Power distribution network energy storage planning method for high-proportion photovoltaic access and user demand response

Abstract

The invention discloses a power distribution network energy storage planning method for high-proportion photovoltaic access and user demand response, and the method comprises the steps: firstly building an energy storage configuration planning model including capacity cost and power cost, analyzing the number of energy storage distribution users, and forming an independent energy storage configuration model and a shared energy storage configuration model; secondly, based on all scenes of the photovoltaic output, establishing an uncertainty set of the photovoltaic output to describe the uncertainty of the photovoltaic output; then, analyzing the electricity / cold / heat load characteristics of the user participating in the demand response, and establishing a demand response model of the electricity / cold / heat load; and finally, considering system uncertainty and regulation and control means of energy storage and demand response in operation, establishing an energy storage configuration optimization model considering high-proportion photovoltaic access and user demand response, and solving the energy storage configuration optimization model. According to the method, the photovoltaic absorption amount is effectively increased, the energy storage operation cost is reduced, the flexibility and reliability of a power grid system are enhanced, multi-side energy sources are complementary and fused, and the utilization efficiency of the energy sources is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network energy storage planning, and in particular to a distribution network energy storage planning method considering high-proportion photovoltaic access and user demand response. Background Art

[0002] The high proportion of distributed photovoltaic power grid connection has become a general trend. Due to the intermittent and random characteristics of photovoltaic power generation, the pressure on conventional power supply regulation has increased, and it has also brought about power quality problems to a certain extent. The energy storage system has the ability to quickly adjust power and supply and storage, which helps to improve the local consumption capacity of photovoltaic power, and can also improve the support capacity and power quality of the power grid. The energy storage system has the advantages of fast charging and discharging speed, high efficiency, low requirements for geographical conditions, and long service life, so it has been widely used. At present, photovoltaic access and energy storage configuration have brought many risks and challenges to the planning of distribution networks and the safe operation of power grids. Distributed photovoltaic systems are consumed nearby, with small scale, large number, and wide spatial distribution. The energy storage capacity required by ordinary users is small, and they cannot enjoy the cost advantages brought by scale effects. The dual uncertainty of distributed photovoltaics and loads makes it difficult to accurately predict the actual operation of the distribution network. The uncertainty of photovoltaic output and load demand often makes it difficult to achieve the expected optimization configuration of the energy storage system, and in many cases, the utilization rate of energy storage is low. Summary of the invention

[0003] The present invention proposes a distribution network energy storage planning method that takes into account a high proportion of photovoltaic access and user demand response, which can at least solve one of the technical problems in the background technology.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A distribution network energy storage planning method with high-proportion photovoltaic access and user demand response, characterized in that the specific steps are as follows:

[0006] S100, establishing an energy storage configuration planning model including capacity cost and power cost, analyzing the number of energy storage allocation users, and further optimizing it into an independent energy storage configuration model and a shared energy storage configuration model;

[0007] S200, analyzing and counting historical data of photovoltaic output, determining a continuous time period that can cover all continuous scenarios of photovoltaic output, and establishing an uncertainty set of photovoltaic output based on all photovoltaic output scenarios to describe the uncertainty of photovoltaic output;

[0008] S300, analyzing the electricity / cooling / heating load characteristics of users participating in demand response, forming a three-model collaborative analysis method based on a curtailable load model, a transferable load model, and an interruptible load model, and realizing a comprehensive analysis of the user's electricity / cooling / heating load;

[0009] S400, based on S100, S200, and S300, taking into account system uncertainty and the means of regulating energy storage and demand response during operation, establishes and solves an energy storage configuration optimization model that considers a high proportion of photovoltaic access and user demand response.

