A multi-objective optimization scheduling method and system for user-supplied emergency power supplies for peak shaving and valley filling

Abstract

This invention relates to a multi-objective optimization scheduling method and system for user-provided emergency power sources for peak shaving and valley filling. It includes: establishing a system architecture; building optimized operation models based on the energy storage characteristics of user-provided emergency power sources and time-of-use pricing based on real-time load changes; establishing a multi-objective optimization scheduling model for emergency power sources for peak shaving and valley filling, with the objectives of minimizing the user's overall load cost and the degradation loss cost of the user-provided emergency power source, and minimizing the peak-valley difference and fluctuation of the overall load; optimizing the user's overall load curve after scheduling; and based on the determined optimization scheduling model, transforming the multi-objective function into a single-objective problem by assigning weights, and using a solver to solve the optimization problem of the user's overall load. This invention not only improves the power dispatch efficiency and power supply reliability of the power system, but also fully taps the potential of user-provided emergency power sources, effectively reducing power supply costs and energy waste.

Description

Technical Field

[0001] This invention relates to the field of energy and power economic optimization dispatching technology, and in particular to a multi-objective optimization dispatching method and system for user-provided emergency power supplies for peak shaving and valley filling. Background Technology

[0002] With economic development and increasing population density, energy demand is growing, leading to increasingly tight power supply. In critical facilities such as data centers, transportation hubs, financial institutions, and industrial parks, seasonal and diurnal variations in power supply and demand, as well as unpredictable emergencies, result in significant peak-to-valley load differences in the power grid. Traditional power dispatching systems often employ uniform dispatching strategies, which struggle to address uneven energy utilization and supply-demand imbalances. Furthermore, the large-scale integration of renewable energy in recent years has exacerbated grid stability issues, posing unprecedented challenges to power dispatching systems. Against this backdrop, proposing a multi-objective optimization dispatching method and system for user-provided emergency power sources, oriented towards peak shaving and valley filling, is particularly important.

[0003] Traditional power dispatching methods often overlook user-side backup emergency power sources, which have enormous potential for grid stability, peak shaving and valley filling, and improved power supply reliability. For example, any power outage can cause irreparable damage to data center servers, lighting and elevators in transportation hubs, settlement systems in financial institutions, and production lines in industrial parks. Therefore, fully utilizing these backup emergency power sources to ensure stable power supply is a pressing issue. However, existing dispatching methods fail to fully utilize these backup emergency power sources, leading to energy waste and low power supply efficiency.

[0004] Furthermore, existing dispatching systems typically consider only a single optimization objective, such as the economic efficiency of power supply. However, in critical infrastructure, actual power dispatching often requires considering multiple objectives simultaneously, such as battery degradation costs and power supply reliability. Single-objective optimization methods are insufficient to meet practical needs; therefore, a dispatching method capable of handling multi-objective optimization problems is required. Summary of the Invention

[0005] The purpose of this invention is to solve the problems of uneven energy utilization, supply and demand imbalance, and insufficient multi-objective optimization capabilities in the existing power dispatching system. It provides a multi-objective optimization dispatching method and system for user-provided emergency power supply for peak shaving and valley filling, which can not only make full use of the potential of user-provided emergency power supply, but also improve the efficiency and power supply reliability of the power dispatching system.

[0006] This invention discloses a multi-objective optimization scheduling method and system for user-supplied emergency power sources for peak shaving and valley filling. Targeting scenarios where user-supplied emergency power sources participate in grid peak shaving and valley filling, it establishes optimized operation models based on both the energy storage characteristics of user-supplied emergency power sources and time-of-use pricing based on real-time load changes. Within the framework of the grid-side time-of-use pricing strategy, it comprehensively considers user-side demand response and emergency power source energy storage mechanisms to establish a multi-objective optimization scheduling model for emergency power sources for peak shaving and valley filling, achieving more efficient, economical, and stable power dispatch.

[0007] The multi-objective optimization scheduling model includes: minimizing the user's overall load cost and the cost of degradation loss of self-provided emergency power supply, and minimizing the peak-valley difference and fluctuation of the overall load. It optimizes the user's overall load curve after scheduling, and comprehensively considers the constraints of emergency power supply state of charge, emergency power supply SOC continuity, emergency power supply charging and discharging power, emergency power supply maximum allowable charging power, battery cycle life, and user power balance. Finally, it integrates the multi-objective functions into a single-objective problem by assigning weights, and uses the Cplex12.10 solver to solve the optimization problem of the user's overall load.

[0008] To achieve the above objectives, the technical solution of the present invention is: a multi-objective optimization scheduling method for user-supplied emergency power supplies for peak shaving and valley filling, comprising the following steps:

[0009] S1. Establish the system architecture, which includes a user microgrid architecture with self-provided emergency power supply and the architecture of each user's self-provided emergency power supply.

