New energy micro-grid load optimization method for residential buildings

Abstract

The application discloses a new energy micro-grid load optimization method for residential houses, comprising the following steps: step 1, modeling the new energy micro-grid for residential houses; step 2, establishing a residential life electricity load optimization model; and step 3, establishing an electric vehicle charging load optimization model. The application studies the electric vehicle participating in the'vehicle-to-grid' incentive mechanism and the problem of guaranteeing residential electricity in the new energy micro-grid, and constructs a new energy micro-grid load optimization model for residential houses. The residential life electricity load and the electric vehicle charging plan are optimized respectively with the maximization of electric vehicle charging benefits and the maximization of residential electricity satisfaction as the target, so as to pursue the improvement of the reliability of the new energy micro-grid while reducing the residential electricity experience.

Description

Technical Field

[0001] This invention belongs to the field of power system technology, specifically relating to a method for optimizing the load of a residential renewable energy microgrid. Background Technology

[0002] With a large influx of people migrating to cities, urban electricity demand will increase significantly, making the active development of distributed energy systems on the user side essential. The large-scale integration of user-side distributed energy systems will promote the large-scale construction of microgrid subsystems incorporating renewable energy sources. However, given the significant challenges in simulation and data analysis for large-scale user-side renewable energy microgrid systems, focusing on residential renewable energy microgrids can reduce the microgrid size and the complexity of system model operation analysis. This approach enhances the absorption of renewable energy sources while ensuring stable power generation from distributed energy sources and meeting user energy demands.

[0003] Residential electricity demand is related to residents' daily routines. Therefore, when establishing a new energy microgrid model to better serve residential communities, it is necessary to analyze the source-load characteristics of residential buildings and formulate more effective dispatch strategies based on previous research. Previous surveys and studies show that the conventional electricity consumption scenario in residential buildings is for daily living. However, with the popularization of electric vehicles, large-scale smart charging stations have become standard in new residential community planning to ensure that electric car users can enjoy the convenience of home charging.

[0004] The main electricity consumption scenarios in residential life include lighting, bathing, elevators, entertainment, dining, air conditioning, and hot water, involving both rigid and flexible loads in residential buildings. Numerous studies have indicated that by considering load demand response in residential buildings, it is possible to improve residents' electricity consumption patterns, reduce energy consumption and costs, and positively impact residential electricity load through peak shaving and valley filling. However, existing research has not fully considered the uncertainties of load and demand response models. In reality, the load results after residents participate in demand response are uncertain due to a combination of factors, including weather and differences in consumer psychology. Therefore, some scholars have conducted in-depth research on the uncertainties of traditional electricity load demand response. However, in current research on the uncertainty of flexible load demand response, many scholars often overlook the importance of striking a balance between reducing user experience losses on the demand side and improving system operational reliability.

[0005] As a crucial load in residential renewable energy microgrids, the integration of electric vehicles (EVs) presents new challenges to power dispatching. Currently, many scholars both domestically and internationally have conducted in-depth research on the dispatching issues following EV integration into renewable energy microgrids. Implementing vehicle-to-grid (V2G) charging and discharging dispatching technology plays a vital role in improving the economic efficiency of renewable energy microgrids; however, current research rarely focuses on the interests of EVs themselves, which is crucial for improving residents' electricity experience. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a load optimization method for residential new energy microgrids. This method analyzes the electricity consumption characteristics of residential buildings and proposes corresponding solutions to various problems encountered during residential electricity consumption, thereby improving the reliability of system operation and enhancing the residents' electricity experience.

[0007] The present invention adopts the following technical solution:

[0008] A method for optimizing the load of a residential renewable energy microgrid, with the specific design scheme as follows:

[0009] Step 1: Model the residential renewable energy microgrid;

[0010] Step 2: Establish an optimization model for residential electricity load;

[0011] Step 3: Establish an electric vehicle charging load optimization model.

[0012] Furthermore, the residential new energy microgrid structure in step 1 is as follows: the source side includes gas turbines, photovoltaic generators, and energy storage devices; the load side consists of the residential basic electrical load and the electric vehicle charging load of grid-connected smart charging piles; in addition, the new energy microgrid can also meet the system's power supply and demand balance by buying and selling electricity from the upper-level grid.