[0010] Furthermore, the method for building the independent energy storage configuration model and the shared energy storage configuration model in S100 of the present invention is as follows:

[0011] S110, establishing an energy storage configuration planning model including capacity cost and power cost;

[0012] Energy storage configuration planning needs to comprehensively consider capacity cost and power cost. The energy storage configuration planning model and constraints are as follows:

[0013] C S,i =θ E E S,i +θ P P S,i (1)

[0014]

[0015] Among them, C S,i is the planning cost of the i-th energy storage, including capacity cost and power cost, θ E and θ P are the unit capacity cost and unit power cost respectively, E S,i and P S,i are the capacity and charging / discharging power of the i-th energy storage, respectively. and are the upper capacity limit and power limit of the i-th energy storage respectively;

[0016] The operation model of the i-th energy storage is:

[0017]

[0018] In the formula, is the storage capacity of the i-th energy storage at time h, μ S is the self-loss rate of energy storage capacity, and are charging power and discharging power respectively, ρ IN and ρ OUT are the efficiency during the charging and discharging process, ρ min and ρ max are the upper and lower limits of the energy storage state of charge, is a 0-1 variable representing the charging state, with a value of 1 for charging and a value of 0 for discharging;

[0019] S120, further optimizing into an independent energy storage configuration model and a shared energy storage configuration model according to the number of energy storage allocation users;

[0020] According to the number of energy storage allocation users, the energy storage configurations allocated to a single user and multiple users are divided into independent energy storage configurations and shared energy storage configurations; the set of independent energy storage configuration users is represented as I S = {I S1 ,LI SI}, I is the number of independent energy storage configuration users; the set of shared energy storage configuration users is represented by C S ={C S1 ,LC SC}, C is the number of shared energy storage; based on the energy storage configuration planning model and its constraints described in S110, it is regarded as an independent energy storage configuration model;

[0021] Based on the energy storage configuration planning model and its constraints, the allocation of energy storage power to all energy storage participating users is further considered. The shared energy storage configuration must meet the following requirements:

[0022]

[0023] In the formula is the charging power of the jth user in the i-th shared energy storage participating user set at the h-th moment, is the discharge power of the jth user in the i-th shared energy storage participating user set at the h-th moment. The sum of the charging / discharging power of all users in the set should be equal to the charging / discharging power of the shared energy storage.

[0024] Furthermore, the uncertainty set determination method of photovoltaic output in S200 of the present invention is as follows:

[0025] S210, conducting statistical analysis on historical data of photovoltaic output;

[0026] S220, selecting a certain time period, and selecting a continuous range covering all photovoltaic output scenarios within the time period,

[0027] S230, generating an uncertainty set under a certain radius constraint;

[0028]

[0029] Where: is the uncertain set of photovoltaic power output; is the actual output of the photovoltaic unit in the T period; and They represent the minimum and maximum output of the photovoltaic unit in the T period respectively; T is the number of periods in a day. This paper takes 1 hour as the unit and decomposes each day into 24 periods; Λ is the robustness factor with a value range of [0, 24], which is used to control the interval range of the uncertainty set. The larger the value, the larger the range covered by the uncertainty set.

[0030] Furthermore, the three-model collaborative analysis method in S300 of the present invention has the following specific steps:

[0031] S310, analyze the characteristics of electricity / cooling / heating loads of users participating in demand response, and form a load reduction model; from the user's perspective, divide a day into N load reduction periods T, and the load reduction model is as follows:

[0032]

[0033] In the formula, ρ LAr,t is the load reduction factor at time t; S t is the user load capacity P at time t LAr,t is the load reduction power at time t; W LAr,T is the amount of load reduction that can be reduced in period T; t LAr and are the start and end time of load shedding, respectively; W LAr,T and They are the lower and upper limits of the load reduction capacity in period T respectively;

[0034] S320, analyzing the characteristics of the electricity / cooling / heating loads of users participating in the demand response, and forming a transferable load model; the transferable load model is as follows:

[0035]

[0036] In the formula, ρ LAs,t is the transferability coefficient of the load at time t; P LAs,t is the load transferable power at time t; t LAs and is the start and end time of load transfer, W LAs,T is the transferable amount of load in period T; and W LAs,T They are the upper and lower limits of the load transferable capacity in period T respectively.

