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 PDFInfo
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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
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:
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;represents the power transfer to the ith BEV at the c-th charging point during time period t;indicating power transfer from the ith BEV at the c-th charging point during the time period t;andrespectively 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:
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:
wherein the content of the first and second substances,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:
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;the energy capacity of the ith BEV at the c-th charging point at arrival;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:
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:
wherein the content of the first and second substances,respectively representing binary variables ui,c,tPositive and negative differences between the ON and OFF states of (a);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:
2.1.2) photovoltaic Power Generation constraints
Wherein η MPPT is the DC-DC converter efficiency; etainvTo the inverter efficiency;represents the power transfer from photovoltaic power generation to the ith BEV at the c-th charging point of the time period t;represents the power transmission from the photovoltaic system to the grid during a time period t;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:
wherein the content of the first and second substances,indicating that during the time period t, the c-th chargeThe electric point transmits power from the power grid to the ith BEV;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.Representing the transfer of power from the ith BEV at the c-th charging point to the grid during a time period t;representing the transmitted power from the photovoltaic generation to the grid during a time period t;represents the maximum transmission power from the c-charge point to the grid;
power balance in the charging process of the electric automobile:
wherein eta isinvIs the grid-connected inverter efficiency;
2.2 setting the objective function
Wherein, CtotThe total cost generated in the process of charging and discharging;marginal price for charging electric vehicles during t;the marginal price of photovoltaic power generation at the time t;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:
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;represents the power transfer to the ith BEV at the c-th charging point during time period t;indicating power transfer from the ith BEV at the c-th charging point during the time period t;andrespectively 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:
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 pointThe 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:
wherein the content of the first and second substances,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:
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;the energy capacity of the ith BEV at the c-th charging point at arrival;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:
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:
wherein the content of the first and second substances,respectively representing binary variables ui,c,tAnd ON andpositive and negative differences between the OFF states;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:
2.1.2) photovoltaic Power Generation constraints
Wherein η MPPT is the DC-DC converter efficiency; etainvTo the inverter efficiency;represents the power transfer from photovoltaic power generation to the ith BEV at the c-th charging point of the time period t;represents the power transmission from the photovoltaic system to the grid during a time period t;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:
wherein the content of the first and second substances,represents the transfer of power from the grid to the ith BEV at the c-th charging point during the time period t;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.Representing the transfer of power from the ith BEV at the c-th charging point to the grid during a time period t;representing the transmitted power from the photovoltaic generation to the grid during a time period t;represents the maximum transmission power from the c-charge point to the grid;
power balance in the charging process of the electric automobile:
wherein eta isinvIs the grid-connected inverter efficiency;
2.2 setting the objective function
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
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:
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;represents the power transfer to the ith BEV at the c-th charging point during time period t;indicating power transfer from the ith BEV at the c-th charging point during the time period t;andrespectively 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:
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:
wherein the content of the first and second substances,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:
wherein E isictIs the energy capacity of the battery at the ith BEV at the c-th charging point in time period t;the energy capacity of the ith BEV at the c-th charging point at arrival;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:
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:
wherein the content of the first and second substances,respectively representing binary variables ui,c,tPositive and negative differences between the ON and OFF states of (a);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:
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:
wherein η MPPT is the DC-DC converter efficiency; etainvTo the inverter efficiency;represents the power transfer from photovoltaic power generation to the ith BEV at the c-th charging point of the time period t;represents the power transmission from the photovoltaic system to the grid during a time period t;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:
wherein the content of the first and second substances,represents the transfer of power from the grid to the ith BEV at the c-th charging point during the time period t;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.Representing the transfer of power from the ith BEV at the c-th charging point to the grid during a time period t;representing the transmitted power from the photovoltaic generation to the grid during a time period t;represents the maximum transmission power from the c-charge point to the grid;
power balance in the charging process of the electric automobile:
wherein eta isinvIs the grid-tied inverter efficiency.
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