CN113746105A

CN113746105A - Optimal control method, device, equipment and storage medium for power demand response

Info

Publication number: CN113746105A
Application number: CN202111160695.6A
Authority: CN
Inventors: 李童佳; 贺建豪; 李秋硕; 林俊宏; 朱贤文
Original assignee: Southern Power Grid Digital Grid Research Institute Co Ltd
Current assignee: China Southern Power Grid Digital Grid Technology Guangdong Co ltd
Priority date: 2021-09-30
Filing date: 2021-09-30
Publication date: 2021-12-03

Abstract

The application relates to the technical field of power optimization control, and provides an optimization control method, device, equipment and storage medium for power demand response, which can give consideration to power demand and generating equipment utilization rate, and comprises the following steps: the power utilization point is adjusted on the basis of a power utilization load characteristic curve obtained by power utilization data of the power utilization point; combining a plurality of power supply points with geographical position closeness smaller than a threshold value into an equivalent power supply point according to the respective geographical position information of the power utilization point and the power supply point, and combining a plurality of power utilization points with geographical position closeness smaller than the threshold value into the equivalent power utilization point; performing power flow calculation aiming at the equivalent power supply points and the equivalent power consumption points on the basis of various possible power consumptions of the power consumption points after power consumption adjustment, and determining the optimal power flow; determining the electric energy required to be provided by each power supply point which is combined to form the equivalent power supply point according to the electric energy required to be provided by the equivalent power supply point in the optimal power flow; and controlling each power supply point to generate power according to the electric energy required to be provided by each power supply point.

Description

Optimal control method, device, equipment and storage medium for power demand response

Technical Field

The present application relates to the field of power optimization control technologies, and in particular, to an optimization control method and apparatus for power demand response, a computer device, and a storage medium.

Background

The power system generally includes a power supply point, a power consumption point, and a line for transmitting electric energy between the power supply point and the power consumption point; with the large-scale layout of the power system, many areas are incorporated into the power system, and the number of power supply points and power consumption points of the power system is enormous. The power supply point can generate electricity and carry the electric energy to the power consumption point through the circuit, if the electric energy that the power supply point provided is too little then be difficult to satisfy the power consumption demand, if the power supply point provides the electric energy too much then the lower condition of power generation facility utilization ratio of power supply point appears easily, consequently how to confirm the electric energy that the power supply point needs to provide in the electric power system to compromise the power consumption demand and the power generation facility utilization ratio of power consumption point, be comparatively crucial problem.

Disclosure of Invention

In view of the above, it is necessary to provide a method, an apparatus, a computer device and a storage medium for optimal control of power demand response.

A method of optimizing control of power demand response, the method comprising:

the power utilization point is subjected to power utilization adjustment based on a power utilization load characteristic curve of the power utilization point obtained by power utilization data of the power utilization point;

combining a plurality of power supply points with geographical position closeness smaller than a threshold value into an equivalent power supply point according to the geographical position information of the power utilization point and the geographical position information of the power supply points, and combining a plurality of power utilization points with geographical position closeness smaller than the threshold value into an equivalent power utilization point;

performing power flow calculation aiming at the equivalent power supply points and the equivalent power utilization points on the basis of various possible power consumptions of the power utilization points after the power utilization adjustment, and determining an optimal power flow;

determining the electric energy required to be provided by each power supply point which is combined to form the equivalent power supply point according to the electric energy required to be provided by the equivalent power supply point in the optimal power flow;

and controlling each power supply point to generate power according to the electric energy required to be provided by each power supply point.

In one embodiment, the power consumption load characteristic curve of the power consumption point obtained based on the power consumption data of the power consumption point includes:

performing abnormal data processing on the electricity utilization data, performing standardized processing on the electricity utilization data after the abnormal data processing, and taking the electricity utilization data after the standardized processing as electricity utilization load data;

and clustering the power load data by using a fuzzy C-means clustering algorithm to obtain a power load characteristic curve.

In one embodiment, the electricity usage data includes at least one of electricity meter data and gas meter data.

In one embodiment, the equivalent power supply point comprises an equivalent generator and an equivalent temperature control machine;

the determining an optimal power flow by performing power flow calculation for the equivalent power supply point and the equivalent power consumption point based on various possible power consumptions of the power consumption point after the power consumption adjustment comprises:

under the condition of power demand response, determining the minimum total loss of the equivalent generator, the equivalent temperature control machine and the power utilization point participating in the demand response by utilizing a first objective function;

obtaining an optimal power flow based on the energy required to be supplied by the equivalent generator and the equivalent temperature controller under the minimum total loss and the energy saved by the power utilization point;

the first objective function is:

wherein HG is the number of equivalent generators, HL is the number of equivalent user points, HT is the number of equivalent temperature control machines, B_HGFor the cost of electricity generation per kilowatt-hour equivalent generator, P_HGTo equivalent generated power of the generator, B_HLCost savings for equivalent power usage points to participate in demand response per kilowatt-hour，P_HLPower participating in demand response for equivalent points of use, B_HTControlling the cost, P, of the equivalent temperature of refrigerating and heating capacity per unit_HTThe cold and heat power is equivalent to the temperature control mechanism; the requirements meet the constraint conditions of supply and demand balance:

wherein, P_HLTCooling/heating power of heat energy used for each equivalent power consumption point.

In one embodiment, determining the electric energy required to be provided by the power supply points which are combined to form the equivalent power supply point according to the electric energy required to be provided by the equivalent power supply point in the optimal power flow includes:

calculating the internal power distribution of each equivalent generator and the equivalent temperature control machine by utilizing a second objective function, determining the electric energy required by each generator combined to form each equivalent generator, and combining the electric energy required by each temperature control machine to form the equivalent temperature control machine; the generator comprises a photovoltaic generator set and a wind generating set; the temperature controller comprises a cogeneration unit, a gas boiler, an electric refrigerating unit and an absorption refrigerating unit;

the second objective function is:

wherein, C_HGCost per hour for an equivalent generator, C_HTThe cost per hour for each temperature controller;

wherein λ is_HG1For the operating state of the photovoltaic generator set, λ_HG2For the operating state of the wind turbine generator system, λ_HG3For the operating state of cogeneration units, lambda_HT1Is a gas-fired boilerOperating conditions of the furnace, λ_HT2For operating conditions of electric refrigerating units, lambda_HT3The operation state of the absorption refrigerating unit is shown.

