A method and system for determining the decision-making of residents' bounded rational electricity demand response considering multi-dimensional living needs

Abstract

The present invention discloses a method and a system for determining a bounded rationality electricity demand response decision of residents considering multi-dimensional living needs. The method establishes a corresponding load power model according to the production and living needs of users; constructs an electricity cost and comfort evaluation model under each mental account according to the non-substitutability of the "mental account"; uses prospect theory to correct each mental account evaluation model and constructs a comprehensive electricity consumption prospect function for different mental accounts; combines the power model of household appliances and the social responsibility demand constraint to construct a bounded rationality electricity demand response decision model for residents; solves the model to obtain the bounded rationality electricity demand response decision behavior of users under the change of market signals. The present invention considers the bounded rationality psychology of residential users when making electricity use decisions under the demand response mechanism, can truly reflect the electricity use response decision behavior of users, and further promotes the exploration of the adjustable potential of residential users.

Description

Technical Field

[0001] The present invention relates to the field of power demand response, and particularly to a method and system for determining the limited-rational electricity demand response decision of residents considering multi-dimensional living needs. Background Art

[0002] With the continuous advancement of the urbanization process in China, the electricity consumption of urban and rural residents has increased rapidly year by year. The peak loads in summer in many provinces and cities across the country have reached new highs year after year, triggering a series of power supply and demand imbalance problems. It is urgent to strengthen the interaction between the demand side and the power grid to relieve the pressure of power grid construction. Among them, urban residents account for the highest proportion among demand-side users and have great potential for demand response. With the gradual development of smart grid technology, the informatization 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 relatively effective demand response mechanism to guide their electricity consumption behavior. Under this background, 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 to be faced in the formulation and implementation of demand response projects for residential users.

[0003] At present, most of the optimization methods for residents' electricity consumption response are based on the assumption of "economic man" with perfect rationality, pursuing the minimization of electricity consumption cost or the maximization of comprehensive utility; there are also some optimization methods that consider the bounded rationality psychology of residents in the process of electricity consumption cost evaluation and use relevant behavioral economics theories to characterize residents' perception of the gains and losses of electricity consumption costs. However, these optimization methods do not involve the behavioral economics theory representation of the subjective feelings of users' other living needs and social needs, etc., affecting electricity consumption behavior, and cannot effectively characterize the real electricity consumption decision-making behavior of residents. Therefore, establishing a limited-rational electricity demand response decision for residents considering multi-dimensional living needs, comprehensively evaluating electricity consumption cost and comfort, realizing the characterization of the real electricity consumption behavior of residential users under the demand response mechanism, and formulating electricity consumption optimization strategies on this basis to promote the excavation of users' adjustable potential. Summary of the Invention

[0004] Object of the Invention: The present invention aims to provide a method for determining a limited-rational electricity demand response decision that can reasonably characterize the real electricity demand response decision of users under the demand response mechanism considering multi-dimensional living needs; another object of the present invention is to provide a system for determining the limited-rational electricity demand response decision of residents considering multi-dimensional living needs.

[0005] Technical Solution: The method for determining the limited-rational electricity demand response decision of residents considering multi-dimensional living needs according to the present invention includes 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 regulation characteristics of electrical demand and the physical characteristics of the equipment;

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

[0008] (3) Establish typical electrical usage scenarios considering the randomness of equipment usage time, outdoor temperature, and driving mileage, and use prospect theory to correct the evaluation models for electricity costs and comfort under each mental account in step (2) to build a comprehensive electrical usage prospect function for different mental accounts;

[0009] (4) Considering the adjustment means of the operating states of various household appliances by residential users and the evaluation methods of electricity costs and comfort, with the load power model in step (1) and social responsibility requirements as constraints, and the maximization of the comprehensive electrical usage prospect function in step (3) as the goal, establish a user bounded rationality demand response decision-making model;

[0010] (5) Use the genetic algorithm to solve the user bounded rationality demand response decision-making model in step (4) to obtain the bounded rationality electrical usage demand response decision-making behavior of users under market signal changes.

