New energy distribution, construction and storage combined operation optimization system and method

Abstract

The invention provides a new energy configuration, construction and energy storage combined operation optimization system and method, and relates to the technical field of new energy configuration and energy storage. According to electricity price prediction data, a single wind power station output prediction result, a single photovoltaic power station output prediction result, an AGC active power instruction and new energy real-time active power, overall income maximization is taken as a target function, new energy output constraint and energy storage operation cost are considered, and a new energy output prediction result is obtained according to a general principle of low-price charging and high-price discharging. And an energy storage charging and discharging strategy in a future period is given, the charging and discharging strategy is issued to the energy storage EMS, and automatic charging and discharging are controlled by the energy storage EMS. By predicting the node electricity price, the energy storage charging and discharging time is reasonably planned, the energy storage charging and discharging actions are automatically controlled, and the utilization rate of new energy distribution and storage is improved. By predicting the node electricity price, the energy storage charging and discharging time is reasonably planned, the energy storage charging and discharging actions are automatically controlled, and the utilization rate of new energy distribution and storage is improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy configuration and energy storage technology, and in particular to a new energy configuration and energy storage joint operation optimization system and method. Background Art

[0002] Energy storage with new energy refers to the energy storage facilities built in conjunction with new energy power generation projects, combining new energy with energy storage technology to achieve effective utilization and storage of new energy. This strategy uses energy storage technology to solve the instability and intermittency problems of new energy power generation, improve the stability and reliability of the power system, and thus meet the growing energy demand.

[0003] The combination of "new energy + energy storage" has gradually become an important force in participating in electricity spot transactions. At present, how to reduce the new energy forecast deviation assessment and maximize the overall profit through strategy adjustments and overall operation optimization of joint declarations of energy storage and its affiliated power sources is a very concerned issue in the current electricity spot market.

[0004] However, the energy storage configured with existing new energy sources is generally charged and discharged during periods of power abandonment, and at most operates on a "charge-and-discharge" basis. The electrochemical energy storage configured in some areas is basically not called upon, and the utilization rate of new energy storage is low. Summary of the invention

[0005] 1. Technical issues to be solved

[0006] In view of the deficiencies in the prior art, the present invention provides a system and method for optimizing the joint operation of new energy and energy storage, which solves the technical problem of low utilization rate of new energy and energy storage in the prior art.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] In a first aspect, the present invention provides a new energy and energy storage combined operation optimization system, comprising:

[0010] A prediction result acquisition module is used to execute S1, obtain the output prediction result of a single wind power station, the output prediction result of a single photovoltaic power station and the supply and demand prediction result of a region;

[0011] The power restriction judgment module is used to execute S2, and judge whether the new energy power station is in a power restriction state according to the output prediction results of a single wind power station, the output prediction results of a single photovoltaic power station, the supply and demand prediction results of the region, the real-time power grid operation status and the real-time meteorological data. If so, the steps in the energy storage capacity judgment module are executed first and then the steps in the electricity price prediction module are executed; otherwise, the steps in the electricity price prediction module are directly executed;

[0012] The energy storage capacity judgment module is used to execute S3 and judge whether the energy storage is full. If not, the energy storage EMS system performs charging. If yes, the energy storage EMS system does not perform charging.

[0013] The electricity price prediction module is used to execute S4, perform node electricity price prediction based on the disclosed data of the power trading center and the power grid dispatching center, meteorological data, and historical power trading data, and obtain electricity price prediction data;

[0014] The charging and discharging strategy acquisition module is used to execute S5, based on the electricity price forecast data, the output forecast results of a single wind power station, the output forecast results of a single photovoltaic power station, the AGC active power command and the real-time active power of new energy, with the overall benefit maximization as the objective function, considering the output constraints of new energy and the operating cost of energy storage, and according to the general principle of low-price charging and high-price discharging, it gives the energy storage charging and discharging strategy for the future period, and sends the charging and discharging strategy to the energy storage EMS, which controls automatic charging and discharging.

[0015] Preferably, the method for obtaining the supply and demand forecast result of the region includes:

[0016] Based on the data publicly disclosed by the power trading center and the power grid dispatching center, based on the time series method or the artificial neural network method, analyze the historical power load data, draw the daily load curve, monthly load curve, and seasonal load curve, identify the load change patterns and characteristics, and based on the load change patterns and characteristics, combine future holidays and temperature forecast information to predict the load trend and obtain the regional supply and demand forecast results.

[0017] Preferably, the node electricity price forecasting based on the disclosed data of the power trading center and the power grid dispatching center, meteorological data, and historical power trading data includes:

[0018] Based on the data disclosed by the power trading center, power grid dispatching center, meteorological data, and historical power trading data, node electricity prices are predicted through neural network models or time series models.

