CN116523193B - Virtual power plant energy storage scheduling method and device, electronic equipment and storage medium - Google Patents
Virtual power plant energy storage scheduling method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the application discloses a virtual power plant energy storage scheduling method, a virtual power plant energy storage scheduling device, electronic equipment and a storage medium. In the embodiment of the application, the energy storage scheduling historical data of the virtual power plant can be obtained; predicting energy storage related parameters of the virtual power plant in a future preset time period according to the energy storage scheduling history data of the virtual power plant; constructing a charge-discharge energy storage scheduling target model according to the energy storage related parameters of the virtual power plant; and solving the charge-discharge energy storage scheduling target model to obtain the optimal charge-discharge energy storage scheduling decision. In the whole energy storage scheduling strategy for the virtual power plant, the influence of the battery charge-discharge strategy, the photovoltaic and the load on the whole income is comprehensively considered, the modeling is performed by adopting the linear programming method, the operation efficiency is high, the prediction is accurate, the system robustness is good, and the economical efficiency and the reliability of the operation of the virtual power plant are very good.
Description
Technical Field
The application relates to the technical field of energy storage charge and discharge scheduling, in particular to a virtual power plant energy storage scheduling method, device, electronic equipment and storage medium.
Background
The virtual power plant (virtual power plant, VPP) technology can provide a solution to the problem of combined heat and power dispatching, and the power system and the thermodynamic system are fused to be dispatched uniformly by gathering the CHP unit, the energy storage system and the clean energy source to realize coordinated optimization between the heat system and the electric system, so that fluctuation of the clean energy source power generation can be reduced, wind power generation and photovoltaic power generation can be fully consumed, the utilization rate of renewable energy sources can be improved, the economic benefit of the system can be improved, and safe and stable operation of the power system can be promoted.
At present, in the existing integral energy storage scheduling strategy for the virtual power plant, due to the fact that consideration factors are single and a calculation method is rough, system robustness is poor, and economical efficiency and reliability of operation of the virtual power plant are low. Therefore, how to better perform virtual power plant overall energy storage scheduling has become a problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the foregoing, the present specification is directed to providing a virtual power plant energy storage scheduling method, apparatus, electronic device, and storage medium that overcome or at least partially solve the foregoing problems.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
In a first aspect, an embodiment of the present application provides a method for energy storage scheduling of a virtual power plant, including obtaining energy storage scheduling history data of the virtual power plant; predicting energy storage related parameters of the virtual power plant in a future preset time period according to the energy storage scheduling history data of the virtual power plant; constructing a charge-discharge energy storage scheduling target model according to the energy storage related parameters of the virtual power plant; and solving the charge-discharge energy storage scheduling target model to obtain the optimal charge-discharge energy storage scheduling decision.
In some embodiments, obtaining virtual power plant energy storage scheduling history data includes: based on the virtual power plant, acquiring the electricity price corresponding to the virtual power plant in a preset historical time period; obtaining the load consumption corresponding to the virtual power plant in a preset historical time period; and obtaining the photovoltaic manufacturing quantity corresponding to the virtual power plant in a preset historical time period.
In some embodiments, predicting energy storage related parameters of the virtual power plant for a future preset period of time based on the virtual power plant energy storage scheduling history data, the energy storage related parameters of the virtual power plant including electricity prices, load consumption, and photovoltaic manufacturing quantities includes: dividing a future preset time period into particles to obtain n time granularity; based on a first artificial intelligent prediction model, predicting the electricity price of the virtual power plant in a future preset time period according to the electricity price corresponding to the virtual power plant in the preset historical time period to obtain an electricity price P corresponding to the ith time granularity i I.e {1,2,..n }; based on a second artificial intelligence prediction model, predicting the load consumption of the virtual power plant in a future preset time period according to the load consumption corresponding to the virtual power plant in the preset historical time period to obtain the load consumption corresponding to the ith time granularityL i The method comprises the steps of carrying out a first treatment on the surface of the Based on a third artificial intelligence prediction model, according to the photovoltaic manufacturing amount corresponding to the virtual power plant in the preset historical time period, predicting the photovoltaic manufacturing amount of the virtual power plant in the future preset time period to obtain the photovoltaic manufacturing amount PV corresponding to the ith time granularity i 。
In some embodiments, constructing a charge-discharge energy storage scheduling target model according to energy storage related parameters of the virtual power plant includes: the charge and discharge capacity corresponding to the ith time granularity is recorded as X i The method comprises the steps of carrying out a first treatment on the surface of the Load consumption L corresponding to the ith time granularity i Photovoltaic manufacturing amount PV corresponding to ith time granularity i Charging and discharging quantity X corresponding to ith time granularity i Correcting to obtain a photovoltaic manufacturing quantity correction value y corresponding to the ith time granularity 1i Correction value y of charge and discharge amount 2i Load consumption correction value y 3i The method comprises the steps of carrying out a first treatment on the surface of the Electricity price P corresponding to ith time granularity i According to the photovoltaic manufacturing quantity correction value y corresponding to the ith time granularity 1i Correction value y of charge and discharge amount 2i Load consumption correction value y 3i Constructing a charge-discharge energy storage scheduling target model, wherein the charge-discharge energy storage scheduling target model is as follows
