Optical storage micro-grid economical efficiency optimization scheduling method considering demand response and source load uncertainty

Abstract

The invention discloses an optical storage micro-grid economical efficiency optimization scheduling method considering demand response and source load uncertainty, and the method comprises the steps: firstly generating a plurality of probability models, namely scenes, of photovoltaic power generation power and user load power based on a Monte Carlo method, and then carrying out the k-means clustering, scene reduction and expectation solving, so as to deal with the source load uncertainty; and obtaining the photovoltaic power generation power and the user load power in each time period after considering the uncertainty of the source load. On this basis, the constraint condition and the objective function of the energy scheduling model of the energy storage system of the optical storage micro-grid are determined, and a flexible load and demand response mechanism is introduced, so that the optical storage micro-grid has the capability of adjusting demand fluctuation, and the economic optimization scheduling of the micro-grid is further realized. The high-proportion access of the optical storage micro-grid to the main power grid and the efficient utilization of the optical storage micro-grid are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic microgrids with energy storage, and more specifically, relates to an economic optimization scheduling method for photovoltaic microgrids with energy storage taking into account demand response and source-load uncertainty. Background Art

[0002] With the construction of a low-carbon power system, renewable energy plays an increasingly important role in the new power system, and demand response technology is becoming a key means to promote the efficient use of renewable energy. As the proportion of intermittent renewable energy in the new power system continues to rise, its proportion in the energy supply structure also increases. In this way, in the process of switching from reliance on fossil fuels to renewable energy, the stability of power energy is weakened and more uncertainties are introduced, which adds difficulty to economic dispatch and ensuring stable operation.

[0003] The popularity of distributed energy has given rise to the concept of photovoltaic microgrids with storage and prosumers with both production and consumption functions. The traditional distribution network operation model has been unable to cope with the rapid growth of distributed energy, which has brought new challenges to the grid connection of distributed photovoltaic microgrids with storage and prosumers. The access of distributed photovoltaic microgrids with storage to the main grid may cause serious imbalances in supply and demand, such as power fluctuations and peak-to-valley differences. The development of energy dispatching and management methods that can cope with uncertainty and achieve friendly interaction with the upper-level power grid is of far-reaching significance for ensuring the economical and reliable operation of photovoltaic microgrids with storage and prosumers.

[0004] Considering the uncertainty of source and load, how to effectively manage and dispatch resources to ensure the stable and economical operation of the main power grid has become the focus of current research. Especially in the scenarios of photovoltaic storage microgrids and prosumers, the development of energy management systems must not only consider economic benefits, but also take into account multiple goals such as system reliability, smoothing power fluctuations, environmental protection, and compliance with power generation plan curves, so as to achieve high-proportion access and efficient utilization of photovoltaic storage microgrids. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for economic optimization scheduling of a photovoltaic storage microgrid taking into account demand response and source-load uncertainty, so as to achieve a high proportion of photovoltaic storage microgrid access to the main grid and efficient use of the photovoltaic storage microgrid.

[0006] To achieve the above-mentioned object of the invention, the present invention provides a method for optimizing the economic efficiency of a photovoltaic microgrid with energy storage, taking into account demand response and source-load uncertainty, and is characterized by comprising the following steps:

[0007] (1) Dividing the scheduling interval of the photovoltaic and energy storage microgrid

[0008] A day is divided into NT periods, each period is a PV-storage microgrid dispatching interval consisting of Δt, where Δt = 24 hours / NT. The PV-storage microgrid includes a photovoltaic system and an energy storage system.

[0009] (2) Generate scenarios of photovoltaic power generation and user load power based on the Monte Carlo method

[0010] 2.1) Based on the photovoltaic power generation in NT periods of a day And user load power Constructing sample data sets of power generation and load;

[0011] 2.2) From the power generation and load sample data set, take out NT photovoltaic power generation And user load power t=1,2,...,NT, then the photovoltaic power generation Multiply by the proportionality factor k pv , User load power Multiply by the proportionality factor k ld , and get the photovoltaic power product value User load power product value As the standard deviation of the normal distribution, where the proportionality coefficient k pv and k ld The values ​​of are all in the interval [0.05,0.2];

[0012] 2.3) Standard deviation t×1,2,...,NT constructs NT normal distributions with standard deviation t×1,2,...,NT constructs NT normal distributions, on this basis, generates S based on the Monte Carlo method MC The probability model of photovoltaic power generation and user load power is the scenario;

[0013] (3) Perform k-means clustering based on the preset number of clusters of photovoltaic power generation and user load power, and then perform scene reduction and expectation calculation.

