A multi-objective community energy management method for controllable device groups

Abstract

The present invention belongs to the technical field of community resource energy optimization, and specifically discloses a multi-objective community energy management method for a controllable device group, comprising the following steps: establishing an operation model of a secondary-life battery energy storage system; modeling multiple resident dissatisfactions based on the power consumption behavior of devices with adjustable operating time, power-adjustable devices, and flexible power consumption behavior of air conditioners to obtain a multiple resident dissatisfaction model; wherein the multiple resident dissatisfaction model includes a dissatisfaction model for the use of devices with adjustable operating time, a dissatisfaction model for the use of power-adjustable devices, an auditory discomfort model, and a thermal discomfort model; constructing an objective function with a minimum peak-to-average ratio of a net load curve; establishing a multi-objective community energy management optimization model based on the objective function with a minimum peak-to-average ratio of the net load curve, the operation model of the secondary-life battery energy storage system, and the multiple resident dissatisfaction model; and solving the multi-objective community energy management optimization model using an optimization algorithm solution method.

Description

Technical Field

[0001] The present invention belongs to the technical field of community resource energy optimization, and in particular relates to a multi-objective community energy management method for a controllable device group. Background Art

[0002] As urbanization continues to accelerate, electricity load continues to climb. To mitigate the impact of this growing load on the power grid while improving the economic efficiency of electricity consumption for consumers, community demand-side energy management has become a key research topic in academia and industry in recent years. As a fundamental unit of the residential demand side, research on community energy management will provide key models and methodologies for energy management in larger-scale scenarios (residential buildings / communities), making it a primary issue that needs to be addressed in research on residential demand-side energy management.

[0003] Existing research focuses on selecting energy storage resources that are more affordable for residential users. Most research uses BESS (battery energy storage systems) or EVs (electric vehicles) as energy storage devices to design energy management strategies, leveraging the BESS's or EVs' adjustable charging and discharging capabilities to flexibly alter energy consumption. However, for residential electricity users, especially those with low or middle incomes, the high purchase and replacement costs of BESSs and EVs will remain a significant challenge for years and decades to come.

[0004] In addition, under the current electricity consumption environment, more important residents' satisfaction indicators need to be considered, and a more flexible satisfaction quantification modeling method is needed. Existing research mostly focuses on the user satisfaction of TSA (time-shiftable equipment) and thermal comfort for air-conditioning equipment, and basically does not involve the user satisfaction of PAA (power-adjustable equipment). In addition, the operation of the equipment will generate a certain amount of noise. According to research, indoor auditory comfort has been recognized as an important indicator affecting residents' health. In terms of satisfaction modeling, existing work mostly uses strict constraints to express it, or uses penalty costs to be included in the objective function. However, this does not well reflect the real feelings of the residents. Summary of the Invention

[0005] In order to solve the problems existing in the prior art, the purpose of the present invention is to propose a multi-objective community energy management method for controllable device groups. By reorganizing retired EV power batteries into an energy storage system to support community energy optimization decisions, the present invention proposes a multi-objective community energy management optimization model. The proposed model and the adopted solution strategy can obtain a satisfactory non-dominated solution set in various community energy management optimization applications, thereby achieving an effective trade-off between users' multiple residential needs.

[0006] To achieve the above-mentioned purpose, the present invention adopts the following technical means to achieve it: a multi-objective community energy management method for a controllable device group, comprising the following steps: step 1: establishing an operation model of a secondary life battery energy storage system; step 2: modeling multiple residential dissatisfactions according to the power consumption behavior of the operating time-shiftable device, the power-adjustable device and the flexible power consumption behavior of the air conditioner, and obtaining a multiple residential dissatisfaction model; wherein the multiple residential dissatisfaction model includes a dissatisfaction model for the use of the operating time-shiftable device, a dissatisfaction model for the use of the power-adjustable device, an auditory discomfort model and a thermal discomfort model; step 3: establishing an operation model of a secondary life battery energy storage system; step 4: establishing an operation model of a secondary life battery energy storage system; step 5: establishing an operation model of a secondary life battery energy storage system; step 6: establishing an operation model of a secondary life battery energy storage system; step 7: establishing an operation model of a secondary life battery energy storage system; step 8: establishing an operation model of a secondary life battery energy storage system; step 9: establishing an operation model of a secondary life battery energy storage system; step 10: establishing an operation model of a secondary life battery energy storage system; step 11: establishing an operation model of a secondary life battery energy storage system; step 12: establishing an operation model of a secondary life battery energy storage system; step 13: establishing an operation model of a secondary life battery energy storage system; step 14: establishing an operation model of a secondary life battery energy storage system; step 15: establishing an operation model of a secondary life battery energy storage system; step 16: establishing an operation model of a secondary life battery energy storage system Step 3: Construct an objective function with the minimum peak-to-average ratio of the net load curve; Step 4: Based on the objective function of minimizing the peak-to-average ratio of the net load curve, the operation model of the secondary-life battery energy storage system and the multiple resident dissatisfaction model, establish a multi-objective community energy management optimization model; Step 5: Use the optimization algorithm to solve the multi-objective community energy management optimization model, and optimize the task start time of the equipment with shiftable operating time, the power consumption of the power-adjustable equipment during the operation cycle, the start and stop plan of the air conditioner during the operation cycle, and the charging and discharging operation plan of the secondary-life battery energy storage system through the optimal feasible solution.

[0007] Furthermore, the specific process of establishing the operation model of the secondary life battery energy storage system is as follows:

[0008] Within the scope of the second life use value of retired power batteries, a power function is used to model the capacity decay of the battery after n charge and discharge cycles within the scope of the second life use value. Calculated as:

[0009] Where Q0 represents the initial capacity of the battery; n represents the number of charge and discharge cycles; χ represents the capacity attenuation coefficient; τ is the capacity attenuation power exponent;

[0010] The remaining cycle life of a power battery at retirement is N sec It can be calculated as:

[0011] N sec =l -1 (RC thr )-N retire

[0012] Where RC thr is the minimum capacity retention rate threshold; N retire It represents the cumulative number of charge and discharge cycles of a power battery retired from an electric vehicle, and defines N retire It is related to the annual mileage of electric vehicles, the energy consumption per 100 kilometers of electric vehicles, and the rated energy capacity of the battery. The cumulative number of charge and discharge cycles N of a power battery retired from an electric vehicle is retire It can be calculated as:

[0013] E mile The expected daily mileage of electric vehicles can be calculated as:

[0014] Where, e and Q represent the energy consumption per 100 kilometers of electric vehicles and the rated energy capacity of the battery respectively; Y retire Indicates the cumulative service life of the battery when it is retired; μ D and σ D represent the mean and standard deviation of daily mileage of electric vehicles;

[0015] Define the capacity of the secondary life battery energy storage system as A rate According to the two-stage capacity attenuation law of retired batteries, the remaining available capacity A of the secondary life battery energy storage system is SL The calculation is as follows:

[0016] A SL =A rate ·[l(N retire )-RC thr ]

[0017] Where, l(N retire ) represents the charge and discharge cycle N retire Capacity decay after times;

[0018] Combining the remaining available capacity of the second-life battery energy storage system and the remaining cycle life of the power battery at its retirement, the average attenuation capacity A of the second-life battery energy storage system after a complete charge and discharge cycle is fade Calculated as:

[0019] A fade =A SL / N sec .

