Micro-grid multi-objective optimization scheduling method considering electric vehicle and energy storage

Abstract

The invention discloses a micro-grid multi-objective optimization scheduling method considering an electric vehicle and energy storage, and the method comprises the steps: building mathematical models of a photovoltaic power generation device, a wind power generation device and an energy storage device, constructing a cost function, employing an SOC model for energy storage, and setting the upper and lower limits of SOC and the charging and discharging efficiency; modeling the travel behavior of the electric vehicle based on Monte Carlo simulation to generate a load demand of the electric vehicle; and constructing an optimization model with the goal of minimizing energy loss and voltage deviation, and solving under the conditions of power balance constraint, generator capacity constraint, voltage and phase angle constraint, power factor constraint and energy storage SOC constraint. According to the method, accurate optimization scheduling under complex power demand and power generation conditions is realized, and an intelligent micro-grid solution is provided.

Description

Technical Field

[0001] This invention belongs to the field of power system planning and optimal scheduling, and particularly relates to a multi-objective optimal scheduling method for microgrids that considers electric vehicles and energy storage. Background Technology

[0002] In recent years, renewable energy has developed rapidly and been widely used globally. Clean energy sources such as solar photovoltaic and wind power are gradually replacing traditional energy sources, promoting the construction of new power systems and distributed microgrids. However, the volatility and randomness of renewable energy can easily lead to unstable power generation, uneven voltage distribution, and power mismatch, thereby affecting the stability and reliability of power grid operation.

[0003] At the same time, the rapid increase in the adoption rate of electric vehicles (EVs) and their large-scale grid connection have brought new load characteristics and uncertainties, posing challenges to the scheduling and operation of distribution networks. Battery energy storage systems (BES), as an important flexible adjustment resource, can absorb energy during periods of electricity surplus and release energy during peak demand periods, helping to balance renewable energy output and load demand.

[0004] Existing dispatching methods typically consider only a single factor, such as traditional unit economic dispatching or independent optimization of energy storage systems, failing to fully integrate renewable energy output, electric vehicle random charging demand, and the bidirectional regulation capabilities of energy storage systems. This results in insufficient overall system efficiency, significant power losses, difficulty in controlling voltage deviations, and an inability to meet the operational demands of new power systems with a high proportion of renewable energy penetration. Summary of the Invention

[0005] This invention aims to address the lack of coordinated optimization of the load demand of renewable energy, battery storage, and electric vehicles in microgrids in existing technologies. It provides a multi-objective optimization scheduling method for microgrids that considers electric vehicles and energy storage, and a planning and scheduling method that can simultaneously minimize daily energy loss and voltage deviation.

[0006] According to a first aspect of the embodiments of this application, a multi-objective optimization scheduling method for microgrids considering electric vehicles and energy storage is provided, comprising: (1) Establish mathematical models for photovoltaic power generation devices, wind power generation devices, and energy storage devices, construct cost functions, and adopt the SOC model for energy storage, and set upper and lower limits of SOC and charging and discharging efficiency; (2) Model the travel behavior of electric vehicles based on Monte Carlo simulation to generate the load demand of electric vehicles; (3) Construct an optimization model with the goal of minimizing energy loss and voltage deviation, and solve it under the constraints of power balance, generator capacity, voltage and phase angle, power factor and energy storage SOC.

[0007] Further, step (1) includes: (1.1) Establish mathematical models for the power output of photovoltaic power generation devices and wind power generation devices; The power output model of the photovoltaic power generation device is as follows: , In the formula: The output power of the photovoltaic power generation device. The rated power of PV. For the number of solar panels, This represents the actual solar irradiance. Standard solar irradiance, Inverter efficiency Renewable energy efficiency, Ambient temperature, Temperature coefficient For clarity index; The power output model of the wind power generation device is as follows: , In the formula: It is the rated wind power. It is the output power of the wind power generation device. These are the rated wind speed, cut-in wind speed, cut-out wind speed, and actual wind speed, respectively. (1.2) Establish a charge-discharge model for the energy storage device to calculate the maximum and minimum SOC of the battery: , , , Total number of batteries It is the nominal capacity (kWh). It is the maximum depth of discharge. It is the time step. The charging power of the i-th bus in the k-th time period. Let i be the discharge power of the i-th bus in the k-th time period. For discharge efficiency, Charging efficiency, Minimum charge state, Maximum charge state, Let i be the charging state function of the i-th bus. Voltage angle; Based on charging and discharging efficiency, the limits for energy storage and release are determined, including the minimum charging energy that the BES can store. and minimum discharge energy They are respectively: , , Let i be the active power demand of the load on the i-th bus in the k-th time period. Let i be the active power of the power grid at bus i during time period k. For the active power output of a traditional generator on the i-th bus in the k-th hour, express The rated maximum value, express The rated maximum value; (1.3) A generation cost model is constructed using a quadratic generation function: , in, It is the generator fuel cost. It refers to the ON and OFF states of a single electric vehicle battery cell. It is the output power of the i-th bus at the k-th hour. The cost coefficient.

