V2G Scheduling Model for Revenue Maximization
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Solution Overview
Problem
Existing V2G scheduling methods fail to comprehensively consider the randomness of both V2G scheduling resources and renewable energy power generation, leading to suboptimal decision-making for operator revenue maximization.
Innovation Solution
A two-stage stochastic programming-based V2G scheduling method that integrates the randomness of V2G scheduling resources and renewable energy power generation, involving day-ahead parameter sets, classification of in-agreement and out-of-agreement EVs, scenario generation, and a nonlinear stochastic programming model to maximize operator revenue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional deterministic scheduling methods are used, then the scheduling process is simple and easy to implement, but the operator revenue is suboptimal due to failing to account for randomness in V2G resources and renewable energy generation
Solution Approach 1:
The patent transforms the deterministic scheduling parameters into stochastic parameters by introducing probability distributions for V2G resource availability and renewable energy generation. This allows the model to account for randomness and uncertainty, improving revenue maximization while managing complexity through structured probabilistic formulations.
Solution Approach 2:
The patent segments the scheduling problem into distinct components: V2G charging/discharging decisions, renewable energy generation scenarios, and load demand patterns. By dividing the complex stochastic optimization into manageable segments that can be analyzed separately and combined, the model handles randomness systematically without becoming unmanageably complex.
2Measurement precision
If comprehensive randomness of V2G scheduling resources and renewable energy is considered, then the decision-making accuracy is improved, but the computational complexity and model difficulty increase significantly
Solution Approach 1:
The patent performs preliminary scenario generation and parameter characterization before the main optimization process. By pre-defining probability distributions, generating representative scenarios, and characterizing random variables in advance, the model reduces the difficulty of formulating the complete stochastic program while maintaining high decision-making accuracy.
Solution Approach 2:
The patent introduces scenario sets as intermediary structures that bridge the gap between raw random inputs and the optimization model. These scenarios act as mediators that translate complex randomness into discrete, manageable cases that can be systematically processed by the scheduling algorithm, reducing formulation difficulty.
Data Source
AI summary
A two-stage stochastic programming based V2G scheduling for operator revenue maximization is provided. Said method aims for the charge/discharge scheduling of electric vehicles, and establishes, based on a distributed renewable energy-storage-EVs charge/discharge power system, a V2G two-stage nonlinear stochastic programming model combining the V2G scheduling randomness with the renewable energy power generation randomness. Said model is converted into a mixed integer linear programming model (MILP) by means of constraint linearization. Furthermore, in order to enable random scenarios to cover uncertainty factors comprehensively, a scenario generation and combination method is designed to combine the V2G scheduling resources with the randomness of the renewable energy level. The V2G two-stage stochastic programming model solves an optimal charge/discharge plan of the electric vehicles seeking to adapt the randomness of the V2G scheduling layer and the renewable energy randomness, and increases the revenue of said model participating in power assistance services.