CN115115145B - Demand response scheduling method and system for distributed photovoltaic intelligent residence - Google Patents
Demand response scheduling method and system for distributed photovoltaic intelligent residence Download PDFInfo
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Abstract
The invention provides a demand response scheduling method and system for a distributed photovoltaic intelligent house, and belongs to the technical field of power demand response scheduling control. According to the invention, through the coordination and coordination of the distributed photovoltaic and the demand load resource, the problem of conflict between uncertainty of user behavior and a demand plan is solved; a mathematical model of demand resources is established, a user load is predicted by adopting a generalized regression neural network and a probabilistic neural network to form a pre-scheduling measure, a photovoltaic community demand response integral strategy is formed based on the established model and measure, and a user autonomous response algorithm is provided to improve the comfort level of users.
Description
Technical Field
The invention relates to the technical field of power demand response scheduling control, in particular to a demand response scheduling method and system for a distributed photovoltaic intelligent house.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
With the development of the intelligent power distribution and utilization network technology, the schedulable capacity of load resources represented by electric vehicles and air conditioners is gradually enhanced. Due to the characteristics of large quantity of load resources, low capacity, strong dispersibility and random response, the distribution system generally takes all schedulable loads in the whole photovoltaic community as a response aggregator, and takes the aggregation form as a carrier of a demand response strategy, so as to uniformly schedule the load resources of all users. However, in the process of dispatching load by the aggregator, user behaviors are easy to conflict with demand plans, and even the stability of the whole power distribution system is affected. With the development of distributed renewable energy, the access of the renewable energy is beneficial to relieving the conflict between the user behavior and the demand plan, but the output fluctuation and the randomness of the renewable energy also influence the formulation of the demand plan, and the user behavior of each electric appliance is analyzed independently, so that the method is complicated, and the method does not have the practicability in a large range.
The existing demand response strategy generally utilizes different objective functions to optimize schedulable loads of users and make a scheduling plan from top to bottom, but the method not only has the problems of transmission delay and slow response, but also does not consider the comfort level demands of the users, and if the mandatory scheduling load resources easily cause conflict psychology of the users, the making and implementation of the network side scheduling plan are obviously influenced.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a demand response scheduling method and system for a distributed photovoltaic intelligent house, which can be used for planning the working state of a schedulable load through a user power load prediction result and solving the problem of conflict between user behavior uncertainty and a demand plan.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a demand response scheduling method for a distributed photovoltaic intelligent residence.
A demand response scheduling method of a distributed photovoltaic intelligent house comprises the following processes:
the method comprises the following steps: acquiring current electric power operation parameter data of the distributed photovoltaic intelligent residence and a plurality of prediction models constructed based on historical electric power operation parameter data;
step two: obtaining a current user load prediction result based on the prediction model with the highest accuracy and the current power operation parameter data; aiming at the lowest cost, carrying out single-target optimization through a particle swarm algorithm by combining the current power operation parameter data to obtain the current user load optimization result;
step three: if the prediction result of the user load of half or more than half of the prediction models at the current moment is larger than the user load after particle swarm optimization, taking the prediction result of the current user load as the power upper limit of the current moment, and otherwise, taking the optimization result of the current user load as the power upper limit; calculating the priority value of each electric appliance according to the load prediction result of the current user, judging whether the total power value of the response load meets the upper limit of the current power, if so, ending the demand response of the stage, and feeding the current power utilization behavior back to the prediction model; if not, the switch state of the electric appliance is re-planned, and the electric appliance with the lowest priority is switched off;
step four: calculating the switch indexes of the electric appliances, judging whether the switch indexes of the electric appliances are lower than a set value or not, if so, supplying power to the electric appliances reaching the set values of the switch indexes in a mode of mutually matching energy storage and a power grid, and returning to the step two again; if not, returning to the third step;
step five: when response interruption occurs, executing a user autonomous response algorithm, and judging whether the total amount of the current power is less than or equal to the upper limit of the current power, if so, returning to a demand response algorithm; otherwise, the electric appliance with the lowest turn-off priority value judges again until the total current power is less than or equal to the upper current power limit.
As an alternative implementation, the objective of minimizing the cost includes:
wherein the content of the first and second substances,Nin minute-scale quantities;Fthe total electricity consumption cost of the community is calculated;P t,pv 、P t,grid andP t,ESS is the firsttPhotovoltaic network access power, traditional energy power and bidirectional load power at each moment;C t,price is thattReal-time electricity prices at the moment.
As an optional implementation manner, each appliance priority value includes:
wherein the content of the first and second substances,irepresentLThe number of types of appliances;K t,L,wor representstOf time of dayLThe priority index of the working state of the electric appliance,D t,set andD t,now representtA set value and a real-time value of the time;K t,L,bill representtTime of dayLThe electricity price index of the electric appliance, the denominator is the sum of the real-time cost of all the equipment, and the numerator is the real-time cost of single equipment;C t,price is thattReal-time electricity prices at the moment;K t,L,com representstTime of dayLUser selection indicators for the appliance;nrepresenting the number of all devices participating in the demand response;m i the specific value is determined by the user and represents the on or off willingness of the user to different electrical appliances;K t,L,open representtTime of dayLThe opening priority index of the electric appliance, a, b and c are respectively coefficients, P t,L,i And the predicted value is the electric load predicted value of the ith L-type electric appliance at the moment t.