[0019] Preferably, the strategy for energy storage charging and discharging in the future is given according to the general principle of low-price charging and high-price discharging, including:

[0020] Introduce differential electricity price setting value R 设定值 , when |R 放电 -R 充电 |≥R 设定值 When R 放电 Greater than R 充电 When |R 放电 -R 充电 |≥R 设定值 , and R 放电 Less than R 充电 When the charging strategy is executed.

[0021] Preferably, the new energy and energy storage combined operation optimization system further includes:

[0022] The electricity quantity reported strategy optimization module is used to determine the real-time electricity price based on the electricity price forecast data, and compare the real-time electricity price with the day-ahead market electricity price. When the real-time electricity price is less than the day-ahead market electricity price, the day-ahead reported electricity quantity is the new energy output forecast + energy storage capacity. When the real-time electricity price is greater than the day-ahead market electricity price, the day-ahead reported electricity quantity is the new energy output forecast.

[0023] In a second aspect, the present invention provides a method for optimizing the combined operation of new energy and energy storage, comprising:

[0024] S1. Obtain the output forecast results of a single wind power station, the output forecast results of a single photovoltaic power station, and the supply and demand forecast results of the region;

[0025] S2, judging whether the new energy power station is in a power-limited state according to the output forecast results of a single wind power station, the output forecast results of a single photovoltaic power station, the regional supply and demand forecast results, the real-time power grid operation status and the real-time meteorological data, if so, executing S3 and then executing S4, otherwise, directly executing S4;

[0026] S3, determine whether the energy storage is full, if not, the energy storage EMS system performs charging, if yes, the energy storage EMS system does not perform charging;

[0027] S4. Node electricity price forecasting is performed based on the data disclosed by the power trading center and the power grid dispatching center, meteorological data, and historical power trading data to obtain electricity price forecast data;

[0028] S5. Based on the electricity price forecast data, the output forecast results of a single wind power station, the output forecast results of a single photovoltaic power station, the AGC active power command and the real-time active power of new energy, with the overall profit maximization as the objective function, considering the output constraints of new energy and the operating costs of energy storage, and following the general principle of low-price charging and high-price discharging, a strategy for energy storage charging and discharging in the future is given, and the charging and discharging strategy is issued to the energy storage EMS, which controls automatic charging and discharging.

[0029] Preferably, the node electricity price forecasting based on the disclosed data of the power trading center and the power grid dispatching center, meteorological data, and historical power trading data includes:

[0030] Based on the data disclosed by the power trading center, power grid dispatching center, meteorological data, and historical power trading data, node electricity prices are predicted through neural network models or time series models.

[0031] Preferably, the strategy for energy storage charging and discharging in the future is given according to the general principle of low-price charging and high-price discharging, including:

[0032] Introduce differential electricity price setting value R 设定值 , when |R 放电 -R 充电 |≥R 设定值 When R 放电 Greater than R 充电 When |R 放电 -R 充电 |≥R 设定值 , and R 放电 Less than R 充电 When the charging strategy is executed.

[0033] In a third aspect, the present invention provides a computer-readable storage medium storing a computer program for optimizing the combined operation of new energy and energy storage, wherein the computer program enables a computer to execute the method for optimizing the combined operation of new energy and energy storage as described above.

[0034] In a fourth aspect, the present invention provides an electronic device, comprising:

[0035] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include methods for executing the new energy and energy storage combined operation optimization method as described above.

[0036] (III) Beneficial effects

[0037] The present invention provides a system and method for optimizing the combined operation of new energy and energy storage. Compared with the prior art, it has the following beneficial effects:

[0038] The present invention can take the overall benefit maximization as the objective function, consider the new energy output constraint and the energy storage operation cost, and give the energy storage charging and discharging strategy in the future period according to the general principle of low-price charging and high-price discharging according to the electricity price forecast data, the output forecast result of a single wind power station, the output forecast result of a single photovoltaic power station, the AGC active power instruction and the real-time active power of new energy, and send the charging and discharging strategy to the energy storage EMS, which controls the automatic charging and discharging. The present invention predicts the node electricity price, reasonably plans the energy storage charging and discharging time, automatically controls the energy storage charging and discharging action, and improves the utilization rate of new energy storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 This is a flow chart of the new energy and energy storage joint operation optimization system according to an embodiment of the present invention when performing automatic control of energy storage charging and discharging actions;

[0041] Figure 2 This is a flow chart of the statistical method for abandoned power consumption;

[0042] Figure 3 In order to verify the theoretical benefits in the experiment, a schematic diagram of the monthly benefits of the charging and discharging strategy calculated based on the actual electricity price at different kilowatt-hour benefits is provided. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] The embodiments of the present application solve the technical problem of low utilization rate of energy storage combined with new energy in the prior art by providing a system and method for optimizing the joint operation of energy storage combined with new energy, realize the prediction of node electricity prices, reasonably plan the energy storage charging and discharging time, automatically control the energy storage charging and discharging actions, and improve the utilization rate of energy storage combined with new energy.

