E-SOP optimization scheduling method and system based on unscented transformation and information gap decision theory

Abstract

The invention discloses an E-SOP optimal scheduling method and system based on unscented transformation and an information gap decision theory, and belongs to the field of E-SOP optimal scheduling. According to the method, historical data are obtained, unscented transformation is used for carrying out joint uncertainty modeling on loads and new energy output, fluctuation characteristics and correlation of the loads and the new energy output are captured, and the unscented transformation and the new energy output are combined; and performing information gap uncertainty modeling on the price cost of the transformer substation market by using an information gap decision theory, quantifying the maximum tolerance of the system to the price fluctuation, finally comprehensively representing the characteristics of source-load random fluctuation and price interval fluctuation, solving a function taking the minimum operation cost as an objective function, and obtaining an optimal E-SOP regulation and control strategy. Cooperative processing of source load randomness and price fluctuation is realized, scheduling accuracy of the E-SOP is improved, and the problem of low scheduling accuracy of the E-SOP caused by incapability of cooperatively processing source load randomness and price information gap uncertainty due to independent processing of a certain type of uncertainty in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of E-SOP optimal scheduling, and in particular to an E-SOP optimal scheduling method and system based on unscented transformation and information gap decision theory. Background Technology

[0002] With a high proportion of renewable energy being integrated into the distribution network, the strong uncertainty in load and renewable energy output poses a severe challenge to E-SOP optimal dispatch. Traditional dispatch methods typically assume that source load power and substation market price costs are fixed values. However, in actual operation, fluctuations in photovoltaic / wind power output, load forecasting deviations, and electricity price market fluctuations significantly affect economic efficiency and security, necessitating optimization methods that can simultaneously handle multiple uncertainties.

[0003] Existing technologies primarily employ stochastic programming or robust optimization to handle a single type of uncertainty—for example, generating new energy output scenarios through Monte Carlo simulations or using interval models to describe price fluctuations. However, because these technologies handle a single type of uncertainty individually, they cannot coordinate the randomness of source and load with the uncertainty of price information gaps, resulting in low scheduling accuracy for E-SOPs. Summary of the Invention

[0004] This invention provides an E-SOP optimal scheduling method and system based on unscented transformation and information gap decision theory, which can solve the problem of low scheduling accuracy of E-SOP caused by the inability of existing technologies to coordinate the randomness of source load and price information gap uncertainty due to the inability to handle a certain type of uncertainty in isolation.

[0005] To address the aforementioned technical problems, one embodiment of the present invention provides an E-SOP optimized scheduling method based on unscented transformation and information gap decision theory, comprising:

[0006] Acquire historical load data, historical output data of new energy sources, and historical market price costs of substations;

[0007] Based on historical load data and historical power output data of renewable energy, the uncertainty of load and renewable energy output is jointly modeled and calculated using unscented transformation to obtain the uncertainty results of load and renewable energy output.

[0008] Based on the historical market price cost of substations, the uncertainty of the market price cost of substations is modeled and calculated using the information gap decision theory, and the uncertainty result of the market price cost of substations is obtained.

[0009] Based on the distribution network parameters and equipment operating costs, a function and constraints are constructed with the goal of minimizing operating costs.

[0010] Based on the uncertainties in load, renewable energy output, and substation market price costs, an objective function that minimizes operating costs is solved to obtain the operating results of E-SOP when the constraints are met and the operating costs are minimized. E-SOP is then adjusted based on these operating results.

[0011] Furthermore, the method of using unscented transformation to perform joint uncertainty modeling and calculation of load and new energy output includes:

[0012] Based on historical load data and historical renewable energy output data, a prediction algorithm is used to predict the load and renewable energy output, resulting in load prediction data and renewable energy output prediction data. The load prediction data includes the mean and standard deviation of the load prediction, and the renewable energy output prediction data includes the mean and standard deviation of the renewable energy output prediction.

[0013] Based on load forecast data and new energy output forecast data, several sample points are generated, and the weight of each sample point is calculated to obtain several sample points and their corresponding weights.

[0014] Based on several sample points, the fitness of each sample point is calculated using the fitness function to obtain the fitness of several sample points. Then, based on the fitness of several sample points and the weight corresponding to each sample point, the mean and covariance of the weighted fitness of all sample points are calculated.

[0015] The uncertainty results for load and renewable energy output are determined based on the mean and covariance of the weighted fitness of all sample points.

