EV Fleet Power Procurement Using Route-Coupled Cost Optimization
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Solution Overview
Problem
Optimizing power procurement for enterprises with electric vehicle fleets is challenging due to the interdependence of source selection, fleet routing, and demand profile shaping, especially when considering external charging points and battery storage, and existing methods fail to account for the cost of electricity dependent on EV charging demand and utilize parked EV batteries effectively.
Innovation Solution
A method and system that iteratively solve routing and procurement sub-problems using interior point techniques to optimize EV routes and electricity procurement, considering various constraints and costs, reducing the problem complexity from MINLP to MIQP, and iteratively refine the procurement cost until a predefined tolerance is met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If existing approaches for joint optimization of EV routing and charging are used, then the cost of charging and travel distance can be minimized, but the cost of electricity is assumed to be independent of EV charging demand which does not hold for enterprises with EV fleets
Solution Approach 1:
The patent segments the power procurement optimization into two interconnected sub-problems: (1) EV routing optimization that determines optimal routes and charging schedules, and (2) power procurement optimization that determines optimal power allocation from multiple sources. This segmentation allows each sub-problem to be solved with appropriate assumptions while maintaining overall optimality through iterative coordination.
Solution Approach 2:
The patent introduces dynamic coupling between the routing and procurement sub-problems through an iterative solution framework. The EV charging demand profile from routing optimization becomes input to procurement optimization, which in turn provides updated electricity costs back to routing optimization. This dynamic feedback loop captures the interdependence of charging demand and electricity pricing.
2Ease of manufacture
If existing approaches are used, then routing and charging can be optimized, but the impact of utilizing a parked EV's battery as static storage for reducing electricity cost is not considered
Solution Approach 1:
The patent extends the functionality of EV batteries beyond mere propulsion energy storage to include static energy storage for power procurement optimization. Parked EV batteries are modeled as dispatchable storage resources that can charge or discharge to arbitrage electricity price differences across time and sources, enabling the fleet to actively participate in demand response and cost optimization.
Solution Approach 2:
The fleet's own parked EV batteries are utilized as distributed energy storage resources to serve the enterprise's power procurement needs. Instead of relying solely on external grid power or dedicated charging infrastructure, the system enables the fleet vehicles themselves to provide storage services, reducing overall procurement costs through internal resource utilization.
3Loss of energy
If the overall demand profile is known to procure power optimally, then power procurement cost can be optimized, but the demand profile depends on external charging points which creates mutual dependency
Solution Approach 1:
The patent decomposes the complex mutual dependency problem into two manageable sub-problems with clear interfaces. The routing sub-problem outputs EV charging demand profiles to the procurement sub-problem, which in turn outputs electricity cost information back to routing. This segmentation transforms an intractable simultaneous optimization into an iterative sequential process.
Solution Approach 2:
The patent introduces an iterative solution framework as an intermediary mechanism that coordinates between routing and procurement optimization. This framework exchanges information (charging demand profiles and electricity costs) between the two sub-problems across multiple iterations, gradually converging to a jointly optimal solution without requiring direct simultaneous solution of the coupled problems.
Data Source
AI summary
Accurate estimation of average procurement cost becomes increasingly pivotal to ensure optimal procurement of electricity for an enterprise owning EV fleets. Existing techniques available for joint optimization of EV routing and charging are not usable for enterprise with EV fleet charging loads as cost of electricity is assumed to be independent of the EV charging demand. Further, they require total demand to be known a priori. Present disclosure provides a method and a system for optimizing power procurement for enterprises with electric vehicle fleet charging load. The system first performs a route optimization by modelling set of routing constraints which are then used to obtain optimal EV routes. Thereafter, system performs a procurement cost optimization by modelling set of procurement constraints using the plurality of inputs and the optimal EV routes to obtain an allocation information of each external energy source which is then utilized to obtain final procurement cost.