An Optimization Method for Thermoelectric Integrated Demand Response Considering Multiple Influencing Factors

Abstract

The present invention relates to a multi-objective optimization method for integrated thermoelectric demand response considering multiple influencing factors. The method includes: introducing a load aggregator and dividing residential users into several categories of residential users with different energy consumption habits; determining the influencing factors that affect residential users' participation in integrated demand response; classifying the energy consumption loads of residential users' participation in integrated demand response; according to the determined influencing factors and the classification of energy consumption loads, for each category of residential users, respectively establishing a multi-objective optimization mathematical model for residential users' participation in integrated demand response; solving the multi-objective optimization mathematical model to obtain the best reward mechanism that maximizes the interests of the load aggregator and residential users, including the allowable scheduling load amount for each category of energy consumption load of residential users and the unit price for obtaining corresponding rewards. Compared with the prior art, the present invention fully excavates adjustable resources, and can help users change their energy consumption patterns, reduce energy consumption, and save energy costs without affecting the comfort of residential users.

Description

Technical Field

[0001] The present invention relates to the technical field of energy optimization, and in particular to a comprehensive thermoelectric demand response optimization method considering multiple influencing factors. Background Art

[0002] With the proposal of the "dual carbon" policy, people's attention to renewable energy has been increasing. Due to the inherent intermittency and uncertainty of renewable energy, a high proportion of renewable energy access will affect the security and stability of the operation of the energy system. To solve this problem, from the supply side, most research has been done at home and abroad on modeling the uncertainty of renewable energy, modeling production capacity equipment, and coupling modeling between the two. With the upgrading of the energy system and energy technology, the demand side gradually dominates in the supply-demand system. Thus, the concept of demand response has emerged. Exploring the energy consumption characteristics of the demand side, understanding the energy consumption behavior of end-users, and flexibly allocating demand-side resources are effective ways to solve the supply-demand balance problem and improve system energy efficiency. Due to the characteristics of single residential users such as insufficient response ability, serious energy consumption waste, and accounting for more than one-third of the total electricity consumption in the whole society, in view of this phenomenon, introducing a load aggregator to aggregate a large number of adjustable resources of residential users and exploring the demand response potential of residential users can effectively relieve the pressure on the power grid and improve the consumption rate of renewable energy.

[0003] Most of the mechanism research on improving demand response ability is divided into price-based demand response and incentive-based demand response. Price-based demand response is further divided into time-of-use electricity price mechanism and real-time electricity price mechanism. Mainly, users spontaneously carry out load curtailment and load transfer according to the different prices at each time period or each moment. However, for price-based demand response, there are characteristics such as insufficient response ability or unstable response, with large randomness and uncertainty. In comparison, incentive-based demand response can better mobilize the enthusiasm of users to participate. However, in the existing research on demand response, most consider single influencing factors or conduct demand response on single electrical load or thermal load, and the mechanism research on comprehensive thermoelectric demand response is still in the development stage. Summary of the Invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned existing technologies and provide a comprehensive thermoelectric demand response optimization method considering multiple influencing factors.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A multi-objective optimization method for comprehensive thermoelectric demand response considering multiple influencing factors, the method includes:

[0007] Introduce a load aggregator to conduct clustering analysis on the energy consumption loads of residential users, and divide the residential users into several types of residential users with different energy consumption habits;

[0008] Determine the influencing factors that affect residential users' participation in integrated demand response, including: the energy consumption comfort of residential users, the consumption psychological curve driven by price, and the dissatisfaction brought about by participating in integrated demand response;

[0009] Classify the energy consumption loads of residential users participating in integrated demand response, including reducible electric loads, shiftable electric loads, flexibility-regulated electric loads, and reducible heat loads;

[0010] According to the determined influencing factors and the classification of energy consumption loads, establish a multi-objective optimization mathematical model for each type of residential user to participate in integrated demand response;

[0011] Solve the multi-objective optimization mathematical model to obtain the best reward mechanism that maximizes the interests of load aggregators and residential users, including the allowable dispatching load volume of each type of energy consumption load of residential users and the unit price of the corresponding rewards obtained.

[0012] Preferably, the multi-objective optimization mathematical model includes an objective function and constraint conditions, and the objective function includes an objective function for maximizing the profit of the load aggregator and an objective function for maximizing the welfare of residential users.

