Elastic power grid energy scheduling optimization method and system

Abstract

The invention provides an elastic power grid energy scheduling optimization method and system, and relates to the field of power grid scheduling optimization, and the method comprises the steps: obtaining basic parameters and real-time electricity price of a to-be-optimized elastic power grid; utilizing a calculation model based on prediction errors in a bilateral super-quantile confidence interval to calculate a prediction error expected value of an electrical load of the elastic power grid and a prediction error expected value of photovoltaic power generation power; the basic parameters, the real-time electricity price and the calculated prediction error expected value are substituted into an elastic power grid grid-connected multi-target energy optimization model considering the prediction error, solving is carried out with the lowest operation cost and the minimum elastic power grid grid-connected load fluctuation total value as the target, and an energy optimization scheduling strategy is obtained; according to the invention, by optimizing the energy scheduling strategy and utilizing the unique advantages of the lithium battery energy storage system, the negative influence of the prediction error on the day-ahead energy optimization scheduling of the elastic power grid is reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of power grid dispatch optimization, and in particular to a method and system for optimizing energy dispatch of a flexible power grid. Background Art

[0002] The scale and development of global renewable energy have entered the fast lane mode. With the access of distributed renewable energy represented by wind power and photovoltaic power to traditional distribution networks, the current power system control strategy has become more and more complex, and the problems of reliability and economy have become prominent. Facing new challenges, new energy power generation has the characteristics of large-scale, multi-type, and high discreteness. The power grid's ability to absorb new energy still cannot match the demand. The existing distribution network cannot fully utilize new energy, and its support capacity for the load within the distribution network is limited. To solve the above problems, the concept of elastic power grid came into being. The construction of elastic power grid can facilitate the local absorption of power generation and improve the support capacity for load. However, in the elastic power grid, solar power generation is obviously unstable and intermittent due to natural conditions. In addition, the uncertainty of power load and photovoltaic power generation forecast within the microgrid also affects the stable operation and development of the power grid. Summary of the invention

[0003] In order to solve the above problems, the present disclosure proposes a method and system for optimizing energy scheduling of a flexible power grid, which optimizes the energy scheduling strategy and utilizes the unique advantages of the lithium battery energy storage system to reduce the negative impact of prediction errors on the day-ahead energy optimization scheduling of the flexible power grid.

[0004] According to some embodiments, the present disclosure adopts the following technical solutions:

[0005] A method for optimizing energy dispatching of a flexible power grid, comprising:

[0006] Obtain the basic parameters of the elastic power grid to be optimized and the real-time electricity price;

[0007] The expected value of the forecast error of the flexible grid load and the expected value of the forecast error of the photovoltaic power generation power are calculated using the calculation model based on the forecast error within the bilateral superquantile confidence interval.

[0008] The basic parameters, real-time electricity prices and the expected value of the calculated prediction error are substituted into the multi-objective energy optimization model of the elastic grid grid-connected with prediction error. The model is solved with the goal of minimizing the operating cost and the total value of the elastic grid-connected load fluctuation to obtain the energy optimization scheduling strategy.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions:

[0010] A system for optimizing energy dispatching of a flexible power grid includes a data acquisition module, an error calculation module and an optimization solution module:

[0011] The data acquisition module is configured to: acquire basic parameters of the elastic power grid to be optimized and real-time electricity prices;

[0012] The error calculation module is configured to: calculate the expected value of the prediction error of the elastic power grid power load and the expected value of the prediction error of the photovoltaic power generation power by using a calculation model based on the prediction error within the bilateral superquantile confidence interval;

[0013] The optimization solution module is configured to substitute the basic parameters, real-time electricity prices and the expected value of the calculated prediction error into the multi-objective energy optimization model of the elastic power grid connection taking into account the prediction error, and solve the model with the goal of minimizing the operating cost and the total value of the elastic power grid connection load fluctuation to obtain the energy optimization scheduling strategy.

