Multi-scenario Photovoltaic Energy Storage Flow Optimization and Capacity Configuration Methods for Industrial and Commercial Users

Abstract

This invention relates to a multi-scenario photovoltaic (PV) energy storage flow optimization and capacity configuration method for industrial and commercial users, comprising: collecting raw data; obtaining reduced scenarios; establishing a physical operation model of the energy storage equipment, a user demand response model, and a power balance model of the PV-storage integrated system; formulating a PV-storage integration interaction strategy; constructing a robust capacity configuration operation model for the PV-storage power station; and obtaining the optimal equipment configuration scheme and the optimal equipment power allocation strategy. This invention considers the output characteristics of photovoltaics, generates and reduces scenarios to the four seasons (spring, summer, autumn, and winter), and proposes multi-scenario PV-storage energy flow optimization strategies considering the uncertainty of photovoltaic output according to different seasons. The effectiveness of the PV-storage integrated system is verified through scenario settings, and the photovoltaic-storage-charging system configuration effectively improves the photovoltaic absorption capacity, realizing optimized energy flow between industrial and commercial users and the PV-storage system.

Description

Technical Field

[0001] This invention relates to the field of capacity configuration and energy management technology for optical fusion systems for industrial and commercial users, and in particular to a method for optimizing the flow of optical energy storage and configuring capacity for industrial and commercial users in multiple scenarios. Background Technology

[0002] With the explosive growth of distributed photovoltaic (PV) capacity, time-of-use (TOU) pricing in various regions has shifted to off-peak hours during midday, coinciding with peak PV generation. Whether distributed PV is fed into the electricity market or not, curtailment significantly impacts its profitability. While industry researchers have explored PV-storage synergy strategies using intelligent algorithms or machine learning-based approaches, these efforts have failed to optimize energy flow between PV and storage, resulting in irrational capacity allocation and significantly extended investment payback periods. Simultaneously, the continuous increase in distributed PV capacity across regions is causing a decline in grid capacity, leading to grid connection shortages. Consequently, various regions have proposed management measures for distributed PV to adapt to this evolving situation, encouraging PV-storage synergy and flexible dispatching to ensure healthy industry development and stable grid operation.

[0003] Currently, research on control strategies and capacity configuration strategies for photovoltaics and energy storage is relatively comprehensive. However, most studies focus on only one specific area, with few exploring the application of integrated photovoltaic-energy storage systems in industrial and commercial user scenarios as small microgrids to investigate energy interaction strategies and capacity configuration schemes between the two. Current industry research does not consider the uncertainty of renewable energy output or the impact of photovoltaic-energy storage flow optimization on the coordinated capacity configuration under current policies. Furthermore, existing intelligent algorithms such as particle swarm optimization are prone to getting stuck in local optima when solving complex models; more rigorous mathematical solutions can yield accurate results. Therefore, optimizing the energy flow and capacity configuration strategies for photovoltaic-energy storage is a crucial means to address the current problems of low grid capacity, insufficient distributed photovoltaic power transmission, inadequate absorption capacity, and low investment returns, and it is also a pressing technical challenge that needs to be solved. Summary of the Invention

[0004] To address the uncertainties in the configuration of photovoltaic and energy storage equipment for industrial and commercial users and the uniformity of energy flow under the new circumstances, the present invention aims to provide a multi-scenario photovoltaic and energy storage flow optimization strategy that considers the uncertainty of photovoltaic output based on different seasons. While ensuring the safe and reliable operation of the entire microgrid, this method realizes the energy flow optimization for industrial and commercial users and the photovoltaic and energy storage system, and is oriented towards multi-scenario photovoltaic and energy storage flow optimization and capacity configuration.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for multi-scenario photovoltaic energy storage flow optimization and capacity configuration for industrial and commercial users, the method comprising the following sequential steps:

[0006] (1) Collect original data;

[0007] (2) Using the collected raw data, the initial photovoltaic power output scenario is generated based on the Latin hypersolution method according to the day-ahead photovoltaic power output value, and the K-means algorithm is used to reduce the scenario to obtain the reduced scenario;

[0008] (3) Build a topology diagram of the photovoltaic-storage integration system for industrial and commercial users, establish a physical operation model of the energy storage equipment, a demand response model for users, and a power balance model for the photovoltaic-storage integration system, and formulate a photovoltaic-storage integration interaction strategy based on the latest time-of-use electricity price in the region.

[0009] (4) Based on the physical operation model of energy storage equipment, the user's demand response model, the power balance model of the photovoltaic-storage system, and the photovoltaic-storage integration interaction strategy, the robust idea is adopted to further consider the uncertainty of photovoltaic output and construct a robust capacity configuration operation model for photovoltaic-storage power station.

[0010] (5) The column sum constraint algorithm is used to solve the robust capacity configuration operation model of the photovoltaic-storage power station to obtain the optimal equipment configuration scheme and the optimal equipment power allocation strategy.

[0011] Step (1) specifically refers to the following: The original data includes historical regional temperature, irradiance, wind speed, atmospheric pressure and other meteorological data, as well as the area within the land boundary where photovoltaics can be installed, the 15-minute resolution load data of industrial and commercial users, the latest regional time-of-use electricity price, the user's annual electricity bill, the status of power construction, the user's regional land boundary status, and the technical and economic parameters of photovoltaic and energy storage equipment.

[0012] Step (2) specifically includes the following steps in sequence:

[0013] (2a) The day-ahead photovoltaic power output is calculated using the following formula. :

[0014] ;

[0015] in, Number of photovoltaic panels installed The rated power of the photovoltaic system. Let be the irradiance at time t. Standard irradiance, For power temperature coefficient, Let be the surface temperature of the photovoltaic panel at time t. For reference temperature;

[0016] (2b) Based on the current day's photovoltaic power output forecast The actual photovoltaic output value is obtained after considering the photovoltaic output error. :

[0017] ;

[0018] In the formula, For photovoltaic power output prediction error; The expected value is 0 and the standard deviation is 0. follows a standard normal distribution; where, The calculation formula is:

[0019] ;

[0020] In the formula, This refers to the total capacity where photovoltaic panels can be installed.

