Virtual power plant control method and device under peak regulation-carbon market coupling

Abstract

The invention relates to a virtual power plant control method and device under peak regulation-carbon market coupling. The method comprises the following steps: S1, acquiring wind power data, gas turbine data and electricity price data of a virtual power plant in different scenes; s2, according to preset constraint data, the wind power data, the gas turbine data and the electricity price data, constructing a peak regulation-carbon market coupled virtual power plant scheduling model; and S3, solving the virtual power plant scheduling model to obtain an operation strategy of the virtual power plant. Clean energy represented by a wind turbine generator and a traditional unit represented by a gas turbine are aggregated to participate in the electric power peak regulation market and the carbon market at the same time. The benefit maximization of the virtual power plant under the condition of participating in the carbon market and the peak regulation market at the same time can be supported; in addition, the method also supports the virtual power plant to reduce the carbon emission to the minimum under the condition of ensuring a certain income. Therefore, the regulation and control requirements of the virtual power plant are met.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to a virtual power plant control method and device under peak load regulation-carbon market coupling. Background Art

[0002] Under the new situation of the advancement of power market reform and the increasingly improved carbon market construction, the market environment faced by virtual power plants will also undergo tremendous changes. In the future electricity-carbon joint market environment, the operation strategy of virtual power plants needs to change. At present, domestic virtual power plants mainly participate in the power peaking market. With the booming development of carbon markets in various places, virtual power plant users will face carbon quota constraint assessments and participate in carbon market transactions at the same time.

[0003] At present, there are two main problems with the operation strategy of virtual power plants: First, the existing virtual power plant solutions aggregate distributed resources in a relatively simple manner. For example, some virtual power plants only aggregate adjustable loads to participate in demand response. Second, the existing virtual power plant solutions do not take carbon market constraints into consideration: whether it is a traditional unit or a new energy unit, its operation is bound to be subject to carbon market constraints.

[0004] Chinese patent application publication number CN112257903A discloses a control method and device for a virtual power plant. Aiming at the optimization control problem of a virtual power plant, a virtual power plant scheduling model is established to provide theoretical guidance for improving the value of virtual power plants and optimizing the behavioral decision-making of virtual power plant scheduling. However, the above application does not take carbon market constraints into consideration.

[0005] In summary, there is currently a lack of a virtual power plant control method to solve or partially solve the aforementioned problems. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a virtual power plant control method and device under peak load regulation-carbon market coupling, so as to solve or partially solve the problem of single aggregation of distributed resources in existing virtual power plant operation schemes.

[0007] One aspect of the present invention provides a virtual power plant control method under peak load regulation and carbon market coupling, which realizes the maximization of the benefits of the virtual power plant when participating in the carbon market and the peak load regulation market at the same time, and includes the following steps:

[0008] Step S1, obtaining wind power data, gas turbine data and electricity price data of the virtual power plant in different scenarios;

[0009] Step S2, constructing a peak load-carbon market coupled virtual power plant dispatching model according to preset constraint data, the wind power data, the gas turbine data and the electricity price data;

[0010] Step S3, obtaining the operation strategy of the virtual power plant by solving the virtual power plant scheduling model.

[0011] As a preferred technical solution, in step S2, the process of constructing a peak load regulation-carbon market coupled virtual power plant dispatching model includes the following steps:

[0012] Step S201, obtaining the objective function of the virtual power plant;

[0013] Step S202, construct wind turbine constraints that take into account wind turbine uncertainties, gas turbine constraints, virtual power plant participation in peak load regulation constraints, virtual power plant participation in carbon market constraints, and power constraints.

[0014] As a preferred technical solution, in step S201, the objective function of the virtual power plant is:

[0015]

[0016] Among them, P is the value of the virtual power plant, R F is the value of virtual power plant participating in peak load regulation, R CM is the value of virtual power plants participating in the carbon market, C qd is the cost of power curtailment of the virtual power plant, C MT is the gas turbine cost, P t G and They respectively represent the power purchased by the virtual power plant from the power grid at time t and the power purchase price.

