Microgrid energy storage battery planning method, device, equipment and medium

Abstract

The invention discloses a micro-grid energy storage battery planning method, device, equipment and medium, and the method comprises the steps: determining the battery planning of the last year of a planning period based on a preset micro-grid energy storage battery planning model through the typical day data of the last year of the planning period; based on the battery planning of the last year of the planning period and the micro-grid energy storage battery planning model, starting from the last year, determining the battery planning of the previous year according to the battery planning of each year in sequence until the battery planning of all years in the planning period is obtained; and based on the battery planning of all years, considering the degradation effect of the battery capacity and the energy-power ratio requirement of the battery, solving the micro-grid energy storage battery planning model from the first year of the planning period backwards year by year so as to correct the battery planning of each year and obtain the optimal battery planning of each year in the planning period. According to the invention, the energy storage battery can be planned comprehensively for many years, and the planning accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage planning, and in particular to a method, device, equipment and medium for planning energy storage batteries in a microgrid. Background Art

[0002] As a new type of distributed energy system, microgrid integrates multiple energy forms, such as wind power, photovoltaics and energy storage equipment, and can operate independently or be connected to the large power grid. In order to improve the stability of microgrid operation and the efficient use of energy, battery energy storage system has become an important part of microgrid. Battery energy storage system can provide power support during peak power demand, smooth the fluctuation of renewable energy output, and improve the self-regulation ability of microgrid. However, the accuracy of existing technology in microgrid energy storage battery planning for many years needs to be improved. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a microgrid energy storage battery planning method, device, equipment and medium. Through the iterative operation of the back propagation method and the forward propagation method, the battery planning is continuously corrected, and comprehensive energy storage battery planning for many years can be performed to improve the accuracy.

[0004] In a first aspect, an embodiment of the present invention provides a microgrid energy storage battery planning method, comprising:

[0005] Obtain historical load data in the microgrid and historical power generation data of distributed energy, and perform clustering to obtain four types of typical daily historical data corresponding to the four seasons;

[0006] Obtain the natural growth rate of the power load of the microgrid, and predict the typical daily data of the last year of the planning period based on the natural growth rate of the power load and the typical daily historical data;

[0007] Based on the preset microgrid energy storage battery planning model, the battery planning for the last year of the planning period is determined using the typical daily data of the last year of the planning period;

[0008] Based on the battery planning for the last year of the planning period and the microgrid energy storage battery planning model, starting from the last year, the battery planning for the previous year is determined in turn according to the battery planning for each year until the battery planning for all years in the planning period is obtained;

[0009] Based on the battery planning of all the years, considering the degradation effect of the battery capacity and the energy-to-power ratio requirements of the battery, the microgrid energy storage battery planning model is solved year by year starting from the first year of the planning period to correct the annual battery planning and obtain the optimal battery planning for each year within the planning period.

[0010] As an improvement of the above solution, the preset microgrid energy storage battery planning model uses the typical daily data of the last year of the planning period to determine the battery planning for the last year of the planning period, including:

[0011] Input the typical daily data of the last year of the planning period into a preset microgrid energy storage battery planning model to obtain the corresponding battery power and energy capacity, and determine the battery model and battery quantity according to the battery power and energy capacity;

[0012] Obtaining sufficiency data and capacity data of the microgrid, and combining the battery power and energy capacity to calculate the load loss probability;

[0013] Determine whether the load loss probability is less than a preset threshold; if so, obtain the battery plan for the last year of the planning period; if not, increase the number of batteries, re-solve the microgrid energy storage battery planning model and calculate the corresponding load loss probability until the load loss probability is less than the preset threshold, and obtain the battery plan for the last year of the planning period; the battery plan includes: battery power, energy capacity, battery number and battery model.

[0014] As an improvement of the above solution, the method of determining the battery plan for the previous year based on the annual battery plan includes:

[0015] Calculate the corresponding load loss probability according to the annual battery plan and the sufficiency data and capacity data of the microgrid;

[0016] By gradually reducing the battery power in the battery planning and recalculating the corresponding load loss probability, determining the minimum value of the battery power when the load loss probability is less than a preset threshold;

[0017] Based on the minimum value of the battery power, the annual battery plan and the typical day data of the previous year, the microgrid energy storage battery planning model is solved to obtain the battery plan of the previous year; wherein the typical day data is predicted by the natural growth rate of the power load and the typical day historical data.

[0018] As an improvement to the above scheme, the annual battery planning is revised to include:

[0019] According to the annual battery planning, determine whether the energy-to-power ratio of the battery is greater than the preset threshold; if so, determine the optimal battery planning for that year; if not, based on the battery planning for that year, increase the energy capacity of the battery, re-solve the microgrid energy storage battery planning model, update the battery planning, until the energy-to-power ratio of the battery is greater than the preset threshold, and determine the optimal battery planning for that year.

[0020] As an improvement of the above scheme, the objective function of the microgrid energy storage battery planning model aims to minimize the annualized total investment and operating cost;

[0021] When generating a battery plan, the objective function is the sum of the total operating cost of the microgrid, the installation cost of the energy storage battery unit, and the operation and maintenance cost;

[0022] When the battery planning is revised, the objective function is the sum of the total operating cost of the microgrid, the installation cost of the energy storage battery unit, the operation and maintenance cost, and the replacement cost of the energy storage battery.