[0010] S2. Establish optimized operation models for user-provided emergency power storage characteristics and time-of-use pricing based on real-time load changes, respectively.

[0011] S3. Based on the system architecture proposed in step S1 and the optimized operation model established in step S2, with the goal of minimizing the user's comprehensive load cost and the cost of degradation loss of self-provided emergency power supply, and minimizing the comprehensive load peak-valley difference and fluctuation, establish a multi-objective optimized scheduling model for emergency power supply oriented to peak shaving and valley filling, and optimize the user's comprehensive load curve after scheduling.

[0012] S4. Based on the multi-objective optimization scheduling model for emergency power supply for peak shaving and valley filling determined in step S3, the multi-objective functions are integrated into a single-objective problem by assigning weights, and the optimization problem of the user's comprehensive load is solved using the Cplex12.10 solver.

[0013] Preferably, in step S1, the method for building the system architecture is as follows:

[0014] The aforementioned establishment of a user microgrid architecture with self-provided emergency power supply refers to the creation of a comprehensive microgrid system that integrates user-provided emergency power supplies with other distributed energy sources, forming an energy network that can operate independently or be connected to the grid. Users connect to the external power grid via transformers and tie lines, and also connect to the emergency power supply themselves. Users purchase electricity from the external grid and store and draw energy from the emergency power supply.

[0015] The architecture for establishing user-owned emergency power supplies refers to selecting appropriate emergency power types and capacities based on user power needs and available energy resources, including battery storage systems, fuel cells, and micro gas turbines. Each user's emergency power system is designed and established, including installation, connection, and control systems. The user's power bus is connected to the emergency power supply's AC / DC module, with the AC / DC module's DC terminal connected to the emergency power supply's DC terminal. The battery is connected to the DC bus via the DC / DC module and a DC isolator. If the battery is not fully charged, the unused energy will be stored in the emergency power supply battery, ensuring that users can maintain power supply to critical facilities and guarantee the normal operation of important equipment in the event of a main grid power outage. Furthermore, this architecture considers the different power needs and energy configurations of each user to achieve personalized energy management and dispatch.

[0016] Preferably, in step S2, the user-provided emergency power storage characteristic model includes: an emergency power supply state of charge model, an emergency power supply SOC continuity model, an emergency power supply charge / discharge power model, an emergency power supply maximum allowable charging power model, and a battery cycle life model; wherein:

[0017] The aforementioned emergency power charging and discharging power upper and lower limit model refers to the following: to avoid overcharging or discharging of the emergency power supply and to ensure that the emergency power supply can provide sufficient power at critical moments, its power has certain upper and lower limits during the charging and discharging process, satisfying...

[0018]

[0019]

[0020] In the formula: p i,t P represents the charging and discharging power of emergency power supply i during time period t, in kW. char and P dis These are the maximum charging power and discharging power, respectively, in kW; N SPEP N represents the number of nodes connected to the emergency power supply; N is the total number of nodes.

[0021] The maximum allowable charging power model for the emergency power supply refers to the maximum allowable charging power that the emergency power supply can deliver during the charging process to ensure its safe operation and extend its service life.

[0022]

[0023]

[0024]

[0025] Furthermore, the charging and discharging power of the emergency power supply is limited by the grid's allowable power at any given time, thus meeting the requirements.

[0026]

[0027] In the formula: E i,init The initial electrical energy set for the emergency power supply during time period t, in kWh; η is the charge / discharge efficiency of the emergency power supply battery; E i,des The user's expected electrical energy for emergency power supply i, kWh; SOC i,t E represents the state of charge of emergency power supply i during time period t. i,max E represents the maximum capacity of the emergency power supply i, in kWh. i,t Let t represent the electrical energy of emergency power supply i during time period t, in kWh.

[0028] The emergency power supply state-of-charge model refers to the ratio of the remaining charge to the fully charged charge of the battery in the emergency power supply, which satisfies the following conditions:

[0029]

[0030] Where: SOC i,max With SOC i,min These are the upper and lower limits of the state of charge of emergency power supply i, respectively.

[0031] The emergency power supply SOC continuity model refers to the following: the battery state of charge (SOC) in the emergency power supply changes over time according to the following conditions:

[0032]

[0033] In the formula: p i,char,t With p i,dis,t Let be the charging power and discharging power of emergency power supply i during time period t, respectively, in kW.

[0034] The emergency power supply battery cycle life model refers to the relationship between the number of cycles an emergency power supply battery can withstand after one charge and discharge cycle and its performance, in order to help users better understand the performance and reliability of emergency power supply batteries and thus formulate more reasonable maintenance plans and usage strategies.

[0035]

[0036]

[0037]

[0038] Where: SOC i,min The minimum permissible state of charge for emergency power supply i; ΔSOC i,t The difference between the minimum allowable state of charge of emergency power supply i during time period t and the current state of charge.