[0014] According to some embodiments, the present disclosure adopts the following technical solutions:

[0015] A computer program product includes a computer program, and when the computer program is executed by a processor, the method for optimizing the energy dispatching of a flexible power grid is implemented.

[0016] According to some embodiments, the present disclosure adopts the following technical solutions:

[0017] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, a method for optimizing energy scheduling of a flexible power grid is implemented.

[0018] According to some embodiments, the present disclosure adopts the following technical solutions:

[0019] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the method for optimizing the energy scheduling of a flexible power grid.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] The present invention discloses a method for optimizing energy dispatching of a flexible power grid. By optimizing the energy dispatching strategy and utilizing the unique advantages of a lithium battery energy storage system, the negative impact of prediction errors on the day-ahead energy optimization dispatching of the flexible power grid is reduced. The problem of prediction errors is fully considered. Considering that prediction errors can improve the optimization effect of the power grid, the optimized microgrid energy storage system can achieve a certain degree of "peak shaving and valley filling" effect, and the operating cost of the microgrid connected to the grid is significantly reduced, thereby ensuring the stable and safe operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0023] Figure 1 This is a flow chart of the method of Example 1.

[0024] Figure 2 This is the normal distribution fitting diagram of the power load prediction error in Example 1.

[0025] Figure 3 This is a load curve diagram of the elastic power grid in Example 1.

[0026] Figure 4 This is a graph showing the load fluctuation curve of the elastic power grid in Example 1. DETAILED DESCRIPTION

[0027] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0028] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0029] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0030] Example 1

[0031] In one embodiment of the present disclosure, a method for optimizing energy dispatch of a flexible power grid is provided. Figure 1 As shown, including:

[0032] Obtain the basic parameters of the elastic power grid to be optimized and the real-time electricity price;

[0033] The expected value of the forecast error of the flexible grid load and the expected value of the forecast error of the photovoltaic power generation power are calculated using the calculation model based on the forecast error within the bilateral superquantile confidence interval.

[0034] The basic parameters, real-time electricity prices and the expected value of the calculated prediction error are substituted into the multi-objective energy optimization model of the elastic grid grid-connected with prediction error. The model is solved with the goal of minimizing the operating cost and the total value of the elastic grid-connected load fluctuation to obtain the energy optimization scheduling strategy.

[0035] As an embodiment, the present invention discloses a method for optimizing energy scheduling of a flexible power grid, which optimizes the energy scheduling strategy and utilizes the unique advantages of a lithium battery energy storage system to reduce the negative impact of prediction errors on the day-ahead energy optimization scheduling of the flexible power grid. The specific implementation process is described below.

[0036] The elastic power grid of this embodiment is an integrated photovoltaic storage and charging elastic power grid that integrates a photovoltaic power generation system and a lithium battery energy storage system. It utilizes the characteristics of the lithium battery energy storage system to achieve the purpose of "peak shaving and valley filling" within the entire elastic power grid.

[0037] This embodiment adopts a common elastic power grid, and its main hardware structure includes a 480kw charging cabinet composed of 8 groups of 60kw charging stacks, a lithium battery energy storage system with a capacity of 215kWh, 8 groups of charging piles, a photovoltaic power generation unit with a photovoltaic capacity of 120kW, a photovoltaic inverter, a junction box, etc. The external 0.3kv power bus is connected to the 20kv power bus of the external network through a transformer, and the internal 0.3kv power bus is connected to the load unit and the return box, which can transmit electric energy to the load unit, as well as receive electric energy from the junction box or transmit electric energy to the junction box. The junction box is electrically connected to the photovoltaic power generation unit, the lithium battery energy storage system, and the 480kw bidirectional charging cabinet. According to existing research results, it has been clearly pointed out that the prediction errors of power load and photovoltaic power generation usually follow the normal distribution law.

[0038] This embodiment adopts a two-sided super quantile method to estimate the expected value of the power load and photovoltaic power generation forecast errors within the confidence interval. This method will serve as the core data basis for the day-ahead energy dispatch control strategy of the flexible power grid that integrates the forecast error.

