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Smart Flexibility Management for Electric Grid Optimization

Explore advanced concepts and opportunities in managing balancing reserves, random production from renewables, and intelligent consumption for peak power optimization. Understand the benefits of Smart Load Shifting (LS) and Demand Response (DR) solutions based on physical models. Enhance control and optimization with real-time data monitoring, bidding strategies, and financial models.

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Smart Flexibility Management for Electric Grid Optimization

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  1. Making Things Smart Advanced flexibility management: concepts and opportunities

  2. Major issues for the electricgrid W 1/ PEAK 2/ BALANCING

  3. Management of balancingreserves

  4. The world changes Random production fromrenewables Intelligent consumption

  5. So, do weneed to build more peak power plants???

  6. The upcomingparadigm Demand israndom but canbepredicted Production canbeplanned

  7. Smart-LS and smart-DR Both solutions perform a bottom-up quantitative analysis based on a physical model of the process under control The solutions cover load shifting (LS) and demand response (DR).

  8. Automated Demand Response Bids Monitor state Simulate • Exact energy estimates • Exact cost estimates • Always-on bidding • Proof of D/R energy volumes • Quantitative Back Testing

  9. From process to model

  10. Real time parameter data - volume, temperature, consumption, availability… • Statistical analysis • Database of adjustment prices/volumes • Forecast models OTC interface Internal market prior to TSO bidding • Aggregation engine • Summation of offers • Safety margins • Financial models, optimal adjustment bid offers. • Physical models • Constrained optimization • Purchasing/Selling optimization • Industrial process simulation model • Computes: • Production/consumption nominal schedule • Maximum adjustment volume, upward and downward • Minimal adjustment offer prices • TSO/tech interface • Bid submission and management • Periodic bid updates. • TSO/finance interface • Consolidation of payments • Penalties • De-aggregation engine • Split activations to individual commands • Reporting • Dashboard : predicted vs actual schedule, gains and penalties per activated bid. • Post-mortem analysis of won and lost bids. • Adjustment potential estimations. • Optimization of nominal schedule Real-time control

  11. Engagement model : back-testing

  12. Gain estimates Dynamicpredictive tarif optimization: 5 to 10% (on top of classicalmanualoptimizations) % of electricity bill (energy and distribution) Long termflexibility options: about 5% (peakconsumersonly!) Online flexibility sales/purchases: 10 to 15% (all consumers)  For certain industries, thismayrepresent a doubling of operationalmargin

  13. Immediate activation request on participant dashboard Automatic handling of failures & intra-aggregatereserve power

  14. olivier.hersent@actility.com THANK YOU

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