[0020] Furthermore, the specific process of establishing the usage dissatisfaction model of the runtime-shiftable device is as follows: according to the flexible power consumption behavior of the runtime-shiftable device, the usage dissatisfaction of the kth runtime-shiftable device is calculated. It can be calculated as:

[0021]

[0022]

[0023] Where, It represents the time required for the kth running time translatable device to complete its running task; and They represent the earliest and latest task completion times expected by the user respectively; the set of controllable devices is Φ tsa ; Indicates the completion time of the task actually scheduled by a runtime mobile device. The adjustable variable is the startup time of a runtime mobile device. Δt is the unit time interval, γ tsa Indicates the user-specified usage satisfaction factor, which changes with the user's subjective feelings; Indicates the user's expected task completion time.

[0024] Furthermore, the specific process of establishing the dissatisfaction model of power adjustable equipment is as follows: according to the flexible power consumption behavior of the adjustable equipment, the dissatisfaction of the kth power adjustable equipment is It can be calculated as:

[0025]

[0026] Where, represents the expected power consumption of a user of a power-adjustable device, γ paa It represents the user-specified satisfaction factor, which changes with the user's subjective feelings. The set of power-adjustable devices is specified as Φ paa ; Indicates the minimum power consumption of the device specified by the user; Indicates the maximum power consumption of the device expected by the user. To ensure the device is in the best operating state, Specified as the rated power consumption of the equipment; Indicates that the adjustable variable is power consumption; Indicates the power consumption expected by the user.

[0027] Furthermore, the specific process of establishing the auditory discomfort model is as follows: the controllable device group includes devices with shiftable operating time, power adjustable devices and air conditioners. It is stipulated that in time period t, the controllable device group, uncontrollable devices and outdoor environmental noise together form the aggregate noise sound pressure level, the aggregate noise sound pressure level SPL t It can be calculated as:

[0028]

[0029] Where, Respectively represent the noise pressure levels released by equipment with adjustable operating time, equipment with adjustable power, and air conditioner operation; Indicates the sound pressure level of noise from uncontrollable equipment and outdoor environment; The start and stop state variables and power consumption of the air conditioner AC; Indicates the start and stop status of the power adjustable device; Indicates the start and stop status of the runtime shiftable device;

[0030] Stevens power law is used to convert the aggregate noise sound pressure level SPL t and noise sound pressure level threshold Converted into loudness values ​​respectively, the loudness value LU of the user in time period t t Can be calculated as: LU t =(SPL t ) 0.6 7

[0031] The maximum loudness of the user in time period t It can be calculated as:

[0032] The user's auditory discomfort AD in time period t t It can be calculated as:

[0033]

[0034] Where κ represents the auditory comfort factor.

[0035] Furthermore, the specific process of establishing the thermal discomfort model is as follows:

[0036] The user's thermal discomfort TD at time period t t It can be calculated as:

[0037]

[0038] Where σ represents the thermal comfort factor, T t ind Indicates the indoor room temperature; ΔT hy Indicates the temperature 'dead zone' bandwidth or temperature comfort zone bandwidth, T set represents the user's expected indoor temperature, [T set -ΔT hy , T set +ΔT hy ] is the temperature comfort range; T max Indicates the maximum allowable temperature, T min Indicates the lowest allowable temperature.

[0039] Furthermore, the specific process of the established multi-objective community energy management optimization model is as follows: (1) The objective function F1 with the goal of minimizing the total energy cost of the community unit within the scheduling period is expressed as:

[0040] Where, ζ t represents the grid electricity price in period t, represents the net power consumption of the community unit in time period t, represents the uncontrollable load of the community unit in time period t, represents the charge and discharge power of the secondary life battery energy storage system in time period t, where indicates charging, otherwise it indicates discharging, T indicates the scheduling period; P t solar represents the photovoltaic power generation output during time period t; represents the power consumption of the kth time-shiftable device at time t; represents the power consumption of the air conditioner in time period t;

[0041] (2) The objective function F2, which aims to minimize user dissatisfaction with the use of devices with shiftable runtime and adjustable power, is expressed as:

[0042]

[0043] Where, It represents the dissatisfaction with the use of the k-th running time-shiftable device, It represents the dissatisfaction with the use of the k-th power-adjustable device; and They represent the operation start time and end time of the kth power adjustable device respectively;

[0044] (3) The objective function F3, which aims to minimize the user's thermal discomfort, is expressed as:

[0045]

[0046] In the formula, TD t represents the thermal discomfort of the user in time period t; t ac，1 and t ac，2 They represent the start time and end time of the kth air conditioner respectively;

[0047] (4) The objective function F4, which aims to minimize the user's auditory discomfort, is expressed as:

[0048]

[0049] Where AD t represents the user's auditory discomfort in time period t;

[0050] (5) The objective function F5, which aims to minimize the capacity attenuation of the secondary life battery energy storage system, is expressed as:

[0051]

[0052] Where, represents the depth of discharge of the secondary-life battery energy storage system in the kth half-cycle of the charge and discharge portion; Ξ represents the set of all charge and discharge half-cycles of the secondary-life battery energy storage system in the charge and discharge scheduling period; and They represent the energy levels of the secondary life battery energy storage system at the end of the kth and k-1th partial charge and discharge half cycles respectively; E slbess，rated Indicates the rated energy capacity of the secondary life battery energy storage system; A fade It represents the average attenuation capacity of a secondary life battery energy storage system after a complete charge and discharge cycle; the parameter kp represents the capacity attenuation exponential coefficient of the secondary life battery;

[0053] (6) The objective function F6, which aims to minimize the peak-to-average ratio of the net load curve, is expressed as:

[0054]

[0055] Where, P t net represents the net power consumption of the community unit in time period t.

[0056] Furthermore, constraints are set for the established multi-objective community energy management optimization model:

[0057] (1) Charge and discharge operation constraints of secondary life battery energy storage system:

[0058]

[0059]

[0060]

[0061]

[0062]

[0063] Where, Indicates the state of charge of the secondary life battery energy storage system at time period t, SOC slbess,min and SOC slbess,max They represent the minimum state of charge and maximum state of charge allowed by the secondary life battery energy storage system; represents the energy level of the secondary life battery energy storage system at time period t, η slbess，c and η slbess,d They represent the charging efficiency and discharging efficiency of the secondary life battery energy storage system respectively; represents the charge and discharge power of the secondary life battery energy storage system in time period t; P slbbess，rated Indicates the rated charge and discharge power of the secondary life battery energy storage system; Represents the battery load constraint of the secondary life battery energy storage system during the charging and discharging process; SOC desire Indicates the specified threshold value of the state of charge of the retired battery; E slbess，ratedIndicates the rated energy level of a second-life battery energy storage system.

[0064] Furthermore, the constraints also include: runtime shiftable device operation constraints:

[0065]

[0066]

[0067]

[0068] Where T represents the scheduling period, Δt represents the unit time interval;

[0069] Ramp rate constraints for power consumption up / down regulation:

[0070]

[0071] Where, Indicates the power change threshold specified by the k-th power-adjustable device; It represents the power consumption of the power-adjustable device at time t-1;

[0072] Air conditioning operation constraints:

[0073]

[0074]

[0075]

[0076] Where, t on,min , t off,min Respectively represent the minimum online and offline time requirements of the AC; Indicates the cumulative opening time; Indicates the cumulative off time.

[0077] Furthermore, the solution method of NSGA-III algorithm is used to solve the multi-objective community energy management optimization model. In NSGA-III algorithm, each individual is encoded as a 1×N dim A vector where N dim is the number of variables in the individual vector, calculated as follows:

[0078]

[0079] In the formula, |Φ tsa | represents the number of devices with shiftable runtime in the community unit; Indicates the total number of operating hours of all power-adjustable devices; |DR ac| / Δt represents the number of operating periods of the air conditioner; T represents the number of operating periods of the secondary life battery energy storage system.

