CN116151436B - Household-user-oriented photovoltaic building energy planning method and system - Google Patents

Household-user-oriented photovoltaic building energy planning method and system Download PDF

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CN116151436B
CN116151436B CN202310028423.3A CN202310028423A CN116151436B CN 116151436 B CN116151436 B CN 116151436B CN 202310028423 A CN202310028423 A CN 202310028423A CN 116151436 B CN116151436 B CN 116151436B
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CN116151436A (en
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谢方静
李兆瑜
郭幸
林海
张渊晟
曾鸣
王晶晶
廖艳芬
马晓茜
谭福太
陈庆文
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Guangzhou Huijin Energy Efficiency Technology Co ltd
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Abstract

The invention provides a photovoltaic building energy planning method and system for home users, comprising the following steps: building a building photovoltaic power generation power prediction model; establishing a power supply condition prediction model; building a building load demand prediction model; planning the capacity of energy storage equipment accessed to the building energy system by taking minimum investment and running cost in the project period of the building energy system as targets; based on personalized customization information of the user, turning to the second step or the third step or the fourth step to perform model adjustment; an intra-project planning scheme is generated, including annual investment costs and returns. The invention can assist the user to carry out investment planning and income prediction, ensures the electricity demand and pursues the maximization of income, and also considers the personalized demand of the user, thereby being beneficial to the popularization and application of the photovoltaic building energy system of the user.

Description

Household-user-oriented photovoltaic building energy planning method and system
Technical Field
The invention belongs to the technical field of photovoltaic buildings, and relates to a photovoltaic building energy planning method and system for household users.
Background
The distributed photovoltaic power generation system fully utilizes solar energy resources in a running mode of self-power-consumption and redundant power surfing on the user side, so that stone energy consumption can be reduced, and the energy cost of the user is saved. According to measurement and calculation, the development potential of the distributed photovoltaic technology in 2025 is 14.9 hundred million kilowatts, wherein the photovoltaic and industrial and mining factory buildings at the top of a house account for 89% of the total development potential of the technology, and the distributed photovoltaic technology is an important support for achieving the aim of double carbon. However, due to the small investment and decentralized locations of individual user-level building photovoltaic projects, related operators are less promoted for such projects, resulting in many potential users lacking an efficient way of knowing the photovoltaic building energy system.
In a photovoltaic building energy system, the load is relatively small, and the instability of the photovoltaic output has a large influence on energy supply. The storage battery is arranged for the photovoltaic building energy system, so that the fluctuation of the photovoltaic output can be smoothed, the electricity selling benefits of the low-valley energy storage peak can be realized when the price difference of the electricity price peak in the valley period is large, and the uninterrupted power supply can be provided for the building energy system when the power is cut off. However, the cost of the battery is high, and the number of charge and discharge times can affect the life of the battery. Currently, the selection of battery capacity in engineering operation is generally based on empirical values or local grid policies, and a method for finely planning a photovoltaic building energy system is still lacking.
Therefore, how to provide a method and a system for finely planning photovoltaic building energy for home users is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a photovoltaic building energy planning method and system for home users, which solve the technical problems in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the invention discloses a photovoltaic building energy planning method for a household user, which comprises the following steps of:
s1: building a building photovoltaic power generation power prediction model based on historical meteorological data and historical photovoltaic power generation power recording data of an area where a user is located, and obtaining building photovoltaic power generation power period-by-period prediction data under a set time scale;
S2: based on electricity price information, electric energy supply data and historical power failure data of the region where the user is located, and combining a power failure occurrence period, establishing a power supply condition prediction model to obtain predicted electricity price under a set time scale;
s3: based on the use record data of the electric equipment of the user history, building a building load demand prediction model by combining the energy consumption habit data of the user, and obtaining the prediction data of the building load demand in a time scale period by period;
s4: according to the time-period-by-time prediction data of building photovoltaic power generation power, the time-period-by-time prediction data of building load demand and the predicted electricity price under the set time scale, performing preliminary planning on the capacity of a storage battery accessed to a building energy system by taking the minimum investment and operation cost in the project period of the building energy system as an objective function;
s5: based on the personalized setting parameters of the user and the risk preference coefficient of the power failure event, turning to S2 to adjust the power supply condition prediction model; according to the long-term planning parameters, turning to S3 to adjust the building load demand prediction model; according to the environment-friendly preference coefficient, turning to S4 to adjust the objective function; and updating and optimizing the preliminary planning of the capacity of the storage battery accessed to the building energy system based on the adjusted power supply condition prediction model, the building load demand prediction model and the objective function.
Preferably, the building of the building photovoltaic power generation power prediction model in S1 includes:
s11: according to the solar radiation actual measurement data or global meteorological database data of the region where the user is located, the historical radiation quantity data of the region where the user is located is fitted to obtain predicted irradiance data W of time intervals 0 (t), t being a selected period of time;
s12: according to the area of the building, which can be paved with the photovoltaic, the installed capacity P of the photovoltaic is determined, and according to the angle of the building roof, the inclination angle O fixed by the component and the solar irradiance data W of the area where the user is located are selected 0 (t) predicting time-by-time-period inclined-plane irradiance data W (t);
s13: collecting the geographical position and commissioned data of the same type of distributed photovoltaic engineering in the region; building L for distance users i According to the average annual radiation quantity, the installed photovoltaic capacity and the actual annual energy production of the photovoltaic on the inclined plane of the photovoltaic engineering, the comprehensive power generation efficiency K of the current photovoltaic engineering is calculated i ：
Wherein i is the number of the distributed photovoltaic engineering; p (P) i Is the annual total power generation of the put-into-operation photovoltaic engineering; p (P) i 0 Is the installed capacity of the photovoltaic; w (W) i Is the annual inclined plane radiation quantity of the put-into-operation photovoltaic engineering;
s14: calculating the comprehensive efficiency coefficient K of the photovoltaic engineering of the user building according to the power generation comprehensive efficiency of the selected n operated photovoltaic engineering:
S15: calculating the generated energy P (pv, t) of the photovoltaic power generation system according to the comprehensive efficiency coefficient K, the photovoltaic installed capacity P and the predicted period-by-period inclined plane irradiance data W (t):
preferably, the establishing a power supply condition prediction model in S2 includes:
every time a power failure event occurs, the electricity price in the power failure event time period is increased by a set multiple epsilon of the original electricity price:
C(grid,t)＝C 0 (grid,t)+ε×C 0 (grid,t)
wherein C is 0 (grid, t) is the original time-of-use electricity price; epsilon is the electricity price multiple of the power outage event time period.
