Smart community load matching using stochastic demand modeling and historical production data

Research output: Chapter in Book/Report/Conference proceedingsChapterpeer-review

Abstract

The current upward trend of the residential energy demand and the high penetration of new renewable resources have changed the conception of the electrical grid. The centralized distribution scheme is currently moving forward to a distributed layout where the paradigm of Smart Energy Communities has emerged, meaning a set of households that share a Microgrid, have tied renewable production and can be either connected or disconnected from the main grid. In this context, due to the reduced dispatchability of the renewable generation, the planning of the installed PV power as well as the storage capacity is the cornerstone in order to achieve a high degree of both self-generation and self-consumption. However, the lack of detailed hourly or sub-hourly data makes it difficult. Therefore, this paper aims to present a high-resolution simulation method for evaluating the PV power and storage capacity requirements for a Smart Community based on a stochastic demand model and real PV production data, so the interplay between consumption and generation can be better understood.

Original languageEnglish
Title of host publicationEEEIC 2016 - International Conference on Environment and Electrical Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509023196
DOIs
Publication statusPublished - 29 Aug 2016
Externally publishedYes
Event16th International Conference on Environment and Electrical Engineering, EEEIC 2016 - Florence, Italy
Duration: 7 Jun 201610 Jun 2016

Publication series

NameEEEIC 2016 - International Conference on Environment and Electrical Engineering

Conference

Conference16th International Conference on Environment and Electrical Engineering, EEEIC 2016
Country/TerritoryItaly
CityFlorence
Period7/06/1610/06/16

Keywords

  • Energy Consumption
  • energy storage
  • load modeling
  • solar power generation

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