[0016] Furthermore, several sample points are generated using the following formula:

[0017]

[0018] in, and They are respectively the lth and l+|Ω up |and 2|Ω up |+1 sample points, where the sample points are joint sample points of uncertain parameters; μ x The Ω is the joint vector of the mean values ​​of the uncertain parameters, composed of the mean values ​​of the load and the predicted output of new energy sources; up | represents the number of uncertain parameters; Ω up For uncertain parameters, namely load and renewable energy output; W0 is a preset weight; C xx The covariance matrix is ​​composed of the standard deviations of load and new energy output forecasts, representing uncertain parameters.

[0019] Furthermore, the weight for each sample point is calculated using the following formula:

[0020]

[0021] In the formula, W l , and They are respectively the lth and l+|Ω up |and 2|Ω up |+1 sample point weight;|Ω up | represents the number of uncertain parameters; Ω up The parameters are uncertain, namely load and renewable energy output; W0 is a preset weight; the sum of the weights of each sample point is 1.

[0022] Furthermore, the formulas for calculating the mean and covariance of the weighted fitness of all sample points are as follows:

[0023]

[0024] In the formula, μ y C is the mean of the weighted fitness of all sample points. yy The covariance of the weighted fitness for all sample points; W l Y represents the weight of the l-th sample point; l Let the fitness of the l-th sample point be ,

[0025] Furthermore, based on the historical market price cost of substations, the uncertainty modeling and calculation of the market price cost of substations are performed using information gap decision theory to obtain the uncertainty result of the market price cost of substations, including:

[0026] Based on the historical market price cost of substations, the market price cost of substations is predicted using a prediction algorithm, thus obtaining the market price cost prediction data for substations.

[0027] Based on the market price cost forecast data of substations, uncertainty constraints on the market price cost of substations are constructed using information gap decision theory, generating uncertainty constraints on the market price cost of substations; the uncertainty constraints on the market price cost of substations are the uncertainty results of the market price cost of substations.

[0028] Furthermore, the expression for the uncertainty constraint of the market price cost of the substation is as follows:

[0029]

[0030] in, The market price cost of the substation; Forecast data on market price costs for substations; Γ price The maximum permissible deviation percentage of the market price of the substation; Ωtime For time sets; Ω price This is a collection of market price cost forecast data for substations.

[0031] Furthermore, the expression for the objective function is:

[0032]

[0033] Where OC is the operating cost; C SS C represents the cost of substations. DG For diesel generator operating costs; The market price cost of the substation; This is the operating cost coefficient for diesel generators; The power input to the s-th substation; To generate active power for the d-th diesel generator; Ω time For time sets; Ω sub For substation collection; Ω dg This is a collection of diesel generators.

[0034] Furthermore, the constraints include: distribution network operation constraints, photovoltaic inverter constraints, ESOP operation and capacity constraints, and diesel generator operation constraints;

[0035] The operating constraints of the power distribution network are:

[0036]

[0037] In the formula, Let n be the sum of the demand and supply of active power at node n. This represents the active power transmitted by line mn. Let n be the sum of the reactive power demand and supply at node n. The reactive power transmitted by line mn. Let be the voltage at node n. Let be the resistance of line mn. Let mn be the reactance of the line. The root substation voltage of line mn; The active power demand of the load at node n; The sop transmission power at node n; Ω represents the output of the new energy source at node n. bus For the system node set; This represents the upper limit of the line's transmission capacity. The set of parent nodes; It is a set of child nodes;

[0038] The constraints of the photovoltaic inverter are:

[0039]

[0040] In the formula, Let p be the reactive power generated by the p-th photovoltaic inverter. Let p be the active power generated by the p-th photovoltaic inverter. The capacity of the p-th photovoltaic inverter; Ω PV This refers to the set of nodes connected to the photovoltaic system.