[0080] Compared with the prior art, the present invention has at least the following beneficial effects:

[0081] The secondary-life battery energy storage system operation model established in the present invention uses retired batteries. Compared with existing studies that mostly use BESS or EV as energy storage equipment to design energy management strategies, retired batteries have significant cost advantages and can effectively reduce energy costs. It solves the problem of high purchase and replacement costs of battery energy storage systems or electric vehicles for middle- and low-income users, and greatly reduces the energy management costs of middle- and low-income users. The multi-objective community energy management optimization model established in the present invention takes into account more important residents' living satisfaction indicators, creatively constructs a more flexible satisfaction quantification modeling method, and improves the application scenarios and feasibility of energy scheduling management solutions.

[0082] This paper proposes a multi-objective community energy management method for controllable device clusters. This method integrates the utilization of retired electric vehicle batteries into community energy operation and scheduling. Based on the two-stage capacity decay characteristics of retired power batteries in the inflection point mode, a secondary battery energy storage system model is established. A fuzzy membership function is used to quantify multiple resident dissatisfaction factors. A multi-objective optimization model for community energy management is established and solved using NSGA-III. This multi-objective community energy management optimization model and its solution strategy can obtain satisfactory non-dominated solutions in various community energy management optimization applications, effectively balancing users' multiple residential needs. The optimal compromise solution can be selected based on user preferences and other practical considerations.

[0083] Retired batteries have significant cost advantages and good application value and prospects on the residential side. To this end, this invention will explore the application of retired batteries in the energy scheduling and management environment. The challenge lies in how to construct a suitable retired battery energy storage model. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0085] Figure 1 is a flow chart of the present invention;

[0086] Figure 2 Schematic diagram of the community energy management environment;

[0087] Figure 3 The capacity decay characteristic curves of new batteries and retired batteries;

[0088] Figure 4 Quantification function of usage dissatisfaction of runtime-transferable devices

[0089] Figure 5 Quantification function of dissatisfaction with the use of power-adjustable equipment

[0090] Figure 6 Auditory discomfort quantification function

[0091] Figure 7 Indoor thermal discomfort quantification function

[0092] Figure 8 Predict data curves for ambient temperature and light intensity curves;

[0093] Figure 9 is the ambient noise sound pressure level;

[0094] Figure 10 It is the uncontrollable load data;

[0095] Figure 11 is the mapping result of the six-dimensional Pareto hypersurface in three-dimensional space;

[0096] Figure 12 The operation scheduling result of the device that can be translated during runtime;

[0097] Figure 13 The operation scheduling results of the power adjustable equipment;

[0098] Figure 14 Optimization results of charge and discharge operation of the secondary life battery energy storage system in case 1;

[0099] Figure 15 Optimization results of charge and discharge operation of the secondary life battery energy storage system in scenario 4. DETAILED DESCRIPTION

[0100] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other.

[0101] The following detailed description is an exemplary description and is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention.

[0102] This paper studies multi-objective community energy management for controllable device groups to solve two existing problems:

[0103] By applying retired batteries to energy scheduling and management solutions and building a storage model suitable for retired batteries around them, we provide energy storage resource solutions that make it easier for residential users to afford them financially.

[0104] At the same time, by introducing the user satisfaction and auditory discomfort of power-adjustable equipment, more important residents' living satisfaction indicators are taken into consideration, and a more flexible satisfaction quantification modeling method is creatively constructed, which improves the application scenarios and feasibility of the energy scheduling management solution.

[0105] Figure 2 A schematic diagram of the community energy management environment is given. The flexible energy resources of the energy management environment include a controllable device group and a second-life battery energy storage system (SL-BESS).

[0106] The controllable device group includes multiple TSAs (time-shiftable devices), multiple PAAs (power-adjustable devices) and an air conditioner (AC).

[0107] First, the flexible power consumption behavior of these controllable devices needs to be modeled and then integrated into the energy management system (EMS).

[0108] The controllable devices are electrically connected to the SL-BESS and integrated into the energy flow bus. Combined with rooftop photovoltaic power generation resources, the energy management environment forms a micro-energy grid with power generation and consumption capabilities and computational decision-making capabilities. The EMS (Energy Management System) is responsible for collecting photovoltaic power generation data, as well as data collection, operation scheduling, and control signal distribution for the controllable devices and the SL-BESS. Upon receiving control signals, the intelligent control terminals of the controllable devices and the SL-BESS controller control device operations, such as TSA (Time Shifting Module) activation, PAA power consumption, AC startup and shutdown, and SL-BESS charging and discharging. The community interacts with the external power grid and purchases electricity from it. Therefore, the EMS also communicates with the external power grid and receives grid electricity price signals.

[0109] Taking the next day as the scheduling cycle, EMS will predict the photovoltaic power generation output.

[0110] Based on the received grid electricity price signals, the EMS optimizes the startup time of TSAs (shiftable operating time equipment), power consumption of PAAs (power adjustable operating time equipment), AC start-up and shutdown schemes during the operation cycle, and the charge and discharge operation plan of the SL-BESS, while meeting relevant constraints. This is done with the goals of maximizing energy economy, minimizing dissatisfaction with the use of TSAs (shiftable operating time equipment) and PAAs (power adjustable operating time equipment), minimizing indoor thermal discomfort, minimizing indoor auditory discomfort, minimizing the attenuation of the SL-BESS charge and discharge cycle life, and minimizing the peak-to-average ratio (PAR) of the power load curve.

[0111] like Figure 1 As shown, the present invention provides a multi-objective community energy management method for a controllable device group, which specifically includes the following steps:

[0112] Step 1: Establish a second-life battery energy storage system operation model:

[0113] like Figure 3 As shown in the figure, retired batteries exhibit a two-stage capacity decay characteristic, also known as the inflection point decay mode: in the first stage, the capacity decays at a slower rate; in the second stage, that is, after crossing the inflection point, the remaining available capacity of the battery drops sharply. Not only is the remaining available capacity limited, but it will also bring some operational safety issues such as battery overheating or even combustion. Therefore, after the remaining capacity of the battery crosses the inflection point, it is considered that it no longer has secondary use value. Its secondary use value is as follows: Figure 2 As shown in the shaded area on the left side of the dotted line, the inflection point is Figure 2 The dotted line indicates that the capacity retention rate CRR is the ratio of the current remaining capacity of the battery to the rated capacity. The capacity retention rate CRR provides a basis for judging whether the battery has secondary use value at a certain point in time. For retired power batteries of electric vehicles, batteries above the minimum CRR threshold are required to be used for reorganization into a second-life battery energy storage system. The threshold is Figure 2 The capacity retention rate CRR value corresponding to the mid-inflection point.

[0114] Battery capacity decay is closely related to its charge and discharge behavior. Within the scope of the second life use value of retired power batteries, a power function is used to model the capacity decay of the battery after n charge and discharge cycles within the scope of the second life use value. Calculated as:

[0115]

[0116] Model (1) can be rewritten as follows:

[0117]

[0118] Where Q0 represents the initial capacity of the battery, and the unit of Q0 is mAh; n represents the number of charge and discharge cycles; χ represents the capacity attenuation coefficient; τ is the capacity attenuation power exponent;

[0119] According to model (2), the remaining cycle life N of a power battery at the time of retirement is sec It can be calculated as:

[0120]

[0121] Where RC thr is the minimum capacity retention rate threshold; N retire It represents the cumulative number of charge and discharge cycles of a power battery retired from an electric vehicle, and defines N retire It is related to the annual mileage of electric vehicles, the energy consumption per 100 kilometers of electric vehicles, and the rated energy capacity of the battery. The cumulative number of charge and discharge cycles N of a power battery retired from EV is retire It can be calculated as:

[0122]

[0123] E mile The expected daily mileage of electric vehicles can be calculated as:

[0124]

[0125] Where, e and Q represent the energy consumption per 100 kilometers of electric vehicles and the rated energy capacity of the battery respectively; Y retire Indicates the cumulative service life of the battery when it is retired; μ D and σ D represent the mean and standard deviation of the daily mileage of electric vehicles respectively. It should be noted that model (5) is established based on the EV travel statistics released by the United States in 2001. Other EV travel statistics can also be used to estimate the expected value of EV daily mileage.

