CN112215641A - Control method and system for intelligent building type virtual power plant to participate in energy frequency modulation - Google Patents

Control method and system for intelligent building type virtual power plant to participate in energy frequency modulation Download PDF

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CN112215641A
CN112215641A CN202011077665.4A CN202011077665A CN112215641A CN 112215641 A CN112215641 A CN 112215641A CN 202011077665 A CN202011077665 A CN 202011077665A CN 112215641 A CN112215641 A CN 112215641A
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power plant
virtual power
frequency modulation
market
energy
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杜洋
郭灵瑜
杨心刚
苏磊
刘琦
孙沛
梁伟朋
曹博源
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State Grid Shanghai Electric Power Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention relates to a control method and a system for an intelligent building type virtual power plant to participate in energy frequency modulation, wherein the intelligent building type virtual power plant comprises photovoltaic and air conditioner load virtual energy storage, and the method comprises the following steps: establishing a random planning model with the minimum daily operation cost as a target; different scenes and probabilities of the day-ahead market price and the predicted photovoltaic output are obtained by adopting a time-aligned Markov chain; solving the stochastic programming model based on the day-ahead market price, different scenes and the probability thereof; and controlling the running state of the intelligent building type virtual power plant according to the solved result. Compared with the prior art, the invention has the advantages of high stability, high regulation and control flexibility and the like.

Description

Control method and system for intelligent building type virtual power plant to participate in energy frequency modulation
Technical Field
The invention relates to a power grid control method, in particular to a control method and a control system for an intelligent building type virtual power plant to participate in energy frequency modulation.
Background
Under the background of the vigorous development of distributed renewable energy and load control technologies, virtual power plants become a main medium for demand-side resources to participate in power market regulation. The virtual power plant effectively reduces the scheduling burden of the power grid by effectively aggregating distributed resources, and realizes the win-win of the power system. The intelligent building has higher research value as a special virtual power plant comprising a distributed power supply and a controllable load. At present, the research aiming at virtual power plants, particularly building type virtual power plants, participating in the energy-frequency modulation market at the same time is relatively deficient.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a control method and a control system for an intelligent building type virtual power plant to participate in energy frequency modulation, wherein the control method and the control system have high stability and high regulation flexibility.
The purpose of the invention can be realized by the following technical scheme:
a control method for an intelligent building type virtual power plant to participate in energy frequency modulation is provided, the intelligent building type virtual power plant comprises photovoltaic and air conditioner load virtual energy storage, and the method comprises the following steps:
establishing a random planning model with the minimum daily operation cost as a target;
different scenes and probabilities of the day-ahead market price and the predicted photovoltaic output are obtained by adopting a time-aligned Markov chain;
solving the stochastic programming model based on the day-ahead market price, different scenes and the probability thereof;
and controlling the running state of the intelligent building type virtual power plant according to the solved result.
Further, the objective function of the stochastic programming model is:
Figure BDA0002717804570000011
wherein, I is the total scene number selected by random planning; omegaiProbability of scene i;
Figure BDA0002717804570000012
participating in the total trading cost of the energy and frequency modulation market for the virtual power plant under the scene i;
Figure BDA0002717804570000013
punishment cost of the virtual power plant failing to respond to the market scheduling signal under the scene i; f. ofPVAnd fVESThe expected regulation and control costs of the photovoltaic and the virtual energy storage due to participation in the virtual power plant are respectively.
Further, the virtual power plant under the scene i participates in the total transaction cost of the energy and frequency modulation market
Figure BDA0002717804570000021
The calculation formula of (2) is as follows:
Figure BDA0002717804570000022
wherein the content of the first and second substances,
Figure BDA0002717804570000023
the price of the energy market in the scene i in the time period t;
Figure BDA0002717804570000024
capacity prices of the frequency modulation market and the frequency modulation market at the time t in the scene i are respectively set;
Figure BDA0002717804570000025
the competitive bidding amount of the virtual power plant in the energy market in the period t;
Figure BDA0002717804570000026
Figure BDA0002717804570000027
respectively competitive upper and lower capacity of the virtual power plant in the frequency modulation market in the t time period;
Figure BDA0002717804570000028
and respectively the bid winning probability of the virtual power plant in the upper and lower frequency modulation markets in the t period in the scene i.
