Optimal Energy Management in buildings: sizing, anticipative and reactive management

Transcription

1 Optimal Energy Management in buildings: sizing, anticipative and reactive management Frédéric Wurtz LEG - Laboratoire d'electrotechnique de Grenoble Institut National Polytechnique de Grenoble (France) 1

2 Frédéric Wurtz He took his degrees in Electrical Engineering in 1993 and the PH-D in 1996 at the National Polytechnique de Grenoble. He is now researcher at the CNRS at the Grenoble Electrical Engineering Laboratory. His research regards the design process of electromagnetic devices and develops methods and tools in this area. In the last years he works in the area of design of systems for energy management: typically cars, flights and buildings. He is actually time at head of the MAGE team of the G2ELAB: this team, composed of 12 permanents researchers and 25 phd students, is specialized in tools, software and methodologies for the design and simulation of electromagnetic systems, and systems that use electrical energy. 2

3 Optimal Energy Management in buildings: sizing, anticipative and reactive management S. Bacha, H. Pham, G. Foggia, D. Roye, G. Warkosek, F. Wurtz, S. Ploix,E. Zamaï, M. Jacomino, 3

4 Outline Presentation of Grenoble actors The context Application: optimal energy management in buildings Sizing Anticipative management Optimization Neural Networks Reactive management 4

5 Presentation of Grenoble Actors INPG (Institut National Polytechnique de Grenoble) first French engineering university for training: 1100 engineers graduated research (30 affiliated laboratories) 350 students receive a Master of Science degree 150 students their PHD every year Important research topics: ICT and Energy G2ELAB Electrical systems & Management of energy G-SCOP Mechanical Engineering, Operational Research, Control of Discrete Event Systems, Computer Science and Safety and Security Engineering Goal of the presentation: give an overview of some works done by those partners in management of energy in buildings 5

6 The context The importance of energy in buildings in France Transport; 50.4; 31% Argriculture; 2.9; 2% Sidérurgie; 5.5; 3% Résidentiel- Tertiaire; 68.2; 43% Industrie; 33.6; 21% % of buildings: 43% French final energy consumption 2005 (Mtep) Sidérurgie Industrie Résidentiel-Tertiaire Argriculture Transport A new global context for energy Competition on energy sale Déregulation Résidentiel- Tertiaire; 273; 64% Transport; 12; 3% Sidérurgie; 10; 2% Argriculture; 3; 1% % of buildings: 64% Source: Industrie; 126; 30% French electricity consumption 2005 (TWh) Optimal management Sources/loads Sidérurgie Industrie Résidentiel-Tertiaire Argriculture Transport Ancillary services Dynamic pricing Open, actors Active and intelligent Contribution to ancillary services 6

7 Primary sources Application: optimal energy management in buildings Energy fluxes control Energetic architecture optimization? Management Efficiency Sources Loads Devices & Envelope Weather Market 7

8 A typical project Project ANR MULTISOL (G2ELab, G-SCOP, INES, Schneider Electric, Armines) optimal sources and loads co-management Diesel engine Storage WG Power production control board Power delivery control board Controllable loads Grid Solar Panels = ~ FC H 2 = = = = = = = ~ MODULE ; Coupling and sources management MODULE : Demand Side Management Exogen factors (weather, market ) DC Electrical Flux AC Electrical Flux Measurement Flux Command Flux 8 Measurements + Forcasting EXPERT and PREDICTIVE system Control board References Parameters Power command orders Graphical User Interface

9 The problematic Local sources Optimal Energy Management Storage OF* Ecology OF* Economy Network OF* Autonomy Loads Management of the Quality of Energy Weather Current, Voltage Market *: OF = Objective Function Optimal sizing Optimal anticipation of the management of energy (Optimal scheduling) Optimal reactive management User 9

10 Sizing The goal : Finding size of batteries, Photovoltaic Panels, Sizing of the installation Objective- Minimization of the global cost CG = I + MC + R + CEC I: Investment cost I = I PV + I PE + I B + I GS I PV : Photovoltaic investment I PE : Power electronic investment I B : Battery investment I GS : Generating set investment MC: Maintenance Cost R: Replacement cost CEC: Cost of Energy Consumption 10

11 Sizing Formulation : Mainly Mixed Linear Programming Objective function to minimize : f T x Under constraints : With : Ax b A eq.x = b eq lb x ub x are the variables (continue, binary or integers) A, A eq are matrixes; f, b, b eq are vectors But also : MILP, SQP and dynamic approaches Solved with : Matlab CPLEX (Ilog) Own developed tools 11

