CN116663933A - Method and system for determining charge and discharge strategy of energy storage system - Google Patents

Abstract

The invention discloses a method and a system for determining a charging and discharging strategy of an optical storage charging station, which are used for acquiring parameters and historical data of optical storage charging station equipment, respectively calculating the historical photovoltaic power generation data, illumination intensity, illumination angle, historical charging data and other data by utilizing a plurality of preset prediction models to obtain a plurality of prediction results, solving a day-ahead transaction model of an electric power market according to the plurality of prediction results, the parameters and the day-ahead declaration plan of the optical storage charging station equipment to obtain a day-ahead charging and discharging plan curve, and solving the real-time transaction model of the electric power market according to the day-ahead charging and discharging plan curve, the plurality of prediction results and the day-ahead preset electric quantity to obtain a real-time charging and discharging plan curve. According to the method, the charging and discharging strategy of the energy storage system is obtained by constructing the electric power market transaction optimization model to solve, so that the dispatching efficiency and accuracy of electric power resources in the photo-storage charging station are improved, and the operation cost of the photo-storage charging station is reduced.

Description

Method and system for determining charge and discharge strategy of energy storage system

Technical Field

The invention relates to the technical field of energy storage charging and discharging, in particular to a method and a system for determining a charging and discharging strategy of an energy storage system.

Background

With the gradual perfection of the power market system in China, the participation of industrial and commercial power consumers in the power market trade becomes a necessary trend. In the power spot market environment, the price of the electric energy can be changed in real time under the influence of factors such as supply and demand conditions, weather and the like of the whole market. Charging and discharging strategies of the energy storage system in the electric power market environment of the charging station with the photovoltaic power generation system and the energy storage system are changed.

Before the electricity market is participated, the prices of the industrial and commercial electricity prices exist in peak, flat and valley periods, the energy storage system in the light storage charging station is mainly used for peak Gu Taoli, namely valley period charging, peak period discharging, earning price difference to obtain benefits, or a self-power-consumption and surplus electricity surfing mode is adopted, the generated energy of the photovoltaic system is preferentially supplied to the charging station for use, and the energy storage system selects a charging and discharging mode according to the generated energy and the load condition of the charging station. After the electric energy is added into the electric power market, the price of the electric energy in the spot market is not in three types of peaks, flat and valley, but fluctuates up and down in real time along with the supply and demand conditions of the market, the price of the electric energy is cleared in one market per hour, and an accurate charging and discharging strategy is difficult to obtain.

Disclosure of Invention

The invention provides a method and a system for determining a charging and discharging strategy of an energy storage system, wherein the charging and discharging strategy of the energy storage system is obtained by establishing an electric power market transaction optimization model to solve, so that the scheduling efficiency and accuracy of electric power resources in an optical storage station are improved, and the operation cost of the optical storage station is reduced.

A first aspect of an embodiment of the present invention provides a method and a system for determining a charging and discharging policy of an energy storage system, where the method includes:

acquiring optical storage charging station equipment parameters, historical photovoltaic power generation data, illumination intensity, illumination angle, historical charging data, historical day-ahead price-clearing data, historical real-time price-clearing and date;

calculating historical photovoltaic power generation data, illumination intensity, illumination angle, historical charging data, historical day-ahead price clearing data, historical real-time price clearing and date by utilizing a plurality of preset prediction models to obtain a plurality of prediction results;

establishing an optical storage charging station system operation model according to optical storage charging station equipment parameters, and establishing an electric power market transaction optimization model by utilizing the optical storage charging station system operation model, wherein the electric power market transaction optimization model comprises an electric power market day-ahead transaction model and an electric power market real-time transaction model;

and solving the daily transaction model of the electric power market according to the plurality of prediction results, the optical storage charging station equipment parameters and the daily declaration plan, obtaining a daily charge and discharge plan curve, and then solving the real-time transaction model of the electric power market according to the daily charge and discharge plan curve, the plurality of prediction results and the daily preset electric quantity to obtain a real-time charge and discharge plan curve, so that the energy management system sends the real-time charge and discharge plan curve to the energy storage system, and the energy storage system charges or discharges according to the charge and discharge plan curve.

According to the embodiment, after the parameters and the historical data of the photo-electricity storage charging station are obtained, a plurality of preset prediction models are utilized to respectively calculate historical photovoltaic power generation data, illumination intensity, illumination angle, historical charging data, historical day-ahead price-reading data, historical real-time price-reading and date to obtain a plurality of prediction results, then a photo-electricity storage charging station system operation model is built according to the parameters of the photo-electricity storage charging station, an electric market day-ahead transaction model and an electric market real-time transaction model are built according to the operation models of the photo-electricity storage charging station, the day-ahead transaction model is solved according to the plurality of prediction results, the parameters of the photo-electricity storage charging station and the day-ahead declaration plan, a day-ahead charge-discharge plan curve is obtained, and then the electric market real-time transaction model is solved according to the day-ahead charge-discharge plan curve, the plurality of prediction results and day-ahead preset electric quantity, so that the energy management system sends the real-time charge-discharge plan curve to the energy storage system to charge or discharge according to the charge-discharge plan curve. According to the method, the charging and discharging strategy of the energy storage system is obtained by constructing the electric power market transaction optimization model to solve, so that the dispatching efficiency and accuracy of electric power resources in the photo-storage charging station are improved, and the operation cost of the photo-storage charging station is reduced.

In a possible implementation manner of the first aspect, a plurality of preset prediction models are utilized to calculate historical photovoltaic power generation data, illumination intensity, illumination angle, historical charging data, historical day-ahead price clearing data, historical real-time price clearing data and date respectively to obtain a plurality of prediction results, which specifically are:

inputting historical photovoltaic power generation data, illumination intensity and illumination angle into a preset photovoltaic power prediction model for calculation to obtain a photovoltaic power generation power prediction curve;

inputting historical charging data and date into a preset charging load prediction model for calculation to obtain a charging load prediction curve;

and inputting the historical daily price-out and price-out data into a preset spot market electricity price prediction module for calculation to obtain a short-term daily price-out curve and a real-time price-out curve.

In a possible implementation manner of the first aspect, the optical storage charging station system operation model is built according to optical storage charging station equipment parameters, specifically:

an energy storage system model is constructed by utilizing energy storage system parameters, wherein the charge and discharge power of the energy storage system is as follows:

the state of charge is:

wherein ,P_batt.t 、P _charge.t 、P _diischa.t Respectively represents the power, the charging power and the discharging power of the energy storage system, eta _batt Indicating charge-discharge efficiency, delta _charge.t 、δ _discha.t The energy storage charge and discharge states are 0 or 1;

the constraint conditions of the energy storage system are as follows:

P _battmin ＜P _batt.t ＜P _battmax

SOC _battmin ＜SOC _batt.t ＜SOC _battmax

wherein ,P_battmin 、P _battmax Indicating upper and lower limits of energy storage output and SOC _battmin 、SOC _battmax Indicating upper and lower limits of energy storage SOC；

And constructing a photovoltaic power generation system model by utilizing parameters of the photovoltaic power generation system, wherein the electric power of the photovoltaic power generation system is as follows:

P _pv.t ＝f _pv S _pv G _pv.t /G _s

wherein ,P_pv.t Representing the electric power of the photovoltaic power generation system, f _pv Representing a photovoltaic system power derating factor; s is S _pv Representing the installed capacity of the photovoltaic system; g _pv.t Representing the illumination radiation intensity in the t period; g _s Represents the radiation intensity under standard illumination conditions of 1kW/m ² ；

The output constraint conditions of the photovoltaic power generation system are as follows:

0＜P _pv.t ＜P _pvmax

wherein ,P_pvmax Indicating the upper photovoltaic output limit.