[0013] The conventional electricity consumption scenario in residential buildings is residential electricity use, which mainly includes lighting, bathing, elevators, entertainment, dining, air conditioning, and hot water, involving both rigid and flexible loads of the building. However, with the increasing popularity of electric vehicles, large-scale smart charging stations have become standard in modern residential community planning to ensure electric vehicle users can enjoy the convenience of home charging. Therefore, this invention divides residential electricity consumption scenarios into residential electricity use and electric vehicle charging.

[0014] Furthermore, the residential electricity load optimization model in step 2 is specifically as follows:

[0015] The transferable load demand response model describes how users change their electricity consumption periods in response to the time-of-use pricing strategy of microgrid electricity sales; however, due to the diversity of user consumption habits, the degree of user response is uncertain. This uncertainty is reflected in the load transfer rate, which is the proportion of load transferred from high-price periods to low-price periods. The load transfer rate is an important indicator reflecting the degree of user response, as detailed below:

[0016]

[0017]

[0018] in, and These represent the upper and lower limits of the response invalid zone threshold, response extreme zone threshold, and maximum load transfer rate for the demand response model of transferable load under different time periods.

[0019] After obtaining all possible load transfer rates for the three different time periods from the above formula, the load for each time period after transfer is:

[0020]

[0021] in, and The electrical load corresponding to time period t before and after the implementation of the demand response strategy; and To calculate the average electrical load during peak and off-peak periods before demand response; t g t p and t f These represent the off-peak, average, and peak periods of residential electricity load, respectively.

[0022] By establishing an uncertainty model for demand response of transferable loads, and based on the time-of-use (TOU) electricity price of the renewable energy microgrid, the load distribution range after demand response can be obtained. On this basis, to balance the relationship between demand-side user experience loss and system operational reliability, this invention proposes a multi-objective optimization model based on TOU pricing, aiming to balance system operational reliability and user experience loss, thereby seeking a reasonable TOU electricity price.

[0023] The first objective of the multi-objective optimization model based on time-of-use pricing is to minimize the square of the load peak-to-valley difference, thereby improving the peak-shaving and valley-filling effect and the operational stability of the renewable energy microgrid. The second objective is to maximize residential electricity satisfaction, taking into account both the subjective willingness and objective benefits of users participating in demand response. This model assumes that user satisfaction is inversely proportional to load changes; that is, when users do not change their electricity consumption habits, the load shift rate for each time period is 0, and the satisfaction rate is 1. When users adjust their electricity consumption habits completely according to the demand response model, the load shift rate for each time period is 1, and the satisfaction rate is 0. The model is shown below:

[0024]

[0025] J max 1-κJ

[0026]

[0027] Where, p g p p and p f These represent the time-of-use electricity prices during off-peak, normal, and peak periods, and they are the decision variables for this multi-objective optimization model. and Let represent the upper and lower limits of the time-of-use electricity price for period i, respectively; κ is the weighting coefficient; κJ represents the normalized index of load change, reflecting the degree of adjustment in users' electricity consumption habits. When the load change in each period reaches its maximum possible value, the maximum value is taken, meaning that users have completely changed their original electricity consumption habits, at which point user satisfaction is 0. The specific form of κJ is as follows:

[0028]

[0029] Furthermore, the electric vehicle charging load optimization model in step 3 is as follows:

[0030] This invention establishes a multi-objective optimization model based on electric vehicle charging and discharging scheduling, aiming to maximize the charging benefits for all electric vehicle owners in residential areas and optimize the charging and discharging load distribution of electric vehicles. The objective function of this model is:

[0031] The first objective function is to maximize the overall satisfaction of electric vehicle owners during the charging process, which is determined by... and The composition is as follows:

[0032]

[0033] in, and Let N represent the satisfaction with charging costs and the satisfaction with travel convenience for the i-th electric vehicle, respectively;ev The number of electric vehicles; κ p and κ c These are the weighting coefficients.

[0034] (1) Satisfaction with charging costs

[0035]

[0036] in, and Let $\mathbf$ and $\mathbf$ represent the maximum and minimum charging costs that the owner of the $i$ electric vehicle must bear during the parking period, respectively. The charging and discharging cost of the i-th electric vehicle is shown below:

[0037]

[0038] Among them, T ev The charging cycle for electric vehicles. Let be the charge / discharge amount of the i-th electric vehicle at time t. A positive value indicates charging, and a negative value indicates discharging; and These represent the charging and discharging states of the i-th electric vehicle at time t, respectively. When the electric vehicle is in charging or discharging mode, the corresponding state variable takes the value of 1. and Let t be the time-of-use electricity price for charging and discharging of electric vehicles.