[0023] As an improvement of the above solution, the constraints of the microgrid energy storage battery planning model include:

[0024] Energy storage battery capital budget constraints, energy storage battery power size constraints, energy storage battery energy capacity constraints, energy storage battery operation constraints, energy storage battery replacement constraints, and microgrid system operation constraints.

[0025] As an improvement of the above solution, the formula for the total operating cost J1 of the microgrid is:

[0026]

[0027] Where g is a single generator set, G is a collection of generator sets; d is a single typical day, D is a collection of selected typical days; t is a single time step within a typical day, T is a collection of time steps within a typical day; CND is the number of selected typical days; Pg g,d,t is the generator output power; W g,d,t is the decision variable for generator operation; b g 、c g are the power generation cost and input cost of the generator respectively; U g,d,t 、V g,d,t are the decision variables for generator shutdown and startup; SUP g 、SDN g They are the generator shutdown and startup costs respectively;

[0028] The formula for the installation cost J2 of the energy storage battery unit is:

[0029]

[0030] Among them, k is a single energy storage battery, and K is a collection of energy storage batteries; The nominal power capacity of the energy storage battery at the time of installation; The annual installation cost per unit power of the energy storage battery; The nominal energy capacity of the energy storage battery at the time of installation; The annual installation cost per unit capacity of energy storage batteries; B kThe decision variables for energy storage battery installation; C fx The annual fixed installation cost of the energy storage battery;

[0031] The formula for the operation and maintenance cost J3 is:

[0032]

[0033] Among them, OMC fx Pb is the annual fixed operation and maintenance cost of the energy storage battery; k is the power of the energy storage battery; OMC v is the annual variable operation and maintenance cost of the energy storage battery; η ch , η dch They are respectively the charging and discharging efficiency of energy storage batteries; k,d,t Charge / discharge power for energy storage batteries.

[0034] The formula for the replacement cost J4 of the energy storage battery is:

[0035]

[0036] in, The nominal energy capacity of the energy storage battery when installed; RY k is the year when the energy storage battery is replaced; RC is the replacement cost of the energy storage battery.

[0037] In a second aspect, an embodiment of the present invention further provides a microgrid energy storage battery planning device, comprising:

[0038] The historical data acquisition module is used to obtain the historical load data in the microgrid and the historical power generation data of distributed energy, and cluster them to obtain four types of typical daily historical data corresponding to the four seasons;

[0039] A typical day data module is used to obtain the natural growth rate of the power load of the microgrid, and predict the typical day data of the last year of the planning period based on the natural growth rate of the power load and the typical day historical data;

[0040] A battery planning generation module is used to determine the battery planning for the last year of the planning period based on a preset microgrid energy storage battery planning model and the typical daily data of the last year of the planning period; based on the battery planning for the last year of the planning period and the microgrid energy storage battery planning model, starting from the last year, the battery planning for the previous year is determined in turn according to the battery planning for each year until the battery planning for all years in the planning period is obtained;

[0041] The optimal battery planning module is used to solve the microgrid energy storage battery planning model year by year starting from the first year of the planning period based on the battery planning of all years, taking into account the degradation effect of the battery capacity and the energy-to-power ratio requirements of the battery, so as to correct the annual battery planning and obtain the optimal battery planning for each year within the planning period.

[0042] Furthermore, the preset microgrid energy storage battery planning model uses the typical daily data of the last year of the planning period to determine the battery planning for the last year of the planning period, including:

[0043] Input the typical daily data of the last year of the planning period into a preset microgrid energy storage battery planning model to obtain the corresponding battery power and energy capacity, and determine the battery model and battery quantity according to the battery power and energy capacity;

[0044] Obtaining sufficiency data and capacity data of the microgrid, and combining the battery power and energy capacity to calculate the load loss probability;

[0045] Determining whether the load loss probability is less than a preset threshold;

[0046] If so, the battery plan for the last year of the planning period is obtained;

[0047] If not, increase the number of batteries, re-solve the microgrid energy storage battery planning model and calculate the corresponding load loss probability until the load loss probability is less than the preset threshold, and obtain the battery planning for the last year of the planning period; the battery planning includes: battery power, energy capacity, battery quantity and battery model.

[0048] Furthermore, determining the battery plan for the previous year based on the annual battery plan includes:

[0049] Calculate the corresponding load loss probability according to the annual battery plan and the sufficiency data and capacity data of the microgrid;

[0050] By gradually reducing the battery power in the battery planning and recalculating the corresponding load loss probability, determining the minimum value of the battery power when the load loss probability is less than a preset threshold;

[0051] Based on the minimum value of the battery power, the annual battery plan and the typical day data of the previous year, the microgrid energy storage battery planning model is solved to obtain the battery plan of the previous year; wherein the typical day data is predicted by the natural growth rate of the power load and the typical day historical data.

[0052] Furthermore, the annual battery plan is revised, including:

[0053] According to the annual battery plan, determine whether the energy-to-power ratio of the battery is greater than the preset threshold;

[0054] If so, determine the optimal battery plan for that year;

[0055] If not, based on the battery planning for that year, increase the energy capacity of the battery, re-solve the microgrid energy storage battery planning model, update the battery planning until the battery's energy-to-power ratio is greater than a preset threshold, and determine the optimal battery planning for that year.

[0056] Furthermore, the objective function of the microgrid energy storage battery planning model aims to minimize the annualized total investment and operating cost;

[0057] When generating a battery plan, the objective function is the sum of the total operating cost of the microgrid, the installation cost of the energy storage battery unit, and the operation and maintenance cost;

[0058] When the battery planning is revised, the objective function is the sum of the total operating cost of the microgrid, the installation cost of the energy storage battery unit, the operation and maintenance cost, and the replacement cost of the energy storage battery.

