Microgrid Energy Optimization Using User Preference Forecasting
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Solution Overview
Problem
Existing energy control systems for microgrids operate sub-optimally due to a lack of insight into factors affecting energy usage, leading to excessive or insufficient energy generation, resulting in waste or improper operation of connected systems.
Innovation Solution
An energy optimization platform that receives user preferences and normalizes them with additional information, using an energy model to determine optimal asset operations that meet electrical loads while considering constraints such as cost, carbon emissions, and resilience, with an optimization component to refine these operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If energy control systems operate based on minimal user insight or no insight into factors affecting energy usage, then the system is easier to operate, but the energy generation becomes sub-optimal leading to waste or insufficient supply
Solution Approach 1:
The energy optimization platform automatically performs load forecasting, normalization of user preferences, and optimization of asset operations without requiring user expertise. The system self-manages the complex tasks of analyzing weather conditions, historical loads, capacities, and user preferences to determine optimal operating states, allowing users to simply provide preferences while the system handles the technical optimization independently
Solution Approach 2:
The energy optimization platform acts as an intermediary between user preferences and microgrid asset operations. It receives high-level user preferences, processes them through normalization and load forecasting components, and translates them into specific operating states for assets. This intermediary layer bridges the gap between simple user input and complex operational decisions, maintaining ease of operation while achieving optimal productivity
2Reliability
If the microgrid generates excessive energy based on sub-optimal control, then the energy supply is sufficient, but the excessive energy is underutilized and wasted
Solution Approach 1:
The energy optimization platform continuously monitors actual energy usage, weather conditions, and load patterns, then uses this feedback to adjust and refine load forecasts and operating state recommendations. This closed-loop feedback mechanism ensures that energy generation closely matches actual demand, preventing both excessive generation and insufficient supply, thereby reducing energy waste while maintaining reliable supply
Solution Approach 2:
The system dynamically adjusts asset operating states based on real-time conditions including weather forecasts, historical data, and user preferences. Rather than using static control rules, the optimization platform continuously adapts its recommendations to changing conditions, allowing the microgrid to respond flexibly to actual energy needs and avoid generating excessive energy that would go underutilized
3Loss of energy
If the microgrid generates insufficient energy based on sub-optimal control, then the energy generation reduces waste, but the connected systems operate improperly
Solution Approach 1:
The continuous monitoring and feedback mechanism ensures that the optimization platform detects when energy generation is approaching insufficient levels and adjusts recommendations accordingly. By comparing forecasted demand with available asset capacity and actual consumption patterns, the system prevents under-generation that would cause systems to operate improperly, while still avoiding excessive generation through precise demand matching
Solution Approach 2:
The load forecasting component performs preliminary analysis of weather conditions, historical loads, and capacities to predict future energy demands before they occur. This advance planning allows the optimization platform to recommend operating states that ensure sufficient energy generation is available, preventing situations where connected systems would operate improperly due to insufficient energy supply
4Productivity
If the energy control system uses complex optimization with multiple factors, then the energy usage is optimized, but the device complexity increases
Solution Approach 1:
The energy optimization platform is segmented into distinct functional components: user preference input interface, normalization component for processing preferences, load forecasting component for predicting demand, optimization component for determining operating states, and output interface for communicating recommendations. This segmentation allows each component to handle specific tasks independently, managing overall system complexity while achieving comprehensive energy optimization through coordinated operation of specialized modules
Data Source
AI summary
An energy optimization platform may receive user preference information associated with operations of assets of a microgrid. The user preference information may identify user preferences regarding reducing cost, reducing an emission of carbon dioxide, and a resilience against a power outage. The assets may include at least one of one or more solar panels, one or more generators, or one or more batteries. The energy optimization platform may receive load forecast information regarding an electrical load. The energy optimization platform may provide the user preference information and the load forecast information as inputs to an energy model. The energy model predicts, as an output, operating states of the assets based on the user preference information and the load forecast information. The energy optimization platform may determine an optimized combination of the operating states and may provide, to a microgrid controller, optimization information regarding the optimized combination of the operating states.