Preferably, the usage record data of the consumer in the history of the user in S3 includes: recording data of the electric load of the historical building electric appliance and the driving mileage of the electric automobile; wherein,,
the building electric load comprises the steps of obtaining user electric power consumption information and electric appliance information, wherein the user electric power consumption information comprises time-by-time electric power data and/or month electric power bills;
converting the driving mileage of the electric automobile into power consumption, and adding the power consumption as building load in the electricity price valley period into the building electric load;
the user energy consumption habit data comprise application setting data of the user on the electric appliance;
dividing the building electric load into translatable load and non-translatable load according to the building electric load and the user energy consumption habit, subtracting the average solar photovoltaic power generation amount from the non-translatable load to obtain time-by-time electric quantity surplus and shortage, scheduling the translatable load, and summarizing the non-translatable load and the scheduled translatable load to generate scheduled building load demand time-by-time prediction data.
Preferably, in S4, a single-objective optimization method is adopted to plan the electricity storage device in the building, and the objective function is:
wherein C is pv Is a photovoltaic project around the projectInvestment and maintenance operation costs during the period;
is the investment cost of the unit storage battery in the project period;
alpha is the number of the invested storage battery packs; Δt is the time interval of each period of the peak-to-valley period of the electricity price;
y is the years of the project cycle;
t is the total number of time periods within one year;
p (bt, t) is the charge-discharge power of the battery pack in the t-th period, P (bt, t) >0 when the battery is charged, and P (bt, t) <0 when the battery is discharged;
c (bt, t) is the unit price of the sum of the maintenance cost and the charge decay cost of the battery pack;
p (ev, t) is the charge-discharge power of the electric vehicle in the t-th period, P (ev, t) >0 when the electric vehicle is charged, and P (ev, t) <0 when the electric vehicle is discharged;
c (ev, t) is the electric quantity attenuation cost unit price of the electric automobile;
p (grid, t) is the exchange power between the building and the external power grid in the t period, when the building purchases electricity from the external power grid, P (grid, t) is more than 0, and when the building sells electricity to the external power grid, P (grid, t) is less than 0;
c (grid, t) is the cost or revenue unit price between the t-th time building and the external grid.
Preferably, the objective function C satisfies the following constraint:
(1) Power balance constraint:
P(pv,t)+P(grid,t)＝P(load,t)+β(ev,t)×P(ev,t)
wherein P (load, t) is the required power of all loads of the building in the t period; β (ev, t) is a connection state of the electric vehicle and the building energy system in the t-th period, if the electric vehicle is connected to the building energy system, β (ev, t) =1, if the electric vehicle is disconnected from the building energy system, β (ev, t) =0;
(2) Investment constraint of storage battery:
α≤α max
wherein alpha is max The maximum allowable investment quantity of the storage battery pack is set;
(3) And (3) restraining the charge and discharge power of the storage battery:
in the method, in the process of the invention,is the capacity of the unit storage battery pack, and χ is the ratio of the limit power of the storage battery pack charged or discharged in the period deltat;
(4) Battery charge-discharge capacity constraint:
in the method, in the process of the invention,is the capacity of a unit battery pack; />Is the lowest capacity ratio allowed by the unit battery, < >>Is the highest capacity ratio allowed by the unit storage battery pack;
(5) Electric automobile charge-discharge power constraint:
in the method, in the process of the invention,the battery capacity of the electric automobile, and gamma is the limit proportion of the charging or discharging power of the battery of the electric automobile in a period delta t;
(6) Constraint of charge and discharge capacity of electric automobile:
in phi min Is the highest capacity proportion allowed by the electric automobile, phi max Is the highest capacity ratio allowed by the electric automobile.
Preferably, in the step S5,
the personalized setting parameters comprise coefficients with uncertainty, such as blackout time risk preference coefficients, long-term planning parameters and environment-friendly preference coefficients, which are provided by a user;
the power failure event risk preference coefficient is converted into electricity price multiple epsilon in the power failure period;
the long-term planning parameters are converted into annual load coefficients eta;
the environment-friendly preference coefficient is converted into a carbon emission cost coefficient lambda of the electricity purchase or selling cost part of the power grid in the objective function, and the objective function is adjusted as follows:
the invention also provides a photovoltaic building energy planning system facing the household user, which comprises: the system comprises a user information acquisition module, a photovoltaic power generation power prediction module, a power supply information module, a building load demand prediction module, a capacity planning module and a database;
the user information acquisition module is used for acquiring geographical positions of areas where users are located, historical photovoltaic power generation power recording data, historical electric equipment use recording data and personalized setting parameters;
the photovoltaic power generation prediction module is used for establishing a building photovoltaic power generation power prediction model based on historical meteorological data and historical photovoltaic power generation power recording data of an area where a user is located, and obtaining building photovoltaic power generation power period-by-period prediction data under a set time scale;
The power supply information module is used for establishing a power supply condition prediction model based on power price information, power supply data and historical power failure data of the region where the user is located and combining the power failure occurrence time period to obtain a predicted power price under a set time scale;
the building load demand prediction module is used for establishing a building load demand prediction model based on the use record data of the historical electric equipment of the user and combining the energy consumption habit data of the user to obtain the time-period-by-time prediction data of the building load demand under a set time scale;
the capacity planning module is used for generating an energy storage equipment capacity planning scheme connected with the building energy system by taking minimum investment and running cost in the project period of the building energy system as objective functions according to the building photovoltaic power generation power time-interval prediction data, the building load demand time-interval prediction data, the electricity price information and the local power failure distribution data under the set time scale;
the database is used for storing photovoltaic power generation power record data of each region, the geographical position of the same type distributed photovoltaic engineering of the region, commissioned data, electricity price information, power failure data and a generated planning scheme.
