Direct current micro-grid group power protection scheduling method based on mobile energy storage transfer frequency modeling

Abstract

The invention belongs to the technical field of power systems, and particularly relates to a direct-current micro-grid group power protection scheduling method based on mobile energy storage transfer frequency modeling. Comprising the following steps: constructing a mobile energy storage MES model; during the peak period of power demand, the MES flexibly moves through charging and discharging to realize energy scheduling among DCMG groups; a scheduling objective function is constructed, and a scheduling strategy with the purpose of minimizing the total operation cost is achieved; constructing a space-time transfer model of the mobile energy storage MES, and defining transfer time, residence time and frequency constraints; the nonlinear constraint is converted into a mixed integer linear programming problem, and the problem is solved through a commercial solver. According to the method, energy sharing among the DCMGs is realized through the MES, and the DC micro-grid group energy scheduling model integrating the MES under the power limiting condition is established, so that the load interruption cost during the power limiting period is reduced, and the overall elasticity of the micro-grid group is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and particularly relates to a power protection scheduling method for a DC microgrid group based on mobile energy storage transfer frequency modeling, and is particularly applicable to realizing energy sharing and optimal scheduling among multiple DC microgrids (DCMGs) through mobile energy storage (MES) in the case of power shortage. Background Art

[0002] During the peak load period throughout the year in China, the power balance is relatively tight. When extreme weather conditions and low renewable energy generation occur simultaneously, power outages and power rationing measures may be implemented in the park microgrid, resulting in significant load interruptions and economic losses. Therefore, there is an increasing demand to develop microgrid operation strategies to improve their resilience. In the prior art, the scheduling strategies for individual microgrids have been relatively mature, but they only rely on internal resource optimization and are difficult to further enhance the system resilience. Although the coordinated scheduling of microgrid groups has been studied, it mostly focuses on fixed energy storage or energy complementarity, lacking the effective utilization of the dynamic spatio-temporal transfer ability of mobile energy storage (MES), making it difficult to achieve effective energy sharing and scheduling, resulting in low energy utilization efficiency and inability to meet the power demand of microgrids. The development of mobile energy storage technology provides a new idea for solving this problem. Summary of the Invention

[0003] The purpose of the present invention is to provide a power protection scheduling method for a DC microgrid group based on mobile energy storage transfer frequency modeling, to achieve energy sharing among multiple DCMGs through MES, and to establish an energy scheduling model for a DC microgrid group integrating MES under power rationing conditions, so as to reduce the load interruption cost during power rationing and enhance the overall resilience of the microgrid group.

[0004] To solve the above technical problems, the present invention provides a power protection scheduling method for a DC microgrid group based on mobile energy storage transfer frequency modeling, including the following steps:

[0005] Step 1: Construct a mobile energy storage MES model; during the peak power demand period, MES flexibly moves through charging and discharging to achieve energy scheduling among the DC microgrid DCMG groups.

[0006] Step 2: Construct a scheduling objective function to achieve a scheduling strategy with the goal of minimizing the total operating cost.

[0007] Step 3: Construct a spatio-temporal transfer model of the mobile energy storage MES, and define transfer time, residence time, and frequency constraints.

[0008] Step 4: Convert the non-linear constraints into a mixed integer linear programming problem and solve it through a commercial solver.

[0009] Preferably, the specific steps of step one include: defining that three DCMGs are distributed in different spaces to form an equilateral triangle route layout and losing the external power supply; at the same time, wind power generation, photovoltaic power generation and energy storage batteries are installed in each DCMG; the three DCMGs are interconnected through MES dynamic transfer.

[0010] Preferably, the scheduling objective function in step two is set as the following equation:

[0011]

[0012] Among them, F cut represents the load interruption price, represents the load interruption volume of microgrid i at time t, F mes represents the MES charge and discharge price, F battery represents the battery charge and discharge price, and represent the charging and discharging power of MES in microgrid i at time t, while and represent the charging and discharging power of the battery, η e represents the charge and discharge efficiency of MES, η b represents the charge and discharge efficiency of the battery, F trans represents the vehicle transfer cost, D trans represents the number of vehicle transfers, and T represents the scheduling period.

[0013] Preferably, the total operating cost of the DCMG group in step two includes load interruption cost, battery charge and discharge cost, MES charge and discharge cost, and MES transfer cost.

