CN112215641B - Control method and system for participating in energy frequency modulation of intelligent building type virtual power plant - Google Patents
Control method and system for participating in energy frequency modulation of intelligent building type virtual power plant Download PDFInfo
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Abstract
The invention relates to a control method and a system for participating in energy frequency modulation of an intelligent building type virtual power plant, 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 of market price and predicted photovoltaic output before the day and probability thereof are obtained by adopting a time-aligned Markov chain; solving the random programming model based on the market price before the day, different scenes and probability thereof; and controlling the running state of the intelligent building type virtual power plant according to the solving result. Compared with the prior art, the invention has the advantages of high stability, high control flexibility and the like.
Description
Technical Field
The invention relates to a power grid control method, in particular to a control method and a control system for participating in energy frequency modulation of an intelligent building type virtual power plant.
Background
Under the background of the strong development of distributed renewable energy and load control technology, virtual power plants become main media for the demand side resources to participate in the regulation of the electric power market. The virtual power plant effectively reduces the power grid dispatching burden by effectively aggregating the distributed resources, thereby realizing the win-win of the power system. The intelligent building is used as a special virtual power plant comprising distributed power sources and controllable loads, and has higher research value. At present, the research of participating in the energy-frequency modulation market at the same time of a virtual power plant, especially a building type virtual power plant is relatively lacking.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a control method and a control system for participating in energy frequency modulation of an intelligent building type virtual power plant, wherein the control method and the control system have high stability and high modulation and control flexibility.
The aim of the invention can be achieved by the following technical scheme:
the intelligent building type virtual power plant comprises photovoltaic and air conditioner load virtual energy storage, and the method comprises the following steps of:
Establishing a random planning model with the minimum daily operation cost as a target;
Different scenes of market price and predicted photovoltaic output before the day and probability thereof are obtained by adopting a time-aligned Markov chain;
Solving the random programming model based on the market price before the day, different scenes and probability thereof;
And controlling the running state of the intelligent building type virtual power plant according to the solving result.
Further, the objective function of the stochastic programming model is:
Wherein I is the total scene number selected by random planning; omega i is the probability of scene i; The method comprises the steps that the total transaction cost of the virtual power plant participating in the energy and frequency modulation market in a scene i is calculated; /(I) Penalty cost for virtual power plants failing to respond to market dispatch signals in scenario i; f PV and f VES are the expected regulatory costs of photovoltaic and virtual energy storage, respectively, from participating in a virtual power plant.
Further, the virtual power plant participates in the total transaction cost of energy and frequency modulation markets in the scene iThe calculation formula of (2) is as follows:
Wherein, The price of the energy market in scene i at time t; /(I)The capacity prices of the up-frequency modulation market and the down-frequency modulation market in the t period in the scene i are respectively; /(I)The competitive bidding quantity of the virtual power plant in the energy market is t time periods; /(I) The capacity of the virtual power plant in the frequency modulation market bidding is respectively adjusted up and down in the period t; /(I)And respectively determining winning probabilities of the up-down frequency modulation market of the virtual power plant in the scene i at the t period.
Further, the penalty cost of the virtual power plant failing to respond to the market dispatch signal in scenario iThe calculation formula of (2) is as follows:
Wherein, Penalty cost for the expected response of a virtual power plant to participate in the energy market,/>Penalty cost for insufficient invocations of virtual power plants to participate in the frequency modulated market,/>The cost is punished for the virtual power plant to participate in the expectations of the down-frequency market.
Further, the calculation formula of the expected regulation cost f PV generated by participating in the virtual power plant is as follows:
Wherein, Marginal cost of providing frequency modulation service and energy supply for the photovoltaic respectively; /(I)And/>Photovoltaic, which is a virtual power plant decision, participates in the capacity of the frequency modulation and energy market, respectively.
Further, the calculation formula of the expected regulation cost f VES generated by the participation of the virtual energy storage in the virtual power plant is as follows:
Wherein, Providing marginal cost of frequency modulation service for virtual energy storage; /(I)And/>Photovoltaic, which is a decision for virtual power plants, participates in the capacity of the frequency modulation market.