In a possible implementation manner of the first aspect, the electricity market transaction optimization model is constructed by using the operation model of the light storage charging station system, specifically:

utilizing the light storage charging station system operation model to construct an electric power market day-ahead transaction model and an electric power market real-time transaction model, wherein an objective function of the electric power market day-ahead transaction model is as follows:

wherein ,representing the unit power operation and maintenance cost of the energy storage system, < >>Power of the energy storage system in period t representing the day-ahead phase,/- >Representing the operation and maintenance cost of the unit power of the photovoltaic system, < >>Representing the power generated by the photovoltaic power generation system in the period t of the day-ahead stage;

the objective function of the real-time transaction model of the electric power market is as follows:

wherein ,representing the power of the energy storage system in a t period of the real-time phase;Representing the generated power of the photovoltaic power generation system in a t period of the real-time period;

the electricity purchasing cost of the daily transaction model of the electric power market is as follows:

C _dayMarket.t ＝C _long.t +C _day.t

C _long.t ＝q _long.t ×p _long.t

C _day.t ＝(q _day.t -q _long.t )p _day.t

wherein ,C_dayMarket.t Representing electricity purchasing cost of daily transaction model of electric power market, C _long.t Electric charge, q, representing middle-long term contract decomposed electric quantity within t period _long.t 、p _long.t Respectively representing the decomposition electric quantity and price of the medium and long term in the period t, C _day.t Electric charge, q representing time period t before day _day.t 、p _day.t Respectively representing the declaration electric quantity and unified price in the period t before the day;

the electricity purchasing cost of the real-time transaction model of the electric power market is as follows:

C _realMarket.t ＝C _real.t +C _err.t +C _apportion

C _real.t ＝(q _real.t -q _day.t )p _real.t

C _err.t ＝{max[0，|q _real.t -q _day.t |-q _real.t ×λ ₀ ]}×p _check

C _apportion.t ＝q _real.t ×p _apportion

wherein ,C_real.t Represents the real-time electricity charge in the period t, q _real.t 、p _real.t Respectively representing the actual electricity consumption in the period t and the real-time unified price, C _err.t Indicating the declaration deviation checking cost of t period lambda ₀ Representing allowable declaration deviation scaling factor, p _check Indicating the electricity price of the deviation checking degree, C _apportion.t Representing the apportionment costs of the period t, p _apportion Represents the monthly electricity allocation price, q _day.t The declaration electric quantity of the period t before the day is represented;

the constraint conditions of the electricity purchasing cost of the electric power market are as follows:

q _long.t 、q _day.t 、q _real.t ≥0

wherein ,q_long.t Represents the decomposition price of the middle and long periods in the t period, q _day.t Unified price showing time period t before day, q _real.t And the actual power consumption in the period t is represented.

In one possible implementation manner of the first aspect, according to the plurality of prediction results, the optical storage charging station equipment parameters and the day-ahead declaration plan, a day-ahead transaction model of the electric power market is solved, after a day-ahead charging and discharging plan curve is obtained, a real-time transaction model of the electric power market is solved according to the day-ahead charging and discharging plan curve, the plurality of prediction results and the day-ahead preset electric quantity, and a real-time charging and discharging plan curve is obtained, which specifically includes:

inputting a photovoltaic power generation power prediction curve, a charging load prediction curve, a short-term day-ahead clearing curve and a preset contract decomposition electric quantity curve into a day-ahead transaction model of the electric power market for solving, and obtaining a day-ahead charging and discharging plan curve;

and (3) inputting the daily charge and discharge planning curve, the charge load prediction curve, the real-time out-of-price clearing curve and the date preset electric quantity into the electric power market real-time transaction model for solving, so as to obtain the real-time charge and discharge planning curve.

A second aspect of an embodiment of the present invention provides a charging and discharging policy determining system of an optical storage charging station, the system including:

The acquisition module is used for acquiring basic information of industries to be analyzed, wherein the basic information of the industries comprises product names and company names;

the acquisition module is used for acquiring the parameters of the photovoltaic storage charging station equipment, the historical photovoltaic power generation data, the illumination intensity, the illumination angle, the historical charging data, the historical day-ahead price clearing data, the historical real-time price clearing and the date;

the prediction module is used for calculating historical photovoltaic power generation data, illumination intensity, illumination angle, historical charging data, historical day-ahead price-clearing data, historical real-time price-clearing data and date by utilizing a plurality of preset prediction models to obtain a plurality of prediction results;

the construction module is used for constructing an optical storage charging station system operation model according to the optical storage charging station equipment parameters and constructing an electric power market transaction optimization model by utilizing the optical storage charging station system operation model, wherein the electric power market transaction optimization model comprises an electric power market day-ahead transaction model and an electric power market real-time transaction model;

the solving module is used for solving the day-ahead transaction model of the electric power market according to the plurality of prediction results, the optical storage station equipment parameters and the day-ahead declaration plan, solving the real-time transaction model of the electric power market according to the day-ahead charge-discharge plan curve, the plurality of prediction results and the day-ahead preset electric quantity after the day-ahead charge-discharge plan curve is obtained, and obtaining the real-time charge-discharge plan curve, so that the energy management system sends the real-time charge-discharge plan curve to the energy storage system, and the energy storage system charges or discharges according to the charge-discharge plan curve.

In a possible implementation manner of the second aspect, the prediction module includes a power prediction unit, a load prediction unit and a price prediction unit,

the power prediction unit is used for inputting historical photovoltaic power generation data, illumination intensity and illumination angle into a preset photovoltaic power prediction model for calculation to obtain a photovoltaic power generation power prediction curve;

the load prediction unit is used for inputting the historical charging data and the historical daily output price clearing data into a preset charging load prediction model for calculation to obtain a charging load prediction curve;

the price prediction unit is used for inputting historical daily price-output and real-time price-output data into the preset spot market electricity price prediction module for calculation to obtain a short-term daily price-output curve and a real-time price-output curve.

In a possible implementation manner of the second aspect, the building module is further configured to build an energy storage system model using energy storage system parameters, where a charge and discharge power of the energy storage system is:

the state of charge is:

wherein ,P_batt.t 、P _charge.t 、P _discha.t Respectively represents the power, the charging power and the discharging power of the energy storage system, eta _batt Indicating charge-discharge efficiency, delta _charge.t 、δ _discha.t The energy storage charge and discharge states are 0 or 1;

the constraint conditions of the energy storage system are as follows:

P _battmin ＜P _batt . _t ＜P _battmax

SOC _battmin ＜SOC _batt.t ＜SOC _battmax

wherein ,P_battmin 、P _battmax Indicating upper and lower limits of energy storage output and SOC _battmin 、SOC _battmax Representing upper and lower limits of the energy storage SOC;

and constructing a photovoltaic power generation system model by utilizing parameters of the photovoltaic power generation system, wherein the electric power of the photovoltaic power generation system is as follows:

P _pv.t ＝f _pv S _pv G _pv.t /G _s

wherein ,P_pv.t Representing the electric power of the photovoltaic power generation system, f _pv Representing a photovoltaic system power derating factor; s is S _pv Representing the installed capacity of the photovoltaic system; g _pv.t Representing the illumination radiation intensity in the t period; g _s Represents the radiation intensity under standard illumination conditions of 1kW/m ² ；

The output constraint conditions of the photovoltaic power generation system are as follows:

0＜P _pv.t ＜P _pvmax

wherein ,P_pvmax Indicating the upper photovoltaic output limit.