As an optional implementation manner, the switch index includes:
wherein, t 1 、t 2 Respectively representing the starting and stopping time of the change of the L electrical appliance switch; t represents a demand response period.
As an optional implementation manner, the particle swarm algorithm includes:
initializing a load model, setting the random position and the iteration times of a particle swarm according to the running characteristics of a load, respectively calculating the fitness value Fi of each particle, wherein the fitness value in single-target optimization is equal to the cost;
comparing the fitness value of each particle with the individual optimal position, namely the fitness value at each moment, and taking the optimal comparison result as the optimal working state of the current electric appliance;
comparing the fitness value of each particle with a global optimal value, namely the fitness value of all the time of the demand response, and taking the optimal comparison result as the most appropriate working time of each current electric appliance in all the time periods of the demand response;
if the maximum iteration times are exceeded, re-optimizing the individual optimal positions of the particles; if not, outputting the result.
As an alternative implementation, with the goal of lowest cost, the method further includes the constraint:
wherein the content of the first and second substances,P t,out is thattThe output power of the photovoltaic power source at a moment,SOC t,n is shown asnElectric automobiletState of charge at time of day，SOC exp Is the rated capacity of the SOC (state of charge),SOC max,n is the maximum value of the SOC, and the SOC is the maximum value,P t,Lmax representing a controllable loadtAn upper power limit at a time;P t,n_cmax andP t,n_dmax respectively represent the upper limits of the charging power and the discharging power of the bidirectional load,P t,n_c to charge the power for the bi-directional load,P t,n_d discharging power for the bi-directional load.
As an optional implementation party, the user autonomous response algorithm includes:
switching on the electric appliance selected by the user;
judging whether the total amount of the current power is less than or equal to the current power upper limit, if so, keeping the on-off states of other electrical appliances unchanged, recording user behaviors, and returning to a demand response algorithm; otherwise, calculating the priority value of the electric appliance at the current moment, and judging the electric appliance with the lowest priority value again until the total current power is less than or equal to the upper current power limit.
The invention provides a demand response scheduling system of a distributed photovoltaic intelligent house.
A demand response dispatch system for a distributed photovoltaic smart home, comprising:
a data acquisition module configured to: acquiring current electric power operation parameter data of the distributed photovoltaic intelligent residence and a plurality of prediction models constructed based on historical electric power operation parameter data;
a load prediction and optimization module configured to: obtaining a current user load prediction result based on the prediction model with the highest accuracy and the current power operation parameter data; aiming at the lowest cost, performing single-target optimization by combining the current power operation parameter data through a particle swarm algorithm to obtain a current user load optimization result;
an appliance optimization control module configured to: if the prediction result of half or more than half of the prediction models at the current time is larger than the user load after particle swarm optimization, taking the prediction result of the current user load as the upper power limit of the current time, or taking the optimization result of the current user load as the upper power limit; calculating the priority value of each electric appliance according to the load prediction result of the current user, judging whether the total power value of the response load meets the upper limit of the current power, if so, ending the demand response of the stage, and feeding the current power utilization behavior back to the prediction model; if not, the switch state of the electric appliance is re-planned, and the electric appliance with the lowest priority is switched off;
an appliance switch control module configured to: calculating the switch indexes of the electric appliances, judging whether the switch indexes of the electric appliances are lower than a set value or not, if so, supplying power to the electric appliances reaching the set values of the switch indexes in a way of mutually matching energy storage and a power grid, and returning to the load prediction and optimization module again; if not, returning to the electrical appliance optimization control module;
an interrupt response control module configured to: when response interruption occurs, executing a user autonomous response algorithm, and judging whether the total amount of the current power is less than or equal to the upper limit of the current power, if so, returning to a demand response algorithm; otherwise, the electric appliance with the lowest turn-off priority value judges again until the total current power is less than or equal to the upper current power limit.
As an alternative implementation, the objective of minimizing the cost includes:
wherein the content of the first and second substances,Nin minute-scale quantities;Fthe total electricity consumption cost of the community is calculated;P t,pv 、P t,grid andP t,ESS is the firsttPhotovoltaic network access power, traditional energy power and bidirectional load power at each moment;C t,price is thattReal-time electricity prices at the moment.