In some embodiments, the load consumption L for the ith time granularity i Photovoltaic manufacturing amount PV corresponding to ith time granularity i Charging and discharging quantity X corresponding to ith time granularity i Correcting to obtain a photovoltaic manufacturing quantity correction value y corresponding to the ith time granularity 1i Correction value y of charge and discharge amount 2i Load consumption correction value y 3i Comprising: obtaining a photovoltaic manufacturing quantity correction coefficient K corresponding to the ith time granularity 1i Correction coefficient K of charge and discharge amount 2i Load consumption correction coefficient K 3i The method comprises the steps of carrying out a first treatment on the surface of the Correcting the photovoltaic manufacturing quantity correction coefficient K corresponding to the ith time granularity 1i Photovoltaic production amount PV corresponding to ith time granularity i Multiplying to obtain the ith time particlePhotovoltaic manufacturing amount correction value y corresponding to degree 1i ＝K 1i *PV i The method comprises the steps of carrying out a first treatment on the surface of the Charging and discharging quantity correction coefficient K corresponding to ith time granularity 2i Charge and discharge amount X corresponding to the ith time granularity i Multiplying to obtain the charge-discharge correction value y corresponding to the ith time granularity 2i ＝K 2i *X i The method comprises the steps of carrying out a first treatment on the surface of the Load consumption coefficient K corresponding to ith time granularity 3i Load consumption L corresponding to the ith time granularity i Multiplying to obtain the i-th time granularity corresponding load consumption correction value y 2i ＝K 3i *L i 。
In some embodiments, constructing the charge-discharge energy storage scheduling target model according to the energy storage related parameters of the virtual power plant further comprises: constructing a capacity limiting constraint equation condition, wherein the capacity limiting constraint equation condition is as followsWherein C is Rated for Is the rated capacity of the battery; constructing a power limiting constraint condition, wherein the power limiting constraint condition is as followsWherein P is 1max At maximum discharge power, P 2max Is the maximum charging power.
In some embodiments, the method solves the charge-discharge energy storage scheduling target model to obtain an optimal charge-discharge energy storage scheduling decision, and further includes: and under the condition of a capacity limiting constraint equation and a power limiting constraint condition, solving a charge-discharge energy storage scheduling target model to obtain charge-discharge quantities corresponding to n time granularities respectively, wherein the charge-discharge quantities corresponding to the n time granularities respectively are optimal decisions of charge-discharge energy storage scheduling.
In a second aspect, an embodiment of the present application provides a virtual power plant energy storage scheduling device, including: the data acquisition module is used for acquiring the energy storage scheduling historical data of the virtual power plant; the prediction module is used for predicting energy storage related parameters of the virtual power plant in a future preset time period based on the energy storage scheduling historical data of the virtual power plant; the model construction module is used for constructing a charge-discharge energy storage scheduling target model according to the energy storage related parameters of the virtual power plant; and the solving module is used for solving the charge-discharge energy storage scheduling target model to obtain an optimal charge-discharge energy storage scheduling decision.
In a third aspect, embodiments of the present application provide an electronic device, including a memory storing a plurality of instructions; the processor loads instructions from the memory to perform steps in any of the virtual power plant energy storage scheduling methods provided in embodiments of the present application.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a plurality of instructions adapted to be loaded by a processor to perform steps in any of the virtual power plant energy storage scheduling methods provided by embodiments of the present application.
According to the embodiment of the application, the energy storage scheduling historical data of the virtual power plant can be acquired firstly; then, predicting the electricity price, the load consumption and the photovoltaic manufacturing amount of the virtual power plant in a preset time period in the future based on the energy storage scheduling historical data of the virtual power plant; then constructing a charge-discharge energy storage scheduling target model according to electricity price, load consumption and photovoltaic manufacturing quantity; and finally solving the charge-discharge energy storage scheduling target model to obtain the optimal decision of charge-discharge energy storage scheduling.
According to the method, the influence of the battery charge-discharge strategy on the benefits of the virtual power plant is considered, the influence of the photovoltaic and the load on the overall benefits is considered, the power dispatching can be adjusted according to the predicted electricity price, the predicted photovoltaic and the predicted load, and meanwhile, the linear programming method is utilized to achieve the maximization of economic benefits. In the whole energy storage scheduling strategy aiming at the virtual power plant, the influence of the battery charging and discharging strategy, the photovoltaic and the load on the whole income is comprehensively considered, and the method has the advantages of high operation efficiency, accurate prediction, good system robustness and very good economical efficiency and reliability of the operation of the virtual power plant.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic view of a scenario of a virtual power plant energy storage scheduling method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a virtual power plant energy storage scheduling method according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a virtual power plant energy storage scheduling device according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The virtual power plant is an efficient management system of distributed energy, and participates in the optimized operation and market transaction of the system by aggregating distributed energy such as distributed electricity, controllable load, energy storage and the like. Because the distributed power supply output has stronger randomness and intermittence, the configuration of energy storage with certain capacity in the virtual power plant is necessary, and further, the realization of optimal scheduling of the energy storage of the virtual power plant becomes a hot research direction in the field.
The embodiment of the application provides a virtual power plant energy storage scheduling method, a virtual power plant energy storage scheduling device, electronic equipment and a storage medium.
