Methods and related products for electrochemical energy storage planning in comprehensive energy planning

Abstract

The embodiment of the present application provides a method and related products for energy planning based on an electric power system, the method comprising: obtaining data to be processed; calculating and processing the data to be processed through an upper-level planning model to obtain the total power of the battery energy storage of the electric power system and the total capacity of the battery energy storage of the electric power system, wherein the electric power system comprises a distribution network; processing the total power of the battery energy storage of the electric power system and the total capacity of the battery energy storage of the electric power system through a middle-level planning model to obtain the power and capacity of each node of the distribution network; processing the power and capacity of each node of the distribution network through a lower-level planning model to obtain an operating curve of each node of the distribution network, thereby improving the economic benefits of the electric power system.

Description

Technical Field

[0001] The present application relates to the field of energy technology, and in particular to a method and related products for energy planning based on a power system. Background Art

[0002] With the development of science and technology, the energy consumption of modern life is increasing day by day. Among the energy currently used, electric energy is the most convenient to produce, transport, distribute and utilize, and it is also the most widely used energy in modern production and life. Therefore, planning for electrochemical energy storage is an important part of comprehensive energy planning.

[0003] At present, electrochemical energy storage planning is usually estimated by professional technicians based on experience and electricity consumption. However, since the above planning method is easily affected by various factors, it may cause a large deviation between the estimated results and actual needs, resulting in waste of resources and increased costs.

[0004] Therefore, exploring more stable and cost-effective electrochemical energy storage planning methods is an urgent issue that needs to be addressed. Summary of the invention

[0005] The embodiments of the present application disclose a method and related products for energy planning based on an electric power system. The method constructs a three-layer planning model by comprehensively considering the three factors of electrochemical energy storage installation, site selection, and operation, and obtains the optimal planning method for electrochemical energy storage based on the model, which can improve the overall economic benefits and reduce the waste of resources.

[0006] In a first aspect, the present application provides a method for energy planning based on an electric power system, the method comprising: obtaining data to be processed, the data to be processed being used to represent initial data of energy planning; processing the data to be processed through an upper-level planning model to obtain the total power of the battery energy storage of the electric power system and the total capacity of the battery energy storage of the electric power system, wherein the electric power system includes a distribution network; processing the total power of the battery energy storage of the electric power system and the total capacity of the battery energy storage of the electric power system through a middle-level planning model to obtain the power and capacity of each node of the distribution network; processing the power and capacity of each node of the distribution network through a lower-level planning model to obtain an operating curve of each node of the distribution network.

[0007] In the process of energy planning, since the electricity consumption in different regions is different in different time periods, in order to avoid the waste of resources and costs, the power system is planned from three aspects: installation, site selection, and operation. The upper-level planning model is used to calculate the total capacity and total power of the power system under the condition of optimal economic benefits of the distribution network throughout the life cycle, and the installed capacity and power are reasonably configured; the middle-level planning model is used to calculate the energy storage power and capacity of each node under the condition of the lowest load rate of the distribution network; the lower-level model is used to calculate the operating curve under the condition of the highest peak-to-valley difference benefit of energy storage operation. Improve the overall economic benefits and reduce the waste of resources.

[0008] In an optional implementation of the first aspect, the processing of the data to be processed by the upper-level planning model includes: processing the data to be processed according to a first objective function, a first algorithm model and a first constraint condition, wherein the first objective function represents the relationship between total revenue and cost, and the first constraint condition is used to limit the value range of the total power of the battery energy storage of the power system and the total capacity of the battery energy storage of the power system. It can be understood that the various parts of the revenue and cost constituting the total revenue can be represented by any one or more of the total power of the battery energy storage of the power system and the total capacity of the battery energy storage of the power system.

[0009] In energy planning, since the benefits of the power system are affected by many factors, the economic benefits of the power system can be improved by adjusting and analyzing the impact of different factors on the benefits and adjusting the planning scheme.

[0010] In an optional implementation of the first aspect, the processing of the total power of the battery energy storage of the power system and the total capacity of the battery energy storage of the power system through the middle-level planning model includes: processing the total power of the battery energy storage of the power system and the total capacity of the battery energy storage of the power system according to a second objective function, a second algorithm model and a second constraint condition, the second objective function represents the relationship between the comprehensive load rate and the load rate of each line, the second constraint is used to limit the power and capacity of each node in the distribution network, and the second algorithm model is a sequential quadratic programming algorithm.