[0008] Further, step (2) includes: (2.1) By modeling the travel behavior of electric vehicles, the characteristics of electric vehicles are represented by probability distribution functions, including but not limited to driving frequency, mileage, and driving duration; (2.2) Based on the modeling results obtained in step (2.1), the load demand of electric vehicles is simulated using Monte Carlo simulation: , in, This refers to the electricity demand of plug-in electric vehicles. This refers to the power demand of the PEV charging station.

[0009] Furthermore, the travel behavior model for electric vehicles is as follows: (i) Traffic frequency model , , in For a single simulated sample, mileage or number of trips is represented by μ, expectation factor, and standard deviation. Shape parameters m and v are determined through statistical analysis of the sample. (ii) PEV mileage modeling , , in The shape parameters m and v are determined through statistical analysis of the samples to represent the mileage of a single simulated sample. (iii) Modeling of PEV driving duration , in This represents the travel time in a single simulated sample. (iv) Probability distribution function of PEV driving duration , Where n is the number of samples, h is the bandwidth, and k is the time period index. (v) PDF of PEVs arrival and departure times As shown below, the Poisson distribution function is used to model arrival and departure times: , in, For time points, This represents the average arrival / departure rate.

[0010] Furthermore, in step (3), the objective function is: , , , , Where TB represents the total benefit. For market benefits, It is the output power of the i-th bus at the k-th hour. The cost coefficient, For the active power output of a traditional generator on the i-th bus in the k-th hour, This represents the voltage amplitude of the i-th bus at time k. Let the conductance of the line admittance between buses i and j be the value at time k. Let n represent the voltage phase angle of the i-th and j-th buses at time k, and n be the total number of buses.

[0011] Furthermore, in step (3), the constraints are as follows: (i) Power balance between active and reactive power , , in, The active power balance of the i-th bus at time k. The active power of the generator on the i-th bus at time k (including conventional units, renewable energy, and energy storage discharge). The active power of the ZIP load section of the i-th bus at time k. The active charging power demand of the electric vehicle at time k on the i-th bus. This represents the voltage magnitude of the i-th and j-th buses at time k. This represents the voltage phase angle of the i-th and j-th buses at time k. Let the conductance of the line admittance between buses i and j be the value at time k. At time k, the susceptance portion of the line admittance between bus i and j. Let be the reactive power balance of the i buses at time k. The reactive power of the generator on the i-th bus at time k. The reactive power demand of the ZIP load on the i-th bus at time k. This refers to the bus cluster where energy storage devices (BES) are installed. This represents the set of all time periods considered in the scheduling / optimization process; (ii) Power generation equation , , in, The i-th bus always has active power injected at time k. The active power output of a conventional generator at bus i, The number of fans installed on bus i. The active power output of a single wind turbine on the i-th bus at time k. The number of photovoltaic units installed on bus i. The active power output of a single photovoltaic panel on the i-th bus at time k is... The total reactive power injection of the i-th bus at time k is The reactive power output of a conventional generator at bus i, The reactive power output of a single wind turbine on the i-th bus at time k is zero. This represents the charging and discharging power of the energy storage at time k. (iii) Power loss , (iv) Voltage and angle limitations: , , in, These are the maximum and minimum allowable values ​​for the bus voltage. These are the maximum and minimum allowable values ​​for the bus phase angle; (v) Capacity limitations of the power generation system: , , in, The generator's maximum / minimum active power output The generator's maximum / minimum reactive power output Busbar assembly of all generators; (vi) Power factor limits: , in, The upper and lower limits of the power factor; (vii) Energy storage constraints: , , , , , in, These represent the upper and lower limits of battery SOC.

[0012] According to a second aspect of the embodiments of this application, a multi-objective optimization scheduling device for microgrids considering electric vehicles and energy storage is provided, comprising: The component and energy storage modeling module is used to establish mathematical models of photovoltaic power generation devices, wind power generation devices, energy storage devices and conventional units. The energy storage adopts the SOC model and sets the upper and lower limits of SOC and the charging and discharging efficiency. The electric vehicle demand load modeling module is used to model the travel behavior of electric vehicles based on Monte Carlo simulation and generate the load demand of electric vehicles. The optimization solution module is used to construct an optimization model with the goal of minimizing energy loss and voltage deviation, and to solve it under the constraints of power balance, generator capacity, voltage and phase angle, power factor, and energy storage SOC.