The virtual power plant energy storage scheduling device can be integrated in electronic equipment, and the electronic equipment can be a terminal, a server and other equipment. The terminal can be a mobile phone, a tablet computer, an intelligent Bluetooth device, a notebook computer, a personal computer (Personal Computer, PC) or the like; the server may be a single server or a server cluster composed of a plurality of servers.
In some embodiments, the virtual power plant energy storage scheduling device may also be integrated in a plurality of electronic devices, for example, the virtual power plant energy storage scheduling device may be integrated in a plurality of servers, and the virtual power plant energy storage scheduling method is implemented by the plurality of servers.
In some embodiments, the server may also be implemented in the form of a terminal.
For example, referring to fig. 1, the electronic device may include a virtual power plant 10, a storage terminal 11, a server 12, etc., the control object of the virtual power plant 10 may include various distributed generator sets (distributed generation, DG), an energy storage system, a controllable load, an electric automobile, etc., the storage terminal 11 may store virtual power plant energy storage scheduling history data, etc., and the virtual power plant 10, the storage terminal 11, the server 12 are in communication connection with each other, which is not described herein.
Wherein the server 10 may include a processor, memory, and the like. The server 10 may first obtain the energy storage scheduling history data of the virtual power plant; predicting energy storage related parameters of the virtual power plant in a future preset time period according to the energy storage scheduling history data of the virtual power plant; constructing a charge-discharge energy storage scheduling target model according to the energy storage related parameters of the virtual power plant; and solving the charge-discharge energy storage scheduling target model to obtain an optimal charge-discharge energy storage scheduling decision and the like.
The following will describe in detail. The numbers of the following examples are not intended to limit the preferred order of the examples.
In this embodiment, a virtual power plant energy storage scheduling method related to an energy storage charging and discharging scheduling technology is provided, as shown in fig. 2, and the specific flow of the virtual power plant energy storage scheduling method may be as follows:
200. and the server acquires the energy storage scheduling historical data of the virtual power plant.
In some embodiments, obtaining virtual power plant energy storage scheduling history data includes: based on the virtual power plant, acquiring the electricity price corresponding to the virtual power plant in a preset historical time period; obtaining the load consumption corresponding to the virtual power plant in a preset historical time period; and obtaining the photovoltaic manufacturing quantity corresponding to the virtual power plant in a preset historical time period.
210. And predicting the energy storage related parameters of the virtual power plant in a preset time period in the future according to the energy storage scheduling history data of the virtual power plant by the server.
In the embodiment of the application, the energy storage related parameters of the virtual power plant may include electricity price, load consumption, photovoltaic manufacturing amount and the like.
In some embodiments, predicting energy storage related parameters of the virtual power plant within a future preset time period based on the virtual power plant energy storage scheduling history data, the energy storage related parameters including electricity price, load consumption and photovoltaic manufacturing amount, includes: dividing a future preset time period into particles to obtain n time granularity; based on a first artificial intelligent prediction model, predicting the electricity price of the virtual power plant in a future preset time period according to the electricity price corresponding to the virtual power plant in the preset historical time period to obtain an electricity price P corresponding to the ith time granularity i I.e {1,2,..n }; based on a second artificial intelligence prediction model, predicting the load consumption of the virtual power plant in a future preset time period according to the load consumption corresponding to the virtual power plant in the preset historical time period to obtain the load consumption L corresponding to the ith time granularity i The method comprises the steps of carrying out a first treatment on the surface of the Based on a third artificial intelligence prediction model, according to the photovoltaic manufacturing amount corresponding to the virtual power plant in the preset historical time period, predicting the photovoltaic manufacturing amount of the virtual power plant in the future preset time period to obtain the photovoltaic manufacturing amount PV corresponding to the ith time granularity i 。
In the embodiment of the application, the first artificial intelligence prediction model, the second artificial intelligence prediction model and the third artificial intelligence prediction model can be machine learning models, deep learning models and the like, and the embodiment of the application does not limit the structure of the artificial intelligence prediction models and can realize the data prediction function.
Specifically, for example, according to the embodiment of the application, according to the electricity prices corresponding to the virtual power plant in the preset historical time period, machine learning or deep learning can be used for predicting the future 24-hour electricity prices of the virtual power plant; according to the load consumption corresponding to the virtual power plant in the preset historical time period, machine learning or deep learning can be used for predicting the load consumption of the virtual power plant for 24 hours in the future; and according to the photovoltaic manufacturing quantity corresponding to the virtual power plant in the preset historical time period, machine learning or deep learning can be used for predicting the future 24-hour photovoltaic manufacturing quantity of the virtual power plant.
In the present embodiment, a time granularity of v minutes isFor example, if one time granularity is 60 minutes, the virtual power plant is divided into particles for 24 hours in the future, and 24 time granularities can be obtained.
220. And the server constructs a charge-discharge energy storage scheduling target model according to the energy storage related parameters of the virtual power plant.
In the embodiment of the application, the server can construct the charge-discharge energy storage scheduling target model according to the electricity price, the load consumption and the photovoltaic manufacturing quantity of the virtual power plant.