In a possible implementation manner of the second aspect, the construction module is further configured to construct an electric market day-ahead transaction model and an electric market real-time transaction model using the optical storage charging station system operation model, where an objective function of the electric market day-ahead transaction model is:

wherein ,representing the unit power operation and maintenance cost of the energy storage system, < >>Power of the energy storage system in period t representing the day-ahead phase,/->Representing the operation and maintenance cost of the unit power of the photovoltaic system, < >>Representing the power generated by the photovoltaic power generation system in the period t of the day-ahead stage;

the objective function of the real-time transaction model of the electric power market is as follows:

wherein ,power of the energy storage system in period t representing the real time phase,/- >Representing the generated power of the photovoltaic power generation system in a t period of the real-time period;

the electricity purchasing cost of the daily transaction model of the electric power market is as follows:

C _dayMarket.t ＝C _long.t +C _day.t

C _long.t ＝q _long.t ×p _long.t

C _day.t ＝(q _day.t -q _long.t )p _day.t

wherein ,C_dayMarket.t Representing electricity purchasing cost of daily transaction model of electric power market, C _long.t Electric charge, q, representing middle-long term contract decomposed electric quantity within t period _long.t 、p _long.t Respectively representing the decomposition electric quantity and price of the medium and long term in the period t, C _day.t Electric charge, q representing time period t before day _day.t 、p _day.t Respectively representing the declaration electric quantity and unified price in the period t before the day;

the electricity purchasing cost of the real-time transaction model of the electric power market is as follows:

C _realMarket.t ＝C _real.t +C _err.t +C _apportion

C _real.t ＝(q _real.t -q _day.t )p _real.t

C _err.t ＝{max[0，|q _real.t -q _day.t |-q _real.t ×λ ₀ ]}×p _check

C _apportion.t ＝q _real.t ×p _apportion

wherein ,C_real.t Represents the real-time electricity charge in the period t, q _real.t 、p _real.t Respectively representing the actual electricity consumption in the period t and the real-time unified price, C _err.t Indicating the declaration deviation checking cost of t period lambda ₀ Representing allowable declaration deviation scaling factor, p _check Indicating the electricity price of the deviation checking degree, C _apportion.t Representing the apportionment costs of the period t, p _apportion Represents the monthly electricity allocation price, q _day.t The declaration electric quantity of the period t before the day is represented;

the constraint conditions of the electricity purchasing cost of the electric power market are as follows:

q _long.t 、q _day.t 、q _real.t ≥0

wherein ,q_long.t Represents the decomposition price of the middle and long periods in the t period, q _day.t Unified price showing time period t before day, q _real.t And the actual power consumption in the period t is represented.

In one possible implementation manner of the second aspect, the solving module includes a day-ahead computing module and a real-time computing module,

Wherein the day-ahead calculation module is used for inputting a photovoltaic power generation power prediction curve, a charging load prediction curve, a short-term day-ahead price clearing curve and a preset contract decomposition electric quantity curve into the day-ahead transaction model of the electric power market for solving, obtaining a day-ahead charge-discharge plan curve;

the real-time calculation module is used for inputting the daily charge and discharge planning curve, the charge load prediction curve, the real-time price-clearing curve and the date preset electric quantity into the electric power market real-time transaction model to solve, so that the real-time charge and discharge planning curve is obtained.

Drawings

Fig. 1 is a schematic flow chart of an embodiment of a method for determining a charging and discharging strategy of an optical storage station according to the present invention;

fig. 2 is a schematic diagram of a topology structure of an optical storage charging station according to an embodiment of a method for determining a charging and discharging strategy of the optical storage charging station provided by the present invention;

fig. 3 is a schematic diagram of a charge-discharge strategy optimization flow of an embodiment of a charge-discharge strategy determination method of an optical storage station according to the present invention;

fig. 4 is a schematic system structure diagram of another embodiment of a method for determining a charging and discharging strategy of an optical storage station according to 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.

Example 1

Referring to fig. 1, fig. 1 is a flowchart of an embodiment of a method for determining a charging and discharging strategy of an optical storage station according to the present invention, which includes steps S11 to S14. The method comprises the following steps:

s11, acquiring parameters of the photovoltaic storage charging station equipment, historical photovoltaic power generation data, illumination intensity, illumination angle, historical charging data, historical day-ahead price clearing data, historical real-time price clearing and date.

In this embodiment, the topology structure of the photovoltaic power generation system, the energy storage system and the DC charging station are connected to a DC bus of 750Vdc via a DC converter, and the DC bus is connected to 10kV mains via a DC/AC converter in a grid connection manner, as shown in fig. 2. The whole light storage station realizes the plan management and the equipment scheduling of equipment such as photovoltaic equipment, energy storage equipment and the like through an energy management system, and optimizes the equipment resource allocation and the application.

Firstly, information such as optical storage charging station equipment parameters, historical photovoltaic power generation data, illumination intensity, illumination angle, historical charging data, historical day-ahead price-clearing data, historical real-time price-clearing and date is obtained. And inputting the information into each prediction model in the energy management system for calculation to obtain a prediction result. And the system energy management module calculates a charge and discharge operation curve to the energy storage system according to the prediction results of the modules and the current operation state of the energy storage system and with the aim of maximizing the income of the charging station, and an optimization flow chart is shown in fig. 3.

It should be noted that the obtained parameters and data include, but are not limited to, the above information, and any data required when the charging station calculates the charging and discharging policy may be obtained according to the actual situation.

And S12, respectively calculating historical photovoltaic power generation data, illumination intensity, illumination angle, historical charging data, historical day-ahead price-clearing data, historical real-time price-clearing and date by utilizing a plurality of preset prediction models to obtain a plurality of prediction results.

In a preferred embodiment, a plurality of preset prediction models are utilized to calculate historical photovoltaic power generation data, illumination intensity, illumination angle, historical charging data, historical day-ahead price clearing data, historical real-time price clearing data and date respectively to obtain a plurality of prediction results, specifically:

inputting historical photovoltaic power generation data, illumination intensity and illumination angle into a preset photovoltaic power prediction model for calculation to obtain a photovoltaic power generation power prediction curve;

inputting historical charging data and date into a preset charging load prediction model for calculation to obtain a charging load prediction curve;

and inputting the historical daily price-output data and the historical real-time price-output data into a preset spot market electricity price prediction module for calculation to obtain a short-term daily price-output curve and a real-time price-output curve.

In this embodiment, the energy management system includes a photovoltaic power prediction module, a charging station load prediction module, a spot market electricity price prediction module, a system energy management module, and an energy storage operation control module. The photovoltaic power prediction module predicts the photovoltaic power generation power of a certain period in the future through historical photovoltaic power generation data, illumination intensity, illumination angle and other data, and obtains photovoltaic power generation power prediction upper and lower limit curves. The charging station load prediction module predicts a charging load curve of a period of time according to parameters such as historical charging data and date of a charging station. And the spot market electricity price prediction module predicts the daily price and the real-time price of the future period according to the historical daily price and the historical real-time price and obtains a daily price and a real-time price curve of the future period. The system energy management module receives the prediction results of the other modules and the current running state of the energy storage system, and calculates a charge and discharge running curve to the energy storage system with the aim of maximizing the income of the charging station.

S13, building an optical storage charging station system operation model according to the optical storage charging station equipment parameters, and building an electric power market transaction optimization model by utilizing the optical storage charging station system operation model, wherein the electric power market transaction optimization model comprises an electric power market day-ahead transaction model and an electric power market real-time transaction model.

In a preferred embodiment, the optical storage charging station system operation model is built according to the optical storage charging station equipment parameters, specifically:

an energy storage system model is constructed by utilizing energy storage system parameters, wherein the charge and discharge power of the energy storage system is as follows:

the state of charge is:

wherein ,P_batt.t 、P _charge.t 、P _discha.t Respectively represents the power, the charging power and the discharging power of the energy storage system, eta _batt Indicating charge-discharge efficiency, delta _charge.t 、δ _discha.t The energy storage charge and discharge states are 0 or 1;

the constraint conditions of the energy storage system are as follows:

P _battmin ＜P _batt.t ＜P _battmax

SOC _battmin ＜SOC _batt.t ＜SOC _battmax

wherein ,P_battmin 、P _battmax Indicating upper and lower limits of energy storage output and SOC _battmin 、SOC _battmax Representing upper and lower limits of the energy storage SOC;

and constructing a photovoltaic power generation system model by utilizing parameters of the photovoltaic power generation system, wherein the electric power of the photovoltaic power generation system is as follows:

P _pv.t ＝f _pv S _pv G _pv.t /G _s

wherein ,P_pv.t Representing the electric power of the photovoltaic power generation system, f _pv Representing a photovoltaic system power derating factor; s is S _pv Representing the installed capacity of the photovoltaic system; g _pv.t Representing the illumination radiation intensity in the t period; g _s Represents the radiation intensity under standard illumination conditions of 1kW/m ² ；

The output constraint conditions of the photovoltaic power generation system are as follows:

0＜P _pv.t ＜P _pvmax

wherein ,P_pvmax Indicating the upper photovoltaic output limit.

