A new energy system energy storage planning method, system, device and storage medium

Abstract

This invention discloses an energy storage planning method, system, device, and storage medium for new energy systems. The energy storage planning method for new energy systems constructs a typical scenario set including operating scenarios under extreme disaster environments and operating scenarios under normal environments, and constructs an objective function to minimize a first cost and a second cost, thus optimizing the cost of the new energy system while taking into account both operating scenarios under extreme disaster environments and normal environments. By constructing constraints considering the first and second factors, and combining the objective function and constraints to obtain the energy storage planning results, the ability of new energy systems to cope with extreme disaster environments is improved, while also ensuring the efficient absorption of new energy by the system under normal environments. This invention can be widely applied in the field of power system safety planning.

Description

Technical Field

[0001] This application relates to the field of power system safety planning, and in particular to a method, system, device and storage medium for energy storage planning of a new energy system. Background Technology

[0002] With the development of new power systems based on new energy sources, the ever-increasing scale of new energy power generation places higher demands on the large-scale optimization and allocation of new energy power generation and the flexibility of the power system. At the same time, due to the continuous growth of new energy power generation, new energy systems exhibit characteristics of uncertainty, openness, and complexity, making them difficult to withstand extreme natural disasters and human attacks, and drastically increasing operational risks.

[0003] In recent years, energy storage technology has matured, becoming a potential solution for improving the stability and flexibility of new energy systems and promoting the integration of new energy sources. However, current energy storage planning methods are still unable to cope with extreme disaster environments, and their effectiveness in reducing the cost of new energy systems is poor when considering both operational scenarios under extreme disaster and conventional environments. Summary of the Invention

[0004] The purpose of this invention is to at least partially solve one of the technical problems existing in the prior art.

[0005] To this end, embodiments of the present invention provide an energy storage planning method, system, device, and storage medium for a new energy system, achieving cost minimization for a new energy system that takes into account both extreme disaster environments and conventional environments.

[0006] To achieve the above-mentioned technical objectives, the technical solutions adopted in the embodiments of the present invention include:

[0007] On one hand, embodiments of the present invention provide an energy storage planning method for a new energy system, comprising the following steps:

[0008] A set of typical scenarios is constructed, which includes a first set of scenarios and a second set of scenarios. The first set of scenarios is a collection of various operating scenarios of the new energy system under normal conditions, and the second set of scenarios is a collection of various operating scenarios of the new energy system under extreme disaster conditions.

[0009] The first cost is calculated based on the preset energy storage configuration information. The first cost is the energy storage investment cost of the new energy system in each operating scenario of the first scenario set.

[0010] Calculate the product of the load shedding of the new energy system in each operating scenario of the second scenario set and the preset unit load shedding cost to obtain multiple second costs;

[0011] An objective function is constructed based on the first cost and a plurality of second costs, the objective function being used to minimize the first cost and the second costs;

[0012] Constraints are constructed based on the first factor and the second factor. The first factor is the time-series characteristics of the energy storage configuration information and the operation data of the new energy system. The operation data includes the acquired load data and output characteristics. The second factor includes the curtailment rate of the new energy system in each operation scenario of the first scenario set and the network topology reconstruction information of the new energy system in each operation scenario of the second scenario set.

[0013] The energy storage planning results are obtained based on the objective function and the constraints.

[0014] In addition, the energy storage planning method for a new energy system according to the above embodiments of the present invention may also have the following additional technical features:

[0015] Furthermore, in a new energy system energy storage planning method according to an embodiment of the present invention, the construction of a typical scenario set includes:

[0016] Generate the first scene set;

[0017] Generate the second scene set;

[0018] The typical scene set is obtained by summing the first scene set and the second scene set.

[0019] Furthermore, in one embodiment of the present invention, generating the first scene set includes:

[0020] The new energy information is processed using Weibull distribution and stratified sampling to generate a first energy sequence set. The first energy sequence set includes multiple energy sequences composed of the new energy information, where the new energy information is the value of energy collected by the new energy system under normal conditions.

[0021] The number of energy sequences in the first energy sequence set is reduced by using the K-means clustering algorithm to generate a second energy sequence set.

[0022] Based on the relationship between the new energy information and the output power of the new energy system driven by the new energy information, each energy sequence in the second energy sequence set is converted into an output power sequence to obtain the first scenario set.

[0023] Furthermore, in one embodiment of the present invention, generating the second scene set includes:

[0024] Extreme disaster information is generated using the Batts model, which includes the disaster occurrence path and disaster severity.

[0025] A third scenario set is generated using Monte Carlo simulation, which is a set of failure scenarios of the new energy system corresponding to the extreme disaster information.

[0026] The third scene set is reduced using the K-means clustering algorithm to obtain the second scene set.