Preferably, the photovoltaic building energy system comprises a photovoltaic array, a storage battery pack, a photovoltaic inverter, a power distribution control cabinet, building load equipment and a charging and discharging pile; the electric energy generated by the photovoltaic array is supplied to building load equipment through the photovoltaic inverter and the power distribution control cabinet, and the redundant electric energy of the photovoltaic array and the electric energy below a set electricity price threshold period are supplied to the storage battery pack and the charging and discharging pile for charging; the electric energy of the building load equipment higher than the set electricity price threshold time period is supplied by the storage battery pack and the charging and discharging piles in a discharging way; the electric automobile is charged or discharged through the charging and discharging pile, and the capacity of the electric automobile is fixed; the capacity of the storage battery is planned through a photovoltaic building energy planning system;
The electric energy generated by the photovoltaic array is supplied to building load or directly supplied to a storage battery through a power distribution control cabinet, and the storage battery and the electric automobile are supplied to building internal load in a period that the electricity price information is higher than a set electricity price threshold value;
the capacity of the storage battery is planned through a photovoltaic building energy planning system.
Preferably, the user information acquisition module acquires user information in stages, including:
the method comprises the steps of firstly, obtaining geographical position of a region where a user is located and building basic information, and calculating building photovoltaic power generation time-period prediction data under a set time scale according to a photovoltaic power generation prediction module;
the second stage, acquiring user electricity load data and electric appliance use information, and calculating building load demand time-period-by-time-period prediction data under a set time scale according to a building load demand prediction module;
and thirdly, acquiring user personalized setting parameters, and generating a capacity planning scheme of the energy storage equipment according to the capacity planning module.
Compared with the prior art, the technical scheme has the beneficial effects that:
according to the invention, through the prediction of the photovoltaic output and load demand of the photovoltaic building energy system, capacity planning is carried out on the energy storage system containing the energy storage of the electric automobile, and personalized staged customization of a planning scheme is implemented, so that investment planning and income prediction are assisted for users, the electricity demand is ensured, meanwhile, income maximization is pursued, the personalized demand of the users is considered, and popularization and application of the photovoltaic building energy system of the users are facilitated.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, it will be apparent that the drawings in the following description are only embodiments of the present invention, and other drawings can be obtained according to the provided drawings without inventive effort to a person skilled in the art;
fig. 1 is a flowchart of a photovoltaic building energy planning method for a home user according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a photovoltaic building energy planning system for a home user according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the first aspect of the present invention discloses a photovoltaic building energy planning method for a home user, which comprises the following steps:
S1: and building a building photovoltaic power generation power prediction model based on historical meteorological data of the region where the user is located and photovoltaic power generation power recording data of the historical existing engineering to obtain building photovoltaic power generation power period-by-period prediction data under a set time scale.
S2: based on electricity price information, electric energy supply data and historical power failure data of the region where the user is located, and combining the power failure occurrence time period, a power supply condition prediction model is built, and the predicted electricity price under a set time scale is obtained. The historical power outage data includes historical power outage times and durations.
S3: based on the use record data of the electric equipment of the user history, building a building load demand prediction model by combining the energy consumption habit data of the user, and obtaining the prediction data of the building load demand in a time scale according to time periods.
S4: and performing preliminary planning on the capacity of a storage battery accessed to the building energy system by taking the minimum investment and operation cost in the project period of the building energy system as an objective function according to the building photovoltaic power generation time-interval prediction data, the building load demand time-interval prediction data and the predicted electricity price under the set time scale.
S5: based on the personalized setting parameters of the user and the risk preference coefficient of the power failure event, turning to S2 to adjust the power supply condition prediction model; according to the long-term planning parameters, turning to S3 to adjust the building load demand prediction model; according to the environment-friendly preference coefficient, turning to S4 to adjust the objective function; and updating and optimizing the preliminary planning of the storage battery capacity of the access building energy system based on the adjusted power supply condition prediction model, the building load demand prediction model and the objective function to obtain a final project period planning scheme including annual investment cost and income.
In one embodiment, it is assumed that the comprehensive efficiency of the photovoltaic engineering is determined by natural factors and equipment factors, and the solar radiation amount, temperature, rainfall and other natural factors in the same area can be regarded as basically unchanged in the area, and the equipment factors remain unchanged after the engineering is built, so that the building of the photovoltaic power generation prediction model in S1 includes:
s11: according to solar radiation actual measurement data of weather stations near project sites or global weather database data such as Meteonetwork, historical radiation data of the light resources in the region for 3-10 years are fitted, the predicted radiation is more accurate as the data is closer to reality and the annual span is wider, but the acquisition and calculation difficulty is correspondingly increased, so that a balance point can be obtained between model difficulty and accuracy according to requirements; and fitting the historical radiation quantity data to obtain irradiance data of one year in the future.
S12: according to the area of the building, which can be paved with the photovoltaic, the installed photovoltaic capacity P is determined, according to the angle of the building roof, the inclination angle O fixed by the component and solar irradiance data of the area where the user is located in the next year are selected, and the inclination surface irradiance data W (t) is calculated through photovoltaic design software.
S13: according to the geographic position, acquiring photovoltaic power generation power record data of the existing project, and calculating the comprehensive power generation efficiency of the photovoltaic project: collecting the geographical position and commissioned data of the same type of distributed photovoltaic engineering in the region; the principle of selecting the existing engineering is that in the distributed photovoltaic engineering which is put into operation in the region, the installation mode and the inclined plane angle of the distributed photovoltaic engineering are consistent with those of the photovoltaic building to be planned; the photovoltaic power generation power recording data of the existing engineering comprises the number n of photovoltaic engineering and the distance L from the user building i Photovoltaic installed capacity P i 0 Average annual radiation quantity W of inclined plane i Annual energy production P i The method comprises the steps of carrying out a first treatment on the surface of the Respectively calculating the comprehensive power generation efficiency K of n photovoltaic projects i ：
Wherein i is the number of the distributed photovoltaic engineering; p (P) i Is the annual total power generation of the put-into-operation photovoltaic engineering; p (P) i 0 Is the installed capacity of the photovoltaic; w (W) i Is the total radiation quantity of the annual inclined plane of the put-into-operation photovoltaic engineering.
S14: the comprehensive efficiency coefficient K of the photovoltaic engineering of the photovoltaic building to be planned is calculated according to the comprehensive power generation efficiency of the photovoltaic engineering which is put into operation, so that deviation caused by calculation of a power generation capacity calculation formula or simulation software can be avoided; the engineering in the same area is selected, so that deviation caused by a meteorological data acquisition range can be reduced; considering the geographic distance between engineering projects is based on the amount of radiation and the inherent association of weather factors with geographic distance; calculating the comprehensive efficiency coefficient K of the photovoltaic engineering of the user building according to the power generation comprehensive efficiency coefficients of the selected n operated photovoltaic engineering:
s15: calculating the generated energy P (pv, t) of the photovoltaic power generation system according to the comprehensive efficiency coefficient K, the photovoltaic installed capacity P and the predicted period-by-period inclined plane irradiance data W (t):
in one embodiment, the power supply condition prediction model in S2 is to translate the likelihood of a power outage event to an additional price of electricity over the outage period.