In this embodiment, an operation model of the entire optical storage charging station system is first built. The mathematical model of the energy storage system and the operation and maintenance cost thereof are as follows, and the charge and discharge power of the energy storage system is as follows:

The state of charge is:

wherein ,P_batt.t 、P _charge.t 、P _discha.t Respectively represents the power, the charging power and the discharging power of the energy storage system, eta _batt Indicating charge-discharge efficiency, delta _charge.t 、δ _discha.t The energy storage charge and discharge states are 0 or 1;

the mathematical model of the photovoltaic power generation system is as follows:

P _pv.t ＝f _pv S _pv G ^p _v.t /G _s

wherein ,P_pv.t Representing the electric power of the photovoltaic power generation system, f _pv Representing a photovoltaic system power derating factor; s is S _pv Representing the installed capacity of the photovoltaic system; g _pv.t Representing the illumination radiation intensity in the t period; g _s Represents the radiation intensity under standard illumination conditions of 1kW/m ² ；

In normal operation, the entire optical storage charging station satisfies the electric power balance:

P _pv.t +P _batt.t +P _gird.t ＝P _load.t

wherein ,P_pv.t Representing the power of the photovoltaic in the period t, P _batt.t Representing the power stored in t time period, P _gird.t Representing the power of the power grid in the period t, P _load.t Representing charging station load power.

The whole electric power of the light storage charging station needs to be balanced for a certain period of time.

The energy storage system operation constraint is:

P _battmin ＜P _batt.t ＜P _battmax

SOC _battmin ＜SOC _batt . _t ＜SOC _battmax

wherein ,P_battmin 、P _battmax Representing upper and lower limits of energy storage output; SOC (State of Charge) _battmin 、SOC _battmax Indicating the upper and lower limits of the stored energy SOC.

The output constraint conditions of the photovoltaic power generation system are as follows:

0＜P _pv.t ＜P _pvmax

wherein ,P_pvmax Indicating the upper photovoltaic output limit.

In a preferred embodiment, an electricity market transaction optimization model is built by using an optical storage charging station system operation model, specifically:

And constructing an electric market day-ahead transaction model and an electric market real-time transaction model by using the optical storage charging station system operation model, wherein the objective function of the electric market day-ahead transaction model is as follows:

wherein ,representing the unit power operation and maintenance cost of the energy storage system, < >>Power of the energy storage system in period t representing the day-ahead phase,/->Representing the operation and maintenance cost of the unit power of the photovoltaic system, < >>Representing the power generated by the photovoltaic power generation system in the period t of the day-ahead stage;

the objective function of the real-time transaction model of the electric power market is as follows:

wherein ,representing the power of the energy storage system in a t period of the real-time phase;Representing the generated power of the photovoltaic power generation system in a t period of the real-time period;

the electricity purchasing cost of the daily transaction model of the electric power market is as follows:

C _dayMarket.t ＝C _long.t +C _day.t

C _long.t ＝q _long.t ×p _long.t

C _day.t ＝(q _day.t -q _long.t )p _day.t

wherein ,C_dayMarket.t Representing electricity purchasing cost of daily transaction model of electric power market, C _long.t Electric charge, q, representing middle-long term contract decomposed electric quantity within t period _long.t 、p _long.t Respectively representing the decomposition electric quantity and price of the medium and long term in the period t, C _day.t Electric charge, q representing time period t before day _day.t 、p _day.t Respectively representing the declaration electric quantity and unified price in the period t before the day;

the electricity purchasing cost of the real-time transaction model of the electric power market is as follows:

C _realMarket.t ＝C _real.t +C _err.t +C _apportion

C _real.t ＝(q _real.t -q _day.t )p _real.t

C _err.t ＝{max[0，|q _real.t -q _day.t |-q _real.t ×λ ₀ ]}×p _check

C _apportion.t ＝q _real.t ×p _apportion

wherein ,C_real.t Represents the real-time electricity charge in the period t, q _real.t 、p _real.t Respectively representing the actual electricity consumption in the period t and the real-time unified price, C _err.t Indicating the declaration deviation checking cost of t period lambda ₀ Representing allowable declaration deviation scaling factor, p _check Indicating the electricity price of the deviation checking degree, C _apportion.t Representing the apportionment costs of the period t, p _apportion Represents the monthly electricity allocation price, q _day.t The declaration electric quantity of the period t before the day is represented;

the constraint conditions of the electricity purchasing cost of the electric power market are as follows:

q _long.t 、q _day.t 、q _real.t ≥0

wherein ,q_long.t Represents the decomposition price of the middle and long periods in the t period, q _day.t Unified price showing time period t before day, q _real.t And the actual power consumption in the period t is represented.

In this embodiment, the energy storage system in the optical storage station operates and optimizes in two stages of day-ahead and real-time, and an electric power market day-ahead transaction model and an electric power market real-time transaction model are constructed by using an optical storage station system operation model, wherein an objective function of the electric power market day-ahead transaction model is as follows:

wherein ,representing the unit power operation and maintenance cost of the energy storage system, < >>Power of the energy storage system in period t representing the day-ahead phase,/->Representing the operation and maintenance cost of the unit power of the photovoltaic system, < >>Representing the power generated by the photovoltaic power generation system in the period t of the day-ahead stage;

the objective function of the real-time transaction model of the electric power market is as follows:

wherein ,representing the power of the energy storage system in a t period of the real-time phase;Representing the generated power of the photovoltaic power generation system in the t period of the real-time phase.

The electricity purchasing cost of the daily transaction model of the electric power market is as follows:

C _dayMarket.t ＝C _long.t +C _day.t

C _long.t ＝q _long.t ×p _long.t

C _day.t ＝(q _day.t -q _long.t )p _day.t

wherein ,C_dayMarket.t Representing electricity purchasing cost of daily transaction model of electric power market, C _long.t Electric charge, q, representing middle-long term contract decomposed electric quantity within t period _long.t 、p _long.t Respectively representing the decomposition electric quantity and price of the medium and long term in the period t, C _day.t Electric charge, q representing time period t before day _day.t 、p _day.t Respectively representing the declaration electric quantity and unified price in the period t before the day;

the electricity purchasing cost of the real-time transaction model of the electric power market is as follows:

C _realMarket.t ＝C _real.t +C _err.t +C _apportion

C _real.t ＝(q _real.t -q _day.t )p _real.t

C _err.t ＝{max[0，|q _real.t -q _day.t |-q _real.t ×λ ₀ ]}×p _check

C _apportion.t ＝q _real.t ×p _apportion

wherein ,C_real.t Represents the real-time electricity charge in the period t, q _real.t 、p _real.t Respectively representing the actual electricity consumption in the period t and the real-time unified price, C _err.t Indicating the declaration deviation checking cost of t period lambda ₀ Representing allowable declaration deviation scaling factor, p _check Indicating the electricity price of the deviation checking degree, C _apportion.t Representing the apportionment costs of the period t, p _apportion Represents the monthly electricity allocation price, q _day.t The declaration electric quantity of the period t before the day is represented;

the constraint conditions of the electricity purchasing cost of the electric power market are as follows:

q _long.t 、q _day.t 、q _real.t ≥0

wherein ,q_long.t Represents the decomposition price of the middle and long periods in the t period, q _day.t Indicating time t before dayUnified let-off price of segments, q _real.t And the actual power consumption in the period t is represented.