[0014] 3.1) The square error δ MSE As a criterion function:

[0015]

[0016] Among them, X i is the probability model set of photovoltaic power generation and user load power of the i-th cluster, k is the number of clusters, P is the probability model of photovoltaic power generation and user load power, m i is the cluster mean vector of the i-th class, i.e., the class center:

[0017]

[0018] Among them, N i is the probability model set X i The number of probability models in ;

[0019] Use k-means algorithm to perform S MC The probability model of photovoltaic power generation and user load power is clustered according to the scenario;

[0020] 3.2) For each cluster, use the Euclidean distance function and perform scene reduction, and add the probability of the reduced probability model, i.e., the scene, to the probability of the closest probability model, i.e., the scene;

[0021] 3.3) Calculate the expectation of the probability model in all clusters after scene reduction, and use it as the final scene generation result: t×1,2,...,NT, where They are the photovoltaic power generation power and user load power in the tth period after considering the uncertainty of source and load;

[0022] (4) Determine the constraints and objective function of the energy scheduling model of the photovoltaic microgrid energy storage system

[0023] 4.1) Determine the constraints of the energy dispatch model of the photovoltaic microgrid energy storage system, including: power balance constraints, energy storage charging and discharging power constraints, SOC constraints, flexible load constraints, and grid transaction constraints. The constraints are:

[0024]

[0025] in, The power supplied by the photovoltaic system to the energy storage system in the tth period, is the power supplied by the photovoltaic system to the main grid during the tth period, is the power supplied by the photovoltaic system to the user in the tth period, P j,disc (t) is the discharge power of the jth energy storage device in the energy storage system in the tth period, is the power purchased by the user in the tth period, is the total load power after demand response in the tth period, is the total power supplied to the main grid in the tth period, The charging power of the main grid to the jth energy storage device in the energy storage system in the tth period, is the power purchased by the energy storage system in the tth period, P is the charging power of the photovoltaic system to the jth energy storage device in the energy storage system during the tth period, j,char (t) is the charging power of the jth energy storage device in the energy storage system in the tth period, U j,cis the charge and discharge flag of the jth energy storage device in the energy storage system, 1 indicates charging, 0 indicates discharging, P j,disc (t) is the discharge power of the jth energy storage device in the energy storage system in the tth period, P j N is the rated power of the jth energy storage device in the energy storage system, is the SOC state of the jth energy storage device in the energy storage system in the tth period, is the SOC state of the jth energy storage device in the energy storage system in the t+1th period, η j,Ch and η j,Dis are the charging and discharging efficiencies of the jth energy storage device in the energy storage system, cbat j is the capacity of the jth energy storage device in the energy storage system, E j,max and E j,min are the charge and discharge depths of the jth energy storage device in the energy storage system, is the load power transferred in the tth period, T trans is the load transfer rate, The load power reduction in the tth period, T cut is the load reduction rate;

[0026] 4.2) Determine the objective function of the energy dispatch model of the photovoltaic microgrid energy storage system, including the total operating loss cost, operation and maintenance cost, depreciation cost, grid transaction cost and demand response incentive cost. The objective function is:

[0027] min(C loss +C oprt +C f +C trade +C cut )

[0028] In the formula, C loss is the total loss cost of the photovoltaic and energy storage microgrid operation, C oprt is the operation and maintenance cost, C f is the depreciation cost, C trade is the grid transaction cost, C cut Incentivizing costs for demand response;

[0029] Among them, the total operating loss cost C loss for:

[0030]

[0031] In the formula, kl j is the power loss coefficient of the jth energy storage device in the energy storage system;

[0032] Operation and maintenance cost C oprt for:

[0033]

[0034] In the formula, γ j is the aging coefficient of the jth energy storage device in the energy storage system, δ j is the operation and maintenance coefficient of the jth energy storage device in the energy storage system, LLT j is the design service life of the jth energy storage device in the energy storage system, θ j,t is the total cycle index of the jth energy storage device in the energy storage system;

[0035] Depreciation cost C f for:

[0036]