In some embodiments, constructing a charge-discharge energy storage scheduling target model from electricity prices, load consumption, and photovoltaic manufacturing quantities includes: the charge and discharge capacity corresponding to the ith time granularity is recorded as X i The method comprises the steps of carrying out a first treatment on the surface of the Load consumption L corresponding to the ith time granularity i Photovoltaic manufacturing amount PV corresponding to ith time granularity i Charging and discharging quantity X corresponding to ith time granularity i Correcting to obtain a photovoltaic manufacturing quantity correction value y corresponding to the ith time granularity 1i Correction value y of charge and discharge amount 2i Load consumption correction value y 3i The method comprises the steps of carrying out a first treatment on the surface of the Electricity price P corresponding to ith time granularity i According to the photovoltaic manufacturing quantity correction value y corresponding to the ith time granularity 1i Correction value y of charge and discharge amount 2i Load consumption correction value y 3i Constructing a charge-discharge energy storage scheduling target model, wherein the charge-discharge energy storage scheduling target model is as follows
In some embodiments, the load consumption L for the ith time granularity i Photovoltaic manufacturing amount PV corresponding to ith time granularity i Charging and discharging quantity X corresponding to ith time granularity i Correcting to obtain a photovoltaic manufacturing quantity correction value y corresponding to the ith time granularity 1i Correction value y of charge and discharge amount 2i Load consumption correction value y 3i Comprising: obtaining a photovoltaic manufacturing quantity correction coefficient K corresponding to the ith time granularity 1i Correction coefficient K of charge and discharge amount 2i Load consumption correction coefficient K 3i The method comprises the steps of carrying out a first treatment on the surface of the Correcting the photovoltaic manufacturing quantity correction coefficient K corresponding to the ith time granularity 1i Photovoltaic production amount PV corresponding to ith time granularity i Multiplying to obtain photovoltaic manufacture quantity correction value y corresponding to ith time granularity 1i ＝K 1i *PV i The method comprises the steps of carrying out a first treatment on the surface of the Charging and discharging quantity correction coefficient K corresponding to ith time granularity 2i Charge and discharge amount X corresponding to the ith time granularity i Multiplying to obtain the charge-discharge correction value y corresponding to the ith time granularity 2i ＝K 2i *X i The method comprises the steps of carrying out a first treatment on the surface of the Load consumption coefficient K corresponding to ith time granularity 3i Load consumption L corresponding to the ith time granularity i Multiplying to obtain the i-th time granularity corresponding load consumption correction value y 2i ＝K 3i *L i 。
In the embodiment of the application, modeling is performed by adopting a linear programming method. When the photovoltaic manufacturing quantity correction coefficient and the load consumption coefficient are both set to 0, the charge-discharge energy storage scheduling target model is as followsThe charge-discharge energy storage scheduling target model considers the relationship between the price and the charge-discharge of the battery. When the photovoltaic manufacturing amount correction coefficient, the load consumption coefficient and the charge and discharge amount correction coefficient are all not 0, the charge and discharge energy storage scheduling target model in the embodiment of the application is as followsThe model not only considers the relation between the price and the charge and discharge of the battery, but also considers the relation between the photovoltaic manufacturing amount and the load consumption amount, namely, not only considers the influence of the photovoltaic and the load on the overall benefit, but also considers the influence of the charge and discharge strategy of the battery on the benefit.
In some embodiments, constructing the charge-discharge energy storage scheduling target model according to the electricity price, the load consumption and the photovoltaic manufacturing amount further comprises: constructing a capacity limiting constraint equation condition, wherein the capacity limiting constraint equation condition is as followsWherein C is Rated for Is the rated capacity of the battery; constructing a power limiting constraint condition, wherein the power limiting constraint condition is +.>Wherein P is 1max At maximum discharge power, P 2max Is the maximum charging power.
230. And solving the charge and discharge energy storage scheduling target model by the server to obtain an optimal charge and discharge energy storage scheduling decision.
In some embodiments, the method solves the charge-discharge energy storage scheduling target model to obtain an optimal charge-discharge energy storage scheduling decision, and further includes: and under the condition of a capacity limiting constraint equation and a power limiting constraint condition, solving a charge-discharge energy storage scheduling target model to obtain charge-discharge quantities corresponding to n time granularities respectively, wherein the charge-discharge quantities corresponding to the n time granularities respectively are optimal decisions of charge-discharge energy storage scheduling.
In the process of optimizing and solving, the prior art generally adopts a genetic algorithm to solve the problem, and although the optimal solution can be found, the method consumes too much calculation resources, and the result is uncertain and can be converged to the optimal solution. In the prior art, a greedy algorithm is adopted to calculate the optimal solution, but the greedy algorithm is complicated in design, difficult in code writing and easy to obtain local optimization instead of global optimization when being integrated into a charge-discharge strategy. Embodiments of the present application are directed to the prior artThe method is characterized in that a linear programming method is used for modeling the charge-discharge scheduling of the virtual power plant, a charge-discharge energy storage scheduling target model is built, limiting conditions are determined, a linear solving method is used for solving the charge-discharge energy storage scheduling target model, and the charge-discharge capacity X is solved i I.e {1,2,..n }, the optimal charge-discharge strategy is obtained. The algorithm has high operation efficiency, does not need to consume too much computing resources, and does not fall into local optimum in the solving process.
