A micro-grid energy storage battery planning method, device, equipment and medium

Abstract

The application discloses a micro-grid energy storage battery planning method, device, equipment and medium, and the method comprises the steps of: based on a preset micro-grid energy storage battery planning model, using the typical day data of the last year of the planning period, determining the battery planning 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 turn, and until the battery planning of all years in the planning period is obtained; 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, starting from the first year of the planning period, solving the micro-grid energy storage battery planning model year by year to the rear to correct the battery planning of each year, and obtaining the optimal battery planning of each year in the planning period. The application can perform comprehensive multi-year energy storage battery planning, and improves the planning accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage planning, and particularly relates to a micro-grid energy storage battery planning method, device, equipment and medium. BACKGROUND

[0002] As a new type of distributed energy system, a micro-grid integrates multiple energy forms such as wind energy, photovoltaic and energy storage devices, and can operate independently or be connected to a large power grid. In order to improve the stability of micro-grid operation and the efficient use of energy, the battery energy storage system has become an important part of the micro-grid. The battery energy storage system can provide power support during peak power demand, smooth the fluctuations of renewable energy output, and improve the self-regulating ability of the micro-grid. However, the accuracy of the existing technology needs to be improved when planning the micro-grid energy storage battery for many years. SUMMARY

[0003] To solve the above technical problems, the present application provides a micro-grid energy storage battery planning method, device, equipment and medium, which iteratively runs through back propagation and forward propagation to continuously correct the battery planning, can plan the energy storage battery for many years, and improves the accuracy.

[0004] In a first aspect, the embodiments of the present application provide a micro-grid energy storage battery planning method, comprising:

[0005] Obtain historical load data in the micro-grid and historical power generation data of distributed energy, and cluster to obtain four types of typical day historical data corresponding to four seasons;

[0006] Obtain the natural growth rate of electricity load of the micro-grid, and according to the natural growth rate of electricity load and the typical day historical data, predict to obtain the typical day data of the last year of the planning period;

[0007] Based on a preset micro-grid energy storage battery planning model, use the typical day data of the last year of the planning period to determine the battery planning of the last year of the planning period;

[0008] 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, the battery planning of each year is determined in turn to determine the battery planning of the previous year, until the battery planning of all years in the planning period is obtained;

[0009] Based on the battery planning of all years, considering the degradation effect of battery capacity and the energy power ratio requirement of the battery, the micro-grid energy storage battery planning model is solved year by year from the first year of the planning period to the back to correct the battery planning of each year, and the optimal battery planning of each year in the planning period is obtained.

[0010] As an improvement of the above scheme, the preset micro-grid energy storage battery planning model is used to determine the battery planning of the last year of the planning period, including:

[0011] The typical day data of the last year of the planning period is input into the preset micro-grid energy storage battery planning model to obtain corresponding battery power and energy capacity, and the battery model and the number of batteries are determined according to the battery power and energy capacity.

[0012] The sufficiency data and capacity data of the micro-grid are obtained, and the load loss probability is calculated in combination with the battery power and energy capacity.

[0013] It is judged whether the load loss probability is less than a preset threshold value; if yes, the battery planning of the last year of the planning period is obtained; if no, the number of batteries is increased, the micro-grid energy storage battery planning model is solved again and the corresponding load loss probability is calculated until the load loss probability is less than the preset threshold value, and the battery planning of the last year of the planning period is obtained; the battery planning includes battery power, energy capacity, number of batteries and battery model.

[0014] As an improvement of the above scheme, the battery planning of the previous year is determined according to the battery planning of each year, including:

[0015] According to the battery planning of each year and the sufficiency data and capacity data of the micro-grid, the corresponding load loss probability is calculated.

[0016] The minimum value of the battery power when the load loss probability is less than the preset threshold value is determined by gradually reducing the battery power in the battery planning and recalculating the corresponding load loss probability.

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

[0018] As an improvement of the above scheme, the battery planning of each year is corrected, including:

[0019] According to the battery planning of each year, it is judged whether the energy power ratio of the battery is greater than a preset threshold value; if yes, the optimal battery planning of the year is determined; if no, the energy capacity of the battery is increased based on the battery planning of the year, and the micro-grid energy storage battery planning model is solved again to update the battery planning until the energy power ratio of the battery is greater than the preset threshold value, and the optimal battery planning of the year is determined.

[0020] As an improvement of the above scheme, the objective function of the micro-grid energy storage battery planning model aims to minimize the total annual investment and operation cost;

[0021] When generating the battery planning, the objective function is the sum of the total operation cost of the micro-grid, the installation cost of the energy storage battery unit, the operation and maintenance cost;

[0022] When the battery planning is corrected, the objective function is the sum of the total operation cost of the micro-grid, 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 scheme, the constraint conditions of the micro-grid energy storage battery planning model include:

[0024] Energy storage battery capital budget constraint, energy storage battery power size constraint, energy storage battery energy capacity constraint, energy storage battery operation constraint, energy storage battery replacement constraint, micro-grid system operation constraint.