As an example of the present embodiment, the power market in guangdong province is exemplified as a medium-to-long-term market and a spot market. Market bodies participating in electric power market trading need to purchase approximately 90% of electric energy in a future period of time by contracting for medium-long term contracts in medium-long term markets so as to avoid risks caused by fluctuation of electricity prices due to supply and demand mismatch in electric power spot markets in advance. The market in spot market is divided into a daily market and a real-time market. In the day-ahead market, the user side needs to declare the electric quantity required by the next day plan in advance, and in the real-time market, according to the difference between the actual practical electric quantity and the day-ahead declaration, the electric energy cost is calculated by adopting the day-ahead price and the real-time price.

The electricity purchasing cost of a certain period of the day in the market before the day is as follows:

C _dayMarket.t ＝C _long.t +C _day.t

C _long.t ＝q _long.t ×p _long.t

C _day.t ＝(q _day.t -q _long.t )p _day.t

wherein ,C_dayMarket.t Representing electricity purchasing cost of daily transaction model of electric power market, C _long.t Represents the electricity charge of the middle-long term contract decomposition electricity quantity in the t period, _qlong.t 、 _plong.t respectively representing the decomposition electric quantity and price of the medium and long term in the period t, C _day.t Electric charge, q representing time period t before day _day.t 、p _day.t Respectively representing the declaration electric quantity and unified price in the period t before the day;

the electricity purchasing cost of the real-time transaction model of the electric power market is as follows:

C _realMarket.t ＝C _real.t +C _err.t +C _apportion

C _real.t ＝(q _real.t -q _day.t )p _real.t

C _err.t ＝{max[0，|q _real.t -q _day.t |-q _real.t ×λ ₀ ]}×p _check

C _apportion.t ＝q _real.t ×p _ap p _ortion

wherein ,C_real.t Represents the real-time electricity charge in the period t, q _real.t 、p _real.t Respectively representing the actual electricity consumption in the period t and the real-time unified price, C _err.t Indicating the declaration deviation checking cost of t period lambda ₀ Representing allowable declaration deviation scaling factor, p _check Indicating the electricity price of the deviation checking degree, C _apportion.t Representing the apportionment costs of the period t, p _apportion Represents the monthly electricity allocation price, q _day.t The declaration electric quantity of the period t before the day is represented;

the constraint conditions of the electricity purchasing cost of the electric power market are as follows:

q _long.t 、q _day.t 、q _real.t ≥0

wherein ,q_long.t Represents the decomposition price of the middle and long periods in the t period, q _day.t Unified price showing time period t before day, q _real.t And the actual power consumption in the period t is represented.

The light storage charging station can also improve economic benefits in an electric market environment by participating in demand side responses. The demand response consists of several steps of response offer, response capability confirmation, response execution, corresponding assessment and settlement. The gain in participation in demand response is primarily determined by the actual response power, i.e., the difference between the baseline load and the actual detected load.

C _DR.t ＝(q _base.t -q _real.t )p _DR.t

wherein ,C_DR.t Representing the benefit of the t period involved in demand response, q _real.t Represents the actual power consumption in the period t, q _base.t Represents the basic electricity consumption in the period t, p _DR.t And represents the electricity price of the period t.

S14, solving a day-ahead transaction model of the electric power market according to a plurality of prediction results, the optical storage charging station equipment parameters and a day-ahead declaration plan, obtaining a day-ahead charging and discharging plan curve, and then solving a real-time transaction model of the electric power market according to the day-ahead charging and discharging plan curve, a plurality of prediction results and a day-ahead preset electric quantity to obtain a real-time charging and discharging plan curve, so that the energy management system sends the real-time charging and discharging plan curve to the energy storage system, and the energy storage system charges or discharges according to the charging and discharging plan curve.

In a preferred embodiment, according to a plurality of prediction results, parameters of the optical storage charging station equipment and a day-ahead declaration plan, a day-ahead transaction model of the electric power market is solved, after a day-ahead charging and discharging plan curve is obtained, a real-time transaction model of the electric power market is solved according to the day-ahead charging and discharging plan curve, a plurality of prediction results and a day-ahead preset electric quantity, and a real-time charging and discharging plan curve is obtained, specifically:

inputting a photovoltaic power generation power prediction curve, a charging load prediction curve, a short-term day-ahead clearing curve and a preset contract decomposition electric quantity curve into a day-ahead transaction model of an electric power market for solving, and obtaining a day-ahead charging and discharging plan curve;

And (3) inputting the daily charge and discharge planning curve, the charge load prediction curve, the real-time out-of-price clearing curve and the date preset electric quantity into the electric power market real-time transaction model for solving, so as to obtain the real-time charge and discharge planning curve.

In this embodiment, in the early day stage, that is, the day before the formal operation day, the system energy management module receives the photovoltaic system power prediction result, the charging station load prediction result, and the early day electricity price prediction information, and calculates and obtains the next day charge and discharge plan curve of the energy storage system with the lowest charging station operation cost as the target according to the decomposition condition of the medium-and-long-term contract electricity quantity, the deviation of the contract electricity price and the early day predicted electricity price, and the charging station supply and demand balance condition, and forms an optimal electricity reporting strategy for the operation day, so as to participate in the electricity energy transaction of the early day market. The objective function of the day-ahead stage is:

wherein ,representation storeCost of operation and maintenance of unit power of energy system, < >>Power of the energy storage system in period t representing the day-ahead phase,/->Representing the operation and maintenance cost of the unit power of the photovoltaic system, < >>Representing the power generated by the photovoltaic power generation system in the period t of the day-ahead stage;

and after the day-ahead stage is finished, a day-ahead clearing result is generated, and the day-ahead market electricity price and the winning curve are determined, namely the day-ahead electricity price and the day-ahead declaration electric quantity corresponding to each period of the operation day are included. In a real-time operation stage, taking a power generation plan curve formed in a day-ahead stage as a basis, taking 15min as a period, a system energy management module optimizes the actual use energy of the whole charging station by taking the cost minimization of the charging station energy into account factors such as a photovoltaic real-time output curve, a real-time SOC state of an energy storage system and a charging and discharging power limit in a price deviation allowable range and taking the factors such as the real-time charging and discharging power limit of the energy storage system into consideration, wherein the 15min is a period, and the real-time optimization stage has the objective function that:

wherein ,representing the power of the energy storage system in a t period of the real-time phase;Representing the generated power of the photovoltaic power generation system in the t period of the real-time phase.

And then solving an objective function of day-ahead stage optimization and real-time stage optimization by adopting a Cplex solver in Matlab.

First, solving a day-ahead stage optimization model. Prediction result curve P of photovoltaic power generation power _pv (t) day-ahead Price prediction result Price _day (t) charging station Load pre-curve Load _day (t) and Medium-and-Long-term contract resolution Electrical quantity Curve Q _long Parameters such as (t) are input as a model, the maximum iteration number is set, the total operation cost of the optical storage station is taken as a target, and conditions such as electric power balance, photovoltaic and energy storage output constraint are considered for solving, so that a daily declaration electric quantity curve and a daily charge-discharge planning curve of the energy storage system can be obtained.

And (5) solving an optimization model in a real-time stage. In the real-time stage, the clearing result of the day-ahead market is released, and the operation strategy of the energy storage system in the real-time stage is required to be adjusted according to the day-ahead clearing result. In the energy management system, optimization is performed every 15 minutes, and a short-term Load prediction result Load of the charging station is obtained _real (t), real-time Price prediction result Price _real (t) amount of bid-winning electric quantity Q in the daytime market _day And (t) taking parameters such as a charging and discharging plan curve before the energy storage day as input, taking constraint conditions such as photovoltaic, energy storage output constraint, real-time balance of system electric power and the like into consideration, and solving by taking the lowest comprehensive energy utilization cost of the charging station as a target, so that a real-time charging and discharging plan of the energy storage system can be obtained. And the energy storage system charge-discharge curve obtained through optimization solution is sent to the energy storage system in real time by the energy management system so as to control the charge and discharge of the energy storage system.

