A power distribution network flexibility evaluation and resource configuration method and system

Abstract

The application discloses a power distribution network flexibility evaluation and resource configuration method and system, considers the characteristics presented in different time scales during the operation of the power distribution network, solves corresponding operation scene sets by using specific uncertainty methods, then comprehensively considers micro gas turbines, energy storage devices, demand response loads and other types of flexible resources, establishes respective flexibility capability models, finally designs and plans a flexible resource optimal configuration method combined with operation, realizes decoupling and information transmission of the planning layer and the operation layer by using a target cascade method, and realizes flexible configuration of multiple types of resources under the condition of meeting flexible supply and demand balance through repeated iteration.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network resource configuration, in particular to a power distribution network flexibility evaluation and resource configuration method and system. BACKGROUND

[0002] With the intensification of global energy crisis, climate change and environmental pollution problems, the society of each country is accelerating the transformation of the power system to clean and intelligent. The China Electric Power Industry Annual Development Report 2022 pointed out that in 2021, the national power generation reached 83959 billion kWh, an increase of 10.1% over the previous year. Among them, pumped storage power generation increased by 16.3% year-on-year, grid-connected wind power generation increased by 40.6% year-on-year, and grid-connected solar power generation increased by 25.2% year-on-year. It can be seen that China continues to actively promote energy transformation and power system reform, and accelerates the implementation of the energy strategy to promote renewable distributed power generation. However, renewable distributed energy represented by wind turbines and photovoltaic power has intermittency, randomness and volatility, and its superposition with terminal load after grid connection makes the net load in adjacent time present a greater climbing amplitude. The superposition of net load fluctuation, renewable energy output prediction error and load prediction error intensifies the uncertainty of the operation of the distribution network and poses a severe test to the flexible adjustment capability of the distribution network. The flexibility of the power system refers to the ability to respond quickly to random fluctuations in power on the generation side or the load side by dispatching various types of response resources (such as conventional generating units with deep peak shaving capability) under economic and operating condition constraints. The flexible adjustment capability of the traditional distribution network is provided by the upper main network through the transformer station node, but due to the influence of node voltage and network transmission capacity, it is difficult to respond to the net load fluctuation of nodes with long electrical distance in a timely and effective manner. Therefore, it is necessary to study the optimal configuration method of multiple types of flexible resources in the distribution network.

[0003] At present, the research on the flexible resource configuration of the distribution network can be divided into energy storage configuration and demand side resource configuration according to the configuration subject, and can be divided into scenario-based configuration and robustness-based configuration according to the time scale of uncertainty. Liguomi established a local constraint and overall calculation model of distribution network flexibility, and proposed a storage configuration method considering the interaction of flexible resources; Li Zhihao and other scholars quantified the improvement effect of demand response configuration on the flexibility of the distribution network based on a multi-stage elasticity evaluation model of mechanical mapping; Ren Zhijun and other scholars established a double-layer model combining planning and operation, introduced the flexibility deficiency rate as an evaluation index in the operation layer, and introduced the comprehensive safety index in the planning layer to evaluate the safety of the system, and constructed a configuration model of micro gas turbine, energy storage device and demand response load for a single scenario; Zhu Xiaorong and other scholars fully considered the multi-time scale characteristics of the flexibility of the distribution network, and established a set of uncertain operation scenarios of the distribution network for long and short time scales respectively, and on this basis, studied the optimal configuration method of energy storage of the distribution network.

[0004] Most of the existing researches focus on the configuration of a certain specific flexible resource, ignoring the complementary characteristics of multiple types of resources, and when coordinating the configuration of multiple flexible resources, only the uncertainty of a single time scale of the distribution network is considered to simplify the model or the solving process, so that the power supply reliability of the configuration scheme is low or the resource redundancy is high. SUMMARY

[0005] The technical problem to be solved by the present application is that the existing flexible resource configuration method of the distribution network is limited to a single subject or a single time scale of uncertainty, resulting in low power supply reliability of the configuration scheme or high resource redundancy. The purpose is to provide a distribution network flexibility evaluation and resource configuration method and system, considering the characteristics presented by the distribution network in different time scales during operation, using a specific uncertainty method to solve the corresponding operation scenario set respectively; then considering multiple types of flexible resources such as micro gas turbines, energy storage devices, and demand response loads, establishing their respective flexibility capability models; finally, designing a flexible resource optimization configuration method combined with operation, using the target cascade method to realize the decoupling and information transmission of the planning layer and the operation layer, and through repeated iteration, realizing the flexible configuration of multiple types of resources under the condition of meeting the flexibility supply and demand balance.

[0006] The present application is implemented by the following technical solutions:

[0007] On the one hand, the present application provides a distribution network flexibility evaluation and resource configuration method, comprising the following steps:

[0008] Obtain the historical annual daily operation data of the distribution network, use the K-means clustering algorithm to cluster the original scenarios of the historical annual daily operation data to obtain a typical operation scenario set; according to the typical operation scenario set, establish a typical joint scenario set of wind turbine, photovoltaic and load curve; use a polyhedral set to represent the prediction error of the typical joint scenario set to obtain a distribution network operation scenario set;

[0009] Establish a flexibility demand model, and establish a flexibility regulation capability model according to the flexibility demand model;

[0010] According to the distribution network operation scenario set and the flexibility regulation capability model, establish a distribution network flexibility resource configuration model;

[0011] According to the distribution network flexibility resource configuration model, configure the flexibility resource of the distribution network.

[0012] Further, the model expression of the typical operation scenario set is: In formula (1), S1 represents the number of typical operation scenarios, n is the number of clustering in the iteration process; l nLoss index caused by clustering; M is the end value of clustering number; s is the number of original scene; P s Power vector of original scene s; P s Cluster center of the cluster where the original scene s is located;

[0013] The model expression of the typical joint scene set is: In formula (2), P

[0014] Further, the model expression of the power distribution network operation scene set is:

[0015] In formula (3), Σ|a| represents the information of each time period within a scheduling period, P P Γ ζ Indicates the conservativeness index.