[0027] Furthermore, in one embodiment of the present invention, the acquisition of the load data specifically includes the following steps:

[0028] Construct the IEEE 33-node power distribution system corresponding to the new energy system;

[0029] The load data is generated by adding random load multipliers to the load of each node in the IEEE 33-node power distribution system.

[0030] Furthermore, in one embodiment of the present invention, the energy storage configuration information includes the energy storage power, energy storage capacity, and energy storage life information configured for the nodes in the IEEE 33-node power distribution system;

[0031] The calculation of the first cost based on the preset energy storage configuration information includes:

[0032] The third cost is obtained by multiplying the energy storage power by the preset unit energy storage power investment cost;

[0033] The fourth cost is obtained by multiplying the energy storage capacity by the preset unit energy storage capacity investment cost.

[0034] The fifth cost is obtained by summing the third cost and the fourth cost.

[0035] The first cost is obtained by multiplying the fifth cost by the energy storage life information.

[0036] Furthermore, in one embodiment of the present invention, obtaining the energy storage planning result based on the objective function and the constraints includes:

[0037] Generate an energy storage planning model based on the objective function and the constraints;

[0038] Solve the energy storage planning model to obtain the energy storage planning results.

[0039] On the other hand, embodiments of the present invention propose an energy storage planning system for a new energy system, comprising:

[0040] A scenario set construction module is used to construct a typical scenario set, which includes a first scenario set and a second scenario set. The first scenario set is a collection of various operating scenarios of the new energy system under normal conditions, and the second scenario set is a collection of various operating scenarios of the new energy system under extreme disaster conditions.

[0041] The first cost calculation module is used to calculate the first cost based on the preset energy storage configuration information. The first cost is the energy storage investment cost of the new energy system in each operating scenario of the first scenario set.

[0042] The second cost calculation module is used to calculate the product of the load shedding of the new energy system and the preset unit load shedding cost in each operating scenario of the second scenario set, so as to obtain multiple second costs;

[0043] An objective function construction module is configured to construct an objective function based on the first cost and a plurality of second costs, the objective function being configured to minimize the first cost and the second costs;

[0044] The constraint construction module is used to construct constraints based on a first factor and a second factor. The first factor is the time-series characteristics of the energy storage configuration information and the operation data of the new energy system. The operation data includes the acquired load data and output characteristics. The second factor includes the curtailment rate of the new energy system in each operation scenario of the first scenario set and the network topology reconstruction information of the new energy system in each operation scenario of the second scenario set.

[0045] The energy storage planning result acquisition module is used to obtain the energy storage planning result based on the objective function and the constraints.

[0046] On the other hand, embodiments of the present invention provide an energy storage planning device for a new energy system, comprising:

[0047] At least one processor;

[0048] At least one memory for storing at least one program;

[0049] When the at least one program is executed by the at least one processor, the at least one processor implements the energy storage planning method for a new energy system.

[0050] On the other hand, embodiments of the present invention provide a storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the energy storage planning method for a new energy system.

[0051] Advantages and beneficial effects of the present invention:

[0052] This invention constructs a typical scenario set including operating scenarios under extreme disaster environments and operating scenarios under normal environments, and constructs an objective function to minimize the energy storage investment cost and the second cost of the new energy system in each operating scenario of the first scenario set. This optimizes the cost of the new energy system while taking into account both operating scenarios under extreme disaster environments and operating scenarios under normal environments. By constructing constraints that consider the time-series characteristics of energy storage configuration information and the operating data of the new energy system, as well as the curtailment rate of the new energy system in each operating scenario of the first scenario set and the network topology reconstruction information of the new energy system in each operating scenario of the second scenario set, and combining the objective function and constraints to obtain energy storage planning results, the ability of the new energy system to cope with extreme disaster environments is improved, while also taking into account the efficient consumption of the new energy system in normal environments. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following description is provided with accompanying drawings of the relevant technical solutions in the embodiments of this application or the prior art. It should be understood that the accompanying drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions in this application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0054] Figure 1 This is a flowchart illustrating a specific embodiment of an energy storage planning method for a new energy system according to the present invention;

[0055] Figure 2 This is a schematic diagram of the load multiplier of a specific embodiment of the energy storage planning method for a new energy system according to the present invention;

[0056] Figure 3 This is a schematic diagram of a conventional operation scenario of a specific embodiment of the energy storage planning method for a new energy system according to the present invention;

[0057] Figure 4 This is a schematic diagram of a specific embodiment of an energy storage planning system for a new energy system according to the present invention;

[0058] Figure 5 This is a schematic diagram of a specific embodiment of an energy storage planning device for a new energy system according to the present invention. Detailed Implementation

