Power consumer-oriented multi-objective-based energy storage capacity double-layer optimization configuration method

Abstract

The invention provides a power consumer-oriented multi-objective-based energy storage capacity double-layer optimization configuration method. The method comprises the following steps of collecting historical load data, electricity price information and battery technical economic parameters of power consumers; clustering the monthly load data; determining the most representative load curve in each class as a typical day of the class; aiming at hard physical requirements of the battery, establishing an energy storage battery model, including constructing a performance constraint condition and an operation constraint condition; the outer-layer optimization model takes the power capacity and the energy capacity of energy storage as optimization variables; the inner-layer optimization model takes the battery electric quantity state of the energy storage system in the typical day in different time periods as an optimization variable SOCp +; energy storage capacity configuration in the inner-layer optimization model is obtained according to the outer-layer optimization model, and an economical efficiency target in the outer-layer optimization model is influenced by an optimization result of the inner-layer optimization model. According to the invention, more efficient energy storage optimization capacity configuration can be realized under different user and different electricity price scenes.

Description

Technical Field

[0001] The present application relates to the technical field of electric energy storage systems of novel power systems, and in particular to a multi-objective based double-layer optimization configuration method for energy storage capacity for power users. Background Art

[0002] The cost of electricity is an important part of the operating cost of large power users. With the continuous increase in energy demand of users, under the time-of-use electricity price strategy, large power users need to improve their energy flexibility in order to reduce operating expenses such as electricity bills and improve their own operating efficiency. By controlling the reasonable charging and discharging of energy storage batteries, the energy load at different times can be changed without changing the user's electricity consumption behavior, thereby improving the user's benefits. At present, the research on energy storage configuration and operation optimization is divided into the grid side and the user side. Optimizing grid-side energy storage can improve the reliability of grid operation, while optimizing user-side energy storage can improve the economic efficiency of users after installing energy storage by reasonably transferring loads. Some user-side energy storage optimization studies adopt a two-layer optimization mode, that is, optimizing energy storage capacity configuration based on optimizing energy storage scheduling. However, if energy storage scheduling optimization is performed based on all historical loads, the computational cost is huge. Therefore, there is currently a lack of energy storage optimization research that can simultaneously consider energy storage capacity configuration and operation optimization and greatly reduce the amount of calculation. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a multi-objective dual-layer optimization configuration method for energy storage capacity for power users, which takes into account the technical and economic issues in the energy storage system capacity optimization process, and can achieve more efficient energy storage optimization capacity configuration under different users and different electricity price scenarios.

[0004] To achieve the above objectives, this application provides the following technical solutions:

[0005] The embodiment of the present application provides a multi-objective dual-layer optimization configuration method for energy storage capacity for power users, comprising the following steps:

[0006] Step 1: Collect historical load data, electricity price information, and battery technical and economic parameters of power users to prepare for energy storage capacity configuration optimization;

[0007] Step 2: Cluster the monthly load data using density-based spatial clustering with noise;

[0008] Step 3: Based on the center point of each cluster, determine the most representative load curve in each class as the typical day of that class;

[0009] Step 4: Establish an energy storage battery model based on the hard physical requirements of the battery, including building performance constraints and operation constraints;

[0010] Step 5: The outer optimization model uses the power capacity and energy capacity of energy storage as optimization variables, calculates the benefits and payback period based on several typical days and the number of days in the monthly load data, and optimizes the multi-objective function through the multi-objective simulated annealing algorithm to obtain the optimal energy storage capacity configuration results, the power capacity P of energy storage and the energy capacity E of energy storage;

[0011] Step 6: The inner optimization model uses the battery power status of the energy storage system in different time periods during a typical day as the optimization variable , based on typical day load data, calculate the benefits, optimize the objective function through the particle swarm optimization algorithm, and obtain the operation results of the energy storage system in a typical day;

[0012] Step 7: The energy storage capacity configuration in the inner optimization model is obtained according to the outer optimization model, and the economic objectives in the outer optimization model are affected by the optimization results of the inner optimization model.