[0126] It is stipulated that the consistency (battery voltage, internal resistance) of retired single cells used for reorganization into a second-life battery energy storage system is consistent, and the capacity of the second-life battery energy storage system is defined as A rate According to the two-stage capacity attenuation law of retired batteries, the remaining available capacity A of the secondary life battery energy storage system is SL The calculation is as follows:

[0127]

[0128] Where, Indicates charge and discharge cycle N retireCapacity decay after times;

[0129] Combined model (3) and model (6), that is, combining the remaining available capacity of the secondary life battery energy storage system and the remaining cycle life of the power battery at its retirement, the average attenuation capacity A of the secondary life battery energy storage system after a complete charge and discharge cycle is fade Calculated as:

[0130] A fade =A SL / N sec (7).

[0131] Step 2: Model multiple occupancy dissatisfaction based on the electricity consumption behavior of time-shiftable devices, power-adjustable devices, and air conditioning flexible devices to obtain a multiple occupancy dissatisfaction model. The multiple occupancy dissatisfaction model includes a time-shiftable device usage dissatisfaction model, a power-adjustable device usage dissatisfaction model, an auditory discomfort model, and a thermal discomfort model.

[0132] Resident satisfaction is a key criterion for determining the success of an EMS design. Resident satisfaction is influenced by a variety of metrics. This paper considers the following three: user satisfaction with TSA (Time Shifting Components) and PAA (Power Adjustable Components) operation; indoor auditory comfort; and thermal comfort. Taking into account the fuzziness of subjective human experience, this paper employs a fuzzy membership function approach to establish a quantitative model for resident satisfaction. The detailed modeling process is as follows:

[0133] (1) Runtime-translatable device usage dissatisfaction model

[0134] According to the flexible power consumption behavior of the runtime-shiftable device, the properties of a runtime-shiftable device are: The adjustable variable is the startup time of the device The user's expected task completion time is Specifies the time to complete a task scheduled by a runtime shifting device. Deviating from the user's expected task completion time This will bring a certain degree of dissatisfaction to the user; when the deviation time exceeds a certain threshold, it will become unacceptable to the user and the degree of dissatisfaction will reach the maximum; Figure 4 As shown, the above process can be quantified as follows: The dissatisfaction of the use of the kth running time shiftable device It can be calculated as:

[0135]

[0136]

[0137] Where, It represents the time required for the kth running time translatable device to complete its running task; and Specified by the user, they represent the earliest and latest task completion times expected by the user; the set of controllable devices is Φ tsa ; Indicates the completion time of the task actually scheduled by a runtime mobile device. The adjustable variable is the startup time of a runtime mobile device. Δt is the unit time interval, γ tsa represents the user-specified satisfaction factor, γ tsa control Figure 4 The shape of the satisfaction function curve shown, this value changes with the user's subjective feelings; Indicates the user's expected task completion time;

[0138] (2) Dissatisfaction model of power-adjustable equipment usage

[0139] According to the flexible power consumption behavior of the adjustable device, the properties of a power adjustable device are: The adjustable variable is power consumption The user's expected power consumption is Specifies that during a period of time, when the power consumption of the actually scheduled power-adjustable device deviates from the user's expected power consumption This will cause a certain degree of dissatisfaction to the user, and the dissatisfaction will gradually increase with the increase of power deviation. When the power deviation reaches a certain threshold, the user will find it unacceptable and the dissatisfaction will reach the maximum value. Figure 5 As shown, the above process can be quantified as follows: The dissatisfaction with the use of the k-th power adjustable device It can be calculated as:

[0140]

[0141] Where, represents the expected power consumption of a user of a power-adjustable device, γ paa represents the user-specified satisfaction factor, γ paa control Figure 5 The shape of the satisfaction function curve shown in the figure changes with the user's subjective feeling. The set of power adjustable devices is specified as Φ paa ; Indicates the minimum power consumption of the device specified by the user; Indicates the maximum power consumption of the device expected by the user. To ensure the device is in the best operating state, it is usually Specified as the rated power consumption of the equipment; Indicates that the adjustable variable is power consumption; Indicates the power consumption expected by the user;

[0142] (3) Auditory discomfort model

[0143] The present invention stipulates that each device will release noise with a certain sound pressure level (in dB) during operation. The noise from uncontrollable devices and the outdoor environment is considered as uncontrollable. The controllable device group includes devices with adjustable operating time, power adjustable devices and air conditioners. Considering the impact of outdoor environmental noise on the indoor environment, it is stipulated that in time period t, the controllable device group, uncontrollable devices and outdoor environmental noise together form an aggregate noise sound pressure level, the aggregate noise sound pressure level SPL t It can be calculated as:

[0144]

[0145] Where, Respectively represent the noise pressure levels released by equipment with adjustable operating time, equipment with adjustable power, and air conditioner operation; Indicates the sound pressure level of noise from uncontrollable equipment and outdoor environment; The start and stop state variables and power consumption of the air conditioner AC; Indicates the start and stop status of the power adjustable device; Indicates the start and stop status of the runtime shiftable device;

[0146] At different times of the day, users have different tolerance levels for ambient noise, which depends on their daily routine. The physiological impact of noise on humans is mainly reflected in the loudness of the noise (unit: sones). That is, the louder the ambient noise, the more uncomfortable the human body will feel. To this end, Stevens power law is used to aggregate the noise sound pressure level SPL t and noise sound pressure level threshold Converted into loudness values ​​respectively as follows:

[0147] The loudness value LU of the user in time period t t It can be calculated as:

[0148] LU t =(SPL t ) 0.67 (12)

[0149] The maximum loudness of the user in time period t It can be calculated as:

[0150]

[0151] like Figure 6As shown, the user's auditory discomfort will gradually increase with the increase of indoor noise loudness level; if the indoor loudness level exceeds a certain threshold during a certain period of time, the user's auditory discomfort will reach a maximum value and become unacceptable. Figure 6 is the relationship between the change in indoor noise loudness level and the user's auditory discomfort. Therefore, the model is built using formula (14), and the user's auditory discomfort AD in time period t is t It can be calculated as:

[0152]

[0153] In the formula, κ represents the auditory comfort factor, and κ controls Figure 6 Middle auditory comfort curve shape.