[0059] Furthermore, the constraints of the microgrid energy storage battery planning model include:

[0060] Energy storage battery capital budget constraints, energy storage battery power size constraints, energy storage battery energy capacity constraints, energy storage battery operation constraints, energy storage battery replacement constraints, and microgrid system operation constraints.

[0061] In a third aspect, an embodiment of the present invention further provides a computer device, comprising a processor and a memory, wherein a computer program is stored in the memory, and the computer program is configured to be executed by the processor, and when the processor executes the computer program, the microgrid energy storage battery planning method described in any one of the above items is implemented.

[0062] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any of the microgrid energy storage battery planning methods described above.

[0063] Compared with the prior art, the beneficial effects of a microgrid energy storage battery planning method, device, equipment and medium provided by the embodiments of the present invention are: by obtaining the historical load data in the microgrid and the historical power generation data of distributed energy, and clustering them to obtain four types of typical daily historical data corresponding to the four seasons; obtaining the natural growth rate of the power load of the microgrid, and predicting the typical daily data of each year in the planning period based on the natural growth rate of the power load and the typical daily historical data; based on the preset microgrid energy storage battery planning model, using the typical daily data of the last year of the planning period, determine the battery planning for the last year of the planning period; based on the battery planning for the last year of the planning period and the microgrid energy storage battery planning model, starting from the last year, the battery planning for each year is determined in sequence. The battery planning of the previous year is determined by planning until the battery planning of all years in the planning period is obtained; based on the battery planning of all years, the degradation effect of battery capacity and the energy-to-power ratio requirement of the battery are considered, and the microgrid energy storage battery planning model is solved year by year from the first year of the planning period to correct the annual battery planning and obtain the optimal battery planning for each year in the planning period; the present invention can improve the overall economy of the system under the premise of ensuring the safety of system operation and sufficient capacity, realize accurate energy storage battery planning, and can more reasonably determine the optimal configuration of energy storage batteries, ensuring that the microgrid continues to meet the capacity requirements throughout the planning cycle, avoiding the errors caused by the traditional planning method due to the decision-making at a single time point, and ensuring the long-term reliability and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a flow chart of a microgrid energy storage battery planning method provided by an embodiment of the present invention;

[0065] Figure 2 It is a structural schematic diagram of a microgrid energy storage battery planning device provided by an embodiment of the present invention;

[0066] Figure 3 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] See also Figure 1 , Figure 1 1 is a flow chart of a microgrid energy storage battery planning method provided by an embodiment of the present invention. The microgrid energy storage battery planning method comprises:

[0069] S1: Obtain historical load data in the microgrid and historical power generation data of distributed energy, and perform clustering to obtain four types of typical daily historical data corresponding to four seasons;

[0070] Specifically, historical wind power generation data, historical photovoltaic power generation data and historical load data in the microgrid are obtained, and clustered into four categories of typical daily historical data, where each category of typical daily data represents a season, and the typical daily historical data include typical daily data of historical wind power generation power, typical daily data of historical photovoltaic power generation power, and typical daily data of historical load data.

[0071] S2: Obtain the natural growth rate of the power load of the microgrid, and predict the typical daily data of the last year of the planning period based on the natural growth rate of the power load and the typical daily historical data;

[0072] Specifically, according to the corresponding natural growth rate of electricity load each year during the planning period, such as the year-on-year growth of electricity consumption in the whole society by 6.7% in 2023, the typical daily data of each year in the future planning period is calculated. Among them, the electricity load in the current period = the electricity load in the previous period × (1 + natural growth rate of electricity load).

[0073] S3: Based on the preset microgrid energy storage battery planning model, the battery planning for the last year of the planning period is determined using the typical daily data of the last year of the planning period;

[0074] As one of the optional embodiments, the preset microgrid energy storage battery planning model uses the typical daily data of the last year of the planning period to determine the battery planning for the last year of the planning period, including:

[0075] Input the typical daily data of the last year of the planning period into a preset microgrid energy storage battery planning model to obtain the corresponding battery power and energy capacity, and determine the battery model and battery quantity according to the battery power and energy capacity;

[0076] Obtaining sufficiency data and capacity data of the microgrid, and combining the battery power and energy capacity to calculate the load loss probability;

[0077] Determine whether the load loss probability is less than a preset threshold; if so, obtain the battery plan for the last year of the planning period; if not, increase the number of batteries, re-solve the microgrid energy storage battery planning model and calculate the corresponding load loss probability until the load loss probability is less than the preset threshold, and obtain the battery plan for the last year of the planning period; the battery plan includes: battery power, energy capacity, battery number and battery model.

[0078] Specifically, assuming that the planning period is a total of Y years, obtain the typical daily data of the last year Y, including the typical daily data of wind power generation power, photovoltaic power generation power, and load data of the last year Y, and input them into the microgrid energy storage battery planning model; solve the planning model, determine the battery power and energy capacity of the last year Y, and thereby determine the battery model and installation quantity of the energy storage battery, and obtain the battery planning for the last year; obtain the sufficiency data and capacity data of the system components in the microgrid, and combine the battery planning for the last year Y to calculate the corresponding load loss probability and judge whether it meets the microgrid sufficiency requirements, that is, the load loss probability is less than the preset threshold; if it meets, the current battery planning is the energy storage battery planning scheme for the last year Y that meets the microgrid sufficiency index; if it does not meet, reject the current battery planning, and increase the capacity of the energy storage battery according to the existing standards, that is, increase the number of installed energy storage batteries, re-solve the planning model and judge whether the battery planning meets the microgrid sufficiency requirements, until the battery planning meets the microgrid sufficiency requirements, and obtain the energy storage battery planning scheme for the last year Y.

