Optimization method and device of power system, storage medium and electronic equipment

Abstract

The application discloses an optimization method and device of a power system, a storage medium and an electronic device. It relates to the technical field of data processing. The method comprises the following steps: obtaining a first output prediction curve of a thermal power generating unit in a target power system and obtaining a second output prediction curve of a distributed power generation device in the target power system; obtaining a target power consumption load prediction curve by predicting historical power consumption load data on the user side; calculating the target power consumption load prediction curve according to the first output prediction curve, the second output prediction curve and the target power consumption load prediction curve to obtain a power consumption gap curve; and performing power supply optimization on the target power system according to the power consumption gap curve. The application solves the problem of poor power supply optimization effect caused by manually adjusting the power supply of the power system in the related art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to an optimization method and device of a power system, a storage medium and an electronic device. BACKGROUND

[0002] It is an important goal of the power department to provide customers with stable, uninterrupted and high-quality power to meet production needs. In order to meet the growing load, the installed capacity needs to be continuously increased, but blindly increasing the installed capacity will lead to a decrease in the annual utilization hours of power transmission equipment and a continuous rise in power supply costs, which is extremely detrimental to the rational use of social resources. Therefore, peak-shaving control of electricity has important significance for the efficient and stable operation of the power system. In the prior art, power supply optimization of the power system is often performed through manual adjustment to realize stable operation of the power grid. However, this method has the problem of poor power supply optimization effect.

[0003] In view of the problem in the related art that power supply optimization of the power system is performed through manual adjustment, resulting in poor power supply optimization effect, no effective solution has been proposed so far. SUMMARY

[0004] The main purpose of the present application is to provide an optimization method and device of a power system, a storage medium and an electronic device to solve the problem in the related art that power supply optimization of the power system is performed through manual adjustment, resulting in poor power supply optimization effect.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, an optimization method of a power system is provided. The method comprises: obtaining a first output prediction curve of a thermal power generating unit in a target power system and obtaining a second output prediction curve of a distributed power generation device in the target power system; obtaining a target electricity load prediction curve by predicting according to historical electricity load data on the user side; calculating a electricity gap curve according to the first output prediction curve, the second output prediction curve and the target electricity load prediction curve; and performing power supply optimization on the target power system according to the electricity gap curve.

[0006] Further, obtaining the second output prediction curve of the distributed power generation device in the target power system comprises: obtaining historical environmental data and historical output data of the region where the distributed power generation device is located, and constructing a training sample set according to the historical environmental data and the historical output data; training an initial output prediction model according to the training sample set to obtain a target output prediction model; and processing current environmental data of the distributed power generation device through the target output prediction model to obtain the second output prediction curve.

[0007] Further, the target power load prediction curve is obtained according to historical power load data of the user side, and the obtaining comprises: clustering the historical power load data to obtain first historical power load data and second historical power load data; processing the first historical power load data by using a first sequence prediction model to obtain a first power load prediction curve in a target time period; processing the second historical power load data by using a second sequence prediction model to obtain a second power load prediction curve in the target time period; and determining the target power load prediction curve according to the first power load prediction curve and the second power load prediction curve.

[0008] Further, the power consumption gap curve is obtained according to the first output prediction curve, the second output prediction curve and the target power load prediction curve, and the obtaining comprises: obtaining a power purchase plan curve of the target power system to an upper-level power grid; and calculating the power consumption gap curve according to the first output prediction curve, the second output prediction curve, the target power load prediction curve and the power purchase plan curve.

[0009] Further, the power supply optimization of the target power system is performed according to the power consumption gap curve, and the performing comprises: constructing a power supply optimization function according to the power consumption gap curve; solving the power supply optimization function to obtain a power supply optimization strategy of the target power system; and performing power supply optimization of the target power system according to the power supply optimization strategy.

[0010] Further, the power supply optimization function is constructed according to the power consumption gap curve, and the constructing comprises: obtaining a demand response device of the user side, and determining a load adjustment curve of the demand response device according to a type of the demand response device, wherein the demand response device is a device capable of adjusting power load in response to a demand of the target power system; obtaining a capacity parameter of an energy storage device of the target power system, an operation cost of the thermal power generator unit, an operation cost of the distributed power generation device and a power purchase cost of the target power system to the upper-level power grid; and constructing the power supply optimization function according to the load adjustment curve, the capacity parameter, the operation cost of the thermal power generator unit, the operation cost of the distributed power generation device, the power purchase cost of the target power system to the upper-level power grid and the power consumption gap curve.

[0011] Further, the power supply optimization function is solved to obtain the power supply optimization strategy of the target power system, including: obtaining the first output upper limit value and the first output lower limit value of the thermal power generating unit and the second output upper limit value and the second output lower limit value of the distributed power generation device, and determining a first output constraint condition according to the first output upper limit value, the first output lower limit value, the second output upper limit value and the second output lower limit value; obtaining a dispatch load threshold of the demand response device according to the load adjustment curve, and determining a response load constraint condition according to the dispatch load threshold; obtaining a second output constraint condition corresponding to the energy storage device according to the capacity parameter; and solving the power supply optimization function according to the first output constraint condition, the response load constraint condition and the second output constraint condition to obtain the power supply optimization strategy.

[0012] To achieve the above-mentioned purpose, according to another aspect of the present application, an optimization device of a power system is provided. The device comprises: an acquisition unit configured to acquire a first output prediction curve of a thermal power generating unit in a target power system and acquire a second output prediction curve of a distributed power generation device in the target power system; a prediction unit configured to obtain a target power consumption load prediction curve by prediction according to historical power consumption load data of a user side; a calculation unit configured to obtain a power consumption gap curve by calculation according to the first output prediction curve, the second output prediction curve and the target power consumption load prediction curve; and an optimization unit configured to perform power supply optimization on the target power system according to the power consumption gap curve.

[0013] Further, the acquisition unit comprises: a first acquisition module configured to acquire historical environmental data and historical output data of a region where the distributed power generation device is located, and construct a training sample set according to the historical environmental data and the historical output data; a training module configured to train an initial output prediction model according to the training sample set to obtain a target output prediction model; and a first processing module configured to process current environmental data of the distributed power generation device by the target output prediction model to obtain the second output prediction curve.

[0014] Further, the prediction unit comprises: a clustering module, configured to cluster the historical power consumption load data to obtain first type historical power consumption load data and second type historical power consumption load data; a second processing module, configured to process the first type historical power consumption load data by using a first type sequence prediction model to obtain a first power consumption load prediction curve in the target time period; a third processing module, configured to process the second type historical power consumption load data by using a second type sequence prediction model to obtain a second power consumption load prediction curve in the target time period; and a determination module, configured to determine the target power consumption load prediction curve according to the first power consumption load prediction curve and the second power consumption load prediction curve.

[0015] Further, the calculation unit comprises: a second acquisition module, configured to acquire a power purchase plan curve of the target power system to an upper-level power grid; and a calculation module, configured to calculate the power consumption gap curve according to the first output prediction curve, the second output prediction curve, the target power consumption load prediction curve and the power purchase plan curve.