Further, penalty cost of virtual power plant failing to respond to market scheduling signal under the scenario i
Figure BDA0002717804570000029
The calculation formula of (2) is as follows:
Figure BDA00027178045700000210
wherein the content of the first and second substances,
Figure BDA00027178045700000211
penalizes costs for the expected response of the virtual power plant to participate in the energy market,
Figure BDA00027178045700000212
penalty cost is not enough for the calling amount of the virtual power plant participating in the frequency modulation market,
Figure BDA00027178045700000213
and the expected penalty cost of the virtual power plant participating in the frequency modulation market is obtained.
Further, the expected regulation cost f of the photovoltaic due to participation in the virtual power plantPVThe calculation formula of (2) is as follows:
Figure BDA00027178045700000214
wherein the content of the first and second substances,
Figure BDA00027178045700000215
marginal costs of providing frequency modulation service and energy supply for the photovoltaic cells respectively;
Figure BDA00027178045700000216
and
Figure BDA00027178045700000217
and photovoltaic decision-making for the virtual power plant participates in frequency modulation and capacity of an energy market.
Further, the expected regulation cost f of the virtual energy storage due to participation in the virtual power plantVESThe calculation formula of (a) is:
Figure BDA00027178045700000218
wherein the content of the first and second substances,
Figure BDA00027178045700000219
marginal cost of providing frequency modulation service for virtual energy storage;
Figure BDA00027178045700000220
and
Figure BDA00027178045700000221
and (4) capacity of photovoltaic participating in frequency modulation market for virtual power plant decision.
Furthermore, the constraint conditions of the stochastic programming model comprise capacity constraints of the photovoltaic in an energy market and a frequency modulation auxiliary service market, upper and lower frequency modulation capacity constraints which can be contributed by the photovoltaic under the virtual energy storage characteristic of the air conditioner load, total frequency modulation capacity constraints of the virtual power plant, expected response deviation constraints of the virtual power plant participating in the energy market and maximum frequency modulation capacity deviation constraints which cannot be met by the virtual power plant participating in the frequency modulation market.
The invention also provides a control system for the intelligent building type virtual power plant to participate in energy frequency modulation, wherein the intelligent building type virtual power plant comprises photovoltaic and air conditioner load virtual energy storage, and the system comprises:
the model building module is used for building a random planning model with the minimum daily operation cost as a target;
the data preparation module is used for obtaining different scenes and probabilities of the day-ahead market price and the predicted photovoltaic output by adopting a time-aligned Markov chain;
the solving module is used for solving the stochastic programming model based on the day-ahead market price, different scenes and the probability of the scenes;
and the control module is used for controlling the running state of the intelligent building type virtual power plant according to the solving result.
The present invention also provides a computer apparatus comprising:
a processor;
a memory storing processor-executable instructions;
wherein the processor is coupled to the memory for reading the program instructions stored by the memory and, in response, performing the steps of the method described above.
The invention also provides a computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method described above.
Compared with the prior art, the invention has the following beneficial effects:
the virtual power plant participates in the capacity control of the energy-frequency modulation market at the same time, so that the stability of a power network is effectively improved, and compared with the method of independently participating in the energy market, the virtual power plant has obvious economic advantages, and the participation of the virtual energy storage of the air conditioner load provides more regulation and control flexibility for the virtual power plant.
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FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a graph of a load baseline and a virtual energy storage charge-discharge capacity of a virtual power plant;
FIG. 3 is a competitive bidding capacity map of virtual power plants for each time period participating in the frequency modulation market;
FIG. 4 is a comparison graph of the competitive bidding amount of the virtual power plant under two strategies.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Example 1
The embodiment provides a control method for an intelligent building type virtual power plant to participate in energy frequency modulation, wherein the intelligent building type virtual power plant comprises photovoltaic and air-conditioning load virtual energy storage, and as shown in fig. 1, the method comprises the following steps:
step 1: and establishing a stochastic programming model with the aim of minimizing daily operation cost.