Further, constraint conditions of the stochastic programming model include capacity constraint of the photovoltaic in an energy market and a frequency modulation auxiliary service market, up-down frequency modulation capacity constraint which can be contributed by the air conditioner load under virtual energy storage characteristics, total frequency modulation capacity constraint of the virtual power plant, expected response deviation constraint of the virtual power plant participating in the energy market and maximum frequency modulation capacity deviation constraint which cannot be met by the virtual power plant participating in the frequency modulation market.
The invention also provides a control system for participating in energy frequency modulation of the intelligent building type virtual power plant, wherein the intelligent building type virtual power plant comprises photovoltaic and air conditioner load virtual energy storage, and the system comprises:
the model construction 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 of market price and predicted photovoltaic output in the future and probability thereof by adopting a time-aligned Markov chain;
The solving module is used for solving the random programming model based on the market price before the day, different scenes and the probability thereof;
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 device 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 having stored thereon a computer program which, when executed by a processor, implements the steps of the method described above.
Compared with the prior art, the invention has the following beneficial effects:
According to the invention, the virtual power plant participates in capacity control of the energy-frequency modulation market at the same time, so that the stability of the power network is effectively improved, and compared with the independent participation of the virtual power plant in the energy market, the virtual energy storage of the air conditioner load has obvious economic advantages, and more regulation and control flexibility is provided for the virtual power plant.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a graph of virtual power plant load baseline versus virtual energy storage charge-discharge capacity;
FIG. 3 is a graph of bidding capacities of virtual power plants participating in a frequency modulation market for each period;
FIG. 4 is a graph comparing energy market bids for virtual power plants under two strategies.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present example is implemented on the premise of the technical scheme of the present invention, and a detailed implementation and a specific operation procedure are given, but the scope of protection of the present invention is not limited to the following examples.
Example 1
The embodiment provides a control method for participating in energy frequency modulation of an intelligent building type virtual power plant, wherein the intelligent building type virtual power plant comprises photovoltaic and air conditioner load virtual energy storage, as shown in fig. 1, and the method comprises the following steps:
Step 1: and establishing a random programming model aiming at the minimum daily operation cost.
The objective function of the stochastic programming model is:
Wherein I is the total scene number selected by random planning; omega i is the probability of scene i; The method comprises the steps that the total transaction cost of the virtual power plant participating in the energy and frequency modulation market in a scene i is calculated; /(I) Penalty cost for virtual power plants failing to respond to market dispatch signals in scenario i; f PV and f VES are the expected regulatory costs of photovoltaic and virtual energy storage, respectively, from participating in a virtual power plant. The specific expression of each cost is as follows:
total transaction cost of virtual power plants participating in energy and frequency modulation markets in scenario i The calculation formula of (2) is as follows:
Wherein, The price of the energy market in scene i at time t; /(I)The capacity prices of the up-frequency modulation market and the down-frequency modulation market in the t period in the scene i are respectively; /(I)The competitive bidding quantity of the virtual power plant in the energy market is t time periods; /(I) The capacity of the virtual power plant in the frequency modulation market bidding is respectively adjusted up and down in the period t; /(I)And respectively determining winning probabilities of the up-down frequency modulation market of the virtual power plant in the scene i at the t period.
Penalty cost for virtual power plants failing to respond to market dispatch signals in scenario iThe calculation formula of (2) is as follows:
Wherein, Penalty cost for the expected response of a virtual power plant to participate in the energy market,/>Penalty cost for insufficient invocations of virtual power plants to participate in the frequency modulated market,/>The cost is punished for the virtual power plant to participate in the expectations of the down-frequency market.
The calculation formula of the expected regulation cost f PV generated by the participation of the photovoltaic in the virtual power plant is as follows:
Wherein, Marginal cost of providing frequency modulation service and energy supply for the photovoltaic respectively; /(I)And/>Photovoltaic, which is a virtual power plant decision, participates in the capacity of the frequency modulation and energy market, respectively.
The calculation formula of the expected regulation cost f VES generated by the participation of the virtual energy storage in the virtual power plant is as follows:
Wherein, Providing marginal cost of frequency modulation service for virtual energy storage; /(I)And/>Photovoltaic, which is a decision for virtual power plants, participates in the capacity of the frequency modulation market.
