An active distribution network optimization and coordination control method and system

Abstract

This invention discloses an active distribution network optimization and coordination control method and system, comprising: establishing an uncertainty set of distributed generation output in the active distribution network based on the output of distributed generation sources, and constructing a first model based on the uncertainty factors of the uncertainty set; constructing a second model based on the power balance relationship between load and distributed generation sources; constructing a third model of the active distribution network based on the dynamic characteristics of the load; constructing a fourth model based on the energy storage state of charge of the active distribution network; constructing an active distribution network optimization and coordination control model with the minimization of the overall cost of the active distribution network as the objective function and in combination with the model setting constraints; and optimizing the parameters based on the active distribution network optimization and coordination control model to obtain the active distribution network optimization and coordination control strategy. This invention, through a four-dimensional integrated and interactive power balance model, can improve the stability, flexibility, and energy utilization efficiency of the power system, better adapting to the development needs of new power systems.

Description

Technical Field

[0001] This invention relates to the technical field of smart grids, and in particular to an active distribution network optimization and coordination control method and system. Background Technology

[0002] With the widespread integration of distributed energy resources into power distribution networks and the development of energy storage and flexible load control technologies, an imbalance has emerged between load power and distributed energy output. The reasons for this imbalance are threefold: first, the uncertainty of distributed energy output, as its capacity is significantly affected by natural conditions; second, the dynamic changes in load demand, with users exhibiting distinct temporal and random characteristics in their electricity consumption; and third, the relatively high cost of energy storage technology, with large-scale deployment increasing the construction and operation costs of power distribution networks, and the limited energy density and cycle life of energy storage systems making it difficult to fully meet the network's energy storage needs. This imbalance between load power and distributed energy output leads to fluctuations in grid operating parameters such as voltage and frequency. A sudden increase in distributed energy output while the load power remains constant may cause a voltage rise, and conversely, a decrease in distributed energy output while the load power is high may lead to a voltage drop. The imbalance also results in a decline in power quality, primarily manifested as voltage deviation, harmonic distortion, and flicker. When the output of distributed energy resources exceeds the load capacity, the excess electricity is wasted if it cannot be effectively utilized. Conversely, when the output of distributed energy resources is insufficient, more electricity needs to be drawn from the upstream power grid to meet the load demand, increasing transmission losses. This imbalance leads to the inability to rationally allocate and efficiently utilize energy, reducing the overall efficiency of the energy system.

[0003] Traditional power balancing primarily revolves around peak load demand. In this model, power system planning and operation focus on ensuring sufficient generation capacity to meet demand during peak load periods. The power supply side is dominated by large, centralized power plants, with generation resources constructed and dispatched based on predicted peak loads, such as building sufficient thermal power plants and large hydropower stations. Its advantage lies in its relative simplicity and directness; by analyzing and predicting historical electricity consumption data, it can, to some extent, ensure the stability of the power supply. However, this model has significant limitations. It relies too heavily on the accuracy of load forecasting; if the forecast is inaccurate, it may lead to overcapacity or undercapacity. Furthermore, it does not adequately consider the integration of new power resources such as distributed energy, cannot effectively utilize the flexibility of the load side, has a slow response time to rapid load changes, and the utilization efficiency of generation resources may be lower during off-peak periods. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides an active distribution network optimization and coordination control method and system to solve the problems mentioned in the background art.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, embodiments of the present invention provide an active distribution network optimization and coordination control method, comprising:

[0009] Based on the output of distributed generation, an uncertainty set of distributed generation output in the active distribution network is established, and a first model is constructed by combining the uncertainty factors of the uncertainty set;

[0010] A second model is constructed based on the power balance relationship between load and distributed power sources;

[0011] Based on the dynamic characteristics of the load, a third model of the active distribution network is constructed;

[0012] A fourth model is constructed based on the state of charge of energy storage in an active distribution network;

[0013] The active distribution network optimization and coordination control model is constructed by taking the minimization of the overall cost of the active distribution network as the objective function and setting constraints in combination with the model. Based on the active distribution network optimization and coordination control model, the parameters are optimized to obtain the active distribution network optimization and coordination control strategy.

