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Competitive Energy Generation Scheduling in Microgrids

Competitive Energy Generation Scheduling in Microgrids. Lian Lu*, Jinlong Tu *, Minghua Chen. Chi-Kin Chau. Xiaojun Lin. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A A. Power Grid Landscape. generation. transmission.

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Competitive Energy Generation Scheduling in Microgrids

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  1. Competitive Energy Generation Scheduling in Microgrids LianLu*, JinlongTu*, Minghua Chen Chi-Kin Chau Xiaojun Lin TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAAAAAAAA

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

  3. Power Grid Abstraction and Challenges Generation Efficiency 62% energy loses in conversion Grid Reliability Economic loss due to outage: 150 billion USD / year Households Electricity Electricity Wind Energy Electricity Renewable Integration Intermittent generation makes it difficult to balance supply and demand Electricity Grid Power Generator Microgrid opens up new design space here! Source: U.S. Energy Information Administration, Annual Energy Review 2011, 2012. DoE Intro to SG, 2008

  4. Microgrid: A New Paradigm Microgrid Households • 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)) Households Elec-tricity Electricity Electricity Wind Energy Electricity Grid Electricity Grid Elec-tricity Heating Heating Gas Combined Heat and Power Natural Gas Local Heating

  5. Potential Benefits of Microgrids • Worldwide deployment of pilot microgrids in the US, Japan, Europe, • and more.

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

  7. A Key Problem in Microgrid Operation • Real-timebalancing supply and demand with minimum cost • Electricity cannot be stored cheaply, yet Electricity demand Heat demand Energy Generation Decision Module Grid electricity and natural gas tariffs Wind/solar generation and CHP generation Picture source: Lawrence Berkeley National Laboratory, 2007.

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

  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

  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

  11. Our Contributions CHASE– Competitive Heuristic Algorithms for Scheduling Energy-generation

  12. Problem Formulation Microgrid operational cost in [0,T] • 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 capacity constraints supply-demand constraints generator operating constraints

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

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

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

  16. Solutions and Intuitions Arrivals of demand Net electricity demand Heat demand type-start type-1 type-2 type-1 type-end (t) Function captures “sparseness/denseness” of the demand 0 - Offline optimal solution CHASE ‘s solution without look-head CHASE’s solution with look-head

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

  18. CHASE Is the Best Deterministic Algorithm • Theorem: For the problem with multiple fast-responding generators, CHASE achieves the best competitive ratio (CR) of all deterministic online algorithms:CR(CHASE) ,where captures maximum price discrepancy between local generation and external generation. • Price of uncertaintyis at most 3

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

  20. Look-Ahead Improves Performance • Theorem: For the problem with multiple fast-responding generators and look-ahead window size ,CR(CHASE) ,where captures the benefit of look-ahead. 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 (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 (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

  24. Conclusions Microgrid Households • 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 Elec-tricity Electricity Wind Energy Electricity Grid Elec-tricity Heating Heating Gas Combined Heat and Power Natural Gas Local Heating

  25. Thank you! CHASE– Competitive Heuristic Algorithms for Scheduling Energy-generation CHASE– Track the offline optimal in an online fashion • Minghua Chen (minghua@ie.cuhk.edu.hk) • http://www.ie.cuhk.edu.hk/~mhchen

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