As an optional implementation manner, each appliance priority value includes:
wherein the content of the first and second substances,irepresentsLThe number of types of appliances;K t,L,wor representstOf time of dayLThe priority index of the working state of the electric appliance,D t,set andD t,now representtA set value and a real-time value of the time;K t,L,bill to representtTime of dayLThe electricity price index of the electric appliance, the denominator is the sum of the real-time cost of all the equipment, and the numerator is the real-time cost of the single equipment;K t,L,com representstTime of dayLUser selection indicators for the appliance;nrepresenting the number of all devices participating in the demand response;m i the specific value is determined by the user and represents the on or off willingness of the user to different electrical appliances;K t,L,open representtTime of dayLThe opening priority index of the electric appliance, a, b and c are respectively coefficients, P t,L,i The predicted value is the electricity load predicted value of the ith L-type electric appliance at the moment t;C t,price is thattReal-time electricity prices at the moment.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the demand response scheduling method and system for the distributed photovoltaic intelligent residence, the working state of the schedulable load is planned through the power utilization load prediction result of the user, and the problem of conflict between user behavior uncertainty and a demand plan is solved.
2. According to the demand response scheduling method and system for the distributed photovoltaic intelligent residence, a prediction model based on a generalized neural network or a probabilistic neural network is established, and photovoltaic output and user power utilization behaviors can be accurately predicted.
3. According to the demand response scheduling method and system for the distributed photovoltaic intelligent residence, a user autonomous response algorithm is established, the real-time demand of a user can be met, and the comfort level of the user is improved.
4. The demand response scheduling method and system of the distributed photovoltaic intelligent residence establish a photovoltaic community demand response overall strategy based on user load prediction, can achieve the aim of peak clipping and valley filling of the overall load in the overall load optimization scheduling of the community, and reduce the power consumption cost of users.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flow chart of a photovoltaic community demand response strategy based on user load prediction according to embodiment 1 of the present invention.
Fig. 2 is a residential diagram of a photovoltaic-connected hybrid energy system provided in embodiment 1 of the present invention.
Fig. 3 is a diagram of a result of photovoltaic output and user load prediction provided in embodiment 1 of the present invention.
Fig. 4 is a load comparison diagram before and after optimization of the demand response policy provided in embodiment 1 of the present invention.
Fig. 5 is a flowchart of a user autonomous response algorithm provided in embodiment 1 of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1:
as shown in fig. 1, embodiment 1 of the present invention provides a demand response scheduling method for a distributed photovoltaic intelligent residence, including the following processes:
s1: demand side resource model
The demand side resources generally comprise distributed power generation resources, load resources and energy storage resources, and in order to deeply mine the response potential of the demand side resources in the power distribution network, a mathematical model of the demand side resources needs to be established.
S1.1: distributed power generation resource model
The output of the photovoltaic power supply is related to the illumination intensity and the temperature of the environment, and the simulation of the formula (1) is adoptedtPhotovoltaic output at the moment:
wherein the content of the first and second substances,P t,out is thattThe output power of the photovoltaic power supply at each moment; p set The default output power of the photovoltaic power supply is obtained; w set A default illumination intensity for the current illumination; t is set Is a default temperature of the environment; t is a unit of t,out Is the ambient temperature at time t;W t is thattThe illumination intensity at that moment;αfor a set temperature parameter by adjustingαThe magnitude of (a) simulates the influence of the ambient temperature on the output power of the photovoltaic power supply,αan increase represents a greater influence of temperature change on photovoltaic output;αthe reduction represents less influence of temperature variation on the photovoltaic output.
S1.2: load resource model
The controllable load is divided into three types of basic load, temperature control load and bidirectional flexible load according to the power requirement of the load, and corresponding mathematical models are established according to different load characteristics.
S1.2.1: base load
The power of the basic load is a constant value, the power is consumed in a single direction, and the switching time is transferred by responding to the requirements, such as electric appliances such as a water heater, a washing machine and the like, and a mathematical model is established as follows:
wherein, the first and the second end of the pipe are connected with each other,P t,L representstTime of dayLThe power value of the electric appliance is set,P def representing the rated power of the electric appliance;k t,L representstTime of dayLSwitch of electric appliance.
S1.2.2: temperature controlled load
The power of the temperature-controlled load is affected by the ambient temperature and consumes power in one direction, and the most typical equipment is an air conditioner. The set air-conditioning model is a stepped power model, the power gear is influenced by the room temperature, and a mathematical formula of the room temperature model is obtained by the following formula (3):
wherein the content of the first and second substances,T t,in 、T t,out 、T low andT hign are respectively astThe indoor temperature and the outdoor temperature at the moment, the lower temperature limit and the upper temperature limit;T t-1,in is composed oft-1The temperature of the room at the moment of time,αthe influence coefficient of outdoor temperature on the indoor is taken as the coefficient;βis the influence coefficient of the electricity consumption on the room temperature in a unit time period,e t is the amount of electricity used per unit time period.
S1.2.3: bidirectional flexible load
The bidirectional flexible load can provide electric energy and consume the electric energy, the power of the load can flow in two directions, for example, an electric Vehicle with a Vehicle to grid (V2G) function, the V2G model is shown as the following formula:
wherein the content of the first and second substances,SOC t,n andSOC t ,n-1 is shown asnElectric automobiletTime of day andt-state of charge at time 1;P t,n_c andP t,n_d respectively representing the charging and discharging power of the electric automobile;η EV the charge-discharge efficiency is shown in the figure,SOC exp represents a rated capacity; lower limit of electric vehicle charge state for ensuring autonomy of userSOC set The charge state is set by the user independently and meets the travel requirement of the user at any time. The bidirectional flexible load and the energy storage resource have the same functions, and the mathematical model expressions of the bidirectional flexible load and the energy storage resource are also the same.