[0014] As a preferred embodiment of the active distribution network optimization and coordination control method of the present invention, the step of establishing an uncertain set of distributed power output in the active distribution network and constructing a first model in combination with the uncertainty factors of the uncertain set includes: the distributed power output includes wind power output, photovoltaic power output and hydropower output; the uncertainty factors of wind power output include wind speed, the uncertainty factors of photovoltaic power output include light intensity and temperature, and the uncertainty factors of hydropower output include inflow water flow.

[0015] As a preferred embodiment of the active distribution network optimization and coordinated control method of the present invention, it further includes: the uncertainty set of the wind speed is Ω. w ={v min ≤v≤v max}, where vmin and v max Let these be the lower and upper limits of wind speed, respectively. The wind power generation output model is represented as follows:

[0016]

[0017] Among them, P w (v) represents the power of the wind turbine, P r The rated power of the wind turbine, v ci To cut off the wind speed, v r For the rated wind speed, v co Initial wind speed;

[0018] The uncertainty set of photovoltaic power output is

[0019] The photovoltaic power generation output model considering the uncertainties of light intensity and temperature is expressed as follows:

[0020]

[0021] Among them, P pv (I,T) represents the output of photovoltaic power generation. T represents the rated power of the photovoltaic power generation, and T represents the actual temperature of the photovoltaic cell. r The reference temperature is γ, and the power temperature coefficient is γ.

[0022] The uncertainty set of hydropower output is

[0023] The power output model for hydroelectric power generation is represented as follows:

[0024] P h (Q,H,η)=k h QH η ,(Q,H,η)∈Ω h

[0025] Among them, P h (Q,H,η) represents the processing parameters for hydroelectric power generation, k h is a coefficient.

[0026] As a preferred embodiment of the active distribution network optimization and coordinated control method described in this invention, the method involves: constructing a second model based on the power balance relationship between loads and distributed generation sources; and constructing a third active distribution network model based on load dynamic characteristics, including: performing power flow calculations on each node of the distribution network; and for any connected node and node branch, the expressions for its active power and reactive power are as follows:

[0027]

[0028] Among them, P ij and Qij These represent the active and reactive power of branch ij, respectively. ij and b ij The conductance and susceptance of branch ij are respectively, θ ij =θ i -θ j Let be the voltage phase angle difference between node i and node j.

[0029] As a preferred embodiment of the active distribution network optimization and coordinated control method described in this invention, the load characteristic model established using time series modeling and cluster analysis is expressed as follows:

[0030]

[0031] in, It is a p-order autoregressive model. Here are the autoregressive coefficients, e(t) is a coefficient with a mean of 0 and a variance of σ. 2 White noise sequence, θ i μ is the moving average coefficient. j For cluster C j The centroid is determined by iteratively updating the cluster partition and centroid position until J converges.

[0032] As a preferred embodiment of the active distribution network optimization and coordination control method described in this invention, the fourth model constructed based on the energy storage state of charge of the active distribution network is expressed as follows:

[0033]

[0034] Where SOC(t) is the state of charge of the stored energy at time t, SOC(t0) is the state of charge at the initial time t0, and C rated For the rated capacity of energy storage, I c (τ) and I d (τ) represents the charging current and the discharging current, respectively.

[0035] As a preferred embodiment of the active distribution network optimization and coordination control method of the present invention, the active distribution network optimization and coordination control model is constructed with the goal of minimizing the comprehensive cost of the active distribution network and the constraints set in the model. The comprehensive cost of the active distribution network includes the cost of distributed power generation, energy storage cost and grid operation cost.

[0036] The objective function for total cost is expressed as:

[0037] minC=C DG +C S +C G

[0038] Among them, C GFor grid operating costs, C DG For the cost of distributed power sources, C S Total cost of energy storage;

[0039] The output constraints and installed capacity constraints of wind power generation are expressed as follows:

[0040] 0≤P w (v)≤P r

[0041]

[0042] Where v must satisfy the uncertainty set condition related to the wind speed probability distribution, C w Let i represent the installed capacity of a single wind farm, and i∈wind represent all wind turbines.

