CN114239956A - Resource allocation method and device of virtual power plant and terminal - Google Patents
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Abstract
The application provides a resource allocation method, a resource allocation device and a terminal of a virtual power plant, wherein the resource allocation method of the virtual power plant comprises the following steps: acquiring electric heating user equipment data, weather data, bidding capacity of a virtual power plant and time-of-use electricity price initial data; calculating the power consumption of the electric heating load and constructing an operation scene data set according to the electric heating user equipment data, the weather data and the time-of-use electricity price initial data; calculating the net load power of each period of the virtual power plant and updating the peak-valley period according to the power consumption power, the operation scene data set and the bidding capacity; constructing a daily operation income evaluation objective function and constraint conditions of the virtual power plant, and determining a dynamic time-of-use electricity price scheme; constructing a demand response model of the electric heating load according to the dynamic time-of-use electricity price and the time-of-use electricity price initial data; and determining the resource allocation capacity of the electric heating of the virtual power plant according to the demand response model.
Description
Technical Field
The application relates to the technical field of resource allocation of virtual power plants, in particular to a resource allocation method, a resource allocation device and a resource allocation terminal of a virtual power plant.
Background
The load spiking trend in China is more and more obvious, and under the condition that the coal-electricity development is limited, the functions of demand-side resources in peak clipping and valley filling, electric power supply and demand contradiction alleviation and clean energy consumption promotion are urgently needed to be fully excavated and exerted. The virtual power plant is an important organization form which gives full play to the flexible adjustment response capability of resources on the demand side, and can participate in peak shaving of the power grid and reduce the power generation cost.
The research of the existing peak regulation type virtual power plant regulation and control method mostly focuses on the optimized scheduling of various resources, static time-of-use electricity price is generally adopted for controllable load excitation, and if an excitation mechanism of the static time-of-use electricity price cannot be dynamically adjusted along with peak-valley time, it is difficult to effectively guide excitation electric heating users to adjust electricity consumption behaviors to participate in power grid scheduling. Therefore, reasonable day-ahead dynamic time-of-day electricity prices need to be formulated to guide the electricity consumption behavior of the electric heating load, so that the resource allocation of a virtual power plant is realized, and the requirements of economy and power grid peak regulation are met.
Disclosure of Invention
The embodiment of the application aims to provide a resource allocation method, a resource allocation device and a resource allocation terminal of a virtual power plant, so as to solve the technical problem that the virtual power plant resource allocation cannot be realized by effectively guiding a heating load by adopting an excitation mechanism of static time-of-use electricity price.
In order to solve the above technical problem, an embodiment of the present application provides the following technical solutions:
the application provides a resource allocation method of a virtual power plant in a first aspect, and the resource allocation method of the virtual power plant comprises the following steps: acquiring electric heating user equipment data, weather data, bidding capacity of a virtual power plant and time-of-use electricity price initial data;
calculating the power consumption of the electric heating load and constructing an operation scene data set according to the electric heating user equipment data, the weather data and the time-of-use electricity price initial data;
calculating the net load power of each period of the virtual power plant and updating the peak-valley period according to the power consumption power, the operation scene data set and the bidding capacity;
according to the updated peak-valley period, a daily operation income evaluation objective function and a constraint condition of the virtual power plant are constructed;
determining a dynamic time-of-use electricity price scheme according to the daily operation income evaluation objective function and the constraint condition;
constructing a demand response model of the electric heating load according to the dynamic time-of-use electricity price and the time-of-use electricity price initial data;
determining the resource allocation capacity of the electric heating of the virtual power plant according to the demand response model;
wherein the peak-to-valley period comprises: a valley period, a flat period, and a peak period.
In some variations of the first aspect of the present application, the electric heating user equipment data includes at least: number of users, equipment configuration electrical power, photovoltaic configuration capacity, and user thermal demand;
the weather data at least includes: temperature and light intensity;
the time-of-use electricity price initial data at least comprises: the initial value of the peak-valley period and the initial value of the electricity price of each period.
In some modified embodiments of the first aspect of the present application, the calculating the power consumption of the electric heating load includes:
according to
Calculating the electricity power of the electric heating load in the valley period;
wherein, PE(t) the electric power, P, of the electric heating load for a period tPV(t) photovoltaic power generation power, Q, for the period of th(t) represents the load heat demand of the user for the t period,
representing the accumulated power, Q, of said period tW(t) represents the heat storage amount of the electric heating in the period of t, QW,maxIndicating the maximum heat storage capacity, T, of the electric heatingVRepresenting the trough period;
according to
Calculating the power consumption of the electric heating load in the flat time period;
wherein, TFRepresenting the flat period;
according to
Calculating the power usage of the electrical heating load during the peak hours;
wherein, TPRepresenting the peak hours.
In some variations of the first aspect of the present application, the calculating the payload power for each period of the virtual power plant and updating the peak-to-valley period comprises:
calculating the power consumption of the electric heating load at each time period before the day according to the power consumption, the operation scene data set and the time-of-use electricity price initial data;
according to the power consumption and the bidding capacity and according to PVPP(t)＝PVPP,F(t)+PE,B(t) calculating the net load power for each period of the virtual power plant;
wherein, PVPP,F(t) represents the bid capacity, P, of the virtual power plant to participate in a peak shaving assistance service during a period tE,B(t) represents the purchased electric power of the consumer electric heating load for the period of time t.