[0021] (2c) Based on the Latin hypersolution method, N photovoltaic power output scenarios are obtained:

[0022] First, due to the error in photovoltaic power generation prediction The expected value is 0, and the standard deviation is 0. The standard normal distribution, therefore The cumulative probability distribution function F( The probability is divided into N equal probability intervals; for any one of these intervals... ,in, Random sampling of probability values ​​yields probability values. ,in, It follows a uniform distribution between [0, 1].

[0023] According to the cumulative probability distribution function F( The inverse distribution function F is obtained. -1 (R), obtained by sampling 24 time periods. The sample matrix; T represents the time period;

[0024] (2d) According to The sample matrix is ​​used to reduce the N photovoltaic power output scenarios using the K-means algorithm to obtain the reduced scenarios.

[0025] In step (3), establishing the physical operation model of the energy storage equipment in the photovoltaic-energy storage integrated system specifically refers to:

[0026] Considering the user-side energy storage system, which adopts electrochemical energy storage, a physical operation model of the energy storage equipment in the photovoltaic-energy storage integrated system is established:

[0027] ;

[0028] ;

[0029] ;

[0030] ;

[0031] ;

[0032] ;

[0033] ;

[0034] ;

[0035] In the formula, , These represent the electrical energy storage capacity values ​​at times t and t-1, respectively. , Let represent the charging power and discharging power of the energy storage at times t and t-1, respectively. , These represent the charging and discharging efficiencies of electrical energy storage, respectively. A flag variable indicating whether electrical energy is in operation or in a charging state. , This indicates the maximum charging and discharging power of the electrical energy storage. This indicates the capacity value of the energy storage device. , These represent the energy storage capacity values ​​at the initial moment and at the end of the scheduling cycle, respectively. , These represent the minimum and maximum capacity values ​​of the energy storage device, respectively. This represents the maximum monthly demand for industrial and commercial users. This represents the load value for industrial and commercial users during off-peak hours. This represents the load value for industrial and commercial users during peak hours. Indicates the rated power of the energy storage;

[0036] Establish the load reduction response of equation (1) and the load transfer response of equation (2):

[0037] (1);

[0038] (2);

[0039] In the formula, This indicates that participation at time t in scenario s can reduce the load after demand response. This indicates that participation at time t in scenario s can reduce the load before demand response. This represents the load reduction at time t in scenario s. This indicates the minimum load that can be reduced. Indicates the maximum load that can be reduced; This represents the load at time t after the participation in the transferable demand response in scenario s. This represents the load at time t in scenario s before the participation in the transferable demand response. This represents the amount of load transferred in at time t under scenario s. This represents the outgoing load at time t in scenario s. This represents the maximum transferred load at time t in scenario s; T is the time period.

[0040] Formulas (1) and (2) together form the user's demand response model;

[0041] The power balance model of the photovoltaic-storage system is established as follows:

[0042] ;

[0043] ;

[0044] ;

[0045] ;

[0046] ;

[0047] In the formula, This represents the photovoltaic output at time t in scenario s; This represents the basic user load at time t in scenario s; The variable represents the flag variable for purchasing electricity from the public grid at time t in scenario s, where 0 indicates no purchase and 1 indicates purchase; Let $\frac{ ... This represents the electricity sales power at time t in scenario s; This represents the power purchased at time t in scenario s; This represents the user load at time t in scenario s.

[0048] In step (3), the photovoltaic-storage fusion interaction strategy specifically refers to deciding the capacity configuration of user-side energy storage by setting different application scenarios, and setting the optimal photovoltaic-storage optimization operation strategy in different scenarios; the application scenarios include the first, second, third and fourth scenarios;

[0049] The first scenario is that industrial and commercial users only consider the installation of photovoltaics: the photovoltaic capacity is determined entirely based on the available installation area, industrial and commercial users only consider installing photovoltaics, supplying electricity to users during the photovoltaic power generation period, and the surplus electricity is fed into the grid, with the grid-connected portion participating in the electricity market transaction;

[0050] The second scenario involves industrial and commercial users considering only the installation of electrochemical energy storage: the charging power of the energy storage comes entirely from the mains power, and the discharging power is supplied to the user. In this scenario, the first step is to determine whether the energy storage capacity is sufficient for charging. Energy storage can be used during the nighttime and midday low periods in summer and winter. Charge; if Energy storage can then be used during afternoon and evening peak hours. Discharge; if Moreover, it falls during the nighttime and midday low periods in spring and autumn. Charge during the morning, evening, and nighttime peak hours. Discharge; , Let represent the charging power and discharging power of the energy storage at times t and t-1, respectively. This represents the electrical energy storage capacity at time t. This indicates the capacity value of the energy storage device;

[0051] The third scenario involves industrial and commercial users considering a photovoltaic-storage integrated system where time-of-use pricing policies do not yet include midday off-peak periods: during the nighttime off-peak periods, if... At this time, the electricity demand of industrial and commercial users is met by purchasing electricity from the external power grid, and energy storage devices are used to meet this demand. The power is drawn from the external power grid for charging; during normal and peak periods in summer and winter, when At times, photovoltaic power generation meets electricity demand. During normal periods, energy storage determines whether to recharge based on its own state of charge. and Energy storage will utilize surplus photovoltaic power and grid electricity to charge the battery until it reaches full capacity; if the electricity price is normal and Then the photovoltaic power will be connected to the grid and participate in the electricity market transaction; if it is during peak hours and the electricity price is low... Then energy storage is Charge until the power is sufficient. If the value is negative, the energy storage stops working; during the peak hours of time-of-use electricity pricing at night, when there is no photovoltaic output, if... Then, energy storage will supply electricity to industrial and commercial users until... ,like Then, electricity is purchased from the external power grid; during the off-peak hours at night in spring and autumn, energy storage devices... Charging at the specified power, during morning peak hours and midday off-peak hours, if and At that time, the energy storage device is still in the charging state, storing the surplus photovoltaic power. and At peak times, the energy storage device discharges to meet the user's energy demand; however, during peak hours at midday and night, the photovoltaic equipment outputs zero, and the energy storage device... Discharge power to meet users' electricity needs; This represents the photovoltaic output at time t in scenario s; This represents the load value at time t in scenario s;