Acquiring time-of-use electricity price information of the region according to the geographic position; according to the geographic position, acquiring power failure event information of the region, and converting the possibility of the power failure event into additional electricity prices in a power failure time period, wherein the power failure event is supposed to repeatedly occur based on the same reason; the power outage event information generated in the area comprises power outage frequency, occurrence time period, duration time and the like; in a modern power grid system, the power failure event caused by line faults and the like is fewer or corresponding compensation measures are provided (for example, the power failure caused by the power supply reliability of a power grid company exceeds a certain number of times and the time length is corresponding to the compensation of users), the power failure event is generally local time and local area, so that the power price is taken as a key factor, on the basis of the original time-sharing power price, when the power failure event occurs once, the power price in the power failure event time period is increased by a certain multiple epsilon of the original power price, the influence of the power failure on civil load is not great or can be supplemented (for example, the power failure is used for charging the area without power failure when an electric automobile is driven), and the power price epsilon default value of the power failure time period is 4; the corrected time-sharing electricity price of each period is as follows:
C(grid,t)＝C 0 (grid,t)+ε×C 0 (grid,t)
wherein C is 0 (grid, t) is the original time-of-use electricity price; epsilon is the electricity price multiple of the power outage event time period.
In one embodiment, the historical usage record data of the consumer in S3 includes: recording data of the electric load of the historical building electric appliance and the driving mileage of the electric automobile; and carrying out ideal scheduling and predicting the building load demand according to the historical electric power consumption data and the electric vehicle driving mileage provided by the user. Wherein,,
the historical electric appliance power consumption data can be time-by-time electric quantity data obtained from a power grid company or a smart electric meter, and also can be a user monthly electric quantity bill and household electric appliance information. It is more common that historical electricity usage data is missing or that the user can only provide coarse-grained time data. When the user provides the monthly electric quantity data, the monthly electric quantity is averaged into daily electric quantity according to the stability of household electricity, and the daily electric quantity is ideally scheduled according to the characteristics of household appliances and the energy consumption habit of the user, and the method comprises the following specific steps:
dividing an electric appliance into translatable load and non-translatable load according to electric load of the building electric appliance and energy habit of a user, selecting an average value of time-by-time power generation of each solar volt in a month, subtracting the average solar volt power generation from the non-translatable load to obtain time-by-time power surplus and shortage, dispatching the translatable load, and summarizing the non-translatable load and the dispatched translatable load to generate dispatching building load demand time-by-time prediction data; the scheduling rules are as follows:
The translatable loads in the electricity price peak period are ordered according to the descending order of the electricity consumption, and sequentially translated to the period of the surplus electricity quantity; the translatable loads in the electricity price flat period are ordered according to the descending order of the electricity consumption, and sequentially translated to the period of surplus electricity; and when the translatable load of the electricity price peak period or the flat period exceeds the surplus electric quantity, the surplus electric quantity translates to the electricity price valley period.
The electricity price information obtained by the embodiment is time-of-use electricity price, and the peak time period is 10:00-12:00 and 14:00-19:00; flat period is 8:00-10:00;12:00-14:00;19:00-00:00; the trough period is 00:00-800 a day. The electricity prices of the peaks and valleys are 0.995, 0.589 and 0.229 yuan respectively.
The power consumption scheduling conditions of the electric device in this embodiment are as follows:
table 1 scheduling of daily Power for Electrical appliances
It should be noted that, in this embodiment, 1 hour is selected as a period, so the working power of the electrical appliance is converted from the rated power and the working time period into the working power within 1 hour, for example, the rated power of the dust collector is 1000W, and the working time period is 0.5 hour, and the working power of the dust collector is 500W.
Electric vehicles have the dual properties of energy storage and load, and in photovoltaic buildings, electric vehicles are typically charged during night off-hours, so the charging load of electric vehicles is transferred as a schedulable load to off-hours. The charging load of the electric automobile is converted through the relation between the historical driving mileage and the charging amount, so that the electric automobile is generally stable, and an automobile enterprise has accurate statistical data.
In the project period, the annual building electricity load demand is considered to be related to the previous annual electricity load demand, and the annual building electricity load demand is predicted as:
P(load,y+1)＝η×P(load,y)
where η is an annual load factor and the default value is 1.
In this embodiment, the user energy consumption habit data includes application setting data of the user on the electric appliance, that is, specific requirements of the user on the electric appliance, for example, the user requires that a certain or certain electric appliance load is uninterruptible.
In one embodiment, in S4, a single-objective optimization method is adopted to plan the electricity storage device in the building, and the objective function is:
wherein C is pv The investment and maintenance operation cost of the photovoltaic project in the project period is as follows;
is the investment cost of the unit storage battery in the project period;
alpha is the number of the invested storage battery packs; Δt is the time interval of each period of the peak-to-valley period of the electricity price;
y is the years of the project cycle;
t is the total number of time periods within one year;
p (bt, t) is the charge-discharge power of the battery pack in the t-th period, P (bt, t) >0 when the battery is charged, and P (bt, t) <0 when the battery is discharged;
c (bt, t) is the unit price of the sum of the maintenance cost and the charge decay cost of the battery pack;
p (ev, t) is the charge-discharge power of the electric vehicle in the t-th period, P (ev, t) >0 when the electric vehicle is charged, and P (ev, t) <0 when the electric vehicle is discharged;
C (ev, t) is the electric quantity attenuation cost unit price of the electric automobile;
p (grid, t) is the exchange power between the building and the external power grid in the t period, when the building purchases electricity from the external power grid, P (grid, t) is more than 0, and when the building sells electricity to the external power grid, P (grid, t) is less than 0;
c (grid, t) is the cost or revenue unit price between the t-th time building and the external grid.
Wherein, the objective function comprises photovoltaic projects (including photovoltaic modules, inverters and the like), investment and maintenance operation cost of the storage battery; the electric quantity attenuation cost caused by the charge and discharge of the storage battery and the electric automobile; and the purchase cost or the sales income of the external power grid.