[0079] Among them, the calculation method of load loss probability includes: expressing the daily load with the daily peak load value, and then arranging the daily peak load values ​​in descending order to obtain the daily peak load change curve; then comparing the system's outage capacity and the daily peak load change curve, the probability that the daily peak load exceeds the system's available power generation capacity can be obtained, which is called the power shortage probability, also known as the load loss probability, and this is used as the system reliability indicator.

[0080] S4: Based on the battery planning for the last year of the planning period and the microgrid energy storage battery planning model, starting from the last year, the battery planning for the previous year is determined in turn according to the battery planning for each year until the battery planning for all years in the planning period is obtained;

[0081] As one of the optional embodiments, determining the battery plan for the previous year according to the annual battery plan includes:

[0082] Calculate the corresponding load loss probability according to the annual battery plan and the sufficiency data and capacity data of the microgrid;

[0083] By gradually reducing the battery power in the battery planning and recalculating the corresponding load loss probability, determining the minimum value of the battery power when the load loss probability is less than a preset threshold;

[0084] Based on the minimum value of the battery power, the annual battery plan and the typical day data of the previous year, the microgrid energy storage battery planning model is solved to obtain the battery plan of the previous year; wherein the typical day data is predicted by the natural growth rate of the power load and the typical day historical data.

[0085] Specifically, obtain the typical daily data of the corresponding year and the battery planning of the Yth year (i.e., the last year) as the input of the microgrid energy storage battery planning model; set y = Y, i.e., start from the last year;

[0086] Let y = y-1; based on the battery planning for year Y, continuously reduce the number of energy storage batteries installed and test the adequacy requirements of the microgrid at the same time, so as to obtain the minimum number of energy storage batteries installed while meeting the adequacy requirements of year y. Specifically, based on the battery planning for year Y, the adequacy data of system components and their capacity data, evaluate the probability of load loss; reduce the battery power and determine the minimum value of the battery power when the probability of load loss is less than the preset threshold; fix the minimum battery power value, solve the planning model, and determine the battery planning for year y;

[0087] The annual battery plan is obtained year by year from the last year forward until y = 1, and the energy storage battery planning scheme for all years in the planning period that meets the microgrid adequacy index is obtained.

[0088] S5: Based on the battery planning of all the years, considering the degradation effect of the battery capacity and the energy-to-power ratio requirements of the battery, the microgrid energy storage battery planning model is solved year by year starting from the first year of the planning period to correct the annual battery planning and obtain the optimal battery planning for each year within the planning period.

[0089] As one of the optional embodiments, the annual battery planning is revised, including:

[0090] According to the annual battery planning, determine whether the energy-to-power ratio of the battery is greater than the preset threshold; if so, determine the optimal battery planning for that year; if not, based on the battery planning for that year, increase the energy capacity of the battery, re-solve the microgrid energy storage battery planning model, update the battery planning, until the energy-to-power ratio of the battery is greater than the preset threshold, and determine the optimal battery planning for that year.

[0091] Specifically, based on the battery planning of all years obtained, the microgrid energy storage battery planning model is used to revise the battery planning year by year from the beginning to the end.

[0092] Let y = 0;

[0093] Let y = y - 1;

[0094] Solve the microgrid energy storage battery planning model that includes the operation of the energy storage battery, and determine the health status of the energy storage battery based on the operating status of the energy storage battery;

[0095] Determine whether it is satisfied Among them, E k is the energy capacity of the energy storage battery / kWh, The nominal power capacity of the energy storage battery at the time of installation / kW;

[0096] If so, the optimal battery plan for year y is obtained;

[0097] If not, based on the current battery plan for that year y, increase And return to the step of solving the planning model;

[0098] The optimal battery plan is obtained year by year until the optimal battery plan for all years within the planning period is obtained.

[0099] As one of the optional embodiments, the objective function of the microgrid energy storage battery planning model aims to minimize the annualized total investment and operating cost;

[0100] When generating a battery plan, the objective function is the sum of the total operating cost of the microgrid, the installation cost of the energy storage battery unit, and the operation and maintenance cost;

[0101] When the battery planning is revised, the objective function is the sum of the total operating cost of the microgrid, the installation cost of the energy storage battery unit, the operation and maintenance cost, and the replacement cost of the energy storage battery.

[0102] Specifically, the objective function J of the microgrid energy storage battery planning model aims to minimize the annualized total investment and operating cost, as shown below:

[0103]

[0104] Among them, J1 is the total operating cost of the microgrid, J2 is the installation cost of the energy storage battery unit, J3 is the operation and maintenance cost, and J4 is the replacement cost of the energy storage battery. It should be noted that J4 is only applied when the energy storage battery reaches the end of its life, that is, it is only used in the planning model in step S5.

