Extracting value from data sharing for RES forecasting: Privacy aspects & data monetization
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Recording at: https://youtu.be/cXWOE7RDO6M Recent works in renewable energy sources (RES) forecasting, have shown the interest of using spatially distributed time series and assumed that data could be gathered centrally and used, either at the RES power plant level, or at the level of a system operator. However, data is distributed in terms of ownership, limitation in data transfer capabilities and with agents being reluctant to share their data anyway.
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Extracting value from data sharing for RES forecasting: Privacy aspects & data monetization
- 1. Extracting value from data sharing for RES forecasting Privacy aspects & data monetization Ricardo Jorge Bessa, INESC TEC Liyang Han, DTU December 2020 ISGAN Academy webinar #25 Recorded webinars available at: https://www.iea-isgan.org/our-work/annex-8/
- 2. ISGAN in a Nutshell Created under the auspices of: the Implementing Agreement for a Co-operative Programme on Smart Grids ISGAN IN A NUTSHELL 2 Strategic platform to support high-level government knowledge transfer and action for the accelerated development and deployment of smarter, cleaner electricity grids around the world International Smart Grid Action Network is the only global government-to- government forum on smart grids. an initiative of the Clean Energy Ministerial (CEM) Annexes Annex 1 Global Smart Grid Inventory Annex 2 Smart Grid Case Studies Annex 3 Benefit- Cost Analyses and Toolkits Annex 4 Synthesis of Insights for Decision Makers Annex 5 Smart Grid Internation al Research Facility Network Annex 6 Power T&D Systems Annex 7 Smart Grids Transitions Annex 8: ISGAN Academy on Smart Grids
- 3. ISGAN’s worldwide presence 1/8/2018 ISGAN, PRESENTATION TITLE 3
- 4. Value proposition 4 ISGAN Conference presentations Policy briefs Technology briefs Technical papers Discussion papers Webinars Casebooks Workshops Broad international expert network Knowledge sharing, technical assistance, project coordination Global, regional & national policy support Strategic partnerships IEA, CEM, GSGF, Mission Innovation, etc. Visit our website: www.iea-isgan.org
- 5. Agenda • Smart4RES in a nutshell • Motivation for data sharing & collaborative analytics • Collaborative learning for RES forecasting • Data markets : Basics and Smart4RES proposal 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 5
- 6. Smart4RES in a nutshell EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 6
- 7. Smart4RES in a nutshell EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 7 Smart4RES vision Achieve outstanding improvement in RES predictability through a holistic approach, that covers the whole model and value chain related to RES forecasting Improvement from collaborative RES forecasting Potential for improvement with spatial-temporal approaches up to 20% for 6h ahead for solar energy and up to 15-20% for wind energy Lack of privacy guarantees and price-based incentives to share data
- 8. Smart4RES consortium EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 8 6 countries 12 partners 11/2019-4/2023 Funds: H2020 programme Budget: 4 Mio€ Duration: 3.5 years End-users Universities Research Industry Meteorologists
- 9. Motivation for data sharing & collaborative analytics EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 9
- 10. Data Sharing: Motivation • Increasing volume of geographically distributed data • Improvement in forecasting accuracy by this data • Main barriers • Data privacy and confidentiality • Lack of economic signals for sharing (collaborating with) data • Lack of business cases for collaborative analytics EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 10
- 11. Collaborative Analytics EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 11 Model from centralized to distributed learning Master Node Model Worker Node ΔW1 Aggr(ΔW1+ΔW2+ ΔW3) Worker Node ΔW2 Worker Node ΔW3 Federated learning as industry standard • Exchange raw data → NO • Exchange model coefficients → NO (partially) • Exchange model outputs → YES • Data divided by features (not by observations) DATA SHARING IN
- 12. Possible Use cases for Data Sharing EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 12 RES Forecasting Benefit: Improve forecasting skill in minutes to day-ahead time horizon & exploit heterogenous data sources Weather Modelling Benefit: Liberalization of weather data trading → access to large-scale weather data Privacy & monetization Monetization Source: Data Basin How to price weather data?