[0059] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0060] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0061] In this invention, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0062] In recent years, energy storage technology has matured, becoming a potential solution for improving the stability and flexibility of new energy systems and promoting the consumption of new energy. However, current energy storage planning methods are still unable to cope with extreme disaster environments, and their effectiveness in reducing the cost of new energy systems is poor when considering both extreme disaster and conventional operating scenarios. To address this, this invention proposes an energy storage planning method, system, device, and storage medium for new energy systems. It constructs a typical scenario set including operating scenarios under extreme disaster and conventional environments, and establishes an objective function to minimize the energy storage investment cost and secondary cost of the new energy system in each operating scenario of the first scenario set. This optimizes the cost of the new energy system while considering both extreme disaster and conventional operating scenarios. By constructing constraints that consider the time-series characteristics of energy storage configuration information and the operating data of the new energy system, as well as the curtailment rate of the new energy system in each operating scenario of the first scenario set and the network topology reconstruction information of the new energy system in each operating scenario of the second scenario set, and combining the objective function and constraints, the energy storage planning results are obtained. This improves the ability of new energy systems to cope with extreme disaster environments while also ensuring efficient consumption of new energy systems in conventional environments.

[0063] The following describes in detail, with reference to the accompanying drawings, an energy storage planning method, system, device, and storage medium for a new energy system according to an embodiment of the present invention. First, the energy storage planning method for a new energy system according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0064] Reference Figure 1 This invention provides a method for energy storage planning in a new energy system. This method can be applied to a terminal, a server, or software running on either a terminal or server. The terminal can be a tablet, laptop, desktop computer, etc., but is not limited to these. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms. The method for energy storage planning in a new energy system mainly includes the following steps S101-S106:

[0065] S101. Construct a set of typical scenarios;

[0066] The typical scenario set includes a first scenario set and a second scenario set. In the embodiments of the present invention, the first scenario set is a collection of various operating scenarios of the new energy system under normal environment, and the second scenario set is a collection of various operating scenarios of the new energy system under extreme disaster environment.

[0067] The energy storage planning method for a new energy system in this embodiment of the invention further includes a step of acquiring load data of the new energy system. Specifically, acquiring load data of the new energy system includes the following steps:

[0068] 1) Based on the IEEE 33-node power distribution system corresponding to the new energy system;

[0069] Based on prior knowledge, the IEEE 33-node distribution system is a classic distribution network model (radial network model) abstracted from the actual system. In this embodiment of the invention, the IEEE 33-node distribution system is constructed based on the new energy system to obtain load data and to perform subsequent energy storage planning simulation.

[0070] 2) Add random load multipliers to the loads of each node in the IEEE 33-node power distribution system to generate load data.

[0071] Specifically, in this embodiment of the invention, a random load multiplier is added to the load of each node in the IEEE 33-node power distribution system, such as... Figure 2 As shown, the load of node i at time t in scenario s is generated:

[0072]

[0073] τ i (s)~N(1,0,1 2 )

[0074] Among them, M p (t) is the load multiplier, P i To obtain the coincidence size at node i, τ i (s) represents a random number that follows a normal distribution in scenario s.

[0075] Specifically, in some embodiments of the present invention, a first scene set and a second scene set are generated respectively, thereby constructing a typical scene set.

[0076] S101 can be further divided into the following steps S1011-S1013:

[0077] Step S1011: Generate the first scene set;

[0078] In some embodiments, a set of energy sequences is obtained by processing the energy values ​​collected by the new energy system under normal conditions, and the energy sequences are clustered. Then, the clustered energy sequences are transformed into the output power sequences of the new energy system to obtain the first scenario set.

[0079] Specifically, in some embodiments, the generation of the first scene set includes the following steps:

[0080] 1) The Weibull distribution and stratified sampling are used to process the new energy information to generate the first energy sequence set;

[0081] The first energy sequence set includes multiple energy sequences composed of new energy information, which are the values ​​of energy collected by the new energy system under normal conditions.

[0082] Specifically, some embodiments of the present invention use the Weibull distribution to obtain the probability value of new energy information, and use the probability value to infer the distribution parameters of new energy information, thereby characterizing the probability distribution of new energy information.

[0083] Based on prior knowledge, stratified sampling is a method of extracting samples from a statistical subject / population. In some embodiments of the present invention, the probability distribution of new energy information is divided into different strata, and samples are then extracted independently and randomly from different strata. The samples extracted from the same stratum are then used to construct energy sequences, thereby obtaining a first set of energy sequences.

[0084] Optionally, taking a wind turbine generator as an example of a new energy system, some embodiments of the present invention use a Weibull distribution to characterize the probability distribution of wind speed, and perform stratified sampling on the probability distribution of wind speed to generate a set of wind speed sequences, namely the first energy sequence set.