[0037] S330, analyzing the electricity / cooling / heating load characteristics of users participating in demand response, and forming an interruptible load model;

[0038] The interruptible load model is as follows:

[0039]

[0040] Where P LAt,t is the interruptible power of the load at time t; ρ LAt,t is the interruptibility factor of the load at time t; t LAt and is the start and end time of load interruption; W LAt,T is the interruptible amount of load in period T; and W LAt,T The upper and lower limits of the interruption capacity of the interruptible load, and the load interruption time shall not be less than the minimum duration.

[0041] S340: Comprehensively analyze the user's electricity / cooling / heating loads based on the curtailable load model, transferable load model, and interruptible load model.

[0042] Furthermore, the method for solving the energy storage configuration optimization model for user demand response in S400 of the present invention is as follows:

[0043] S410, taking into account system uncertainty and the means of regulating energy storage and demand response during operation, a three-layer optimization model for energy storage configuration is proposed;

[0044] S420, solving an energy storage configuration optimization model considering high proportion of photovoltaic access and user demand response;

[0045] S430, solving the main model and the sub-model.

[0046] Furthermore, the three-layer optimization model of energy storage configuration in S410 of the present invention is specifically as follows:

[0047]

[0048] Where D is the number of days in the planning cycle; τ B and τ L They are the system node set and line set respectively.

[0049]

[0050] In the formula, is the electricity purchase cost of the ith node at the hth moment, is the power injected into the node by the i-th node at the h-th moment, is the purchase price of electricity from the power grid, is the electricity selling price of the power grid. In this paper, the time-of-use electricity price is adopted.

[0051] Furthermore, the method for solving the energy storage configuration optimization model in S420 of the present invention is as follows:

[0052] The three-layer optimization model of energy storage configuration is decomposed into a main model and sub-models, where the main model is expressed as follows:

[0053]

[0054] For a set of system uncertainty scenarios The kth uncertainty scenario The corresponding decision variable is

[0055] The sub-model is an optimization problem in the operation phase. For a given energy storage planning scheme

[0056] i∈I S ∪C S Its optimization model can be expressed as follows:

[0057]

[0058] in,

[0059]

[0060] In the formula, is the amount of electricity purchased by node i at time h, is the electricity sales amount of node i at time h.

[0061] Furthermore, the method for solving the main model and the sub-model in S430 of the present invention is as follows:

[0062] The column and constraint generation algorithm (C&CG) suitable for solving the two-level robust optimization problem is adopted. The specific steps are as follows:

[0063] S431, set optimization target lower limit B L =-∞, upper limit B U =∞, set the uncertain set to empty and the number of iterations to k=1;

[0064] S432, solve the optimization main problem and obtain the optimal value of the objective function O MP And the corresponding optimal solution Using the solved O MP Update B U The value of

[0065] S433, Planning solution obtained based on the solution of the main problem Solve the optimization subproblem and obtain the optimal value of the objective function O SP (including the objective function value in the planning stage) and the corresponding uncertain scenario solution results Use O SP Update B L The value of

[0066] S434, if | B U -B L ∣ / B L <δ, the solution process ends, otherwise proceed to the next step;

[0067] S435, Join Set In the main optimization problem, Corresponding variables Add corresponding constraints, set k=k+1 and go to step S432.

[0068] It can be seen from the above technical solutions that the present invention establishes an energy storage configuration planning model including capacity cost and power cost to form an independent energy storage configuration model and a shared energy storage configuration model. Based on all scenarios of photovoltaic output, an uncertainty set of photovoltaic output is established to describe the uncertainty of photovoltaic output. The characteristics of the electric / cold / heat loads of users participating in demand response are analyzed, and a demand response model for electric / cold / heat loads is established. Taking into account system uncertainty and the means of regulating energy storage and demand response during operation, an energy storage configuration optimization model considering a high proportion of photovoltaic access and user demand response is established and solved. The first layer of the solution model aims to minimize the investment cost of energy storage operation in the energy storage planning stage; the second layer aims to establish the optimal operating cost considering photovoltaic and load uncertainties in the operation stage; the third layer optimizes the energy storage charging and discharging strategy and the demand side response strategy to minimize the system operating cost. This method can effectively increase the photovoltaic absorption capacity, reduce the operating cost of energy storage, enhance the flexibility and reliability of the system, make multi-side energy complementary and integrated, and improve the efficiency of energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is a flow chart of the method of the present invention;

[0070] Figure 2 Flowchart of the solution of the optimization run model. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments.