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

[0012] The load power model of shiftable non-interruptible loads is

[0013]

[0014] In the formula, L j,TL represents the power consumption of shiftable non-interruptible load j during operation; P j,TL represents the operating power of load j; S j,TL (t) represents the operating state of load j at time t; and respectively represent the start operating time and end operating time of load j, τ j,TL represents the set operating duration; and respectively represent the earliest start time and latest stop time that the user can accept;

[0015] The load power model of shiftable interruptible loads is

[0016]

[0017] Wherein, L j,IL represents the power consumption of the transferable and interruptible load j during operation; P j,IL represents the operating power of the load j; S j,IL (t) represents the operating state of the load j at time t; and respectively represent the starting operating time and the ending operating time of the load j, τ j,IL represents the operating duration required to complete the task; and respectively represent the earliest start time and the latest stop time acceptable to the user; θ j,IL is the working duration that the interruptible load j must maintain each time it starts;

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

[0019]

[0020] Wherein, P b (t) represents the operating power of the temperature control load at time t; represents the upper limit of the operating power; T in (t), T out (t) respectively represent the internal temperature and the external temperature of the load at time t; ε represents the inertia coefficient of the change in 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 offset; +, - respectively represent the heating and cooling modes of temperature control;

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

[0022]

[0023] Wherein, 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 battery of the energy storage load; is the maximum charging power of the energy storage load; SOC ub , SOC lb are respectively the maximum state of charge and the minimum state of charge of the energy storage load.

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

[0025] The electrical equipment corresponding to the hygiene demand includes vacuum cleaners, water heaters and washing machines;

[0026] The electrical equipment corresponding to the temperature demand includes air conditioners;

[0027] The electrical equipment corresponding to the food demand includes induction cookers and rice cookers;

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

[0029] Furthermore, the electricity consumption cost and comfort evaluation models under each mental account in step (2) are as follows:

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

[0031]

[0032] In the formula, represents the start 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 state 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 the time offset ratio to represent the comfort U com is

[0034]

[0035] In the formula, represents the habitual start time of home appliance a; and respectively represent the earliest start time and the latest start time that the user can accept for home appliance a;

[0036] Water heaters use the time delay ratio to represent the comfort U com is

[0037]

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

[0039] Air conditioners use the indoor temperature offset ratio to represent the comfort U com is

[0040]

[0041] Wherein, T(t) represents the actual temperature at time t; T habit represents the temperature value of the user's habit; T max and T min respectively represent the maximum temperature and the minimum temperature that the user can accept;

[0042] The electric vehicle uses the state of charge offset ratio to represent the comfort U com is

[0043]

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

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

[0046] Statistically obtain N types of typical electricity consumption scenarios. When residents evaluate the comfort of the mental account i that needs to be accounted K times a day under each electricity consumption scenario, the perceived utility is The corresponding probability is The generated electricity cost is

[0047] Construct a comfort value function according to the typical electricity consumption scenarios, and obtain the comfort value function

[0048]

[0049] Wherein, K represents the number of times the user accounts for the mental account i in a day; represents the comfort of the mental account i at the k-th accounting in the electricity consumption scenario n; α i represents the risk preference coefficient of the user for evaluating the comfort of the mental account i;

[0050] Construct a comfort weight function according to the occurrence probability of each electricity consumption scenario

[0051]

[0052] Wherein, represents the probability of the occurrence of the electricity consumption scenario n when the user evaluates the comfort of the mental account i; γ i represents the risk attitude coefficient of the user when evaluating the comfort of the mental account i; Construct an electricity cost value function for different mental accounts according to the historical electricity costs of various household appliances

[0053]

[0054] Wherein, C i represents the electricity consumption cost of the household appliances corresponding to the mental account i after the user participates in the demand response; α cost represents the risk aversion coefficient of the electricity cost; λ cost and β cost represent the loss aversion coefficient and the risk preference coefficient of the electricity cost respectively;

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

[0056]

[0057] Wherein, the cumulative weight function π cost (1)=1; is the probability that the electricity consumption scenario m occurs when the user conducts a comfort evaluation on the mental account i; is the comfort value function of the electricity consumption scenario m when the user conducts a comfort evaluation on the mental account i; the cumulative weight function of comfort

[0058] Furthermore, in step (4), the user's bounded - rationality demand response decision - making model is as follows:

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

[0060]

[0061] Wherein, P all (t) represents the total power of the equipment used by the user at time t; represents the average power of the user in a day; P sys (t) represents the total power of the system load at time t; is the average power of the system in a day; H0(·) and H(·) represent the contributions before and after the user's response respectively; x a =1 indicates that the user has a social responsibility demand;

[0062] Taking the maximum of the comprehensive electricity consumption prospect functions of each mental account in step (3) as the goal, the objective function is as follows:

[0063]

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

[0065] Furthermore, the determination method further includes

[0066] (6) Under the assumption of perfect rationality, construct a user's perfectly rational demand response decision-making model;

[0067] Use the genetic algorithm to solve the user's perfectly rational demand response decision-making model, obtain the perfectly rational electricity demand response decision-making behavior of the user under the change of market signals, and verify the rationality of the user's bounded rationality response decision-making model.