S2: photovoltaic output and user load prediction model
In the demand response process, the electricity utilization behavior of the user can change along with factors such as weather and temperature, and random fluctuation occurs in the regulation and control of the controllable load. The uncertainty of the user behavior is finally reflected in the randomness of the change of the user load, and in order to coordinate the uncertainty of the user behavior and the demand plan, a prediction method for simulating the photovoltaic output and the user load in the next demand response period based on historical data is provided, so that the comfort degree and the overall response speed of the user are effectively improved.
The SHPCS adopts a neural network algorithm as a core algorithm of a prediction system, and a Radial Basis Function (RBF) is a neural network proposed by Moody and Darken, and the structure of the RBF is divided into an input layer, a radial base layer and a linear layer, whereinRInputting variable quantity including factors such as weather, temperature, illumination intensity and the like;S1、S2, the number of neurons in the first layer and the second layer is shown, and one neuron corresponds to one training;LWthe power consumption behavior of the user is a weight coefficient, namely a coefficient that the power consumption behavior of the user is influenced by different factors;distis the width phasor, the euclidean norm, from the origin.
Based on RBF, two kinds of guiding training models of General Regression Neural Network (GRNN) and Probability Neural Network (PNN) are respectively extended, wherein GRNN replaces the weight of an output layer and a hidden layer by an output matrix of a training set, PNN changes a linear layer into a competition layer, and replaces the linear function of the output layer by the competition function. Under the condition that the reference sample data size is large, the accuracy rate of GRNN prediction is high; and under the condition that the division reference variable is an integer, the PNN prediction accuracy is high. In order to improve the prediction accuracy and compare the advantages and disadvantages of the two prediction models, the SHPCS sets that the two prediction systems have the same input variable and reference variable, and selects the prediction model with higher accuracy.
S2.1: GRNN neural network
Historical data of photovoltaic output and illumination intensity are used as input variablesxTime, output variableyPredicting the photovoltaic output in tomorrow; historical data of temperature, photovoltaic output, power grid load and user power consumption are used as input variablesxTime, output variableyThe influence coefficient of each input variable on the electricity consumption of the user is shown. And (3) replacing an LW matrix in the RBF with an output matrix y to construct a GRNN neural network:
wherein the content of the first and second substances,nthe number of samples represents the sampling rate of input quantity, such as half an hour of sampling data adopted by photovoltaic output;f x y(,) is a probability density function;x 0 the current observed value, such as the current temperature value;pis composed ofxX is a column vector having a dimension of P, x 0j Line j element, x, representing the current time ij The element of the jth row representing the ith instant,σis the standard deviation of the gaussian function. Output variableyFormula (6) can be obtained by simplifying formula (5):
wherein the output valueyIs a calculated influence coefficienty i Is calculated as a weighted sum of. By taking user load prediction as an example, the predicted weather and temperature of a meteorological data network are input, the system load and photovoltaic output predicted by a prediction system are input, and input variables are linearized through an influence coefficient to obtain the predicted user power consumption.
S2.2: PNN neural network
In order to analyze the characteristics of the two prediction models, the input variables and the output variables which are the same as GRNN (generalized regression neural network), namely the historical data of photovoltaic output and illumination intensity are used as the input variablesxTime, output variableyPredicting the photovoltaic output in the tomorrow; historical data of temperature, photovoltaic output, power grid load and user power consumption are used as input variablesxTime, output variableyIs the electricity consumption of the user. After inputting the reference variable of the historical data, the SHPCS calculates the Euclidean distance between the input variable and the power consumption of the user, and the weighted sum is taken as the parameter of the Gaussian functionσInitial probability density matrixPThe following were used:
wherein, the first and the second end of the pipe are connected with each other,E pm represents the normalized secondpPredicted photovoltaic output and firstmEuclidean distance between historical illumination intensities;P pm represents the firstpPredicted photovoltaic output and firstmInitial probability between the two of the historical photovoltaic outputs.
Taking photovoltaic output prediction as an example, assume that the illumination intensity data is an input sample and the column dimension ismIs divided intoCA number of samples of integer orderKA first, thenm=CK. The probability sum of each type of sample is obtained through the initial probability matrix as shown in the following formula:
wherein the content of the first and second substances,S pc representing the sum of probabilities of all samples based on the initial probability sum matrix,and calculating the probability sum of all elements of the matrix, and taking the maximum value of each row as the photovoltaic output data of the current row.
S3: demand response strategy based on SHPCS
S3.1: demand response monolithic framework
In order to solve the problems of information interaction and individual dispersion of demand response among three parties of distributed photovoltaic, traditional energy and community aggregators, a demand response overall framework is established, as shown in fig. 2. The framework comprises distributed photovoltaic and traditional energy, a power transmission and distribution system, a load aggregator and an SHPCS, a prediction system, schedulable loads and the like, the intelligent ammeter and an internet communication system can be used for connecting the community load aggregator and individual users, and the community loads can be uniformly scheduled through the SHPCS and the load aggregator.
