Energy Storage and DER Aggregators: Energy Arbitrage, Retail Market - PowerPoint PPT Presentation

Market Participation of Energy Storage and DER Aggregators: Energy Arbitrage, Retail Market Design, and Electricity Price Forecasting Meng Wu Arizona State University (mwu@asu.edu) PSERC Webinar March 31, 2020 1

Acknowledgements PSERC Support • M-41 [Ongoing]: The Stacked Value of Battery Energy Storage Systems (BESSs) • M-42 [Starting Soon]: Modeling and Coordinating DERs in Power Systems and Markets ASU Students Reza Khalili Senobari Mohammad Mousavi Zhongxia Zhang Battery Operation + Planning Market Design + DER Aggregators Machine Learning + Price Forecasting Summer Intern @ Dominion Available for Summer Intern 2

Challenges & Opportunities Batteries Wholesale Market DER Aggregators • Source: AutoGrid • Source: Greentech Media • Source: PJM Market Operation Market Participation • Energy arbitrage behavior of batteries? • Market bidding/offering strategies? • Forecast Electricity price? • Batteries’ impact on market operation? • Offer multiple services? • Coordinate T&D, DER aggregators, and DERs? 3

Proposed Solutions Market + Batteries : Optimal Battery Participation in Energy & Ancillary Services Markets Market + DER Aggregators : A DSO Design for Wholesale & Retail Markets with DER Aggregators Market Participation : Machine Learning for System-Wide Electricity Price Forecasting 4

Proposed Solutions Market + Batteries : Optimal Battery Participation in Energy & Ancillary Services Markets Market + DER Aggregators : A DSO Design for Wholesale & Retail Markets with DER Aggregators Market Participation : Machine Learning for System-Wide Electricity Price Forecasting 5

Background & Motivation ❖ Understand the role of utility-scale batteries in daily system operations and economics Sustainability Policy Technology • CO 2 Reduction • FERC Order 841 • BESS: fast ramping, • Renewables • BESS → Markets multiple services 6

Background & Motivation The Role of Utility-Scale Batteries in System Operations & Economics • The impact of utility-scale batteries on daily market operations • Utility-scale batteries’ capability of multiple services provision (energy arbitrage, spinning reserve, frequency regulation services, etc.) • Operating patterns of merchant batteries in energy, reserve, and pay-as-performance regulation markets • Interaction between battery owner’s profit maximization strategies and system operator’s joint operating cost minimization activities (via the market clearing process) Bi-Level Optimization: Battery Owner & System Operator 7

Problem Formulation: Bi-Level Optimization Framework • Upper-level Problem: Battery owner’s profit maximization from real-time energy, reserve, and pay-as-performance regulation markets • Lower-level Problem: System operator’s joint market clearing process for real-time energy, reserve, and pay-as-performance regulation markets 8

The Upper-Level Problem 𝐶,𝑇 − 𝑄 𝐶,𝐸 + 𝜌 𝑢 𝐹 𝑄 𝐶,𝑆𝑡 𝑆𝑡 𝑄 𝜌 𝑗,𝑢 • Upper-Level Objective: Battery owner’s profit 𝑗,𝑢 𝑗,𝑢 𝑗,𝑢 𝑛𝑏𝑦 ෍ ෍ ∆𝑢 𝑆𝑕𝐷 𝑄 𝐶,𝑆𝑕𝐷 +𝜌 𝑢 𝑆𝑕𝑁 𝑄 𝐶,𝑆𝑕𝑁 maximization from real-time energy, reserve, +𝜌 𝑢 𝑗,𝑢 𝑗,𝑢 𝑢∈𝑈 𝑗∈𝐶 and pay-as-performance regulation markets Subject to: 𝐹,𝑇 ≤ 𝑣 𝑗 𝑄 𝑗 𝑆𝑏𝑢𝑓 0 ≤ 𝑅 𝑗,𝑢 • Constraints-1: Battery output power limits 𝐹,𝐸 ≤ 1 − 𝑣 𝑗 𝑄 𝑗 𝑆𝑏𝑢𝑓 0 ≤ 𝑅 𝑗,𝑢 𝑆𝑡 ≤ 𝑄 𝑗 𝑆𝑏𝑢𝑓 0 ≤ 𝑅 𝑗,𝑢 𝑆𝑕𝐷 ≤ 𝑄 𝑗 𝑆𝑏𝑢𝑓 0 ≤ 𝑅 𝑗,𝑢 𝐶,𝑆𝑕𝐷 ≤ 𝑄 𝐶,𝐸 − 𝑄 𝐶,𝑇 − 𝑄 𝐶,𝑆𝑡 ≤ 𝑄 𝑗 𝑆𝑏𝑢𝑓 + 𝑄 𝑆𝑏𝑢𝑓 − 𝑄 𝐶,𝑆𝑕𝐷 −𝑄 𝑗 𝑗,𝑢 𝑗,𝑢 𝑗,𝑢 𝑗,𝑢 𝑗,𝑢 𝐶,𝑇 − 𝑄 𝐶,𝐸 ∆𝑢 𝐽𝑜𝑗𝑢 + σ 𝑙=1 𝑢 𝑇𝑃𝐷 𝑗,𝑢 = 𝑇𝑃𝐷 𝑗 𝑄 • Constraints-2: Battery state of charge (SOC) 𝑗,𝑙 𝑗,𝑙 𝐶,𝑆𝑕𝐷 ∆𝑢 ≤ 𝑇𝑃𝐷 𝑗,𝑢 ≤ 𝑇𝑃𝐷 𝑗 limits 𝐶,𝑆𝑡 + 𝑄 𝑁𝑗𝑜 + 𝑄 𝑁𝑏𝑦 − 𝑄 𝐶,𝑆𝑕𝐷 ∆𝑢 𝑇𝑃𝐷 𝑗 𝑗,𝑢 𝑗,𝑢 𝑗,𝑢 9

