Cornwall LEM Flexibility Market Platform
Thursday 19 November 2020 LEM Platform Overview
LEM trial models
Trials results Next steps and LEM platform solutionMilestones
Market clearing
LEM platform demo Cornwall Local Energy Market Platform Overview
• Part-funded by European Regional Development Fund, part-funded by Centrica • Cornwall an area with significant penetration of renewables and consequent grid management issues • The LEM Programme aims to: • Develop and demonstrate a highly automated and “production ready” market-based solution to DSO & TSO flexibility procurement. • Demonstrate a transparent means of coordinating DSO and ESO flexibility procurement. • Save at least 5,600 tonnes of greenhouse gas emissions per year by fostering the installation of domestic and commercial smart energy infrastructure in Cornwall.
Programme started in Q1 2017, will finish end of 2020.
Project partners An integrated auction platform to unlock flexibility across transmission and distribution
DSO TSO Balancing market Wholesale market
Buys from
DERMS DSO systems Cornwall Local Cornwall Energy Market Energy
Sells to (Aggregated / Direct)
Generation Flexible demand Storage / EVs LEM Platform Overview
LEM trial models
Trials results Next steps and LEM platform solutionMilestones
Market clearing
LEM platform demo Phase 1 & 2 Market models
Phase 1 Trial Phase 2 Trial Market Quote and Tender (one sided) Market Spot Market (two sided) Model Model • Electronic version of existing purchasing • Separate reservation and Utilisation auctions mechanism • Can reserve significantly in advance – reserved services • Asset reservation & dispatch confirmation must participate in utilisation auction close to delivery • Concurrent purchasing by ESO and DSO with grid secure • DSO places Flex request - providers respond optimisation and independent conflict avoidance with offers functions • DSO optimises the offers to accept and has • Price cap can be put in place where required due to lack advance visibility of prices of liquidity
Feature Phase 1 Trial Feature Phase 2 Trial Purchasers WPD as DSO only Purchasers DSO and ESO individually and concurrently
Optimisation Selection of offers by DNO – separate Excel Optimisation Optimised for all purchasers and providers - Solver based tool developed commercial optimisation tool Conflict management Not part of trial Conflict Management Built in to optimisation, avoid services that overload Flex provider capacity 11 Locations, capacity c. 25MW, varied during network the trial. Flex provider capacity 11 Locations, capacity c. 25MW, varied during the trial. Flex provider types Diesel generators, gas turbine, flow battery, Flex provider types As per Phase 1, - flow battery +ice manufacturer domestic battery clusters. When Sept – Dec 2019 When May – August 2019 Where 3 BSPs plus 3 primaries Where 4 BSPs plus 4 primaries Service types Pre-fault congestion management and post fault Service types Pre-fault congestion management or dispatch maintenance window extension 6 Auction Model Benefits
Benefits of the auction model • Allows for complex bids and ensures a better execution/matching of blocks • Allows generators to offer energy in consecutive hours taking into account technical and economic requirements • Enables T/D co-ordination • optimal allocation of network capacity • Increased market depth (likely due to not withholding capacity for potential price increases) • Lower barriers to entry (for small market players not equipped for continuous 24/7 trading) • Typically allows for a longer calculation time • Fallback and backup procedures are easier to implement LEM Platform Overview
LEM trial models
Trials results Next steps and LEM platform solutionMilestones
Market clearing
LEM platform demo Cornwall trial results Phase 1 Trial Results – Prices & Delivery
11.5 MW 12 Bids Registered 8 Contracts (16.5 MWh) 7 Sites / 17 Offers Locations
Average Offer Price Delivery Proportion per Event 14 3 12 10 2 8 6 1 4
Number of Number Occurrences 2 of Number Occurrences 0 0 220 300 305 320 600 0 to 0.2 0.2 to 0.4 0.4 to 0.6 0.6 to 0.8 0.8 to 1 Utilisation Price [£/MWh] [lower bound] Delivery Proportion Range
9 Cornwall trial results Phase 2 Trial Results – Prices & Delivery
30.91 MW 381 Bids Registered 77 Reserve 49 Utilisation Contracts Contracts (210 MWh) (100 MWh) 12 Sites / 107 Offers Locations
Average Utilisation Price per Contract Delivery Proportion per Event 12 25 10 20 8 15 6 10 4
2 5 Number of Number Occurrences Number of Number Occurrences 0 0 160 180 200 220 240 260 280 300 320 0 to 0.2 0.2 to 0.4 0.4 to 0.6 0.6 to 0.8 0.8 to 1 Utilisation Price [£/MWh] [lower bound] Delivery Proportion Range
10 Cornwall trial results Key Learning Points • Network Data (required improvements) • Network topology under abnormal running arrangements • Customer-to-network mapping • Constraint forecasts • Power flow analysis or power transfer distribution factors