[0016] Further, the optimization unit comprises: a construction module, configured to construct a power supply optimization function according to the power consumption gap curve; a solving module, configured to solve the power supply optimization function to obtain a power supply optimization strategy for the target power system; and an optimization module, configured to optimize power supply of the target power system according to the power supply optimization strategy.

[0017] Further, the construction module comprises: a first acquisition submodule, configured to acquire a demand response device on a user side, and determine a load adjustment curve of the demand response device according to a type of the demand response device, wherein the demand response device is a device capable of adjusting power consumption load in response to a demand of the target power system; a second acquisition submodule, configured to acquire a capacity parameter of an energy storage device of the target power system, an operation cost of the thermal power generator unit, an operation cost of the distributed power generation device and a power purchase cost of the target power system to an upper-level power grid; and a construction submodule, configured to construct the power supply optimization function according to the load adjustment curve, the capacity parameter, the operation cost of the thermal power generator unit, the operation cost of the distributed power generation device, the power purchase cost of the target power system to the upper-level power grid and the power consumption gap curve.

[0018] Further, the solving module comprises: a third obtaining sub-module, configured to obtain a first output upper limit value and a first output lower limit value of the thermal power generating unit and a second output upper limit value and a second output lower limit value of the distributed power generation device, and determine a first output constraint condition according to the first output upper limit value, the first output lower limit value, the second output upper limit value and the second output lower limit value; a fourth obtaining sub-module, configured to obtain a dispatch load threshold of the demand response device according to the load adjustment curve, and determine a response load constraint condition according to the dispatch load threshold; a fifth obtaining sub-module, configured to obtain a second output constraint condition corresponding to the energy storage device according to the capacity parameter; and a solving sub-module, configured to solve the power supply optimization function according to the first output constraint condition, the response load constraint condition and the second output constraint condition, to obtain the power supply optimization strategy.

[0019] In order to achieve the above object, according to an aspect of the present application, a computer readable storage medium is provided, the storage medium stores a program, wherein the program controls the device where the storage medium is located to execute the power system optimization method of any one of the above when the program is running.

[0020] In order to achieve the above object, according to another aspect of the present application, an electronic device is also provided, the electronic device comprises one or more processors and a memory, the memory is used to store the power system optimization method of any one of the above which is realized by the one or more processors.

[0021] By the present application, the following steps are adopted: obtaining a first output prediction curve of a thermal power generating unit in a target power system and obtaining a second output prediction curve of a distributed power generation device in the target power system; obtaining a target power consumption load prediction curve according to historical power consumption load data of a user side; and calculating according to the first output prediction curve, the second output prediction curve and the target power consumption load prediction curve to obtain a power consumption gap curve; and performing power supply optimization on the target power system according to the power consumption gap curve, thereby solving the problem of poor power supply optimization effect caused by manually adjusting the power supply of the power system in the related art. In the present solution, the first output prediction curve of the thermal power generating unit and the second output prediction curve of the distributed power generation device are obtained, the target power consumption load prediction curve is obtained according to the historical power consumption load data of the user side, the current power consumption gap curve is then determined according to the target power consumption load prediction curve, the first output prediction curve and the second output prediction curve, and finally the power supply optimization is performed on the target power system according to the power consumption gap curve. The utilization rate of the distributed power generation device and the thermal power generating unit can be improved through power supply control and optimization according to the power consumption gap curve, thereby achieving the effect of improving the power supply quality. BRIEF DESCRIPTION OF DRAWINGS

[0022] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The embodiments of this application, and of the description of the embodiments, are to explain the application and are not to be construed as the only way of implementing the present application. In the drawings:

[0023] Figure 1 is a flow of the optimization method of the power system according to the embodiment of the present application Figure 1 ;

[0024] Figure 2 is a flow of the optimization method of the power system according to the embodiment of the present application Figure 2 ;

[0025] Figure 3 is a schematic diagram of the optimization device of the power system according to the embodiment of the present application

[0026] Figure 4 is a schematic diagram of the dispatch automation system according to the embodiment of the present application

[0027] Figure 5 is a schematic diagram of the electronic device according to the embodiment of the present application. DETAILED DESCRIPTION

[0028] It should be noted that the embodiments and the features in the embodiments in the present application can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0029] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the protection scope of the present application.

[0030] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0031] It should be noted that the related information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, analyzed data, etc.) involved in the present disclosure are information and data authorized by the user or authorized by all parties. For example, an interface is provided between the system and the related user or institution. Before obtaining the related information, the interface needs to send a request to the aforementioned user or institution, and after receiving the consent information feedback from the aforementioned user or institution, the related information is obtained.

[0032] The application will be described below in conjunction with preferred implementation steps, Figure 1 is a flow of the optimization method of the power system provided by the embodiment of the present application Figure 1 As shown in the figure, Figure 1 The method comprises the following steps:

[0033] Step S101, obtaining a first output prediction curve of a thermal power generating unit in a target power system and obtaining a second output prediction curve of a distributed power generation device in the target power system.

[0034] Optionally, the target power system comprises a distributed power generation device, a thermal power generating unit, an energy storage device, a conventional energy consumption device on the user side, and a demand response device on the user side. It should be noted that the demand response device on the user side is a device that can adjust the power load in response to the demand of the target power system. Demand response refers to when the power supply is tight (or surplus), there is a period of power shortage (or surplus), the power grid enterprise invites the electricity customers with load adjustment capacity to actively reduce (or increase) the power load in the specific period agreed by both parties according to the supply and demand situation through economic incentives, effectively realizes peak load shifting, and alleviates the contradiction between power supply and demand.

[0035] Optionally, the first output prediction curve of the thermal power generating unit and the second output prediction curve of the distributed power generation device in the target power system can be obtained through a data acquisition module. It should be noted that the first output prediction curve can be determined by the historical output of the thermal power generating unit, and the second output prediction curve can be determined according to the historical output of the distributed power generation device. It should be noted that the first output prediction curve can be the output prediction curve of the thermal power generating unit in the future one week. The first output prediction curve and the second output prediction curve can accurately predict the power output in the future period of time.

[0036] Step S102, predicting according to the historical power load data on the user side to obtain a target power load prediction curve.

[0037] Optionally, the historical power consumption load data of the user side can be collected by the data collection module, and the power consumption of the user side in a future period of time (for example, in a week), that is, the target power consumption load prediction curve, can be calculated by fitting the historical power consumption load data. It should be noted that the target power consumption load prediction curve is the predicted power consumption of the regular energy-consuming equipment of the user side.

[0038] In step S103, the power consumption gap curve is obtained by calculating according to the first output prediction curve, the second output prediction curve and the target power consumption load prediction curve.

[0039] Optionally, the power consumption gap curve in a future period of time is obtained by calculating according to the first output prediction curve, the second output prediction curve and the target power consumption load prediction curve.

[0040] In step S104, the power supply optimization of the target power system is performed according to the power consumption gap curve.

[0041] Optionally, the power supply optimization of the target power system is performed according to the power consumption gap curve, for example, the output (power output) of the distributed power generation equipment and the thermal power generator set, the charging and discharging of the energy storage, and the power of the demand response equipment are optimized and adjusted.

