1. Ændr 2. linje i overskriften til AU Passata Light 3 MARCH 2015COMMUNICATIONS SYSTEMS RUNE HYLSBERG JACOBSEN AARHUS UNIVERSITY DEPARTMENT OF ENGINEERING AU TO SHIFT OR NOT TO SHIFT? Towards an Urban Demand Response AU Smart Cities Internal Symposium Godsbanen, Remisen, Aarhus

2. 3 MARCH 2015COMMUNICATIONS SYSTEMS RUNE HYLSBERG JACOBSEN DEPARTMENT OF ENGINEERING AARHUS UNIVERSITY AU Overskrift én linje Bold eller Regular RES INTEGRATION Direct load control  Smart energy ready buildings with automation  Automated residential demand response In-direct load control  Residential demand response with the resident in the loop  Facilitating changed human behavior to exploit flexibilities

3. 3 MARCH 2015COMMUNICATIONS SYSTEMS RUNE HYLSBERG JACOBSEN DEPARTMENT OF ENGINEERING AARHUS UNIVERSITY AU THE SEMIAH MOTIVATION Demand response potentials  The operation of wet appliances can be shifted in time without affecting user comfort.  Postponing the operation of a heat pump for a small amount of time (up to 30 min.) does not affect the comfort but creates a considerable amount of flexibility.  The operation of a micro CHP plant can be shifted in the same manner as the heat pump (shifting operation in time; operate at partial output e.g., 70%; or full power).  Hot water boilers represents an energy buffer, which provide flexibility to the overall power system. 3 (Source: Eurostat) Up to 40% of household electricity consumption can be shifted Residential electricity consumption breakdown in the EU-27 An aggregated demand response need many small contributions of flexibility

4. 3 MARCH 2015COMMUNICATIONS SYSTEMS RUNE HYLSBERG JACOBSEN DEPARTMENT OF ENGINEERING AARHUS UNIVERSITY AU THE SEMIAH SYSTEM 4 But most people confuse the concept of energy shifting with energy reduction!

5. 3 MARCH 2015COMMUNICATIONS SYSTEMS RUNE HYLSBERG JACOBSEN DEPARTMENT OF ENGINEERING AARHUS UNIVERSITY AU LOAD SCHEDULING OF THE DEMAND RESPONSE SYSTEM (DRS) The DRS utilizes a centralized back-end service to control electricity loads of home appliances. The scheduling algorithm concerns the shiftability and interruptability of loads. A single-objective optimization technique is employed by using a cost metric that combines CO 2 emission and the electricity cost. The algorithm runs continuously in time. A Knapsack approach is used to define the set of appliances to run in the next time interval. Household scenarios: Scheduler Output Demand peak flattening can be observed from the aggregation and shifting of appliance loads in 100 residential households. Input LOAD SCHEDULING OF THE DEMAND RESPONSE SYSTEM (DRS) The DRS utilizes a centralized back-end service to control electricity loads of home appliances. The scheduling algorithm concerns the shiftability and interruptability of loads. A single-objective optimization technique is employed by using a cost metric that combines CO 2 emission and the electricity cost. The algorithm runs continuously in time. A Knapsack approach is used to define the set of appliances to run in the next time interval. Household scenarios: Scheduler Output Demand peak flattening can be observed from the aggregation and shifting of appliance loads in 100 residential households. Input LOAD SCHEDULING OF THE DEMAND RESPONSE SYSTEM (DRS) The DRS utilizes a centralized back-end service to control electricity loads of home appliances. The scheduling algorithm concerns the shiftability and interruptability of loads. A single-objective optimization technique is employed by using a cost metric that combines CO 2 emission and the electricity cost. The algorithm runs continuously in time. A Knapsack approach is used to define the set of appliances to run in the next time interval. House hold scenari os: Sched uler Output Demand peak flattening can be observed from the aggregation and shifting of appliance loads in 100 residential households. In p u t LOAD SCHEDULING OF THE DEMAND RESPONSE SYSTEM (DRS) The DRS utilizes a centralized back-end service to control electricity loads of home appliances. The scheduling algorithm concerns the shiftability and interruptability of loads. A single-objective optimization technique is employed by using a cost metric that combines CO 2 emission and the electricity cost. The algorithm runs continuously in time. A Knapsack approach is used to define the set of appliances to run in the next time interval. Household scenarios: Scheduler Outp ut Demand peak flattening can be observed from the aggregation and shifting of appliance loads in 100 residential households. Inp ut

6. 3 MARCH 2015COMMUNICATIONS SYSTEMS RUNE HYLSBERG JACOBSEN DEPARTMENT OF ENGINEERING AARHUS UNIVERSITY AU For at få punktopstilling på teksten (flere niveauer findes), brug ‘Forøg listeniveau’ For at få venstrestillet tekst uden punktopstilling, brug ‘Formindsk listeniveau’ Ændr 2. linje i overskriften til AU Passata Light 12 Floors; 159 Apartments (~40 m 2 ) 12 Floors; 159 Apartments (~40 m 2 ) Grundfos Dormitory Living Lab AN INTERDISCIPLINARY RESEARCH EXPERIMENT

7. 3 MARCH 2015COMMUNICATIONS SYSTEMS RUNE HYLSBERG JACOBSEN DEPARTMENT OF ENGINEERING AARHUS UNIVERSITY AU For at få punktopstilling på teksten (flere niveauer findes), brug ‘Forøg listeniveau’ For at få venstrestillet tekst uden punktopstilling, brug ‘Formindsk listeniveau’ Ændr 2. linje i overskriften til AU Passata Light  There is a need to adapt the electricity consumption side to cope with the fluctuations in generation from renewable energy sources (wind and PV).  ICT can be utilized as a cost-effective way to exploit flexibilities in consumption through Demand Response programs:  The European research FP7 project SEMIAH investigates scalable demand response in residential households;  The ForskEL project VPP4SGR looks at the integration of building automation using a combination of direct and in-direct electricity load control ;  Key challenges in demand response research: user involvement, scalability, quality of a demand response, new business models – trading flexibility. SUMMARY & OUTLOOK

8. AARHUS UNIVERSITY AU