[0039] (2) Satisfaction with travel convenience

[0040] Satisfaction with travel convenience is related to the state of charge (SOC) of electric vehicle batteries: when an electric vehicle is fully charged as quickly as possible without considering cost factors, the SOC will rapidly reach its upper limit at the maximum growth rate and remain stable. At this time, the sum of the accumulated SOC of the electric vehicle during the parking time will be the maximum, and the travel convenience will also reach its highest level.

[0041]

[0042] in, This represents the state of charge of the i-th electric vehicle at time t; This represents the upper limit of the accumulated state of charge value of the i-th electric vehicle during the parking time when charged at the fastest speed.

[0043] The second objective function considers the stability of the new energy microgrid, with the optimization objective of minimizing the charging power fluctuation of charging piles, in order to reduce the impact on the new energy microgrid and prevent the occurrence of the "tragedy of the commons".

[0044]

[0045] The constraints of this multi-objective optimization model are as follows:

[0046] (1) Constraints on the charging and discharging capacity of electric vehicles

[0047]

[0048] in, and These are the maximum discharge capacity and maximum charging capacity of an electric vehicle, respectively.

[0049] (2) Constraints on the charging and discharging states of electric vehicles

[0050]

[0051] (3) Charge state constraints of electric vehicles

[0052]

[0053]

[0054] in, and These are the upper and lower limits of the electric vehicle's state of charge, respectively; η ev,chr and η ev,dis These represent the charging and discharging efficiency of electric vehicles; E ev This refers to the rated capacity of the electric vehicle battery.

[0055] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0056] (1) This invention analyzes the potential electricity consumption characteristics of residential buildings and establishes a corresponding residential new energy microgrid model based on this.

[0057] (2) This invention takes into account the impact of residential load fluctuations and the uncertainty of flexible load demand response, and proposes a multi-objective optimization model based on time-of-use pricing to alleviate the contradiction between system reliability and residential electricity loss. While pursuing significant load peak shaving and valley filling benefits, it also focuses on the electricity experience of residential users.

[0058] (3) This invention focuses on the charging benefits of electric vehicle owners during the charging process and proposes a multi-objective optimization model for electric vehicle charging load, so as to maximize the charging benefits of electric vehicle owners in residential areas while avoiding the occurrence of the "tragedy of the commons" phenomenon caused by excessive occupation of charging resources. Attached Figure Description

[0059] Figure 1 This is a structural diagram of a residential new energy microgrid in an example of the present invention;

[0060] Figure 2This is a schematic diagram of the load optimization method in an example of the present invention;

[0061] Figure 3 This is a schematic diagram illustrating the uncertainty of demand response to transferable loads in an example of the present invention;

[0062] Figure 4 This is a schematic diagram of the Pareto front solution distribution of the multi-objective optimization model based on time-of-use pricing in an example of the present invention;

[0063] Figure 5 This is a schematic diagram of the electrical load distribution of a new energy microgrid under load demand response in an example of the present invention;

[0064] Figure 6 This is a schematic diagram of the load curve distribution under different electric vehicle charging strategies in an example of the present invention. Detailed Implementation

[0065] The invention is further illustrated below with specific implementation examples and accompanying drawings. This invention proposes a method for optimizing the load of a residential renewable energy microgrid. The structure of the residential renewable energy microgrid is as follows: Figure 1 As shown, to improve the system's operational reliability, a system load optimization strategy was established. The system load optimization strategy process is as follows: Figure 2 As shown, the specific implementation steps are as follows:

[0066] (1) Modeling of new energy microgrids

[0067] Residential building new energy microgrid structure diagram as follows Figure 1 As shown, the source side includes devices such as gas turbines, photovoltaic generators, and energy storage; the load side consists of residential basic electrical loads and electric vehicle charging loads from grid-connected smart charging piles; in addition, the new energy microgrid can also meet the system's power supply and demand balance by buying and selling electricity from the upper-level grid.

[0068] The conventional electricity consumption scenario in residential buildings is residential electricity use, which mainly includes lighting, bathing, elevators, entertainment, dining, air conditioning, and hot water, involving both rigid and flexible loads of the building. However, with the increasing popularity of electric vehicles, large-scale smart charging stations have become standard in modern residential community planning to ensure electric vehicle users can enjoy the convenience of home charging. Therefore, this invention divides residential electricity consumption scenarios into residential electricity use and electric vehicle charging.

