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US 12,726,028 B2Granted
Capacity configuration method and system of energy storage in microgrid
AssigneeHefei University of Technology
InventorsKaile Zhou, Kunshu Zhou, Shanlin Yang
Life of this patent
Estimating the term…
Abstract
A capacity configuration method and system of energy storage in a microgrid. In this application, the time-series data related to photovoltaic power generation is acquired and processed to obtain the preprocessed time-series data; a time-series generative adversarial network (Time GAN) is trained based on the preprocessed time-series data to perform data enhancement to obtain enhanced time-series data; and based on the enhanced time-series data, a distributionally robust optimization model is used to perform capacity configuration of energy storage.
Claims3
An electronic device, comprising:
- one or more processors;
- a memory;
- and
- one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the programs may be configured to execute methods, comprising,
- acquiring, by one or more sensors and one or more data collectors, time-series data related to photovoltaic power generation, and performing, by the one or more processors, preprocessing of the time-series data to obtain preprocessed time-series data;
- training a time-series generative adversarial network (Time GAN) based on the preprocessed time-series data to perform data enhancement to obtain enhanced time-series data;
- wherein the Time GAN comprises an embedded network and a generative adversarial network (GAN);
- and
- based on the enhanced time-series data, performing capacity configuration of energy storage by using a distributionally robust optimization model;
- and
- the distributionally robust optimization model comprises:
- an objective function of the distributionally robust optimization model is expressed as follows:
- C
- =
- C
- 1
- +
- C
- 2
- ;
-
- C
- 1
- =
- r
- e
- ×
- C
- 1
- ′
- ×
- S
- D
- ;
-
- r
- e
- =
- d
-
- (
- 1
- +
- d
- )
- y
- 365
- ×
- [
- (
- 1
- +
- d
- )
- y
- -
- 1
- ]
- ;
- C
- 2
- =
- p
- g
- ,
- t
- ×
- ∑
- t
- =
- 1
- T
-
- (
- V
- chp
- ,
- t
- )
- +
- p
- t
- ×
- ∑
- t
- =
- 1
- T
-
- (
- P
- grid
- ,
- t
- )
- +
- [
- ∑
- t
- =
- 1
- 24
-
- (
- ❘
- "\[LeftBracketingBar]"
- p
- t
- c
- ❘
- "\[RightBracketingBar]"
- +
- ❘
- "\[LeftBracketingBar]"
- p
- t
- d
- ❘
- "\[RightBracketingBar]"
- )
-
- Δ
-
- t
- ×
- C
- ess
- ]
- ;
- wherein C is a total investment cost of energy storage;
- C1 indicates a daily average investment cost of energy storage battery;
- C2 indicates a daily operating cost;
- re indicates a fund recovery factor;
- C
- 1
- ′
- indicates an investment cost per unit capacity of energy storage battery;
- S indicates energy storage configuration capacity;
- D indicates a depth of discharge of the energy storage battery;
- d indicates a discount rate;
- y indicates an investment life of the energy storage battery;
- pg,t indicates a price of natural gas at time t;
- Vchp,t indicates the amount of natural gas purchased at time t;
- pt indicates a price of purchased electricity at time t;
- Pgrid,t indicates the amount of electricity purchased at moment t;
- p
- t
- c
-
- and
-
- p
- t
- d
- indicate charging anu discharging power of energy storage at moment t, respectively;
- and Cess indicates a cost per unit charge and discharge of the energy storage battery;
- constraints of the objective function comprise:
- a. an operation constraint of energy storage equipment is expressed as follows:
- {
- SOC
- t
- +
- 1
- =
- SOC
- t
- (
- 1
- -
- φ
- )
- +
- (
- p
- t
- c
- ·
- η
- -
- p
- t
- d
- 1
- -
- η