[0045] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0046] The existing new energy storage operation has the following main defects:

[0047] 1. Low utilization rate of energy storage with new energy. Energy storage with new energy is one of the various energy storage application methods. The call frequency, equivalent utilization coefficient and utilization rate of energy storage with new energy are lower than those of energy storage with thermal power plants, grid energy storage and user energy storage. In terms of call frequency, at present, energy storage with new energy is generally charged and discharged during periods of power abandonment, and at most operates in a "one charge and one discharge" mode. The electrochemical energy storage configured in some areas is basically not called.

[0048] 2. Manual control is the main method. For the effective and rational use of energy storage, various new energy power stations have different methods, and most of them are mainly based on manual control. The disadvantages of manual control are: there is a lot of information to pay attention to, including electricity price information, energy storage status, photovoltaic active output, power limit judgment, etc., there is a certain technical threshold, there is no accurate profit guarantee, and the control is intermittent and uncertain.

[0049] 3. Single operation mode. In the spot market, how to control the optimal operation of energy storage and maximize revenue is a question that all new energy power stations are very concerned about. Currently, there are three scenarios for manual control of charging and discharging: (1) low-price charging and high-price discharging; (2) limited-power charging; and (3) dispatching control.

[0050] In order to solve the above problems, the embodiments of the present invention provide a system and method for optimizing the joint operation of energy storage with new energy. When the system operates in the spot market, it can combine the price fluctuation patterns of the power market, dispatch instructions, and the actual active power, wind speed and other data of the new energy power station to perform real-time profit measurement by predicting electricity prices, thereby realizing intelligent charging and discharging control of energy storage in multiple scenarios, maximizing the utilization rate of energy storage with new energy, exploring the profit space of energy storage, improving the response speed and accuracy of the system, reducing the misjudgment rate, and ultimately maximizing the overall profit of the new energy power station.

[0051] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0052] An embodiment of the present invention provides a new energy and energy storage combined operation optimization system, the system comprising:

[0053] A prediction result acquisition module is used to execute S1, obtain the output prediction result of a single wind power station, the output prediction result of a single photovoltaic power station and the supply and demand prediction result of a region;

[0054] The power restriction judgment module is used to execute S2, and judge whether the new energy power station is in a power restriction state according to the output prediction results of a single wind power station, the output prediction results of a single photovoltaic power station, the supply and demand prediction results of the region, the real-time power grid operation status and the real-time meteorological data. If so, the steps in the energy storage capacity judgment module are executed first and then the steps in the electricity price prediction module are executed; otherwise, the steps in the electricity price prediction module are directly executed;

[0055] The energy storage capacity judgment module is used to execute S3 and judge whether the energy storage is full. If not, the energy storage EMS system performs charging. If yes, the energy storage EMS system does not perform charging.

[0056] The electricity price prediction module is used to execute S4, perform node electricity price prediction based on the disclosed data of the power trading center and the power grid dispatching center, meteorological data, and historical power trading data, and obtain electricity price prediction data;

[0057] The charging and discharging strategy acquisition module is used to execute S5, based on the electricity price forecast data, the output forecast results of a single wind power station, the output forecast results of a single photovoltaic power station, the AGC active power command and the real-time active power of new energy, with the overall benefit maximization as the objective function, considering the output constraints of new energy and the operating cost of energy storage, and according to the general principle of low-price charging and high-price discharging, it gives the energy storage charging and discharging strategy for the future period, and sends the charging and discharging strategy to the energy storage EMS, which controls automatic charging and discharging.

[0058] The embodiment of the present invention predicts node electricity prices, reasonably plans energy storage charging and discharging time, automatically controls energy storage charging and discharging actions, and improves the utilization rate of new energy storage.

[0059] In the specific implementation process, the new energy and energy storage joint operation optimization system also includes:

[0060] The electricity quantity declared strategy optimization module determines the real-time electricity price based on the electricity price forecast data, and compares the real-time electricity price with the day-ahead market electricity price. When the real-time electricity price is less than the day-ahead market electricity price, the electricity quantity declared the day-ahead is the new energy output forecast + energy storage capacity. When the real-time electricity price is greater than the day-ahead market electricity price, the electricity quantity declared the day-ahead is the new energy output forecast. The details are as follows:

[0061] When the day-ahead market electricity price is greater than the real-time electricity price, the electricity reported on the day-ahead is the new energy output forecast + energy storage capacity, and the energy storage simultaneously performs new energy output power smoothing; when the day-ahead market electricity price is less than the real-time electricity price, the electricity reported on the day-ahead is the new energy output forecast, and the energy storage performs discharge arbitrage in the real-time market, while performing new energy output power smoothing. This process takes into account that when the spot market electricity price is higher than the medium- and long-term CFD electricity price, more electricity can be generated at the new energy station in the spot market, and at the same time, under the premise of considering excess recovery, the new energy storage strategy in the spot market is a discharge strategy. The arbitrage formula is:

[0062] Day-ahead arbitrage = (Q 日前 -Q 合约 )×(P 日前 -P 合约 )

[0063] in:

[0064] Q 日前 Indicates the electricity settled on the previous day, Q 合约 Indicates the power of medium and long-term trading contracts, P 合约 represents the medium- and long-term transaction contract price, P 日前 Represents the day-ahead market electricity price.

