Optimal allocation method for stored energy coordinating electric vehicles to participate in ancillary service market

Patent metadata

Field	Value
Publication number	US-12159090-B2
Application number	US-202117338990-A
Country	US
Kind code	B2
Filing date	Jun 4, 2021
Priority date	Nov 6, 2020
Publication date	Dec 3, 2024
Grant date	Dec 3, 2024

Abstract

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.

First claim

What is claimed is:

1. An optimal allocation method for stored energy coordinating electric vehicles (EVs) to participate in ancillary service market (ASM), comprising the following steps:
step 1: the historical load of EVs is collected, and the reported capacity of 96 points per day for EVs to participate in ASM is predicted by a least square support vector machine (LSSVM);
step 2: through the historical total load distribution of EVs collected in step 1, the daily total load distribution of EVs is fitted;
step 3: after differentiating the actual load and the capacity results predicted in step 1 for daily EV participation in ASM to obtain the response error, an error distribution between reported capacity and actual response capacity are determined, according to the total daily load distribution function of EVs obtained in step 2, the response capacity scale of EV load in the future is simulated with Monte Carlo;
step 4: based on the simulation results of the response capacity scale of the future EV load obtained in step 3, the energy storage capacity required by EVs daily participating in ASM is calculated with conditional value at risk (CVaR);
step 5: based on the daily 96 points reported capacity curve from step 1 and the daily EV response capacity scale from in step 3, combined with the complementary capacity of the energy storage capacity allocation in step 4, the objective function to minimize the scheduling risk of auxiliary service is constructed, and the risk loss under different response errors is considered to realize the optimal allocation of energy storage capacity;
step 6: the particle swarm optimization algorithm is used to solve the energy storage model in step 5, and the optimal configuration results of energy storage capacity and energy storage power are output;
wherein the specific methods in the step 5 are as follows:
(1) in the process of coordinating EVs to participate in the optimal allocation of stored energy in ASM, in order to minimize the annual scheduling risk for aggregators in ASM, the objective function of the optimal allocation model of stored energy is established as Formula (14):

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in the function, Pro is the calculation formula for the aggregator's annual scheduling risk, W d refers to the daily risk loss of EVs participating in auxiliary services on Day d, A e_t d is the daily risk cost of the EVs participating in auxiliary services at the time of t on day d, W u d is the energy storage risk loss for directly participating in auxiliary services on day d and W l d is the energy storage risk loss for coordinating EVs on day d, A b is the annual cost of energy storage configuration; through optimization operation, the energy storage capacity Q is optimized as follows:
Q=Q u +Q l   (15)

(2) considering that the energy storage power configuration results affect the climbing rate, the constraint function of energy storage power P is set as follows:
P≥E 1 ×λ 2   (16)

E i is the total charging power of EVs at time t, λ 2 is the critical proportion of charging power of EVs that do not participate in valley filling auxiliary service;
the calculation method of parameters is as follows:
{circle around (1)} the daily risk loss of EVs participating in auxiliary services W d
after participating in ASM, EV companies obtain compensation from auxiliary services and compensate risks through market scheduling so as to create risk losses, which is calculated as Formula (17):

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Assignees

• Univ North China Electric Power
• State Grid Electric Vehicle Service Ltd

Inventors

• Liu Dunnan
• Liu Mingguang
• Peng Xiaofeng
• Jia Heping
• Wang Wen
• Wang Lingxiang
• Zou Mengjiao
• Zhang Yue
• Yang Ye
• Su Shu
• Bai Desheng

Classifications

• H02J3/322

  the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging · CPC title

• G06Q50/06

  Energy or water supply · CPC title

• G06Q10/0635

  Risk analysis of enterprise or organisation activities · CPC title

• G06Q10/06315

  Needs-based resource requirements planning or analysis · CPC title

• G06Q10/06313

  Resource planning in a project environment · CPC title