Active power distribution network optimization coordination control method and system

Abstract

The invention discloses an active power distribution network optimization coordination control method and system, and the method comprises the steps: building an uncertain set of the output of a distributed power supply of an active power distribution network based on the output condition of the distributed power supply, and building a first model through combining the uncertain factors of the uncertain set; constructing a second model based on the power balance relationship between the load and the distributed power supply; constructing a third model of the active power distribution network according to the dynamic characteristics of the load; constructing a fourth model based on the energy storage charge state of the active power distribution network; and taking the minimization of the comprehensive cost of the active power distribution network as a target function, constructing an active power distribution network optimization coordination control model by combining with a model setting constraint condition, and performing parameter optimization based on the active power distribution network optimization coordination control model to obtain an active power distribution network optimization coordination control strategy. The stability, flexibility and energy utilization efficiency of the electric power system can be improved through the four-dimensional fusion interactive electric power and electric quantity balance mode, and the development requirements of the novel electric power system can be better met.

Description

Technical Field

[0001] The present 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 Art

[0002] With the widespread access of distributed energy in the distribution network, as well as the development of energy storage technology and flexible load control technology, there is an imbalance between load power and distributed power output. The reasons for the imbalance are: first, the uncertainty of distributed energy output, that is, the output of distributed energy such as solar energy and wind energy is significantly affected by natural conditions; second, the dynamic change of load demand, the user's power load has obvious time characteristics and randomness; third, the cost of energy storage technology is relatively high, and large-scale configuration of energy storage systems will increase the construction and operation costs of distribution networks. In addition, the energy density and cycle life of energy storage systems are limited, and it is difficult to fully meet the distribution network's demand for energy storage. The imbalance between load power and distributed energy output will cause fluctuations in grid voltage, frequency and other operating parameters. When the output of distributed energy suddenly increases and the load power remains unchanged, it may cause the voltage to rise. Conversely, when the output of distributed energy decreases and the load power is large, it may cause the voltage to drop. The imbalance problem will also lead to a decrease in power quality, mainly manifested in voltage deviation, harmonic distortion, flicker and other problems. When the output of distributed energy is greater than the load power, if the excess power cannot be effectively utilized, it will cause energy waste; when the output of distributed energy is insufficient, more power needs to be obtained from the upper power grid to meet the load demand, which increases the transmission loss of the power grid. This imbalance leads to the inability to reasonably allocate and efficiently utilize energy, reducing the utilization efficiency of the entire energy system.

[0003] Traditional power balance mainly revolves around maximum load demand. In this mode, power system planning and operation mainly focus on ensuring sufficient power generation capacity to meet power demand during peak load periods. The power supply side is mainly based on large centralized power stations, and power generation resources are built and dispatched according to the predicted maximum load, such as building thermal power plants and large hydropower stations with sufficient capacity. Its advantage is that it is relatively simple and direct. By analyzing and predicting historical power consumption data, it can guarantee the stability of power supply to a certain extent. However, this model has obvious limitations. It relies too much on the accuracy of load forecasting. If the forecast is biased, it may lead to excess or insufficient power generation capacity. At the same time, it does not fully consider the access of new power resources such as distributed energy, cannot effectively utilize the flexibility of the load side, has a slow response speed when facing rapid load changes, and the utilization efficiency of power generation resources may be low during non-peak hours. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above 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 technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides an active distribution network optimization and coordinated control method, comprising:

[0009] Based on the output of distributed power sources, an uncertainty set of the output of distributed power sources in the active distribution network is established, and a first model is constructed in combination with uncertainty factors of the uncertainty set;

[0010] Building a second model based on the power balance relationship between the load and the distributed generation;

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

[0012] Constructing a fourth model based on the energy storage charge state of the active distribution network;

[0013] Taking minimization of the comprehensive cost of the active distribution network as the objective function and combining the constraint conditions set by the model, an active distribution network optimization and coordination control model is constructed, and based on the active distribution network optimization and coordination control model, parameter optimization is performed to obtain an active distribution network optimization and coordination control strategy.

