CN116384155A - Household energy management optimization method considering uncertainty of user behavior - Google Patents
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Abstract
The invention relates to the technical field of intelligent energy conservation, and aims to provide a new household energy management model which can ensure the electricity economy and improve the comfort level of users. The household energy management optimization method is characterized in that uncertainty of user behavior is considered, the uncertainty of the user is quantified on the basis of a household energy management system model, the allowable start-stop time, the environmental temperature preference and the temporary hot water consumption of the user are represented by comfort level deviation coefficients, the uncertainty parameters of the user and the electricity cost are jointly used as objective functions to solve, meanwhile, the household energy management system model is operated in a rolling optimization mode in the day by adopting a model prediction control method, and an operation plan is adjusted in real time along with the operation state and prediction information of the system to realize household energy management optimization. The invention is mainly applied to intelligent energy-saving occasions.
Description
Technical Field
The invention relates to the technical field of intelligent energy conservation, in particular to a household energy management optimization method considering uncertainty of user behaviors.
Background
With the development of internet technology and communication technology, the intelligent power grid solves the problems of slow electric energy information transmission, insufficient system stability and the like existing in the current power grid by combining with the infrastructure of the power grid. The home intelligent micro-grid represented by the home energy management system is gradually developed, and requirements of people on the aspects of electric energy quality, comfort level requirements, environmental protection and the like are better met. The optimization of the household energy management system plays an important role in reducing the cost of users, meeting the comfort level of the users, promoting the on-site consumption of renewable energy sources and the like, and is beneficial to improving the intelligent electricity utilization level of household users. In the process of intelligent household electricity utilization, as the behavior parameters of the user have larger randomness, the user can only know the probability distribution or the upper and lower limits of the distribution interval, and the user cannot accurately solve the probability distribution or the upper and lower limits of the distribution interval. If such uncertainty factors are not considered, the expected optimization scheme will deviate from the actual situation, and in severe cases, the optimal scheduling scheme cannot be obtained against the actual constraint, and the electricity economy and the comfort of the user are greatly influenced.
Disclosure of Invention
In order to overcome the defects of the prior art and solve the influence of the subjective behavior of a user on the operation of equipment in a household energy management system, the invention aims to provide a novel household energy management model, and the comfort level of the user is improved while the electricity economy is ensured. The household energy management optimization method is characterized in that uncertainty of user behavior is considered, the uncertainty of the user is quantified on the basis of a household energy management system model, the allowable start-stop time, the environmental temperature preference and the temporary hot water consumption of the user are represented by comfort level deviation coefficients, the uncertainty parameters of the user and the electricity cost are jointly used as objective functions to solve, meanwhile, the household energy management system model is operated in a rolling optimization mode in the day by adopting a model prediction control method, and an operation plan is adjusted in real time along with the operation state and prediction information of the system to realize household energy management optimization.
The method comprises the following specific steps:
1) According to the electricity utilization characteristics of the household appliances, dividing the household appliances into distributed power generation equipment, energy storage equipment and household electricity utilization loads, and establishing a running model of household electric equipment;
2) Analyzing subjective behaviors of a user, expressing the allowable start-stop time, the environmental temperature preference and the temporary hot water consumption of the electrical equipment of the user by using a comfort level deviation coefficient, and modeling the behaviors of the user;
3) Aiming at user behaviors and household user cost, a household energy management model considering uncertainty of the user behaviors is established, and a user net electricity charge and comfort = violation coefficient are selected as objective functions to solve a multi-objective optimization problem;
4) And (3) carrying out rolling optimization solving on the established daily household operation model by using a model prediction control method to obtain a simulation optimization result.