[0072] like Figure 1 As shown in FIG. 1 , the distribution network energy storage planning method considering high proportion of photovoltaic access and user demand response includes the following steps:

[0073] S100, establishing an energy storage configuration planning model including capacity cost and power cost, analyzing the number of energy storage allocation users, and further optimizing it into an independent energy storage configuration model and a shared energy storage configuration model;

[0074] S200, analyzing and counting historical data of photovoltaic output, determining a continuous time period that can cover all continuous scenarios of photovoltaic output, and establishing an uncertainty set of photovoltaic output based on all photovoltaic output scenarios to describe the uncertainty of photovoltaic output;

[0075] S300, analyzing the electricity / cooling / heating load characteristics of users participating in demand response, forming a three-model collaborative analysis method based on a curtailable load model, a transferable load model, and an interruptible load model, and realizing a comprehensive analysis of the user's electricity / cooling / heating load;

[0076] S400, based on S100, S200, and S300, taking into account system uncertainty and the means of regulating energy storage and demand response during operation, establishes and solves an energy storage configuration optimization model that considers a high proportion of photovoltaic access and user demand response.

[0077] The following are specific instructions:

[0078] S100: Establish an energy storage configuration planning model including capacity cost and power cost, analyze the number of energy storage allocation users, and further optimize it into an independent energy storage configuration model and a shared energy storage configuration model.

[0079] The independent energy storage configuration model and the shared energy storage configuration model are constructed as follows:

[0080] S110, establishing an energy storage configuration planning model including capacity cost and power cost;

[0081] Energy storage configuration planning needs to comprehensively consider capacity cost and power cost. The energy storage configuration planning model and constraints are as follows:

[0082] C S,i =θ E E S,i +θ P P S,i (1)

[0083]

[0084] Among them, C S,i is the planning cost of the i-th energy storage, including capacity cost and power cost, θ E and θ P are the unit capacity cost and unit power cost respectively, E S,i and P S,i are the capacity and charging / discharging power of the i-th energy storage, respectively. and are the capacity upper limit and power upper limit of the i-th energy storage respectively. The operation model of the i-th energy storage is:

[0085]

[0086] In the formula, is the storage capacity of the i-th energy storage at time h, μ S is the self-loss rate of energy storage capacity, and are charging power and discharging power respectively, ρ IN and ρ OUT are the efficiency during the charging and discharging process, ρ min and ρ max are the upper and lower limits of the energy storage state of charge, A 0-1 variable representing the charging state. When the value is 1, it is charging, and when the value is 0, it is discharging.

[0087] S120, further optimizing into an independent energy storage configuration model and a shared energy storage configuration model according to the number of energy storage allocation users;

[0088] According to the number of energy storage allocation users, the energy storage configurations allocated to a single user and multiple users are divided into independent energy storage configurations and shared energy storage configurations. The set of independent energy storage configuration users is represented as I S = {I S1 ,LI SI}, I is the number of independent energy storage configuration users; the set of shared energy storage configuration users is represented by C S ={C S1 ,LC SC}, C is the number of shared energy storage. Based on the energy storage configuration model and its constraints described in S110, it is regarded as an independent energy storage configuration model.

[0089] Based on the energy storage configuration model and its constraints, the allocation of energy storage power by all energy storage participating users is further considered. The shared energy storage configuration must meet the following requirements:

[0090]

[0091] In the formula is the charging power of the jth user in the i-th shared energy storage participating user set at the h-th moment, is the discharge power of the jth user in the i-th shared energy storage participating user set at the h-th moment. The sum of the charging / discharging power of all users in the set should be equal to the charging / discharging power of the shared energy storage.