[0014] As a preferred scheme of the active distribution network optimization and coordinated control method described in the present invention, wherein: the establishment of an uncertainty set of distributed power output of the active distribution network, and the construction of a first model in combination with the uncertainty factors of the uncertainty set include: the distributed power output includes wind power output, photovoltaic power output and hydropower output; the uncertainty factors of the wind power output include wind speed, the uncertainty factors of the photovoltaic power output include light intensity and temperature, and the uncertainty factors of the hydropower output include water flow.

[0015] As a preferred solution of the active distribution network optimization and coordinated control method described in the present invention, it also includes: the uncertainty set of wind speed is Ω w = {v min ≤v≤v max}, where vmin and v max are the lower and upper limits of wind speed respectively. The wind power generation output model is expressed as:

[0016]

[0017] Among them, P w (v) is the power of the wind turbine generator, P r is the rated power of the wind turbine, v ci is the cut-in wind speed, v r is the rated wind speed, v co is the initial wind speed;

[0018] The uncertainty set of photovoltaic power generation output is

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

[0020]

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

[0022] The uncertainty set of hydropower output is

[0023] The hydropower output model is expressed as:

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

[0025] Among them, P h (Q,H,η) is the treatment of hydropower generation, k h is the coefficient.

[0026] As a preferred solution of the active distribution network optimization and coordinated control method described in the present invention, the second model is constructed based on the power balance relationship between the load and the distributed power source, and the third model of the active distribution network is constructed based on the dynamic characteristics of the load, including: performing flow calculation on each node of the distribution network, and for any connecting node and node branch, the expression of its active power and reactive power is:

[0027]

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

[0029] As a preferred solution of the active distribution network optimization and coordinated control method of the present invention, a load characteristic model is established by using a time series model and a cluster analysis method, which is expressed as:

[0030]

[0031] in, is a p-order autoregressive model, is the autoregressive coefficient, e(t) has a mean of 0 and a variance of σ 2 The white noise sequence, θ i is the moving average coefficient, μ j Cluster C j The centroid of the cluster is updated through continuous iteration until J converges.

[0032] As a preferred solution of the active distribution network optimization and coordinated control method of the present invention, the fourth model constructed based on the energy storage charge state of the active distribution network is expressed as:

[0033]

[0034] Among them, SOC(t) is the state of charge of the energy storage at time t, SOC(t 0 ) is the initial time t 0 The state of charge, C rated is the rated capacity of energy storage, I c (τ) and I d (τ) are the charging current and discharging current respectively.

[0035] As a preferred solution of the active distribution network optimization and coordination control method described in the present invention, wherein: taking the minimization of the comprehensive cost of the active distribution network as the objective function, and setting the constraint conditions in combination with the model to construct the active distribution network optimization and coordination control model includes: the comprehensive cost of the active distribution network includes the distributed power source cost, the energy storage cost and the grid operation cost;

[0036] The objective function of the comprehensive cost is expressed as:

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

[0038] Among them, C G is the grid operation cost, C DG is the distributed generation cost, C S is the total cost of energy storage;

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

[0040] 0≤P w (v)≤P r

[0041]

[0042] Among them, v needs to satisfy the uncertainty set conditions related to the wind speed probability distribution, C w is the installed capacity of a single wind farm, i∈wind represents all wind turbines;

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

[0044]

[0045] Where I and T are light intensity and temperature, C pv is the installed capacity of the PV power station, j∈pv represents all PV panels or PV power station units;

[0046] The output constraint of hydropower generation and the water balance constraint of reservoir are expressed as:

[0047] 0≤P h (Q,H,η)≤P h,max

[0048]

[0049] Among them, P h,max is the maximum possible output of hydroelectric power, V min and V max are the upper and lower limits of the reservoir’s water storage capacity, V 0 is the initial water volume of the reservoir, Q in (t) is the inflow flow, Q out (t) is the amount of water used for power generation;

[0050] The grid-side constraints are expressed as:

[0051] V i,min ≤|V i |≤V i,max

[0052] The load characteristic constraint is expressed as:

[0053] L min ≤L(t)≤Lmax

[0054] Among them, L min and L max are the lower and upper limits determined according to 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 are the minimum and maximum states of charge allowed for energy storage.