[0041] The ESOP's operational and capacity constraints are as follows:

[0042]

[0043] In the formula, For the charging power of the e-th ESOP, For the e-th ESOP discharge power, Inject active power into the ESOP of node i. Let be the ESOP power loss of node i. The active power flowing to node i, The active power flowing out of node i; Inject active power into the ESOP of node j. Let be the ESOP power loss of node j. The active power flowing to node j, B represents the active power flowing out of node j; i,t For binary variables, ESOP active power limit Inject reactive power into the ESOP of node i. and Let $\overall$ be the lower and upper limits of the reactive power injected into the ESOP of node $i$. The maximum capacity of the ESOP at the i-th node is ESOP. e,t Let ρ be the state of charge of ESOP at time t. ch ρ is the ESOP charging efficiency coefficient. dis Δt is the ESOP discharge efficiency coefficient, where Δt is the time interval. e,min and ESOP e,max Let the upper and lower limits of the state of charge of the e-th ESOP be defined. and These are the lower and upper limits of the charging power of the e-th ESOP. and These are the lower and upper limits of the discharge power of the e-th ESOP. e,initial and ESOP e,finalFor the initial and final states of charge of ESOP; Ω esop It is the set of nodes connected to esop;

[0044] The operating constraints of the diesel generator are:

[0045]

[0046] In the formula, For the active power output of the d-th diesel generator, and These are the lower and upper limits of the active power output of the d-th diesel generator, respectively.

[0047] Based on the above method embodiments, the present invention provides corresponding system embodiments;

[0048] One embodiment of the present invention provides an E-SOP optimization scheduling system based on unscented transformation and information gap decision theory, comprising: a data acquisition module, a first uncertainty modeling module, a second uncertainty modeling module, an objective function and constraint construction module, and an E-SOP control module;

[0049] The data acquisition module is used to acquire historical load data, historical output data of new energy sources, and historical market price costs of substations;

[0050] The first uncertainty modeling module is used to perform joint uncertainty modeling calculation on load and new energy output based on historical load data and historical new energy output data, using unscented transformation, to obtain the uncertainty results of load and new energy output.

[0051] The second uncertainty modeling module is used to perform uncertainty modeling calculation on the market price cost of substations based on historical substation market price costs and using information gap decision theory to obtain the uncertainty result of the substation market price cost.

[0052] The objective function and constraint construction module is used to construct an objective function and constraint conditions with the minimum operating cost as the objective function, based on the distribution network parameters and equipment operating costs.

[0053] The E-SOP control module is used to solve the objective function that minimizes operating cost based on the uncertainty of load, renewable energy output and substation market price cost, to obtain the operating result of E-SOP when the constraints are met and the operating cost is minimized, and to control E-SOP based on the operating result of E-SOP.

[0054] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0055] This invention acquires historical data and uses unscented transformation to perform joint uncertainty modeling of load and renewable energy output, and uses information gap decision theory to model the information gap uncertainty of substation market price costs. It comprehensively characterizes the random fluctuations of load and price range fluctuations, and solves for the optimal E-SOP control strategy with the minimum operating cost as the objective function. Specifically, it uses unscented transformation to jointly model the uncertainties of load and renewable energy output, simultaneously capturing their fluctuation characteristics and correlations. Furthermore, it uses information gap decision theory to model market price costs, quantifying the system's maximum tolerance for price fluctuations. Finally, it solves for the constructed objective function by combining the two types of uncertainty results, achieving coordinated processing of load randomness and price fluctuations, improving the scheduling accuracy of E-SOP. This solves the problem of low scheduling accuracy in existing technologies that fail to coordinate the processing of load randomness and price information gap uncertainty due to the inability to address only one type of uncertainty. Attached Figure Description

[0056] Figure 1 A flowchart illustrating the steps of an E-SOP optimized scheduling method based on unscented transformation and information gap decision theory, provided for embodiments of the present invention;

[0057] Figure 2 This is a topology diagram of the power distribution network structure in the planning area provided in an embodiment of the present invention;

[0058] Figure 3 A block diagram of an E-SOP optimized scheduling system based on unscented transformation and information gap decision theory is provided for embodiments of the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0061] Example 1:

[0062] Reference Figure 1This document presents a flowchart of an E-SOP optimization scheduling method based on unscented transformation and information gap decision theory, as provided in an embodiment of the present invention. To address the problem of low scheduling accuracy in existing technologies due to the inability to coordinate the handling of source load randomness and price information gap uncertainty by addressing only one type of uncertainty, this method includes at least the following steps:

[0063] Step S1: Obtain historical load data, historical output data of new energy sources, and historical market price costs of substations;

[0064] In this embodiment, the historical load data is obtained through smart meters and electricity consumption information collection systems; the historical output data of new energy sources can be obtained from the SCADA systems of wind farms or photovoltaic power plants; and the historical market price cost of substations is obtained from the day-ahead market and real-time market time-of-use electricity price data of the power trading center, and combined with the agreed electricity price in the substation power purchase contract to form a complete cost time series.