• Market Design • Short-term vs long-term procurement • Need to address the imbalance effect on Balance Responsible Parties when providing a DSO service
• Performance Assessment • Reliability – Average 58.3% throughout the trials • Metering data resolution has little impact on results • Site vs Asset level metering data can significantly effect results
11 LEM Platform Overview
LEM trial models
Trials results Next steps and LEM Platform solutionMilestones
Market clearing
LEM platform demo Flexibility market functions The platform manages the end-to-end-process
Grid model import Bids and Auction Contracts Delivery Baselining Settlement offers
Resources registered End-to-end process
Grid model import Network Topology/Hierarchy • Radial network assumed for the trials
GSP
BSP
Primary Substations
Headroom Limits • “Headroom” represents the available capacity on the network • Specified per grid node • Per settlement period • For both demand and generation Flexibility market functions The platform manages the end-to-end-process
Grid model import Bids and Auction Contracts Delivery Baselining Settlement offers
Resources registered End-to-end process
Resources registered Sites Registered Assets Registered • MPAN’s used to verify location • Generation / Storage / DSR • Site associated to grid node • Technical parameters of the assets • Import / Export capacity • Ramp rates • Contact Information • Max / Min runtimes • Recovery time • Maximum energy Flexibility market functions The platform manages the end-to-end-process
Grid model import Bids and Auction Contracts Delivery Baselining Settlement offers
Resources registered End-to-end process
Bids and offers
Bids = Buy Orders Offers = Sell Orders • Time • Time • Volume (MW) • Volume (MW) • Location • Location • Price (£/MW/h) or (£/MWh) • Price (£/MW/h) • Min acceptance vol (MW) • Max energy (MWh) (opt.) (opt.) • Min activation time (opt.) • Time block (Boolean) (opt.) • Max activation time (opt.) • Min recovery time (opt.) • Min acceptance volume (MW) • Time block (Boolean) (opt.) • Ramping rate (kW/min) (up and down) (opt.) Flexibility market functions The platform manages the end-to-end-process
Grid model import Bids and Auction Contracts Delivery Baselining Settlement offers
Resources registered End-to-end process
Auction
Reserve and/or Energy Decreasing uncertainty on flexibility needs and Auction Schedule congestion states Uncertainty M-3 Reserve M-1 Reserve DSO LT auctions DA auctions ID market W-1 Reserve Congestion DA Utilisation management ID Utilisation and post-fault response Reserve Utilisation Utilisation Can easily add other auctions or ESO merge reserve + Balancing and utilisation Algorithm auctions congestion Reserve Utilisation Utilisation automatically management clears the market
✓ Fairness ✓ Transparency ✓ Max Social Welfare ✓ Robustness Flexibility market functions The platform manages the end-to-end-process
Grid model import Bids and Auction Contracts Delivery Baselining Settlement offers
Resources registered End-to-end process
Contracts
Compliant with Maximum asset constraints social welfare
Grid secure
T/D Co-ordination Flexibility market functions The platform manages the end-to-end-process
Grid model import Bids and Auction Contracts Delivery Baselining Settlement offers
Resources registered End-to-end process
Baselining
30-minute site level metering data uploaded
Based on the ISONE similar days approach • Calculate average generation/consumption for the ten previous similar days • Adjustment factor based on usage on the event day • Calculates the predicted generation/consumption over the event window Flexibility market functions The platform manages the end-to-end-process
Grid model import Bids and Auction Contracts Delivery Baselining Settlement offers
Resources registered End-to-end process
Settlement
Monthly settlement service • Delivery percentage calculated based on the predicted baseline and the actual metered data • Payment percentage calculated based on DP • Buyers are then invoiced and payment is dispersed to the sellers User Interface and API’s
Reduce barriers to entry • Easy to use web based User Interface allows small and less sophisticated participants to enter
Complex functionality • Tried to keep the complex functionality required for a market • Sophisticated participants can automate systems by connecting directly with our API. LEM Platform Overview
LEM trial models
Trials results Next steps and LEM platform solutionMilestones
Market clearing
LEM platform demo N-SIDE is providing the Clearing Algorithm
Sell Buy Advance Analytic
Orders Books price Demand
cleared price
Supply cleared quantity quantity
Volumes Prices (MegaWatt) (pounds) Our mathematical model is based on demand and supply curves
Demand curve price • Relation between the price and the quantity that the buyers want to get. Demand • This function is decreasing.