[0092] S200, analyzing and counting historical data of photovoltaic output, determining a continuous time period that can cover all continuous scenarios of photovoltaic output, and establishing an uncertainty set of photovoltaic output based on all photovoltaic output scenarios to describe the uncertainty of photovoltaic output;

[0093] The uncertainty set determination method of the photovoltaic output is as follows:

[0094] S210, conducting statistical analysis on historical data of photovoltaic output;

[0095] S220, selecting a certain time period, and selecting a continuous range covering all photovoltaic output scenarios within the time period,

[0096] S230, generating an uncertainty set under a certain radius constraint;

[0097]

[0098] Where: is the uncertain set of photovoltaic power output; is the actual output of the photovoltaic unit in the T period; and They represent the minimum and maximum output of the photovoltaic unit in the T period respectively; T is the number of periods in a day. This paper takes 1 hour as the unit and decomposes each day into 24 periods; Λ is the robustness factor with a value range of [0, 24], which is used to control the interval range of the uncertainty set. The larger the value, the larger the range covered by the uncertainty set.

[0099] S300, analyzing the characteristics of the electricity / cooling / heating loads of users participating in demand response, establishing a curtailable load model, a transferable load model, and an interruptible load model, and analyzing the electricity / cooling / heating load demand response of users;

[0100] The specific steps are as follows:

[0101] S310, analyzing the characteristics of the electricity / cooling / heating loads of users participating in demand response, and forming a model based on the load that can be reduced;

[0102] Part of the user's electricity / cooling / heating load can be reduced to a certain extent while meeting basic energy needs. This is called reducible load. Its control cost is low, but the capacity available for dispatch is relatively small and flexible.

[0103] From the user's perspective, a day is divided into N load-reducible periods T. The load-reducible model is as follows:

[0104]

[0105] In the formula, ρ LAr,t is the load reduction factor at time t; St is the user load capacity P at time t LAr,t is the load reduction power at time t; W LAr,T is the amount of load reduction that can be reduced in period T; t LAr and are the start and end time of load shedding, respectively; W LAr,T and They are the lower and upper limits of the load reduction capacity in period T respectively.

[0106] S320, analyzing the electricity / cooling / heating load characteristics of users participating in demand response, and forming a transferable load model;

[0107] Part of the user's electricity / cooling / heating load is moved from the peak load period to the valley period under the condition of meeting the rigid demand for energy. This is called transferable load. Its control cost is slightly higher, but its adjustable capacity is relatively large and its flexibility is slightly weaker. Its function is similar to that of energy storage equipment. The transferable load model is as follows:

[0108]

[0109] In the formula, ρ LAs,t is the transferability coefficient of the load at time t; P LAs,t is the load transferable power at time t; t LAs and is the start and end time of load transfer, W LAs,T is the transferable amount of load in period T; and W LAs,T They are the upper and lower limits of the load transferable capacity in period T respectively.

[0110] S330, analyzing the electricity / cooling / heating load characteristics of users participating in demand response, and forming an interruptible load model;

[0111] Under the condition of meeting the rigid demand of energy users, some of the user's cold / heat / electric load can be interrupted to a certain extent, which is called interruptible load. Compared with the load that can be cut / transferred, the interruptible load subsidy cost is the lowest, the flexibility is strong, and the adjustable capacity is large. The interruptible load model is as follows:

[0112]

[0113] Where P LAt,t is the interruptible power of the load at time t; ρ LAt,t is the interruptibility factor of the load at time t; t LAt and is the start and end time of load interruption; W LAt,Tis the interruptible amount of load in period T; and W LAt,T The upper and lower limits of the interruption capacity of the interruptible load, and the load interruption time shall not be less than the minimum duration.

[0114]

[0115] The above formula is the comfort constraint. and T LAt is the upper and lower limits of the comfortable temperature change, T in is room temperature, T obj The habitable temperature.