[0065] Step S2: Based on historical load data and historical power output data of new energy sources, perform joint uncertainty modeling and calculation on load and power output of new energy sources using unscented transformation to obtain the uncertainty results of load and power output of new energy sources;

[0066] In this embodiment, the uncertainty joint modeling calculation of load and new energy output using unscented transformation includes:

[0067] Based on historical load data and historical renewable energy output data, a prediction algorithm is used to predict the load and renewable energy output, resulting in load prediction data and renewable energy output prediction data. The load prediction data includes the mean and standard deviation of the load prediction, and the renewable energy output prediction data includes the mean and standard deviation of the renewable energy output prediction.

[0068] Based on load forecast data and new energy output forecast data, several sample points are generated, and the weight of each sample point is calculated to obtain several sample points and their corresponding weights.

[0069] Based on several sample points, the fitness of each sample point is calculated using the fitness function to obtain the fitness of several sample points. Then, based on the fitness of several sample points and the weight corresponding to each sample point, the mean and covariance of the weighted fitness of all sample points are calculated.

[0070] The uncertainty results for load and renewable energy output are determined based on the mean and covariance of the weighted fitness of all sample points.

[0071] In this embodiment, the prediction algorithm includes, but is not limited to, time series prediction algorithm (ARIMA / SARIMA), machine learning algorithm (LightGBM / XGBoost), and deep learning algorithm (LSTM / Transformer).

[0072] In this embodiment, several sample points are generated using the following formula:

[0073]

[0074] in, and They are respectively the lth and l+|Ω up |and 2|Ω up |+1 sample points, where the sample points are joint sample points of uncertain parameters; μ x The Ω is the joint vector of the mean values ​​of the uncertain parameters, composed of the mean values ​​of the load and the predicted output of new energy sources; up | represents the number of uncertain parameters; Ω up For uncertain parameters, namely load and renewable energy output; W0 is a preset weight; C xx The covariance matrix is ​​composed of the standard deviations of load and new energy output forecasts, representing uncertain parameters.

[0075] In this embodiment, the weight for each sample point is calculated using the following formula:

[0076]

[0077] In the formula, W l , and They are respectively the lth and l+|Ω up |and 2|Ω up |+1 sample point weight;|Ω up | represents the number of uncertain parameters; Ω up The parameters are uncertain, namely load and renewable energy output; W0 is a preset weight; the sum of the weights of each sample point is 1.

[0078] In this embodiment, the formulas for calculating the mean and covariance of the weighted fitness of all sample points are as follows:

[0079]

[0080] In the formula, μ y C is the mean of the weighted fitness of all sample points. yy The covariance of the weighted fitness for all sample points; W l Y represents the weight of the l-th sample point; l Let the fitness of the l-th sample point be ,

[0081] Step S3: Based on the historical market price cost of substations, use the information gap decision theory to perform uncertainty modeling and calculation on the market price cost of substations, and obtain the uncertainty result of the market price cost of substations;

[0082] In this embodiment, the step of performing uncertainty modeling and calculation on the market price cost of the substation based on historical substation market price costs and using information gap decision theory to obtain the uncertainty result of the substation market price cost includes:

[0083] Based on the historical market price cost of substations, the market price cost of substations is predicted using a prediction algorithm, thus obtaining the market price cost prediction data for substations.

[0084] Based on the market price cost forecast data of substations, uncertainty constraints on the market price cost of substations are constructed using information gap decision theory, generating uncertainty constraints on the market price cost of substations; the uncertainty constraints on the market price cost of substations are the uncertainty results of the market price cost of substations.

[0085] In this embodiment, the prediction algorithm includes, but is not limited to, time series prediction algorithm (ARIMA / SARIMA), machine learning algorithm (LightGBM / XGBoost), and deep learning algorithm (LSTM / Transformer).

[0086] In this embodiment, the theoretical framework of the information gap decision theory is specifically as follows:

[0087] The general form of the optimization problem is:

[0088]

[0089] In the formula, OF is the objective function, EC is the equality constraint, IC is the inequality constraint, and Ξ is the decision variable of the optimization problem. To control the deviation factor of uncertain budgets, Ω uncertainties For the set of uncertain parameters under robust methods;

[0090] Based on the objective function of the optimization problem, there are two inputs: decision variables (Ξ) and uncertain parameters. Each problem contains equality constraints and inequality constraints, as well as a set of uncertain parameters, represented as follows:

[0091] Based on the general form of the optimization problem described above, the set of uncertain parameters is defined using the envelope constraint method as follows:

[0092]

[0093] In the formula, Let Γ be the predicted value of the uncertain parameter, and Γ be the radius of uncertainty of the uncertain parameter.