[0011] In energy planning, since the energy consumption of each node in the power system is different, by analyzing the load rate of each line and adjusting the power and capacity of each node, resources can be saved and economic benefits can be improved.

[0012] In an optional implementation of the first aspect, the processing of the power and capacity of each node of the distribution network through the lower-level planning model includes: processing the power and capacity of each node of the distribution network according to a third objective function, a third algorithm model and a third constraint condition, the third objective function represents the relationship between the power of each node in different time periods and the peak-valley price difference profit, the third constraint condition is used to limit the power of different nodes in the power system in different time periods, and the third algorithm model is a mixed integer linear programming algorithm.

[0013] In energy planning, since energy consumption and electricity prices are different in different time periods, the peak-valley price difference benefits can be maximized by adjusting the operation plan, which is beneficial to reducing peaks and filling valleys and improving economic benefits.

[0014] In an optional implementation of the first aspect, the first objective function is:

[0015] max f=R delay +R sub +R env +R oper -C BESS -C DSR ;

[0016] The R delay represents the benefit of delaying the upgrade of the distribution network, and the R sub represents the income from the government subsidy, the R env represents the environmental benefits brought by reducing the peak output of thermal power units, R oper represents the benefits of the battery energy storage grid-connected operation, C BESS represents the battery energy storage investment and operating cost, C DSR Describe the cost of demand-side response for the user.

[0017] In an optional implementation of the first aspect, the second objective function is:

[0018]

[0019] N represents the total number of power supply lines, and δ i represents the load rate of the i-th line, the aver(δ i ) represents the average load rate of the power system.

[0020] In an optional implementation of the first aspect, the third objective function is:

[0021] RE tu，k =∑T∑N se P tu，k (t)P s，i，k (t)Δt;

[0022] The P tu，k (t) represents the time-of-use electricity price of the kth typical day period t, Δt represents the time interval, and N se represents the number of nodes to be selected in the power system, T is the number of time periods within a day, and P s，i，k (t) is the active power of the power system at node i in the kth typical day period t.

[0023] In a second aspect, the present application provides a device for energy planning based on an electric power system, comprising: an acquisition unit, used to acquire data to be processed, wherein the data to be processed is used to represent initial data of energy planning; a first processing unit, used to process the data to be processed through an upper-level planning model, to obtain the total power of the battery energy storage of the electric power system and the total capacity of the battery energy storage of the electric power system, wherein the electric power system includes a distribution network; a second processing unit, used to process the total power of the battery energy storage of the electric power system and the total capacity of the battery energy storage of the electric power system through a middle-level planning model, to obtain the power and capacity of each node of the distribution network; a third processing unit, used to process the power and capacity of each node of the distribution network through a lower-level planning model, to obtain an operating curve of each node of the distribution network.

[0024] In a third aspect, an embodiment of the present application provides a terminal, comprising: a memory for storing programs; a processor for executing the computer program or instructions stored in the memory, wherein when the computer program or instructions are executed, the method in the first aspect or any possible implementation manner of the first aspect is executed.

[0025] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed, the method in the first aspect or any possible implementation manner of the first aspect is executed.

[0026] In a fifth aspect, an embodiment of the present application provides a computer program comprising instructions, which, when executed on a computer, enables the computer to execute a method according to the first aspect or any possible implementation of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the following is a brief introduction to the drawings required for use in the embodiments of the present application or the background technology.

[0028] Figure 1 A schematic diagram of a method for energy planning based on a power system provided in an embodiment of the present application;

[0029] Figure 2 A flow chart of another method for energy planning based on a power system provided in an embodiment of the present application;

[0030] Figure 3 A schematic diagram of a model for energy planning based on a power system provided in an embodiment of the present application;

[0031] Figure 4 A schematic diagram of the structure of a device for energy planning based on a power system provided in an embodiment of the present application;

[0032] Figure 5 A schematic diagram of the structure of another device for energy planning based on a power system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described below in conjunction with the accompanying drawings.