The detailed steps are as follows:
1. household energy management system model
In a household energy management system, household intelligent electric equipment is a main control object, the influence of the household intelligent electric equipment on household electricity is analyzed by classifying and modeling the electric characteristics of different types of equipment, a scheduling period is set to be 24h, the scheduling period is set to be N time periods, the time duration of each time period is deltat, and each time period is recorded as i, and then the operation model of various electric equipment is as follows:
(one) interruptible load: p (P) ICL,i Operating power for an interruptible load during an i-th period; p is p ICL,r To interrupt rated power of load x ICL,i The load interruption state is an operation state in which the load can be interrupted in the ith period, wherein 0 is not operated, and 1 is operated; [ b ] ICL ,e ICL ]For the permitted operating period of the interruptible load, l ICL For the total duration of the operation of the interruptible load, the interruptible load operation constraint is as follows:
P ICL,i ＝x ICL,i ·p ICL,r (1)
(II) uninterruptible load: the operating constraints including interruptible loads while adding supplemental constraints that guarantee non-interruptible characteristics are:
P UICL,i operating power for the uninterruptible load during the i-th period; p is p UICL,r Is the rated power of the load. X is x UICL,i An operating state for the i-th period of the load; [ b ] UICL ,e UICL ]For the allowed period of operation, l UICL For a total length of time of operation;
and (III) temperature control load: the temperature-controlled load refers to equipment for optimizing load operation by controlling temperature, and T is set in,i And T out,i Indoor and outdoor air temperatures respectively representing the i-th period; c (C) ac Is the heat capacity of the indoor air; p is p ac,i The operation power of the air conditioner in the ith period, R is the thermal resistance of the house, and the operation model and the upper and lower temperature limits of the air conditioner are constrained as follows:
T in,min ≤T in,i ≤T in,max (6)
let T be st,i Indicating the hot water temperature of the electric water heater in the ith period;
for the temperature of the cold water injected into the tank; v (V) cold,i The volume of cold water to be injected in the ith period; v (V) total Is the total capacity of the water tank; c (C) st Conversion coefficients of kilowatt-hours and joules; p is p st,i When the operating power of the electric water heater is the ith period of time, the water heaterThe operating model and the upper and lower temperature limits of (a) are as follows:
T st,min ≤T st,i ≤T st,max (8)
(IV) distributed photovoltaic device: in the household energy management system, photovoltaic equipment is converted into electric energy through light energy, and is converted into power frequency current which can be used by household electric equipment through a converter and an inverter, so that the power frequency current is provided for the household electric equipment or stored in energy storage equipment, and p is set pv,i Represents the photovoltaic output power, eta pv Represents the photoelectric conversion efficiency of the photovoltaic system, S pv Representing the receiving area m of the photovoltaic panel 2 ，I pv,i Representing the solar radiation intensity (kW/m) of a photovoltaic system 2 )，
Indicating the outdoor temperature (c), the power characteristics of the photovoltaic cell are:
and (V) energy storage equipment: let the SOC be the state of charge of the energy storage device,
respectively representing the charging and discharging power and the charging and discharging working state of the household energy storage equipment; η (eta) ch Is the charging efficiency; η (eta) dch Is the discharge efficiency; />
And->
Representing maximum power of charge and discharge; epsilon represents the self-discharge rate, Q r Charging and discharging household energy storage equipment for rated capacity of storage batteryThe electrical characteristics and operational constraints are shown in the formulas (10) - (15):
SOC min ≤SOC i+1 ≤SOC max (11)
SOC 96 ≤SOC ini (15)
2. user-uncertain behavior analysis
(1) Uninterruptible load uncertainty behavior
In actual operation, because the user behavior can lead to the delay of the work permission starting time or the advance of the work permission deadline, the two conditions can lead to the incapacitation of the original operation plan, the change of the actual permission work time and the actual deadline work time of the unbreakable load obeys normal distribution, thereby meeting the requirements of
And->
Simulating N different scenes by adopting Monte Carlo sampling method, and setting b UICL,j For the initial working period e in scene j UICL,j For the ending working period in scene j, x UICL,i For uninterrupted load operation, p UICL,i For the power consumption of the uninterruptible load, the obtained uninterruptible load is comfortableDegree deviation C UICL The following is shown:
(2) Interruptible load uncertainty behavior
Similar to the non-interruptible load, the running time of the interruptible load is also affected by the behavior of the user, the actual working start time b of the interruptible load ICL,j And actual working cut-off time e ICL,j Also satisfies normal distribution, adopts Monte Carlo simulation method, and is provided with b ICL,j E is the actual allowed run time of the interruptible load in scenario j ICL,j For the actual cut-off run time of interruptible load in scenario j, x ICL,i To interrupt the load operation state, p ICL,i For interrupting the power consumption of the load, the resulting interruption-load comfort deviation C ICL The following is shown:
(3) Uncertain temperature preference
User preference temperature occurrence time i ac,b,j Evenly distributed in a prescribed period of time, the change time is 30 minutes, and the user prefers the temperature T in,j,i Obeying normal distribution, keeping constant in the occurrence time, adopting Monte-Cart sampling method, setting T in,j,t Preference comfort temperature, T, for user of the ith period in scene j in,i The obtained user temperature comfort level deviation is that for the indoor temperature of the ith period:
(4) Uncertain hot water dosage