According to the method, after a prediction result is obtained through calculation according to a preset prediction model of energy storage of the light storage charging station, an operation model of the light storage charging station system is built according to parameters of light storage charging station equipment, a daily transaction model of an electric market and a real-time transaction model of the electric market are built according to the operation model of the light storage charging station, the daily transaction model of the electric market is solved according to a plurality of prediction results, parameters of the light storage charging station equipment and a daily declaration plan, a daily charge-discharge plan curve is obtained, the real-time transaction model of the electric market is solved according to the daily charge-discharge plan curve, a plurality of prediction results and daily preset electric quantity, and a real-time charge-discharge plan curve is obtained, so that the energy management system sends the real-time charge-discharge plan curve to the energy storage system, and the energy storage system is charged or discharged according to the charge-discharge plan curve. According to the method, the charging and discharging strategy of the energy storage system is obtained by constructing the electric power market transaction optimization model to solve, so that the dispatching efficiency and accuracy of electric power resources in the photo-storage charging station are improved, and the operation cost of the photo-storage charging station is reduced.

Example two

Accordingly, referring to fig. 4, fig. 4 is a charge and discharge policy determining system of an optical storage charging station according to the present invention, as shown in the drawing, the charge and discharge policy determining system of the optical storage charging station includes: the acquisition module 401 is configured to acquire optical storage charging station equipment parameters, historical photovoltaic power generation data, illumination intensity, illumination angle, historical charging data, historical day-ahead price clearing data, historical real-time price clearing and date;

the prediction module 402 is configured to calculate, using a plurality of preset prediction models, historical photovoltaic power generation data, illumination intensity, illumination angle, historical charging data, historical day-ahead price-clearing data, historical real-time price-clearing data, and date to obtain a plurality of prediction results;

the building module 403 is configured to build an optical storage charging station system operation model according to the optical storage charging station device parameter, and build an electric power market transaction optimization model by using the optical storage charging station system operation model, where the electric power market transaction optimization model includes an electric power market day-ahead transaction model and an electric power market real-time transaction model;

the solving module 404 is configured to solve the day-ahead transaction model of the electric power market according to the plurality of prediction results, the optical storage charging station device parameters and the day-ahead declaration plan, and then solve the real-time transaction model of the electric power market according to the day-ahead charging and discharging plan curve, the plurality of prediction results and the day-ahead preset electric quantity after obtaining the day-ahead charging and discharging plan curve, so as to obtain a real-time charging and discharging plan curve, so that the energy management system sends the real-time charging and discharging plan curve to the energy storage system, and the energy storage system charges or discharges according to the charging and discharging plan curve.

In a preferred embodiment, the prediction module 402 includes a power prediction unit 4021, a load prediction unit 4022 and a price prediction unit 4023,

the power prediction unit 4021 is configured to input historical photovoltaic power generation data, illumination intensity and illumination angle into a preset photovoltaic power prediction model for calculation, so as to obtain a photovoltaic power generation power prediction curve;

the load prediction unit 4022 is configured to input historical charging data and date into a preset charging load prediction model for calculation, so as to obtain a charging load prediction curve;

the price prediction unit 4023 is configured to input the historical daily output price data and the historical real-time output price data into the preset spot market electricity price prediction module to calculate, so as to obtain a short-term daily output price curve and a real-time output price curve.

In a preferred embodiment, the construction module 403 is further configured to construct an energy storage system model using energy storage system parameters, where the charge and discharge power of the energy storage system is:

the state of charge is:

wherein ,P_batt.t 、P _charge.t 、P _discha.t Respectively represents the power, the charging power and the discharging power of the energy storage system, eta _batt Indicating charge-discharge efficiency, delta _charge.t 、δ _discha.t The energy storage charge and discharge states are 0 or 1;

the constraint conditions of the energy storage system are as follows:

P _battmin ＜P _batt.t ＜P _battmax

SOC _battmin ＜SOC _batt.t ＜SOC _battmax

wherein ,P_battmin 、P _battmax Indicating upper and lower limits of energy storage output and SOC _battmin 、SOC _battmax Representing upper and lower limits of the energy storage SOC;

and constructing a photovoltaic power generation system model by utilizing parameters of the photovoltaic power generation system, wherein the electric power of the photovoltaic power generation system is as follows:

P _pv.t ＝f _pv S _pv G _pv.t /G _s

wherein ,P_pv.t Representing the electric power of the photovoltaic power generation system, f _pv Representing a photovoltaic system power derating factor; s is S _pv Representing the installed capacity of the photovoltaic system; g _pv.t Representing the illumination radiation intensity in the t period; g _s Represents the radiation intensity under standard illumination conditions of 1kW/m ² ；

The output constraint conditions of the photovoltaic power generation system are as follows:

0＜P _pv.t ＜P _pvmax

wherein ,P_pvmax Indicating the upper photovoltaic output limit.

In a preferred embodiment, the building module 403 is further configured to build a power market day-ahead transaction model and a power market real-time transaction model using the optical storage charging station system operation model, wherein an objective function of the power market day-ahead transaction model is:

wherein ,representing the unit power operation and maintenance cost of the energy storage system, < >>Power of the energy storage system in period t representing the day-ahead phase,/->Representing the operation and maintenance cost of the unit power of the photovoltaic system, < >>Representing the power generated by the photovoltaic power generation system in the period t of the day-ahead stage;

the objective function of the real-time transaction model of the electric power market is as follows:

wherein ,power of the energy storage system in period t representing the real time phase,/- >Representing the generated power of the photovoltaic power generation system in a t period of the real-time period;

the electricity purchasing cost of the daily transaction model of the electric power market is as follows:

C _dayMarket.t ＝C _long.t +C _day.t

C _long.t ＝q _long.t ×p _long.t

C _day.t ＝(q _day.t -q _long.t )p _day.t

wherein ,C_dayMarket.t Representing electricity purchasing cost of daily transaction model of electric power market, C _long.t Electric charge, q, representing middle-long term contract decomposed electric quantity within t period _long.t 、p _long.t Respectively representing the decomposition electric quantity and price of the medium and long term in the period t, C _day.t Electric charge, q representing time period t before day _day.t 、p _day.t Respectively representing the declaration electric quantity and unified price in the period t before the day;

the electricity purchasing cost of the real-time transaction model of the electric power market is as follows:

C _realMarket.t ＝C _real.t +C _err.t +C _apportion

C _real.t ＝(q _real.t -q _day.t )p _real.t

C _err.t ＝{max[0，|q _real.t -q _day.t |-q _real.t ×λ ₀ ]}×p _check

C _apportion.t ＝q _real.t ×p _apportion

wherein ,C_real.t Represents the real-time electricity charge in the period t, q _real.t 、p _real.t Respectively representing the actual electricity consumption in the period t and the real-time unified price, C _err.t Indicating the declaration deviation checking cost of t period lambda ₀ Representing allowable declaration deviation scaling factor, p _check Indicating the electricity price of the deviation checking degree, C _apportion.t Representing the apportionment costs of the period t, p _apportion Represents the monthly electricity allocation price, q _day.t The declaration electric quantity of the period t before the day is represented;

the constraint conditions of the electricity purchasing cost of the electric power market are as follows:

q _long.t 、q _day.t 、q _real.t ≥0

wherein ,q_long.t Represents the decomposition price of the middle and long periods in the t period, q _day.t Unified price showing time period t before day, q _real.t And the actual power consumption in the period t is represented.

In a preferred embodiment, the solving module 404 includes a day-ahead computing module 4041 and a real-time computing module 4042,

The day-ahead calculation module 4041 is configured to input a photovoltaic power generation power prediction curve, a charging load prediction curve, a short-term day-ahead price clearing curve and a preset contract decomposition electric quantity curve into a day-ahead transaction model of the electric power market for solving, so as to obtain a day-ahead charging and discharging plan curve;

the real-time calculation module 4042 is configured to input the day-ahead charge-discharge planning curve, the charge load prediction curve, the real-time price-clearing curve, and the day preset electric quantity into the electric power market real-time transaction model for solving, so as to obtain the real-time charge-discharge planning curve.

