahraei-Ardakani, Mostafa

doi:10.1049/iet-gtd.2018.0376

Cited by 28 publications

(34 citation statements)

References 25 publications

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“…To compromise between accuracy and tractability, the proposed MINLP optimisation problem in (1)-(31) is recast into an MILP problem by linearising the non-linear and non-convex constraints in (7)-(9). To obtain a linear approximation for the non-linear and non-convex nodal active and reactive power balance equations in (7) and (8), it is assumed that the voltage magnitudes are close to 1 pu, the differences of voltage angles between adjacent buses are within [ − π/6, π/6], the reactance/resistance ratios for transmission lines are considerably high (the reactance/resistance ratio is >10) [32]. Hence, given a specific xl i,j , the linear approximation of the nodal active and reactive power flow equations in (7) and (8) can be obtained by the proposed linearisation technique in [32] with sufficient accuracy as follows: (see (32)) (see (33)) where δ i, j in (32) and (33) is equal to θ n − θ j .…”

Section: Linear Coordinated Planning Modelmentioning

confidence: 99%

“…To obtain a linear approximation for the non-linear and non-convex nodal active and reactive power balance equations in (7) and (8), it is assumed that the voltage magnitudes are close to 1 pu, the differences of voltage angles between adjacent buses are within [ − π/6, π/6], the reactance/resistance ratios for transmission lines are considerably high (the reactance/resistance ratio is >10) [32]. Hence, given a specific xl i,j , the linear approximation of the nodal active and reactive power flow equations in (7) and (8) can be obtained by the proposed linearisation technique in [32] with sufficient accuracy as follows: (see (32)) (see (33)) where δ i, j in (32) and (33) is equal to θ n − θ j . Also, the parameters α, α, β, and β in (32) and (33) can be obtained from (34) and (35) where Δδ is the deviation of the voltage angle and the functions Φ and Ψ are defined in the constraints (7) and (8), respectively.…”

Section: Linear Coordinated Planning Modelmentioning

confidence: 99%

“…Hence, given a specific xl i,j , the linear approximation of the nodal active and reactive power flow equations in (7) and (8) can be obtained by the proposed linearisation technique in [32] with sufficient accuracy as follows: (see (32)) (see (33)) where δ i, j in (32) and (33) is equal to θ n − θ j . Also, the parameters α, α, β, and β in (32) and (33) can be obtained from (34) and (35) where Δδ is the deviation of the voltage angle and the functions Φ and Ψ are defined in the constraints (7) and (8), respectively. Although the constraints (32) and (33) are linear for a specific value of xl i,j , in general, these equations are non-linear due to…”

Section: Linear Coordinated Planning Modelmentioning

confidence: 99%

“…Also, the parameters α, α, β, and β in (32) and (33) can be obtained from (34) and (35) where Δδ is the deviation of the voltage angle and the functions Φ and Ψ are defined in the constraints (7) and (8), respectively. Although the constraints (32) and (33) are linear for a specific value of xl i,j , in general, these equations are non-linear due to…”

Section: Linear Coordinated Planning Modelmentioning

confidence: 99%

See 3 more Smart Citations

Flexible, reliable, and renewable power system resource expansion planning considering energy storage systems and demand response programs

Hamidpour¹,

Aghaei²,

Pirouzi

et al. 2019

IET Renewable Power Generation

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This study presents a flexible, reliable, and renewable power system resource planning approach to coordinate generation, transmission, and energy storage (ES) expansion planning in the presence of demand response (DR). The flexibility and reliability of the optimal resource expansion planning are ensured by means of appropriate constraints incorporated into the proposed planning tool where thermal generation units, ES systems, and DR programs are considered as flexibility resources. The proposed planning tool is a mixed-integer non-linear programming (MINLP) problem due to the non-linear and non-convex constraints of AC power flow equations. Accordingly, to linearise the proposed MINLP problem, the AC nodal power balance constraints are linearised by means of the first-order expansion of Taylor's series and the line flow equations are linearised by means of a polygon. Additionally, the stochastic programming is used to characterise the uncertainty of loads, a maximum available power of wind farms, forecasted energy price, and availability/unavailability of generation units and transmission lines by means of a sufficient number of scenarios. The proposed planning tool is implemented on the IEEE 6-bus and the IEEE 30bus test systems under different conditions. Case studies illustrate the effectiveness of the proposed approach based on both flexibility and reliability criteria.