In one embodiment, λ_HG1、λ_HG2、λ_HG3、λ_HT1、λ_HT2And λ_HT3The coefficient representing the operation state of the corresponding equipment takes 0 or 1 according to the actual working state of the corresponding equipment.

According to the optimal control method, device, computer equipment and storage medium for power demand response, power utilization adjustment is performed on the power utilization point based on the power utilization load characteristic curve of the power utilization point obtained by the power utilization data of the power utilization point; combining a plurality of power supply points with geographical position closeness smaller than a threshold value into an equivalent power supply point according to the geographical position information of the power utilization point and the geographical position information of the power supply points, and combining a plurality of power utilization points with geographical position closeness smaller than the threshold value into an equivalent power utilization point; performing power flow calculation aiming at the equivalent power supply points and the equivalent power utilization points on the basis of various possible power consumptions of the power utilization points after the power utilization adjustment, and determining an optimal power flow; determining the electric energy required to be provided by each power supply point which is combined to form the equivalent power supply point according to the electric energy required to be provided by the equivalent power supply point in the optimal power flow; and controlling each power supply point to generate power according to the electric energy required to be provided by each power supply point. In the application, on one side of the power utilization point, a power utilization load characteristic curve is obtained based on power utilization data, power utilization adjustment is carried out on the power utilization point, equivalent combination is carried out according to geographic position information of the power utilization point and geographic position information of the power supply point, power flow calculation aiming at the equivalent power supply point and the equivalent power utilization point is carried out according to various possible power consumptions of the power utilization point after power utilization adjustment, the optimal power flow is obtained, the accuracy of electric energy required to be provided by the equivalent power supply point in the optimal power flow is guaranteed, the accuracy of the electric energy required to be provided by each power supply point is further improved, and meanwhile, the power utilization requirements and the utilization rate of power generation equipment are considered.

Drawings

FIG. 1 is a schematic flow chart diagram illustrating a method for optimizing control of power demand response according to one embodiment;

FIG. 2 is a schematic flow diagram illustrating the flow of power traffic and information flow in one embodiment;

FIG. 3 is a schematic flow diagram for constructing a representation of a power consumption point based on big data mining in one embodiment;

FIG. 4 is a schematic flow chart diagram illustrating a method for optimizing control of power demand response according to one embodiment;

FIG. 5 is a schematic diagram of an electrical model of a load aggregate in one embodiment;

FIG. 6 is a simplified architectural diagram of a power supply point and a power consumption point in one embodiment;

FIG. 7 is a graphical comparison of the cost of the lowest daily grid power supply in one embodiment;

FIG. 8 is a diagram illustrating a ratio of power consumption to total power consumption of a user during a peak electricity rate period, a flat electricity rate period, and a valley electricity rate period, respectively, before performing an energy management policy according to an embodiment;

FIG. 9 is a diagram illustrating a ratio of power consumption to total power consumption of a user during a peak electricity rate period, a flat electricity rate period, and a valley electricity rate period, respectively, after performing an energy management policy according to an embodiment;

FIG. 10 is a schematic illustration of the daily electricity rate change after adjustments are made in one embodiment;

FIG. 11 is a block diagram showing the configuration of an apparatus for optimizing control of electric power demand response according to an embodiment;

FIG. 12 is a diagram illustrating an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. 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.

The optimal control method for power demand response provided by the present application is introduced with reference to fig. 1, and mainly includes the following steps:

and step S101, power utilization adjustment is carried out on the power utilization point based on the power utilization load characteristic curve of the power utilization point obtained by the power utilization data of the power utilization point.

In this step, the electricity consumption data includes at least one of meter data and gas meter data, which is obtained mainly by a data acquisition and monitoring control System (SCADA), an Advanced Metering Infrastructure (AMI), and a smart meter. In an electric power system, an SCADA system is most widely applied and the technical development is the most mature, and the SCADA system plays an important role in a telecontrol system, can monitor and control on-site operating equipment so as to realize various functions of data acquisition, equipment control, measurement, parameter adjustment, various signal alarms and the like, and plays a very important role in the comprehensive automation construction of the current transformer substation.

The power utilization points can be industrial and commercial users, and the main related departments comprise an enterprise power utilization management department, a power utilization management department in an industrial and commercial gathering area, a regional power supply bureau and the like. The service flow and the information flow up and down as shown in fig. 2, and interaction is performed continuously, and the energy flow mainly flows from the power grid to the load. The applied data mainly comprises basic attribute information of the user, power consumption behavior information of the user, interaction information between the user and the power grid and the like, and the information is acquired by depending on an intelligent electric meter arranged at the user side and basic information reported by all users accessing the power grid. In the aspect of demand response regulation and control, the power utilization behavior of users is mainly interfered by price signals, hierarchical management is achieved, and the load requirement of a superior power grid is met to the maximum extent by a subordinate power grid through demand response. A demand response decision department of a power grid company should issue a load adjustment demand to a lower-level power grid within a certain period of time in the future according to the load state of a current power system, so that the overall load is as stable as possible, and the problem of peak rebound possibly caused by demand response is considered. After receiving the price adjustment signal of the demand response administrator, the industrial and commercial users respond by reducing or transferring partial load on the basis of ensuring the normal operation of the enterprise.

