Exempt Supply Matching
Optimizing energy distribution with ML algorithms
Project Details
Industry
Energy
Client
Confidential Utility Partner
Technologies
Project Overview
Created a unique matching algorithm operating under the UK's Supplier Exempt Class A and BSC Modification P442 regulations. This innovative solution optimally pairs SME energy consumers with local sub–5 MW generators, unlocking approximately £50/MWh in non-commodity cost savings.
This algorithm has generated millions in new revenue streams and savings for businesses, while promoting more sustainable, localized energy consumption patterns.
Benefits
Cost Savings
in non-commodity costs
Value Generated
Unprecedented revenue stream for SMEs and utilities
Successful Pairings
35% match success rate achieved
Annual Savings Calculation
Annual Generation
Cost Savings
Total Annual Benefit
The Challenge
UK renewable energy regulations offer significant cost-saving opportunities through "exempt supply" arrangements, but establishing these partnerships presents complex challenges:
Regulatory Complexity
UK energy regulations permit exemptions from certain non-commodity costs when generators supply nearby businesses directly, but navigating these regulations requires specialized expertise and careful compliance management.
Matching Difficulty
Finding viable generator-consumer pairs requires analyzing multiple complex factors: compatibility criteria, load profiles, suitable connection points, and technical feasibility.
Scale & Efficiency
Manually identifying and evaluating potential matches across thousands of sites is prohibitively time-consuming and prone to missed opportunities.
Data Integration
Combining and analyzing fragmented data from generation profiles, consumption records, grid infrastructure, and regulatory requirements presents significant technical challenges.
Solution
I developed a comprehensive solution to address the complex challenge of matching exempt renewable generators with nearby businesses, creating efficient and cost-effective energy partnerships that leverage UK electricity regulations.
Technical Approach
- Advanced matching algorithm to identify optimal generator-consumer pairings
- Real-time regulatory compliance verification system
- Advanced load profiling to match generation and consumption patterns
Key Innovations
- Proprietary scoring algorithm for optimal matching
- Dynamic regulatory compliance engine with real-time updates
- AI-powered consumption forecasting for maximizing exemption value
- Automated contractual agreement generation with legal validation
Regulatory Framework
Supplier Exempt Class A
Regulatory classification that allows for certain exemptions from standard energy supply obligations when specific conditions are met between generators and consumers.
BSC Modification P442
Balancing and Settlement Code modification that enables specific matching arrangements between small-scale generators and consumers, supporting localized energy markets.
Key Regulatory Requirements
- •Generators must be sub-5 MW capacity to qualify for exemptions
- •Supply must meet regulatory requirements for direct supply
- •Matching must be documented and reported to regulatory authorities
- •Balancing responsibilities must be properly assigned and managed
System Architecture
Data Inputs
Processing Layer
Output Systems
Continuous Optimization Loop
Application Scenario
Example Pairing
Solar Installation
~4.8 MW capacity solar farm with 7 GWh annual generation
Business Complex
A collection of 20-25 SMEs with varied energy needs
Cost Calculation
Annual generation: 7 GWh × £50/MWh savings = £350,000 potential annual benefit
Key Benefits
- ✓Cost Reduction
Annual savings of £350,000 based on 7 GWh generation
- ✓Efficient Energy Use
Up to 85% of generated power consumed locally
- ✓Revenue Stability
More stable revenue streams for renewable generators
- ✓Environmental Impact
Carbon footprint reduction equivalent to removing 150-200 cars from roads
Business Impact
Cost Savings
Unlocked approximately £50/MWh in non-commodity cost savings for participating businesses.
Revenue Generation
Generated millions in new revenue streams through this innovative matching service.
Sustainability
Promoted more sustainable, localized energy consumption patterns, reducing transmission losses.
Market Advantage
Provided significant competitive advantage in the energy supply market with this unique offering.
Future Developments
Platform Scaling
Expanding the platform to handle larger volumes of participants and more complex matching scenarios.
Enhanced AI
Implementing more advanced machine learning algorithms to improve matching efficiency and forecast accuracy.
Marketplace Expansion
Developing a broader marketplace model that supports additional energy services and participant types.