Multi-objective distributed generation planning in distribution network considering correlations among uncertainties

Zhang, Shenxi; Cheng, Haozhong; Li, Ke; Tai, Nengling; Wang, Dan; Li, Furong

doi:10.1016/j.apenergy.2018.06.049

Cited by 100 publications

(44 citation statements)

References 33 publications

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“…(5) It shows that the receiving end voltage will suffer voltage drop where the voltage drop is affected by the branch current and the branch impedance. The real power loss, P loss and reactive power loss, Q loss can be illustrated in (6) and 7respectively [13].…”

Section: Fig 1 Two Bus Modelmentioning

confidence: 99%

Optimal Placement and Sizing of Distributed Generation in Distribution System using Analytical Method

Seet

Pasupuleti²,

Khan

2019

IJRTE

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The reliability of distribution network can be improved with the penetration of small scale distributed generation (DG) unit to the distribution grid. Nevertheless, the location and sizing of the DG in the distribution network have always become a topic of debate. This problem arises as different capacity of DG at various location can affect the performance of the entire system. The main objective of this study is to recommend a suitable size of DG to be placed at the most appropriate location for better voltage profile and minimum power loss. Therefore, this paper presents an analytical approach with a fixed DG step size of 500 kW up to 4500 kW DG to analyses the effect of a single P-type DG in IEEE 33 bus system with consideration of system power loss and voltage profile. Four scenarios have been selected for discussions where Scenario 1: 3500 kW DG placed at node 3; Scenario 2: 2500 kW DG placed at node 6; Scenario 3: 1000 kW DG placed at node 18 and Scenario 4: 3000 kW DG placed at node 7. Results show that all the four scenarios are able to reduce the power loss and improve the voltage profile however Scenario 4 has better performance where it complies with minimum voltage requirement and minimizing the system power loss.

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Section: Fig 1 Two Bus Modelmentioning

confidence: 99%

Optimal Placement and Sizing of Distributed Generation in Distribution System using Analytical Method

Seet

Pasupuleti²,

Khan

2019

IJRTE

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“…Stochastic PV generation scenarios are introduced in the multi-objective PSO algorithm of [24] assuming that solar irradiance follows the beta distribution, but no reactive support is provided by the PV units. Reactive power support based on a constant power factor is introduced in [25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…On the other hand, the works in [20,31,32] do not account for the clipping effect of the PV unit, which pertains to the PV inverter limiting the AC power output under high solar irradiance conditions. The clipping effect is accommodated in [25], but the design is limited to optimizing only the active power rating of the PV unit as a consequence of considering only constant-power-factor reactive power support.…”

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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“…The increased concerns over carbon dioxide emission and energy saving have led to the drastic integration of DGs into the distribution networks [1,2]. The strategical deployment integration of DGs brings some technical and economic benefits [3], including improvement of voltage profile [4], reduction of power loss [5], and enhancement of system reliability [6]. However, the excessive penetration of DGs introduces more uncertainties into the systems and may alter the normal operational behavior of distribution networks.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Assessment of Distribution Network with High Penetration of Distributed Generators

Zhou¹,

Tang

Liu

et al. 2020

Sustainability

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Over the past decades, the deployment of distributed generations (DGs) in distribution systems has grown dramatically due to the concerns of environment and carbon emission. However, a large number of DGs have introduced more uncertainties and challenges into the operation of distribution networks. Due to the stochastic nature of renewable energy resources, probabilistic tools are needed to assist systems operators in analyzing operating states of systems. To address this issue, we develop a probabilistic framework for the assessment of systems. In the proposed framework, the uncertainties of DGs outputs are modeled using short term forecast values and errors. Moreover, an adaptive cluster-based cumulant method is developed for probabilistic load flow calculation. The performance of the proposed framework is evaluated in the IEEE 33-bus system and PG&E 69-bus system. The results indicate that the proposed framework could yield accurate results with a reasonable computational burden. The excellent performance of the proposed framework in estimating technological violations can help system operators underlying the potential risks of systems.

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Multi-objective distributed generation planning in distribution network considering correlations among uncertainties

Cited by 100 publications

References 33 publications

Optimal Placement and Sizing of Distributed Generation in Distribution System using Analytical Method

Optimal Placement and Sizing of Distributed Generation in Distribution System using Analytical Method

Stochastic Planning of Distributed PV Generation

Probabilistic Assessment of Distribution Network with High Penetration of Distributed Generators

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