[0116] S400, taking into account system uncertainty and the means of regulating energy storage and demand response during operation, establish and solve the energy storage configuration optimization model considering high proportion of photovoltaic access and user demand response;

[0117] like Figure 2 As shown in the figure, the solution method of the energy storage configuration optimization model for user demand response is as follows:

[0118] S410, taking into account system uncertainty and the means of regulating energy storage and demand response during operation, a three-layer optimization model for energy storage configuration is proposed:

[0119]

[0120] Where D is the number of days in the planning cycle; τ B and τ L They are the system node set and line set respectively.

[0121]

[0122] In the formula, is the electricity purchase cost of the ith node at the hth moment, is the power injected into the node by the i-th node at the h-th moment, is the purchase price of electricity from the power grid, is the electricity selling price of the power grid. In this paper, the time-of-use electricity price is adopted.

[0123] S420, solving an energy storage configuration optimization model considering high proportion of photovoltaic access and user demand response;

[0124] The three-layer optimization model of energy storage configuration is decomposed into a main model and sub-models, where the main model is expressed as follows:

[0125]

[0126] For a set of system uncertainty scenarios The kth uncertainty scenario The corresponding decision variable is

[0127] The sub-model is an optimization problem in the operation phase. For a given energy storage planning scheme

[0128] i∈I S ∪C S Its optimization model can be expressed as follows:

[0129]

[0130] in,

[0131]

[0132] In the formula, is the amount of electricity purchased by node i at time h, is the electricity sales amount of node i at time h.

[0133] S430, solving the main model and the sub-model;

[0134] The column and constraint generation algorithm (C&CG) suitable for solving the two-level robust optimization problem is adopted. The specific steps are as follows:

[0135] S431, set optimization target lower limit B L =-∞, upper limit B U =∞, set the uncertain set to empty and the number of iterations to k=1;

[0136] S432, solve the optimization main problem and obtain the optimal value of the objective function O MP And the corresponding optimal solution Using the solved O MP Update B U The value of

[0137] S433, Planning solution obtained based on the solution of the main problem Solve the optimization subproblem and obtain the optimal value of the objective function O SP (including the objective function value in the planning stage) and the corresponding uncertain scenario solution results Use O SP Update B L The value of

[0138] S434, if | B U -B L ∣ / B L <δ, the solution process ends, otherwise proceed to the next step;

[0139] S435, Join Set In the main optimization problem, Corresponding variables Add corresponding constraints, set k=k+1 and go to step S432.

[0140] In summary, the present invention establishes an energy storage configuration planning model including capacity cost and power cost to form an independent energy storage configuration model and a shared energy storage configuration model. Based on all scenarios of photovoltaic output, an uncertainty set of photovoltaic output is established to describe the uncertainty of photovoltaic output. The characteristics of the electric / cold / heat loads of users participating in demand response are analyzed, and a demand response model for electric / cold / heat loads is established. Taking into account system uncertainties and the means of regulating energy storage and demand response during operation, an energy storage configuration optimization model that considers a high proportion of photovoltaic access and user demand response is established and solved. The first layer of the solution model aims to minimize the investment cost of energy storage operation in the energy storage planning stage; the second layer aims to establish the optimal operating cost considering photovoltaic and load uncertainties in the operation stage; the third layer optimizes the energy storage charging and discharging strategy and the demand-side response strategy to minimize the system operating cost.

[0141] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0142] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0143] In another embodiment provided in the present application, a computer program product comprising instructions is also provided. When the computer is executed on a computer, the computer executes any of the distribution network energy storage planning methods considering a high proportion of photovoltaic access and user demand response in the above embodiments.

[0144] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above methods.

