Method and system for determining limited rationality electricity demand response decision of residents in consideration of multi-dimensional living demands

Abstract

The invention discloses a method and a system for determining a resident bounded rational electricity demand response decision by considering multi-dimensional life demands, and the method comprises the steps: building a corresponding load power model according to the production and life demands of a user; according to the non-substitutability of the psychological account, constructing a power utilization cost and comfort evaluation model under each psychological account; correcting each psychological account evaluation model by using a foreground theory, and constructing a comprehensive electricity utilization foreground function of different psychological accounts; combining the power model of the household electrical appliance and the social liability demand constraint to construct a resident finite rationality electricity demand response decision model; and solving the model, and obtaining a bounded rationality electricity demand response decision behavior of the user under the market signal change. According to the method, the limited rationality and psychology of the resident user making the electricity utilization decision under the demand response mechanism are considered, the electricity utilization response decision behavior of the user can be truly reflected, and mining of the adjustable potential of the resident user is promoted.

Description

Technical Field

[0001] The present invention relates to the field of power demand response, and in particular to a method and system for determining residents' limited rational power demand response decision taking into account multi-dimensional life needs. Background Art

[0002] With the continuous advancement of my country's urbanization process, the electricity consumption of urban and rural residents has increased sharply year by year. The summer peak load in many provinces and cities has hit new highs year after year, causing a series of imbalances in electricity supply and demand. It is urgent to strengthen the interaction between the demand side and the power grid to alleviate the pressure on power grid construction. Among them, urban residents account for the highest proportion of demand-side users and have huge demand response potential. With the gradual development of smart grid technology, the information technology of the power system and related smart home appliances have been popularized, providing technical support for residents to participate in demand response. However, due to the complexity of residents' electricity consumption behavior, there is currently no effective demand response mechanism to guide their electricity consumption behavior. In this context, how to effectively characterize the real electricity consumption decision-making behavior of urban residents and then explore the influencing mechanism of residents' response behavior under the demand response mechanism is an important issue that needs to be faced in the formulation and implementation of demand response projects for residential users.

[0003] At present, most of the optimization methods for residential electricity consumption response are based on the assumption of a completely rational "economic man", pursuing the minimization of electricity costs or the maximization of comprehensive utility; some optimization methods also take into account the limited rational psychology of residents in the process of electricity cost evaluation, and apply relevant behavioral economics theories to characterize residents' perception of the gains and losses of electricity costs. However, these optimization methods have not yet involved the behavioral economics theoretical characterization of the subjective feelings of users in other life needs and social needs that affect electricity consumption behavior, and cannot effectively characterize the real electricity decision-making behavior of residents. Therefore, it is necessary to establish a limited rational electricity demand response decision for residents that considers multi-dimensional life needs, conduct a comprehensive evaluation of electricity costs and comfort, and achieve the characterization of the real electricity behavior of residential users under the demand response mechanism. On this basis, an electricity optimization strategy is formulated to promote the exploration of users' adjustable potential. Summary of the invention

[0004] Purpose of the invention: The present invention aims to provide a method for determining the limited rationality electricity demand response decision of users under the demand response mechanism, which takes into account multi-dimensional life needs and can reasonably characterize the real limited rationality of users under the demand response mechanism; another purpose of the present invention is to provide a system for determining the limited rationality electricity demand response decision of residents, which takes into account multi-dimensional life needs.

[0005] Technical solution: The method for determining residents' limited rational electricity demand response decision considering multi-dimensional life needs of the present invention comprises the following steps:

[0006] (1) Classify electrical equipment according to hygiene needs, temperature needs, food needs, and travel needs, and build a load power model based on the power demand regulation characteristics and equipment physical characteristics;

[0007] (2) Establish corresponding mental accounts for hygiene needs, temperature needs, food needs, and travel needs, and construct electricity cost and comfort evaluation models under each mental account;

[0008] (3) Considering the randomness of equipment usage time, outdoor temperature, and mileage, a typical electricity consumption scenario is established. The prospect theory is used to modify the electricity cost and comfort evaluation model under each mental account in step (2) to construct a comprehensive electricity consumption prospect function for different mental accounts.

[0009] (4) Considering the means for residential users to adjust the operating status of each household appliance and the evaluation method of electricity cost and comfort, with the load power model in step (1) and social responsibility requirements as constraints and the maximization of the comprehensive electricity consumption prospect function in step (3) as the goal, a user limited rationality demand response decision model is established;

[0010] (5) A genetic algorithm is used to solve the user's limited rational demand response decision model in step (4) to obtain the user's limited rational electricity demand response decision behavior under changes in market signals.