[0065] It should be noted that, in the embodiment of the present invention, the area to be optimized is taken as a province as an example.

[0066] It should be noted that before describing in detail the various modules in the new energy and energy storage joint operation optimization system, the composition of the total benefit of new energy and energy storage is described in detail:

[0067] In the case of new energy and energy storage, the electricity charges are composed as follows:

[0068] The electricity price R includes the interprovincial contract electricity price R a 、Day-ahead electricity charges between provinces R b , save the daily electricity cost R c , the electricity price in the province today is R d , real-time electricity charges in the province R e , Provincial contract electricity fee R f .

[0069] R=R a +R b +R c +R d +R e +R f

[0070] Among them, the daily electricity price in the province is R d The calculation method is as follows:

[0071] The unit calculates the electricity fee based on the provincial day-ahead market settlement volume and the provincial day-ahead market node electricity price. The specific calculation formula is as follows:

[0072]

[0073] Among them, the spot market trading day will be divided into 96 points, that is, t = 0, 1, 2, 3 ... 95, Q d,t P is the unit's provincial day-ahead settlement electricity in period t, that is, the difference between the unit's bid electricity in the provincial day-ahead market in period t and the electricity decomposed in the medium- and long-term trading contract; d,t is the provincial day-ahead node electricity price of the unit in period t.

[0074] Real-time electricity charges in the province e The calculation method is as follows:

[0075] The unit calculates the electricity fee based on the real-time market settlement volume and the real-time market node electricity price within the province. The specific calculation formula is as follows:

[0076] R e =∑(Q e,t ×P e,t )

[0077] Q e,tis the real-time settlement electricity of the unit in the province during period t, that is, the difference between the actual grid-connected electricity of the unit in period t and the decomposed electricity of the inter-provincial medium- and long-term trading contract, the inter-provincial day-ahead settlement electricity, the inter-provincial intra-day settlement electricity, the provincial day-ahead settlement electricity, and the decomposed electricity of the inter-provincial medium- and long-term trading contract; P et is the real-time node electricity price of the unit in the province during period t.

[0078] Combine the following Figure 1 The flowchart shown describes in detail the steps performed in each module:

[0079] The prediction result acquisition module is used to execute S1, obtain the output prediction results of a single wind power station, the output prediction results of a single photovoltaic power station, and the supply and demand prediction results of the region. The specific implementation process is as follows:

[0080] The output prediction methods of a single wind power station are mainly divided into physical methods, statistical methods and machine learning methods:

[0081] Physical method: Modeling based on the physical characteristics of the wind farm and the surrounding geographical and meteorological environment. First, collect topography, roughness, temperature, air pressure, wind direction and wind speed and other meteorological data, use tools such as computational fluid dynamics (CFD) to simulate the airflow in the wind farm, predict the wind speed at different locations, and then calculate the wind turbine output.

[0082] Statistical method: Rely on historical wind power output data and meteorological data to explore the patterns and correlations between data.

[0083] Machine learning method: Artificial neural network (ANN): Imitate human brain neurons to build a network, input wind speed, wind direction, temperature and other influencing factors, and learn the complex mapping relationship between input and output to predict output through a large number of sample training.

[0084] Support Vector Machine (SVM): Based on the principle of structural risk minimization, it finds the optimal classification hyperplane to classify or regress sample data.

[0085] Deep learning method: Convolutional neural network (CNN) and long short-term memory network (LSTM) are used to predict the power of a single wind power station.

[0086] The output prediction methods of a single photovoltaic power station are mainly divided into IoT methods, statistical methods and machine learning methods:

[0087] Physical method: Modeling is based on meteorological principles, optical theory, and the physical characteristics of photovoltaic modules. The longitude and latitude of the site, as well as the altitude, are collected, and the solar radiation is calculated by combining the atmospheric transmittance, scattering, and direct radiation models. Then, factors such as module temperature and aging are considered, and the output is calculated based on the power curve.

[0088] Statistical method: Based on historical photovoltaic power station output data and meteorological data, explore the statistical laws between data.

[0089] Machine learning methods: Artificial neural network (ANN): Build a network consisting of input layer, hidden layer, and output layer, input meteorological data, time information, etc., and learn the nonlinear mapping relationship between input and output through massive sample training. Multilayer perceptron (MLP) is often used for photovoltaic prediction. It has high accuracy but time-consuming training and is prone to overfitting.