[0145] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0146] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0147] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0148] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distribution network energy storage planning method with high proportion of photovoltaic access and user demand response, characterized in that: The specific steps are as follows: S100, establishing an energy storage configuration planning model including capacity cost and power cost, analyzing the number of energy storage allocation users, and further optimizing it into an independent energy storage configuration model and a shared energy storage configuration model; S200, analyzing and counting the historical data of photovoltaic output, determining a continuous time period that can cover all continuous scenarios of photovoltaic output, and establishing an uncertainty set of photovoltaic output based on all the photovoltaic output scenarios to describe the uncertainty of photovoltaic output; S300, analyzing the electricity / cooling / heating load characteristics of users participating in demand response, forming a three-model collaborative analysis method based on a curtailable load model, a transferable load model, and an interruptible load model, and realizing a comprehensive analysis of the user's electricity / cooling / heating load; S400, based on S100, S200, and S300, taking into account system uncertainty and the means of regulating energy storage and demand response during operation, establishes and solves an energy storage configuration optimization model that considers a high proportion of photovoltaic access and user demand response.

2. The distribution network energy storage planning method considering high proportion of photovoltaic access and user demand response according to claim 1 is characterized in that: The method for building the independent energy storage configuration model and the shared energy storage configuration model in S100 is as follows: S110, establishing an energy storage configuration planning model including capacity cost and power cost; Energy storage configuration planning needs to comprehensively consider capacity cost and power cost. The energy storage configuration planning model and constraints are as follows: C S,i =θ E E S,i +θ P P S,i (1) in, is the planning cost of the i-th energy storage, including capacity cost and power cost, θ E and θ P are the unit capacity cost and unit power cost respectively, E S,i and P S,i are the capacity and charging / discharging power of the i-th energy storage, respectively. and are the upper capacity limit and power limit of the i-th energy storage respectively; The operation model of the i-th energy storage is: In the formula, is the storage capacity of the i-th energy storage at time h, μ S is the self-loss rate of energy storage capacity, and are charging power and discharging power respectively, ρ IN and ρ OUT are the efficiency during the charging and discharging process, ρ min and ρ max are the upper and lower limits of the energy storage state of charge, is a 0-1 variable representing the charging state, with a value of 1 for charging and a value of 0 for discharging; S120, further optimizing into an independent energy storage configuration model and a shared energy storage configuration model according to the number of energy storage allocation users; According to the number of energy storage allocation users, the energy storage configurations allocated to a single user and multiple users are divided into independent energy storage configurations and shared energy storage configurations; the set of independent energy storage configuration users is represented as I S = {I S1 ,LI SI }, I is the number of independent energy storage configuration users; the set of shared energy storage configuration users is represented by C S ={C S1 ,LC SC }, C is the number of shared energy storage; based on the energy storage configuration planning model and its constraints described in S110, it is regarded as an independent energy storage configuration model; Based on the energy storage configuration planning model and its constraints, the allocation of energy storage power to all energy storage participating users is further considered. The shared energy storage configuration must meet the following requirements: In the formula is the charging power of the jth user in the i-th shared energy storage participating user set at the h-th moment, is the discharge power of the jth user in the i-th shared energy storage participating user set at the h-th moment. The sum of the charging / discharging power of all users in the set should be equal to the charging / discharging power of the shared energy storage.

3. The distribution network energy storage planning method considering high proportion of photovoltaic access and user demand response according to claim 1 is characterized in that: The method for determining the uncertainty set of photovoltaic output in S200 is as follows: S210, conducting statistical analysis on historical data of photovoltaic output; S220, selecting a certain time period, and selecting a continuous range covering all photovoltaic output scenarios within the time period, S230, generating an uncertainty set under a certain radius constraint; Where: is the uncertain set of photovoltaic power output; is the actual output of the photovoltaic unit in the T period; and They represent the minimum and maximum output of the photovoltaic unit in the T period respectively; T is the number of periods in a day. This paper takes 1 hour as the unit and decomposes each day into 24 periods; Λ is the robustness factor, with a value range of [0, 24], which is used to control the interval range of the uncertainty set. The larger the value, the larger the range covered by the uncertainty set.