The management model of fig. 2 aggregates the power consumers participating in the demand response service and reports the total amount of demand response that can be provided to the corresponding units of the grid system, the manager being mainly a local power supply office. On the other hand, the manager charges a demand response fee priced at a certain hourly rate from the power grid company and compensates participation of the members at the certain hourly rate while obtaining a management fee therefrom.

Step S102, combining a plurality of power supply points with geographic position closeness smaller than a threshold value into an equivalent power supply point according to the geographic position information of the power utilization point and the geographic position information of the power supply points, and combining a plurality of power utilization points with geographic position closeness smaller than the threshold value into an equivalent power utilization point;

that is, according to the geographical position information of the power utilization points and the geographical position information of the power supply points, combining a plurality of power supply points with close geographical positions into equivalent power supply points, and combining a plurality of power utilization points with close geographical positions into equivalent power utilization points; the equivalent power supply point may include an equivalent generator and an equivalent temperature control machine.

Step S103, performing power flow calculation aiming at the equivalent power supply points and the equivalent power consumption points based on various possible power consumption of the power consumption points after power consumption adjustment, and determining an optimal power flow;

step S104, determining the electric energy required to be provided by each power supply point which is combined to form the equivalent power supply point according to the electric energy required to be provided by the equivalent power supply point in the optimal power flow;

and step S105, controlling each power supply point to generate power according to the electric energy required to be provided by each power supply point.

According to the method, on one side of a power consumption point, a power load characteristic curve is obtained based on power consumption data, power consumption adjustment is carried out on the power consumption point, equivalent combination is carried out according to geographic position information of the power consumption point and geographic position information of a power supply point, power flow calculation aiming at the equivalent power supply point and the equivalent power consumption point is carried out according to various possible power consumptions of the power consumption point after power consumption adjustment, an optimal power flow is obtained, accuracy of electric energy required to be provided by the equivalent power supply point in the optimal power flow is guaranteed, accuracy of the electric energy required to be provided by each power supply point is further improved, and meanwhile power consumption requirements and the utilization rate of power generation equipment are considered.

In one embodiment, the process of building a representation of a power consumption point based on big data mining is shown in FIG. 3:

step S301: and collecting and sorting the electricity consumption data required when the portrait is established for the application points, and adding the collected and sorted electricity consumption data into a corresponding label system, wherein no electricity consumption data exists at the moment.

Step S302: and data preprocessing, namely performing abnormal data processing on the electricity utilization data uploaded by the intelligent ammeter and standardizing the data, so that the problem that users with different capacities cannot make strategies together is solved.

Step S303: and clustering the power consumption load data by using a fuzzy C-means clustering algorithm (also called FCM algorithm), and extracting a power consumption load characteristic curve.

That is, the step S101 may specifically include: performing abnormal data processing on the electricity utilization data, performing standardized processing on the electricity utilization data after the abnormal data processing, and taking the electricity utilization data after the standardized processing as electricity utilization load data; and clustering the power load data by using a fuzzy C-means clustering algorithm to obtain a power load characteristic curve.

Step S304: an image is formed based on the power load characteristic curve and other information of the power consumption point, and the image created at this time is still a temporary version and needs to be corrected.

Step S305: and carrying out power utilization adjustment on the power utilization point according to the image of the power utilization point and the power utilization load characteristic curve, and enabling the power utilization point to continuously run for a period of time based on the strategy after the power utilization adjustment.

Step S306: judging whether the power load characteristic curve in the user portrait tends to be stable or not, if so, repeating the steps S302 to S305 and judging again, and if not, judging that the default curve is not stable when the curve is established for the first time; if stabilized, proceed to the next step.

Step S307: and outputting the result of the portrait establishment of the power consumption points as the basis for the formulation of the energy management strategy.

Further, the detailed processing manner of step S302 is as follows:

the reality and the integrity of the electricity load data are the premise of analysis and prediction, but as various intelligent electric meters are more in types and different in network access modes, the data measurement and transmission precision is different, and the obtained data may have more five types of error data, namely null data, zero-value data, constant value data, abnormal boundary-crossing data and abnormal step value data. For processing these data, abnormal data should be located first, positioning should be performed by applying the principle, and first, the mean and standard deviation of the load are calculated according to the following formula:

wherein x is_i96 data are provided for original load data at a certain time of a processing date every day; mu is the mean value of the original load data; sigma²Is the variance of the original load data.

And (3) judging the data by applying the following formula, and positioning abnormal or error data:

|x_i-μ|>3σε (1-2)

wherein epsilon is a threshold value and is 1-1.5.

After the positioning of the abnormal data is completed, correcting the abnormal data according to the following formula:

wherein x is_{i, front}And x_{i, after}Load data, x, at the time of the previous and subsequent days, respectively_i-1And x_i+1Respectively the previous and the next data of the same day at the moment, alpha,Beta and gamma are both weight coefficients.

When the user data are found to be abnormal, the abnormal data are recorded, if the abnormal data are too many in the detection time period, the information is pushed to the user, the user is guided to check the working condition of the local intelligent electric meter, and operation and maintenance personnel in the region are informed to carry out door-to-door maintenance on the electric meter.

After the preprocessing of abnormal data is finished, the Z-score method is applied to carry out standardized processing on the electric load data, and the main purpose is to enable users with different energy utilization levels to carry out analysis and comparison under a unified standard and enable parameters to be fast converged in the optimization process. The Z-score standardization method is used for standardizing data by applying the mean value and the standard deviation of load data, so that the processed data meet normal distribution, the standard deviation is 1, the mean value is 0, and the corresponding calculation formula is as follows:

wherein y is original load data; y is^*The load data after being processed; sigma is the standard deviation of the load data; μ is the mean of the load data.

