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Distributed energy resources integration and demand response: the role of stochastic demand modelling

Distributed energy resources integration and demand response: the role of stochastic demand modelling

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One of the main problems with it comes to the integration of distributed energy resources is the estimation and prediction of the energetic demand that those energy sources must be able to supply [1]. This is especially difficult nowadays due to the upward trend observed in all the projections for the energy consumption for major energy end-use sectors (residential, commercial, industrial, and transportation) [2]. In this context, stochastic modelling techniques have been presented as the most suitable ones as they are able to create the diversity needed to take into account the behaviour of the residents while keeping the general observed statistical trends [7]. These methods are based on the individual modelling of each appliance and the consumer behaviour patterns so not can only high-resolution profiles be generated, but also the end-use of the energy can be specifically determined. That is not even possible nowadays with the new advanced metering infrastructure, unless non-intrusive load monitoring programmes are applied, which also makes these models a great tool for the assessment of energy policies and the impact of including new appliances at home [8]. In this chapter, the usage and application of stochastic demand models will be addressed, with a focus on the prediction and estimation of the energy consumption, the assessment of different demand response policies and the integration of distributed energy resources at small scale. For this aim, first, a short overview of the main modelling techniques employed in the residential sector will be given to the reader in order to contextualise this type of models. Subsequently, the methodology used in these models will be exposed, showing the different blocks that usually built up the estimation process. Finally, the usability for assessing energy policies and integrating distributed energy resources will be discussed.

Chapter Contents:

  • 8.1 Introduction
  • 8.2 Overview of modelling techniques for energy demand prediction
  • 8.2.1 Top-down models
  • 8.2.2 Bottom-up models
  • 8.2.3 Comparison
  • 8.3 Time of use based bottom-up models
  • 8.3.1 Occupancy and consumers' behaviour
  • 8.3.1.1 Model basics
  • 8.3.1.2 Input parameters
  • 8.3.1.3 Simulation algorithm
  • 8.3.2 Lighting system consumption
  • 8.3.2.1 Model basics
  • 8.3.2.2 Input data
  • 8.3.2.3 Simulation algorithm
  • 8.3.3 General appliances consumption
  • 8.3.3.1 Model basics
  • 8.3.3.2 Input data
  • 8.3.3.3 Simulation algorithm
  • 8.3.4 Heating and cooling consumption
  • 8.3.4.1 Model basics
  • 8.3.4.2 Input data
  • 8.3.4.3 Simulation algorithm
  • 8.3.5 Remarks on the model
  • 8.4 Applications of bottom-up stochastic models
  • 8.4.1 Demand prediction
  • 8.4.2 Energy policies and demand response strategies assessment
  • 8.4.3 Distributed resources integration
  • 8.5 Conclusion
  • References

Inspec keywords: renewable energy sources; government policies; estimation theory; demand side management; distributed power generation; stochastic processes

Other keywords: energy demand estimation; stochastic modelling techniques; distributed energy resources integration; stochastic demand modelling; energy consumption; energy end-use sectors; advanced metering infrastructure; demand response policy; energy policy; energy demand prediction

Subjects: Distributed power generation; Other topics in statistics; Probability theory, stochastic processes, and statistics; Energy and environmental policy, economics and legislation; Energy resources and fuels; Power system management, operation and economics; Energy resources

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