Updating the peak-valley period according to the payload power.
In some modified embodiments of the first aspect of the present application, the updating the peak-valley period specifically includes:
dividing membership function according to the peak-valley time period
Wherein, PVPP(ti) Predicted net load power, delta, of the virtual power plant at each time interval representing a prediction of the day aheadPRepresenting the degree of membership, δ, of said peak periodVRepresenting a degree of membership of the trough period;
will deltaP[PVPP(ti)]>εPIs divided into said peak periods, δP[PVPP(ti)]<εVThe time interval of (1) is divided into the valley time interval, and the rest time intervals are divided into the flat time intervals;
wherein, deltaPA membership threshold, δ, representing said peak periodP∈[0，1]，εVA membership threshold, ε, representing the valley periodV∈[0，1]。
In some modified embodiments of the first aspect of the present application, the daily operating yield evaluation objective function of the virtual power plant is specifically:
maxF＝F1+F2-C1-C2；
wherein F represents the daily operating yield evaluation objective function of the virtual power plant, F1Representing the peak shaving yield of the virtual power plant, F2Representing the district electricity sales revenue of said virtual power plant, C1Representing the market purchase cost, C, of the virtual power plant2Representing the photovoltaic surplus electricity recovery cost of a user;
the component is a daily operation income evaluation objective function of the virtual power plant, and further comprises:
according to
Calculating the peak shaving yield of the virtual power plant;
wherein λ isVPP(t)、PVPP,F(t) respectively representing the peak shaving quotes and the medium and medium capacity of the virtual power plant participating in the peak shaving auxiliary service in the t period;
according to
Calculating the district electricity selling income of the virtual power plant;
wherein λ isP、λF、λVRespectively representing the electricity prices of the peak time period, the flat time period and the valley time period established by the virtual power plant, and m, n and k respectively representing the number of the peak time period, the flat time period and the valley time period divided in one day;
according to
Calculating the market electricity purchase cost of the virtual power plant;
wherein λ isM(t)、PVPP,B(t) respectively representing spot market electricity prices and VPP market electricity purchasing powers before the t period day;
according to
Calculating the photovoltaic surplus electricity recovery cost of the user;
wherein λ isPV(t)、PPV,SAnd (t) respectively representing the photovoltaic internet surfing subsidy electricity price and the photovoltaic internet surfing power of the user in the time period t.
In some variations of the first aspect of the present application, the constraints comprise: the system comprises a house heat balance constraint and a heat demand constraint of an electric heating user, a power balance constraint and a peak shaving constraint of the virtual power plant, an electric heating heat storage amount constraint and a time-of-use electricity price constraint;
the constructing of the constraint conditions of the virtual power plant comprises the following steps:
the house heat balance constraint of the electric heating user is specifically as follows:
wherein, Delta TinRepresenting the amount of change in temperature, Q, of the building interiorhIndicating that the building maintains an indoor preset temperatureHeat output by electric heating equipment, QSRepresenting the amount of heat of the sun impinging on the building, QC、QVRespectively representing the heat conducted by the enclosure of said building and the heat exchanged with the outdoor air, CairRepresents the total heat capacity of air;
the heat demand constraints of the electric heating users are specifically: t isin,min≤Tin(t)≤Tin,max；
Wherein, Tin(T) represents the indoor temperature of the electric heating user for the period T, Tin,max、Tin,minRespectively representing the indoor temperature of the highest heat demand and the indoor temperature of the lowest demand acceptable by the electric heating user;
the power balance constraint of the virtual power plant is specifically as follows: pE,B(t)+PVPP,F(t)＝ PVPP,B(t)+PPV,S(t)；
The peak regulation constraint of the virtual power plant is specifically as follows: i PVPP,F(t)|≤PVPP,F,max；
Wherein, PVPP,F,maxRepresenting a maximum regulatory capacity of the virtual power plant;
the electric heating heat storage capacity constraint is as follows: qW(1)＝QW(T)；
Wherein Q isW(1)、QW(T) represents the heat storage amount of the heat accumulator at the beginning and the end of a scheduling period respectively;
the time-of-use electricity price constraint specifically comprises the following steps:
wherein θ represents a constant of a peak-to-valley electrovalence ratio, λmaxIndicating the maximum allowed electricity prices.
In some modified embodiments of the first aspect of the present application, the demand response model specifically includes:
wherein Q ish(t0)、Qh(t) respectively represents the heat demand of the electric heating user before and after the time-of-use electricity price change, s and t respectively represent the time period, and lambda (t)0) λ (t) represents the time-of-use electricity price before and after the adjustment of the t period, λ(s)0) And lambda(s) respectively represent the time-of-use electricity price before and after the adjustment of the s time interval, epsilonttRepresenting the value of the coefficient of self-elasticity of the price, generally a positive value, epsilonstThe elastic coefficient of price cross is shown, s is not equal to T and is generally a negative value, and T represents the last peak time period, the last valley time period or the ordinary time period of the electric heating load.