[0094] Since the goal of gap-of-information decision theory (IGDT) is to maximize the resilience of uncertain parameters under the constraints of a given objective function, the robust model can be represented by a risk-averse strategy, as shown in the following equation:

[0095]

[0096] In the formula, ROF is the robustness value of the objective function; (1±Γ) can use positive and negative operators, for example, negative for renewable resources and positive for energy prices and loads, to consider the worst case of different uncertain parameters; OF0 represents the random value of the objective function.

[0097] In this embodiment, based on the theoretical framework of information gap decision theory, the market price is modeled, and the expression for the uncertainty constraint of the market price cost of the substation is generated as follows:

[0098]

[0099] in, The market price cost of the substation; Forecast data on market price costs for substations; Γ price The maximum permissible deviation percentage of the market price of the substation; Ω time For time sets; Ω price This is a collection of market price cost forecasting data for substations.

[0100] The main purpose of IGDT is to determine the level of uncertainty that the objective function will satisfy, such as Γ. price Therefore, the robust model structure employing a risk-averse strategy is as follows:

[0101]

[0102] In the formula, OC(·) represents the operating cost function;

[0103] As can be seen from the above formula, the uncertainty of market prices can be... To determine the optimal level of uncertainty while satisfying a specified cost objective, risk aversion techniques aim to identify the highest resistance to market price increases. As described in the proposed objective function, firstly, the value of the objective function under deterministic conditions is obtained and stored in a variable OC0. Then, the uncertainty budget ρ is increased, and robustness exponents (such as Γ) are used. price (As shown) maximize.

[0104] Step S4: Based on the distribution network parameters and equipment operating costs, construct an objective function and constraints with the minimum operating cost as the objective;

[0105] In this embodiment, the expression for the objective function is:

[0106]

[0107] Where OC is the operating cost; C SS C represents the cost of substations. DG For diesel generator operating costs; The market price cost of the substation; This is the operating cost coefficient for diesel generators; The power input to the s-th substation; To generate active power for the d-th diesel generator; Ω time For time sets; Ω sub For substation collection; Ω dg This is a collection of diesel generators.

[0108] In this embodiment, the constraints include: distribution network operation constraints, photovoltaic inverter constraints, ESOP operation and capacity constraints, and diesel generator operation constraints.

[0109] The operating constraints of the power distribution network are:

[0110]

[0111] In the formula, Let n be the sum of the demand and supply of active power at node n. This represents the active power transmitted by line mn. Let n be the sum of the reactive power demand and supply at node n. The reactive power transmitted by line mn. Let be the voltage at node n. Let be the resistance of line mn. Let mn be the reactance of the line. The root substation voltage of line mn; The active power demand of the load at node n; The sop transmission power at node n; Ω represents the output of the new energy source at node n. bus For the system node set; This represents the upper limit of the line's transmission capacity. The set of parent nodes; It is a set of child nodes;

[0112] The constraints of the photovoltaic inverter are:

[0113]

[0114] In the formula, Let p be the reactive power generated by the p-th photovoltaic inverter. Let p be the active power generated by the p-th photovoltaic inverter. The capacity of the p-th photovoltaic inverter; Ω PV This refers to the set of nodes connected to the photovoltaic system.

[0115] The ESOP's operational and capacity constraints are as follows:

[0116]

[0117]

[0118] In the formula, For the charging power of the e-th ESOP, For the e-th ESOP discharge power, Inject active power into the ESOP of node i. Let be the ESOP power loss of node i. The active power flowing to node i, The active power flowing out of node i; Inject active power into the ESOP of node j. Let be the ESOP power loss of node j. The active power flowing to node j, B represents the active power flowing out of node j; i,t For binary variables, ESOP active power limit Inject reactive power into the ESOP of node i. and Let $\overall$ be the lower and upper limits of the reactive power injected into the ESOP of node $i$. The maximum capacity of the ESOP at the i-th node is ESOP. e,t Let ρ be the state of charge of ESOP at time t. ch ρ is the ESOP charging efficiency coefficient. dis Δt is the ESOP discharge efficiency coefficient, where Δt is the time interval. e,min and ESOP e,max Let the upper and lower limits of the state of charge of the e-th ESOP be defined. and These are the lower and upper limits of the charging power of the e-th ESOP. and These are the lower and upper limits of the discharge power of the e-th ESOP. e,initial and ESOP e,final For the initial and final states of charge of ESOP; Ω esop It is the set of nodes connected to esop;

[0119] The operating constraints of the diesel generator are:

[0120]

[0121] In the formula, For the active power output of the d-th diesel generator, and These are the lower and upper limits of the active power output of the d-th diesel generator, respectively.