[0016] Further, the expression of the flexibility demand model is:

[0017]

[0018] In formula (4), F D (t,τ) is the flexibility demand of the power distribution network, P NL (t+τ) and P NL (t) are the actual power of the net load of the power distribution network at t and t+τ, respectively, P NL (t+τ)-P NL (t) is the fluctuation of the net load of the power distribution network from t to t+τ; formula (5) represents the upward flexibility demand generated by the power distribution network; formula (6) represents the downward flexibility demand generated by the power distribution network.

[0019] Further, the flexibility regulation capability model includes: a flexibility regulation capability model of a micro gas turbine, a flexibility regulation capability model of an interruptible load, and a flexibility regulation capability model of an energy storage device.

[0020] The expression of the flexibility regulation capability model of the micro gas turbine is: In formula (7), and P and respectively represent the maximum and minimum output of the gas turbine, P gt,t represents the real-time output power of the gas turbine, and respectively represent the upward and downward ramping rates of the gas turbine;

[0021] The expression of the flexible regulation capability model of the micro gas turbine is: In formula (8), P il,t and respectively represent the output and maximum output of the controllable load at the current time, and respectively represent the maximum limit of the system to cut off the controllable load and restore the load;

[0022] The expression of the flexible regulation capability model of the energy storage device is: In formula (9), respectively represent the flexible upward and downward regulation capabilities of the energy storage device. respectively represent the real-time charging / discharging power of the energy storage device. respectively represent the maximum charging / discharging power of the energy storage device.E ess,t represents the real-time power of the energy storage device; respectively represent the minimum and maximum storage power of the energy storage device.

[0023] Further, after the flexible regulation capability model is established, the following steps are included: establishing a flexible resource adequacy model and a branch flexible response capability model according to the flexible regulation capability model;

[0024] The expression of the flexible resource adequacy model is: In formula (10), I fl represents the adequacy level of the distribution network flexible resource in an operating cycle;

[0025] The expression of the branch flexible response capability model is: In formula (11), L ij,t is the real-time load margin of the branch transmission channel, I br represents the power flow balance degree of the distribution network line in the entire operating cycle.

[0026] Further, the objective function of the distribution network flexible resource configuration model is: min F = C inv + C ope (12), in formula (12), C inv is the planning investment of the equalized output, C ope represents the equalized output operation;

[0027]

[0028] C p,t =P p,t π e,t (16),

[0029] C wt,t =P wt,t π wt (17),

[0030] C pv,t =P pv,t π pv (18),

[0031] C il,t =P il,t π il (19),

[0032]

[0033] C loss,t =P loss,t π e,t (22),

[0034] In formula (13) to (22), τ a represents the equivalent annual factor of equipment a, r is the discount rate, y a is the economic service life of equipment a.c gt is the investment cost of micro gas turbine; c il , c ess are the unit investment costs of interruptible load contract power and energy storage configuration capacity, respectively.x gt,i , x il,i , x ess,i are the investment decision variables of gas turbine, interruptible load and energy storage equipment, respectively.Ψ gt , Ψ il , Ψ ess are the configuration candidate nodes of gas turbine, interruptible load and energy storage equipment, respectively.P p,t is the power purchased by the distribution network from the upper-level power grid; P wt,t , P pv,t are the actual output of wind and solar power; P loss,t is the network power flow transmission loss.π e,t , π g,t are the real-time electricity price and gas price, respectively; π wt , π pv are the unit costs of wind and solar power; π il is the interruptible load calling cost; are the charging / discharging unit costs of energy storage device, respectively.η gt is the power generation efficiency of gas turbine; Qlhv The natural gas heat value.

[0035] Further, the constraint conditions of the power distribution network flexibility resource configuration model include: power distribution network safe operation constraint, wind and light output constraint, gas turbine constraint, energy storage device constraint, interruptible load constraint, reactive power compensation device constraint and flexibility constraint.

[0036] The expression of the power distribution network safe operation constraint is:

[0037]

[0038] V j,t 2 =V i,t 2 -2(P ij,t r ij +Q ij,t x ij )+I ij,t 2 (r ij 2 +x ij 2 ) (25),

[0039]

[0040] In formula (23) to (28), π(j) and δ(j) respectively represent the first node and the last node set of the branch connected with node j; B represents the node set; r ij and x ij respectively represent the resistance and reactance of the branch ij; P ij,t and Q ij,t represent the branch flow at time t; V j,t is the node voltage; Q cb,j,t represents the reactive power provided by the switchable capacitor. tanθ ij is the power factor of the interruptible load;

[0041] The expression of the wind and light output constraint is:

[0042]

[0043] In formula (29) and formula (30), and are respectively the upper limit of the output of the wind turbine and the upper limit of the output of the photovoltaic unit;

[0044] The expression of the gas turbine constraint is:

[0045]

[0046] In equations (31) to (33), and These represent the uphill and downhill ramp rates of the gas turbine, respectively.

[0047] The expression for the constraint of the energy storage device is:

[0048]

[0049] E ess,T =E ess,0 (35),

[0050]

[0051] Equation (34) is the charging and discharging model of the energy storage device; Equation (35) indicates that the state of charge of the energy storage remains unchanged before and after a scheduling cycle; Equations (36), (37) and (38) represent the charging power constraint, discharging power constraint and energy constraint, respectively; in Equations (34) to (39), n loss This represents the self-loss rate of the energy storage device, describing the inherent energy loss of energy storage; and These represent the charging efficiency and discharging efficiency of the energy storage device, respectively. E ess,0 E represents the initial state of charge of the energy storage device. ess,T This indicates the state of charge of the energy storage device at the end of the scheduling cycle. ess,t This represents the charge / discharge flag of the energy storage device, and is a 0-1 type variable; x ess The decision variable representing whether to configure an energy storage device is an integer variable;

[0052] The expression for the interruptible load constraint is:

[0053]

[0054] The expression for the constraints of the reactive power compensation device is:

[0055]

[0056] n cb,j,t ≤N cb,j (42),

[0057]

[0058] In equations (41) to (43), n cb,j,t The number of capacitor groups to be switched on and off. The reactive power compensation provided for each capacitor bank; N cb,j This represents the upper limit on the number of capacitor banks that can be put into operation. This is the upper limit of the number of capacitor switching operations that can be performed within one operating cycle;

[0059] The expression of the flexibility constraint is:

[0060]

[0061] Further, the safe operation constraint of the power distribution network comprises a node voltage constraint and a line flow constraint.