[0013] Preferably, the objective function for maximizing the profit of the load aggregator is expressed as:

[0014]

[0015]

[0016]

[0017] Pen C =Pen ELC +Pen ELS +Pen EFCL +Pen HLC

[0018]

[0019]

[0020]

[0021]

[0022] Among them, F1 is the profit of the load aggregator, L bid is the bidding load volume of the load aggregator in the load market, ρda is the price set by the load market for the bidding volume of the load aggregator, are the rewards obtained by the user's demand response for participating in the curtailable electric load, shiftable electric load, flexibility-regulated electric load, and curtailable heat load at time t, respectively. Pen C is the total income for punishing the user, Pen ELC Pen ELS Pen EFCL Pen HLC are the penalty incomes for the curtailable electric load, shiftable electric load, flexibility-regulated electric load, and curtailable heat load to the user, respectively. ε ELC ε ELS ε EFCL ε HLC are the penalty coefficients for the curtailable electric load, shiftable electric load, flexibility-regulated electric load, and curtailable heat load to the user, respectively. Pen A is the penalty cost of the load market to the load aggregator, ε A is the penalty coefficient for the load aggregator due to incorrect bidding in the load market, are the planned curtailable electric load response volume, planned shiftable electric load response volume, planned flexibility-regulated electric load response volume, and planned curtailable heat load response volume at time t, respectively. are the actual curtailable electric load response volume, actual shiftable electric load response volume, actual flexibility-regulated electric load response volume, and actual curtailable heat load response volume at time t, respectively. T represents the 24-hour cycle of a day, and the time interval is one hour.

[0023] Preferably, the welfare maximization objective function of the residential user is expressed as:

[0024]

[0025]

[0026] where F2 is the welfare of the residential user, are the rewards obtained by the user's demand response for participating in the curtailable electric load, shiftable electric load, flexibility-regulated electric load, and curtailable heat load at time t, respectively. μ ELC μ ELS μ HLC are the dissatisfaction coefficients for participating in the demand response of the curtailable electric load, the dissatisfaction coefficient for participating in the demand response of the shiftable electric load, and the dissatisfaction coefficient for participating in the demand response of the flexibility-regulated electric load, respectively.

[0027] Preferably, the reward obtained by the user for participating in the demand response of the curtailable electric load is obtained in the following way:

[0028]

[0029]

[0030]

[0031]

[0032] Among them, is the original electricity load of the user at time t, is the slope of the consumption psychology curve of the user for the curtailable electricity load, is the real-time curtailable electricity load price determined by the reward mechanism at time t, is the lower limit of the curtailable electricity load price, is the upper limit of the curtailable electricity load price, is the maximum load transfer rate of the curtailable electricity load, α ELC is the real response coefficient of the curtailable electricity load, is the reward unit price of the curtailable electricity load for the user to participate in the demand response at time t determined by the reward mechanism, is the electricity price of the original electricity load of the user at time t.

[0033] Preferably, the reward obtained by the user for participating in the demand response of the transferable electricity load is obtained in the following way:

[0034]

[0035]

[0036]

[0037]

[0038] Among them, is the original electricity load of the user at time t, is the slope of the consumption psychology curve of the user for the transferable electricity load, is the real-time transferable electricity load price determined by the reward mechanism at time t, is the lower limit of the transferable electricity load price, is the upper limit of the transferable electricity load price, is the maximum load transfer rate of the transferable electricity load, α ELS is the real response coefficient of the transferable electricity load, is the reward unit price of the transferable electricity load for the user to participate in the demand response at time t determined by the reward mechanism.

[0039] Preferably, the reward obtained by the user participating in the flexible regulation of the electrical load demand response is obtained in the following manner:

[0040]

[0041]

[0042] where is the reward unit price for the user's participation in the demand response at time t for the flexible regulation of the electrical load determined by the reward mechanism.

[0043] Preferably, the reward obtained by the user participating in the flexible regulation of the heat load demand response is obtained in the following manner:

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] where is the planned flexible heat load response volume at time t, is the actual flexible heat load response volume at time t, is the heat load after reduction at time t, is the heat load of residential users at time t, T t out is the outdoor temperature at time t, T t in is the indoor temperature at time t, is the indoor temperature at time t + 1, R is the equivalent thermal resistance of the building, C air is the specific heat capacity of indoor air, Δt is the time interval, α HLC is the actual response coefficient of the flexible heat load, is the reward unit price for the user's participation in the demand response at time t for the flexible heat load determined by the reward mechanism.