[0039] Preferably, in step S2, the time-of-use electricity price based on real-time load changes includes: a user power balance model and a user electricity cost model; wherein:

[0040] The user power balance model refers to optimizing scheduling to achieve peak shaving and valley filling effects for user-provided emergency power supplies while meeting electricity demand, thereby improving the stability and economy of the power system. The balance between the power output of user-provided emergency power supplies and electricity demand satisfies...

[0041]

[0042]

[0043] In the formula: P base,t For time period t, the user's basic electrical load excluding emergency power supply is expressed in kW and P. SPEP,t The charging and discharging power of the self-contained emergency power supply after integration during time period t is expressed in kW; P t The total load of users during time period t is in kW.

[0044] The user electricity cost model refers to reducing user electricity costs and improving the economic efficiency of the power system through optimized scheduling. The relationship between user electricity costs and factors such as electricity demand and the operational status of backup emergency power supplies satisfies...

[0045] C(P t )=aP t +b

[0046]

[0047] In the formula: C(P) t ) is; a and b are the increased cost and additional cost per unit charge / discharge power, respectively; C user The total cost of the user's overall load is in yuan; C user,t Let be the comprehensive load cost for the user at time t, expressed in yuan.

[0048] Preferably, in step S3, the objective function for the charging and discharging optimization scheduling of the model includes: minimizing the overall user electricity load cost and the cost of degradation loss of the backup emergency power supply battery, and minimizing the overall load peak-to-valley difference and fluctuation, wherein:

[0049] Minimizing the overall user electricity load cost and the degradation cost of the backup emergency power battery refers to minimizing the overall user electricity load cost and the degradation cost of the backup emergency power battery in the multi-objective optimization scheduling model for peak shaving and valley filling. The degradation cost of the backup emergency power battery refers to the performance degradation and loss cost caused by the increased number of charge-discharge cycles during use.

[0050]

[0051]

[0052]

[0053] In the formula: F1 is the comprehensive load and emergency power battery degradation cost for the user within one scheduling cycle, in yuan; C i,SPEP,t Let be the battery degradation cost of emergency power supply i at time t, in yuan; c is the battery degradation rate. Let C be the charge / discharge capacity of the emergency power supply at time t (i cycles), in kWh. change The cost of battery replacement is [amount in yuan].

[0054] The lowest combined load peak-to-valley difference and fluctuation refers to the existence of a certain difference between the peak and valley periods of electricity load in a power system, i.e., the peak-to-valley difference. Simultaneously, low electricity load fluctuation indicates higher power system stability.

[0055] F2=min[max(P t )-min(P t )]

[0056]

[0057]

[0058] In the formula: F2 is the peak-to-valley difference of the user's comprehensive load, kW; F3 is the fluctuation value of the user's comprehensive load, kW; P ave The average total load for users, in kW.

[0059] Preferably, in step S4, the objective function considers both the overall user load and the cost of battery degradation, as well as minimizing the peak-to-valley difference and load fluctuation during peak shaving and valley filling. This constitutes a multi-objective constraint. Finally, through weighting and simplification, the multi-objective problem is integrated into a single-objective problem for solution.

[0060]

[0061]

[0062] F 2M =max(P t )-min(P t )

[0063]

[0064] In the formula: F is the overall objective after integrating the three objectives; ω1, ω2, and ω3 are the weight coefficients of the three objectives, respectively; F 1M F 2M F 3M These are the reference values ​​for F1, F2, and F3, respectively.

[0065] The preferred scheduling period is 24 hours, i.e., T=24;

[0066] Preferably, the unit time period of the scheduling cycle time period set is 1 hour, i.e., Δt = 1.

[0067] The present invention also provides a multi-objective optimization scheduling system for user-supplied emergency power sources for peak shaving and valley filling, including a memory, a processor, and computer program instructions stored in the memory and executable by the processor. When the processor executes the computer program instructions, it can implement the method steps described above.

[0068] Compared to existing technologies, this invention offers the following advantages: By collecting grid load data and user-provided emergency power supply data, this invention constructs a multi-objective optimization model and employs a multi-objective optimization algorithm to solve it, thereby achieving peak shaving and valley filling scheduling of user-provided emergency power supplies. This method and system can simultaneously consider multiple objectives, such as cost and power supply reliability, improving the efficiency and reliability of the power dispatching system. Furthermore, it fully utilizes the potential of user-provided emergency power supplies, reducing energy waste and power supply costs. Attached Figure Description

[0069] Figure 1 This is a diagram illustrating the state transition characteristics of the user-provided emergency power supply in the example.

[0070] Figure 2 This is the system runtime framework in the example;

[0071] Figure 3 The charging and discharging power curves of the emergency power supply in the example;

[0072] Figure 4 The examples show the state-of-charge curves of each emergency power source.