[0013] The objective function of the outer model is:

[0014] ,

[0015] in, is the revenue of the energy storage system in that month; is the weight of monthly income in the total objective function; is the payback period of the energy storage system; is the weight of the battery payback period in the overall objective function; is the net present value of the energy storage system; is the weight of the net present value of the energy storage system in the overall objective function;

[0016] ,

[0017] in, The cost saved by operating the energy storage system for the user in that month; is the initial investment cost of the energy storage system; is the assumed service life of the energy storage system;

[0018] ,

[0019] ,

[0020] in The annual cost savings after operating the energy storage system for the user;

[0021] ,

[0022] in, The net present value of the energy storage system over its entire life cycle; The service life of the energy storage system; is the discount rate, which is usually determined based on the bank loan interest rate of the year; is the age of the energy storage system;

[0023] ,

[0024] in, is the battery power capacity; is the unit power capacity cost of the energy storage battery system, is the battery energy capacity; is the unit energy capacity cost of the energy storage battery system;

[0025] ,

[0026] in, The total monthly electricity bill of the user before allocation and storage; It is the total monthly electricity bill of the user after allocation and storage.

[0027] The total monthly electricity fee before and after the allocation and storage is composed of the kilowatt-hour electricity fee and the demand electricity fee, and the calculation formula is:

[0028] ,

[0029] in, The monthly electricity bill; The monthly demand electricity fee.

[0030] The original electricity fee is calculated by the electricity price and the original hourly load. The electricity fee after storage is obtained by the electricity price, the typical day load after storage and the weight of the typical day. The calculation formula is:

[0031] ,

[0032] in, is the number of days of monthly load; is the duration of the daily load; Real-time electricity prices for users; For users in The real-time electricity consumption of the day, where the superscript 1 represents the original load before the energy storage system is used; is the number of categories for a typical day; For user Real-time electricity consumption on a typical day; Indicates The number of days that are typical days.

[0033] The calculation formula of the demand electricity fee is:

[0034] ,

[0035] in, It is the demand electricity charge; is the capacity electricity fee; is the demand price; is the capacity price; Indicates the transformer capacity.

[0036] The objective function of the inner model is the difference in typical daily total electricity costs before and after energy storage operation scheduling, which is specifically expressed as:

[0037] ,

[0038] Among them, the superscripts 1 and 2 represent the scenes before and after energy storage, respectively; The total electricity cost of the user on a typical day before the energy storage operation; The total electricity cost of the user on a typical day after the energy storage is in operation;

[0039] The total electricity cost for a typical day of energy storage operation dispatch is:

[0040] ,

[0041] in, It is the real-time electricity consumption of users on a typical day.

[0042] The inner optimization model must meet the energy storage operation constraints, including:

[0043] (a) The battery charge and discharge capacity, charge and discharge power, and remaining battery capacity of the energy storage system at each time period are:

[0044] ,

[0045] in, The amount of electricity changed by the energy storage system is positive during charging and negative during discharging; is the self-discharge coefficient of the energy storage system; For energy storage systems The remaining power at any time; For batteries The charging and discharging power at the moment; For energy storage systems The state of charge at the moment is also the optimization variable of the inner model;

[0046] (b) The real-time state of charge constraint of the energy storage system is:

[0047] ,

[0048] in, is the lower limit of the state of charge of the energy storage system; is the upper limit of the state of charge of the energy storage system;

[0049] (c) Real-time power capacity constraints of energy storage systems:

[0050] ,

[0051] in, is the maximum discharge power of the energy storage system; The maximum charging power for the energy storage system;

[0052] (d) Real-time energy capacity constraints of energy storage systems:

[0053] ,

[0054] in, is the discharge depth of the battery.

[0055] The SOC value of the battery in each time period obtained based on the optimization is used to determine the real-time load condition of the user after energy storage. The specific formula is as follows:

[0056] ,

[0057] in, The real-time electricity consumption of users on a typical day;

[0058] The outer model is optimized based on the simulated annealing algorithm, specifically including taking the power capacity and energy capacity of the energy storage system as optimization variables and optimizing the revenue, payback period and net present value as multi-objective functions.

[0059] The inner model is optimized based on the particle swarm optimization algorithm, which includes taking the SOC at the end of each period in a typical day as the optimization variable and maximizing the daily profit as the objective function.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows: the present application takes into account the impact of real-time optimization and scheduling of energy storage systems on typical days on the energy storage capacity demand on the user side. The amount of calculation of the inner optimization is reduced by clustering load data to obtain typical days and weights. At the same time, the amount of calculation of the inner optimization model is further reduced by optimizing the SOC during the period. The computational cost of the energy storage capacity optimization method can be reduced, the real-time feasibility of the method can be improved, and a guiding basis can be provided for the storage allocation on the user side. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 This is a flow chart of a method according to an embodiment of the present application. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0064] The terms "comprises," "comprising," or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0065] The terms "first", "second", etc. are only used to distinguish one entity or operation from another entity or operation, and should not be understood as indicating or implying relative importance, nor should they be understood as requiring or implying any such actual relationship or order between these entities or operations.

