Small-Scale Communities Are Sufficient for Cost- and Data-Efficient - - PowerPoint PPT Presentation

Small-Scale Communities Are Sufficient for Cost- and Data-Efficient Peer-to-Peer Energy Sharing

Romaric Duvignau (duvignau@chalmers.se) 1 Verena Heinisch 2 Lisa G¨

• ransson 2

Vincenzo Gulisano 1 Marina Papatriantafilou 1 e-Energy’20, Virtual Event, Australia, June 23 2020.

1 Chalmers, CSE, Networks and Systems; 1 Chalmers, SEE, Energy Technology.

Introduction

Introduction: Context & Motivation

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Introduction: Context & Motivation

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Introduction: Context & Motivation

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Introduction: Research Questions

Research Questions

• 1. Cooperation: to understand which configurations lead to

noticeable cost savings.

• 2. Capacity: to identify ranges of sizes for energy production,

where cooperation becomes interesting.

• 3. Size: to identify from which community sizes the gain starts

to become important.

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Introduction: Contributions

Contributions

• Forecast Range: replace perfect foresight by limited

prediction (online decision-making problem).

• Community Compositions: use different local generation

and storage capacities.

• Gain-sharing Mechanisms: show how to split the

cooperative gain (average financial advantage of cooperating).

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Model

Optimization Model

Individual

• Objective: minimize yearly electricity bill of each household h.
• Parameters for h:
• PV and Battery capacities.
• Hourly consumption.
• Parameters for all:
• Solar profile.
• Electricity prices.

Cooperative

• Same as individual but with aggregated consumptions,

generation and storage capacities.

• Assumptions: no battery degradation, transmission losses nor

constraints on connection capacities or communication faults.

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Our case study: 100 households

• Dataset: consumption for 100 swedish households with wide

range of consumption (0.33-3.36 kWh average consumption).

• Production levels:
• ALR (Array to Load Ratio): controls PV panels size.
• BDR (Battery to Demand Ratio): controls Battery size.

5 Scenarios, avg. # PV (min-max):

• 1. Very Small – 3 PVs (1-6)
• 2. Small – 9 PVs (2-17)
• 3. Medium – 18 PVs (3-33)
• 4. Large – 27 PVs (5-50)
• 5. Very Large – 36 PVs (7-67)

None Very Small Small Medium Large Very Large 500 1500 2500 3500 Yearly Electricity Bill (€/year) 5 10 15 Battery-To-Demand-Ratio −50 50 100 150 200 Average Saving including Investment (€/year) None Very Small Small Medium Large Very Large ALR = 0 ALR = 0.5 ALR = 1.5 ALR = 3 ALR = 4.5 ALR = 6 ALR = 9 ALR = 12

(ALR,BDR): Very Small (0.5,1), Small (1.5,2.5), Medium (3,5), Large (4.5,10), Very Large (6,15).

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Results

Result 1. We need pure-consumers as well!

1 2 3 4 Average ALR of the 100-Community 50 100 150 200 250 300 Avg Coop. Gain (€/household) 0% prosumers 25% prosumers 50% prosumers 75% prosumers 100% prosumers 10 20 30 40 50 60 70 80 90 100 Number of Equipped Household (over 100 Households) 0.00 0.02 0.04 0.06 0.08 0.10

• Avg. Rel. Coop. Gain (€/Household)

Very Large Large Medium Small Very Small

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Result 2. Small-scale communities are enough!

100/100 1/5 1/4 1/3 1/2 2/4 2/5 4/10 10/25 20/50 40/100 Size of the Community in Prosumers/Total (2-100 peers) 25 50 75 100 125 150 175

• Avg. Coop. Gain (€/household)

Very Large Large Medium Small Very Small 10 20 30 Self-Cons. (%) 100-community Small-scale com. Individual 20% 25% 33% 40% 50% 60% 66% 75% 80% 100% Fraction of Prosumers in the Community 0.0 2.5 5.0

• Diff. with Ind. (%)

1/5 1/4 1/3 2/5 1/22/4 3/5 2/3 3/4 4/5 2/23/34/45/5 Small-scale com. 100-community 100-community Small-scale com. Individual

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Result 3. Forming the right pairs is important!

2 4 6 8 10 12 14 16 Generation power of the paired Prosumer (kWp) 50 100 150 200

• Avg. Cooperative Gain (€/Household)

0.4kWh 1.1kWh 1.3kWh 1.4kWh 1.8kWh 1.9kWh 2.1kWh 2.2kWh 2.5kWh 2.9kWh Random Worst Best Greedy-Largest Greedy Single Community Pairing of 10/20 and 10/100 prosumer/consumer households 25 50 75 100 125 150

• Avg. Coop. Gain (€/household)

88% 80% 96% 95% 96% 100% 71% 41% 100% 75% 99% 39% 10/20 pairing 10/100 pairing 20-com. 100-com.

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Result 4. We don’t need much prediction power!

10 20 30 40 50 Number of forecasted hours 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of optimal saving Optimal Solution Greedy Individual Truth Predictor Average Predictor Linear Predictor 2 4 6 8 10 12 Generation power of the paired prosumer (kWp) 50 100

• Avg. Coop. Gain (€/household)

Perfect Foresight Truth Predictor Average Predictor Linear Predictor Greedy Coalition

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Result 5. Consumers should also get rewarded!

200 400 600 Max Avg. Coop. Gain No-Split 1/2-Split 3/4-Split Even-Split Individual 1 2 3 4 5 6 ALR (Production Level) for Prosumers in the community 25 50 75 100 125 Max Avg. Coop. Gain

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For Prosumers For Consumers

Conclusion

Take Home Messages

• 1. Small-scale communities obtain up to 88-97% of the same

benefits of any larger community → large reduction in the amount of data to share over the network!

• 2. Matching prosumers with pure-consumers in the right way

can lead to up to 59% improvement on the coop. benefit!

• 3. No need for very accurate predictions: you can achieve up

to 90% of the optimal cooperative gain with inaccurate and limited foresight of only 8h, and 96% with 16h!

• 4. How the gain is split among the peers influence motivations

both on investing in energy resources and participating in the sharing process!

• Future Work: Can we organize households (matching

problem) into a data- and cost-efficient P2P network in a distributed and continuous fashion?

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Thank you for your attention,

and take care!

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