[0154] (5) Thermal discomfort model

[0155] Indoor thermal comfort and indoor room temperature Directly related. Figure 7 As shown, the provisions In the air conditioning temperature 'dead zone', it will not cause thermal discomfort to the user; the 'dead zone' is the comfort constraint set by the user; when If the air conditioner exceeds the temperature 'dead zone' within a certain range, it will cause users to feel uncomfortable; Beyond the user-specified temperature threshold, thermal discomfort reaches its maximum and becomes unacceptable; Figure 7 The relationship between indoor temperature change and user thermal discomfort change is shown in the figure. The above process can be quantified as follows:

[0156] The user's thermal discomfort TD at time period t t It can be calculated as:

[0157]

[0158] Where σ represents the thermal comfort factor, Indicates the indoor room temperature; ΔT hy Indicates the temperature 'dead zone' bandwidth or temperature comfort zone bandwidth (℃), T set represents the user's expected indoor temperature, [T set -ΔT hy , T set +ΔT hy ] is the temperature comfort range; T max Indicates the maximum allowable temperature, T min Indicates the minimum allowable temperature;

[0159] Step 3: Construct an objective function to minimize the peak-to-average ratio of the net load curve;

[0160] Step 4: Establish a multi-objective community energy management optimization model based on the objective function of minimizing the peak-to-average ratio of the net load curve, the second-life battery energy storage system operation model, and the multiple resident dissatisfaction model;

[0161] The multi-objective community energy management optimization model uses the following operating objectives to represent users' multiple residential considerations:

[0162] The objective functions of the multi-objective community energy management optimization model include:

[0163] (1) The objective function F1, which aims to minimize the total energy cost of the community unit within the scheduling period, is expressed as:

[0164] Where, ζ t represents the grid electricity price in period t; P t net represents the net power consumption of the community unit in time period t; P t nca represents the uncontrollable load of the community unit in time period t; P t slbess represents the charge and discharge power of the secondary life battery energy storage system in time period t, where P t slbess ≥0 means charging, represents discharge; T represents the scheduling period; P t solar It represents the photovoltaic power generation output in time period t, which is related to the photovoltaic panel array area, ambient temperature and light intensity; represents the power consumption of the kth time-shiftable device at time t; represents the power consumption of the air conditioner in time period t;

[0165] (2) The objective function F2, which aims to minimize user dissatisfaction with the use of devices with shiftable runtime and adjustable power, is expressed as:

[0166]

[0167] Where, It represents the dissatisfaction with the use of the k-th running time-shiftable device, It represents the dissatisfaction with the use of the k-th power-adjustable device; and They represent the operation start time and end time of the kth power adjustable device respectively;

[0168] (3) The objective function F3, which aims to minimize the user's thermal discomfort, is expressed as:

[0169]

[0170] In the formula, TD t represents the thermal discomfort of the user in time period t; t ac，1 and t ac，2 They represent the start time and end time of the kth air conditioner respectively;

[0171] (4) The objective function F4, which aims to minimize the user's auditory discomfort, is expressed as:

[0172]

[0173] Where AD t represents the user's auditory discomfort in time period t;

[0174] (5) During the implementation of EMS, the service life degradation of SL-BESS is a factor that must be considered. Although frequent charge and discharge control using the secondary life battery energy storage system can reduce energy costs, excessive charge and discharge behavior will lead to serious capacity degradation of the secondary life battery energy storage system, which is not conducive to the long-term use of the secondary life battery energy storage system and to some extent weakens its secondary use value.

[0175] Therefore, the objective function F5, which aims to minimize the capacity attenuation of the secondary life battery energy storage system, is expressed as:

[0176]

[0177] Wherein, the capacity decay of SL-BESS is mainly related to the number of charge and discharge cycles and the depth of discharge (DOD) of SL-BESS; represents the depth of discharge of the secondary-life battery energy storage system in the kth half-cycle of the charge and discharge portion; Ξ represents the set of all charge and discharge half-cycles of the secondary-life battery energy storage system in the charge and discharge scheduling period; and They represent the energy levels of the secondary life battery energy storage system at the end of the kth and k-1th partial charge and discharge half cycles respectively; E slbess，rated Indicates the rated energy capacity of the secondary life battery energy storage system; A fade Indicates the average attenuation capacity of a secondary life battery energy storage system after a complete charge and discharge cycle, A fade The value of can be calculated using the second-life battery energy storage system operation model (1) to (7); the parameter kp represents the capacity attenuation index coefficient of the second-life battery, which takes a value in the range [0.8, 2.1] and mainly depends on the battery used;

[0178] (6) Considerations for home-to-grid (H2G) integration:

[0179] As a coordinator between the power grid and residential end-users, EMS should not only ensure user satisfaction and energy economy, but also consider the impact of electricity load on the power grid.

[0180] Therefore, the objective function F6, which aims to minimize the peak-to-average ratio (PAR) of the net load curve, is expressed as:

[0181]

[0182] Where, P t net represents the net power consumption of the community unit in time period t;

[0183] During the execution of the proposed EMS, four constraints need to be met: SL-BESS charging and discharging operation constraints, TSA operation constraints, PAA operation constraints, and AC operation constraints. The established multi-objective energy management model sets the following constraints:

[0184] (1) Charge and discharge operation constraints of secondary life battery energy storage system:

[0185]

[0186]

[0187]

[0188]

[0189]

[0190] Constraint (22) represents the charge and discharge power boundary limit of the secondary life battery energy storage system, where P slbess，rated represents the rated charge and discharge power of the secondary life battery energy storage system; constraint (23) represents the SOC constraint of the secondary life battery energy storage system during the charge and discharge process, where represents the state of charge of the secondary life battery energy storage system in time period t, calculated using formula (24); SOC slbess，min and SOC slbess，max They represent the minimum state of charge and maximum state of charge allowed by the secondary life battery energy storage system; represents the energy level of the secondary life battery energy storage system in time period t; η slbess，c and η slbess，d They represent the charging efficiency and discharging efficiency of the secondary life battery energy storage system respectively; represents the charge and discharge power of the secondary life battery energy storage system in time period t; P slbess，rated Indicates the rated charge and discharge power of the secondary life battery energy storage system; Represents the battery load constraint of the secondary life battery energy storage system during the charging and discharging process; SOC desire Indicates the specified threshold value of the state of charge of the retired battery; E slbess，rated represents the rated energy level of the secondary life battery energy storage system; constraint (26) represents the energy level at the end of the charge and discharge scheduling of the secondary life battery energy storage system Should not be less than a specified threshold SOC desire To ensure the continuous availability of the secondary life battery energy storage system for next day operation and dispatch;

[0191] It should be pointed out that and Both represent the energy level of a secondary life battery energy storage system. The difference is that the former It describes the energy level change of the secondary life battery energy storage system during the charging and discharging process from the perspective of time dimension, which is expressed by formula (25); the latter It describes the energy level changes of the secondary life battery energy storage system from the perspective of charge and discharge cycles.

[0192] (2) Runtime shiftable device operation constraints:

[0193]

[0194]

[0195]

[0196] Where T represents the scheduling period, Δt represents the unit time interval;

[0197] Where constraint (27) represents the adjustable range of task start time for each TSA, and the user-specified allowable task completion time range The larger the value is, the greater the flexible and adjustable potential of the runtime shiftable device is; constraint (28) ensures that each TSA must be turned off after completing the running task; constraint (29) indicates that once the TSA is turned on, it cannot be interrupted during the running process until the running task is completed.

[0198] (3) Ramp-up / Ramp-down Constraints for Power Consumption: For each PAA, a ramp-up / ramp-down constraint (30) is introduced to ensure that the power variation of the PAA in adjacent time periods is sufficiently smooth. The introduction of constraint (30) is mainly based on user comfort considerations. The power consumption of the PAA can reflect its external characteristics, such as the brightness of the light and the loudness of the audio. By applying constraint (30), it is possible to avoid drastic changes in the power consumption of the PAA in adjacent time periods, such as sudden increases or decreases, which may cause drastic fluctuations in the external characteristics of the PAA, such as the light suddenly becoming brighter or the audio volume suddenly increasing, causing comfort disturbances to the user.

