CN109840631B - Electricity utilization scheduling optimization method for residential building group - Google Patents
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Abstract
The invention discloses a residential community-oriented electricity dispatching optimization method, which comprises the following steps: dividing the resident controllable electric appliances into power adjustable electric appliances and time adjustable electric appliances, and respectively establishing user uncomfortable degree models related to the user electric comfort degree; determining an objective function of residential group power utilization scheduling by taking the minimized power utilization discomfort degree, the user power utilization cost and the power grid load variance as overall objectives; and solving the optimal value of power utilization scheduling of the multi-user controllable power utilization equipment in each time period under the residential community by adopting an improved cooperative particle swarm algorithm. The method can be applied to optimization of power utilization scheduling of large-scale resident user groups under demand response, so that the power utilization cost and the peak load of a power grid are reduced on the premise of ensuring the power utilization comfort of users, and the purposes of economy, safety, energy conservation and emission reduction are achieved.
Description
Technical Field
The invention belongs to the technical field of electricity utilization service, and particularly relates to an electricity utilization scheduling optimization method for residential building groups.
Background
The increase in the number and capacity of the resident high-power electric appliances will become an important cause of the peak load and even the peak load of the power grid. For example, in the continuous high-temperature weather in summer, the temperature reduction of the air conditioner is used as the main power consumption, and the large-scale use in the same time period can lead the load of the power grid to continuously rise, thereby bringing about serious hidden danger to the safety of the power grid. In order to avoid the power grid fault caused by overload, a power supply company can only limit the power in sequence at present, so that great inconvenience is caused to the life of power-limited residents. However, according to the user preference, the current environmental parameters and the power grid load data, the residential users can reduce the peak load of the power grid together by properly reducing the power of the electric appliances or transferring the working time of the electric appliances during the peak period of the power grid power consumption in a cooperative response mode, so that the virtual energy storage system is realized.
Although controllable load accounts for a large proportion of total load of residents and has strong controllability, compared with commercial power utilization and industrial power utilization, the demand response effect of individual residential users is little. In order to achieve effective load balancing of the power grid, the load balancing needs to be achieved through the aggregation effect of large-scale residential electricity. However, at present, the research on optimizing the residential power dispatching under demand response mainly focuses on the direct load control strategy of a single user, and the research on optimizing the power dispatching of multiple users under the residential community is not comprehensive.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a residential building group-oriented electricity utilization scheduling optimization method so as to reduce electricity utilization cost and peak load of a power grid under the condition of ensuring the comfort level of residential electricity utilization.
In order to achieve the purpose, the invention adopts the following technical scheme:
a residential community-oriented electricity utilization scheduling optimization method comprises the following steps:
s1: dividing the household electric equipment into controllable electric appliances and uncontrollable electric appliances, dividing the controllable electric appliances into power-adjustable electric appliances and time-adjustable electric appliances, and establishing user uncomfortable degree models of the power-adjustable electric appliances and the time-adjustable electric appliances;
s2: determining an objective function of the residential group power utilization scheduling by taking the minimized power utilization discomfort degree, the user power utilization cost and the power grid load variance as overall objectives by using the model obtained in the S1;
s3: and (3) solving an optimal value of power utilization scheduling of the multi-user controllable electric appliances in each time period under the residential group by adopting an improved cooperative particle swarm algorithm and taking the objective function obtained in the step (S2) as a fitness function, wherein the obtained optimal value is an optimal power utilization scheduling strategy of the controllable electric appliances.
In S1, for the controllable electrical appliance, the user discomfort degree model of the temperature-related power-adjustable electrical appliance is as follows:
wherein, delta ij (T) is the current temperature T at time T ij (t) and optimum temperature
The distance of (a) to (b),
η ij an amount of discomfort function offset for the user preference; theta.theta. ij The magnitude of the discomfort level function is preferred by the user.
In S1, for the time-adjustable electric appliance, the user discomfort degree model is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the user-defined time period of using the electric appliance i can generate uncomfortable degree;
indicating the schedulable period of time for the time adjustable appliance,
α ij to the start time, β ij Is the end time.