[0085] Optionally, some embodiments of the present invention employ Latin hypercube sampling to perform stratified sampling processing of the probability distribution of new energy information.

[0086] 2) The number of energy sequences in the first energy sequence set is reduced by using the K-means clustering algorithm to generate the second energy sequence set;

[0087] Based on prior knowledge, the K-means clustering algorithm is an iterative clustering analysis algorithm. In some embodiments of this invention, the K-means clustering algorithm is used to select K energy sequences from the energy sequences in the first energy sequence set as initial cluster centers, and these initial cluster centers are considered as the current cluster centers. Then, the distance between each energy sequence and each current cluster center is calculated, and each energy sequence is assigned to the nearest current cluster center, thereby forming multiple clusters. It is understood that during the energy sequence assignment process, the current cluster center of each cluster is recalculated based on the existing objects (energy sequences) in the cluster, and the distance between each energy sequence and each current cluster center is recalculated. Then, each energy sequence is assigned to the nearest current cluster center again, until a preset termination condition is met.

[0088] Optionally, in some embodiments of the present invention, the termination condition may be one of the following:

[0089] a) No (or a predetermined minimum number) energy sequences were reassigned to different clusters;

[0090] b) No (or the preset minimum number) cluster centers change again;

[0091] c) The sum of squared errors of clustering is locally minimized.

[0092] In this embodiment of the invention, the energy sequences in the first energy sequence set are clustered using the K-means clustering algorithm to obtain multiple clusters, thereby reducing the number of energy sequences in the first energy sequence set and generating a second energy sequence set.

[0093] 3) Based on the relationship between new energy information and the output power of new energy systems driven by new energy information, each energy sequence in the second energy sequence set is transformed into an output power sequence to obtain the first scenario set.

[0094] It is understandable that each operating scenario in the first scenario set is an output power sequence obtained by converting each energy sequence in the second energy sequence set, and the output power sequence is a sequence composed of the output power of the new energy system driven by new energy information.

[0095] Optionally, taking a wind turbine generator as an example of a new energy system, some embodiments of the present invention, based on the relationship between wind speed and wind turbine generator power, transform each wind speed sequence (energy sequence) in the second energy sequence set into an output power sequence of the wind turbine generator, such as... Figure 3 As shown.

[0096] Step S1012: Generate the second scene set;

[0097] In some embodiments, extreme disaster information is first generated through a model, then multiple failure scenarios of the new energy system are simulated based on the generated extreme disaster information, and a clustering algorithm is used to reduce the set of failure scenarios to obtain a second scenario set.

[0098] Specifically, in some embodiments, the generation of the second scene set includes the following steps:

[0099] 1) Use the Batts model to generate extreme disaster information;

[0100] Extreme disaster information includes the disaster's path and severity.

[0101] Optionally, in some embodiments, the new energy unit is a distributed wind turbine generator, and the extreme disaster can be a typhoon. Based on prior knowledge, the Batts model superimposes the gradient wind speed and moving wind speed within a cyclone, and determines the wind speed value at the study point based on the positional relationship between the typhoon center and the study point. This embodiment of the invention uses the Batts model to extract parameters describing the typhoon and establishes a probability distribution model corresponding to these parameters, thereby simulating the typhoon's occurrence path and wind speed (disaster occurrence path and disaster severity).

[0102] 2) A third scene set was generated using Monte Carlo simulation;

[0103] The third scenario set is the set of failure scenarios of the new energy system corresponding to the extreme disaster information.

[0104] It is understood that Monte Carlo simulation is a random sampling or statistical experiment method that can realistically simulate actual physical processes. In this embodiment of the invention, Monte Carlo simulation can generate a set of failure scenarios of new energy systems when extreme disasters occur, describing the information on extreme disasters.

[0105] 3) The third scene set is reduced by K-means clustering algorithm to obtain the second scene set.

[0106] As described in steps S1011-2), some embodiments of the present invention employ the K-means clustering algorithm to select K fault scenarios from the fault scenarios in the third scenario set as initial cluster centers, and regard the initial cluster centers as the current cluster centers. Then, the distance between each fault scenario and each current cluster center is calculated, and each fault scenario is assigned to the nearest current cluster center, thereby forming multiple clusters. It is understood that during the fault scenario assignment process, the current cluster center of each cluster is recalculated based on the existing objects (fault scenarios) in the cluster, and the distance between each fault scenario and each current cluster center is recalculated. Then, each fault scenario is assigned to the nearest current cluster center again, until a preset termination condition is met.

[0107] Optionally, in some embodiments of the present invention, the termination condition may be one of the following:

[0108] a) No (or a preset minimum number) fault scenarios were reassigned to different clusters;

[0109] b) No (or the preset minimum number) cluster centers change again;

[0110] c) The sum of squared errors of clustering is locally minimized.