A second aspect of the present application provides a resource allocation device of a virtual power plant, including: the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring electric heating user equipment data, weather data, and bidding capacity and time-of-use electricity price initial data of a virtual power plant;
the first construction module is used for calculating the power consumption of the electric heating load and constructing an operation scene data set according to the electric heating user equipment data, the weather data and the time-of-use electricity price initial data;
the second construction module is used for calculating the net load power of each period of the virtual power plant and updating the peak-valley period by using the power, the operation scene data set and the bid capacity;
the third construction module is used for constructing a daily operation income evaluation objective function and a constraint condition of the virtual power plant according to the updated peak-valley period and constructing a demand response model of the electric heating load;
and the determining module is used for determining the resource allocation capacity of the electric heating of the virtual power plant according to the load demand response model.
A third aspect of the present application provides a terminal, comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the method described above when executing the computer program.
Compared with the prior art, the resource allocation method, the resource allocation device and the resource allocation terminal of the virtual power plant provided by the application utilize the characteristic that the dynamic time-of-use electricity price can be adjusted along with the peak-valley period, effectively guide and stimulate the electric heating users to participate in power grid scheduling, realize resource allocation of the virtual power plant, comprehensively consider the operation economy and meet the requirement of power grid peak regulation.
Drawings
The above and other objects, features and advantages of exemplary embodiments of the present application will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the present application are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings and in which like reference numerals refer to similar or corresponding parts and in which:
FIG. 1 is a schematic flow chart illustrating a resource allocation method for a virtual power plant according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a dynamic time-of-use electricity price scheme implemented by the resource allocation method for a virtual power plant according to the embodiment of the present invention;
fig. 3(a) schematically shows graphs of outdoor temperatures of four typical weather scenarios in the resource allocation method of the virtual power plant according to the embodiment of the present invention;
fig. 3(b) schematically shows a graph of outdoor lighting in each time period per day in four typical weather scenes in the resource allocation method for a virtual power plant provided by the embodiment of the invention;
FIG. 4 is a diagram schematically illustrating an implementation effect of a resource allocation method for applying a virtual power plant in a certain area according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a resource allocation device of a virtual power plant provided by an embodiment of the invention;
fig. 6 schematically shows a schematic diagram of a terminal provided by an embodiment of the present invention;
the reference numbers illustrate:
the resource configuration device 1 of the virtual power plant, the acquisition module 101, the first construction module 102, the second construction module 103, the third construction module 104, the determination module 105, the terminal 2, the memory 201, the processor 202 and the computer program 203.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are merely used to more clearly illustrate the technical solutions of the present application, and therefore are only examples, and the protection scope of the present application is not limited thereby.
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 application belongs; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions.
In the description of the embodiments of the present application, the technical terms "first", "second", and the like are used only for distinguishing different objects, and are not to be construed as indicating or implying relative importance or implicitly indicating the number, specific order, or primary-secondary relationship of the technical features indicated. In the description of the embodiments of the present application, "a plurality" means two or more unless specifically defined otherwise.
Reference herein to "an embodiment" means that a particular feature or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Example one
Referring to fig. 1 and fig. 2, in an embodiment of the present invention, a resource allocation method for a virtual power plant is provided, where the resource allocation method for the virtual power plant includes:
step S1: and acquiring electric heating user equipment data, weather data, the bidding capacity of the virtual power plant and time-of-use electricity price initial data.
Specifically, in the data, the electric heating user equipment data, the bidding capacity of the virtual power plant and the time-of-use electricity price initial data can be obtained from the power grid, and the weather data can be obtained from the power grid, or can be obtained through other ways; the electric heating user equipment data may include, but is not limited to: the number of users for electric heating, the equipment configuration electric power, the photovoltaic configuration capacity, the user heat demand and the like, and the weather data can comprise: temperature and illumination intensity, and the time-of-use price initial data at least comprises: an initial value of a peak-valley period and an initial value of a power rate for each period, where the peak-valley period includes: the low ebb time period, the ordinary time period and the peak time period can be divided according to the power utilization requirements of users.
Step S2: calculating the power consumption of the electric heating load and constructing an operation scene data set according to the electric heating user equipment data, the weather data and the time-of-use electricity price initial data;
specifically, in this step, the formula for calculating the power consumption of the electric heating load is:
in the valley period, the photovoltaic heat storage is preferentially utilized, the heat is supplied in real time, and the power consumption P in the periodE(t) is:
wherein, PE(t) electric power for electric heating load in t period, PPV(t) photovoltaic power generation power in t period, Qh(t) represents the load heat demand of the user for a period of t,
representing the accumulated power of the period t, QW(t) represents the heat storage amount of electric heating in the period of t, QW,maxIndicating the maximum heat storage capacity, T, of the electric heatingVRepresents a trough period;
at ordinary times, the electric heating preferentially utilizes the photovoltaic power generation to carry out the electric heating, if the heat accumulator can not be fully stored in the valley electricity time period at the moment, the time period can be utilized to further store energy, and the electric power P in the time periodE(t) is:
wherein, TFRepresents a flat period;
at peak time, the electric heating preferentially utilizes the heat storage equipment to supply heat at the time, when the energy storage body can not meet the heat supply demand, the photovoltaic is utilized to supply the heat, and the power consumption P at the time isE(t) is:
wherein, TPIndicating peak hours;
specifically, in this step, an operation scene data set is established according to the electric heating user equipment data, the weather data and the time-of-use electricity price initial value, where the operation scene data set may include the peak time period, the valley time period and the divided time periods of the flat time period of the power grid, the corresponding electricity price data, the temperature and the illumination intensity, and each time period divides every 24 hours into the peak time period, the valley time period and the flat time period to combine the temperature and the illumination intensity, so as to construct an operation scene data set of the electric heating virtual power plant.