[0050] Preferably, the constraints include:

[0051] Constraints on the reward unit price determined by the reward mechanism:

[0052]

[0053]

[0054]

[0055]

[0056] Among them, are respectively the reward unit prices of the reducible electricity load, transferable electricity load, flexibility-regulated electricity load, and reducible heat load that can be participated by users in demand response determined by the reward mechanism at time t, are respectively the minimum values of the reward unit prices of the reducible electricity load, transferable electricity load, flexibility-regulated electricity load, and reducible heat load, are respectively the maximum values of the reward unit prices of the reducible electricity load, transferable electricity load, flexibility-regulated electricity load, and reducible heat load;

[0057] When residential users participate in the demand response of the reducible heat load, considering the energy consumption comfort of residential users, a constraint on the energy consumption comfort of residential users is formed:

[0058] T t min ≤T t in

[0059] T t min is the minimum indoor temperature to ensure user comfort at time t, is the indoor temperature at time t;

[0060] When residential users participate in the demand response of the reducible electricity load and transferable electricity load, considering the consumption psychological curve, a constraint on the consumption psychological curve is formed:

[0061]

[0062]

[0063] is the maximum load transfer rate of the reducible electricity load, is the maximum load transfer rate of the transferable electricity load, λ 1 、λ 2 are the maximum load transfer rate constraint constants.

[0064] Preferably, the NSGA-II genetic algorithm is used to solve the multi-objective function.

[0065] Compared with the prior art, the present invention has the following advantages:

[0066] On the one hand, the present invention fully exploits and aggregates the adjustable resources of residential users, which can effectively relieve the peak pressure of the power grid and improve the consumption rate of renewable energy. On the other hand, without affecting the comfort of residential users, it can help users change their energy consumption patterns, reduce energy consumption, and save energy costs. Description of the Drawings

[0067] Figure 1 It is a flowchart of a multi-objective optimization method for integrated heat and power demand response considering multiple influencing factors according to the present invention;

[0068] Figure 2 It is an elbow graph for determining the optimal number of clusters in the clustering analysis of the energy consumption load of residential users according to the present invention;

[0069] Figure 3 It is the original electrical load of four different types of residential users after clustering analysis according to the embodiments of the present invention and the average electrical load of residential users without clustering analysis.

[0070] Figure 4 It is the original heat load of four different types of residential users after clustering analysis according to the embodiments of the present invention and the average heat load of residential users without clustering analysis;

[0071] Figure 5 It is the reward mechanism and response volume for the reducible electrical load taking the first type of residential user as an example according to the present invention;

[0072] Figure 6 It is the reward mechanism and response volume for the shiftable electrical load taking the first type of residential user as an example according to the present invention;

[0073] Figure 7 It is the reward mechanism and response volume for the flexibility-regulated electrical load taking the first type of residential user as an example according to the present invention;

[0074] Figure 8 It is the reward mechanism and response volume for the reducible heat load taking the first type of residential user as an example according to the present invention;

[0075] Figure 9 It is a Pareto solution set graph of a dual-objective under maximizing the interests of load aggregators and user welfare according to the embodiments of the present invention. Detailed Embodiments

[0076] The present invention will be described in detail below with reference to the drawings and specific embodiments. Note that the following description of the embodiments is only illustrative in nature, and the present invention is not intended to limit the objects to which it is applicable or its uses, and the present invention is not limited to the following embodiments.

[0077] Embodiment

[0078] The technical problems solved by the present invention are as follows: ignoring the psychological and behavioral changes of residential users in participating in integrated demand response in actual situations; not considering the excavation of the response potential of residential users, and how to help users reduce energy consumption and save costs without affecting the normal energy use of users.

[0079] To solve the above technical problems, the present invention provides the following technical solutions: considering the comfort level of residential users when participating in integrated demand response, the actual consumption psychology driven by price, and the dissatisfaction after participating in integrated demand response, transforming the three into the form of functions, integrating them into the multi-objective optimization model, and dividing the energy consumption load of residential users into reducible electric load, transferable electric load, flexible regulation electric load, and reducible heat load, so as to more deeply excavate the response potential of residential users.