- 13. Possible Use cases for Data Sharing EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 13 Peer-to-peer (P2P) Trading Benefit: Secure analytics with personal data → better decision-making & more trust Power Transformer Condition Benefit: Data augmentation (faults, dissolved gas analysis, sensors) → improved maintenance policies Utility A Utility C Utility B € € enable sharing of energy transactions and usage data
- 14. Collaborative learning for RES forecasting 1/8/2018 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 14
- 15. RES Collaborative Forecasting Formulation EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 15 Vector Autoregressive Model (VAR) 𝒀 = 𝒄 + 𝑩𝒁 + 𝑬 Z: lagged power observations 1 Y1,tY1,t-1Y1,t-2 … 2 Yt,2Y2,t-1Y2,t-2 … N YN,tYN,t-1YN,t-2 … Example for 2 PV sites 𝑌1,𝑡 𝑌2,𝑡 = 𝑐1 𝑐2 + 𝐵1,1 1 𝐵1,2 1 𝐵1,1 2 𝐵1,2 2 𝐵2,1 1 𝐵2,2 1 𝐵2,1 2 𝐵2,2 2 ∙ 𝑌1,𝑡−1 𝑌2,𝑡−1 𝑌1,𝑡−2 𝑌2,𝑡−2 + 𝐸1,𝑡 𝐸2,𝑡 B: coefficients matrix (to estimate) constant terms (to estimate) white noiseforecasted power multivariate linear model power forecasts for multiple sites as a function of past power observations from all sites
- 16. Iterative estimation RES Collaborative Forecasting State-of-the-art distributed learning 16 Distributed coefficients estimation = B2= 𝐶𝑜𝑒𝑓 𝑊𝐹1 𝑘+1 = 𝑔( 𝐶𝑜𝑒𝑓 𝑊𝐹2 𝑘+1 = 𝑔( 𝐶𝑜𝑒𝑓 𝑊𝐹3 𝑘+1 = 𝑔( ) ) ) , , , , , , 1 2 3 data from WF3 PowerWF1 PowerWF2 PowerWF3 data from WF1 data from WF2 data from WF3 PowerWF1 PowerWF2 PowerWF3 data from WF1 data from WF2 Coef WF1 Coef WF2 Coef WF3 Power for multiple locations lagged power observations Coefficients Limitation: access to private/confidential data Conciliation k Conciliation k Conciliation k . C. Gonçalves, R.J. Bessa, P. Pinson, “A critical overview of privacy- preserving approaches for collaborative forecasting,” International Journal of Forecasting, vol. 37, no. 1, pp. 322-342, Jan-Mar 2021. Conciliation k data from WF1 𝑪𝒐𝒆𝒇 𝑾𝑭 𝟏 𝒌 + data from WF2 𝑪𝒐𝒆𝒇 𝑾𝑭 𝟐 𝒌 data from WF3 𝑪𝒐𝒆𝒇 𝑾𝑭 𝟑 𝒌+= ℎ Power WF1Power WF2Power , Forecast after k iterations . . . EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING Distributed learning with ADMM (Alternating Direction Method of Multipliers)
- 17. RES Collaborative Forecasting Privacy-preserving Protocol 17 == data from WF3 PowerWF1 PowerWF2 PowerWF3 data from WF1 data from WF2 data from WF3 PowerWF1 PowerWF2 PowerWF3 data from WF1 data from WF2 Power for multiple locations Lagged power observations Coefficients (1) Power data encryption with linear algebra (M: random matrix – unknown but built by all agents) PowerWF1 Private matrices M1 M2 M3. . . Coef WF1 Coef WF2 Coef WF3 . Encrypted data == PowerWF1 PowerWF2 PowerWF3 data from WF2 Enc data from WF3 EncPWF1 EncPWF2 EncPWF3 Enc data from WF1 Enc data from WF2 Enc Coef WF1 Enc Coef WF2 Enc Coef WF3 . EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING
- 18. RES Collaborative Forecasting Privacy-preserving Protocol 18 Encrypted data == data from WF3 PowerWF1 PowerWF2 PowerWF3 data from WF1 data from WF2 data from WF3 PowerWF1 PowerWF2 PowerWF3 data from WF1 data from WF2 Power for multiple locations Lagged power observations == PowerWF1 PowerWF2 PowerWF3 data from WF2 Enc data from WF3 EncPWF1 EncPWF2 EncPWF3 Enc data from WF1 Enc data from WF2 M1 M2 M3 PowerWF2 Enc Coef WF1 Enc Coef WF2 Enc Coef WF3 Coef WF1 Coef WF2 Coef WF3 . . . . . Coefficients EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING (1) Power data encryption with linear algebra (M: random matrix – unknown but built by all agents)