[0105] Furthermore, the total operating cost J1 of the microgrid is composed of the power generation cost of each unit and the unserved demand cost, and its formula is:

[0106]

[0107] Where g is a single generator set, G is a collection of generator sets; d is a single typical day, D is a collection of selected typical days; t is a single time step within a typical day, T is a collection of time steps within a typical day; CND is the number of selected typical days; Pg g,d,t is the generator output power; W g,d,t is the generator operation decision variable. When the variable value is 0, the generator stops running. When the variable value is 1, the generator starts running. g 、c gare the power generation cost and input cost of the generator respectively; U g,d,t 、V g,d,t are the decision variables for generator shutdown and startup; SUP g 、SDN g They are the generator shutdown and startup costs respectively;

[0108] Furthermore, the installation cost J2 of the energy storage battery unit includes its power capacity cost, energy capacity cost and fixed installation cost, and its formula is:

[0109]

[0110] Among them, k is a single energy storage battery, and K is a collection of energy storage batteries; The nominal power capacity of the energy storage battery at the time of installation; The annual installation cost per unit power of the energy storage battery; The nominal energy capacity of the energy storage battery at the time of installation; The annual installation cost per unit capacity of energy storage batteries; B k The decision variables for energy storage battery installation; C fx The annual fixed installation cost of the energy storage battery;

[0111] Furthermore, the operation and maintenance cost J3 includes the fixed and variable operation and maintenance costs of the energy storage battery, and its formula is:

[0112]

[0113] Among them, OMC fx Pb is the annual fixed operation and maintenance cost of the energy storage battery; k is the energy storage battery power; OMC v is the annual variable operation and maintenance cost of the energy storage battery; η ch , η dch They are respectively the charging and discharging efficiency of energy storage batteries; Pba k,d,t Charge / discharge power for energy storage batteries.

[0114] Furthermore, the formula for the replacement cost J4 of the energy storage battery is:

[0115]

[0116] in, The nominal energy capacity of the energy storage battery when installed; RY k is the year when the energy storage battery is replaced; RC is the replacement cost of the energy storage battery.

[0117] As one of the optional embodiments, the constraints of the microgrid energy storage battery planning model include:

[0118] Energy storage battery capital budget constraints, energy storage battery power size constraints, energy storage battery energy capacity constraints, energy storage battery operation constraints, energy storage battery replacement constraints, and microgrid system operation constraints.

[0119] Specifically, the capital budget constraints for energy storage batteries are:

[0120]

[0121] This limit sets an upper limit on the capital expenditure of the microgrid on energy storage battery installation and replacement during the planning year, with BL being the specified budget limit.

[0122] Energy storage battery power size constraints:

[0123]

[0124] These restrictions are related to the installation limits of the power capacity of the energy storage battery. it is the power capacity setting used as the lower limit when the adequacy check is not satisfied in the previous iteration; M is a large constant; is the number of power components installed in the energy storage battery, which is an integer variable; Λ P It is the market standard for energy storage battery power capacity.

[0125] Energy storage battery energy capacity constraints:

[0126]

[0127] If there is an installation during the planning interval, the binary variable B k Takes the unit value. is the number of installed energy storage battery capacity components, which is an integer variable; Λ E It is the market standard for energy storage battery energy capacity; are the energy storage battery capacity loss due to cycle and calendar degradation, respectively; The change of SoC of the energy storage battery during discharge; E / P , are the minimum / maximum energy-to-power ratio of the energy storage battery, respectively; To increase the energy storage capacity / kWh; α k is the capacity degradation coefficient of the energy storage battery.

[0128] Energy storage battery operation constraints:

[0129]

[0130] That is, the operating constraints of the energy storage battery charging and discharging process. The energy balance equation of the above energy storage battery determines the state of charge (SoC) level of each unit, which must be within the capacity limit of the battery, and the lower limit depends on the depth of discharge (DoD) allowed each time. k,d,t is the state of charge of the kth energy storage battery in the tth time period on a typical day d; η ch , η dch are the charging and discharging efficiencies of the energy storage battery respectively.

[0131] The energy balance equation of the energy storage battery above determines the state of charge (SoC) level of each unit, which must be within the capacity limit of the battery; its lower limit depends on the depth of discharge (DoD) allowed each time.

[0132]

[0133] It should be noted that the energy balance equation of the above energy storage battery is a nonlinear equation, which is linearized using the Big M method.

[0134]

[0135] in, Respectively represent the binary variables of the charge and discharge process; Pba k,d,t It is the charging / discharging power, which is limited by the installed power size of the energy storage battery device; The manufacturer's specified limit for cycles to failure.

[0136] Energy storage battery replacement constraints, once the energy capacity of the energy storage battery reaches a lower threshold, the following related constraints will be activated:

[0137]

[0138] Among them, σ k CRB is the lower limit threshold of the health status of the energy storage battery; k CF is the remaining energy capacity of the energy storage battery; k is a binary variable of the remaining energy capacity of the energy storage battery; Decision variables for energy storage battery replacement; Replacement year for energy storage battery; y The relative position of the year when the energy storage battery was replaced;

[0139] Microgrid system operation constraints:

[0140]

[0141] The above microgrid supply and demand balance constraints include the total discharge and total charging power of all energy storage battery units; the reserve constraint ensures that the generator and all installed energy storage battery units provide sufficient capacity to meet the peak demand of the system and maintain the capacity margin. χ is the reserve allocation factor taking into account the uncertainty of renewable energy; P g,d,t Output power for the generator; Predict photovoltaic output power for microgrids; Pd is the wind power output power prediction of microgrid; d,t is the microgrid load demand; W g,d,t Run decision variables for generators;

[0142] The embodiment of the present invention provides a multi-year integrated microgrid energy storage battery planning method to determine the optimal power, energy size and replacement year of the energy storage battery, while considering the capacity degradation effect of the energy storage battery. A new heuristic algorithm is introduced, including:

[0143] Backward propagation method: Starting from the last year of the planning cycle, backpropagation is performed to the initial year to ensure that the microgrid meets the capacity adequacy requirements in all years. If the adequacy constraint is not met in any year, the energy storage battery planning scheme is appropriately modified;

[0144] Forward propagation method: Based on the operation of energy storage battery devices, the degradation effect of their capacity is taken into account, so as to appropriately select their replacement years and make appropriate modifications to the energy storage battery planning scheme.