In this embodiment, the single-objective optimization planning model needs to satisfy constraints such as power balance of the system, investment of the storage battery, charge and discharge power and charge and discharge capacity of the storage battery and the electric vehicle, that is, the objective function C satisfies the following constraint conditions:
(1) Power balance constraint:
P(pv,t)+P(grid,t)＝P(load,t)+β(ev,t)×P(ev,t)
wherein P (load, t) is the required power of all loads of the building in the t period; β (ev, t) is a connection state of the electric vehicle and the building energy system in the t-th period, if the electric vehicle is connected to the building energy system, β (ev, t) =1, if the electric vehicle is disconnected from the building energy system, β (ev, t) =0;
(2) Investment constraint of storage battery:
wherein alpha is max The maximum allowable investment quantity of the storage battery pack is set;
(3) And (3) restraining the charge and discharge power of the storage battery:
in the method, in the process of the invention,is the capacity of the unit storage battery pack, and χ is the storage batteryThe ratio of the limit power of the charging or discharging of the battery pack in the period delta t;
(4) Battery charge-discharge capacity constraint:
in the method, in the process of the invention,is the capacity of a unit battery pack; />Is the lowest capacity ratio allowed by the unit battery, < >>Is the highest capacity ratio allowed by the unit storage battery pack;
(5) Electric automobile charge-discharge power constraint:
in the method, in the process of the invention,the battery capacity of the electric automobile, and gamma is the limit proportion of the charging or discharging power of the battery of the electric automobile in a period delta t;
(6) Constraint of charge and discharge capacity of electric automobile:
in phi min Is the highest capacity proportion allowed by the electric automobile, phi max Is the highest capacity ratio allowed by the electric automobile.
When the electric automobile is used as a charging load, the electric automobile is connected with a building energy system only in the electricity price valley period; the electric automobile can be used as energy storage equipment only when being connected with a building energy system.
In this embodiment, the electric vehicle is connected to the building energy system on weekdays 18:00-8:00 the next day and holidays as a commuter. In order to avoid the influence of insufficient electric quantity on traveling, the electric automobile is always higher than the minimum electric quantity when in stop, and a certain margin is provided, and the margin is usually higher than the electric quantity required by household appliances, so that the energy storage spare capacity can be provided for connecting a building energy system at night by using the peak power. It is assumed here that the battery life of an electric vehicle is related only to the amount of charge and discharge, and is independent of the number of charge and discharge.
In one embodiment, the optimization planning model is modified based on personalized customization information of the user. The personalized customization information is a coefficient with uncertainty provided by a user, and comprises a blackout time risk preference coefficient, a long-term planning parameter and an environment protection preference coefficient, and the specific steps are as follows:
the risk preference is reflected as the risk aversion degree to the power failure event, the risk preference coefficient is converted into the electricity price multiple epsilon of the power failure period, and the power failure period is converted into an S2 corrected power supply condition prediction model; for example, the user cannot tolerate the outage event, the electricity price multiple epsilon of the outage period is set to 20, so that the energy storage capacity is increased to reduce the power supply amount of the power grid of the period because the electricity price of the period is too high to minimize the total cost, for example, the user has higher tolerance to the outage event, and the influence of the outage event on the total cost is reduced without increasing more energy storage capacity because the electricity price multiple epsilon of the outage period is set to 1;
the long-term planning is embodied in the prediction of the change of the household load in the project period provided by the user, such as the load increase caused by the increase of the estimated household members, the increase and decrease of the power consumption requirements corresponding to different age stages of the household members, and the future trend prediction of the power consumption of the household appliances; the service life of the equipment of the photovoltaic building energy system can reach 15-25 years, so that the future uncertainty is included in the planning and is necessary; the long-term planning parameters are converted into annual load coefficients eta, and S3 is carried out;
The environmental preference is reflected in the hope of reducing the electric quantity of the electric network taking coal power generation as a main body as much as possible, and the photovoltaic power generation process is considered to not cause carbon emission, so that the environmental preference coefficient is converted into a carbon emission cost coefficient lambda related to the electricity purchasing or selling cost part of the electric network in an objective function, the process is transferred to S4, and the objective function is corrected as follows:
further comprising S6: an intra-project planning scheme is generated, including annual investment costs and returns.
As shown in fig. 2, a second aspect of the embodiment of the present invention further provides a photovoltaic building energy planning system for a home user, including: the system comprises a user information acquisition module, a photovoltaic power generation power prediction module, a power supply information module, a building load demand prediction module, a capacity planning module and a database;
the user information acquisition module is used for acquiring geographical positions of areas where users are located, historical photovoltaic power generation power recording data, historical electric equipment use recording data and personalized setting parameters; the historical photovoltaic power generation power record data includes: the building can be paved with photovoltaic areas and building roof angles.
The photovoltaic power generation prediction module is used for establishing a building photovoltaic power generation power prediction model based on historical meteorological data and historical photovoltaic power generation power recording data of an area where a user is located, and obtaining building photovoltaic power generation power period-by-period prediction data under a set time scale;
The power supply information module is used for establishing a power supply condition prediction model based on power price information, power supply data and historical power failure data of the region where the user is located and combining the power failure occurrence time period to obtain a predicted power price under a set time scale;
the building load demand prediction module is used for establishing a building load demand prediction model based on the use record data of the historical electric equipment of the user and combining the energy consumption habit data of the user to obtain the time-period-by-time prediction data of the building load demand under a set time scale;
the capacity planning module is used for generating an energy storage equipment capacity planning scheme connected with the building energy system by taking minimum investment and running cost in the project period of the building energy system as objective functions according to the building photovoltaic power generation power time-interval prediction data, the building load demand time-interval prediction data, the electricity price information and the local power failure distribution data under the set time scale;
the database is used for storing photovoltaic power generation power record data of each region, the geographical position of the same type distributed photovoltaic engineering of the region, commissioned data, electricity price information, power failure data and a generated planning scheme.