[0043] The output constraints and installed capacity constraints of photovoltaic power generation are expressed as follows:

[0044]

[0045] Where I and T represent light intensity and temperature, and C represents temperature. pv For the installed capacity of the photovoltaic power station, j∈pv represents all photovoltaic panels or photovoltaic power station units;

[0046] The power output constraints and reservoir water balance constraints of hydropower generation are expressed as follows:

[0047] 0≤P h (Q,H,η)≤P h,max

[0048]

[0049] Among them, P h,max V is the maximum possible output of hydroelectric power generation. min and V max These represent the upper and lower limits of the reservoir's water storage capacity, respectively; V0 represents the initial water volume of the reservoir; and Q represents the initial water volume of the reservoir. in (t) represents the inflow rate, Q out (t) represents the water consumption for power generation;

[0050] The network-side constraints are expressed as follows:

[0051] V i,min ≤|V i |≤V i,max

[0052] The load characteristic constraint is expressed as:

[0053] L min ≤L(t)≤L max

[0054] Among them, Lmin and L max These are the lower and upper limits, determined based on load type and historical data, respectively.

[0055] The energy storage state of charge constraint is expressed as:

[0056] SOC min ≤SOC(t)≤SOC max

[0057] Among them, SOC min and SOC max These represent the minimum and maximum states of charge that energy storage can tolerate, respectively.

[0058] Secondly, the present invention provides an active distribution network optimization and coordination control system, comprising:

[0059] The first model building module is used to construct an uncertain set model of the output of distributed power sources in the active distribution network based on the output of distributed power sources, and to construct the first model in combination with the uncertainty factors of the uncertain set model.

[0060] The second model building module is used to construct a second model based on the power balance relationship between load and distributed power sources.

[0061] The third model building module is used to construct a third model of the active distribution network based on the dynamic characteristics of the load.

[0062] The fourth model building module is used to construct a fourth model based on the energy storage state of charge of the active distribution network.

[0063] The control module is used to construct an active distribution network optimization and coordination control model with the objective function of minimizing the overall cost of the active distribution network and the constraints set in the model. Based on the active distribution network optimization and coordination control model, the parameters are optimized to obtain the active distribution network optimization and coordination control strategy.

[0064] Thirdly, the present invention provides an electronic device, comprising:

[0065] Memory and processor;

[0066] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the active distribution network optimization and coordination control method.

[0067] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the active distribution network optimization and coordination control method.

[0068] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention analyzes and processes four-dimensional distribution network data (source-grid-load-storage) to formulate scheduling optimization strategies. Simultaneously, a high-speed and reliable communication network ensures real-time information transmission between all parties, guaranteeing the coordinated operation of the entire system. This invention, through a four-dimensional integrated and interactive power balance model, can improve the stability, flexibility, and energy utilization efficiency of the power system, better adapting to the development needs of new power systems. Attached Figure Description

[0069] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0070] Figure 1 This is a schematic diagram of the process flow of an active distribution network optimization and coordination control method and system according to an embodiment of the present invention. Detailed Implementation

[0071] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0072] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0073] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0074] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0075] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0076] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0077] Example 1

[0078] Reference Figure 1 As one embodiment of the present invention, this embodiment provides an active distribution network optimization and coordination control method, comprising:

[0079] S100: Based on the output of distributed generation, establish the uncertainty set of distributed generation output in the active distribution network, and construct the first model by combining the uncertainty factors of the uncertainty set;

[0080] S200: Construct a second model based on the power balance relationship between load and distributed power sources;

[0081] S300: Construct a third model of an active distribution network based on load dynamic characteristics;

[0082] S400: Constructing a fourth model based on the state of charge of energy storage in an active distribution network;

[0083] S500: The objective function is to minimize the overall cost of the active distribution network. An active distribution network optimization and coordination control model is constructed by combining the model setting constraints. The active distribution network optimization and coordination control strategy is obtained by optimizing the parameters based on the active distribution network optimization and coordination control model.