The renewable energy photovoltaic panel array and the traditional energy thermal power unit transmit electric energy to the SHPCS through a power transmission and distribution network, and the microgrid provides real-time power grid load data for load aggregators. The prediction system carries out prediction based on historical data such as temperature, illumination intensity and the like, and transmits the prediction result to the SHPCS. The load aggregator and the SHPCS issue scheduling instructions according to information of each party, and the schedulable load coordinates the power utilization state according to the instructions: by changing the operation conditions, such as changing the refrigeration temperature of the air conditioner; by shifting the operating time, such as shifting the operating time of a washing machine; the electric automobile is charged through intermittent working time, such as intermittent. The information transmission is carried out in the system through an internet communication system.
S3.2: SHPCS solving strategy
In order to realize the control of an intelligent power utilization system on adjustable resources and reduce the power utilization cost of a photovoltaic community, a single-target optimizing function of the formula (9) is established, and a solving strategy is used for optimizing the schedulable load through a particle swarm algorithm.
Wherein, the first and the second end of the pipe are connected with each other,Nin minute-scale quantities;Fthe total electricity consumption cost of the community is calculated;P t,pv 、P t,grid andP t,ESS is the firsttPhotovoltaic network access power, traditional energy power and bidirectional load power at each moment;C t,price is thattReal-time electricity prices at the moment.
The constraint conditions in the solving process are as follows:
wherein the content of the first and second substances,P t,outmax represents the photovoltaic access upper limit;P t,Lmax representing a controllable loadtPower and upper power limit at a time;P t,n_cmax andP t,n_dmax representing the upper limit for bi-directional load charging and discharging.
The steps for solving the lowest cost through the particle swarm optimization are as follows:
the method comprises the following steps: initializing a load model, setting the random position and the iteration times of the particle swarm according to the running characteristics of the load, and respectively calculating the fitness value of each particleF i The fitness value in the single-target optimization is equal to the cost;
step two: comparing the fitness value of each particle with the individual optimal position, namely the fitness value at each moment, and taking the optimal comparison result as the optimal working state of the current electric appliance;
step three: comparing the fitness value of each particle with a global optimal value, namely the fitness value of all the time of the demand response, and taking the optimal comparison result as the demand response of each current electric appliance;
optimum working time in all time periods;
step four: if the maximum iteration times are exceeded, re-optimizing the individual optimal positions of the particles; if not, outputting the result.
S3.3: priority assessment index based on user comfort
In order to ensure the comfort of the user in the demand response process, the method is provided from four angles of the working state of the electric appliance, the electricity price coefficient, the user selection inertia and the user selection rightAnd evaluating indexes by priority. Formula (11) index calculation formula pass coefficienta、b、cThe weighted priority index yields:
wherein the content of the first and second substances,irepresentsLThe number of types of appliances;K t,L,wor representstOf time of dayLThe priority index of the working state of the electric appliance,D t,set andD t,now representtA set value and a real-time value of the time;K t,L,bill to representtTime of dayLThe electricity price index of the electric appliance, the denominator is the sum of the real-time cost of all the equipment, and the numerator is the real-time cost of single equipment;K t,L,com representstTime of dayLA user selection indicator of the appliance;nrepresenting the number of all devices participating in the demand response;mithe specific value is determined by the user and represents the on or off willingness of the user to different electrical appliances;K t,L,open to representtTime of dayLThe opening priority index of the electric appliance. The user selection weight is embodied on the setting of the weighting coefficient and the on-off willingness coefficient.
In order to solve the problem that the user electrical appliance is frequently switched in the demand response process, a switching index shown in formula (12) is introduced. The SHPCS supplies power to the electrical appliance with higher switch index by adopting a mode of mutually matching the power grid and the energy storage, so that the electrical appliance can still normally work when the power supply of the power grid is cut off.
Wherein, the first and the second end of the pipe are connected with each other,K t L switch,, represents a switch index;t 1 、t 2 respectively representLThe starting and stopping time of the change of the electric switch;Trepresenting a demand response cycle. The switch index is associated with a sampling time of the demand response strategy, the shorter the sampling,t 1 and witht 2 BetweenThe shorter the time difference (i.e. the smaller the switch index, the more frequently the appliance switches. When the sampling time of the demand response strategy is longer, the problem of frequent switching of the electric appliance does not exist, but the comfort level of a user is reduced, and the electricity utilization cost is increased.