[0017] As a preferred technical solution, the wind turbine constraints considering the uncertainty of the wind turbine are:

[0018]

[0019] P t W,min ≤P t W ≤P t W,max

[0020] Where v is the actual wind speed, c and k are the scale parameter and shape parameter respectively, v n Indicates rated wind speed, v in and v out Respectively represent the cut-in and cut-out wind speeds, P * and P t W Represents rated power and output power respectively, P t W,min , P t W,max are the minimum output power and the maximum output power respectively.

[0021] As a preferred technical solution, the benefits of the virtual power plant participating in peak load regulation are:

[0022]

[0023] in, is the flexible peak-shaving price at time t, is the peak load capacity at time t, is the baseline capacity at time t, is the peak shift penalty coefficient, are gas turbine output power, wind power output power and purchased power respectively, R F The value of participating in power peak regulation.

[0024] As a preferred technical solution, the benefits of the virtual power plant participating in the carbon market are:

[0025]

[0026] Among them, c CM is the carbon quota price, x a1 Issuing carbon quota factor for gas turbines, x a2 Apply for CCER for wind turbines to obtain carbon offset quota coefficient, x e is the carbon emission coefficient of the gas turbine, R CM To participate in the value of carbon market, They are gas turbine output power and wind power output power respectively, and C represents the overall carbon emissions of virtual operations.

[0027] As a preferred technical solution, the power constraint is:

[0028]

[0029] in, are gas turbine output power, wind power output power and purchased power respectively. is the peak load capacity at time t.

[0030] As a preferred technical solution, the gas turbine constraints are:

[0031]

[0032] Among them, P MT,max and P MT,min They represent the upper and lower limits of gas turbine output respectively, and They represent the ramp rate and ramp rate of the gas turbine respectively. is a Boolean variable, indicating the power generation status of the gas turbine, 1 for operation and 0 for shutdown. and Respectively represent the continuous power generation and shutdown time of the gas turbine, M on and M off Respectively represent the minimum continuous power generation and shutdown time of the unit, P t MT is the power generation capacity of the gas turbine.

[0033] Another aspect of the present invention provides another virtual power plant control method under peak load regulation-carbon market coupling. This method can reduce carbon emissions to a minimum while ensuring a certain income of the virtual power plant, so as to meet the regulation and control needs of the virtual power plant. The difference from the above method is that in step S201, the objective function is:

[0034] minC

[0035] Among them, C represents the overall carbon emissions of virtual operations.

[0036] The step S202 also includes a virtual power plant revenue constraint:

[0037] P>P 0

[0038]

[0039] Among them, P is the value of the virtual power plant, P 0 represents the minimum profit of the virtual power plant, R F is the value of virtual power plant participating in peak load regulation, R CM is the value of virtual power plants participating in the carbon market, C qd is the cost of power curtailment of the virtual power plant, C MT is the gas turbine cost, P t G and They respectively represent the power purchased by the virtual power plant from the power grid at time t and the power purchase price.

[0040] Another aspect of the present invention provides a virtual power plant control device under peak load regulation-carbon market coupling, characterized in that it includes:

[0041] Data acquisition module, used to obtain wind power data, gas turbine data and electricity price data of virtual power plants in different scenarios;

[0042] A model building module, used to build a peak load-carbon market coupled virtual power plant dispatch model according to preset constraint data, the wind power data, the gas turbine data and the electricity price data;

[0043] A solution module is used to obtain the operation strategy of the virtual power plant by solving the virtual power plant scheduling model.

[0044] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0045] (1) In order to solve the problem that the existing virtual power plant operation scheme aggregates a single distributed resource, the present invention aggregates clean energy represented by wind turbines and traditional units represented by gas turbines, and obtains the operation strategy of the virtual power plant by solving the virtual power plant scheduling model.

[0046] (2) As existing studies have not considered the carbon market constraints on virtual power plants, a virtual power plant operation model under the peak-shaving-carbon coupling market is proposed, and the model is used to solve the operation strategy of the virtual power plant.