The Lower-Level Problem 𝐻,𝑇 + 𝛽 𝑘,𝑢 𝐻,𝑆𝑡 + 𝐹,𝑇 𝑄 𝑆𝑡 𝑄 𝛽 𝑘,𝑢 𝑘,𝑢 𝑘,𝑢 • Lower-Level Objective: System operator’s joint ෍ 𝐻,𝑆𝑕𝐷 + 𝛽 𝑘,𝑢 𝑆𝑕𝐷 𝑄 𝑆𝑕𝑁 𝑄 𝐻,𝑆𝑕𝑁 𝛽 𝑘,𝑢 market clearing process for real-time energy, reserve, 𝑘,𝑢 𝑘,𝑢 𝑘∈𝐻 𝑛𝑏𝑦 ෍ ∆𝑢 𝐶,𝑇 − 𝛾 𝑗,𝑢 𝐶,𝐸 + 𝛾 𝑗,𝑢 and pay-as-performance regulation markets 𝐹,𝑇 𝑄 𝐹,𝐸 𝑄 𝐶,𝑆𝑡 𝑆𝑡 𝑄 𝛾 𝑗,𝑢 𝑗,𝑢 𝑗,𝑢 𝑗,𝑢 𝑢∈𝑈 ෍ 𝑆𝑕𝐷 𝑄 𝐶,𝑆𝑕𝐷 +𝛾 𝑗,𝑢 𝑆𝑕𝑁 𝑄 𝐶,𝑆𝑕𝑁 +𝛾 𝑗,𝑢 𝑗,𝑢 𝑗,𝑢 𝑗∈𝐶 Subject to: • Constraints-1: Operating limits of batteries 𝐶,𝑇 ≤ 𝑅 𝑗,𝑢 𝐹,𝑇 0 ≤ 𝑄 𝑗,𝑢 𝐶,𝐸 ≤ 𝑅 𝑗,𝑢 𝐹,𝐸 0 ≤ 𝑄 𝑗,𝑢 𝐶,𝑆𝑡 ≤ 𝑅 𝑗,𝑢 𝑆𝑡 0 ≤ 𝑄 𝑗,𝑢 𝐶,𝑆𝑕𝐷 ≤ 𝑅 𝑗,𝑢 𝑆𝑕𝐷 0 ≤ 𝑄 𝑗,𝑢 𝐻,𝑆𝑕𝐷 ≤ 𝑄 𝐻,𝑇 ≤ 𝑄 𝐻,𝑆𝑡 − 𝑄 𝑁𝑗𝑜 + 𝑄 𝑁𝑏𝑦 − 𝑄 𝐻,𝑆𝑕𝐷 𝑄 𝑘 𝑘 𝑘,𝑢 𝑘,𝑢 𝑘,𝑢 𝑘,𝑢 𝐻,𝑆𝑡 ≤ 𝑄 𝑆𝑡,𝑠𝑏𝑛𝑞 • Constraints-2: Operating limits of generators 0 ≤ 𝑄 𝑘,𝑢 𝑘 𝐻,𝑆𝑕𝐷 ≤ 𝑄 𝑆𝑕,𝑠𝑏𝑛𝑞 0 ≤ 𝑄 𝑘,𝑢 𝑘 10

