V2B Charge Scheduling Using MILP and MIQP Under EVSE Constraints
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Solution Overview
Problem
Existing electric vehicle (EV) technologies face challenges in stabilizing power supply and demand in the grid due to the instability of renewable energy sources and the high cost and installation limitations of energy storage systems, necessitating efficient charge/discharge scheduling methods.
Innovation Solution
A V2B charge/discharge scheduling device and method using mixed-integer linear programming (MILP) and mixed-integer quadratic programming (MIQP) to optimize charge/discharge scheduling based on the number of electric vehicles and charging stations, incorporating EV information, building power use, and EVSE information to maximize profit and minimize load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If V2B charge/discharge scheduling is implemented to stabilize power supply and demand, then grid stability is improved, but scheduling complexity increases due to the need to coordinate multiple EVs and EVSEs
Solution Approach 1:
The scheduling problem is segmented into two distinct models based on the relationship between EV and EVSE counts: when EV count ≤ EVSE count, a simpler scheduling model is applied; when EV count > EVSE count, a more complex model with waiting time considerations is used. This segmentation allows the system to adapt its complexity to the actual operational needs, improving grid stability without unnecessarily increasing scheduling complexity in all scenarios.
Solution Approach 2:
The scheduling model dynamically adjusts its parameters and constraints based on real-time conditions, specifically the relative numbers of EVs and EVSEs. The system transitions between different scheduling approaches depending on whether EVs are scarce or abundant, allowing flexible adaptation to changing grid conditions while maintaining optimal performance.
2Productivity
If the number of electric vehicles exceeds the number of EVSEs, then more power can be supplied to the grid, but scheduling difficulty increases due to EV waiting and connectivity constraints
Solution Approach 1:
The scheduling model performs preliminary calculations and predictions about EV arrivals, departures, and charging needs. By anticipating future states and pre-planning schedules, the system can accommodate more EVs than EVSEs without excessive waiting times, thereby increasing power supply capacity while managing scheduling complexity through proactive rather than reactive planning.
Solution Approach 2:
The system creates virtual representations or copies of scheduling scenarios to evaluate different scheduling strategies without affecting actual operations. This allows complex scheduling optimizations to be tested and refined in silico before implementation, reducing the actual scheduling difficulty while maximizing power supply capacity.
Data Source
AI summary
A vehicle to building (V2B) charge/discharge scheduling method can include inputting input data including at least one of electric vehicle information related to battery charging and discharging of electric vehicles, building information related to power use of a building, and electric vehicle supply equipment (EVSE) information related to EVSEs connected to the electric vehicles and charging and discharging a battery; setting a scheduling model using the input data and an objective function; outputting optimization data using the scheduling model; and performing charge/discharge scheduling of the electric vehicles using the optimization data, wherein the setting a scheduling model includes setting the scheduling model using mixed-integer linear programming (MILP) if a number of electric vehicles is not greater than a number of EVSEs; and setting the scheduling model using mixed-integer quadratic programming (MIQP) if the number of electric vehicles is greater than the number of EVSEs.