[0199]

[0200] Where, Indicates the power change threshold specified by the k-th power-adjustable device; It represents the power consumption of the power-adjustable device at time t-1;

[0201] (4) Air conditioning operation constraints:

[0202]

[0203]

[0204]

[0205]

[0206] Where, t on，min , t off,min The minimum online and offline time requirements for the AC are to avoid mechanical wear and tear caused by frequent AC starts and stops, thereby extending the AC's service life. Indicates the cumulative opening time; Indicates the cumulative shutdown time; Indicates the cumulative opening time at time t-1; represents the cumulative off-time at time t-1; constraint (31) represents The value of should ensure that the indoor temperature does not exceed the limit in each time period, that is, the AC disconnection should ensure that the indoor temperature does not exceed the limit in each time period; Constraint (32) indicates that the AC is uncontrollable outside the user's use time range and is in standby mode; Constraint (33) ensures that the cumulative time of AC on and off should not be less than the minimum online and offline time requirements of AC, where and Formula (34) is used for calculation.

[0207] Step 5: Use the optimization algorithm to solve the multi-objective community energy management optimization model. Through the optimal feasible solution, optimize the task start time of the equipment with shiftable operation time, the power consumption of the equipment with adjustable power during the operation cycle, the start and stop plan of the air conditioner during the operation cycle, and the charging and discharging operation plan of the secondary life battery energy storage system.

[0208] In a specific embodiment of the present invention, the multi-objective community energy management optimization model is solved using the NSGA-III algorithm solution method.

[0209] For the proposed multi-objective EMS model, its decision variables include: the mission start time of multiple TSAs is a discrete integer variable; the operating power consumption of multiple PAAs is a continuous real variable; the start and stop state of AC is a 0-1 binary integer variable; and the charging and discharging power of SL-BESS is a continuous real number variable.

[0210] In the NSGA-III algorithm, each individual is encoded as a 1×N dim A vector where N dim is the number of variables in the individual vector, calculated as follows:

[0211]

[0212] In the formula, |Φ tsa | represents the number of runtime-shiftable devices in the community unit, where each dimension represents the task startup time of a runtime-shiftable device; Indicates the total number of operating time periods of all power-adjustable devices, where each dimension represents the operating power of a power-adjustable device in a time period; |DR ac | / Δt represents the number of operating periods of the air conditioner, where each dimension represents the start and stop status of the air conditioner; T represents the number of operating periods of the secondary-life battery energy storage system, where each dimension represents the charge and discharge power of the secondary-life battery energy storage system in a period.

[0213] The NSGA-III algorithm solves the proposed multi-objective community energy management optimization model using the following algorithm: First, the energy management system parameters are initialized to generate an initial population within NSGA-III. NSGA-III then performs a series of individual selection operations based on the reference point, including reference point design, problem space normalization, association operations, and niche protection. The algorithm then iteratively searches for the optimal feasible solution. The optimization process ends when the maximum iteration time or other convergence criteria are met, resulting in the output of a six-dimensional Pareto hypersurface.

[0214] The specific solution steps are known to those skilled in the art and will not be described in detail in the embodiments of the present invention.

[0215] To facilitate a better understanding for those skilled in the art, in a specific embodiment of the present invention, a multi-objective community energy management method for a controllable device group is used. A numerical simulation is performed using a community unit on a summer weekday as an example. All programs are coded using the MATLAB platform and executed on a personal computer with an 8-core 64-bit Dell Workstation, an Intel Core i5-8250U CPU, and 8GB of RAM.

[0216] This paper reorganizes retired electric vehicle power batteries into an energy storage system to support community energy optimization decision-making. It explores the collaborative optimization scheduling problem of the charging and discharging behavior of the secondary life battery energy storage system, the power consumption behavior of the controllable equipment group, and the local photovoltaic power generation. It proposes a multi-objective community energy management optimization model, which integrates multiple different operating objectives into the energy operation scheduling environment, including energy consumption cost and user multiple residential satisfaction. It designs a solution method based on the NSGA-III algorithm to solve the proposed multi-objective optimization problem. The proposed model and the adopted solution strategy can obtain a satisfactory non-dominated solution set in various community energy management optimization applications, realize the effective trade-off of users' multiple residential needs, and select the optimal compromise solution based on user preferences and other practical considerations.

[0217] This example assumes that the controllable devices include seven devices with shiftable runtimes, three devices with adjustable power, and one air conditioner. Specifically, these devices with shiftable runtimes include a dishwasher, a rice cooker, a washing machine, a clothes dryer, a coffee maker, an oven, and a bread maker; and these devices with adjustable power include a food blender and two lamps. Assume that the rooftop photovoltaic system is configured as follows: the effective area of ​​the photovoltaic panel array is 15m2, and the photovoltaic cell power generation efficiency is 0.164; time-of-use electricity prices are used in the simulation, as shown in Table 2; the controllable device group includes devices with shiftable operating time, power adjustable devices and air conditioners. The flexible power consumption behaviors of these controllable devices are modeled and integrated into the energy management system. The parameter configuration of the controllable energy resources of the energy management system in this embodiment is given in Table 1, where most of the controllable device attribute parameters are derived from the device technical specifications on the official websites of Amazon and Ebay. For the convenience of simulation, some other device parameters will be set in Table 1. DW represents a dishwasher, RC represents an electric rice cooker, WM represents a washing machine, CD represents a clothes dryer, CM represents a coffee machine, OV represents an oven, BM represents a bread maker, FD represents a food blender, and the two lamps are represented as L-1 and L-2 respectively. Figure 8 The prediction data curves of outdoor environment temperature and light intensity curve are given.

[0218] Table 1 Controllable energy resource parameters of energy management system

[0219]

[0220] Table 2 Time-of-use electricity price data

[0221]

[0222] In this embodiment, the energy management scheduling cycle is set to 24 hours (1 day). It is assumed that the user gets up and starts his day from 07:00 in the morning. The unit time interval is 5 minutes, so there are a total of 288 time periods for the entire 24-hour scheduling cycle. Figure 10 In addition, for each runtime-shiftable device, the task completion time limit that the user can tolerate is set to The room heating cycle, that is, the air conditioner usage time range is set from 16:00 in the afternoon to 07:00 the next morning, a total of 15 hours. The maximum noise pressure level that users can tolerate at different times of the day is given in Table 3. The outdoor environmental noise pressure level is as follows Figure 9 shown.

[0223] Table 3 Maximum noise tolerance data for users

[0224]

[0225] In this embodiment, for the NSGA-III algorithm control parameters, the maximum number of iterations and the number of populations are set to 1,000 and 200, respectively, and the crossover and mutation factors are both set to 30.

[0226] To verify the effectiveness of the multi-objective community energy management method in this embodiment, simulation analysis is performed on the following four different scenarios:

[0227] Case 1: The proposed multi-objective community energy management optimization model;

[0228] Scenario 2: Utilizing a second-life battery energy storage system and local renewable energy, and without energy optimization management, each device with shiftable runtime operates at the user's desired task completion time, each device with adjustable power operates at the user's desired power consumption, and the air conditioner operates in free cooling mode.

[0229] Case 3: Without considering the use of second-life battery energy storage systems and local renewable energy, but implementing energy management. Therefore, the energy management system only contains five goals, namely F1, F2, F3, F4 and F6;

[0230] Case 4: Based on Case 1, the capacity decay of the secondary life battery energy storage system is not considered;

[0231] Scenario 5: Traditional methods are used to control the operation of controllable devices. For devices with variable operating times, the operating time of each device is strictly limited to the permitted operating time range. For power-adjustable devices, power is considered adjustable within the specified operating time range. For air conditioners, indoor temperature is strictly controlled within the temperature comfort "dead zone." In this scenario, user dissatisfaction is not calculated during the optimization process; the proposed model is used to quantify dissatisfaction only after the optimization is completed.