[0090] Support Vector Machine (SVM): Maps low-dimensional data to high-dimensional space through kernel functions, finds the optimal classification or regression hyperplane, achieves better prediction results with small sample data, has strong generalization ability, but parameter tuning is more troublesome.

[0091] Deep learning methods: Long short-term memory network (LSTM) and convolutional neural network (CNN) methods are used to predict the output of a single photovoltaic power station.

[0092] Regional supply and demand forecast: Based on the publicly disclosed data of the power trading center and the power grid dispatching center, the historical power load data is analyzed based on the time series method and artificial neural network method, and the daily load curve, monthly load curve, seasonal load curve, etc. are drawn to identify the load change rules and characteristics, such as industrial electricity consumption is concentrated during the daytime on weekdays, and residential electricity consumption is at its peak in the evening. Based on this characteristic, combined with simple information such as future holidays and temperature forecasts, the load trend is extrapolated and predicted. The method is simple and intuitive, but it is difficult to cope with sudden and complex changes.

[0093] The power restriction judgment module is used to execute S2, and judge whether the new energy power station is in a power restriction state according to the output prediction results of a single wind power station, the output prediction results of a single photovoltaic power station, the supply and demand prediction results of the region, the real-time power grid operation status and the real-time meteorological data. If so, the steps in the energy storage capacity judgment module are executed first and then the steps in the electricity price prediction module are executed. Otherwise, the steps in the electricity price prediction module are directly executed. The specific implementation process is as follows:

[0094] In the embodiment of the present invention, whether a new energy power station will be power-limited is predicted by a new energy power station power-limited prediction strategy, as follows:

[0095] The calculation method of the abandoned power limit of new energy power stations can divide the new energy power stations into different regions, select the model units by region (county), and use the model units in each region to estimate the overall power limit after the power limit, and then count the off-grid new energy capacity to convert the abandoned power limit. In this way, the error caused by different solar radiation can be minimized. The calculation method is as follows:

[0096]

[0097] Where: P j,mis the estimated power generation capacity of the mth household in area j after the new energy power restriction; k is the number of sample new energy households; m is the number of controlled new energy households; N j,m M is the new energy capacity of the mth household in area j; k The capacity of the first household model new energy; P j,k is the actual power of the kth sample new energy user in area j; P j is the estimated power generation capacity of the new energy region j; P is the estimated power generation capacity of the new energy in the entire region after power restriction.

[0098] The sample machine estimation method is a typical stratified sampling statistical method, which has the advantages of high practicality, simple calculation and easy operation. However, due to the wide distribution of new energy sources and the large difference in power generation efficiency, there is a certain amount of error in the formula. In the future, it is necessary to use artificial intelligence and machine learning methods, combined with historical data, to approximate the actual power and the estimated power.

[0099] The model machine method mainly uses samples to estimate the overall power generation, and then uses the whole to estimate the abandoned power after the distributed off-grid. The error of this method is mainly that the power generation efficiency of the model machine will change over time, and the comprehensive coefficient K will vary greatly. The model is difficult to accurately represent the whole. The embodiment of the present invention adds the historical curve of the overall power generation efficiency of new energy and uses the regression algorithm to make corrections. Take a region as an example, first calculate the abandoned power of each region, and then add all the regions.

[0100] The process of the statistical method of power abandonment and restriction considering the correction of regression algorithm is as follows: Figure 2 shown.

[0101] Judgment of power restriction in new energy power stations: Cross-judgment of power restriction in new energy power stations based on multi-source data. Integrate multi-dimensional data such as meteorological data of the node where the new energy power station is located, real-time power generation power, AGC system scheduling target value, model machine operation data, maintenance shutdown capacity, etc. By comparing the actual power generation power with the theoretical meteorological curve (such as the relationship between wind speed / irradiance and power), combined with the deviation analysis of the scheduling target value, the probability of power restriction is judged. The concept of "dead zone" is introduced. For example, when the actual power of a new energy power station is continuously lower than 1% of the command power, it is judged to be in a power restriction state. This method captures instantaneous fluctuations through high-frequency sampling (1-10 second intervals), and dynamically adjusts the threshold value in combination with the capacity of new energy power stations to reduce misjudgment.

[0102] The energy storage capacity judgment module is used to execute S3 and judge whether the energy storage has remaining capacity. If not, the energy storage EMS system does not perform charging. If so, the energy storage EMS system performs charging. The specific implementation process is as follows:

[0103] Based on the power limit prediction and energy storage operation status, the energy storage battery state of charge (SOC) is judged. When SOC ≥ 100% (usually set to 95%-100% considering the calibration error), it is judged as full capacity. If not, the energy storage EMS system performs charging. If yes, the energy storage EMS system does not perform charging.

[0104] The electricity price prediction module is used to execute S4, perform node electricity price prediction based on the data disclosed by the power trading center and the power grid dispatching center, meteorological data, and historical power trading data to obtain electricity price prediction data. The specific implementation process is as follows:

[0105] Based on the data disclosed by the power trading center and the power grid dispatching center, meteorological data, and historical power trading data, node electricity prices are predicted through neural network models, time series models, etc.