After initial data preprocessing is completed, clustering operation is started, in the previous power utilization data clustering operation, due to the fact that load characteristic data are large in change amplitude and poor in regularity, a plurality of clustering centers may exist, data are possibly discrete and not suitable for a K-means hard clustering algorithm, an FCM clustering algorithm is applied in the stage, the membership function of the method is suitable for reflecting uncertainty in actual power utilization, and clustering accuracy is high. Firstly, processing load data by applying an FCM algorithm at each time point, solving clustering centers, performing the operations at 96 times to obtain 96 clustering centers, and sequentially connecting all the clustering centers according to a time sequence to be used as a power load characteristic curve.

If the optimal clustering number is 1 after judgment through the formula (1-4) of the FCM algorithm, the K-means algorithm is adopted to perform clustering operation on the data, and the load characteristic curve tends to be gentle and has certain regularity, so that the method is suitable for data processing by applying the K-means algorithm with low complexity, the calculation efficiency is high, and the total time of the clustering operation is saved.

Further, the detailed processing manner of step S305 is as follows:

assuming that there are two load peaks, respectively from 8 hours to 11 hours and from 13 hours to 17 hours, and a load valley at night, for the whole power system, in order to reduce the influence of the load change of the users on the power grid as much as possible and to enable the large industrial and commercial users to participate in the demand response, the goal of the power utilization strategy adjustment in this stage is:

(1) for industrial users, the load characteristic curve in the daytime period is more gentle, the load rate in the nighttime period is higher, and for commercial users, the load is reduced more;

(2) applying the transferable load to fill the valley of the minimum value point in the load characteristic curve;

(3) clipping the peak of the maximum point in the load characteristic curve by applying the reducible load and the transferable load;

(4) for peak load which cannot be transferred, the load descending time is shortened, the influence of the peak load on other users in subsequent time periods is reduced, namely the change of the curve is closer to on and off, and the demand response speed is accelerated (the significance of the target is to reduce the uncertainty existing in the change of the load curve and enable the curve to change at a unique speed so as to ensure that the final load characteristic curve is unique, and the subsequent optimization operation is convenient).

In order to achieve the adjustment target, according to partial information of the large-scale industrial and commercial user portrait, the power utilization characteristics of the large-scale user are interfered by using a demand response method such as an incentive policy, and the demand response plan is popularized and used in a large user group. The labels applied are mainly:

(1) basic properties: load grades (I, II and III), public service participation of demand response and power supply types;

(2) the electricity utilization action comprises the following steps: daily power consumption peak time, daily power consumption valley time, monthly power consumption peak date, monthly power consumption valley date, directly-controllable interrupt capacity, participation demand response times and power consumption behavior rating;

(3) consumption behaviors: participating in demand response incentive rates.

How to make a demand response strategy according to the above information is described below item by item, and the tags are sorted according to the priority of the order in which demand responses are made, that is, if users with similar characteristics are encountered during making demand responses, the tags are compared item by item according to the order to determine the priority of demand responses:

(1) load grade: the I and II-class loads have high requirements on power supply reliability, and power supply reduction or power supply interruption is not allowed, so that whether the loads of the two types of loads can be transferred to participate in demand response or not is only considered, but no mandatory requirement is imposed on the load time; the III-grade load has no special requirement on the power supply reliability, and the participation in demand response through load reduction and load transfer can be considered;

(2) demand response commonweal participation: the label determines when a power grid and an enterprise sign a demand response protocol, the numerical value is the proportion of the demand response capacity to the full load power, and the higher the demand response participation of the enterprise is, the priority is given to the demand response capacity during optimization;

(3) the power supply type: in order to improve the consumption rate of renewable resources, the higher the proportion of wind power generation and photovoltaic power generation in the total power consumption of the enterprise is, the lower the priority of demand response adjustment;

(4) daily power consumption peak time and daily power consumption valley time: the peak value and the valley time of daily power consumption of an enterprise are adjusted, the valley time of the enterprise can be coincided with the peak value time of a power grid, and the peak value time of the enterprise can be coincided with the valley time of the power grid, so that the purposes of peak clipping and valley filling are achieved;

(5) monthly electricity peak date, monthly electricity valley date: in order to enable more users to participate in demand response, the peak and valley dates of power consumption of different enterprises are adjusted to be complementary;

(6) interrupt capacity can be controlled directly: the partial capacity can be directly stopped during the peak time of electricity utilization, and the higher the capacity, the higher the priority of the enterprise is;

(7) number of participating demand responses: the more the demand response participation frequency of the enterprise in the previous month is, the priority is given to the participation of the enterprise in the demand response;

(8) and (3) grading the power consumption behavior: the higher the value is, the higher the overall load rate of the user is, and correspondingly, the lower the possibility of participation in demand response is, the enterprise is not considered preferentially;

(9) participation in demand response incentive line: the higher this value, the higher the incentive the grid will pay to incentivize its participation in demand response, and the higher this value will not give priority to the business in order to reduce the operating costs of the grid.

After the user portrait establishing process is completed, the power load curve of the large industrial and commercial users is subjected to peak clipping and valley filling to the maximum extent, and the first-stage adjustment of the demand response optimization decision method is completed.

In an embodiment, electric energy, heat energy, natural gas and the like can be modeled respectively to obtain equivalent energy supply points of different energy sources, each equivalent energy supply point is subjected to optimization calculation, an optimal power flow is determined, and a user demand response optimization control method is constructed, wherein the complete flow of the method is shown in fig. 4, and the specific steps are as follows:

step S401: establishing a model of an energy supply point and an energy consumption point;

step S402: determining an optimal power flow;

step S403: performing optimization calculation on each equivalent temperature control machine and each equivalent generator;

step S404: and outputting the result of the optimization calculation, and applying the result to an equivalent temperature control machine and an equivalent generator.