In S2, the objective function of the residential community electricity utilization scheduling is as follows:
wherein the content of the first and second substances,
λ and μ are two parameters used to adjust the weight of each sub-targeting function; y is ij (t) represents the on-off state of the time adjustable appliance; u shape ij (t) discomfort associated with operating the appliance, including user discomfort associated with the power-adjustable appliance
User discomfort of time-adjustable electrical appliance
ρ (t) is the real-time electricity price at time t;
is a set of resident residents,
is a set of controllable electric appliances, and the electric appliances are controlled by the controller,
and
is the cardinality of the respective set; p is ij (t) represents the power of the electrical equipment in all time intervals at the moment t,
i is the serial number of the resident user, j is the serial number of the controllable electric appliance,
in order to schedule a set of time intervals,
is the cardinality of the set.
S3 specifically comprises the following processes:
s3.1: for each family, a plurality of particle swarms are set according to the type and the number of controllable electric appliances to optimize the electric power dispatching of the controllable electric appliances;
s3.2: initializing the particle group in S3.1, and setting a maximum iteration number value;
s3.3: calculating the diversity of each particle swarm particle;
s3.4: calculating a random attraction/repulsion signal q;
s3.5: calculating the fitness values of all the particles, and solving individual historical optimal values and global optimal values of the particles in each particle swarm;
s3.6: updating the particle speed and the position according to the results of S3.3-S3.5;
s3.7: judging whether the maximum iteration times is reached, if so, performing S3.8, and if not, performing S3.3;
s3.8: outputting global optimal values g of all particle swarms best And obtaining the optimal power utilization scheduling strategy of each controllable electric appliance.
S3.1, regarding the power adjustable electric appliances, the power of each power adjustable electric appliance in each time period is a control variable; for time-adjustable electrical appliances, the starting time of each time-adjustable electrical appliance is a control variable.
In S3.3, the diversity of each particle group is calculated according to the following formula:
wherein the content of the first and second substances,
an r particle population for a k iteration; m and M are respectively the serial number of the particles and the total number of the particles; d and D are respectively a dimension serial number and a dimension number; x is the position of the particle;
for the kth iterationIs the average of all particles in d-dimension.
In S3.4, the random attraction/repulsion signal q is calculated by:
wherein, the first and the second end of the pipe are connected with each other,
s3.5, calculating the fitness values of all particles by taking the objective function of the residential community power utilization scheduling as a particle fitness function, and solving individual historical optimal values and global optimal values of the particles in each particle swarm; in the calculation process, the value of a sub-objective function Variance in an objective function of the residential community power utilization scheduling needs to be calculated through a particle swarm cooperative formula, wherein the particle swarm cooperative formula is as follows:
c(j,z)≡(s 1 .g best ,s 2 .g best ,...,s j-1 .g best ,z,s j+1 .g best ,...,s m .g best )
wherein c (j, z) represents the current global optimum g in a given population of other particles best Next, a vector of z values is inserted at position j.
In S3.6, the particle velocity is updated according to the following equation:
the position of the particle is updated as follows:
wherein w is a weight; c. C 1 And c 2 Setting parameters for the two in advance; p is a radical of formula bestm An individual historical optimum value for the mth particle; g best The global optimal value of the particle swarm is obtained;
is the position of the particle m at the kth iteration;
the velocity of the particle m at the k +1 iteration.
Compared with the prior art, the invention has the following beneficial effects:
compared with other researches aiming at the resident electricity dispatching optimization under demand response, the resident electricity dispatching optimization method for the resident residential group firstly divides the resident electricity utilization equipment into controllable electric appliances and uncontrollable electric appliances, the controllable electric appliances are power adjustable electric appliances and time adjustable electric appliances, and a user uncomfortable degree model of the power adjustable electric appliances and a user uncomfortable degree model of the time adjustable electric appliances are established; then, determining an objective function of the residential group power utilization scheduling by taking the minimum power utilization discomfort degree, the user power utilization cost and the power grid load variance as overall objectives by using the obtained model; and finally, solving an optimal value of power utilization scheduling of the multi-user controllable electric appliances in each time period under the residential group by adopting an improved cooperative particle swarm algorithm and taking the obtained objective function as a fitness function, wherein the obtained optimal value is an optimal power utilization scheduling strategy of the controllable electric appliances. According to the technical scheme, the power dispatching condition of multiple users in a residential community is comprehensively considered, and the condition that the demand response of a single residential user is very little is avoided; the household controllable electric appliance is divided into a power adjustable electric appliance and a time adjustable electric appliance, and load models associated with the user electricity utilization comfort level are respectively established; the cooperative particle swarm optimization algorithm can avoid dimension disasters and particle precocity and is suitable for large-scale residential electricity dispatching. By optimizing the electricity utilization scheduling of the resident household group, the electricity utilization cost and the peak load of the power grid are reduced on the premise of ensuring the electricity utilization comfort of the user, so that the purposes of economy, safety, energy conservation and emission reduction are achieved.