[0011] Furthermore, the load power model in step (1) includes a load power model of a transferable non-interruptible load, a load power model of a transferable interruptible load, a load power model of a temperature-controlled load, and a load power model of an energy storage load, which are specifically as follows:

[0012] The load power model of transferable non-interruptible load is:

[0013]

[0014] Where, L j,TL represents the power consumption of the transferable and non-interruptible load j during operation; P j,TL represents the operating power of load j; S j,TL (t) represents the operating status of load j at time t; and They represent the start and end time of load j, τ j,TL Indicates the set running time; and They represent the earliest start time and the latest stop time that the user can accept respectively;

[0015] The load power model of transferable and interruptible load is:

[0016]

[0017] Where, L j,IL represents the power consumption of the transferable and interruptible load j during operation; P j,IL represents the operating power of load j; S j,IL (t) represents the operating status of load j at time t; and They represent the start and end time of load j, τ j,IL Indicates the runtime required to complete the task; and They represent the earliest start time and the latest stop time that the user can accept respectively; θ j,IL The working time that the interruptible load j must maintain each time it starts;

[0018] The load power model of the temperature control load is:

[0019]

[0020] Where P b (t) represents the operating power of the temperature control load at time t; Indicates the upper limit of operating power; T in (t), T out (t) represents the internal temperature and external temperature of the load at time t, respectively; ε represents the inertia coefficient of the change of the internal temperature of the load; η represents the heat conduction efficiency; A represents the thermal conductivity; T set (t) represents the set temperature at time t; ΔT represents the maximum allowable temperature deviation; + and - represent temperature control heating and cooling modes, respectively;

[0021] The load power model of energy storage load is:

[0022]

[0023] Where SOC(t) is the state of charge of the energy storage load at time t; P c (t) is the charging power of the energy storage load at time t; η c is the charging efficiency; E is the rated capacity of the energy storage load battery; is the maximum charging power of the energy storage load; SOC ub , SOC lb They are the maximum state of charge and minimum state of charge of the energy storage load respectively.

[0024] Furthermore, in step (1), the electrical equipment is classified according to hygiene requirements, temperature requirements, food requirements and travel requirements, as follows:

[0025] Electrical appliances corresponding to sanitation needs include vacuum cleaners, water heaters, and washing machines;

[0026] Electrical equipment corresponding to temperature requirements include air conditioners;

[0027] Electrical appliances corresponding to food needs include induction cookers and rice cookers;

[0028] The electrical equipment corresponding to travel needs includes electric vehicles.

[0029] Furthermore, the electricity cost and comfort evaluation model under each psychological account in step (2) is as follows:

[0030] The electricity cost C of each mental account is

[0031]

[0032] In the formula, Indicates the startup time of device j; represents the stop time of device j; P j (t) represents the operating power of device j at time t; S j (t) represents the operating status of device j at time t; c(t) represents the time-of-use electricity price at time t;

[0033] Vacuum cleaners, washing machines, rice cookers, induction cookers and range hoods use time offset ratios to represent comfort U com for

[0034]

[0035] In the formula, Indicates the customary start-up time of appliance a; and They represent the earliest start-up time and the latest start-up time that the user can accept for home appliance a respectively;

[0036] The water heater uses the time delay ratio to express the comfort level U com for

[0037]

[0038] In the formula, t wh Indicates the time when the water heater completes its operation task; Indicates the time when the user expects the water heater to complete the operation task; Indicates the latest time that the user can accept for the water heater to complete its operation task;

[0039] The air conditioner uses the indoor temperature deviation ratio to express the comfort level U com for

[0040]

[0041] Where, T(t) represents the actual temperature at time t; T habit Indicates the temperature value that the user is accustomed to; T max and T min Respectively represent the highest and lowest temperatures that the user can accept;

[0042] Electric vehicles use the charge state deviation ratio to express the comfort level U com for

[0043]

[0044] In the formula, SOC represents the actual state of charge of the energy storage load; SOC exp Indicates the user's expected state of charge for the energy storage load; SOC max and SOC min They are the maximum state of charge and minimum state of charge of the energy storage load battery respectively.