[0025] As an improvement of the above scheme, the formula of the total operation cost J1 of the micro-grid is:

[0026]

[0027] Where g is a single generator unit, G is a set of generator units; d is a single typical day, D is a set of selected typical days; t is a single time step within a typical day, T is a set 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; b g , c g are the generation cost and investment cost of the generator, respectively; U g,d,t , V g,d,t are the decision variables of generator shutdown and startup, respectively; SUP g , SDN g are the shutdown and startup costs of the generator, respectively;

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

[0029]

[0030] Where k is a single energy storage battery, K is a set of energy storage batteries; is the nominal power capacity of the energy storage battery at installation; is the annual unit power installation cost of the energy storage battery; is the nominal energy capacity of the energy storage battery at installation; is the annual unit capacity installation cost of the energy storage battery; B kDecision variable for installing energy storage battery; C fx Annual fixed installation cost of energy storage battery;

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

[0032]

[0033] Wherein, OMC fx Annual fixed operation and maintenance cost of energy storage battery; Pb k Power of energy storage battery; OMC v Annual variable operation and maintenance cost of energy storage battery; η ch η dch Charging and discharging efficiency of energy storage battery respectively; Pba k,d,t Charging / discharging power of energy storage battery.

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

[0035]

[0036] Wherein, Nominal energy capacity of energy storage battery when installed; RY k Replacement year of energy storage battery; RC is the replacement cost of energy storage battery.

[0037] In a second aspect, the embodiment of the present application further provides a micro-grid energy storage battery planning device, comprising:

[0038] A historical data acquisition module is configured to acquire historical load data in the micro-grid and historical power generation data of distributed energy, and to cluster to obtain four types of typical day historical data corresponding to four seasons;

[0039] A typical day data module is configured to acquire a natural growth rate of electricity consumption load of the micro-grid, and to predict typical day data of the last year of the planning period according to the natural growth rate of electricity consumption load and the typical day historical data;

[0040] A battery planning generation module is configured to determine battery planning of the last year of the planning period based on the typical day data of the last year of the planning period and a preset micro-grid energy storage battery planning model, to determine battery planning of a previous year according to battery planning of each year from the last year based on the battery planning of the last year of the planning period and the micro-grid energy storage battery planning model, and to obtain battery planning of all years in the planning period.

[0041] An optimal battery planning module is configured to correct the battery planning of each year based on the battery planning of all years, considering the degradation effect of battery capacity and the energy-to-power ratio requirement of the battery, and solve the micro-grid energy storage battery planning model from the first year of the planning period to the last year to obtain the optimal battery planning of each year in the planning period.

[0042] Further, the preset micro-grid energy storage battery planning model is used to determine the battery planning of the last year of the planning period based on the typical day data of the last year of the planning period, including:

[0043] The typical day data of the last year of the planning period is input into the preset micro-grid energy storage battery planning model to obtain the corresponding battery power and energy capacity, and the battery model and the number of batteries are determined according to the battery power and energy capacity.

[0044] The sufficiency data and capacity data of the micro-grid are obtained, and the load loss probability is calculated based on the battery power and energy capacity.

[0045] It is determined whether the load loss probability is less than a preset threshold.

[0046] If yes, the battery planning of the last year of the planning period is obtained.

[0047] If no, the number of batteries is increased, the micro-grid energy storage battery planning model is solved again, and the corresponding load loss probability is calculated until the load loss probability is less than the preset threshold, and the battery planning of the last year of the planning period is obtained; the battery planning includes battery power, energy capacity, battery number and battery model.

[0048] Further, the battery planning of each year is determined according to the battery planning of each year, including:

[0049] The corresponding load loss probability is calculated according to the battery planning of each year and the sufficiency data and capacity data of the micro-grid.

[0050] The minimum value of the battery power when the load loss probability is less than the preset threshold is determined by gradually reducing the battery power in the battery planning and recalculating the corresponding load loss probability.

[0051] Based on the minimum value of the battery power, the battery planning of each year, and the typical day data of the previous year, the micro-grid energy storage battery planning model is solved to obtain the battery planning of the previous year; wherein the typical day data is obtained from the natural growth rate of the electricity load and the typical day historical data.

[0052] Further, the battery planning of each year is corrected, including:

[0053] According to the battery planning of each year, it is judged whether the energy power ratio of the battery is greater than a preset threshold value;

[0054] If yes, the optimal battery planning of the year is determined.

[0055] If no, the energy capacity of the battery is increased based on the battery planning of the year, and the micro-grid energy storage battery planning model is re-solved, the battery planning is updated until the energy power ratio of the battery is greater than the preset threshold value, and the optimal battery planning of the year is determined.