[0039] When formulating the day-ahead energy optimization scheduling plan, this embodiment aims to achieve two core goals: minimizing operating costs and minimizing grid-connected load fluctuations. By considering factors such as grid-connected power purchase adjustment, system load fluctuation control, and safe operation of the lithium battery energy storage system, corresponding constraints are set, and the embodiment is committed to optimizing the two operating indicators of load fluctuation and operating costs when the elastic power grid is connected to the grid. At the same time, the unique advantages of the lithium battery energy storage system, such as high energy density and fast charging and discharging, are utilized to reduce the negative impact of prediction errors on the day-ahead energy optimization scheduling of the elastic power grid.

[0040] The following explains this method from three aspects: objective function, constraint conditions and solution method of the multi-objective energy optimization model of elastic grid connection.

[0041] 1. Objective Function

[0042] This embodiment simplifies the elastic grid operation cost and load fluctuation into the following multi-objective optimization model, minimizing the operation cost of the photovoltaic unit, the operation cost of the energy storage system, the cost of purchasing electricity when connected to the grid, and the charging cost of the V2G vehicle.

[0043] Without considering the construction cost, equipment depreciation cost and power transmission loss cost, it is assumed that the power transmission efficiency between the elastic power grid and the distribution network, the charging and discharging efficiency of the energy storage system and the charging and discharging efficiency of the V2G vehicles are all 100%, and the V2G vehicles can participate in the energy optimization scheduling of the elastic power grid on time.

[0044] The general multi-objective optimization model can be simplified as shown below:

[0045]

[0046] In the formula, the objective function is to find the maximum or minimum value, g i (x) is the i-th objective function, k i represents the i-th equality constraint, n represents the number of equality constraints, f j represents the jth inequality constraint, and m is the number of inequality constraints.

[0047] The optimization objectives are to minimize the operating cost of the flexible power grid and minimize the total value of the flexible power grid load fluctuation.

[0048] (1) Minimizing operating costs is the optimization goal

[0049] Taking one day as a cycle, setting a time interval Δt, and dividing one day into T time periods according to Δt, the operating cost of the elastic grid in one cycle is minimized, which is referred to as the minimization of the elastic grid operation cost, which can be expressed by the formula:

[0050]

[0051] Among them, C P (i) is the operating cost of the photovoltaic power generation unit in the i-th time period, C cost (i) represents the operating cost of the energy storage system in the i-th time period, C fe (i) represents the cost of electricity purchase and sale in the i-th time period of the elastic power grid and the distribution network, g1 represents the operating cost function model of the elastic power grid within one cycle, and T represents the total number of time periods.

[0052] Among them, the cost of the photovoltaic power generation unit in the i-th time period is C P (i) The calculation is as follows:

[0053] C P(i) = K P P p (i)Δt (3)

[0054] In the formula, K P represents the photovoltaic power generation operation cost coefficient, P P (i) represents the photovoltaic power generation value in the i-th time period, Δt = 15min.

[0055] The operating cost C of the energy storage system in the i-th time period cost (i) The calculation is as follows:

[0056] C cost (i) = K cost |P B (i)|Δt (4)

[0057] In the formula, K cost Represents the operating cost coefficient of the energy storage system, P B (i) represents the charging and discharging power of the energy storage system in the i-th time period. A positive value represents charging, and a negative value represents discharging.

[0058] The cost of electricity purchase and sale in the flexible grid and distribution network in the i-th time period C fe (i) The calculation is as follows:

[0059] C fe (i) = c fe (i)P fe (i)Δt (5)

[0060] In the formula, c fe (i) represents the electricity purchase or sale price when the flexible grid and the distribution grid trade in the i-th time period, P fe (i) represents the power transmitted when the flexible grid trades with the distribution network in the i-th time period considering the prediction error.