User at [ i ] st,min ,i st,max ]Temporary water use occurs in the water tank, and the starting time i st,b,j And water consumption V st,j Obeying uniform distribution, setting Q by using a Monte-Cart sampling method, wherein the water consumption time is 30 minutes st,j,i Heat consumption of the water heater in the ith period of the scene j is T ws,j,i Is the hot water temperature of the i-th period in scene j. Q (Q) i For the heat consumption of the water heater in the period i, the obtained user hot water consumption comfort level deviation C st The following is shown:
Q st,j,i ＝c water ·V total ·(T st,j,i+1 -T st,j,i ) (20)
Q i ＝c water ·V total ·(T st,i+1 -T st,i ) (22)
3. household energy management optimization model considering user behavior uncertainty
Selecting two targets of the net electricity charge and the comfort degree violation coefficient of the user as target functions of an optimization model, solving by adopting a weighted sum method, and omega 1 And omega 2 Weight coefficient, ω, for user economy and comfort 1 +ω 2 =1; by normalization method, set C cost C for household energy management model taking user behavior into consideration cost of electricity used for one day com Sum of user comfort violation indexes in one day of model, C cost,max And C cost,min C is the maximum and minimum of electricity costs occurring in the iteration com,max And C com,min For maximum and minimum values of user comfort violation indices to occur in an iteration,
p grid,i representing interaction power and price of family energy management system and power grid in ith period b,i Price and price of electricity purchase representing user in ith period s,i The price of electricity selling of the user in the ith period is represented, ρ is a punishment factor, ε n The occurrence probability of the uncertain behavior n is represented by B, and the occurrence probability of the uncertain behavior n is represented by B. The objective function of the obtained optimization model of the household energy management system is as follows:
in a family energy optimization scheduling model, besides the constraint of operation equipment, the electric power balance constraint is added, and P is set pv,i For the i-th period of the generated power of the distributed photovoltaic device, P δ,i The electric power used in the ith period of the delta type load comprises energy storage equipment and various electric loads. The electric power balance constraint is as follows
4. Real-time scroll optimization based on model predictive control
The model predictive control is performed by a scroll optimization process by changing the operation period t=1..n in the formula to t=i..n. And if t=i, carrying real operation data such as water temperature, indoor temperature and the like of the water heater at i into the household energy management system together with predicted data in the next optimizing time domain T to obtain an optimizing scheduling result of the next optimizing time domain T. And (3) in the period (i, i+1), adopting an optimized scheduling result to perform optimized scheduling on the household electric equipment, and repeating the process until the day is ended after t=i+1.
The invention has the characteristics and beneficial effects that:
the method for optimizing the power consumption of the user by adopting the multiple targets can improve the comfort level of the user while ensuring lower power consumption cost, and reduce the influence of uncertain behaviors on the power consumption of the user. Compared with the common household energy management optimization with the aim of user cost, the invention can better meet the comfort level requirement of the user, can automatically adjust according to the preference of the user on economy and comfort level, and sets different preference factors to obtain the optimal power consumption scheme of the user.
Description of the drawings:
FIG. 1 illustrates the behavior of the uninterruptible load uncertainty.
FIG. 2 may interrupt load uncertainty behavior.
FIG. 3 is a time frame diagram of a scroll optimization.
Fig. 4 electricity selling price curve.
Fig. 5 photovoltaic power curve.
Fig. 6 outdoor temperature profile.
FIG. 7 is a graph of hot water usage.
FIG. 8 different ω 1 The following operation results.
Detailed Description
In order to solve the influence of the subjective behavior of a user in a household energy management system on the operation of equipment, a random optimization method is introduced on the basis of a household energy management system model, uncertain behaviors of the user are quantified, the allowable start-stop time, the environmental temperature preference, the temporary hot water consumption and the like of the electrical equipment of the user are represented by comfort level deviation coefficients, uncertainty parameters of the user and electricity cost are jointly used as an objective function for solving, and the comfort level of the user is improved while the electricity economy is ensured. Meanwhile, the model is operated in a rolling optimization mode in the day by adopting a model prediction control method, and an operation plan is adjusted in real time along with the operation state and prediction information of the system, so that the method is more suitable for an actual operation optimization system.
1) According to the electricity utilization characteristics of the household appliances, the household appliances are divided into distributed power generation equipment, energy storage equipment and household electricity utilization loads, and a household electric equipment operation model is built.
2) The subjective behavior of the user is analyzed, the allowable start-stop time of the electrical equipment of the user, the environmental temperature preference and the temporary hot water consumption are represented by comfort level deviation coefficients, and the behavior of the user is modeled.
3) Aiming at user behaviors and household user cost, a household energy management model considering uncertainty of the user behaviors is established, and a user net electricity charge and comfort = violation coefficient are selected as objective functions to solve a multi-objective optimization problem.
4) And (3) carrying out rolling optimization solving on the established daily household operation model by using a model prediction control method to obtain a simulation optimization result.
The present invention will be described in detail below.