[0122] In this embodiment, after constructing the constraints, the method further includes: converting the nonlinear constraints into linear constraints;

[0123] Specifically, nonlinear constraints are converted into linear constraints, and the converted linear constraints replace the original nonlinear constraints. They can be used for direct solution and are used in the solution process in step five.

[0124] In this embodiment, the conversion of nonlinear constraints into linear constraints specifically involves:

[0125] First of all Convert to:

[0126] Redefinition: have After conversion:

[0127] Therefore, it can be converted into the following linear expression:

[0128]

[0129] Step S5: Based on the uncertainty of load, renewable energy output and substation market price cost, solve the objective function with minimum operating cost to obtain the operating result of E-SOP when the constraints are met and the operating cost is minimized, and adjust E-SOP according to the operating result of E-SOP.

[0130] In this embodiment, the E-SOP's control operations include off-peak charging (low-price periods), peak discharging (high-price periods), and adjusting active / reactive power to smooth voltage fluctuations and reduce network losses.

[0131] Example 2:

[0132] Step 1: The following describes a typical IEEE 33-bus system of a medium-voltage 12.66kV line as the planning object. Two sets of photovoltaic (PV) grid connection examples are selected, with connection nodes 5 and 26; two sets of wind power grid connection examples are selected, with connection nodes 3 and 18; and two 200kW diesel generator grid connection examples are selected, with connection nodes 9 and 31. Two 500kVA ESOPs are connected at the connection lines between nodes 12-21 and 17-32, as per [reference / reference / reference]. Figure 2 This is a topology diagram of the power distribution network structure in the planning area.

[0133] Step 2: Set up 3 comparison scenarios:

[0134] Option 1: Set up the system so that no equipment is connected to the power distribution network;

[0135] Option 2: Simultaneously connect diesel generators, photovoltaic power, and wind power;

[0136] Option 3: A solution that simultaneously connects to diesel generators, photovoltaics, wind power, and ESOP.

[0137] Step 3: Calculate and compare the daily operating costs of the three schemes. The results are shown in Table 1.

[0138] Table 1. Cost of the Solution

[0139]

[0140] As shown in Table 2, compared to the optimized scheme without any equipment connected to the distribution network and considering the connection of diesel generators, photovoltaics, and wind power, the scheme that simultaneously considers the connection of diesel generators, photovoltaics, wind power, and ESOPs significantly reduces the annual operating cost. The optimization results demonstrate that effective management of distributed energy resources can substantially reduce operating costs. Adding RSOPs further enhances economic efficiency, as ESOPs can store electricity during off-peak hours and discharge it during peak hours. Therefore, the collaborative optimization scheduling scheme proposed in this invention can significantly reduce the operating cost of the distribution network, ultimately improving the overall return on investment.

[0141] Example 3:

[0142] Reference Figure 3This is a block diagram of an E-SOP optimization scheduling system based on unscented transformation and information gap decision theory provided by an embodiment of the present invention. In order to solve the problem that the scheduling accuracy of E-SOP is low due to the inability of existing technologies to coordinate the handling of source load randomness and price information gap uncertainty because they handle a certain type of uncertainty alone, the system includes at least the following modules: a data acquisition module, a first uncertainty modeling module, a second uncertainty modeling module, an objective function and constraint construction module, and an E-SOP control module.

[0143] The data acquisition module is used to acquire historical load data, historical output data of new energy sources, and historical market price costs of substations;

[0144] The first uncertainty modeling module is used to perform joint uncertainty modeling calculation on load and new energy output based on historical load data and historical new energy output data, using unscented transformation, to obtain the uncertainty results of load and new energy output.

[0145] The second uncertainty modeling module is used to perform uncertainty modeling calculation on the market price cost of substations based on historical substation market price costs and using information gap decision theory to obtain the uncertainty result of the substation market price cost.

[0146] The objective function and constraint construction module is used to construct an objective function and constraint conditions with the minimum operating cost as the objective function, based on the distribution network parameters and equipment operating costs.

