FACTS devices and energy storage in unit commitment

Luburić, Zora; Pandžić, Hrvoje

doi:10.1016/j.ijepes.2018.07.013

Cited by 32 publications

(20 citation statements)

References 29 publications

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“…In this model, due to the elimination of reactive power, voltage drop, and power loss variables, the computational error was expected to be significant. To address this issue, a SCUC model was presented in accordance with AC power flow (AC-PF) in [16][17][18]. The AC-SCUC modeling was described in [16], and it explored the flexibility of the power system in the presence of wind farms by controlling the structure of the transmission network that used a linearized AC-PF to achieve low computational time.…”

Section: B Literature Reviewmentioning

confidence: 99%

Evolutionary Algorithm-Based Adaptive Robust Optimization for AC Security Constrained Unit Commitment Considering Renewable Energy Sources and Shunt FACTS Devices

Baziar

Ghotbabadi

et al. 2021

IEEE Access

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An AC security constrained unit commitment (AC-SCUC) in the presence of the renewable energy sources (RESs) and parallel flexible AC transmission system (FACTS) devices is conventionally modeled as a deterministic optimization problem to minimize the operation cost of conventional generation units (CGUs) subject to AC optimal power flow (AC-OPF) equations, operation constraints of RESs, parallel FACTS devices, and CGUs. To cope with the uncertainties of load and RES generation, robust and stochastic optimization and linearized formulation have been used to achieve a sub-optimal solution. To achieve the optimal solution, this paper proposed an evolutionary algorithm-based adaptive robust optimization (EA-ARO) approach to solve the non-linear and non-convex optimization problem. A hybrid solver of grey wolf optimization (GWO) and teaching learning-based optimization (TLBO) obtained the robust and reliable optimal solution for the proposed AC-SCUC in the worst-case scenario. Finally, the proposed method was simulated on standard IEEE test systems to demonstrate its capabilities, and the results showed the proposed hybrid solver obtained robust optimal solutions with reduced computation time and standard deviation. Moreover, the numerical results proved the proposed strategy's capabilities of improving the economics of generation units, such as lower operational cost, and technical performance of the transmission networks, such as improved voltage profile and reduced energy losses.INDEX TERMS AC security constrained unit commitment, Evolutionary algorithm-based adaptive robust optimization, Renewable energy sources, Parallel FACTS devices.

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Section: B Literature Reviewmentioning

confidence: 99%

Evolutionary Algorithm-Based Adaptive Robust Optimization for AC Security Constrained Unit Commitment Considering Renewable Energy Sources and Shunt FACTS Devices

Baziar

Ghotbabadi

et al. 2021

IEEE Access

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“…One of the most attractive solutions that avoid the mentioned problems is FACTS devices, which can increase the transmission system's transfer limits, enhance the system controllability, and minimize the LMP differences 5‐7 . However, planning of the FACTS devices should be done with some considerations to benefit from utilizing them.…”

Section: Introductionmentioning

confidence: 99%

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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“…Whether TAST and UPFC can pull excess power of a heavily loaded transmission line or push more power in another lightly loaded one. However, to pull excess power of heavily loaded transmission lines, many research works [20][21][22][23][24] have recommended allocating series FACTS controllers at most congested transmission lines. In [20], Esmaili et al proposed a multi-objective framework for congestion management.…”

Section: Introductionmentioning

confidence: 99%

Power flow control capability of the power transistor‐assisted Sen transformer and the unified power flow controller: a close comparison

Mohamed

2020

IET Generation, Transmission & Distribution

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FACTS devices and energy storage in unit commitment

Cited by 32 publications

References 29 publications

Evolutionary Algorithm-Based Adaptive Robust Optimization for AC Security Constrained Unit Commitment Considering Renewable Energy Sources and Shunt FACTS Devices

Evolutionary Algorithm-Based Adaptive Robust Optimization for AC Security Constrained Unit Commitment Considering Renewable Energy Sources and Shunt FACTS Devices

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

Power flow control capability of the power transistor‐assisted Sen transformer and the unified power flow controller: a close comparison

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