[0037] Where Bd j is the initial construction investment cost of the jth energy storage device in the energy storage system;

[0038] Grid transaction cost C trade for:

[0039]

[0040] In the formula, The power purchased from the main grid in the tth period is: and are the time-of-use electricity prices for purchasing and selling electricity from the main grid in the tth period;

[0041] Demand response incentive cost C cut for:

[0042]

[0043] In the formula, λ cp is the compensation coefficient;

[0044] (5) First, determine the decision variables of the energy scheduling model of the photovoltaic microgrid energy storage system. The decision variables include the charging power P of the jth energy storage device in the tth period j,char (t), discharge power P j,disc (t), the interaction power between the photovoltaic microgrid and the main grid: the power purchased by the energy storage system in the tth period Total power supplied to the main grid Power purchased by users And the demand response power in the tth period: the transfer load power Reduce load power Then, the optimization solver is used to solve the problem and obtain the decision variables, that is, the economic optimization scheduling plan of the photovoltaic and energy storage microgrid.

[0045] The object of the invention of the present invention is achieved in this way:

[0046] The economic optimization scheduling method of the photovoltaic storage microgrid considering demand response and source-load uncertainty of the present invention is to cope with source-load uncertainty. First, based on the Monte Carlo method, a plurality of probability models of photovoltaic power generation and user load power, i.e., scenarios, are generated. Then, k-means clustering, scenario reduction and expectation are performed to obtain photovoltaic power generation and user load power in each time period after considering source-load uncertainty. On this basis, the constraint conditions and objective function of the energy scheduling model of the photovoltaic storage microgrid energy storage system are determined, and a flexible load and demand response mechanism is introduced to enable the photovoltaic storage microgrid to have the ability to adjust demand fluctuations, thereby realizing the economic optimization scheduling of the microgrid, realizing the high proportion of photovoltaic storage microgrid access to the main power grid and the efficient use of the photovoltaic storage microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flowchart of a specific implementation method of the method for optimizing the economic efficiency of a photovoltaic storage microgrid considering demand response and source-load uncertainty of the present invention;

[0048] Figure 2 The present invention generates photovoltaic power generation and user load power, i.e., a scene instance diagram;

[0049] Figure 3 This is an example diagram of the results of optimizing the scheduling of a photovoltaic and energy-storage microgrid according to the present invention. DETAILED DESCRIPTION

[0050] The specific implementation of the present invention is described below in conjunction with the accompanying drawings so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when the detailed description of known functions and designs may dilute the main content of the present invention, these descriptions will be omitted here.

[0051] Example

[0052] Figure 1 It is a flowchart of a specific implementation method of the method for optimizing the economic efficiency of a photovoltaic storage microgrid considering demand response and source-load uncertainty of the present invention;

[0053] In this embodiment, if Figure 1 As shown, the economic optimization scheduling method of the photovoltaic storage microgrid considering demand response and source-load uncertainty includes the following steps:

[0054] Step S1: Divide the PV-storage microgrid dispatching interval

[0055] A day is divided into NT time periods, each of which is a photovoltaic and energy storage microgrid scheduling interval consisting of Δt, where Δt = 24 hours / NT. The photovoltaic and energy storage microgrid includes a photovoltaic system and an energy storage system.

[0056] In this embodiment, a day is divided into NT=24 time periods, each of which lasts for 1 hour, i.e., Δt=1 hour, so as to analyze and optimize the power supply of the photovoltaic microgrid in each time period.

[0057] Step S2: Generate scenarios of photovoltaic power generation and user load power based on the Monte Carlo method

[0058] Step S2.1: Photovoltaic power generation based on NT time periods in a day And user load power Construct a sample data set of generation and load.

[0059] Step S2.2: From the power generation and load sample data set, extract NT photovoltaic power generation And user load power t=1,2,...,NT, then the photovoltaic power generation Multiply by the proportionality factor k pv , User load power Multiply by the proportionality factor k ld , and get the photovoltaic power product value User load power product value As the standard deviation of the normal distribution, where the proportionality coefficient k pv and k ld The values ​​of are all in the range of [0.05, 0.2]. In this embodiment, the proportionality coefficient k pv , proportionality coefficient k ld Both are 0.1.