In the step S401, energy supply sites with a close geographical position are combined into an equivalent generator and an equivalent temperature controller according to the collected power consumption information and geographical position information of the user side and the power grid side, energy consumption sites with a close geographical position are combined into an equivalent user, and an optimization model is established according to a corresponding formula. The specific contents for establishing the optimization model comprise:

(1) model of photovoltaic power generation equipment

The photovoltaic device changes the output power along with the change of the solar illumination intensity and the local environment temperature, has strong nonlinear characteristics, and can quickly change the output power in a short time under the cloudy meteorological condition. The model of the photovoltaic power generation apparatus is as follows:

wherein, B_PVCost per kilowatt-hour of wind power generation; p_PVThe unit kW is the power of photovoltaic power generation; t is_PVThe photovoltaic power generation time is; eta is the photoelectric conversion efficiency of photovoltaic power generation; n is the number of photovoltaic panels; s is the area of the photovoltaic cell panel, unit m²(ii) a K is the illumination intensity in lux (Lx); θ is the outdoor temperature in degrees celsius. The constraint conditions of photovoltaic power generation are as follows:

wherein the content of the first and second substances,

the maximum photovoltaic power generation power is in kW.

(2) Model of wind power plant

The wind power generation equipment changes the output power along with the change of the wind speed, and also has strong nonlinear characteristics, and the wind power generator outputs the rated power when running at the wind speed between the rated wind speed and the cut-out wind speed. The model of the wind power plant is: c_W＝B_WT_W(C_W1+C_W2v+C_W3v²) Wherein B is_WCost per kilowatt-hour of wind power generation; t is_WThe wind power generation duration is; c_W1、C_W2、C_W3To be controlled by the cutting-in speed v_cCutting speed v_sAnd rated power P_WRThe determined constant and the unit of wind speed are all m/s. The constraint conditions of wind power generation are as follows: v. of_c≤v≤v_s。

(3) Storage battery model

The storage battery is an important device in a renewable energy power plant, and when the storage battery is matched with wind power generation, photovoltaic power generation and the like to run together, the absorption rate of renewable energy can be improved, the balance of power can be maintained, and the economic benefit of a renewable power supply in power supply is higher. When the generated power is insufficient, the storage battery and the renewable energy source supply electric energy to the power grid together; when the generated power is excessive, the renewable energy sources charge the storage battery, and the waste of resources is reduced.

When the storage battery operates normally, the battery state is determined by the charge and discharge power of the previous time interval and the current time interval, and the model is as follows:

wherein, state (t) is the state of charge of the storage battery at the time t; STATE (t-1) is the STATE of charge of the storage battery at the t-1 moment; p_ES(t) the charging/discharging power of the storage battery at the moment t, in kW; m_ES(t) is a storage battery charging/discharging state mark at the time t, and 1 (discharging) or-1 (charging) is selected; ESS_RIs the rated capacity of the storage battery, and has a unit of kWh; eta_cCharging efficiency for the battery; eta_fDischarging efficiency for the battery; Δ t is the time interval.

The output characteristic equation of the storage battery is as follows:

wherein, t₀The generated power is excessive at any moment, and charging is started; t is t₂The power generation is insufficient at the moment, and the discharge is started.

In order to simplify the model of wind and light power generation, wind and light power generation units are integrated and a storage battery is configured, and the average output power per hour is used as the output characteristic.

(4) Model of combined heat and power generation unit

The cogeneration unit can integrate the waste heat generated by the prime motor and can simultaneously supply electric energy and heat energy, and the model is as follows:

wherein, B_CHP,EThe power generation cost of the cogeneration unit is every kilowatt-hour; t is_CHPThe working time of the wind-heat electricity cogeneration unit is long; eta_CHP,EThe generating efficiency of the cogeneration unit; b is_CHP,HThe heating cost of the unit heat cogeneration unit is; eta_CHP,HThe heating efficiency of the cogeneration unit is obtained;

the heat value of methane is expressed in megajoules per cubic meter (MJ/m)³)；V_CHPIs the consumption of natural gas per unit time, m³。

(5) Model of gas boiler

A gas boiler is a plant that consumes natural gas and produces heat energy, and is modeled as follows:

wherein, B_GBThe heating cost of the unit heat energy gas boiler is; t is_GBThe working time of the gas boiler is the working time of the gas boiler; eta_GBThe heating efficiency of the gas boiler;

the heat value of methane is expressed in megajoules per cubic meter (MJ/m)³)；V_GBIs the consumption of natural gas per unit time, m³。

(6) Model of electric refrigerator

The electric refrigerator is a device which consumes electric energy and performs refrigeration, and the model thereof is as follows:

C_EC＝B_EC·T_EC·P_EC·η_EC，

wherein, B_ECIs a unit cold gas generatorRefrigeration cost of the refrigerator; t is_ECThe working time of the electric refrigerator is the working time of the electric refrigerator; p_ECIs the electric power of the electric refrigerator, and has unit kW; eta_ECIs the refrigeration coefficient of the electric refrigerator.

(7) Model of absorption refrigerator

The absorption refrigerator can utilize low-temperature heat energy of a cogeneration unit and a gas boiler and is used for refrigerating equipment, and the absorption refrigerator is modeled as follows:

C_AC＝B_AC·T_AC·H_AC·η_AC，

wherein, B_ACThe refrigeration cost of a unit cold air absorption refrigerator is taken as the unit; t is_ACThe operating time of the absorption refrigerator is the operating time of the absorption refrigerator; h_ACThe absorption refrigerator consumes heat energy power in kW; eta_ACThe refrigeration coefficient of the absorption refrigerator.

(8) Model of load aggregate

In the case of load-side joint demand response, the power plans of all members are optimized together to maximize the revenue of all members, with the revenue or loss being divided equally by the members. An electrical model of the load assembly is shown in fig. 5.