[0080] Specifically, as Figure 1 shown, this embodiment provides a multi-objective optimization method for integrated thermoelectric demand response considering multiple influencing factors, and the method includes:

[0081] Introduce a load aggregator to conduct clustering analysis on the energy consumption load of residential users, and divide residential users into several types of residential users with different energy use habits;

[0082] Determine the influencing factors that affect residential users' participation in integrated demand response, including: the energy use comfort level of residential users, the consumption psychological curve driven by price, and the dissatisfaction brought about by participating in integrated demand response;

[0083] Classify the energy consumption load of residential users participating in integrated demand response, including reducible electric load, transferable electric load, flexible regulation electric load, and reducible heat load;

[0084] According to the determined influencing factors and the classification of energy consumption load, for each type of residential user, establish a multi-objective optimization mathematical model for residential users to participate in integrated demand response;

[0085] Solve the multi-objective optimization mathematical model to obtain the best reward mechanism that maximizes the interests of the load aggregator and residential users, including the allowable dispatching load amount of each type of energy consumption load of residential users and the unit price for obtaining rewards corresponding thereto.

[0086] First, use the K-Means theory to conduct clustering analysis on users according to the energy consumption load of residential users, and select the elbow method to determine the optimal number of clusters,

[0087]

[0088] where k is the number of clusters, C i is the i-th cluster, p is the sample point in C i i.e., the energy consumption load of users, mi is the centroid of C i (the mean value of all samples in C i , SSE is the clustering error of all samples, representing the quality of the clustering effect. As the number of clusters k increases, the sample division will be more detailed. When the number of clusters reaches the optimum, the decline rate of SSE will suddenly decrease. As the value of k increases, SSE will continue to increase and tend to level off. Specifically, as shown in Figure 2 .

[0089] In this embodiment, the residential users are divided into four categories through clustering. The electricity load demand curves of the four categories of users are as shown in Figure 3 . The electricity load demand curves of the four categories of users are as shown in Figure 4 .

[0090] As the determination of the method of the integrated heat and electricity comprehensive demand response reward mechanism for residential users proposed in the present invention considering various influencing factors, it mainly includes three influencing factors: user comfort, the consumption psychology curve driven by price, and dissatisfaction

[0091] 1. User comfort

[0092] When no load curtailment is carried out, the indoor temperature remains at the ideal temperature. After curtailment, the temperature decreases. Considering the user's comfort, a lower temperature limit should be set when curtailing the heat load

[0093] T t min ≤T t in

[0094] wherein, T t min is the minimum indoor temperature to ensure user comfort, and T t in is the indoor temperature. In the embodiment of the present invention, the minimum indoor temperature is determined to be 18°C

[0095] 2. For the consumption psychology curve driven by price

[0096] According to the principles of consumer psychology, there is a just noticeable difference (difference threshold) for the stimulation of users. Within this difference threshold range, users basically have no response or a very small response, that is, the insensitive period (equivalent to the dead zone); when exceeding this difference threshold range, users will have a response, and it is related to the degree of stimulation, that is, the normal response period (equivalent to the linear region); users also have a saturation value for the stimulation. When exceeding this value, users will not have a further response, that is, the response limit period (equivalent to the saturation region)

[0097] The concept of load transfer rate is introduced. The load transfer rate is defined as the ratio of the amount of user load transferred from high - price periods to low - price periods to the high - price - period load after the implementation of time - of - use electricity prices. The user response model based on the load transfer rate can be approximately fitted into a piece - wise linear function.

[0098]

[0099] Among them, j represents the j - th type of user, and λ pv is the transfer rate from the peak period to the valley period, and Δρ pv is the difference between the peak - period electricity price and the valley - period electricity price, and a pv is the dead - zone threshold, is the maximum load transfer rate from the peak period to the valley period under the change of the peak - valley electricity price difference, and K pv is the slope of the linear region of the piece - wise linear peak - valley period transfer rate curve. In the embodiment of the present invention, K pv is determined to be 0.26, a pv is determined to be 0.021, and is determined to be 0.08.

[0100] 3. Dissatisfaction with the inconvenience caused by participating in the integrated demand response

[0101] Dis = ∑Dis ELC + Dis ELS + Dis HLC

[0102]

[0103]

[0104]

[0105] Among them, Dis is the sum of the dissatisfaction with the reducible electricity and heat loads and the transferable electricity load. Dis ELC , Dis ELS , Dis HLC are the dissatisfaction with the reducible electricity load, the transferable electricity load, and the reducible heat load respectively. Since the flexible regulation of the electricity load is controlled by the user himself, its dissatisfaction is 0. μ ELC , μ ELS , μ HLC are the dissatisfaction coefficients of the reducible electricity load, the transferable electricity load, and the flexibly regulated electricity load respectively, is the actual response amount at time t, is the planned response amount at time t. In the embodiment of the present invention, μ ELC , μ ELS , μ HLC are determined to be 0.01.