[0056] Further, the objective function of the micro-grid energy storage battery planning model aims to minimize the total investment and operation cost per year.

[0057] When the battery planning is generated, the objective function is the sum of the total operation cost of the micro-grid, the installation cost of the energy storage battery unit, and the operation and maintenance cost.

[0058] When the battery planning is corrected, the objective function is the sum of the total operation cost of the micro-grid, the installation cost of the energy storage battery unit, the operation and maintenance cost, and the replacement cost of the energy storage battery.

[0059] Further, the constraint conditions of the micro-grid energy storage battery planning model include:

[0060] The energy storage battery capital budget constraint, the energy storage battery power size constraint, the energy storage battery energy capacity constraint, the energy storage battery operation constraint, the energy storage battery replacement constraint, and the micro-grid system operation constraint.

[0061] In a third aspect, an embodiment of the present application further provides a computer device, including a processor and a memory, the memory stores a computer program, and the computer program is configured to be executed by the processor, and the processor executes the computer program to implement the micro-grid energy storage battery planning method of any one of the above.

[0062] In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, which stores a computer program, wherein the computer program controls the device where the computer readable storage medium is located to execute the micro-grid energy storage battery planning method of any one of the above when the computer program runs.

[0063] Compared to existing technologies, the beneficial effects of the microgrid energy storage battery planning method, apparatus, equipment, and medium provided in this embodiment of the invention are as follows: By acquiring historical load data and distributed energy generation data in the microgrid, and performing clustering, four types of typical daily historical data corresponding to the four seasons are obtained; the natural growth rate of the microgrid's electricity load is acquired, and based on the natural growth rate of the electricity load and the typical daily historical data, the typical daily data for each year within the planning period is predicted; based on a 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; 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 is sequentially determined according to the annual battery planning... The invention involves determining the battery plan for the previous year and refining it to obtain the battery plans for all years within the planning period. Based on the battery plans for all years, considering the degradation effect of battery capacity and the energy-power ratio requirements of the batteries, the microgrid energy storage battery planning model is solved year by year starting from the first year of the planning period to revise the battery plan for each year and obtain the optimal battery plan for each year within the planning period. This invention can improve the overall economic efficiency of the system while ensuring system operation safety and sufficient capacity. It achieves accurate energy storage battery planning, can more rationally determine the optimal configuration of energy storage batteries, ensures that the microgrid continuously meets the capacity requirements throughout the planning cycle, avoids the errors caused by single-point-in-time decisions in traditional planning methods, and ensures the long-term reliability and stability of the system. Attached Figure Description

[0064] Figure 1 This is a flowchart illustrating a microgrid energy storage battery planning method provided in an embodiment of the present invention;

[0065] Figure 2 This is a schematic diagram of the structure of a microgrid energy storage battery planning device provided in an embodiment of the present invention;

[0066] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] Please see Figure 1 , Figure 1 This is a flowchart illustrating a microgrid energy storage battery planning method provided by an embodiment of the present invention. The microgrid energy storage battery planning method includes:

[0069] S1: obtaining historical load data in the micro-grid and historical power generation data of distributed energy, and 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 micro-grid are obtained, and they are clustered into four types of typical daily historical data, wherein each type of typical daily data represents a season, and the typical daily historical data includes typical daily data of historical wind power generation, typical daily data of historical photovoltaic power generation, and typical daily data of historical load data.

[0071] S2: obtaining the natural growth rate of electricity load in the micro-grid, and predicting the typical daily data of the last year of the planning period according to the natural growth rate of electricity load and the typical daily historical data;

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

[0073] S3: based on a preset micro-grid energy storage battery planning model, using the typical daily data of the last year of the planning period to determine the battery planning of the last year of the planning period;

[0074] As one of the optional embodiments, the micro-grid energy storage battery planning model based on the preset micro-grid energy storage battery planning model, using the typical daily data of the last year of the planning period to determine the battery planning of the last year of the planning period, includes:

[0075] The typical daily data of the last year of the planning period is input into the preset micro-grid energy storage battery planning model to obtain the corresponding battery power and energy capacity, and the battery model and the number of batteries are determined according to the battery power and energy capacity;

[0076] Obtaining the sufficiency data and capacity data of the micro-grid, and combining the battery power and energy capacity, the load loss probability is calculated;

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

[0078] Specifically, assuming that the planning period has Y years, typical day data of the last year Y is obtained, including typical day data of wind power generation, typical day data of photovoltaic power generation, typical day data of load, and the micro-grid energy storage battery planning model is input; the planning model is solved to determine the battery power and energy capacity of the last year Y, and the battery model and the installation quantity of the energy storage battery are determined based on the battery power and energy capacity of the last year Y, and the battery planning of the last year is obtained; the sufficiency data and the capacity data of the system components in the micro-grid are obtained, and the corresponding load loss probability is calculated combined with the battery planning of the last year Y, and it is judged whether the load loss probability meets the sufficiency demand of the micro-grid, i.e. the load loss probability is less than a preset threshold; if yes, the current battery planning is the energy storage battery planning scheme of the last year Y that meets the sufficiency index of the micro-grid; if not, the current battery planning is rejected, and the capacity of the energy storage battery is increased according to the existing standard, i.e. the installation quantity of the energy storage battery is increased, the planning model is solved again, and it is judged whether the battery planning meets the sufficiency demand of the micro-grid, until the battery planning meets the sufficiency demand of the micro-grid, and the energy storage battery planning scheme of the last year Y is obtained.

