A Mixed-Integer Linear Programming Model for Simultaneous Optimal Reconfiguration and Optimal Placement of Capacitor Banks in Distribution Networks

Pareja, Luis Alfonso Gallego; López-Lezama, Jesús M.; Carmona, Oscar Gómez

doi:10.1109/access.2022.3175189

Cited by 33 publications

(25 citation statements)

References 77 publications

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“…The reconfiguration of other circuit elements to accommodate new capacitor arrangements has also been considered in literature, including by system topology alterations through changes in tie states and sectionalizing switches [21], [22]. Other studies have incorporated distributed generators (DGs) with their capacitor planning approaches, as renewable DGs like local solar PV are becoming more prevalent in modern power distribution systems [23]- [26].…”

Section: Figurementioning

confidence: 99%

Optimal Capacitor Placement and Rating for Large-Scale Utility Power Distribution Systems Employing Load-Tap-Changing Transformer Control

et al. 2023

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Significant opportunity for savings in energy and investment through improved performance of power distribution systems exists in the optimal placement and rating of capacitors, a conventionally costeffective and popular reactive power compensating technology. A novel optimal capacitor planning (OCP) procedure is proposed for large-scale utility power distribution systems, which is exemplified on an existing utility circuit of approximately 4,000 buses. An initial sensitivity analysis is employed to intelligently reduce OCP computation time and maintain quality of optimal configurations. Three optimization objectives are considered, including the minimization of total system active power losses, standard deviation of node voltages, and investment in total capacitor power rating. Eight multi-objective optimization methods that employ the non-dominated sorting algorithm III (NSGA-III) concept are compared to determine individual merits. Differences between the methods include the incorporation of a penalty constraint for voltage violations and the automatic readjustment of load-tap-changing (LTC) transformer tap settings for proposed capacitor re-configurations concurrently within the optimizer, which ensures peak system performance and fair comparison to the reference case. A multi-step model conversion process was developed with OpenDSS to enable the OCP procedure to be generally applicable to real large-scale utility circuits. OCP is performed for three example sub-circuits served by a substation with a 48MW, 9Mvar peak load, which represents the most extreme case and offers the best opportunity for savings. Example configurations from the resulting Pareto sets through a pseudo-weight vector approach are also analyzed through a systematic procedure of comparison between the most extreme configuration types to inform configuration selection.

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Section: Figurementioning

confidence: 99%

Optimal Capacitor Placement and Rating for Large-Scale Utility Power Distribution Systems Employing Load-Tap-Changing Transformer Control

et al. 2023

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“…Different focuses of research lead to different presentation of objective in DNR studies. An approach for DNR and the optimal placement of capacitors based on a mixed-integer linear programming (MILP) model was shown in [18]. This work emphasizes the loss of DNR, enhance reliability and improve the voltage profile.…”

Section: B Development and Research Status Of Dnrmentioning

confidence: 99%

Multi-Objective Optimization for Distribution Network Reconfiguration With Reactive Power Optimization of New Energy and EVs

2023

IEEE Access

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The rise of new energy and the wide application of electric vehicles (EVs) have led to the substantial expansion of distribution network in recent years. The problems such as the decline of transmission reliability and the rise of power loss in distribution network are becoming increasingly serious. Therefore, distribution network can greatly improve its reliability and quality of power supply voltage through changing topology. This work built a distribution network reconfiguration (DNR) model with new energy and EVs firstly. Then, the position of bus tie switches and the reactive power regulation range of new energy and EVs were proposed as decision variables. A multi-objective evolutionary algorithm would be applied to this DNR model and the optimization results will be obtained when considering the line loss and voltage deviation as the objective function. In order to get different optimal compromise solutions with the changes of actual environment, this work employed a new decision-making method named Prevalence Effect Method (PEM). Finally, a high-quality strategy of DNR and reactive power regulation will be obtained.