The objective function of the stochastic programming model is:
Figure BDA0002717804570000041
wherein, I is the total scene number selected by random planning; omegaiProbability of scene i;
Figure BDA0002717804570000042
participating in the total trading cost of the energy and frequency modulation market for the virtual power plant under the scene i;
Figure BDA0002717804570000043
punishment cost of the virtual power plant failing to respond to the market scheduling signal under the scene i; f. ofPVAnd fVESThe expected regulation and control costs of the photovoltaic and the virtual energy storage due to participation in the virtual power plant are respectively. The specific expressions of the costs are as follows:
total trading cost of virtual power plant participating in energy and frequency modulation market under scene i
Figure BDA0002717804570000044
The calculation formula of (2) is as follows:
Figure BDA0002717804570000045
wherein the content of the first and second substances,
Figure BDA0002717804570000046
the price of the energy market in the scene i in the time period t;
Figure BDA0002717804570000047
capacity prices of the frequency modulation market and the frequency modulation market at the time t in the scene i are respectively set;
Figure BDA0002717804570000048
the competitive bidding amount of the virtual power plant in the energy market in the period t;
Figure BDA0002717804570000049
Figure BDA00027178045700000410
respectively competitive upper and lower capacity of the virtual power plant in the frequency modulation market in the t time period;
Figure BDA00027178045700000411
and respectively the bid winning probability of the virtual power plant in the upper and lower frequency modulation markets in the t period in the scene i.
Penalty cost of virtual power plant failing to respond to market scheduling signal under the scene i
Figure BDA00027178045700000412
The calculation formula of (2) is as follows:
Figure BDA00027178045700000413
wherein the content of the first and second substances,
Figure BDA00027178045700000414
for virtual power plants participating in the energy marketThe cost of the response penalty is expected to be,
Figure BDA00027178045700000415
penalty cost is not enough for the calling amount of the virtual power plant participating in the frequency modulation market,
Figure BDA00027178045700000416
and the expected penalty cost of the virtual power plant participating in the frequency modulation market is obtained.
Expected regulation and control cost f of the photovoltaic due to participation in a virtual power plantPVThe calculation formula of (2) is as follows:
Figure BDA00027178045700000417
wherein the content of the first and second substances,
Figure BDA00027178045700000418
marginal costs of providing frequency modulation service and energy supply for the photovoltaic cells respectively;
Figure BDA00027178045700000419
and
Figure BDA00027178045700000420
and photovoltaic decision-making for the virtual power plant participates in frequency modulation and capacity of an energy market.
The expected regulation and control cost f of the virtual energy storage due to participation in the virtual power plantVESThe calculation formula of (2) is as follows:
Figure BDA00027178045700000421
wherein the content of the first and second substances,
Figure BDA00027178045700000422
marginal cost of providing frequency modulation service for virtual energy storage;
Figure BDA00027178045700000423
and
Figure BDA00027178045700000424
and (4) capacity of photovoltaic participating in frequency modulation market for virtual power plant decision.
Figure BDA00027178045700000425
The specific formula for obtaining the three penalty costs on the right side of the equation in the calculation formula is as follows:
Figure BDA00027178045700000426
Figure BDA0002717804570000051
Figure BDA0002717804570000052
in the formula (6), the reaction mixture is,
Figure BDA0002717804570000053
respectively the punishment unit price of the actual demand capacity higher than or lower than the scalar quantity in the energy market; in the formula (7), the reaction mixture is,
Figure BDA0002717804570000054
the calling rate of the medium frequency modulation capacity in the scene i in the t time period is obtained;
Figure BDA0002717804570000055
the penalty price mean value when the actual call capacity is not satisfied (assuming that the actual call capacity is uniformly distributed in the medium scalar range); the symbols in the formula (8) have meanings similar to those in the formula (7) and correspond to the parameters of the frequency modulation.
The constraint conditions of the stochastic programming model comprise capacity constraints of the photovoltaic in an energy market and a frequency modulation auxiliary service market, upper and lower frequency modulation capacity constraints which can be contributed by the photovoltaic under the virtual energy storage characteristic of the air conditioner load, total frequency modulation capacity constraints of a virtual power plant, expected response deviation constraints of the virtual power plant participating in the energy market and maximum frequency modulation capacity deviation constraints which cannot be met by the virtual power plant participating in the frequency modulation market.