[0062] In another aspect, the present application provides a power distribution network flexibility evaluation and resource configuration system, comprising:

[0063] A first scenario set construction module is configured to cluster original scenarios of the historical annual daily operation data by using a K-means clustering algorithm, so as to obtain a typical joint scenario set of wind turbine, photovoltaic and load curves;

[0064] A second scenario set construction module is configured to represent prediction errors of the typical joint scenario set by using a polyhedral set, so as to obtain a power distribution network operation scenario set;

[0065] A first model construction module is configured to establish a flexibility demand model;

[0066] A second model construction module is configured to establish a flexibility adjustment capability model and a flexibility evaluation index according to the flexibility demand model;

[0067] A third model construction module is configured to establish a power distribution network flexibility resource configuration model according to the power distribution network operation scenario set and the flexibility adjustment capability model;

[0068] A resource configuration module is configured to perform power distribution network flexibility resource configuration according to the power distribution network flexibility resource configuration model.

[0069] Compared with the prior art, the present application has the following advantages and beneficial effects: the characteristics of the power distribution network in operation at different time scales and multiple types of flexibility resources are considered, and by establishing a power distribution network operation scenario set, a flexibility adjustment capability model and a power distribution network flexibility resource configuration model, the configuration of multiple types of resources is realized under the condition of meeting the flexibility supply and demand balance, and the defects that the current power distribution network flexibility resource configuration method is limited to a single subject or a single time scale uncertainty are improved. In addition, the flexibility of the power distribution network is evaluated by using flexibility resource adequacy and branch flexibility response capability, so that the flexibility attack relative to the demand is always at an adequate level in the operation process. BRIEF DESCRIPTION OF DRAWINGS

[0070] In order to make the technical solutions of the exemplary embodiments of the present application clearer, the drawings needed in the embodiments will be briefly introduced as follows. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be considered as limiting the scope. Other related drawings can also be obtained by those of ordinary skill in the art without creative effort.

[0071] Figure 1 A robust optimization uncertainty set diagram in different forms for the embodiment 1 of the present application;

[0072] Figure 2 A power distribution network uncertainty scenario calculation flow diagram for the embodiment 1 of the present application;

[0073] Figure 3 A power distribution network uncertainty operation scenario diagram for the embodiment 2 of the present application;

[0074] Figure 4 An IEEE33 node test system diagram for the embodiment 2 of the present application;

[0075] Figure 5 A time-of-use price and gas price diagram for the embodiment 2 of the present application;

[0076] Figure 6 A scheme 1 flexibility resource configuration situation diagram for the embodiment 2 of the present application;

[0077] Figure 7 A scheme 2 flexibility resource configuration situation diagram for the embodiment 2 of the present application. DETAILED DESCRIPTION

[0078] In order to make the technical solutions of the exemplary embodiments of the present application clearer, the drawings needed in the embodiments will be briefly introduced as follows. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be considered as limiting the scope. Other related drawings can also be obtained by those of ordinary skill in the art without creative effort.

[0079] Embodiment 1

[0080] At present, most of the power distribution network flexibility resource configuration methods are configured for a certain specific flexibility resource, ignoring the complementary characteristics of multiple types of resources. The existing power distribution network flexibility resource configuration method often only considers the uncertainty of the power distribution network in a single time scale when configuring multiple flexibility resources, in order to simplify the model or solve the process, so that the power supply reliability of the configuration scheme is low or the resource redundancy is too high.

[0081] In view of the above technical problems, the embodiment provides a power distribution network flexibility resource configuration method considering long-term and short-term uncertainty.

[0082] Firstly, the uncertainty of distribution network operation is analyzed and processed. The uncertainty of distribution network is different in long time scale and short time scale. In long time scale, it is affected by seasonal weather changes, and in short time scale, it is affected by prediction accuracy. For seasonal differences, a scenario analysis method is introduced to cluster historical data to obtain typical operating scenarios. For prediction errors, a polyhedral uncertainty model is introduced to robustly optimize each typical scenario to obtain a set of distribution network operating scenarios considering long-term and short-term uncertainties.

[0083] Then, starting from the net load fluctuation caused by the uncertainty of distribution network, a directional flexibility demand model is constructed. In view of the single problem of distribution network flexibility resources, flexibility adjustment capability models of micro gas turbine, interruptible load and energy storage device are established from the aspects of source, load and storage. In addition, in order to ensure that the flexibility supply is always in a sufficient level relative to the demand during operation, a flexibility evaluation index is constructed.

[0084] Finally, taking the sum of the annual total construction cost and the distribution network operation cost as the minimum target, considering the safe operation of distribution network, the output and climbing of various resources, and the balance constraint of flexibility supply and demand, a flexible resource integrated configuration method combining planning and operation is proposed, and Gurobi is used to solve the integer linear programming problem.

[0085] Specifically, the distribution network flexibility evaluation and resource configuration method provided in the embodiment includes the following steps:

[0086] Step 1: Obtain the historical annual daily operation data of the distribution network, and use the K-means clustering algorithm to cluster the original scenarios of the historical annual daily operation data to obtain a set of typical operating scenarios.