S3.4: demand response holistic approach
The specific response steps of the overall scheme applicable to the SHPCS are as follows:
the method comprises the following steps: and (3) analyzing the output power of the power grid based on the detection data, calculating the photovoltaic output power according to the formula (1), providing the photovoltaic output power to a prediction model and a power utilization system as a data base, and establishing output models of different loads of a user according to the formulas (2) to (4). And determining input and output variables based on historical data, and respectively establishing GRNN and PNN neural network prediction models according to the formulas (5) - (8). Adjusting the weight value of input data of the neural network to obtain ten prediction models;
step two: based on the user load power of the neural network prediction model with the highest accuracy, establishing a target function with the lowest cost according to the formula (9), establishing a constraint function according to the formula (10), and performing single-target optimization on the SHPCS through a particle swarm optimization to obtain an optimized user load curve;
step three: comparing the user load based on ten prediction models with the particle swarm optimization result, and judging whether half of the prediction models are in the processtAnd (3) comparing the power of the particle swarm optimization result at the moment with the power rise of the particle swarm optimization result, setting the SHPCS to be the power upper limit of the current moment, and taking the optimization result as the power upper limit if the SHPCS does not set the prediction result to be the power upper limit of the current moment. And calculating the priority value of the electric appliance according to the formula (11) based on the result of the prediction model, and scheduling the corresponding load. The SHPCS judges whether the total power value of the response load meets the current demand response power limit value or not, if so, the demand response of the stage is ended, and the current power utilization behavior is fed back to the neural network prediction model; if not, the switch state of the electric appliance is re-planned, and the electric appliance with a lower priority value is turned off;
step four: the appliance switch index is calculated according to equation (12). Judging whether the switch index of the electric appliance is lower than a set value, if so, supplying power to the electric appliance reaching the switch index set value by the SHPCS in a way of mutually matching the stored energy with the power grid, and returning to the step two again; if not, returning to the third step;
step five: when the interrupt response occurs, the SHPCS executes a user autonomous response algorithm and judges whether the current total power meets the current limit index, if so, the SHPCS returns to the demand response algorithm; if the current value is not consistent with the limit value, the electric appliance with the lower switching-off priority value is judged again until the limit value is met.
Compared with the accuracy and the speed of the prediction system used in the embodiment, the illumination intensity of 4 continuous days and the load of 3 continuous days are selected as the data basis of the prediction system, a result graph of a photovoltaic prediction model and a user load prediction model is shown in fig. 3, the generalized neural network prediction accuracy is high under the condition that the reference sample data quantity is large, and the probabilistic neural network prediction accuracy is high under the condition that the division reference variable is an integer.
By using the model provided by the embodiment, 50 intelligent residential users with photovoltaic access are taken as a typical aggregator community, each household is provided with 2 photovoltaic panels with the area of 0.3m2, the conversion efficiency of the photovoltaic cell is 18%, loads which can participate in scheduling in the system are an air conditioner, a refrigerator, a washing machine, a lighting system, a water heater, a fan and an electric vehicle, and the simulation time is set to be 0-00. Load pairs before and after optimization are shown in fig. 4, which shows that the optimized user load can better track a main load curve of the microgrid, and the power utilization curve of the user is reduced at the moment of a main load peak, namely the peak time of the power price; and at the time of the low valley of the main load, namely the electricity price valley period, the electricity consumption of the user is increased. Compared with the situation without control, the method has the advantages that the user electricity load in the noon time period is reduced, the user electricity load in the evening time period is improved, and the effects of peak clipping and valley filling are achieved.
Considering the comparison of loads before and after the optimization of the user autonomous response, the user generates the autonomous response, namely, the electric automobile is actively charged, and the control system improves the comfort level of the user by weakening part of demand response difference values. The user load prediction result based on the historical data changes in a part of time periods, so that the user load transfer amount in the evening period is reduced, and the electric automobile is charged in the noon, so that the charge state is good, and continuous charging in the evening is not needed; secondly, at the noon time, the prediction result shows that the photovoltaic output is reduced at the time, namely the electricity of the user is reduced; and thirdly, the user load level at the evening time rises, the photovoltaic prediction result shows that the light intensity rises and the temperature rises at the moment, and the historical data of the user load shows that the user has frequent response interruption and the air conditioner switching-on times rise in the period.
Example 2:
the embodiment 2 of the invention provides a demand response scheduling system of a distributed photovoltaic intelligent house, which comprises:
a data acquisition module configured to: acquiring current electric power operation parameter data of the distributed photovoltaic intelligent residence and a plurality of prediction models constructed based on historical electric power operation parameter data;
a load prediction and optimization module configured to: obtaining a current user load prediction result based on the prediction model with the highest accuracy and the current power operation parameter data; aiming at the lowest cost, carrying out single-target optimization through a particle swarm algorithm by combining the current power operation parameter data to obtain the current user load optimization result;
an appliance optimization control module configured to: if the prediction result of half or more than half of the prediction models at the current time is larger than the user load after particle swarm optimization, taking the prediction result of the current user load as the upper power limit of the current time, or taking the optimization result of the current user load as the upper power limit; calculating the priority value of each electric appliance according to the load prediction result of the current user, judging whether the total power value of the response load meets the upper limit of the current power, if so, ending the demand response of the stage, and feeding the current power utilization behavior back to the prediction model; if not, the switch state of the electric appliance is re-planned, and the electric appliance with the lowest priority is switched off;
an appliance switch control module configured to: calculating the switch indexes of the electric appliances, judging whether the switch indexes of the electric appliances are lower than a set value or not, if so, supplying power to the electric appliances reaching the set values of the switch indexes in a way of mutually matching energy storage and a power grid, and returning to the load prediction and optimization module again; if not, returning to the electrical appliance optimization control module;
an interrupt response control module configured to: when the response interruption occurs, executing a user autonomous response algorithm, and judging whether the current power sum is less than or equal to the current power upper limit, if so, returning to a demand response algorithm; otherwise, the electric appliance with the lowest turn-off priority value judges again until the total current power is less than or equal to the upper current power limit.