The three penalty cost specific acquisition formulas on the right of the equation in the calculation formulas of (a) are as follows:
In the formula (6), the amino acid sequence of the compound, Penalty unit price of the actual electricity demand is higher than or lower than scalar in the energy market respectively; in formula (7)/>The calling rate of the up-regulation capacity in the period t in the scene i is set; /(I)The penalty price average value when the actual calling capacity is not satisfied (the actual calling capacity is assumed to be uniformly distributed in the middle label range); the meaning of each symbol in the formula (8) is similar to that of the formula (7), and each parameter of the frequency modulation corresponds to each parameter.
Constraint conditions of the stochastic programming model include capacity constraint of the energy market and the frequency modulation auxiliary service market, up-and-down frequency modulation capacity constraint which can be contributed by the photovoltaic under the virtual energy storage characteristic of the air conditioner load, total frequency modulation capacity constraint of the virtual power plant, expected response deviation constraint of the virtual power plant participating in the energy market and maximum frequency modulation capacity deviation constraint which cannot be met by the virtual power plant participating in the frequency modulation market.
Taking uncertainty of photovoltaic output into consideration, and introducing auxiliary decision variablesThe upper limit and the lower limit of the photovoltaic output prediction are respectively set, and bidding quantity constraint of the photovoltaic in the energy market and the frequency modulation auxiliary service market is as follows:
the upper and lower frequency modulation capacity value range which can be contributed under the virtual energy storage characteristic of the air conditioner load is as follows:
Wherein, alpha VES is 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 the frequency modulation capacity provided by the roof photovoltaic and air conditioning load virtual energy storage:
Considering the calling rate of the frequency modulation market, the expected response deviation of the virtual power plant participating in the energy market And/>The linearization in the optimization model can be expressed as:
Wherein L t is the load base value of the virtual power plant in the t period; p PV,i,t is the actual power of the photovoltaic in scenario i over period t.
The maximum fm capacity deviation that cannot be met by a virtual power plant participating in the fm market can be expressed as the following constraint:
notably, since the accuracy of load prediction is higher than the accuracy of photovoltaic output prediction, the method incorporates the error of load prediction into the photovoltaic prediction error, and is not considered separately.
Step 2: different scenes of day-ahead market price and predicted photovoltaic output and probabilities thereof are obtained by adopting a time-aligned Markov chain (time-homogeneous Markov chain).
Step 3: and solving the random programming model based on the market price before the day, different scenes and the probability thereof.
Step 4: and controlling the running state of the intelligent building type virtual power plant according to the solving result.
In the embodiment, a modern commercial area is taken as an example to evaluate the effectiveness of the intelligent building type virtual power plant in participating in energy-frequency modulation market bidding and realizing energy control. The load baseline of the business area in summer and the virtual energy storage charge and discharge capacity of the air conditioner load in each period calculated according to the outdoor temperature and the air conditioner power are shown in figure 2. The unit frequency modulation capacity cost of the 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 power generation marginal cost is 0.005USD/kWh, and the participation frequency modulation cost is 0.002USD/kW. The probability of participation of the virtual power plant in the frequency modulation auxiliary service market is 0.6, and the probability of the final invoking of the frequency modulation capacity is 0.7.
The bidding capacities of the intelligent building type virtual power plant which is obtained by solving the data and participates in the up-down frequency modulation market and the composition relation of the bidding capacities are shown in figure 3. The energy market bid amount comparison of a virtual power plant participating in the energy-frequency modulated market (Case 1) simultaneously with the energy market only (Case 2) is given by fig. 4.
As can be seen from fig. 3, the bidding period of the virtual power plant participating in the fm auxiliary service market is mainly focused on the daytime due to the correlation of the photovoltaic output and the air conditioning load over time. Meanwhile, the upper frequency modulation capacity and the lower frequency modulation capacity provided by the air conditioner load virtual energy storage are not greatly different, and the photovoltaic can selectively provide one frequency modulation capacity in the same period. In addition, the total capacity of the down-frequency modulation provided by the photovoltaic all day is obviously larger than the total capacity of the up-frequency modulation, which is mainly because the opportunity cost generated by light rejection when the photovoltaic provides the down-frequency modulation is smaller than the opportunity cost that the photovoltaic provides the up-frequency modulation and cannot acquire the benefit of the energy market, even if the frequency modulation market is clear, the loss caused by the failure of participating in the energy market can be considered.