[0084] It should be noted that traditional power balancing primarily revolves around maximum load demand. In this model, power system planning and operation mainly focus on ensuring sufficient generation capacity to meet power demand during peak load periods. The power supply side is dominated by large-scale centralized power plants, with power generation resources constructed and dispatched based on predicted maximum load, such as building sufficient capacity thermal power plants and large hydropower stations. Its advantage lies in its relative simplicity and directness; by analyzing and predicting historical electricity consumption data, it can, to some extent, ensure the stability of power supply. However, this model has significant limitations. It relies too heavily on the accuracy of load forecasting; if the forecast is inaccurate, it may lead to overcapacity or undercapacity. Furthermore, it does not fully consider the integration of new power resources such as distributed energy sources, cannot effectively utilize the flexibility of the load side, has a slow response speed to rapid load changes, and the utilization efficiency of power generation resources may be lower during off-peak periods. This invention collects and analyzes data from power sources, grids, loads, and storage, including real-time output of distributed power sources, grid operating parameters, load electricity consumption, and the charging and discharging status of energy storage. Based on this data analysis and processing, an optimized dispatching strategy is formulated. Meanwhile, a high-speed and reliable communication network ensures real-time information transmission between all parties, guaranteeing the coordinated operation of the entire system. This four-dimensional integrated and interactive power balance model can improve the stability, flexibility, and energy efficiency of the power system, better adapting to the development needs of new power systems.

[0085] In this application embodiment, an uncertainty set of distributed power output in an active distribution network is established, and a first model is constructed in combination with the uncertainty factors of the uncertainty set. The distributed power output includes wind power output, photovoltaic power output and hydropower output; the uncertainty factors of wind power output include wind speed, the uncertainty factors of photovoltaic power output include light intensity and temperature, and the uncertainty factors of hydropower output include inflow water flow.

[0086] In an optional embodiment, based on the output of distributed generation, the source side of the active distribution network is analyzed to construct uncertainty sets for wind power output, photovoltaic power output, and hydropower output. Uncertainty factors are considered to improve the prediction of distributed generation output. The main uncertainties affecting wind power output include random variations in wind speed, wind farm wake effects, and wind turbine failures. Among these, the uncertainty of wind speed is the most critical factor. Let the uncertainty set of wind speed be Ω. w ={v min ≤v≤v max}, where v min and v max These are the lower and upper limits of wind speed, respectively. Considering the probability distribution characteristics of wind speed, the uncertainty set can be further represented as a basic probability distribution.

[0087] For example, based on the wind speed probability density function of the Weibull distribution, a wind speed interval corresponding to a certain confidence level α is constructed as an uncertainty set, and the wind speed probability density function is expressed as:

[0088]

[0089] That is, to solve for the lower limit wind speed value v l and the upper limit wind speed value v u , making The uncertain set is Ω w ={v l ≤v≤v u}

[0090] In this embodiment of the application, the uncertainty set of the wind speed is further defined as Ω. w ={v min ≤v≤v max}, where v min and v max Let these be the lower and upper limits of wind speed, respectively. The wind power generation output model is represented as follows:

[0091]

[0092] Among them, P w (v) represents the power of the wind turbine, P r The rated power of the wind turbine, v ci To cut off the wind speed, v r For the rated wind speed, v co This refers to the initial wind speed.

[0093] In one alternative embodiment, the uncertainty in photovoltaic power output mainly stems from random variations in irradiance, temperature fluctuations, aging of photovoltaic modules, and shading. Among these, irradiance and temperature are the two primary uncertainties.

[0094] For light intensity, similar to the uncertainty set construction method for wind speed in wind power generation, the probability distribution function of light intensity is expressed as:

[0095]

[0096] Where I is the normalized value of light intensity, α and β are the shape parameters of the Beta distribution, and Γ(·) is the gamma function.

[0097] The uncertainty set is determined by the range of light intensity corresponding to a certain confidence level α. For temperature, an uncertainty set can be constructed based on statistical analysis of historical temperature data:

[0098]

[0099] In this embodiment of the application, considering the uncertainties of both light intensity and temperature, the uncertainty set of photovoltaic power generation output is:

[0100] The photovoltaic power generation output model considering the uncertainties of light intensity and temperature is expressed as:

[0101]

[0102] Among them, P pv (I,T) represents the output of photovoltaic power generation. T represents the rated power of the photovoltaic power generation, and T represents the actual temperature of the photovoltaic cell. r The reference temperature is γ, and the power temperature coefficient is γ.

[0103] In one optional embodiment, the uncertainty of hydropower output is mainly related to factors such as changes in inflow, fluctuations in reservoir water level, changes in turbine efficiency, and grid dispatch requirements. Among these, the uncertainty of inflow is the key factor affecting hydropower output.