[0047] (3) In the carbon market, both the free carbon quota obtained by traditional gas turbine cleaning and the carbon quota offset obtained by distributed wind turbines applying for CCER are taken into account, and the actual carbon emissions of gas turbines are incorporated, and the optimization problem is solved on this basis. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of virtual power plant control under peak load regulation-carbon market coupling in an embodiment. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0050] Example 1

[0051] In response to the problems existing in the aforementioned prior art, this embodiment provides a virtual power plant control method under the peak-shaving-carbon market coupling, which aims to aggregate multiple types of users in the virtual power plant to participate in the peak-shaving-carbon coupling market, and support the virtual power plant to maximize its profits while participating in the carbon market and the peak-shaving market. In the aggregated resources, both traditional units represented by gas turbines and new energy units represented by distributed wind power are considered. In addition, in addition to the electricity market with power peaking as a typical example, the constraints of the carbon market in the operation of virtual power plants are also fully considered. Not only the carbon quotas obtained and consumed by traditional units are taken into consideration, but also the carbon quota offset obtained by emission reduction of new energy units is taken into consideration. On this basis, the operation plan is optimized to obtain the operation strategy of the virtual power plant under the peak-shaving-carbon market coupling.

[0052] Specifically, this method mainly includes the following steps:

[0053] Step S1, obtaining wind power data, gas turbine data and electricity price data of the virtual power plant in different scenarios;

[0054] Step S2, constructing a peak load-carbon market coupled virtual power plant dispatching model based on preset constraint data, the aforementioned wind power data, gas turbine data, and electricity price data;

[0055] Step S3, obtaining the operation strategy of the virtual power plant by solving the virtual power plant scheduling model.

[0056] For this embodiment, virtual power plant modeling is the most important, and the modeling is mainly divided into two parts: objective function construction and constraint establishment.

[0057] (1) Objective function construction.

[0058] Virtual power plants participate in the market with the goal of maximizing their own profits:

[0059]

[0060] Where P represents the virtual power plant revenue, R F is the peak load-shaving income of the virtual power plant, R CM The benefits of virtual power plants participating in the carbon market; qd is the cost of power curtailment of the virtual power plant, C MT is the economic cost of gas turbine; P t G and They respectively represent the power purchased by the virtual power plant from the power grid at time t and the power purchase price.

[0061] (2) Virtual power plant aggregates resource operating costs.

[0062] gas turbine:

[0063] 1) Gas turbine operating costs.

[0064] C MT =C f +C qd

[0065]

[0066] Among them C f is the gas turbine fuel cost, C qd is the gas turbine start-up and shutdown cost; P t MT is the power generation capacity of the gas turbine; a, b, c are the power generation cost coefficients of the gas turbine; SU and SD are the start-up and shutdown costs of the unit; It is a Boolean variable, indicating the power generation status of the gas turbine, 1 for operation and 0 for shutdown.

[0067] 2) Gas turbine constraints.

[0068] Gas turbines mainly use natural gas, propane, gasoline, and diesel as fuel, and have short start-stop times and fast power regulation. During operation, the main considerations are output constraints, ramp constraints, and start-stop constraints.

[0069]

[0070] Where P MT,max and P MT,min Respectively represent the upper and lower limits of its output; and They represent the ramp rate and ramp rate of the gas turbine respectively. It is a Boolean variable, indicating the power generation status of the gas turbine, 1 for operation and 0 for shutdown. and Respectively represent the continuous power generation and shutdown time of the gas turbine; M on and M off Respectively represent the minimum continuous power generation and shutdown time of the unit.

[0071] Wind turbines:

[0072] 1) Cost of wind turbines.

[0073] Wind turbines are clean energy, and their operation only considers the cost of wind abandonment.

[0074]

[0075] 2) Uncertainty of wind turbines.

[0076] It is generally assumed that wind speed follows a Weibull distribution

[0077]

[0078] Where v is the actual wind speed, c and k are the scale parameter and shape parameter respectively.

[0079] Use the scenario generation method to generate scenarios that satisfy the probability distribution model

[0080]

[0081] where v n Indicates rated wind speed, v in and v out Respectively represent the cut-in and cut-out wind speeds, P * and P t W Represents rated power and output power respectively.

[0082] 3) Wind turbine constraints.

[0083] Affected by the natural environment, the actual operation of wind turbines is subject to upper and lower limits on output.