[0079] The calculation method of the load loss probability comprises: representing the daily load by daily peak load values, arranging the daily peak load values in descending order to obtain a daily peak load change curve; and then comparing the outage capacity of the system with the daily peak load change curve to obtain the probability that the daily peak load exceeds the available power generation capacity of the system, which is referred to as the power shortage probability, also referred to as the load loss probability, and is used as the reliability index of the system.

[0080] S4: based on the battery planning of the last year of the planning period and the micro-grid energy storage battery planning model, the battery planning of each year is determined in sequence from the last year to obtain the battery planning of all years in the planning period;

[0081] As one of the optional embodiments, the determination of the battery planning of each year comprises:

[0082] According to the battery planning of each year and the sufficiency data and the capacity data of the micro-grid, the corresponding load loss probability is calculated;

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

[0084] Based on the minimum value of the battery power, the battery planning of each year and the typical day data of the previous year, the micro-grid energy storage battery planning model is solved to obtain the battery planning of the previous year; wherein the typical day data is obtained by the natural growth rate of the electricity load and the typical day historical data.

[0085] Specifically, the typical day data of the corresponding year, the battery planning of the Yth year (i.e., the last year) are obtained as the input of the microgrid energy storage battery planning model; y=Y, i.e., starting from the last year;

[0086] y=y-1; based on the battery planning of the Yth year, the number of installed energy storage batteries is continuously reduced while the sufficiency requirement of the microgrid is tested to obtain the minimum number of installed energy storage batteries under the condition of meeting the sufficiency requirement of the year y. Specifically, the load loss probability is evaluated according to the battery planning of the Yth year, the sufficiency data and the capacity data of the system components; the battery power is reduced to determine the minimum battery power when the load loss probability is less than the preset threshold; the minimum battery power is fixed, the planning model is solved, and the battery planning of the year y is determined;

[0087] The battery planning of each year is obtained from the last year to the first year until y=1, and the energy storage battery planning scheme of all years in the planning period that meets the microgrid sufficiency index is obtained.

[0088] S5: based on the battery planning of all years, considering the degradation effect of battery capacity and the energy-to-power ratio requirement of the battery, the microgrid energy storage battery planning model is solved from the first year of the planning period to the last year to correct the battery planning of each year to obtain the optimal battery planning of each year in the planning period.

[0089] As one of the optional embodiments, the correction of the battery planning of each year includes:

[0090] According to the battery planning of each year, it is judged whether the energy-to-power ratio of the battery is greater than the preset threshold; if yes, the optimal battery planning of the year is determined; if not, based on the battery planning of the year, the energy capacity of the battery is increased, and the microgrid energy storage battery planning model is solved again to update the battery planning until the energy-to-power ratio of the battery is greater than the preset threshold, and the optimal battery planning of the year is determined.

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

[0092] y=0;

[0093] y=y-1;

[0094] The microgrid energy storage battery planning model containing the operation of the energy storage battery is solved, and the health status of the energy storage battery is determined according to the operation state of the energy storage battery;

[0095] It is judged whether the following condition is met wherein, E k is the energy capacity of the energy storage battery / kWh, Nominal power capacity of the energy storage battery installed / kW;

[0096] If yes, the optimal battery planning of the year y is obtained;

[0097] If no, based on the current battery planning of the year y, increase And return to the step of solving the planning model;

[0098] Obtain the optimal battery planning of each year year by year until the optimal battery planning scheme of all years in 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 total annual investment and operating cost;

[0100] When generating the battery planning, 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;

[0101] When the battery planning is corrected, 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 total annual investment and operating cost, as shown below:

[0103]

[0104] Wherein, 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 its life, i.e. only used in the planning model in step S5.