In one embodiment, a photovoltaic building energy system includes a photovoltaic array, a battery pack, a photovoltaic inverter, a power distribution control cabinet, a building load device, and a charging and discharging pile; the photovoltaic building energy planning system executes a photovoltaic building energy system planning method facing to a household user. Wherein,,
The electric energy generated by the photovoltaic array is supplied to building load equipment through the photovoltaic inverter and the power distribution control cabinet, and the redundant electric energy of the photovoltaic array and the electric energy below a set electricity price threshold period are supplied to the storage battery pack and the charging and discharging pile for charging; the electric energy of the building load equipment higher than the set electricity price threshold time period is supplied by the storage battery pack and the charging and discharging piles in a discharging way; the electric automobile is charged or discharged through the charging and discharging pile, and the capacity of the electric automobile is fixed; the capacity of the storage battery is planned through a photovoltaic building energy planning system;
the electric energy generated by the photovoltaic array is supplied to building load or directly supplied to a storage battery through a power distribution control cabinet, and the storage battery and the electric automobile are supplied to building internal load in a period that the electricity price information is higher than a set electricity price threshold value;
the capacity of the storage battery is planned through a photovoltaic building energy planning system.
In one embodiment, providing detailed information by a user helps build a more accurate model, but when facing a wide range of users, the difficulty and patience of providing information by a new user need to be considered. Therefore, the user information acquisition module can acquire the user information according to stages, including:
the method comprises the steps of firstly, obtaining building basic information such as geographical positions of areas where users are located and areas where photovoltaic can be paved, and calculating building photovoltaic power generation time-period prediction data under a set time scale according to a photovoltaic power generation prediction module;
The second stage, acquiring user electricity load data and electric appliance use information, selecting average values of users in the region from a database by the missing data, calculating prediction data of building load demands in a set time scale according to a building load demand prediction module, and taking default values by uncertain parameters;
and in the third stage, the personalized setting parameters of the user are acquired, a planning scheme and a profit level are generated according to the capacity planning module, and meanwhile, the capacity planning module can provide a planning scheme which is typical of the region where the user is located for the user to compare.
By the method, the energy storage capacity is determined by solving the planning model by taking the optimal scheduling of household appliances and the dual attribute of electricity storage of the electric automobile into consideration, and the decision support is provided for users by stages.
The method and system for planning the photovoltaic building energy for the home user provided by the invention are described in detail, and specific examples are applied in the embodiment to explain the principle and implementation mode of the invention, and the description of the above examples is only used for helping to understand the method and core idea of the invention; meanwhile, as those skilled in the art will vary in the specific embodiments and application scope according to the idea of the present invention, the present disclosure should not be construed as limiting the present invention in summary.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined in this embodiment may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (8)

1. The photovoltaic building energy planning method for the home user is characterized by comprising the following steps of:
s1: building a building photovoltaic power generation power prediction model based on historical meteorological data and historical photovoltaic power generation power recording data of an area where a user is located, and obtaining building photovoltaic power generation power period-by-period prediction data under a set time scale;
s2: based on electricity price information, electric energy supply data and historical power failure data of the region where the user is located, and combining a power failure occurrence period, establishing a power supply condition prediction model to obtain predicted electricity price under a set time scale;
s3: based on the use record data of the electric equipment of the user history, building a building load demand prediction model by combining the energy consumption habit data of the user, and obtaining the prediction data of the building load demand in a time scale period by period;
S4: according to the time-period-by-time prediction data of building photovoltaic power generation power, the time-period-by-time prediction data of building load demand and the predicted electricity price under the set time scale, performing preliminary planning on the capacity of a storage battery accessed to a building energy system by taking the minimum investment and operation cost in the project period of the building energy system as an objective function;
s5: based on the personalized setting parameters of the user and the risk preference coefficient of the power failure event, turning to S2 to adjust the power supply condition prediction model; according to the long-term planning parameters, turning to S3 to adjust the building load demand prediction model; according to the environment-friendly preference coefficient, turning to S4 to adjust the objective function; updating and optimizing the preliminary planning of the capacity of the storage battery accessed to the building energy system based on the adjusted power supply condition prediction model, the building load demand prediction model and the objective function;
and S4, planning the electricity storage equipment in the building by adopting a single-objective optimization method, wherein the objective function is as follows:
wherein C is pv The investment and maintenance operation cost of the photovoltaic project in the project period is as follows;
is the investment cost of the unit storage battery in the project period;
alpha is the number of the invested storage battery packs; Δt is the time interval of each period of the peak-to-valley period of the electricity price;
Y is the years of the project cycle;
t is the total number of time periods within one year;
p (bt, t) is the charge-discharge power of the battery pack in the t-th period, P (bt, t) >0 when the battery is charged, and P (bt, t) <0 when the battery is discharged;
c (bt, t) is the unit price of the sum of the maintenance cost and the charge decay cost of the battery pack;
p (ev, t) is the charge-discharge power of the electric vehicle in the t-th period, P (ev, t) >0 when the electric vehicle is charged, and P (ev, t) <0 when the electric vehicle is discharged;
c (ev, t) is the electric quantity attenuation cost unit price of the electric automobile;
p (grid, t) is the exchange power between the building and the external power grid in the t period, when the building purchases electricity from the external power grid, P (grid, t) is more than 0, and when the building sells electricity to the external power grid, P (grid, t) is less than 0;
c (grid, t) is the cost or revenue unit price between the construction of period t and the external grid;
the objective function C satisfies the following constraint:
(1) Power balance constraint:
P(pv,t)+P(grid,t)＝P(load,t)+β(ev,t)×P(ev,t)
wherein P (pv, t) is the generated energy of the photovoltaic power generation system in the t period, and P (load, t) is the required power of all loads of the building in the t period; β (ev, t) is a connection state of the electric vehicle and the building energy system in the t-th period, if the electric vehicle is connected to the building energy system, β (ev, t) =1, if the electric vehicle is disconnected from the building energy system, β (ev, t) =0;
(2) Investment constraint of storage battery:
α≤α max
wherein alpha is max The maximum allowable investment quantity of the storage battery pack is set;
(3) And (3) restraining the charge and discharge power of the storage battery:
in the method, in the process of the invention,is the capacity of the unit storage battery pack, and χ is the ratio of the limit power of the storage battery pack charged or discharged in the period deltat;
(4) Battery charge-discharge capacity constraint:
in the method, in the process of the invention,is the capacity of a unit battery pack; />Is the lowest capacity ratio allowed by the unit battery, < >>Is the highest capacity ratio allowed by the unit storage battery pack;
(5) Electric automobile charge-discharge power constraint:
in the method, in the process of the invention,the battery capacity of the electric automobile, and gamma is the limit proportion of the charging or discharging power of the battery of the electric automobile in a period delta t;
(6) Constraint of charge and discharge capacity of electric automobile:
in phi min Is the highest capacity proportion allowed by the electric automobile, phi max Is the highest capacity ratio allowed by the electric automobile.