[0014] Preferably, the spatio-temporal transfer model of MES in step three is set as the following equation:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] Among them, equation (2) is used to represent the time correlation between the arrival time, transfer time and stay time; equation (3) indicates that MES can only arrive at one microgrid at any time; equation (4) establishes and The relationship between; Equation (5) and Equation (6) constrain the actual time that the MES stays in Microgrid i; Equation (7) gives and I i,t The relationship between;

[0022] Define T i→j Indicates the normal transfer time of the MES from Microgrid i to Microgrid j; The integer variable Z n Indicates the time when the MES arrives at the microgrid for the nth time; The boolean variable Indicates that the MES arrives at Microgrid j for the nth time; Indicates that the MES stays at Microgrid i when it arrives for the nth time at t hours; I i,t = 1 indicates that the MES stays in Microgrid i at t hours; M is a sufficiently large positive number.

[0023] Preferably, the third step further includes determining the distribution system constraints, and the distribution system constraints include:

[0024] The operating power constraint of the MES, that is, the MES participates in the power regulation of the DCMG group through charge and discharge, and the specific equation is set as follows:

[0025]

[0026]

[0027]

[0028]

[0029]

[0030] Among them, the boolean variables and Indicate that the MES charges and discharges in Microgrid i at time t; and Indicate the charging power and discharging power of the MES at time t; and Are used to mark the maximum charging and discharging power of the MES; Indicates the energy storage state of the MES; Indicates the energy storage capacity of the MES;

[0031] The operating power constraint of the battery, that is, the battery participates in the power regulation of the DCMG group through charge and discharge, and the specific equation is set as follows:

[0032]

[0033]

[0034]

[0035]

[0036]

[0037] Among them, the Boolean variables and represent the charging and discharging of the battery in the microgrid i at time t; and represent the charging or discharging power and the maximum charging or discharging power of the battery; represents the state of charge of the battery; represents the energy storage capacity of the battery;

[0038] The power balance constraint of the microgrid is specifically set as the following equation:

[0039]

[0040]

[0041]

[0042]

[0043] Among them, represents the power demand of the microgrid i at t hours; and represent the wind power and photovoltaic power generation used, respectively; and represent the available wind power generation and photovoltaic power generation, respectively.

[0044] Preferably, the specific steps of step four include:

[0045] Using the big M method to handle the non-linear constraint, namely equation (2), includes the following process;

[0046] By analyzing the Boolean variables and Using the variable to replace, the constraints are as follows:

[0047]

[0048] The non-linear constraint, namely equation (2), can be converted to:

[0049]

[0050] Through the above process, the scheduling model is converted into a mixed integer linear programming problem.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. Improve energy utilization efficiency: Through the flexible scheduling of MES, energy sharing between multiple DC microgrids is achieved, improving the energy utilization efficiency and reducing energy waste.

[0053] 2. Reduce operating costs: Optimize the scheduling strategy to reduce the total operating costs of the DCMG group, including load interruption costs, battery charge and discharge costs, MES charge and discharge costs, and transfer costs, etc.

[0054] 3. Enhance the stability of the microgrid: Through reasonable constraint conditions and algorithm solving, ensure the safe and stable operation of the DC microgrid group and improve the reliability of the microgrid.

[0055] 4. Promote the accommodation of new energy: This method is conducive to promoting the accommodation of new energy such as solar energy and wind energy, and improving the economic operation efficiency of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a schematic flow chart of a power protection scheduling method for a DC microgrid group based on mobile energy storage transfer frequency modeling according to the present invention.

[0057] Figure 2 is a schematic diagram of the transfer mode of the MES of the present invention between DCMGs.

[0058] Figure 3 is a load and renewable energy generation curve diagram of the DCMG of the present invention.

[0059] Figure 4 is a curve diagram of the transfer path and SOC change of the MES of the present invention.

[0060] Figure 5 is a charge and discharge power curve diagram of the MES of the present invention in three DCMGs.

[0061] Figure 6 is a schematic diagram of the scheduling result of the MES of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0062] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. According to the following description, the advantages and features of the present invention will be clearer. It should be noted that the accompanying drawings are all in very simplified forms and use non-precise scales, only for the purpose of conveniently and clearly assisting in explaining the purpose of the embodiments of the present invention.