[0066] See also Figure 1 The present invention provides an implementation method of a multi-objective energy storage capacity double-layer optimization configuration method for power users, comprising the following steps:

[0067] Step 1: Collect historical load data, electricity price information, and battery technical and economic parameters of power users to prepare for energy storage capacity configuration optimization;

[0068] Step 2: Cluster the monthly load data using the density-based spatial clustering of applications with noise (DBSCAN);

[0069] Step 3: Based on the center point of each cluster, determine the most representative load curve in each class as the typical day of that class;

[0070] Step 4: In the optimization model, establish an energy storage battery model based on the hard physical requirements of the battery, including building performance constraints and operation constraints.

[0071] Step 5: The outer optimization model is to achieve monthly energy storage capacity optimization. This model uses the power capacity and energy capacity of energy storage as optimization variables, calculates the benefits, payback period and net present value based on several typical days and the number of days in the monthly load data, and optimizes the multi-objective function through the multi-objective simulated annealing algorithm to obtain the optimal energy storage capacity configuration result, namely P and E;

[0072] Step 6: The inner optimization model is to optimize the energy storage capacity based on a typical day. This model uses the battery power status of the energy storage system in different time periods during a typical day as the optimization variable, calculates the benefits based on the typical day load data, and optimizes the objective function through the particle swarm optimization algorithm to obtain the operation results of the energy storage system in a typical day, that is, ;

[0073] Step 7: The energy storage capacity configuration in the inner optimization model is obtained according to the outer optimization model, and the economic target in the outer optimization model is affected by the optimization result of the inner optimization model.

[0074] The objective function of the outer model is:

[0075] ,

[0076] in, is the revenue of the energy storage system in that month; is the weight of monthly income in the total objective function; is the payback period of the energy storage system; is the weight of the battery payback period in the overall objective function; is the net present value of the energy storage system; is the weight of the net present value of the energy storage system in the overall objective function;

[0077] ,

[0078] in, The cost saved by operating the energy storage system for the user in that month; is the initial investment cost of the energy storage system; is the assumed service life of the energy storage system;

[0079] ,

[0080] ,

[0081] in The annual cost savings after operating the energy storage system for the user;

[0082] ,

[0083] in, The net present value of the energy storage system over its entire life cycle; The service life of the energy storage system; is the discount rate, which is usually determined based on the bank loan interest rate of the year; is the age of the energy storage system;

[0084] ,

[0085] in, is the battery power capacity; is the unit power capacity cost of the energy storage battery system, is the battery energy capacity; is the unit energy capacity cost of the energy storage battery system;

[0086] ,

[0087] in, The total monthly electricity bill of the user before allocation and storage; It is the total monthly electricity bill of the user after allocation and storage.

[0088] The total monthly electricity fee before and after the allocation and storage is composed of the kilowatt-hour electricity fee and the demand electricity fee, and the calculation formula is:

[0089] ,

[0090] in, The monthly electricity bill; The monthly demand electricity fee.

[0091] The original electricity fee is calculated by the electricity price and the original hourly load. The electricity fee after storage is obtained by the electricity price, the typical day load after storage and the weight of the typical day. The calculation formula is:

[0092] ,

[0093] in, is the number of days of monthly load; is the duration of the daily load; Real-time electricity prices for users; For users in The real-time electricity consumption of the day, where the superscript 1 represents the original load before the energy storage system is used; is the number of categories for a typical day; For user Real-time electricity consumption on a typical day; Indicates The number of days that are typical days.

[0094] The calculation formula of the demand electricity fee is:

[0095] ,

[0096] in, It is the demand electricity charge; is the capacity electricity fee; is the demand price; is the capacity price; Indicates the transformer capacity.