1. Household energy management system model
In a household energy management system, household intelligent electric equipment is a main control object, and an optimization model can be more conveniently constructed by classifying and modeling the electric characteristics of different types of equipment so as to analyze the influence of the household intelligent electric equipment on household electricity. Setting a scheduling period to be 24h, and equally setting the scheduling period to be N time periods, wherein the duration of each time period is deltat, and each time period is marked as i, and then the operation model of various electrical equipment is as follows:
(one) interruptible load: for the interruptible load, the running period is fixed, but the running mode is flexible, the interruptible load can be randomly scheduled in the running period, and the suspension in the running process can not cause great influence on equipment. Let P be ICL,i Operating power for an interruptible load during an i-th period; p is p ICL,r Is rated for the power of the interruptible load. X is x ICL,i The load interruption state is an operation state in which the load can be interrupted in the ith period, wherein 0 is not operated, and 1 is operated; [ b ] ICL ,e ICL ]For the permitted operating period of the interruptible load, l ICL For the total duration of the operation of the interruptible load. The interruptible load operation constraint is:
P ICL,i ＝x ICL,i ·p ICL,r (28)
(II) uninterruptible load: the operation model of the uninterruptible load is similar to that of the interruptible load, but because of the characteristic that the operation cannot be suspended and stopped during operation, supplementary constraint for ensuring the uninterruptible characteristic is added besides the operation constraint of the interruptible load. Let P be UICL,i Operating power for the uninterruptible load during the i-th period; p is p UICL,r Is the rated power of the load. X is x UICL,i An operating state for the i-th period of the load; [ b ] UICL ,e UICL ]For the allowed period of operation, l UICL For the total duration of operation. The supplemental constraints that guarantee its uninterruptible nature are:
and (III) temperature control load: temperature-controlled loads refer to those devices that optimize load operation by controlling temperature. Air conditioning and electric water heaters are chosen as representative examples herein. Let T be in,i And T out,i Indoor and outdoor air temperatures respectively representing the i-th period; c (C) ac Is the heat capacity of the indoor air; p is p ac,i Is the operating power of the air conditioner in the i-th period. R is the thermal resistance of the house, the operation model and the upper and lower temperature limits of the air conditioner are constrained as follows:
T in,min ≤T in,i ≤T in,max (33)
similar to the operation model of the air conditioner, the operation state of the electric water heater is closely related to the temperature of the hot water. Let T be st,i Indicating the hot water temperature of the electric water heater in the ith period;
for the temperature of the cold water injected into the tank; v (V) cold,i The volume of cold water to be injected in the ith period; v (V) total Is the total capacity of the water tank; c (C) st Conversion coefficients of kilowatt-hours and joules; p is p st,i The operation power of the electric water heater in the ith period. The operational model and upper and lower temperature limits of the water heater are constrained as follows:
T st,min ≤T st,i ≤T st,max (35)
(IV) distributed photovoltaic device: in the household energy management system, the photovoltaic equipment is converted into electric energy through light energy, and is converted into power frequency current which can be used by household electric equipment through a converter and an inverter, so that the power frequency current is provided for the household electric equipment or stored in the energy storage equipment. Let p be pv,i Represents the photovoltaic output power, eta pv Represents the photoelectric conversion efficiency of the photovoltaic system, S pv Representing the receiving area m of the photovoltaic panel 2 ，I pv,i Representing the solar radiation intensity (kW/m) of a photovoltaic system 2 )，
Indicating the outdoor temperature (c), the power characteristics of the photovoltaic cell are:
and (V) energy storage equipment: the household energy storage device represented by the lead-acid battery mainly utilizes the charging and discharging functions of the household energy storage device to supply power to a house in a high-electricity-price period and to buy electricity from a power grid in a low-electricity-price period, so that the purpose of reducing electricity cost is achieved. Let the SOC be the state of charge of the energy storage device,
respectively representing the charging and discharging power and the charging and discharging working state of the household energy storage equipment; η (eta) ch Is the charging efficiency; η (eta) dch Is the discharge efficiency; />
And->
Representing maximum power of charge and discharge; epsilon represents the self-discharge rate, Q r For the rated capacity of the storage battery, the charge and discharge characteristics and the operation constraint of the household energy storage device are shown as the following formula (10) -formula (15):
SOC min ≤SOC i+1 ≤SOC max (38)
SOC 96 ≤SOC ini (42)
2. user-uncertain behavior analysis
In the actual household electricity utilization process, the electricity utilization behavior of the user has a certain subjective uncertainty, so that the actual operation scheme does not accord with the optimal electricity utilization plan of the household energy management system, and the robustness and reliability of the household energy management system are reduced. Therefore, quantitative analysis of the uncertainty behavior of the user is required, modeling of the behavior of the user is performed, and influence on the comfort of the user is reduced. The user's uncertain behavior is analyzed as follows:
(1) Uninterruptible load uncertainty behavior
In actual operation, the work permission start time is delayed or the work permission deadline is advanced due to user behavior, and the two situations can cause that the original operation plan cannot be completed normally. FIG. 1 is an uncertainty behavior of an uninterruptible load. Therefore, the variation of the actual allowable operation time and the actual cut-off operation time of the uninterruptible load is assumed to follow the normal distribution, and the requirement is satisfied
And->
Simulating N different scenes by adopting Monte Carlo sampling method, and setting b UICL,j For the initial working period e in scene j UICL,j For the ending working period in scene j, x UICL,i For uninterrupted load operation, p UICL,i For the power consumption of the uninterruptible load, the obtained uninterruptible load comfort deviation C UICL The following is shown:
(2) Interruptible load uncertainty behavior
Like the uninterruptible load, the runtime of the interruptible load is also affected by the behavior of the user. FIG. 2 is an uncertainty behavior of interruptible loads. Assuming actual work start time b of interruptible load ICL,j And actual working cut-off time e ICL,j Also satisfies normal distribution, adopts Monte Carlo simulation method, and is provided with b ICL,j E is the actual allowed run time of the interruptible load in scenario j ICL,j For the actual cut-off run time of interruptible load in scenario j, x ICL,i To interrupt the load operation state, p ICL,i For interrupting the power of the load, the resulting interruptible loadComfort deviation C ICL The following is shown:
(3) Uncertain temperature preference
The user's preference for temperature will also have an impact on the user's comfort, mainly in terms of the user's change in the upper and lower temperature limits of preference. Let it be assumed that the user's preference temperature occurrence time i ac,b,j Evenly distributed in a prescribed period of time, the change time is 30 minutes, and the user prefers the temperature T in,j,i Obeys normal distribution and remains constant over the time of occurrence. Method for sampling Monte Cart is adopted, and T is set in,j,t Preference comfort temperature, T, for user of the ith period in scene j in,i The obtained user temperature comfort level deviation is that for the indoor temperature of the ith period:
(4) Uncertain hot water dosage
The operation of the electric water heater is accompanied by heat exchange and consumption and supplement of water consumption. When a great amount of water is used outside a planned scheme, a great amount of cold water is injected into the water heater suddenly, and the electric water heater can not quickly heat the cold water to reach the lower temperature limit due to constant power, so that the temperature in the water heater is further reduced and finally lower than the lower temperature limit, and the comfort of the user is influenced. Thus, assume that the user is at [ i ] st,min ,i st,max ]Temporary water use occurs in the water tank, and the starting time i st,b,j And water consumption V st,j Obeying uniform distribution, setting Q by using a Monte-Cart sampling method, wherein the water consumption time is 30 minutes st,j,i Heat consumption of the water heater in the ith period of the scene j is T ws,j,i Is the hot water temperature of the i-th period in scene j. Q (Q) i For the heat consumption of the water heater in the period i, the obtained user hot water consumption comfort level deviation C st The following is shown:
Q st,j,i ＝c water ·V total ·(T st,j,i+1 -T st,j,i ) (47)
Q i ＝c water ·V total ·(T st,i+1 -T st,i ) (49)
3. household energy management optimization model considering user behavior uncertainty
Home energy management systems are generally intended to better manage the operation plan of the home consumer. Meanwhile, in order to reduce the influence of the user behavior on the power consumption plan, the comfort violation coefficient of the user needs to be minimized. Therefore, the economical efficiency and the comfort of the user are comprehensively considered, and two targets of the net electricity charge and the comfort violation coefficient of the user are selected as the objective functions of the optimization model. In order to solve the multi-objective optimization problem better, a weighted sum method is adopted for solving. Let ω be 1 And omega 2 Weight coefficient, ω, for user economy and comfort 1 +ω 2 =1; because the order of magnitude difference of the economical efficiency target and the comfort target can not directly participate in calculation, C is set by a normalization method cost C for household energy management model taking user behavior into consideration cost of electricity used for one day com Sum of user comfort violation indexes in one day of model, C cost,max And C cost,min C is the maximum and minimum of electricity costs occurring in the iteration com,max And C com,min For maximum and minimum values of user comfort violation indices to occur in an iteration,
p grid,i representing interaction power and price of family energy management system and power grid in ith period b,i Price and price of electricity purchase representing user in ith period s,i Sales representative of period i userElectric price, ρ is penalty factor, ε n The occurrence probability of the uncertain behavior n is represented by B, and the occurrence probability of the uncertain behavior n is represented by B. The objective function of the obtained optimization model of the household energy management system is as follows:
in a family energy optimization scheduling model, besides the constraint of operation equipment, the electric power balance constraint is added, and P is set pv,i For the i-th period of the generated power of the distributed photovoltaic device, P δ,i The electric power used in the ith period of the delta type load comprises energy storage equipment and various electric loads. The electric power balance constraint is as follows
4. Real-time scroll optimization based on model predictive control
And (3) the established scheduling optimization model utilizes the prediction data to make an operation plan within 24 hours at the beginning of one-day operation, so as to obtain one-day operation results. However, in actual operation, due to the prediction data error, real-time parameters need to be considered, and the control strategy is corrected according to the actual operation state of the electric equipment. And the model predictive control changes the operation period t=1..n in the formula to t=i..n by the scroll optimization process. And if t=i, carrying real operation data such as water temperature, indoor temperature and the like of the water heater at i into the household energy management system together with predicted data in the next optimizing time domain T to obtain an optimizing scheduling result of the next optimizing time domain T. And in the period (i, i+1), adopting an optimal scheduling result to optimally schedule the household electric equipment. After t=i+1, the above process is repeated until the end of one day. Thus, only the operation result of the first period of each optimization scheme will actually operate. Along with the real-time adjustment of the running plan of the system running state and the prediction information, the influence of uncertainty can be reduced, and the method is more suitable for an actual running optimization system, and an optimal household energy management running scheme is obtained. Fig. 3 is a frame diagram of a scroll optimization time.