- 19. RES Collaborative Forecasting Privacy-preserving Protocol 19 Encrypted data == data from WF3 PowerWF1 PowerWF2 PowerWF3 data from WF1 data from WF2 data from WF3 PowerWF1 PowerWF2 PowerWF3 data from WF1 data from WF2 Power for multiple locations Lagged power observations == PowerWF1 PowerWF2 PowerWF3 data from WF2 Enc data from WF3 EncPWF1 EncPWF2 EncPWF3 Enc data from WF1 Enc data from WF2 PowerWF1 Q1 Private matrix for encrypting coefficients from WF1 Enc Coef WF1 Enc Coef WF2 Enc Coef WF3 Coef WF1 Coef WF2 Coef WF3 . M1 M2 M3. . . . . Coefficients EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING (2) Coefficients encryption with linear algebra (Q: random matrix – own by each agent)
- 20. RES Collaborative Forecasting Privacy-preserving Protocol 20 Encrypted data == data from WF3 PowerWF1 PowerWF2 PowerWF3 data from WF1 data from WF2 data from WF3 PowerWF1 PowerWF2 PowerWF3 data from WF1 data from WF2 Power for multiple locations Lagged power observations == PowerWF1 PowerWF2 PowerWF3 data from WF2 Enc data from WF3 EncPWF1 EncPWF2 EncPWF3 Enc data from WF1 Enc data from WF2 Q2 PowerWF2 Private matrix for encrypting coefficients from WF2 Enc Coef WF1 Enc Coef WF2 Enc Coef WF3 Coef WF1 Coef WF2 Coef WF3 M1 M2 M3. . . . .. Coefficients EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING (2) Coefficients encryption with linear algebra (Q: random matrix – own by each agent)
- 21. RES Collaborative Forecasting Privacy-preserving Protocol 21 Encrypted data Distributed computation == data from WF3 PowerWF1 PowerWF2 PowerWF3 data from WF1 data from WF2 data from WF3 PowerWF1 PowerWF2 PowerWF3 data from WF1 data from WF2 Power for multiple locations Lagged power observations == PowerWF1 PowerWF2 PowerWF3 EncPWF1 EncPWF2 EncPWF3 Enc Coef WF1 Enc Coef WF2 Enc Coef WF3 Enc data from WF3 Enc data from WF1 Enc data from WF2 Enc Coef WF1 Enc Coef WF2 Enc Coef WF3 Coef WF1 Coef WF2 Coef WF3 .. Coefficients EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING (3) Distributed computation of coefficients with ADMM
- 22. RES Collaborative Forecasting Privacy-preserving Protocol 22 Encrypted data Distributed computation == data from WF3 PowerWF1 PowerWF2 PowerWF3 data from WF1 data from WF2 data from WF3 PowerWF1 PowerWF2 PowerWF3 data from WF1 data from WF2 Power for multiple locations Lagged power observations == PowerWF1 PowerWF2 PowerWF3 EncPWF1 EncPWF2 EncPWF3 Original coefficients Enc Coef WF2 Enc Coef WF3 Q1 Q2 Q3 Enc Coef WF1 Enc Coef WF2 Enc Coef WF3 Enc data from WF3 Enc data from WF1 Enc data from WF2 Enc Coef WF1 Enc Coef WF1 Enc Coef WF2 Enc Coef WF3 Coef WF1 Coef WF2 Coef WF3 .. . . . Coefficients EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING (4) Obtain original coefficients with Q matrix (same coefficients with privacy protocol)
- 23. RES Collaborative Forecasting Communication Schemes 23 Agents (RES Producers) Central node (hub) Centralized Model Peer-to-Peer Model EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING
- 24. RES Collaborative Forecasting Communication Schemes 24 Step 1. Agents compute their encrypted coefficients 𝐸𝑛𝑐 𝐶𝑜𝑒𝑓 𝑗 𝑘+1 = 𝑔( ), , Conciliation k EncPj Enc data from agent 𝒋 Initialized with zeros Centralized Model Peer-to-Peer Model EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING
- 25. RES Collaborative Forecasting Communication Schemes 25 Centralized Model Peer-to-Peer Model Step 2. Agents share their encrypted contribution 𝑬𝒏𝒄 𝑪𝒐𝒆𝒇 𝑾𝑭𝒋 𝒌 Enc data from agent 𝒋 . EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING
- 26. 𝑗 RES Collaborative Forecasting Communication Schemes 26 Centralized Model Peer-to-Peer Model Step 3. Computation of Conciliation k+1 , … 𝑬 𝒏𝒄 𝑪𝒐𝒆𝒇 𝑾𝑭𝒋 𝒌 Enc data from agent 𝒋 = ℎ . EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING
- 27. RES Collaborative Forecasting Communication Schemes 27 Centralized Model Peer-to-Peer Model Step 4. Agents obtain conciliation matrix and proceed to Step 1 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING
- 28. RES Collaborative Forecasting Results for Évora PV Dataset EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 28 Asynchronous communication: equal failure probabilities are assumed for all agents ❑ Better performance of the P2P scheme ▪ Centralized: if one agent fails the algorithm proceeds without its information ▪ P2P: agent communicates its contribution to some peers → probability of information lost is smaller ❑ Computational performance ▪ Privacy protocol: 65.5s ▪ 0.05s (centralized) and 0.12s (P2P) for model fitting ÉVORA 44 Domestic PV
- 29. RES Collaborative Forecasting Results for Évora PV Dataset EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 29 RMSE improvement of Privacy-VAR over AR (autoregressive) & Analogs search (collaborative w/ privacy) Some data owners contribute to improve competitors’ forecast without getting the same benefit (error improvement) ↓ Even if privacy is ensured, such agents may be unwilling to collaborate → data monetization (data markets)
- 30. Data Markets 30
- 31. Data Market Basics • Market components • Key players • Data buyer(s) • Data seller(s) • Monetary Values • Seller’s cost of offering data • Buyer’s profit • Data payment • Market procedure 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 31
- 32. Data Market Basics • Market components • Key players • Data buyer(s) B with known data 𝐷 • Data seller(s) • Monetary Values • Seller’s cost of offering data • Buyer’s profit 𝐹(𝐷) • Data payment • Market procedure 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 32 B 𝐷 𝐹(𝐷) Buyer profit 0
- 33. Data Market Basics • Market components • Key players • Data buyer(s) B with known data 𝐷 • Data seller(s) S with data ∆𝐷 • Monetary Values • Seller’s cost of offering data • Buyer’s profit 𝐹(𝐷) • Data payment • Market procedure 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 33 BS ∆𝐷 𝐷 𝐹(𝐷) profit BuyerSeller 0
- 34. Data Market Basics • Market components • Key players • Data buyer(s) B with known data 𝐷 • Data seller(s) S with data ∆𝐷 • Monetary Values • Seller’s cost of offering data • Buyer’s profit 𝐹 𝐷 • Data payment • Market procedure 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 34 BS ∆𝐷 𝐷 𝐹(𝐷) profit BuyerSeller 0
- 35. Data Market Basics • Market components • Key players • Data buyer(s) B with known data 𝐷 • Data seller(s) S with data ∆𝐷 • Monetary Values • Seller’s cost of offering data • Buyer’s profit 𝐹(𝐷) < 𝐹(𝐷 + ∆𝐷) • Data payment • Market procedure 1) Buyer can profit from seller’s data 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 35 BS ∆𝐷 𝐷 𝐹(𝐷) 𝐹 𝐷 + ∆𝐷 ∆𝐹 profit BuyerSeller 0 *Note: the use of data is not necessarily volumetric!
- 36. Data Market Basics • Market components • Key players • Data buyer(s) B with known data 𝐷 • Data seller(s) S with data ∆𝐷 • Monetary Values • Seller’s cost of offering data C(∆𝐷) • Buyer’s profit 𝐹(𝐷) < 𝐹(𝐷 + ∆𝐷) • Data payment • Market procedure 1) Buyer can profit from seller’s data 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 36 BS 𝐶 ∆𝐷 𝐷 𝐹(𝐷) 𝐹 𝐷 + ∆𝐷 * ∆𝐹 𝐶 profit BuyerSeller 0 *Note: the use of data is not necessarily volumetric!