The detailed working method of the system is the same as that in embodiment 1, and is not described again here.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program, which may be stored in a computer readable storage medium and executed by a computer to implement the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A demand response scheduling method for a distributed photovoltaic intelligent house is characterized by comprising the following steps:
the method comprises the following steps:
the method comprises the following steps: acquiring current electric power operation parameter data of the distributed photovoltaic intelligent residence and a plurality of prediction models constructed based on historical electric power operation parameter data;
step two: obtaining a current user load prediction result based on the prediction model with the highest accuracy and the current power operation parameter data; aiming at the lowest cost, carrying out single-target optimization through a particle swarm algorithm by combining the current power operation parameter data to obtain the current user load optimization result;
step three: if the prediction result of half or more than half of the prediction models at the current time is larger than the user load after particle swarm optimization, taking the prediction result of the current user load as the upper power limit of the current time, or taking the optimization result of the current user load as the upper power limit; calculating the priority value of each electric appliance according to the load prediction result of the current user, judging whether the total power value of the response load meets the upper limit of the current power, if so, ending the demand response of the stage, and feeding the current power utilization behavior back to the prediction model; if not, the switch state of the electric appliance is re-planned, and the electric appliance with the lowest priority is switched off;
step four: calculating the switch indexes of the electric appliances, judging whether the switch indexes of the electric appliances are lower than a set value or not, if so, supplying power to the electric appliances reaching the set values of the switch indexes in a way of mutually matching energy storage and a power grid, and returning to the step two again; if not, returning to the third step;
step five: when response interruption occurs, executing a user autonomous response algorithm, and judging whether the total amount of the current power is less than or equal to the upper limit of the current power, if so, returning to a demand response algorithm; otherwise, the electric appliance with the lowest turn-off priority value judges again until the total current power is less than or equal to the upper current power limit.
2. The demand response scheduling method of the distributed photovoltaic smart home of claim 1, wherein:
aiming at the lowest cost, the method comprises the following steps:
wherein the content of the first and second substances,Nin minute-scale quantities;Fthe total electricity consumption cost of the community is calculated;P t,pv 、P t,grid andP t,ESS is the firsttPhotovoltaic network access power, traditional energy power and bidirectional load power at each moment;C t,price is thattReal-time electricity prices at the moment.
3. The demand response scheduling method of a distributed photovoltaic intelligent home of claim 1, wherein:
each electric appliance priority value comprises:
wherein the content of the first and second substances,irepresentsLThe number of types of appliances;K t,L,wor representstOf time of dayLThe priority index of the working state of the electric appliance,D t,set andD t,now representstA set value and a real-time value of the time;K t,L,bill to representtTime of dayLThe electricity price index of the electric appliance, the denominator is the sum of the real-time cost of all the equipment, and the numerator is the real-time cost of single equipment;K t,L,com representstTime of dayLUser selection indicators for the appliance;C t,price is thattReal-time electricity prices at the moment;nrepresenting the number of all devices participating in the demand response;m i the specific value is determined by the user and represents the on or off willingness of the user to different electrical appliances;K t,L,open representtTime of dayLThe opening priority index of the electric appliance, a, b and c are respectively coefficients, P t,L,i And the predicted value is the electric load predicted value of the ith L-type electric appliance at the moment t.
4. The demand response scheduling method of a distributed photovoltaic intelligent home of claim 1, wherein:
a switch indicator comprising:
wherein, t 1 、t 2 Respectively representing L electrical appliancesOff-change start-stop times; t represents a demand response period.
5. The demand response scheduling method of a distributed photovoltaic intelligent home of claim 1, wherein:
a particle swarm algorithm, comprising:
initializing a load model, setting the random position and the iteration times of a particle swarm according to the running characteristics of a load, respectively calculating the fitness value Fi of each particle, wherein the fitness value in single-target optimization is equal to the cost;
comparing the fitness value of each particle with the individual optimal position, namely the fitness value at each moment, and taking the optimal comparison result as the optimal working state of the current electric appliance;
comparing the fitness value of each particle with a global optimal value, namely the fitness value of all the time of the demand response, and taking the optimal comparison result as the most appropriate working time of each current electric appliance in all the time periods of the demand response;
if the maximum iteration times are exceeded, re-optimizing the individual optimal positions of the particles; if not, outputting the result.