[0042] In summary, the first output prediction curve and the second output prediction curve of the thermal power generator set and the distributed power generation equipment are obtained, the target power consumption load prediction curve is obtained according to the historical power consumption load data of the user side, the current power consumption gap curve is determined by the target power consumption load prediction curve, the first output prediction curve and the second output prediction curve, and finally the power supply optimization of the target power system is performed according to the power consumption gap curve. The utilization rate of the distributed power generation equipment and the thermal power generator set can be improved by the power supply control and optimization of the power consumption gap curve, and the effect of improving the power supply quality is achieved.

[0043] Optionally, in the power system optimization method provided in the embodiments of the present application, the second output prediction curve of the distributed power generation equipment in the target power system comprises: obtaining the historical environmental data and the historical output data of the region where the distributed power generation equipment is located, and constructing a training sample set according to the historical environmental data and the historical output data; training the initial output prediction model according to the training sample set to obtain a target output prediction model; and processing the current environmental data of the distributed power generation equipment by the target output prediction model to obtain the second output prediction curve.

[0044] In an optional embodiment, the second output prediction curve can be obtained by the following steps: collecting historical environmental data and historical output data of the area where the distributed power generation device is located, it should be noted that the historical environmental data includes but is not limited to historical wind speed, historical irradiation and the like of the area where the distributed power generation device is located, then, constructing a training sample set by the historical environmental data and the historical output data, training the distributed power generation device prediction model (i.e. the initial output prediction model) by using the training sample set, thereby obtaining the optimal model parameters.

[0045] Finally, the trained distributed power generation device prediction model (i.e. the target output prediction model) is used for power prediction of the distributed power generation device, that is, the current environmental data of the distributed power generation device is processed by the target output prediction model to obtain the second output prediction curve.

[0046] The target output prediction model can accurately evaluate and predict the output prediction curve of the distributed power generation device.

[0047] Optionally, in the power system optimization method provided in the embodiments of the present application, the target power consumption load prediction curve is obtained by predicting the historical power consumption load data of the user side, including: clustering the historical power consumption load data to obtain first type historical power consumption load data and second type historical power consumption load data; processing the first type historical power consumption load data by the first type sequence prediction model to obtain a first power consumption load prediction curve in the target time period; processing the second type historical power consumption load data by the second type sequence prediction model to obtain a second power consumption load prediction curve in the target time period; determining the target power consumption load prediction curve according to the first power consumption load prediction curve and the second power consumption load prediction curve.

[0048] In an optional embodiment, in order to improve the accurate prediction of the power consumption of the user side, the power system optimization method provided in the embodiments of the present application further includes: collecting historical power consumption load data of the user side, it should be noted that the historical power consumption load data is the historical power consumption of the conventional energy consumption equipment of the user side.

[0049] After obtaining the historical power consumption load data of the user side, the historical power consumption load data can be clustered into first type historical power consumption load data and second type historical power consumption load data by a clustering algorithm, for example, clustered into two types of weekdays and non-weekdays, and a sequence prediction model is established respectively, the historical power consumption load data is fused with local meteorological data as training samples, a CNN is used to extract feature vectors from the training samples, the extracted feature vectors are constructed in a time sequence sequence manner and the constructed data are used as input data of an LSTM network (i.e. the sequence prediction model mentioned above), and then the LSTM network is used for short-term load prediction. That is, the first type sequence prediction model is used to process the first type historical power consumption load data to obtain a first power consumption load prediction curve in a target time period; the second type sequence prediction model is used to process the second type historical power consumption load data to obtain a second power consumption load prediction curve in the target time period.

[0050] Finally, the final target power consumption load prediction curve is obtained by using the first power consumption load prediction curve and the second power consumption load prediction curve.

[0051] Optionally, in the power system optimization method provided in the embodiments of the present application, the calculation based on the first output prediction curve, the second output prediction curve and the target power consumption load prediction curve to obtain the power consumption gap curve comprises: obtaining a power purchase plan curve of the target power system to the upper-level power grid; and calculating based on the first output prediction curve, the second output prediction curve, the target power consumption load prediction curve and the power purchase plan curve to obtain the power consumption gap curve.

[0052] In an optional embodiment, during actual power supply, the target power system can need to purchase a certain amount of power from the upper-level power grid in order to maintain the stability of power supply, and therefore, when calculating the power consumption gap curve, the following steps are included: obtaining a power purchase plan curve of the target power system to the upper-level power grid, and then calculating based on the first output prediction curve, the second output prediction curve, the target power consumption load prediction curve and the power purchase plan curve to obtain the power consumption gap curve.

[0053] In an optional embodiment, the power consumption gap curve mentioned above can be calculated by the following formula (1):

[0054] P q,t = P load,t -P buy,t -P dg,t -P gt,t (1)

[0055] Wherein, P q,t is the power grid power gap power at time t (i.e. the power consumption gap curve mentioned above), P load,tP is the predicted power of the user-side conventional energy-consuming equipment load at time t (i.e., the target power consumption load prediction curve described above) buy,t P is the power purchase plan at time t (i.e., the power purchase plan curve described above) dg,t P is the power purchase plan at time t (i.e., the power purchase plan curve described above) gt,t P and P are the output of the distributed power generation equipment and the output of the thermal power generator unit at time t (i.e., the first output prediction curve and the second output prediction curve described above).

[0056] Optionally, in the power system optimization method provided in the embodiments of the present application, the power supply optimization of the target power system according to the electricity consumption gap curve comprises: constructing a power supply optimization function according to the electricity consumption gap curve; solving the power supply optimization function to obtain a power supply optimization strategy for the target power system; and performing power supply optimization on the target power system according to the power supply optimization strategy.

[0057] In an optional embodiment, the power supply optimization of the target power system is performed by the following steps: constructing a power supply optimization function according to the electricity consumption gap curve, for example, constructing a power deviation minimum function according to the electricity consumption gap curve, and taking the power deviation minimum function as the power supply optimization function, then solving the power supply optimization function to obtain a power supply optimization strategy, i.e., how to adjust the output (power output) of the distributed power generator unit and the thermal power generator unit of the power equipment layer, the charge and discharge of the energy storage, and the power of the demand response equipment. Finally, the power supply optimization of the target power system is performed according to the power supply optimization strategy.

[0058] The power supply optimization function can be used to more accurately and efficiently determine how to perform power supply optimization on the target power system.

[0059] Optionally, in the power system optimization method provided in the embodiments of the present application, the construction of the power supply optimization function according to the electricity consumption gap curve comprises: obtaining a demand response equipment on the user side, and determining a load adjustment curve of the demand response equipment according to the type of the demand response equipment, wherein the demand response equipment is an equipment capable of adjusting the power consumption load in response to the demand of the target power system; obtaining a capacity parameter of an energy storage equipment of the target power system, an operation cost of a thermal power generator unit, an operation cost of a distributed power generation equipment, and a power purchase cost of the target power system to an upper-level power grid; and constructing the power supply optimization function according to the load adjustment curve, the capacity parameter, the operation cost of the thermal power generator unit, the operation cost of the distributed power generation equipment, the power purchase cost of the target power system to the upper-level power grid, and the electricity consumption gap curve.