- 37. Data Market Basics • Market components • Key players • Data buyer(s) B with known data 𝐷 • Data seller(s) S with data ∆𝐷 • Monetary Values • Seller’s cost of offering data C(∆𝐷) • Buyer’s profit 𝐹(𝐷) < 𝐹(𝐷 + ∆𝐷) • Data payment 𝑅 < ∆𝐹 = 𝐹 𝐷 + ∆𝐷 − 𝐹(𝐷) • Market procedure 1) Buyer can profit from seller’s data 2) Buyer offers seller monetary rewards 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 37 BS 𝑅 𝐶 ∆𝐷 𝐷 𝐹(𝐷) 𝐹 𝐷 + ∆𝐷 * ∆𝐹 𝑅 𝐶 profit BuyerSeller 0 ? *Note: the use of data is not necessarily volumetric!
- 38. Data Market Basics • Market components • Key players • Data buyer(s) B with known data 𝐷 • Data seller(s) S with data ∆𝐷 • Monetary Values • Seller’s cost of offering data C(∆𝐷) • Buyer’s profit 𝐹(𝐷) < 𝐹(𝐷 + ∆𝐷) • Data payment 𝑅 < ∆𝐹 = 𝐹 𝐷 + ∆𝐷 − 𝐹(𝐷) • Market procedure 1) Buyer can profit from seller’s data 2) Buyer offers seller monetary rewards 3) Seller either rejects or accepts offer 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 38 BS 𝑅 𝐶 ∆𝐷 If R < 𝐶 × 𝐷 𝐹(𝐷) 𝐹 𝐷 + ∆𝐷 * ∆𝐹 𝑅 𝐶 𝑅 Reject offer ×profit BuyerSeller 0 *Note: the use of data is not necessarily volumetric!
- 39. Data Market Basics • Market components • Key players • Data buyer(s) B with known data 𝐷 • Data seller(s) S with data ∆𝐷 • Monetary Values • Seller’s cost of offering data C(∆𝐷) < 𝑅 • Buyer’s profit 𝐹(𝐷) < 𝐹(𝐷 + ∆𝐷) • Data payment 𝑅 < ∆𝐹 = 𝐹 𝐷 + ∆𝐷 − 𝐹(𝐷) • Market procedure 1) Buyer can profit from seller’s data 2) Buyer offers seller monetary rewards 3) Seller either rejects or accepts offer 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 39 BS 𝑅 𝐶 Requirement: 𝐶 < 𝑅 ∆𝐷 √ 𝐷 𝐹(𝐷) 𝐹 𝐷 + ∆𝐷 * ∆𝐹 𝑅 𝑅𝐶 Accept offer √ profit BuyerSeller 0 *Note: the use of data is not necessarily volumetric!
- 40. Data Market Basics • Market components • Key players • Data buyer(s) B with known data 𝐷 • Data seller(s) S with data ∆𝐷 • Monetary Values • Seller’s cost of offering data C(∆𝐷) < 𝑅 • Buyer’s profit 𝐹(𝐷) < 𝐹(𝐷 + ∆𝐷) • Data payment 𝑅 < ∆𝐹 = 𝐹 𝐷 + ∆𝐷 − 𝐹(𝐷) • Market procedure 1) Buyer can profit from seller’s data 2) Buyer offers seller monetary rewards 3) Seller either rejects or accepts offer 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 40 BS 𝑅 𝐶 Requirement: 𝐶 < 𝑅 ∆𝐷 (𝐶 includes the value of privacy) √ 𝐷 𝐹(𝐷) 𝐹 𝐷 + ∆𝐷 * ∆𝐹 𝑅 𝑅𝐶 Accept offer √ profit BuyerSeller 0 *Note: the use of data is not necessarily volumetric!
- 41. Data Market Basics • Market components • Key players • Data buyer(s) B with known data 𝐷 • Data seller(s) S with data ∆𝐷 • Monetary Values • Seller’s cost of offering data 𝐂(∆𝑫) < 𝑹 • Buyer’s profit 𝑭(𝑫) < 𝑭(𝑫 + ∆𝑫) • Data payment 𝑹 < ∆𝑭 = 𝑭 𝑫 + ∆𝑫 − 𝑭(𝑫) • Market procedure 1) Buyer can profit from seller’s data 2) Buyer offers seller monetary rewards 3) Seller either rejects or accepts offer 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 41 BS 𝑅 𝐶 Requirement: 𝐶 < 𝑅 ∆𝐷 𝐷 𝐹(𝐷) 𝐹 𝐷 + ∆𝐷 * ∆𝐹 𝑅 𝐶 𝑅 Seller Payoff = 𝑅 − 𝐶 Buyer Payoff = ∆𝐹 − 𝑅 profit Seller Buyer (𝐶 includes the value of privacy) 0 *Note: the use of data is not necessarily volumetric!