[0104] Based on the probability distribution of incoming water flow:

[0105]

[0106] Where Q is the inflow rate, μ Q σ represents the average inflow rate. Q This represents the standard deviation of the incoming water flow rate.

[0107] Uncertain set of water flow rate determined at a certain confidence level α Simultaneously considering the range of changes in reservoir water level and turbine efficiency, a corresponding uncertainty set is constructed. and

[0108] In this embodiment of the application, the uncertainty set of hydropower output is:

[0109]

[0110] The power output model for hydroelectric power generation is represented as follows:

[0111] P h (Q,H,η)=k h QH η ,(Q,H,η)∈Ω h

[0112] Among them, P h (Q,H,η) represents the processing parameters for hydroelectric power generation, k h is a coefficient.

[0113] It should be noted that power flow calculation models are fundamental tools for analyzing the operating status of distribution networks and play an important role in studying the balance between load power and distributed generation output. Through power flow calculations, information such as voltage amplitude, phase angle, and power distribution of each branch at each node in the distribution network can be obtained, thus providing a direct understanding of the power balance between loads and distributed generation.

[0114] In an optional embodiment, for a distribution network with n nodes, its node voltage equations can be expressed as:

[0115] I = YV

[0116] Where I is the node injection current column vector, V is the node voltage column vector, and Y is the node admittance matrix. For node i, the relationship between its injection current and node voltage is:

[0117]

[0118] According to Kirchhoff's laws, the branch power flow equations can be obtained.

[0119] In this embodiment, a second model is constructed based on the power balance relationship between loads and distributed generation. A third model of the active distribution network is constructed based on the load dynamic characteristics, including: performing power flow calculations on each node of the distribution network. For any connected node and node branch, the expressions for its active and reactive power are as follows:

[0120]

[0121] Among them, P ij and Q ij These represent the active and reactive power of branch ij, respectively. ij and b ij The conductance and susceptance of branch ij are respectively, θ ij =θ i -θ j Let be the voltage phase angle difference between node i and node j.

[0122] In this embodiment of the application, the load characteristic model established using time series modeling and cluster analysis methods is expressed as follows:

[0123]

[0124] in, For a p-order autoregressive model, Here are the autoregressive coefficients, e(t) is a coefficient with a mean of 0 and a variance of σ. 2 White noise sequence, θ i μ is the moving average coefficient. j For cluster C jThe centroid is determined by iteratively updating the cluster partition and centroid position until J converges.

[0125] In an optional embodiment, the value is estimated using methods such as least squares. value; It is a q-order moving average model.

[0126] It should be noted that the above model can more accurately describe the dynamic characteristics of load changes over time, and is especially suitable for short-term load forecasting.

[0127] In an optional embodiment, the load data is viewed as points in a high-dimensional space, and clustering is performed based on the similarity of load curves. Let the load dataset X = {x1, x2, ..., x...} n}, where x i It is a vector representing a load curve. For k-means clustering, the goal is to divide X into k clusters C1, C2, ..., Ck. k This minimizes the sum of squared errors within the cluster, i.e.:

[0128]

[0129] Where, μ j It is cluster C j By iteratively updating the cluster partitioning and centroid position until J converges, different types of loads can be distinguished and their characteristics analyzed.

[0130] By calculating the similarity between load data, a clustering tree is gradually constructed. This can be either agglomerative or divisive. When calculating similarity, methods such as Euclidean distance and Pearson correlation coefficient can be used. This method does not require pre-specifying the number of clusters and can intuitively display the hierarchical structure of the load data.

[0131] In this embodiment of the application, the fourth model based on the energy storage state of charge of the active distribution network is represented as follows:

[0132]

[0133] Where SOC(t) is the state of charge of the stored energy at time t, SOC(t0) is the state of charge at the initial time t0, and C rated For the rated capacity of energy storage, I c (τ) and I d (τ) represents the charging current and the discharging current, respectively.

[0134] It should be noted that the fourth model is based on the principle of energy conservation and determines the state of charge (SOC) by calculating the change in energy during the charging and discharging process through integration.