- )
-
- Δ
-
- t
- p
- min
- c
- ≤
- p
- t
- c
- ≤
- p
- max
- c
- p
- min
- d
- ≤
- p
- t
- d
- ≤
- p
- max
- d
- SOC
- t
- ,
- min
- ≤
- SOC
- t
- ≤
- SOC
- t
- ,
- max
- ;
- wherein SOCt is a capacity of a battery at time t;
- φ is a self-discharging rate of the battery;
- η is a charging efficiency of the battery;
- p
- t
- c
- is a charging power of the battery at time t;
- p
- t
- d
- is a discharging power of the battery at time t;
- p
- min
- c
- is a minimum charging power of the battery;
- p
- max
- c
- is a maximum charging power of the battery;
- p
- min
- d
- is a minimum discharging power of the battery;
- p
- max
- d
- is a maximum discharging power of the battery;
- and Δt is dispatching time interval;
- b. an operation constraint of a gas turbine is expressed as follows:
- {
- P
- g
-
- t
- ,
- t
- =
- V
- chp
- ,
- t
- ×
- J
- ×
- ω
- H
- g
-
- t
- ,
- t
- =
- V
- chp
- ,
- t
- ×
- J
- ×
- (
- 1
- -
- ω
- )
- P
- g
-
- t
- min
- ≤
- P
- g
-
- t
- ,
- t
- ≤
- P
- g
-
- t
- max
- ;
- wherein Vchp,t is the amount of natural gas purchased at time t;
- ω is an electrical efficiency of the gas turbine;
- J is a heat value of the natural gas;
- Hgt,t is a thermal power output by the gas turbine at time t;
- P
- g
-
- t
- min
- is a lower limit or electrical power of the gas turbine, and
- P
- g
-
- t
- max
- is an upper limit of the electrical power of the gas turbine;
- c. a constraint of power balance is expressed as follows:
- {
- P
- grid
- ,
- t
- +
- P
- pv
- ,
- t
- +
- p
- t
- d
- -
- p
- t
- c
- +
- P
- gt
- ,
- t
- =
- P
- load
- ,
- t
- H
- gt
- ,
- t
- =
- H
- load
- ,
- t
- ;
- wherein Pgrid,t represents the power transmission between the microgrid and a main grid at time t;
- Ppv,t is a power output of the photovoltaic power generation at time t;
- p
- t
- c
- is a charging power of an energy storage battery at time t;
- p
- t
- d
- is a discharging power of the of the energy storage battery at time t, Pload,t is a total electrical load demand of the microgrid at time t;
- Hgt,t is a thermal power output of the gas turbine at time t;
- and Hload,t is a total heat load demand of the microgrid at time t;
- and
- an ambiguity set Mε used for measuring an uncertainty of the distributionally robust optimization model is shown as follows:
- M
- ε
- =
- {
- P
- p
- ˆ
- ∈
- M
-
- (
- ξ
- )
- :
- d
- W
- (
- P
- p
- ~
- ,
- P
- ^
- )
- ≤
- ε
- }
- ;
- wherein P{circumflex over (p)} is a probability distribution of an actual output power of the photovoltaic power generation;
- {circumflex over (P)} is an empirical distribution of the photovoltaic power generation;
- M(ξ) is all probability distribution spaces defined by Wasserstein distance dW;
- and ε is radius of an ambiguity set W;
- and
- wherein the step of using a distributionally robust optimization model to perform capacity configuration of energy storage comprises:
- solving the distributionally robust optimization model by the one or more processors to obtain a solution result comprising the energy storage configuration capacity;
- transmitting the solution result to an energy management system of the microgrid;
- and
- performing, by the energy management system, the capacity configuration of energy storage based on the solution result;
- the performing capacity configuration of energy storage comprises configuring at least one of energy storage configuration capacity, a charging power of the energy storage, a discharging power of the energy storage, state of charge limits of the energy storage and charge and discharge limits of the energy storage.
413 more elements
Dependencies are inferred from the claim text.