According to the embodiment of the application, the energy storage scheduling historical data of the virtual power plant can be acquired firstly; then, predicting the electricity price, the load consumption and the photovoltaic manufacturing amount of the virtual power plant in a preset time period in the future based on the energy storage scheduling historical data of the virtual power plant; then constructing a charge-discharge energy storage scheduling target model according to electricity price, load consumption and photovoltaic manufacturing quantity; and finally solving the charge-discharge energy storage scheduling target model to obtain the optimal decision of charge-discharge energy storage scheduling.
According to the method, the influence of the battery charge-discharge strategy on the benefits of the virtual power plant is considered, the influence of the photovoltaic and the load on the overall benefits is considered, the power dispatching can be adjusted according to the predicted electricity price, the predicted photovoltaic and the predicted load, and meanwhile, the linear programming method is utilized to achieve the maximization of economic benefits. In the whole energy storage scheduling strategy aiming at the virtual power plant, the influence of the battery charging and discharging strategy, the photovoltaic and the load on the whole income is comprehensively considered, and the method has the advantages of high operation efficiency, accurate prediction, good system robustness and very good economical efficiency and reliability of the operation of the virtual power plant.
In order to better implement the method, the embodiment of the application also provides a virtual power plant energy storage scheduling device, which can be integrated in electronic equipment, wherein the electronic equipment can be a terminal, a server and other equipment. The terminal can be a mobile phone, a tablet personal computer, an intelligent Bluetooth device, a notebook computer, a personal computer and other devices; the server may be a single server or a server cluster composed of a plurality of servers.
For example, in this embodiment, a method of the embodiment of the present application will be described in detail by taking a specific integration of a virtual power plant energy storage scheduling device in an electronic device as an example.
For example, as shown in fig. 3, the virtual power plant energy storage scheduling apparatus may include: a data acquisition module 310, a prediction module 320, a model construction module 330, and a solution module 340. The data acquisition module 310 is configured to acquire energy storage scheduling history data of the virtual power plant; the prediction module 320 is configured to predict energy storage related parameters of the virtual power plant in a future preset period of time based on the energy storage scheduling history data of the virtual power plant; the model construction module 330 is configured to construct a charge-discharge energy storage scheduling target model according to the energy storage related parameters of the virtual power plant; and the solving module 340 is configured to solve the charge-discharge energy storage scheduling target model to obtain an optimal charge-discharge energy storage scheduling decision.
In some embodiments, the data acquisition module 310 includes a data acquisition sub-module configured to: based on the virtual power plant, acquiring the electricity price corresponding to the virtual power plant in a preset historical time period; obtaining the load consumption corresponding to the virtual power plant in a preset historical time period; and obtaining the photovoltaic manufacturing quantity corresponding to the virtual power plant in a preset historical time period.
In some embodiments, the energy storage related parameters of the virtual power plant include electricity prices, load consumption, and photovoltaic production, and the prediction module 320 includes a prediction submodule configured to: dividing a future preset time period into particles to obtain n time granularity; based on a first artificial intelligent prediction model, predicting the electricity price of the virtual power plant in a future preset time period according to the electricity price corresponding to the virtual power plant in the preset historical time period to obtain an electricity price P corresponding to the ith time granularity i I.e {1,2,..n }; based on a second artificial intelligence prediction model, predicting the load consumption of the virtual power plant in a future preset time period according to the load consumption corresponding to the virtual power plant in the preset historical time period to obtain the load consumption L corresponding to the ith time granularity i The method comprises the steps of carrying out a first treatment on the surface of the Based on a third artificial intelligence prediction model, predicting the photovoltaic manufacturing amount of the virtual power plant in a future preset time period according to the photovoltaic manufacturing amount corresponding to the virtual power plant in the preset historical time period to obtain an ith photovoltaic manufacturing amountPhotovoltaic manufacturing amount PV corresponding to time granularity i 。
In some embodiments, model building module 330 includes a target model building module configured to: the charge and discharge capacity corresponding to the ith time granularity is recorded as X i The method comprises the steps of carrying out a first treatment on the surface of the Load consumption L corresponding to the ith time granularity i Photovoltaic manufacturing amount PV corresponding to ith time granularity i Charging and discharging quantity X corresponding to ith time granularity i Correcting to obtain a photovoltaic manufacturing quantity correction value y corresponding to the ith time granularity 1i Correction value y of charge and discharge amount 2i Load consumption correction value y 3i The method comprises the steps of carrying out a first treatment on the surface of the Electricity price P corresponding to ith time granularity i According to the photovoltaic manufacturing quantity correction value y corresponding to the ith time granularity 1i Correction value y of charge and discharge amount 2i Load consumption correction value y 3i Constructing a charge-discharge energy storage scheduling target model, wherein the charge-discharge energy storage scheduling target model is as follows
In some embodiments, the object model construction module includes a correction module configured to: obtaining a photovoltaic manufacturing quantity correction coefficient K corresponding to the ith time granularity 1i Correction coefficient K of charge and discharge amount 2i Load consumption correction coefficient K 3i The method comprises the steps of carrying out a first treatment on the surface of the Correcting the photovoltaic manufacturing quantity correction coefficient K corresponding to the ith time granularity 1i Photovoltaic production amount PV corresponding to ith time granularity i Multiplying to obtain photovoltaic manufacture quantity correction value y corresponding to ith time granularity 1i ＝K 1i *PV i The method comprises the steps of carrying out a first treatment on the surface of the Charging and discharging quantity correction coefficient K corresponding to ith time granularity 2i Charge and discharge amount X corresponding to the ith time granularity i Multiplying to obtain the charge-discharge correction value y corresponding to the ith time granularity 2i ＝K 2i *X i The method comprises the steps of carrying out a first treatment on the surface of the Load consumption coefficient K corresponding to ith time granularity 3i Load consumption L corresponding to the ith time granularity i Multiplying to obtain the ith time granularityCorresponding load consumption correction value y 2i ＝K 3i *L i 。
In some embodiments, model building module 330 further includes a constraint building module that includes: constructing a capacity limiting constraint equation condition, wherein the capacity limiting constraint equation condition is as followsWherein C is Rated for Is the rated capacity of the battery; constructing a power limiting constraint condition, wherein the power limiting constraint condition is as followsWherein P is 1max At maximum discharge power, P 2max Is the maximum charging power.