- 42. B Data Market Models BS B 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 42 𝑅 ∆𝐷 Data Seller(s) Data Buyer(s) Data Market Models: S S 𝐶 𝐷 MoneyData
- 43. B Data Market Models BS B 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 43 Data Market Models: • Monopolistic Data Seller Data Seller(s) Data Buyer(s) MoneyData
- 44. Data Market Models BS 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 44 Data Seller(s) Data Buyer(s) S S Data Market Models: • Monopolistic Data Seller • Monopolistic Data Buyer MoneyData
- 45. B Data Market Models B S S S B 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 45 Data Market Models: • Monopolistic Data Seller • Monopolistic Data Buyer • Peer-to-Peer Multi-Seller Multi-Buyer Data Seller(s) Data Buyer(s) MoneyData
- 46. B Data Market Models B S S S B 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 46 Data Market Models: • Monopolistic Data Seller • Monopolistic Data Buyer • Peer-to-Peer Multi-Seller Multi-Buyer • Centralized Model Data Seller(s) Data Buyer(s) MoneyData Central Data Platform
- 47. B Data Market Models B S S S B 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 47 Data Market Models: • Monopolistic Data Seller • Monopolistic Data Buyer • Peer-to-Peer Multi-Seller Multi-Buyer • Centralized Model Data Seller(s) Data Buyer(s) MoneyData Central Data Platform How can this be applied to Smart4RES in an energy market?
- 48. B Data Market Models B S S S B 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 48 Data Market Models: • Monopolistic Data Seller • Monopolistic Data Buyer • Peer-to-Peer Multi-Seller Multi-Buyer • Centralized Model Data Seller(s) Data Buyer(s) MoneyData Central Data Platform Similar to an energy wholesale market! How can this be applied to Smart4RES in an energy market?
- 49. R Energy-Data Market R G G G Generators Electric Energy Pool R Retailers Energy Wholesale Market 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 49 MoneyData Energy
- 50. R Energy-Data Market R C C C G G G Generators End-Customers Electric Energy Pool R Retailers Energy Wholesale Market Energy Retail Market 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 50 MoneyData Energy
- 51. R Energy-Data Market R C C C G G G Generators End-Customers Electric Energy Pool R Retailers 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 51 MoneyData Energy
- 52. R Energy-Data Market R C C C G G G Generators End-Customers Electric Energy Pool R Retailers 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 52 MoneyData Energy e.g., RES forecasting 0 Gen(MW) Fore -cast Real -time 𝐺 Generator
- 53. R Energy-Data Market R C C C G G G Generators End-Customers Electric Energy Pool R Retailers 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 53 MoneyData Energy e.g., customer load forecasting e.g., RES forecasting 0 Gen(MW) Fore -cast Real -time 𝐺 Generator 0 Load(MW) Fore -cast Real -time 𝐿 Retailer
- 54. R Energy-Data Market R C C C G G G Generators End-Customers Electric Energy Pool R Retailers 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 54 MoneyData Energy e.g., customer load forecasting e.g., RES forecasting 0 Gen(MW) Fore -cast Real -time 𝐺 𝐺 Generator ∆𝐺 0 Load(MW) Fore -cast Real -time 𝐿 𝐿 Retailer ∆𝐿
- 55. R Energy-Data Market R C C C G G G Generators End-Customers Electric Energy Pool R Retailers 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 55 MoneyData Energy e.g., customer load forecasting e.g., RES forecasting Reduce cost of real-time energy imbalance 0 Gen(MW) Fore -cast Real -time 𝐺 𝐺 Generator Imbalance Cost = priceimb × ∆𝐺 ∆𝐺 0 Load(MW) Fore -cast Real -time 𝐿 𝐿 Retailer Imbalance Cost = priceimb × ∆𝐿 ∆𝐿
- 56. R Energy-Data Market R C C C G G G Generators End-Customers Electric Energy Pool R Retailers 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 56 MoneyData Energy e.g., customer load forecasting e.g., RES forecasting
- 57. R Energy-Data Market R G G G Generators End-Customers Gen-Side Data Pool Electric Energy Pool R Retailers Data Seller Data Buyer Data Seller/Buyer 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 57 Data + Money MoneyData Energy C C C Con-Side Data Pool X Privacy Concerns e.g., customer load forecasting e.g., RES forecasting
- 58. R Re.g., RES forecasting Energy-Data Market G G G Generators Gen-Side Data Pool Data Seller Data Buyer Data Seller/Buyer 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 58 Data + Money MoneyData Energy R Electric Energy Pool End-Customers Retailers C C C Con-Side Data Pool X e.g., customer load forecasting Privacy Concerns Con-Side Data Market
- 59. R Re.g., RES forecasting Energy-Data Market G G G Generators Gen-Side Data Pool Data Seller Data Buyer Data Seller/Buyer 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 59 Data + Money MoneyData Energy R Electric Energy Pool End-Customers Retailers C C C Con-Side Data Pool X e.g., customer load forecasting Privacy Concerns Con-Side Data Market L. Han, J. Kazempour, P. Pinson, “Monetizing Customer Load Data for an Energy Retailer: A Cooperative Game Approach,” arXiv e-prints, 2020.
