Decentralized Energy Management via Optimiser State Simulation
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Solution Overview
Problem
Current demand management systems for energy networks face challenges in accurately and reliably managing energy demand across multiple properties, particularly with the integration of renewable energy sources, as they rely on outdated forecasting methods and centralized control systems that fail to account for real-time conditions and local optimizations, leading to inefficiencies and discomfort for consumers.
Innovation Solution
A system that utilizes energy management devices at each property to collect and share Optimiser State data with a central controller, allowing for simulation and adjustment of energy usage rules to meet aggregate demand requirements, enabling decentralized control and real-time optimization of energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized control systems with statistical forecasting models are used to manage energy demand, then system-wide demand can be controlled, but real-time local conditions and consumer comfort requirements cannot be adequately accounted for
Solution Approach 1:
The system divides the centralized energy management into autonomous local energy management devices at each property. Each device independently manages its own energy consumption based on local conditions, while still contributing to overall demand management goals. This segmentation allows simultaneous achievement of system-wide control and local adaptability.
Solution Approach 2:
Each energy management device is equipped with sensors and processors that detect and respond to local conditions specific to its property (occupancy patterns, ambient temperature, appliance usage). This local quality enables each device to make context-aware decisions that statistical models cannot capture, improving both reliability and adaptability.
2Loss of time
If statistical forecasting models based on historical data are used, then long-term demand patterns can be predicted, but real-time energy optimization and complex system responses cannot be captured
Solution Approach 1:
The energy management devices continuously monitor real-time energy consumption, appliance states, and environmental conditions, then adjust their control strategies accordingly. This closed-loop feedback mechanism enables precise real-time measurement and optimization that historical statistical models cannot provide, while maintaining long-term forecasting capabilities through accumulated operational data.
Solution Approach 2:
The system performs preliminary optimization by pre-cooling or pre-heating properties during low-demand periods, and by scheduling appliance operations in advance based on predicted demand patterns. This allows the system to account for thermal inertia and complex system responses ahead of time, improving both forecasting accuracy and real-time performance.
3Productivity
If demand management rules are applied to reduce peak demand, then aggregate energy consumption can be controlled, but consumer comfort and individual property requirements may be compromised
Solution Approach 1:
The energy management devices dynamically adjust control strategies based on real-time conditions, allowing flexible balancing of demand reduction goals with consumer comfort requirements. The system can adapt its aggressiveness in demand management based on current property states, occupancy patterns, and external factors, ensuring comfort is maintained while achieving efficiency goals.
Solution Approach 2:
The system changes operational parameters of energy-consuming devices (temperature setpoints, scheduling times, power levels) to optimize the balance between demand reduction and comfort maintenance. By dynamically adjusting these parameters based on local conditions and demand signals, the system achieves both productivity improvement and ease of operation.
Data Source
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AI summary
We provide a system for controlling energy supply to and/or consumption of energy by multiple properties from an energy network so as to cause an aggregate of the energy consumed at the properties to comply with one or more requirements, each property having an associated energy management device operable to control consumption of energy at its associated property by controlling one or more energy devices associated with the property, the system including a controller configured to: receive first Optimiser State data from a first energy management device, and second Optimiser State data from a second energy management device, each including a set of logic parameters; simulate the operation of the first and second energy management devices using the received first and second Optimiser State data, to determine a first aggregate forecast energy usage for first and second properties associated with the first and second energy management devices; determine first and second energy management rules to be imposed on the energy management devices each defining an updated set of logic parameters; simulate the operation of the first and second energy management devices using the updated logic parameters, and determine that the updated aggregate forecast complies with the requirements; and communicate the updated logic parameters to the energy management devices.