[0052] The fourth scenario involves industrial and commercial users considering a photovoltaic-storage integrated system where the time-of-use pricing policy applies during off-peak hours at midday: this occurs during off-peak hours when photovoltaic power generation begins and during both summer / winter and regular periods. and At that time, energy storage devices utilize the surplus electricity from photovoltaic power to... The power is used for charging. Due to the current electricity pricing policy, there is a two-hour off-peak period during the midday hours in spring and winter. By introducing energy storage, the surplus photovoltaic power generated during these two off-peak hours is stored and released during the nighttime peak hours and peak periods to meet users' electricity demand. During the morning peak hours in spring and autumn, if... and The energy storage device operates in a charging state and uses The power is used for charging, and during the three off-peak hours in spring and autumn, the surplus power of the photovoltaic system is utilized. Charging power is applied during peak hours in the evening and at night. Discharge using the discharge power.

[0053] Step (4) specifically includes the following steps in sequence:

[0054] (4a) Due to the strong uncertainty of photovoltaic power output, the objective function of the robust capacity configuration operation model of the photovoltaic-storage power station is constructed by considering the uncertainty of photovoltaic power output and using the following formula:

[0055] ;

[0056] ;

[0057] ;

[0058]

[0059] ;

[0060] ;

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] in, This indicates the investment cost of photovoltaic energy storage. This indicates the installed capacity of photovoltaic power generation. Indicates the installed capacity of energy storage equipment; Indicates the number of photovoltaic panels installed. This indicates the capacity of a single photovoltaic panel. This indicates the price of a single photovoltaic panel. Indicates the lifespan of the photovoltaic panel; Indicates the capacity of the energy storage device. This indicates the price per unit of electrical energy storage capacity. Indicates the lifespan of the energy storage device; Represents the probability of scenario s; This indicates the cost of purchasing electricity from the power grid. This represents the power purchased at time t in scenario s. This represents the time-of-use electricity price at time t; This indicates the maintenance cost of optical storage. This indicates the operation and maintenance cost of photovoltaic systems. This represents the photovoltaic output at time t in scenario s; This indicates the operation and maintenance cost of energy storage. This represents the energy storage charging power at time t in scenario s. This represents the energy storage discharge power at time t in scenario s; This represents the revenue from selling electricity to the power grid. This represents the electricity sales power at time t in scenario s. This represents the on-grid electricity price at time t; This indicates the carbon reduction benefits of photovoltaic, energy storage, and charging equipment. Indicates the carbon emission factor of the power grid. This indicates the benefit per unit of carbon emission reduction; This indicates the benefits that building users receive from participating in demand response. This indicates the benefits of reducing load demand response. This indicates the benefits of responding to transferable loads. This represents the unit revenue from load reduction. This represents the load reduction at time t in scenario s. This represents the unit revenue from load transfer. This represents the amount of load transferred in at time t under scenario s. This represents the outflow load at time t in scenario s; T represents the time period; r represents the discount rate; Indicates the number of scenes; Represents the uncertain set of photovoltaic power output; This represents the load reduction at time t in scenario s;

[0067] (4b) The constraints of the robust capacity configuration operation model for photovoltaic-storage power stations are as follows:

[0068] ;

[0069] ;

[0070] ;

[0071] In the formula, This represents the photovoltaic prediction value at time t under scenario s; This variable, ranging from 0 to 1, indicates whether the photovoltaic output at time t in scenario s is in a positive deviation. Indicates the degree of deviation in photovoltaic power output; This variable, ranging from 0 to 1, indicates whether the photovoltaic output at time t in scenario s is in a negative deviation. The value is an uncertain variable, with a maximum value of 24.

[0072] Step (5) specifically includes the following steps in sequence:

[0073] (5a) First, the robust capacity configuration operation model of the photovoltaic-storage power station is rewritten into a general matrix form:

[0074] (3);

[0075] In the formula, A, C, E, b, c, d, and f are all coefficient matrices; x represents the capacity variable of the first stage; y represents the operating variable of the second stage, i.e., the optimal equipment power allocation strategy; u represents the value of the uncertain variable; and U represents the set of uncertain variables.

[0076] (5b) Using a column and constraint generation algorithm, equation (3) is decomposed into the main problem of equation (4) and the subproblems of equation (5), specifically including the following steps:

[0077] (4);

[0078] (5);

[0079] In the formula, Variables introduced; Let y be the value of the m-th iteration in scenario s; This represents the value of the inner objective function in the m-th iteration;

[0080] (5b1) First, initialize the maximum number of iterations m. max The number of iterations m=1, the convergence accuracy, and the initial output of the photovoltaic power generation;

[0081] (5b2) Taking the initial output of photovoltaic power as the worst-case scenario for the first iteration, solve equation (3) to obtain the optimal equipment capacity configuration x. * Update the Nether ;

[0082] (5b3) Solve the main problem to obtain the optimal equipment capacity configuration x * By substituting the values ​​into the subproblem and solving it, we obtain the worst-case photovoltaic output value u* and the optimal equipment power allocation strategy y, and update the upper bound value. ;

[0083] (5b4) Determine whether the condition is met. If the conditions are met, the iteration stops and the iteration result is returned to obtain the optimal device capacity configuration x. * If not satisfied, let u m+1,* =u, m=m+1, return to step (5b2) to continue iterative calculation; The gap value is a constant.

