EV Cluster Charging Scheduling Under Grid and Cost Constraints
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Solution Overview
Problem
Existing methods for large-scale electric vehicle charging are inefficient in handling complex constraints and take too long to find optimal charging solutions, leading to issues like peak load fluctuations and high charging costs.
Innovation Solution
A method using a greedy repair genetic algorithm to optimize electric vehicle cluster charging, incorporating discrete-time systems, constraint conditions, and 0/1 integer linear programming to minimize charging costs while ensuring stable power distribution network operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing methods are used for large-scale electric vehicle charging optimization, then the charging scheduling can be performed, but the solution speed is slow and the constraint handling is difficult
Solution Approach 1:
The patent segments the charging optimization problem into discrete time slots and individual vehicle charging decisions, representing the solution as a binary vector where each element corresponds to a specific vehicle-charging-slot assignment. This segmentation enables the genetic algorithm to efficiently explore the solution space while maintaining constraint satisfaction through structured representation.
Solution Approach 2:
The patent transforms the continuous charging optimization problem into a discrete 0/1 integer linear programming problem by parameterizing vehicle charging decisions as binary variables. This parameter transformation allows the use of genetic algorithms with efficient constraint handling mechanisms, significantly improving both solution speed and constraint satisfaction reliability.
2Stability of the object's composition
If coordinated charging is implemented to balance load and avoid peak loads, then the power distribution network stability is improved, but the charging cost optimization becomes more complex
Solution Approach 1:
The patent incorporates load balance constraints and peak avoidance requirements as preliminary constraints in the optimization model before executing the genetic algorithm. By pre-defining acceptable load ranges and phase balance requirements, the algorithm efficiently searches only within feasible regions, reducing computational complexity while ensuring network stability.
Solution Approach 2:
The patent introduces a constraint handling mechanism that acts as an intermediary between the optimization objective and the power distribution constraints. This mechanism transforms complex physical constraints into simplified penalty functions or feasibility checks, allowing the genetic algorithm to maintain network stability requirements without being overwhelmed by computational complexity.
3Ease of operation
If user charging freedom is maintained to reduce charging costs, then the user satisfaction is improved, but the coordinated charging control becomes more difficult
Solution Approach 1:
The patent implements dynamic charging scheduling that adapts to user preferences and grid conditions in real-time. The genetic algorithm dynamically adjusts charging assignments based on user-defined flexibility parameters, allowing users to express their charging preferences while the system coordinates overall load management. This dynamic approach maintains user freedom without requiring overly complex centralized control.
Data Source
AI summary
The disclosure relates to the technical field of power system management, and in particular to a large-scale electric vehicle cluster coordinated charging optimization method based on greedy repair genetic algorithm. the method includes the following steps: Si, establishing a task model: establishing a task model of electric vehicle cluster coordinated charging in large charging stations; S2, constructing constraint conditions: constructing constraint conditions for the coordinated charging optimization problem; S3, constructing a constrained optimization problem: designing an objective function with the minimum charging cost, performing linear mathematical transformation on non-linear constraint conditions, and constructing a 0/1 integer linear programming problem; and S4, solving the constrained optimization problem with a custom genetic algorithm based method: carrying out genetic coding on a decision model of the charging station, and specifically designing a genetic algorithm with greedy repair operators to solve the constructed constrained optimization problem.