And verifying the proposed model by the related data of the household intelligent power utilization system of a user in spring. The scheduling duration is 0:00 to 24:00, the scheduling step length is 15 minutes, 96 time periods are total, each time period is represented by i epsilon {1,2, …,95,96}, and a family energy optimization scheduling model is built. The electricity selling price is shown in fig. 4, and the electricity purchasing price is one half of the electricity selling price. The photovoltaic output power curve is shown in fig. 5. The operating parameters of the various interruptible and non-interruptible loads are shown in table 1 and the operating parameters of the energy storage device are shown in table 2.
TABLE 1 interruptible, uninterrupted, controllable load operating parameters
Table 2 operating parameters of energy storage devices
The rated power of the household air conditioner is 1.8kW, and the heat capacity C ac 0.426 (kW.h/. Degree.C.) and house thermal resistance R of75.71 kJ/. Degree.C. The rated power of the household electric water heater is 3.6kW, and the specific heat C of water is selected water 4.26 kJ/(kg. DEG C.). The predicted curves of the outdoor temperature and the hot water usage are shown in fig. 6 and 7.
In scene simulation of uncertain behavior, the number of scenes n=100 is set. The temporary water use period and the temperature preference change time which are possible for the user are set to 18:00-22:00, ω 1 And omega 2 All take 0.5. The probability distribution of possible uncertain actions of the user is shown in Table 3, and the occurrence probability epsilon of the uncertain actions n 0.16667 penalty factor ρ=100, ω 1 ＝ω 2 ＝0.5。
TABLE 3 uncertain behavior probability distribution parameters
In order to simulate the actual data measured in actual operation, the prediction curve of the past is adjusted, and the actual data is simulated by adding a normally distributed deviation to the prediction data. And assuming that the prediction error after 8 periods of the prediction data is more obvious, the prediction error is regarded as a far prediction error, and the prediction error within 8 periods is regarded as a near prediction error. The set parameter prediction errors are shown in table 4.
By using the scheduling method of the invention, three different optimization scenes of families are simulated in order to verify the proposed optimization model considering the uncertainty of the user behavior. Scene 1 is an objective function in which only electricity cost is considered; scene 2 only considers user uncertainty behavior for the objective function; scenario 3 is an objective function, the electricity cost and the user uncertainty behavior are considered at the same time, and the conclusions in different scenarios are shown in table 4.
TABLE 4 parameter prediction error
As can be seen from table 4, scenario 3 reduces the user's comfort deviation index from 7.05 to 0.72 by quantifying the user's behavior, as compared to scenario 1, which only considers electricity costs. In the optimization scheme considering uncertain behaviors of users, in order to avoid that the starting time of controllable loads exceeds the actual allowed starting time, the optimization scheme can transfer the working time of the loads to the middle of the allowed operation period as much as possible, and although the part of electricity cost is sacrificed, the comfort of the users can be improved very effectively. In order to avoid the temperature being too low, the temperature control load can keep the hot water temperature and the indoor temperature in a higher range in the occurrence time of temperature change so as to cope with the change of temporary hot water quantity and temperature preference over time. By timely adjusting the two types of loads, the influence of uncertain behaviors of the user on the comfort level of the user can be well reduced. Analysis of scenario 2 and scenario 3 shows that when the comfort bias index is fully used as the objective function, the influence of uncertain behavior on the comfort of the user cannot be effectively reduced, but the electricity cost of the user is significantly increased, and the willingness of paying a higher electricity cost for a lower comfort bias index is relatively low for the user. Therefore, the multi-objective optimization method can improve the comfort level of the user and reduce the influence of uncertain behaviors on the power consumption of the user while ensuring lower power consumption cost.
Considering that different users have different preferences for economy and comfort, different weight factors are set, and the household electricity cost coefficient and the comfort deviation coefficient are simulated by taking 0.1 as a scale, and the obtained conclusion is shown in fig. 8. As can be seen from the above, as the electricity cost coefficient increases, the comfort deviation index increases continuously, the electricity cost decreases gradually, and the operation result is mainly divided into three parts: at omega 1 At [0.1,0.3]During the period omega 1 The variation of (2) has a larger influence on the electricity cost, but has a small influence on the comfort deviation index, and belongs to the applicable scheme of comfort preference type users, wherein the requirements of the users on comfort are far higher than the requirements on economy. At omega 1 At [0.7,0.9 ]]During the period omega 1 The variation of (2) has a larger influence on the comfort deviation index of the user, but has a smaller influence on the electricity cost, and belongs to an applicable scheme of the economic preference type user, and the user hopes to obtain lower electricity cost. And at omega 1 At [0.4,0.6 ]]During the period omega 1 The variation of (2) has a great influence on the comfort deviation index of the user and the electricity cost, belongs to neutral preference type users, and generally selects a proper coefficient according to the experience after adopting an optimization scheme. According to the results, compared with the general household energy management optimization with the user cost, the household energy management optimization method can better meet the comfort level requirements of users, can automatically adjust according to the preference of the users on economy and comfort level, and sets different preference factors to obtain the power consumption scheme which is most suitable for the users.