[0013] According to a third aspect of the embodiments of this application, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the method described in the first aspect.

[0014] According to a fourth aspect of the embodiments of this application, an electronic device is provided, comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the method as described in the first aspect.

[0015] According to a fifth aspect of the embodiments of this application, a computer-readable storage medium is provided that stores computer instructions thereon, which, when executed by a processor, implement the steps of the method as described in the first aspect.

[0016] The technical solution of this invention can be summarized as follows: 1. An optimization model for microgrid generation dispatch is proposed. By mathematically modeling the load demand of solar photovoltaic, wind power, energy storage devices (BES), and electric vehicles, and combining historical data and simulation analysis, the power output, charging status, and load demand of each component are pre-calculated to provide data support for optimized dispatch and maximize system benefits. 2. A real-time optimization method considering electric vehicle load demand and energy storage scheduling is proposed. Based on Monte Carlo simulation (MCS) to simulate the load demand of electric vehicles, the optimal energy storage charging and discharging plan is calculated and selected in real time according to the driving mode of electric vehicles, charging demand and the availability of charging piles, to ensure the stable operation of the microgrid under different load conditions and maximize economic benefits. 3. A multi-objective optimization scheduling method is proposed. Combining nonlinear programming (NLP), this method comprehensively optimizes the generation scheduling, energy storage devices, and electric vehicle load demand by minimizing energy losses and voltage deviations, so as to ensure the efficient and stable operation of the microgrid, while minimizing the system's power loss and voltage fluctuations.

[0017] The technical solutions provided by the embodiments of this application may include the following beneficial effects: As demonstrated by the above embodiments, this application improves system energy efficiency and reduces power loss by comprehensively optimizing energy storage devices (BES), electric vehicle load demand, and renewable energy (RES) generation scheduling. Simultaneously, minimizing voltage deviation ensures stable microgrid operation and enhances system reliability. Accurate modeling of electric vehicle load demand using Monte Carlo simulation (MCS) optimizes electric vehicle charging scheduling and improves load management capabilities. Efficient energy storage device scheduling not only optimizes battery utilization but also extends the lifespan of energy storage devices. Combined with nonlinear programming (NLP) technology, this invention achieves precise optimized scheduling under complex power demand and generation conditions, providing an intelligent microgrid solution.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] Figure 1 This is a flowchart of a multi-objective optimization scheduling method for microgrids that considers electric vehicles and energy storage.

[0021] Figure 2 This is a schematic diagram of the load modeling process for plug-in electric vehicles (PEVs) based on Monte Carlo simulation (MCS).

[0022] Figure 3 This is a comparison chart of the total benefits under different options.

[0023] Figure 4 This is a comparison chart of energy loss under different schemes.

[0024] Figure 5 This is a block diagram of a multi-objective optimization scheduling device for a microgrid that considers electric vehicles and energy storage.

[0025] Figure 6 This is a schematic diagram of an electronic device. Detailed Implementation

[0026] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0027] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0028] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0029] like Figure 1As shown, this application provides a multi-objective optimization scheduling method for microgrids that considers electric vehicles and energy storage. In a microgrid environment, the method comprehensively considers the generation scheduling impact of renewable energy generation, battery energy storage systems, and electric vehicle load demand. The method may include the following steps: (1) Mathematical modeling of components: Establish mathematical models of photovoltaic power generation devices, wind power generation devices, and energy storage devices, construct the energy storage model using the State of Charge (SOC) model, and set upper and lower limits of SOC and charging and discharging efficiency. Specifically, this step may include the following sub-steps: (1.1) Establish mathematical models for the power output of photovoltaic power generation devices and wind power generation devices; Specifically, the power output of a photovoltaic power generation device depends on factors such as solar irradiance and ambient temperature, as shown in the following mathematical model: (1) In the formula: The output power of the photovoltaic power generation device. The rated power of PV. For the number of solar panels, This represents the actual solar irradiance. Standard solar irradiance, Inverter efficiency Renewable energy efficiency, Ambient temperature, Temperature coefficient This refers to the clarity index.