[0063] As Figure 1 shown, the embodiment of the present invention discloses a power protection scheduling method for a DC microgrid group based on mobile energy storage transfer frequency modeling, including the following steps:

[0064] Step 1: Construct a mobile energy storage MES model; during peak power demand periods, the MES moves flexibly through charging and discharging to achieve energy scheduling between DC microgrid DCMG clusters.

[0065] Step 2: Construct a scheduling objective function to achieve a scheduling strategy with the goal of minimizing the total operating cost.

[0066] Step 3: Construct a spatio-temporal transfer model for the mobile energy storage MES, defining transfer time, residence time, and frequency constraints.

[0067] Step 4: Convert the non-linear constraints into a mixed-integer linear programming problem and solve it using a commercial solver.

[0068] As Figure 2 shown, during peak power demand periods, power supply imbalances may lead to power outages and power rationing. Three DCMGs are distributed in different spaces and have lost external power supply. Wind power generation, photovoltaic power generation, and batteries are installed in the DC microgrid. The MES moves flexibly through charging and discharging to achieve energy scheduling between DCMGs. The specific content of Step 1 includes: defining that three DCMGs are distributed in different spaces to form an equilateral triangle route layout and have lost external power supply; at the same time, wind power generation, photovoltaic power generation, and energy storage batteries are installed in each DCMG; the three DCMGs are interconnected through dynamic transfer of the MES.

[0069] The scheduling objective function in Step 2 is set as the following equation:

[0070]

[0071] Among them, F cut represents the load interruption price, represents the load interruption volume of microgrid i at time t, F mes represents the MES charging and discharging price, F battery represents the battery charging and discharging price, and represent the charging and discharging power of the MES at microgrid i and time t, while and represent the charging and discharging power of the battery, η e represents the charging and discharging efficiency of the MES, η b represents the charging and discharging efficiency of the battery, F trans represents the vehicle transfer cost, D trans represents the number of vehicle transfers, and T represents the scheduling period.

[0072] The total operating cost of the DCMG cluster in Step 2 includes load interruption cost, battery charging and discharging cost, MES charging and discharging cost, and MES transfer cost.

[0073] In the third step, the spatio-temporal transfer model of MES is set as the following equations:

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080] Among them, Equation (2) is used to represent the time correlation between the arrival time, transfer time, and residence time; Equation (3) indicates that MES can only reach one microgrid at any time; Equation (4) establishes the and relationship; Equations (5) and (6) constrain the actual time that MES stays in microgrid i; Equation (7) gives the and I i,t relationship;

[0081] Define T i→j to represent the normal transfer time of MES from microgrid i to microgrid j; the integer variable Z n represents the time when MES arrives at the microgrid for the nth time; the boolean variable represents that MES arrives at microgrid j for the nth time; represents that MES stays at microgrid i for the nth time at t hours; I i,t = 1 indicates that MES stays at microgrid i at t hours; M is a sufficiently large positive number.

[0082] The third step further includes determining the distribution system constraints, and the distribution system constraints include:

[0083] The operating power constraint of MES, that is, MES participates in the power regulation of the DCMG group through charging and discharging, and is specifically set as the following equation:

[0084]

[0085]

[0086]

[0087]

[0088]

[0089] Among them, the Boolean variables and represent the charging and discharging of the MES in microgrid i at time t; and represent the charging power and discharging power of the MES at time t; and are used to mark the maximum charging and discharging power of the MES; represents the energy storage state of the MES; represents the energy storage capacity of the MES;

[0090] The operating power constraint of the battery, that is, the battery participates in the power regulation of the DCMG group through charging and discharging. The specific setting is as follows:

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] Among them, the Boolean variables and represent the charging and discharging of the battery in microgrid i at time t; and represent the charging or discharging power and the maximum charging or discharging power of the battery; represents the energy storage state of the battery; represents the energy storage capacity of the battery;

[0097] The power balance constraint of the microgrid is specifically set as follows:

[0098]

[0099]

[0100]

[0101]

[0102] Among them, represents the power demand of microgrid i at t hours; and represent the wind power and photovoltaic power generation used respectively; and respectively represent available wind power generation and photovoltaic power generation.