[0111] In this embodiment of the invention, the fault scenarios in the third scenario set are clustered using the K-means clustering algorithm to obtain multiple clusters, thereby reducing the number of fault scenarios in the third scenario set and generating a second scenario set.

[0112] Optionally, in some embodiments, the line fault scenarios (second scenario set) simulated and clustered in the IEEE 33-node distribution system built on a new energy system are shown in Table 1.

[0113] Table 1

[0114]

[0115] Step S1013: Summarize the first scene set and the second scene set to obtain the typical scene set.

[0116] Specifically, in the embodiments of the present invention, the first scenario set generated in step S1011 and the second scenario set generated in step S1012 are combined to obtain a typical scenario set that takes into account various operating scenarios of the new energy system in normal environment and various operating scenarios of the new energy system in extreme disaster environment, so that the objective function and constraint conditions constructed in subsequent steps can also take into account both normal environment and extreme disaster environment.

[0117] S102. Calculate the first cost based on the preset energy storage configuration information;

[0118] Among them, the first cost is the energy storage investment cost of the new energy system in each operating scenario of the first scenario set.

[0119] In some embodiments, the energy storage configuration information includes the energy storage power, energy storage capacity, and energy storage life of the nodes configured in the IEEE 33-node power distribution system.

[0120] Specifically, in this embodiment of the invention, the cost corresponding to the energy storage power and energy storage capacity configured in the nodes of the IEEE 33-node power distribution system is calculated, and the first cost is obtained by combining the energy storage life information.

[0121] S102 can be further divided into the following steps S1021-S1024:

[0122] Step S1021: Calculate the product of the energy storage power and the preset unit energy storage power investment cost to obtain the third cost;

[0123] Step S1022: Calculate the product of the energy storage capacity and the preset unit energy storage capacity investment cost to obtain the fourth cost;

[0124] Step S1023: Calculate the sum of the third cost and the fourth cost to obtain the fifth cost;

[0125] Step S1024: Calculate the product of the fifth cost and the energy storage life information to obtain the first cost.

[0126] In some embodiments, the specific expression for the first cost is as follows:

[0127]

[0128] in, For energy storage lifespan information, r is the discount rate, and T is the storage period. EES C represents the lifespan of the energy storage system. ess C represents the investment cost per unit of energy storage capacity. pss The investment cost per unit of energy storage capacity; P ess,i The energy storage power configured for node i; E ess,i The energy storage capacity configured for node i; A binary variable specifying whether node i is configured with energy storage.

[0129] S103. Calculate the product of the load shedding of the new energy system in each operating scenario of the second scenario set and the preset unit load shedding cost to obtain multiple second costs;

[0130] It is understood that the load shedding in the embodiments of the present invention refers to the loads that the new energy system needs to disconnect from the grid in each operating scenario (fault scenario) of the second scenario set in order to maintain power balance and stability.

[0131] In some embodiments, the specific expression for the second cost is as follows:

[0132]

[0133] Among them, C l The preset unit load shedding cost; Let t be the load size of node j in scenario s at time t.

[0134] S104. Construct an objective function based on the first cost and multiple second costs;

[0135] The objective function is used to minimize the first cost and the second cost.

[0136] Specifically, some embodiments of the present invention construct an objective function with the goal of minimizing the first cost and the second cost.

[0137] Optionally, in some embodiments, the annual probability of occurrence of extreme disasters corresponding to the failure scenarios involved in each second cost also needs to be considered in the objective function. The specific expression of the objective function is as follows:

[0138] minC inv +N ave E s [C los (s)]

[0139] Where, N ave Let C be the annual probability of the extreme disaster corresponding to fault scenario s. loss (s) represents the second cost of the new energy system under fault scenario s.

[0140] It is understood that embodiments of the present invention use the objective function minC inv +N ave E s [C loss [s] Minimize the first cost and the second cost under a single running scenario (failure scenario s) of the second scenario set, through the objective function minC inv +N ave E m [C loss [m] Minimize the second cost under a single operating scenario (failure scenario m) of the first cost and the second scenario set, thereby minimizing the second cost under each operating scenario of the first cost and the second scenario set respectively.

[0141] S105. Construct constraints based on the first and second factors;

[0142] The first factor is the time-series characteristics of energy storage configuration information and the operation data of the new energy system. The operation data includes the acquired load data and output characteristics. The second factor includes the curtailment rate of the new energy system in each operation scenario of the first scenario set and the network topology reconstruction information of the new energy system in each operation scenario of the second scenario set.

