Adaptive Energy Flow Management for Photovoltaic Storage
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Solution Overview
Problem
Current photovoltaic storage systems lack adaptive operational management, failing to account for system inefficiencies and market changes, leading to suboptimal energy distribution and grid stability.
Innovation Solution
An adaptive operational management method for photovoltaic storage systems that uses stochastic optimization and probability distributions to forecast energy production and consumption, determining optimal energy flows based on power demand parameters, thereby optimizing energy distribution and grid stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current operational management is used in photovoltaic storage systems, then the system operates with simple control logic, but the system fails to account for inefficiencies and market changes resulting in suboptimal energy distribution and reduced productivity
Solution Approach 1:
The operational management system transitions from static to dynamic by continuously adapting energy flow decisions based on real-time stochastic forecasts of production, consumption, and market prices. The system dynamically adjusts operating modes (self-consumption, battery charging, grid feed-in) according to predicted future conditions rather than following fixed rules
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual production and consumption data, comparing it with stochastic forecasts, and using the results to refine future operational decisions. The energy management system receives feedback on system inefficiencies and market changes, then adjusts energy flow optimization accordingly
2Loss of information
If statistical values from the past are used to forecast production and consumption, then no additional data needs to be supplied from outside, but the system must process multiple sets of future characteristics with probability values increasing computational complexity
Solution Approach 1:
The system performs self-service by generating all necessary forecast data internally using historical statistical values stored in its own memory. The energy management system autonomously creates multiple sets of future characteristics with probability values without requiring external data input, processing everything through its internal stochastic model
Solution Approach 2:
The system manages computational complexity by parameterizing the stochastic model with a limited set of historical statistical parameters. By changing the representation from raw historical data to condensed statistical parameters (mean, variance, probability distributions), the system reduces the dimensionality of the computational problem while retaining essential information
3Reliability
If the system determines multiple possible energy flows with expected values for each set of future characteristics, then optimal energy flow can be selected, but the processing time and computational load increase
Solution Approach 1:
The system performs preliminary action by pre-calculating multiple sets of future characteristics with their probability values and expected energy flows before making operational decisions. The energy management system prepares a range of possible scenarios and their corresponding optimal energy flows in advance, then selects the best option based on current conditions
Solution Approach 2:
The system applies partial action by determining expected energy flows for only the most probable sets of future characteristics rather than exhaustively analyzing all possible scenarios. By focusing computational resources on the most relevant probability distributions, the system achieves sufficient optimization accuracy without excessive processing time
Data Source
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AI summary
The invention relates to a method (1300) for adaptive management for a memory system (104) for a photovoltaic system (100), wherein the photovoltaic system (100) comprises, as components, at least one solar generator (102), the memory system (104), an energy management system (116) and also a domestic connection (106) and a mains connection (108). The method (1300) comprises a step of reading in (1310) present characteristic data from the photovoltaic system (100) and previous characteristic data from the photovoltaic system (100), a step of creating (1320) a plurality of sets of future characteristic data using a stochastic model, a step of determining (1330) a multiplicity of possible energy flows (114) between the components of the photovoltaic system (100) for the at least one future time interval using the plurality of sets of future characteristic data and determining an expected value per set of future characteristic data using a power requirement parameter and the multiplicity of possible energy flows (114), and a step of selecting (1340) an energy flow (114) from the multiplicity of possible energy flows (114) for the at least one future time interval, wherein the present characteristic data represent production values and/or consumption values and/or a state of charge (122) for the memory system (104) in a present time interval, wherein the previous characteristic data represent position values and/or consumption values and/or a state of charge (122) in at least one previous time interval, wherein each set of the future characteristic data comprises an expected production value and/or an expected consumption value for at least one future time interval, wherein the expected production values and/or the expected consumption values each have a probability value assigned to them and wherein a difference between the expected value of the energy flow (114) and the mean value of the expected values of the multiplicity of energy flows (114) is minimal, with the energy flow (114) determining the adaptive management.