[0106] The charging and discharging strategy acquisition module is used to execute S5, based on the electricity price forecast data, new energy output data, AGC active power instructions and new energy real-time active power, with overall profit maximization as the objective function, considering the new energy output constraints (meteorological constraints) and energy storage operation costs, and in the spot market, according to the general principle of low-price charging and high-price discharging at the node, give the energy storage charging and discharging strategy for the future period according to the set charging and discharging price difference greater than or equal to the set electricity price, and send the charging and discharging strategy to the energy storage EMS, which controls automatic charging and discharging. The specific implementation process is as follows:

[0107] Based on the predicted node electricity price, energy storage charging and discharging power and energy storage SOC, photovoltaic predicted output and other constraints, using the optimal solver including dynamic programming, nonlinear programming and other algorithms, with the optimal total energy storage benefit as the objective function, an optimization model is established to automatically generate the optimal energy storage charging and discharging strategy for the future day.

[0108] The external network data pushes electricity price forecast data to the system, and generates energy storage charging and discharging strategies based on the electricity price forecast results, realizing peak-valley arbitrage by low charging and high discharging. At the same time, the system obtains AGC active power instructions and real-time active power of new energy through the photovoltaic EMS system interface, and obtains data such as the current SOC and charging and discharging power of energy storage through the energy storage EMS system interface.

[0109] Ultimately, the system will combine electricity price forecast data, new energy output data, AGC active power instructions and new energy real-time active power, take overall profit maximization as the objective function, consider new energy output constraints and energy storage operating costs, and follow the general principle of low-price charging and high-price discharging to give energy storage charging and discharging strategies for the future period. The charging and discharging strategies will be sent down to the energy storage EMS, which will control automatic charging and discharging.

[0110] In the specific implementation process, in order to prevent the frequent execution of energy storage EMS, the differential electricity price setting value R is introduced 设定值 Only when the discharge price R放电 and charging price R 充电 The difference between them is greater than or equal to the set R 设定值 When the energy storage EMS executes control to automatically charge and discharge, it effectively reduces the frequency of operation and improves the stability and efficiency of system operation. The details are as follows:

[0111] When | R 放电 -R 充电 |≥R 设定值 When |R 放电 -R 充电 |≥R 设定值 , and R 放电 Greater than R 充电 When |R 放电 -R 充电 |≥R 设定值 , and R 放电 Less than R 充电 When the charging strategy is executed.

[0112] The effectiveness of the embodiment of the present invention is verified by data below:

[0113] The new energy and energy storage combined operation optimization system of the embodiment of the present invention uses different kilowatt-hour revenue settings and combines the node electricity price forecast results to derive the optimal charging and discharging time of energy storage with the goal of maximizing revenue through the optimization model, basically achieving two charges and two discharges per day. The results of the simulation calculation of the historical data of a new energy power station in the strong wind season in December 2022 are as follows: Figure 3 As shown, during the verification process, the total energy storage capacity is 2MWh, the total installed capacity of the wind farm is 19.5MW, the charging and discharging loss is 0.86, and the maximum / minimum SOC is: 90% / 10%.

[0114] Figure 3 The theoretical income in the table is the monthly income of the charging and discharging strategy calculated based on the actual electricity price under different kilowatt-hour income. The predicted income is the monthly income of the charging and discharging strategy calculated based on the predicted electricity price under different kilowatt-hour income. The predicted income is about 55% of the theoretical income. Under the screening condition of kilowatt-hour income of 10 yuan / MWh, the monthly cumulative predicted income is 20,785 yuan.

[0115] It can be found from the monthly transaction report of the Electricity Trading Center that after the trial operation in September 2022, there were 25 effective operating days in that month. Due to energy storage problems from September 7 to September 14, there were 5 days when the strategy was not executed normally. A total of 25 charging and discharging times were performed, with actual income totaling 20,945.8 yuan, an average daily income of 837.8 yuan, an average increase of 0.62 yuan per kilowatt-hour, and the cumulative discharge of energy storage in September was 33,682.87 kWh.

[0116] Because September is a low wind season, there are certain constraints on energy storage operations, and therefore certain restrictions on revenue. Based on this, it is estimated that the annual revenue should be greater than 250,000 yuan.

[0117] The original annual operation frequency of energy storage was about 50 times. Based on the trial operation data in September, the total annual operation frequency of the system after operation is predicted to be about 400 times, which is about 8 times higher than before.

[0118] Through the analysis of historical charging and discharging data, the charging and discharging strategy model can be further optimized. At the same time, by optimizing the power restriction logic judgment, the power utilization rate during the power restriction period will be further improved, and the benefits can be further improved.