[0061] (2) Taking the total value of microgrid grid-connected load fluctuation as the minimum as the optimization goal

[0062] Considering the randomness and intermittency of the output of distributed power sources in the elastic grid, the power transmission between the elastic grid and the distribution network may cause power fluctuations. In order to maintain the stable operation of the power system inside the elastic grid and reduce the impact on the distribution network, optimizing the load fluctuation when the elastic grid is connected to the grid is taken as one of the goals. Its expression is as follows:

[0063]

[0064] Where g2 is the minimum total amount of the elastic grid-connected load fluctuation within a cycle, referred to as the minimum total value of the elastic grid-connected load fluctuation, P fe(i) represents the power transmitted when the elastic grid and the distribution network trade in the i-th time period considering the prediction error. 2. Constraints

[0065] (1) Power balance constraints:

[0066] The power balance of the elastic grid, that is, the balance of power supply and demand within the elastic grid, is expressed as follows:

[0067] P+ΔP V =P P +ΔP P +P ev -P B +P fe (7)

[0068] In the formula, photovoltaic power generation units, energy storage systems, V2G vehicles, and distribution networks are all power supply units of the flexible power grid.

[0069] (2) Grid-connected power boundary constraints:

[0070] In order to avoid excessive impact on the distribution network power system when the elastic grid is connected to the grid, the power transmission power of the elastic grid is constrained:

[0071] |P fe |≤max(P fe ) (8)

[0072] In the formula, max(P fe ) represents the maximum power transmission limit value of the flexible grid connection.

[0073] (3) Grid-connected power fluctuation constraints:

[0074] In the objective function, the total amount of grid-connected load fluctuation of the elastic power grid is set to be the minimum. However, in order to avoid the elastic power grid bringing large fluctuations to the distribution network within a certain period of time, the grid-connected power fluctuation constraint is set:

[0075] |P fe (i+1)-P fe (i)|≤θ (9)

[0076] Where θ represents the maximum allowable grid-connected power fluctuation.

[0077] (4) Energy storage system charging and discharging power constraints:

[0078] If the energy storage system is in an overcharged or over-discharged state for a long time, it will cause irreversible loss of its service life. Therefore, the maximum charge and discharge of the energy storage system is restricted:

[0079] min(P B )≤P B ≤max(PB ) (10)

[0080] In the formula, min(P B ) represents the minimum charging and discharging power of the energy storage system, which is a negative value, max(P B ) represents the maximum charging and discharging power of the energy storage system, which is a positive value.

[0081] (5) Energy storage system SOC constraints:

[0082] The deep discharge and overcharge of the energy storage system will affect its service life and performance. Therefore, the state of charge (SOC) of the energy storage system needs to be limited to a certain range. The constraint expression of SOC is as follows:

[0083] min(SOC)≤SOC≤max(SOC) (11)

[0084] In the formula, min(SOC) indicates the minimum SOC that the energy storage battery should maintain, and max(SOC) indicates the upper limit of the SOC of the energy storage battery. The SOC calculation formula of the energy storage battery is as follows:

[0085]

[0086] In the formula, S B is the capacity of the energy storage system.

[0087] P involved in the above objective function and constraints fe It is the power transmitted when the elastic power grid trades with the distribution network considering the prediction error. The prediction error here, this application uses a calculation model based on the prediction error within the bilateral super quantile confidence interval to calculate the expected value of the prediction error of the elastic power grid power load and the expected value of the prediction error of the photovoltaic power generation power.

[0088] In the two-sided super-quantile model, the quantile on the left side of the normal distribution model fitted by the prediction error is g(x)1, and the quantile on the right side is g(x)2. The corresponding super-quantile interval areas are β1 and β2, respectively. According to the definition of the cumulative distribution density function, the quantile and super-quantile interval areas should satisfy the following formula:

[0089]

[0090] The super quantile on the left side of the confidence interval indicates that the actual power system forecast error is lower than the minimum lower confidence level, while the super quantile on the right side indicates that the actual power system forecast error is higher than the minimum higher confidence level. The quantiles g(x)1 and g(x)2 on the left and right sides are obtained by the following formula

[0091] g(x)1=F -1 (β1) (15)

[0092] g(x)2=F -1 (1-β2) (16)

[0093] In the formula, F -1 It is the inverse function of the cumulative distribution function F(x).

