EV Charging Network Pricing With Forecast-Based Energy Shifting
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Solution Overview
Problem
Current electric vehicle (EV) charging technologies lack efficient management of energy costs and renewable energy utilization, leading to suboptimal energy distribution and increased greenhouse gas emissions.
Innovation Solution
A system comprising a computer-readable storage medium with executable instructions and hardware processors that optimize EV charging by determining dynamic pricing based on demand and energy cost forecasts, managing local energy storage systems, and coordinating energy use across multiple EV charging stations to maximize earnings and reduce operational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If EV charging is scheduled when energy costs are low, then energy costs are reduced, but charging convenience and user satisfaction may deteriorate
Solution Approach 1:
The system implements dynamic pricing that adjusts charging rates in real-time based on energy cost fluctuations, demand conditions, and renewable energy availability. This allows the charging station to offer lower prices during low-cost periods while maintaining competitive pricing during peak periods, thereby reducing overall energy costs while preserving user convenience through flexible pricing options.
Solution Approach 2:
The system changes the pricing parameter dynamically based on multiple factors including energy cost forecasts, demand forecasts, and renewable energy generation predictions. By adjusting the price parameter in response to changing conditions, the system achieves cost reduction without sacrificing user satisfaction, as users can choose to charge when prices are lowest.
2Productivity
If dynamic optimized EV charging prices are implemented, then earnings are maximized, but system complexity increases
Solution Approach 1:
The system performs preliminary forecasting of energy costs, demand, and renewable energy generation before making pricing decisions. By having these forecasts ready in advance, the optimization process becomes more straightforward and less computationally intensive, reducing system complexity while still achieving earnings maximization through informed dynamic pricing.
Solution Approach 2:
The system introduces forecasted information as an intermediary layer between raw data and pricing decisions. Rather than directly optimizing based on complex real-time data streams, the forecasted energy cost information, demand information, and renewable energy information serve as simplified inputs that guide the optimization process, reducing computational complexity while maintaining earnings maximization.
3Measurement precision
If forecasted information is used for optimization, then decision accuracy is improved, but data processing requirements increase
Solution Approach 1:
The system extracts only the most critical forecasted information elements (energy cost forecasts, demand forecasts, renewable energy generation forecasts) needed for optimization decisions. By selecting and processing only these essential data elements rather than all available data, the system achieves improved decision accuracy while keeping data processing requirements manageable.
4Use of energy by moving object
If local energy storage systems are managed dynamically, then energy utilization efficiency is improved, but control complexity increases
Solution Approach 1:
The system merges the management of local energy storage with the dynamic pricing and optimization functions. By combining energy storage control with the existing forecast-based optimization framework, the system achieves improved energy utilization efficiency without adding separate complex control systems, as the storage management is integrated into the unified optimization process.
Data Source
AI summary
Improvements in energy distribution for electric vehicle (EV) energy delivery technologies are provided. An EV charging station management system optimizes the use of various sources of power for charging EVs. The optimizing may be based on current and/or forecasted EV charging demand, and the amount of greenhouse gas emissions produced by various sources to generate the power used for EV charging. In another embodiment, the optimizing may be based on current and/or forecasted EV charging demand, and current or forecasted cost of acquiring power from a power grid, which varies over time. The system may be configured to maximize earnings from EV charging at one or more charging stations.