[0106] Based on the above, the established multi-objective optimization mathematical model of the present invention includes an objective function and constraint conditions. The objective function includes an objective function for maximizing the profit of the load aggregator and an objective function for maximizing the welfare of residential users.

[0107] The objective function for maximizing the profit of the load aggregator is expressed as:

[0108]

[0109]

[0110]

[0111] Pen C = Pen ELC + Pen ELS + Pen EFCL + Pen HLC

[0112]

[0113]

[0114]

[0115]

[0116] Among them, F1 is the profit of the load aggregator, L bid is the bid load quantity of the load aggregator in the load market, ρ da is the price set by the load market for the bid quantity of the load aggregator, are the rewards obtained by users participating in demand response for curtailable electric load, shiftable electric load, flexible regulation electric load, and curtailable heat load at time t respectively. Pen C is the total income for punishing users, Pen ELC , Pen ELS , Pen EFCL , Pen HLC are the penalty incomes for curtailable electric load, shiftable electric load, flexible regulation electric load, and curtailable heat load to users respectively. ε ELC , ε ELS , ε EFCL , ε HLC are the penalty coefficients for curtailable electric load, shiftable electric load, flexible regulation electric load, and curtailable heat load to users respectively. Pen A is the penalty cost of the load market to the load aggregator, ε A is the penalty coefficient for the load aggregator due to incorrect bidding in the load market, They are the planned reducible electricity load response volume, planned transferable electricity load response volume, planned flexible regulation electricity load response volume, and planned reducible heat load response volume at time t, respectively. They are the actual reducible electricity load response volume, actual transferable electricity load response volume, actual flexible regulation electricity load response volume, and actual reducible heat load response volume at time t, respectively. T represents the 24-hour cycle of a day, and the time interval is one hour.

[0117] The objective function for maximizing the welfare of residential users is expressed as:

[0118]

[0119]

[0120] Among them, F2 is the welfare of residential users. They are the rewards obtained by users participating in the demand response of reducible electricity load, transferable electricity load, flexible regulation electricity load, and reducible heat load at time t, respectively. μ ELC 、μ ELS 、μ HLC They are the dissatisfaction coefficients for participating in the demand response of reducible electricity load, transferable electricity load, and flexible regulation electricity load, respectively.

[0121] The reward obtained by users participating in the demand response of reducible electricity load is obtained in the following way:

[0122]

[0123]

[0124]

[0125]

[0126] Among them, is the original electricity load volume of users at time t, is the slope of the consumption psychology curve of users for reducible electricity load, is the real-time reducible electricity load price determined by the reward mechanism at time t, is the lower limit of the reducible electricity load price, is the upper limit of the reducible electricity load price, is the maximum load transfer rate of reducible electricity load, α ELC is the real response coefficient of reducible electricity load, The reward unit price for the reducible electricity load at time t determined by the reward mechanism for user participation in demand response is the original electricity load price of the user at time t. In this embodiment, α ELC is determined to be 0.8, is determined to be 0.26, is determined to be 0.021.

[0127] The reward obtained by the user for participating in the demand response of transferable electricity load is obtained in the following way:

[0128]

[0129]

[0130]

[0131]

[0132] wherein, is the original electricity load of the user at time t, is the slope of the consumption psychological curve of the transferable electricity load of the user, is the real-time transferable electricity load price determined by the reward mechanism at time t, is the lower limit of the transferable electricity load price, is the upper limit of the transferable electricity load price, is the maximum load transfer rate of the transferable electricity load, α ELS is the true response coefficient of the transferable electricity load, is the reward unit price of the transferable electricity load for the user to participate in the demand response at time t determined by the reward mechanism. In this embodiment, α ELS is determined to be 0.8, is determined to be 0.27, is determined to be 0.023.

[0133] The reward obtained by the user for participating in the demand response of flexible regulation of electricity load is obtained in the following way:

[0134]

[0135]

[0136] wherein, is the reward unit price of the flexible regulation of electricity load for the user to participate in the demand response at time t determined by the reward mechanism.