[0034] The terms "first" and "second" in the specification, claims and drawings of this application are only used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0035] The "embodiment" mentioned in this article means that the specific features, structures or characteristics described in conjunction with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It can be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0036] In the present application, "at least one (item)" means one or more, "more than one" means two or more, "at least two (items)" means two or three and more than three, and "and / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c".

[0037] The embodiments of the present invention provide energy scheduling methods, devices, equipment and storage media. In order to more clearly describe the scheme of the present invention, some knowledge related to the energy scheduling methods, devices, equipment and storage media provided by the embodiments of the present application is first introduced below.

[0038] Distribution network: Distribution network refers to the power grid used to receive electrical energy and distribute it to users through distribution facilities. It consists of overhead lines, cables, poles, distribution transformers, disconnectors, reactive power compensators and some ancillary facilities.

[0039] Peak shaving of thermal power units: Peak shaving of thermal power units refers to the use of thermal power generating units to regulate peak electricity consumption. Thermal power generating units mainly use coal, oil or combustible gas as fuel to heat water, turning the water into steam with a certain temperature and pressure to drive the turbine to generate electricity.

[0040] Demand-side response: Demand-side response refers to users actively adjusting their electricity demand based on real-time electricity prices and related incentives.

[0041] Time-of-use electricity price: Time-of-use electricity price means dividing the 24 hours of a day into several time periods based on the system operation conditions, and charging electricity fees for each time period based on the average marginal cost of system operation.

[0042] Objective function and constraints: Linear programming problems consist of a linear objective function and linear constraints (including non-negative variables). The set of all solutions that satisfy the constraints is called the feasible solution region. The solution that satisfies the constraints and makes the objective function reach the extreme value is called the optimal solution.

[0043] See also Figure 1 , Figure 1 Schematic diagram of a method for energy planning based on a power system provided in an embodiment of the present application. Figure 1As shown, professionals plan the power system based on experience and different situations. Considering the benefits of the overall installed capacity, plan 1 with the highest overall benefits is selected for planning. Considering the benefits of the site selection of each node, plan 2 with the highest overall benefits is selected for planning. Considering the benefits of different nodes and different time periods, plan 3 with the highest overall benefits is selected for planning.

[0044] In view of the problems existing in the above methods, the present application embodiment provides a method for energy planning based on a power system. Figure 2 As shown, the method may include the following steps:

[0045] 201. Obtain data to be processed.

[0046] Specifically, the data to be processed is used to represent the initial data of energy planning, and the initial data of energy planning includes but is not limited to any one or more of distribution network data, parameter matrix of candidate lines, distributed photovoltaic planning results, and load forecast level. The distribution network data includes but is not limited to one or more of original load parameters, newly added load parameters, generator parameters, original line parameters, and newly added candidate line parameters.

[0047] It can be understood that the terminal obtains the data to be processed. The above-mentioned terminal can be a mobile phone, a computer with data sending and receiving functions (such as a laptop computer, a PDA, etc.), a mobile Internet device (mobile internet device, MID), a terminal in industrial control, a wearable device (such as a smart watch, a smart bracelet, etc.), or other terminal devices with storage and computing capabilities, which are not limited in the embodiments of the present application.

[0048] 202. Process the above-mentioned data to be processed through the upper-level planning model to obtain the total power of the battery energy storage of the power system and the total capacity of the battery energy storage of the above-mentioned power system.

[0049] It is understandable that the above-mentioned data to be processed is input into the above-mentioned upper-level planning model, and the calculation is performed with the goal of optimizing the economic benefits of energy storage and distribution network in the whole life cycle to obtain the total power of the battery energy storage of the power system and the total capacity of the battery energy storage of the above-mentioned power system. The above-mentioned upper-level planning model can be optimized using a genetic algorithm. The above-mentioned data to be processed include but are not limited to any one or more of load forecast data, photovoltaic planning data, time-of-use electricity price data, power grid data, and energy-saving transformation power.

[0050] In some embodiments, the data to be processed is processed according to a first objective function, a first algorithm model and a first constraint condition, wherein the first objective function represents the relationship between total revenue and cost, and the first constraint condition is used to limit the total power of the battery energy storage of the power system and the total capacity of the battery energy storage of the power system.

[0051] Exemplarily, the above-mentioned first constraint condition includes, but is not limited to, one or more of a constraint condition for limiting the total power of battery energy storage, a constraint condition for limiting the total capacity of battery energy storage, a constraint condition for limiting the demand side response power, and a constraint condition for limiting the demand side response power, and the embodiment of the present application is not limited to this.