[0058] In a second aspect, the present invention provides an active distribution network optimization and coordination control system, comprising:

[0059] A first model building module is used to build 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 build a first model in combination with uncertainty factors of the uncertain set model;

[0060] A second model building module, used to build a second model based on the power balance relationship between the load and the distributed power source;

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

[0062] A fourth model building module, used to build a fourth model based on the energy storage charge state of the active distribution network;

[0063] The control module is used to take the minimization of the comprehensive cost of the active distribution network as the objective function, and to construct an active distribution network optimization and coordination control model in combination with the constraint conditions set by the model, and to obtain an active distribution network optimization and coordination control strategy by performing parameter optimization based on the active distribution network optimization and coordination control model.

[0064] In a third aspect, 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, the steps of the active power distribution network optimization and coordinated control method are implemented.

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

[0068] Compared with the prior art, the present invention has the following beneficial effects: the present invention analyzes and processes the four-dimensional distribution network data of source-grid-load-storage to formulate a dispatch optimization strategy. At the same time, the real-time transmission of information between the parties is ensured through a high-speed and reliable communication network to ensure the coordinated operation of the entire system. The present invention can improve the stability, flexibility and energy utilization efficiency of the power system through a four-dimensional fusion and interactive power balance mode, and better adapt to the development needs of new power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0070] Figure 1 The present invention is a flowchart of an active power distribution network optimization and coordination control method and system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0071] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0072] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present 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 term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0074] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0075] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0076] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0077] Example 1

[0078] Reference Figure 1 , is an embodiment of the present invention, which provides an active distribution network optimization and coordinated control method, including:

[0079] S100: Based on the output of distributed power sources, an uncertainty set of the output of distributed power sources of the active distribution network is established, and a first model is constructed in combination with uncertainty factors of the uncertainty set;

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

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

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

[0083] S500: Taking the minimization of the comprehensive cost of the active distribution network as the objective function, and combining the model setting constraints to build an active distribution network optimization coordination control model, based on the active distribution network optimization coordination control model, the parameters are optimized to obtain the active distribution network optimization coordination control strategy.

[0084] It should be noted that the traditional power balance is mainly carried out around the maximum load demand. In this mode, the planning and operation of the power system mainly focus on ensuring sufficient power generation capacity to meet the power demand during the peak load period. The power supply side is mainly based on large centralized power stations, and power generation resources are constructed and dispatched according to the predicted maximum load, such as building thermal power plants and large hydropower stations with sufficient capacity. Its advantage is that it is relatively simple and direct. By analyzing and predicting historical power consumption data, it can guarantee the stability of power supply to a certain extent. However, this model has obvious limitations. It relies too much on the accuracy of load forecasting. If the forecast is biased, it may lead to excess or insufficient power generation capacity. At the same time, it does not fully consider the access of new power resources such as distributed energy, and cannot effectively utilize the flexibility of the load side. It has a slow response speed when facing rapid changes in load, and the utilization efficiency of power generation resources may be low during non-peak hours. The present invention collects and analyzes data from the source, network, load, and storage parties, including the real-time output of distributed power sources, the operating parameters of the power grid, the power consumption of the load, and the charging and discharging status of the energy storage, and then formulates an optimized scheduling strategy based on these data after analysis and processing. At the same time, the high-speed and reliable communication network ensures the real-time transmission of information between all parties and guarantees the coordinated operation of the entire system. This four-dimensional integrated and interactive power and electricity balance mode can improve the stability, flexibility and energy efficiency of the power system and better adapt to the development needs of the new power system.

