Comparison of Mixed-Integer Programming and Genetic Algorithm Methods for Distributed Generation Planning

Foster, James; Berry, Adam; Boland, Natashia; Waterer, Hamish

doi:10.1109/tpwrs.2013.2287880

Cited by 61 publications

(36 citation statements)

References 47 publications

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“…Due to the efficiency and flexibility of GA, it has been extensively used in numerous fields in recent years. There are many successful GA applications in the power systems, such as optimal power flow, unit commitment, economic dispatch, capacitor placement, distributed generations planning, distribution networks reconfiguration, energy management, supply restoration, volt/var/total harmonic distortion control, maintenance strategy, placement of power quality monitors, and voltage regulator placement …”

Section: Optimizing Based On Gamentioning

confidence: 99%

A practical approach for distribution network load balancing by optimal re‐phasing of single phase customers using discrete genetic algorithm

Homaee

Najafi

Dehghanian

et al. 2019

Int Trans Electr Energ Syst

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Summary Voltage and current imbalance have adverse impacts on power systems such as power loss increase, communication interference, and component lifetime reduction. This paper attempts to look at unbalance impacts from distribution system operator's (DSO) viewpoint in order to understand them and then mitigate the impacts. Since solving the unbalance conditions is influenced by the way the imbalance is defined, standard unbalance indexes are studied, and new indexes are proposed in this paper. Re‐phasing of the customers is selected to reduce the level of unbalance in distribution feeders. Due to the huge number of customers, a wide variety of choices can be selected for re‐phasing of customers, which makes the solution questionable. Therefore, discrete genetic algorithm (DGA) as a metaheuristic method has been utilized in order to distribute customers among the network phases optimally considering the fact that DSO has a limitation for the re‐phasing practice. The aim is to reduce the unbalance indexes and power losses throughout the network. Simulations have been carried out on a real test case network, which shows the importance of load balancing and its effects on the power losses, voltage profile, and current flow in that network. The effectiveness of the proposed indexes has also been demonstrated for the four‐wire multigrounded distribution system. Since re‐phasing of the majority of customers in distribution networks seems impractical, a re‐phasing limitation is also investigated in this paper, and some practical suggestions of optimal load balancing in a real‐world low‐voltage distribution system have been presented here. Results show the importance of load balancing in power loss reduction and voltage unbalance improvement in the low‐voltage four‐wire multigrounded distribution system. They also illustrate that by changing phases of a few customers, power losses and unbalance indexes will be improved significantly.

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Section: Optimizing Based On Gamentioning

confidence: 99%

A practical approach for distribution network load balancing by optimal re‐phasing of single phase customers using discrete genetic algorithm

Homaee

Najafi

Dehghanian

et al. 2019

Int Trans Electr Energ Syst

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“…Comparison between MIP methods and the genetic algorithm in [26] reveals that MIP methods yield improved solutions at the expense of a longer computation time. Mixed-integer linear programming (MILP) is pursued in [27] for robust placement and sizing of dispatchable and intermittent DGs where a set of polyhedral constraints model the uncertainty in wind, PV, and loads.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic Planning of Distributed PV Generation

Bazrafshan

Yalamanchili²,

Gatsis

et al. 2019

Energies

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Recent studies by electric utility companies indicate that maximum benefits of distributed solar photovoltaic (PV) units can be reaped when siting and sizing of PV systems is optimized. This paper develops a two-stage stochastic program that serves as a tool for optimally determining the placing and sizing of PV units in distribution systems. The PV model incorporates the mapping from solar irradiance to AC power injection. By modeling the uncertainty of solar irradiance and loads by a finite set of scenarios, the goal is to achieve minimum installation and network operation costs while satisfying necessary operational constraints. First-stage decisions are scenario-independent and include binary variables that represent the existence of PV units, the area of the PV panel, and the apparent power capability of the inverter. Second-stage decisions are scenario-dependent and entail reactive power support from PV inverters, real and reactive power flows, and nodal voltages. Optimization constraints account for inverter’s capacity, PV module area limits, the power flow equations, as well as voltage regulation. A comparison between two designs, one where the DC:AC ratio is pre-specified, and the other where the maximum DC:AC ratio is specified based on historical data, is carried out. It turns out that the latter design reduces costs and allows further reduction of the panel area. The applicability and efficiency of the proposed formulation are numerically demonstrated on the IEEE 34-node feeder, while the output power of PV systems is modeled using the publicly available PVWatts software developed by the National Renewable Energy Laboratory. The overall framework developed in this paper can guide electric utility companies in identifying optimal locations for PV placement and sizing, assist with targeting customers with appropriate incentives, and encourage solar adoption.

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“…Son restringidos a la hora de aplicarlos a problemas reales, debido a la alta complejidad que presentan; sin embargo, son eficientes en problemas sencillos. Para solucionar el problema de GD en el SD, los métodos más utilizados son [34]: programación lineal [40], programación no lineal [41], programación dinámica [42], programación entera [43] y programación estocástica [44].…”

Section: Métodos Numéricosunclassified

Ubicación y dimensionamiento de generación distribuida: Una revisión

Grisales

Cuestas

Jaramillo

2017

Cien.Ing.Neogranadina

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La generación distribuida (GD) se presenta como un apoyo para suplir la demanda del sistema de forma localizada, lo que permite además como alternativas de generación el uso de fuentes de energía renovables a pequeña escala. Como aporte principal, este artículo presenta las consideraciones que se deben tener en cuenta al momento de analizar, modelar y solucionar el problema de ubicación y dimensionamiento de GD en la red eléctrica de distribución. Inicialmente, se enuncian los aspectos técnicos de la red, el modelado de las cargas e impactos de la GD; seguido de la formulación del modelo matemático del problema, principales técnicas de solución y metodologías aplicadas en la literatura; finalmente, se muestran las investigaciones futuras en este campo de estudio. Todo lo anterior en busca de mejorar las condiciones operativas de la red. Dentro de la revisión de los trabajos presentados por los diferentes autores, se encontró que una gran parte de los estudios se enfocan en los aspectos técnicos, sin tener en cuenta los costos asociados a la integración de GD, ni los incentivos económicos a los que puede ser susceptibles este tipo de proyectos, que son de vital importancia para evaluar la viabilidad de la propuesta de integración.

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Comparison of Mixed-Integer Programming and Genetic Algorithm Methods for Distributed Generation Planning

Cited by 61 publications

References 47 publications

A practical approach for distribution network load balancing by optimal re‐phasing of single phase customers using discrete genetic algorithm

A practical approach for distribution network load balancing by optimal re‐phasing of single phase customers using discrete genetic algorithm

Stochastic Planning of Distributed PV Generation

Ubicación y dimensionamiento de generación distribuida: Una revisión

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