[0060] In an optional embodiment, the power supply optimization function is obtained by the following steps: obtaining the demand response device on the user side, and then determining the load adjustment curve of the demand response device according to the type of the demand response device. It should be noted that the type of the demand response device mainly includes price type response and incentive type response load. The incentive type load response generally formulates a response scheduling plan for the load according to the supply and demand situation in the microgrid, and gives a certain compensation fee to the load participating in the response. The price type load response generally guides the user to formulate a reasonable power consumption plan by setting a time-of-use price, so as to reduce the load peak in some period of the microgrid.

[0061] In an optional embodiment, the load adjustment curve of the price type demand response device is shown in formula (2):

[0062]

[0063] wherein, is the price type demand response load value at the initial time, is the price type demand response load value at t time after participating in the load response, is the price type demand response adjustable load value at t time, η t is the load response rate of the price type demand response at t time, are respectively the minimum incentive price and the maximum incentive price, ε is the price incentive coefficient of the price type demand response, and α is the power index.

[0064] In an optional embodiment, the load adjustment curve of the price type demand response device is shown in formula (3):

[0065]

[0066] wherein, is the incentive type load response load value at the initial time, is the incentive type load response load value at t time after participating in the load response, is the incentive type load response adjustable load value at t time, is the proportion of load transfer out at t time, is the proportion of load transfer in at t time, is the proportion of load cut-off at t time.

[0067] After obtaining the load adjustment curve, the capacity parameter of the energy storage device of the target power system, the operation cost of the thermal power generator set, the operation cost of the distributed power generation device, and the purchase cost of the target power system to the upper-level power grid are obtained, and finally, the power supply optimization function is constructed according to the load adjustment curve, the capacity parameter, the operation cost of the thermal power generator set, the operation cost of the distributed power generation device, the purchase cost of the target power system to the upper-level power grid, and the power gap curve.

[0068] In an optional embodiment, the minimum peak-shaving regulation cost F1 and the minimum peak-shaving control power deviation F2 are used as the power supply optimization function. Specifically, the power supply optimization function is as follows:

[0069]

[0070] wherein T represents the number of time periods, C gt,t , C dg,t represent the operation cost of the distributed power generation device and the thermal power generator set in the t time period, respectively, λ grid,t , P grid,t represent the purchase electricity price and the purchase power (i.e., the purchase amount) of the upper-level power grid in the t time period, respectively, λ sc,t is the charging and discharging price of the energy storage device in the t time period, P dis,t , P chr,t represent the discharging power and the charging power of the energy storage device in the t time period, respectively, λ ibdr,t , λ pbdr,t represent the incentive load response price and the price-type demand response price in the t time period, respectively, P ibdr,t , P pbdr,t represent the incentive load response adjustment power (obtained according to the load adjustment curve) and the price-type demand response adjustment power in the t time period, respectively; and P q,t is the power gap power of the power grid at the t time.

[0071] The minimum peak-shaving regulation cost F1 and the minimum peak-shaving control power deviation F2 are used as the power supply optimization function, which can more accurately and reasonably determine the power supply optimization strategy and improve the power supply stability of the power system.

[0072] Optionally, in the power system optimization method provided in the embodiments of the present application, the power supply optimization function is solved to obtain a power supply optimization strategy for the target power system, which comprises: obtaining a first upper limit value and a first lower limit value of the output of the thermal power generator set and a second upper limit value and a second lower limit value of the output of the distributed power generation device, and determining a first output constraint condition according to the first upper limit value, the first lower limit value, the second upper limit value and the second lower limit value; obtaining a dispatch load threshold of the demand response device according to the load adjustment curve, and determining a response load constraint condition according to the dispatch load threshold; obtaining a second output constraint condition corresponding to the energy storage device according to the capacity parameter; and solving the power supply optimization function according to the first output constraint condition, the response load constraint condition and the second output constraint condition to obtain the power supply optimization strategy.

[0073] In an optional embodiment, when the power supply optimization function is solved, the following steps are adopted to avoid unreasonable power supply optimization strategies:

[0074] First, the first upper limit value and the first lower limit value of the output of the thermal power generator set and the second upper limit value and the second lower limit value of the output of the distributed power generation device are obtained, and then the first output constraint condition is determined according to the first upper limit value, the first lower limit value, the second upper limit value and the second lower limit value.

[0075] In an optional embodiment, the first output constraint condition can be shown in formula (5):

[0076] P gt,min ≤P gt,t ≤P gt,max

[0077] P dg,min ≤P dg,t ≤P dg,max (5)

[0078] Wherein, P gt,min , P gt,max and P dg,min , P dg,max are the upper limit and lower limit of the output of the thermal power generator set and the distributed power generator set, i.e. the first upper limit value, the first lower limit value, the second upper limit value and the second lower limit value of the output of the thermal power generator set and the distributed power generator set.

[0079] Then, the dispatch load threshold of the demand response device is determined according to the load adjustment curve, and the response load constraint condition is determined according to the dispatch load threshold.

[0080] In an optional embodiment, the response load constraint condition is shown in formula (6):

[0081] P ibdr,min <P ibdr,t<P ibdr,max

[0082] P pbdr,min <P pbdr,t <P pbdr,max (6)

[0083] wherein, P ibdr,min , P ibdr,max and P pbdr,min , P pbdr,max represent the minimum and maximum values of the dispatchable power of the incentive-based load response equipment and the price-based demand response equipment (i.e. the dispatch load threshold mentioned above) respectively.

[0084] Finally, according to the capacity parameter of the energy storage equipment, the second output constraint condition corresponding to the energy storage equipment is obtained. And the power supply optimization function is solved based on the first output constraint condition, the load constraint condition and the second output constraint condition, to obtain the power supply optimization strategy.

[0085] In an optional embodiment, the second output constraint condition is shown in formula (7):

[0086]

[0087] wherein, SOC t , SOC t-1 represent the SOC values of the energy storage equipment at time t and time t-1 respectively; represent the charging and discharging electric quantities of the energy storage equipment at time t respectively; η chr , η dis represent the charging and discharging efficiencies of the energy storage equipment at time t respectively; SOC min , SOC max represent the minimum and maximum values of the SOC of the energy storage equipment respectively; represent the minimum and maximum values of the discharging of the energy storage equipment at time t; represent the minimum and maximum values of the charging of the energy storage equipment at time t.

[0088] In an optional embodiment, when the power supply optimization function is solved, the constraint of the transmission power value of the power grid and the superior power grid tie line can also be considered, for example, the constraint is shown in formula (8):

[0089] 0 < P tline,t < P tline,max (8)

[0090] wherein, P tline,t represents the transmission power value of the power grid and the superior power grid tie line at time t, P tline,max represents the maximum value of the transmission power limit of the power grid and the superior power grid tie line

[0091] In an optional embodiment, the control of the power system can be implemented by the flow chart shown in Figure 2 The control of the power system can be implemented by the flow chart shown in FIG. 1, which specifically includes: obtaining the output prediction curve of the thermal power generating unit at the power grid equipment layer, the capacity configuration of the energy storage equipment, and the adjustable curve of the demand response equipment at the user side, and obtaining the output prediction curve of the distributed power generation equipment and the load prediction curve of the conventional energy consumption equipment at the user side. The power purchase plan curve of the upper-level power grid is obtained. Then, the power consumption gap curve is determined according to the power purchase plan curve, the output prediction curve of the distributed power generation equipment, the output prediction curve of the thermal power generating unit, and the load prediction curve. Finally, peak shaving optimization is performed based on the power consumption gap curve.