[0143] Specifically, the constraints constructed in this embodiment of the invention comprehensively consider the time-series characteristics of energy storage configuration information, load data of the new energy system, and output characteristics of the new energy system, as well as the curtailment rate of the new energy system in each operating scenario of the first scenario set and the network topology reconstruction information of the new energy system in each operating scenario of the second scenario set. The charging and discharging strategies and distribution network operation strategies corresponding to the energy storage configuration information are treated as variables for optimization, thereby constructing constraints that take into account both normal and extreme disaster environments.

[0144] Optionally, in some embodiments, the constraints include energy storage construction constraints, distribution network operation constraints, distribution network topology constraints, energy storage operation constraints, and renewable energy system curtailment constraints.

[0145] Among them, the constraints on energy storage investment and construction are:

[0146]

[0147]

[0148]

[0149] Where, N ESS In some embodiments of the invention, the maximum number of nodes that can be configured for energy storage is set to 2. The maximum energy storage capacity configured for a node. The maximum energy storage capacity configured for the node.

[0150] Distribution network operation constraints:

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159]

[0160]

[0161]

[0162]

[0163] Where δ(j) is the set of child nodes of a node, and π(j) is the set of parent nodes of a node. The active power flowing through line ij is The reactive power flowing through line ij is Let the active power demand of node j be the load. For the reactive power demand of node j, To remove reactive power from the load at node j, Let i be the voltage at node i. Let r be the voltage at node j. ij Let x be the resistance of line ij. ij Let U be the reactance of line ij, U0 be the rated voltage, and M be a large number. Let represent the state of line ij at time t in scenario s. and Contribute to new energy power generation units and It provides power to the substation.

[0164] Distribution network topology constraints:

[0165]

[0166]

[0167]

[0168] in, and To constrain the radial topology of the distribution network using binary variables.

[0169] Energy storage operation constraints:

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176] Among them, SOC max and SOC min These are the upper and lower limits of the energy storage state of charge, respectively. Let be the charging and discharging power of the energy stored at node i at time t. Let be the energy stored at node i at time t.

[0177] Curtailment constraints of renewable energy systems:

[0178]

[0179]

[0180] in, Let j be the active power of renewable energy generating unit j at time t. Let j be the reactive power of the renewable energy unit at time t. The maximum permissible amount of abandoned active power. This represents the maximum permissible reactive power to be abandoned.

[0181] S106. Obtain the energy storage planning results based on the objective function and constraints.

[0182] Specifically, in this embodiment of the invention, energy storage planning is carried out based on the objective function constructed in step S104 and the constraints constructed in step S105, so as to obtain energy storage planning results that take into account both extreme disaster environments and normal environments.

[0183] S106 can be further divided into the following steps S1061-S1062:

[0184] Step S1061: Generate an energy storage planning model based on the objective function and constraints;

[0185] In this embodiment of the invention, the energy storage planning model is a mixed integer linear programming (MILP) model.

[0186] Step S1062: Solve the energy storage planning model to obtain the energy storage planning results.

[0187] It is understood that solving the energy storage planning model in this embodiment of the invention is equivalent to solving a mixed integer programming problem. According to prior knowledge, solving a mixed integer programming problem is an NP-hard problem, and an exact solution cannot be obtained in polynomial time.

[0188] Optionally, in some embodiments of the present invention, by relaxing the requirement for the accuracy of the solution (energy storage planning result), a more efficient branch and bound algorithm is adopted to solve the energy storage planning model. In this embodiment, the energy storage planning model is divided into multiple linear programming problems using the branch and bound algorithm. The solutions to these linear programming problems can determine the upper and lower bounds of the solution to the energy storage planning model. As the branch and bound algorithm iterates, the interval between the upper and lower bounds of the solution is gradually narrowed, ultimately yielding a suboptimal solution whose distance from the accurate solution (accurate energy storage planning result) is less than a preset threshold.

[0189] Optionally, taking a 10kW distributed wind turbine generator set as an example of a new energy system, in some embodiments, based on the IEEE 33-node example (the IEEE 33-node power distribution system constructed based on the new energy system in this embodiment of the invention), the base capacity is 10MVA and the base voltage is 12.66KV. The distributed wind turbine generator set is installed at node 3 of the IEEE 33-node power distribution system. The energy storage planning results are obtained by solving the energy storage planning model corresponding to the distributed wind turbine generator set using GUROBI in MATLAB, as shown in Table 2.

[0190] Table 2

[0191]

[0192] As can be seen from the energy storage planning method for a new energy system described in steps S101-S106, this invention constructs a typical scenario set including operating scenarios under extreme disaster environments and operating scenarios under normal environments, and constructs an objective function to minimize the energy storage investment cost and the second cost of the new energy system under each operating scenario in the first scenario set. This optimizes the cost of the new energy system while taking into account both operating scenarios under extreme disaster environments and operating scenarios under normal environments. By constructing constraints that consider the time-series characteristics of energy storage configuration information and the operating data of the new energy system, as well as the curtailment rate of the new energy system under each operating scenario in the first scenario set and the network topology reconstruction information of the new energy system under each operating scenario in the second scenario set, and combining the objective function and constraints to obtain the energy storage planning results, the ability of the new energy system to cope with extreme disaster environments is improved, while also taking into account the efficient consumption of the new energy system under normal environments.