[0103] The non - linear constraints (i.e., Equation (2)) and complex Boolean variables make it difficult to directly solve the scheduling model. To solve this problem, Step 4 specifically includes:

[0104] Using the Big - M method to handle the non - linear constraint, i.e., Equation (2), includes the following process;

[0105] By analyzing the Boolean variable and using an auxiliary variable constraints are as follows:

[0106]

[0107] The non - linear constraint, i.e., Equation (2), can be converted to:

[0108]

[0109] Through the above process, the scheduling model is converted into a mixed - integer linear programming problem.

[0110] It also includes the following simulation results and analysis:

[0111] Basic data: The proposed scheduling strategy was tested and verified in three DCMGs. As Figure 2 shown, the distances between the three DCMGs are equal. Figure 3 shows the renewable energy generation and load power, and their values are scaled. The cost of shedding load is 10 yuan / kWh. The upper limit of MES and battery energy storage is 2 MW, and the charge - discharge cost is 0.3 yuan / kW. The scheduling cost for MES to transfer from one DC micro - grid to another is 100 yuan. The scheduling period is 24 hours. The SOC of MES and the battery is allowed to vary between 0 and 1. The initial SOC is set to 0.5, and MES is initially located in DCMG 3.

[0112] The embodiments of the present invention compare the following two cases to highlight the advantages of MES in the power - shedding energy guarantee scheduling of the micro - grid group: Case 1: MES participates in the power - shedding energy guarantee scheduling strategy of the DC micro - grid group. Case 2: The power - shedding energy guarantee scheduling strategy of the DC micro - grid group without MES participation.

[0113] All numerical simulations were coded in MATLAB and solved using Gurobi.

[0114] Energy scheduling results: Based on the simulation data, the power scheduling results of the model can be obtained, and the detailed explanations are as follows: Figure 4shows the transfer path of MES and the SOC change. Assume that it represents the acting direction of the electric vehicle. From Figure 4 it can be seen that MES can transfer between three DCMGs. During the scheduling period, the driving trajectory is 3→1→3→2→3. At the same time, the SOC is always changing between 0 and 1, which means that the charge and discharge behavior of MES is feasible.

[0115] Figure 5 shows the charge and discharge power of MES in three DCMGs. By charging in DCMG 1 and DCMG 2 and discharging in DCMG 3, a reasonable scheduling of power resources is achieved. Figure 6 shows the power balance result of the scheduling model, where positive and negative values represent power supply and demand respectively. By flexibly storing or releasing electrical energy through MES, the hourly power supply and demand are balanced.

[0116] Economic analysis: Table I shows the operating costs during the scheduling period. In Case 1, MES releases the excess power of DCMG 1 and DCMG 2 to DCMG 3, meeting all the power demands of the load in DCMG 3 without cutting off the load. The total cost is 4338.4 yuan. In Case 2, due to the lack of MES participation in power regulation, DCMG 3 has to cut off the excess load, and the total cost is 39176.7 yuan. This shows that MES participation in scheduling has significant economic benefits.

[0117] Table I: Operating costs of Case 1 and Case 2

[0118]

[0119] In summary, the present invention uses mobile energy storage (MES) to achieve energy sharing between multiple DC microgrids. First, a spatio-temporal transfer model of MES based on transfer frequency is established, and flexible charge and discharge constraints of MES are developed. On this basis, an energy scheduling strategy for a group of DC microgrids integrating MES under power rationing is designed, considering battery operation limitations and power balance constraints. This scheduling model is directly solved using a commercial solver. By MES participating in the collaborative optimal scheduling of a group of DC microgrids, the utilization rate of new energy generation is improved, and at the same time, the economic losses caused by common power load cuts are reduced. The simulation results show that the strategy proposed by the present invention has practical feasibility and significant economic benefits.

[0120] The above description is only a description of the preferred embodiments of the present invention, and does not limit the scope of the present invention in any way. Any changes and modifications made by those of ordinary skill in the art of the present invention according to the above disclosure shall fall within the scope of protection of the claims.

Claims

1. A power guarantee scheduling method for a DC microgrid cluster based on mobile energy storage transfer frequency modeling, characterized in that It includes the following steps: Step 1: Construct a mobile energy storage MES model; during peak power demand periods, MES flexibly moves through charging and discharging to achieve energy scheduling between DC microgrid DCMG groups. Step 2: Construct a scheduling objective function to achieve a scheduling strategy with the goal of minimizing the total operating cost. Step 3: Construct a spatio-temporal transfer model of the mobile energy storage MES, and define transfer time, residence time, and frequency constraints. Step 4: Convert the non-linear constraints into a mixed-integer linear programming problem and solve it using a commercial solver.