6. The demand response scheduling method of the distributed photovoltaic smart home of claim 1, wherein:
with the goal of lowest cost, also include the constraints:
wherein, the first and the second end of the pipe are connected with each other,P t,out is thattThe output power of the photovoltaic power source at a moment,SOC t,n denotes the firstnElectric automobiletThe state of charge at the moment in time,SOC exp is the rated capacity of the SOC (state of charge),SOC max,n is the maximum value of the SOC, and the SOC is the maximum value,P t,Lmax representing a controllable loadtAn upper power limit at a time;P t,n_cmax andP t,n_dmax representing the upper limits of the bi-directional load charging power and discharging power respectively,P t,n_c to charge the power for the bi-directional load,P t,n_d discharging power for the bi-directional load.
7. The demand response scheduling method of a distributed photovoltaic intelligent home of claim 1, wherein:
a user autonomous response algorithm comprising:
switching on the electric appliance selected by the user;
judging whether the total amount of the current power is less than or equal to the upper limit of the current power, if so, keeping the on-off states of other electrical appliances unchanged, recording user behaviors, and returning to a demand response algorithm; otherwise, calculating the priority value of the electric appliance at the current moment, and judging the electric appliance with the lowest priority value again until the total current power is less than or equal to the upper current power limit.
8. The utility model provides a demand response dispatch system of distributed photovoltaic intelligence house which characterized in that:
the method comprises the following steps:
a data acquisition module configured to: acquiring current electric power operation parameter data of the distributed photovoltaic intelligent residence and a plurality of prediction models constructed based on historical electric power operation parameter data;
a load prediction and optimization module configured to: obtaining a current user load prediction result based on the prediction model with the highest accuracy and the current power operation parameter data; aiming at the lowest cost, carrying out single-target optimization through a particle swarm algorithm by combining the current power operation parameter data to obtain the current user load optimization result;
an appliance optimization control module configured to: if the prediction result of the user load of half or more than half of the prediction models at the current moment is larger than the user load after particle swarm optimization, taking the prediction result of the current user load as the power upper limit of the current moment, and otherwise, taking the optimization result of the current user load as the power upper limit; calculating the priority value of each electric appliance according to the load prediction result of the current user, judging whether the total power value of the response load meets the upper limit of the current power, if so, ending the demand response of the stage, and feeding the current power utilization behavior back to the prediction model; if not, the switch state of the electric appliance is re-planned, and the electric appliance with the lowest priority is switched off;
an appliance switch control module configured to: calculating the switch indexes of the electric appliances, judging whether the switch indexes of the electric appliances are lower than a set value or not, if so, supplying power to the electric appliances reaching the set values of the switch indexes in a way of mutually matching energy storage and a power grid, and returning to the load prediction and optimization module again; if not, returning to the electrical appliance optimization control module;
an interrupt response control module configured to: when response interruption occurs, executing a user autonomous response algorithm, and judging whether the total amount of the current power is less than or equal to the upper limit of the current power, if so, returning to a demand response algorithm; otherwise, the electric appliance with the lowest turn-off priority value judges again until the current power sum is less than or equal to the current power upper limit.
9. The demand response scheduling system of a distributed photovoltaic smart home of claim 8, wherein:
the aim is to minimize the cost, including:
wherein the content of the first and second substances,Nin minute-scale quantities;Fthe total electricity consumption cost of the community is calculated;P t,pv 、P t,grid andP t,ESS is the firsttPhotovoltaic network access power, traditional energy power and bidirectional load power at each moment;C t,price is thattReal-time electricity prices at the moment.
10. The demand response scheduling system of a distributed photovoltaic smart home of claim 8, wherein:
each electric appliance priority value comprises:
wherein the content of the first and second substances,irepresentLThe number of types of appliances;K t,L,wor representstOf time of dayLThe priority index of the working state of the electric appliance,D t,set andD t,now representstA set value and a real-time value of the time;K t,L,bill to representtTime of dayLThe electricity price index of the electric appliance, the denominator is the sum of the real-time cost of all the equipment, and the numerator is the real-time cost of the single equipment;K t,L,com representstTime of dayLUser selection indicators for the appliance;nrepresenting the number of all devices participating in the demand response;m i the specific value is determined by the user and represents the on or off willingness of the user to different electrical appliances;K t,L,open to representtTime of dayLThe opening priority index of the electric appliance, a, b and c are respectively coefficients, P t,L,i The electricity load prediction value at the t moment of the ith L-type electric appliance is obtained;C t,price is thattReal-time electricity prices at the moment.
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| CN109740846A (en) * | 2018-11-30 | 2019-05-10 | 国网江苏省电力有限公司电力科学研究院 | Intelligent residential district demand response dispatching method and system |
| CN109818373A (en) * | 2019-04-02 | 2019-05-28 | 国网黑龙江省电力有限公司电力科学研究院 | The fault recovery reconstructing method of the distributed power distribution network containing Thief zone |
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| CN109818373A (en) * | 2019-04-02 | 2019-05-28 | 国网黑龙江省电力有限公司电力科学研究院 | The fault recovery reconstructing method of the distributed power distribution network containing Thief zone |
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