CN112149960A - Photo-electricity-micro coordination control method based on prediction of photovoltaic power generation - Google Patents

Photo-electricity-micro coordination control method based on prediction of photovoltaic power generation Download PDF

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CN112149960A
CN112149960A CN202010876565.1A CN202010876565A CN112149960A CN 112149960 A CN112149960 A CN 112149960A CN 202010876565 A CN202010876565 A CN 202010876565A CN 112149960 A CN112149960 A CN 112149960A
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郁家麟
金海�
施海峰
雷象兵
黄旻
张群艳
高嘉豪
顾一懿
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Haining Jinneng Power Industry Co ltd
Haining Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Haining Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Abstract

A photo-electric-micro coordination control method based on prediction of photovoltaic power generation comprises the following steps: step 1, establishing a time series model for predicting photovoltaic power generation, and providing a time series model-difference integration moving average autoregressive model ARIMA for predicting photovoltaic power generation, wherein the model is used as input data of an Energy Management System (EMS) model; step 2, establishing a mixed integer linear programming model, wherein the process is as follows: 2.1, the constraint conditions comprise electric automobile constraint, photovoltaic power generation constraint and power grid constraint; and 2.2, setting an objective function. The invention can predict the inflow of PV power, and then optimally plan the parking lot for PV power generation and distribute the power flow, thereby reducing the charging cost and relieving the pressure on the main power grid; a differential integrated moving average autoregressive model for predicting PV power, and another mixed integer linear programming approach, the framework can optimally allocate power to minimize charging costs.

Description

Photo-electricity-micro coordination control method based on prediction of photovoltaic power generation
Technical Field
The invention relates to an Energy Management System (EMS) with prediction of photovoltaic power generation, in particular to a photo-electric-micro coordination control method based on prediction of photovoltaic power generation, which can predict the power generation amount of Photovoltaic (PV) and optimize the power flow between photovoltaic, micro-grid and electric vehicle (BEV) in a workplace. The sustainability of BEVs is improved by increasing PV self-consumption, thereby minimizing charging costs.
Background
Due to the increasing severity of energy crisis and environmental pollution problems, the vigorous development of renewable energy and electric vehicle technologies becomes an important measure for energy conservation and emission reduction. With the rapid development of electric automobile technology at home and abroad, the proportion of electric automobiles occupying conventional automobiles is continuously increased. The electric automobile contains a large amount of storage battery inside, is equivalent to the mobile energy storage, and the storage battery has the energy bidirectional flow characteristic simultaneously, both can regard as the load, can regard as the power again, consequently can not only reduce static energy storage configuration capacity through carrying out effectual control to the large-scale electric automobile who inserts based on this characteristic, can provide auxiliary service for the electric wire netting simultaneously, therefore electric automobile mobile energy storage participated in the electric wire netting and interacted and have obtained extensive concern. In recent years, the advantages of few energy conversion times, high efficiency, simple control structure, easy photovoltaic and energy storage access and the like of the direct-current micro-grid are rapidly developed. Therefore, how to effectively improve the energy utilization rate among electric vehicles, photovoltaic systems and micro-grids becomes one of the hot spots of the current research.
At present, research aiming at a joint optimization scheduling strategy containing distributed new energy, electric vehicles and a microgrid is a research focus of students in various countries, documents are provided for establishing an electric vehicle double-layer economic scheduling model based on electric vehicle space distribution characteristics and network optimal trend, and economic scheduling of the electric vehicles in the microgrid is realized from 2 levels of space and time. Aiming at the joint optimization scheduling of photovoltaic power generation, electric vehicles and a microgrid, a scholars proposes that the grid loss is minimum, a double particle swarm algorithm is adopted to carry out the joint optimization scheduling of the electric vehicles and the photovoltaic, and the economic operation of the microgrid is realized. Furthermore, previous studies aimed at studying minimization of emissions, minimization of penalty costs, minimization of operational costs, improvement of self-consumption or maximization of profit, etc., while achieving satisfactory results in terms of respective objective functions and settings, daily optimization was performed without prediction. Still other scholars have proposed heuristic strategies and fuzzy logic controller methods with predictive capabilities to coordinate control of photovoltaic power generation, electric vehicles and microgrids. These control methods, however, do not achieve global optimality.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a photo-electric-micro coordination control method based on prediction of photovoltaic power generation, which can predict the inflow of PV power and then optimally plan a parking lot for PV power generation and distribute power flow, thereby reducing charging cost and relieving pressure on a main power grid; one differential integrated moving average autoregressive model (ARIMA) for predicting PV power, and another Mixed Integer Linear Programming (MILP) framework that optimally distributes power to minimize charging cost.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a photo-electric-micro coordination control method based on prediction of photovoltaic power generation comprises the following steps:
