A novel efficient model for the power flow analysis of power systems

Safdarian, Amir; Fotuhi‐Firuzabad, Mahmud; Aminifar, Farrokh

doi:10.3906/elk-1204-56

Cited by 18 publications

(4 citation statements)

References 28 publications

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“…The constraints of the optimal placement of FACTS devices are represented by the energy balance, the network security constraints and the constraints related to the FACTS devices [78][79][80][81], such as:…”

Section: Constraints Of the Problemmentioning

confidence: 99%

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Ismail¹,

Guesmi²,

Alshammari³

et al. 2023

Preprint

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Using flexible AC transmission system (FACTS) devices in power systems while adhering to some equality and inequality constraints, researchers around the world sought to address this issue with the objectives of improving the voltage profile, reducing power losses in transmission lines, and increasing system reliability and safety. The recent development of FACTS controllers opens up new perspectives for safer and more efficient operation of electrical power networks by continuous and rapid action on power systems parameters, such as phase angle shifting, voltage injection and line impedance compensation. Thus, an improvement on voltage profile and enhancement of power transfer capability can be obtained. It is for that, the idea behind the FACTS concept is to enable the transmission system to be an active element in increasing the flexibility of power transfer requirements and in securing stability of integrated power system. It may also be effective in transient stability improvement, power oscillations damping and balancing power flow in parallel lines. The primary issue that has significantly piqued the interest of a number of researchers working in this field is the FACTS optimization problem, which involves determining the optimal type, location, and size of FACTS devices in electrical power systems. For solving this mixed integer, nonlinear and non-convex optimization problem, this paper provides an in-depth and comprehensive review of the various optimization techniques covered in published works in the field. In this review, a classification of optimization techniques in five main groups that are widely used, such as classical optimization techniques or conventional optimization approaches, Meta heuristic methods, analytic methods or sensitive index methods and mixed or hybrid methods, is summarized. In addition, a performance descriptions and comparison of these different optimization techniques are discussed in this study. Finally, some advice is offered for future research in this field.

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Section: Constraints Of the Problemmentioning

confidence: 99%

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Ismail¹,

Guesmi²,

Alshammari³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The constraints of the optimal placement of FACTS devices are represented by the energy balance, the network security constraints, and the constraints related to the FACTS devices [78][79][80][81], such as:…”

Section: Constraints Of the Problemmentioning

confidence: 99%

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

et al. 2023

View full text Add to dashboard Cite

The use of FACTS devices in power systems has become increasingly popular in recent years, as they offer a number of benefits, including improved voltage profile, reduced power losses, and increased system reliability and safety. However, determining the optimal type, location, and size of FACTS devices can be a challenging optimization problem, as it involves mixed integer, nonlinear, and nonconvex constraints. To address this issue, researchers have applied various optimization techniques to determine the optimal configuration of FACTS devices in power systems. The paper provides an in-depth and comprehensive review of the various optimization techniques that have been used in published works in this field. The review classifies the optimization techniques into four main groups: classical optimization techniques, metaheuristic methods, analytic methods, and mixed or hybrid methods. Classical optimization techniques are conventional optimization approaches that are widely used in optimization problems. Metaheuristic methods are stochastic search algorithms that can be effective for nonconvex constraints. Analytic methods involve sensitivity analysis and gradient-based optimization techniques. Mixed or hybrid methods combine different optimization techniques to improve the solution quality. The paper also provides a performance comparison of these different optimization techniques, which can be useful in selecting an appropriate method for a specific problem. Finally, the paper offers some advice for future research in this field, such as developing new optimization techniques that can handle the complexity of the optimization problem and incorporating uncertainties into the optimization model. Overall, the paper provides a valuable resource for researchers and practitioners in the field of power systems optimization, as it summarizes the various optimization techniques that have been used to solve the FACTS optimization problem and provides insights into their performance and applicability.

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“…The resulted equations are linear with respect to square of voltage amplitudes. For detailed information about calculating the above two equations, inspired readers are referred to [25].…”

Section: Distribution Network Constraintsmentioning

confidence: 99%

“…Amplitude of bus voltages should be mounted between lower and upper limits which is adhered by (24). Furthermore, the angle of bus voltages should satisfy constraint (25). It should be explained that relations (22) and (23) are linear in respect to the square of voltage amplitude.…”

Section: Distribution Network Constraintsmentioning

confidence: 99%

Residential Demand Response Coordination for Distribution Network Reliability Enhancement

2021

Self Cite

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This paper establishes a centralized model to activate residential demand response in order to improve distribution network reliability. The model aims at minimizing the damage cost imposed by load curtailments following occurrence of unexpected events. In this model, distribution system operator (DSO) and responsive customers have already signed a contract authorizing the DSO alters the operation of responsive appliances whenever system reliability is jeopardized. The model addresses consumers' preferences and guarantees that the operation of appliances is displaced within the bounds defined by the owners. Once an unexpected event occurs, the DSO commits responsive appliances to avoid likely violations in the network operational limits and costly load curtailments. The proposed model is mathematically formulated in the form of mixed integer linear programming (MILP) and its capability is depicted via applying to a real-world distribution network with some residential consumers. The comparison of service reliability indices after and before utilizing demand response potentials illustrates the effectiveness of the model.

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A novel efficient model for the power flow analysis of power systems

Cited by 18 publications

References 28 publications

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Residential Demand Response Coordination for Distribution Network Reliability Enhancement

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