[0085] In an embodiment of the present application, an uncertainty set of the output of distributed power sources in the active distribution network is established, and a first model is constructed in combination with the uncertainty factors of the uncertainty set, including: the output of distributed power sources 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 water flow.

[0086] In an optional embodiment, based on the output of distributed power sources, the source side of the active distribution network is analyzed to construct uncertainty sets of wind power output, photovoltaic power output, and hydropower output, and the prediction of distributed power output is improved by considering uncertainty factors; the main uncertain factors affecting wind power output include random changes in wind speed, wind farm wake effects, wind turbine failures, etc. Among them, 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 are the lower limit and upper limit of wind speed respectively. Considering the probability distribution characteristics of wind speed, the uncertainty set can be further expressed in the form of basic probability distribution.

[0087] Exemplarily, a wind speed interval corresponding to a certain confidence level α is constructed as an uncertainty set according to the wind speed probability density function of the Weibull distribution. The wind speed probability density function is expressed as:

[0088]

[0089] That is, solve the lower limit wind speed value v l and the upper wind speed value v u , so that Then the uncertainty set is Ω w = {v l ≤v≤v u}.

[0090] In the embodiment of the present application, it also includes: the uncertainty set of wind speed is Ω w = {v min ≤v≤v max}, where v min and v max are the lower and upper limits of wind speed respectively. The wind power generation output model is expressed as:

[0091]

[0092] Among them, P w (v) is the power of the wind turbine generator, P r is the rated power of the wind turbine, v ci is the cut-in wind speed, v r is the rated wind speed, v co It is the initial wind speed.

[0093] In an optional embodiment, the uncertainty of photovoltaic power generation output mainly comes from random changes in light intensity, temperature fluctuations, aging and shading of photovoltaic modules, etc. Among them, light intensity and temperature are two main uncertain factors.

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

[0095]

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

[0097] Determine the illumination intensity interval corresponding to a certain confidence level α as the uncertainty set For temperature, we can construct the uncertainty set of temperature based on the statistical analysis of historical temperature data:

[0098]

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

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

[0101]

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

[0103] In an optional embodiment, the uncertainty of hydroelectric power generation is mainly related to factors such as changes in water flow, fluctuations in reservoir water levels, changes in turbine efficiency, and grid dispatching requirements, among which the uncertainty of water flow is a key factor affecting hydroelectric power generation.

[0104] According to the probability distribution of water flow:

[0105]

[0106] Among them, Q is the inflow flow, μ Q is the mean of the inflow flow, σ Q is the standard deviation of the inflow flow.

[0107] Determine the uncertainty set of the water flow with a certain confidence level α At the same time, considering the variation range of reservoir water level and turbine efficiency, the corresponding uncertainty set is constructed. and

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

[0109]

[0110] The hydropower output model is expressed as:

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

[0112] Among them, P h (Q,H,η) is the treatment of hydropower generation, k h is the coefficient.

[0113] It should be noted that the power flow calculation model is a basic tool for analyzing the operating status of the distribution network, and plays an important role in studying the balance between load power and distributed power output. Through power flow calculation, we can obtain information such as the voltage amplitude and phase angle of each node in the distribution network and the power distribution of each branch, so as to intuitively understand the power balance between the load and the distributed power.

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

[0115] I=YV

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

[0117]

[0118] According to Kirchhoff's law, the branch flow equation can be obtained.

[0119] In the embodiment of the present application, the second model is constructed based on the power balance relationship between the load and the distributed power source, and the third model of the active distribution network is constructed based on the dynamic characteristics of the load, including: performing flow calculation on each node of the distribution network, and for any connecting node and node branch, the expression of its active power and reactive power is:

[0120]

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

[0122] In the embodiment of the present application, a load characteristic model is established by using a time series model and a cluster analysis method, and is expressed as:

[0123]

[0124] in, is a p-order autoregressive model, is the autoregressive coefficient, e(t) has a mean of 0 and a variance of σ 2 The white noise sequence, θ i is the moving average coefficient, μ j Cluster C jThe centroid of the cluster is updated through continuous iteration until J converges.