[0145] The back-propagation method and the forward-propagation method are iteratively run to continuously revise the energy storage battery planning scheme, and ultimately the optimal installed power and energy scale of the microgrid energy storage battery, as well as the replacement and investment schedule, are obtained.

[0146] Compared with the prior art, the embodiments of the present invention can improve the overall economy of the system while ensuring safe operation of the system and sufficient capacity, realize accurate energy storage battery planning, and more reasonably determine the optimal configuration of the energy storage battery, ensuring that the microgrid continues to meet capacity requirements throughout the entire planning cycle, avoiding errors caused by single-point-in-time decisions in traditional planning methods, and ensuring the long-term reliability and stability of the system.

[0147] Correspondingly, the present invention also provides a microgrid energy storage battery planning device, which can implement all processes of the microgrid energy storage battery planning method in the above embodiment.

[0148] See also Figure 2 , Figure 2 1 is a schematic diagram of a microgrid energy storage battery planning device provided by an embodiment of the present invention. The microgrid energy storage battery planning device comprises:

[0149] The historical data acquisition module 201 is used to acquire the historical load data in the microgrid and the historical power generation data of distributed energy, and cluster them to obtain four types of typical daily historical data corresponding to four seasons;

[0150] The typical day data module 202 is used to obtain the natural growth rate of the power load of the microgrid, and predict the typical day data of the last year of the planning period based on the natural growth rate of the power load and the typical day historical data;

[0151] The battery planning generation module 203 is used to determine the battery planning for the last year of the planning period based on the preset microgrid energy storage battery planning model and the typical daily data of the last year of the planning period; based on the battery planning for the last year of the planning period and the microgrid energy storage battery planning model, starting from the last year, the battery planning for the previous year is determined in turn according to the battery planning for each year until the battery planning for all years in the planning period is obtained;

[0152] The optimal battery planning module 204 is used to solve the microgrid energy storage battery planning model year by year starting from the first year of the planning period based on the battery planning of all years, taking into account the degradation effect of the battery capacity and the energy-to-power ratio requirements of the battery, so as to correct the annual battery planning and obtain the optimal battery planning for each year within the planning period.

[0153] Preferably, the preset microgrid energy storage battery planning model uses the typical daily data of the last year of the planning period to determine the battery planning for the last year of the planning period, including:

[0154] Input the typical daily data of the last year of the planning period into a preset microgrid energy storage battery planning model to obtain the corresponding battery power and energy capacity, and determine the battery model and battery quantity according to the battery power and energy capacity;

[0155] Obtaining sufficiency data and capacity data of the microgrid, and combining the battery power and energy capacity to calculate the load loss probability;

[0156] Determining whether the load loss probability is less than a preset threshold;

[0157] If so, the battery plan for the last year of the planning period is obtained;

[0158] If not, increase the number of batteries, re-solve the microgrid energy storage battery planning model and calculate the corresponding load loss probability until the load loss probability is less than the preset threshold, and obtain the battery planning for the last year of the planning period; the battery planning includes: battery power, energy capacity, battery quantity and battery model.

[0159] Preferably, determining the battery plan for the previous year according to the annual battery plan includes:

[0160] Calculate the corresponding load loss probability according to the annual battery plan and the sufficiency data and capacity data of the microgrid;

[0161] By gradually reducing the battery power in the battery planning and recalculating the corresponding load loss probability, determining the minimum value of the battery power when the load loss probability is less than a preset threshold;

[0162] Based on the minimum value of the battery power, the annual battery plan and the typical day data of the previous year, the microgrid energy storage battery planning model is solved to obtain the battery plan of the previous year; wherein the typical day data is predicted by the natural growth rate of the power load and the typical day historical data.

[0163] Preferably, the revision of the annual battery plan includes:

[0164] According to the annual battery plan, determine whether the energy-to-power ratio of the battery is greater than the preset threshold;

[0165] If so, determine the optimal battery plan for that year;

[0166] If not, based on the battery planning for that year, increase the energy capacity of the battery, re-solve the microgrid energy storage battery planning model, update the battery planning until the battery's energy-to-power ratio is greater than a preset threshold, and determine the optimal battery planning for that year.

[0167] Preferably, the objective function of the microgrid energy storage battery planning model aims to minimize the annualized total investment and operating cost;

[0168] When generating a battery plan, the objective function is the sum of the total operating cost of the microgrid, the installation cost of the energy storage battery unit, and the operation and maintenance cost;

[0169] When the battery planning is revised, the objective function is the sum of the total operating cost of the microgrid, the installation cost of the energy storage battery unit, the operation and maintenance cost, and the replacement cost of the energy storage battery.

[0170] Preferably, the constraints of the microgrid energy storage battery planning model include:

[0171] Energy storage battery capital budget constraints, energy storage battery power size constraints, energy storage battery energy capacity constraints, energy storage battery operation constraints, energy storage battery replacement constraints, and microgrid system operation constraints.