The uncertainty of photovoltaic output is considered, and an auxiliary decision variable is introduced
Figure BDA0002717804570000056
Respectively, the photovoltaic output is predicted to have upper and lower limits, and the competitive bidding amount of the photovoltaic in the energy market and the frequency modulation auxiliary service market is constrained as follows:
Figure BDA0002717804570000057
Figure BDA0002717804570000058
Figure BDA0002717804570000059
the value ranges of the upper and lower frequency modulation capacities which can be contributed under the virtual energy storage characteristic of the air conditioner load are as follows:
Figure BDA00027178045700000510
Figure BDA00027178045700000511
wherein alpha isVESAnd the response probability of the virtual energy storage frequency modulation signal.
The total frequency modulation capacity of the virtual power plant is the sum of frequency modulation capacities provided by roof photovoltaic and air conditioner load virtual energy storage:
Figure BDA00027178045700000512
Figure BDA00027178045700000513
deviation of expected response of virtual power plant participating in energy market by considering calling rate of frequency modulation market
Figure BDA00027178045700000514
And
Figure BDA00027178045700000515
the linearization can be expressed in the optimization model as:
Figure BDA00027178045700000516
Figure BDA00027178045700000517
wherein L istThe load base value of the virtual power plant is in the period t; pPV,i,tIs the actual power of the photovoltaic in scene i during time t.
The maximum deviation of the capacity for frequency modulation that cannot be met by the virtual power plant participating in the frequency modulation market can be expressed as the following constraint:
Figure BDA0002717804570000061
Figure BDA0002717804570000062
it is noted that, since the accuracy of the load prediction is higher than that of the photovoltaic output prediction, the method takes the error of the load prediction into the photovoltaic prediction error and does not need to be considered separately.
Step 2: and obtaining different scenes and probabilities of the day-ahead market price and the predicted photovoltaic output by adopting a time-homogeneous Markov chain (time-homogeneous Markov chain).
And step 3: solving the stochastic programming model based on the day-ahead market price, the different scenarios, and the probabilities thereof.
And 4, step 4: and controlling the running state of the intelligent building type virtual power plant according to the solved result.
In the embodiment, a certain modern commercial district is taken as an example to evaluate the effectiveness of the intelligent building type virtual power plant participating in the competitive bidding of the energy-frequency modulation market and realizing energy control at the same time. The load base line of a commercial district in summer and the virtual energy storage charging and discharging capacity of the air conditioner load in each period calculated according to the outdoor temperature and the air conditioner power are shown in fig. 2. The cost of unit frequency modulation capacity of virtual energy storage is 0.0015USD/kW, and the frequency modulation response rate is 80%. The installation capacity of the roof photovoltaic is 4MW, the marginal cost of power generation is 0.005USD/kWh, and the cost of participating in frequency modulation is 0.002 USD/kW. The successful bid probability of the virtual power plant participating in the frequency modulation auxiliary service market is 0.6, and the final called probability of the frequency modulation capacity is 0.7.
The bidding capacities of the intelligent building type virtual power plant participating in the upper and lower frequency modulation markets and the composition relationship thereof obtained by solving the data are shown in fig. 3. A scalar comparison of the energy market when the virtual power plant is simultaneously participating in the energy-frequency modulated market (Case 1) versus the energy market when it is only participating in the energy market (Case 2) is given by fig. 4.
As can be seen from fig. 3, due to the temporal correlation between the photovoltaic output and the air conditioning load, the bidding period of the virtual power plant participating in the frequency modulation auxiliary service market is mainly concentrated in the daytime. Meanwhile, the difference between the upper frequency modulation capacity and the lower frequency modulation capacity provided by the air conditioner load virtual energy storage is not large, and the photovoltaic can selectively provide a frequency modulation capacity in the same time period. In addition, the total capacity of the lower frequency modulation provided by the photovoltaic all day is obviously larger than that of the upper frequency modulation, which is mainly because the opportunity cost generated by light abandoning when the photovoltaic provides the lower frequency modulation is generally lower than the opportunity cost generated by the upper frequency modulation provided by the photovoltaic and cannot obtain the energy market income, and even if the frequency modulation market is out of date, the loss caused by the incapability of participating in the energy market can be considered.