[0084] As can be seen from the above technical solution, the beneficial effects of the present invention are as follows: First, under the background of building a new power system, the present invention considers the output characteristics of photovoltaics, generates and reduces scenarios, reduces the scenarios to spring, summer, autumn and winter, and proposes multi-scenario photovoltaic-storage energy flow optimization strategies considering the uncertainty of photovoltaic output according to different seasons. The effectiveness of the photovoltaic-storage integrated system is verified by setting scenarios, and the photovoltaic-storage-charging system is effectively improved by configuring photovoltaic-storage-charging systems. On the basis of ensuring the safe and reliable operation of the entire microgrid, the energy flow optimization of industrial and commercial users and photovoltaic-storage systems is realized. Second, the present invention taps into the demand response potential of industrial and commercial users, further optimizes the energy flow of industrial and commercial users and photovoltaic-storage equipment, and on the basis of optimizing the energy flow of the entire photovoltaic-storage microgrid, realizes the capacity optimization configuration of photovoltaic-storage systems, further improving the socio-economic benefits of photovoltaic-storage systems and energy systems. Attached Figure Description

[0085] Figure 1 This is a topology diagram of the photovoltaic-storage fusion system for industrial and commercial users in this invention;

[0086] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0087] like Figure 2 As shown, a method for optimizing the flow and configuring the capacity of photovoltaic energy storage in multiple scenarios for industrial and commercial users includes the following steps in sequence:

[0088] (1) Collect original data;

[0089] (2) Using the collected raw data, the initial photovoltaic power output scenario is generated based on the Latin hypersolution method according to the day-ahead photovoltaic power output value, and the K-means algorithm is used to reduce the scenario to obtain the reduced scenario;

[0090] (3) such as Figure 1 As shown, a topology diagram of the photovoltaic-storage integration system for industrial and commercial users is constructed. A physical operation model of the energy storage equipment, a demand response model of the user, and a power balance model of the photovoltaic-storage integration system are established. Based on the latest time-of-use electricity price in the region, a photovoltaic-storage integration interaction strategy is formulated.

[0091] (4) Based on the physical operation model of energy storage equipment, the user's demand response model, the power balance model of the photovoltaic-storage system, and the photovoltaic-storage integration interaction strategy, the robust idea is adopted to further consider the uncertainty of photovoltaic output and construct a robust capacity configuration operation model for photovoltaic-storage power station.

[0092] (5) The column sum constraint algorithm is used to solve the robust capacity configuration operation model of the photovoltaic-storage power station to obtain the optimal equipment configuration scheme and the optimal equipment power allocation strategy.

[0093] Step (1) specifically refers to the following: The original data includes historical regional temperature, irradiance, wind speed, atmospheric pressure and other meteorological data, as well as the area within the land boundary where photovoltaics can be installed, the 15-minute resolution load data of industrial and commercial users, the latest regional time-of-use electricity price, the user's annual electricity bill, the status of power construction, the user's regional land boundary status, and the technical and economic parameters of photovoltaic and energy storage equipment.

[0094] Step (2) specifically includes the following steps in sequence:

[0095] (2a) The day-ahead photovoltaic power output is calculated using the following formula. :

[0096] ;

[0097] in, Number of photovoltaic panels installed The rated power of the photovoltaic system. Let be the irradiance at time t. Standard irradiance, For power temperature coefficient, Let be the surface temperature of the photovoltaic panel at time t. For reference temperature;

[0098] (2b) Based on the current day's photovoltaic power output forecast The actual photovoltaic output value is obtained after considering the photovoltaic output error. :

[0099] ;

[0100] In the formula, For photovoltaic power output prediction error; The expected value is 0 and the standard deviation is 0. follows a standard normal distribution; where, The calculation formula is:

[0101] ;

[0102] In the formula, This refers to the total capacity where photovoltaic panels can be installed.

[0103] (2c) Based on the Latin hypersolution method, N photovoltaic power output scenarios are obtained:

[0104] First, due to the error in photovoltaic power generation prediction The expected value is 0, and the standard deviation is 0. The standard normal distribution, therefore The cumulative probability distribution function F( The probability is divided into N equal probability intervals; for any one of these intervals... ,in, Random sampling of probability values ​​yields probability values. ,in, It follows a uniform distribution between [0, 1].

[0105] According to the cumulative probability distribution function F( The inverse distribution function F is obtained. -1 (R), obtained by sampling 24 time periods. The sample matrix; T represents the time period;

[0106] (2d) According to The sample matrix is ​​used to reduce the N photovoltaic power output scenarios using the K-means algorithm to obtain the reduced scenarios.

[0107] In step (3), establishing the physical operation model of the energy storage equipment in the photovoltaic-energy storage integrated system specifically refers to:

[0108] Considering the user-side energy storage system, which adopts electrochemical energy storage, a physical operation model of the energy storage equipment in the photovoltaic-energy storage integrated system is established:

[0109] ;

[0110] ;

[0111] ;

[0112] ;

[0113] ;

[0114] ;

[0115] ;

[0116] ;

[0117] In the formula, , These represent the electrical energy storage capacity values ​​at times t and t-1, respectively. , Let represent the charging power and discharging power of the energy storage at times t and t-1, respectively. , These represent the charging and discharging efficiencies of electrical energy storage, respectively. A flag variable indicating whether electrical energy is in operation or in a charging state. , This indicates the maximum charging and discharging power of the electrical energy storage. This indicates the capacity value of the energy storage device. , These represent the energy storage capacity values ​​at the initial moment and at the end of the scheduling cycle, respectively. , These represent the minimum and maximum capacity values ​​of the energy storage device, respectively. This represents the maximum monthly demand for industrial and commercial users. This represents the load value for industrial and commercial users during off-peak hours. This represents the load value for industrial and commercial users during peak hours. Indicates the rated power of the energy storage;

[0118] Establish the load reduction response of equation (1) and the load transfer response of equation (2):

[0119] (1);

[0120] (2);

[0121] In the formula, This indicates that participation at time t in scenario s can reduce the load after demand response. This indicates that participation at time t in scenario s can reduce the load before demand response. This represents the load reduction at time t in scenario s. This indicates the minimum load that can be reduced. Indicates the maximum load that can be reduced; This represents the load at time t after the participation in the transferable demand response in scenario s. This represents the load at time t in scenario s before the participation in the transferable demand response. This represents the amount of load transferred in at time t under scenario s. This represents the outgoing load at time t in scenario s. This represents the maximum transferred load at time t in scenario s; T is the time period.