[0073] Figure 5 The curves show the comparison of user load before and after optimization in the example. Detailed Implementation

[0074] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0075] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0076] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0077] This invention provides a multi-objective optimization scheduling method for user-provided emergency power supplies for peak shaving and valley filling, comprising the following steps:

[0078] S1. Establish the system architecture, which includes a user microgrid architecture with self-provided emergency power supply and the architecture of each user's self-provided emergency power supply.

[0079] S2. Establish optimized operation models for user-provided emergency power storage characteristics and time-of-use pricing based on real-time load changes, respectively.

[0080] S3. Based on the system architecture proposed in step S1 and the optimized operation model established in step S2, with the goal of minimizing the user's comprehensive load cost and the cost of degradation loss of self-provided emergency power supply, and minimizing the comprehensive load peak-valley difference and fluctuation, establish a multi-objective optimized scheduling model for emergency power supply oriented to peak shaving and valley filling, and optimize the user's comprehensive load curve after scheduling.

[0081] S4. Based on the multi-objective optimization scheduling model for emergency power supply for peak shaving and valley filling determined in step S3, the multi-objective functions are integrated into a single-objective problem by assigning weights, and the optimization problem of the user's comprehensive load is solved using the Cplex12.10 solver.

[0082] The following is a detailed implementation process of the present invention.

[0083] Please see Figures 1 to 4 The present invention includes: a multi-objective optimization scheduling method for user-supplied emergency power supplies for peak shaving and valley filling, comprising the following steps:

[0084] S1. Establish the system architecture, which includes a user microgrid architecture with self-provided emergency power supply and the architecture of each user's self-provided emergency power supply.

[0085] S2. Establish optimized operation models for user-provided emergency power storage characteristics and time-of-use pricing based on real-time load changes, respectively.

[0086] S3. Based on the system architecture proposed in step S1 and the operation model established in step S2, a multi-objective optimization scheduling model for emergency power supply oriented towards peak shaving and valley filling is established, with the objectives of minimizing the user's comprehensive load cost and the degradation loss cost of self-provided emergency power supply, and minimizing the comprehensive load peak-valley difference and fluctuation. The optimized user comprehensive load curve is shown in the figure.

[0087] S4. Based on the optimization scheduling model determined in step S3, the multi-objective functions are integrated into a single-objective problem by assigning weights, and the optimization problem of the user's comprehensive load is solved using the Cplex12.10 solver.

[0088] First, in step S1, the method for building the system architecture is as follows:

[0089] The aforementioned establishment of a user microgrid architecture with self-provided emergency power supply refers to the creation of a comprehensive microgrid system that integrates user-provided emergency power supplies with other distributed energy sources, forming an energy network that can operate independently or be connected to the grid. Users connect to the external power grid via transformers and tie lines, and also connect to the emergency power supply themselves. Users purchase electricity from the external grid and store and draw energy from the emergency power supply.

[0090] The architecture for establishing user-owned emergency power supplies refers to selecting appropriate emergency power types and capacities based on user power needs and available energy resources, including battery storage systems, fuel cells, and micro gas turbines. Each user's emergency power system is designed and established, including installation, connection, and control systems. The user's power bus is connected to the emergency power supply's AC / DC module, with the AC / DC module's DC terminal connected to the emergency power supply's DC terminal. The battery is connected to the DC bus via the DC / DC module and a DC isolator. If the battery is not fully charged, the unused energy will be stored in the emergency power supply battery, ensuring that users can maintain power supply to critical facilities and guarantee the normal operation of important equipment in the event of a main grid power outage. Furthermore, this architecture considers the different power needs and energy configurations of each user to achieve personalized energy management and dispatch.

[0091] Secondly, in step S2, the user-provided emergency power storage characteristic model includes: emergency power state of charge model, emergency power SOC continuity model, emergency power charge / discharge power model, emergency power maximum allowable charging power model, and battery cycle life model; wherein:

[0092] The aforementioned emergency power charging and discharging power upper and lower limit model refers to the following: to avoid overcharging or discharging of the emergency power supply and to ensure that the emergency power supply can provide sufficient power at critical moments, its power has certain upper and lower limits during the charging and discharging process, satisfying...

[0093]

[0094]

[0095] In the formula: p i,t P represents the charging and discharging power of emergency power supply i during time period t, in kW. char and P dis These are the maximum charging power and discharging power, respectively, in kW; N SPEP N represents the number of nodes connected to the emergency power supply; N is the total number of nodes.

[0096] The maximum allowable charging power model for the emergency power supply refers to the maximum allowable charging power that the emergency power supply can deliver during the charging process to ensure its safe operation and extend its service life.

[0097]

[0098]

[0099]

[0100] Furthermore, the charging and discharging power of the emergency power supply is limited by the grid's allowable power at any given time, thus meeting the requirements.