[0125] In an optional embodiment, the least square method is used to estimate 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 particularly suitable for short-term load forecasting.

[0127] In an optional embodiment, the load data is regarded as points in a high-dimensional space and clustered according to the similarity of the load curves. 1 ,x 2 ,...,x n}, where x i is a vector representing a load curve. For k-means clustering, the goal is to divide X into k clusters C 1 , C 2 ,...,C k , so that the sum of squared errors within the cluster is minimized, that is:

[0128]

[0129] Among them, μ j It is cluster C j The centroid of the cluster is continuously updated through iteration until J converges. In this way, different types of loads can be distinguished and the characteristics of each type of load can be analyzed.

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

[0131] In the embodiment of the present application, the fourth model constructed based on the energy storage charge state of the active distribution network is expressed as:

[0132]

[0133] Among them, SOC(t) is the state of charge of the energy storage at time t, SOC(t 0 ) is the initial time t 0 The state of charge, C rated is the rated capacity of energy storage, I c (τ) and I d (τ) are the charging current and discharging current respectively.

[0134] It should be noted that the fourth model is based on the principle of conservation of charge and determines the SOC by integrating the change in charge during the charging and discharging process.

[0135] In the embodiment of the present application, the objective function is to minimize the comprehensive cost of the active distribution network, and the active distribution network optimization coordination control model is constructed in combination with the model setting constraint conditions, including: the comprehensive cost of the active distribution network includes the distributed power source cost, the energy storage cost and the grid operation cost;

[0136] The objective function of the comprehensive cost is expressed as:

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

[0138] Among them, C G is the grid operation cost, C DG is the distributed generation cost, C S is the total cost of energy storage.

[0139] Specifically, the distributed power cost C DG It is expressed 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 is the initial investment cost of the i-th distributed generation, C om,DG,i (P DG ,i) is the operation and maintenance cost of the i-th distributed generation, C fuel,DG,i (P DG ,i) is the fuel cost of the i-th distributed generation.

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

[0143]

[0144] In an optional embodiment, suppose there are m energy storage devices in the distribution network. For the jth energy storage device, the cost C S,j (P S ,j) can be expressed 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 is the initial investment cost of the jth energy storage device, C om,j (P S ,j) is the operation and maintenance cost of the jth energy storage device, C loss,j (P S ,j) is the charge and discharge loss cost of the jth energy storage device, C life,j (P S ,j) is the life loss cost of the jth energy storage device.

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

[0148]

[0149] Grid operation cost C G It is expressed as:

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

[0151] Among them, C loss,G is the network loss cost, C d is the depreciation cost of power grid equipment, C m It is the maintenance cost of power grid equipment.

[0152] In an optional embodiment, assuming that there are n nodes in the distribution network, the load power of the 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 the embodiment of the present application, the output constraint and installed capacity constraint of wind power generation constraint are expressed as:

[0155]

[0156] Among them, v needs to satisfy the uncertainty set conditions related to the wind speed probability distribution, C w is the installed capacity of a single wind farm, i∈wind represents all wind turbines;

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

[0158]

[0159] Where I and T are light intensity and temperature, C pv is the installed capacity of the PV power station, j∈pv represents all PV panels or PV power station units;

[0160] The output constraint of hydropower generation and the water balance constraint of reservoir are expressed as:

[0161] 0≤P h (Q,H,η)≤P h,max

[0162]

[0163] Among them, P h,max is the maximum possible output of hydroelectric power, V min and V max are the upper and lower limits of the reservoir’s water storage capacity, V 0 is the initial water volume of the reservoir, Q in (t) is the inflow flow, Q out (t) is the amount of water used for power generation;

[0164] The grid-side constraints are expressed as:

[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 are the lower and upper limits determined according to 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 are the minimum and maximum states of charge allowed for energy storage.