In summary, the embodiment of the invention has the following beneficial effects:

after the parameters and the historical data of the photo-storage charging station equipment are obtained, a plurality of preset prediction models are utilized to respectively calculate historical photovoltaic power generation data, illumination intensity, illumination angle, historical charging data, historical day-ahead price-giving data, historical real-time price-giving data and date to obtain a plurality of prediction results, then a photo-storage charging station system operation model is built according to the parameters of the photo-storage charging station equipment, an electric market day-ahead transaction model and an electric market real-time transaction model are built according to the operation models of the photo-storage charging station system, the plurality of prediction results, the photo-storage charging station equipment parameters and the day-ahead reporting plan are utilized to solve the electric market day-ahead transaction model to obtain a day-ahead charging and discharging plan curve, and then the electric market real-time transaction model is solved according to the day-ahead charging and discharging plan curve, the plurality of prediction results and the day-ahead preset electric quantity to obtain a real-time charging and discharging plan curve, so that the energy management system sends the real-time charging and discharging plan curve to the energy storage system to charge or discharge according to the charging and discharging plan curve. According to the method, the charging and discharging strategy of the energy storage system is obtained by constructing the electric power market transaction optimization model to solve, so that the dispatching efficiency and accuracy of electric power resources in the photo-storage charging station are improved, and the operation cost of the photo-storage charging station is reduced.

While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.

Claims (10)

1. A method for determining a charge-discharge strategy of an optical storage station, comprising:

acquiring optical storage charging station equipment parameters, historical photovoltaic power generation data, illumination intensity, illumination angle, historical charging data, historical day-ahead price-clearing data, historical real-time price-clearing and date;

calculating the historical photovoltaic power generation data, the illumination intensity, the illumination angle, the historical charging data, the historical day-ahead price-taking data, the historical real-time price-taking data and the date by utilizing a plurality of preset prediction models to obtain a plurality of prediction results;

establishing an optical storage charging station system operation model according to the optical storage charging station equipment parameters, and establishing an electric power market transaction optimization model by utilizing the optical storage charging station system operation model, wherein the electric power market transaction optimization model comprises an electric power market day-ahead transaction model and an electric power market real-time transaction model;

And solving a day-ahead transaction model of the electric power market according to the plurality of prediction results, the optical storage station equipment parameters and the day-ahead declaration plan, obtaining a day-ahead charge-discharge plan curve, and then solving a real-time transaction model of the electric power market according to the day-ahead charge-discharge plan curve, the plurality of prediction results and the day-ahead preset electric quantity to obtain a real-time charge-discharge plan curve, so that an energy management system sends the real-time charge-discharge plan curve to an energy storage system, and the energy storage system charges or discharges according to the charge-discharge plan curve.

2. The method for determining a charging and discharging strategy of an optical storage charging station according to claim 1, wherein the calculating the historical photovoltaic power generation data, the illumination intensity, the illumination angle, the historical charging data, the historical day-ahead price clearing data, the historical real-time price clearing data and the date by using a plurality of preset prediction models respectively obtains a plurality of prediction results, specifically:

inputting the historical photovoltaic power generation data, the illumination intensity and the illumination angle into a preset photovoltaic power prediction model for calculation to obtain a photovoltaic power generation power prediction curve;

Inputting the historical charging data and the date into a preset charging load prediction model for calculation to obtain a charging load prediction curve;

and inputting the historical daily price-output data and the historical real-time price-output data into a preset spot market electricity price prediction module for calculation to obtain a short-term daily price-output curve and a real-time price-output curve.

3. The method for determining a charging and discharging strategy of an optical storage charging station according to claim 1, wherein the establishing an optical storage charging station system operation model according to the optical storage charging station equipment parameters specifically comprises:

an energy storage system model is constructed by utilizing energy storage system parameters, wherein the charge and discharge power of the energy storage system is as follows:

the state of charge is:

wherein ,P_batt.t 、P _charge.t 、P _discha.t Respectively represents the power, the charging power and the discharging power of the energy storage system, eta _batt Indicating charge-discharge efficiency, delta _charge.t 、δ _discha.tx The energy storage charge and discharge state is 0 or 1;

the constraint conditions of the energy storage system are as follows:

P _battmin ＜P _batt.t ＜P _battmax

SOC _battmin ＜SOC _batt.t ＜SOC _battmax

wherein ,P_battmin 、P _battmax Indicating upper and lower limits of energy storage output and SOC _battmin 、SOC _battmax Representing upper and lower limits of the energy storage SOC;

building a photovoltaic power generation system model by utilizing parameters of the photovoltaic power generation system, wherein the electric power of the photovoltaic power generation system is as follows:

P _pv.t ＝f _pv S _pv G _pv.t /G _s

wherein ,P_pv.t Representing the electric power of the photovoltaic power generation system, f _pv Representing a photovoltaic system power derating factor; s is S _pv Representing the installed capacity of the photovoltaic system; g _pv.t Representing the illumination radiation intensity in the t period; g _s Represents the radiation intensity under standard illumination conditions of 1kW/m ² ；

The output constraint conditions of the photovoltaic power generation system are as follows:

0＜P _pv.t ＜P _pvmax

wherein ,P_pvmax Indicating the upper photovoltaic output limit.

4. The method for determining a charging and discharging strategy of an optical storage charging station according to claim 1, wherein the constructing an electric market transaction optimization model by using the optical storage charging station system operation model comprises the following steps:

utilizing the light storage charging station system operation model to construct an electric power market day-ahead transaction model and an electric power market real-time transaction model, wherein an objective function of the electric power market day-ahead transaction model is as follows:

wherein ,representing the unit power operation and maintenance cost of the energy storage system, < >>Power of the energy storage system in period t representing the day-ahead phase,/->Representing the operation and maintenance cost of the unit power of the photovoltaic system, < >>Representing the power generated by the photovoltaic power generation system in the period t of the day-ahead stage;

the objective function of the real-time transaction model of the electric power market is as follows:

wherein ,representing the power of the energy storage system in a t period of the real-time phase;Representing the generated power of the photovoltaic power generation system in a t period of the real-time period;

The electricity purchasing cost of the electricity market day-ahead transaction model is as follows:

C _dayMarket.t ＝C _long.t +C _day.t

C _long.t ＝q _long.t ×p _long.t

C _day.t ＝(q _day.t -q _long.t )p _day.t

wherein ,C_dayMarket.t Representing electricity purchasing cost of daily transaction model of electric power market, C _long.t Electric charge, q, representing middle-long term contract decomposed electric quantity within t period _long.t 、p _long.t Respectively representing the decomposition electric quantity and price of the medium and long term in the period t, C _day.t Electric charge, q representing time period t before day _day.t 、p _day.t Respectively representing the declaration electric quantity and unified price in the period t before the day;

the electricity purchasing cost of the real-time transaction model of the electric power market is as follows:

C _realMarket. t＝C _real.t +C _err.t +C _apportion

C _real.t ＝(q _real.t -q _day.t )p _real.t

C _err.t ＝{max[0，|q _real.t -q _day.t |-q _real.t ×λ ₀ ]}×p _check

C _apportion.t ＝q _real.t ×p _apportion

wherein ,C_real.t Represents the real-time electricity charge in the period t, q _real.t 、p _real.t Respectively representing the actual electricity consumption in the period t and the real-time unified price, C _err.t Indicating the declaration deviation checking cost of t period lambda ₀ Representing allowable declaration deviation scaling factor, p _check Indicating the electricity price of the deviation checking degree, C _apportion.t Representing the apportionment costs of the period t, p _apportion Represents the monthly electricity allocation price, q _day.t The declaration electric quantity of the period t before the day is represented;

the electricity purchasing cost constraint conditions of the electric power market are as follows:

q _long.t 、q _day.t 、q _real.t ≥0

wherein ,q_long.t Represents the decomposition price of the middle and long periods in the t period, q _day.t Unified price showing time period t before day, q _real.t And the actual power consumption in the period t is represented.