[0087] Step 2: According to the set of typical operating scenarios, a set of typical joint scenarios of wind turbine, photovoltaic and load curves is established.

[0088] Step 3: The prediction error of the set of typical joint scenarios is represented by a polyhedral set to obtain a set of distribution network operating scenarios.

[0089] The steps 1 to 3 are specifically explained as follows:

[0090] The uncertainty of distribution network mainly comes from the random characteristics of the actual operation state of the components in the network, mainly including the uncertainty of terminal load and the uncertainty of distributed renewable energy. Whether it is load or distributed renewable energy represented by wind and light, it is influenced by climate resources and other environments, and presents random fluctuation characteristics at different time scales. For the output difference of seasonal variation at long time scale, the non-time sequence uncertainty research method based on probability density is often used, and the probability characteristics of the research object with statistical significance are analyzed according to the historical operation data. For the error at short time scale influenced by prediction accuracy, the time sequence characteristic research method is mainly used, and the research object is simulated according to the time sequence simulation method. In this embodiment, the scene analysis method and the robust optimization theory are used to process the uncertainty of wind, light and load at two time scales.

[0091] 1. Generation of typical scenes based on clustering algorithm

[0092] The optimal configuration of flexible resources is a random planning problem, and the scene analysis method is often used to solve the random planning problem in the process. The ideal configuration method should be based on the analysis of all operation scenes of the distribution network, so that the accuracy of simulating the actual distribution system is best and the economy of the configuration scheme is best. However, if all original scenes are simulated in the configuration model, due to the increase of decision variables and constraint conditions, it will undoubtedly produce huge calculation cost, and even appear "dimension disaster". In order to effectively solve the uncertainty at long time scale, based on the comprehensive consideration of the full time sequence scene of distribution network and the solution efficiency of configuration model, the generation method of typical scenes based on clustering algorithm has been proved to be an efficient and accurate uncertainty planning method.

[0093] The climate conditions of the same region every year have regularity. It is assumed that the future wind and light conditions and user energy behavior of the distribution area are the same as those of the same time of the previous year, that is, it is considered that the future wind power and user load have the same distribution as the historical data, so the historical annual daily operation data statistically obtained is directly used as the original scene. Then the K-means clustering algorithm is used to cluster the original operation scenes of the historical data, and S1 typical scenes are obtained, as shown in formula (1). At the same time, the probability p corresponding to the typical scene is calculated according to the number of elements in each cluster in the clustering result s1 .

[0094] The model expression of the typical operation scene set is: In formula (1), S1 represents the number of typical operation scenes, n is the clustering number in the iteration process; l n is the loss index caused by clustering; M is the end value of the clustering number; s is the number of original scenes; P s is the power vector of the original scene s; is the clustering center of the cluster where P s is located.

[0095] In actual operation, it is considered that the wind and solar power output and the load curve have the same seasonal characteristics, that is, they show a certain correlation in a long time scale. Therefore, a typical joint scene set of wind turbines, photovoltaic and load curves is directly established, which can be represented as: In formula (2), is the power vector of the wind turbine, photovoltaic and load curve at each time in the original scene s.

[0096] 2. Robust optimization-based prediction error processing

[0097] Due to the real-time changes of wind and light conditions, user behavior and prediction accuracy, the typical joint scenes generated based on the K-means clustering algorithm are difficult to describe the uncertainty of wind and light and load in a short time scale, that is, the prediction error. If the predicted output of the typical scene wind, light and load is directly used as the operation data, it may cause a large deviation in the planning and configuration scheme of the flexible resource of the distribution network, and the uncertainty optimization theory in a short time scale needs to be introduced to reduce the influence of the prediction error.

[0098] Robust optimization is one of the effective means to deal with prediction errors, and the key lies in establishing an uncertainty set containing all uncertain parameters in a short time scale on the basis of the aforementioned typical scenes. The uncertainty parameter refers to the error influencing factor information including the prediction accuracy, which reflects the degree of conservatism of the corresponding configuration model decision result. The more accurate the uncertainty set, the lower the degree of conservatism of the configuration scheme decision result. The uncertainty set mainly has box, ellipsoid and polyhedron forms. Compared with the box set with too high degree of conservatism and the ellipsoid set with nonlinear structure, the polyhedron set is favored by researchers due to its moderate degree of conservatism and unique linear structure. Its form is shown in formula (3) as follows: Figure 1 C1C5C3C7 encloses a unique polyhedral linear structure.

[0099] The polyhedral set is used to represent the prediction error of the above-mentioned typical joint scene, and the distribution network operation scene set containing the worst uncertainty range in a short time scale is obtained, which can be represented as: In formula (3), all variables are the power at a certain operation time, and the time vector t is omitted for convenience. ∑|a| represents the sum of the information of each period in a scheduling period. respectively represent the actual power of the wind turbine, photovoltaic and load in the s-th typical scene; respectively represent the predicted output of the wind turbine, photovoltaic and load in the s-th typical scene, that is, the power value of the typical scene generated based on the clustering algorithm; respectively represent the worst uncertainty range of the prediction error of the wind and light and load in the s-th typical scene; Γ ζIt represents the index of the conservation degree, reflecting the strength of robustness and the degree of conservation of the scenario information.

[0100] 3. Set of uncertain operation scenarios

[0101] To ensure that the flexible resource configuration scheme takes into account the robustness while ensuring faster calculation rate, the end-point scenario method is used to screen the multiple scenarios generated by the polyhedron set, so that the selected scenarios contain more extreme points. The calculation process of the set of power distribution network operation scenarios considering long-term and short-term uncertainties is shown in Figure 2 , and the generated scenario set is shown in Figure 3 , wherein the robust scenario set is illustrated by taking typical scenario 3 as an example.

[0102] Step 4: Establish a flexible demand model.

[0103] Step 5: Establish a flexible regulation capability model according to the flexible demand model.

