BUILDING HOT WATER MODELS FOR URBAN AND REGIONAL ENERGY INTEGRATION ALEX BERTRAND RIAD AGGOUNE FRANÇOIS MARÉCHAL BIWAES 2015, STOCKHOLM

-Urban and regional energy integration approaches based on Mixed Integer Linear and Non-Linear Programming (MILP, MINLP) for the definition of energy optimisation scenario -Optimised parameters: operating and investment costs, energy consumption, emissions -Urban projects focus on building energy demand: space heating and cooling, electricity, hot water consumption * Weber C., 2008, Multi-objective design and optimization of district energy systems including polygeneration energy conversion technologies, Thesis, EPFL, Lausanne, Switzerland CONTEXT -Use of simplified hot water demand models: constant demand during the day (70 l/day*capita), instead of peak demand Energy integration outcomes for the city of Geneva (CH)*

-Determination and characterisation of hot water streams (end- uses) in different residential and non-residential buildings (offices, hospitals, hotels restaurants and swimming pools) : temperature levels, load, availability rate and number of appliances ([Blokker et al. (2010)], [Pieterse-Quirijns et al. (2010)], [Blokker et al. (2011)], [Pieterse-Quirijns et al. (2013)] ) -Definition of an aggregation factor, the simultaneity factor, for buildings with several households or units (e.g. rooms in hospitals) -Comparison of simplified and detailed load models for hot water applied to the city of Esch-Alzette (Luxembourg) APPROACH

HOT WATER MODELS End use types

HOT WATER MODELS Building and district aggregation

Assumptions: 70 l/d*capita, 60/10°C, 5:00-23:00, households CASE STUDY – SIMPLIFIED MODEL

CASE STUDY – DETAILED MODEL

DISCUSSION -The hot water power requirements in single-family house using the detailed model is between 6 to 135 times higher than with the simplified model  The detailed models show a large range of values, due to the consideration of various hot water end-uses and not the number of inhabitants -The use of a simultaneity factor for utility sizing leads to a load reduction of 83 % for buildings with more than 50 inhabitants -The thermal load of well-insulated buildings is mostly defined by hot water demand, while offices are not affected much as space heating is more relevant in terms of power requirements

CONCLUSIONS -The use of detailed hot water models will provide more realistic investment cost estimations -The utility selection following the energy integration approach will lead to different technology solutions. -Geographical clustering methods used for district heat network design could profit from simultaneity factors to reduce district load requirements -The application of a randomizing function distributing various end-use types better reflects the stochastic equipment distribution -Next Steps: inclusion of use patterns for multi-time problems, application of hot water models for national energy integration approach including both building and industrial hot and cold streams

THANK YOU FOR YOUR ATTENTION ANY QUESTIONS?