[0092] The optimization method of the power system provided in the embodiments of the present application includes the following steps: obtaining a first output prediction curve of a thermal power generating unit in a target power system; obtaining a second output prediction curve of a distributed power generation equipment in the target power system; predicting a target load prediction curve of a user side according to historical load data of the user side; calculating a power consumption gap curve according to the first output prediction curve, the second output prediction curve, and the target load prediction curve; and performing power supply optimization on the target power system according to the power consumption gap curve. The power supply optimization method of the power system in the related art is performed by manual adjustment, which leads to poor power supply optimization effect. In the present solution, the first output prediction curve of the thermal power generating unit and the second output prediction curve of the distributed power generation equipment are obtained, the target load prediction curve of the user side is obtained according to the historical load data of the user side, the current power consumption gap curve is determined by the target load prediction curve, the first output prediction curve, and the second output prediction curve, and finally the power supply optimization is performed on the target power system according to the power consumption gap curve. The power supply control and optimization performed by the power consumption gap curve can improve the utilization rate of the distributed power generation equipment and the thermal power generating unit, thereby improving the power supply quality.

[0093] It should be noted that the steps shown in the flow chart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flow chart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0094] The embodiments of the present application also provide an optimization device of a power system. It should be noted that the optimization device of the power system of the embodiments of the present application can be used to execute the optimization method of the power system provided in the embodiments of the present application. The optimization device of the power system provided in the embodiments of the present application is introduced as follows.

[0095] Figure 3 FIG. 1 is a schematic diagram of an optimization device of a power system according to the embodiments of the present application. As shown in Figure 3As shown, the device comprises an acquisition unit 301, a prediction unit 302, a calculation unit 303 and an optimization unit 304.

[0096] The acquisition unit 301 is configured to acquire a first output prediction curve of a thermal power generating unit in a target power system and acquire a second output prediction curve of a distributed power generation device in the target power system.

[0097] The prediction unit 302 is configured to perform prediction according to historical power consumption load data on the user side to obtain a target power consumption load prediction curve.

[0098] The calculation unit 303 is configured to perform calculation according to the first output prediction curve, the second output prediction curve and the target power consumption load prediction curve to obtain a power consumption gap curve.

[0099] The optimization unit 304 is configured to perform power supply optimization on the target power system according to the power consumption gap curve.

[0100] The power system optimization device provided by the embodiment of the present application acquires the first output prediction curve of the thermal power generating unit in the target power system and acquires the second output prediction curve of the distributed power generation device in the target power system through the acquisition unit 301; the prediction unit 302 performs prediction according to the historical power consumption load data on the user side to obtain the target power consumption load prediction curve; the calculation unit 303 performs calculation according to the first output prediction curve, the second output prediction curve and the target power consumption load prediction curve to obtain the power consumption gap curve; and the optimization unit 304 performs power supply optimization on the target power system according to the power consumption gap curve, thereby solving the problem that the power supply optimization of the power system is performed in a manual adjustment manner in the related art, resulting in poor power supply optimization effect. In the present scheme, the first output prediction curve and the second output prediction curve of the thermal power generating unit and the distributed power generation device are acquired, the target power consumption load prediction curve is obtained according to the historical power consumption load data on the user side, the current power consumption gap curve is then determined through the target power consumption load prediction curve, the first output prediction curve and the second output prediction curve, and finally the power supply optimization is performed on the target power system according to the power consumption gap curve. The utilization rate of the distributed power generation device and the thermal power generating unit can be improved through power supply control and optimization according to the power consumption gap curve, thereby achieving the effect of improving the power supply quality.

[0101] Optionally, in the optimization device for the power system provided by the embodiment of the present application, the obtaining unit comprises: a first obtaining module, configured to obtain historical environmental data and historical output data of a region where the distributed power generation device is located, and construct a training sample set according to the historical environmental data and the historical output data; a training module, configured to train an initial output prediction model according to the training sample set to obtain a target output prediction model; and a first processing module, configured to process current environmental data of the distributed power generation device by using the target output prediction model to obtain a second output prediction curve.

[0102] Optionally, in the optimization device for the power system provided by the embodiment of the present application, the prediction unit comprises: a clustering module, configured to cluster historical power consumption load data to obtain first type historical power consumption load data and second type historical power consumption load data; a second processing module, configured to process the first type historical power consumption load data by using the first type sequence prediction model to obtain a first power consumption load prediction curve in the target time period; a third processing module, configured to process the second type historical power consumption load data by using the second type sequence prediction model to obtain a second power consumption load prediction curve in the target time period; and a determination module, configured to determine a target power consumption load prediction curve according to the first power consumption load prediction curve and the second power consumption load prediction curve.

[0103] Optionally, in the optimization device for the power system provided by the embodiment of the present application, the calculation unit comprises: a second obtaining module, configured to obtain a power purchase plan curve of the target power system to an upper-level power grid; and a calculation module, configured to calculate according to the first output prediction curve, the second output prediction curve, the target power consumption load prediction curve and the power purchase plan curve to obtain a power consumption gap curve.

[0104] Optionally, in the optimization device for the power system provided by the embodiment of the present application, the optimization unit comprises: a construction module, configured to construct a power supply optimization function according to the power consumption gap curve; a solving module, configured to solve the power supply optimization function to obtain a power supply optimization strategy for the target power system; and an optimization module, configured to perform power supply optimization on the target power system according to the power supply optimization strategy.

[0105] Optionally, in the optimization device for the power system provided by the embodiment of the present application, the constructing module comprises: a first obtaining sub-module, configured to obtain a demand response device on a user side, and determine a load adjustment curve of the demand response device according to a type of the demand response device, wherein the demand response device is a device capable of adjusting an electricity load in response to a demand of a target power system; a second obtaining sub-module, configured to obtain a capacity parameter of an energy storage device of the target power system, an operation cost of a thermal power generator unit, an operation cost of a distributed power generation device, and a power purchase cost of the target power system from a superior power grid; and a constructing sub-module, configured to construct a power supply optimization function according to the load adjustment curve, the capacity parameter, the operation cost of the thermal power generator unit, the operation cost of the distributed power generation device, the power purchase cost of the target power system from the superior power grid, and the electricity gap curve.

