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Competitive Energy Generation Scheduling in Microgrids TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A AA A AA A.

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Presentation on theme: "Competitive Energy Generation Scheduling in Microgrids TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A AA A AA A."— Presentation transcript:

1 Competitive Energy Generation Scheduling in Microgrids TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AA A AA A AA A A A Lian Lu*, Jinlong Tu*, Minghua Chen Chi-Kin ChauXiaojun Lin

2 Power Grid Landscape 2 Transcos Transmission: enerator to substation generation transmission distribution Transmission: from generator to substation, long distance, high voltage Distribution: from substation to customer, shorter distance, lower voltage generation substation

3 Power Grid Abstraction and Challenges 3 Households Electricity Grid Electricity Power Generator Wind Energy Electricity Grid Reliability Economic loss due to outage: 150 billion USD / year Renewable Integration Intermittent generation makes it difficult to balance supply and demand Generation Efficiency62% energy loses in conversion Microgrid opens up new design space here! Source: U.S. Energy Information Administration, Annual Energy Review 2011, DoE Intro to SG, 2008Annual Energy Review 2011

4 Microgrid: A New Paradigm □Microgrid is a distributed power system that satisfies heat and electricity demand with two sources of supply: – External electricity and heat supply – Local generation (renewable, combined heat and power(CHP)) 4 Households Wind Energy Combined Heat and Power Local Heating Electricity Grid Natural Gas Microgrid Heating Elec- tricity Heating Electricity Gas Households Electricity Grid Electricity

5 Potential Benefits of Microgrids Today’s ChallengeThe Microgrid Solution ReliabilityLocal generation provides heterogeneous power quality and reliability Renewable Integration Absorb the renewable uncertainty locally, reducing its negative impact on grid stability Generation Efficiency CHP-generation improves generation efficiency by 2-3x 5 Worldwide deployment of pilot microgrids in the US, Japan, Europe, and more.

6 Worldwide Deployment of Pilot Microgrids □Lawrence Berkeley Lab project of a San Francisco hotel – Cost reduced by 12.4% & carbon footprint by 13.7% 6 NTT’s microgrid, Japan Bornholm Microgrid, Denmark Scheneider microgrid, Berlin, Germany UCSD microgrid, San Diego, US

7 A Key Problem in Microgrid Operation □Real-time balancing supply and demand with minimum cost – Electricity cannot be stored cheaply, yet 7 Picture source: Lawrence Berkeley National Laboratory, Energy Generation Decision Module Electricity demand Heat demand Grid electricity and natural gas tariffs Wind/solar generation and CHP generation

8 Prior Approaches: Predict-and-Optimize □Assumption: Demand is predictable □Applications: Unit commitment and economic dispatching Time Electricity Demand Electricity Supply Supply Capacity 8

9 Microgrid Demands Are Difficult to Predict: Prior Approaches Fail □Prior assumption: – Demand is predictable □Local (net) demands are difficult to predict – Net electricity demand inherits uncertainty from wind and solar – Electricity and heat demands express different uncertainty pattern 9

10 Conventional Wisdom and Our Perspective □Model-centric perspective: Model the future, then optimize accordingly. □Optimal but (usually) rely on accurate modeling/prediction □Solution-centric perspective: □Do not rely on modeling/prediction □Characterize the value of modeling/prediction □Can explore prediction to further improve performance 10

11 Our Contributions 11 Prior Art: Offline Our Competitive Approach: Online – CHASE Predict and optimize No performance guarantee in the presence of uncertainty CHASE – Competitive Heuristic Algorithms for Scheduling Energy-generation

12 Problem Formulation □Challenges: – Generator characteristics makes decisions across slots coupled: on/off and output-level decisions depend on future input – Future demand and renewable generation are highly volatile and unpredictable 12 Microgrid operational cost in [0,T] generator operating constraints supply-demand constraints capacity constraints

13 “Make It Simple, But Not Simpler.” 13 Problem with multiple generators Problem with multiple fast-responding generators Problem with single fast-responding generator Solve the simplified problem Problem with single fast-responding generator Find an optimal offline solution CHASE the offline optimal in an online fashion Characterize the Competitiveness

14 Problem with Single Fast-Responding Generator 14 CHP generator External generation: on-demand but expensive Local CHP generation: cheap but has start- up cost Electricity Grid Separate Heating Net electricity demand Heat demand Input: electricity and heat demand in [0,T] Decision output: which generation produce what portion of the demand in [0,T]?

15 Dense Demand: Local Generation Sparse Demand: External Generation 15 CHP generator External generation: on-demand but expensive Local CHP generation: cheap but has start- up cost Electricity Grid Separate Heating Net electricity demand Heat demand Net electricity demand Heat demand Sparse demand Dense demand

16 Offline optimal solution CHASE ‘s solution without look-head Solutions and Intuitions 16 Arrivals of demand  (t) type-start type-1 type-2 type-1 type-end -- 0 Net electricity demand Heat demand

17 Extending CHASE to Multiple Generators □Theorem: Layered partition induces zero optimality loss. 17 Gen. #1 Gen. #2 Electricity Grid Separate Heating External generation

18 CHASE Is the Best Deterministic Algorithm 18

19 Worst Demand vs. Real-world Demand a(t) h(t) Worst Case Demand Real-world Demand 19

20 Look-Ahead Improves Performance 20 look-ahead window

21 Numerical Evaluation □Electricity/heat demand traces from a San Francisco college □Power output from a nearby wind station □Compare OFFLINE, CHASE, and an alternative RHC 21 (a) Summer week (b) Winter week

22 Benefit of Combined Heat and Power (CHP) □CHP leads to additional 10% cost saving □CHASE performs close to the offline optimal □RHC performs badly and may give zero cost saving 22 (a) With CHP (b) Without CHP

23 Benefit of Looking Ahead □CHASE saves 20% cost even without look-ahead □CHASE is robust to prediction error 23

24 Conclusions □We propose CHASE: a paradigm-shift solution for energy generation scheduling in microgrids – Does not rely on demand prediction – Achieves the best competitive ratio without look-ahead – Performance improves with look-ahead – Leads to 20% cost reduction in case studies 24 Households Wind Energy Combined Heat and Power Local Heating Electricity Grid Natural Gas Microgrid Heating Elec- tricity Heating Electricity Gas

25 Minghua Chen Thank you! CHASE – Competitive Heuristic Algorithms for Scheduling Energy-generation CHASE – Track the offline optimal in an online fashion

26 RHC – Receding Horizon Control Prediction window Rolling frequency (implement length) WILMAR: WInd Power Integrated in Liberalized Electricity Markets, Risoe National Laboratory 26

27 OFFLINE To Predict or Not to Predict OFFLINE 27

28 Behavior of CHASE: A Closer Look □CHASE tracks the offline optimal in an online fashion □Local generation saves cost when demand is dense 28

29 Combined Heat and Power (CHP) □CHP is most efficient when heat can be used on-site or very close to it. Source: United States Environmental Protection Agency Users heat exchanger 29

30 CHP Generation Improves Efficiency □US electricity end-usage efficiency: 33% □Local CHP-generation improves generation efficiency by 2-3x 30 Picture source: Lawrence Berkeley Lab, 2007


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