Step S3: and calculating the net load power of each period of the virtual power plant and updating the peak-valley period according to the power consumption power, the operation scene data set and the bidding capacity.
Specifically, the net load power of each time period of the virtual power plant is calculated according to the power consumption power of the electric heating load, the operation scene data and the bid capacity of the virtual power plant, and the peak-valley time period of each time period is updated according to the net load power of each time period of the virtual power plant;
specifically, the net load power of each period of the virtual power plant in this step can be according to PVPP(t)＝PVPP,F(t)+PE,B(t) is calculated, wherein PVPP,F(t) shows the bid capacity of the virtual power plant to participate in the peak shaving auxiliary service during the period t, PE,B(t) the power purchase of the consumer electric heating load for the period t;
wherein, aiming at the updating peak-valley time period in the step, the membership function can be divided according to the peak-valley time period
Will deltaP[PVPP(ti)]>εPIs divided into peak periods, δP[PVPP(ti)]<εVThe time interval of the time interval is divided into a low valley time interval, and the rest time intervals are divided into flat time intervals; wherein, PVPP(ti) Predicted payload power, delta, of the virtual power plant at each time interval representing a prediction of the day aheadPRepresenting degree of membership, delta, at peak hoursVRepresenting degree of membership, δ, of the trough periodPRepresenting a membership threshold, δ, for peak hoursP∈[0，1]，εVMembership threshold, ε, representing valley periodsV∈[0，1]。
Step S4: according to the updated peak-valley period, a daily operation income evaluation objective function and a constraint condition of the virtual power plant are constructed, and a dynamic time-of-use electricity price scheme is determined;
specifically, the daily operation income evaluation objective function of the virtual power plant is maxF ═ F1+F2-C1- C2Wherein F represents a daily operating yield evaluation objective function of the virtual power plant, F1Representing the peak shaving yield of the virtual power plant, F2Representing the district electric sales revenue of the virtual plant, C1Representing the market purchase cost of the virtual power plant, C2Representing the photovoltaic surplus electricity recovery cost of a user;
wherein can be according to
Calculating the peak shaving income of the virtual power plant; wherein λ isVPP(t)、PVPP,F(t) respectively representing the peak regulation quotation and the medium-grade capacity of the virtual power plant participating in the peak regulation auxiliary service in the t period;
wherein can be according to
Calculating the district electricity selling income of the virtual power plant; wherein λ isP、λF、λVRespectively representing the electricity prices of the peak time period, the flat time period and the valley time period set by the virtual power plant, and respectively representing the number of the peak time period, the flat time period and the valley time period divided in one day by m, n and k;
wherein can be according to
Calculating the market electricity purchasing cost of the virtual power plant; wherein λ isM(t)、PVPP,B(t) respectively representing spot market electricity price and VPP market electricity purchasing power before the day of the t period;
wherein can be according to
Calculating the photovoltaic surplus electricity recovery cost of a user; wherein λ isPV(t)、PPV,SAnd (t) respectively representing the photovoltaic internet surfing subsidy electricity price and the photovoltaic internet surfing power of the user in the t period.