[0122] Formulas (1) and (2) together form the user's demand response model;

[0123] The power balance model of the photovoltaic-storage system is established as follows:

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] ;

[0129] In the formula, This represents the photovoltaic output at time t in scenario s; This represents the basic user load at time t in scenario s; The variable represents the flag variable for purchasing electricity from the public grid at time t in scenario s, where 0 indicates no purchase and 1 indicates purchase; Let $\frac{ ... This represents the electricity sales power at time t in scenario s; This represents the power purchased at time t in scenario s; This represents the user load at time t in scenario s.

[0130] In step (3), the photovoltaic-storage fusion interaction strategy specifically refers to deciding the capacity configuration of user-side energy storage by setting different application scenarios, and setting the optimal photovoltaic-storage optimization operation strategy in different scenarios; the application scenarios include the first, second, third and fourth scenarios;

[0131] The first scenario is that industrial and commercial users only consider the installation of photovoltaics: the photovoltaic capacity is determined entirely based on the available installation area, industrial and commercial users only consider installing photovoltaics, supplying electricity to users during the photovoltaic power generation period, and the surplus electricity is fed into the grid, with the grid-connected portion participating in the electricity market transaction;

[0132] The second scenario involves industrial and commercial users considering only the installation of electrochemical energy storage: the charging power of the energy storage comes entirely from the mains power, and the discharging power is supplied to the user. In this scenario, the first step is to determine whether the energy storage capacity is sufficient for charging. Energy storage can be used during the nighttime and midday low periods in summer and winter. Charge; if Energy storage can then be used during afternoon and evening peak hours. Discharge; if Moreover, it falls during the nighttime and midday low periods in spring and autumn. Charge during the morning, evening, and nighttime peak hours. Discharge; , Let represent the charging power and discharging power of the energy storage at times t and t-1, respectively. This represents the electrical energy storage capacity at time t. This indicates the capacity value of the energy storage device;

[0133] The third scenario involves industrial and commercial users considering a photovoltaic-storage integrated system where time-of-use pricing policies do not yet include midday off-peak periods: during the nighttime off-peak periods, if... At this time, the electricity demand of industrial and commercial users is met by purchasing electricity from the external power grid, and energy storage devices are used to meet this demand. The power is drawn from the external power grid for charging; during normal and peak periods in summer and winter, when At times, photovoltaic power generation meets electricity demand. During normal periods, energy storage determines whether to recharge based on its own state of charge. and Energy storage will utilize surplus photovoltaic power and grid electricity to charge the battery until it reaches full capacity; if the electricity price is normal and Then the photovoltaic power will be connected to the grid and participate in the electricity market transaction; if it is during peak hours and the electricity price is low... Then energy storage is Charge until the power is sufficient. If the value is negative, the energy storage stops working; during the peak hours of time-of-use electricity pricing at night, when there is no photovoltaic output, if... Then, energy storage will supply electricity to industrial and commercial users until... ,like Then, electricity is purchased from the external power grid; during the off-peak hours at night in spring and autumn, energy storage devices... Charging at the specified power, during morning peak hours and midday off-peak hours, if and At that time, the energy storage device is still in the charging state, storing the surplus photovoltaic power. and At peak times, the energy storage device discharges to meet the user's energy demand; however, during peak hours at midday and night, the photovoltaic equipment outputs zero, and the energy storage device... Discharge power to meet users' electricity needs; This represents the photovoltaic output at time t in scenario s; This represents the load value at time t in scenario s;

[0134] The fourth scenario involves industrial and commercial users considering a photovoltaic-storage integrated system where the time-of-use pricing policy applies during off-peak hours at midday: this occurs during off-peak hours when photovoltaic power generation begins and during both summer / winter and regular periods. and At that time, energy storage devices utilize the surplus electricity from photovoltaic power to... The power is used for charging. Due to the current electricity pricing policy, there is a two-hour off-peak period during the midday hours in spring and winter. By introducing energy storage, the surplus photovoltaic power generated during these two off-peak hours is stored and released during the nighttime peak hours and peak periods to meet users' electricity demand. During the morning peak hours in spring and autumn, if... and The energy storage device operates in a charging state and uses The power is used for charging, and during the three off-peak hours in spring and autumn, the surplus power of the photovoltaic system is utilized. Charging power is applied during peak hours in the evening and at night. Discharge using the discharge power.

[0135] Step (4) specifically includes the following steps in sequence:

[0136] (4a) Due to the strong uncertainty of photovoltaic power output, the objective function of the robust capacity configuration operation model of the photovoltaic-storage power station is constructed by considering the uncertainty of photovoltaic power output and using the following formula:

[0137] ;

[0138] ;

[0139] ;

[0140]

[0141] ;

[0142] ;

[0143] ;

[0144] ;

[0145] ;

[0146] ;

[0147] ;