[0104] The steps 4 and 5 are specifically explained as follows:

[0105] NERC defines flexibility as the ability of power system resources to meet the change in net load. The flexible regulation capability of the traditional distribution network is provided by the upper main network through the transformer nodes, however, due to the influence of node voltage and network transmission capacity, it is difficult to respond to the net load fluctuation of nodes with long electrical distance in time and effectively.

[0106] 1. Flexible demand model

[0107] The flexible demand of the distribution network is derived from the change of the state of each component in the distribution area, such as the fault maintenance of power supply and use equipment, as well as the volatility and uncertainty of source and load. This embodiment mainly considers the flexible demand caused by the net load fluctuation, which can be expressed as: In formula (4), P NL (t+τ) and P NL (t) are the actual power of the system at t and t+τ, respectively, P NL (t+τ)-P NL (t) is the net load fluctuation from t to t+τ. F D (t,τ) is the flexible demand of the distribution network, and its positive and negative nature reflects the directionality of the flexible demand. Positive indicates upward flexible demand of the distribution system, and vice versa. It can be expressed as:

[0108] 2. Flexible resource model

[0109] The flexible resource considered in this embodiment is the node-side resource, mainly including micro gas turbines, demand response loads and energy storage devices installed at each node.

[0110] (1)Microturbine

[0111] The gas turbine belongs to the conventional generator set, which can adjust the output power at a certain rate (ramp rate) before the maximum and minimum output. In other words, the gas turbine can provide flexibility in a certain proportion of its installed capacity, which can be expressed as: In formula (7), respectively represent the up-regulation and down-regulation ability of the flexibility provided by the gas turbine. respectively represent the maximum and minimum output of the gas turbine. gt,t represents the real-time output power of the gas turbine. respectively represent the up and down ramp rates of the gas turbine.

[0112] (2) Interruptible load

[0113] Demand response refers to the response of users to change their inherent power consumption patterns in response to changes in electricity market price signals or incentives from power suppliers to ensure stable operation of the power grid, which is generally divided into price-based demand response and incentive-based demand response. Among them, interruptible load has the advantages of more timely and stable response ability and high user selectivity, and is often used as one of the user-side flexibility resources. Its flexible regulation capacity can be expressed as:

[0114] In formula (8), P il,t , are the output and maximum output of the controllable load at the current time, respectively; are the maximum limits of the system for cutting off and restoring the controllable load, respectively.

[0115] (3) Energy storage device

[0116] At present, the most widely used energy storage technology in distribution network is battery energy storage, which has the advantages of flexible configuration, rapid response, high energy density and power density. The flexible regulation capacity of the energy storage system can be expressed as: In formula (9), respectively represent the up-regulation and down-regulation ability of the flexibility provided by the energy storage device. respectively represent the real-time charging / discharging power of the energy storage device. respectively represent the maximum charging / discharging power of the energy storage device. ess,t represents the real-time power of the energy storage device; respectively represent the minimum and maximum storage power of the energy storage device.

[0117] 3. Distribution network flexibility evaluation index

[0118] To keep the balance between supply and demand in the process of power distribution network operation, not only the flexible resource supply is required to be sufficient, but also the power grid structure is required to provide sufficient transmission capacity margin to ensure that the flexible resource can be called in time and quickly when needed. Therefore, two flexible evaluation indexes of flexible resource sufficiency and branch flexible response capacity are defined.

[0119] (1) Flexible resource sufficiency

[0120] The regulation capacity of flexible demand and flexible resource has directionality, so the defined flexible sufficiency is composed of two parts, which can be expressed as: In formula (10), I fl represents the sufficiency level of the power distribution network in an operation cycle, and the value reflects the resource response capacity of the power distribution network to the net load fluctuation.

[0121] (2) Branch flexible response capacity

[0122] The response of various flexible resources to the flexible demand of the distribution network requires sufficient grid transmission channels. As the support platform for power supply and demand balance and flexible supply and demand balance, the branch needs to reserve a certain load margin to cope with the uncertain changes of net load fluctuation. Therefore, the branch flexible response capacity is defined as the standard deviation of the branch load margin, which can be expressed as: In formula (11), L ij,t is the real-time load margin of the branch transmission channel, and the value reflects the possibility of power flow congestion in the grid channel. I br represents the power flow balance degree of the distribution network line in the entire operation cycle, reflecting the channel support capacity of the distribution network to the net load fluctuation.

[0123] Step 6: Establish a flexible resource configuration model of the power distribution network according to the set of power distribution network operation scenarios and the flexible regulation capacity model.

[0124] The specific description of step 6 is as follows:

[0125] On the basis of the above-mentioned power distribution network uncertainty operation scenario constructed by clustering algorithm and robust optimization theory, a flexible resource configuration model of the power distribution network is established, which comprehensively considers the optimization configuration of micro gas turbine, interruptible load and energy storage device and the simulation operation in the scenario set.

[0126] 1. Objective function

[0127] The flexible resource optimization configuration model takes the minimum annual comprehensive cost of the power distribution network in the planning period as the objective function, including the planning investment cost C inv and the operation cost C ope of the equalized output, which can be expressed as: min F = C inv +Cope (12), in formula (12), C inv represents the planning investment of the equalized output, C ope represents the equalized output operation; wherein,

[0128]

[0129] C p,t = P p,t π e,t (16),

[0130] C wt,t = P wt,t π wt (17),

[0131] C pv,t = P pv,t π pv (18),

[0132] C il,t = P il,t π il (19),

[0133]

[0134] C loss,t = P loss,t π e,t (22),

[0135] in formula (13) to (22), τ a represents the equalized coefficient of the device a, r is the discount rate, y a is the economic service life of the device a.c gt is the investment cost of the micro gas turbine; c il , c ess are the unit investment costs of the interruptible load contract power and the energy storage configuration capacity, respectively.x gt,i , x il,i , x ess,i are the investment decision variables of the gas turbine, the interruptible load and the energy storage device, respectively.Ψ gt , Ψ il , Ψ ess are the configuration candidate nodes of the gas turbine, the interruptible load and the energy storage device, respectively.P p,t is the power purchased by the distribution network from the upper-level power grid; P wt,t , P pv,t are the actual outputs of the wind and solar power generation; P loss,t is the network power flow transmission loss.π e,t , π g,t are the real-time electricity price and the gas price, respectively; π wt , πpv is the unit cost of wind power generation; π il is the calling cost of interruptible load; are the unit costs of charging / discharging of energy storage device, respectively. gt is the power generation efficiency of gas turbine; Q lhv is the calorific value of natural gas.