[0101]

[0102] In the formula: E i,init The initial electrical energy set for the emergency power supply during time period t, in kWh; η is the charge / discharge efficiency of the emergency power supply battery; E i,des The user's expected electrical energy for emergency power supply i, kWh; SOC i,t E represents the state of charge of emergency power supply i during time period t. i,max E represents the maximum capacity of the emergency power supply i, in kWh. i,t Let t represent the electrical energy of emergency power supply i during time period t, in kWh.

[0103] The emergency power supply state-of-charge model refers to the ratio of the remaining charge to the fully charged charge of the battery in the emergency power supply, which satisfies the following conditions:

[0104]

[0105] Where: SOC i,max With SOC i,min These are the upper and lower limits of the state of charge of emergency power supply i, respectively.

[0106] The emergency power supply SOC continuity model refers to the following: the battery state of charge (SOC) in the emergency power supply changes over time according to the following conditions:

[0107]

[0108] In the formula: p i,char,t With p i,dis,t Let be the charging power and discharging power of emergency power supply i during time period t, respectively, in kW.

[0109] The emergency power supply battery cycle life model refers to the relationship between the number of cycles an emergency power supply battery can withstand after one charge and discharge cycle and its performance, in order to help users better understand the performance and reliability of emergency power supply batteries and thus formulate more reasonable maintenance plans and usage strategies.

[0110]

[0111]

[0112]

[0113] Where: SOC i,min The minimum permissible state of charge for emergency power supply i; ΔSOC i,t The difference between the minimum allowable state of charge of emergency power supply i during time period t and the current state of charge.

[0114] Furthermore, in step S2, the time-of-use electricity price based on real-time load changes includes: a user power balance model and a user electricity cost model; wherein:

[0115] The user power balance model refers to optimizing scheduling to achieve peak shaving and valley filling effects for user-provided emergency power supplies while meeting electricity demand, thereby improving the stability and economy of the power system. The balance between the power output of user-provided emergency power supplies and electricity demand satisfies...

[0116]

[0117]

[0118] In the formula: Pbase,t For time period t, the user's basic electrical load excluding emergency power supply is expressed in kW and P. SPEP,t The charging and discharging power of the self-contained emergency power supply after integration during time period t is expressed in kW; P t The total load of users during time period t is in kW.

[0119] The user electricity cost model refers to reducing user electricity costs and improving the economic efficiency of the power system through optimized scheduling. The relationship between user electricity costs and factors such as electricity demand and the operational status of backup emergency power supplies satisfies...

[0120] C(P t )=aP t +b

[0121]

[0122] In the formula: C(P) t ) represents the cost factor and additional cost factor per unit charge / discharge power, respectively; C represents the additional cost factor and additional cost factor per unit charge / discharge power. user The total cost of the user's overall load is in yuan; C user,t Let be the comprehensive load cost for the user at time t, expressed in yuan.

[0123] Then, in step S3, the objective function for the charging and discharging optimization scheduling of the model includes: minimizing the comprehensive user electricity load cost and the cost of degradation loss of backup emergency power battery, and minimizing the comprehensive load peak-valley difference and fluctuation, wherein:

[0124] Minimizing the overall user electricity load cost and the degradation cost of the backup emergency power battery refers to minimizing the overall user electricity load cost and the degradation cost of the backup emergency power battery in the multi-objective optimization scheduling model for peak shaving and valley filling. The degradation cost of the backup emergency power battery refers to the performance degradation and loss cost caused by the increased number of charge-discharge cycles during use.

[0125]

[0126]

[0127]

[0128] In the formula: F1 is the comprehensive load and emergency power battery degradation cost for the user within one scheduling cycle, in yuan; C i,SPEP,t Let be the battery degradation cost of emergency power supply i at time t, in yuan; c is the battery degradation rate. Let C be the charge / discharge capacity of the emergency power supply at time t (i cycles), in kWh. change The cost of battery replacement is [amount in yuan].

[0129] The lowest combined load peak-to-valley difference and fluctuation refers to the existence of a certain difference between the peak and valley periods of electricity load in a power system, i.e., the peak-to-valley difference. Simultaneously, low electricity load fluctuation indicates higher power system stability.

[0130] F2=min[max(P t )-min(P t )]

[0131]

[0132]

[0133] In the formula: F2 is the peak-to-valley difference of the user's comprehensive load, kW; F3 is the fluctuation value of the user's comprehensive load, kW; P ave The average total load for users, in kW.

[0134] Finally, in step S4, the objective function considers both the overall user load and the cost of battery degradation, as well as minimizing the peak-to-valley difference and load fluctuation during peak shaving and valley filling. This constitutes a multi-objective constraint. Finally, through weighting and simplification, the multi-objective problem is integrated into a single-objective problem for solution.