[0106] Optionally, in the optimization device for the power system provided by the embodiment of the present application, the solving module comprises: a third obtaining sub-module, configured to obtain a first upper limit value and a first lower limit value of an output of the thermal power generator unit and a second upper limit value and a second lower limit value of an output of the distributed power generation device, and determine a first output constraint condition according to the first upper limit value, the first lower limit value, the second upper limit value, and the second lower limit value; a fourth obtaining sub-module, configured to obtain a dispatching load threshold of the demand response device according to the load adjustment curve, and determine a response load constraint condition according to the dispatching load threshold; a fifth obtaining sub-module, configured to obtain a second output constraint condition corresponding to the energy storage device according to the capacity parameter; and a solving sub-module, configured to solve the power supply optimization function according to the first output constraint condition, the response load constraint condition, and the second output constraint condition, to obtain a power supply optimization strategy.

[0107] The optimization device for the power system comprises a processor and a memory, the obtaining unit 301, the prediction unit 302, the calculation unit 303, and the optimization unit 304 are all stored in the memory as program units, and the corresponding functions are realized by the processor executing the program units stored in the memory.

[0108] The processor comprises a core, and the core calls the corresponding program units from the memory. The core can be set as one or more, and the efficient optimization of the power system is realized by adjusting the core parameters.

[0109] The memory can comprise a non-permanent memory in a computer readable medium, a random access memory (RAM), and / or a non-volatile memory such as a read-only memory (ROM) or a flash memory (flash RAM), and the memory comprises at least one memory chip.

[0110] In an optional embodiment, an automatic dispatching system is also provided, which comprises Figure 4As shown, the dispatch automation system specifically comprises: a power equipment layer 40, a data terminal layer 41, a data transmission layer 42 and a power grid dispatch layer 43; the power equipment layer 40 comprises distributed generator sets, thermal power generator sets, energy storage devices, user-side conventional energy consumption devices and user-side demand response devices.

[0111] The data terminal layer 41 comprises a data acquisition module 410, an output prediction module 411, a load prediction module 412 and a device control module 413, the data acquisition module 410 is used for acquiring the output curve of the thermal power generator set of the power grid power equipment layer, the capacity configuration of the energy storage device and the adjustable curve of the user-side demand response device, the output prediction module 411 is used for acquiring the output prediction curve of the distributed power generation device, the load prediction module 412 is used for acquiring the load prediction curve of the user-side conventional energy consumption device, and the device control module 413 is used for receiving the peak shaving control instruction of the power grid dispatch layer, and the device control module is also used for controlling the devices of the power equipment layer.

[0112] The data transmission layer 42 is used for providing data communication services for the data acquisition layer 41 and the power grid dispatch layer 43, and uploads the output curve of the thermal power generator set and the output prediction curve of the distributed power generation device, the load prediction curve of the user-side conventional energy consumption device and the adjustable curve of the user-side demand response device collected by the data acquisition layer to the power grid dispatch layer, and downloads the control instruction of the power grid dispatch layer to the power equipment layer 40;

[0113] The power grid dispatch layer 43 comprises a determination of electricity gap curve module 430 and a power grid peak shaving control optimization module 431, the determination of electricity gap curve module 430 is used for analyzing and saving the data from the data transmission layer, the determination of electricity gap curve module 430 is also used for acquiring the power purchase plan curve of the upper-level power grid, and determining the electricity gap curve according to the power purchase plan curve, the output prediction curve of the distributed power generation device, the output curve of the thermal power generator set and the conventional load consumption curve, the power grid peak shaving control optimization module 431 performs peak shaving optimization based on the electricity gap curve, forms a peak shaving control instruction and issues the instruction through the data transmission layer.

[0114] The embodiment of the application provides a computer readable storage medium, which stores a program, and the program is executed by a processor to realize the optimization method of the power system.

[0115] The embodiment of the application provides a processor, which is used for running a program, and the program is executed to realize the optimization method of the power system.

[0116] As Figure 5As shown, the embodiment of the present application provides an electronic device, the device comprising a processor, a memory, and a program stored in the memory and executable on the processor, and the processor implements the following steps when executing the program: obtaining a first output prediction curve of a thermal power generating unit in a target power system and obtaining a second output prediction curve of a distributed power generation device in the target power system; obtaining a target power consumption load prediction curve according to prediction of historical power consumption load data on the user side; calculating a power consumption gap curve according to the first output prediction curve, the second output prediction curve, and the target power consumption load prediction curve; and performing power supply optimization on the target power system according to the power consumption gap curve.

[0117] Optionally, obtaining the second output prediction curve of the distributed power generation device in the target power system comprises: obtaining historical environmental data and historical output data of a region where the distributed power generation device is located, and constructing a training sample set according to the historical environmental data and the historical output data; training an initial output prediction model according to the training sample set to obtain a target output prediction model; and processing current environmental data of the distributed power generation device through the target output prediction model to obtain the second output prediction curve.

[0118] Optionally, obtaining the target power consumption load prediction curve according to prediction of the historical power consumption load data on the user side comprises: clustering the historical power consumption load data to obtain first-type historical power consumption load data and second-type historical power consumption load data; processing the first-type historical power consumption load data through a first-type sequence prediction model to obtain a first power consumption load prediction curve in a target time period; processing the second-type historical power consumption load data through a second-type sequence prediction model to obtain a second power consumption load prediction curve in the target time period; and determining the target power consumption load prediction curve according to the first power consumption load prediction curve and the second power consumption load prediction curve.

[0119] Optionally, calculating the power consumption gap curve according to the first output prediction curve, the second output prediction curve, and the target power consumption load prediction curve comprises: obtaining a power purchase plan curve of the target power system to an upper-level power grid; and calculating the power consumption gap curve according to the first output prediction curve, the second output prediction curve, the target power consumption load prediction curve, and the power purchase plan curve.

[0120] Optionally, performing power supply optimization on the target power system according to the power consumption gap curve comprises: constructing a power supply optimization function according to the power consumption gap curve; solving the power supply optimization function to obtain a power supply optimization strategy for the target power system; and performing power supply optimization on the target power system according to the power supply optimization strategy.

[0121] Optionally, constructing the power supply optimization function according to the electricity gap curve comprises: obtaining a demand response device on the user side, and determining a load adjustment curve of the demand response device according to the type of the demand response device, wherein the demand response device is a device capable of adjusting the electricity load in response to the demand of the target power system; obtaining a capacity parameter of an energy storage device of the target power system, an operation cost of a thermal power generator unit, an operation cost of a distributed power generation device, and a power purchase cost of the target power system from a superior power grid; and constructing the power supply optimization function according to the load adjustment curve, the capacity parameter, the operation cost of the thermal power generator unit, the operation cost of the distributed power generation device, the power purchase cost of the target power system from the superior power grid, and the electricity gap curve.

[0122] Optionally, solving the power supply optimization function to obtain the power supply optimization strategy for the target power system comprises: obtaining a first upper limit value and a first lower limit value of the output of the thermal power generator unit and a second upper limit value and a second lower limit value of the output of the distributed power generation device, and determining a first output constraint condition according to the first upper limit value, the first lower limit value, the second upper limit value, and the second lower limit value; obtaining a dispatching load threshold of the demand response device according to the load adjustment curve, and determining a response load constraint condition according to the dispatching load threshold; obtaining a second output constraint condition corresponding to the energy storage device according to the capacity parameter; and solving the power supply optimization function according to the first output constraint condition, the response load constraint condition, and the second output constraint condition to obtain the power supply optimization strategy.