In one embodiment, the equivalent power supply point comprises an equivalent generator and an equivalent temperature control machine; step S103 may include the steps of: under the condition of only considering the power demand response, determining the minimum total loss of the equivalent generator, the equivalent temperature control machine and the power utilization point participating in the demand response by using the first objective function (in some scenarios, the loss can be characterized by cost, and the minimum total loss refers to the minimum sum of the cost of supplying cold, heat and electricity on the energy supply side and the excitation cost of the user participating in the demand response); obtaining an optimal power flow based on the energy required to be supplied by the equivalent generator and the equivalent temperature controller under the minimum total loss and the energy saved by the power utilization point; the first objective function is:

wherein HG is the number of equivalent generators, HL is the number of equivalent user points, HT is the number of equivalent temperature controllers,B_HGfor the cost of electricity generation per kilowatt-hour equivalent generator, P_HGTo equivalent generated power of the generator, B_HLCost savings for participating in demand response per kilowatt-hour equivalent power consumption point, P_HLPower participating in demand response for equivalent points of use, B_HTControlling the cost, P, of the equivalent temperature of refrigerating and heating capacity per unit_HTThe cold and heat power is equivalent to the temperature control mechanism; the requirements meet the constraint conditions of supply and demand balance:

And completing optimization calculation through the model to obtain the optimal power utilization load flow and the optimal equivalent temperature to control the refrigerating and heating power of the machine, performing load flow calculation on various possible power utilization conditions, and finding out the power scheduling scheme with the minimum total loss.

In an embodiment, the step S104 may specifically include the following steps: calculating the internal power distribution of each equivalent generator and the equivalent temperature control machine by utilizing a second objective function, determining the electric energy required by each generator combined to form each equivalent generator, and combining the electric energy required by each temperature control machine to form the equivalent temperature control machine; the generator comprises a photovoltaic generator set and a wind generating set; the temperature controller comprises a cogeneration unit, a gas boiler, an electric refrigerating unit and an absorption refrigerating unit; the second objective function is:

wherein λ is_HG1For the operating state of the photovoltaic generator set, λ_HG2For the operating state of the wind turbine generator system, λ_HG3For operating the cogeneration unit，λ_HT1For the operating conditions of gas-fired boilers, lambda_HT2For operating conditions of electric refrigerating units, lambda_HT3The operation state of the absorption refrigerating unit is shown.

Further, λ_HG1、λ_HG2、λ_HG3、λ_HT1、λ_HT2And λ_HT3The coefficient representing the operation state of the corresponding equipment takes 0 or 1 according to the actual working state of the corresponding equipment.

Because the heat power supply is usually not in the whole system intranet, and the power can be scheduled in the interconnected system, on the basis of the step S103, optimization calculation is firstly performed on each equivalent temperature control machine, so that the operating cost is the lowest under the condition of meeting the user requirements, and at the moment, the problem of energy supply cost increase caused by geographical position is mainly considered, and the 'nearby supply' is sought to reduce the cost; on the basis, each equivalent generator is subjected to optimization calculation, the problem of network loss caused by the output of different generator sets in the equivalent generator is mainly considered, and the power of the wind and light generator sets is required to be consumed preferentially.

The above step S404: and outputting the result of the optimization calculation, and transmitting specific operation scheduling data to each equivalent temperature controller and each equivalent generator.

Based on the flow shown in fig. 4, a primary energy management strategy can be established, but due to the increase or decrease of the user-side equipment, the change of the power consumption information and the continuous increase of the power generation capacity of the power generation side are caused, and new users are added continuously, so that the whole power system is in the process of continuous change, user portrait needs to be updated regularly according to the complexity of the power grid, and a demand response decision is made continuously, so that the effectiveness of the algorithm can be better exerted.

Based on the optimal control method for power demand response provided by the application, the embodiment sets specific example analysis, and the loss of the embodiment is represented by cost, specifically as follows:

the premise hypothesis is that: considering the combined load data of the equivalent power users as a horizontal straight line and the energy supply points as one; the whole heat load of the city is borne by the cogeneration unit, so that a gas boiler is not considered; the refrigeration load is borne by the electric refrigerator, and the electric refrigerator is placed on the load side without considering the absorption refrigerator; an example analysis is performed based on a 10kV distribution network, and the simplified structure of the 10kV distribution network is shown in fig. 6.

The data of the optimal power flow obtained after the power flow calculation (the objective function is the minimum loss of the power system) is P₁＝1000MW,P₂＝300MW，P₁＝1000MW,P₂300 MW. Wherein, P₁Is a power supply point, P₂And P₃For industrial equivalent power consumers, P₄Is the equivalent power consumer of the business.

The composition of the equivalent generator comprises:

(1) the wind-solar complementary generating set can obtain stable output due to wind-solar complementary power generation, and an energy storage battery is added, so that the calculation is simplified, the output characteristic of the wind-solar complementary generating set is considered to be a horizontal straight line, the total electric load is 200MW, and the power generation cost is 0.28 yuan/kWh (since the power generation cost of the wind-solar complementary generating set is different in different regions and fluctuates between 0.25 yuan/kWh and 0.25 yuan/kWh, but the whole is lower than the grid electricity price of thermal power, the electricity price is 0.28 yuan/kWh in the calculation example);

(2) in a traditional thermal power plant, 2 power plants are arranged at power supply points, and the power generation cost is 0.3 yuan/kWh;

(3) the power supply points of the thermal power plant are 10 power plants of the type, the heat load is preferentially supplied by the cogeneration unit, the total heat load is 1000t/h, the total electric load is 350MW, the calculation is simplified, the power generating units of 10 thermal power plants are considered to have the same structure, the power generating cost is 0.27 yuan/kWh, and the specific data are shown in Table 1.