[0045] Furthermore, step (3) is specifically as follows:

[0046] Statistics show that N typical electricity consumption scenarios are obtained. Under each electricity consumption scenario, the perceived utility of residential users when evaluating the comfort level of the psychological account i that needs to be calculated K times a day is: The corresponding probability is The electricity cost is

[0047] According to the typical electricity consumption scenario, the comfort value function is constructed to obtain the comfort value function

[0048]

[0049] In the formula, K represents the number of times a user calculates mental account i in a day; represents the comfort level of mental account i during the kth calculation under electricity usage scenario n; α i The risk preference coefficient representing the user's comfort level assessment of mental account i;

[0050] Construct a comfort weight function based on the probability of each electricity usage scenario

[0051]

[0052] In the formula, represents the probability of electricity usage scenario n occurring when the user evaluates the comfort level of psychological account i; γ i represents the risk attitude coefficient when the user evaluates the comfort level of mental account i; based on the historical electricity costs of various household appliances, the electricity cost value function of different mental accounts is constructed

[0053]

[0054] In the formula, C i represents the electricity cost of household appliances corresponding to the psychological account i after the user participates in demand response; α cost represents the risk aversion coefficient of electricity costs; cost and β cost They represent the loss aversion coefficient and risk preference coefficient of electricity cost respectively;

[0055] For mental account i, the results of the comfort evaluation value function are reordered from small to large and represented by a set {1,…,m,…M}. Combined with the comfort weight function and the electricity cost value function, the comprehensive electricity consumption prospect function V of mental account i is obtained. i :

[0056]

[0057] In the formula, the cumulative weight function of cost assessment is cost (1) = 1; The probability of electricity usage scenario m occurring when the user evaluates the comfort level of psychological account i; is the comfort value function of the electricity usage scenario m when the user evaluates the comfort of the psychological account i; the cumulative weight function of the comfort

[0058] Furthermore, the user's limited rational demand response decision model in step (4) is as follows:

[0059] Taking the load power model of step (1) as the constraint and social responsibility demand as the constraint, the user's contribution to the peak load shifting of the power grid is used to represent the social responsibility demand. The user's contribution index H(P all (t)) is

[0060]

[0061] Where P all (t) represents the total power of the device used by the user during period t; Indicates the average power of the user in a day; P sys (t) represents the total power of the system load during period t; is the average power of the system in one day; H0(·) and H(·) represent the contribution of the user before and after the response, respectively; x a =1 indicates that the user has social responsibility needs;

[0062] The goal is to maximize the comprehensive electricity consumption prospect function of each psychological account in step (3). The objective function is as follows:

[0063]

[0064] In the formula, and are the electricity cost weight and comfort weight of mental account i respectively, and

[0065] Furthermore, the determination method also includes

[0066] (6) Under the assumption of complete rationality, a user's completely rational demand response decision model is constructed;

[0067] A genetic algorithm is used to solve the user's completely rational demand response decision model, obtain the user's completely rational electricity demand response decision-making behavior under changes in market signals, and verify the rationality of the user's limited rational response decision model.

[0068] Furthermore, in step (6), the user's fully rational demand response decision model makes electricity consumption decisions with the goal of maximizing utility, as follows:

[0069] maxV=max(-ω c C all +ω u U all )

[0070] Where V is the total utility function; C all The total electricity cost of all household appliances in one day; U all is the normalized sum of comfort levels of all home appliances in a day; ω c and ω u are the weights of the two, ω c +ω u =1.

[0071] Furthermore, the constraints of the user's completely rational demand response decision model and the user's limited rational demand response decision model in step (4) are the same.

[0072] The system for determining residents' limited rationality electricity demand response decision taking into account multi-dimensional life needs of the present invention comprises:

[0073] Model building module, used to build load power model, electricity cost and comfort evaluation model under each psychological account, and limited rational demand response decision model;

[0074] The decision-solving module is used to modify the electricity cost and comfort evaluation model under each psychological account according to the prospect theory, and obtain the comprehensive electricity prospect function of different psychological accounts; considering the means for residential users to adjust the operating status of each household appliance and the electricity cost and comfort evaluation method, with the load power model as the constraint and the maximization of the comprehensive electricity prospect function as the goal, a user limited rational demand response decision model is established; the genetic algorithm is used to solve the user limited rational demand response decision model to obtain the user's optimal limited rational electricity demand response decision behavior under the change of market signals.

[0075] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: 1. The present invention simultaneously takes into account the limited rational psychology of residential users when evaluating electricity costs and electricity comfort, as well as the social responsibility needs of residential users, and combines the mental accounting theory and prospect theory to construct a limited rational electricity demand response optimization model for residents with multi-dimensional life needs. The model can characterize the real decision-making preference behavior of residential users when choosing electricity strategies under the demand response mechanism, thereby promoting the mining of the adjustable potential of residential users and the effective regulation of resources by aggregation subjects in a complex market environment; 2. The present invention also constructs a user's completely rational demand response decision model under the assumption of complete rationality to verify the rationality of the user's limited rational response decision model. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 is a flow chart of the present invention;

[0077] Figure 2 It is a line graph of rigid load power of household appliances;

[0078] Figure 3 A schematic diagram of the usage habits of household appliances;

[0079] Figure 4 It is a schematic diagram of the operation mode of various dispatchable electrical appliances before and after the limited rational demand response of residential users;

[0080] Figure 5 Comparison chart of total load of residential users before and after response. DETAILED DESCRIPTION

[0081] The technical solution of the present invention is further illustrated below in conjunction with the accompanying drawings and specific embodiments.

