Probabilistic prosumer node modeling for estimating planning parameters in distribution networks with renewable energy sources

Robert John Millar, Jussi Ekström, Matti Lehtonen, Matti Koivisto, Eero Saarijärvi, Merkebu Degefa

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)
147 Downloads (Pure)

Abstract

With the increase in distributed generation, the demand-only nature of many secondary substation nodes in medium voltage networks is becoming a mix of temporally varying consumption and generation with significant stochastic components. Traditional planning, however, has often assumed that the maximum demands of all connected substations are fully coincident, and in cases where there is local generation, the conditions of maximum consumption and minimum generation, and maximum generation and minimum consumption are checked, again assuming unity coincidence. Statistical modelling is used in this paper to produce network solutions that optimize investment, running and interruption costs, assessed from a societal perspective. The decoupled utilization of expected consumption profiles and stochastic generation models enables a more detailed estimation of the driving parameters using the Monte Carlo simulation method. A planning algorithm that optimally places backup connections and three layers of switching has, for real-scale distribution networks, to make millions of iterations within iterations to form a solution, and therefore cannot computationally afford millions of parallel load flows in each iteration. The interface that decouples the full statistical modelling of the combinatorial challenge of prosumer nodes with such a planning algorithm is the main offering of this paper.
Original languageEnglish
Title of host publicationProceedings of the 58th IEEE International Scientific Conference on Power and Electrical Engineering of Riga Technical University, RTUCON 2017
PublisherIEEE
Number of pages8
ISBN (Electronic)978-1-5386-3846-0
DOIs
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventInternational Scientific Conference on Power and Electrical Engineering of Riga Technical University - Riga, Latvia
Duration: 12 Oct 201713 Oct 2017
Conference number: 58

Conference

ConferenceInternational Scientific Conference on Power and Electrical Engineering of Riga Technical University
Abbreviated titleRTUCON
CountryLatvia
CityRiga
Period12/10/201713/10/2017

Keywords

  • distributed generation
  • distribution network planning
  • Monte Carlo simulation
  • statistical load analysis
  • wind generation analysis

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