[0052] It can be understood that, according to the above-mentioned first constraint condition and the above-mentioned first objective function, the total power of the battery energy storage of the above-mentioned power system and the total capacity of the battery energy storage of the power system can be output.

[0053] In some embodiments, the first objective function may satisfy the following conditions:

[0054] max f=R delay +R sub +R env +R oper -C BESS -C DSR ;

[0055] The above R delay represents the benefit of delaying the upgrade of the distribution network, and the above R sub represents the income from the above government subsidies, the above R env represents the environmental benefits brought by reducing the peak output of thermal power units, R oper represents the benefits of the above-mentioned battery energy storage grid-connected operation, C BESS represents the above battery energy storage investment and operating cost, C DSR It expresses the cost of demand-side response for the above users, and the above f represents the economic benefits of the entire life cycle including energy storage and distribution network.

[0056] Specifically, the above R delay The benefit model of delaying grid upgrade can be established to calculate the benefit. The above delaying grid upgrade model can be expressed as: C inv represents the upgrade cost of the unit capacity of the power grid, λ represents the peak load regulation rate of energy storage, and τ represents the load growth rate. sub The calculation can be done by establishing a government subsidy benefit model. The above government subsidy benefit model can be expressed as R sub =ΔP BESS *S unit ; The above S unitTo configure the unit battery energy storage, ΔP BESS Unit capacity financial subsidies given for local peak load ancillary service policies. env The environmental benefits brought by reducing the peak output of thermal power units can be calculated by establishing a model. The environmental benefits brought by reducing the peak output of thermal power units can be expressed as R env = k*b*ΔE PEAK ; the above ΔE PEAK represents the total peak-shaving electricity of battery energy storage throughout the year, b represents the unit coal consumption of thermal power units during peak-shaving output, and k represents the environmental cost caused by unit coal consumption.

[0057] Exemplarily, the above constraint conditions for limiting the total power of battery energy storage may satisfy the following conditions:

[0058] 0≤ΔP BESS ≤P max ;

[0059] The above ΔP BESS is the total power of battery energy storage, the above P max is the maximum power of the battery;

[0060] The above constraints for limiting the total capacity of battery energy storage can meet the following conditions:

[0061] 0≤ΔE BESS ≤E max ;

[0062] The above ΔE BESS is the total capacity of battery energy storage, the above E max is the maximum capacity of the battery;

[0063] The above constraints for limiting the demand-side response power can meet the following conditions:

[0064] 0≤ΔP DSR ≤P max ;

[0065] The above ΔP DSR is the power of demand-side response;

[0066] The above constraints for limiting the amount of demand-side response power can meet the following conditions:

[0067] 0≤ΔE DSR ≤E max ;

[0068] The above ΔE DSR is the capacity of demand-side response.

[0069] Understandably, due to the rapid development of renewable energy power generation in my country, the structure of my country's power market has also undergone significant changes. New energy sources such as wind power and solar power generation will gradually become the main energy source. However, due to the intermittent and random nature of renewable energy, the power system often requires more backup resources and frequency regulation resources from generators, and the efficiency of power system operation is low. Using thermal power units with regulation capabilities to peak load and provide power guarantee for the power system is one of the solutions. However, using thermal power units for peak load regulation will increase the coal consumption and environmental costs of the power system. Therefore, through reasonable energy planning and reducing the peak load regulation of thermal power units, economic benefits can be improved.

[0070] When the load of a certain line exceeds the range, the power grid needs to be upgraded, but upgrading the power grid consumes a lot of costs. By rationally planning the power system and delaying the upgrade of the power grid, the economic benefits of the power system can be improved and the cost consumption can be reduced.

[0071] The large-scale development of new energy requires a supporting energy storage system. Battery energy storage grid connection connects battery energy storage to the power grid and transmits electricity to users through the power grid. It can effectively improve the stability of power generation, reduce the peak load of the power grid, and increase economic benefits.

[0072] Demand-side response promotes market stability and grid reliability by adjusting price signals and adjusting load or electricity consumption patterns.

[0073] 203. The total power of the battery energy storage of the above power system and the total capacity of the battery energy storage of the above power system are processed through the middle-level planning model to obtain the power and capacity of each node of the distribution network.