[0094] From the above, we can get the normal distribution expected value ΔP of the prediction error within the confidence interval:

[0095]

[0096] β=1-β1-β2 (18)

[0097] Based on the calculation method of the normal distribution expected value ΔP, the expected value of the prediction error of photovoltaic power generation can be obtained: p and the expected value of power load forecast error ΔP V , and then the power P of power transmission when the flexible power grid and the distribution network trade is obtained fe , expressed as:

[0098] P fe =P+ΔP V +P B (i)+P ev -P p (i)-ΔP p (19)

[0099] Where P ev It represents the charging and discharging power of V2G vehicles when participating in the energy optimization scheduling of microgrids. Its positive value indicates that V2G vehicles are discharging as distributed units, and its negative value indicates that V2G vehicles are charging as power consumption units. ΔP p represents the expected value of the prediction error of photovoltaic power generation, P represents the power load power of the microgrid, ΔP V Represents the expected value of power load forecast error.

[0100] 3. Solution method

[0101] In the multi-objective energy optimization model for the elastic grid connection proposed in this embodiment, the prediction error is taken into account, and the optimization objectives are to minimize the elastic grid connection operation cost and minimize the total grid connection fluctuation. For this purpose, a correction coefficient λ is introduced to unify the order of magnitude and unit of the two objective functions, and its expression is as follows:

[0102]

[0103] The present disclosure adopts a multi-objective particle swarm algorithm (MOPSO) to solve the multi-objective energy optimization model of microgrid grid connection considering prediction errors. MOPSO is a multi-objective optimization algorithm proposed by Carlos A. Coello et al. It improves the traditional particle swarm algorithm so that it can handle multi-objective optimization problems. Compared with combining multiple objectives into a weighted sum of an objective function, the MOPSO algorithm can provide better solution diversity and is more in line with the decision maker's preferences. The specific multi-objective particle swarm algorithm parameters are shown in Table 1:

[0104] Table 1 Multi-objective particle swarm algorithm parameter settings

[0105]

[0106] The specific solution steps and the basic process are as follows:

[0107] (1) Obtain the basic parameters of the elastic power grid and the real-time electricity price; the basic parameters include the elastic power grid power load, photovoltaic power generation power, V2G vehicle dispatchable resources, power balance range, grid-connected power boundary range, grid-connected power fluctuation range, energy storage system charging and discharging power range, and energy storage system charge state range.

[0108] (2) A calculation model based on the prediction error within the bilateral super-quantile confidence interval and a multi-objective energy optimization model for the elastic power grid connection considering the prediction error are constructed respectively.

[0109] (3) Using the calculation model based on the prediction error within the bilateral superquantile confidence interval, the expected value of the prediction error of the flexible grid power load and the expected value of the prediction error of the photovoltaic power generation power are calculated.

[0110] (4) Substitute the basic parameters, the data obtained in step 3, and the real-time electricity price data into the multi-objective energy optimization model of the elastic grid connection considering the prediction error, and use the multi-objective particle swarm algorithm to solve it, and obtain the optimal charging and discharging power curve of the energy storage system in one cycle. When solving the power system energy management optimization model, it is a common solution method to combine multiple objective functions into a single objective function. However, considering the problem that the optimization result is single and difficult to adjust, and the weights of each objective function are difficult to select, the multi-objective energy optimization model of the microgrid connection considering the prediction error is solved.