Specifically, the electric heating virtual power plant is to follow certain constraint conditions during operation, where the constraint conditions may include, but are not limited to: the method comprises the following steps of (1) restraining house heat balance and heat demand of an electric heating user, restraining power balance and peak regulation of a virtual power plant, restraining electric heating heat storage capacity and restraining time-of-use electricity price;
the house heat balance constraint of the electric heating user is as follows:
wherein, Delta TinRepresenting the amount of change in temperature, Q, of the building interiorhRepresents the heat quantity, Q, output by the electric heating equipment required by the building to maintain the indoor preset temperatureSRepresenting the amount of heat of the sun striking the building, QC、QVRespectively representing the heat conducted by the enclosure of the building and the heat exchanged with the outdoor air, CairRepresents the total heat capacity of air;
wherein, the thermal demand constraint of the electric heating user is specifically as follows: t isin,min≤Tin(t)≤Tin,max(ii) a Wherein, Tin(T) represents the indoor temperature of the electric heating user for a period of T, Tin,max、Tin,minRespectively representing the indoor temperature of the highest heat demand and the indoor temperature of the lowest demand acceptable by an electric heating user; a
Wherein, the power balance constraint of the virtual power plant is specifically as follows:PE,B(t)+PVPP,F(t)＝ PVPP,B(t)+PPV,S(t)；
wherein, the peak regulation restraint of virtual power plant specifically is: i PVPP,F(t)|≤PVPP,F,max(ii) a Wherein, PVPP,F,maxRepresenting a maximum regulatory capacity of the virtual power plant;
specifically, the electric heating heat storage capacity constraint specifically comprises: qW(1)＝QW(T); wherein Q isW(1)、 QW(T) represents the heat storage amount of the heat accumulator at the beginning and the end of a scheduling period respectively;
the time-of-use electricity price constraint specifically comprises the following steps:
wherein θ represents a constant of the peak-to-valley electrovalence ratio, and θ is set to 5 and λ is referred to the hair diversion notificationmaxRepresents the maximum allowed electricity price;
further, a target function and a constraint condition can be evaluated according to daily operation income of the virtual power plant, the particle swarm algorithm is adopted to solve the target function, and then a dynamic time-of-use electricity price scheme is determined according to decision variables of the obtained target function value, wherein the scheme is an optimal scheme, and the optimal dynamic electricity price scheme is optimal, namely the daily operation income of the virtual power plant is optimal;
further, in a specific implementation, referring to fig. 2, the evaluating an objective function and a constraint condition of daily operation income of the virtual power plant in this step, and determining a dynamic time-of-use electricity price scheme may specifically include:
step S41: calculating initial values of daily operation income evaluation objective functions of the virtual power plants corresponding to time-of-use electricity price schemes at different dynamic peak-valley periods on the virtual power plants according to the daily operation income evaluation objective functions and the constraint conditions of the virtual power plants;
specifically, the dynamic peak-valley period is a period that 24 hours a day is divided into a peak period, a valley period, a flat period and other periods according to the net load power of each period of the virtual power plant, and can be according to F1、F2、C1、C2Corresponding initial values are calculated by corresponding formulas, and finally virtual is determinedEvaluating an initial value of a target function of daily operation income of the power plant;
step S42: calculating final values of the daily operation income evaluation objective function of the virtual power plant corresponding to the time-of-use electricity price schemes at different dynamic peak-valley periods on the virtual power plant according to the initial value and the constraint conditions of the daily operation income evaluation objective function of the virtual power plant;
specifically, when calculating the final value corresponding to the objective function, a particle algorithm may be used for calculation, and the specific steps are as follows:
s421: selecting the electricity prices lambda of the peak time period, the flat time period and the off-peak time period in the objective function of a certain one-time electricity price scheme in the dynamic time-of-use electricity price scheme of the electric heating virtual power plantP、λF、λVAs the particles, the initial values thereof are calculated according to step S41;
s422: setting the initial iteration number K to 1 and the maximum iteration number Kmax＝500；
S423: according to λP、λF、λVCalculating the numerical value of each particle by a corresponding formula and limiting the numerical value of each particle to meet each constraint condition;
s424: update the velocity and position of the particle:
wherein K represents the number of iterations, xKRepresenting the spatial position of the particle, x, at the number of iterations KK+1Representing the spatial position of the particle at the number of iterations K +1, vKRepresenting the velocity of the particle, v, at the number of iterations KK+1Denotes the velocity of the particle at the number of iterations K +1, c1、c2Representing a learning factor, and taking a value of between 0 and 4, r1、r2Denotes a random number uniformly distributed between (0, 1), ω denotes an inertia factor, ωmax、ωminRepresenting maximum, minimum, pbest of the inertia factorkRepresents the self-optimal solution, gbest, of the particle for the number of iterations KkRepresenting the global optimal solution of the particles when the iteration number is K;
s425: if K>KmaxThen each objective function is outputOtherwise, let K be K +1, return to step S424;
step S43: and determining the optimal dynamic time-of-use electricity price scheme in the dynamic peak-valley time-of-use electricity price scheme set according to the final value of the daily operation income evaluation objective function of the virtual power plant corresponding to different dynamic peak-valley time-of-use electricity price schemes on the virtual power plant.
Step S5: and constructing a demand response model of the electric heating load according to the dynamic time-of-use electricity price and the time-of-use electricity price initial data, and determining the resource allocation capacity of the electric heating of the virtual power plant.
Further, the demand response model of the electric heating load is
Wherein Q ish(t0)、Qh(t) respectively represents the heat demand of the electric heating user before and after the time-of-use electricity price change, s and t respectively represent the time period, and lambda (t)0) λ (t) represents the time-of-use electricity price before and after the adjustment of the t period, λ(s)0) And lambda(s) respectively represent the time-of-use electricity price before and after the adjustment of the s time interval, epsilonttRepresenting the value of the coefficient of self-elasticity of the price, generally a positive value, epsilonstThe price difference elastic coefficient is shown, s is not equal to T and is generally a negative value, and T shows the last peak time period, the last valley time period or the ordinary time period of the electric heating load;
specifically, the configuration capacity of each time period of the electric heating load is determined according to a demand response model of the electric heating load, dynamic time-of-use electricity price obtained through solving by an optimization algorithm and original time-of-use electricity price data obtained from a power grid.