step 1, establishing a time series model for predicting photovoltaic power generation
Giving a time series model-difference integration moving average autoregressive model ARIMA for predicting photovoltaic power generation capacity, wherein the model is used as input data of an Energy Management System (EMS) model;
ARIMA consists of two parts: an autoregressive model of order p (ar (p)) and a moving average model of order q (ma (q)), both of which describe a stationary process; however, since irradiance data exhibits non-stationary characteristics, a difference method is required to achieve smoothing of the time series; furthermore, the data collected is seasonal data with s time periods, in which case the ARIMA model can be extended to a seasonal ARIMA (sarima) model, introducing B as a backward shift operator, so that BXt＝Xt-1The SARIMA model is expressed in polynomial form as follows:
φ(B)Φ(Bs)(1-B)d(1-Bs)DXt＝θ(B)Θ(Bs)
wherein phi (B) is 1-phi1B-φ2B2-L-φPBPDescribe by phi1LφPA non-seasonal ar (p) process that is a parameter; theta (B) 1+ phi1B+φ2B2+L+φPBPIs measured by phi1LφPA non-seasonal ma (q) process that is a parameter; the first difference is represented as (X)t-Xt-1)＝(1-B)XtThus, the d-th difference is represented as (1-B)dXtt, expressing the seasonal difference D as (X)t-Xt-s)＝(1-Bs)Xt；
Step 2, establishing a mixed integer linear programming model, wherein the process is as follows:
2.1, constraints, as follows:
2.1.1) electric vehicle restraint
Electric vehicle charging and discharging power limitation:
Figure BDA0002652768720000031
Figure BDA0002652768720000032
wherein u isi,c,tIs a binary variable (0/1) indicating whether the ith BEV for the c-th charging point is chargeable during the t-period; v. ofi,c,tA binary variable (0/1) indicating whether the ith BEV of the c-th charging point in the time period t is discharging;
Figure BDA0002652768720000033
represents the power transfer to the ith BEV at the c-th charging point during time period t;
Figure BDA0002652768720000034
indicating power transfer from the ith BEV at the c-th charging point during the time period t;
Figure BDA0002652768720000035
and
Figure BDA0002652768720000036
respectively represent the maximum power transmitted to and from the ith BEV;
it is physically impossible to charge multiple electric vehicles at the same charging point, and to reduce costs, a modular converter topology is used, thus introducing the following constraints:
Figure BDA0002652768720000037
using two sets of binary variables ui,c,tAnd vi,c,tTo ensure that only one electric vehicle can be charged or discharged at a given charging point in a given time;
calculating the charging power of the electric automobile according to the following formula:
Figure BDA0002652768720000038
wherein the content of the first and second substances,
Figure BDA0002652768720000039
is the total transmission power of the c-th charging point to the i-th BEV in the time period t; etach，ηdisRespectively the charge and discharge efficiency of the electric automobile;
the battery capacity of the electric automobile is as follows:
Figure BDA0002652768720000041
wherein E isi,c,tIs the energy capacity of the battery at the ith BEV at the c-th charging point in time period t;
Figure BDA0002652768720000042
the energy capacity of the ith BEV at the c-th charging point at arrival;
Figure BDA0002652768720000043
the energy capacity of the ith BEV at the c-th charging point at departure;
to extend battery life and prevent deep discharge and overcharge of automotive batteries, the following constraints were introduced:
Figure BDA0002652768720000044
Figure BDA0002652768720000045
respectively representing the minimum and maximum battery capacities of the ith BEV at the c-th charging point in all time periods t;
to reduce the adverse impact of intermittent charge/discharge on battery capacity fade, the EMS is allowed to initiate a maximum NmaxThe charge/discharge process, as follows:
Figure BDA0002652768720000046
Figure BDA0002652768720000047
Figure BDA0002652768720000048
Figure BDA0002652768720000049
wherein the content of the first and second substances,
Figure BDA00026527687200000410
respectively representing binary variables ui,c,tPositive and negative differences between the ON and OFF states of (a);
Figure BDA00026527687200000411
respectively representing binary variables vi,c,tPositive and negative differences between the ON and OFF states of (a); n is a radical ofmaxRepresents the maximum number of charging and discharging times;
furthermore, it is specified when to disconnect the electric vehicle from the charging point:
ui,c,t＝0,
Figure BDA00026527687200000412
or
Figure BDA00026527687200000413
vi,c,t＝0,
Figure BDA00026527687200000414
Or
Figure BDA00026527687200000415
2.1.2) photovoltaic Power Generation constraints
Figure BDA0002652768720000051
Wherein η MPPT is the DC-DC converter efficiency; etainvTo the inverter efficiency;
Figure BDA0002652768720000052
represents the power transfer from photovoltaic power generation to the ith BEV at the c-th charging point of the time period t;
Figure BDA0002652768720000053
represents the power transmission from the photovoltaic system to the grid during a time period t;
Figure BDA0002652768720000054
represents the maximum photovoltaic power generation power over the time period t;
2.1.3) grid constraints
The EV-PV charger is a three-port charger that limits charge and discharge power according to the following equation:
Figure BDA0002652768720000055
Figure BDA0002652768720000056
wherein the content of the first and second substances,
Figure BDA0002652768720000057
indicating that during the time period t, the c-th chargeThe electric point transmits power from the power grid to the ith BEV;
Figure BDA0002652768720000058
represents the maximum power transfer from the grid to the c-charge point; si,c,tThe binary variable (0/1) indicates that power is prevented from being supplied to the grid while power is being drawn from the grid.