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“…As clearly shown in the figures, bus voltages and VSIs are significantly improved for scenarios 2 to 6. For further validation of the proposed APSO technique, the observed simulation results in this study are objectively compared with the results of improved binary particle swarm optimization (IBPSO) [14], modified flower pollinated algorithm (MFPA) [20] and mixed integer linear programming (MILP) [7], which are some of the recent literatures that considered the same scenarios. The results of the comparative analysis presented in Table 2 show that the proposed APSO method outperforms the IBPSO, MFPA and MILP in most of the considered scenarios in terms of percentage power loss reduction.…”

Section: Ieee 33-bus Rdnmentioning

confidence: 99%

“…The fundamental loops obtained for the Ayepe 34-bus RDN are depicted in Table 3. The tie-switches displayed in the 9) 1032( 14) 344 (23) 433 (7) 783 (12) 736 (26) 531 ( 9) 1032 ( 14) 344 (23) 833 ( 12) 640 ( 21) 358 ( 28 The voltage profiles and VSIs for all the considered scenarios are displayed in Figures 9 and 10. It is clear from the figures that the bus voltages and VSIs significantly improve in scenario 2 to 6.…”

Section: Nigerian Ayepe 34-bus Rdnmentioning

confidence: 99%

“…Juan et al proposed mixed-integer second-order cone programming formulation for the optimization problem using voltage dependent models [14]. Other optimization methods like mixed integer linear programming (MILP) [7], ordination optimization [15], bat algorithm [16], oppositional krill herd algorithm (OKHA) [17], cat swarm optimization (CSO) [18], modified flower pollinated algorithm (MFPA) [19], moth swarm algorithm (MSA) [20], chemical reaction optimization (CRO) [21], modified particle swarm optimization (MPSO) [22], and autonomous group particle swarm optimization (AGPSO) [23] have also been deployed to solve RDNR and SCA combinatorial optimization problem. The aforementioned studies in [7] - [23] implemented their solution techniques on standard IEEE radial distribution networks.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive particle swarm optimization approach to simultaneous reconfiguration and shunt capacitor allocation in radial distribution network

Adeyemo¹,

AKINROGUNDE²,

Salimon³

et al. 2022

Global J. Eng. Technol. Adv.

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Simultaneous radial distribution network reconfiguration (RDNR) and shunt capacitor allocation (SCA) is one of the compensation techniques that are used for getting an improved radial structure with reduced real power loss and enhanced voltage stability. This study presents a novel adaptive particle swarm optimisation (APSO) technique for the simultaneous RDNR and SCA, which is a complex and nonlinear optimisation problem. Unlike the conventional particle swarm optimization (PSO) technique in which an initial population of particles is randomly generated, the fundamental loop concept is used to populate the search space of APSO with the candidate branches for each tie switch (open branch) in the loop. The candidate branches are preselected with the graph theory. This is done to mitigate infeasible configurations in the optimization process and also to ensure that the conditions for radiality of the network are satisfied. The effectiveness of the proposed APSO technique for simultaneous RDNR and SCA is demonstrated on the standard IEEE 33-bus and Nigerian Ayepe 34-bus RDNs using six event cases. The efficacy of the proposed APSO technique is further validated with the comparison of the observed simulation results with the reported results of similar work implemented with established algorithms like improved binary particle swarm optimization (IBPSO), modified pollinated flower algorithm (MFPA) and mixed integer linear programming (MILP). The result of the comparative study reveals that the proposed APSO technique outperforms the selected algorithms in most of the considered event cases.

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A Mixed-Integer Linear Programming Model for Simultaneous Optimal Reconfiguration and Optimal Placement of Capacitor Banks in Distribution Networks

Cited by 33 publications

References 77 publications

Optimal Capacitor Placement and Rating for Large-Scale Utility Power Distribution Systems Employing Load-Tap-Changing Transformer Control

Optimal Capacitor Placement and Rating for Large-Scale Utility Power Distribution Systems Employing Load-Tap-Changing Transformer Control

Multi-Objective Optimization for Distribution Network Reconfiguration With Reactive Power Optimization of New Energy and EVs

Adaptive particle swarm optimization approach to simultaneous reconfiguration and shunt capacitor allocation in radial distribution network

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