According to the above list, the embodiment of the invention provides a resource allocation method for a virtual power plant, which effectively guides an excitation electric heating user to participate in power grid scheduling by utilizing the characteristic that dynamic time-of-use electricity price can be adjusted along with peak-valley time periods, realizes resource allocation of the virtual power plant, comprehensively considers operation economy (namely a daily operation income evaluation objective function of the virtual power plant), and meets the requirement of power grid peak regulation.
The implementation of this solution is illustrated below in a specific embodiment:
the implementation process of the scheme is described by taking the actual power distribution network in a certain area as an example. The power distribution network comprises large-scale electric heating loads and distributed photovoltaics, and the electric heating users can be integrated into a peak regulation type virtual power plant. The method comprises the steps that device data, weather data and time-of-use electricity price data of electric heating users are obtained from a power grid, 762 total users of the regional electric heating users are obtained through statistics, electric power is allocated to heat accumulating type electric heating according to 60-120W/m 2, the heat accumulation capacity is full of 8h, the photovoltaic allocation capacity of each user is 10-20 kW, 4 typical house characteristics of the regional electric heating users are provided, and house heat characteristic parameters of the typical users are shown in a table I; the region is composed of four typical weather scenes of sunny days, cloudy days and snowy days in winter, and outdoor temperature and illumination curves of each time period of each day under the four typical scenes based on historical outdoor temperature and illumination data refer to the accompanying drawings 3(a) and 3(b) respectively; the power distribution network in the area has the following load peak time: 9: 00-17: 00, electricity price of 1.12 yuan/kW h, and a low-valley period: 1: 00-8: 00. 18: 00 to 24: 00, the electricity price is 0.38 yuan/kW h, the rest time periods are flat time periods, and the electricity price is 0.38 yuan/kW h. It should be noted that the data in table one and fig. 3 are only exemplary data.
By adopting the virtual power plant resource allocation method provided by the application, the net load power of the virtual power plant in each time period is formed according to the electric power consumption in the peak time period, the valley time period and the flat time period of the electric heating load and the bid capacity of the virtual power plant in each time period, and the bid capacity of the virtual power plant is shown in a table II:
watch two
Continuation table two
Dividing membership functions according to dynamic peak-valley periods, dividing the dynamic peak-valley periods, evaluating the final value of a target function according to daily operation income of a virtual power plant by adopting the method provided by the application, determining the optimal dynamic time-of-use electricity price scheme in which the dynamic peak-valley period time-of-use electricity price schemes are concentrated, wherein the optimized dynamic peak-valley periods and the optimized electricity prices are shown in table three:
watch III
Continued table III
And finally, determining the electric heating resource allocation capacity meeting the peak shaving requirement according to the electric heating load demand response model, referring to the attached figure 4.
It should be noted that, the sequence numbers of the steps in the foregoing embodiments do not mean the execution sequence, and the execution sequence of each process should be determined by the function and the internal logic of the process, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
Example two
Referring to fig. 5, a second embodiment of the present invention provides a resource allocation device 1 of a virtual power plant, where the resource allocation device 1 of the virtual power plant is applied to the resource allocation method of the virtual power plant, and the method includes: the system comprises an acquisition module 101, a data processing module and a data processing module, wherein the acquisition module 101 is used for acquiring electric heating user equipment data, weather data, bidding capacity of a virtual power plant and time-of-use electricity price initial data; the first construction module 102 is configured to calculate power consumption of an electric heating load and construct an operation scene data set according to electric heating user equipment data, weather data and time-of-use electricity price initial data; the second construction module 103 is used for calculating the net load power of each period of the virtual power plant and updating the peak-valley period by using the power consumption, the operation scene data set and the bidding capacity; the third construction module 104 is used for constructing a daily operation income evaluation objective function and constraint conditions of the virtual power plant according to the updated peak-valley period, and constructing a demand response model of the electric heating load; the determining module 105 is configured to determine the resource allocation capacity of the electric heating of the virtual power plant according to the load demand response model.
EXAMPLE III
Referring to fig. 6, a second embodiment of the present invention provides a terminal, where the terminal 2 includes: the system comprises a memory 201, a processor 202 and a computer program 203 stored in the memory 201 and capable of running on the processor 202, wherein the processor 202 can implement the resource allocation method of the virtual power plant when executing the computer program 203.
Further, the present invention may be implemented in a manner that the computer program 203 is divided into one or more modules/units, and the one or more modules/units are stored in the memory 201 and executed by the processor 202 to implement the present solution. One or more of the modules/units herein may be a series of instruction segments of the computer program 203 capable of performing specific functions, which are used to describe the execution process of the computer program 203 in the terminal 2. For example, the computer program 203 may be divided into the modules/units 101 to 104 shown in fig. 5.
Specifically, the terminal 2 provided in this embodiment may be a desktop computer, a notebook, a palm computer, a cloud server, and other computing devices; the terminal 2 may include, but is not limited to: a processor 202, a memory 201; those skilled in the art will appreciate that fig. 6 is merely an example of a terminal 2 and does not constitute a limitation of terminal 2, and may include more or less components than those shown, or some components in combination, or different components, for example: the terminal 2 may also include input output devices, network access devices, buses, etc.