[0136] 2. Constraints

[0137] The constraints of the flexibility resource configuration model considered in this embodiment include power distribution network safe operation constraints, photovoltaic power generation constraints, energy storage device operation constraints, gas turbine operation constraints, and flexibility constraints.

[0138] (1) Power distribution network safe operation constraints

[0139] The branch power flow model of the second-order cone relaxation is adopted in the present technology, and the constraints are as follows:

[0140]

[0141] V j,t 2 = V i,t 2 - 2 (P ij,t r ij + Q ij,t x ij ) + I ij,t 2 (r ij 2 +x ij 2 ) (25),

[0142]

[0143] In formulae (23) to (28), π(j) and δ(j) represent the first and last node sets of the branch connected with node j, respectively; B represents the node set; r ij , x ij represent the resistance and reactance of branch ij, respectively; P ij,t , Q ij,t represent the branch power flow at time period t; V j,t is the node voltage; Q cb,j,t represents the reactive power provided by the switchable capacitor. tanθ ij is the power factor of the interruptible load.

[0144] In addition, the power grid safe operation constraints also include node voltage constraints, line power flow constraints, and other conventional constraints, which are not shown here.

[0145] (2) Wind and solar power output constraints

[0146] The power distribution network can control the output of the distributed power source, and the related constraints are as follows:

[0147]

[0148] In formula (29) and formula (30), and respectively represent the upper limit of the output of the wind turbine and the upper limit of the output of the photovoltaic unit.

[0149] (3) Gas turbine constraints

[0150] The output of the gas turbine is affected by technical parameters such as rated capacity, maximum and minimum technical output, inherent ramp rate, etc., and the related constraint conditions are as follows:

[0151]

[0152] In formula (31) to (33), and respectively represent the upper ramp rate and the lower ramp rate of the gas turbine.

[0153] (4) Energy storage device constraints

[0154] The energy storage device is affected by its capacity, state of charge, charge / discharge speed, and the corresponding constraint conditions are as follows:

[0155]

[0156] E ess,T = E ess,0 (35),

[0157]

[0158] Formula (34) is the charge / discharge model of the energy storage device; formula (35) indicates that the state of charge of the energy storage device remains unchanged before and after a scheduling period; formula (36), (37) and (38) respectively represent the charge power constraint, the discharge power constraint and the electric quantity constraint; in formula (34) to (39), n loss represents the self-loss rate of the energy storage device, which describes the inherent electric quantity loss of the energy storage; respectively represent the charge / discharge efficiency of the energy storage device. E ess,0 represents the initial state of charge of the energy storage device, E ess,T represents the state of charge of the energy storage device after the end of the scheduling period. c ess,t represents the charge / discharge identification bit of the energy storage device, which is a 0-1 type variable; x ess represents the decision variable of whether the energy storage device is configured, which is an integer type variable.

[0159] (5) Interruptible load constraints

[0160] Interruptible load is mainly constrained by contracted capacity during operation:

[0161] (6) Reactive power compensation device constraint

[0162] During operation, the reactive power compensation device is constrained by the maximum technical output:

[0163]

[0164] n cb,j,t ≤N cb,j (42),

[0165]

[0166] In formula (41) to (43), n cb,j,t is the number of groups of switched capacitors in operation, is the reactive power compensation power provided by each group of capacitors; N cb,j is the upper limit of the number of groups of capacitors that can be put into operation; is the upper limit of the number of times of switching operation of the capacitor in one operation cycle.

[0167] (7) Flexibility constraint

[0168] In addition to the above distribution network safe operation constraints and network component configuration and operation constraints, the flexibility constraints of the distribution network also need to be considered, which can be represented as:

[0169]

[0170] The above considers the characteristics presented by the distribution network during operation at different time scales, and the corresponding operation scenario set is obtained by using a specific uncertainty method; then the micro gas turbine, energy storage device, demand response load and other types of flexible resources are considered, and the flexibility capability model of each type is established; finally, the flexible resource optimization configuration method combined with operation is designed and planned, and the target cascade method is used to realize the decoupling and information transmission of the planning layer and the operation layer, and through repeated iteration, the flexible configuration of multiple types of resources is realized under the condition of meeting the flexible supply and demand balance.

[0171] Embodiment 2

[0172] In order to verify the effectiveness of the distribution network flexible resource configuration method considering long-term and short-term uncertainty given in embodiment 1, the IEEE33 node test system is used as an example for analysis in this patent.

[0173] 1. Scenario description

[0174] The wind, light and load curve prediction data use the operation uncertainty scenario set established as described above, such asFigure 3 The fan access positions in the test system are 9 / 16 / 22 / 24 / 30 / 31, the photovoltaic unit access positions are 14 / 17 / 20 / 23 / 27, the reactive power compensation device is connected at nodes 15 / 29, and node 1 is the substation node connected to the upper-level power grid, as shown in Figure 4 The time-of-use electricity price and gas price are shown in Figure 5 The related parameters of the flexible resources to be configured in the test system are shown in Table 1.

[0175]

[0176] Table 1 Related parameters of flexible resources

[0177] Two comparison schemes are set for simulation testing, and the effectiveness of the method is verified:

[0178] Scheme 1: Only the typical operation scenario is considered, and the prediction error is not considered.