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

[0106]

[0107] Wherein, g is a single generator unit, G is a set of generator units; d is a single typical day, D is a set of selected typical days; t is a single time step within a typical day, T is a set 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 is out of operation, and when the variable value is 1, the generator is put into operation; b g , c grespectively, are the generation cost and the input cost of the generator; U g,d,t g,d,t respectively, are the decision variables of the generator shutdown and startup; SUP g g respectively, are the shutdown and startup costs of the generator;

[0108] Further, 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] wherein k is a single energy storage battery, and K is a set of energy storage batteries; is the nominal power capacity of the energy storage battery at the time of installation; is the annual unit power installation cost of the energy storage battery; is the nominal energy capacity of the energy storage battery at the time of installation; is the annual unit capacity installation cost of the energy storage battery; B k is the decision variable of the installation of the energy storage battery; C fx is the annual fixed installation cost of the energy storage battery;

[0111] Further, 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] wherein OMC fx is the annual fixed operation and maintenance cost of the energy storage battery; Pb 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 are the charging and discharging efficiencies of the energy storage battery; Pba k,d,t is the charging / discharging power of the energy storage battery.

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

[0115]

[0116] wherein, is the nominal energy capacity of the energy storage battery at the time of installation; RY k is the replacement year of the energy storage battery; and RC is the replacement cost of the energy storage battery.

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

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

[0119] Specifically, the energy storage battery capital budget constraint is:

[0120]

[0121] This limit specifies the upper limit of the capital expenditure of the microgrid on the installation and replacement of the energy storage battery in the planning year, and BL is the specified budget limit.

[0122] Energy storage battery power size constraint:

[0123]

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

[0125] Energy storage battery energy capacity constraint:

[0126]

[0127] is a binary variable B k takes a unit value. is the number of capacity components installed for the energy storage battery, which is an integer variable; Λ E is the market standard of the energy capacity of the energy storage battery; respectively, the capacity loss of the energy storage battery due to cycling and calendar degradation; is the change in the SoC of the energy storage battery when discharging; E / P , respectively, the minimum / maximum energy-to-power ratio of the energy storage battery; is the increase in energy storage capacity / kWh; α k is the capacity degradation coefficient of the energy storage battery.

[0128] Energy storage battery operation constraint:

[0129]

[0130] i.e. the operational 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; the lower limit depends on the allowed depth of discharge (DoD) at each time. k,d,t SoCk,t,d is the state of charge of the kth energy storage battery at the tth time period of a typical day d; η ch ηk,t,d is the state of charge of the kth energy storage battery at the tth time period of a typical day d; η dch are the charging and discharging efficiencies of the energy storage battery, respectively.

[0131] 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; the lower limit depends on the allowed depth of discharge (DoD) at each time.

[0132]

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

[0134]

[0135] where, Pba k,t,d and Pbc k,t,d represent the binary variables of the charging and discharging process, respectively; Pba k,t,d k,d,t Pba k,t,d and Pbc k,t,d represent the binary variables of the charging and discharging process, respectively; Pba k,t,d Pba k,t,d and Pbc k,t,d represent the binary variables of the charging and discharging process, respectively; Pba k,t,d

[0136] Energy storage battery replacement constraint, once the energy capacity of the energy storage battery reaches a lower threshold, the relevant constraint given below will be activated:

[0137]

[0138] where, σ k σ is the lower threshold of the health status of the energy storage battery; CRB k CRB is the remaining energy capacity of the energy storage battery; CF k CF is the binary variable of the remaining energy capacity of the energy storage battery; is the decision variable of the replacement of the energy storage battery; ord is the replacement year of the energy storage battery; ord y ord is the relative position of the replacement year of the energy storage battery;

[0139] Microgrid system operation constraints:

[0140]

[0141] The above micro-grid supply-demand balance constraints include total discharge and total charge 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 a reserve allocation factor considering the uncertainty of renewable energy; P g,d,t is the generator output power; is the micro-grid photovoltaic output power prediction; is the micro-grid wind power output power prediction; Pd d,t is the micro-grid load demand; W g,d,t is the generator operation decision variable;

[0142] The embodiment of the present application provides a comprehensive multi-year micro-grid energy storage battery planning method to determine the optimal power, energy size and replacement year of the energy storage battery, considering the capacity degradation effect of the energy storage battery, and introduces a new heuristic algorithm, including:

[0143] Back propagation method: starting from the last year of the planning period, propagating backward to the initial year to ensure that the micro-grid meets the capacity adequacy requirement in all years, and 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 the energy storage battery device, considering the degradation effect of their capacity, the replacement year of the energy storage battery device is appropriately selected, and the energy storage battery planning scheme is appropriately modified.

[0145] The back propagation method and the forward propagation method are iteratively operated, and the energy storage battery planning scheme is continuously modified, and finally the optimal installation power and energy size of the micro-grid energy storage battery and the replacement and investment schedule are obtained.

[0146] Compared with the prior art, the embodiment of the present application can improve the overall economy of the system under the premise of ensuring the safe operation and capacity adequacy of the system, realizes accurate energy storage battery planning, can more reasonably determine the optimal configuration of the energy storage battery, ensures that the micro-grid continuously meets the capacity requirement in the entire planning period, avoids the error caused by single time point decision in the traditional planning method, and guarantees the long-term reliability and stability of the system.