Figure BDA0002652768720000059
Representing the transfer of power from the ith BEV at the c-th charging point to the grid during a time period t;
Figure BDA00026527687200000510
representing the transmitted power from the photovoltaic generation to the grid during a time period t;
Figure BDA00026527687200000511
represents the maximum transmission power from the c-charge point to the grid;
power balance in the charging process of the electric automobile:
Figure BDA00026527687200000512
wherein eta isinvIs the grid-connected inverter efficiency;
2.2 setting the objective function
Figure BDA00026527687200000513
Wherein, CtotThe total cost generated in the process of charging and discharging;
Figure BDA0002652768720000061
marginal price for charging electric vehicles during t;
Figure BDA0002652768720000062
the marginal price of photovoltaic power generation at the time t;
Figure BDA0002652768720000063
on-line electricity price lambda in time period tdeg。
The invention has the following beneficial effects: the inflow of PV power can be predicted and then the parking lot for PV power generation can be optimally planned and the power flow distributed, thereby reducing charging costs and relieving stress on the main grid.
Drawings
Fig. 1 is a flowchart of a photo-electric-micro coordination control method based on prediction of photovoltaic power generation.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, a photo-electro-micro coordination control method based on prediction of photovoltaic power generation includes the following steps:
step 1, establishing a time series model for predicting photovoltaic power generation
Giving a time series model-difference integration moving average autoregressive model ARIMA for predicting photovoltaic power generation capacity, wherein the model is used as input data of an Energy Management System (EMS) model;
ARIMA consists of two parts: an autoregressive model of order p (ar (p)) and a moving average model of order q (ma (q)), both of which describe a stationary process; however, since irradiance data exhibits non-stationary characteristics, a difference method is required to achieve smoothing of the time series; furthermore, the data collected is seasonal data with s time periods, in which case the ARIMA model can be extended to a seasonal ARIMA (sarima) model, introducing B as a backward shift operator, so that BXt＝Xt-1The SARIMA model is expressed in polynomial form as follows:
φ(B)Φ(Bs)(1-B)d(1-Bs)DXt＝θ(B)Θ(Bs)
wherein phi (B) is 1-phi1B-φ2B2-L-φPBPDescribe by phi1LφPA non-seasonal ar (p) process that is a parameter; theta (B) 1+ phi1B+φ2B2+L+φPBPIs measured by phi1LφPA non-seasonal ma (q) process that is a parameter; the first difference is represented as (X)t-Xt-1)＝(1-B)XtThus, the d-th difference is represented as (1-B)dXtt, expressing the seasonal difference D as (X)t-Xt-s)＝(1-Bs)Xt；
Step 2, establishing a mixed integer linear programming model, wherein the process is as follows:
2.1, constraints, as follows:
2.1.1) electric vehicle restraint
Electric vehicle charging and discharging power limitation:
Figure BDA0002652768720000071
Figure BDA0002652768720000072
wherein u isi,c,tIs a binary variable (0/1) indicating whether the ith BEV for the c-th charging point is chargeable during the t-period; v. ofi,c,tA binary variable (0/1) indicating whether the ith BEV of the c-th charging point in the time period t is discharging;
Figure BDA0002652768720000073
represents the power transfer to the ith BEV at the c-th charging point during time period t;
Figure BDA0002652768720000074
indicating power transfer from the ith BEV at the c-th charging point during the time period t;
Figure BDA0002652768720000075
and
Figure BDA0002652768720000076
respectively represent the maximum power transmitted to and from the ith BEV;
it is physically impossible to charge multiple electric vehicles at the same charging point, and to reduce costs, a modular converter topology is used, thus introducing the following constraints:
Figure BDA0002652768720000077
using two sets of binary variables ui,c,tAnd vi,c,tTo ensure that only one electric vehicle can be charged or discharged at a given charging point in a given time;
due to the loss, the electric quantity received by the electric automobile is less than the charging point
Figure BDA0002652768720000078
The available amount of electricity, assuming that the round trip efficiency of the electric vehicle battery is 0.92, and therefore the efficiency of a single trip is 0.96, and assuming that the charger efficiency is 0.94, and therefore the charge/discharge efficiency can be calculated to be 0.90, then the electric vehicle charging power is calculated according to the following formula:
Figure BDA0002652768720000079
wherein the content of the first and second substances,
Figure BDA00026527687200000710
is the total transmission power of the c-th charging point to the i-th BEV in the time period t; etach，ηdisRespectively the charge and discharge efficiency of the electric automobile;
the battery capacity of the electric automobile is as follows:
Figure BDA0002652768720000081
wherein E isi,c,tIs the energy capacity of the battery at the ith BEV at the c-th charging point in time period t;
Figure BDA0002652768720000082
the energy capacity of the ith BEV at the c-th charging point at arrival;
Figure BDA0002652768720000083
the energy capacity of the ith BEV at the c-th charging point at departure;
to extend battery life and prevent deep discharge and overcharge of automotive batteries, the following constraints were introduced:
Figure BDA0002652768720000084
Figure BDA0002652768720000085
respectively representing the minimum and maximum battery capacities of the ith BEV at the c-th charging point in all time periods t;
to reduce the adverse impact of intermittent charge/discharge on battery capacity fade, the EMS is allowed to initiate a maximum NmaxThe charge/discharge process, as follows:
Figure BDA0002652768720000086
Figure BDA0002652768720000087
Figure BDA0002652768720000088
Figure BDA0002652768720000089
wherein the content of the first and second substances,
Figure BDA00026527687200000810
respectively representing binary variables ui,c,tAnd ON andpositive and negative differences between the OFF states;
Figure BDA00026527687200000811
respectively representing binary variables vi,c,tPositive and negative differences between the ON and OFF states of (a); n is a radical ofmaxRepresents the maximum number of charging and discharging times;
furthermore, it is specified when to disconnect the electric vehicle from the charging point:
ui,c,t＝0,
Figure BDA00026527687200000812
or
Figure BDA00026527687200000813
vi,c,t＝0,
Figure BDA00026527687200000814
Or
Figure BDA00026527687200000815
2.1.2) photovoltaic Power Generation constraints
Figure BDA0002652768720000091
Wherein η MPPT is the DC-DC converter efficiency; etainvTo the inverter efficiency;
Figure BDA0002652768720000092
represents the power transfer from photovoltaic power generation to the ith BEV at the c-th charging point of the time period t;
Figure BDA0002652768720000093
represents the power transmission from the photovoltaic system to the grid during a time period t;
Figure BDA0002652768720000094
represents the maximum photovoltaic power generation power over the time period t;
2.1.3) grid constraints
The EV-PV charger is a three port charger, assuming a rated power of 10kW, thus limiting the charging and discharging power according to the following formula:
Figure BDA0002652768720000095
Figure BDA0002652768720000096
wherein the content of the first and second substances,
Figure BDA0002652768720000097
represents the transfer of power from the grid to the ith BEV at the c-th charging point during the time period t;
Figure BDA0002652768720000098
represents the maximum power transfer from the grid to the c-charge point; si,c,tThe binary variable (0/1) indicates that power is prevented from being supplied to the grid while power is being drawn from the grid.