TABLE 1 thermal power plant Generator set configuration

The equivalent power consumer consists of:

(1)P₂point users are industrial users, participating in the product of demand response itemsThe polarity is not high, so that the electricity fee is high in the demand response, and the power grid charges 0.2 yuan more per degree of electricity from the user as the penalty of the negative participation in the demand response;

(2)P₃the point user is an industrial user, and the enthusiasm for participating in the demand response project is high, so that the electricity charge is low in the demand response, and the power grid charges less 0.2 yuan per degree of electricity from the point user to be used as compensation for actively participating in the demand response;

(3)P₄the point user is a commercial user, the enthusiasm for participating in the demand response project is high, but the proportion of mandatory loads is high, and the power grid charges less 0.1 yuan per degree of electricity from the point user as compensation for actively participating in the demand response.

If wind power generation and photovoltaic power generation are not added into the consideration range according to the traditional power grid operation mode, all loads are borne by the thermal power plant, but the thermal load of the thermal power plant is fixed, and the cost of the thermal power plant is increased due to more power generation, in this example, if the power generation cost of the original thermal power plant is increased from 0.27 yuan/kWh to a level equivalent to that of the traditional thermal power plant, namely, the power generation cost is 0.3 yuan/kWh, the daily minimum power grid power supply cost when the maximum power is always supplied is as follows:

in order to preferentially absorb wind power and photovoltaic power generation and reduce the pollution of thermal power generation to the environment, the wind-solar complementary generator set is required to output the maximum power, then a thermal power plant with forced power exists, the rest load is borne by the traditional thermal power plant, and the cost of the lowest power grid power supply every day when the maximum power is supplied all the time is as follows:

the results obtained show that the cost of electricity generation is reduced by 4.531% if the preferential consumption of wind power and photovoltaic power generation is taken into account, and the comparison of the daily minimum grid supply costs is shown in fig. 7.

In the following calculation of the electricity fee of the user, in order to simplify the data processing process, it is assumed that the proportion of the electricity consumption of the user in the electricity price peak period, the electricity price average period and the electricity price valley period to the total electricity consumption is as shown in fig. 8 before the adjustment of the first stage of the energy management strategy. The peak time interval industrial electricity price is 1.025 yuan/kWh, the flat time interval electricity price is 0.725 yuan/kWh, the valley time interval is 0.425 yuan/kWh, and the daily electricity fee before the first phase adjustment is as follows:

after the production and operation behavior is adjusted, the peak power usage proportion is decreased, and the average and valley power usage proportions are increased, assuming that the specific distribution after the first stage adjustment is performed is as shown in fig. 9. The daily electricity charge after the first stage adjustment is:

the daily electricity rate was changed after the adjustment was made, as shown in fig. 10.

The corresponding daily rate of change of electricity rates is shown in table 2.

TABLE 2 daily electric charge Change rate table

Equivalent power consumer	Rate of change of electricity charge
		P₂	22.289％
P₃	-36.747％
		P₄	-18.471％

As can be seen from table 2, actively participating in the adjustment project can greatly reduce the electric power fee of the enterprise, otherwise, the electric power fee will be greatly increased, and for the power supply department, 19,704,000 yuan is charged before adjustment every day, 16,680,000 yuan is charged after adjustment every day, the income is reduced by 15.347%, but the construction time of the large peak shaving unit can be delayed, and the stability of the operation of the power system is also increased, which is beneficial for the whole power system.

The demand response optimization control method based on various market trading mechanisms is divided into two stages, wherein the first stage is a production operation plan adjustment stage of large industrial users, a load characteristic curve of each user is obtained through power consumption data cluster analysis of the large industrial users, adjustment strategies are provided for the users according to the load characteristic curve, coarse granularity adjustment is carried out on the power consumption plans of the large users, and the second stage of adjustment is carried out after the load characteristic curve adjustment of the users tends to be stable. The adjustment of the first stage is already finished when the user portrait is established, and the main purpose of the adjustment of the first stage is to reduce the maximum value of the electricity consumption of a large user in the peak period of the electricity consumption as much as possible, fill up the minimum value of the electricity consumption of the large user in the valley period of the electricity consumption, further reduce the influence of the extreme value on the subsequent adjustment, and reduce the economic loss caused by supplying the peak value of the load or stopping supplying the valley value of the load by other energy sources (renewable energy power generation and the like); meanwhile, each large user is enabled to arrange production and operation activities in advance, and the method is beneficial to stable and normal operation of the large user, and enables the user to adapt to the mode and the rule of demand response. In the second stage of adjustment, electric energy (including wind and light and other renewable energy sources for power generation), heat energy (a cogeneration unit and a gas boiler), natural gas and the like are respectively modeled, energy supply sites with close geographic positions are combined into equivalent generators and equivalent temperature control machines, energy consumption sites with close geographic positions are combined into equivalent users, an optimization model is established, trend calculation is carried out on various possible electricity utilization conditions, the optimal trend is determined, then optimization calculation is carried out on each equivalent generator and each equivalent temperature control machine, the operation cost is the lowest under the condition that the user requirements are met, and the power utilization plan of a large user is adjusted in a finer granularity mode so as to achieve the purpose of optimization. The method and the device can improve the user income under the condition of meeting the user demands, improve the utilization rate of the power generation equipment and improve the operation stability of the power system.

It should be understood that, although the steps in the flowcharts of fig. 1 to 10 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1 to 10 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least some of the other steps or stages.

In one embodiment, as shown in fig. 11, there is provided an optimization control device of power demand response, including:

a power consumption adjustment module 1101 configured to perform power consumption adjustment on a power consumption point based on a power consumption load characteristic curve of the power consumption point obtained from power consumption data of the power consumption point;

a merging equivalence module 1102, configured to merge, according to the geographic location information of the power consumption points and the geographic location information of the power supply points, a plurality of power supply points whose geographic location closeness is smaller than a threshold into an equivalent power supply point, and merge a plurality of power consumption points whose geographic location closeness is smaller than the threshold into an equivalent power consumption point;

a power flow processing module 1103, configured to perform power flow calculation for the equivalent power supply point and the equivalent power consumption point based on various possible power consumptions of the power consumption point after the power consumption adjustment, and determine an optimal power flow;

an electric energy determining module 1104, configured to determine, according to the electric energy that needs to be provided by the equivalent power supply point in the optimal power flow, the electric energy that needs to be provided by each power supply point that is combined to form the equivalent power supply point;

and a power generation control module 1105, configured to control the power supply points to generate power according to the electric energy required by the power supply points.