[0135]

[0136]

[0137] F 2M =max(P t )-min(P t )

[0138]

[0139] In the formula: F is the overall objective after integrating the three objectives; ω1, ω2, and ω3 are the weight coefficients of the three objectives, respectively; F 1M F 2M F 3M These are the reference values ​​for F1, F2, and F3, respectively.

[0140] Meanwhile, the scheduling cycle is 24 hours, i.e., T = 24; the unit time period of the scheduling cycle time period set is 1 hour, i.e., Δt = 1;

[0141] The state transition characteristics of the user-provided emergency power supply are shown in the diagram below. Figure 1 As shown. Emergency power supplies only participate in system optimization scheduling during dispatching. When an emergency power supply is connected to the grid, it will switch between the following four states:

[0142] Idle state: The emergency power supply is not connected to the power grid, that is, it is in an idle state. In this example, it is assumed that the emergency power supply is never in an idle state.

[0143] Standby state: The emergency power supply is connected to the power grid, but it is neither charging nor participating in dispatching, i.e., it is in standby state;

[0144] Charging status: When an emergency power supply is connected to the power grid, if the state of charge of emergency power supply i is less than the specified minimum state of charge, then emergency power supply i will not participate in dispatching, i.e., it is in the charging status.

[0145] Dispatch Status: When an emergency power source is connected to the power grid, if the state of charge (SOC) of emergency power source i is not less than the specified minimum SOC, then emergency power source i participates in dispatch, i.e., it is in dispatch status. If, while in dispatch status, its SOC falls below the specified minimum SOC due to participation in dispatch, it transitions from dispatch status to charging status to ensure that emergency power source i has sufficient power for the next use.

[0146] To verify the feasibility of the method and system proposed in this invention, a case study analysis was conducted using user power load and backup emergency power supply data from an industrial park in Fujian Province. The system operation architecture is as follows: Figure 2 As shown in Table 1, the parameter settings are as follows.

[0147] Table 1 System Parameter Settings

[0148] N 7 η 0.9 <![CDATA[N SPEP ]]> 5 a 0.01 <![CDATA[P char ]]> 5 (kW) b 0.01 <![CDATA[P dis ]]> -5(kW) c 0.0157 <![CDATA[E i,max ]]> 48 (kWh) <![CDATA[C change ]]> 41,000 (yuan) <![CDATA[E i,init ]]> 0.35 (kWh) <![CDATA[ω1]]> 0.5 <![CDATA[E i,des ]]> 0.85 (kWh) <![CDATA[ω2]]> 0.25 <![CDATA[SOC i,min ]]> 0.2 <![CDATA[ω3]]> 0.25 <![CDATA[SOC i,max ]]> 0.9

[0149] The external power grid sells electricity to users in the form of peak-flat-valley time-of-use pricing. The peak period is from 6:00 to 11:00 and from 21:00 to 2:00 the next day, the normal period is from 3:00 to 5:00 and from 19:00 to 20:00, and the valley period is from 12:00 to 18:00. The electricity price for each period is shown in Table 2.

[0150] Table 2 Electricity Prices During Peak, Off-Peak, and Valley Periods

[0151]

[0152] The optimization scheduling model was solved using the commercial solver IBM Cplex 12.10 in conjunction with MATLAB R2022b. The results are as follows: Figure 3-5 As shown.

[0153] Depend on Figure 3 It is evident that the emergency power supply's charging periods are concentrated between 3-5 hours and 11-22 hours. During 6-10 hours, the emergency power supply is in a dispatching state, participating in peak shaving and valley filling for user loads. The emergency power supply remains in standby mode during the remaining periods. Figure 4It can be seen that each emergency power supply starts with a state of charge of 0.3-0.4, reaches its peak value after 6-7 hours in the dispatch state, reaches its trough value after 10-12 hours, and finally switches from the dispatch state to the charging state and continues to charge to a state of charge of close to 0.9.

[0154] Depend on Figure 5 As can be seen, after optimization and scheduling by the method and system described in this example, the user's basic load power increases significantly during the off-peak hours of 2h-5h and 11h-20h, and the peak load power is reduced to some extent during 5h-10h. The optimized load curve is smoother than the original load curve, which proves the feasibility and superiority of the method and system proposed in this invention.

[0155] This invention provides a multi-objective optimization scheduling method for user-provided emergency power sources aimed at peak shaving and valley filling. By collecting grid load data and user-provided emergency power source data, a multi-objective optimization model is constructed, and a highly efficient algorithm is used to solve the model, achieving optimized scheduling of user-provided emergency power sources. This method not only improves the efficiency and reliability of the power dispatching system but also fully utilizes the potential of user-provided emergency power sources, effectively reducing energy waste and power supply costs. The technical solution of this invention undoubtedly provides a new solution to existing power dispatching technology problems, possessing broad application prospects and promotional value.