[0074] It can be understood that the total power of the battery energy storage of the above power system and the total capacity of the battery energy storage of the above power system are input into the middle-level planning model, and the power and capacity of each node of the above distribution network are calculated with the lowest system comprehensive load rate as the goal. Each node of the above distribution network is a combination of several physical points of equal potential in the distribution network, which may include but is not limited to any one or more of the switch station and the distribution room. The upper-level planning model can be optimized using the sequential quadratic programming method.

[0075] In some embodiments, the total power of the battery energy storage of the above-mentioned power system and the total capacity of the battery energy storage of the above-mentioned power system are processed according to the second objective function, the second algorithm model and the second constraint condition, the above-mentioned second objective function represents the relationship between the comprehensive load rate and the load rate of each line, the above-mentioned second constraint condition is used to limit the power and capacity of each node of the distribution network, and the above-mentioned second algorithm model is a sequential quadratic programming algorithm.

[0076] Exemplarily, the second constraint condition includes but is not limited to a power flow equation and a constraint condition for limiting branch power, which is not limited in the embodiments of the present application. The power flow equation is used to calculate the power distribution of the power system in each branch, the power loss of each branch, the voltage of each node, and the voltage loss of each branch.

[0077] It can be understood that, according to the above second constraint condition and the above second objective function, the power and capacity of each node of the above distribution network can be output.

[0078] In some embodiments, the second objective function may satisfy the following conditions:

[0079]

[0080] The above N represents the total number of power supply lines, and the above δ i represents the load rate of the i-th line. The above aver(δ i ) represents the average load factor of the above power system, and the above f1 represents the comprehensive load factor of the above system.

[0081] It can be understood that the load factor is calculated by the annual maximum load and the line rated power. By calculating the load factor of different lines, the capacity and power of each node in the power system can be calculated.

[0082] 204. Process the power and capacity of each node of the distribution network through the lower-level planning model to obtain an operation curve of each node of the distribution network.

[0083] It can be understood that the power and capacity of each node in the above distribution network are input into the above lower-level planning model, and the peak-to-valley price difference benefit is calculated with the goal of maximizing the battery energy storage operation process to obtain the operation curve of each node in the above distribution network, and the network loss benefit reduced by battery energy storage is calculated based on this, and the sum of the two is taken as the total benefit of battery energy storage operation R oper , and returns to the upper optimization model for iteration. The above lower planning model can be optimized using a mixed integer linear programming algorithm. The above operation curve is a curve representing the relationship between the operation time period and power.

[0084] The power and capacity of each node of the above-mentioned distribution network are processed according to the third objective function, the third algorithm model and the third constraint condition. The above-mentioned third objective function represents the relationship between the power of each node in different time periods and the peak-valley price difference profit. The above-mentioned third constraint condition is used to limit the power of different nodes in the power system in different time periods. The above-mentioned third algorithm model is a mixed integer linear programming algorithm.

[0085] Exemplarily, the third constraint condition includes but is not limited to a constraint condition for limiting node voltage, a constraint condition for limiting branch power, a constraint condition for peak load regulation and a power flow equation, which is not limited in this solution.

[0086] It can be understood that according to the third constraint condition and the third objective function, the operation curve of each node of the distribution network and the annual operation income of the battery energy storage can be output.

[0087] In some embodiments, the third objective function may satisfy the following conditions:

[0088] RE tu，k =∑T∑N se P tu，k (t)P s，i，k (t)Δt;

[0089] The above P tu，k (t) represents the time-of-use electricity price of the kth typical day period t, the above Δt represents the time interval, and the above N se represents the number of nodes to be selected in the above power system, the above T is the number of time periods within a day, and the above P s，i，k (t) is the active power of the power system at node i in the kth typical day period t, and the above RE tu，k Represents the peak-valley price difference profit of battery energy storage operation.

[0090] The embodiment of the present application calculates the total power and total capacity of the power system configuration when the upper model has the highest profit, inputs the calculated total capacity and total power into the lower model, calculates the power and capacity of each node when the distribution network load rate is the lowest, and then inputs the capacity and power of each node into the lower model to calculate the operation plan of each node in different time periods when the peak-valley price difference profit is the largest. By rationally planning the power system, costs can be reduced, profits can be increased, and resource waste can be reduced.

