Probabilistic Energy Tariff Planning for Grid Load Optimization
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Solution Overview
Problem
Current energy management systems infringe on consumer privacy, lack flexibility in tariff issuance, and struggle with planning energy consumption and network loading, especially with renewable energy sources, due to inadequate data protection and inflexible tariff structures.
Innovation Solution
A method that allows consumers to receive probabilistic information about energy tariffs and network loads, enabling flexible energy planning by continuously updating probability values, allowing consumers to choose energy sources based on environmental conditions and personal preferences, while maintaining data protection and allowing for better network utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bidirectional communication is implemented to collect consumer energy requirements, then network utilization can be optimized and overload avoided, but consumer privacy and data protection are compromised
Solution Approach 1:
The patent extracts only the necessary energy requirement data from consumer communications while leaving private information (living conditions, device details, personal habits) outside the transmitted data set. This selective extraction allows network optimization without privacy infringement.
Solution Approach 2:
The patent introduces an intermediary data processing layer that receives consumer energy requirements, processes them to extract only network-relevant parameters, and transmits only this processed information to the energy supply company. This intermediary protects consumer privacy while enabling network utilization optimization.
2Loss of time
If binding tariffs are announced in advance, then consumers can plan energy consumption, but flexibility in issuing new tariffs is reduced
Solution Approach 1:
The patent implements dynamic tariff structures where energy requirements and corresponding tariffs can be adjusted in real-time based on current network conditions, renewable energy availability, and demand patterns. This allows the system to adapt to changing conditions while still providing consumers with planning capabilities through advance notification of tariff changes.
Solution Approach 2:
The patent changes the parameter of tariff binding from static to semi-dynamic, where tariffs are announced in advance for planning purposes but can be updated based on objective criteria such as renewable energy production forecasts and network load conditions. This maintains consumer planning capability while enabling operational flexibility.
3Object-affected harmful factors
If renewable energy production is used, then environmental sustainability is improved, but planning reliability is reduced due to environmental dependency
Solution Approach 1:
The patent implements feedback mechanisms where renewable energy production forecasts, network load conditions, and actual delivery performance are continuously monitored and used to adjust future production plans and tariff offerings. This feedback loop improves reliability by learning from past performance while maintaining environmental sustainability.
Solution Approach 2:
The patent applies beforehand cushioning by creating buffer capacities and alternative supply options in advance to compensate for potential renewable energy delivery shortfalls due to environmental factors. This ensures reliability is maintained even when renewable production does not meet forecasts.
4Productivity
If detailed consumer data is collected for personalized energy management, then energy consumption optimization is improved, but data security risks increase
Solution Approach 1:
The patent extracts only the essential energy management parameters (consumption patterns, load profiles, energy requirements) from consumer data while leaving sensitive personal information outside the processed data set. This minimizes data security risks while maintaining energy optimization capabilities.
Solution Approach 2:
The patent uses disposable, anonymized data representations for energy optimization purposes, where personal identifiers are removed and data is used in aggregated or pseudonymized form. This allows energy optimization without creating long-term security vulnerabilities from storing detailed personal data.
Data Source
Figure 1~2
AI summary
The invention relates to a method for planning and/or controlling an energy output to a consumer (2) and/or an energy supply to an energy distribution network (1). According to said method, first messages relating to expected current and/or planned tariffs and/or network loads, and relating to a value for a probability of the input of the related tariffs and/or network loads are created and sent by a first energy supplier.