[0060] Step S2.3: Standard deviation t=1,2,...,NT construct NT normal distributions with standard deviation t=1,2,...,NT construct NT normal distributions, on this basis, generate S based on the Monte Carlo method MC The probability model of photovoltaic power generation and user load power is the scenario. MC =500, i.e., 500 scenarios, each of which consists of 24 pairs of photovoltaic power generation and user load power.

[0061] Step S3: Perform k-means clustering according to the preset photovoltaic power generation and user load power clustering numbers, and then perform scene reduction and expectation calculation

[0062] Step S3.1: Substituting the square error δ MSE As a criterion function, it is also called minimum squared error (MSE):

[0063]

[0064] Among them, X i is the probability model set of photovoltaic power generation and user load power of the i-th cluster, k is the number of clusters, P is the probability model of photovoltaic power generation and user load power, m i is the cluster mean vector of the i-th class, i.e., the class center:

[0065]

[0066] Among them, N i is the probability model set X i The number of probability models in .

[0067] Use k-means algorithm to perform S MC The probability model of photovoltaic power generation and user load power is clustered according to the scenario.

[0068] Step S3.2: For each cluster, a Euclidean distance function is used and scene reduction is performed, and the probability of the reduced probability model, i.e., the scene, is added to the probability of the nearest probability model, i.e., the scene.

[0069] Step S3.3: Calculate the expectation of the probability model in all clusters after scene reduction, and use it as the final scene generation result: t=1,2,...,NT, where They are the photovoltaic power generation power and user load power in the tth period after considering the uncertainty of source and load, respectively. In this embodiment, the photovoltaic power generation power and user load power in the tth period after considering the uncertainty of source and load, that is, the final scenario is as follows Figure 2 shown.

[0070] Step S4: Determine the constraints and objective function of the energy scheduling model of the photovoltaic microgrid energy storage system

[0071] Step S4.1: Determine the constraints of the energy dispatch model of the photovoltaic microgrid energy storage system, including: power balance constraints, energy storage charging and discharging power constraints, SOC constraints, flexible load constraints, and grid transaction constraints. The constraints are:

[0072]

[0073] in, The power supplied by the photovoltaic system to the energy storage system in the tth period, is the power supplied by the photovoltaic system to the main grid during the tth period, is the power supplied by the photovoltaic system to the user in the tth period, P j,disc (t) is the discharge power of the jth energy storage device in the energy storage system in the tth period, is the power purchased by the user in the tth period, is the total load power after demand response in the tth period, is the total power supplied to the main grid in the tth period, The charging power of the main grid to the jth energy storage device in the energy storage system in the tth period, is the power purchased by the energy storage system in the tth period, P is the charging power of the photovoltaic system to the jth energy storage device in the energy storage system during the tth period, j,char (t) is the charging power of the jth energy storage device in the energy storage system in the tth period, U j,c is the charge and discharge flag of the jth energy storage device in the energy storage system, 1 indicates charging, 0 indicates discharging, P j,disc (t) is the discharge power of the jth energy storage device in the energy storage system in the tth period, P j N is the rated power of the jth energy storage device in the energy storage system, is the SOC state of the jth energy storage device in the energy storage system in the tth period, is the SOC state of the jth energy storage device in the energy storage system in the t+1th period, η j,Ch and η j,Dis are the charging and discharging efficiencies of the jth energy storage device in the energy storage system, cbat j is the capacity of the jth energy storage device in the energy storage system, E j,max and E j,min are the charge and discharge depths of the jth energy storage device in the energy storage system, is the load power transferred in the tth period, T trans is the load transfer rate, The load power reduction in the tth period, T cut is the load reduction rate.

[0074] Step S4.2: Determine the objective function of the energy dispatch model of the photovoltaic microgrid energy storage system, including the total operating loss cost, operation and maintenance cost, depreciation cost, grid transaction cost and demand response incentive cost. The objective function is:

[0075] min(C loss +C oprt +C f +C trade +C cut )

[0076] In the formula, C loss is the total loss cost of the photovoltaic and energy storage microgrid operation, C oprt is the operation and maintenance cost, C f is the depreciation cost, C trade is the grid transaction cost, C cut Incentivizing costs for demand response;

[0077] Among them, the total operating loss cost C loss for:

[0078]

[0079] In the formula, kl j is the power loss coefficient of the jth energy storage device in the energy storage system;