[0135] In this embodiment of the application, the active distribution network optimization and coordination control model is constructed with the goal of minimizing the overall cost of the active distribution network and the model setting constraints. The overall cost of the active distribution network includes the cost of distributed power generation, energy storage cost and grid operation cost.

[0136] The objective function for total cost is expressed as:

[0137] minC=C DG +C S +C G

[0138] Among them, C G For grid operating costs, C DG For the cost of distributed power sources, C S This represents the total cost of energy storage.

[0139] Specifically, the cost C of distributed power sources DG Represented as:

[0140] C DG,i (P DG ,i)=C inv,DG,i +C om,DG,i (P DG ,i)+C fuel,DG,i (P DG ,i)

[0141] Among them, C inv,DG,i Let C be the initial investment cost of the i-th distributed power source. om,DG,i (P DG Let C(i) be the operation and maintenance cost of the i-th distributed power source. fuel,DG,i (P DG ,i) represents the fuel cost of the i-th distributed power source.

[0142] The total cost of distributed power sources is expressed as:

[0143]

[0144] In an optional embodiment, suppose there are m energy storage devices in the distribution network, and for the j-th energy storage device, the cost C S,j (P S ,j) can be represented as:

[0145] C S,j (P S ,j)=C inv,S,j +C om,j (P S ,j)+C loss,j (P S ,j)+C life,j (P S ,j)

[0146] Among them, C inv,S,j Let C be the initial investment cost of the j-th energy storage device. om,j (P S Let C(j) be the operation and maintenance cost of the j-th energy storage device. loss,j (P S Let C(j) be the charging and discharging loss cost of the j-th energy storage device. life,j (P S ,j) represents the lifespan loss cost of the j-th energy storage device.

[0147] The total cost of energy storage is expressed as:

[0148]

[0149] Power grid operating cost C G Represented as:

[0150] C G =C loss,G +C d +C m

[0151] Among them, C loss,G For network loss costs, C d C represents the depreciation cost of power grid equipment. m The cost of maintenance for power grid equipment.

[0152] In an optional embodiment, suppose there are n nodes in the distribution network, the load power of each node is , the output of the distributed power source is , and the charging and discharging power of the energy storage device is , then the power balance constraint is:

[0153]

[0154] In this embodiment of the application, the output constraint and installed capacity constraint of wind power generation are expressed as follows:

[0155]

[0156] Where v must satisfy the uncertainty set condition related to the wind speed probability distribution, C w Let i represent the installed capacity of a single wind farm, and i∈wind represent all wind turbines.

[0157] The output constraints and installed capacity constraints of photovoltaic power generation are expressed as follows:

[0158]

[0159] Where I and T represent light intensity and temperature, and C represents temperature. pv For the installed capacity of the photovoltaic power station, j∈pv represents all photovoltaic panels or photovoltaic power station units;

[0160] The power output constraints and reservoir water balance constraints of hydropower generation are expressed as follows:

[0161] 0≤P h (Q,H,η)≤P h,max

[0162]

[0163] Among them, P h,max V is the maximum possible output of hydroelectric power generation. min and V max These represent the upper and lower limits of the reservoir's water storage capacity, respectively; V0 represents the initial water volume of the reservoir; and Q represents the initial water volume of the reservoir. in (t) represents the inflow rate, Q out (t) represents the water consumption for power generation;

[0164] The network-side constraints are expressed as follows:

[0165] V i,min ≤|V i |≤V i,max

[0166] The load characteristic constraint is expressed as:

[0167] L min ≤L(t)≤L max

[0168] Among them, L min and L max These are the lower and upper limits, determined based on load type and historical data, respectively.

[0169] The energy storage state of charge constraint is expressed as:

[0170] SOC min ≤SOC(t)≤SOC max

[0171] Among them, SOC min and SOC max These represent the minimum and maximum states of charge that energy storage can tolerate, respectively.

[0172] In an optional embodiment, the upper and lower limits of the energy storage device's charging and discharging power are constrained as follows:

[0173] P s,j,min ≤P s,j ≤P s,j,max

[0174] In addition, other relevant constraints, such as node voltage constraints and branch power constraints, are imposed to ensure the safe and stable operation of the distribution network. These constraints together constitute an optimization problem aimed at minimizing the overall cost of the active distribution network while considering the balance between the output of distributed generation and the load power.