[0193] Secondly, with reference to the accompanying drawings, an energy storage planning system for a new energy system according to an embodiment of this application is described.

[0194] Figure 4 This is a schematic diagram of the energy storage planning system structure of a new energy system according to an embodiment of this application.

[0195] The system specifically includes:

[0196] The scenario set construction module 401 is used to construct a typical scenario set, which includes a first scenario set and a second scenario set. The first scenario set is a collection of various operating scenarios of the new energy system under normal environment, and the second scenario set is a collection of various operating scenarios of the new energy system under extreme disaster environment.

[0197] The first cost calculation module 402 is used to calculate the first cost based on the preset energy storage configuration information. The first cost is the energy storage investment cost of the new energy system in each operating scenario of the first scenario set.

[0198] The second cost calculation module 403 is used to calculate the product of the load shedding of the new energy system and the preset unit load shedding cost in each operating scenario of the second scenario set, so as to obtain multiple second costs;

[0199] The objective function construction module 404 is configured to construct an objective function based on the first cost and a plurality of second costs, the objective function being configured to minimize the first cost and the second costs;

[0200] The constraint construction module 405 is used to construct constraint conditions based on a first factor and a second factor. The first factor is the time-series characteristics of the energy storage configuration information and the operation data of the new energy system. The operation data includes the acquired load data and output characteristics. The second factor includes the curtailment rate of the new energy system in each operation scenario of the first scenario set and the network topology reconstruction information of the new energy system in each operation scenario of the second scenario set.

[0201] The energy storage planning result acquisition module 406 is used to obtain the energy storage planning result based on the objective function and the constraints.

[0202] It is evident that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0203] Reference Figure 5 This application provides an energy storage planning device for a new energy system, comprising:

[0204] At least one processor 501;

[0205] At least one memory 502 is used to store at least one program;

[0206] When the at least one program is executed by the at least one processor 501, the at least one processor 501 implements the energy storage planning method for a new energy system described in steps S101-S106.

[0207] Similarly, the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0208] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this application are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0209] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding this application. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional technology for an engineer. Therefore, those skilled in the art can implement the application set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0210] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several programs to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0211] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable programs for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can retrieve and execute a program from or in conjunction with such a program execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with a program execution system, apparatus, or device.

[0212] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0213] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0214] In the foregoing description of this specification, the references to terms such as "one embodiment," "another embodiment," or "some embodiments," etc., indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0215] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

[0216] The above is a detailed description of the preferred embodiments of this application, but this application is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for energy storage planning in a new energy system, characterized in that, Includes the following steps: A set of typical scenarios is constructed, which includes a first set of scenarios and a second set of scenarios. The first set of scenarios is a collection of various operating scenarios of the new energy system under normal conditions, and the second set of scenarios is a collection of various operating scenarios of the new energy system under extreme disaster conditions. The first cost is calculated based on the preset energy storage configuration information. The first cost is the energy storage investment cost of the new energy system in each operating scenario of the first scenario set. Calculate the product of the load shedding of the new energy system in each operating scenario of the second scenario set and the preset unit load shedding cost to obtain multiple second costs; An objective function is constructed based on the first cost and a plurality of second costs, the objective function being used to minimize the first cost and the second costs; Constraints are constructed based on the first factor and the second factor. The first factor is the time-series characteristics of the energy storage configuration information and the operation data of the new energy system. The operation data includes the acquired load data and output characteristics. The second factor includes the curtailment rate of the new energy system in each operation scenario of the first scenario set and the network topology reconstruction information of the new energy system in each operation scenario of the second scenario set. The energy storage planning results are obtained based on the objective function and the constraints. The energy storage configuration information includes the energy storage power, energy storage capacity, and energy storage life of the nodes configured in the IEEE 33-node power distribution system. The calculation of the first cost based on the preset energy storage configuration information includes: The third cost is obtained by multiplying the energy storage power by the preset unit energy storage power investment cost; The fourth cost is obtained by multiplying the energy storage capacity by the preset unit energy storage capacity investment cost. The fifth cost is obtained by summing the third cost and the fourth cost. The first cost is obtained by multiplying the fifth cost by the energy storage life information; The expression for the first cost is: in, For energy storage life information, For the discount rate, This refers to the lifespan of the energy storage device; The cost per unit of energy storage capacity; The cost per unit of energy storage capacity; The energy storage power configured for node i; The energy storage capacity configured for node i; A binary variable specifying whether node i should be configured with energy storage; The second cost is: in, The preset unit load shedding cost; The load size of node j in scenario s at time t; The objective function is: in, Let be the annual probability of the extreme disaster corresponding to fault scenario s. This represents the second cost of the new energy system under fault scenario s.

