Development of new methods for fit-for-purpose solar resource assessment for performance prediction of PV systems in the built environment

Dmitry Surugin

Research output: ThesisPd Eng Thesis

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Abstract

The integration of photovoltaic (PV) systems in the built environment is one of the major elements of high-performance building design. To further stimulate the role of urban PV as a driver in the sustainable energy transition, it is important that risk-informed decision-making takes place to facilitate (i) proper selection of cost-optimal energy-efficient designs among competing alternatives, and (ii) appropriate sizing of such systems considering the multitude of technical and financial uncertainties involved. In this context, simulation-based design support methods are considered a powerful tool, but its success relies on the use of representative boundary conditions, such as weather data.

The mainstream approach for representing weather data in energy simulations, the test reference year (TRY) method, was developed decades ago with a main focus on aiding building designers to reduce heating needs. Considering the growing role of solar energy in the design of building and cities, and the increasing interest in the guaranteed performance of such systems, it is questionable whether the best practices for solar resource assessment are aligned with the relevant challenges of the energy transition.
The focus of this thesis is therefore on critically examining state of the art methods and proposing new approaches for fit-for-purpose solar resource assessment, with regards to (i) improving equitability of energy policy frameworks, (ii) enhancing future-proof design support of low-carbon buildings and (iii) increasing bankability of PV systems in the built environment. The key findings of the PDEng project include:

• developing an innovative weather-driven spatial clustering method and applying it to the case of the Netherlands, which allowed to address the issue of underestimating electrical PV performance by up to 15% in the coastal regions
• proposing two adaptations to the EN-ISO 15792-4 methodology for constructing the test reference years to stimulate fair comparison between different building design alternatives
• suggesting risk-based approaches for developing meteorological datasets intended for performance predictions of PV systems
• exploring accuracy and trustworthiness of the four PVGIS satellite-based approaches to stimulate the use of reliable weather datasets with high spatial granularity allowing geo-specific design support
• creating a method for evaluating impact of intra-monthly sequences of cloudy days on load matching and utilization of on-site renewable energy for dwellings with PV-battery system

The proposed improvements based on the meteorological considerations have then been tested applying a performance-based approach, with case studies under realistic conditions including a typical Dutch dwelling, the forthcoming energy efficient buildings policy framework and a number of available meteorological datasets for the Netherlands. These case studies have highlighted that utilization of more robust methods for design optimization is required.

As the outcome of the work, all findings were synthesized into techno-economic implications for different target groups, including investors, occupants, SMEs from the PV industry, grid operators and society at large. Finally, the suggested actions for policy-makers to put these implications in practice and to move the building simulation industry towards fit-for-purpose utilization of weather data have been proposed.
Original languageEnglish
Supervisors/Advisors
  • Loonen, Roel C.G.M., Supervisor
  • Valckenborg, Roland, External supervisor
Award date28 May 2019
Place of PublicationEindhoven
Publisher
Publication statusPublished - 31 May 2019

Bibliographical note

PDEng thesis

Keywords

  • PV
  • BIPV
  • building performance simulation
  • building energy simulation
  • energy transition
  • built environment
  • solar energy
  • solar resource assessment
  • weather data

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