Drawings
FIG. 1 is a flowchart illustrating a method for optimizing power scheduling according to the present invention;
FIG. 2 is a diagram of three time adjustable appliances during periods of electrical discomfort;
FIG. 3 is a flow chart of the cooperative particle swarm optimization algorithm of the present invention;
FIG. 4 is a graph of outdoor temperature curves according to an embodiment of the present invention;
FIG. 5 is a real-time electricity rate graph in accordance with an embodiment of the present invention;
FIG. 6 is a simulation experiment chart of the total load of 2000 residents under no coordination in the embodiment of the present invention;
FIG. 7 is a simulation experiment chart of the total load of 2000 residents under cooperation in the embodiment of the present invention;
FIG. 8 is a graph illustrating indoor temperature after cooperative scheduling optimization in an embodiment of the present invention;
FIG. 9 is a graph illustrating a hot water temperature curve after cooperative scheduling optimization according to an embodiment of the present invention;
FIG. 10 is a simulation experiment diagram of different loads of the power grid in the embodiment of the present invention;
fig. 11 is a simulation experiment diagram of the total load of the power grid after the cooperative scheduling optimization in the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the figures and examples.
The invention is further described in detail by simulating the winter power utilization condition of a northern community with 2000 resident residents and combining the accompanying drawings:
a large number of users actively cooperate to perform demand response, and the electricity utilization scheduling optimization method facing the residential community is realized. Firstly, the resident controllable electric appliances are divided into power adjustable electric appliances and time adjustable electric appliances, and a load model related to the comfort level of the electricity consumption of a user is established for typical electric appliances. Meanwhile, an objective function is established with the aim of minimizing the power utilization discomfort, the power utilization cost of the user and the power grid load variance. And finally, solving an approximate optimal value of the power utilization scheduling of the multi-user typical controllable electrical appliances in each time period under the residential community by adopting an improved cooperative particle swarm algorithm. The specific working flow is shown in fig. 1. The electricity utilization scheduling optimization method for the residential community of the embodiment specifically comprises the following steps:
(1) Dividing the residential electric equipment into controllable electric appliances and uncontrollable electric appliances, further dividing the controllable electric appliances into power-adjustable electric appliances and time-adjustable electric appliances, and establishing user uncomfortable degree models of the power-adjustable electric appliances and the time-adjustable electric appliances;
(2) Under the constraint condition, determining an objective function of residential group power utilization scheduling by taking the minimized power utilization discomfort degree, the user power utilization cost and the power grid load variance as overall objectives by using the model obtained in the step (1);
(3) And (3) solving an approximate optimal value of the power utilization scheduling of the multi-user typical controllable electrical appliances in each time period under the residential group by adopting an improved cooperative particle swarm algorithm and taking the objective function in the step (2) as a fitness function, wherein the obtained optimal value is an optimal power utilization scheduling strategy of the controllable electrical appliances.