In some embodiments, the solution module 340 further includes a solution submodule configured to: and under the condition of a capacity limiting constraint equation and a power limiting constraint condition, solving a charge-discharge energy storage scheduling target model to obtain charge-discharge quantities corresponding to n time granularities respectively, wherein the charge-discharge quantities corresponding to the n time granularities respectively are optimal decisions of charge-discharge energy storage scheduling.
In the implementation, each unit may be implemented as an independent entity, or may be implemented as the same entity or several entities in any combination, and the implementation of each unit may be referred to the foregoing method embodiment, which is not described herein again.
From the above, the virtual power plant energy storage scheduling device of the embodiment may first obtain the virtual power plant energy storage scheduling history data; then, predicting the electricity price, the load consumption and the photovoltaic manufacturing amount of the virtual power plant in a preset time period in the future based on the energy storage scheduling historical data of the virtual power plant; then constructing a charge-discharge energy storage scheduling target model according to electricity price, load consumption and photovoltaic manufacturing quantity; and finally solving the charge-discharge energy storage scheduling target model to obtain the optimal decision of charge-discharge energy storage scheduling.
According to the method, the influence of the battery charge-discharge strategy on the benefits of the virtual power plant is considered, the influence of the photovoltaic and the load on the overall benefits is considered, the power dispatching can be adjusted according to the predicted electricity price, the predicted photovoltaic and the predicted load, and meanwhile, the linear programming method is utilized to achieve the maximization of economic benefits. In the whole energy storage scheduling strategy aiming at the virtual power plant, the influence of the battery charging and discharging strategy, the photovoltaic and the load on the whole income is comprehensively considered, and the method has the advantages of high operation efficiency, accurate prediction, good system robustness and very good economical efficiency and reliability of the operation of the virtual power plant.
The embodiment of the application also provides electronic equipment which can be a terminal, a server and other equipment. The terminal can be a mobile phone, a tablet computer, an intelligent Bluetooth device, a notebook computer, a personal computer and the like; the server may be a single server, a server cluster composed of a plurality of servers, or the like.
In some embodiments, the virtual power plant energy storage scheduling device may also be integrated in a plurality of electronic devices, for example, the virtual power plant energy storage scheduling device may be integrated in a plurality of servers, and the virtual power plant energy storage scheduling method is implemented by the plurality of servers.
In the present embodiment, a detailed description will be given taking an example in which the electronic device of the present embodiment is a server, for example, as shown in fig. 4, which shows a schematic structural diagram of the server according to the embodiment of the present application, specifically:
the server may include one or more processors 401 of a processing core, memory 402 of one or more computer readable storage media, a power supply 403, an input module 404, and a communication module 405, among other components. Those skilled in the art will appreciate that the server architecture shown in fig. 4 is not limiting of the server and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components. Wherein:
the processor 401 is a control center of the server, connects respective portions of the entire server using various interfaces and lines, and performs various functions of the server and processes data by running or executing software programs and/or modules stored in the memory 402 and calling data stored in the memory 402, thereby performing overall monitoring of the server. In some embodiments, processor 401 may include one or more processing cores; in some embodiments, processor 401 may integrate an application processor that primarily processes operating systems, user interfaces, applications, and the like, with a modem processor that primarily processes wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 401.
The memory 402 may be used to store software programs and modules, and the processor 401 executes various functional applications and data processing by executing the software programs and modules stored in the memory 402. The memory 402 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data created according to the use of the server, etc. In addition, memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.
The server also includes a power supply 403 for powering the various components, and in some embodiments, the power supply 403 may be logically connected to the processor 401 by a power management system, such that charge, discharge, and power consumption management functions are performed by the power management system. The power supply 403 may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
The server may also include an input module 404, which input module 404 may be used to receive entered numeric or character information and to generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
The server may also include a communication module 405, and in some embodiments the communication module 405 may include a wireless module, through which the server may wirelessly transmit over short distances, thereby providing wireless broadband internet access to the user. For example, the communication module 405 may be used to assist a user in e-mail, browsing web pages, accessing streaming media, and so forth.
