Control Center Feedback for Local Energy Exchange Prediction
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Solution Overview
Problem
Small energy systems, such as individual households, face challenges in accurately predicting energy generation and consumption due to stochastic fluctuations, leading to inefficiencies and additional costs in participating in local energy markets.
Innovation Solution
A control center system that utilizes an interface module to collect and process prediction profiles and component-specific data from energy systems, determining control data to optimize energy exchange through continuous optimization and closed-loop control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If small energy systems participate in local energy markets with stochastic consumption profiles, then energy exchange opportunities increase, but prediction accuracy deteriorates due to large stochastic fluctuations
Solution Approach 1:
The patent introduces a local energy market platform as an intermediary between small energy systems and the energy network. This platform aggregates energy exchange data from multiple participants, allowing individual systems with poor prediction accuracy to benefit from collective data patterns. The platform mediates the matching of energy offers and demands, enabling participation despite individual stochastic fluctuations.
Solution Approach 2:
The patent combines energy exchange operations of multiple small energy systems through a centralized platform. By merging prediction data from multiple participants, the system achieves better overall prediction accuracy than individual systems could achieve alone. The aggregation effect reduces the impact of individual stochastic fluctuations on the collective energy exchange optimization.
2Reliability
If energy reserves are planned to compensate for inaccurate predictions, then reliability of energy exchange improves, but utilization of local energy market potential deteriorates and additional costs arise
Solution Approach 1:
The patent implements a feedback mechanism where the local energy market platform continuously monitors actual energy exchanges against predicted values. This feedback loop allows the system to learn from prediction deviations and improve future predictions. Instead of relying on static energy reserves, the system dynamically adjusts based on actual performance, maintaining reliability while improving market utilization over time.
Solution Approach 2:
The patent applies partial action by having the platform determine optimal energy exchange quantities that account for prediction uncertainties without requiring full energy reserve planning. The control center calculates precise exchange amounts based on aggregated data, achieving sufficient reliability through coordinated partial exchanges rather than conservative full-reserve approaches.
3Measurement precision
If accurate predictions are achieved for small energy systems, then energy exchange optimization improves, but system complexity increases due to difficulty in handling stochastic fluctuations
Solution Approach 1:
The patent enables energy systems to self-report their energy offers, demands, and consumption profiles to the local energy market platform. Each participant autonomously provides their data without requiring complex external measurement or verification systems. The platform then processes this self-reported data to determine optimized exchanges, reducing the complexity burden on individual systems while maintaining accurate predictions through aggregated information.
Data Source
AI summary
Various embodiments include a method for controlling an energy exchange among a plurality of energy systems using a control center, wherein a component of one of the plurality of energy systems is coupled to the control center via an interface module for data exchange. The method includes: transmitting a first data set to the interface module with a prediction profile regarding an energy exchange of the component; transmitting a second data set to the control center using the interface module including the first data set and component-specific data of the component; determining control data using the control center using the prediction profile and the component-specific data and data communicated to the control center by further energy systems to determine the control data; transmitting the determined control data to the interface module; and operating the component based on the control data.