Smart Grid Opportunities: Reducing Emissions and Cutting Energy Costs

Transcription

1 Smart Grid Opportunities: Reducing Emissions and Cutting Energy Costs Kartik B. Ariyur School of Mechanical Engineering Purdue University Lehigh University ME Graduate Seminar April 16, 2010

2 16 April 2010 PURDUE UNIVERSITY 2 Acknowledgements Graduate Student: Qi Luo Commercial facility data from Terrafore Inc. Discussions with Anoop Mathur, CTO of Terrafore. Real Time Prices of electricity from Con Edison Inc. Solar radiation data from National Solar Radiation Data Base Natural gas price data from Energy Information Administration

3 What Does the Smart Grid Imply? Real time prices of electricity (and perhaps gas) Real time monitoring of consumption Will need active energy management By utilities And by consumers System stability needs to be studied What will the changes in consumption patterns be? Can Solar and Wind plants be plugged in? Can we reduce the need for expensive grid storage? How much can we save? 16 April 2010 PURDUE UNIVERSITY 3

4 16 April 2010 PURDUE UNIVERSITY 4 Pattern of US Energy Usage Source: Department of Energy

5 Building Energy Consumption Electric Power Consumption Sources: buildingsdatabook.eren.doe.gov/ 16 April 2010 PURDUE UNIVERSITY 5

6 16 April 2010 PURDUE UNIVERSITY 6 Motivating the Consumer Determine conditions where local sources can both lower environmental impact and cut power costs. Reduce resistance to Real Time Pricing (RTP) through showing how power bills and their uncertainty can be reduced in an RTP regime If local source generation can track local consumption as with cogeneration in winter and solar in summer, we can end up with a stabler grid. The inclusion of financial considerations into engineering optimization has a greater potential to reduce costs than simplification of the engineering for financial optimization.

7 16 April 2010 PURDUE UNIVERSITY 7 Tools Needed: Finance, Optimization and Control Hedging the risks due of Real Time Prices Hedging and local cogeneration in winter Hedging and local CSP (Concentrated Solar Power) in summer Active energy management With cogeneration With CSP

8 Lehigh ME Seminar PURDUE UNIVERSITY 8 Hedging Theory Hedging equation: C q p ( q q) P t RTP q kwhrs of electricity purchased in advance p advance purchase price/kwhr in dollars q instantaneous energy consumption in kwhrs t P RTP Real Time Price of electricity We assume feed-in tariffs, i.e., the power company purchases local power at Real-Time Prices. Hedging percentage

9 Lehigh ME Seminar PURDUE UNIVERSITY 9 Hedging and Cogeneration Overall power bill to optimize: Bill D P V P coge ' electric electric gas gas D Plant Plant varcost fixcost Constraints: Dtotal D ' electricity ( t) Dcoge ( t) D ' electricity V Qheat Qgas Qelectricity Vgas Q electricity 0 Qgas 10.8 Vgas max Dcoge Dcoge max gas

10 Lehigh ME Seminar PURDUE UNIVERSITY 10 Hedging and Cogeneration Background information

11 16 April 2010 PURDUE UNIVERSITY 11 Hedging and Cogeneration Cogeneration cuts overall power costs and bill variation

12 16 April 2010 PURDUE UNIVERSITY 12 Hedging and Cogeneration Hedging is beneficial in reducing bill variation

13 Lehigh ME Seminar PURDUE UNIVERSITY 13 Hedging and Concentrated Solar Power Overall bill to optimize: Bill D' electric P electric D solar CSP varcost CSP fixcost Constraints: Dtotal D ' electricity ( t) Dsolar ( t) Dsolar Dsolarmax

14 16 April 2010 PURDUE UNIVERSITY 14 Hedging and Concentrated Solar Power Installed solar power saves money only in summer

15 16 April 2010 PURDUE UNIVERSITY 15 Hedging and Concentrated Solar Power Hedging is beneficial only in reducing bill variation

16 16 April 2010 PURDUE UNIVERSITY 16 Building Model for Active Energy Management Dynamic model mc d 1 ( ) L hin A hout A ka in p out in dt Q

17 16 April 2010 PURDUE UNIVERSITY 17 Active Energy Management + Cogeneration To maintain temperature in a range consistent with human comfort, we use F F K To limit purchase of gas, we adapt the set point as follows And apply on-off strategy cost ( K) expected actual,gas set, k 1 set, k min 0, UAp gas set cost Q max set in & set oldin Q in = 0 set in

18 16 April 2010 PURDUE UNIVERSITY 18 Active Energy Management + Cogeneration Hedging for the strategic time scale, cogeneration for the tactical time scale and set-point adaptation for real time cost control.

19 16 April 2010 PURDUE UNIVERSITY 19 Active Energy Management + CSP To maintain temperature in a range consistent with human comfort, we use F F K To limit purchase of grid electricity, we adapt the set point as follows cost And apply on-off strategy ( K) expected actual.electricity set, k 1 set, k min 0, UAp electricity set cost Qmax in set & oldin set Q in = 0 set in

20 16 April 2010 PURDUE UNIVERSITY 20 Active Energy Management + CSP Hedging for the strategic time scale, solar for the tactical time scale and set-point adaptation for real time cost control.