[0069] (2) Establish an optimization model for residential electricity load.

[0070] The transferable load demand response model describes how users change their electricity consumption periods in response to time-of-use pricing strategies in microgrid electricity sales; however, the diversity of user consumption habits creates uncertainty in the degree of user response. This uncertainty is reflected in the load transfer rate, which is the proportion of load transferred from high-price periods to low-price periods. The load transfer rate is an important indicator reflecting the degree of user response.

[0071] Figure 3 This demonstrates the characteristics of the uncertainty in demand response to transferable loads, with the horizontal axis Δp. l The vertical axis represents the electricity price difference at different times, λ. l The corresponding load shift rate is represented by 'l', indicating three types of electricity load periods: peak-valley (fg), peak-flat (fp), and flat-valley (pg). Each curve in the figure represents the load shift rate variation of a user within the range of electricity price differences. Each curve can be divided into three regions: the ineffective response region, the linear response region, and the extreme response region. Due to the differences in users' electricity consumption behavior, the ranges of the ineffective region, extreme region, and linear region of each curve are also different.

[0072] like Figure 3 As shown, after determining the electricity price difference Δp0 between two time periods, all possible load transfer rates within the corresponding two time periods can be obtained. The calculation method is as follows:

[0073]

[0074]

[0075] in, and These represent the upper and lower limits of the response invalid zone threshold, response extreme zone threshold, and maximum load transfer rate for the demand response model of transferable load under different time periods.

[0076] After obtaining all possible load transfer rates for the three different time periods from the above formula, the load for each time period after transfer is:

[0077]

[0078] in, and The electrical load corresponding to time period t before and after the implementation of the demand response strategy; and To calculate the average electrical load during peak and off-peak periods before demand response; t g t p and t f These represent the off-peak, average, and peak periods of residential electricity load, respectively.

[0079] By establishing an uncertainty model for demand response of transferable loads, and based on the time-of-use (TOU) electricity price of the renewable energy microgrid, the load distribution range after demand response can be obtained. On this basis, to balance the relationship between demand-side user experience loss and system operational reliability, this invention proposes a multi-objective optimization model based on TOU pricing, aiming to balance system operational reliability and user experience loss, thereby seeking a reasonable TOU electricity price.

[0080] The first objective of the multi-objective optimization model based on time-of-use pricing is to minimize the square of the load peak-to-valley difference, thereby improving the peak-shaving and valley-filling effect and the operational stability of the renewable energy microgrid. The second objective is to maximize residential electricity satisfaction, taking into account both the subjective willingness and objective benefits of users participating in demand response. This model assumes that user satisfaction is inversely proportional to load changes; that is, when users do not change their electricity consumption habits, the load shift rate for each time period is 0, and the satisfaction rate is 1. When users adjust their electricity consumption habits completely according to the demand response model, the load shift rate for each time period is 1, and the satisfaction rate is 0. The model is shown below:

[0081]

[0082] J max 1-κJ

[0083]

[0084] Where, p g p p and p f These represent the time-of-use electricity prices during off-peak, normal, and peak periods, and they are the decision variables for this multi-objective optimization model. and Let represent the upper and lower limits of the time-of-use electricity price for period i, respectively; κ is the weighting coefficient; κJ represents the normalized index of load change, reflecting the degree of adjustment in users' electricity consumption habits. When the load change in each period reaches its maximum possible value, the maximum value is taken, meaning that users have completely changed their original electricity consumption habits, at which point user satisfaction is 0. The specific form of κJ is as follows:

[0085]

[0086] (3) Establish an electric vehicle charging load optimization model

[0087] This invention establishes a multi-objective optimization model based on electric vehicle charging and discharging scheduling, aiming to maximize the charging benefits for all electric vehicle owners in residential areas and optimize the charging and discharging load distribution of electric vehicles. The objective function of this model is:

[0088] The first objective function is to maximize the overall satisfaction of electric vehicle owners during the charging process, which is determined by... and The composition is as follows:

[0089]

[0090] in, and Let N represent the satisfaction with charging costs and the satisfaction with travel convenience for the i-th electric vehicle, respectively; ev The number of electric vehicles; κ p and κ c These are the weighting coefficients.