[0232] By executing Algorithm 1, the Pareto hypersurface of the proposed multi-objective community energy management optimization problem is obtained in six-dimensional space. Considering the application scenario as a day-ahead energy management problem, the solution time is acceptable. To visualize the output Pareto hypersurface, Figure 11 The mapping results of the six-dimensional Pareto hypersurface in three-dimensional space are shown. It can be seen that the non-dominated solutions obtained by the proposed solution method are relatively evenly distributed in space and show good diversity.

[0233] Non-dominated solutions represent different compromises among all objectives. The final compromise solution is typically selected based on the decision maker's subjective preferences or other practical considerations regarding how to balance different objectives. In the absence of any prior knowledge, several effective methods can be used to select the final compromise solution from non-dominated solutions.

[0234] In this embodiment, by adopting the fuzzy satisfactory decision-making method, the final compromise solution of the proposed multi-objective community energy management optimization model is as follows: Figure 11 Table 4 shows the numerical simulation results for five scenarios. In the table, F1 indicates that the total energy cost of the community unit is minimized during the dispatch period, F2 indicates that users are least dissatisfied with the use of equipment with shiftable operating hours and adjustable power, F3 indicates that users experience minimal thermal discomfort, F4 indicates that users experience minimal auditory discomfort, F5 indicates that the capacity attenuation of the secondary battery energy storage system is minimized, and F6 indicates that the peak-to-average ratio of the net load curve is minimized.

[0235] Table 4 Numerical simulation results under different situations

[0236]

[0237] As can be seen, the proposed method in Scenario 1 effectively balances all user operational considerations. Compared to Scenario 2, which ignores the use of secondary battery energy storage systems and local renewable energy, and does not implement energy management, the proposed energy management system in Scenario 1 significantly reduces energy costs by slightly sacrificing user satisfaction and indoor thermal comfort for runtime-shifting and power-adjustable devices. The cost reduction is approximately 34.62% compared to Scenario 2.

[0238] like Figure 14 and Figure 15 As shown, since Scenario 4 does not consider the capacity degradation of the second-life battery energy storage system, it can be seen that Scenario 4 has lower energy costs, better resident satisfaction, and a lower peak-to-average ratio of the net load curve compared to Scenario 1. However, this results in a larger charge and discharge capacity and a greater number of charge and discharge cycles for the second-life battery energy storage system. These results indicate that while fully utilizing the charge and discharge control of the second-life battery energy storage system can effectively achieve short-term operational goals, it will also result in greater capacity degradation of the second-life battery energy storage system, which is detrimental to long-term economic operation. The numerical results in Table 4 show that for Scenario 2, if the controllable device operation is not optimized, the indoor noise loudness level is the highest compared to Scenarios 1, 3, and 4. The indoor loudness levels in Scenarios 1, 3, and 4 exhibit similar trends: by optimizing the operating time and power consumption of the controllable devices, the indoor noise loudness level can be systematically controlled, minimizing occupant auditory discomfort. According to the numerical results in Table 4, it can be found that for Case 5, although the energy cost and the peak-to-average ratio of the net load curve are lower than those in Case 1 of the proposed method, the residents' dissatisfaction with their living conditions increases significantly.

[0239] In a preferred embodiment of the present invention, the optimization algorithm part can be replaced by a different algorithm.

[0240] This paper proposes a multi-objective community energy management method for controllable device clusters, which has broad application prospects. By reorganizing retired EV power batteries into an energy storage system to support community energy optimization decision-making, this method explores the coordinated optimization scheduling of SL-BESS charging and discharging behaviors, the power consumption behavior of controllable device clusters, and local photovoltaic power generation. A multi-objective community energy management optimization model is proposed, integrating multiple different operational objectives into the energy operation scheduling environment, including energy costs and user satisfaction with multiple residences. A solution method based on the NSGA-III algorithm is designed to solve the proposed multi-objective optimization problem. The proposed model and solution strategy can obtain satisfactory non-dominated solutions in various community energy management optimization applications, achieving effective trade-offs between users' multiple residence needs. The optimal compromise solution can be selected based on user preferences and other practical considerations.

[0241] It is understood from common technical knowledge that the present invention may be implemented by other embodiments that do not depart from its spirit or essential features. Therefore, the embodiments disclosed above are, in all respects, merely illustrative and not exclusive. All modifications within the scope of the present invention or equivalent to the scope of the present invention are intended to be encompassed by the present invention.

[0242] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A multi-objective community energy management method for a controllable device group, characterized in that: The following steps are involved: Step 1: Establish a second-life battery energy storage system operation model; Step 2: Model multiple occupancy dissatisfaction based on the electricity consumption behavior of time-shiftable devices, power-adjustable devices, and air conditioning flexible devices to obtain a multiple occupancy dissatisfaction model. The multiple occupancy dissatisfaction model includes a time-shiftable device usage dissatisfaction model, a power-adjustable device usage dissatisfaction model, an auditory discomfort model, and a thermal discomfort model. Step 3: Construct an objective function to minimize the peak-to-average ratio of the net load curve; Step 4: Establish a multi-objective community energy management optimization model based on the objective function of minimizing the peak-to-average ratio of the net load curve, the second-life battery energy storage system operation model, and the multiple resident dissatisfaction model; Step 5: Use an optimization algorithm to solve the multi-objective community energy management optimization model. Using the optimal feasible solution, optimize the task start time of time-shiftable devices, the power consumption of power-adjustable devices during their operating cycles, the start and stop schedules for air conditioners during their operating cycles, and the charge and discharge schedules for the secondary battery energy storage system. The specific process of establishing the usage dissatisfaction model of runtime-transferable equipment is as follows: According to the flexible power consumption behavior of the runtime-shiftable device, the usage dissatisfaction of the kth runtime-shiftable device It can be calculated as: Where, It represents the time required for the kth running time translatable device to complete its running task; and They represent the earliest and latest task completion times expected by the user respectively; the set of controllable devices is Φ tsa ; Indicates the completion time of the task actually scheduled by a runtime mobile device. The adjustable variable is the startup time of a runtime mobile device. Δt is the unit time interval, γ tsa Indicates the user-specified usage satisfaction factor, which changes with the user's subjective feelings; Indicates the user's expected task completion time.