[0082] like Figure 1 As shown, the method for determining residents' limited rationality electricity demand response decision considering multi-dimensional life needs according to the present invention includes the following steps:

[0083] (1) Classify electrical equipment according to hygiene needs, temperature needs, food needs, and travel needs, and build a load power model based on the power demand regulation characteristics and equipment physical characteristics. The specific contents are as follows:

[0084] First, the residents' hygiene needs, temperature needs, food needs and travel needs are summarized and the related main household appliances are listed, as shown in Table 1. And according to the different electricity usage habits of users and the operating characteristics of household appliances, these household loads are divided into five categories from the perspective of operation and scheduling: basic load, transferable non-interruptible load, transferable interruptible load, temperature control load and energy storage load. Then, the corresponding physical model is established according to the different operating characteristics of the above five types of loads.

[0085] Consider optimizing the operating status of adjustable household appliances in the next day, and divide the day into T time periods on average, with each time period lasting Δt. Since the scheduling interval is generally 15 minutes during the demand response period, Δt = 15 minutes, then T = 96.

[0086] Table 1 Household appliances corresponding to various living needs

[0087]

[0088] Vacuum cleaners, washing machines, rice cookers, and induction cookers are transferable and non-interruptible loads, and the load power model is

[0089]

[0090] Where, L j,TL represents the power consumption of the transferable and non-interruptible load j during operation; P j,TL represents the operating power of load j; S j,TL (t) represents the operating status of load j at time t; and They represent the start and end time of load j, τ j,TL Indicates the set running time; and They represent the earliest start time and the latest stop time that the user can accept.

[0091] The water heater is a transferable and interruptible load, and the load power model is:

[0092]

[0093] Where, L j,IL represents the power consumption of the transferable and interruptible load j during operation; P j,IL represents the operating power of load j; S j,IL (t) represents the operating status of load j at time t; τj,IL Indicates the runtime required to complete the task; and They represent the earliest start time and the latest stop time that the user can accept respectively; θ j,IL The working time that the interruptible load j must maintain each time it is started.

[0094] Air conditioning belongs to temperature control load, and the load power model is as follows:

[0095]

[0096] Where P b (t) represents the operating power of the temperature control load at time t; Indicates the upper limit of operating power; T in (t), T out (t) represents the internal temperature and external temperature of the load at time t, respectively; ε represents the inertia coefficient of the change of the internal temperature of the load; η represents the heat conduction efficiency; A represents the thermal conductivity; T set (t) represents the set temperature at time t; ΔT represents the maximum allowable temperature deviation; + and - represent temperature control heating and cooling modes, respectively.

[0097] Electric vehicles belong to energy storage loads, and the load power model is

[0098]

[0099] Where SOC(t) is the state of charge of the energy storage load at time t; P c (t) is the charging power of the energy storage load at time t; η c is the charging efficiency; E is the rated capacity of the energy storage load battery; is the maximum charging power of the energy storage load; SOC ub , SOC lb They are the maximum state of charge and minimum state of charge of the energy storage load respectively.

[0100] (2) Based on the non-substitutability of "mental accounts", corresponding mental accounts are established for hygiene needs, temperature needs, food needs, and travel needs, and electricity cost and comfort evaluation models under each mental account are constructed. The specific contents are as follows:

[0101] First of all, according to the theory of mental accounting, since the electricity costs and demand satisfaction of different household appliances are non-substitutable, it is necessary to establish corresponding mental accounts for each type of household appliances, including food needs (induction cookers, rice cookers, etc.), temperature needs (air conditioners, etc.), hygiene needs (water heaters, washing machines, etc.), travel needs (electric cars), and other psychological accounts.

[0102] Secondly, the electricity cost evaluation model of each psychological account is constructed. The electricity cost C of each psychological account is

[0103]

[0104] In the formula, Indicates the startup time of device j; represents the stop time of device j; P j (t) represents the operating power of device j at time t; S j (t) represents the operating status of device j at time t; c(t) represents the time-of-use electricity price at time t.

[0105] Then, according to the differences in the way residents perceive the comfort of using different home appliances, a comfort evaluation model for each psychological account is constructed;

[0106] The comfort level will decrease when the vacuum cleaner, washing machine, rice cooker, induction cooker and range hood deviate from the user's usual operating time. Therefore, the time offset ratio is used to represent the comfort level U com for

[0107]

[0108] In the formula, Indicates the customary start-up time of appliance a; and They respectively represent the earliest startup time and the latest startup time of home appliance a that the user can accept.