[0030] Using wind turbines as wind power generation devices, their power output depends on factors such as wind speed. The mathematical model is as follows: (2) In the formula: It is the rated wind power. It is the output power of the wind power generation device. These are the rated wind speed, cut-in wind speed, cut-out wind speed, and actual wind speed.

[0031] (1.2) Establish a charge and discharge model for the energy storage device to describe the state of charge (SOC) of the battery. This model calculates the maximum and minimum SOC of the battery and determines the limits of energy storage and release based on the charging and discharging efficiency. In one embodiment, a battery energy storage device (BES) is used, and a lithium-ion battery is used for energy storage. The control parameter is the charge / discharge power limit. For the battery, the minimum and maximum SOC are calculated using formulas (3) and (4), respectively, and the charge / discharge process of battery energy storage is linked to the state of energy using formula (5): (3) (4) (5) Total number of batteries It is the nominal capacity (kWh). It is the maximum depth of discharge. It is the time step. The charging power of the i-th bus in the k-th time period. Let i be the discharge power of the i-th bus in the k-th time period. For discharge efficiency, Charging efficiency, Minimum charge state, Maximum charge state, Let i be the charging state function of the i-th bus. This refers to the voltage angle.

[0032] BES charge / discharge schedule depends on the net available power after power generation meets demand.

[0033] (6) (7) Minimum charging energy stored in BES and minimum discharge energy They are given in (6) and (7) respectively. Let i be the active power demand of the load on the i-th bus in the k-th time period. Let i be the active power of the power grid at bus i during time period k. For the active power output of a traditional generator on the i-th bus in the k-th hour, express The rated maximum value (upper limit). Similarly, based on this, subsequent scheduling optimization calculations are performed, with the optimizer determining the charging and discharging sequence based on available net power plus the objective function.

[0034] (1.3) A power generation cost model is constructed using a quadratic generation function. Specifically, traditional thermal power generation systems use a quadratic cost function for economic dispatch, calculating the optimal fuel cost and power output of generators. This model helps optimize the output power of traditional power generation to reduce costs.

[0035] Economic dispatch takes into account traditional thermal power generation. For typical power generation costs, a quadratic cost function is adopted, as follows (8): (8) in, It is the generator fuel cost. It refers to the ON and OFF states of a single electric vehicle (EV) battery cell. It is the output power of the i-th bus at the k-th hour. The cost coefficient.

[0036] (2) Model the travel behavior of electric vehicles based on Monte Carlo simulation to generate the load demand of electric vehicles; Specifically, such as Figure 2 As shown, this step may include the following sub-steps: (2.1) By modeling the travel behavior of electric vehicles (EVs), probability density functions (PDFs) are used to represent characteristics such as driving frequency, mileage, and driving duration of EVs. These PDFs are based on real data, such as annual PEVs (private passenger vehicle data) data of a certain region, and statistical analysis of 1000 samples was carried out, as shown below.

[0037] It should be noted that the research variables are determined according to the characteristics of the electric vehicle being studied. For example, when studying the driving frequency of electric vehicles, the research variable represents the mileage or number of trips in a single simulated sample; when studying the duration of a trip, the research variable represents the time required for the trip; when studying arrival / departure time, the research variable represents a point in time (usually in hours).

[0038] (i) Traffic frequency model For a single simulated sample, the mileage or number of trips is determined by variables. Maintaining a positive value, the log-normal PDF has been used to model the uncertainty of travel frequency.

[0039] (9) Where μ is the expectation factor and σ is the standard deviation.

[0040] The variance (var) is: (10) in (11) The shape parameters m and v were set to 3 and 0.6, respectively, and were determined through statistical analysis of the samples.

[0041] (ii) PEV mileage modeling The same logic applies as the driving frequency model: (12) The log-normal distribution function has been considered as follows: m = 13.8 and v = 15.6.

[0042] (iii) Modeling of PEV driving duration For the travel time in a single simulated sample, a normal distribution is considered to obtain the travel duration because... Keeping it positive, the expected value μ = 17.6, and the standard deviation σ = 2.93; (13) (iv) PDF of PEV driving duration To avoid assumptions about dates, a kernel distribution function has been implemented for driving duration, as shown below: (14) Where the number of samples (n) is 1000, h = 6.6 is the bandwidth, and k is the time period index. (v) PDF of PEVs arrival and departure times As shown below, the Poisson distribution function is used to model arrival and departure times: (15) in, This represents the average arrival / departure rate.

