EV Microgrid Scheduling with Battery Depreciation and PSO
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing microgrid scheduling methods fail to consider the depreciation cost of electric vehicle (EV) batteries and the varying states of charge when EVs access the microgrid, leading to suboptimal load scheduling and economic management.
Innovation Solution
A charging and discharging scheduling method for EVs in microgrids under time-of-use prices, which determines the system structure, optimal scheduling objective function, and constraint conditions, including depreciation costs, using the particle swarm optimization (PSO) algorithm to calculate optimal charge and discharge powers, incorporating photovoltaic, wind turbine, diesel generator, and micro turbine units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing optimal scheduling methods are used for EVs accessing microgrid, then the scheduling process is simple, but the depreciation cost of EV battery is not considered, leading to poor economic management
Solution Approach 1:
The patent introduces new parameters including depreciation cost coefficients, battery cycle life parameters, and state of charge constraints to transform the scheduling model from a simple cost-based approach to a comprehensive economic model that accounts for battery degradation and replacement costs over time
Solution Approach 2:
The patent performs preliminary calculations of battery depreciation costs and establishes constraint conditions before the actual scheduling optimization, allowing the model to pre-compute economic parameters and incorporate them into the objective function for more accurate economic management
2Device complexity
If existing optimal scheduling methods are used for EVs accessing microgrid, then the model is easy to establish, but the varying states of charge when EVs access are not considered, leading to suboptimal load scheduling
Solution Approach 1:
The patent applies different state of charge constraints to different EVs based on their specific conditions (arrival time, departure time, initial charge state), allowing each EV to have customized scheduling constraints that reflect its local operational requirements rather than applying uniform constraints to all vehicles
Solution Approach 2:
The patent transforms the scheduling model from a static framework to a dynamic one by incorporating time-varying state of charge constraints and flexible charge/discharge power adjustments that adapt to real-time EV access patterns and microgrid conditions
3Device complexity
If depreciation cost of EV battery is not considered in scheduling, then the calculation is simpler, but the economic management of EV battery is negative
Solution Approach 1:
The patent implements a feedback mechanism where the scheduling results and battery usage patterns are used to update depreciation cost calculations, which then feed back into the objective function for iterative optimization, allowing the model to learn from operational data and improve economic management over time
Solution Approach 2:
The patent replaces simple arithmetic cost calculations with a more sophisticated economic model that uses optimization algorithms and economic parameters to automatically compute depreciation costs and integrate them into the scheduling decision-making process
Data Source
AI summary
A charging and discharging scheduling method for electric vehicles in microgrid under time-of-use price includes: determining the system structure of the microgrid and the characters of each unit; establishing the optimal scheduling objective function of the microgrid considering the depreciation cost of the electric vehicle (EV) battery under time-of-use price; determining the constraints of each distributed generator and EV battery, and forming an optimal scheduling model of the microgrid together with the optimal scheduling objective function of the microgrid; determining the amount, starting and ending time, starting and ending charge state, and other basic calculating data of the EV accessing the microgrid under time-of-use price; determining the charge and discharge power of the EV when accessing the grid, by solving the optimal scheduling model of the microgrid with a particle swarm optimization algorithm.