[0091] (1) Satisfaction with charging costs

[0092]

[0093] in, and Let $\mathbf$ and $\mathbf$ represent the maximum and minimum charging costs that the owner of the $i$ electric vehicle must bear during the parking period, respectively. The charging and discharging cost of the i-th electric vehicle is shown below:

[0094]

[0095] Among them, T ev The charging cycle for electric vehicles. Let be the charge / discharge amount of the i-th electric vehicle at time t. A positive value indicates charging, and a negative value indicates discharging; and These represent the charging and discharging states of the i-th electric vehicle at time t, respectively. When the electric vehicle is in charging or discharging mode, the corresponding state variable takes the value of 1. and Let t be the time-of-use electricity price for charging and discharging of electric vehicles.

[0096] (2) Satisfaction with travel convenience

[0097] Satisfaction with travel convenience is related to the state of charge (SOC) of electric vehicle batteries: when an electric vehicle is fully charged as quickly as possible without considering cost factors, the SOC will rapidly reach its upper limit at the maximum growth rate and remain stable. At this time, the sum of the accumulated SOC of the electric vehicle during the parking time will be the maximum, and the travel convenience will also reach its highest level.

[0098]

[0099] in, This represents the state of charge of the i-th electric vehicle at time t; This represents the upper limit of the accumulated state of charge value of the i-th electric vehicle during the parking time when charged at the fastest speed.

[0100] The second objective function considers the stability of the new energy microgrid, with the optimization objective of minimizing the charging power fluctuation of charging piles, in order to reduce the impact on the new energy microgrid and prevent the occurrence of the "tragedy of the commons".

[0101]

[0102] The constraints of this multi-objective optimization model are as follows:

[0103] (1) Constraints on the charging and discharging capacity of electric vehicles

[0104]

[0105] in, and These are the maximum discharge capacity and maximum charging capacity of an electric vehicle, respectively.

[0106] (2) Constraints on the charging and discharging states of electric vehicles

[0107]

[0108] (3) Charge state constraints of electric vehicles

[0109]

[0110]

[0111] in, and These are the upper and lower limits of the electric vehicle's state of charge, respectively; η ev,chr and η ev,dis These represent the charging and discharging efficiency of electric vehicles; E ev This refers to the rated capacity of the electric vehicle battery.

[0112] To verify the effectiveness of the proposed multi-objective optimization model based on time-of-use pricing, the NSGA-II multi-objective algorithm was used for solving the problem. The NSGA-II multi-objective algorithm has a population size of 50 and a maximum generation count of 100. Figure 4 The Pareto front solution set of a multi-objective model based on time-of-use pricing is presented. Subsequently, the optimal solution is selected from the Pareto front solution using the entropy-weighted TOPSIS method to obtain the time-of-use electricity price for electricity sales. Finally, the range of residential electricity load is obtained through a transferable load demand response model. Figure 5 As shown.

[0113] To better analyze the changes in system performance after implementing the proposed demand response, this invention compares the electrical load optimization at three points in the Pareto chart: high bias point, low bias point, and optimal solution point, as shown in Table 1.

[0114] Table 1. Changes in system performance after implementing demand response.

[0115]

[0116] Table 1 shows the absolute dominance of high-skewness and low-skewness points in individual indicators: the load transfer rate at the high-skewness point is close to zero, reducing the interference of demand response on user experience and reflecting its emphasis on maintaining a good electricity experience for residents; the peak-to-valley difference of the electricity load at the low-skewness point is reduced by about 17.5%, achieving a good peak-shaving and valley-filling effect, but significantly reducing the electricity experience for residents. The objectively selected optimal solution balances the trade-offs and coordination between the two objectives: on the one hand, its result can reduce the peak-to-valley difference of the electricity load by about 9.49%, such as... Figure 5 As shown, it has achieved certain peak shaving and valley filling effects. On the other hand, the results also maintain an electricity satisfaction rate of 73.4%, taking into account the electricity experience of most residents.

[0117] In summary, the above analysis highlights the necessity of considering user experience when implementing demand response strategies, as well as the effectiveness of the proposed multi-objective optimization model based on time-of-use pricing.

[0118] To verify the effectiveness of the proposed multi-objective optimization model for electric vehicle charging, this invention proposes five different electric vehicle charging strategies for comparative analysis, as shown in Table 2. Strategy 1 is the model proposed in this invention.