4. The distribution network energy storage planning method considering high proportion of photovoltaic access and user demand response according to claim 1 is characterized in that: The specific steps of the three-model collaborative analysis method in S300 are as follows: S310, analyzing the characteristics of the electricity / cooling / heating loads of users participating in demand response, and forming a model based on the load that can be reduced; From the user's perspective, a day is divided into N load-reducible periods T. The load-reducible model is as follows: In the formula, ρ LAr,t is the load reduction factor at time t; S t is the user load capacity P at time t LAr,t is the load reduction power at time t; W LAr,T is the amount of load reduction that can be reduced in period T; t LAr and are the start and end time of load shedding, respectively; W LAr,T and They are the lower and upper limits of the load reduction capacity in period T respectively; S320, analyzing the electricity / cooling / heating load characteristics of users participating in demand response, and forming a transferable load model; The transferable load model is as follows: In the formula, ρ LAs,t is the transferability coefficient of the load at time t; P LAs,t is the load transferable power at time t; t LAs and is the start and end time of load transfer, W LAs,T is the transferable amount of load in period T; and W LAs,T are the upper and lower limits of load transfer capacity in period T respectively; S330, analyzing the electricity / cooling / heating load characteristics of users participating in demand response, and forming an interruptible load model; The interruptible load model is as follows: Where P LAt,t is the interruptible power of the load at time t; ρ LAt,t is the interruptibility factor of the load at time t; t LAt and is the start and end time of load interruption; W LAt,T is the interruptible amount of load in period T; and W LAt,T The upper and lower limits of the interruption capacity of the interruptible load, and the load interruption time is not less than the minimum duration; S340: Comprehensively analyze the user's electricity / cooling / heating loads based on the curtailable load model, transferable load model, and interruptible load model.

5. The distribution network energy storage planning method considering high proportion of photovoltaic access and user demand response according to claim 1 is characterized in that: The method for solving the energy storage configuration optimization model for user demand response in S400 is as follows: S410, taking into account system uncertainty and the means of regulating energy storage and demand response during operation, a three-layer optimization model for energy storage configuration is proposed; S420, solving an energy storage configuration optimization model considering high proportion of photovoltaic access and user demand response; S430, solving the main model and the sub-model.

6. The distribution network energy storage planning method considering high-proportion photovoltaic access and user demand response according to claim 5 is characterized in that: The three-layer optimization model of energy storage configuration in S410 is as follows: Where D is the number of days in the planning cycle; τ B and τ L They are the system node set and line set respectively; In the formula, is the electricity purchase cost of the ith node at the hth moment, is the power injected into the node by the i-th node at the h-th moment, is the purchase price of electricity from the power grid, is the electricity selling price of the power grid. In this paper, the time-of-use electricity price is adopted.

7. The distribution network energy storage planning method considering high-proportion photovoltaic access and user demand response according to claim 5 is characterized in that: The solution method of the energy storage configuration optimization model in S420 is as follows: The three-layer optimization model of energy storage configuration is decomposed into a main model and sub-models, where the main model is expressed as follows: For a set of system uncertainty scenarios The kth uncertainty scenario The corresponding decision variable is The sub-model is an optimization problem in the operation phase. For a given energy storage planning scheme i∈I S ∪C S Its optimization model can be expressed as follows: in, In the formula, is the amount of electricity purchased by node i at time h, is the electricity sales amount of node i at time h.

8. The distribution network energy storage planning method considering high-proportion photovoltaic access and user demand response according to claim 5 is characterized by: The method for solving the main model and the sub-model in S430 is as follows: The column and constraint generation algorithm (C&CG) suitable for solving the two-level robust optimization problem is adopted. The specific steps are as follows: S431, set optimization target lower limit B L =-∞, upper limit B U =∞, set the uncertain set to empty and the number of iterations to k=1; S432, solve the optimization main problem and obtain the optimal value of the objective function O MP And the corresponding optimal solution Using the solved O MP Update B u The value of S433, Planning solution obtained based on the solution of the main problem Solve the optimization subproblem and obtain the optimal value of the objective function O SP (including the objective function value in the planning stage) and the corresponding uncertain scenario solution results Use O SP Update B L The value of S434, if | B U -B L ∣ / B L <δ, the solution process ends, otherwise proceed to the next step; S435, Join Set In the main optimization problem, Corresponding variables Add corresponding constraints, set k=k+1 and go to step S432.

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