Supply curve
• Relation between the price and the quantity that the suppliers want to sell. Supply • This function is increasing. quantity The goal is to maximize the social welfare What is our global objective when selecting the accepted bids ?
Maximize Social Welfare
We want to maximize: Area below the Demand – Area below the Supply
price Demand
cleared price Welfare
Supply quantity We need to find the best welfare while respecting the constraints
The objective function
Maximize: Social welfare Constraint Constraint
Constraint
Under some conditions Solution set Constraint • Grid constraints • Order constraints • Economic constraints • … Constraint Objective function In our case, we have three categories of constraints
Order constraints Grid constraints Economic constraints • Maximum energy • Maximum line capacity • No accepted order must • Ramping • Radial network “out of the money” • Minimum duration • Rejected orders must • Recovery not be economically • Maximum duration interesting • Time block Usually, we use the simplex method to solve linear problems
Simplex algorithm
• Cleverly explore the potential best solutions • Fast in practice • Do not work with binary variables
Our problem involves binary variables (due to blocks), so we need to be smart. The complexity of the problem lies in the block orders
Block orders • Fill-or-kill constraint This solution need to be rejected • Fixed price limit and volume If the block is accepted then the order will loss money
price Data price Optimal Solution Demand Demand
cleared price Block Block Supply Supply quantity cleared quantity quantity Solving the problem with “Branch and cut” and “dual formulation”
Not integer relaxed solution
Infeasible/sub-optimal relaxed solution
Integer relaxed solution, dual feasible
Integer relaxed solution , dual infeasible
High level view • Exploring the solution tree • “Relaxing” (i.e. forget) the binary constraints • We only explore nodes that potentially give an optimal solution. Finally, we obtain an optimal solution
The clearing engine provides the clearing price/volume and accepted orders
The algorithm takes into account order, grid and economic constraints
The existence of block orders makes the problem challenging
Using advance optimization theory, we were able to deliver the results in an efficient way LEM Platform Overview
LEM trial models
Trials results Next steps and LEM platform solutionMilestones
Market clearing
LEM platform demo Practical use cases demonstration Presentation of the grid topology
GSP level Indian Queens GSP
BSP level Fraddon BSP Truro BSP St Austell BSP
Primary . substations Road Road Major Bugle Fowey Trebal FraddonNewquay Drinnick PadstowMawgan Newquay Devoran St Agnes Harbour Blackpool Roseland Probus 33 St Austell LostwithielMegagissey St Trencreek Trevemper Perranporth TreyewTwelveheads Par Sawles Columb Shortlansend St Truro Truro Practical use cases demonstration Winter peak consumption National Grid bids 3.5MW downward reserve
Indian Queens GSP - 3.5MW | £110/ MWh 10:00-20:00 TSO bid
The line is congested due to excess consumption
+2.5 MW | £200/ MWh Truro BSP St Austell BSP DSO bid 16:30-20:00
Headroom = 2.7 MW
Truro Bugle Truro Treyew Shortlanesend Road - 4MW | £60/MW/h + 2MW | £80/MW/h + 4MW | £100/MW/h 12:00-19:00 17:00-20:30 17:00-21:00 Practical use cases demonstration Winter peak consumption National Grid bids 3.5MW downward reserve
Indian Queens GSP • The headroom constraint at Bugle limits - 3.5MW | £110/ MWh the cleared volume 10:00-20:00 TSO bid • The bids in Truro Shortlanesend and Treyew Road are cleared in price order
The line is congested due to excess consumption
+2.5 MW | £200/ MWh Truro BSP St Austell BSP DSO bid 16:30-20:00
Headroom = 2.7 MW
Truro Bugle Truro Treyew Shortlanesend Road - 4MW | £60/MW/h + 2MW | £80/MW/h + 4MW | £100/MW/h 12:00-19:00 17:00-20:30 17:00-21:00 Platform demo Thanks for listening