As can be easily seen from fig. 4, when the virtual power plant adopts bidding strategies of participating in energy and frequency modulation markets at the same time, electricity higher than actual demand can be purchased from the energy market in a period of larger photovoltaic output in the middle of the day so as to meet the requirement of providing frequency modulation capacity, which means that the polarity of the virtual power plant participating in the frequency modulation auxiliary service market is higher under the condition of having the same resource, thereby further expanding the economic benefit of the virtual power plant. However, if the actual response index of the virtual power plant participating in the frequency modulation market is poor, resulting in a decrease in the capacity percentage in the frequency modulation market, a completely different result will occur.
The above analysis compares the different behaviors of the virtual power plant when participating in the energy market in both cases. To further illustrate the strategic advantages of the intelligent building type virtual power plant in considering the air conditioning load virtual energy storage while participating in the energy, frequency modulated market, the present embodiment compares the expected operating costs of the virtual power plant for three situations, as shown in table 1. The Case 1 is a scheme provided by the invention, considers the virtual energy storage of the air conditioner load, 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 Power plant operating costs under different market policies
| 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 the virtual power plant when the virtual power plant only participates in the energy market, and the fact that the adjustability of the utilization resources of the virtual power plant participates in the frequency modulation auxiliary service market has obvious economic benefit is demonstrated. And simultaneously, the virtual energy storage characteristic of the air conditioner load is utilized to provide the frequency modulation capacity, so that the economic advantage of the virtual power plant in participating in the frequency modulation market can be further enlarged.
Example 2
The embodiment provides a control system for participating in energy frequency modulation of an intelligent building type virtual power plant, wherein the intelligent building type virtual power plant comprises photovoltaic and air conditioner load virtual energy storage, and the system comprises: the model construction module is used for constructing a random planning model with the minimum daily operation cost as a target; the data preparation module is used for obtaining different scenes of the market price before the day and the predicted photovoltaic output and the probability thereof by adopting a time alignment Ma Erke Fu chain; the solving module is used for solving the random programming model based on the market price before the day, different scenes and the probability thereof; 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 procedure is as in example 1.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (4)
1. The control method for participating in energy frequency modulation of the intelligent building type virtual power plant 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 of market price and predicted photovoltaic output before the day and probability thereof are obtained by adopting a time-aligned Markov chain;
Solving the random programming model based on the market price before the day, different scenes and probability thereof;
controlling the running state of the intelligent building type virtual power plant according to the solving result;
The objective function of the stochastic programming model is:
Wherein I is the total scene number selected by random planning; omega i is the probability of scene i; f i mk is the total transaction cost of the virtual power plant participating in the energy and frequency modulation market in scene i; f i pen is penalty cost for virtual power plants failing to respond to market dispatch signals in scenario i; f PV and f VES are respectively expected regulation and control costs of photovoltaic and virtual energy storage due to participation in a virtual power plant;
The calculation formula of the total transaction cost f i mk of the virtual power plant participating in the energy and frequency modulation market in the scene i is as follows:
Wherein, The price of the energy market in scene i at time t; /(I)The capacity prices of the up-frequency modulation market and the down-frequency modulation market in the t period in the scene i are respectively; p t e delta T is the bidding quantity of the virtual power plant in the energy market in the T period; p t ru、Pt rd is the up-and-down capacity of the virtual power plant in the frequency modulation market bidding at the t period respectively; /(I)The probability of winning a bid of the virtual power plant in the scene i at the time period of up and down frequency modulation market is respectively calculated;
The calculation formula of the penalty cost f i pen that the virtual power plant fails to respond to the market dispatching signal in the scene i is as follows:
fi pen＝fi pe+fi pru+fi prd
Wherein f i pe is the expected response penalty cost of the virtual power plant participating in the energy market, f i pru is the insufficient call volume penalty cost of the virtual power plant participating in the frequency up market, and f i prd is the expected penalty cost of the virtual power plant participating in the frequency down market;
the calculation formula of the expected regulation cost f PV generated by the participation of the photovoltaic in the virtual power plant is as follows:
Wherein, Marginal cost of providing frequency modulation service and energy supply for the photovoltaic respectively; /(I)And/>The capacity of the photovoltaic participation frequency modulation and energy market which are respectively decided by the virtual power plant;
The calculation formula of the expected regulation cost f VES generated by the participation of the virtual energy storage in the virtual power plant is as follows:
Wherein, Providing marginal cost of frequency modulation service for virtual energy storage; /(I)And/>The capacity of the photovoltaic participation frequency modulation market for virtual power plant decision-making;
Constraint conditions of the stochastic programming model comprise capacity constraint of the energy market and the frequency modulation auxiliary service market, up-down frequency modulation capacity constraint which can be contributed by the photovoltaic under the virtual energy storage characteristic of the air conditioner load, total frequency modulation capacity constraint of the virtual power plant, expected response deviation constraint of the virtual power plant participating in the energy market and maximum frequency modulation capacity deviation constraint which cannot be met by the virtual power plant participating in the frequency modulation market.