Specifically, the Processor 202 may be a Central Processing Unit (CPU), or may also be other general-purpose Processor 202, a Digital Signal Processor 202 (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like; the general purpose processor 202 may be a microprocessor 202 or the processor 202 may be any conventional processor 202 or the like.
Specifically, the memory 201 may be an internal storage unit of the terminal 2, such as: the storage 201 may be a hard disk or a memory of the terminal 2; alternatively, the memory 201 may be an external storage device of the terminal 2, such as: the memory 201 may be one of a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the terminal 2; alternatively, the memory 201 may include both an internal storage unit of the terminal 2 and an external storage device. The memory 201 is used for storing the computer program 203 and other programs and data required by the terminal 2, and the memory 201 may also be used for temporarily storing data that has been output or is to be output.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present disclosure, and the present disclosure should be construed as being covered by the claims and the specification. In particular, the technical features mentioned in the embodiments can be combined in any way as long as there is no structural conflict. The present application is not intended to be limited to the particular embodiments disclosed herein but is to cover all embodiments that may fall within the scope of the appended claims.
Claims (10)
1. A resource allocation method of a virtual power plant is characterized by comprising the following steps:
acquiring electric heating user equipment data, weather data, bidding capacity of a virtual power plant and time-of-use electricity price initial data;
calculating the power consumption of the electric heating load and constructing an operation scene data set according to the electric heating user equipment data, the weather data and the time-of-use electricity price initial data;
calculating the net load power of each period of the virtual power plant and updating the peak-valley period according to the power consumption power, the operation scene data set and the bidding capacity;
according to the updated peak-valley period, a daily operation income evaluation objective function and a constraint condition of the virtual power plant are constructed;
determining a dynamic time-of-use electricity price scheme according to the daily operation income evaluation objective function and the constraint condition;
constructing a demand response model of the electric heating load according to the dynamic time-of-use electricity price and the time-of-use electricity price initial data;
determining the resource allocation capacity of the electric heating of the virtual power plant according to the demand response model;
wherein the peak-to-valley period comprises: a valley period, a flat period, and a peak period.
2. The resource allocation method of a virtual power plant according to claim 1,
the electric heating user equipment data at least comprises: number of users, equipment configuration electrical power, photovoltaic configuration capacity, and user thermal demand;
the weather data at least includes: temperature and light intensity;
the time-of-use electricity price initial data at least comprises: the initial value of the peak-valley period and the initial value of the electricity price of each period.
3. The resource allocation method of a virtual power plant according to claim 2,
the calculating of the power consumption of the electric heating load comprises the following steps:
according to
Calculating the electricity power of the electric heating load in the valley period;
wherein, PE(t) the electric power, P, of the electric heating load for a period tPV(t) photovoltaic power generation power, Q, for the period of th(t) represents the load heat demand of the user for the t period,
representing the accumulated power, Q, of said period tW(t) represents the heat storage amount of the electric heating in the period of t, QW，maxIndicating the maximum heat storage capacity, T, of the electric heatingVRepresenting the trough period;
according to
Calculating the power consumption of the electric heating load in the flat time period;
wherein, TFRepresenting the flat period;
according to
Calculating the power usage of the electrical heating load during the peak hours;
wherein, TPRepresenting the peak hours.
4. The resource allocation method of a virtual power plant according to claim 3,
the calculating the net load power of each period of the virtual power plant and updating the peak-valley period comprises:
calculating the power consumption of the electric heating load at each current time period according to the power consumption, the operation scene data set and the time-of-use electricity price initial data;
according to the power consumption and the bidding capacity and according to PVPP(t)＝PVPP，F(t)+PE，B(t) calculating the net load power for each period of the virtual power plant;
wherein, PVPP，F(t) represents the bid capacity, P, of the virtual power plant to participate in a peak shaving assistance service during a period tE，B(t) the power purchase of the consumer electric heating load for the period t;
updating the peak-valley period according to the payload power.
5. The resource allocation method of a virtual power plant according to claim 4,
the updating the peak-valley period specifically includes:
dividing membership function according to the peak-valley time period
Wherein, PVPP(ti) Predicted net load power, delta, of the virtual power plant at each time interval representing a prediction of the day aheadPRepresenting the degree of membership, δ, of said peak periodVRepresenting a degree of membership of the trough period;
will deltaP[PVPP(ti)]＞εPIs divided into said peak periods, δP[PVPP(ti)]＜εVThe time interval of (1) is divided into the valley time interval, and the rest time intervals are divided into the flat time intervals;
wherein, deltaPA membership threshold, δ, representing said peak periodP∈[0，1]，εVA membership threshold, ε, representing the valley periodV∈[0，1]。
6. The resource allocation method of a virtual power plant according to claim 4,
the daily operation income evaluation objective function of the virtual power plant specifically comprises the following steps:
maxF＝F1+F2-C1-C2；
wherein F represents the daily operating yield evaluation objective function of the virtual power plant, F1Representing the peak shaving yield of the virtual power plant, F2Representing the district electricity sales revenue of said virtual power plant, C1Representing the market purchase cost, C, of the virtual power plant2Representing the photovoltaic surplus electricity recovery cost of a user;
the component is a daily operation income evaluation objective function of the virtual power plant, and further comprises:
according to
Calculating the peak shaving yield of the virtual power plant;
wherein λ isVPP(t)、PVPP，F(t) respectively representing the peak shaving quotes and the medium and medium capacity of the virtual power plant participating in the peak shaving auxiliary service in the t period;
according to
Calculating the district electricity selling income of the virtual power plant;
wherein λ isP、λF、λVRespectively representing the electricity prices of the peak time period, the flat time period and the valley time period established by the virtual power plant, and m, n and k respectively representing the number of the peak time period, the flat time period and the valley time period divided in one day;
according to
Calculating the market electricity purchase cost of the virtual power plant;
wherein，λM(t)、PVPP，B(t) respectively representing spot market electricity prices and VPP market electricity purchasing powers before the t period day;
according to
Calculating the photovoltaic surplus electricity recovery cost of the user;
wherein λ isPV(t)、PPV，SAnd (t) respectively representing the photovoltaic internet surfing subsidy electricity price and the photovoltaic internet surfing power of the user in the time period t.