12 Sizing User Geographical localization Preferences of the user Typical loads (devices, typical practices ) Some typical results Optimizer Option : Ecology Option : Economy Option : Autonomy Data base Dépenses ( ) Costs Sizes: Power of the photovoltaic panels: 4 kw Capacity of the batter y: 1140 Ah Power subscription on network: 9 kw Rated power of generating set : 0 kw Study of Economical return Rentabilité d'une installation en fonction du prix de revente Profitability of the installation with several options of selling of photovoltaic energy Cost without optimizer Photovoltaic energy price = price energy on the network Photovoltaic energy price = 0 Photovoltaic price = public grant Cas 1 : réseau uniquement - consommation standard Cas 5 : réseau & PV revente excedent avec prix conso Cas 5 : réseau & PV revente excedent avec prix 0 Cas 5 : réseau & PV revente excedent au prix "avantage PV" Année Years Economical Break point event 12

13 Anticipative management The goal : Anticipate optimal dispatching of locally produced energy Stored, sold to the network, locally used, 2 approaches presented 1 Optimization with different possible objective functions Objective - Ecology Objective - Economy Objective - Autonomy 2 Use of neural networks 13

14 Anticipative management First approach : Optimization Formulation : Mainly Mixed Linear Programming Objective function to minimize : f T x Under constraints : With : Ax b A eq.x = b eq lb x ub x are the variables (continue, binary or integers) A, A eq are matrixes; f, b, b eq are vectors But also : MILP, MINLP, SQP and dynamic approaches Solved with : Matlab CPLEX (Ilog) Own developed tools 14

15 Anticipative management Some typical results Option Ecology Forecast data Price of energy Information (buy/sell) User Preferences Optimizer Option : Ecology Option : Economy Option : Autonomy Data base Solar energy locally used Energy going through the battery 15 Energy coming from the network Energy stored in the battery kwh kwh kwh kwh Solar energy not used locally hours hours hours hours hours kwh

16 Anticipative management Some typical results Option Economy Solar energy locally used Forecast data Price of energy Information (buy/sell) User Preferences Optimizer Option : Ecology Option : Economy Option : Autonomy Data base Option Gain in cost % of solar energy sold kwh kwh kwh kwh Energy going through the battery Energy coming from the network Energy stored in the battery Solar energy not used locally hours hours hours hours OFEcology /day % OFEconomy /day % kwh hours 16

17 Anticipative management Second approach : Neural Networks Inputs for anticipation (Hour, day, loads, temperatures, sunlight, presence of inhabitants, etc ) Neural Network P_ch(t-1) T_ext(t) T_int(t) E(t) P_ch(t) P_pv(t) Recognition of sequences of dispatching of energy Sequence 1 Sequence 2 Biais C* Find the more nearest curve Sequence N t Optimal sequence of dispatching of energy t 17

18 Anticipative management Second approach : Neural Networks Loads over a day Loads over a day Optimizer Optimal dispatching of energy Used to build training examples Neural Network (Ps, Pb, Pr, Eacc) Estimation of optimal dispatching by the neural network (Ps*, Pb*, Pr*, Eacc*) 18 Kind of architectures explored: - Perceptron multi layers - Fuzzy ARTMAP

19 kwh Anticipative management Second approach : Neural Networks Some typical results Optimal anticipation of energy that must be stored in the battery RED: Reference BLUE: Neural Network (system Photovoltaic Panel-Battery-Network) hours kwh hours Neural Network Perceptron multi layer: -learning : 90 days number of hidden neurons : 120) kwh hours Neural Network successful in 70 to 90% but with some absurd results 19

20 Reactive management Actual internal management I guarantee the voltage I consume power when I need it I cannot get any more power! All is well... I have to Switch off! I (still) consume power when I need it and overload! 20

21 Reactive management Towards a new scheme management I should reduce my production in 1 hour. Power cost is increasing and user is becoming unsatisfied. Negotiations based on user satisfaction. User would accept that I pause the washing in 1 hour. Loads and sources = Agent that can negotiate in reactive time User would accept that I shift the cooking up to one hour. I need more power because user is becoming unsatisfied. 21

22 Reactive management Some typical results A prototype that implements the approach 22

23 Conclusion Cost of energy that will increase Increase of renewable energy in buildings Liberalization of market of energy Necessity to manage dynamic aspects like weather dynamic variation of cost of energy Need of Optimal energy management in buildings for - Sizing - Anticipation - Reactive management 23

24 Perspective: from the building to the residential micro-grid Micro-grid concept application: Participation to ancillary services To optimize the economic exploitation To improve reliability: islanding Contribution to Smart Micro-grid, virtual plants 24

25 Perspective: from the building to a new scheme for distribution of energy 1.5 kv - 50 kv Old scheme 63 kv kv 400 V Production Network Distribution and repartition Network for distribution Loads Usual way for distribution of energy 25

26 Perspective: from the building to a new scheme for distribution of energy New scheme Active distribution CHP 1.5 kv - 50 kv 63 kv kv Production Network Distribution and repartition 400 V Network for distribution FC HTA BT Small Size Hydrolic Classic generation Fully controllable Wind Farms PV Intermittent or random generation 26