[0109] Changes in the water heater start-up time or temperature setting may affect the user's bathing plan and reduce the comfort of electricity consumption. Therefore, the time delay ratio is used to represent the water heater U com for

[0110]

[0111] In the formula, t wh Indicates the time when the water heater completes its operation task; Indicates the time when the user expects the water heater to complete the operation task; Indicates the latest time that the user can accept for the water heater to complete its operating task.

[0112] Residential users usually have a habitual temperature setting value, and they will be disgusted when the indoor temperature deviates from the setting value. Therefore, the indoor temperature deviation ratio is used to express the comfort level U com for

[0113]

[0114] Where, T(t) represents the actual temperature at time t; T habitIndicates the most suitable temperature perceived by the user; T max and T min They respectively represent the highest and lowest temperatures that the user can accept.

[0115] Considering the user's mileage anxiety, the state of charge of the electric vehicle during travel will affect the user's travel satisfaction. Therefore, the state of charge deviation ratio is used to express the comfort level U com for

[0116]

[0117] In the formula, SOC represents the actual state of charge of the energy storage load; SOC exp Indicates the user's expected state of charge for the energy storage load; SOC max and SOC min They are the maximum state of charge and minimum state of charge of the energy storage load battery respectively.

[0118] (3) Considering the randomness of equipment usage time, outdoor temperature and mileage, a typical electricity consumption scenario is established. The prospect theory is used to modify the electricity cost and comfort evaluation model under each mental account in step (2), and then a comprehensive electricity consumption prospect function of different mental accounts is constructed. The specific contents are as follows:

[0119] First, based on the randomness of appliance usage time, outdoor temperature, and mileage, corresponding typical electricity usage scenarios were obtained through statistical analysis;

[0120] The randomness of household appliance usage time is modeled as

[0121] Assume that through statistical analysis, it is found that a certain residential user has N typical electricity usage habits, where the startup time of household appliance j under each habit is The corresponding probability is The electricity cost is

[0122] The outdoor temperature randomness is modeled as

[0123] Assume that the air conditioner is running for The typical outdoor temperature set is divided into time intervals of Δt. The corresponding probability is

[0124] The randomness of daily travel distance is modeled as

[0125] Assume that a resident user has N typical travel plans corresponding to daily mileage (d 1 ,…,d n ,…,d N ), and then infer the expected state of charge when traveling the next day The corresponding probability is

[0126] Secondly, the electricity comfort value function is constructed based on typical electricity consumption scenarios. Since residential users have different calculation frequencies for each mental account, the demand satisfaction evaluation value function is obtained as follows:

[0127]

[0128] In the formula, K represents the number of times a user calculates mental account i in a day; represents the comfort utility of mental account i in the kth calculation under electricity usage scenario n; α i The risk preference coefficient representing the user's comfort level assessment of mental account i;

[0129] According to the occurrence probability of each electricity consumption scenario, the comfort weight function is constructed as follows:

[0130]

[0131] In the formula, represents the probability of electricity usage scenario n occurring when the user evaluates the comfort level of psychological account i; γ i It represents the risk attitude coefficient when the user evaluates the comfort level of psychological account i;

[0132] Furthermore, based on the historical electricity costs of various household appliances, the electricity cost value functions of different mental accounts are constructed as follows:

[0133]

[0134] In the formula, C i represents the electricity cost of household appliances corresponding to the psychological account i after the user participates in demand response; α cost represents the risk aversion coefficient of electricity costs; cost and β cost They represent the loss aversion coefficient and risk preference coefficient of electricity cost respectively;

[0135] For mental account i, the results of the comfort evaluation value function are reordered from small to large and represented by a set {1,…,m,…M}. Combined with the comfort weight function and the electricity cost value function, the comprehensive electricity consumption prospect function V of mental account i is obtained. i for

[0136]

[0137] In the formula, the cumulative weight function of cost assessment is cost (1) = 1; The probability of electricity usage scenario m occurring when the user evaluates the comfort level of psychological account i; is the comfort value function of the electricity usage scenario m when the user evaluates the comfort of the psychological account i; the cumulative weight function of the comfort

[0138] (4) Considering the differences in the means by which residential users adjust the operating status of each household appliance and the differences in the way they evaluate electricity costs and comfort, a user limited rationality demand response decision model is established with the load power model in step (1) as a constraint and the maximization of the comprehensive electricity consumption prospect function in step (3) as the goal. The specific contents are as follows:

[0139] Taking the load power model of step (1) as the constraint and social responsibility demand as the constraint, the user's contribution to the peak load shifting of the power grid is used to represent the social responsibility demand. The user's contribution index H(P all (t)) is