[0156] The present invention also provides a multi-objective optimization scheduling system for user-supplied emergency power sources for peak shaving and valley filling, including a memory, a processor, and computer program instructions stored in the memory and executable by the processor. When the processor executes the computer program instructions, it can implement the method steps described above.

[0157] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor, wherein when the processor executes the computer program instructions, it can implement the method steps described above.

[0158] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0159] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0160] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0161] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0162] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A multi-objective optimization scheduling method for user-supplied emergency power supplies for peak shaving and valley filling, characterized in that, include: S1. Establish the system architecture, which includes a user microgrid architecture with self-provided emergency power supply and the architecture of each user's self-provided emergency power supply. S2. Establish optimized operation models for user-provided emergency power storage characteristics and time-of-use pricing based on real-time load changes, respectively. S3. Based on the system architecture proposed in step S1 and the optimized operation model established in step S2, with the goal of minimizing the user's comprehensive load cost and the cost of degradation loss of self-provided emergency power supply, and minimizing the comprehensive load peak-valley difference and fluctuation, establish a multi-objective optimized scheduling model for emergency power supply oriented to peak shaving and valley filling, and optimize the user's comprehensive load curve after scheduling. S4. Based on the multi-objective optimization scheduling model for emergency power supply for peak shaving and valley filling determined in step S3, the multi-objective function is integrated into a single-objective problem by assigning weights to solve the optimization problem of the user's comprehensive load. In step S1, the method for building the system architecture is as follows: Establish a user microgrid architecture with self-provided emergency power supply: Establish a comprehensive microgrid system that integrates user-provided emergency power supply with other distributed energy sources to form an energy network that can operate independently or be connected to the grid; users connect to the external power grid through transformers and tie lines, and connect to the emergency power supply through their own connections; users purchase electricity from the external power grid and store and retrieve energy from the emergency power supply. Establish an architecture for each user's self-contained emergency power supply: Based on the user's power demand and available energy, select the type and capacity of emergency power supply, including battery storage systems, fuel cells, and micro gas turbines; design and establish each user's emergency power supply system, including the installation, connection, and control system of the power supply; the user's power bus is connected to the AC / DC module of the emergency power supply, and the DC terminal of the AC / DC module is connected to the DC terminal of the emergency power supply; the battery is connected to the DC bus through the DC / DC module and DC isolator; if the battery is not fully charged, the power that the user cannot consume will be stored in the emergency power supply battery, ensuring that in the event of a power outage from the main grid, the user can rely on their self-contained power supply to maintain the power supply of critical facilities and ensure the normal operation of important equipment; in addition, the architecture of each user's self-contained emergency power supply also considers the different power demands and energy configurations of each user to achieve personalized energy management and dispatch.

2. The multi-objective optimization scheduling method for user-supplied emergency power supplies for peak shaving and valley filling as described in claim 1, characterized in that, In step S2, the user-provided emergency power storage characteristic model includes: an emergency power supply state of charge model, an emergency power supply SOC continuity model, an emergency power supply charge / discharge power upper and lower limit model, an emergency power supply maximum allowable charging power model, and an emergency power supply battery cycle life model; wherein, The emergency power supply state-of-charge model refers to the ratio of the remaining charge to the fully charged charge of the battery in the emergency power supply, which satisfies the following conditions: Where: SOC i,max With SOC i,min These are the upper and lower limits of the state of charge of emergency power supply i, respectively; The emergency power supply SOC continuity model refers to the following: the change of battery state of charge (SOC) in the emergency power supply over time satisfies... In the formula: p i,char,t With p i,dis,t These represent the charging power and discharging power of emergency power supply i during time period t, respectively. The aforementioned emergency power charging and discharging power upper and lower limit model refers to the following: to avoid overcharging or discharging of the emergency power supply and to ensure that the emergency power supply can provide sufficient power at critical moments, its power has an upper and lower limit during the charging and discharging process, satisfying the following conditions: In the formula: p i,t P represents the charging and discharging power of emergency power supply i during time period t; char and P dis These are the maximum charging power and the maximum discharging power, respectively; N SPEP N represents the number of nodes connected to the emergency power supply; N is the total number of nodes. The maximum allowable charging power model for the emergency power supply refers to the maximum allowable charging power that the emergency power supply can deliver during the charging process to ensure its safe operation and extend its service life. Furthermore, the charging and discharging power of the emergency power supply is limited by the grid's allowable power at any given time, thus meeting the requirements. In the formula: E i,init The initial electrical energy set for the emergency power supply during time period t; η is the charge / discharge efficiency of the emergency power supply battery; E i,des For emergency power supply i, the user's expected electrical energy; SOC i,t E represents the state of charge of emergency power supply i during time period t. i,max E represents the maximum capacity of the emergency power supply i. i,t The electrical energy of emergency power supply i during time period t; The emergency power battery cycle life model refers to the relationship between the number of cycles an emergency power battery can withstand after one charge and discharge cycle and its performance, satisfying the following conditions: Where: SOC i,min The minimum permissible state of charge for emergency power supply i; ΔSOC i,t The difference between the minimum allowable state of charge of emergency power supply i during time period t and the current state of charge.