[0119] This verification experiment shows that by predicting the spot clearing electricity price, rationally planning the energy storage charging and discharging time, and automatically controlling the energy storage charging and discharging actions, it is possible to maximize the profitability of new energy power stations in the spot market and effectively utilize energy storage resources.

[0120] It should be noted that, at present, some new energy power stations will control energy storage charging through manual control during low-price periods and power-limited periods, but the two are not fully combined. It may happen that the power is limited immediately after it is fully charged during the low-price period, and the abandoned wind power during the power-limited period cannot be used normally, thus causing energy waste. This system can identify the characteristics of power-limited situations through AGC instructions, wind turbine power, wind speed and other data. At the same time, it can use market supply and demand data, line maintenance conditions, etc. to predict power-limited situations, automatically optimize and control energy storage charging and discharging time, reasonably arrange charging time during power-limited and low-price periods, give priority to charging during power-limited periods, maximize the use of abandoned wind / abandoned solar power, and further expand energy storage benefits.

[0121] The embodiment of the present invention also provides a method for optimizing the combined operation of new energy and energy storage, including:

[0122] S1. Obtain the output forecast results of a single wind power station, the output forecast results of a single photovoltaic power station, and the supply and demand forecast results of the region;

[0123] S2, judging whether the new energy power station is in a power-limited state according to the output forecast results of a single wind power station, the output forecast results of a single photovoltaic power station, the regional supply and demand forecast results, the real-time power grid operation status and the real-time meteorological data, if so, executing S3 and then executing S4, otherwise, directly executing S4;

[0124] S3, determine whether the energy storage is full, if not, the energy storage EMS system performs charging, if yes, the energy storage EMS system does not perform charging;

[0125] S4. Node electricity price forecasting is performed based on the data disclosed by the power trading center and the power grid dispatching center, meteorological data, and historical power trading data to obtain electricity price forecast data;

[0126] S5. Based on the electricity price forecast data, the output forecast results of a single wind power station, the output forecast results of a single photovoltaic power station, the AGC active power command and the real-time active power of new energy, with the overall profit maximization as the objective function, considering the output constraints of new energy and the operating costs of energy storage, and following the general principle of low-price charging and high-price discharging, a strategy for energy storage charging and discharging in the future is given, and the charging and discharging strategy is issued to the energy storage EMS, which controls automatic charging and discharging.

[0127] It is understandable that the new energy and energy storage joint operation optimization method provided in the embodiment of the present invention corresponds to the above-mentioned new energy and energy storage joint operation optimization system. The explanations, examples, beneficial effects and other parts of its relevant contents can refer to the corresponding contents in the new energy and energy storage joint operation optimization system, and will not be repeated here.

[0128] An embodiment of the present invention also provides a computer-readable storage medium storing a computer program for optimizing the combined operation of new energy and energy storage, wherein the computer program enables a computer to execute the method for optimizing the combined operation of new energy and energy storage as described above.

[0129] An embodiment of the present invention also provides an electronic device, comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include methods for executing the new energy and energy storage combined operation optimization method as described above.

[0130] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0131] 1. The embodiment of the invention predicts node electricity prices, rationally plans energy storage charging and discharging time, automatically controls energy storage charging and discharging actions, and improves the utilization rate of new energy storage.

[0132] 2. The present invention cooperates with new energy to participate in the auxiliary service market in real time, utilizes the rapid response characteristics of energy storage, participates in the primary frequency regulation control of new energy power stations, and reduces the electric field deviation assessment.

[0133] 3. The present invention participates in power station spot market transactions in real time: utilizing the storage characteristics of energy storage to participate in power station spot transactions, increase electricity sales revenue, and reduce or exempt deviation assessments.

[0134] 4. The system of the embodiment of the present invention performs energy storage charging during power-limited periods and orderly discharges energy storage during power-unlimited periods; at the same time, the accuracy of AGC tracking scheduling instructions is improved.

[0135] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0136] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A new energy and energy storage joint operation optimization system, characterized in that: include: A prediction result acquisition module is used to execute S1, obtain the output prediction result of a single wind power station, the output prediction result of a single photovoltaic power station and the supply and demand prediction result of a region; The power restriction judgment module is used to execute S2, and judge whether the new energy power station is in a power restriction state according to the output prediction results of a single wind power station, the output prediction results of a single photovoltaic power station, the supply and demand prediction results of the region, the real-time power grid operation status and the real-time meteorological data. If so, the steps in the energy storage capacity judgment module are executed first and then the steps in the electricity price prediction module are executed; otherwise, the steps in the electricity price prediction module are directly executed; The energy storage capacity judgment module is used to execute S3 and judge whether the energy storage is full. If not, the energy storage EMS system performs charging. If yes, the energy storage EMS system does not perform charging. The electricity price prediction module is used to execute S4, perform node electricity price prediction based on the data disclosed by the power trading center and the power grid dispatching center, meteorological data, and historical power trading data, and obtain electricity price prediction data; The charging and discharging strategy acquisition module is used to execute S5, based on the electricity price forecast data, the output forecast results of a single wind power station, the output forecast results of a single photovoltaic power station, the AGC active power command and the real-time active power of new energy, with the overall benefit maximization as the objective function, considering the output constraints of new energy and the operating cost of energy storage, and according to the general principle of low-price charging and high-price discharging, it gives the energy storage charging and discharging strategy for the future period, and sends the charging and discharging strategy to the energy storage EMS, which controls automatic charging and discharging.