[0084] P t W,min ≤P t W ≤P t W,max

[0085] 4) Market value of virtual power plants:

[0086] Power peaking market.

[0087]

[0088] in is the flexible peak-shaving price at time t, is the peak load capacity at time t, is the baseline capacity at time t, is the peak shift penalty coefficient.

[0089] Carbon market.

[0090]

[0091] where c CM is the carbon quota price, x a1 Issuing carbon quota factor for gas turbines, x a2 Apply for CCER for wind turbines to obtain carbon offset quota coefficient, x e is the carbon emission coefficient of the gas turbine.

[0092] Power constraints.

[0093]

[0094] In summary, this embodiment considers the participation of virtual power plants in wind turbines and gas turbines in the peak load regulation market and the carbon quota market. Assuming that the wind speed follows the Weibull distribution, a wind turbine model that takes into account the uncertainty of wind output is constructed, and the cost of abandoned wind is included in the cost. A gas turbine model is constructed in combination with the start and stop of the unit and the cost of raw materials. After the unit cost and operation model are constructed, the peak load regulation market is participated in. In the carbon market, both the free carbon quota obtained by traditional gas turbine cleaning and the carbon quota offset obtained by distributed wind turbines applying for CCER are considered, and the actual carbon emissions of gas turbines are also included.

[0095] Example 2

[0096] Compared with Example 1, this embodiment provides another virtual power plant control method under peak regulation-carbon market coupling. The difference is that the goal of the virtual power plant operation in this embodiment is to minimize carbon emissions while meeting a certain target profit of the virtual power plant. It supports the virtual power plant to reduce carbon emissions to a minimum while ensuring a certain profit, so as to adapt to the regulation and control needs of the virtual power plant.

[0097] Specifically in this embodiment, the objectives of the virtual power plant are:

[0098] minC

[0099] Where C represents the overall carbon emissions of virtual operations.

[0100] At the same time, the preconditions also include virtual power plant revenue constraints:

[0101] P>P 0

[0102] Where P 0 Represents the minimum profit target for virtual power plant operation.

[0103] This embodiment can form an operation strategy for the virtual power plant under the peak load regulation-carbon market coupling, so that the virtual power plant can reduce the overall carbon emissions to a minimum while meeting certain target returns.

[0104] This method has the following advantages:

[0105] (1) In view of the single aggregation of distributed resources in the existing virtual power plant operation plan, it is proposed to aggregate clean energy represented by wind turbines and traditional units represented by gas turbines.

[0106] (2) As existing studies have not considered that virtual power plants are subject to carbon market constraints, a virtual power plant operation model under the peak-shaving-carbon coupling market is proposed.

[0107] Example 3

[0108] Based on the above-mentioned embodiment, this embodiment provides a virtual power plant control device under peak load regulation-carbon market coupling, including:

[0109] Data acquisition module, used to obtain wind power data, gas turbine data and electricity price data of virtual power plants in different scenarios;

[0110] A model building module, used to build a peak load-carbon market coupled virtual power plant dispatching model according to preset constraint data, the previous wind power data, the gas turbine data and the electricity price data;

[0111] The solution module is used to obtain the operation strategy of the virtual power plant by solving the virtual power plant scheduling model.

[0112] See also Figure 1 The virtual power plant aggregates distributed wind power and gas turbines, and as a virtual power plant, it purchases electricity from the grid as a whole, and is coupled with the power peak regulation and carbon market to form a market linkage. The wind power and gas turbine modules simulate actual generator sets, and the market is used to simulate the actual market.

[0113] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A virtual power plant control method under peak load regulation and carbon market coupling, characterized in that: The steps include: Step S1, obtaining wind power data, gas turbine data and electricity price data of the virtual power plant in different scenarios; Step S2, constructing a peak load-carbon market coupled virtual power plant dispatching model according to preset constraint data, the wind power data, the gas turbine data and the electricity price data; Step S3, obtaining the operation strategy of the virtual power plant by solving the virtual power plant scheduling model.