3. The multi-objective optimization scheduling method for user-supplied emergency power supplies for peak shaving and valley filling as described in claim 1, characterized in that, In step S2, the time-of-use electricity price based on real-time load changes includes: a user power balance model and a user electricity cost model; wherein: The user power balance model refers to: through optimized scheduling, enabling users' self-provided emergency power supplies to meet electricity demand while achieving peak shaving and valley filling, thereby improving the stability and economy of the power system; and ensuring a balance between the power output of users' self-provided emergency power supplies and electricity demand. In the formula: P base,t For time period t, the user's basic electrical load excluding emergency power supply; P SPEP,t P represents the charging and discharging power of the self-contained emergency power supply after integration during time period t; t The total load of users during time period t; The user electricity cost model refers to: reducing user electricity costs and improving the economic efficiency of the power system through optimized scheduling; and ensuring that the relationship between user electricity costs and factors including electricity demand and the operating status of backup emergency power sources satisfies... C(P t )=aP t +b In the formula: C(P) t ) is; a and b are the increased cost and additional cost per unit charge / discharge power, respectively; C user The total cost of the user's overall load; C user,t Let t be the user's overall load cost at time t.

4. The multi-objective optimization scheduling method for user-supplied emergency power supplies for peak shaving and valley filling as described in claim 1, characterized in that, In step S3, the charging and discharging optimization scheduling objective function of the emergency power supply multi-objective optimization scheduling model for peak shaving and valley filling includes: minimizing the comprehensive load cost of user electricity consumption and the cost of degradation loss of self-provided emergency power supply batteries, and minimizing the comprehensive load peak-valley difference and fluctuation, wherein: Minimizing the overall user electricity load cost and the degradation cost of the backup emergency power supply battery means, in the multi-objective optimization scheduling model for user-provided backup emergency power supplies oriented towards peak shaving and valley filling, minimizing both the overall user electricity load cost and the degradation cost of the backup emergency power supply battery. The degradation cost of the backup emergency power supply battery refers to the performance degradation and loss cost caused by the increased number of charge-discharge cycles during use. In the formula: F1 represents the comprehensive load and emergency power battery degradation cost for the user within one scheduling cycle; C i,SPEP,t Let be the battery degradation cost of emergency power supply i at time t; c is the battery degradation rate. C represents the charge / discharge cycle quantity of the emergency power supply at time t; change Battery replacement costs; The lowest combined load peak-to-valley difference and fluctuation refers to the following: in a power system, there is a certain difference between the peak and valley periods of electricity load, i.e., the peak-to-valley difference; at the same time, the fluctuation of electricity load is also low, indicating that the power system has high stability. F2=min[max(P t )-min(P t )] In the formula: F2 is the peak-to-valley difference of the user's comprehensive load; F3 is the fluctuation value of the user's comprehensive load; P ave This represents the average overall load for users.

5. A multi-objective optimization scheduling method for user-supplied emergency power supplies for peak shaving and valley filling as described in claim 4, characterized in that, In step S4, the multi-objective function considers both the overall user load and the cost of battery degradation from backup power sources, as well as minimizing the peak-valley difference and load fluctuation during peak shaving and valley filling. These are the multi-objective constraints. Finally, through weighting and simplification, the multi-objective problem is integrated into a single-objective problem for solution. F 2M =max(P t )-min(P t ) In the formula: F is the overall objective after integrating the three objectives; ω1, ω2, and ω3 are the weight coefficients of the three objectives, respectively; F 1M F 2M F 3M These are the reference values ​​for F1, F2, and F3, respectively.

6. A multi-objective optimization scheduling method for user-supplied emergency power supplies for peak shaving and valley filling as described in claim 2, characterized in that, The scheduling cycle is 24 hours, i.e., T = 24.

7. A multi-objective optimization scheduling method for user-supplied emergency power supplies for peak shaving and valley filling as described in claim 2, characterized in that, The unit time period of the scheduling cycle time period set is 1 hour, that is, Δt = 1.

8. A multi-objective optimization scheduling method for user-supplied emergency power supplies for peak shaving and valley filling as described in claim 1, characterized in that, In step S4, the Cplex12.10 solver is used to solve the optimization problem of the user's comprehensive load.

9. A multi-objective optimization scheduling system for user-supplied emergency power supplies for peak shaving and valley filling, characterized in that, It includes a memory, a processor, and computer program instructions stored in the memory and executable by the processor, which, when executed by the processor, enable the implementation of the method as described in any one of claims 1-8.

Images