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

[0173] P s,j,min ≤P s,j ≤P s,j,max

[0174] As well as other related constraints, such as node voltage constraints, branch power constraints, etc., to ensure the safe and stable operation of the distribution network. These constraints together constitute the optimization problem with the goal of minimizing the comprehensive cost of the active distribution network while considering the output of distributed generation and load power balance.

[0175] It should be noted that the embodiment of the present application uses a mathematical optimization algorithm to perform parameter optimization on the above-mentioned objective function and constraint conditions. This step can use linear programming, nonlinear programming or mixed integer programming and other methods to optimize the objective function and constraint conditions. No specific limitation is made here. One of the algorithms can be selected for optimization to obtain an active distribution network optimization coordination control strategy.

[0176] Example 2

[0177] The above embodiment is a schematic scheme of an active distribution network optimization and coordination control method. It should be noted that the technical scheme of this active distribution network optimization and coordination control system and the technical scheme of the above active distribution network optimization and coordination control method belong to the same concept, and the details of the technical scheme of the active distribution network optimization and coordination control system not described in detail in this embodiment can be referred to the description of the technical scheme of the above active distribution network optimization and coordination control method.

[0178] In this embodiment, an active distribution network optimization and coordination control system includes:

[0179] The first model building module is used to build 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 build a first model in combination with uncertainty factors of the uncertain set model;

[0180] A second model building module, used to build a second model based on the power balance relationship between the load and the distributed power source;

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

[0182] A fourth model building module, used to build a fourth model based on the energy storage charge state of the active distribution network;

[0183] The control module is used to take the minimization of the comprehensive cost of the active distribution network as the objective function, and to construct an active distribution network optimization and coordination control model in combination with the model setting constraints. Based on the active distribution network optimization and coordination control model, parameter optimization is performed to obtain an active distribution network optimization and coordination control strategy.

[0184] This embodiment further provides an electronic device, which is applicable to the case of the active distribution network optimization and coordinated control method, including:

[0185] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the active distribution network optimization and coordinated control method proposed in the above embodiment.

[0186] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing active distribution network optimization and coordinated control as proposed in the above embodiment is implemented.

[0187] The storage medium proposed in this embodiment and the method for realizing active distribution network optimization and coordinated control proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0188] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of 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 rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An active distribution network optimization and coordination control method, characterized in that: include: Based on the output of distributed power sources, an uncertainty set of the output of distributed power sources in the active distribution network is established, and a first model is constructed in combination with uncertainty factors of the uncertainty set; Building a second model based on the power balance relationship between the load and the distributed generation; Based on the dynamic characteristics of loads, the third model of active distribution network is constructed; Constructing a fourth model based on the energy storage charge state of the active distribution network; Taking minimization of the comprehensive cost of the active distribution network as the objective function and combining the constraint conditions set by the model, an active distribution network optimization and coordination control model is constructed, and based on the active distribution network optimization and coordination control model, parameter optimization is performed to obtain an active distribution network optimization and coordination control strategy.

2. The active distribution network optimization and coordination control method according to claim 1, characterized in that: The method of establishing an uncertainty set of distributed power output of an active power distribution network and constructing a 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 the wind power output include wind speed, the uncertainty factors of the photovoltaic power output include light intensity and temperature, and the uncertainty factors of the hydropower output include water flow.

3. The active distribution network optimization and coordination control method according to claim 2, characterized in that: Also includes: The uncertainty set of wind speed is Ω w = {v min ≤v≤v max }, where v min and v max are the lower and upper limits of wind speed respectively. The wind power generation output model is expressed as: Among them, P w (v) is the power of the wind turbine generator, P r is the rated power of the wind turbine, v ci is the cut-in wind speed, v r is the rated wind speed, v co is the initial wind speed; The uncertainty set of photovoltaic power generation output is The photovoltaic power generation model considering the uncertainty of light intensity and temperature is expressed as: Among them, P pv (I,T) is the output of photovoltaic power generation, is the rated power of photovoltaic power generation, T is the actual temperature of the photovoltaic cell, T r is the reference temperature, γ is the power temperature coefficient; The uncertainty set of hydropower output is The hydropower output model is expressed as: P h (Q,H,η)=k h QH η ,(Q,H,η)∈Ω h Among them, P h (Q,H,η) is the treatment of hydropower generation, k h is the coefficient.

