EV Charging Load Optimization for Wind Power Consumption
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Solution Overview
Problem
The integration of large-scale wind power into the grid leads to uncertainty and randomness in power generation, resulting in wind abandonment phenomena and disorderly charging of electric vehicles, which intensifies peak loads and traffic jams, and reduces the utilization of wind power due to inadequate peak down-regulation capacity of conventional power supplies.
Innovation Solution
A method for optimizing the dispatching of electric vehicle charging loads to minimize remaining blocked wind power and reduce total charging costs, using an adaptive mutation particle swarm optimization algorithm to determine target charging and discharging quantities and powers, while considering power balance, wind power plant output, and electric vehicle constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional power supply is used for peak regulation, then the power supply stability is maintained, but the peak down-regulation capacity is insufficient leading to wind power abandonment
Solution Approach 1:
The patent combines conventional power supply with electric vehicle charging loads to form a hybrid peak regulation system. The optimization model coordinates traditional power plants with EV charging/discharging operations, allowing the system to leverage both the stability of conventional supply and the flexible down-regulation capacity of EV batteries, thereby resolving the contradiction between maintaining stability and achieving sufficient peak down-regulation.
Solution Approach 2:
Electric vehicles are assigned multiple functions: they serve both as transportation tools and as mobile energy storage units for peak regulation. The optimization model enables EVs to participate in wind power consumption while maintaining their primary transportation function, thus achieving multi-functionality that addresses both the stability requirement and the peak regulation capacity need.
2Ease of operation
If electric vehicles are charged disorderly, then the charging convenience for users is improved, but the peak load problem is intensified and traffic jams occur
Solution Approach 1:
The patent implements dynamic charging scheduling that adapts to real-time grid conditions, wind power availability, and EV user requirements. The optimization model continuously adjusts charging/discharging strategies based on changing conditions, allowing the system to maintain charging convenience while dynamically managing peak loads to prevent grid overload and traffic congestion.
Solution Approach 2:
The system incorporates feedback mechanisms where the optimization model receives real-time information about grid load, wind power generation, and EV charging status, then adjusts charging schedules accordingly. This feedback loop enables the system to prevent peak load intensification while maintaining user convenience through adaptive charging recommendations.
3Productivity
If electric vehicles participate in wind power consumption, then the wind power utilization is improved, but the charging cost for users increases
Solution Approach 1:
The optimization model dynamically adjusts charging parameters such as charging power, timing, and duration based on wind power availability and grid conditions. By changing these parameters optimally, the system maximizes wind power consumption while minimizing user charging costs through strategic charging during periods of high wind generation and low electricity prices.
Solution Approach 2:
The system enables EV users to automatically participate in wind power consumption through the optimization model, which autonomously schedules charging/discharging operations. Users benefit from reduced charging costs and increased wind power utilization without manual intervention, as the system self-optimizes based on predefined objectives and real-time conditions.
4Productivity
If the output of wind power is increased, then the renewable energy consumption is improved, but the power grid stability is affected by uncertainty and randomness
Solution Approach 1:
Electric vehicle batteries serve as an intermediary between wind power generation and the power grid. The optimization model uses EV batteries to buffer the uncertainty and randomness of wind power output, absorbing excess generation when available and providing stable power delivery when needed, thus enabling increased renewable energy consumption while maintaining grid stability.
Solution Approach 2:
The system performs beforehand cushioning by using EV batteries to store energy in advance during periods of high wind generation, preparing for future periods when wind power may be insufficient. This proactive energy storage approach cushions the grid against wind power uncertainty and enables higher renewable energy penetration while maintaining stability.
Data Source
AI summary
A method for optimizing dispatching of charging loads of electric vehicles to promote wind power consumption includes: acquiring blocked electric quantity of wind power at a peak down-regulation period; acquiring a curve of disorderly charging loads of the electric vehicles; establishing a model for optimizing the charging loads of the electric vehicles to promote wind power consumption, wherein an objective function of the model refers to that the electric vehicles participate in wind power consumption to minimize the remaining blocked quantity of the wind power, and the total charging cost of the electric vehicles is lowest, and acquiring constraint conditions of the model; and solving the optimization model by adopting an adaptive mutation particle swarm optimization algorithm, to obtain the target charging/discharging electric quantity and the target charging/discharging power of the electric vehicles.