Table 5 cost of electricity and user comfort in different scenarios
Those skilled in the art will appreciate that the drawings are schematic representations of only one preferred embodiment, and that the above-described embodiment numbers are merely for illustration purposes and do not represent advantages or disadvantages of the embodiments.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (3)
1. A household energy management optimization method considering user behavior uncertainty is characterized in that on the basis of a household energy management system model, user uncertainty behavior is quantized, the allowable start-stop time, environment temperature preference and temporary hot water consumption of electric equipment of a user are represented by comfort level deviation coefficients, user uncertainty parameters and electricity cost are jointly used as objective functions to solve, meanwhile, the household energy management system model is operated in a rolling optimization mode in the day by adopting a model prediction control method, and an operation plan is adjusted in real time along with system operation states and prediction information to realize household energy management optimization.
2. The home energy management optimization method considering user behavior uncertainty as claimed in claim 1, wherein the specific steps are as follows:
1) According to the electricity utilization characteristics of the household appliances, dividing the household appliances into distributed power generation equipment, energy storage equipment and household electricity utilization loads, and establishing a running model of household electric equipment;
2) Analyzing subjective behaviors of a user, expressing the allowable start-stop time, the environmental temperature preference and the temporary hot water consumption of the electrical equipment of the user by using a comfort level deviation coefficient, and modeling the behaviors of the user;
3) Aiming at user behaviors and household user cost, a household energy management model considering uncertainty of the user behaviors is established, and a user net electricity charge and comfort = violation coefficient are selected as objective functions to solve a multi-objective optimization problem;
4) And (3) carrying out rolling optimization solving on the established daily household operation model by using a model prediction control method to obtain a simulation optimization result.
3. The home energy management optimization method considering user behavior uncertainty as claimed in claim 1, wherein the detailed steps are as follows:
1. household energy management system model
In a household energy management system, household intelligent electric equipment is a main control object, the influence of the household intelligent electric equipment on household electricity is analyzed by classifying and modeling the electric characteristics of different types of equipment, a scheduling period is set to be 24h, the scheduling period is set to be N time periods, the time duration of each time period is deltat, and each time period is recorded as i, and then the operation model of various electric equipment is as follows:
(one) interruptible load: p (P) ICL,i Operating power for an interruptible load during an i-th period; p is p ICL,r To interrupt rated power of load x ICL,i The load interruption state is an operation state in which the load can be interrupted in the ith period, wherein 0 is not operated, and 1 is operated; [ b ] ICL ,e ICL ]For the permitted operating period of the interruptible load, l ICL For the total duration of the operation of the interruptible load, the interruptible load operation constraint is as follows:
P ICL,i ＝x ICL,i ·p ICL,r (1)
(II) uninterruptible load: the operating constraints including interruptible loads while adding supplemental constraints that guarantee non-interruptible characteristics are:
P UICL,i operating power for the uninterruptible load during the i-th period; p is p UICL,r Is the rated power of the load. X is x UICL,i An operating state for the i-th period of the load; [ b ] UICL ,e UICL ]For the allowed period of operation, l UICL For a total length of time of operation;
and (III) temperature control load: the temperature-controlled load refers to equipment for optimizing load operation by controlling temperature, and T is set in,i And T out,i Indoor and outdoor air temperatures respectively representing the i-th period; c (C) ac Is the heat capacity of the indoor air; p is p ac,i The operation power of the air conditioner in the ith period, R is the thermal resistance of the house, and the operation model and the upper and lower temperature limits of the air conditioner are constrained as follows:
T in,min ≤T in,i ≤T in,max (6)
let T be st,i Indicating the hot water temperature of the electric water heater in the ith period;
for the temperature of the cold water injected into the tank; v (V) cold,i The volume of cold water to be injected in the ith period; v (V) total Is the total capacity of the water tank; c (C) st Conversion coefficients of kilowatt-hours and joules; p is p st,i For the operation power of the electric water heater in the ith period, the operation model and the upper and lower temperature limits of the water heater are constrained as follows:
T st,min ≤T st,i ≤T st,max (8)
(IV) distributed photovoltaic device: in the household energy management system, photovoltaic equipment is converted into electric energy through light energy, and is converted into power frequency current which can be used by household electric equipment through a converter and an inverter, so that the power frequency current is provided for the household electric equipment or stored in energy storage equipment, and p is set pv,i Represents the photovoltaic output power, eta pv Represents the photoelectric conversion efficiency of the photovoltaic system, S pv Representing the receiving area m of the photovoltaic panel 2 ，I pv,i Representing the solar radiation intensity (kW/m) of a photovoltaic system 2 )，
Indicating the outdoor temperature (c), the power characteristics of the photovoltaic cell are:
and (V) energy storage equipment: let the SOC be the state of charge of the energy storage device,
respectively representing the charging and discharging power and the charging and discharging working state of the household energy storage equipment; η (eta) ch Is the charging efficiency; η (eta) dch Is the discharge efficiency; />
And->
Representing maximum power of charge and discharge; epsilon represents the self-discharge rate, Q r For the rated capacity of the storage battery, the charge and discharge characteristics and the operation constraint of the household energy storage device are shown as the following formula (10) -formula (15):
SOC min ≤SOC i+1 ≤SOC max (11)
SOC 96 ≤SOC ini (15)
2. user-uncertain behavior analysis
(1) Uninterruptible load uncertainty behavior
In actual operation, because the user behavior can lead to the delay of the work permission starting time or the advance of the work permission deadline, the two conditions can lead to the incapacitation of the original operation plan, the change of the actual permission work time and the actual deadline work time of the unbreakable load obeys normal distribution, thereby meeting the requirements of
And->
Simulating N different scenes by adopting Monte Carlo sampling method, and setting b UICL,j For the initial working period e in scene j UICL,j For the ending working period in scene j, x UICL,i For uninterrupted load operation, p UICL I is the electric power of the uninterruptible load, and the obtained uninterruptible load comfort level deviation C UICL The following is shown:
(2) Interruptible load uncertainty behavior
Similar to the non-interruptible load, the running time of the interruptible load is also affected by the behavior of the user, the actual working start time b of the interruptible load ICL,j And actual working cut-off time e ICL,j Also satisfies normal distribution, adopts Monte Carlo simulation method, and is provided with b ICL,j E is the actual allowed run time of the interruptible load in scenario j ICL,j For the actual cut-off run time of interruptible load in scenario j, x ICL,i To interrupt the load operation state, p ICL,i For interrupting the power consumption of the load, the resulting interruption-load comfort deviation C ICL The following is shown:
(3) Uncertain temperature preference
User preference temperature occurrence time i ac,b,j Evenly distributed in a prescribed period of time, the change time is 30 minutes, and the user prefers the temperature T in,j,i Obeying normal distribution, keeping constant in the occurrence time, adopting Monte-Cart sampling method, setting T in,j,t For the ith period in scene jUser preference comfort temperature, T in,i The obtained user temperature comfort level deviation is that for the indoor temperature of the ith period:
(4) Uncertain hot water dosage
User at [ i ] st,min ,i st,max ]Temporary water use occurs in the water tank, and the starting time i st,b,j And water consumption V st,j Obeying uniform distribution, setting Q by using a Monte-Cart sampling method, wherein the water consumption time is 30 minutes st,j,i Heat consumption of the water heater in the ith period of the scene j is T ws,j,i Is the hot water temperature of the i-th period in scene j. Q (Q) i For the heat consumption of the water heater in the period i, the obtained user hot water consumption comfort level deviation C st The following is shown:
Q st,j,i ＝c water ·V total ·(T st,j,i+1 -T st,j,i ) (20)
Q i ＝c water ·V total ·(T st，i+1 -T st，i ) (22)
3. household energy management optimization model considering user behavior uncertainty
Selecting two targets of the net electricity charge and the comfort degree violation coefficient of the user as target functions of an optimization model, solving by adopting a weighted sum method, and omega 1 And omega 2 Weight coefficient, ω, for user economy and comfort 1 +ω 2 =1; by normalization method, set C cost Family energy management model one for considering user behaviorCost of electricity consumption in the sky, C com Sum of user comfort violation indexes in one day of model, C cost,max And C cost,min C is the maximum and minimum of electricity costs occurring in the iteration com,max And C com,min For maximum and minimum values of user comfort violation indices to occur in an iteration,
p grid,i representing interaction power and price of family energy management system and power grid in ith period b,i Price and price of electricity purchase representing user in ith period s,i The price of electricity selling of the user in the ith period is represented, ρ is a punishment factor, ε n The occurrence probability of the uncertain behavior n is represented by B, and the occurrence probability of the uncertain behavior n is represented by B. The objective function of the obtained optimization model of the household energy management system is as follows:
in a family energy optimization scheduling model, besides the constraint of operation equipment, the electric power balance constraint is added, and P is set pv,i For the i-th period of the generated power of the distributed photovoltaic device, P δ,i The electric power used in the ith period of the delta type load comprises energy storage equipment and various electric loads. The electric power balance constraint is as follows
4. Real-time scroll optimization based on model predictive control
The model predictive control is performed by a scroll optimization process by changing the operation period t=1..n in the formula to t=i..n. And if t=i, carrying real operation data such as water temperature, indoor temperature and the like of the water heater at i into the household energy management system together with predicted data in the next optimizing time domain T to obtain an optimizing scheduling result of the next optimizing time domain T. And (3) in the period (i, i+1), adopting an optimized scheduling result to perform optimized scheduling on the household electric equipment, and repeating the process until the day is ended after t=i+1.
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