[0137] The reward obtained by the user for participating in the demand response of reducible heat load is obtained in the following way:

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] Among them, is the planned reducible heat load response at time t, is the actual reducible heat load response at time t, is the heat load after reduction at time t, is the heat load of residential users at time t, T t out is the outdoor temperature at time t, T t in is the indoor temperature at time t, is the indoor temperature at time t + 1, R is the equivalent thermal resistance of the building, C air is the specific heat capacity of indoor air, Δt is the time interval, α HLC is the actual response coefficient of the reducible heat load, is the reward unit price for the reducible heat load that users participate in demand response determined by the reward mechanism at time t. In this embodiment, R is determined to be 18 °C / kW, C air is determined to be 0.525 kW·h / °C, and Δt is determined to be 1 h.

[0144] Constraints include:

[0145] Constraints on the reward unit price determined by the reward mechanism:

[0146]

[0147]

[0148]

[0149]

[0150] Among them, are respectively the reward unit price for the reducible electric load, the reward unit price for the shiftable electric load, the reward unit price for the flexible regulation electric load, and the reward unit price for the reducible heat load that users participate in demand response determined by the reward mechanism, are respectively the minimum values of the reward unit prices for the reducible electric load, the shiftable electric load, the flexible regulation electric load, and the reducible heat load, They are the maximum reward unit prices for the electricity load that can be reduced, the electricity load that can be transferred, the flexible regulated electricity load, and the heat load that can be reduced, respectively.

[0151] When residential users participate in the demand response of the heat load that can be reduced, the energy consumption comfort of residential users is considered to form the energy consumption comfort constraint of residential users:

[0152] T t min ≤T t in

[0153] T t min is the lowest indoor temperature to ensure user comfort at time t. is the indoor temperature at time t. In this embodiment, the lowest indoor temperature T t min is determined to be 18 °C.

[0154] When residential users participate in the demand response of the electricity load that can be reduced and the electricity load that can be transferred, the consumption psychological curve is considered to form the consumption psychological curve constraint:

[0155]

[0156]

[0157] is the maximum load transfer rate of the electricity load that can be reduced. is the maximum load transfer rate of the electricity load that can be transferred, λ 1 、λ 2 are the maximum load transfer rate constraint constants.

[0158] The dissatisfaction constraint is mainly reflected in that both the user's contract load and the actual load are related to the consumption psychological curve, and at the same time, there is an additional dissatisfaction coefficient of the user.

[0159] The NSGA-II genetic algorithm is used to solve the multi-objective function. Specifically, as Figure 9 shown:

[0160] The NSGA-II genetic algorithm is used to solve the multi-objective function to obtain the Pareto front. The Pareto front can present the trade-off between each objective. Each solution in the Pareto front solution set has its own advantages. However, some solutions tend to be beneficial to a specific objective. To ensure fairness, the optimal solution F * can be obtained, where F is the solution from the solution set Sol.

[0161]

[0162]

[0163]

[0164]

[0165]

[0166] Among them, F * is the optimal solution in the solution set of the multi-objective mathematical model, F is all the solutions in the solution set of the multi-objective mathematical model, Sol is the Pareto solution set of the multi-objective mathematical model, F1 is the first objective function of the multi-objective mathematical model, F2 is the second objective function of the multi-objective mathematical model, and F1 max , F2 max are respectively the maximum solutions in the first and second objective functions, and F1 min , F2 min are respectively the minimum solutions in the first and second objective functions.

[0167] In this embodiment, taking the first type of users as an example, a multi-objective optimization mathematical model is established to obtain the best reward mechanism that maximizes the interests of the load aggregator and residential users, including the allowable scheduling load of each type of energy consumption load of residential users and the unit price for obtaining rewards accordingly, as specifically shown in FIGS. 5 to 8. In the figures, the unit price is the unit price for residential users to obtain rewards by participating in demand response determined by the reward mechanism.

[0168] To illustrate the practicability and reliability of the present invention, the present invention conducts an index comparison and analysis under different conditions in this part, and the details are shown in Table 1.

[0169] The first case is the energy consumption load and energy consumption bill of residential users under two package forms of fixed electricity price and time-of-use electricity price without comprehensive demand response.

[0170] The second case is the energy consumption load and energy consumption bill of residential users under two comprehensive demand forms of time-of-use electricity price and real-time electricity price with price-based comprehensive demand response.

[0171] The third case is the energy consumption load and energy consumption bill of residential users under two forms of non-cluster analysis and cluster analysis with incentive-based comprehensive demand response.