2. The power protection dispatching method for a DC microgrid group based on mobile energy storage transfer frequency modeling according to claim 1, characterized in that The specific content of Step 1 includes: defining a route layout where three DCMGs are distributed in different spaces to form an equilateral triangle and the external power supply is lost; at the same time, wind power generation, photovoltaic power generation, and energy storage batteries are installed in each DCMG; the three DCMGs are interconnected through dynamic transfer of MES.

3. A power guarantee scheduling method for a DC microgrid group based on mobile energy storage transfer frequency modeling according to claim 1, characterized in that The scheduling objective function in Step 2 is set as the following equation: Among them, F cut represents the load interruption price, represents the load interruption amount of microgrid i at time t, F mes represents the charge and discharge price of MES, F battery represents the charge and discharge price of the battery, and represent the charging and discharging power of MES in microgrid i at time t, while and represent the charging and discharging power of the battery, η e represents the charge and discharge efficiency of MES, η b represents the charge and discharge efficiency of the battery, F trans represents the vehicle transfer cost, D trans represents the number of vehicle transfers, and T represents the scheduling period.

4. The power protection scheduling method for a DC microgrid group based on mobile energy storage transfer frequency modeling according to claim 1, characterized in that The total operating cost of the DCMG group in Step 2 includes load interruption cost, battery charge and discharge cost, MES charge and discharge cost, and MES transfer cost.

5. The power guarantee scheduling method for a DC microgrid group based on mobile energy storage transfer frequency modeling according to claim 1, wherein The spatio-temporal transfer model of MES in Step 3 is set as the following equation: Among them, Equation (2) is used to represent the time correlation among the arrival time, transfer time, and residence time; Equation (3) indicates that the MES can only reach one microgrid at any time; Equation (4) establishes the and relationship; Equations (5) and (6) constrain the actual residence time of the MES in microgrid i; Equation (7) gives the and I i,t relationship; Define T i→j Indicates the normal transfer time of the MES from microgrid i to microgrid j; integer variable Z n Indicates the time when the MES arrives at the microgrid for the nth time; boolean variable Indicates that the MES arrives at microgrid j for the nth time; Indicates that the MES stays at microgrid i for the nth time at t hours; I i,t = 1 indicates that the MES stays at microgrid i at t hours; M is a sufficiently large positive number.

6. The power guarantee scheduling method for a DC microgrid group based on mobile energy storage transfer frequency modeling according to claim 1, characterized in that Step 3 also includes determining distribution system constraints, and the distribution system constraints include: The operating power constraint of MES, that is, MES participates in power regulation of the DCMG group through charging and discharging, and is specifically set as the following equation: Among them, the Boolean variables and represent the charging and discharging of the MES in the microgrid i at time t; and represent the charging power and discharging power of the MES at time t; and are used to mark the maximum charging and discharging power of the MES; represents the energy storage state of the MES; represents the energy storage capacity of the MES; The operating power constraint of the battery, that is, the battery participates in power regulation of the DCMG group through charging and discharging, and is specifically set as the following equation: Among them, the Boolean variables and represent the charging and discharging of the battery in the microgrid i at time t; and represent the charging or discharging power and the maximum charging or discharging power of the battery; represents the state of charge of the battery; represents the energy storage capacity of the battery; The power balance constraint of the microgrid is specifically set as the following equation: Among them, represents the power demand of microgrid i at t hours; and respectively represent the wind power and photovoltaic power generation used; and respectively represent the available wind power generation and photovoltaic power generation.

7. The power protection dispatching method for a DC microgrid group based on mobile energy storage transfer frequency modeling according to claim 1, characterized in that The specific content of Step 4 includes: Using the big M method to handle the non-linear constraint, that is, Equation (2), including the following process; By analyzing Boolean variables and Using an auxiliary variable The constraints are as follows: The non-linear constraint, that is, Equation (2), can be converted to: Through the above process, the scheduling model is converted into a mixed-integer linear programming problem.

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