| Vasilis Zois USC 1

|  Dynamic and sophisticated demand control – Direct control over household appliances  Curtailment Reasons – Reactive Curtailment » Loss of power generation » Renewable sources don’t work at full capacity – Proactive » Maximize profits » Reduced power consumption overweigh customer compensation  Customer Satisfaction – Discounted plan  Valuation Function – Plan connected to customer load elasticity 2

|  Dynamic pricing – Direct control achieved by monetary incentives  Cost & valuation functions – Convex cost functions – Concave valuation functions  Optimal Curtailment – Component failure as subject of attack – Quantify severity by the amount of the curtailed power  Frequency stability – Locally measured frequency – Centralized approach » Physical constraints » Low computational cost 3

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|  Reactive curtailment – Fixed amount of supply reduction – Match the supply loss while minimizing compensation  Proactive curtailment – Supply reduction » Savings outweigh curtailment costs 6

|  Curtailment Period – Fixed (e.g 15 minutes) – Optimization at the beginning – Cost savings and profits for one period  Comparison of valuation functions – Linear vs concave  Effect of line capacity in optimization 7

|  Concave function – Line capacities limit load shedding on specific busses  Linear function – Same curtailment for different capacities  Comparison – Better distribution of curtailment with concave function 8

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| 10  Capacity effect – Profits always increase in contrast to power supply  Comparison – Higher profit than in reactive curtailment by optimizing supply reduction

|  Additional constraints – Limit curtailed load on each bus – Preserved convexity of optimization problem  Effect of limits – Reduced profits – Limited power reduction » Limit is not reached 11

|  Fast response – Critical in reactive curtailment – Primary control within 5- 30s  Experiments – 14,57 or 118 bus systems – Average time from 100 iterations 12

| Thank you! Questions? 13