[0172] Through the comparison and analysis of the three cases and different indexes, the practicability of the present invention can be proved.

[0173] Table 1 Index comparison and analysis under different conditions

[0174]

[0175] In summary, through the comparison of the first, second, and third cases, it can be concluded that for residential users, without participating in the integrated demand response, time-of-use electricity prices can effectively help residential users save energy costs compared to fixed electricity prices;

[0176] In the case of participating in the integrated demand response, according to the analysis of the "total load" indicator, for price-based integrated demand response, users will spontaneously reduce peak loads or shift them to off-peak and valley periods according to the different electricity prices in each stage or each hour, and the energy consumption load will fluctuate within a small range. Compared with the price-based type, the energy consumption of users with the incentive-based type will be significantly reduced. Thus, it can be seen that the incentive-based reward mechanism promotes more active participation of residential users in the response; according to the analysis of the "total cost" indicator, the method of the incentive-based reward mechanism saves more costs than the price-based method considering the three influencing factors; according to the analysis of the "load transfer rate" indicator, compared with the price-based type, the load transfer rate of the incentive-based type is relatively stable, and the response volume of residential users is more stable, increasing the feasibility and practicality of this reward mechanism.

[0177] In the third case, through the comprehensive analysis of the three indicators of "total load", "total cost", and "load transfer rate" and performing cluster analysis, the response load of residential users can be more accurately regulated, the response potential of users can be better explored, and the energy consumption pattern of users can be changed, so as to help users meet the requirements of participating in the demand response and reduce the total energy consumption cost.

[0178] The above embodiments are only examples and do not represent limitations on the scope of the present invention. These embodiments can also be implemented in various other ways and can be subject to various omissions, substitutions, and changes without departing from the technical idea of the present invention.

Claims

1. A multi-objective optimization method for integrated thermoelectric demand response considering multiple influencing factors, characterized in that, the method includes: Introduce a load aggregator to conduct cluster analysis on the energy consumption loads of residential users, and divide residential users into several types of residential users with different energy consumption habits; Determine the influencing factors that affect residential users' participation in integrated demand response, including: the energy consumption comfort of residential users, the consumption psychology curve driven by price, and the dissatisfaction brought about by participating in integrated demand response; Classify the energy consumption loads of residential users participating in integrated demand response, including reducible electric loads, transferable electric loads, flexible regulation electric loads, and reducible heat loads; According to the determined influencing factors and energy consumption load classification, for each type of residential user, establish a multi-objective optimization mathematical model for residential users to participate in integrated demand response respectively; Solve the multi-objective optimization mathematical model to obtain the best reward mechanism that maximizes the interests of load aggregators and residential users, including the allowable scheduling load of each type of energy consumption load of residential users and the unit price of corresponding rewards; The multi-objective optimization mathematical model includes an objective function and constraint conditions. The objective function includes an objective function for maximizing the profit of the load aggregator and an objective function for maximizing the welfare of residential users; The objective function for maximizing the profit of the load aggregator is expressed as: Among them, F1 is the profit of the load aggregator, and L bid is the bid load quantity of the load aggregator in the load market, and ρ da is the price set by the load market for the bid quantity of the load aggregator. are the rewards obtained by users participating in demand response for curtailable electricity load, shiftable electricity load, flexible regulation electricity load, and curtailable heat load at time t, respectively. Pen C is the total income of punishing users, and Pen ELC , Pen ELS , Pen EFCL , Pen HLC are the penalty incomes for users from curtailable electricity load, shiftable electricity load, flexible regulation electricity load, and curtailable heat load, respectively. ε ELC , ε ELS , ε EFCL , ε HLC are the penalty coefficients for users from curtailable electricity load, shiftable electricity load, flexible regulation electricity load, and curtailable heat load, respectively. Pen A is the penalty cost of the load market for the load aggregator, and ε A is the penalty coefficient for the load aggregator due to incorrect bidding in the load market. are the planned curtailable electricity load response quantity, planned shiftable electricity load response quantity, planned flexible regulation electricity load response quantity, and planned curtailable heat load response quantity at time t, respectively. are the actual curtailable electricity load response quantity, actual shiftable electricity load response quantity, actual flexible regulation electricity load response quantity, and actual curtailable heat load response quantity at time t, respectively. T represents a 24-hour cycle of a day, and the time interval is one hour.