[0123] The device in the present application can be a server, a PC, a PAD, a mobile phone, etc.

[0124] The present application also provides a computer program product adapted to execute the program of the following method steps when executed on a data processing device: obtaining a first output prediction curve of a thermal power generator unit in a target power system and obtaining a second output prediction curve of a distributed power generation device in the target power system; obtaining a target electricity load prediction curve by predicting according to historical electricity load data on the user side; calculating the electricity gap curve according to the first output prediction curve, the second output prediction curve, and the target electricity load prediction curve; and performing power supply optimization on the target power system according to the electricity gap curve.

[0125] Optionally, obtaining the second output prediction curve of the distributed power generation device in the target power system comprises: obtaining historical environmental data and historical output data of the region where the distributed power generation device is located, and constructing a training sample set according to the historical environmental data and the historical output data; training an initial output prediction model according to the training sample set to obtain a target output prediction model; and processing current environmental data of the distributed power generation device through the target output prediction model to obtain the second output prediction curve.

[0126] Optionally, the obtaining the target power consumption load prediction curve according to the historical power consumption load data of the user side comprises: clustering the historical power consumption load data to obtain first type historical power consumption load data and second type historical power consumption load data; processing the first type historical power consumption load data through a first type sequence prediction model to obtain a first power consumption load prediction curve in the target time period; processing the second type historical power consumption load data through a second type sequence prediction model to obtain a second power consumption load prediction curve in the target time period; and determining the target power consumption load prediction curve according to the first power consumption load prediction curve and the second power consumption load prediction curve.

[0127] Optionally, the obtaining the power consumption gap curve according to the first output prediction curve, the second output prediction curve and the target power consumption load prediction curve comprises: obtaining a power purchase plan curve of the target power system to the upper-level power grid; and calculating the power consumption gap curve according to the first output prediction curve, the second output prediction curve, the target power consumption load prediction curve and the power purchase plan curve.

[0128] Optionally, the power supply optimization of the target power system according to the power consumption gap curve comprises: constructing a power supply optimization function according to the power consumption gap curve; solving the power supply optimization function to obtain a power supply optimization strategy of the target power system; and performing power supply optimization of the target power system according to the power supply optimization strategy.

[0129] Optionally, the constructing the power supply optimization function according to the power consumption gap curve comprises: obtaining a demand response device of the user side, and determining a load adjustment curve of the demand response device according to a type of the demand response device, wherein the demand response device is a device capable of adjusting power consumption load in response to a demand of the target power system; obtaining a capacity parameter of an energy storage device of the target power system, an operation cost of a thermal power generator unit, an operation cost of a distributed power generation device and a power purchase cost of the target power system to the upper-level power grid; and constructing the power supply optimization function according to the load adjustment curve, the capacity parameter, the operation cost of the thermal power generator unit, the operation cost of the distributed power generation device, the power purchase cost of the target power system to the upper-level power grid and the power consumption gap curve.

[0130] Optionally, the power supply optimization function is solved to obtain the power supply optimization strategy for the target power system, including: obtaining the first output upper limit value and the first output lower limit value of the thermal power generating unit and the second output upper limit value and the second output lower limit value of the distributed power generation device, and determining the first output constraint condition according to the first output upper limit value, the first output lower limit value, the second output upper limit value and the second output lower limit value; obtaining the dispatching load threshold of the demand response device according to the load adjustment curve, and determining the response load constraint condition according to the dispatching load threshold; obtaining the second output constraint condition corresponding to the energy storage device according to the capacity parameter; and solving the power supply optimization function according to the first output constraint condition, the response load constraint condition and the second output constraint condition to obtain the power supply optimization strategy.

[0131] Those skilled in the art will understand that embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) having computer-usable program code embodied therein.

[0132] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce an apparatus that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0133] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in a flow or multiple flows and / or blocks Figure 1 The functions specified in a flow or multiple flows and / or blocks

[0134] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0135] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0136] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the processor can execute instructions. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), flash memory, or a combination of non-volatile memories in different types. The memory can also include a compact disk read only memory (CD-ROM), digital versatile disk (DVD), Blu-ray, or another non-transitory computer readable medium, which is non-volatile and non-transitory in nature, but volatile in that it can lose its content if the power to the computer is turned off or if the computer crashes. The memory is an example of a computer readable medium.

[0137] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD), or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media such as modulated data signals and carriers.

[0138] It should also be noted that the terms "comprising," "including," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements recited, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0139] ​​Those skilled in the art will appreciate that embodiments of the present application can be devised for a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer-readable program code thereon for use by or in connection with an instruction execution system. For the purposes of this description, a computer-usable or computer readable storage medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The medium can be electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. The computer-usable or computer readable program code can be downloaded from an Internet website, server, or other remote source via a network or a data stream communication path. From the Internet website, server, or other remote source, the code can be downloaded into the instruction execution system, apparatus, or device where execution of the same can take place. The present application is directed to any number and type of computer-usable storage media, apparatuses, and devices self-evidently known to one of ordinary skill in the art.

[0140] The foregoing is merely illustrative of the embodiments of this application, and is not intended to limit the application. Numerous variations and modifications can be made to the embodiments of the present application without departing from the spirit and scope of the application. Any equivalent modifications, variations, and improvements that are within the spirit and scope of the present application are intended to be included within the scope of

Claims

1. A method of optimization of an electric power system, characterized in that, The method comprises the following steps: obtaining a first output prediction curve of a thermal power generator set in a target power system and a second output prediction curve of a distributed power generation device in the target power system; predicting a target power consumption load prediction curve according to historical power consumption load data of a user side; calculating the target power consumption load prediction curve according to the first output prediction curve, the second output prediction curve and the target power consumption load prediction curve to obtain a power consumption gap curve; optimizing power supply of the target power system according to the power consumption gap curve; wherein the power supply optimization of the target power system according to the power consumption gap curve comprises: constructing a power supply optimization function according to the power consumption gap curve; solving the power supply optimization function to obtain a power supply optimization strategy for the target power system; optimizing power supply of the target power system according to the power supply optimization strategy; wherein the construction of the power supply optimization function according to the power consumption gap curve comprises: obtaining a demand response device of the user side, and determining a load adjustment curve of the demand response device according to a type of the demand response device, wherein the demand response device is a device capable of adjusting power consumption load in response to a demand of the target power system; the type of the demand response device comprises an incentive type load response device and a price type demand response device; the load adjustment curve of the price type demand response device is determined according to an initial price type demand response load value, a price type demand response load value after participating in load response, a price type demand response adjustable load value at t time, a load response rate of the price type demand response at t time, a minimum incentive price and a maximum incentive price, a price incentive coefficient of the price type demand response, and a power index; the load adjustment curve of the incentive type load response device is determined according to an initial incentive type load response load value, an incentive type load response load value after participating in load response, an incentive type load response adjustable load value at t time, a proportion of load transfer out at t time, a proportion of load transfer in at t time, and a proportion of load cut-off at t time.