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

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

[0070] In the formula, V is the total utility function; C all is the total electricity cost of all household appliances in a day; U all is the total normalized comfort of all household appliances in a day; ω c and ω u are respectively the weights of the two, ω c + ω u = 1.

[0071] Furthermore, the constraint conditions of the user's perfectly rational demand response decision-making model are the same as those of the user's bounded rationality demand response decision-making model described in step (4).

[0072] The determination system for the bounded rational electricity demand response decision-making of residents considering multi-dimensional living needs according to the present invention includes

[0073] A model construction module, configured to construct a load power model, an electricity cost and comfort evaluation model under each mental account, and a bounded rationality demand response decision-making model;

[0074] A decision-making and solving module is used to correct the electricity consumption cost and comfort evaluation models under each mental account according to prospect theory to obtain the comprehensive electricity consumption prospect function of different mental accounts; considering the adjustment means of the operating states of various household appliances for residential users and the evaluation methods of electricity consumption costs and comfort, with the load power model as a constraint and the maximization of the comprehensive electricity consumption prospect function as the goal, a decision-making model for the limited rational demand response of users is established; a genetic algorithm is used to solve the decision-making model for the limited rational demand response of users to obtain the optimal limited rational electricity demand response decision-making behavior of users under the change of market signals.

[0075] Beneficial effects: Compared with the prior art, the remarkable advantages of the present invention are as follows: 1. The present invention simultaneously considers the limited rational psychology of residential users when evaluating electricity consumption costs and electricity consumption comfort and the social responsibility requirements of residential users, and constructs an optimization model for the limited rational electricity demand response of residents with multi-dimensional living needs by combining the mental account theory and prospect theory. This model can depict the real decision-making preference behavior of residential users when choosing electricity consumption strategies under the demand response mechanism, thereby promoting the excavation of the adjustable potential of residential users and enabling the effective regulation of resources by aggregating entities in a complex market environment; 2. The present invention also constructs a decision-making model for the complete rational demand response of users under the assumption of complete rationality to verify the rationality of the decision-making model for the limited rational response of users. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

[0080] Figure 5 is a comparison graph of the total load of residential users before and after the response. DETAILED DESCRIPTION OF THE INVENTION

[0081] The technical solution of the present invention will be further clarified below with reference to the drawings and specific embodiments.

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

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

[0084] First, summarize and list the main household appliances related to residents' hygiene needs, temperature needs, food needs, and travel needs, as shown in Table 1. And according to the different electricity consumption habits of users and the operating characteristics of household appliances, these household loads are divided into 5 categories from the perspective of operation scheduling: basic load, transferable non-interruptible load, transferable interruptible load, temperature control load, and energy storage load. Then, establish corresponding physical models 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. Divide a day into T time periods on average, and the duration of each time period is Δt. Since within the demand response period, generally 15 minutes is used as a scheduling interval, so take Δt = 15 min, then T = 96.

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

[0087]

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

[0089]

[0090] In the formula, L j,TL represents the power consumption of the transferable 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 respectively represent the starting operating time and the ending operating time of load j, and τ j,TL represents the set operating duration; and respectively represent the earliest start time and the latest stop time that the user can accept.

[0091] Water heaters belong to transferable interruptible loads, and the load power model is

[0092]

[0093] In the formula, L j,IL represents the power consumption of the transferable 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 running duration required to complete the task; and respectively represent the earliest start time and the latest stop time that the user can accept; θ j,IL is the working duration that the interruptible load j must maintain each time it starts.

[0094] Air conditioners belong to temperature-controlled loads, and the load power model is shown as follows:

[0095]

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

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

[0098]

[0099] In the formula, 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 battery of the energy storage load; is the maximum charging power of the energy storage load; SOC ub 、SOC lb represent the maximum state of charge and the minimum state of charge of the energy storage load respectively.

