Electric Distribution Network Expansion Under Load-Evolution Uncertainty Using an Immune System Inspired Algorithm

Carrano, Eduardo G.; Guimarães, Frederico Gadelha; Takahashi, Ricardo H. C.; Neto, Oriane M.; Campelo, Felipe

doi:10.1109/tpwrs.2007.894847

Cited by 87 publications

(73 citation statements)

References 15 publications

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“…There are several models for uncertainties, but few have been found to model the DG scenarios. In [15] uncertain variables are produced by Monte Carlo analysis. Since the DSO will receive the application for connection from DG developers, the DSO can predict probable generation and load for the coming period.…”

Section: Challengementioning

confidence: 99%

“…The main advantages of this search algorithm as stated in [17] are: (i) it obtains multi-objective non-dominated solutions to the three objective functions, (ii) it intensifies the search by ranking lists of the best network nodes of the distribution and stores visited network nodes avoiding unwanted movements, (iii) it diversifies the search to obtain a proper distribution of solutions. An immune-based evolutionary optimization algorithm proposed in [15] considers the uncertainties in the evolution of load and energy tax in each node in a time horizon. Artificial immune systems (AIS) is applied as computational techniques, which deliver not only a single solution at the end of the optimization procedure, but also an entire set of suboptimal solutions [25].…”

Section: B Phase 2: Optimization Algorithms Applied To Distribution mentioning

confidence: 99%

“…Artificial immune systems (AIS) is applied as computational techniques, which deliver not only a single solution at the end of the optimization procedure, but also an entire set of suboptimal solutions [25]. The objective function in [15] is a monetary representative of minimization of energy losses and minimization of new investment. The variables represent possible connection between two nodes in the network.…”

Section: B Phase 2: Optimization Algorithms Applied To Distribution mentioning

confidence: 99%

“…Due to some conflicting objectives, infeasibility rate can be used as the first filter to analyze the results [15]. After applying the filter a group of non dominated solutions will be left.…”

Section: Phase 3: Decision Makingmentioning

confidence: 99%

“…Later in [17] a min-max approach is preferred. In [15] a sensitivity analysis is conducted to the candidate-solution set under a set of load levels . The final solution is chosen as the one that minimizes the cost under certain constraints of feasibility and reliability.…”

Section: Phase 3: Decision Makingmentioning

confidence: 99%

See 4 more Smart Citations

Regulation impact on distribution systems with distributed generation

Huang

Alvehag

Söder

2012

2012 9th International Conference on the European Energy Market

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Abstract-Distribution system operators (DSOs) are facing new challenges when more distributed generation (DG) is connected to the network. In this new operating environment the DSO has to be able to plan an efficient network topology, which consists of reinforcement and extensions. In addition, the DSO has to finance the investment from tariffs. The methods to solve network planning problems are reviewed in this paper. The studied network planning problem is the case when the DSO has no influence on the location of DG due to the unbundling between DSOs and production. Furthermore, the regulation for how the DSOs are allowed to design the tariffs in systems with DG vary between countries, a comparison of how the DSOs design their tariffs under different regulations is presented. This paper ends with a case study on methods that Swedish DSOs use to plan the networks when considering the uncertainties caused by wind power and the regulation impact on distribution network planning and network tariffs in Sweden.

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Section: Challengementioning

confidence: 99%

Section: B Phase 2: Optimization Algorithms Applied To Distribution mentioning

confidence: 99%

Section: B Phase 2: Optimization Algorithms Applied To Distribution mentioning

confidence: 99%

Section: Phase 3: Decision Makingmentioning

confidence: 99%

Section: Phase 3: Decision Makingmentioning

confidence: 99%

See 3 more Smart Citations

Regulation impact on distribution systems with distributed generation

Huang

Alvehag

Söder

2012

2012 9th International Conference on the European Energy Market

View full text Add to dashboard Cite

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New Electricity Distribution Network Planning Approaches for Integrating Renewables

Pilo

Celli

Ghiani

et al. 2019

Advances in Energy Systems

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New electricity distribution network planning approaches for integrating renewable

Pilo

Celli

Ghiani

et al. 2013

WIREs Energy & Environment

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The electricity distribution business is experiencing a tremendous and challenging transformation. The use of renewable energy sources is moving generation from the top to the bottom of power systems, where traditionally only loads existed. Active demand, distribution energy-storage devices, and electric vehicles are going to change even more drastically the way the distribution system will be operated. Finally, several stakeholders will share the responsibility for system operation while they often pursue opposite objectives. In contrast to conventional approaches, modern distribution planning algorithms should emulate the new environment to produce expansion and strategic plans for guiding the evolution of system in times of financial restrictions. Probabilistic methods are necessary to capture the intrinsically stochastic behavior of renewable generation, whereas the multiobjective programming is recognized to be the most effective way for planning, transparently and objectively, the system evolution, taking into account\ud the multiple needs of different stakeholders. Finally, the integration of smart grid operation within planning algorithms is the key point for a proper distribution planning that allows integrating renewable resources and minimizing the cost for new electrical infrastructures

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Electric Distribution Network Expansion Under Load-Evolution Uncertainty Using an Immune System Inspired Algorithm

Cited by 87 publications

References 15 publications

Regulation impact on distribution systems with distributed generation

Regulation impact on distribution systems with distributed generation

New Electricity Distribution Network Planning Approaches for Integrating Renewables

New electricity distribution network planning approaches for integrating renewable

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