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Section: Linear Coordinated Planning Modelmentioning

confidence: 99%

Section: Linear Coordinated Planning Modelmentioning

confidence: 99%

Section: Linear Coordinated Planning Modelmentioning

confidence: 99%

Section: Linear Coordinated Planning Modelmentioning

confidence: 99%

See 2 more Smart Citations

Flexible, reliable, and renewable power system resource expansion planning considering energy storage systems and demand response programs

Hamidpour¹,

Aghaei²,

Pirouzi

et al. 2019

IET Renewable Power Generation

Self Cite

View full text Add to dashboard Cite

show abstract

“…The first stage problem, Equations (1)‐(21), includes uncertain parameters such as active and reactive load, P D , Q D and D MG , energy price, λ , photovoltaic power, P PV , wind system power, P W , EVs charge/discharge rate, CR EV / DR EV , and EVs plugged‐in/plugged‐out time energy, E arr / E dep . Hence, the scenario‐based stochastic programing is implemented to model the load based on normal probability distribution function (PDF), 35 photovoltaic and wind system power model according to Beta and Weibull PDF, 36 and EVs parameters model depending on the Rayleigh PDF 37 . Furthermore, the Roulette Wheel Mechanism (RWM) and Kantorovich method are applied to generate and reduce the scenario samples, respectively, 38 where more details of this technique were expressed in 38 …”

Section: Two‐stage Model Of the Coupling Of Cema And Shs In Sdnmentioning

confidence: 99%

Two‐stage coupling of coordinated energy management and self‐healing strategies in a smart distribution network considering coupling neighbouring microgrids according to autonomous decentralized restoration technique

Zaheri

Khederzadeh

2020

Int Trans Electr Energ Syst

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One of the central approaches in the smart grid is to restore the smart distribution network (SDN) including different renewable sources and storage systems as microgrid formwork under fault conditions. Therefore, the automatic restoration processes need optimal power flow results in the SDN at fault times. Hence, this study proposed the optimal two‐stage coordinated energy management approach (CEMA) and self‐healing strategy (SHS) in the SDN in the presence of coupling neighbouring microgrids (CNMGs) according to autonomous decentralized restoration technique (ADRT). In the first stage, the SDN operation cost receiving from the upstream grid was minimized to obtain flexible and reliable network, followed by addressing network power flow equations, CNMG constraints including the renewable energy sources (RESs), energy storage systems (ESSs) and electric vehicles (EVs), and SDN operating limits. In the second stage, the optimal model of the ADRT formwork was defined based on the multi‐agent system (MAS). Also, the suitable restoration path was obtained with different zone agents distributed across the network CNMGs considering the minimization of the difference between the number of switching operation and priority loads restored as the normalized objective function. Furthermore, the scenario‐based stochastic programming was used to model the uncertainty of energy price, load, RES power, and EVs parameters. Finally, the proposed problem was solved by the Grey Wolf Optimization (GWO) algorithm and simulated on 33‐bus radial distribution network using MATLAB software. According to numerical results, the proposed strategy can offer the self‐healing and flexible‐reliable SDN in addition to the well‐suited overloading management for CNMGs.

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Profit‐based evaluation of optimal FACTS devices planning for the large consumers and TRANSCO considering uncertainty

Aghdam

Ghaemi

Safari

et al. 2021

Int Trans Electr Energ Syst

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Summary This paper seeks to address the impact of optimal FACTS devices planning on the obtained profit of Transmission Company (TRANSCO) and large consumers. An economic investigation is done to verify how the investment cost of FACTS devices can be compensated using the obtained benefits of TRANSCO and large consumers. It is shown that the gained benefit by the consumers might be considered as an effective policy for the participation of the demand side in the FACTS devices' investment costs. Additionally, in this paper, a new hybrid approach, consisting of both mathematical and heuristic methods for solving the problem, is presented. The mentioned algorithm is a combination of the hunting search (HuS) algorithm as a heuristic algorithm and non‐linear programming (NLP) as a mathematical approach. Also, the stochastic natures of renewable energy sources and load variations have been considered. For modeling the stochastic parameters, two points estimation method has been employed. Also, the spatial correlation among uncertain parameters has been considered. Finally, the proposed approach has been tested on a modified IEEE 57‐bus and IEEE 118‐bus power systems to confirm its efficiency and capability. The results indicate that by this methodology and procedure for the planning of FACTS, there is a reduction of 4.6474 × 105$ in payment of large consumers in 57‐bus system and 3.8089 × 108$ in 118‐bus system. Also, the respected values of power loss reduction cost of 57‐bus and 118‐bus test systems are 2.6246 × 105$ and 2.8250 × 107$.

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Contribution of FACTS devices in power systems security using MILP‐based OPF

Cited by 28 publications

References 25 publications

Flexible, reliable, and renewable power system resource expansion planning considering energy storage systems and demand response programs

Flexible, reliable, and renewable power system resource expansion planning considering energy storage systems and demand response programs

Two‐stage coupling of coordinated energy management and self‐healing strategies in a smart distribution network considering coupling neighbouring microgrids according to autonomous decentralized restoration technique

Profit‐based evaluation of optimal FACTS devices planning for the large consumers and TRANSCO considering uncertainty

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