[0043] The cumulative probability distribution function (CPDF) was used to evaluate 2,400 PEVs, which were then further categorized into four classes or PEV levels. Level 1 included two-wheeled vehicles, electric rickshaws, and tricycles; Level 2 included electric rickshaws or tricycles; Level 3 included medium / four-wheeled vehicles; and Level 4 included heavy vehicles. Driving data was approximated after determining the PDF of consumer travel behavior. Driving behavior was modeled using equations (9) through (15) for a day's schedule. The PDF of PEV driving behavior has been described previously.

[0044] By taking the PEV battery capacity, the maximum and minimum charging times of the PEV were obtained using (17). The load of the PEV charging station was checked for 15-minute time intervals, resulting in a total of 96 15-minute time intervals for a day simulation. On the other hand, it varies with time and consumer daily behavior. The arrival time of each PEV at the charging station is determined by the PEV's start time, end time, and driving duration. MCS creates the PDF by randomly selecting a number between 0 and 1.

[0045] (2.2) Based on the PDF modeling results obtained in step (2.1), the load demand of electric vehicles, mainly the changes in charging demand, is simulated using Monte Carlo simulation. This includes determining the arrival time, charging time, and usage of charging stations for each PEV. The SOC (State of Charge) of the PEV and its charging time are calculated through simulation to ensure the efficiency and time management of the charging process; After modeling is complete, a random number between 0 and 1 is generated to determine when the PEV arrives at the charging station. If the State of Charge (SOC) exceeds the lower limit, the PEV will be charged. The SOC limits for PEVs are as follows: (16) in, It is a plug-in PEV SOC.

[0046] The required time for each PEV has been obtained by fully charging the PEV battery as follows: (17) in, It refers to charging capacity. It refers to the battery capacity of PEV. The total energy storage of a single electric vehicle battery. n is the power of the charging station, and Nev is the number of charging stations. n = 1, 2, ..., Nev.

[0047] Solving (13) and (14) yields the PEV's driving duration. The driving duration essentially determines the energy consumption and arrival / departure times of this trip, thus determining the minimum and maximum charging times. Assume the maximum capacity of the charging station is 100 kWh. The time required for the next PEV to arrive depends on the charging time required for the previous PEV. According to the M / M / s charging theory, based on the availability of the charging stations, PEVs are distributed at each station. When a PEV arrives at a charging station, if the first charging station is unavailable, it will move to the next upcoming charging station. The load of the entire PEV charging station is determined based on the total number of PEVs arriving at the station each day. Therefore, the load demand of the electric vehicle is expressed as follows: (18) in, This refers to the electricity demand of plug-in electric vehicles. This refers to the power demand of the PEV charging station.

[0048] Since the constant impedance (Z), constant current (I), and constant power (P) of the ZIP load model accurately describe the behavior of actual loads, it has been used for analysis. The active and reactive components of the ZIP load have been analyzed from the total apparent ZIP load.

[0049] For the purpose of analysis, voltage-dependent time-varying load models are given as (19) and (20).

[0050] (19) (20) (twenty one) in, and These are the active and reactive components of the ZIP load at the k-th hour of the i-th bus. and These represent the constant impedance, constant current, and constant voltage for the active and reactive components, respectively. It is the skewness of the i-th bus load. It is the voltage at time k on the i-th bus. It is the minimum voltage at time k.

[0051] The simulation of charging stations considers the capacity limitations of the charging stations and the scenario of multiple electric vehicles charging simultaneously. Using the M / M / s queuing theory and the availability of charging stations, the charging time for each electric vehicle is simulated, and the use of charging stations is optimized to reduce energy waste and waiting time during the charging process.

[0052] (3) Optimize the objective function: Construct an optimization model with the goal of minimizing energy loss and voltage deviation, and solve it under the constraints of power balance, generator capacity, voltage and phase angle, power factor and energy storage SOC. Step (3) specifically involves maximizing economic benefits through reasonable scheduling. This includes considering the charging needs of plug-in electric vehicles (PEVs), the generation of renewable energy, and the utilization efficiency of battery energy storage (BES) devices to maximize the overall benefits of the microgrid; reducing power transmission losses and ensuring the effective utilization of energy in the microgrid by optimizing the coordination between generation, storage, and load demand; and minimizing voltage deviations to ensure grid stability by optimizing grid scheduling. Minimizing voltage deviations helps avoid power system instability and ensures that the voltage at each node in the grid is within a safe range.