Specifically, in step (1), divide into controllable electrical apparatus and uncontrollable electrical apparatus with resident's consumer, to controllable electrical apparatus, show the electrical apparatus power in all time intervals with formula (1):
wherein i is the serial number of the resident user, j is the serial number of the controllable electric appliance,
in order to schedule a set of time intervals,
is the cardinality of the set. The controllable electrical appliances are further divided into power-adjustable electrical appliances and time-adjustable electrical appliances. The power-adjustable electrical appliance can change the power consumption according to the change of environment and requirements, and can reduce the load of a power grid by reducing the power consumption in the peak power consumption period, and the electrical appliances mainly comprise an air conditioner, an electric heater, an electric water heater and the like. Only electric heaters and electric water heaters are considered in this embodiment. Time adjustableThe initial working time of the electric appliances can be flexibly selected, and the electric appliances mainly comprise a washing machine, a dryer and a dish washing machine. The utility function of the temperature-dependent power-tunable appliance, i.e., the electrical discomfort level, is represented by equation (2) and equation (3):
wherein, delta ij (T) is the current temperature T at time T ij (t) and optimum temperature
Equation (3) maps this difference value to the electrical discomfort value by Sigmoid function
In the above, it is shown that a larger gap value causes a larger discomfort, while a smaller gap causes a negligible discomfort. Eta ij An amount of discomfort function offset for the user preference; theta ij The magnitude of the discomfort level function is preferred by the user. In the present embodiment, the optimum temperature ± 2 ℃ is set to an acceptable room temperature, and the optimum temperature (70 ℃) ± 5 ℃ is set to an acceptable hot water temperature. A thermodynamic model for generating a change in indoor temperature using an air conditioner or an electric heater is represented by equation (4):
T in (t+1)＝T in (t)e -1/δ +R·P ij (t)·(1-e -1/δ )+T out (t)·(1-e -1/δ ) (4)
wherein, T in (T) is the indoor temperature at time T, T out (t) is the outdoor temperature at time t, as shown in FIG. 4, in units of; r is thermal resistance of a residential building, and the unit is ℃/kW; δ = R · C, C being the air heat capacity in kWh/C. In this embodiment, the corresponding parameter values are shown in table 1.
TABLE 1
The thermodynamic model for changing the water temperature using an electric water heater is shown in equation (5):
wherein, T cold (T) and T hot (t) the temperature of cold water and hot water at time t, in units of; v total Is the volume of the water tank, and the unit is L; v outflow (t) hot water flow in unit time at time t, the unit being L; c water The heat capacity of water is in kW/(. Degree. C. L). In this embodiment, the corresponding parameter values are shown in table 1.
For time-adjustable electrical appliances, in equation (6)
Indicating the period of time during which it can be scheduled, alpha ij To the start time, β ij Is the end time. When it starts to operate, it will continuously work ij A time interval to complete its task:
the utility function of a time-tunable electrical appliance is represented by equation (7):
wherein the content of the first and second substances,
the customized use of the appliance i for the user may result in periods of discomfort. In this embodiment, all time adjustable electrical appliances α ij ＝0，β ij ＝24，
As shown in fig. 2, the other corresponding parameter values are shown in table 1.
Specifically, in the step (3), an objective function of the residential community electricity utilization scheduling with the overall objective of minimizing the discomfort of electricity utilization, the electricity utilization cost of the user and the power grid load variance is established:
wherein:
in the formulas (8) - (9), λ and μ are two parameters, which are used to adjust the weight of each sub-objective function to 0.8 and 0.2, respectively; y is ij (t) represents the on-off state of the time adjustable appliance; u shape ij (t) is the degree of discomfort of electricity consumption caused by operating the electrical appliance; ρ (t) is the real-time electricity price at time t, as shown in fig. 5;
is a set of resident residents,
is a set of controllable electric appliances, and the electric appliances are controlled by the controller,
and
is the cardinality of the corresponding set.
Specifically, in the step (4), because controllable electric appliances of a plurality of users in the residential group are considered, the number of control variables is huge, and therefore, a cooperative particle swarm algorithm is adopted to avoid dimension disasters. Meanwhile, in order to avoid the problem of particle precocity, the original cooperative particle swarm algorithm is improved, and a random attraction/repulsion mechanism is introduced to increase the diversity of particles and maintain the searching capability of the algorithm in a solution space. The improved cooperative particle swarm algorithm comprises the following steps (as shown in fig. 3), and specifically comprises the following steps:
a. and a plurality of particle swarms are set for each family to optimize the power utilization scheduling of the controllable electric appliances. For the power-adjustable electric appliances, the power of each electric appliance in each time period is a control variable; for time-adjustable electrical appliances, the starting time of each electrical appliance is a control variable. The number of particle clusters and the dimension of the particles need to be balanced so that the dimension of each particle is not too large. In this embodiment, each family sets 9 particle swarms for power consumption scheduling optimization: 4 particle swarms are responsible for electric heater power consumption scheduling, and the dimension of each particle is 6;4 particle swarms are responsible for the electric water heater, the dimension of each particle is 6; and 1 particle swarm is responsible for the three time-adjustable electric appliances, and the dimension of each particle is 3.