[0147] Correspondingly, the present application also provides a micro-grid energy storage battery planning device which can realize all processes of the micro-grid energy storage battery planning method in the above embodiment.

[0148] Please refer to Figure 2 , Figure 2 is a structural schematic diagram of a micro-grid energy storage battery planning device provided by the embodiment of the present application. The micro-grid energy storage battery planning device comprises:

[0149] The historical data acquisition module 201 is configured to acquire historical load data in the micro-grid and historical power generation data of the distributed energy, and cluster to obtain four types of typical day historical data corresponding to four seasons.

[0150] The typical day data module 202 is configured to acquire a natural growth rate of the electricity load of the micro-grid, and predict typical day data of the last year of the planning period according to the natural growth rate of the electricity load and the typical day historical data.

[0151] The battery planning generation module 203 is configured to determine the battery planning of the last year of the planning period based on a preset micro-grid energy storage battery planning model and the typical day data of the last year of the planning period, and determine the battery planning of each previous year according to the battery planning of the next year 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, until the battery planning of all years in the planning period is obtained.

[0152] The optimal battery planning module 204 is configured to correct the battery planning of each year by solving the micro-grid energy storage battery planning model from the first year of the planning period to the last year, based on the battery planning of all years, considering the degradation effect of the battery capacity and the energy-to-power ratio requirement of the battery, to obtain the optimal battery planning of each year in the planning period.

[0153] Preferably, the determination of the battery planning of the last year of the planning period based on the preset micro-grid energy storage battery planning model and the typical day data of the last year of the planning period comprises:

[0154] inputting the typical day data of the last year of the planning period into the preset micro-grid energy storage battery planning model to obtain corresponding battery power and energy capacity, and determining a battery model and a battery quantity according to the battery power and the energy capacity;

[0155] acquiring sufficiency data and capacity data of the micro-grid, and calculating a load loss probability in combination with the battery power and the energy capacity;

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

[0157] if yes, the battery planning of the last year of the planning period is obtained;

[0158] if no, the battery quantity is increased, the micro-grid energy storage battery planning model is solved again, and a corresponding load loss probability is calculated, until the load loss probability is less than the preset threshold value, and the battery planning of the last year of the planning period is obtained; the battery planning comprises battery power, energy capacity, battery quantity and battery model.

[0159] Preferably, the determination of the battery planning of each previous year according to the battery planning of the next year comprises:

[0160] According to the battery planning of each year and the sufficiency data and the capacity data of the micro-grid, a corresponding load loss probability is calculated;

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

[0162] Based on the minimum value of the battery power, the battery planning of each year and the typical day data of the previous year, the micro-grid energy storage battery planning model is solved to obtain the battery planning of the previous year; wherein the typical day data is obtained by the natural growth rate of the electricity load and the typical day historical data.

[0163] Preferably, the battery planning of each year is corrected, including:

[0164] According to the battery planning of each year, it is judged whether the energy power ratio of the battery is greater than a preset threshold value;

[0165] If yes, the optimal battery planning of the year is determined;

[0166] If no, based on the battery planning of the year, the energy capacity of the battery is increased, and the micro-grid energy storage battery planning model is solved again to update the battery planning until the energy power ratio of the battery is greater than the preset threshold value, and the optimal battery planning of the year is determined.

[0167] Preferably, the objective function of the micro-grid energy storage battery planning model aims to minimize the total investment and operation cost per year;

[0168] When generating the battery planning, the objective function is the sum of the total operation cost of the micro-grid, the installation cost of the energy storage battery unit, and the operation and maintenance cost;

[0169] When the battery planning is corrected, the objective function is the sum of the total operation cost of the micro-grid, 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 constraint conditions of the micro-grid energy storage battery planning model include:

[0171] The energy storage battery capital budget constraint, the energy storage battery power size constraint, the energy storage battery energy capacity constraint, the energy storage battery operation constraint, the energy storage battery replacement constraint, and the micro-grid system operation constraint.

[0172] In specific implementation, the working principle, control process and technical effects of the micro-grid energy storage battery planning device provided by the embodiments of the present application are the same as the micro-grid energy storage battery planning method in the above embodiments, and will not be repeated here.

[0173] Referring to Figure 3 , Figure 3 is a structural block diagram of a computer device provided by an embodiment of the present application, which includes a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. The processor 301 implements the steps in the micro-grid energy storage battery planning method embodiment described above when executing the computer program. Alternatively, the processor 301 implements the functions of each module / unit in each device embodiment described above when executing the computer program.

[0174] For example, the computer program can be divided into one or more modules / units, which are stored in the memory 302 and executed by the processor 301 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the computer device.

[0175] The computer device can include, but is not limited to, the processor 301 and the memory 302. Those skilled in the art can understand that the schematic diagram is only an example of the computer device and does not limit the computer device, which can include more or fewer components than the diagram, or combine certain components, or different components, for example, the computer device can also include an input / output device, a network access device, a bus, etc.