Although not shown, the server may further include a display unit or the like, which is not described herein. In this embodiment, the processor 401 in the server loads executable files corresponding to the processes of one or more application programs into the memory 402 according to the following instructions, and the processor 401 runs the application programs stored in the memory 402, so as to implement various functions in the energy storage scheduling device of the virtual power plant.
In some embodiments, a computer program product is also presented, comprising a computer program or instructions which, when executed by a processor, implement the steps of any of the virtual power plant energy storage scheduling methods described above.
The specific implementation of each operation above may be referred to the previous embodiments, and will not be described herein.
From the above, the embodiment of the application considers not only the influence of the battery charge-discharge strategy on the virtual power plant income, but also the influence of the photovoltaic and the load on the overall income, and can adjust the power dispatching according to the predicted electricity price, the predicted photovoltaic and the predicted load by using the linear programming method, thereby realizing the maximization of economic benefit. In the whole energy storage scheduling strategy aiming at the virtual power plant, the influence of the battery charging and discharging strategy, the photovoltaic and the load on the whole income is comprehensively considered, and the method has the advantages of high operation efficiency, accurate prediction, good system robustness and very good economical efficiency and reliability of the operation of the virtual power plant.
Those of ordinary skill in the art will appreciate that all or a portion of the steps of the various methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, which may be stored in a computer-readable storage medium and loaded and executed by a processor.
To this end, embodiments of the present application provide a computer readable storage medium having stored therein a plurality of instructions capable of being loaded by a processor to perform steps in any of the virtual power plant energy storage scheduling methods provided by embodiments of the present application. For example, the instructions may perform the steps of: firstly, acquiring energy storage scheduling historical data of a virtual power plant; then, predicting the electricity price, the load consumption and the photovoltaic manufacturing amount of the virtual power plant in a preset time period in the future based on the energy storage scheduling historical data of the virtual power plant; then constructing a charge-discharge energy storage scheduling target model according to electricity price, load consumption and photovoltaic manufacturing quantity; and finally solving the charge-discharge energy storage scheduling target model to obtain an optimal charge-discharge energy storage scheduling decision and the like.
Wherein the storage medium may include: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.
According to one aspect of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, cause the computer device to perform the method provided in various alternative implementations of the aspect of service cluster elastic scaling or the aspect of service cluster elastic scaling provided in the above-described embodiments.
The steps in any of the virtual power plant energy storage scheduling methods provided in the embodiments of the present application may be executed due to the instructions stored in the storage medium, so that the beneficial effects that any of the virtual power plant energy storage scheduling methods provided in the embodiments of the present application may be achieved are detailed in the previous embodiments, and are not repeated herein.
The foregoing describes in detail a virtual power plant energy storage scheduling method, apparatus, server and computer readable storage medium provided in the embodiments of the present application, and specific examples are applied to illustrate the principles and embodiments of the present application, where the foregoing description of the embodiments is only for helping to understand the method and core ideas of the present application; meanwhile, as those skilled in the art will vary in the specific embodiments and application scope according to the ideas of the present application, the contents of the present specification should not be construed as limiting the present application in summary.
Claims (5)
1. The energy storage scheduling method for the virtual power plant is characterized by comprising the following steps of:
acquiring electricity prices corresponding to the virtual power plants in a preset historical time period based on the virtual power plants;
acquiring the load consumption corresponding to the virtual power plant in the preset historical time period;
obtaining the photovoltaic manufacturing quantity corresponding to the virtual power plant in the preset historical time period;
dividing the future preset time period into particles to obtain n time particles;
based on a first artificial intelligent prediction model, predicting the electricity price of the virtual power plant in the future preset time period according to the electricity price corresponding to the virtual power plant in the preset historical time period to obtain an electricity price P corresponding to the ith time granularity i ，i∈{1,2,In}；
Based on a second artificial intelligence prediction model, predicting the load consumption of the virtual power plant in the future preset time period according to the load consumption corresponding to the virtual power plant in the preset historical time period to obtain the load consumption L corresponding to the ith time granularity i ；
Based on a third artificial intelligence prediction model, according to the photovoltaic manufacturing amount corresponding to the virtual power plant in the preset historical time period, predicting the photovoltaic manufacturing amount of the virtual power plant in the future preset time period to obtain a photovoltaic manufacturing amount PV corresponding to the ith time granularity i ；
The charge and discharge capacity corresponding to the ith time granularity is recorded as X i ；
Load consumption L corresponding to the ith time granularity i The ith time particlePhotovoltaic manufacturing amount PV corresponding to degree i The charging and discharging quantity X corresponding to the ith time granularity i Correcting to obtain a photovoltaic manufacturing amount correction value y corresponding to the ith time granularity 1i Correction value y of charge and discharge amount 2i Load consumption correction value y 3i ；
Based on the electricity price P corresponding to the ith time granularity i According to the photovoltaic manufacturing quantity correction value y corresponding to the ith time granularity 1i Correction value y of charge and discharge amount 2i Load consumption correction value y 3i Constructing a charge-discharge energy storage scheduling target model, wherein the charge-discharge energy storage scheduling target model is as follows
Constructing a capacity limiting constraint equation condition, wherein the capacity limiting constraint equation condition is thatWherein C is Rated for Is the rated capacity of the battery;
constructing a power limiting constraint condition, wherein the power limiting constraint condition is thatWherein P is 1max At maximum discharge power, P 2max Is the maximum charging power;
and under the capacity limiting constraint equation condition and the power limiting constraint condition, solving the charge-discharge energy storage scheduling target model to obtain charge-discharge capacity corresponding to n time granularities, wherein the charge-discharge capacity corresponding to the n time granularities is the optimal decision of charge-discharge energy storage scheduling.