2. The virtual power plant control method under peak load regulation and carbon market coupling according to claim 1 is characterized in that: In step S2, the process of constructing a peak load regulation-carbon market coupled virtual power plant dispatching model includes the following steps: Step S201, obtaining the objective function of the virtual power plant; Step S202, construct wind turbine constraints that take into account wind turbine uncertainties, gas turbine constraints, virtual power plant participation in peak load regulation constraints, virtual power plant participation in carbon market constraints, and power constraints.

3. The virtual power plant control method under peak load regulation and carbon market coupling according to claim 2 is characterized in that: In step S201, the objective function of the virtual power plant is: Among them, P is the value of the virtual power plant, R F is the value of virtual power plant participating in peak load regulation, R CM is the value of virtual power plants participating in the carbon market, C qd is the cost of power curtailment of the virtual power plant, C MT is the gas turbine cost, P t G and They respectively represent the power purchased by the virtual power plant from the power grid at time t and the power purchase price.

4. The virtual power plant control method under peak load regulation and carbon market coupling according to claim 2 is characterized in that: The wind turbine constraints considering the uncertainty of the wind turbine are: P t W,min ≤P t W ≤P t W,max Where v is the actual wind speed, c and k are the scale parameter and shape parameter respectively, v n Indicates rated wind speed, v in and v out Respectively represent the cut-in and cut-out wind speeds, P * and P t W Represents rated power and output power respectively, P t W,min , P t W,max are the minimum output power and the maximum output power respectively.

5. The virtual power plant control method under peak load regulation and carbon market coupling according to claim 2 is characterized in that: The benefits of the virtual power plant participating in peak load regulation are: in, is the flexible peak-shaving price at time t, is the peak load capacity at time t, is the baseline capacity at time t, is the peak shift penalty coefficient, are gas turbine output power, wind power output power and purchased power respectively, R F The value of participating in power peak regulation.

6. The virtual power plant control method under peak load regulation and carbon market coupling according to claim 2 is characterized in that: The benefits of the virtual power plant participating in the carbon market are: Among them, c CM is the carbon quota price, x a1 Issuing carbon quota factor for gas turbines, x a2 Apply for CCER for wind turbines to obtain carbon offset quota coefficient, x e is the carbon emission coefficient of the gas turbine, R CM To participate in the value of carbon market, They are gas turbine output power and wind power output power respectively, and C represents the overall carbon emissions of virtual operations.

7. The virtual power plant control method under peak load regulation and carbon market coupling according to claim 2 is characterized in that: The power constraints are: in, are gas turbine output power, wind power output power and purchased power respectively. is the peak load capacity at time t.

8. The virtual power plant control method under peak load regulation and carbon market coupling according to claim 2 is characterized in that: The gas turbine constraints are: Among them, P MT,max and P MT,min They represent the upper and lower limits of gas turbine output respectively, and They represent the ramp rate and ramp rate of the gas turbine respectively. is a Boolean variable, indicating the power generation status of the gas turbine, 1 for operation and 0 for shutdown. and Respectively represent the continuous power generation and shutdown time of the gas turbine, M on and M off Respectively represent the minimum continuous power generation and shutdown time of the unit, P t MT is the power generation capacity of the gas turbine.

9. The virtual power plant control method under peak load regulation and carbon market coupling according to claim 2 is characterized in that: In step S201, the objective function is: min C Among them, C represents the overall carbon emissions of virtual operations. The step S202 also includes a virtual power plant revenue constraint: P>P 0 Among them, P is the value of the virtual power plant, P 0 represents the minimum profit of the virtual power plant, R F is the value of virtual power plant participating in peak load regulation, R CM is the value of virtual power plants participating in the carbon market, C qd is the cost of power curtailment of the virtual power plant, C MT is the gas turbine cost, P t G and They respectively represent the power purchased by the virtual power plant from the power grid at time t and the power purchase price.

10. A virtual power plant control device under peak load regulation and carbon market coupling, characterized in that: include: Data acquisition module, used to obtain wind power data, gas turbine data and electricity price data of virtual power plants in different scenarios; A model building module, used to build a peak load-carbon market coupled virtual power plant dispatch model according to preset constraint data, the wind power data, the gas turbine data and the electricity price data; A solution module is used to obtain the operation strategy of the virtual power plant by solving the virtual power plant scheduling model.

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