[0111] (5) Based on the optimal charging and discharging power curve of the energy storage system in one cycle in step 4, its data is substituted into the multi-objective energy optimization model of the elastic power grid that takes into account the prediction error, and the power transmission power when the elastic power grid and the distribution network are trading is calculated. Based on this power, the optimized results of various operating indicators of the load fluctuation and operating cost when the elastic power grid is connected to the grid are calculated, and an energy optimization scheduling strategy with the lowest operating cost of the elastic power grid and the smallest total value of the load fluctuation of the elastic power grid is obtained.

[0112] 4. Example Analysis

[0113] In order to verify the feasibility of the flexible grid-connected energy optimization scheduling model considering prediction errors designed in the present invention, as well as the impact of load errors on the optimization effect, two groups of optimal solutions with and without considering errors under the same conditions were selected for analysis.

[0114] according to Figure 2 As shown in the figure, the power error of the power load in the period of 9:00-9:45 is fitted with a normal distribution. It can be observed from the figure that the prediction error of the power load shows the characteristics of the normal distribution of the load. According to the data in the figure, the prediction error is mainly concentrated in the interval of [-10,20], among which the prediction error interval with the highest probability is [2,6].

[0115] like Figure 3 The graph shows the microgrid grid-connected load curve, which depicts the grid-connected load power before and after optimization without considering the prediction error and considering the prediction error. The optimized grid-connected load power curve shown in the figure is calculated based on the optimal charging and discharging power of energy storage obtained by using the prediction data into the optimization model and the actual power load and photovoltaic power generation data. It can better reflect the impact of the optimization on various indicators of the microgrid power system.

[0116] according to Figure 4 The three curves in the figure represent the microgrid grid-connected load power fluctuation curves before and after optimization without considering the prediction error and considering the prediction error. It can be observed from the figure that the maximum value of the load fluctuation is significantly reduced after optimization, and in most periods, the grid-connected load fluctuation is reduced to a certain extent after the optimization considering the prediction error. By comparing the optimization effects without considering the prediction error and considering the prediction error, it can be seen that in most periods, the optimization effect of considering the prediction error is more obvious.

[0117] Example 2

[0118] In one embodiment of the present disclosure, a system for optimizing energy dispatching of a flexible power grid is provided, including a data acquisition module, an error calculation module, and an optimization solution module:

[0119] The data acquisition module is configured to: acquire basic parameters of the elastic power grid to be optimized and real-time electricity prices;

[0120] The error calculation module is configured to: calculate the expected value of the prediction error of the elastic power grid power load and the expected value of the prediction error of the photovoltaic power generation power by using a calculation model based on the prediction error within the bilateral superquantile confidence interval;

[0121] The optimization solution module is configured to substitute the basic parameters, real-time electricity prices and the expected value of the calculated prediction error into the multi-objective energy optimization model of the elastic power grid connection taking into account the prediction error, and solve the model with the goal of minimizing the operating cost and the total value of the elastic power grid connection load fluctuation to obtain the energy optimization scheduling strategy.

[0122] Example 3

[0123] In one embodiment of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method for optimizing energy scheduling of a flexible power grid is implemented.

[0124] Example 4

[0125] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method for optimizing energy scheduling of a flexible power grid is implemented.

[0126] Example 5

[0127] In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the method for optimizing energy scheduling of a flexible power grid.

[0128] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0130] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A method for optimizing energy dispatching of a flexible power grid, characterized in that: include: Obtain the basic parameters of the elastic power grid to be optimized and the real-time electricity price; The expected value of the forecast error of the flexible grid load and the expected value of the forecast error of the photovoltaic power generation power are calculated using the calculation model based on the forecast error within the bilateral superquantile confidence interval. The basic parameters, real-time electricity prices and the expected value of the calculated prediction error are substituted into the multi-objective energy optimization model of the elastic grid grid-connected with prediction error. The model is solved with the goal of minimizing the operating cost and the total value of the elastic grid-connected load fluctuation to obtain the energy optimization scheduling strategy.