2. The new energy and energy storage combined operation optimization system according to claim 1 is characterized in that: The supply and demand forecast results of the region are obtained by: Based on the data publicly disclosed by the power trading center and the power grid dispatching center, based on the time series method or the artificial neural network method, analyze the historical power load data, draw the daily load curve, monthly load curve, and seasonal load curve, identify the load change patterns and characteristics, and based on the load change patterns and characteristics, combine future holidays and temperature forecast information to predict the load trend and obtain the regional supply and demand forecast results.

3. The new energy and energy storage combined operation optimization system according to claim 1 is characterized in that: The node electricity price forecast based on the disclosed data of the power trading center and the power grid dispatching center, meteorological data, and historical power trading data includes: Based on the data disclosed by the power trading center, power grid dispatching center, meteorological data, and historical power trading data, node electricity prices are predicted through neural network models or time series models.

4. The new energy and energy storage combined operation optimization system according to claim 1, characterized in that: According to the general principle of low-price charging and high-price discharging, the strategy for energy storage charging and discharging in the future is given, including: Introduce differential electricity price setting value R 设定值 , when |R 放电 -R 充电 |≥R 设定值 When R 放电 Greater than R 充电 When |R 放电 -R 充电 |≥R 设定值 , and R 放电 Less than R 充电 When the charging strategy is executed.

5. The new energy and energy storage combined operation optimization system according to any one of claims 1 to 4, characterized in that: The new energy and energy storage combined operation optimization system also includes: The electricity quantity reported strategy optimization module is used to determine the real-time electricity price based on the electricity price forecast data, and compare the real-time electricity price with the day-ahead market electricity price. When the real-time electricity price is less than the day-ahead market electricity price, the day-ahead reported electricity quantity is the new energy output forecast + energy storage capacity. When the real-time electricity price is greater than the day-ahead market electricity price, the day-ahead reported electricity quantity is the new energy output forecast.

6. A method for optimizing the combined operation of new energy and energy storage, characterized in that: include: S1. Obtain the output forecast results of a single wind power station, the output forecast results of a single photovoltaic power station, and the supply and demand forecast results of the region; S2, judging whether the new energy power station is in a power-limited state according to the output forecast results of a single wind power station, the output forecast results of a single photovoltaic power station, the regional supply and demand forecast results, the real-time power grid operation status and the real-time meteorological data, if so, executing S3 and then executing S4, otherwise, directly executing S4; S3, determine whether the energy storage is full, if not, the energy storage EMS system performs charging, if yes, the energy storage EMS system does not perform charging; S4. Node electricity price forecasting is performed based on the data disclosed by the power trading center and the power grid dispatching center, meteorological data, and historical power trading data to obtain electricity price forecast data; S5. Based on the electricity price forecast data, the output forecast results of a single wind power station, the output forecast results of a single photovoltaic power station, the AGC active power command and the real-time active power of new energy, with the overall profit maximization as the objective function, considering the output constraints of new energy and the operating costs of energy storage, and following the general principle of low-price charging and high-price discharging, a strategy for energy storage charging and discharging in the future is given, and the charging and discharging strategy is issued to the energy storage EMS, which controls automatic charging and discharging.

7. The method for optimizing the combined operation of new energy and energy storage as claimed in claim 6, characterized in that: The node electricity price forecast based on the disclosed data of the power trading center and the power grid dispatching center, meteorological data, and historical power trading data includes: Based on the data disclosed by the power trading center, power grid dispatching center, meteorological data, and historical power trading data, node electricity prices are predicted through neural network models or time series models.

8. The method for optimizing the combined operation of new energy and energy storage as claimed in claim 7, characterized in that: According to the general principle of low-price charging and high-price discharging, the strategy for energy storage charging and discharging in the future is given, including: Introduce differential electricity price setting value R 设定值 , when |R 放电 -R 充电 |≥R 设定值 When R 放电 Greater than R 充电 When |R 放电 -R 充电 |≥R 设定值 , and R 放电 Less than R 充电 When the charging strategy is executed.

9. A computer-readable storage medium, characterized in that: It stores a computer program for optimizing the combined operation of new energy and energy storage, wherein the computer program enables the computer to execute the method for optimizing the combined operation of new energy and energy storage as described in any one of claims 6 to 8.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include a method for executing the new energy and energy storage combined operation optimization method as described in any one of claims 6 to 8.

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