Anticipative Energy Management System with MILP Set-Point Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing energy management systems in living places fail to balance occupant satisfaction and cost optimization while accommodating varying energy sources and appliance constraints, lacking flexibility and ability to adapt to new controllable sources.
Innovation Solution
A system utilizing mixed integer linear programming (MILP) to compute set-point data for services, incorporating prediction systems for energy and weather data, and enabling 'plug-and-play' functionality for new services, with a three-layer architecture that includes anticipative and reactive mechanisms for efficient energy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If binary control (starting or stopping) of energy sources is used, then the control mechanism is simple, but the range of solutions is limited and cannot optimize set-points of new controllable sources
Solution Approach 1:
The patent transitions from binary control (on/off) to continuous parameter control by introducing set-point optimization capabilities. The system now adjusts operational parameters such as temperature set-points, power levels, and timing continuously rather than simply switching devices on or off, enabling fine-grained optimization of energy sources while maintaining reasonable control complexity.
2Device complexity
If cost optimization is the only criterion, then the control system is simple, but occupant satisfaction is not taken into account
Solution Approach 1:
The control system is designed to simultaneously perform multiple functions: optimizing energy costs, maintaining occupant satisfaction, and adapting to new services. The multi-criteria optimization framework integrates both economic objectives and comfort requirements, allowing the system to balance competing goals without requiring separate control mechanisms for each objective.
3Reliability
If frequent adjustments are made to avoid constraint violations, then occupant satisfaction is maintained, but the system requires frequent reactive interventions
Solution Approach 1:
The system performs preliminary optimization by computing optimal set-points in advance using anticipative algorithms that consider future energy prices, weather forecasts, and occupancy patterns. This proactive approach allows the system to prepare optimal schedules before constraints become critical, reducing the need for frequent reactive adjustments while maintaining reliability and occupant satisfaction.
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
The invention concerns a system for managing services in a living place, comprising: - a supervision system, - an anticipative solving system, said anticipative solving system comprising processing and memory means for: ° storing service type problem generators and powers and occupants' satisfaction- related transversal problem generator; ° collecting service type and related parameters for each service; ° generating instantiated constraint data for each service according to its type and an anticipative service type problem data based on said constraint data; ° generating global anticipative problem data; ° computing set-point data for each service based on the solved global anticipative problem; ° sending set-point data to the supervision system, - a reactive system adapted to adjust the set-point data computed by the anticipative system depending on the current state of each service.