[0119] Table 2 Five different electric vehicle charging strategies

[0120]

[0121] Comparing the load stacking and performance optimization under these five strategies, as follows: Figure 6 As shown in Table 3.

[0122] Table 3 Optimization performance under different electric vehicle charging strategies

[0123]

[0124] like Figure 6As shown in Table 3, Strategy 4 focuses solely on reducing the impact of charging power fluctuations, neglecting the charging benefits of electric vehicle owners. Therefore, while meeting the charging needs of electric vehicle owners, it attempts to arrange a uniform and stable charging schedule. However, this approach leads to a series of negative effects: on the one hand, it exacerbates the power supply pressure on the renewable energy microgrid during peak load periods, resulting in an overall increase of 163.37 kWh in peak load; on the other hand, it harms the charging cost satisfaction of electric vehicle owners, reducing their willingness to charge. This highlights the importance of considering the charging benefits of electric vehicle owners, as emphasized in this invention.

[0125] While reducing charging costs for electric vehicle (EV) owners, both Strategies 2 and 5 significantly improve the charging experience, with satisfaction scores exceeding 0.7 across various aspects. However, neither strategy effectively guides or regulates charging plans, leading to the "tragedy of the commons." The "tragedy of the commons" refers to the excessive use or consumption of public resources by individuals pursuing their own interests, ultimately resulting in resource depletion or destruction, detrimental to everyone. Here, the "commons" are charging stations, and the "tragedy" is the peak-valley distribution of electricity load. Since most EV owners tend to charge during the first two moments of off-peak hours to save costs and time, this causes load imbalance and supply-demand imbalance in the power grid. Strategy 5, in particular, exacerbates this situation while ensuring travel convenience. Similarly, Strategy 3, focusing solely on the travel convenience of EV owners and lacking orderly guidance on charging plans, coupled with the fact that most EVs are charging during peak hours, leads to large-scale charging later in the peak period. This increases the peak-valley difference of Strategy 3 by 10.13%, significantly intensifying the power supply pressure on the renewable energy microgrid during peak hours. Therefore, the results of the above three strategies all highlight the necessity of an optimization strategy that balances the charging interests of electric vehicle owners and the smoothing of the load curve.

[0126] In fact, the superior performance of Strategy 1 confirms the effectiveness and practicality of the multi-objective optimization model for electric vehicle charging proposed in this invention. Firstly, regarding the distribution of superimposed loads, Strategy 1 exhibits the smallest peak-to-valley difference, reducing it by 19.56% compared to the unoptimized model. Figure 6 It also clearly demonstrates the significant effect of Strategy 1 in peak shaving and valley filling; secondly, in terms of the charging benefits for electric vehicle owners, the average satisfaction levels for charging costs and travel convenience of Strategy 1 reached 0.77 and 0.875 respectively, which means that more than 3 / 4 of electric vehicle owners can enjoy the savings in charging costs and time; finally, in terms of charging power fluctuations, the load distribution of Strategy 1 is relatively stable, and the load change is not significant within the same period.

[0127] Taking into account various indicators, the multi-objective optimization model for electric vehicle charging proposed in this invention can effectively coordinate the contradiction between the charging interests of electric vehicle owners and the energy supply pressure of new energy microgrids, and achieve ideal results.

[0128] The above examples are used to explain the present invention, not to limit it. Any modifications and changes made to the present invention within the spirit and scope of the claims fall within the protection scope of the present invention.

