EV Charging Schedules Using Local Grid Topology Constraints
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Solution Overview
Problem
The existing electric power grid infrastructure faces challenges in efficiently and cost-effectively charging multiple electric vehicles (EVs) simultaneously, as it may lead to large loads on local distribution systems, potentially exceeding the capacity of power grids associated with transformers rated less than 500 KVA.
Innovation Solution
A method and apparatus that utilize a power system controller to determine optimized charging schedules for EVs based on current energy storage levels, energy usage rates, location, and availability of electric power from distribution grids or alternative resources, balancing loads across microgrids and distributing power intelligently to maintain favorable load balances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple EVs are charged simultaneously from the distribution power grid, then charging productivity is improved, but the load on local distribution systems exceeds grid capacity
Solution Approach 1:
The system segments the charging load by dividing it across multiple microgrids based on their available capacity. The power system controller allocates charging schedules to different EVs across different microgrids, preventing any single grid from being overloaded while maintaining overall charging productivity.
Solution Approach 2:
The charging schedule is dynamically adjusted based on real-time microgrid capacity information. The controller monitors the available capacity of each microgrid and modifies charging schedules accordingly, allowing the system to adapt to changing grid conditions and maintain optimal charging rates without exceeding capacity limits.
2Loss of energy
If charging schedules are optimized using microgrid capacity information, then energy distribution efficiency is improved, but system complexity increases
Solution Approach 1:
The power system controller acts as an intermediary between EVs and microgrids, centralizing the complex task of schedule optimization. This intermediary receives capacity information from microgrids, processes charging requests from EVs, and generates optimized schedules, thereby improving energy distribution efficiency while containing system complexity within the controller rather than distributing it across all components.
Data Source
AI summary
An apparatus (240) and method for charging a plurality of mobile energy storage and power consumption devices (202) may control determining a power charging schedule for charging a battery of at least one of the devices (202), in accordance with charger availability information, transactive energy information, the current location of the one device, mobile energy storage and power consumption device information and information indicating predetermined timing for providing a predetermined minimum charge level at the device; and transmitting a charging instruction signal for charging the battery of the at least one device using electric power supplied from a distribution power grid (10, 204) or an alternative power resource (218), according to the power charging schedule.