5. The method for determining a charging and discharging strategy of an optical storage charging station according to claim 1, wherein the method comprises solving a day-ahead transaction model of an electric power market according to the plurality of prediction results, the optical storage charging station equipment parameters and a day-ahead declaration plan to obtain a day-ahead charging and discharging plan curve, and then solving a real-time transaction model of the electric power market according to the day-ahead charging and discharging plan curve, the plurality of prediction results and a day-ahead preset electric quantity to obtain a real-time charging and discharging plan curve, which specifically comprises:

Inputting a photovoltaic power generation power prediction curve, a charging load prediction curve, a short-term day-ahead clearing curve and a preset contract decomposition electric quantity curve into a day-ahead transaction model of the electric power market for solving, and obtaining a day-ahead charging and discharging plan curve;

and (3) inputting the daily charge and discharge planning curve, the charge load prediction curve, the real-time out-of-price clearing curve and the date preset electric quantity into the electric power market real-time transaction model for solving, so as to obtain the real-time charge and discharge planning curve.

6. A charge-discharge strategy determination system of an optical storage charging station, comprising:

the acquisition module is used for acquiring the parameters of the photovoltaic storage charging station equipment, the historical photovoltaic power generation data, the illumination intensity, the illumination angle, the historical charging data, the historical day-ahead price clearing data, the historical real-time price clearing and the date;

the prediction module is used for calculating the historical photovoltaic power generation data, the illumination intensity, the illumination angle, the historical charging data, the historical day-ahead price data, the historical real-time price and the date by utilizing a plurality of preset prediction models to obtain a plurality of prediction results;

the construction module is used for constructing an optical storage charging station system operation model according to the optical storage charging station equipment parameters, and constructing an electric power market transaction optimization model by utilizing the optical storage charging station system operation model, wherein the electric power market transaction optimization model comprises an electric power market day-ahead transaction model and an electric power market real-time transaction model;

The solving module is used for solving the day-ahead transaction model of the electric power market according to the plurality of prediction results, the optical storage charging station equipment parameters and the day-ahead declaration plan, solving the real-time transaction model of the electric power market according to the day-ahead charging and discharging plan curve, the plurality of prediction results and the day-ahead preset electric quantity after the day-ahead charging and discharging plan curve is obtained, and obtaining a real-time charging and discharging plan curve, so that the energy management system sends the real-time charging and discharging plan curve to the energy storage system, and the energy storage system charges or discharges according to the charging and discharging plan curve.

7. The charge-discharge policy determination system of an optical storage station as claimed in claim 6, wherein the prediction module includes a power prediction unit, a load prediction unit, and a price prediction unit,

the power prediction unit is used for inputting the historical photovoltaic power generation data, the illumination intensity and the illumination angle into a preset photovoltaic power prediction model for calculation to obtain a photovoltaic power generation power prediction curve;

the load prediction unit is used for inputting the historical charging data and the date into a preset charging load prediction model for calculation to obtain a charging load prediction curve;

The price prediction unit is used for inputting the historical daily price data and the historical real-time price data into a preset spot market electricity price prediction module for calculation to obtain a short-term daily price curve and a real-time price curve.

8. The charge-discharge policy determination system of an optical storage station of claim 6, wherein the building module is further configured to build an energy storage system model using energy storage system parameters, wherein the charge-discharge power of the energy storage system is:

the state of charge is:

wherein ,P_batt.t 、P _charge.t 、P _discha.t Respectively represents the power, the charging power and the discharging power of the energy storage system, eta _batt Indicating charge-discharge efficiency, delta _charge.t 、δ _discha.t The energy storage charge and discharge states are 0 or 1;

the constraint conditions of the energy storage system are as follows:

P _battmin ＜P _batt.t ＜P _battmax

SOC _battmin ＜SOC _batt.t ＜SOC _battmax

wherein ,P_battmin 、P _battmax Indicating upper and lower limits of energy storage output and SOC _battmin 、SOC _battmax Representing upper and lower limits of the energy storage SOC;

building a photovoltaic power generation system model by utilizing parameters of the photovoltaic power generation system, wherein the electric power of the photovoltaic power generation system is as follows:

P _pv.t ＝f _pv s _pv G _pv.t /G _s

wherein ,P_pv.t Representing the electric power of the photovoltaic power generation system, f _pv Representing a photovoltaic system power derating factor; s is S _pv Representing the installed capacity of the photovoltaic system; g _pv.t Representing the illumination radiation intensity in the t period; g _s Represents the radiation intensity under standard illumination conditions of 1kW/m ² ；

The output constraint conditions of the photovoltaic power generation system are as follows:

0＜P _pv.t ＜P _pvmax

wherein ,P_pvrnax Indicating the upper photovoltaic output limit.

9. The system for determining a charge-discharge strategy of an optical storage charging station of claim 6, wherein the building module is further configured to build an electric market day-ahead transaction model and an electric market real-time transaction model using the optical storage charging station system operational model, wherein an objective function of the electric market day-ahead transaction model is:

wherein ,representing the unit power operation and maintenance cost of the energy storage system, < >>Power of the energy storage system in period t representing the day-ahead phase,/->Representing the operation and maintenance cost of the unit power of the photovoltaic system, < >>Photovoltaic power generation system in period t representing the day-ahead stageGenerating power;

the objective function of the real-time transaction model of the electric power market is as follows:

wherein ,power of the energy storage system in period t representing the real time phase,/->Representing the generated power of the photovoltaic power generation system in a t period of the real-time period;

the electricity purchasing cost of the electricity market day-ahead transaction model is as follows:

C _dayMarket.t ＝C _long.t +C _day.t

C _long.t ＝q _long.t ×p _long.t

C _day.t ＝(q _day.t -q _long.t )p _day.t

wherein ,C_dayMarket.t Representing electricity purchasing cost of daily transaction model of electric power market, C _long.t Electric charge, q, representing middle-long term contract decomposed electric quantity within t period _long.t 、p _long.t Respectively representing the decomposition electric quantity and price of the medium and long term in the period t, C _day.t Electric charge, q representing time period t before day _day.t 、p _day.t Respectively representing the declaration electric quantity and unified price in the period t before the day;

the electricity purchasing cost of the real-time transaction model of the electric power market is as follows:

C _realMarket.t ＝C _real.t +C _err.t +C _apportion

C _real.t ＝(q _real.t -q _day.t )p _real.t

C _err.t ＝{max[0，|q _real.t -q _day.t |-q _real.t ×λ ₀ ]}×p _check

C _apportion.t ＝q _real.t ×p _apportion

wherein ,C_real.t Represents the real-time electricity charge in the period t, q _real.t 、p _real.t Respectively representing the actual electricity consumption in the period t and the real-time unified price, C _err.t Indicating the declaration deviation checking cost of t period lambda ₀ Representing allowable declaration deviation scaling factor, p _check Indicating the electricity price of the deviation checking degree, C _apportion.t Representing the apportionment costs of the period t, p _apportion Represents the monthly electricity allocation price, q _day.t The declaration electric quantity of the period t before the day is represented;

the electricity purchasing cost constraint conditions of the electric power market are as follows:

q _long.t 、q _day.t 、q _real.t ≥0

wherein ,q_long.t Represents the decomposition price of the middle and long periods in the t period, q _day.t Unified price showing time period t before day, q _real.t And the actual power consumption in the period t is represented.

10. The charge and discharge policy determination system of an optical storage station as claimed in claim 6, wherein said solving module comprises a day-ahead computing module and a real-time computing module,

wherein the day-ahead calculation module is used for inputting a photovoltaic power generation power prediction curve, a charging load prediction curve, a short-term day-ahead price clearing curve and a preset contract decomposition electric quantity curve into the day-ahead transaction model of the electric power market for solving, obtaining a day-ahead charge-discharge plan curve;

The real-time calculation module is used for inputting the daily charge and discharge planning curve, the charge load prediction curve, the real-time price-clearing curve and the date preset electric quantity into the electric power market real-time transaction model to solve, so that the real-time charge and discharge planning curve is obtained.