2. The method for planning photovoltaic building energy for home users according to claim 1, wherein the building photovoltaic power generation power prediction model in S1 is established by:
s11: according to the solar radiation actual measurement data or global meteorological database data of the region where the user is located, the historical radiation quantity data of the region where the user is located is fitted to obtain predicted irradiance data W of time intervals 0 (t), t being a selected period of time;
s12: according to the area of the building, which can be paved with the photovoltaic, the installed capacity P of the photovoltaic is determined 0 According to the angle of the building roof, the inclination angle O fixed by the component and the solar irradiance data W of the area where the user is located are selected 0 (t) predicting time-by-time-period inclined-plane irradiance data W (t);
s13: collecting the geographical position and commissioned data of the same type of distributed photovoltaic engineering in the region; building L for distance users i According to the inclined plane annual radiation quantity, the photovoltaic installed capacity and the photovoltaic annual actual power generation quantity of the photovoltaic project, calculating the comprehensive power generation efficiency K of the current photovoltaic project i ：
Wherein i is the number of the distributed photovoltaic engineering; p (P) i Is the annual total power production of the commissioned photovoltaic project; p (P) i 0 Is the photovoltaic installed capacity of the ith distributed photovoltaic project; w (W) i The total radiation quantity of the annual inclined plane of the put-into-operation photovoltaic engineering is calculated;
s14: calculating the comprehensive efficiency coefficient K of the photovoltaic engineering of the user building according to the power generation comprehensive efficiency of the selected n operated photovoltaic engineering:
s15: according to the comprehensive efficiency coefficient K and the photovoltaic installed capacity P 0 And predicting the period-by-period inclined plane irradiance data W (t), and calculating the generated energy P (pv, t) of the photovoltaic power generation system:
P(pv，t)＝K×P 0 ×W(t)。
3. The photovoltaic building energy planning method for home users according to claim 1, wherein the establishing a power supply condition prediction model in S2 includes:
every time a power failure event occurs, the electricity price in the power failure event time period is increased by a set multiple epsilon of the original electricity price:
C(grid,t)＝C 0 (grid,t)+ε×C 0 (grid,t)
wherein C is 0 (grid, t) is the original time-of-use electricity price; epsilon is the power price multiple of the power outage event time period, and C (grid, t) is the time-sharing power price after the power outage time period is corrected.
4. The photovoltaic building energy planning method for home users according to claim 1, wherein the usage record data of the consumer of the user history in S3 includes: recording data of the electric load of the historical building electric appliance and the driving mileage of the electric automobile; wherein,,
the building electric load comprises the steps of obtaining user electric power consumption information and electric appliance information, wherein the user electric power consumption information comprises time-by-time electric power data and/or month electric power bills;
converting the driving mileage of the electric automobile into power consumption, and adding the power consumption as building load in the electricity price valley period into the building electric load;
the user energy consumption habit data comprise application setting data of the user on the electric appliance;
dividing the building electric load into translatable load and non-translatable load according to the building electric load and the user energy consumption habit, subtracting the average solar photovoltaic power generation amount from the non-translatable load to obtain time-by-time electric quantity surplus and shortage, scheduling the translatable load, and summarizing the non-translatable load and the scheduled translatable load to generate scheduled building load demand time-by-time prediction data.
5. The method for planning energy for a photovoltaic building for home subscribers according to claim 1, wherein, in S5,
the personalized setting parameters comprise coefficients with uncertainty, such as blackout time risk preference coefficients, long-term planning parameters and environment-friendly preference coefficients, which are provided by a user;
the power failure event risk preference coefficient is converted into electricity price multiple epsilon in the power failure period;
the long-term planning parameters are converted into annual load coefficients eta;
the environment-friendly preference coefficient is converted into a carbon emission cost coefficient lambda of the electricity purchase or selling cost part of the power grid in the objective function, and the objective function is adjusted as follows:
6. a photovoltaic building energy planning system for home users, comprising: the system comprises a user information acquisition module, a photovoltaic power generation power prediction module, a power supply information module, a building load demand prediction module, a capacity planning module and a database;
the user information acquisition module is used for acquiring geographical positions of areas where users are located, historical photovoltaic power generation power recording data, historical electric equipment use recording data and personalized setting parameters;
the photovoltaic power generation prediction module is used for establishing a building photovoltaic power generation power prediction model based on historical meteorological data and historical photovoltaic power generation power recording data of an area where a user is located, and obtaining building photovoltaic power generation power period-by-period prediction data under a set time scale;
The power supply information module is used for establishing a power supply condition prediction model based on power price information, power supply data and historical power failure data of the region where the user is located and combining the power failure occurrence time period to obtain a predicted power price under a set time scale;
the building load demand prediction module is used for establishing a building load demand prediction model based on the use record data of the historical electric equipment of the user and combining the energy consumption habit data of the user to obtain the time-period-by-time prediction data of the building load demand under a set time scale;
the capacity planning module is used for generating an energy storage equipment capacity planning scheme connected with the building energy system by taking minimum investment and running cost in the project period of the building energy system as objective functions according to the building photovoltaic power generation power time-interval prediction data, the building load demand time-interval prediction data, the electricity price information and the local power failure distribution data under the set time scale;
the database is used for storing photovoltaic power generation power record data of each region, the geographical position of the same type distributed photovoltaic engineering in the region, commissioned data, electricity price information, power failure data and a generated planning scheme;
and S4, planning the electricity storage equipment in the building by adopting a single-objective optimization method, wherein the objective function is as follows:
Wherein C is pv The investment and maintenance operation cost of the photovoltaic project in the project period is as follows;
is the investment cost of the unit storage battery in the project period;
alpha is the number of the invested storage battery packs; Δt is the time interval of each period of the peak-to-valley period of the electricity price;
y is the years of the project cycle;
t is the total number of time periods within one year;
p (bt, t) is the charge-discharge power of the battery pack in the t-th period, P (bt, t) >0 when the battery is charged, and P (bt, t) <0 when the battery is discharged;
c (bt, t) is the unit price of the sum of the maintenance cost and the charge decay cost of the battery pack;
p (ev, t) is the charge-discharge power of the electric vehicle in the t-th period, P (ev, t) >0 when the electric vehicle is charged, and P (ev, t) <0 when the electric vehicle is discharged;
c (ev, t) is the electric quantity attenuation cost unit price of the electric automobile;
p (grid, t) is the exchange power between the building and the external power grid in the t period, when the building purchases electricity from the external power grid, P (grid, t) is more than 0, and when the building sells electricity to the external power grid, P (grid, t) is less than 0;
c (grid, t) is the cost or revenue unit price between the construction of period t and the external grid;
the objective function C satisfies the following constraint:
(1) Power balance constraint:
P(pv,t)+P(grid,t)＝P(load,t)+β(ev,t)×P(ev,t)
wherein P (pv, t) is the generated energy of the photovoltaic power generation system in the t period, and P (load, t) is the required power of all loads of the building in the t period; β (ev, t) is a connection state of the electric vehicle and the building energy system in the t-th period, if the electric vehicle is connected to the building energy system, β (ev, t) =1, if the electric vehicle is disconnected from the building energy system, β (ev, t) =0;
(2) Investment constraint of storage battery:
α≤α max
wherein alpha is max The maximum allowable investment quantity of the storage battery pack is set;
(3) And (3) restraining the charge and discharge power of the storage battery:
in the method, in the process of the invention,is the capacity of the unit storage battery pack, and χ is the ratio of the limit power of the storage battery pack charged or discharged in the period deltat;
(4) Battery charge-discharge capacity constraint:
in the method, in the process of the invention,is the capacity of a unit battery pack; />Is the lowest capacity ratio allowed by the unit battery, < >>Is the highest capacity ratio allowed by the unit storage battery pack;
(5) Electric automobile charge-discharge power constraint:
in the method, in the process of the invention,is the battery capacity of the electric automobile, and gamma is the charging or discharging work of the battery of the electric automobile in the period delta tRatio limit ratio;
(6) Constraint of charge and discharge capacity of electric automobile:
in phi min Is the highest capacity proportion allowed by the electric automobile, phi max Is the highest capacity ratio allowed by the electric automobile.