[0147] The E-SOP control module is used to solve the objective function that minimizes operating cost based on the uncertainty of load, renewable energy output and substation market price cost, to obtain the operating result of E-SOP when the constraints are met and the operating cost is minimized, and to control E-SOP based on the operating result of E-SOP.

[0148] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. An E-SOP optimal scheduling method based on unscented transformation and information gap decision theory, characterized in that, include: Acquire historical load data, historical output data of new energy sources, and historical market price costs of substations; Based on historical load data and historical power output data of renewable energy, the uncertainty of load and renewable energy output is jointly modeled and calculated using unscented transformation to obtain the uncertainty results of load and renewable energy output. Based on the historical market price cost of substations, the uncertainty of the market price cost of substations is modeled and calculated using the information gap decision theory, and the uncertainty result of the market price cost of substations is obtained. Based on the distribution network parameters and equipment operating costs, a function and constraints are constructed with the goal of minimizing operating costs. Based on the uncertainties in load, renewable energy output, and substation market price costs, an objective function that minimizes operating costs is solved to obtain the operating results of E-SOP when the constraints are met and the operating costs are minimized. E-SOP is then adjusted based on these operating results.

2. The E-SOP optimal scheduling method based on unscented transformation and information gap decision theory according to claim 1, characterized in that, The method of using unscented transformation to perform joint uncertainty modeling and calculation of load and new energy output includes: Based on historical load data and historical renewable energy output data, a prediction algorithm is used to predict the load and renewable energy output, resulting in load prediction data and renewable energy output prediction data. The load prediction data includes the mean and standard deviation of the load prediction, and the renewable energy output prediction data includes the mean and standard deviation of the renewable energy output prediction. Based on load forecast data and new energy output forecast data, several sample points are generated, and the weight of each sample point is calculated to obtain several sample points and their corresponding weights. Based on several sample points, the fitness of each sample point is calculated using the fitness function to obtain the fitness of several sample points. Then, based on the fitness of several sample points and the weight corresponding to each sample point, the mean and covariance of the weighted fitness of all sample points are calculated. The uncertainty results for load and renewable energy output are determined based on the mean and covariance of the weighted fitness of all sample points.

3. The E-SOP optimal scheduling method based on unscented transformation and information gap decision theory according to claim 2, characterized in that, Several sample points are generated using the following formula: in, and They are respectively the lth and l+|Ω up |and 2|Ω up |+1 sample points, where the sample points are joint sample points of uncertain parameters; μ x The Ω is the joint vector of the mean values ​​of the uncertain parameters, composed of the mean values ​​of the load and the predicted output of new energy sources; up | represents the number of uncertain parameters; Ω up For uncertain parameters, namely load and renewable energy output; W0 is a preset weight; C xx The covariance matrix is ​​composed of the standard deviations of load and new energy output forecasts, representing uncertain parameters.

4. The E-SOP optimal scheduling method based on unscented transformation and information gap decision theory according to claim 3, characterized in that, The weight for each sample point is calculated using the following formula: In the formula, W l , and They are respectively the lth and l+|Ω up |and 2|Ω up |+1 sample point weight;|Ω up | represents the number of uncertain parameters; Ω up The parameters are uncertain, namely load and renewable energy output; W0 is a preset weight; the sum of the weights of each sample point is 1.

5. The E-SOP optimal scheduling method based on unscented transformation and information gap decision theory according to claim 4, characterized in that, The formulas for calculating the mean and covariance of the weighted fitness of all sample points are as follows: In the formula, μ y C is the mean of the weighted fitness of all sample points; yy The covariance of the weighted fitness for all sample points; W l Y represents the weight of the l-th sample point; l Let the fitness of the l-th sample point be , 6. The E-SOP optimal scheduling method based on unscented transformation and information gap decision theory according to claim 5, characterized in that, The process involves using information gap decision theory to model and calculate the uncertainty of the market price cost of substations based on historical market price costs, resulting in the following uncertainty outcome: Based on the historical market price cost of substations, the market price cost of substations is predicted using a prediction algorithm, thus obtaining the market price cost prediction data for substations. Based on the market price cost forecast data of substations, uncertainty constraints on the market price cost of substations are constructed using information gap decision theory, generating uncertainty constraints on the market price cost of substations; the uncertainty constraints on the market price cost of substations are the uncertainty results of the market price cost of substations.