- 60. e.g., customer load forecasting R Energy-Data Market R R Data Seller Data Buyer Data Seller/Buyer 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 60 Data + Money MoneyData Energy End-Customers Retailers C C C Con-Side Data Pool X Privacy Concerns Electric Energy Pool G G G Generators Gen-Side Data Pool e.g., RES forecasting Gen-Side Data Market
- 61. RES in a Wholesale Market: 2 Agents Electric Energy Pool 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 61
- 62. RES in a Wholesale Market: 2 Agents Electric Energy Pool 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 62
- 63. RES in a Wholesale Market: 2 Agents Electric Energy Pool 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 63 Problem 1. RES generation uncertainty leads to imbalance costs
- 64. RES in a Wholesale Market: 2 Agents Electric Energy Pool 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 64 Problem 1. RES generation uncertainty leads to imbalance costs
- 65. RES in a Wholesale Market: 2 Agents Electric Energy Pool 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 65 Problem 1. RES generation uncertainty leads to imbalance costs Step 1) Data from neighboring plants can help improve forecasts, reducing imbalance costs
- 66. RES in a Wholesale Market: 2 Agents Electric Energy Pool 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 66 Problem 1. RES generation uncertainty leads to imbalance costs Step 1) Data from neighboring plants can help improve forecasts, reducing imbalance costs Problem 2. Potential loss of privacy and competitiveness
- 67. RES in a Wholesale Market: 2 Agents Electric Energy Pool 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 67 Problem 1. RES generation uncertainty leads to imbalance costs Step 1) Data from neighboring plants can help improve forecasts, reducing imbalance costs Problem 2. Potential loss of privacy and competitiveness Step 2). Buyer offers data payment
- 68. RES in a Wholesale Market: 2 Agents Electric Energy Pool 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 68 Step 2). Buyer offers data payment Problem 2. Potential loss of privacy and competitiveness Problem 1. RES generation uncertainty leads to imbalance costs Step 3). Seller accepts payment Step 1) Data from neighboring plants can help improve forecasts, reducing imbalance costs
- 69. RES in a Wholesale Market: 2 Agents Electric Energy Pool 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 69 Outcome 1. Higher profit for data buyer, lower cost for energy supply Problem 1. RES generation uncertainty leads to imbalance costs Step 1) Data from neighboring plants can help improve forecasts, reducing imbalance costs Outcome 2. Higher profit for data seller Step 2). Buyer offers data payment Problem 2. Potential loss of privacy and competitiveness Step 3). Seller accepts payment
- 70. RES in a Wholesale Market: 2 Agents Electric Energy Pool Outcome 2. Higher profit for data seller Outcome 1. Higher profit for data buyer, lower cost for energy supply HIGHER TOTAL SOCIAL WELFARE 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 70 Problem 1. RES generation uncertainty leads to imbalance costs Step 2). Buyer offers data payment Problem 2. Potential loss of privacy and competitiveness Step 3). Seller accepts payment Step 1) Data from neighboring plants can help improve forecasts, reducing imbalance costs
- 71. RES in a Wholesale Market: Many Agents Electric Energy Pool ... 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 71
- 72. RES in a Wholesale Market: Many Agents Electric Energy Pool ... Payment 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 72 Architecture 1: Peer-to-Peer Model
- 73. RES in a Wholesale Market: Many Agents Electric Energy Pool ... Data Pool 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 73 Architecture 2: Centralized Model
- 74. RES in a Wholesale Market: Many Agents Electric Energy Pool ... Data Pool 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 74 Architecture 2: Centralized Model C. Gonçalves, P. Pinson, R.J. Bessa, “Towards data markets in renewable energy forecasting,” IEEE Trans. on Sust. Energy, In Press, 2020