As can be easily seen from fig. 4, when the virtual power plant adopts a bidding strategy of participating in both the energy and frequency modulation markets, the electric quantity higher than the actual demand is purchased from the energy market at a time of a large photovoltaic output in the middle of the day to meet the demand of providing the frequency modulation capacity, which shows that the virtual power plant has a higher enthusiasm for participating in the frequency modulation auxiliary service market under the condition of having the same resource, and thus the economic benefit of the virtual power plant can be further increased. However, if the actual response index of the virtual power plant participating in the frequency modulation market is poor, which leads to the reduction of the capacity medium-to-standard ratio in the frequency modulation market, a completely different result occurs.
The above analysis contrasts the different behaviors of the virtual power plant when participating in the energy market under both conditions. To further illustrate the policy advantage of the intelligent building type virtual power plant participating in the energy and frequency modulation markets simultaneously when the air conditioner load virtual energy storage is considered, the embodiment compares the expected operating costs of the virtual power plant under three conditions, as shown in table 1. Wherein, Case 1 is the proposal provided by the invention, and considers the air-conditioning load virtual energy storage and participates in the energy-frequency modulation market at the same time; the virtual power plant in Case 2 also participates in the energy-frequency modulation market at the same time, but does not consider the virtual energy storage of the air conditioner load; the virtual power plant in Case 3 only participates in the energy market and does not consider virtual energy storage.
TABLE 1 comparison of virtual plant operating costs under different market strategies
Case 1 2 3
Expected cost/USD 1514.62 2026.74 4085.01
As can be seen from the table, the expected operation cost obtained by adopting the virtual power plant optimization control method provided by the invention is reduced by about 63% compared with the expected operation cost obtained by only participating in the energy market in the virtual power plant, and the fact that the virtual power plant utilizes the adjustability of resources to participate in the frequency modulation auxiliary service market has obvious economic benefit. Meanwhile, the economic advantage that the virtual power plant participates in the frequency modulation market can be further expanded by providing the frequency modulation capacity by utilizing the virtual energy storage characteristic of the air conditioner load.
Example 2
This embodiment provides a control system that intelligent building type virtual power plant participated in energy frequency modulation, intelligent building type virtual power plant includes photovoltaic and the virtual energy storage of air conditioner load, and this system includes: the model building module is used for building a random planning model with the minimum daily operation cost as a target; the data preparation module is used for obtaining different scenes and probabilities of the day-ahead market price and the predicted photovoltaic output by adopting a Simultaneous Markov chain; the solving module is used for solving the stochastic programming model based on the day-ahead market price, different scenes and the probability of the scenes; and the control module is used for controlling the running state of the intelligent building type virtual power plant according to the solving result.
The rest is the same as example 1.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (10)

1. A control method for an intelligent building type virtual power plant to participate in energy frequency modulation is characterized in that the intelligent building type virtual power plant comprises photovoltaic and air conditioner load virtual energy storage, and the method comprises the following steps:
establishing a random planning model with the minimum daily operation cost as a target;
different scenes and probabilities of the day-ahead market price and the predicted photovoltaic output are obtained by adopting a time-aligned Markov chain;
solving the stochastic programming model based on the day-ahead market price, different scenes and the probability thereof;
and controlling the running state of the intelligent building type virtual power plant according to the solved result.
2. The method for controlling intelligent building-type virtual power plant to participate in energy frequency modulation according to claim 1, wherein the objective function of the stochastic programming model is as follows:
Figure FDA0002717804560000011
wherein, I is the total scene number selected by random planning; omegaiProbability of scene i; f. ofi mkParticipating in the total trading cost of the energy and frequency modulation market for the virtual power plant under the scene i; f. ofi penPunishment cost of the virtual power plant failing to respond to the market scheduling signal under the scene i; f. ofPVAnd fVESThe expected regulation and control costs of the photovoltaic and the virtual energy storage due to participation in the virtual power plant are respectively.