[0080] Operation and maintenance cost C oprt for:

[0081]

[0082] In the formula, γ j is the aging coefficient of the jth energy storage device in the energy storage system, δ j is the operation and maintenance coefficient of the jth energy storage device in the energy storage system, LLT j is the design service life of the jth energy storage device in the energy storage system, θ j,t is the total cycle index of the jth energy storage device in the energy storage system;

[0083] Depreciation cost C f for:

[0084]

[0085] Where Bd j is the initial construction investment cost of the jth energy storage device in the energy storage system;

[0086] Grid transaction cost C trade for:

[0087]

[0088] In the formula, The power purchased from the main grid in the tth period is: and are the time-of-use electricity prices for purchasing and selling electricity from the main grid in the tth period;

[0089] Demand response incentive cost C cut for:

[0090]

[0091] In the formula, λ cp is the compensation coefficient.

[0092] Step S5: Determine the decision variables of the energy dispatch model of the photovoltaic microgrid energy storage system and solve it using the optimization solver. The decision variables are obtained to obtain the economic optimization dispatch plan of the photovoltaic microgrid energy storage system.

[0093] First, determine the decision variables of the energy scheduling model of the photovoltaic microgrid energy storage system. The decision variables include the charging power P of the jth energy storage device in the tth period j,char (t), discharge power P j,disc (t), the interaction power between the photovoltaic microgrid and the main grid: the power purchased by the energy storage system in the tth period Total power supplied to the main grid Power purchased by users And the demand response power in the tth period: the transfer load power Reduce load power Then, the optimization solver is used to solve the problem and obtain the decision variables, that is, the economic optimization scheduling plan of the photovoltaic and energy storage microgrid.

[0094] In this embodiment, the YALMIP toolbox in MATLAB software is used for mathematical modeling, and the CPLEX commercial solver is used for solving the problem to obtain the final economic optimization scheduling scheme of the photovoltaic storage microgrid. The scheduling curve is as follows: Figure 3 shown.

[0095] Although the above describes the illustrative specific embodiments of the present invention to facilitate the understanding of the present invention by those skilled in the art, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