- 75. • The cost of privacy is highly individual and difficult to quantify. As a result, the value of privacy preserving techniques is difficult to quantify as well. • Data is a unique commodity. The table below compares data and energy*. Challenges Market Production and Replication Value to Buyer Pricing Energy Produced at a certain cost, non- replicable Additive and known Decided a priori Data Usually a side-product that is produced at zero marginal costs, Replicable at no extra costs Combinatorial: the value of a dataset is dependent on all other available data. Dependent on buyer’s valuation of the dataset with a certain prediction task *Concepts from publication: A. Agarwal, M. Dahleh, and T. Sarkar, “A marketplace for data: An algorithmic solution,” ACM EC 2019 - Proceedings of the 2019 ACM Conference on Economics and Computation, pp. 701–726, 2019. 17/12/2020 EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 75
- 76. Conclusion 17/12/2020 76EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING
- 77. Take-Away Messages & Smart4RES Ongoing Research • Collaborative learning improves forecast accuracy, which may yield additional individual or societal value in the market. • Monetizing data promotes data exchanging by redistributing the added value, helping to address concerns about loss of privacy and competitiveness. Smart4RES is planning to • Design a suitable marketplace for data trading; • Develop relevant data market concepts and create prototypes to foster awareness to the value of data markets; • Extend the concept to different use cases from the energy sector; • Collaborate with other domains, such as IoT, blockchain technologies, etc. 77EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING17/12/2020
- 78. References • C. Gonçalves, P. Pinson, R.J. Bessa, “Towards data markets in renewable energy forecasting,” IEEE Trans. on Sust. Energy, In Press, 2020. • C. Gonçalves, R.J. Bessa, P. Pinson, "Privacy-preserving distributed learning for renewable energy forecasting," Working Paper, 2020. • C. Gonçalves, R.J. Bessa, P. Pinson, “A critical overview of privacy-preserving approaches for collaborative forecasting,” Int. J. of Forecasting, vol. 37(1), pp. 322-342, Jan-Mar 2021 • L. Han, J. Kazempour, P. Pinson, “Monetizing Customer Load Data for an Energy Retailer: A Cooperative Game Approach,” arXiv e-prints, 2020. • A. Agarwal, M. Dahleh, and T. Sarkar, “A marketplace for data: An algorithmic solution,” ACM EC 2019 - Proceedings of the 2019 ACM Conference on Economics and Computation, pp. 701–726, 2019. • BESSA, Ricardo Jorge, GONÇALVES, Carla. “Geographically distributed solar power time series [dataset].” 10 September 2020. INESC TEC research data repository. https://doi.org/10.25747/gywm-9457 78EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING17/12/2020
- 79. Smart4RES webinar series EXTRACTING VALUE FROM DATA SHARING FOR RES FORECASTING 79 Season1: Towards a new Standard for the entire RES forecasting value chain 17/12/2020 To stay in touch https://bit.ly/2Ta wgn3
- 80. Thank you Ricardo Jorge Bessa, INESC TEC ricardo.j.bessa@inesctec.pt Liyang Han, DTU liyha@elektro.dtu.dk info@smart4res.eu For more information www.smart4res.eu Follow us on our social media
- 81. COPYRIGHT (except initial slides on ISGAN) Smart4RES H2020 Project Next Generation Modelling and Forecasting of Variable Renewable Generation for Large-scale Integration in Energy Systems and Markets This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864337. DISCLAIMER The present presentation reflects only the authors’ view. The European Commission or the European Innovation and Networks Executive Agency (INEA) is not responsible for any use that may be made of the information it contains. PROJECT COORDINATOR & CONTACTS Georges Kariniotakis, ARMINES/MINES Paris Tech, Centre PERSEE, Sophia-Antipolis France. info@smart4res.eu