Claims

1. A method for optimizing the load of a residential microgrid powered by renewable energy, characterized in that: Includes the following steps: Step 1: Model the residential renewable energy microgrid. The structure of the residential renewable energy microgrid is as follows: the source side includes a gas turbine, a photovoltaic generator, and an energy storage device; the load side consists of the residential basic electrical load and the electric vehicle charging load of the grid-connected smart charging pile; the renewable energy microgrid meets the system's power supply and demand balance by buying and selling electricity from the upper-level grid. Residential electricity consumption scenarios are divided into residential electricity consumption and electric vehicle charging. Step 2: Establish a residential electricity load optimization model. The specific details of the residential electricity load optimization model are as follows: The transferable load demand response model describes how users change their electricity consumption periods in response to the time-of-use pricing strategy of microgrid electricity sales. The uncertainty of user response is reflected in the load transfer rate, which is the proportion of load transferred from high-price periods to low-price periods. The load transfer rate is an important indicator reflecting the degree of user response, as detailed below: ， ， in, , , , , and These represent the upper and lower limits of the response invalid zone threshold, response extreme zone threshold, and maximum load transfer rate for the demand response model of transferable load under different time periods; After obtaining all possible load transfer rates for the three different time periods from the above formula, the load for each time period after transfer is: ， in, and Before and after implementing the demand response strategy The electrical load corresponding to the time period; and To calculate the average electrical load during peak and off-peak periods before implementing demand response; , and These represent the off-peak, average, and peak periods of residential electricity load, respectively. By establishing an uncertainty model for demand response of transferable loads, and based on the time-of-use electricity price of new energy microgrids, the load distribution range after demand response is obtained; on this basis, in order to balance the relationship between user experience loss on the demand side and system operation reliability, a multi-objective optimization model based on time-of-use electricity price is proposed. The first objective of the multi-objective optimization model based on time-of-use pricing is to minimize the square of the peak-valley load difference; the second objective is to maximize residents' satisfaction with electricity consumption. This model assumes that user satisfaction is inversely proportional to load variation. Specifically, when users do not change their electricity consumption habits, the load shift rate for each time period is 0, and the satisfaction rate is 1. When users adjust their electricity consumption habits completely according to the demand response model, the load shift rate for each time period is 1, and the satisfaction rate is 0. The model is shown below: ， in, , and These represent the time-of-use electricity prices during off-peak, normal, and peak periods, and they are the decision variables for this multi-objective optimization model. and They represent Upper and lower limits of time-of-use electricity prices during specific time periods; These are the weighting coefficients; The normalized index representing the load change reflects the degree of adjustment of users' electricity consumption habits. When the load change in each time period reaches the maximum possible value, the maximum value is taken, which means that users have completely changed their original electricity consumption habits. At this time, user satisfaction is 0. The specific form is as follows: ； Step 3: Establish an electric vehicle charging load optimization model.

2. The method for optimizing the load of a residential new energy microgrid according to claim 1, characterized in that: The electric vehicle charging load optimization model in step 3 is as follows: A multi-objective optimization model based on electric vehicle charging and discharging scheduling is established to maximize the charging benefits for all electric vehicle owners in residential areas and optimize the charging and discharging load distribution of electric vehicles. The objective function of this model is: The first objective function is to maximize the overall satisfaction of electric vehicle owners during the charging process, which is determined by... and The composition is as follows: ， in, and They represent the first Satisfaction with charging costs and travel convenience for electric vehicles; The number of electric vehicles; and These are the weighting coefficients; (1) Satisfaction with charging costs ， in, and They represent the first The maximum and minimum charging costs that owners of electric vehicles must bear during the parking period; Indicates the first The charging and discharging costs for an electric vehicle are as follows: ， in, The charging cycle for electric vehicles. For the first electric vehicles The charge / discharge amount at the current moment, A positive value indicates charging, and a negative value indicates discharging; and Representing the first electric vehicles The charging and discharging states at any given time; when the electric vehicle is in charging or discharging mode, the corresponding state variable takes the value of 1. and for The time-of-use electricity price for charging and discharging of electric vehicles at the current time; (2) Satisfaction with travel convenience Travel convenience satisfaction is related to the state of charge (SOC) of electric vehicle batteries: when an electric vehicle is fully charged as quickly as possible without considering cost factors, the SOC will rapidly reach its upper limit at the maximum growth rate and remain stable. At this time, the sum of the accumulated SOC of the electric vehicle during the parking time is the largest, and the travel convenience will also reach its highest level. ， in, Indicates the first electric vehicles The state of charge at any given moment; Indicates the first The upper limit of the accumulated state of charge value of an electric vehicle during the parking period when charged at the fastest speed; The second objective function considers the stability of the new energy microgrid, with the optimization objective being to minimize the fluctuation of charging power of charging piles; ， The constraints of this multi-objective optimization model are as follows: (1) Constraints on the charging and discharging capacity of electric vehicles ， in, and These are the maximum discharge capacity and maximum charging capacity of an electric vehicle, respectively. (2) Constraints on the charging and discharging states of electric vehicles ， (3) Charge state constraints of electric vehicles ， ， in, and These are the upper and lower limits of the state of charge of the electric vehicle, respectively. and These are the charging and discharging efficiencies of electric vehicles, respectively. This refers to the rated capacity of the electric vehicle battery.

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