2. A multi-objective community energy management method for controllable device groups according to claim 1, characterized in that: The specific process of establishing the operation model of the second-life battery energy storage system is as follows: Within the scope of the second life use value of retired power batteries, a power function is used to model the capacity decay of the battery after n charge and discharge cycles within the scope of the second life use value. Calculated as: Where Q0 represents the initial capacity of the battery; n represents the number of charge and discharge cycles; χ represents the capacity attenuation coefficient; τ is the capacity attenuation power exponent; The remaining cycle life of a power battery at retirement is N sec It can be calculated as: Where RC thr is the minimum capacity retention rate threshold; N retire It represents the cumulative number of charge and discharge cycles of a power battery retired from an electric vehicle, and defines N retire It is related to the annual mileage of electric vehicles, the energy consumption per 100 kilometers of electric vehicles, and the rated energy capacity of the battery. The cumulative number of charge and discharge cycles N of a power battery retired from an electric vehicle is retire It can be calculated as: E mile The expected daily mileage of electric vehicles can be calculated as: Where, e and Q represent the energy consumption per 100 kilometers of electric vehicles and the rated energy capacity of the battery respectively; Y retire Indicates the cumulative service life of the battery when it is retired; μ D and σ D represent the mean and standard deviation of daily mileage of electric vehicles; Define the capacity of the secondary life battery energy storage system as A rate According to the two-stage capacity attenuation law of retired batteries, the remaining available capacity A of the secondary life battery energy storage system is SL The calculation is as follows: Where, Indicates charge and discharge cycle N retire Capacity decay after times; Combining the remaining available capacity of the second-life battery energy storage system and the remaining cycle life of the power battery at its retirement, the average attenuation capacity A of the second-life battery energy storage system after a complete charge and discharge cycle is fade Calculated as: A fade =A SL / N sec 。 3. The multi-objective community energy management method for controllable device groups according to claim 1 is characterized in that: The specific process of establishing the dissatisfaction model of power adjustable equipment usage is as follows: According to the flexible power consumption behavior of the adjustable equipment, the dissatisfaction of the use of the kth power adjustable equipment It can be calculated as: Where, represents the expected power consumption of a user of a power-adjustable device, γ paa It represents the user-specified satisfaction factor, which changes with the user's subjective feelings. The set of power-adjustable devices is specified as Φ paa ; Indicates the minimum power consumption of the device specified by the user; Indicates that the adjustable variable is power consumption; Indicates the power consumption expected by the user.

4. The multi-objective community energy management method for controllable device groups according to claim 1, characterized in that: The specific process of establishing the auditory discomfort model is as follows: The controllable equipment group includes equipment with shiftable operating time, power adjustable equipment and air conditioners. It is stipulated that in the time period t, the controllable equipment group, uncontrollable equipment and outdoor environmental noise together form the aggregate noise sound pressure level, the aggregate noise sound pressure level SPL t It can be calculated as: Where, Respectively represent the noise pressure levels released by equipment with adjustable operating time, equipment with adjustable power, and air conditioner operation; Indicates the sound pressure level of noise from uncontrollable equipment and outdoor environment; The start and stop state variables and power consumption of the air conditioner AC; Indicates the start and stop status of the power adjustable device; Indicates the start and stop status of the runtime shiftable device; Stevens power law is used to convert the aggregate noise sound pressure level SPL t and noise sound pressure level threshold Converted into loudness values ​​respectively, the loudness value LU of the user in time period t t It can be calculated as: LU t =(SPL t ) 0.67 The maximum loudness of the user in time period t It can be calculated as: The user's auditory discomfort AD in time period t t It can be calculated as: Where κ represents the auditory comfort factor.

5. The multi-objective community energy management method for controllable device groups according to claim 1 is characterized in that: The specific process of establishing the thermal discomfort model is as follows: The user's thermal discomfort TD at time period t t It can be calculated as: Where σ represents the thermal comfort factor, T t ind Indicates the indoor room temperature; ΔT hy Indicates the temperature 'dead zone' bandwidth or temperature comfort zone bandwidth, T set represents the user's expected indoor temperature, [T set -ΔT hy ,T set +ΔT hy ] is the temperature comfort range; T max Indicates the maximum allowable temperature, T min Indicates the lowest allowable temperature.

6. A multi-objective community energy management method for controllable device groups according to claim 1, characterized in that: The specific process of the established multi-objective community energy management optimization model is as follows: (1) The objective function F1, which aims to minimize the total energy cost of the community unit within the scheduling period, is expressed as: Where, ξ t represents the grid electricity price in period t, P t net represents the net power consumption of the community unit in time period t, P t nca represents the uncontrollable load of the community unit in time period t, P t slbess represents the charge and discharge power of the secondary life battery energy storage system in time period t, where P t slbess ≥0 indicates charging, otherwise it indicates discharging, T indicates the scheduling period; P t solar represents the photovoltaic power generation output during time period t; represents the power consumption of the kth time-shiftable device at time t; represents the power consumption of the air conditioner in time period t; (2) The objective function F2, which aims to minimize user dissatisfaction with the use of devices with shiftable runtime and adjustable power, is expressed as: Where, It represents the dissatisfaction with the use of the k-th running time-shiftable device, It represents the dissatisfaction with the use of the k-th power-adjustable device; and They represent the operation start time and end time of the kth power adjustable device respectively; (3) The objective function F3, which aims to minimize the user's thermal discomfort, is expressed as: In the formula, TD t represents the thermal discomfort of the user in time period t; t ac，1 and t ac，2 They represent the start time and end time of the kth air conditioner respectively; (4) The objective function F4, which aims to minimize the user's auditory discomfort, is expressed as: Where AD t represents the user's auditory discomfort in time period t; (5) The objective function F5, which aims to minimize the capacity attenuation of the secondary life battery energy storage system, is expressed as: Where, represents the depth of discharge of the secondary-life battery energy storage system in the kth half-cycle of the charge and discharge portion; Ξ represents the set of all charge and discharge half-cycles of the secondary-life battery energy storage system in the charge and discharge scheduling period; and They represent the energy levels of the secondary life battery energy storage system at the end of the kth and k-1th partial charge and discharge half cycles respectively; E slbess ,rated Indicates the rated energy capacity of the secondary life battery energy storage system; A fade It represents the average attenuation capacity of a secondary life battery energy storage system after a complete charge and discharge cycle; the parameter kp represents the capacity attenuation exponential coefficient of the secondary life battery; (6) The objective function F6, which aims to minimize the peak-to-average ratio of the net load curve, is expressed as: Where, P t net represents the net power consumption of the community unit in time period t.

7. A multi-objective community energy management method for controllable device groups according to claim 6, characterized in that: Set constraints for the established multi-objective community energy management optimization model: (1) Charge and discharge operation constraints of secondary life battery energy storage system: Where, Indicates the state of charge of the secondary life battery energy storage system at time period t, SOC slbess,min and SOC slbess ,max They represent the minimum state of charge and maximum state of charge allowed by the secondary life battery energy storage system; represents the energy level of the secondary life battery energy storage system at time period t, η slbess,c and η slbess,d They represent the charging efficiency and discharging efficiency of the secondary life battery energy storage system respectively; represents the charge and discharge power of the secondary life battery energy storage system in time period t; P slbess ，rated Indicates the rated charge and discharge power of the secondary life battery energy storage system; Represents the battery load constraint of the secondary life battery energy storage system during the charging and discharging process; SOC desire Indicates the specified threshold value of the state of charge of the retired battery; E slbess，rated Indicates the rated energy level of a second-life battery energy storage system.

8. A multi-objective community energy management method for controllable device groups according to claim 7, characterized in that: The constraints also include: Runtime translatable device operation constraints: Where T represents the scheduling period, Δt represents the unit time interval; Ramp rate constraints for power consumption up / down regulation: Where, Indicates the power change threshold specified by the k-th power-adjustable device; It represents the power consumption of the power-adjustable device at time t-1; Air conditioning operation constraints: Where, t on,min ,t off,min Respectively represent the minimum online and offline time requirements of the AC; Indicates the cumulative opening time; Indicates the cumulative off time.

9. The multi-objective community energy management method for controllable device groups according to claim 1, characterized in that: The multi-objective community energy management optimization model is solved by the NSGA-III algorithm. In the NSGA-III algorithm, each individual is encoded as a 1×N dim A vector where N dim is the number of variables in the individual vector, calculated as follows: In the formula, |Φ tsa | represents the number of devices with shiftable runtime in the community unit; Indicates the total number of operating hours of all power-adjustable devices; |DR ac | / Δt represents the number of operating periods of the air conditioner; T represents the number of operating periods of the secondary life battery energy storage system.

Images