2. A multi-objective optimization method for integrated thermoelectric demand response considering multiple influencing factors according to claim 1, characterized in that, The objective function for maximizing the welfare of residential users is expressed as: Among them, F2 is the welfare of residential users, which are the rewards obtained by the user's participation in the demand response of curtailable electric load, shiftable electric load, flexible regulation electric load, and curtailable heat load at time t, respectively, μ ELC , μ ELS , μ HLC are the dissatisfaction coefficients for participating in the demand response of curtailable electric load, shiftable electric load, and flexible regulation electric load, respectively.

3. A multi-objective optimization method for integrated thermoelectric demand response considering multiple influencing factors according to claim 2, characterized in that, Rewards obtained from user participation in reducing electricity load demand response Obtained in the following ways: Among them, is the original electricity load of the user at time t, is the slope of the consumer psychology curve of the user for the curtailable electricity load, is the real-time price of the curtailable electricity load determined by the reward mechanism at time t, is the lower limit of the price of the curtailable electricity load, is the upper limit of the price of the curtailable electricity load, is the maximum load transfer rate of the curtailable electricity load, α ELC is the true response coefficient of the curtailable electricity load, is the reward unit price of the curtailable electricity load for the user to participate in the demand response at time t determined by the reward mechanism, is the electricity price of the user's original electricity load at time t.

4. A multi-objective optimization method for integrated thermoelectric demand response considering multiple influencing factors according to claim 2, characterized in that, Rewards obtained by users' participation in transferable electric load demand response Ct ELS Obtained in the following ways: Among them, is the original electricity load of the user at time t, is the slope of the consumption psychological curve of the user for the shiftable electricity load, is the real-time shiftable electricity load price determined by the reward mechanism at time t, is the lower limit of the shiftable electricity load price, is the upper limit of the shiftable electricity load price, is the maximum load transfer rate of the shiftable electricity load, α ELS is the true response coefficient of the shiftable electricity load, is the reward unit price of the shiftable electricity load for the user to participate in the demand response at time t determined by the reward mechanism.

5. A multi-objective optimization method for integrated thermoelectric demand response considering multiple influencing factors according to claim 2, characterized in that, Rewards obtained by users' participation in flexible regulation of electricity load demand response Obtained in the following ways: Among them, is the reward unit price for the flexible regulation of electricity load at time t for the user's participation in demand response determined by the reward mechanism.

6. A multi-objective optimization method for integrated thermoelectric demand response considering multiple influencing factors according to claim 2, characterized in that, Rewards obtained from user participation in reducing heat load demand response Obtained in the following ways: Among them, is the planned reducible heat load response at time t, is the actual reducible heat load response at time t, is the heat load after reduction at time t, is the heat load of residential users at time t, T t out is the outdoor temperature at time t, T t in is the indoor temperature at time t, is the indoor temperature at time t + 1, R is the equivalent thermal resistance of the building, C air is the specific heat capacity of indoor air, Δt is the time interval, α HLC is the actual response coefficient of the reducible heat load, is the reward unit price for the reducible heat load that users participate in demand response determined by the reward mechanism at time t.

7. A multi-objective optimization method for integrated thermoelectric demand response considering multiple influencing factors according to claim 1, characterized in that, The constraints include: Constraints on the reward unit price determined by the reward mechanism: Among them, are respectively the reward unit prices of the reducible electric load, the shiftable electric load, the flexibility-regulated electric load, and the reducible heat load that can be determined by the reward mechanism at time t of user participation in demand response, are respectively the minimum values of the reward unit prices of the reducible electric load, the shiftable electric load, the flexibility-regulated electric load, and the reducible heat load, are respectively the maximum values of the reward unit prices of the reducible electric load, the shiftable electric load, the flexibility-regulated electric load, and the reducible heat load; When residential users participate in the demand response of reducible heat loads, consider the energy consumption comfort of residential users to form an energy consumption comfort constraint for residential users; T t min ≤T t in T t min is the minimum indoor temperature to ensure user comfort at time t, is the indoor temperature at time t; When residential users participate in the demand response of reducible electric loads and transferable electric loads, consider the consumption psychology curve to form a consumption psychology curve constraint; For the maximum load transfer rate of the reducible electrical load, For the maximum load transfer rate of the transferable electrical load, λ 1 , λ 2 is the maximum load transfer rate constraint constant.

8. A multi-objective optimization method for integrated thermoelectric demand response considering multiple influencing factors according to claim 1, characterized in that, Use the NSGA-II genetic algorithm to solve the multi-objective function.

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