[0175] It should be noted that the embodiments of this application use mathematical optimization algorithms to optimize the parameters of the above objective function and constraints. This step can use linear programming, nonlinear programming or mixed integer programming methods to optimize the parameters of the objective function and constraints. No specific limitation is made here. One of the algorithms can be selected to optimize and obtain the active distribution network optimization and coordination control strategy.

[0176] Example 2

[0177] The above embodiment is an illustrative scheme of an active distribution network optimization and coordination control method. It should be noted that the technical solution of this active distribution network optimization and coordination control system belongs to the same concept as the technical solution of the aforementioned active distribution network optimization and coordination control method. Details not described in detail in this embodiment can be found in the description of the aforementioned active distribution network optimization and coordination control method.

[0178] This embodiment of an active distribution network optimization and coordination control system includes:

[0179] The first model building module is used to construct an uncertain set model of the output of distributed generation in the active distribution network based on the output of distributed generation, and to construct the first model by combining the uncertainty factors of the uncertain set model.

[0180] The second model building module is used to construct a second model based on the power balance relationship between load and distributed power sources.

[0181] The third model building module is used to construct a third model of the active distribution network based on the dynamic characteristics of the load.

[0182] The fourth model building module is used to construct a fourth model based on the energy storage state of charge of the active distribution network.

[0183] The control module is used to construct an active distribution network optimization and coordination control model with the objective function of minimizing the overall cost of the active distribution network and in combination with model setting constraints. Based on the active distribution network optimization and coordination control model, the parameters are optimized to obtain the active distribution network optimization and coordination control strategy.

[0184] This embodiment also provides an electronic device applicable to active distribution network optimization and coordination control methods, including:

[0185] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the active distribution network optimization and coordination control method proposed in the above embodiments.

[0186] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the active distribution network optimization and coordination control method proposed in the above embodiments.

[0187] The storage medium proposed in this embodiment and the active distribution network optimization and coordination control method proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0188] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0189] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for active distribution network optimization and coordinated control, characterized in that, include: Based on the output of distributed generation, an uncertainty set of distributed generation output in the active distribution network is established, and a first model is constructed by combining the uncertainty factors of the uncertainty set; A second model is constructed based on the power balance relationship between load and distributed power sources; Based on the dynamic characteristics of the load, a third model of the active distribution network is constructed; A fourth model is constructed based on the state of charge of energy storage in an active distribution network; The active distribution network optimization and coordination control model is constructed with the goal of minimizing the overall cost of the active distribution network and the constraints set in the model. Based on the active distribution network optimization and coordination control model, the parameters are optimized to obtain the active distribution network optimization and coordination control strategy. The active distribution network optimization and coordination control model, with the goal of minimizing the overall cost of the active distribution network and the constraints set in the model, includes the following: the overall cost of the active distribution network includes the cost of distributed power generation, energy storage cost, and grid operation cost. The objective function for total cost is expressed as: minC=C DG +C S +C G Among them, C G For grid operating costs, C DG For the cost of distributed power sources, C S Total cost of energy storage; The output constraints and installed capacity constraints of wind power generation are expressed as follows: 0≤P w (v)≤P r Among them, P w (v) represents the power of the wind turbine, and v must satisfy the uncertainty set condition related to the wind speed probability distribution. r C represents the rated power of the wind turbine. w Let i represent the installed capacity of the wind farm, and i∈wind represent all wind turbines. The output constraints and installed capacity constraints of photovoltaic power generation are expressed as follows: Where I and T are light intensity and temperature, and P pv (I,T) represents the output of photovoltaic power generation. T represents the rated power of photovoltaic power generation. r The reference temperature is T, γ is the power temperature coefficient, and T is the reference temperature. z C represents the actual temperature of the photovoltaic cell. pv For the installed capacity of the photovoltaic power station, j∈pv represents all photovoltaic panels or photovoltaic power station units; The power output constraints and reservoir water balance constraints of hydropower generation are expressed as follows: 0≤P h (Q,H,η)≤P h,max Among them, P h (Q,H,η) represents the processing of hydroelectric power generation, P h,max V is the maximum possible output of hydroelectric power generation. min and V max These represent the upper and lower limits of the reservoir's water storage capacity, respectively; V0 represents the initial water volume of the reservoir; and Q represents the initial water volume of the reservoir. in (t) represents the inflow rate, Q out (t) represents the water consumption for power generation; The network-side constraints are expressed as follows: V i,min ≤|V i |≤V i,max The load characteristic constraint is expressed as: L min ≤L(t)≤L max Among them, L min and L max These are the lower and upper limits, determined based on load type and historical data, respectively. The energy storage state of charge constraint is expressed as: SOC min ≤SOC(t)≤SOC max Among them, SOC min and SOC max These represent the minimum and maximum states of charge that energy storage can tolerate, respectively.

