Peak Power Exchange Optimization for Facility Demand Shaving
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Solution Overview
Problem
Current load management systems for large energy consumers fail to fully exploit peak shaving capabilities, resulting in suboptimal reduction of peak power demand, which limits the potential for cost-effective energy cost minimization.
Innovation Solution
A computer-implemented method using an optimization algorithm that determines a mitigated maximum power exchange value by considering various peak shaving capabilities, such as shiftable loads, sheddable loads, energy storage, and on-site generators, to minimize peak power demand, allowing for more realistic and cost-effective supply contracts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If load management systems use traditional peak shaving capabilities, then some reduction of peak power demand is achieved, but the reduction is suboptimal and fails to fully exploit available capabilities
Solution Approach 1:
The system changes the parameter of peak power demand by using an optimization algorithm to determine optimal operating points for peak shaving capabilities, transforming the approach from traditional rule-based control to mathematically optimized control that achieves greater reduction in peak demand
Solution Approach 2:
The optimization algorithm uses feedback from timeseries data defining peak shaving capabilities to continuously adjust and determine the optimal operation strategy, enabling the system to learn from historical data and improve peak shaving performance over time
2Ease of operation
If facilities adhere to higher peak power demand limits in supply contracts, then operational flexibility is maintained, but demand charges increase significantly
Solution Approach 1:
The system performs preliminary action by determining the mitigated peak power demand value in advance through optimization, allowing facilities to negotiate supply contracts with appropriate demand charge limits before entering into agreements, thus avoiding high charges while maintaining operational flexibility
3Loss of energy
If optimization algorithms determine mitigated peak power demand values, then cost-effective contract negotiation is enabled, but computational complexity increases
Solution Approach 1:
The system uses copying by processing timeseries data that represents historical peak shaving capability patterns, allowing the optimization algorithm to learn from replicated historical scenarios without requiring real-time complex computations, thus reducing computational complexity while maintaining accuracy
Data Source
AI summary
A computer-implemented method for determining a mitigated peak power exchange value for power exchange between a supplier and a facility includes receiving a timeseries comprising power exchange values for power exchange between the supplier and the facility over a predetermined time period; executing an optimization algorithm configured to determine a mitigated maximum power exchange value in the timeseries using data defining one or more peak shaving capabilities at the facility as variables; and outputting the determined mitigated maximum power exchange value.