[0179] Scheme 2: The method proposed in the present technology.

[0180] 2. Results and analysis

[0181] (1) Flexible resource configuration results

[0182] Figure 6 and Figure 7 respectively show the flexible resource configuration results of the two schemes. In scheme 1, the gas turbine access positions are nodes 3 / 7 / 18, the energy storage device access positions are 2 / 6 / 13 / 29, and the interruptible load access positions are 6 / 12. In scheme 2, the gas turbine access positions are nodes 3 / 8 / 11 / 26, the energy storage device access positions are 6 / 12 / 14 / 21 / 23 / 28, and the interruptible load access positions are 12 / 20 / 25 / 31. The specific configuration is shown in Table 2.

[0183]

[0184] Table 2 Flexible resource configuration results of the two schemes

[0185] 2. Comparison of flexibility indicators

[0186] Table 3 shows the comparison of configuration cost and flexibility indicators of the two schemes.

[0187]

[0188] Table 3 Comparison of flexibility indicators

[0189] As can be seen from Table 3, compared with scheme 2, scheme 1 has better economy because scheme 1 does not consider short-term uncertainty when configuring flexible resources. As can also be seen from the configuration nodes and quantity of flexible resources, because scheme 1 does not consider the influence of prediction error, the configured resource quantity is also relatively small. As a result, in actual operation, the flexibility resource adequacy level of scheme 1 is low, and there may be a situation of insufficient flexible resources when facing some unexpected conditions. In addition, because the flexible resource configuration of scheme 2 is more uniform, especially the energy storage device, compared with scheme 1, scheme 2 has the characteristics of less number of single-node configuration and more dispersed configuration nodes, and can better achieve "local balance" in actual scheduling, so that the line load rate is more uniform and at a relatively low level, and therefore the branch flexibility support capability is stronger. In summary, the power distribution network flexibility resource configuration method considering long-term and short-term uncertainty provided by the present technology greatly improves the flexibility resource adequacy level and support capability of the power distribution network at the expense of part of the economy.

[0190] Embodiment 3

[0191] Corresponding to embodiment 1, the present embodiment provides a power distribution network flexibility evaluation and resource configuration system, comprising:

[0192] A first scenario set construction module configured to cluster original scenarios of the historical annual daily operation data by using a K-means clustering algorithm to obtain a typical joint scenario set of wind turbine, photovoltaic and load curves;

[0193] A second scenario set construction module configured to represent prediction errors of the typical joint scenario set by using a polyhedral set to obtain a power distribution network operation scenario set;

[0194] A first model construction module configured to establish a flexibility demand model;

[0195] A second model construction module configured to establish a flexibility regulation capability model according to the flexibility demand model, and establish a flexibility evaluation index;

[0196] A third model construction module configured to establish a power distribution network flexibility resource configuration model according to the power distribution network operation scenario set and the flexibility regulation capability model;

[0197] A resource configuration module configured to perform power distribution network flexibility resource configuration according to the power distribution network flexibility resource configuration model.

[0198] The above detailed description of the specific embodiments of the present application has been given to understand the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A power distribution network flexibility assessment and resource configuration method, characterized in that, The method comprises the following steps: obtaining historical annual daily operation data of a power distribution network, clustering original scenes of the historical annual daily operation data by using a K-means clustering algorithm to obtain a typical operation scene set, and establishing a typical joint scene set of wind turbine, photovoltaic and load curves according to the typical operation scene set; establishing a flexibility demand model, and establishing a flexibility regulation capability model according to the flexibility demand model; establishing a power distribution network flexibility resource configuration model according to the power distribution network operation scene set and the flexibility regulation capability model; configuring flexibility resources of the power distribution network according to the power distribution network flexibility resource configuration model; an expression of the flexibility demand model is: In formula (4), F D (t,τ) P NL (t+τ) and P NL (t) are the actual power of the net load of the distribution network at time t and t+τ, respectively, P NL (t+τ) -P NL (t) is the fluctuation of the net load of the distribution network from time t to t+τ; formula (5) represents the upward flexibility demand generated by the distribution network; and formula (6) represents the downward flexibility demand generated by the distribution network. the flexibility regulation capability model comprises a flexibility regulation capability model of a micro gas turbine, a flexibility regulation capability model of interruptible load and a flexibility regulation capability model of an energy storage device; an expression of the flexibility regulation capability model of the micro gas turbine is: in formula (7), and denote the up and down ramping rates of the gas turbine, respectively; and denote the maximum and minimum output of the gas turbine, P gt,t denotes the real-time output power of the gas turbine, and denote the up and down ramping rates of the gas turbine, respectively; an expression of the flexibility regulation capability model of the interruptible load is: In formula (8), and respectively represent the up-regulation and down-regulation ability of the flexibility provided by the controllable load, P il,t and respectively represent the output and maximum output of the controllable load at the current time, and respectively represent the maximum limit of the system to cut off the controllable load and restore the load; an expression of the flexibility regulation capability model of the energy storage device is: in formula (9), respectively represent the flexibility up-regulation and down-regulation ability of the energy storage device, respectively represent the real-time charge / discharge power of the energy storage device, respectively represent the maximum charge / discharge power of the energy storage device, E ess,t represents the real-time power of the energy storage device, respectively represent the minimum and maximum storage power of the energy storage device; The objective function of the power distribution network flexibility resource configuration model is: min F=C inv +C ope (12), in formula (12), C inv represents the planning investment of the equalized output, C ope represents the equalized output operation; C p,t = P p,t π e,t (16), C wt,t = P wt,t π wt (17), C pv,t = P pv,t π pv (18), C il,t = P il,t π il (19), C loss,t = P loss,t π e,t (22), In formula (13) to (22), τ a represents the equivalent annual factor of equipment a, r is the discount rate, y a is the economic service life of equipment a, c gt is the investment cost of the micro gas turbine; c il , c ess are the unit investment costs of interruptible load contract electricity and energy storage configuration capacity, respectively, x gt,i , x il,i , x ess,i are the investment decision variables of the gas turbine, interruptible load, and energy storage equipment, respectively, Ψ gt , Ψil , Ψess are the nodes to be selected for configuration of the gas turbine, interruptible load, and energy storage equipment, respectively, P p,t is the electricity purchased by the distribution network from the upper-level power grid; P wt,t , P pv,t are the actual output of wind and solar power generation; P loss,t is the network power flow transmission loss, π e,t , π g,t are the real-time electricity price and gas price, respectively; π wt , π pv are the unit costs of wind and solar power generation; π il is the interruptible load calling cost; are the unit costs of charging / discharging of the energy storage device, respectively, η gt is the power generation efficiency of the gas turbine; Q lhv is the calorific value of natural gas.