[0148] in, This indicates the investment cost of photovoltaic energy storage. This indicates the installed capacity of photovoltaic power generation. Indicates the installed capacity of energy storage equipment; Indicates the number of photovoltaic panels installed. This indicates the capacity of a single photovoltaic panel. This indicates the price of a single photovoltaic panel. Indicates the lifespan of the photovoltaic panel; Indicates the capacity of the energy storage device. This indicates the price per unit of electrical energy storage capacity. Indicates the lifespan of the energy storage device; Represents the probability of scenario s; This indicates the cost of purchasing electricity from the power grid. This represents the power purchased at time t in scenario s. This represents the time-of-use electricity price at time t; This indicates the maintenance cost of optical storage. This indicates the operation and maintenance cost of photovoltaic systems. This represents the photovoltaic output at time t in scenario s; This indicates the operation and maintenance cost of energy storage. This represents the energy storage charging power at time t in scenario s. This represents the energy storage discharge power at time t in scenario s; This represents the revenue from selling electricity to the power grid. This represents the electricity sales power at time t in scenario s. This represents the on-grid electricity price at time t; This indicates the carbon reduction benefits of photovoltaic, energy storage, and charging equipment. Indicates the carbon emission factor of the power grid. This indicates the benefit per unit of carbon emission reduction; This indicates the benefits that building users receive from participating in demand response. This indicates the benefits of reducing load demand response. This indicates the benefits of responding to transferable loads. This represents the unit revenue from load reduction. This represents the load reduction at time t in scenario s. This represents the unit revenue from load transfer. This represents the amount of load transferred in at time t under scenario s. This represents the outflow load at time t in scenario s; T represents the time period; r represents the discount rate; Indicates the number of scenes; Represents the uncertain set of photovoltaic power output; This represents the load reduction at time t in scenario s;

[0149] (4b) The constraints of the robust capacity configuration operation model for photovoltaic-storage power stations are as follows:

[0150] ;

[0151] ;

[0152] ;

[0153] In the formula, This represents the photovoltaic prediction value at time t under scenario s; This variable, ranging from 0 to 1, indicates whether the photovoltaic output at time t in scenario s is in a positive deviation. Indicates the degree of deviation in photovoltaic power output; This variable, ranging from 0 to 1, indicates whether the photovoltaic output at time t in scenario s is in a negative deviation. The value is an uncertain variable, with a maximum value of 24.

[0154] Step (5) specifically includes the following steps in sequence:

[0155] (5a) First, the robust capacity configuration operation model of the photovoltaic-storage power station is rewritten into a general matrix form:

[0156] (3);

[0157] In the formula, A, C, E, b, c, d, and f are all coefficient matrices; x represents the capacity variable of the first stage; y represents the operating variable of the second stage, i.e., the optimal equipment power allocation strategy; u represents the value of the uncertain variable; and U represents the set of uncertain variables.

[0158] (5b) Using a column and constraint generation algorithm, equation (3) is decomposed into the main problem of equation (4) and the subproblems of equation (5), specifically including the following steps:

[0159] (4);

[0160] (5);

[0161] In the formula, Variables introduced; Let y be the value of the m-th iteration in scenario s; This represents the value of the inner objective function in the m-th iteration;

[0162] (5b1) First, initialize the maximum number of iterations m. max The number of iterations m=1, the convergence accuracy, and the initial output of the photovoltaic power generation;

[0163] (5b2) Taking the initial output of photovoltaic power as the worst-case scenario for the first iteration, solve equation (3) to obtain the optimal equipment capacity configuration x. * Update the Nether ;

[0164] (5b3) Solve the main problem to obtain the optimal equipment capacity configuration x * By substituting the values ​​into the subproblem and solving it, we obtain the worst-case photovoltaic output value u* and the optimal equipment power allocation strategy y, and update the upper bound value. ;

[0165] (5b4) Determine whether the condition is met. If the conditions are met, the iteration stops and the iteration result is returned to obtain the optimal device capacity configuration x. * If not satisfied, let u m+1,* =u, m=m+1, return to step (5b2) to continue iterative calculation; The gap value is a constant.

[0166] In summary, under the background of building a new power system, this invention considers the output characteristics of photovoltaics (PV) to generate and reduce scenarios, narrowing them down to the four seasons. Based on different seasons, it proposes multi-scenario PV-storage energy flow optimization strategies that consider the uncertainty of PV output. The effectiveness of the PV-storage integrated system is verified through scenario settings. The configuration of the PV-storage-charging system effectively improves the PV absorption capacity. While ensuring the safe and reliable operation of the entire microgrid, it achieves optimized energy flow between industrial and commercial users and the PV-storage system. This invention taps into the demand response potential of industrial and commercial users, further optimizing the energy flow between them and the PV-storage equipment. Based on optimizing the energy flow of the entire PV-storage microgrid, it achieves optimized capacity configuration of the PV-storage system, further improving the socio-economic benefits of the PV-storage system and the overall energy system.