7. The resource allocation method of a virtual power plant according to claim 6,
the constraint conditions include: the system comprises a house heat balance constraint and a heat demand constraint of an electric heating user, a power balance constraint and a peak shaving constraint of the virtual power plant, an electric heating heat storage amount constraint and a time-of-use electricity price constraint;
the constructing of the constraint conditions of the virtual power plant comprises the following steps:
the house heat balance constraint of the electric heating user is specifically as follows:
wherein, Delta TinRepresenting the amount of change in temperature, Q, of the building interiorhHeat, Q, output by the electric heating equipment, indicating that the building maintains the indoor preset temperatureSRepresenting the amount of heat of the sun impinging on the building, QC、QVRespectively representing the heat conducted by the enclosure of said building and the heat exchanged with the outdoor air, CairRepresents the total heat capacity of air;
the heat demand constraints of the electric heating users are specifically: t isin，min≤Tin(t)≤Tin，max；
Wherein, Tin(T) represents the indoor temperature of the electric heating user for the period T, Tin，max、Tin，minIndoor temperature and average temperature respectively representing maximum heat demand acceptable by said electric heating userThe minimum required indoor temperature;
the power balance constraint of the virtual power plant is specifically as follows: pE，B(t)+PVPP，F(t)＝PVPP，B(t)+PPV，S(t)；
The peak regulation constraint of the virtual power plant is specifically as follows: i PVPP，F(t)|≤PVPP，F，max；
Wherein, PVPP，F，maxRepresenting a maximum regulatory capacity of the virtual power plant;
the electric heating heat storage capacity constraint is as follows: qW(1)＝QW(T)；
Wherein Q isW(1)、QW(T) represents the heat storage amount of the heat accumulator at the beginning and the end of a scheduling period respectively;
the time-of-use electricity price constraint specifically comprises the following steps:
wherein θ represents a constant of a peak-to-valley electrovalence ratio, λmaxIndicating the maximum allowed electricity prices.
8. The resource allocation method of a virtual power plant according to claim 1,
the demand response model specifically comprises:
wherein Q ish(t0)、Qh(t) respectively represents the heat demand of the electric heating user before and after the time-of-use electricity price change, s and t respectively represent the time period, and lambda (t)0) λ (t) represents the time-of-use electricity price before and after the adjustment of the t period, λ(s)0) And lambda(s) respectively represent the time-of-use electricity price before and after the adjustment of the s time interval, epsilonttRepresenting the value of the coefficient of self-elasticity of the price, generally a positive value, epsilonstIndicating the elastic coefficient of price cross, s ≠ T, which is generally a negative value, and T indicates the last peak time of the electric heating loadA valley period or a flat period.
9. A resource allocation device of a virtual power plant, which is applied to the resource allocation method of the virtual power plant according to any one of claims 1 to 8, and is characterized by comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring electric heating user equipment data, weather data, and bidding capacity and time-of-use electricity price initial data of a virtual power plant;
the first construction module is used for calculating the power consumption of the electric heating load and constructing an operation scene data set according to the electric heating user equipment data, the weather data and the time-of-use electricity price initial data;
the second construction module is used for calculating the net load power of each period of the virtual power plant and updating the peak-valley period by using the power, the operation scene data set and the bid capacity;
the third construction module is used for constructing a daily operation income evaluation objective function and a constraint condition of the virtual power plant according to the updated peak-valley period and constructing a demand response model of the electric heating load;
and the determining module is used for determining the resource allocation capacity of the electric heating of the virtual power plant according to the load demand response model.
10. A terminal, comprising: memory, processor and computer program stored in the memory and executable on the processor, characterized in that the processor when executing the computer program may implement the method according to any of the claims 1-8.
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| CN115940204A (en) * | 2023-01-09 | 2023-04-07 | 佛山电力设计院有限公司 | District electric power energy management system |
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| CN115940204A (en) * | 2023-01-09 | 2023-04-07 | 佛山电力设计院有限公司 | District electric power energy management system |
| CN115796406A (en) * | 2023-02-13 | 2023-03-14 | 浙江浙能能源服务有限公司 | Optimal adjustment method and system for virtual power plant |
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