2. The power distribution network flexibility assessment and resource configuration method of claim 1, wherein, a model expression of the typical operation scene set is: In formula (1), S1 represents the number of typical operation scenarios, n is the number of clusters in the iteration process; l n is the loss index caused by clustering; M is the end value of the number of clusters; s is the number of original scenarios; P s is the power vector of the original scenario s; is the cluster center of the cluster where P s is located. The model expression of the typical joint scenario set is: In formula (2), is the power vector of the fan, photovoltaic and load curves at each time in the original scenario s.

3. The power distribution network flexibility assessment and resource configuration method of claim 2, wherein, a model expression of the power distribution network operation scene set is: In formula (3), ∑|a| represents the sum of the information of each time period within a scheduling period, respectively represent the actual power of the wind turbine, photovoltaic and load in the s-th typical scenario, respectively represent the predicted output of the wind turbine, photovoltaic and load in the s-th typical scenario, respectively represent the worst uncertainty range allowed by the wind turbine, photovoltaic and load prediction error in the s-th typical scenario; Γ ζ represent the conservative degree index.

4. The method of claim 1, wherein, after the flexibility regulation capability model is established, the following steps are included: establishing a flexibility resource adequacy model and a branch flexibility response capability model according to the flexibility regulation capability model; an expression of the flexibility resource adequacy model is: In formula (10), I fl represents the flexibility level of the power distribution network in a running cycle; an expression of the branch flexibility response capability model is: In formula (11), L ij,t is the real-time load margin of the branch transmission channel, I br represents the power flow balance degree of the distribution network line in the entire operation cycle.

5. The method of claim 1, wherein, constraint conditions of the power distribution network flexibility resource configuration model comprise power distribution network safe operation constraints, wind and light output constraints, gas turbine constraints, energy storage device constraints, interruptible load constraints, reactive power compensation device constraints and flexibility constraints; an expression of the power distribution network safe operation constraints is: V j,t 2 = V i,t 2 - 2(P ij,t r ij + Q ij,t x ij ) + I ij,t 2 (r ij 2 + x ij 2 ) (25), In formulas (23) to (28), π(j) and δ(j) represent the set of head nodes and tail nodes of the branches connected to node j, respectively; B represents the set of nodes; r ij and x ij represent the resistance and reactance of branch ij, respectively; P ij,t and Q ij,t represent the branch power flow at time t; V j,t is the node voltage; Q cb,j,t represents the reactive power provided by the switchable capacitor, tanθ ij is the power factor of the interruptible load; an expression of the wind and light output constraints is: In formula (29) and formula (30), and Pmax and Pmaxpv are the upper limits of the output of the wind turbine and the upper limits of the output of the photovoltaic array respectively. an expression of the gas turbine constraints is: in formulas (31) to (33), and respectively represent the upper and lower ramp rates of the gas turbine; an expression of the energy storage device constraints is: E ess,T = E ess,0 (35), Formula (34) is a charge-discharge model of the energy storage device; formula (35) represents that the state of charge of the energy storage remains unchanged before and after a scheduling period; formulas (36), (37) and (38) represent the charging power constraint, the discharging power constraint and the power constraint respectively; in formulas (34) to (39), n loss represents the self-loss rate of the energy storage device, and describes the inherent power loss of the energy storage; and respectively represent the charging efficiency and the discharging efficiency of the energy storage device, E ess,0 represents the initial state of charge of the energy storage device, E ess,T represents the state of charge of the energy storage device after the end of the scheduling period, c ess,t represents the charge-discharge identification bit of the energy storage device, and is a 0-1 type variable; x ess represents the decision variable of whether the energy storage device is configured, and is an integer type variable; an expression of the interruptible load constraints is: an expression of the reactive power compensation device constraints is: n cb,j,t ≤N cb,j (42), In formula (41) to (43), n cb,j,t is the number of groups of switched capacitors, is the reactive compensation power provided for each group of capacitors; N cb,j is the upper limit of the number of groups of capacitors that can be switched on; is the upper limit of the number of switching operations of the capacitors in one operating cycle; an expression of the flexibility constraints is:

6. The power distribution network flexibility assessment and resource configuration method of claim 5, wherein, the power distribution network safe operation constraints comprise node voltage constraints and line current constraints.

7. A system for implementing the power distribution network flexibility assessment and resource configuration method of claim 1, characterized by, The method comprises the following steps: a first scene set construction module is configured to cluster original scenes of the historical annual daily operation data by using a K-means clustering algorithm to obtain a typical joint scene set of wind turbine, photovoltaic and load curves; a second scene set construction module is configured to represent prediction errors of the typical joint scene set by using a polyhedral set to obtain a power distribution network operation scene set; a first model construction module is configured to establish a flexibility demand model; a second model construction module is configured to establish a flexibility regulation capability model according to the flexibility demand model, and establish a flexibility evaluation index; a third model construction module is configured to establish a power distribution network flexibility resource configuration model according to the power distribution network operation scene set and the flexibility regulation capability model; a resource configuration module is configured to configure flexibility resources of the power distribution network according to the power distribution network flexibility resource configuration model.

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