b. Initializing the particle group defined in the step a. In this embodiment, the corresponding parameter values are shown in table 1.
c. Calculating the diversity of each particle group according to the formula (12):
wherein the content of the first and second substances,
an r particle population for a k iteration; m and M are respectively the serial number of the particles and the total number of the particles; d and D are respectively a dimension serial number and a dimension number; x is the position of the particle;
is the average of all particles in the kth iteration in the d-dimension.
d. The random attraction/repulsion signal q is calculated according to equation (13) and equation (14):
first, p ∈ [0, 1) is calculated according to the diversity of the particle group, and then a random number on [0, 1) is generated through rand (),
if less than p, a random attraction/repulsion signal q =1 indicates attraction, otherwise q = -1 indicates repulsion.
e. And (3) calculating the fitness values of all the particles by taking a formula (8) as a particle fitness function, and solving the individual historical optimal value and the global optimal value of the particles in each particle swarm. In the calculation process, the value of the sub-objective function Variance in the objective function (8) can be calculated through a particle swarm cooperative formula (15):
c(j,z)≡(s 1 .g best ,s 2 .g best ,...,s j-1 .g best ,z,s j+1 .g best ,...,s m .g best ) (15)
wherein c (j, z) represents the current global optimum g in the known other particle swarm best Next, a vector of z values is inserted at position j.
f. The particle velocity and position are updated according to equation (16) and equation (17).
Wherein w is a weight set to 0.72; c. C 1 And c 2 Parameters are set for two advance settings, set to 0.49; p is a radical of formula bestm An individual historical optimum value for the mth particle; g best The global optimal value of the particle swarm is obtained;
is the position of the particle m at the kth iteration;
the velocity of the particle m at the (k + 1) th iteration.
g. Judging whether the maximum iteration times (150 times) is reached, if so, executing the step h, and if not, executing the step c;
h. outputting global optimal values g of all particle swarms best And the optimal power utilization scheduling strategy is the optimal power utilization scheduling strategy of each controllable electric appliance.
The effects of the present invention will be further described with reference to the drawings of experiments and experimental results:
in this embodiment, it is assumed that controllable electrical appliances in the residential quarter 2000 can perform scheduling control. If the household electrical appliances do not respond cooperatively, the time-adjustable electrical appliances of each household are scheduled to operate in a low electricity price period (0-3). After the optimization method is optimized, the power utilization scheduling among the users can be coordinated, so that the load of the power grid is balanced, as shown in fig. 7, and meanwhile, the comfort level of the users can also be guaranteed. Fig. 8 shows a graph of indoor temperature of residents after cooperative scheduling optimization, and it can be seen from the graph that indoor temperatures of 2000 residents are within ± 2 ℃ of the optimal temperature, so that thermal comfort of the residents can be guaranteed. FIG. 9 shows the graph of the hot water temperature after the co-scheduling optimization, from which it can be seen that the water temperature is substantially within + -5 ℃ of the optimal temperature (70 ℃), which is an acceptable hot water temperature. In addition, it is assumed in the experiment that the power grid has commercial power and partial peak load transferred from other adjacent power grids besides residential power, as shown in fig. 10. Through the cooperative scheduling optimization algorithm, a residential area can realize a virtual energy storage system, and the load of a power grid is effectively balanced. Fig. 11 shows the overall balancing effect on the various loads in fig. 10 after the residential controllable electric appliance scheduling optimization.