Figure BDA0002652768720000099
Representing the transfer of power from the ith BEV at the c-th charging point to the grid during a time period t;
Figure BDA00026527687200000910
representing the transmitted power from the photovoltaic generation to the grid during a time period t;
Figure BDA00026527687200000911
represents the maximum transmission power from the c-charge point to the grid;
power balance in the charging process of the electric automobile:
Figure BDA00026527687200000912
wherein eta isinvIs the grid-connected inverter efficiency;
2.2 setting the objective function
Figure BDA0002652768720000101
Wherein, CtotThe total cost generated in the process of charging and discharging; lambda [ alpha ]G2VtMarginal price for charging electric vehicles during t;
Figure BDA0002652768720000102
the marginal price of photovoltaic power generation at the time t;
Figure BDA0002652768720000103
on-line electricity price lambda in time period tdeg。

Claims (4)

1. An opto-electro-micro coordination control method based on prediction of photovoltaic power generation, characterized in that the method comprises the following steps:
step 1, establishing a time series model for predicting photovoltaic power generation
Giving a time series model-difference integration moving average autoregressive model ARIMA for predicting photovoltaic power generation capacity, wherein the model is used as input data of an Energy Management System (EMS) model;
ARIMA consists of two parts: an autoregressive model of order p (ar (p)) and a moving average model of order q (ma (q)), both of which describe a stationary process; however, since irradiance data exhibits non-stationary characteristics, a difference method is required to achieve smoothing of the time series; furthermore, the data collected is seasonal data with s time periods, in which case the ARIMA model can be extended to a seasonal ARIMA (sarima) model, introducing B as a backward shift operator, so that BXt＝Xt-1The SARIMA model is expressed in polynomial form as follows:
φ(B)Φ(Bs)(1-B)d(1-Bs)DXt＝θ(B)Θ(Bs)
wherein phi (B) is 1-phi1B-φ2B2-L-φPBPDescribe by phi1LφPA non-seasonal ar (p) process that is a parameter; theta (B) 1+ phi1B+φ2B2+L+φPBPIs measured by phi1LφPA non-seasonal ma (q) process that is a parameter; the first difference is represented as (X)t-Xt-1)＝(1-B)XtThus, the d-th difference is represented as (1-B)dXtt, expressing the seasonal difference D as (X)t-Xt-s)＝(1-Bs)Xt；
Step 2, establishing a mixed integer linear programming model, wherein the process is as follows:
2.1, constraint conditions including 2.1.1) electric vehicle constraint, 2.2.2) photovoltaic power generation constraint and 2.1.3) power grid constraint;
2.2 setting the objective function
Figure FDA0002652768710000011
Wherein, CtotThe total cost generated in the process of charging and discharging;
Figure FDA0002652768710000012
marginal price for charging electric vehicles during t;
Figure FDA0002652768710000013
the marginal price of photovoltaic power generation at the time t;
Figure FDA0002652768710000014
on-line electricity price lambda in time period tdeg。
2. The photo-electric-micro coordination control method based on prediction of photovoltaic power generation as claimed in claim 1, wherein in 2.1.1), electric vehicle constraints are defined as follows:
electric vehicle charging and discharging power limitation:
Figure FDA0002652768710000015
Figure FDA0002652768710000016
wherein u isi,c,tIs a binary variable (0/1) indicating whether the ith BEV for the c-th charging point is chargeable during the t-period; v. ofi,c,tA binary variable (0/1) indicating whether the ith BEV of the c-th charging point in the time period t is discharging;
Figure FDA0002652768710000017
represents the power transfer to the ith BEV at the c-th charging point during time period t;
Figure FDA0002652768710000018
indicating power transfer from the ith BEV at the c-th charging point during the time period t;
Figure FDA0002652768710000021
and
Figure FDA0002652768710000022
respectively represent the maximum power transmitted to and from the ith BEV;
it is physically impossible to charge multiple electric vehicles at the same charging point, and to reduce costs, a modular converter topology is used, thus introducing the following constraints:
Figure FDA0002652768710000023
using two sets of binary variables ui,c,tAnd vi,c,tTo ensure that only one electric vehicle can be charged or discharged at a given charging point in a given time;
calculating the charging power of the electric automobile according to the following formula:
Figure FDA0002652768710000024
wherein the content of the first and second substances,
Figure FDA0002652768710000025
is the total transmission power of the c-th charging point to the i-th BEV in the time period t; etach，ηdisRespectively the charge and discharge efficiency of the electric automobile;
the battery capacity of the electric automobile is as follows:
Figure FDA0002652768710000026
wherein E isictIs the energy capacity of the battery at the ith BEV at the c-th charging point in time period t;
Figure FDA0002652768710000027
the energy capacity of the ith BEV at the c-th charging point at arrival;