[0140]

[0141] Where P all (t) represents the total power of the device used by the user during period t; Indicates the average power of the user in a day; P sys (t) represents the total power of the system load during period t; is the average power of the system in one day; H0(·) and H(·) represent the contribution of the user before and after the response, respectively; x a =1 indicates that the user has social responsibility needs;

[0142] The goal is to maximize the comprehensive electricity consumption prospect function of each psychological account in step (3). The objective function is:

[0143]

[0144] In the formula, and are the electricity cost weight and comfort weight of mental account i respectively, and

[0145] (5) A genetic algorithm is used to solve the user's limited rational demand response decision model in step (4) to obtain the user's limited rational electricity demand response decision behavior under the change of market signals. The specific contents are as follows:

[0146] Under the assumption of complete rationality, a user's completely rational demand response decision model is constructed.

[0147] The electricity consumption decision is made with the goal of maximizing utility, and the objective function is

[0148] maxV=max(-ω c C all +ω u Uall ) (16)

[0149] Where V is the total utility function; C all The total electricity cost of all household appliances in one day; U all is the normalized sum of comfort levels of all home appliances in a day; ω c and ω u are the weights of the two, ω c +ω u =1;

[0150] The constraints of the user's fully rational demand response decision model are the same as those of the user's limited rational demand response decision model in step (4). The constructed model is solved using a genetic algorithm to obtain the user's limited rational and fully rational electricity demand response decision behaviors under market signal changes, and the rationality of the user's limited rational response decision model is verified by comparison.

[0151] The system for determining residents' limited rationality electricity demand response decision taking into account multi-dimensional life needs of the present invention comprises:

[0152] Model building module, used to build load power model, electricity cost and comfort evaluation model under each psychological account, and limited rational demand response decision model;

[0153] The decision-solving module is used to modify the electricity cost and comfort evaluation model under each psychological account according to the cumulative prospect theory, and obtain the comprehensive electricity prospect function of different psychological accounts; considering the means for residential users to adjust the operating status of each household appliance and the electricity cost and comfort evaluation method, with the load power model as the constraint and the maximization of the comprehensive electricity prospect function as the goal, a user limited rational demand response decision model is established; the genetic algorithm is used to solve the user limited rational demand response decision model to obtain the user's optimal limited rational electricity demand response decision behavior under the change of market signals.

[0154] In order to verify the limited rationality electricity demand response decision of residents considering multi-dimensional life needs proposed in the present invention, the optimized operation results of various household appliances under the limited rationality and complete rationality assumptions of residents under time-of-use electricity prices are compared and analyzed. The time-of-use electricity prices are shown in Table 2:

[0155] Table 2 Time-of-use electricity price information

[0156]

[0157] The parameters of household appliances are shown in Table 3:

[0158] Table 3 Parameters of household appliances

[0159]

[0160] Taking the electricity consumption of a single residential user in a certain area of ​​Jiangsu Province in summer as an example, the rigid load power of household appliances is Figure 2 As shown in Figure 2, the usage habits of household appliances are as follows: Figure 3 The genetic algorithm is used to solve the limited rational response decision model of residential users, and the operation mode of various dispatchable electrical appliances before and after the limited rational response of residential users is obtained, as shown in Figure 4 shown.

[0161] The present invention solves the residential user response decision model, and the total load before and after the response is compared. Figure 5 The load characteristics and electricity costs before and after the response are shown in the following table:

[0162] Table 4 Load characteristics and electricity costs before and after response

[0163]

[0164] Depend on Figure 5 From the data in the table, we can see that the limited rationality and completely rational electricity consumption response decisions of residential users under time-of-use electricity prices have significantly reduced the electricity costs of users. Since the limited rationality of users essentially determines that users can only achieve limited optimization to a certain limited rationality level, they have gradually approached the optimal decision-making result of complete rationality.

[0165] The stabilization method and system for residents' limited rational electricity consumption response decision-making considering multi-dimensional life needs described in the present invention can effectively characterize the real electricity consumption behavior of residential users in their daily production and life, proving the effectiveness and feasibility of the method and system proposed in the present invention in describing the real response behavior of residents under the demand response mechanism.