2. The energy storage planning method for a new energy system according to claim 1, characterized in that, The construction of the typical scenario set includes: Generate the first scene set; Generate the second scene set; The typical scene set is obtained by summing the first scene set and the second scene set.

3. The energy storage planning method for a new energy system according to claim 2, characterized in that, Generating the first scene set includes: The new energy information is processed using Weibull distribution and stratified sampling to generate a first energy sequence set. The first energy sequence set includes multiple energy sequences composed of the new energy information, where the new energy information is the value of energy collected by the new energy system under normal conditions. The number of energy sequences in the first energy sequence set is reduced by using the K-means clustering algorithm to generate a second energy sequence set. Based on the relationship between the new energy information and the output power of the new energy system driven by the new energy information, each energy sequence in the second energy sequence set is converted into an output power sequence to obtain the first scenario set.

4. The energy storage planning method for a new energy system according to claim 2, characterized in that, The generation of the second scene set includes: Extreme disaster information is generated using the Batts model, which includes the disaster occurrence path and disaster severity. A third scenario set is generated using Monte Carlo simulation, which is a set of failure scenarios of the new energy system corresponding to the extreme disaster information. The third scene set is reduced using the K-means clustering algorithm to obtain the second scene set.

5. The energy storage planning method for a new energy system according to claim 1, characterized in that, The acquisition of the load data specifically includes the following steps: Construct the IEEE 33-node power distribution system corresponding to the new energy system; The load data is generated by adding random load multipliers to the load of each node in the IEEE 33-node power distribution system.

6. The energy storage planning method for a new energy system according to claim 1, characterized in that, The process of obtaining the energy storage planning result based on the objective function and the constraints includes: Generate an energy storage planning model based on the objective function and the constraints; Solve the energy storage planning model to obtain the energy storage planning results.

7. An energy storage planning system for a new energy system, characterized in that, include: A scenario set construction module is used to construct a typical scenario set, which includes a first scenario set and a second scenario set. The first scenario set is a collection of various operating scenarios of the new energy system under normal conditions, and the second scenario set is a collection of various operating scenarios of the new energy system under extreme disaster conditions. The first cost calculation module is used to calculate the first cost based on the preset energy storage configuration information. The first cost is the energy storage investment cost of the new energy system in each operating scenario of the first scenario set. The second cost calculation module is used to calculate the product of the load shedding of the new energy system and the preset unit load shedding cost in each operating scenario of the second scenario set, so as to obtain multiple second costs; An objective function construction module is configured to construct an objective function based on the first cost and a plurality of second costs, the objective function being configured to minimize the first cost and the second costs; The constraint construction module is used to construct constraints based on a first factor and a second factor. The first factor is the time-series characteristics of the energy storage configuration information and the operation data of the new energy system. The operation data includes the acquired load data and output characteristics. The second factor includes the curtailment rate of the new energy system in each operation scenario of the first scenario set and the network topology reconstruction information of the new energy system in each operation scenario of the second scenario set. An energy storage planning result acquisition module is used to obtain energy storage planning results based on the objective function and the constraints. The energy storage configuration information includes the energy storage power, energy storage capacity, and energy storage life of the nodes configured in the IEEE 33-node power distribution system. The calculation of the first cost based on the preset energy storage configuration information includes: The third cost is obtained by multiplying the energy storage power by the preset unit energy storage power investment cost; The fourth cost is obtained by multiplying the energy storage capacity by the preset unit energy storage capacity investment cost. The fifth cost is obtained by summing the third cost and the fourth cost. The first cost is obtained by multiplying the fifth cost by the energy storage life information; The expression for the first cost is: in, For energy storage life information, For the discount rate, This refers to the lifespan of the energy storage device; The cost per unit of energy storage capacity; The cost per unit of energy storage capacity; The energy storage power configured for node i; The energy storage capacity configured for node i; A binary variable specifying whether node i should be configured with energy storage; The second cost is: in, The preset unit load shedding cost; The load size of node j in scenario s at time t; The objective function is: in, Let be the annual probability of the extreme disaster corresponding to fault scenario s. This represents the second cost of the new energy system under fault scenario s.

8. An energy storage planning device for a new energy system, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements an energy storage planning method for a new energy system as described in any one of claims 1-6.

9. A storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to implement an energy storage planning method for a new energy system as described in any one of claims 1-6.

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