[0167] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for optimizing the flow and configuring the capacity of photovoltaic energy storage in multiple scenarios for industrial and commercial users, characterized in that: The method includes the following steps in sequence: (1) Collect original data; (2) Using the collected raw data, the initial photovoltaic power output scenario is generated based on the Latin hypersolution method according to the day-ahead photovoltaic power output value, and the K-means algorithm is used to reduce the scenario to obtain the reduced scenario; (3) Build a topology diagram of the photovoltaic-storage integration system for industrial and commercial users, establish a physical operation model of the energy storage equipment, a demand response model for users, and a power balance model for the photovoltaic-storage integration system, and formulate a photovoltaic-storage integration interaction strategy based on the latest time-of-use electricity price in the region. (4) Based on the physical operation model of energy storage equipment, the user's demand response model, the power balance model of the photovoltaic-storage system, and the photovoltaic-storage integration interaction strategy, the robust idea is adopted to further consider the uncertainty of photovoltaic output and construct a robust capacity configuration operation model for photovoltaic-storage power station. (5) The column sum constraint algorithm is used to solve the robust capacity configuration operation model of the photovoltaic-storage power station to obtain the optimal equipment configuration scheme and the optimal equipment power allocation strategy; In step (3), establishing the physical operation model of the energy storage equipment in the photovoltaic-energy storage integrated system specifically refers to: Considering the user-side energy storage system, which adopts electrochemical energy storage, a physical operation model of the energy storage equipment in the photovoltaic-energy storage integrated system is established: ； ； ； ； ； ； ； ； In the formula, , These represent the electrical energy storage capacity values ​​at times t and t-1, respectively. , Let represent the charging power and discharging power of the energy storage at time t, respectively. , These represent the charging and discharging efficiencies of electrical energy storage, respectively. A flag variable indicating whether electrical energy is in operation or in a charging state. , This indicates the maximum charging and discharging power of the electrical energy storage. This indicates the capacity value of the energy storage device. , These represent the energy storage capacity values ​​at the initial moment and at the end of the scheduling cycle, respectively. , These represent the minimum and maximum capacity values ​​of the energy storage device, respectively. This represents the maximum monthly demand for industrial and commercial users. This represents the load value for industrial and commercial users during off-peak hours. This represents the load value for industrial and commercial users during peak hours. Indicates the rated power of the energy storage; Establish the load reduction response of equation (1) and the load transfer response of equation (2): （1）； （2）； In the formula, This indicates that participation at time t in scenario s can reduce the load after demand response. This indicates that participation at time t in scenario s can reduce the load before demand response. This represents the load reduction at time t in scenario s. This indicates the minimum load that can be reduced. Indicates the maximum load that can be reduced; This represents the load at time t after the participation in the transferable demand response in scenario s. This represents the load at time t in scenario s before the participation in the transferable demand response. This represents the amount of load transferred in at time t under scenario s. This represents the outgoing load at time t in scenario s. This represents the maximum transferred load at time t in scenario s; T is the time period. Formulas (1) and (2) together form the user's demand response model; The power balance model of the photovoltaic-storage system is established as follows: ； ； ； ； ； In the formula, This represents the photovoltaic output at time t in scenario s; This represents the basic user load at time t in scenario s; The variable represents the flag variable for purchasing electricity from the public grid at time t in scenario s, where 0 indicates no purchase and 1 indicates purchase; Let $\frac{ ... This represents the electricity sales power at time t in scenario s; This represents the power purchased at time t in scenario s; This represents the user load at time t in scenario s; In step (3), the photovoltaic-storage fusion interaction strategy specifically refers to deciding the capacity configuration of user-side energy storage by setting different application scenarios, and setting the optimal photovoltaic-storage optimization operation strategy in different scenarios; the application scenarios include the first, second, third and fourth scenarios; The first scenario is that industrial and commercial users only consider the installation of photovoltaics: the photovoltaic capacity is determined entirely based on the available installation area, industrial and commercial users only consider installing photovoltaics, supplying electricity to users during the photovoltaic power generation period, and the surplus electricity is fed into the grid, with the grid-connected portion participating in the electricity market transaction; The second scenario involves industrial and commercial users considering only the installation of electrochemical energy storage: the charging power of the energy storage comes entirely from the mains power, and the discharging power is supplied to the user. In this scenario, the first step is to determine whether the energy storage capacity is sufficient for charging. Energy storage can be used during the nighttime and midday low periods in summer and winter. Charge; if Energy storage can then be used during afternoon and evening peak hours. Discharge; if Moreover, it falls during the nighttime and midday low periods in spring and autumn. Charge during the morning, evening, and nighttime peak hours. Discharge; , Let represent the charging power and discharging power of the energy storage at time t, respectively. This represents the electrical energy storage capacity at time t. This indicates the capacity value of the energy storage device; The third scenario involves industrial and commercial users considering a photovoltaic-storage integrated system where time-of-use pricing policies do not yet include midday off-peak periods: during the nighttime off-peak periods, if... At this time, the electricity demand of industrial and commercial users is met by purchasing electricity from the external power grid, and energy storage devices are used to meet this demand. The power is drawn from the external power grid for charging; during normal and peak periods in summer and winter, when At times, photovoltaic power generation meets electricity demand. During normal periods, energy storage determines whether to recharge based on its own state of charge. and Energy storage will utilize surplus photovoltaic power and grid electricity to charge the battery until it reaches full capacity; if the electricity price is normal and Then the photovoltaic power will be connected to the grid and participate in the electricity market transaction; if it is during peak hours and the electricity price is low... Then energy storage is Charge until the power is sufficient. If the value is negative, the energy storage stops working; during the peak hours of time-of-use electricity pricing at night, when there is no photovoltaic output, if... Then, energy storage will supply electricity to industrial and commercial users until... ,like Then, electricity is purchased from the external power grid; during the off-peak hours at night in spring and autumn, energy storage devices... Charging at the specified power, during morning peak hours and midday off-peak hours, if and At that time, the energy storage device is still in the charging state, storing the surplus photovoltaic power. and At peak times, the energy storage device discharges to meet the user's energy demand; however, during peak hours at midday and night, the photovoltaic equipment outputs zero, and the energy storage device... Discharge power to meet users' electricity needs; This represents the photovoltaic output at time t in scenario s; This represents the load value at time t in scenario s; The fourth scenario involves industrial and commercial users considering a photovoltaic-storage integrated system where the time-of-use pricing policy applies during off-peak hours at midday: this occurs during off-peak hours when photovoltaic power generation begins and during both summer / winter and regular periods. and At that time, energy storage devices utilize the surplus electricity from photovoltaic power to... The power is used for charging. Due to the current electricity pricing policy, there is a two-hour off-peak period during the midday hours in spring and winter. By introducing energy storage, the surplus photovoltaic power generated during these two off-peak hours is stored and released during the nighttime peak hours and peak periods to meet users' electricity demand. During the morning peak hours in spring and autumn, if... and Then the energy storage device operates in a charging state and uses The power is used for charging, and during the three off-peak hours in spring and autumn, the surplus power of the photovoltaic system is utilized. Charging power is applied during peak hours in the evening and at night. Discharge using the discharge power.

2. The method for multi-scenario photovoltaic energy storage flow optimization and capacity configuration for industrial and commercial users according to claim 1, characterized in that: Step (1) specifically refers to the following: The original data includes historical regional temperature, irradiance, wind speed, atmospheric pressure and other meteorological data, as well as the area within the land boundary where photovoltaics can be installed, the 15-minute resolution load data of industrial and commercial users, the latest regional time-of-use electricity price, the user's annual electricity bill, the status of power construction, the user's regional land boundary status, and the technical and economic parameters of photovoltaic and energy storage equipment.