7. The home subscriber oriented photovoltaic building energy planning system of claim 6, wherein the photovoltaic building energy system comprises a photovoltaic array, a storage battery pack, a photovoltaic inverter, a power distribution control cabinet, building load equipment and charging and discharging piles; the electric energy generated by the photovoltaic array is supplied to building load equipment through the photovoltaic inverter and the power distribution control cabinet, and the redundant electric energy of the photovoltaic array and the electric energy below a set electricity price threshold period are supplied to the storage battery pack and the charging and discharging pile for charging; the electric energy of the building load equipment higher than the set electricity price threshold time period is supplied by the storage battery pack and the charging and discharging piles in a discharging way; the electric automobile is charged or discharged through the charging and discharging pile, and the capacity of the electric automobile is fixed; the capacity of the storage battery is planned through a photovoltaic building energy planning system;
The electric energy generated by the photovoltaic array is supplied to building load or directly supplied to a storage battery through a power distribution control cabinet, and the storage battery and the electric automobile are supplied to building internal load in a period that the electricity price information is higher than a set electricity price threshold value;
the capacity of the storage battery is planned through a photovoltaic building energy planning system.
8. The photovoltaic building energy planning system for home users according to claim 6, wherein the user information acquisition module acquires the user information in stages, comprising:
the method comprises the steps of firstly, obtaining geographical position of a region where a user is located and building basic information, and calculating building photovoltaic power generation time-period prediction data under a set time scale according to a photovoltaic power generation prediction module;
the second stage, acquiring user electricity load data and electric appliance use information, and calculating building load demand time-period-by-time-period prediction data under a set time scale according to a building load demand prediction module;
and thirdly, acquiring user personalized setting parameters, and generating a capacity planning scheme of the energy storage equipment according to the capacity planning module.
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Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116488223A (en) * 2023-06-26 2023-07-25 湖南大学 Household light-storage-flexible double-layer multi-time scale control method, device and medium
CN116579591B (en) * 2023-07-13 2023-09-29 山西景骏建筑工程有限公司 Building photovoltaic installation management method and system based on power prediction
CN116667538B (en) * 2023-07-24 2023-09-29 常州思瑞电力科技有限公司 Electricity consumption management system of household photovoltaic power station
CN116760029B (en) * 2023-08-15 2024-01-12 国网浙江省电力有限公司宁波供电公司 Rural roof photovoltaic power generation and supply method, system, computing equipment and storage medium

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
CN109299829A (en) * 2018-10-11 2019-02-01 国网甘肃省电力公司电力科学研究院 Photovoltaic greenhouse is micro- can the polymorphic energy storage configuration method of net system and system
CN109510224A (en) * 2018-11-16 2019-03-22 上海交通大学 Photovoltaic energy storage and the united capacity configuration of distributed energy and running optimizatin method
CN110119886A (en) * 2019-04-18 2019-08-13 深圳供电局有限公司 A kind of active distribution dynamic programming method
CN113572182A (en) * 2021-07-06 2021-10-29 水发能源集团有限公司 Method for measuring and calculating capacity of storage battery pack of photovoltaic building energy storage system
CN113595063A (en) * 2021-07-12 2021-11-02 国网湖北省电力有限公司电力科学研究院 Energy storage capacity configuration method suitable for smart park
CN114977330A (en) * 2022-07-11 2022-08-30 湖南大学 Building distributed photovoltaic-storage battery system continuous operation scheduling method and platform

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
CN109299829A (en) * 2018-10-11 2019-02-01 国网甘肃省电力公司电力科学研究院 Photovoltaic greenhouse is micro- can the polymorphic energy storage configuration method of net system and system
CN109510224A (en) * 2018-11-16 2019-03-22 上海交通大学 Photovoltaic energy storage and the united capacity configuration of distributed energy and running optimizatin method
CN110119886A (en) * 2019-04-18 2019-08-13 深圳供电局有限公司 A kind of active distribution dynamic programming method
CN113572182A (en) * 2021-07-06 2021-10-29 水发能源集团有限公司 Method for measuring and calculating capacity of storage battery pack of photovoltaic building energy storage system
CN113595063A (en) * 2021-07-12 2021-11-02 国网湖北省电力有限公司电力科学研究院 Energy storage capacity configuration method suitable for smart park
CN114977330A (en) * 2022-07-11 2022-08-30 湖南大学 Building distributed photovoltaic-storage battery system continuous operation scheduling method and platform

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
倪驰昊 ; 刘学智 ; 张雨曼 ; .光储微网的储能优化配置与技术经济分析.上海节能.2020,(第03期),第241-251页. *

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