Claims

1. A method for determining residents' limited rational electricity demand response decision considering multi-dimensional life needs, characterized in that: The following steps are involved: (1) Classify electrical equipment according to hygiene needs, temperature needs, food needs, and travel needs, and build a load power model based on the power demand regulation characteristics and equipment physical characteristics; (2) Establish corresponding mental accounts for hygiene needs, temperature needs, food needs, and travel needs, and construct electricity cost and comfort evaluation models under each mental account; (3) Considering the randomness of equipment usage time, outdoor temperature, and mileage, a typical electricity consumption scenario is established. The prospect theory is used to modify the electricity cost and comfort evaluation model under each mental account in step (2) to construct a comprehensive electricity consumption prospect function for different mental accounts. (4) Considering the means for residential users to adjust the operating status of each household appliance and the evaluation method of electricity cost and comfort, with the load power model in step (1) and social responsibility requirements as constraints and the maximization of the comprehensive electricity consumption prospect function in step (3) as the goal, a user limited rationality demand response decision model is established; (5) A genetic algorithm is used to solve the user's limited rational demand response decision model in step (4) to obtain the user's limited rational electricity demand response decision behavior under changes in market signals.

2. The method for determining residents' limited rationality electricity demand response decision considering multi-dimensional life needs according to claim 1 is characterized in that: The load power model in step (1) includes a load power model of a transferable non-interruptible load, a load power model of a transferable interruptible load, a load power model of a temperature-controlled load, and a load power model of an energy storage load, which are specifically as follows: The load power model of transferable non-interruptible load is: Where, L j,TL represents the power consumption of the transferable and non-interruptible load j during operation; P j,TL represents the operating power of load j; S j,TL (t) represents the operating status of load j at time t; and They represent the start and end time of load j, τ j,TL Indicates the set running time; and They represent the earliest start time and the latest stop time that the user can accept respectively; The load power model of transferable and interruptible load is: Where, L j,IL represents the power consumption of the transferable and interruptible load j during operation; P j,IL represents the operating power of load j; S j,IL (t) represents the operating status of load j at time t; and They represent the start and end time of load j, τ j,IL Indicates the runtime required to complete the task; and They represent the earliest start time and the latest stop time that the user can accept respectively; θ j,IL The working time that the interruptible load j must maintain each time it starts; The load power model of the temperature control load is: Where P b (t) represents the operating power of the temperature control load at time t; Indicates the upper limit of operating power; T in (t), T out (t) represents the internal temperature and external temperature of the load at time t, respectively; ε represents the inertia coefficient of the change of the internal temperature of the load; η represents the heat conduction efficiency; A represents the thermal conductivity; T set (t) represents the set temperature at time t; ΔT represents the maximum allowable temperature deviation; + and - represent temperature control heating and cooling modes, respectively; The load power model of energy storage load is: Where SOC(t) is the state of charge of the energy storage load at time t; P c (t) is the charging power of the energy storage load at time t; η c is the charging efficiency; E is the rated capacity of the energy storage load battery; is the maximum charging power of the energy storage load; SOC ub , SOC lb They are the maximum state of charge and minimum state of charge of the energy storage load respectively.

3. The method for determining residents' limited rationality electricity demand response decision considering multi-dimensional life needs according to claim 2 is characterized in that: In step (1), the electrical equipment is classified according to hygiene requirements, temperature requirements, food requirements and travel requirements, as follows: Electrical appliances corresponding to sanitation needs include vacuum cleaners, water heaters, and washing machines; Electrical equipment corresponding to temperature requirements include air conditioners; Electrical appliances corresponding to food needs include induction cookers and rice cookers; The electrical equipment corresponding to travel needs includes electric vehicles.

4. The method for determining residents' limited rationality electricity demand response decision considering multi-dimensional life needs according to claim 3 is characterized in that: The electricity cost and comfort evaluation model under each psychological account in step (2) is as follows: The electricity cost C of each mental account is In the formula, Indicates the startup time of device j; represents the stop time of device j; P j (t) represents the operating power of device j at time t; S j (t) represents the operating status of device j at time t; c(t) represents the time-of-use electricity price at time t; Vacuum cleaners, washing machines, rice cookers, induction cookers and range hoods use time offset ratios to represent comfort U com for In the formula, Indicates the customary start-up time of appliance a; and They represent the earliest start-up time and the latest start-up time that the user can accept for home appliance a respectively; The water heater uses the time delay ratio to express the comfort level U com for In the formula, t wh Indicates the time when the water heater completes its operation task; Indicates the time when the user expects the water heater to complete the operation task; Indicates the latest time that the user can accept for the water heater to complete its operation task; The air conditioner uses the indoor temperature deviation ratio to express the comfort level U com for Where, T(t) represents the actual temperature at time t; T habit Indicates the temperature value that the user is accustomed to; T max and T min Respectively represent the highest and lowest temperatures that the user can accept; Electric vehicles use the charge state deviation ratio to express the comfort level U com for In the formula, SOC represents the actual state of charge of the energy storage load; SOC exp Indicates the user's expected state of charge for the energy storage load; SOC max and SOC min They are the maximum state of charge and minimum state of charge of the energy storage load battery respectively.