[0053] The objective is to determine the total cost-effectiveness and the size of the battery energy storage (BES) along with the following for RES-based hybrid systems: The objective function is to maximize the total benefit (TB) to obtain the optimal size of the BES: (twenty two) (twenty three) The total cost (TC) has been minimized to obtain the optimal size of the BES; (twenty four) (25) (26) (27) (28) (29) in, Daily cost of battery energy storage systems Let MCT be the cost at time k on the i-th bus of a traditional generator, and MCT be the operation and maintenance cost of photovoltaic, wind turbine, and energy storage. For market benefits, For market price, For electricity purchase costs, The cost coefficient for photovoltaic and wind turbines, For battery size, Electric vehicle time-of-use electricity revenue, Let t be the usage time at time k, rt be the interest rate, yr be the number of years (investment life cycle), and nb be the number of busbars.

[0054] The market benefit in (23) equals the total electricity price, including the PEV's TOU minus the price of electricity purchased from the grid. Furthermore, TC (24) includes the daily battery cost, generation cost, and the total power generated by the WT, solar PV, conventional generator, and BES, as expressed in equation (25). The total maintenance cost of the WT, PV, and BES is expressed in equation (26). The cost of grid electricity, the PEV-TOU price, and the daily cost of the BES are (27), (28), and (29).

[0055] To minimize daily energy loss (30) and power generation cost (31), the following optimal power output plan is obtained: (30) (31) The voltage deviation (VD) of the system has been minimized as follows (32): (32) (i) The power balance equations for active and reactive power are: (33) (34) in, The active power balance of the i-th bus at time k. The active power of the generator on the i-th bus at time k (including conventional units, renewable energy, and energy storage discharge). The active power of the ZIP load (constant resistance Z, constant current I, constant power P) of the i-th bus at time k. The active charging power demand of the electric vehicle at time k for the i-th bus. This represents the voltage magnitude of the i-th and j-th buses at time k. This represents the voltage phase angle of the i-th and j-th buses at time k. Let be the conductance portion of the line admittance between bus i and j at time k. At time k, the susceptance portion of the line admittance between bus i and j. Let be the reactive power balance of the i buses at time k. The reactive power of the generator on the i-th bus at time k. The reactive power demand of the ZIP load on the i-th bus at time k. This refers to the bus group where energy storage equipment (BES) is installed. This represents the set of all time periods considered in the scheduling / optimization process.

[0056] (ii) The power generation equation is: (35) (36) in, The i-th bus has a total active power injection at time k. The active power output of a conventional generator at bus i. The number of fans installed on bus i. The active power output of a single wind turbine on the i-th bus at time k. The number of photovoltaic units installed on bus i. The active power output of a single photovoltaic cell on the i-th bus at time k. The total reactive power injection of the i-th bus at time k. The reactive power output of a conventional generator at bus i. The reactive power output of a single wind turbine on the i-th bus at time k. This represents the charging and discharging power of the energy storage at time k.

[0057] (iii) Power loss equation: (37) (iv) Voltage and angle limitations: (38) (39) here, The maximum and minimum allowable values ​​of bus voltage. The maximum and minimum allowable values ​​for the bus phase angle.

[0058] (v) Capacity limitations of power generation systems (40) (41) here, The maximum / minimum active power output of the generator. The generator's maximum / minimum reactive power output. The busbar assembly of all generators.

[0059] (vi) Power factor limits (42) here, The upper and lower limits of the power factor.

[0060] (vii) Energy storage constraints (43) (44) (45) (46) (47) Battery SOC upper and lower limits.

[0061] Equation (43) represents the upper and lower limits of the SOC setpoint, while equation (44) represents the charging and discharging power BES constraints in the first hour and the 24th hour when the SOC is set to 90%, which are defined as (45), (46) and (47), respectively.

[0062] PDF has been used to calculate PEV load demand and MCS. MATLAB has been used to calculate PV, WT, and ZIP loads. The remainder has been determined using nonlinear programming (NLP) in the General Algebraic Modeling System (GAMS).

[0063] The baseline scheme (ignoring electric vehicle (EV) stochastic load modeling and battery energy storage (BES) optimization scheduling, employing only normal scheduling of traditional generating units and renewable energy) is compared with Scheme 1 (considering BES charge / discharge model, ignoring EV stochastic charging behavior), Scheme 2 (considering EV (EV) Monte Carlo-based stochastic load model, without adding energy storage system), and Scheme 3 (EV stochastic load modeling, BES charge / discharge optimization, multi-objective optimization (energy loss + voltage deviation), and comprehensive global scheduling of renewable energy + energy storage + EV + traditional generating units). Figure 3 The present invention, namely Scheme 3, shows that its overall benefits are far greater than those of the baseline scheme, Scheme 1, and Scheme 2, such as... Figure 4 The energy loss of Scheme 3 shown is lower than that of Scheme 1 and Scheme 2. Scheme 1 and Scheme 2 are both lower than the benchmark scheme. Therefore, the present invention has advantages.

