CN-122000905-A - Electric automobile and energy storage multilayer joint scheduling method and device based on source network charge storage cooperation

CN122000905ACN 122000905 ACN122000905 ACN 122000905ACN-122000905-A

Abstract

The invention relates to the technical field of power system dispatching, in particular to an electric vehicle and energy storage multilayer joint dispatching method and device based on source network and charge storage coordination, comprising the steps of solving a pre-constructed upper layer optimization model to obtain a load curve; substituting the load curve into a pre-built middle-layer optimization model and solving to obtain a source storage scheduling scheme and a power flow result, substituting the source storage scheduling scheme and the power flow result into a pre-built lower-layer optimization model and solving to obtain power flow distribution of a power distribution network, and obtaining a scheduling scheme of the power distribution network based on the power flow distribution of the power distribution network. The technical scheme provided by the invention can promote the consumption of renewable energy sources such as wind power, photovoltaic and the like, optimize the energy resource allocation, improve the stability of a power grid, reduce the loss of a power distribution network and enhance the capability of the power grid to cope with complex scenes.

Inventors

LIU JINCHENG
ZHANG HUIMING
ZHANG JING
JIANG LINRU
WANG PIYU
TANG YANMEI
HUANG XIAOHUA
YAN CHENJIE
GAO PENG
XUE LI
YANG XU
ZHANG YUANXING
TANG PANPAN
LI KANG
Wan Jingfei
PEI JIANCAI
WANG YALING
LI TAOYONG
LI JIANFENG
LI DEZHI
LI BIN
ZHANG LINJUAN
GUO PU
LI WENFENG

Assignees

中国电力科学研究院有限公司
国网河南省电力公司经济技术研究院
国家电网有限公司

Dates

Publication Date: 20260508
Application Date: 20251201

Claims (20)