21 What Have we Shown? There is promise in combining hedging, local power generation and active energy management Facility energy bills can be reduced by up to 30% Bill variation can be reduced by up to 80% With the implementation of smart grid functionality, several performance improvements and cost savings could be realized using these ideas Large commercial facilities would be able to set up local generation capacities that will minimize their costs Residential customers using smart appliances can cut the uncertainty in their bills further through hedging Utilities benefit from less wear to their system as grid fluctuations are reduced Finally, this aligns business with environmental considerations leading to a lower carbon footprint under a simple regime of feed-in tariffs. 16 April 2010 PURDUE UNIVERSITY 21

22 Digging Deeper: Better Models of Building Energy Consumption Residential and commercial facilities account for almost 41% of energy consumption in US. 70% of electricity 50% of natural gas. Heating, ventilation, and air conditioning are the largest consumers of energy in buildings, followed by lighting. Substantial savings in energy consumption that can be obtained through strategies of keeping certain doors and windows closed, different set points for the different rooms depending upon the time of day. Knowing how energy is consumed through more sensing permits better optimization 16 April 2010 PURDUE UNIVERSITY 22

23 16 April 2010 PURDUE UNIVERSITY 23 Modeling the Building as a Thermal Network General Connectivity Matrix between rooms Equivalent Heat Transfer Coefficient Connectivity Matrix(EHCM) Simulation Model for a Simple Four Room building Energy Cost vs Number of Open Doors Inside the Building

24 16 April 2010 PURDUE UNIVERSITY 24 General Connectivity Matrix Size of the connectivity matrix: Involves a number of rows and columns equivalent to the number of nodes in the network. Connection: Each element representing a connection between two nodes receives a value of 1 (e.g. A - B). Non-connection: Each element that does not represent a direct connection gets a value of 0 (e.g. C - D).

25 16 April 2010 PURDUE UNIVERSITY 25 EHCM Equivalent Heat Transfer Coefficient Connectivity Matrix(EHCM) is introduced to define the unique thermal structure of the building. In EHCM, the node represents the rooms within the building, and thermal resistance between the two rooms is represented by matrix elements R_open when the doors/windows are open R_closed when the doors/windows are all closed. All possible EHCMs can be represented by picking elements from the above matrices.

26 16 April 2010 PURDUE UNIVERSITY 26 Simulating a Four Room building Features: It contains four doors connecting: Room1-Ambient environment, Room1-Room 2, Room3-Ambient environment, Room3-Room4. For convenience of discussion, we set the size of the four rooms identical.

27 16 April 2010 PURDUE UNIVERSITY 27 Four Room Building: Simulation Results Inside temperature and energy cost without active control when all doors are closed

28 16 April 2010 PURDUE UNIVERSITY 28 Four Room Building: Simulation Results Inside temperature and energy cost without active control when all doors are open.

29 16 April 2010 PURDUE UNIVERSITY 29 Four Room Building: Simulation Results Inside temperature and energy cost without active control when the probability of each door being open is 0.3.

30 16 April 2010 PURDUE UNIVERSITY 30 Four Room Building: Simulation Results Inside temperature and energy cost with active control when the probability of each door open is 0.3.

31 Energy Cost vs. Number of Open Doors Inside a Large Building 15 room facility We can save up to 30% just by keeping some doors closed! 16 April 2010 PURDUE UNIVERSITY 31

32 16 April 2010 PURDUE UNIVERSITY 32 Handling a More Dynamic Grid Renewables Hydro+Wind+Solar could be 20% of total production Real time prices will drive more dynamic consumption patterns Utilities may not use backup plants as much Lots of local generation, perhaps isolated in micro-grids.

33 16 April 2010 PURDUE UNIVERSITY 33 Method 1: Energy Storage Hydrogen by Electrolysis: up to 93% efficiency Batteries: 90% efficiency, up to 15 minutes Pumped water: 75% efficiency for 5-6 hour storage Molten salt: up to 99% efficiency for 24 hours

34 16 April 2010 PURDUE UNIVERSITY 34 Alternative for Better Stability Geographic distribution Example: Apply the Geographic Distribution in NY. Select four spots rich in solar radiation

35 16 April 2010 PURDUE UNIVERSITY 35 Another way of improving stability Optimization: We chose four different combinations to find the most stable CV: Coefficient of Variation: C v

36 16 April 2010 PURDUE UNIVERSITY 36 Simulating the Grid

37 16 April 2010 PURDUE UNIVERSITY 37 Open Problems How do you find the weakest points on the grid at any time? How does this change when Solar and Wind are added in substantial proportion? What policies will smooth out power usage peaks and reduce the need to build more power plants? What is the best point on the grid for a terrorist to physical or cyber attack? And how best to secure it from such attacks?