Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques

Ehsan, Ali; Yang, Qiang

doi:10.1016/j.apenergy.2017.10.106

Cited by 395 publications

(204 citation statements)

References 87 publications

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“…The losses that occur during the transmission of the energy generated by large central power plants to the consumption areas can be reduced by generation at the distribution level where the consumption mainly takes place [7]. Besides, in the face of rising energy demands, the capacity of transmission In the literature, numerous studies can be found on DG planning [13][14][15][16]. These studies differ from each other in terms of their scope, the approach adopted and the methodology used in DG planning.…”

Section: Motivation and Backgroundmentioning

confidence: 98%

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Maximum Permissible Integration Capacity of Renewable DG Units Based on System Loads

et al. 2018

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Abstract:Increasing demand for electricity, as well as rising environmental and economic concerns have resulted in renewable energy sources being a center of attraction. Integration of these renewable energy resources into power systems is usually achieved through distributed generation (DG) techniques, and the number of such applications increases daily. As conventional power systems do not have an infrastructure that is compatible with these energy sources and generation systems, such integration applications may cause various problems in power systems. Therefore, planning is an essential part of DG integration, especially for power systems with intermittent renewable energy sources with the objective of minimizing problems and maximizing benefits. In this study, a mathematical model is proposed to calculate the maximum permissible DG integration capacity without causing overvoltage problems in the power systems. In the proposed mathematical model, both the minimum loading condition and maximum generation condition are taken into consideration. In order to prove the effectiveness and the consistency of the proposed mathematical model, it is applied to a test system with different case studies, and the results are compared with the results obtained from other models in the literature.

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Section: Motivation and Backgroundmentioning

confidence: 98%

“…Energies 2018, 11, 255 3 of 16 Furthermore, DG planning studies differ from each other according to the solution methodology used. A comprehensive taxonomy has been given in [13].…”

Section: Motivation and Backgroundmentioning

confidence: 99%

Maximum Permissible Integration Capacity of Renewable DG Units Based on System Loads

et al. 2018

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“…with p (1) ...p (n) ...p (m) corresponding to simultaneous converter outputs. Although these three situations are exemplified in the next section, for a better understanding the following explanations are provided:…”

Section: Additional Formulations For the Conversion Modelmentioning

confidence: 99%

“…with d (1) ...d (n) corresponding to non-simultaneous converters or if a device has more than one output (9),…”

Section: Additional Formulations For the Conversion Modelmentioning

confidence: 99%

“…Consequently, renewable energies are expected to play a key role given their relevance in distributed generation either for power networks [1] or combined energy systems [2]. However, because of their intermittent nature, many require storage systems and management strategies that decouple generation from demand in order to be economically viable [3].…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneous resource management in energy hubs with self-consumption: Contributions and application example

et al. 2018

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The energy hub concept and modeling methodology are widely employed tools for solving resource conversion and storage scheduling problems. For instance, industrial clusters might benefit from determining the suitable time to operate their facilities and to sell electricity to the public power grid, according to legal, economic or environmental factors. In this paper, novel elements are introduced in order to more accurately represent real plants and to reduce the amount of decision variables. The major innovation is to consider devices consuming a resource which is not related to the quantity of output produced, by attaching binary decision variables to certain energy hub outputs. Secondly, a path vector is defined to take into account the flows of resources within the system instead of employing a variable for each branch between the components. The third innovation consists of an additional vector to express the amount of output resources sold from the energy hub, including constraints for those resources which are exported and imported through the same medium. An extended energy hub model is first proposed and then applied to a real plant example, including multiple and heterogeneous resources and performing a comparison between days with different demands, weather conditions and electricity prices. The results obtained in the selected scenarios demonstrate a logical operation scheduling, and therefore validate the proposed approach.

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Spider monkey optimization technique–based allocation of distributed generation for demand side management

Deb

Chakraborty

Deb

2019

Int Trans Electr Energ Syst

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Summary Recent research indicates that future power networks will witness a major enhancement in renewable energy–based distributed generation (DG). The impact of DG and distribution management system (DMS) action can be directly and easily implemented in the distribution networks for the improvement of its voltage security states. In the first phase of this paper, the voltage security state of the distribution network is identified using combined Kohonen's self‐organizing feature map (SOFM) and learning vector quantization (LVQ) algorithm. Two indicators namely voltage stability index (VSI) and distribution system stability indicator (DSSI) are used in this paper for verification of classification result. To ensure voltage security, it is essential to improve the voltage profile of the system. In the next phase, genetic algorithm (GA) and spider monkey optimization (SMO) techniques have been applied to find the optimal location and size of DG for voltage security state improvement of the reconfigured distribution system. Accurate allocation of DG can help in demand side management (DSM) for providing better service to the consumers in real time in smart grid scenario. This methodology has been tested on IEEE 33 bus, IEEE 69 bus, and Indian 85 bus practical radial distribution system. Result shows that the proposed methodology can successfully and appropriately identify the voltage security states of power system by combined SOFM and LVQ algorithms, and utilization of suitable amount of DGs at best locations as obtained from SMO algorithm can improve the operating states of distribution network in terms of voltage security.

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Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques

Cited by 395 publications

References 87 publications

Maximum Permissible Integration Capacity of Renewable DG Units Based on System Loads

Maximum Permissible Integration Capacity of Renewable DG Units Based on System Loads

Heterogeneous resource management in energy hubs with self-consumption: Contributions and application example

Spider monkey optimization technique–based allocation of distributed generation for demand side management

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