EV Stored Energy Allocation for Ancillary Service Scheduling Risk
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Solution Overview
Problem
The participation of electric vehicles (EVs) in the ancillary service market (ASM) is uncertain, making it challenging to optimize stored energy allocation effectively, which is crucial for promoting renewable energy consumption and load stability as the number of EVs increases.
Innovation Solution
An optimal allocation method for stored energy that uses LSSVM to predict EV load, Monte Carlo simulation for response capacity, CVaR for energy storage capacity calculation, and particle swarm optimization to minimize scheduling risk, ensuring reliable participation of EVs in ASM.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If EVs participate in ASM with large scale, then the potential for promoting renewable energy consumption and load stability increases, but the uncertainty of participation makes optimal stored energy configuration difficult to determine
Solution Approach 1:
The patent applies preliminary action by predicting EV load characteristics and participation patterns before ASM operations begin. Historical load data is collected and analyzed in advance to establish prediction models that guide stored energy configuration decisions, allowing the system to proactively prepare optimal energy allocation strategies rather than reacting to uncertain participation patterns in real-time
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring actual EV participation patterns and comparing them with predicted patterns. The system uses historical data feedback to refine prediction models and adjust stored energy configuration strategies, creating a closed-loop control system that improves reliability while managing configuration complexity through iterative optimization
2Productivity
If more EVs are coordinated to participate in ASM, then the adjustable capacity increases, but the scheduling risk management becomes more complex
Solution Approach 1:
The patent introduces an intermediary layer of prediction models and optimization algorithms that mediate between EV participation patterns and stored energy configuration decisions. This intermediary system processes large volumes of EV data, aggregates participation patterns, and translates them into coordinated control strategies, thereby increasing adjustable capacity while managing scheduling risk through systematic analysis rather than direct complex control of individual EVs
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting stored energy configuration parameters based on predicted EV participation patterns. The system modifies energy allocation parameters, charging/discharging rates, and timing schedules according to aggregated EV behavior patterns, enabling scalable coordination of large numbers of EVs while maintaining manageable complexity through parameter-based control rather than individual device control
Data Source
AI summary
The invention relates to an optimal allocation method for stored energy coordinating electric vehicles (EVs) to participate in auxiliary service market (ASM), including the following steps: 1. Predict the reported capacity of daily 96 points for EVs to participate in the ASM by least square support vector machine (LSSVM). 2. Fit the daily total load distribution of EVs. 3. Determine the error distribution between the reported capacity and the actual response capacity, and simulate the total daily load capacity of EVs in the future with Monte Carlo method. 4. Calculate the energy storage capacity required by EVs daily participating in ASM. 5. Build the objective function to minimize the scheduling risk of auxiliary service. 6. Solve the energy storage model in step 5 with particle swarm optimization (PSO), and output the configuration results of optimal energy storage capacity and energy storage power. The invention can improve the adjustable capacity of EVs participating in ASM.