4. The active distribution network optimization and coordination control method according to claim 3, characterized in that: The second model is constructed based on the power balance relationship between the load and the distributed power source. The third model of the active distribution network is constructed based on the dynamic characteristics of the load, including: performing flow calculations on each node of the distribution network. For any connecting node and node branch, the expressions of active power and reactive power are: Among them, P ij and Q ij are the active power and reactive power of branch ij respectively, g ij and b ij are the conductance and susceptance of branch ij, θ ij =θ i -θ j is the voltage phase angle difference between node i and node j.

5. The active distribution network optimization and coordination control method according to claim 4, characterized in that: The load characteristic model is established by using the time series model and cluster analysis method and is expressed as: in, is a p-order autoregressive model, is the autoregressive coefficient, e(t) has a mean of 0 and a variance of σ 2 The white noise sequence, θ i is the moving average coefficient, μ j Cluster C j The centroid of the cluster is updated through continuous iteration until J converges.

6. The active distribution network optimization and coordination control method according to claim 5, characterized in that: The fourth model based on the energy storage charge state of the active distribution network is expressed as: Among them, SOC(t) is the state of charge of the energy storage at time t, SOC(t0) is the state of charge at the initial time t0, C rated is the rated capacity of energy storage, I c (τ) and I d (τ) are the charging current and discharging current respectively.

7. The active distribution network optimization and coordination control method according to claim 6, characterized in that: Taking the minimization of the comprehensive cost of the active distribution network as the objective function and combining the constraints set by the model to construct the active distribution network optimization coordination control model includes: the comprehensive cost of the active distribution network includes the cost of distributed power generation, energy storage cost and grid operation cost; The objective function of the comprehensive cost is expressed as: minC=C DG +C S +C G Among them, C G is the grid operation cost, C DG is the distributed generation cost, C S is the total cost of energy storage; The output constraint and installed capacity constraint of wind power generation constraints are expressed as: 0≤P w (v)≤P r Among them, v needs to satisfy the uncertainty set conditions related to the wind speed probability distribution, C w is the installed capacity of a single wind farm, i∈wind represents all wind turbines; The output constraint and installed capacity constraint of photovoltaic power generation are expressed as: Where I and T are light intensity and temperature, C pv is the installed capacity of the PV power station, j∈pv represents all PV panels or PV power station units; The output constraint of hydropower generation and the water balance constraint of reservoir are expressed as: 0≤P h (Q,H,η)≤P h,max Among them, P h,max is the maximum possible output of hydroelectric power, V min and V max are the upper and lower limits of the reservoir’s water storage capacity, V0 is the initial water volume of the reservoir, Q in (t) is the inflow flow, Q out (t) is the amount of water used for power generation; The grid-side constraints are expressed as: 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 are the lower and upper limits determined according to 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 are the minimum and maximum states of charge allowed for energy storage.

8. An active distribution network optimization and coordination control system, characterized in that: include: A first model building module is used to build 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 build a first model in combination with uncertainty factors of the uncertain set model; A second model building module, used to build a second model based on the power balance relationship between the load and the distributed power source; The third model building module is used to build the third model of the active distribution network based on the dynamic characteristics of the load; A fourth model building module, used to build a fourth model based on the energy storage charge state of the active distribution network; The control module is used to take the minimization of the comprehensive cost of the active distribution network as the objective function, and to construct an active distribution network optimization and coordination control model in combination with the constraint conditions set by the model, and to obtain an active distribution network optimization and coordination control strategy by performing parameter optimization based on the active distribution network optimization and coordination control model.

9. 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, the steps of the active distribution network optimization and coordinated control method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the active distribution network optimization and coordinated control method according to any one of claims 1 to 7.

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