[0091] The present application embodiment also provides an energy planning model, such as Figure 3 As shown, the above planning model includes an upper model, a middle model and a lower model.

[0092] The upper model includes: the first objective function, the first algorithm model and the first constraint condition. The first objective function represents the relationship between total revenue and cost. The total power and capacity of energy storage are calculated with the goal of optimizing the economic benefits of the energy storage distribution network throughout the life cycle, and the total power and capacity of energy storage are transmitted to the middle model. The first constraint condition includes the power and capacity limit of electrochemical energy storage installed capacity and the maximum power and capacity limit of demand-side response. The first algorithm model is a genetic algorithm.

[0093] It can be understood that the above upper model can be used to implement Figure 2 The function of step 202 is not described in detail here.

[0094] The above-mentioned middle-level model includes: a second objective function, a second algorithm model and a second constraint condition. The above-mentioned second objective function represents the relationship between the comprehensive load rate and the load rate of each line. The energy storage power and capacity of each node of the distribution network are calculated with the lowest load rate of the distribution network as the goal, and the energy storage power and capacity of each node of the distribution network are transmitted to the lower-level model. The above-mentioned second constraint condition is used to limit the power and capacity of each node of the distribution network, including flow constraints and branch power constraints. The above-mentioned second algorithm model is a sequential quadratic programming algorithm.

[0095] It can be understood that the above upper model can be used to implement Figure 2 The function of step 203 is not described in detail here.

[0096] The above-mentioned lower-level model includes: a third objective function, a third algorithm model and a third constraint condition. The above-mentioned third objective function represents the relationship between the power of each node in different time periods and the peak-valley price difference profit. The operation curve of the power system is calculated with the highest peak-valley price difference profit of electrochemical energy storage operation as the goal; the above-mentioned third constraint condition includes constraints for limiting node voltage, constraints for limiting branch power, constraints for peak shaving and valley filling and power flow equations. The above-mentioned third algorithm model is a mixed integer linear programming algorithm.

[0097] It can be understood that the above upper model can be used to implement Figure 2 The function of step 204 is not described in detail here.

[0098] Figure 4 A schematic diagram of a structure of a device for energy planning based on a power system provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, the device comprises:

[0099] An acquisition unit, used for acquiring data to be processed, wherein the data to be processed is used for representing initial data of energy planning;

[0100] A first processing unit is used to process the data to be processed through an upper-level planning model to obtain a total power of battery energy storage of the power system and a total capacity of battery energy storage of the power system, wherein the power system includes a distribution network;

[0101] A second processing unit is used to process the total power of the battery energy storage of the above-mentioned power system and the total capacity of the battery energy storage of the above-mentioned power system through a middle-level planning model to obtain the power and capacity of each node of the above-mentioned distribution network;

[0102] The third processing unit is used to process the power and capacity of each node of the distribution network through the lower-level planning model to obtain the operation curve of each node of the distribution network.

[0103] In an optional implementation, the first processing unit is specifically used to process the data to be processed according to the first objective function, the first algorithm model and the first constraint condition, wherein the first objective function represents the relationship between total revenue and cost, and the first constraint condition is used to limit the total power of the battery energy storage of the power system and the total capacity of the battery energy storage of the power system. The first constraint condition includes but is not limited to a constraint condition for limiting the total power of the battery energy storage, a constraint condition for limiting the total capacity of the battery energy storage, a constraint condition for limiting the demand side response power and a constraint condition for limiting the demand side response power, and the embodiment of the present application does not impose any restrictions on this. The first algorithm model is a genetic algorithm.

[0104] In an optional implementation, the second processing unit is specifically used to process the total power of the battery energy storage of the power system and the total capacity of the battery energy storage of the power system according to the second objective function, the second algorithm model and the second constraint condition. The second objective function represents the relationship between the comprehensive load rate and the load rate of each line. The second constraint condition is used to limit the power and capacity of each node of the distribution network. The second algorithm model is a sequential quadratic programming algorithm. In an optional implementation,

[0105] In an optional implementation, the third processing unit is specifically used to process the power and capacity of each node of the distribution network according to a third objective function, a third algorithm model and a third constraint condition. The third objective function represents the relationship between the power of each node in different time periods and the peak-valley price difference profit. The third constraint condition includes a constraint condition for limiting the node voltage, a constraint condition for limiting the branch power, a constraint condition and a power flow equation for peak shaving and valley filling. The third algorithm model is a mixed integer linear programming algorithm.