2. The utility model provides a virtual power plant participation energy frequency modulation's of intelligent building control system which characterized in that, the virtual power plant of intelligent building includes photovoltaic and the virtual energy storage of air conditioner load, and this system includes:
the model construction 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 of the market price before the day and the predicted photovoltaic output and the probability of the different scenes by adopting a time-sharing Markov chain;
The solving module is used for solving the random programming model based on the market price before the day, different scenes and the probability thereof;
the control module is used for controlling the running state of the intelligent building type virtual power plant according to the solving result;
The objective function of the stochastic programming model is:
Wherein I is the total scene number selected by random planning; omega i is the probability of scene i; f i mk is the total transaction cost of the virtual power plant participating in the energy and frequency modulation market in scene i; f i pen is penalty cost for virtual power plants failing to respond to market dispatch signals in scenario i; f PV and f VES are respectively expected regulation and control costs of photovoltaic and virtual energy storage due to participation in a virtual power plant;
The calculation formula of the total transaction cost f i mk of the virtual power plant participating in the energy and frequency modulation market in the scene i is as follows:
Wherein, The price of the energy market in scene i at time t; /(I)The capacity prices of the up-frequency modulation market and the down-frequency modulation market in the t period in the scene i are respectively; p t e delta T is the bidding quantity of the virtual power plant in the energy market in the T period; p t ru、Pt rd is the up-and-down capacity of the virtual power plant in the frequency modulation market bidding at the t period respectively; /(I)The probability of winning a bid of the virtual power plant in the scene i at the time period of up and down frequency modulation market is respectively calculated;
The calculation formula of the penalty cost f i pen that the virtual power plant fails to respond to the market dispatching signal in the scene i is as follows:
fi pen＝fi pe+fi pru+fi prd
Wherein f i pe is the expected response penalty cost of the virtual power plant participating in the energy market, f i pru is the insufficient call volume penalty cost of the virtual power plant participating in the frequency up market, and f i prd is the expected penalty cost of the virtual power plant participating in the frequency down market;
the calculation formula of the expected regulation cost f PV generated by the participation of the photovoltaic in the virtual power plant is as follows:
Wherein, Marginal cost of providing frequency modulation service and energy supply for the photovoltaic respectively; /(I)And/>The capacity of the photovoltaic participation frequency modulation and energy market which are respectively decided by the virtual power plant;
The calculation formula of the expected regulation cost f VES generated by the participation of the virtual energy storage in the virtual power plant is as follows:
Wherein, Providing marginal cost of frequency modulation service for virtual energy storage; /(I)And/>The capacity of the photovoltaic participation frequency modulation market for virtual power plant decision-making;
Constraint conditions of the stochastic programming model comprise capacity constraint of the energy market and the frequency modulation auxiliary service market, up-down frequency modulation capacity constraint which can be contributed by the photovoltaic under the virtual energy storage characteristic of the air conditioner load, total frequency modulation capacity constraint of the virtual power plant, expected response deviation constraint of the virtual power plant participating in the energy market and maximum frequency modulation capacity deviation constraint which cannot be met by the virtual power plant participating in the frequency modulation market.
3. A computer device, comprising:
A processor;
A memory storing processor-executable instructions;
Wherein the processor is coupled to the memory for reading the program instructions stored in the memory and, in response, performing the steps of the method of claim 1.
4. A computer readable medium on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to claim 1.
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