3. The method for multi-scenario photovoltaic energy storage flow optimization and capacity configuration for industrial and commercial users according to claim 1, characterized in that: Step (2) specifically includes the following steps in sequence: (2a) The day-ahead photovoltaic power output is calculated using the following formula. : ； in, Number of photovoltaic panels installed The rated power of the photovoltaic system. Let be the irradiance at time t. Standard irradiance, For power temperature coefficient, Let be the surface temperature of the photovoltaic panel at time t. For reference temperature; (2b) Based on the current day's photovoltaic power output forecast The actual photovoltaic output value is obtained after considering the photovoltaic output error. : ； In the formula, For photovoltaic power output prediction error; The expected value is 0 and the standard deviation is 0. follows a standard normal distribution; where, The calculation formula is: ； In the formula, This refers to the total capacity where photovoltaic panels can be installed. (2c) Based on the Latin hypersolution method, N photovoltaic power output scenarios are obtained: First, due to the error in photovoltaic power generation prediction The expected value is 0, and the standard deviation is 0. The standard normal distribution, therefore The cumulative probability distribution function F( The probability is divided into N equal probability intervals; for any one of these intervals... ,in, Random sampling of probability values ​​yields probability values. ,in, It follows a uniform distribution between [0, 1]. According to the cumulative probability distribution function F( The inverse distribution function F is obtained. -1 (R), obtained by sampling 24 time periods. The sample matrix; T represents the time period; (2d) According to The sample matrix is ​​used to reduce the N photovoltaic power output scenarios using the K-means algorithm to obtain the reduced scenarios.

4. The method for multi-scenario photovoltaic energy storage flow optimization and capacity configuration for industrial and commercial users according to claim 1, characterized in that: Step (4) specifically includes the following steps in sequence: (4a) Due to the strong uncertainty of photovoltaic power output, the objective function of the robust capacity configuration operation model of the photovoltaic-storage power station is constructed by considering the uncertainty of photovoltaic power output and using the following formula: ； ； ； ； ； ； ； ； ； ； ； in, This indicates the investment cost of photovoltaic energy storage. This indicates the installed capacity of photovoltaic power generation. Indicates the installed capacity of energy storage equipment; Indicates the number of photovoltaic panels installed. This indicates the capacity of a single photovoltaic panel. This indicates the price of a single photovoltaic panel. Indicates the lifespan of the photovoltaic panel; Indicates the capacity of the energy storage device. This indicates the price per unit of electrical energy storage capacity. Indicates the lifespan of the energy storage device; Represents the probability of scenario s; This indicates the cost of purchasing electricity from the power grid. This represents the power purchased at time t in scenario s. This represents the time-of-use electricity price at time t; This indicates the maintenance cost of optical storage. This indicates the operation and maintenance cost of photovoltaic systems. This represents the photovoltaic output at time t in scenario s; This indicates the operation and maintenance cost of energy storage. , Let represent the charging power and discharging power of the energy storage at time t, respectively. This represents the revenue from selling electricity to the power grid. This represents the electricity sales power at time t in scenario s. This represents the on-grid electricity price at time t; This indicates the carbon reduction benefits of photovoltaic, energy storage, and charging equipment. Indicates the carbon emission factor of the power grid. This indicates the benefit per unit of carbon emission reduction; This indicates the benefits that building users receive from participating in demand response. This indicates the benefits of reducing load demand response. This indicates the benefits of responding to transferable loads. This represents the unit revenue from load reduction. This represents the load reduction at time t in scenario s. This represents the unit revenue from load transfer. This represents the amount of load transferred in at time t under scenario s. This represents the outflow load at time t in scenario s; T represents the time period; r represents the discount rate; Indicates the number of scenes; Represents the uncertain set of photovoltaic power output; This represents the load reduction at time t in scenario s; (4b) The constraints of the robust capacity configuration operation model for photovoltaic-storage power stations are as follows: ； ； ； In the formula, This represents the photovoltaic prediction value at time t under scenario s; This variable, ranging from 0 to 1, indicates whether the photovoltaic output at time t in scenario s is in a positive deviation. Indicates the degree of deviation in photovoltaic power output; This variable, ranging from 0 to 1, indicates whether the photovoltaic output at time t in scenario s is in a negative deviation. The value is an uncertain variable, with a maximum value of 24.

5. The method for multi-scenario photovoltaic energy storage flow optimization and capacity configuration for industrial and commercial users according to claim 1, characterized in that: Step (5) specifically includes the following steps in sequence: (5a) First, the robust capacity configuration operation model of the photovoltaic-storage power station is rewritten into a general matrix form: （3）； In the formula, A, C, E, b, c, d, and f are all coefficient matrices; x represents the capacity variable of the first stage; y represents the operating variable of the second stage, i.e., the optimal equipment power allocation strategy; u represents the value of the uncertain variable; and U represents the set of uncertain variables. (5b) Using a column and constraint generation algorithm, equation (3) is decomposed into the main problem of equation (4) and the subproblems of equation (5), specifically including the following steps: （4）； （5）； In the formula, Variables introduced; Let y be the value of the m-th iteration in scenario s; This represents the value of the inner objective function in the m-th iteration; (5b1) First, initialize the maximum number of iterations m. max The number of iterations m=1, the convergence accuracy, and the initial output of the photovoltaic power generation; (5b2) Taking the initial output of photovoltaic power as the worst-case scenario for the first iteration, solve equation (3) to obtain the optimal equipment capacity configuration x. * Update the Nether ; (5b3) Solve the main problem to obtain the optimal equipment capacity configuration x * By substituting the values ​​into the subproblem and solving it, we obtain the worst-case photovoltaic output value u* and the optimal equipment power allocation strategy y, and update the upper bound value. ; (5b4) Determine whether the condition is met. If the conditions are met, the iteration stops and the iteration result is returned to obtain the optimal device capacity configuration x. * If not satisfied, let u m+1,* =u, m=m+1, return to step (5b2) to continue iterative calculation; The gap value is a constant.

Images