3. The method as claimed in claim 2, wherein the virtual power plant participates in energy frequency modulation under the scenario i, and the total transaction cost f of the virtual power plant participating in energy and frequency modulation market isi mkThe calculation formula of (2) is as follows:
Figure FDA0002717804560000012
wherein the content of the first and second substances,
Figure FDA0002717804560000013
the price of the energy market in the scene i in the time period t;
Figure FDA0002717804560000014
capacity prices of the frequency modulation market and the frequency modulation market at the time t in the scene i are respectively set; pt eDelta T is the competitive bidding amount of the virtual power plant in the energy market at the time period T; pt ru、Pt rdRespectively competitive upper and lower capacity of the virtual power plant in the frequency modulation market in the t time period;
Figure FDA0002717804560000015
and respectively the bid winning probability of the virtual power plant in the upper and lower frequency modulation markets in the t period in the scene i.
4. The method as claimed in claim 2, wherein the penalty cost f of the virtual power plant failing to respond to the market scheduling signal under the scenario i isi penThe calculation formula of (2) is as follows:
fi pen＝fi pe+fi pru+fi prd
wherein f isi pePenalty cost, f, for virtual plant participation in expected response of energy marketi pruPenalty cost, f, for virtual power plant participation in call volume shortage in frequency modulation marketi prdAnd (4) the expected penalty cost of the frequency-down market for the virtual power plant to participate in.
5. The method as claimed in claim 2, wherein the expected regulation cost f of the photovoltaic due to participation in the virtual power plantPVThe calculation formula of (2) is as follows:
Figure FDA0002717804560000021
wherein the content of the first and second substances,
Figure FDA0002717804560000022
marginal costs of providing frequency modulation service and energy supply for the photovoltaic cells respectively;
Figure FDA0002717804560000023
and
Figure FDA0002717804560000024
and (4) capacity of photovoltaic participation frequency modulation and energy market decided for the virtual power plant respectively.
6. The method as claimed in claim 2, wherein the virtual energy storage has an expected regulation cost f due to participation in the virtual power plantVESThe calculation formula of (2) is as follows:
Figure FDA0002717804560000025
wherein the content of the first and second substances,
Figure FDA0002717804560000026
marginal cost of providing frequency modulation service for virtual energy storage;
Figure FDA0002717804560000027
and
Figure FDA0002717804560000028
and (4) capacity of photovoltaic participating in frequency modulation market for virtual power plant decision.
7. The method as claimed in claim 1, wherein the constraint conditions of the stochastic programming model include capacity constraints of the energy market and the frequency modulation auxiliary service market, upper and lower frequency modulation capacity constraints that can be contributed by the photovoltaic in the virtual energy storage characteristic of the air conditioning load, total frequency modulation capacity constraints of the virtual power plant, expected response deviation constraints of the virtual power plant participating in the energy market, and maximum frequency modulation capacity deviation constraints that cannot be satisfied by the virtual power plant participating in the frequency modulation market.
8. The utility model provides a control system that intelligent building type virtual power plant participated in energy frequency modulation, a serial communication port, intelligent building type virtual power plant includes photovoltaic and the virtual energy storage of air conditioner load, and this system includes:
the model building module is used for building a random planning model with the minimum daily operation cost as a target;
the data preparation module is used for obtaining different scenes and probabilities of the day-ahead market price and the predicted photovoltaic output by adopting a time-aligned Markov chain;
the solving module is used for solving the stochastic programming model based on the day-ahead market price, different scenes and the probability of the scenes;
and the control module is used for controlling the running state of the intelligent building type virtual power plant according to the solving result.
9. A computer device, comprising:
a processor;
a memory storing processor-executable instructions;
wherein the processor is coupled to the memory for reading program instructions stored by the memory and, in response, performing the steps of the method of any of claims 1-7.
10. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
CN202011077665.4A 2020-10-10 2020-10-10 Control method and system for intelligent building type virtual power plant to participate in energy frequency modulation Pending CN112215641A (en)

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