[0176] The processor 301 can be a central processing unit (CPU), and can also be 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. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The processor 301 is the control center of the computer device, which connects each part of the computer device through various interfaces and lines.

[0177] The memory 302 can be used to store the computer programs and / or modules, and the processor 301 realizes various functions of the computer device by running or executing the computer programs and / or modules 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, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), and the like; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), and the like. In addition, the memory 302 can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0178] The modules / units integrated in the computer device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can realize the steps of the above-mentioned various method embodiments when executed by the processor 301. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0179] The embodiment of the present application also provides a computer readable storage medium, which comprises a stored computer program, wherein when the computer program runs, the device where the computer readable storage medium is located executes the micro-grid energy storage battery planning method in any of the above-mentioned embodiments.

[0180] The embodiment of the present application provides a micro-grid energy storage battery planning method, device, equipment and medium, which has the beneficial effects that: by obtaining historical load data in the micro-grid and historical power generation data of the distributed energy, four types of typical day historical data corresponding to four seasons are obtained by clustering; the natural growth rate of the power load of the micro-grid is obtained, and according to the natural growth rate of the power load and the typical day historical data, the typical day data of each year in the planning period is predicted; based on the preset micro-grid energy storage battery planning model, the battery planning of the last year of the planning period is determined by using 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, the battery planning of each year is determined in turn according to the battery planning of the last year, until the battery planning of all years in the planning period is obtained; 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, the micro-grid energy storage battery planning model is solved year by year from the first year of the planning period to the last year, so as to correct the battery planning of each year, and the optimal battery planning of each year in the planning period is obtained; the present application can improve the overall economy of the system under the premise of ensuring the safe operation of the system and sufficient capacity, realizes accurate energy storage battery planning, can more reasonably determine the optimal configuration of the energy storage battery, ensures that the micro-grid can continuously meet the capacity requirement in the whole planning period, avoids the error caused by single time point decision in the traditional planning method, and ensures the long-term reliability and stability of the system.

[0181] The above is the preferred embodiment of the present application, it should be noted that for ordinary skilled in the art, without departing from the principles of the present application, can make a number of improvements and refinements, these improvements and refinements are also considered to be within the scope of the present application.

Claims

1. A microgrid energy storage battery planning method, characterized in that, The method comprises the following steps: acquiring historical load data and historical power generation data of distributed energy in a micro-grid, and clustering to obtain four types of typical day historical data corresponding to four seasons; acquiring a natural growth rate of electricity load of the micro-grid, and predicting typical day data of the last year of the planning period according to the natural growth rate of electricity load and the typical day historical data; based on a preset micro-grid energy storage battery planning model, determining the battery planning of the last year of the planning period by using 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, determining the battery planning of the previous year according to the battery planning of each year in turn from the last year, until the battery planning of all years in the planning period is obtained; based on the battery planning of all years, considering the degradation effect of battery capacity and the energy power ratio requirement of the battery, solving the micro-grid energy storage battery planning model year by year from the first year of the planning period to the last year to correct the battery planning of each year, and obtaining the optimal battery planning of each year in the planning period.

2. The microgrid energy storage battery planning method of claim 1, wherein, The method comprises the following steps: inputting the typical day data of the last year of the planning period into the preset micro-grid energy storage battery planning model to obtain corresponding battery power and energy capacity, and determining the battery model and the number of batteries according to the battery power and the energy capacity; acquiring sufficiency data and capacity data of the micro-grid, and calculating the load loss probability by combining the battery power and the energy capacity; determining whether the load loss probability is less than a preset threshold value; if yes, the battery planning of the last year of the planning period is obtained; if no, the number of batteries is increased, the micro-grid energy storage battery planning model is solved again, and the corresponding load loss probability is calculated until the load loss probability is less than the preset threshold value, and the battery planning of the last year of the planning period is obtained; the battery planning comprises battery power, energy capacity, number of batteries and battery model.

3. The microgrid energy storage battery planning method of claim 2, wherein, The method comprises the following steps: calculating the corresponding load loss probability according to the battery planning of each year and the sufficiency data and the capacity data of the micro-grid; determining the minimum value of the battery power when the load loss probability is less than the preset threshold value by gradually reducing the battery power in the battery planning and recalculating the corresponding load loss probability; based on the minimum value of the battery power, the battery planning of each year and the typical day data of the previous year, solving the micro-grid energy storage battery planning model to obtain the battery planning of the previous year; wherein the typical day data is predicted by the natural growth rate of electricity load and the typical day historical data.