[0100] (2) According to the non-substitutability of the "mental account", establish corresponding mental accounts for health needs, temperature needs, food needs, and travel needs, and construct the electricity cost and comfort evaluation models under each mental account. The specific content is as follows:

[0101] First, according to the mental account theory, since the electricity costs and demand satisfaction degrees of different household appliances are all non-substitutable, it is necessary to establish corresponding mental accounts for various types of household appliances respectively, including mental accounts for food needs (induction cookers, rice cookers, etc.), temperature needs (air conditioners, etc.), health needs (water heaters, washing machines, etc.), travel needs (electric vehicles), etc.

[0102] Secondly, an electricity cost evaluation model for each mental account is constructed. The electricity cost C of each mental account is

[0103]

[0104] In the formula, represents the starting time of device j; represents the stopping time of device j; P j (t) represents the operating power of device j at time t; S j (t) represents the operating state 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 perception methods of residential users for the comfort of different household appliances, a comfort evaluation model for each mental account is constructed;

[0106] When the operating time of vacuum cleaners, washing machines, rice cookers, induction cookers, and range hoods deviates from the user's habits, the comfort level will decrease. Therefore, the time deviation ratio is used to represent the comfort U com is

[0107]

[0108] In the formula, represents the habitual starting time of household appliance a; and respectively represent the earliest starting time and the latest starting time that the user can accept for household appliance a.

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

[0110]

[0111] In the formula, t wh represents the moment when the water heater completes its operation task; represents the expected moment when the user expects the water heater to complete its operation task; represents the latest moment when the user can accept the water heater to complete its operation task.

[0112] Residential users usually have a habitual temperature setting value and will feel disgusted with the situation where the indoor temperature deviates from the set value. Therefore, the indoor temperature deviation ratio is used to represent the comfort U com is

[0113]

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

[0115] Considering the user's range anxiety psychology, the state of charge of an electric vehicle during travel affects the user's travel satisfaction. Therefore, the state of charge offset ratio is used to represent the comfort U com is

[0116]

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

[0118] (3) Establish typical power consumption scenarios by considering the randomness of equipment usage time, outdoor temperature, and driving mileage. Use prospect theory to correct the power consumption cost and comfort evaluation models under each psychological account in step (2), and then construct a comprehensive power consumption prospect function for different psychological accounts. The specific content is as follows:

[0119] First, according to the randomness of home appliance usage time, outdoor temperature, and driving mileage, the corresponding typical power consumption scenarios are obtained through statistical analysis;

[0120] Modeling the randomness of home appliance usage time as

[0121] Assume that through statistical analysis, a certain residential user has N types of typical power consumption habits, and the startup time of home appliance j under each habit The corresponding probability is The power consumption cost generated is

[0122] Modeling the randomness of outdoor temperature as

[0123] Assume that the air conditioner operation period is Divided at time intervals of Δt, the set of typical outdoor temperatures The corresponding probability is

[0124] Modeling the randomness of daily travel mileage as

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

[0126] Secondly, construct the value function of power consumption comfort according to typical power consumption scenarios. Since the accounting frequencies of each mental account for residential users are different, the demand satisfaction evaluation value function is obtained as

[0127]

[0128] In the formula, K represents the number of times the user accounts for mental account i in a day; represents the comfort utility of mental account i at the k-th accounting in power consumption scenario n; α i represents the risk preference coefficient of the user for the comfort evaluation of mental account i;

[0129] According to the occurrence probabilities of each power consumption scenario, construct the comfort weight function as

[0130]

[0131] In the formula, represents the probability of the occurrence of power consumption scenario n when the user conducts a comfort evaluation of mental account i; γ i represents the risk attitude coefficient of the user for the comfort evaluation of mental account i;

[0132] Furthermore, according to the historical power consumption costs of various household appliances, construct the power consumption cost value function of different mental accounts as

[0133]

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

[0135] For mental account i, reorder the results obtained from the comfort evaluation value function from small to large, and represent them with a set as {1,…,m,…M}. Combining the comfort weight function and the power consumption cost value function, obtain the comprehensive power consumption prospect function V of mental account i i as

[0136]

[0137] In the formula, the cumulative weight function π of the cost evaluation cost (1) = 1; is the probability of the occurrence of power consumption scenario m when the user conducts a comfort evaluation of mental account i; The comfort value function of the electricity consumption scenario m when the user evaluates the comfort level of the mental account i; the cumulative weight function of comfort

[0138] (4) Considering the differences in the adjustment means of the operating states of various household appliances for residential users, as well as the differences in electricity consumption costs and comfort evaluation methods, taking the load power model in step (1) as the constraint and maximizing the comprehensive electricity consumption prospect function in step (3) as the goal, a user bounded rationality demand response decision-making model is established, and the specific content is as follows:

[0139] Taking the load power model in step (1) as the constraint and the social responsibility demand as the constraint, using the contribution degree of the user's participation in the power grid peak shaving and valley filling to represent the social responsibility demand, and the contribution degree index H(P all (t)) is

[0140]

[0141] In the formula, P all (t) represents the total power of the equipment used by the user at time t; represents the average power of the user in a day; P sys (t) represents the total power of the system load at time t; is the average power of the system in a day; H0(·) and H(·) respectively represent the contribution degrees before and after the user's response; x a =1 indicates that the user has social responsibility demands;

[0142] Taking the maximization of the comprehensive electricity consumption prospect function of each mental account in step (3) as the goal, the objective function is

[0143]

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

[0145] (5) Using the genetic algorithm to solve the user bounded rationality demand response decision-making model in step (4), and obtaining the bounded rational electricity demand response decision-making behavior of the user under the change of market signals, the specific content is as follows:

[0146] Under the assumption of perfect rationality, a user perfect rationality demand response decision-making model is constructed.

[0147] Making electricity consumption decisions with the goal of maximizing utility, the objective function is

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

[0149] Wherein, V is the total utility function; C all is the total electricity cost of all household appliances in a day; U all is the total normalized comfort level of all household appliances in a day; ω c and ω u are the weights of the two respectively, ω c + ω u = 1;

[0150] The constraint conditions of the user's perfectly rational demand response decision-making model are the same as those of the user's bounded rational demand response decision-making model in step (4). The constructed model is solved by using the genetic algorithm to obtain the bounded rationality and perfectly rational electricity demand response decision-making behaviors of the user under the change of market signals, and to compare and verify the rationality of the user's bounded rational response decision-making model.

[0151] The determination system for the bounded rational electricity demand response decision-making of residents considering multi-dimensional living needs according to the present invention includes

[0152] a model construction module for constructing a load power model, an electricity cost and comfort evaluation model under each mental account, and a bounded rational demand response decision-making model;

[0153] a decision-making solution module for correcting the electricity cost and comfort evaluation model under each mental account according to the cumulative prospect theory to obtain a comprehensive electricity prospect function for different mental accounts; considering the adjustment means of the operating states of various household appliances by resident users and the evaluation methods of electricity costs and comfort levels, taking the load power model as a constraint, and aiming at maximizing the comprehensive electricity prospect function, establishing a user's bounded rational demand response decision-making model; using the genetic algorithm to solve the user's bounded rational demand response decision-making model to obtain the optimal bounded rational electricity demand response decision-making behavior of the user under the change of market signals.

[0154] To verify the bounded rational electricity demand response decision-making of residents considering multi-dimensional living needs proposed by the present invention, the optimized operation results of various household appliances under the assumptions of bounded rationality and perfect rationality of resident users under time-of-use electricity prices are compared and analyzed. The time-of-use electricity price is 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 on a summer day as an example, the rigid load power of household appliances is as Figure 2 shown, and the usage habits of household appliances are as Figure 3 shown. The genetic algorithm is used to solve the bounded rationality response decision model of residential users, and the operation modes of various schedulable appliances before and after the bounded rationality response of residential users are obtained, as Figure 4 shown.

[0161] In the present invention, when solving the response decision model of residential users, the comparison of the total load before and after the response is as Figure 5 shown, and the load characteristics and electricity consumption costs before and after the response are shown in the following table:

[0162] Table 4 Load Characteristics and Electricity Consumption Costs before and after the Response

[0163]

[0164] From Figure 5 and the data in the table, it can be seen that under the time-of-use electricity price, the bounded rationality and fully rational electricity response decisions of residential users both significantly reduce the electricity consumption costs of users. Since the bounded rationality of users essentially determines that users can only achieve limited optimization to a certain extent of bounded rationality, and it has gradually approached the optimal decision result of full rationality.

[0165] The method and system for stabilizing the bounded rationality electricity response decision of residents considering multi-dimensional living needs described in the present invention can effectively depict 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) Using a genetic algorithm 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; 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 accounting 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 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 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; a day is evenly divided into T time periods, and the duration of each time period is Δt; 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 Where, 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: 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 H(·)<H0(·),x a =1 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.

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 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.

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: 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.

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: 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.

9. A system for determining a method for determining a resident's limited rational electricity demand response decision taking into account multi-dimensional life needs as described in any one of claims 1 to 8, 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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