7. The E-SOP optimal scheduling method based on unscented transformation and information gap decision theory according to claim 6, characterized in that, The expression for the uncertainty constraint of the market price cost of the substation is as follows: in, The market price cost of the substation; Forecast data on market price costs for substations; Γ price The maximum permissible deviation percentage of the market price of the substation; Ω time For time sets; Ω price This is a collection of market price cost forecast data for substations.

8. The E-SOP optimal scheduling method based on unscented transformation and information gap decision theory according to claim 6, characterized in that, The expression for the objective function is: Where OC is the operating cost; C SS C represents the cost of substations. DG For diesel generator operating costs; The market price cost of the substation; This is the operating cost coefficient for diesel generators; The power input to the s-th substation; To generate active power for the d-th diesel generator; Ω time For time sets; Ω sub For substation collection; Ω dg This is a collection of diesel generators.

9. The E-SOP optimal scheduling method based on unscented transformation and information gap decision theory according to claim 6, characterized in that, The constraints include: distribution network operation constraints, photovoltaic inverter constraints, ESOP operation and capacity constraints, and diesel generator operation constraints; The operating constraints of the power distribution network are: In the formula, Let n be the sum of the demand and supply of active power at node n. This refers to the active power transmitted by line mn. Let n be the sum of the reactive power demand and supply at node n. The reactive power transmitted by line mn. Let be the voltage at node n. Let be the resistance of line mn. Let mn be the reactance of the line. The root substation voltage of line mn; The active power demand of the load at node n; The sop transmission power at node n; Ω represents the output of the new energy source at node n. bus For the system node set; This represents the upper limit of the line's transmission capacity. The set of parent nodes; It is a set of child nodes; The constraints of the photovoltaic inverter are: In the formula, Let p be the reactive power generated by the p-th photovoltaic inverter. Let p be the active power generated by the p-th photovoltaic inverter. The capacity of the p-th photovoltaic inverter; Ω PV The set of nodes connected to photovoltaics; The ESOP's operational and capacity constraints are as follows: In the formula, For the charging power of the e-th ESOP, For the e-th ESOP discharge power, Inject active power into the ESOP of node i. Let be the ESOP power loss of node i. Let i be the active power flowing to node i. The active power flowing out of node i; Inject active power into the ESOP of node j. Let be the ESOP power loss of node j. The active power flowing to node j, B represents the active power flowing out of node j; i,t For binary variables, ESOP active power limit Inject reactive power into the ESOP of node i. and Let $\overall$ be the lower and upper limits of the reactive power injected into the ESOP of node $i$. The maximum capacity of ESOP at node i is given by ESOP. e,t Let ρ be the state of charge of ESOP at time t. ch ρ is the ESOP charging efficiency coefficient. dis Δt is the ESOP discharge efficiency coefficient, where Δt is the time interval. e,min and ESOP e,max Let the upper and lower limits of the state of charge of the e-th ESOP be defined. and These are the lower and upper limits of the charging power of the e-th ESOP. and These are the lower and upper limits of the discharge power of the e-th ESOP. e,initial and ESOP e,final For the initial and final states of charge of ESOP; Ω esop It is the set of nodes connected to esop; The operating constraints of the diesel generator are: In the formula, For the active power output of the d-th diesel generator, and These are the lower and upper limits of the active power output of the d-th diesel generator, respectively.

10. An E-SOP optimized scheduling system based on unscented transformation and information gap decision theory, characterized in that, include: The module includes a data acquisition module, a first uncertainty modeling module, a second uncertainty modeling module, an objective function and constraint construction module, and an E-SOP control module. The data acquisition module is used to acquire historical load data, historical output data of new energy sources, and historical market price costs of substations; The first uncertainty modeling module is used to perform joint uncertainty modeling calculation on load and new energy output based on historical load data and historical new energy output data, using unscented transformation, to obtain the uncertainty results of load and new energy output. The second uncertainty modeling module is used to perform uncertainty modeling calculation on the market price cost of substations based on historical substation market price costs and using information gap decision theory to obtain the uncertainty result of the substation market price cost. The objective function and constraint construction module is used to construct an objective function and constraint conditions with the minimum operating cost as the objective function, based on the distribution network parameters and equipment operating costs. The E-SOP control module is used to solve the objective function that minimizes operating cost based on the uncertainty of load, renewable energy output and substation market price cost, to obtain the operating result of E-SOP when the constraints are met and the operating cost is minimized, and to control E-SOP based on the operating result of E-SOP.

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