Demand response in Quebec s CI buildings: potential and strategies
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1 Demand response in Quebec s CI buildings: potential and strategies Team: Ahmed Daoud, Ph.D, project manager Marie-Andrée Leduc, MSc., ing, task manager Jean Baribeault, ing, researcher Karine Lavigne, MSc.A., ing, researcher Sylvain Chénard, technician Alain Poulin, MSc.A., researcher Sylvain Martel, Ph.D, ing. jr., researcher Adlane Bendaoud, Ph.D, researcher October 8th 213 ICEBO213
2 Project : SGE Building Software Interaction between the BAS (BACnet) and database MySQL Metric buildings and continuous optimisation Automated calibration (collaboration LBNL) Demand response in CI buildings 2 Groupe Technologie, Hydro-Québec ICEBO 213 Calibration of an EnergyPlus building energy model Lavigne, Karine; Sansregret, Simon; Daoud, Ahmed Hydro-Québec SGE Building Software Herve Frank Nouanegue Ahmed Daoud Hydro Québec
3 Demand response in CI buildings
4 Context Winter Three historical peaks (Jan 23rd am/pm, Jan 24th am) ~ 39 MW Ref: État d avancement 212 Proceedings du Plan d approvisionnement of the 13th International Conference , for Enhanced Régie de Building l Énergie Operations, du Québec, Montreal, Quebec, October 8-11, Groupe Technologie, Hydro-Québec
5 What is DR? why use it? Demand response according to FERC: Changes in electric usage by demand-side resources from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized [1] [1] Winter peaking hours 5 Groupe Technologie, Hydro-Québec
6 Why call upon CI customers? more kw/meter some all-electric more control (BAS) centralized management corporate image DR Strategies LBNL, 29, Northwest Open Automated Demand Response Technology Demonstration Project, S. Kiliccote, M.A. Piette, J.H. Dudley, Environmental Energy Technologies Division, LBNL, avril 29 6 Groupe Technologie, Hydro-Québec
7 Previous work > Study real load profiles per activity sector > EstimatetheoreticalDR potentialper activity sector > Building simulation to test DR strategies and study possible ways of optimising setpoint trajectories for these strategies 7 Groupe Technologie, Hydro-Québec
8 Samples > Meter data > ~ 15 meters from CI > Data history (jan, feb, march) 29/211 Studied sample School Small convenience store Groceries store Retail University Arena Office Cinema Library Healthcare (Long term care facility) 8 Groupe Technologie, Hydro-Québec
9 Load profiles batch study Client typique: 36 Peaking days (29, 211).1 Client typique: heures Profils normalisés réels de la classe : 4 (665 bâtiments) - 16 janvier profils normalisés Per activity sector heures Profils normalisés réels de la classe : 3 (466 bâtiments) - 16 janvier 29 profils normalisés Profils normalisés réels de la classe : 2 (48 bâtiments) - 16 janvier 29.4 profils normalisés profils normalisés Profils normalisés réels de la classe : 1 (45 bâtiments) - 16 janvier 29 Client typique: heures Client typique: heures Fuzzy logic clustering algorithm Profils normalisés sous-classe 4a (181 bâtiments).4 Profils normalisés sous-classe 4b (76 bâtiments).4 Client typique: 367 Client typique: Profils normalisés sous-classe 4c (45 bâtiments) Profils normalisés sous-classe 4d (3 bâtiments).4 Client typique: 113 Client typique: Groupe Technologie, Hydro-Québec
10 Load profiles batch study 11 Profils réels typique - 16 janvier 29 kw classe 1 classe 4 classe 3 classe 2 SCHOOLS Typical load profiles heures Typical load profiles used to simulate typical buildings and estimate Impacts on customers (comfort / financial) Load reductions of strategies during peaking hours kw Profils réels, sous-classes de la classe 4-16 janvier classe 4a classe 4b classe 4c classe 4d 1 Groupe Technologie, Hydro-Québec
11 Theoretical DR potential Applicable to large heterogeneous data sample batch analysis Conservative Accounting only for variations during peaking hours kw P P [ am pm] max,peak, x moy,peak, x DR _ POTtheoretical =, x =, P max,peak, x Proceedings of the 13th International Conference for Enhanced heures Building Operations, Montreal, Quebec, October 8-11, Groupe Technologie, Hydro-Québec
12 Global DR theoretical potential Estimating method: DR potentialfor bothpeakperiod, kw/meter, risk * Targeting tool for interesting activity sector* Assumptions: = PTkW / meter PT N global HIGH PROBABLE LOW #building, segmentation per tariff representative sample building Arena Library Office Health care Cinema Retail Small convenience store Groceries store University School 12 Groupe Technologie, Hydro-Québec Résultats du 16 janvier 29
13 DR strategies Multi-objectives geneticalgorithm 6 objectives: 3 BUREAU Stratégie optimisée Stratégie optimisée Référence HQ index Customer index Tairindex IAQ index (3x) 25 A = 26 kw Pareto front 2 Puissance (kw) 15 B = 113 kw 8 moy 1 5 Caractéristiques du bâtiment: 4 pi 2, 1 étages, 5 zones, 3 systèmes PT theoretical = 27 kw 13 PT max_hourly = 17 kw PT max_15min = 184 kw PT max_average = 14 kw PT max_maxima = 147 kw
14 Ongoing work >Aims Improveourestimation of the theoreticalpotentialfor the mostinterestingactivitysectorsand thosenot already addressed Account for variability just outside peaking hours Pre-Segment samplesby takingintoaccoundcustomersize and weather sensitivity Address several weekdays and weather conditions Verifythe feasabilityof DR strategiesin real buildings through a small scale pilot project Studybaselinemethodsand theirimplications 14 Groupe Technologie, Hydro-Québec
15 Pilot Project Objective: Verify the feasability of DR strategies in real buildings through a small scale pilot project 6 buildings, 6 partners School Large retail bank Small retail Small office Building models (physical, non physical), DR strategiessimulation and implementation, reduction evaluation All-electric 15 Groupe Technologie, Hydro-Québec
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