Claims

1. A method for optimizing the economic performance of a photovoltaic and energy storage microgrid considering demand response and source-load uncertainty, characterized in that: The following steps are involved: (1) Dividing the dispatching interval of the photovoltaic and energy storage microgrid A day is divided into NT periods, each period is a PV-storage microgrid dispatching interval consisting of Δt, where Δt = 24 hours / NT. The PV-storage microgrid includes a photovoltaic system and an energy storage system. (2) Generate scenarios of photovoltaic power generation and user load power based on the Monte Carlo method 2.1) Based on the photovoltaic power generation in NT periods of a day And user load power Constructing sample data sets of power generation and load; 2.2) From the power generation and load sample data set, take out NT photovoltaic power generation And user load power Then the photovoltaic power Multiply by the proportionality factor k pv , User load power Multiply by the proportionality factor k ld , and get the photovoltaic power product value User load power product value As the standard deviation of the normal distribution, where the proportionality coefficient k pv and k ld The values ​​of In the interval [0.05,0.2]; 2.3) Standard deviation Construct NT normal distributions with standard deviation Construct NT normal distributions, and on this basis, generate S based on the Monte Carlo method MC The probability model of photovoltaic power generation and user load power is the scenario; (3) Perform k-means clustering based on the preset number of clusters of photovoltaic power generation and user load power, and then perform scene reduction and expectation calculation. 3.1) The square error δ MSE As a criterion function: Among them, X i is the probability model set of photovoltaic power generation and user load power of the i-th cluster, k is the number of clusters, P is the probability model of photovoltaic power generation and user load power, m i is the cluster mean vector of the i-th class, i.e., the class center: Among them, N i is the probability model set X i The number of probability models in ; Use k-means algorithm to perform S MC The probability model of photovoltaic power generation and user load power is clustered according to the scenario; 3.2) For each cluster, use the Euclidean distance function and perform scene reduction, and add the probability of the reduced probability model, i.e., the scene, to the probability of the closest probability model, i.e., the scene; 3.3) Calculate the expectation of the probability model in all clusters after scene reduction, and use it as the final scene generation result: in, They are the photovoltaic power generation power and user load power in the tth period after considering the uncertainty of source and load; (4) Determine the constraints and objective function of the energy scheduling model of the photovoltaic microgrid energy storage system 4.1) Determine the constraints of the energy dispatch model of the photovoltaic microgrid energy storage system, including: power balance constraints, energy storage charging and discharging power constraints, SOC constraints, flexible load constraints, and grid transaction constraints. The constraints are: in, The power supplied by the photovoltaic system to the energy storage system in the tth period, is the power supplied by the photovoltaic system to the main grid during the tth period, is the power supplied by the photovoltaic system to the user in the tth period, P j,disc (t) is the discharge power of the jth energy storage device in the energy storage system in the tth period, is the power purchased by the user in the tth period, is the total load power after demand response in the tth period, is the total power supplied to the main grid in the tth period, The charging power of the main grid to the jth energy storage device in the energy storage system in the tth period, is the power purchased by the energy storage system in the tth period, P is the charging power of the photovoltaic system to the jth energy storage device in the energy storage system during the tth period, j,char (t) is the charging power of the jth energy storage device in the energy storage system in the tth period, U j,c is the charge and discharge flag of the jth energy storage device in the energy storage system, 1 indicates charging, 0 indicates discharging, P j,disc (t) is the discharge power of the jth energy storage device in the energy storage system in the tth period, is the rated power of the jth energy storage device in the energy storage system, is the SOC state of the jth energy storage device in the energy storage system in the tth period, is the SOC state of the jth energy storage device in the energy storage system in the t+1th period, η j,Ch and η j,Dis are the charging and discharging efficiencies of the jth energy storage device in the energy storage system, cbat j is the capacity of the jth energy storage device in the energy storage system, E j,max and E j,min are the charge and discharge depths of the jth energy storage device in the energy storage system, is the load power transferred in the tth period, T trans is the load transfer rate, The load power reduction in the tth period, T cut is the load reduction rate; 4.2) Determine the objective function of the energy dispatch model of the photovoltaic microgrid energy storage system, including the total operating loss cost, operation and maintenance cost, depreciation cost, grid transaction cost and demand response incentive cost. The objective function is: min(C loss +C oprt +C f +C trade +C cut ) In the formula, C loss is the total loss cost of the photovoltaic and energy storage microgrid operation, C oprt is the operation and maintenance cost, C f is the depreciation cost, C trade is the grid transaction cost, C cut Incentivizing costs for demand response; Among them, the total operating loss cost C loss for: In the formula, kl j is the power loss coefficient of the jth energy storage device in the energy storage system; Operation and maintenance cost C oprt for: In the formula, γ j is the aging coefficient of the jth energy storage device in the energy storage system, δ j is the operation and maintenance coefficient of the jth energy storage device in the energy storage system, LLT j is the design service life of the jth energy storage device in the energy storage system, θ j,t is the total cycle index of the jth energy storage device in the energy storage system; Depreciation cost C f for: Where Bd j is the initial construction investment cost of the jth energy storage device in the energy storage system; Grid transaction cost C trade for: In the formula, The power purchased from the main grid in the tth period is: and are the time-of-use electricity prices for purchasing and selling electricity from the main grid in the tth period; Demand response incentive cost C cut for: In the formula, λ cp is the compensation coefficient; (5) First, determine the decision variables of the energy scheduling model of the photovoltaic microgrid energy storage system. The decision variables include the charging power P of the jth energy storage device in the tth period j,char (t), discharge power P j,disc (t), the interaction power between the photovoltaic microgrid and the main grid: the power purchased by the energy storage system in the tth period Total power supplied to the main grid Power purchased by users And the demand response power in the tth period: the transfer load power Reduce load power Then, the optimization solver is used to solve the problem and obtain the decision variables, that is, the economic optimization scheduling plan of the photovoltaic and energy storage microgrid.

2. The method for optimizing the economic efficiency of a photovoltaic microgrid with energy storage considering demand response and source-load uncertainty according to claim 1 is characterized in that: In step (1), NT = 24, Δt = 1 hour, S MC =500.

3. The method for optimizing the economic efficiency of a photovoltaic microgrid with energy storage considering demand response and source-load uncertainty according to claim 1 is characterized in that: The method of solving the problem using an optimization solver in step (5) is to use the YALMIP toolbox in MATLAB software to perform mathematical modeling and use the CPLEX commercial solver to solve the problem.

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