2. A method for optimizing energy dispatching of a flexible power grid according to claim 1, characterized in that: The basic parameters include the power load of the flexible power grid, photovoltaic power generation power, V2G vehicle dispatchable resources, power balance range, grid-connected power boundary range, grid-connected power fluctuation range, energy storage system charging and discharging power range, and energy storage system charge state range.

3. The method for optimizing energy dispatching of a flexible power grid according to claim 1, characterized in that: The objective function of the multi-objective energy optimization model for elastic grid connection is: Among them, g1 is the objective function of minimizing the operating cost of the elastic power grid, g2 is the objective function of minimizing the total value of the elastic power grid load fluctuation, and C P (i) is the cost of the photovoltaic power generation unit in the i-th time period, C cost (i) represents the operating cost of the energy storage system in the i-th time period, T represents the total number of time periods, λ is the correction coefficient, and C fe (i) = c fe (i)P fe (i) Δt represents the cost of electricity purchase and sale between the flexible grid and the distribution network in the oth time period, c fe (i) represents the electricity purchase or sale price when the flexible grid and the distribution grid trade in the i-th time period, P fe (i) represents the power transmitted when the flexible grid trades with the distribution network considering the prediction error.

4. The method for optimizing energy dispatching of a flexible power grid according to claim 3, characterized in that: The power P of power transmission when the elastic power grid considering the prediction error trades with the distribution network fe (i), the specific formula is: P fe (i)=P+ΔP V +P B (i)+P ev -P p (i)-ΔP p (6) Among them, P ev It represents the charging and discharging power of V2G vehicles when participating in the energy optimization scheduling of elastic power grid. Its positive value indicates that V2G vehicles are discharging as distributed units, and its negative value indicates that V2G vehicles are charging as power consumption units. ΔP p represents the expected value of the prediction error of photovoltaic power generation, P represents the power load power of the flexible power grid, ΔP V It represents the expected value of the forecast error of the flexible power grid electricity load.

5. The method for optimizing energy dispatching of a flexible power grid according to claim 1, characterized in that: The elastic power grid grid-connected multi-objective energy optimization model also includes constraints, specifically power balance constraints, grid-connected power boundary constraints, grid-connected power fluctuation constraints, energy storage system charging and discharging power constraints, and energy storage system SOC constraints.

6. The method for optimizing energy dispatching of a flexible power grid according to claim 1, characterized in that: The solution is to use a multi-objective particle swarm algorithm to obtain the optimal charging and discharging power curve of the energy storage system in one cycle, and substitute the optimal charging and discharging power curve into the multi-objective energy optimization model of the elastic power grid grid connection that takes into account the prediction error, calculate the power transmission power when the elastic power grid and the distribution network trade, and calculate the load fluctuation and operating cost optimization results of various operating indicators when the elastic power grid is grid-connected based on this power, and obtain an energy optimization scheduling strategy with the lowest operating cost of the elastic power grid and the smallest total value of the elastic power grid grid connection load fluctuation.

7. A system for optimizing energy dispatching of a flexible power grid, characterized in that: Including data acquisition module, error calculation module and optimization solution module: The data acquisition module is configured to: acquire basic parameters of the elastic power grid to be optimized and real-time electricity prices; The error calculation module is configured to: calculate the expected value of the prediction error of the elastic power grid power load and the expected value of the prediction error of the photovoltaic power generation power by using a calculation model based on the prediction error within the bilateral superquantile confidence interval; The optimization solution module is configured to substitute the basic parameters, real-time electricity prices and the expected value of the calculated prediction error into the multi-objective energy optimization model of the elastic power grid connection taking into account the prediction error, and solve the model with the goal of minimizing the operating cost and the total value of the elastic power grid connection load fluctuation to obtain the energy optimization scheduling strategy.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for optimizing energy scheduling of a flexible power grid as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, a method for optimizing elastic power grid energy scheduling is implemented as described in any one of claims 1-6.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes a method for optimizing energy scheduling of a flexible power grid as described in any one of claims 1-6.

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