In one embodiment, the power consumption adjusting module 1101 is configured to perform abnormal data processing on the power consumption data, perform standardized processing on the power consumption data after the abnormal data processing, and use the standardized power consumption data as power consumption load data; and clustering the power load data by using a fuzzy C-means clustering algorithm to obtain a power load characteristic curve.

the power flow processing module 1103 is further configured to determine, by using the first objective function, a minimum total loss of the equivalent generator, the equivalent temperature control machine, and the power consumption point participating in the demand response under the condition of the power demand response; obtaining an optimal power flow based on the energy required to be supplied by the equivalent generator and the equivalent temperature controller under the minimum total loss and the energy saved by the power utilization point;

the first objective function is:

wherein HG is the number of equivalent generators, HL is the number of equivalent user points, HT is the number of equivalent temperature control machines, B_HGFor the cost of electricity generation per kilowatt-hour equivalent generator, P_HGTo equivalent generated power of the generator, B_HLCost savings for participating in demand response per kilowatt-hour equivalent power consumption point, P_HLPower participating in demand response for equivalent points of use, B_HTControlling the cost, P, of the equivalent temperature of refrigerating and heating capacity per unit_HTThe cold and heat power is equivalent to the temperature control mechanism; the requirements meet the constraint conditions of supply and demand balance:

In an embodiment, the electric energy determining module 1104 is further configured to calculate, by using a second objective function, an internal power allocation of each equivalent generator and the equivalent temperature controller, determine electric energy required to be provided by each generator that is combined to form each equivalent generator, and combine electric energy required to be provided by each temperature controller that is formed into the equivalent temperature controller; the generator comprises a photovoltaic generator set and a wind generating set; the temperature controller comprises a cogeneration unit, a gas boiler, an electric refrigerating unit and an absorption refrigerating unit;

the second objective function is:

wherein λ is_HG1For the operating state of the photovoltaic generator set, λ_HG2For the operating state of the wind turbine generator system, λ_HG3For the operating state of cogeneration units, lambda_HT1For the operating conditions of gas-fired boilers, lambda_HT2For operating conditions of electric refrigerating units, lambda_HT3The operation state of the absorption refrigerating unit is shown.

For specific limitations of the optimization control device for power demand response, reference may be made to the above limitations of the optimization control method for power demand response, which are not described herein again. The respective modules in the above-described optimization control device for electric power demand response may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, the internal structure of which may be as shown in FIG. 12. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing optimized control data of the power demand response. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of optimizing control of power demand response.

Those skilled in the art will appreciate that the architecture shown in fig. 12 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of the above-described method embodiments when executing the computer program.

In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the respective method embodiment as described above.

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 can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. A method for optimizing control of power demand response, the method comprising:

2. The method according to claim 1, wherein the power consumption load characteristic curve of the power consumption point obtained based on the power consumption data of the power consumption point comprises:

3. The method of claim 1, wherein the electricity usage data comprises at least one of electricity meter data and gas meter data.

4. The method of claim 1, wherein the equivalent power supply points comprise an equivalent generator and an equivalent temperature control machine;

the first objective function is:

wherein HG is the number of equivalent generators, HL is the number of equivalent user points, HT is the number of equivalent temperature controlEye, B_HGFor the cost of electricity generation per kilowatt-hour equivalent generator, P_HGTo equivalent generated power of the generator, B_HLCost savings for participating in demand response per kilowatt-hour equivalent power consumption point, P_HLPower participating in demand response for equivalent points of use, B_HTControlling the cost, P, of the equivalent temperature of refrigerating and heating capacity per unit_HTThe cold and heat power is equivalent to the temperature control mechanism; the requirements meet the constraint conditions of supply and demand balance:

5. The method of claim 1, wherein determining the electric energy required to be provided by the power supply points that combine to form the equivalent power supply point according to the electric energy required to be provided by the equivalent power supply point in the optimal power flow comprises:

the second objective function is:

6. Method according to claim 5, characterized in that λ_HG1、λ_HG2、λ_HG3、λ_HT1、λ_HT2And λ_HT3The coefficient representing the operation state of the corresponding equipment takes 0 or 1 according to the actual working state of the corresponding equipment.

7. An apparatus for optimizing control of a demand for electric power, the apparatus comprising:

the power utilization adjusting module is used for carrying out power utilization adjustment on the power utilization point on the basis of a power utilization load characteristic curve of the power utilization point obtained by power utilization data of the power utilization point;

the combined equivalent module is used for combining a plurality of power supply points with geographic position closeness smaller than a threshold value into equivalent power supply points according to the geographic position information of the power utilization points and the geographic position information of the power supply points, and combining a plurality of power utilization points with geographic position closeness smaller than the threshold value into equivalent power utilization points;

the power flow processing module is used for performing power flow calculation aiming at the equivalent power supply point and the equivalent power utilization point based on various possible power consumptions of the power utilization points after the power utilization adjustment, and determining the optimal power flow;

the electric energy determining module is used for determining the electric energy required to be provided by each power supply point which is combined to form the equivalent power supply point according to the electric energy required to be provided by the equivalent power supply point in the optimal power flow;

and the power generation control module is used for controlling each power supply point to generate power according to the electric energy required by each power supply point.

8. The device according to claim 7, wherein the power consumption adjusting module is configured to perform abnormal data processing on the power consumption data, perform standardized processing on the power consumption data after the abnormal data processing, and use the standardized power consumption data as power consumption load data; and clustering the power load data by using a fuzzy C-means clustering algorithm to obtain a power load characteristic curve.

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the method of any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 6.