[0172] In the specific implementation, the working principle, control process and technical effect achieved by the microgrid energy storage battery planning device provided in the embodiment of the present invention are the same as those of the microgrid energy storage battery planning method in the above embodiment, and will not be repeated here.

[0173] See also Figure 3 , Figure 3 3 is a structural block diagram of a computer device provided in an embodiment of the present invention, wherein the computer device comprises: a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, the steps in the above-mentioned microgrid energy storage battery planning method embodiment are implemented. Alternatively, when the processor 301 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0174] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 302 and executed by the processor 301 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the computer device.

[0175] The computer device may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that the schematic diagram is merely an example of a computer device and does not constitute a limitation on the computer device, and may include more or fewer components than shown in the diagram, or may combine certain components, or different components, for example, the computer device may also include an input / output device, a network access device, a bus, etc.

[0176] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 301 is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0177] The memory 302 can be used to store the computer program and / or module. The processor 301 implements various functions of the computer device by running or executing the computer program and / or module stored in the memory 302 and calling the data stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory 302 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0178] Wherein, if the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor 301. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0179] An embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the microgrid energy storage battery planning method described in any of the above embodiments.

[0180] The embodiments of the present invention provide a method, device, equipment and medium for planning a microgrid energy storage battery, and the beneficial effects thereof are: by acquiring historical load data in a microgrid and historical power generation data of distributed energy, and performing clustering to obtain four types of typical daily historical data corresponding to four seasons; obtaining the natural growth rate of the power load of the microgrid, and predicting the typical daily data of each year in the planning period based on the natural growth rate of the power load and the typical daily historical data; based on a preset microgrid energy storage battery planning model, using the typical daily data of the last year of the planning period, determining the battery planning for the last year of the planning period; based on the battery planning for the last year of the planning period and the microgrid energy storage battery planning model, starting from the last year, determining the previous year's battery planning in turn according to each year's battery planning One year's battery planning, until the battery planning for all years within the planning period is obtained; based on the battery planning for all years, the degradation effect of the battery capacity and the energy-to-power ratio requirement of the battery are considered, and the microgrid energy storage battery planning model is solved year by year from the first year of the planning period to correct the annual battery planning and obtain the optimal battery planning for each year within the planning period; the present invention can improve the overall economy of the system under the premise of ensuring the safety of system operation and sufficient capacity, realize accurate energy storage battery planning, and can more reasonably determine the optimal configuration of the energy storage battery, ensuring that the microgrid continues to meet the capacity requirements throughout the planning cycle, avoiding the errors caused by the traditional planning method due to the decision-making at a single time point, and ensuring the long-term reliability and stability of the system.

[0181] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A microgrid energy storage battery planning method, characterized in that: include: Obtain historical load data in the microgrid and historical power generation data of distributed energy, and perform clustering to obtain four types of typical daily historical data corresponding to the four seasons; Obtain the natural growth rate of the power load of the microgrid, and predict the typical daily data of the last year of the planning period based on the natural growth rate of the power load and the typical daily historical data; Based on the preset microgrid energy storage battery planning model, the battery planning for the last year of the planning period is determined using the typical daily data of the last year of the planning period; Based on the battery planning for the last year of the planning period and the microgrid energy storage battery planning model, starting from the last year, the battery planning for the previous year is determined in turn according to the battery planning for each year until the battery planning for all years in the planning period is obtained; Based on the battery planning of all the years, considering the degradation effect of the battery capacity and the energy-to-power ratio requirements of the battery, the microgrid energy storage battery planning model is solved year by year starting from the first year of the planning period to correct the annual battery planning and obtain the optimal battery planning for each year within the planning period.

2. The microgrid energy storage battery planning method according to claim 1, characterized in that: The preset microgrid energy storage battery planning model is based on the typical daily data of the last year of the planning period to determine the battery planning for the last year of the planning period, including: Input the typical daily data of the last year of the planning period into a preset microgrid energy storage battery planning model to obtain the corresponding battery power and energy capacity, and determine the battery model and battery quantity according to the battery power and energy capacity; Obtaining sufficiency data and capacity data of the microgrid, and combining the battery power and energy capacity to calculate the load loss probability; Determining whether the load loss probability is less than a preset threshold; If so, the battery plan for the last year of the planning period is obtained; If not, increase the number of batteries, re-solve the microgrid energy storage battery planning model and calculate the corresponding load loss probability until the load loss probability is less than the preset threshold, and obtain the battery planning for the last year of the planning period; the battery planning includes: battery power, energy capacity, battery quantity and battery model.

3. The microgrid energy storage battery planning method according to claim 2, characterized in that: The battery plan for the previous year is determined based on the annual battery plan, including: Calculate the corresponding load loss probability according to the annual battery plan and the sufficiency data and capacity data of the microgrid; By gradually reducing the battery power in the battery planning and recalculating the corresponding load loss probability, determining the minimum value of the battery power when the load loss probability is less than a preset threshold; Based on the minimum value of the battery power, the annual battery plan and the typical day data of the previous year, the microgrid energy storage battery planning model is solved to obtain the battery plan of the previous year; wherein the typical day data is predicted by the natural growth rate of the power load and the typical day historical data.