4. The microgrid energy storage battery planning method of claim 1, wherein, The method comprises the following steps: determining whether the energy power ratio of the battery is greater than a preset threshold value according to the battery planning of each year; if yes, the optimal battery planning of the year is determined; If not, based on the battery planning of the year, increase the energy capacity of the battery, and re-solve the micro-grid energy storage battery planning model to update the battery planning until the energy power ratio of the battery is greater than a preset threshold to determine the optimal battery planning of the year.

5. The microgrid energy storage battery planning method of claim 1, wherein, The objective function of the micro-grid energy storage battery planning model aims to minimize the total annual investment and operating cost; When generating the battery planning, the objective function is the sum of the total operating cost of the micro-grid, the installation cost of the energy storage battery unit, and the operation and maintenance cost; When the battery planning is corrected, the objective function is the sum of the total operating cost of the micro-grid, 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 of claim 5, wherein, The constraint conditions of the micro-grid energy storage battery planning model include: Energy storage battery capital budget constraint, energy storage battery power size constraint, energy storage battery energy capacity constraint, energy storage battery operation constraint, energy storage battery replacement constraint, and micro-grid system operation constraint.

7. A microgrid energy storage battery planning device, characterized by, It includes: A historical data acquisition module is configured to acquire historical load data in the micro-grid and historical power generation data of distributed energy, and to cluster to obtain four types of typical day historical data corresponding to four seasons; A typical day data module is configured to acquire a natural growth rate of electricity load of the micro-grid, and to predict typical day data of the last year of the planning period according to the natural growth rate of electricity load and the typical day historical data; A battery planning generation module is configured to determine the battery planning of the last year of the planning period based on a preset micro-grid energy storage battery planning model and using 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, the battery planning of each year is determined in turn from the last year to obtain the battery planning of all years in the planning period; An optimal battery planning module is configured to solve the micro-grid energy storage battery planning model from the first year of the planning period to the last year year by year 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, to correct the battery planning of each year to obtain the optimal battery planning of each year in the planning period.

8. The microgrid energy storage battery planning apparatus of claim 7, wherein, The determination of the battery planning of the last year of the planning period based on the preset micro-grid energy storage battery planning model and using the typical day data of the last year of the planning period includes: The typical day data of the last year of the planning period is input into the preset micro-grid energy storage battery planning model to obtain corresponding battery power and energy capacity, and the battery model and the number of batteries are determined according to the battery power and energy capacity; Sufficient data and capacity data of the micro-grid are acquired, and the load loss probability is calculated in combination with the battery power and energy capacity; It is judged whether the load loss probability is less than a preset threshold; If yes, the battery planning of the last year of the planning period is obtained; If not, the number of batteries is increased, the micro-grid energy storage battery planning model is solved again, and the corresponding load loss probability is calculated until the load loss probability is less than a preset threshold, and the battery planning of the last year of the planning period is obtained; the battery planning includes battery power, energy capacity, battery number, and battery model.

9. The microgrid energy storage battery planning apparatus of claim 8, wherein, The battery planning of each year is used to determine the battery planning of the previous year, including: According to the battery planning of each year and the sufficiency data and the capacity data of the micro-grid, the corresponding load loss probability is calculated; By gradually reducing the battery power in the battery planning and recalculating the corresponding load loss probability, the minimum value of the battery power when the load loss probability is less than a preset threshold is determined; Based on the minimum value of the battery power, the battery planning of each year, and the typical day data of the previous year, the micro-grid energy storage battery planning model is solved to obtain the battery planning of the previous year; wherein the typical day data is obtained by the electricity load natural growth rate and the typical day historical data prediction.

10. The microgrid energy storage battery planning apparatus of claim 7, wherein, The battery planning of each year is corrected, including: According to the battery planning of each year, it is judged whether the energy power ratio of the battery is greater than a preset threshold; If yes, the optimal battery planning of the year is determined; If not, based on the battery planning of the year, the energy capacity of the battery is increased, and the micro-grid energy storage battery planning model is solved again to update the battery planning until the energy power ratio of the battery is greater than the preset threshold, and the optimal battery planning of the year is determined.

11. The microgrid energy storage battery planning apparatus of claim 7, wherein, The objective function of the micro-grid energy storage battery planning model aims to minimize the total investment and operating cost per year; When generating the battery planning, the objective function is the sum of the total operating cost of the micro-grid, the installation cost of the energy storage battery unit, and the operation and maintenance cost; When the battery planning is corrected, the objective function is the sum of the total operating cost of the micro-grid, 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 apparatus of claim 11, wherein, The constraint conditions of the micro-grid energy storage battery planning model include: Energy storage battery capital budget constraint, energy storage battery power size constraint, energy storage battery energy capacity constraint, energy storage battery operation constraint, energy storage battery replacement constraint, and micro-grid system operation constraint.

13. A computer device, comprising: The processor and the memory are included, the memory stores a computer program, and the computer program is configured to be executed by the processor, and the processor executes the computer program to realize the micro-grid energy storage battery planning method in any one of claims 1 to 6.

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

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