[0097] The objective function of the inner model is the difference in typical daily total electricity costs before and after energy storage operation scheduling, which is specifically expressed as:

[0098] ,

[0099] Among them, the superscripts 1 and 2 represent the scenes before and after energy storage, respectively; The total electricity cost of the user on a typical day before the energy storage operation; The total electricity cost of the user on a typical day after the energy storage is in operation;

[0100] The total electricity cost for a typical day of energy storage operation dispatch is:

[0101] ,

[0102] in, It is the real-time electricity consumption of users on a typical day.

[0103] The inner optimization model must meet the energy storage operation constraints, including:

[0104] (a) The battery charge and discharge capacity, charge and discharge power, and remaining battery capacity of the energy storage system at each time period are:

[0105] ,

[0106] in, The amount of electricity changed by the energy storage system is positive during charging and negative during discharging; is the self-discharge coefficient of the energy storage system; For energy storage systems The remaining power at any time; For batteries The charging and discharging power at the moment; For energy storage systems The state of charge at the moment is also the optimization variable of the inner model;

[0107] (b) The real-time state of charge constraint of the energy storage system is:

[0108] ,

[0109] in, is the lower limit of the state of charge of the energy storage system; is the upper limit of the state of charge of the energy storage system;

[0110] (c) Real-time power capacity constraints of energy storage systems:

[0111] ,

[0112] in, is the maximum discharge power of the energy storage system; The maximum charging power for the energy storage system;

[0113] (d) Real-time energy capacity constraints of energy storage systems:

[0114] ,

[0115] in, is the discharge depth of the battery.

[0116] The SOC value of the battery in each time period obtained based on the optimization is used to determine the real-time load condition of the user after energy storage. The specific formula is as follows:

[0117] ,

[0118] in, It is the real-time electricity consumption of users on a typical day.

[0119] The typical day of the user is obtained by clustering the original monthly load data through DBSCAN, which can not only represent the overall characteristics of the user's load, but also reduce the amount of inner optimization calculation. The load of each typical day is the mean of the load curve of this category, and the corresponding weight is the number of load days of this category.

[0120] The outer model is based on the simulated annealing algorithm for calculation and optimization. Specifically, it takes the power capacity and energy capacity of the energy storage system as optimization variables, and optimizes the benefits, payback period, and net present value as multi-objective functions. For the simulated annealing algorithm, the appropriate initial temperature and cooling rate are set to obtain the optimal energy storage capacity configuration.

[0121] The inner model is calculated and optimized based on the particle swarm optimization algorithm. Specifically, the SOC at the end of each period in a typical day is used as the optimization variable, and the daily profit maximization is used as the objective function. The particle swarm optimization algorithm is set with appropriate hyperparameters such as the number of particles, inertia weight, acceleration factor, etc., so as to obtain the optimal scheduling result.

[0122] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A multi-objective dual-layer optimization configuration method for energy storage capacity for power users, characterized in that: The following steps are involved: Step 1: Collect historical load data, electricity price information, and battery technical and economic parameters of power users to prepare for energy storage capacity configuration optimization; Step 2: Cluster the monthly load data using density-based spatial clustering with noise; Step 3: Based on the center point of each cluster, determine the most representative load curve in each class as the typical day of that class; Step 4: Establish an energy storage battery model based on the hard physical requirements of the battery, including building performance constraints and operation constraints; Step 5: The outer optimization model uses the power capacity and energy capacity of energy storage as optimization variables, calculates the benefits and payback period based on several typical days and the number of days in the monthly load data, and optimizes the multi-objective function through the multi-objective simulated annealing algorithm to obtain the optimal energy storage capacity configuration results, the power capacity P of energy storage and the energy capacity E of energy storage; Step 6: The inner optimization model uses the battery power status of the energy storage system in different time periods during a typical day as the optimization variable , based on typical day load data, calculate the benefits, optimize the objective function through the particle swarm optimization algorithm, and obtain the operation results of the energy storage system in a typical day; Step 7: The energy storage capacity configuration in the inner optimization model is obtained according to the outer optimization model, and the economic objectives in the outer optimization model are affected by the optimization results of the inner optimization model.