1. An electric automobile and energy storage multilayer joint scheduling method based on source network charge storage coordination is characterized by comprising the following steps: Solving a pre-constructed upper layer optimization model to obtain a load curve; Substituting the load curve into a pre-constructed middle layer optimization model and solving to obtain a source storage scheduling scheme and a tide result; Substituting the source storage scheduling scheme and the power flow result into a pre-constructed lower layer optimization model and solving to obtain power flow distribution of the power distribution network; and obtaining a scheduling scheme of the power distribution network based on the power flow distribution of the power distribution network.
2. The method of claim 1, wherein the pre-constructed upper layer optimization model includes a first objective function and a first constraint that target a minimum of payload fluctuation.
3. The method of claim 2, wherein the first objective function is as follows: In the above-mentioned method, the step of, In order for the net load to fluctuate, As the power load at the time t, For the fan output at time t, For the photovoltaic output at time t, To optimize the period.
4. A method according to claim 3, wherein the first constraint is as follows: In the above formula, alpha is the upper limit of the load response change at each moment, beta is the upper limit of the electricity price change at each moment, As the response power at the time t, For the response electricity price at the time t, For the initial electricity price at time t, Is the fluctuation of electricity price at time t.
5. The method of claim 2, wherein the pre-constructed middle layer optimization model includes a second objective function and a second constraint that target a minimum total cost of the assembly of units.
6. The method of claim 5, wherein the second objective function is as follows: In the above-mentioned method, the step of, The total cost for the unit assembly is, Is the charge and discharge cost of the electric automobile, For the maintenance cost of the wind and light, In order to discard the wind and the light, In order to purchase the cost of electricity, For the charge and discharge costs of the energy storage system, In order to achieve the cost of charge and discharge loss, The charging and discharging basic cost of the electric automobile is realized, The electric discharge compensation is carried out for the electric automobile, 、 The operation and maintenance cost of wind power and photovoltaic output unit power are respectively, 、 Respectively generating actual power generation amount of wind power and photovoltaic at time tsuper, 、 Wind power respectively the wind abandoned penalty cost of the unit generated energy of the photovoltaic, 、 The power generation amount is respectively estimated by wind power and photovoltaic, 、 The electricity price and the electricity power are purchased at the time t, 、、、、 The electricity price, the absolute value of the charging power, the absolute value of the discharging power, the charging efficiency and the discharging efficiency at the time t are respectively, 、 Respectively the charge and discharge power of the energy storage system and the cost coefficient of the unit charge and discharge energy of the energy storage system, In order to provide for the time interval of time, The period is optimized.
7. The method of claim 6, wherein the electric vehicle charge-discharge basis costs are as follows: The electric automobile discharge compensation is as follows: In the above-mentioned method, the step of, 、 The charge and discharge loads of the electric automobile at the time t are respectively, The loss coefficient of the discharge battery of the electric automobile, The charging price of the electric car at the time t, 、 The total number of electric vehicles charged and discharged at time t, 、 The average charging power and the average discharging power of the electric automobile at the time t respectively, As the discharge price of the electric car at time t, And k is the discharge compensation price of the electric automobile, and J is the discharge compensation growth rate of the electric automobile.
8. The method of claim 7, wherein the second constraint is as follows: In the above-mentioned method, the step of, 、 Respectively wind power and photovoltaic maximum rated power, 、 For the maximum number of electric vehicles that can be charged and discharged at time t, For the state of charge of the energy storage system at time t, For time t-1 the state of charge of the energy storage system, 、 Respectively a lower limit and an upper limit of the charge state of the energy storage system, In order to achieve the efficiency of the charge, In order for the discharge efficiency to be high, For the charging power at the time t, For the discharge power at the time t, As the response power at the time t, 、 The upper limit of the charging power and the upper limit of the discharging power are respectively set.
9. The method of claim 2, wherein the pre-constructed middle layer optimization model includes a third objective function and a third constraint that target a minimum loss of the distribution network.
10. The method of claim 9, wherein the third objective function is as follows: In the above-mentioned method, the step of, E is the active network loss of the power distribution network, E is the branch set of the power distribution network, For the current of the branch ij at the moment t, For the resistance of the branch ij, The period is optimized.
11. The method of claim 10, wherein the third constraint is as follows: , In the above-mentioned method, the step of, 、、、、、 The power purchase active power, the number of the discharging vehicles, the charging and discharging power of the electric automobile, the active load, the number of the charging vehicles and the active power transmission of the node i at the moment t, 、 Respectively the average charging power and the average discharging power of the electric automobile, 、、、 The power purchasing reactive power, reactive discharging load, the transmitted reactive power and the reactive power regulation power of the electric automobile at the moment t node i, 、 For the voltage minimum and maximum at node i, 、 For the current minimum and maximum of the branch ij, 、 For the minimum and maximum value of the transmit power of the branch ij, The maximum number of charge-discharge vehicles for node i, For the current of the branch ij at the moment t, The current of branch ij is at time tj.
12. Electric automobile and energy storage multilayer joint scheduling device based on source network lotus stores up cooperately, its characterized in that, the device includes: the first analysis module is used for solving a pre-constructed upper layer optimization model to obtain a load curve; The second analysis module is used for substituting the load curve into a pre-constructed middle-layer optimization model and solving the model to obtain a source storage scheduling scheme and a tide result; the third analysis module is used for substituting the source storage scheduling scheme and the power flow result into a pre-constructed lower layer optimization model and solving to obtain the power flow distribution of the power distribution network; And the scheduling module is used for obtaining a scheduling scheme of the power distribution network based on the power flow distribution of the power distribution network.
13. The apparatus of claim 12, wherein the pre-constructed upper layer optimization model comprises a first objective function and a first constraint that target a minimum of payload fluctuation.
14. The apparatus of claim 13, wherein the first objective function is as follows: In the above-mentioned method, the step of, In order for the net load to fluctuate, As the power load at the time t, For the fan output at time t, For the photovoltaic output at time t, To optimize the period.
15. The apparatus of claim 14, wherein the first constraint is as follows: , In the above formula, alpha is the upper limit of the load response change at each moment, beta is the upper limit of the electricity price change at each moment, As the response power at the time t, For the response electricity price at the time t, For the initial electricity price at time t, Is the fluctuation of electricity price at time t.
16. The apparatus of claim 13, wherein the pre-constructed middle layer optimization model includes a second objective function and a second constraint that target a minimum total cost of the assembly of units.
17. The apparatus of claim 16, wherein the second objective function is as follows: In the above-mentioned method, the step of, The total cost for the unit assembly is, Is the charge and discharge cost of the electric automobile, For the maintenance cost of the wind and light, In order to discard the wind and the light, In order to purchase the cost of electricity, For the charge and discharge costs of the energy storage system, In order to achieve the cost of charge and discharge loss, The charging and discharging basic cost of the electric automobile is realized, The electric discharge compensation is carried out for the electric automobile, 、 The operation and maintenance cost of wind power and photovoltaic output unit power are respectively, 、 Respectively generating actual power generation amount of wind power and photovoltaic at time tsuper, 、 Wind power respectively the wind abandoned penalty cost of the unit generated energy of the photovoltaic, 、 The power generation amount is respectively estimated by wind power and photovoltaic, 、 The electricity price and the electricity power are purchased at the time t, 、、、、 The electricity price, the absolute value of the charging power, the absolute value of the discharging power, the charging efficiency and the discharging efficiency at the time t are respectively, 、 Respectively the charge and discharge power of the energy storage system and the cost coefficient of the unit charge and discharge energy of the energy storage system, In order to provide for the time interval of time, The period is optimized.
18. The apparatus of claim 17, wherein the electric vehicle charge-discharge basis costs are as follows: The electric automobile discharge compensation is as follows: In the above-mentioned method, the step of, 、 The charge and discharge loads of the electric automobile at the time t are respectively, The loss coefficient of the discharge battery of the electric automobile, The charging price of the electric car at the time t, 、 The total number of electric vehicles charged and discharged at time t, 、 The average charging power and the average discharging power of the electric automobile at the time t respectively, As the discharge price of the electric car at time t, And k is the discharge compensation price of the electric automobile, and J is the discharge compensation growth rate of the electric automobile.
19. The apparatus of claim 18, wherein the second constraint is as follows: , In the above-mentioned method, the step of, 、 Respectively wind power and photovoltaic maximum rated power, 、 For the maximum number of electric vehicles that can be charged and discharged at time t, For the state of charge of the energy storage system at time t, For time t-1 the state of charge of the energy storage system, 、 Respectively a lower limit and an upper limit of the charge state of the energy storage system, In order to achieve the efficiency of the charge, In order for the discharge efficiency to be high, For the charging power at the time t, For the discharge power at the time t, As the response power at the time t, 、 The upper limit of the charging power and the upper limit of the discharging power are respectively set.
20. The apparatus of claim 13, wherein the pre-constructed middle layer optimization model includes a third objective function and a third constraint that target a minimum loss of the distribution network.