[0064] Corresponding to the aforementioned embodiments of the multi-objective optimization scheduling method for microgrids that consider electric vehicles and energy storage, this application also provides embodiments of a multi-objective optimization scheduling device for microgrids that consider electric vehicles and energy storage.

[0065] Figure 5 This is a block diagram illustrating a multi-objective optimization scheduling device for a microgrid considering electric vehicles and energy storage, according to an exemplary embodiment. (Refer to...) Figure 5 The device may include: The component and energy storage modeling module 21 is used to establish mathematical models of photovoltaic power generation devices, wind power generation devices, energy storage devices and conventional units. The energy storage adopts the SOC model and sets the upper and lower limits of SOC and the charging and discharging efficiency. Electric vehicle demand load modeling module 22 is used to model the travel behavior of electric vehicles based on Monte Carlo simulation and generate the load demand of electric vehicles. The optimization solution module 23 is used to construct an optimization model with the goal of minimizing energy loss and voltage deviation, and to solve it under the constraints of power balance, generator capacity, voltage and phase angle, power factor, and energy storage SOC.

[0066] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0067] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0068] Accordingly, this application also provides a computer program product, including a computer program / instruction that, when executed by a processor, implements the multi-objective optimization scheduling method for microgrids that considers electric vehicles and energy storage as described above.

[0069] Accordingly, this application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the multi-objective optimization scheduling method for microgrids considering electric vehicles and energy storage as described above. Figure 6 The diagram shown is a hardware structure diagram of any data processing-capable device in a microgrid multi-objective optimization scheduling device considering electric vehicles and energy storage, provided by an embodiment of the present invention. (Except for...) Figure 6 In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0070] Accordingly, this application also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the aforementioned multi-objective optimization scheduling method for microgrids considering electric vehicles and energy storage. The computer-readable storage medium can be an internal storage unit of any data-processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data-processing device, and can also be used to temporarily store data that has been output or will be output.

[0071] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

Claims

1. A multi-objective optimization scheduling method for microgrids considering electric vehicles and energy storage, characterized in that, include: (1) Establish mathematical models for photovoltaic power generation devices, wind power generation devices, and energy storage devices, construct cost functions, and adopt the SOC model for energy storage, and set upper and lower limits of SOC and charging and discharging efficiency; (2) Model the travel behavior of electric vehicles based on Monte Carlo simulation to generate the load demand of electric vehicles; (3) Construct an optimization model with the goal of minimizing energy loss and voltage deviation, and solve it under the constraints of power balance, generator capacity, voltage and phase angle, power factor and energy storage SOC.

2. The method according to claim 1, characterized in that, Step (1) includes: (1.1) Establish mathematical models for the power output of photovoltaic power generation devices and wind power generation devices; The power output model of the photovoltaic power generation device is as follows: ， In the formula: The output power of the photovoltaic power generation device. The rated power of PV. For the number of solar panels, This represents the actual solar irradiance. Standard solar irradiance, Inverter efficiency Renewable energy efficiency, Ambient temperature, Temperature coefficient For clarity index; The power output model of the wind power generation device is as follows: ， In the formula: It is the rated wind power. It is the output power of the wind power generation device. These are the rated wind speed, cut-in wind speed, cut-out wind speed, and actual wind speed, respectively. (1.2) Establish a charge-discharge model for the energy storage device to calculate the maximum and minimum SOC of the battery: ， ， ， Total number of batteries It is the nominal capacity (kWh). It is the maximum depth of discharge. It is the time step. The charging power of the i-th bus in the k-th time period. Let i be the discharge power of the i-th bus in the k-th time period. For discharge efficiency, Charging efficiency, Minimum charge state, Maximum charge state, Let i be the charging state function of the i-th bus. Voltage angle; Based on charging and discharging efficiency, the limits for energy storage and release are determined, including the minimum charging energy that the BES can store. and minimum discharge energy They are respectively: ， ， Let i be the active power demand of the load on the i-th bus in the k-th time period. Let i be the active power of the power grid at bus i during time period k. For the active power output of a traditional generator on the i-th bus in the k-th hour, express The rated maximum value, express The rated maximum value; (1.3) A generation cost model is constructed using a quadratic generation function: ， in, It is the generator fuel cost. It refers to the ON and OFF states of a single electric vehicle battery cell. It is the output power of the i-th bus at the k-th hour. The cost coefficient.