Figure FDA0002652768710000028
the energy capacity of the ith BEV at the c-th charging point at departure;
to extend battery life and prevent deep discharge and overcharge of automotive batteries, the following constraints were introduced:
Figure FDA0002652768710000029
Figure FDA00026527687100000210
respectively representing the minimum and maximum battery capacities of the ith BEV at the c-th charging point in all time periods t;
to is coming toReducing the adverse effects of intermittent charge/discharge on battery capacity fade, allowing EMS to initiate maximum NmaxThe charge/discharge process, as follows:
Figure FDA00026527687100000211
Figure FDA00026527687100000212
Figure FDA00026527687100000213
Figure FDA00026527687100000214
wherein the content of the first and second substances,
Figure FDA00026527687100000215
respectively representing binary variables ui,c,tPositive and negative differences between the ON and OFF states of (a);
Figure FDA00026527687100000216
respectively representing binary variables vi,c,tPositive and negative differences between the ON and OFF states of (a); n is a radical ofmaxRepresents the maximum number of charging and discharging times;
furthermore, it is specified when to disconnect the electric vehicle from the charging point:
ui,c,t＝0,
Figure FDA00026527687100000217
or
Figure FDA00026527687100000218
vi,c,t＝0,
Figure FDA0002652768710000031
Or
Figure FDA0002652768710000032
3. The photo-electro-micro cooperative control method based on prediction of photovoltaic power generation according to claim 1 or 2, wherein in 2.1.2), the photovoltaic power generation constraint is defined as follows:
Figure FDA0002652768710000033
wherein η MPPT is the DC-DC converter efficiency; etainvTo the inverter efficiency;
Figure FDA0002652768710000034
represents the power transfer from photovoltaic power generation to the ith BEV at the c-th charging point of the time period t;
Figure FDA0002652768710000035
represents the power transmission from the photovoltaic system to the grid during a time period t;
Figure FDA0002652768710000036
representing the maximum photovoltaic power generation over the time period t.
4. The photo-electric-micro coordinated control method based on prediction of photovoltaic power generation according to claim 1 or 2, characterized in that in 2.1.3), the grid constraints are defined as follows:
the EV-PV charger is a three-port charger that limits charge and discharge power according to the following equation:
Figure FDA0002652768710000037
Figure FDA0002652768710000038
wherein the content of the first and second substances,
Figure FDA0002652768710000039
represents the transfer of power from the grid to the ith BEV at the c-th charging point during the time period t;
Figure FDA00026527687100000310
represents the maximum power transfer from the grid to the c-charge point; si,c,tThe binary variable (0/1) indicates that power is prevented from being supplied to the grid while power is being drawn from the grid.
Figure FDA00026527687100000311
Representing the transfer of power from the ith BEV at the c-th charging point to the grid during a time period t;
Figure FDA00026527687100000312
representing the transmitted power from the photovoltaic generation to the grid during a time period t;
Figure FDA00026527687100000313
represents the maximum transmission power from the c-charge point to the grid;
power balance in the charging process of the electric automobile:
Figure FDA00026527687100000314
wherein eta isinvIs the grid-tied inverter efficiency.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6928398B1 (en) * 2000-11-09 2005-08-09 Spss, Inc. System and method for building a time series model
CN104269849A (en) * 2014-10-17 2015-01-07 国家电网公司 Energy managing method and system based on building photovoltaic micro-grid
CN109103929A (en) * 2018-09-11 2018-12-28 燕山大学 Based on the power distribution network economic optimization dispatching method for improving dynamic gram Li Sijin model
US20190056451A1 (en) * 2017-08-18 2019-02-21 Nec Laboratories America, Inc. System and method for model predictive energy storage system control
CN111478312A (en) * 2019-11-20 2020-07-31 国网河北省电力有限公司电力科学研究院 Comprehensive energy cluster coordination control method for improving power grid stability

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6928398B1 (en) * 2000-11-09 2005-08-09 Spss, Inc. System and method for building a time series model
CN104269849A (en) * 2014-10-17 2015-01-07 国家电网公司 Energy managing method and system based on building photovoltaic micro-grid
US20190056451A1 (en) * 2017-08-18 2019-02-21 Nec Laboratories America, Inc. System and method for model predictive energy storage system control
CN109103929A (en) * 2018-09-11 2018-12-28 燕山大学 Based on the power distribution network economic optimization dispatching method for improving dynamic gram Li Sijin model
CN111478312A (en) * 2019-11-20 2020-07-31 国网河北省电力有限公司电力科学研究院 Comprehensive energy cluster coordination control method for improving power grid stability

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
杨立波;王亚军;赵利;: "基于云计算的电网预测模型与自适应调度研究", 电子器件, no. 02 *

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