4. The microgrid energy storage battery planning method according to claim 1, characterized in that: The revisions to the annual battery plan include: According to the annual battery plan, determine whether the energy-to-power ratio of the battery is greater than the preset threshold; If so, determine the optimal battery plan for that year; If not, based on the battery planning for that year, increase the energy capacity of the battery, re-solve the microgrid energy storage battery planning model, update the battery planning until the battery's energy-to-power ratio is greater than a preset threshold, and determine the optimal battery planning for that year.

5. The microgrid energy storage battery planning method according to claim 1, characterized in that: The objective function of the microgrid energy storage battery planning model aims to minimize the annualized total investment and operating cost; When generating a battery plan, the objective function is the sum of the total operating cost of the microgrid, the installation cost of the energy storage battery unit, and the operation and maintenance cost; When the battery planning is revised, the objective function is the sum of the total operating cost of the microgrid, the installation cost of the energy storage battery unit, the operation and maintenance cost, and the replacement cost of the energy storage battery.

6. The microgrid energy storage battery planning method according to claim 5, characterized in that: The constraints of the microgrid energy storage battery planning model include: Energy storage battery capital budget constraints, energy storage battery power size constraints, energy storage battery energy capacity constraints, energy storage battery operation constraints, energy storage battery replacement constraints, and microgrid system operation constraints.

7. A microgrid energy storage battery planning device, characterized in that: include: The historical data acquisition module is used to obtain the historical load data in the microgrid and the historical power generation data of distributed energy, and cluster them to obtain four types of typical daily historical data corresponding to the four seasons; A typical day data module is used to obtain the natural growth rate of the power load of the microgrid, and predict the typical day data of the last year of the planning period based on the natural growth rate of the power load and the typical day historical data; A battery planning generation module is used to determine the battery planning for the last year of the planning period based on a preset microgrid energy storage battery planning model and using the typical daily data for the last year of the planning period; Based on the battery planning for the last year of the planning period and the microgrid energy storage battery planning model, starting from the last year, the battery planning for the previous year is determined in turn according to the battery planning for each year until the battery planning for all years in the planning period is obtained; The optimal battery planning module is used to solve the microgrid energy storage battery planning model year by year starting from the first year of the planning period based on the battery planning of all years, taking into account the degradation effect of the battery capacity and the energy-to-power ratio requirements of the battery, so as to correct the annual battery planning and obtain the optimal battery planning for each year within the planning period.

8. The microgrid energy storage battery planning device according to claim 7, characterized in that: The preset microgrid energy storage battery planning model is based on the typical daily data of the last year of the planning period to determine the battery planning for the last year of the planning period, including: Input the typical daily data of the last year of the planning period into a preset microgrid energy storage battery planning model to obtain the corresponding battery power and energy capacity, and determine the battery model and battery quantity according to the battery power and energy capacity; Obtaining sufficiency data and capacity data of the microgrid, and combining the battery power and energy capacity to calculate the load loss probability; Determining whether the load loss probability is less than a preset threshold; If so, the battery plan for the last year of the planning period is obtained; If not, increase the number of batteries, re-solve the microgrid energy storage battery planning model and calculate the corresponding load loss probability until the load loss probability is less than the preset threshold, and obtain the battery planning for the last year of the planning period; the battery planning includes: battery power, energy capacity, battery quantity and battery model.

9. The microgrid energy storage battery planning device according to claim 8, characterized in that: The battery plan for the previous year is determined based on the annual battery plan, including: Calculate the corresponding load loss probability according to the annual battery plan and the sufficiency data and capacity data of the microgrid; By gradually reducing the battery power in the battery planning and recalculating the corresponding load loss probability, determining the minimum value of the battery power when the load loss probability is less than a preset threshold; Based on the minimum value of the battery power, the annual battery plan and the typical day data of the previous year, the microgrid energy storage battery planning model is solved to obtain the battery plan of the previous year; wherein the typical day data is predicted by the natural growth rate of the power load and the typical day historical data.

10. The microgrid energy storage battery planning device according to claim 7, characterized in that: The revisions to the annual battery plan include: According to the annual battery plan, determine whether the energy-to-power ratio of the battery is greater than the preset threshold; If so, determine the optimal battery plan for that year; If not, based on the battery planning for that year, increase the energy capacity of the battery, re-solve the microgrid energy storage battery planning model, update the battery planning until the battery's energy-to-power ratio is greater than a preset threshold, and determine the optimal battery planning for that year.

11. The microgrid energy storage battery planning device according to claim 7, characterized in that: The objective function of the microgrid energy storage battery planning model aims to minimize the annualized total investment and operating cost; When generating a battery plan, the objective function is the sum of the total operating cost of the microgrid, the installation cost of the energy storage battery unit, and the operation and maintenance cost; When the battery planning is revised, the objective function is the sum of the total operating cost of the microgrid, the installation cost of the energy storage battery unit, the operation and maintenance cost, and the replacement cost of the energy storage battery.

12. The microgrid energy storage battery planning device according to claim 11, characterized in that: The constraints of the microgrid energy storage battery planning model include: Energy storage battery capital budget constraints, energy storage battery power size constraints, energy storage battery energy capacity constraints, energy storage battery operation constraints, energy storage battery replacement constraints, and microgrid system operation constraints.

13. A computer device, characterized in that: It comprises a processor and a memory, wherein a computer program is stored in the memory, and the computer program is configured to be executed by the processor, and when the processor executes the computer program, the microgrid energy storage battery planning method as described in any one of claims 1 to 6 is implemented.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the microgrid energy storage battery planning method according to any one of claims 1 to 6 is implemented.

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