An Online Procurement Auction for Power Demand Response in Storage-Assisted Smart Grids Ruiting Zhou †, Zongpeng Li †, Chuan Wu ‡ † University of Calgary ‡ The University of Hong Kong 1

 The central problem in a smart grid is the matching between power supply and demand.  Supply < Demand, procure from energy storage devices  Demand < Supply, store electricity.  This work studies the demand response problem in storage-assisted smart grids. Introduction 2

 Storage crowdsourcing: thousands of batteries co-residing in the same grid can together store and supply an impressive amount of electricity.  How to incentivize storage participation and minimize the cost?  An Online Procurement Auction! Introduction A storage-assisted smart grid 3

 Effectively response to the imbalance  Need no estimation  Discover the “right price”  reduce the cost  Properties:  Online: diurnal cycles, and electricity stored at low-price hours is in finite supply  Procurement: multiple sellers (storage devices) and a single buyer (the grid). Why Online Procurement Auction? 4

 Two main modules  Translating online auction into a series of one-round auctions A online  Design a truthful auction for one-round demand response problem A one  A polynomial-time approximation algorithm  A payment scheme to guarantee truthfulness  Social cost competitive ratio: 2 in typical scenarios Our Contributions 5

Model Auction includes T time slots; M agents, each agent m ∈ [M] submits a set of K bids. Each bid is a pair: 6 Capacity limit Cover power shortage XOR bidding rule Social cost

 What difficulties could the capacity bring?  Greedy vs Optimal Online Problem 7 Agent A C=10 Round 1 $2 4 Round 2 $6 5 Round 3 $3 6 Agent B C=10 Round 1 $4 4 Round 2 $7 5 Round 3 $9 10

Online Problem 8 Agent A C=10 Round 1 $2 4 Remaining Capacity=6 Agent B C=10 Round 1 $4 4 Remaining Capacity=10 D1=4  What difficulties could the capacity bring?  Greedy

Online Problem 9 Agent A C=10 Round 1 $2 4 Round 2 $6 5 Remaining Capacity=1 Agent B C=10 Round 1 $4 4 Round 2 $7 5 Remaining Capacity=10 D2=5  What difficulties could the capacity bring?  Greedy

 What difficulties could the capacity bring?  Greedy social cost=2+6+9=17 Online Problem 10 Agent A C=10 Round 1 $2 4 Round 2 $6 5 Round 3 $3 6 Remaining Capacity=1 Agent B C=10 Round 1 $4 4 Round 2 $7 5 Round 3 $9 10 Remaining Capacity=0 D3=6

Online Problem 11 Agent A C=10 Round 1 $2 4 Round 2 $6 5 Round 3 $3 6 Agent B C=10 Round 1 $4 4 Round 2 $7 5 Round 3 $9 10  What difficulties could the capacity bring?  Optimal social cost=2+7+3=12.Greedy social cost=17

 Lesson Learned  Do not exhaust battery’s capacity early  Lose all the opportunities on this agent  Solution: Higher priority for agent with higher (remaining) capacity  adjust the cost in a bid according to its remaining capacity 12 Our solution

13 The Online Framework A online Increased cost, adjust each round Run A one based on the increased cost. Suppose A one return a good solution For one-round problem. Update the value of Sm, based on the ratio of consumed power and total capacity

 Simulate A online on the previous example  Two bids, A one select the agent with smallest cost. 14 Example 14 Agent A C=10 Round 1 $2 4 Remaining Capacity=6 D1=4 Agent B C=10 Round 1 $4 4 Remaining Capacity=10

 Simulate A online on the previous example  Two bids, A one select the agent with smallest cost. 15 Example 15 Agent A C=10 Round 1 $2 4 Round 2 $6 5 adjust: $7.2 5 Remaining Capacity=6 D2=5 Agent B C=10 Round 1 $4 4 Round 2 $7 5 adjust: $7 5 Remaining Capacity=5

 Greedy algorithm: social cost $17  Optimal solution: social cost $12  A online : social cost $12 16 Example 16 Agent A C=10 Round 1 $2 4 Round 2 $6 5 Round 3 $3 6 adjust: $ Remaining Capacity=0 D3=6 Agent B C=10 Round 1 $4 4 Round 2 $7 5 Round 3 $9 10 adjust: $ Remaining Capacity=5

 Primal-dual approximation algorithm to determine the winners  Approximation ratio=2 when each agent submits one bid only  Payment to winners  key in satisfying truthfulness, provide monetary incentives to encourage truthful bidding  Myerson’s characterization: an auction is truthful iff  (i) the auction result is monotone  (ii) winners are paid threshold payments 17 One-round Auction Design

18 One-round WDP Increased cost of supply Cover power shortage XOR bidding

 We augment the original one-round WDP: introduce a number of redundant inequalities.  Introducing dual variables y, z. 19 One-round WDP Primal ILP Dual ILP

20 One-round Auction Mechanism Initialize the primal and dual variables While loop: updates the primal and dual variables Once a dual constraint becomes tight, the bid corresponding to that constraint is added to the set A Find the threshold bid, Calculate the payment

 Simulation setup  Demand: [10GWh, 50GWh], with reference to information from ieso (Power to Ontario)  Battery capacity [60 kWh, 200 kWh]  Amount of supple: [0, 100]kWh  cost [$0, $20]  1000~ 3000 agents  1~15 rounds  1~10 bids per agent 21 Performance Evaluation

 Approximation ratio approaches 1 towards the bottom- right corner of the surface  A downward trend as the number of bids per agent grows 22 Performance of One-round WDP Algorithm

 The larger number of available agents, the better performance in terms of cost can be achieved  Small values in k and T lead to a lower ratio 23 Performance of Online Algorithm

 One of the first studies on storage power demand response through an online procurement power auction mechanism  The two-stage auction designed is truthful, computationally efficient, and achieves a competitive ratio of 2 in practical scenarios  An online framework which monitors each agent’s capacity  A primal-dual approximation algorithm for one-round problem 24 Conclusions

 Questions? 25 Thank you!