Claims (7)
1. A residential community-oriented electricity utilization scheduling optimization method is characterized by comprising the following steps:
s1: dividing the residential electric equipment into controllable electric appliances and uncontrollable electric appliances, wherein the controllable electric appliances are divided into power-adjustable electric appliances and time-adjustable electric appliances, and establishing user uncomfortable degree models of the power-adjustable electric appliances and the time-adjustable electric appliances;
s2: determining a target function of electricity utilization scheduling of a residential group by taking the minimum electricity utilization discomfort degree, the electricity utilization cost of the user and the power grid load variance as an integral target by using the user discomfort degree model of the power-adjustable electric appliance and the user discomfort degree model of the time-adjustable electric appliance obtained in the step S1;
s3: an improved cooperative particle swarm algorithm is adopted, the objective function obtained in the S2 is used as a fitness function, the optimal value of the power utilization scheduling of the multi-user controllable electric appliance in each time period under the residential community is solved, and the obtained optimal value is the optimal power utilization scheduling strategy of the controllable electric appliance;
in S2, the objective function of the residential community electricity utilization scheduling is as follows:
wherein the content of the first and second substances,
s3 specifically comprises the following processes:
s3.1: for each family, a plurality of particle swarms are set according to the type and the number of controllable electric appliances to optimize the electric power dispatching of the controllable electric appliances;
s3.2: initializing the particle group in S3.1, and setting a maximum iteration number value;
s3.3: calculating the diversity of each particle swarm particle;
s3.4: calculating a random attraction/repulsion signal q;
s3.5: calculating the fitness values of all the particles, and solving individual historical optimal values and global optimal values of the particles in each particle swarm;
s3.6: updating the particle speed and the position according to the results of S3.3-S3.5;
s3.7: judging whether the maximum iteration times is reached, if so, performing S3.8, and if not, performing S3.3;
s3.8: outputting global optimal values g of all particle swarms best Obtaining the optimal power utilization scheduling strategy of each controllable electric appliance;
in S3.4, the random attraction/repulsion signal q is calculated by:
wherein, the first and the second end of the pipe are connected with each other,
the multiplicity of particles per population of particles.
2. The method according to claim 1, wherein in S1, the user discomfort level model of the temperature-dependent power-regulated appliances for the controllable appliances is as follows:
wherein, delta ij (T) is the current temperature T at time T ij (t) and optimum temperature
The distance of (a) to (b),
η ij an amount of discomfort function offset for the user preference; theta ij The magnitude of the discomfort level function is preferred by the user.
3. The method for optimizing power consumption scheduling for residential communities as claimed in claim 1, wherein in S1, the user discomfort degree model for time-adjustable power consumption is as follows:
wherein the content of the first and second substances,
a period of discomfort can be generated when the electric appliance i is used by a user;
indicating a time period during which the time adjustable appliance is schedulable,
α ij to the start time, β ij Is the end time.
4. The method according to claim 1, wherein in S3.1, for the power-adjustable electrical appliances, the power of each power-adjustable electrical appliance in each time period is a control variable; for time-adjustable electrical appliances, the starting time of each time-adjustable electrical appliance is a control variable.
5. The method according to claim 1, wherein in S3.3, the diversity of each particle group is calculated according to the following formula:
wherein, the first and the second end of the pipe are connected with each other,
an r particle population for a k iteration; m and M are respectively the serial number of the particles and the total number of the particles; d and D are respectively a dimension serial number and a dimension number; x is the position of the particle;
is the average of all particles in the kth iteration in the d-dimension.
6. The residential community-oriented electricity dispatching optimization method according to claim 1, wherein in S3.5, the fitness values of all the particles are calculated by taking an objective function of electricity dispatching of the residential community as a particle fitness function, and the individual historical optimal value and the global optimal value of the particles in each particle swarm are solved; in the calculation process, the value of the subobjective function Variance in the objective function of the residential community power dispatching needs to be calculated through a particle swarm cooperative formula, wherein the particle swarm cooperative formula is as follows:
c(j,z)≡(s 1 .g best ,s 2 .g best ,...,s j-1 .g best ,z,s j+1 .g best ,...,s m .g best )
wherein c (j, z) represents the current global optimum g in the known other particle swarm best Next, a vector of z values is inserted at position j.
7. The method for optimizing electric schedule for residential communities as claimed in claim 1, wherein in S3.6, the speed of the particles is updated according to the following formula:
the position of the particle is updated as follows:
wherein w is a weight; c. C 1 And c 2 Setting parameters for two in advance; p is a radical of bestm An individual historical optimum value for the mth particle; g best The global optimal value of the particle swarm is obtained;
is the position of the particle m at the kth iteration;
the velocity of the particle m at the k +1 iteration.
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