2. The active distribution network optimization and coordination control method as described in claim 1, characterized in that, The establishment of the uncertainty set of distributed power output in the active distribution network, and the construction of the first model in combination with the uncertainty factors of the uncertainty set, includes: the distributed power output includes wind power output, photovoltaic power output and hydropower output; the uncertainty factors of wind power output include wind speed, the uncertainty factors of photovoltaic power output include light intensity and temperature, and the uncertainty factors of hydropower output include inflow water flow.

3. The active distribution network optimization and coordination control method as described in claim 2, characterized in that, Also includes: The uncertainty set of the wind speed is Ω w ={v min ≤v≤v max }, where v min and v max Let these be the lower and upper limits of wind speed, respectively. The wind power generation output model is represented as follows: Among them, v ci To cut off the wind speed, v r For the rated wind speed, v co Initial wind speed; The uncertainty set of photovoltaic power output is The photovoltaic power generation output model considering the uncertainties of light intensity and temperature is expressed as: The uncertainty set of hydropower output is The power output model for hydroelectric power generation is represented as follows: P h (Q,H,η)=k h QH η ,(Q,H,η)∈Ω h Where, k h is a coefficient.

4. The active distribution network optimization and coordination control method as described in claim 3, characterized in that, A second model is constructed based on the power balance relationship between loads and distributed generation. A third model of the active distribution network is then built based on the dynamic characteristics of the load, including: power flow calculations for each node in the distribution network; for any connected node and its branch, the expressions for active and reactive power are as follows: Among them, P ij and Q ij These represent the active and reactive power of branch ij, respectively. ij and b ij The conductance and susceptance of branch ij are respectively, θ ij =θ i -θ j Let be the voltage phase angle difference between node i and node j.

5. The active distribution network optimization and coordination control method as described in claim 4, characterized in that, The load characteristic model established using time series modeling and cluster analysis is expressed as follows: in, For a p-order autoregressive model, Here are the autoregressive coefficients, e(t) is a coefficient with a mean of 0 and a variance of σ. 2 White noise sequence, θ i μ is the moving average coefficient. j For cluster C j The centroid is determined by iteratively updating the cluster partition and centroid position until J converges.

6. The active distribution network optimization and coordination control method as described in claim 5, characterized in that, The fourth model, based on the state of charge of energy storage in an active distribution network, is represented as follows: Where SOC(t) is the state of charge of the stored energy at time t, SOC(t0) is the state of charge at the initial time t0, and C rated For the rated capacity of energy storage, I c (τ) and I d (τ) represents the charging current and the discharging current, respectively.

7. An active distribution network optimization and coordination control system, applied to the method described in any one of claims 1-6, characterized in that, include: The first model building module is used to construct an uncertain set model of the output of distributed power sources in the active distribution network based on the output of distributed power sources, and to construct the first model in combination with the uncertainty factors of the uncertain set model. The second model building module is used to construct a second model based on the power balance relationship between load and distributed power sources. The third model building module is used to construct a third model of the active distribution network based on the dynamic characteristics of the load. The fourth model building module is used to construct a fourth model based on the energy storage state of charge of the active distribution network. The control module is used to construct an active distribution network optimization and coordination control model with the objective function of minimizing the overall cost of the active distribution network and the constraints set in the model. Based on the active distribution network optimization and coordination control model, the parameters are optimized to obtain the active distribution network optimization and coordination control strategy.

8. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the active distribution network optimization and coordination control method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the active distribution network optimization and coordination control method according to any one of claims 1 to 6.

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