2. The method of claim 1, wherein, The method comprises the following steps: obtaining a first output prediction curve of a thermal power generator set in a target power system and a second output prediction curve of a distributed power generation device in the target power system; predicting a target power consumption load prediction curve according to historical power consumption load data of a user side; calculating the target power consumption load prediction curve according to the first output prediction curve, the second output prediction curve and the target power consumption load prediction curve to obtain a power consumption gap curve; 3. The method of claim 1, wherein, optimizing power supply of the target power system according to the power consumption gap curve; wherein the power supply optimization of the target power system according to the power consumption gap curve comprises: constructing a power supply optimization function according to the power consumption gap curve; solving the power supply optimization function to obtain a power supply optimization strategy for the target power system; optimizing power supply of the target power system according to the power supply optimization strategy; wherein the construction of the power supply optimization function according to the power consumption gap curve comprises: obtaining a demand response device of the user side, and determining a load adjustment curve of the demand response device according to a type of the demand response device, wherein the demand response device is a device capable of adjusting power consumption load in response to a demand of the target power system; the type of the demand response device comprises an incentive type load response device and a price type demand response device; the load adjustment curve of the price type demand response device is determined according to an initial price type demand response load value, a price type demand response load value after participating in load response, a price type demand response adjustable load value at t time, a load response rate of the price type demand response at t time, a minimum incentive price and a maximum incentive price, a price incentive coefficient of the price type demand response, and a power index; the load adjustment curve of the incentive type load response device is determined according to an initial incentive type load response load value, an incentive type load response load value after participating in load response, an incentive type load response adjustable load value at t time, a proportion of load transfer out at t time, a proportion of load transfer in at t time, and a proportion of load cut-off at t time. The method comprises the following steps: obtaining a first output prediction curve of a thermal power generator set in a target power system and a second output prediction curve of a distributed power generation device in the target power system; predicting a target power consumption load prediction curve according to historical power consumption load data of a user side; calculating the target power consumption load prediction curve according to the first output prediction curve, the second output prediction curve and the target power consumption load prediction curve to obtain a power consumption gap curve; optimizing power supply of the target power system according to the power consumption gap curve; wherein the power supply optimization of the target power system according to the power consumption gap curve comprises: constructing a power supply optimization function according to the power consumption gap curve; solving the power supply optimization function to obtain a power supply optimization strategy for the target power system; optimizing power supply of the target power system according to the power supply optimization strategy; wherein the construction of the power supply optimization function according to the power consumption gap curve comprises: obtaining a demand response device of the user side, and determining a load adjustment curve of the demand response device according to a type of the demand response device, wherein the demand response device is a device capable of adjusting power consumption load in response to a demand of the target power system; the type of the demand response device comprises an incentive type load response device and a price type demand response device; the load adjustment curve of the price type demand response device is determined according to an initial price type demand response load value, a price type demand response load value after participating in load response, a price type demand response adjustable load value at t time, a load response rate of the price type demand response at t time, a minimum incentive price and a maximum incentive price, a price incentive coefficient of the price type demand response, and a power index; the load adjustment curve of the incentive type load response device is determined according to an initial incentive type load response load value, an incentive type load response load value after participating in load response, an incentive type load response adjustable load value at t time, a proportion of load transfer out at t time, a proportion of load transfer in at t time, and a proportion of load cut-off at t time. processing the second type of historical electricity load data by a second type of sequence prediction model to obtain a second electricity load prediction curve in the target time period; determining the target electricity load prediction curve according to the first electricity load prediction curve and the second electricity load prediction curve.

4. The method of claim 1, wherein, calculating to obtain the electricity gap curve according to the first output prediction curve, the second output prediction curve and the target electricity load prediction curve, including: obtaining a power purchase plan curve of the target power system to the upper-level power grid; calculating to obtain the electricity gap curve according to the first output prediction curve, the second output prediction curve, the target electricity load prediction curve and the power purchase plan curve.

5. The method of claim 1, wherein, constructing a power supply optimization function according to the electricity gap curve, including: obtaining a capacity parameter of an energy storage device of the target power system, an operation cost of the thermal power generator unit, an operation cost of the distributed power generation device and a power purchase cost of the target power system to the upper-level power grid; constructing the power supply optimization function according to the load adjustment curve, the capacity parameter, the operation cost of the thermal power generator unit, the operation cost of the distributed power generation device, the power purchase cost of the target power system to the upper-level power grid and the electricity gap curve.

6. The method of claim 5, wherein, solving the power supply optimization function to obtain a power supply optimization strategy for the target power system, including: obtaining a first output upper limit value and a first output lower limit value of the thermal power generator unit and a second output upper limit value and a second output lower limit value of the distributed power generation device, and determining a first output constraint condition according to the first output upper limit value, the first output lower limit value, the second output upper limit value and the second output lower limit value; obtaining a dispatch load threshold of the demand response device according to the load adjustment curve, and determining a response load constraint condition according to the dispatch load threshold; obtaining a second output constraint condition corresponding to the energy storage device according to the capacity parameter; solving the power supply optimization function according to the first output constraint condition, the response load constraint condition and the second output constraint condition to obtain the power supply optimization strategy.

7. An optimization device of a power system characterized by comprising: including: an obtaining unit, configured to obtain a first output prediction curve of a thermal power generator unit in a target power system and obtain a second output prediction curve of a distributed power generation device in the target power system; a prediction unit, configured to obtain a target electricity load prediction curve by predicting according to historical electricity load data of a user side; a calculation unit, configured to calculate to obtain an electricity gap curve according to the first output prediction curve, the second output prediction curve and the target electricity load prediction curve; an optimization unit, configured to perform power supply optimization on the target power system according to the electricity gap curve; wherein the optimization unit is further configured to construct a power supply optimization function according to the electricity gap curve, solve the power supply optimization function to obtain a power supply optimization strategy for the target power system, and perform power supply optimization on the target power system according to the power supply optimization strategy. The device is further configured to acquire a demand response device on the user side, and determine a load adjustment curve of the demand response device according to a type of the demand response device, wherein the demand response device is a device capable of adjusting an electricity load in response to a demand of the target power system; the type of the demand response device includes an incentive type load response device and a price type demand response device; the load adjustment curve of the price type demand response device is determined according to a price type demand response load value at an initial time, a price type demand response load value after participating in load response at time t, a price type demand response adjustable load value at time t, a load response rate of the price type demand response at time t, a minimum incentive price and a maximum incentive price, a price type demand response price incentive coefficient, and a power index; and the load adjustment curve of the incentive type load response device is determined according to an incentive type load response load value at an initial time, an incentive type load response load value after participating in load response at time t, an incentive type load response adjustable load value at time t, a proportion of load transfer out at time t, a proportion of load transfer in at time t, and a proportion of load cut-off at time t.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium includes a stored program, wherein the program, when executed, controls the storage medium to perform the optimization method of the power system according to any one of claims 1 to 6.

9. An electronic device, comprising: The device includes one or more processors and a memory, and the memory is configured to store one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the optimization method of the power system according to any one of claims 1 to 6.

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