2. The method for dispatching energy stored in a virtual power plant according to claim 1, wherein said load consumption L corresponding to said ith time granularity i The photovoltaic manufacturing amount PV corresponding to the ith time granularity i And (C) aCharging and discharging capacity X corresponding to ith time granularity i Correcting to obtain a photovoltaic manufacturing amount correction value y corresponding to the ith time granularity 1i Correction value y of charge and discharge amount 2i Load consumption correction value y 3i Comprising:
obtaining a photovoltaic manufacturing quantity correction coefficient K corresponding to the ith time granularity 1i Correction coefficient K of charge and discharge amount 2i Load consumption correction coefficient K 3i ；
Correcting the photovoltaic manufacturing quantity correction coefficient K corresponding to the ith time granularity 1i Photovoltaic production amount PV corresponding to the ith time granularity i Multiplying to obtain the photovoltaic manufacturing amount correction value y corresponding to the ith time granularity 1i ＝K 1i *PV i ；
Charging and discharging quantity correction coefficient K corresponding to the ith time granularity 2i Charging and discharging quantity X corresponding to the ith time granularity i Multiplying to obtain the charge-discharge correction value y corresponding to the ith time granularity 2i ＝K 2i *X i ；
A load consumption coefficient K corresponding to the ith time granularity 3i Load consumption L corresponding to the ith time granularity i Multiplying to obtain the load consumption correction value y corresponding to the ith time granularity 2i ＝K 3i *L i 。
3. A virtual power plant energy storage scheduling device, comprising:
the data acquisition module is used for acquiring the energy storage scheduling historical data of the virtual power plant; predicting energy storage related parameters of the virtual power plant in a preset time period in the future according to the energy storage scheduling historical data of the virtual power plant; constructing a charge-discharge energy storage scheduling target model according to the energy storage related parameters of the virtual power plant; solving the charge-discharge energy storage scheduling target model to obtain an optimal charge-discharge energy storage scheduling decision;
a prediction module for dividing the future preset time period into particles,obtaining n time granularities; based on a first artificial intelligent prediction model, predicting the electricity price of the virtual power plant in the future preset time period according to the electricity price corresponding to the virtual power plant in the preset historical time period to obtain an electricity price P corresponding to the ith time granularity u I.e {1,2,..n }; based on a second artificial intelligence prediction model, predicting the load consumption of the virtual power plant in the future preset time period according to the load consumption corresponding to the virtual power plant in the preset historical time period to obtain the load consumption L corresponding to the ith time granularity i The method comprises the steps of carrying out a first treatment on the surface of the Based on a third artificial intelligence prediction model, according to the photovoltaic manufacturing amount corresponding to the virtual power plant in the preset historical time period, predicting the photovoltaic manufacturing amount of the virtual power plant in the future preset time period to obtain a photovoltaic manufacturing amount PV corresponding to the ith time granularity i ；
A model construction module for recording the charge and discharge capacity corresponding to the ith time granularity as X i The method comprises the steps of carrying out a first treatment on the surface of the Load consumption L corresponding to the ith time granularity i The photovoltaic manufacturing amount PV corresponding to the ith time granularity i The charging and discharging quantity X corresponding to the ith time granularity i Correcting to obtain a photovoltaic manufacturing amount correction value y corresponding to the ith time granularity 1i Correction value y of charge and discharge amount 2i Load consumption correction value y 3i The method comprises the steps of carrying out a first treatment on the surface of the Based on the electricity price P corresponding to the ith time granularity i According to the photovoltaic manufacturing quantity correction value y corresponding to the ith time granularity 1i Correction value y of charge and discharge amount 2i Load consumption correction value y 3i Constructing a charge-discharge energy storage scheduling target model, wherein the charge-discharge energy storage scheduling target model is as followsConstructing a capacity limiting constraint equation condition, wherein the capacity limiting constraint equation condition is thatWherein C is Rated for Is the rated capacity of the battery; constructing a power limitation constraint condition, wherein the power limitation constraint condition is +.>Wherein P is 1max At maximum discharge power, P 2max Is the maximum charging power;
and the solving module is used for solving the charge-discharge energy storage scheduling target model under the capacity limiting constraint equation condition and the power limiting constraint condition to obtain charge-discharge capacity corresponding to n time granularities respectively, wherein the charge-discharge capacity corresponding to the n time granularities respectively is the optimal decision of charge-discharge energy storage scheduling.
4. An electronic device comprising a processor and a memory, the memory storing a plurality of instructions; the processor loads instructions from the memory to perform the steps in the virtual power plant energy storage scheduling method of any one of claims 1 or 2.
5. A computer readable storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor for performing the steps in the virtual power plant energy storage scheduling method of any one of claims 1 or 2.
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