3. The method according to claim 1, characterized in that, Step (2) includes: (2.1) By modeling the travel behavior of electric vehicles, the characteristics of electric vehicles are represented by probability distribution functions, including but not limited to driving frequency, mileage, and driving duration; (2.2) Based on the modeling results obtained in step (2.1), the load demand of electric vehicles is simulated using Monte Carlo simulation: ， in, This refers to the electricity demand of plug-in electric vehicles. This refers to the power demand of the PEV charging station.

4. The method according to claim 3, characterized in that, The travel behavior model for electric vehicles is as follows: (i) Traffic frequency model ， ， in For a single simulated sample, mileage or number of trips is represented by μ, expectation factor, and standard deviation. Shape parameters m and v are determined through statistical analysis of the sample. (ii) PEV mileage modeling ， ， in The shape parameters m and v are determined through statistical analysis of the samples to represent the mileage of a single simulated sample. (iii) Modeling of PEV driving duration ， in This represents the travel time in a single simulated sample. (iv) Probability distribution function of PEV driving duration ， Where n is the number of samples, h is the bandwidth, and k is the time period index. (v) PDF of PEVs arrival and departure times As shown below, the Poisson distribution function is used to model arrival and departure times: ， in, For time points, This represents the average arrival / departure rate.

5. The method according to claim 1, characterized in that, In step (3), the objective function is: ， ， ， ， Where TB represents the total benefit. For market benefits, It is the output power of the i-th bus at the k-th hour. The cost coefficient, For the active power output of a traditional generator on the i-th bus in the k-th hour, This represents the voltage amplitude of the i-th bus at time k. Let the conductance of the line admittance between buses i and j be the value at time k. Let n represent the voltage phase angle of the i-th and j-th buses at time k, and n be the total number of buses.

6. The method according to claim 1, characterized in that, In step (3), the constraints are as follows: (i) Power balance between active and reactive power ， ， in, The active power balance of the i-th bus at time k. The active power of the generator on the i-th bus at time k (including conventional units, renewable energy, and energy storage discharge). The active power of the ZIP load section of the i-th bus at time k. The active charging power demand of the electric vehicle at time k on the i-th bus. This represents the voltage magnitude of the i-th and j-th buses at time k. This represents the voltage phase angle of the i-th and j-th buses at time k. Let the conductance of the line admittance between buses i and j be the value at time k. At time k, the susceptance portion of the line admittance between bus i and j. Let be the reactive power balance of the i buses at time k. The reactive power of the generator on the i-th bus at time k. The reactive power demand of the ZIP load on the i-th bus at time k. This refers to the bus cluster where energy storage devices (BES) are installed. This represents the set of all time periods considered in the scheduling / optimization process; (ii) Power generation equation ， ， in, The i-th bus always has active power injected at time k. The active power output of a conventional generator at bus i, The number of fans installed on bus i. The active power output of a single wind turbine on the i-th bus at time k. The number of photovoltaic units installed on bus i. The active power output of a single photovoltaic panel on the i-th bus at time k is... The total reactive power injection of the i-th bus at time k is The reactive power output of a conventional generator at bus i, The reactive power output of a single wind turbine on the i-th bus at time k is zero. This represents the charging and discharging power of the energy storage at time k. (iii) Power loss ， (iv) Voltage and angle limitations: ， ， in, These are the maximum and minimum allowable values ​​for the bus voltage. These are the maximum and minimum allowable values ​​for the bus phase angle; (v) Capacity limitations of the power generation system: ， ， in, The generator's maximum / minimum active power output The generator's maximum / minimum reactive power output The busbar assembly of all generators; (vi) Power factor limits: ， in, The upper and lower limits of the power factor; (vii) Energy storage constraints: ， ， ， ， ， in, These represent the upper and lower limits of battery SOC.

7. A multi-objective optimization scheduling device for microgrids considering electric vehicles and energy storage, characterized in that, include: The component and energy storage modeling module is used to establish mathematical models of photovoltaic power generation devices, wind power generation devices, energy storage devices and conventional units. The energy storage adopts the SOC model and sets the upper and lower limits of SOC and the charging and discharging efficiency. The electric vehicle demand load modeling module is used to model the travel behavior of electric vehicles based on Monte Carlo simulation and generate the load demand of electric vehicles. The optimization solution module is used to construct an optimization model with the goal of minimizing energy loss and voltage deviation, and to solve it under the constraints of power balance, generator capacity, voltage and phase angle, power factor, and energy storage SOC.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the method as described in any one of claims 1-6.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-6.

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