Description

Electric automobile and energy storage multilayer joint scheduling method and device based on source network charge storage cooperation Technical Field The invention relates to the technical field of power system dispatching, in particular to an electric vehicle and energy storage multilayer joint dispatching method and device based on source network charge storage coordination. Background Under the background of accelerating the power system to low carbonization and flexible transformation, the installed capacity of renewable energy sources such as wind power, photovoltaic and the like is greatly increased. However, due to strong fluctuation of new energy output and randomness of load demands, the phenomena of transmission loss surge of the power grid in the peak period, wind discarding and light discarding rate rising in the valley period and the like occur in the power distribution network. The traditional mode relying on power supply side regulation has difficulty in adapting to the operation requirement of a novel power system due to response delay and insufficient economy. The Electric Vehicle (EV) is used as a flexible resource with load and energy storage properties, and a new path is provided for power distribution network optimization by large-scale access. The unordered charging can cause 15% -25% of network loss of the power distribution network, and the existing ordered scheduling research has the limitations that firstly, the Demand Response (DR) is analyzed in an isolated mode, the EV scheduling or Energy Storage System (ESS) is controlled, a linkage optimization framework of price-compensation-ESS is lacked, secondly, the design of a battery loss compensation mechanism of EV discharging is extensive, the excitation of deep peak shaving is not enough, and thirdly, the multi-objective cooperative mechanism of load side fluctuation suppression, source storage side economy and network side network loss optimization is not comprehensively arranged. In addition, the demand response mechanism depends on a single electricity price signal, the nonlinear response characteristic of a user to the electricity price is not fully considered, the complementary scheduling potential of the ESS and the EV is not fully mined, and the economic, low-carbon and efficient operation of the power distribution network is difficult to realize. Disclosure of Invention In order to overcome the defects, the invention provides an electric vehicle and energy storage multilayer joint scheduling method and device based on source network charge storage coordination. In a first aspect, an electric vehicle and energy storage multilayer joint scheduling method based on source network charge storage coordination is provided, where the electric vehicle and energy storage multilayer joint scheduling method based on source network charge storage coordination includes: Solving a pre-constructed upper layer optimization model to obtain a load curve; Substituting the load curve into a pre-constructed middle layer optimization model and solving to obtain a source storage scheduling scheme and a tide result; Substituting the source storage scheduling scheme and the power flow result into a pre-constructed lower layer optimization model and solving to obtain power flow distribution of the power distribution network; and obtaining a scheduling scheme of the power distribution network based on the power flow distribution of the power distribution network. Preferably, the pre-constructed upper layer optimization model comprises a first objective function and a first constraint condition aiming at minimum fluctuation of the net load. Further, the first objective function is as follows: In the above-mentioned method, the step of, In order for the net load to fluctuate,As the power load at the time t,For the fan output at time t,For the photovoltaic output at time t,To optimize the period. Further, the first constraint condition is as follows: In the above formula, alpha is the upper limit of the load response change at each moment, beta is the upper limit of the electricity price change at each moment, As the response power at the time t,For the response electricity price at the time t,For the initial electricity price at time t,Is the fluctuation of electricity price at time t. Further, the pre-constructed middle layer optimization model comprises a second objective function and a second constraint condition aiming at the minimum total cost of the unit combination. Further, the second objective function is as follows: In the above-mentioned method, the step of, The total cost for the unit assembly is,Is the charge and discharge cost of the electric automobile,For the maintenance cost of the wind and light,In order to discard the wind and the light,In order to purchase the cost of electricity,For the charge and discharge costs of the energy storage system,In order to achieve the cost of charge and discharge loss,The charging and discharging bas