The Lower-Level Problem 𝐻,𝑇 + 𝛽 𝑘,𝑢 𝐻,𝑆𝑡 + 𝐹,𝑇 𝑄 𝑆𝑡 𝑄 𝛽 𝑘,𝑢 𝑘,𝑢 𝑘,𝑢 • Lower-Level Objective: System operator’s joint ෍ 𝐻,𝑆𝑕𝐷 + 𝛽 𝑘,𝑢 𝑆𝑕𝐷 𝑄 𝑆𝑕𝑁 𝑄 𝐻,𝑆𝑕𝑁 𝛽 𝑘,𝑢 market clearing process for real-time energy, reserve, 𝑘,𝑢 𝑘,𝑢 𝑘∈𝐻 𝑛𝑏𝑦 ෍ ∆𝑢 𝐶,𝑇 − 𝛾 𝑗,𝑢 𝐶,𝐸 + 𝛾 𝑗,𝑢 and pay-as-performance regulation markets 𝐹,𝑇 𝑄 𝐹,𝐸 𝑄 𝐶,𝑆𝑡 𝑆𝑡 𝑄 𝛾 𝑗,𝑢 𝑗,𝑢 𝑗,𝑢 𝑗,𝑢 𝑢∈𝑈 ෍ 𝑆𝑕𝐷 𝑄 𝐶,𝑆𝑕𝐷 +𝛾 𝑗,𝑢 𝑆𝑕𝑁 𝑄 𝐶,𝑆𝑕𝑁 +𝛾 𝑗,𝑢 𝑗,𝑢 𝑗,𝑢 𝑗∈𝐶 Subject to: 𝐻,𝑆𝑕𝐷 ≤ 𝑄 𝐻,𝑆𝑕𝑁 ≤ 𝑛 𝑘 𝑄 • Constraints-3: Operating constraints of 𝐻,𝑆𝑕𝐷 𝑄 𝑘,𝑢 𝑘,𝑢 𝑘,𝑢 pay-as-performance regulation markets 𝐶,𝑆𝑕𝐷 ≤ 𝑄 𝐶,𝑆𝑕𝑁 ≤ 𝑛 𝑗 𝑄 𝐶,𝑆𝑕𝐷 𝑄 𝑗,𝑢 𝑗,𝑢 𝑗,𝑢 𝐶,𝑆𝑡 + σ 𝑘∈𝐻 𝑄 𝐻,𝑆𝑡 ≥ 𝑆 𝑢 𝑆𝑡 σ 𝑗∈𝐶 𝑄 • Constraints-4: System-wide reserve and 𝑗,𝑢 𝑘,𝑢 regulation requirements 𝐶,𝑆𝑕𝐷 + σ 𝑘∈𝐻 𝑄 𝐻,𝑆𝑕𝐷 ≥ 𝑆 𝑢 𝑆𝑕𝐷 σ 𝑗∈𝐶 𝑄 𝑗,𝑢 𝑘,𝑢 𝐶,𝑆𝑕𝑁 + σ 𝑘∈𝐻 𝑄 𝐻,𝑆𝑕𝑁 ≥ 𝑆 𝑢 𝑆𝑕𝑁 σ 𝑗∈𝐶 𝑄 𝑗,𝑢 𝑘,𝑢 𝐶,𝑇 − 𝑄 𝐶,𝐸 + σ 𝑘∈𝐻 𝑄 𝐻,𝑇 = 𝑄 𝑢 • Constraints-5: System power balance 𝑀𝑝𝑏𝑒 σ 𝑗∈𝐶 𝑄 𝑗,𝑢 𝑗,𝑢 𝑘,𝑢 11

Solution Procedure Convert Bi-Level Problem to Single-Level Problem • Lower-level problem: linear and convex • Solve lower-level problem via solving the KKT equations of the lower-level problem • Write KKT conditions of the Lower-level problem as constraints for the upper-level problem Single-Level Problem after Conversion 𝑪,𝑺𝒉𝑵 ∆𝒖 𝑪,𝑻 − 𝑸 𝒋,𝒖 𝑪,𝑬 + 𝝆 𝒖 𝑪,𝑺𝒕 + 𝝆 𝒖 𝑭 𝑸 𝒋,𝒖 𝑺𝒉𝑫 𝑸 𝒋,𝒖 𝑪,𝑺𝒉𝑫 +𝝆 𝒖 𝑺𝒉𝑵 𝑸 𝒋,𝒖 𝑺𝒕 𝑸 𝒋,𝒖 𝒏𝒃𝒚 ෍ ෍ 𝝆 𝒋,𝒖 𝒖∈𝑼 𝒋∈𝑪 𝒕. 𝒖. Battery power output limits Original Constraints of Upper-Level Problem Battery state of charge (SOC) limits KKT conditions of the lower-level problem 12

Case Study: Test System • Modified PJM 5-bus test system (Market clearing interval = 15 min; Simulation time = 24 hours) BESS Capacity: 400 MWh ; BESS Output Power limit: 40 MW • System’s Load: 1000 MW mapped on 2018 PJM load pattern • • System’s Spinning Reserve Requirements: 10% of load in each interval • System’s Regulation Capacity Requirements: 4% of load in each interval • System’s Regulation Mileage Requirements: 1.75 times regulation capacity requirements Peak Hours 13

Case Study Results [Case 1] Modeling Energy Market Only • Energy arbitrage between different market clearing intervals SOC max : 380 MWh Energy Market Revenue Battery State of Charge (SOC) Charge Discharge 14

Case Study Results [Case 2] Modeling Energy & Reserve Markets • Energy arbitrage between different market clearing intervals & between different markets • Energy arbitrage between different markets at the same market clearing interval (during charging period) • Lower state of charge (SOC) compared to Case 1 (with energy market only) Peak Hours SOC max : 265 MWh Energy Market Revenue Reserve Revenue Reserve Market In Charging Period Revenue Battery SOC Charge Discharge 15