5. The method for determining residents' limited rationality electricity demand response decision considering multi-dimensional life needs according to claim 4 is characterized in that: Step (3) is as follows: Statistics show that N typical electricity consumption scenarios are obtained. Under each electricity consumption scenario, the perceived utility of residential users when evaluating the comfort level of the psychological account i that needs to be calculated K times a day is: The corresponding probability is The electricity cost is According to the typical electricity consumption scenario, the comfort value function is constructed to obtain the comfort value function In the formula, K represents the number of times a user calculates mental account i in a day; represents the comfort level of mental account i during the kth calculation under electricity usage scenario n; α i The risk preference coefficient representing the user's comfort level assessment of mental account i; Construct a comfort weight function based on the probability of each electricity usage scenario In the formula, represents the probability of electricity usage scenario n occurring when the user evaluates the comfort level of psychological account i; γ i represents the risk attitude coefficient when the user evaluates the comfort level of mental account i; based on the historical electricity costs of various household appliances, the electricity cost value function of different mental accounts is constructed In the formula, C i represents the electricity cost of household appliances corresponding to the psychological account i after the user participates in demand response; α cost represents the risk aversion coefficient of electricity costs; λ cost and β cost They represent the loss aversion coefficient and risk preference coefficient of electricity cost respectively; For mental account i, the results of the comfort evaluation value function are reordered from small to large and represented by a set {1,…,m,…M}. Combined with the comfort weight function and the electricity cost value function, the comprehensive electricity consumption prospect function V of mental account i is obtained. i : In the formula, the cumulative weight function of cost assessment is cost (1) = 1; The probability of electricity usage scenario m occurring when the user evaluates the comfort level of psychological account i; The comfort value function of the electricity usage scenario m when the user evaluates the comfort of the psychological account i; the cumulative weight function of the comfort 6. The method for determining residents' limited rationality electricity demand response decision considering multi-dimensional life needs according to claim 5 is characterized in that: The user's limited rational demand response decision model in step (4) is as follows: Taking the load power model of step (1) as the constraint and social responsibility demand as the constraint, the user's contribution to the peak load shifting of the power grid is used to represent the social responsibility demand. The user's contribution index H(P all (t)) is Where P all (t) represents the total power of the device used by the user during period t; Indicates the average power of the user in a day; P sys (t) represents the total power of the system load during period t; is the average power of the system in one day; H0(·) and H(·) represent the contribution of the user before and after the response respectively; x a =1 indicates that the user has social responsibility needs; The goal is to maximize the comprehensive electricity consumption prospect function of each psychological account in step (3). The objective function is as follows: In the formula, and are the electricity cost weight and comfort weight of mental account i respectively, and 7. The method for determining residents' limited rationality electricity demand response decision considering multi-dimensional life needs according to claim 6 is characterized in that: The determination method also includes (6) Under the assumption of complete rationality, a user's completely rational demand response decision model is constructed; A genetic algorithm is used to solve the user's completely rational demand response decision model, obtain the user's completely rational electricity demand response decision-making behavior under changes in market signals, and verify the rationality of the user's limited rational response decision model.

8. The method for determining residents' limited rationality electricity demand response decision considering multi-dimensional life needs according to claim 7 is characterized in that: In step (6), the user's completely rational demand response decision model makes electricity consumption decisions with the goal of maximizing utility, as follows: maxV=max(-ω c C all +oh u U all ) Where V is the total utility function; C all The total electricity cost of all household appliances in one day; U all is the normalized sum of comfort levels of all home appliances in a day; ω c and ω u are the weights of the two, ω c +ω u =1.

9. The method for determining residents' limited rationality electricity demand response decision considering multi-dimensional life needs according to claim 8 is characterized in that: The constraints of the user's completely rational demand response decision model and the user's limited rational demand response decision model in step (4) are the same.

10. A system for determining residents' limited rationality electricity demand response decision taking into account multi-dimensional life needs, characterized in that: include Model building module, used to build load power model, electricity cost and comfort evaluation model under each psychological account, and limited rational demand response decision model; The decision-solving module is used to modify the electricity cost and comfort evaluation model under each psychological account according to the prospect theory, and obtain the comprehensive electricity prospect function of different psychological accounts; considering the means for residential users to adjust the operating status of each household appliance and the electricity cost and comfort evaluation method, with the load power model as the constraint and the maximization of the comprehensive electricity prospect function as the goal, a user limited rational demand response decision model is established; the genetic algorithm is used to solve the user limited rational demand response decision model to obtain the user's optimal limited rational electricity demand response decision behavior under the change of market signals.

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