[0106] Understandably, for Figure 4 The product form of the device for energy planning based on the power system shown is not limited in the embodiments of the present application. For example, the device may be a device having a processor and a memory. For another example, the device may be a chip having a memory. For another example, the device may also be a chip system having a chip, etc.

[0107] See also Figure 5 , Figure 5 is a schematic diagram of another device for energy planning based on a power system provided in an embodiment of the present application, which is used to implement the above Figure 2 The device 50 for energy planning based on the power system may be a terminal device or a device for a terminal device.

[0108] The device 50 for energy planning based on the power system includes at least one processor 501, which is used to implement the functions of the terminal in the method provided in the embodiment of the present application. The device 50 may also include a communication interface 503. In the embodiment of the present application, the processor 501 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. In the embodiment of the present application, the communication interface 503 may be a transceiver, circuit, bus, module or other type of communication interface for communicating with other devices through a transmission medium. For example, the communication interface 503 is used for the device in the device 50 to communicate with other devices. The processor 501 uses the communication interface 503 to send and receive data, and is used to implement the above method embodiment. Figures 3 to 5 The above method.

[0109] The energy planning device 50 may also include at least one memory 502 for storing program instructions and / or data. The memory 502 is coupled to the processor 501. The coupling in the embodiment of the present application is an indirect coupling or communication connection between devices, units or modules, which may be electrical, mechanical or other forms, and is used for information exchange between devices, units or modules. The processor 501 may operate in conjunction with the memory 502. The processor 501 may execute program instructions stored in the memory 502. At least one of the above-mentioned at least one memory may be included in the processor.

[0110] When the energy planning device 50 is turned on, the processor 501 can read the software program in the memory 502, interpret and execute the instructions of the software program, and process the data of the software program.

[0111] The specific connection medium between the communication interface 503, the processor 501 and the memory 502 is not limited in the embodiment of the present application. Figure 5 In the figure, the memory 502, the processor 501 and the communication interface 503 are connected via a bus 504. The connection between the components is only for illustrative purposes and is not intended to be limiting. The above bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0112] It should be noted that the device can execute the relevant steps of the terminal in the aforementioned method embodiment. For details, please refer to the implementation methods provided in the above steps, which will not be repeated here.

[0113] For each device or product applied to or integrated in the energy planning device, each module contained therein can be implemented by hardware such as circuits, and different modules can be located in the same component (for example, a chip, circuit module, etc.) or in different components in the terminal, or at least some of the modules can be implemented by a software program that runs on a processor integrated inside the terminal, and the remaining (if any) modules can be implemented by hardware such as circuits.

[0114] The above-mentioned memory may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM) or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static ram (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0115] In an embodiment of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the method for downloading energy planning provided in the aforementioned embodiment is implemented.

[0116] An embodiment of the present application provides a computer program product including instructions, which, when executed on a computer, enables the computer to execute the data processing method provided by the aforementioned embodiment.

