WP3- research status update

Transcription

1 WP3- research status update Panagiota Gianniou PhD student Supervisors: Carsten Rode Per Sieverts Nielsen Alfred Heller

2 Agenda Last meeting brief-up Implementation of city energy model Energy flexibility Study on thermal mass modeling Future work 2 CITIES PhD/PostDoc meeting

3 Last meeting 22/04/2015 Brief-up 3 CITIES PhD/PostDoc meeting

4 Implementation of city energy model Description of the 1st case study - One-floor single-family houses - Located in Sønderborg, Denmark - Constructed mainly in 1960s - Floor areas: m 2 - Connected to local district heating network - No solar heating panels Figure 1. Typical design of the single-family house - No mechanical cooling 4 CITIES PhD/PostDoc meeting

5 Implementation of city energy model 5 CITIES PhD/PostDoc meeting

6 Implementation of city energy model The houses were classified into 5 building types according to construction age. One archetype building represented each type. Heat demand results from all five building types were aggregated according to: Y = total energy demand of the examined building stock [kwh] j = building type N = total number of building types describing the stock EUI = energy demand per floor area [kwh/m 2 ] for each building type A = total floor area [m 2 ] of all buildings included in the respective type 6 CITIES PhD/PostDoc meeting

7 Implementation of city energy model 60 IDA-ICE Aggregate heat demand [MWh] Measurements Termite Month 7 CITIES PhD/PostDoc meeting

8 Energy flexibility 8 CITIES PhD/PostDoc meeting

9 Flexibility and Energy Networks The increased penetration of volatile renewable energy sources (RES) (PV, wind) to the power system calls for flexibility options to match the renewable generation with the demand. Access to short-term thermal energy storage (TES) can increase the overall efficiency for heat generation in District Heating systems and increase the security of supply in the case of an interrupted heat delivery. (Kensby et al., Chalmers University, 2015) Techniques Demand-Side Management (DSM) any activity adopted on the demand side that ultimately changes the utility s system load profile Demand Response (DR) a set of techniques to induce the customer to change their energy demand Electric Load Management (ELM) any policy devised to manage a set of electric loads to obtain the desired goal, such as peak load reduction or energy usage optimization (Benetti et al., University of Pavia, 2015) Demand-Side Management (DSM) includes: Reducing peak loads (peak clipping) Shifting load from on-peak to off-peak (load-shifting) Increasing the flexibility of the load (flexible load shape) Reducing energy consumption in general (strategic conservation) (Müller et al., RWTH Aachen, 2015) 9 CITIES PhD/PostDoc meeting

10 Thermal energy storage (TES) Quantity of thermal energy that can be stored and released depends on: Duration of energy storage Storage medium characteristics (material) Temperature effects TES technologies utilized for load-shifting control strategies: a. Sensible heat storage (water tanks, building structure) b. Latent heat storage (PCMs, eutectics) Source: Kalaiselvam and Parameshwaran, Elsevier, 2014 c. Thermochemical heat storage (thermochemical / adsorption materials) 10 CITIES PhD/PostDoc meeting

11 Study on thermal mass modeling Modeling of the Thermal Mass of Buildings to estimate the Potential for Energy Demand Shifting 11 CITIES PhD/PostDoc meeting

12 Study on thermal mass modeling BSc project - Frederik Lynge Halvorsen Aim: The examination of the heat transfer and heat storage mechanisms in typical structures and materials to help estimate the potential for flexibility that buildings can offer to the surrounding energy systems. BESTEST model Single zone No glazing 12 CITIES PhD/PostDoc meeting

13 Study on thermal mass modeling Comsol finite element method converts the partial differential equations to the integral form and solves it. Ida Ice building performance simulation tool Finite difference method Representation of thermal resistance and wall capacitance in wall models of Ida Ice 13 CITIES PhD/PostDoc meeting

14 Study on thermal mass modeling Implementation Temperature setpoint [degc] Daily heating schedule with preheating periods Time [hours] 1 h preheating 3 h preheating 6 h preheating 14 CITIES PhD/PostDoc meeting

15 Study on thermal mass modeling Different cooling profiles #1: no active cooling of the room #3: cooling to 21 o C #2: cooling to 16 o C and ventilation of 0.5 h -1 #4: cooling to 19 o C CITIES PhD/PostDoc meeting

16 Study on thermal mass modeling Load shifting potential Heat flux (W) h preheating Time (h) Varme fra væggene Varme fra varmelement Heat flux (W) h preheating Time(h) Varme fra væggene Varme fra varmeelementet Heat flux (W) h preheating Time (h) Varme fra væggene Varme fra varmeelementet Heat flux (W) h preheating Varme fra væggene Time (h) Varme fra varmeelement 16 CITIES PhD/PostDoc meeting

17 Future work Which model/accuracy to select when studying flexibility Utilization of thermal mass in buildings Participation in IEA EBC Annex 67 Implement methodology of aggregation of building energy demand to energy flexibility What is the minimum possible level of information to model building stock? How much can building typologies contribute to this? Which time step is the optimum for building energy simulations? What is the main factor that affects building energy performance at large scale? 17 CITIES PhD/PostDoc meeting

18 Thank you! Panagiota Gianniou PhD student DTU Civil Engineering Department 18 CITIES PhD/PostDoc meeting