2. According to claim 1, a multi-objective based double-layer optimization configuration method for energy storage capacity for power users is characterized in that: The objective function of the outer model is: ， in, is the revenue of the energy storage system in that month; is the weight of monthly income in the total objective function; is the payback period of the energy storage system; is the weight of the battery payback period in the overall objective function; is the net present value of the energy storage system; is the weight of the net present value of the energy storage system in the overall objective function; ， in, The cost saved by operating the energy storage system for the user in that month; is the initial investment cost of the energy storage system; is the assumed service life of the energy storage system; ， ， in The annual cost savings after operating the energy storage system for the user; ， in, The net present value of the energy storage system over its entire life cycle; The service life of the energy storage system; is the discount rate, which is usually determined based on the bank loan interest rate of the year; is the age of the energy storage system; ， in, is the battery power capacity; is the unit power capacity cost of the energy storage battery system, is the battery energy capacity; is the unit energy capacity cost of the energy storage battery system; ， in, The total monthly electricity bill of the user before allocation and storage; It is the total monthly electricity bill of the user after allocation and storage.

3. The method for optimizing the energy storage capacity based on multiple objectives for power users according to claim 2 is characterized in that: The total monthly electricity fee before and after the allocation and storage is composed of the kilowatt-hour electricity fee and the demand electricity fee, and the calculation formula is: ， in, The monthly electricity bill; The monthly demand electricity fee.

4. The method for optimizing the energy storage capacity based on multiple objectives for power users according to claim 3 is characterized in that: The original electricity fee is calculated by the electricity price and the original hourly load. The electricity fee after storage is obtained by the electricity price, the typical day load after storage and the weight of the typical day. The calculation formula is: ， in, is the number of days of monthly load; is the duration of the daily load; Real-time electricity prices for users; For users in The real-time electricity consumption of the day, where the superscript 1 represents the original load before the energy storage system is used; is the number of categories for a typical day; For user Real-time electricity consumption on a typical day; Indicates The number of days that are typical days.

5. The method for optimizing the energy storage capacity based on multiple objectives for power users according to claim 3 is characterized in that: The calculation formula of the demand electricity fee is: ， in, It is the demand electricity charge; is the capacity electricity fee; is the demand price; is the capacity price; Indicates the transformer capacity.

6. The method of energy storage capacity dual-layer optimization configuration based on multiple objectives for power users according to claim 1 is characterized in that: The objective function of the inner model is the difference in typical daily total electricity costs before and after energy storage operation scheduling, which is specifically expressed as: ， Among them, the superscripts 1 and 2 represent the scenes before and after energy storage, respectively; The total electricity cost of the user on a typical day before the energy storage operation; The total electricity cost of the user on a typical day after the energy storage is in operation; The total electricity cost for a typical day of energy storage operation dispatch is: ， in, It is the real-time electricity consumption of users on a typical day.

7. The method of energy storage capacity dual-layer optimization configuration based on multiple objectives for power users according to claim 1 is characterized in that: The inner optimization model must meet the energy storage operation constraints, including: (a) The battery charge and discharge capacity, charge and discharge power, and remaining battery capacity of the energy storage system at each time period are: ， in, The amount of electricity changed by the energy storage system is positive during charging and negative during discharging; is the self-discharge coefficient of the energy storage system; For energy storage systems The remaining power at any time; For batteries The charging and discharging power at the moment; For energy storage systems The state of charge at the moment is also the optimization variable of the inner model; (b) The real-time state of charge constraint of the energy storage system is: ， in, is the lower limit of the state of charge of the energy storage system; is the upper limit of the state of charge of the energy storage system; (c) Real-time power capacity constraints of energy storage systems: ， in, is the maximum discharge power of the energy storage system; The maximum charging power for the energy storage system; (d) Real-time energy capacity constraints of energy storage systems: ， in, is the discharge depth of the battery.

8. The method of energy storage capacity double-layer optimization configuration based on multiple objectives for power users according to claim 1 is characterized in that: The SOC value of the battery in each time period obtained based on the optimization is used to determine the real-time load condition of the user after energy storage. The specific formula is as follows: ， in, It is the real-time electricity consumption of users on a typical day.

9. The method of energy storage capacity double-layer optimization configuration based on multiple objectives for power users according to claim 1 is characterized in that: The outer model is optimized based on the simulated annealing algorithm, specifically including taking the power capacity and energy capacity of the energy storage system as optimization variables and optimizing the revenue, payback period and net present value as multi-objective functions.

10. The method of energy storage capacity double-layer optimization configuration based on multiple objectives for power users according to claim 1, characterized in that: The inner model is optimized based on the particle swarm optimization algorithm, specifically, the SOC at the end of each period in a typical day is used as the optimization variable and the maximization of daily profit is used as the objective function.

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