[0117] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for energy planning based on a power system, characterized in that: include: Acquire data to be processed, where the data to be processed is used to represent initial data of energy planning; The data to be processed is processed according to the first objective function, the first algorithm model and the first constraint condition to obtain the total power of the battery energy storage of the power system and the total capacity of the battery energy storage of the power system, wherein the power system includes a distribution network; the first objective function represents the relationship between the total benefit and the cost, the first constraint condition is used to limit the value range of the total power of the battery energy storage of the power system and the total capacity of the battery energy storage of the power system, and the first algorithm model is a genetic algorithm; the first objective function satisfies the following conditions: maxf=R delay +R sub +R env +R oper -C BESS -C DSR ; The R delay represents the benefit of delaying the upgrade of the distribution network, the R sub Represents the subsidy received, the R env represents the environmental benefits brought by reducing the peak output of thermal power units, R oper represents the benefits of the battery energy storage grid-connected operation, C BESS represents the battery energy storage investment and operating cost, C DSR represents the cost of user demand-side response, and f represents the economic benefits of energy storage and distribution network over the entire life cycle; The total power of the battery energy storage of the power system and the total capacity of the battery energy storage of the power system are processed according to the second objective function, the second algorithm model and the second constraint condition to obtain the power and capacity of each node of the distribution network; the second objective function represents the relationship between the comprehensive load rate and the load rate of each line, the second constraint condition is used to limit the power and capacity of each node of the distribution network, and the second algorithm model is a sequential quadratic programming algorithm; the second objective function satisfies the following conditions: N represents the total number of power supply lines, and δ i represents the load rate of the i-th line, the aver(δ i ) represents the average load rate of the power system, and f1 represents the comprehensive load rate of the power system; The power and capacity of each node of the distribution network are processed according to the third objective function, the third algorithm model and the third constraint condition to obtain the operation curve of each node of the distribution network; the third objective function represents the relationship between the power of each node in different time periods and the peak-valley price difference income, the third constraint condition is used to limit the power of different nodes in the power system in different time periods, and the third algorithm model is a mixed integer linear programming algorithm; the third objective function satisfies the following conditions: RE tu，k =∑T∑N se P tu，k (t)P s,i,k (t)Δt; The P tu，k (t) represents the time-of-use electricity price of the kth typical day period t, Δt represents the time interval, and N se represents the number of nodes to be selected in the power system, T is the number of time periods within a day, and P s,i,k (t) is the active power of the power system at node i in the kth typical day period t, the RE tu，k Represents the peak-valley price difference profit of battery energy storage operation.

2. A device for energy planning based on a power system, characterized in that: include: An acquisition unit, used for acquiring data to be processed, wherein the data to be processed is used for representing initial data of energy planning; A first processing unit is used to process the data to be processed according to a first objective function, a first algorithm model and a first constraint condition to obtain a total power of battery energy storage of a power system and a total capacity of battery energy storage of the power system, wherein the power system includes a distribution network; the first objective function represents a relationship between total revenue and cost, the first constraint condition is used to limit a value range of the total power of battery energy storage of a power system and a total capacity of battery energy storage of the power system, and the first algorithm model is a genetic algorithm; the first objective function satisfies the following conditions: maxf=R delay +R sub +R env +R oper -C BESS -C DSR ; The R delay represents the benefit of delaying the upgrade of the distribution network, the R sub Represents the subsidy received, the R env represents the environmental benefits brought by reducing the peak output of thermal power units, R oper represents the benefits of the battery energy storage grid-connected operation, C BESS represents the battery energy storage investment and operating cost, C DSR represents the cost of user demand-side response, and f represents the economic benefits of energy storage and distribution network over the entire life cycle; The second processing unit is used to process the total power of the battery energy storage of the power system and the total capacity of the battery energy storage of the power system according to the second objective function, the second algorithm model and the second constraint condition to obtain the power and capacity of each node of the distribution network; the second objective function represents the relationship between the comprehensive load rate and the load rate of each line, the second constraint condition is used to limit the power and capacity of each node of the distribution network, and the second algorithm model is a sequential quadratic programming algorithm; the second objective function satisfies the following conditions: N represents the total number of power supply lines, and δ i represents the load rate of the i-th line, the aver(δ i ) represents the average load rate of the power system, and f1 represents the comprehensive load rate of the power system; The third processing unit is used to process the power and capacity of each node of the distribution network according to the third objective function, the third algorithm model and the third constraint condition to obtain the operation curve of each node of the distribution network; the third objective function represents the relationship between the power of each node in different time periods and the peak-valley price difference income, the third constraint condition is used to limit the power of different nodes in the power system in different time periods, and the third algorithm model is a mixed integer linear programming algorithm; the third objective function satisfies the following conditions: RE tu，k =∑T∑N se P tu，k (t)P s,i,k (t)Δt; The P tu，k (t) represents the time-of-use electricity price of the kth typical day period t, Δt represents the time interval, and N se represents the number of nodes to be selected in the power system, T is the number of time periods within a day, and P s,i,k (t) is the active power of the power system at node i in the kth typical day period t, the RE tu，k Represents the peak-valley price difference profit of battery energy storage operation.

3. A terminal, characterized in that: include: Memory, used to store programs; A processor is used to execute the program stored in the memory, and when the program is executed, the processor performs the method according to claim 1.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the method according to claim 1.

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