Virtual power plant electric energy frequency modulation method and system

Abstract

The invention relates to the technical field of power system energy scheduling, and discloses a virtual power plant electric energy frequency modulation method and system. According to the method, under the combined clearing mode of the electric energy market and the frequency modulation auxiliary service market, the virtual power plant participates in the electric energy frequency modulation market transaction model, the bidding capacity in the two types of markets can be reasonably distributed, a basis is established for the optimal bidding scheme of the virtual power plant, and meanwhile, framework support is provided for the virtual power plant to participate in multi-market coupling transaction. The virtual power plant can obtain more bid-winning opportunities in the electric energy market at a relatively low quoted price at the moment of large generation of wind and light; by aggregating high-quality frequency modulation resources such as energy storage, the virtual power plant can improve the comprehensive frequency modulation performance, and the competitiveness in the frequency modulation market is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system energy dispatching, and in particular relates to a virtual power plant electric energy frequency modulation method and system. Background Art

[0002] With the rapid development of the economy, the living standards of residents are constantly improving, and people's demand for electricity is increasing. Virtual power plants participate in the joint clearing of the electric energy market and the frequency regulation market as independent entities. On the one hand, virtual power plants are a profit-oriented aggregation mechanism. On the other hand, the electric energy market and the frequency regulation market are related. Therefore, virtual power plants need to coordinate their bidding capacity in the two markets. While declaring electricity and electricity prices in the electric energy market, they also declare frequency regulation capacity, frequency regulation mileage, frequency regulation capacity price and frequency regulation mileage price in the frequency regulation auxiliary service market, striving to maximize their own benefits; the power dispatching agency conducts joint clearing of the electric energy market and the frequency regulation market based on the declared information of each participant and the load demand and frequency regulation demand of the system, while meeting the power constraints, winning bid quantity constraints and network security constraints within the system. For frequency regulation signals, traditional power generators inevitably have the disadvantages of long response time, low unit ramp rate, adjustment delay, and inability to accurately track the frequency regulation instructions issued by the power grid. Summary of the invention

[0003] In order to overcome the deficiencies and defects mentioned in the above background technology, the present application provides a virtual power plant electric energy frequency regulation method and system.

[0004] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0005] In a first aspect, the present application provides a method for frequency modulation of electric energy in a virtual power plant, comprising:

[0006] S1: Construct the objective function of the virtual power plant decision model and construct the constraints of various types of costs;

[0007] S2: Solving the objective function using the whale algorithm method with adaptive weights based on the constraint conditions;

[0008] S3: Execute frequency modulation according to the solution result.

[0009] In a second aspect, the present application provides a virtual power plant electric energy frequency regulation system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the computer program.

[0010] Compared with the prior art, the present invention has the following beneficial effects:

[0011] The virtual power plant electric energy frequency regulation method provided in this application, under the joint clearing mode of the electric energy market and the frequency regulation auxiliary service market, the virtual power plant participates in the electric energy frequency regulation market transaction model, which can reasonably allocate the bidding capacity in the two types of markets, establish the foundation for the optimal bidding plan of the virtual power plant, and provide framework support for the virtual power plant to participate in multi-market coupling transactions. At the moment of strong wind and solar power generation, the virtual power plant can obtain more bidding opportunities in the electric energy market with a lower bid; by aggregating high-quality frequency regulation resources such as energy storage, the virtual power plant can improve the comprehensive frequency regulation performance and enhance its competitiveness in the frequency regulation market.

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0013] Figure 1 It is a flow chart of a method for frequency modulation of electric energy in a virtual power plant provided by the present application;

[0014] Figure 2 This is the calculation process of the whale algorithm based on adaptive weights provided by this application. DETAILED DESCRIPTION

[0015] In order to facilitate the understanding of the present invention, the present invention will be described more comprehensively and meticulously below in conjunction with the accompanying drawings and preferred embodiments of the present invention, but the protection scope of the present invention is not limited to the following specific embodiments.

[0016] Unless otherwise defined, all professional terms used below have the same meanings as those generally understood by those skilled in the art. The professional terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the scope of protection of the present invention.

[0017] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the usual meanings understood by persons with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one" or "one" do not indicate quantity restrictions, but indicate the existence of at least one. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship also changes accordingly.

[0018] See also Figure 1 The present application provides a virtual power plant electric energy frequency modulation method, comprising:

[0019] S1: Construct the objective function of the virtual power plant decision model and construct the constraints of various types of costs;

[0020] S2: Solving the objective function using the whale algorithm method with adaptive weights based on the constraint conditions;

[0021] S3: Execute frequency modulation according to the solution result.

[0022] The above-mentioned virtual power plant electric energy frequency regulation method, under the joint clearing mode of the electric energy market and the frequency regulation auxiliary service market, the virtual power plant participates in the electric energy frequency regulation market transaction model, which can reasonably allocate the bidding capacity in the two types of markets, establish the foundation for the optimal bidding plan of the virtual power plant, and provide a framework support for the virtual power plant to participate in multi-market coupling transactions. At the time of strong wind and solar power generation, the virtual power plant can obtain more bidding opportunities in the electric energy market with a lower bid; by aggregating high-quality frequency regulation resources such as energy storage, the virtual power plant can improve the comprehensive frequency regulation performance and enhance its competitiveness in the frequency regulation market.

[0023] Below, the steps of the above-mentioned virtual power plant electric energy frequency modulation method are described in detail with a complete example:

[0024] First, build a virtual power plant decision model:

[0025] The goal of a virtual power plant is to maximize its own interests. The objective function is the opposite of the original objective function, that is, the cost minus the benefit, and the minimum value is obtained. The objective function is as follows:

[0026]

[0027] in, is the operating cost of the virtual power plant in period t, is the total revenue of the virtual power plant in period t.

[0028] The operating costs of a virtual power plant are as follows:

[0029]

[0030] in, is the power generation cost of the gas unit in period t, is the operating cost of energy storage in period t, is the wind curtailment cost in period t, is the cost of abandoned light in time period t, The user demand response fee can be shifted during time period t.

[0031] Gas turbine costs and constraints are as follows:

[0032]

[0033] Where x is the fixed cost of the gas turbine, c start is the gas turbine startup cost, c end is the gas turbine downtime cost, Indicates whether the gas turbine is in working state during period t. Indicates whether the gas turbine is started during period t. Indicates whether the gas turbine stops during period t, N i is the gas turbine output segment set, y i is the power generation cost slope of the gas turbine in the i-th segment, is the output of the gas turbine at the kth stage during period t, is the sum of the output of each section of the gas turbine during period t, represents the maximum output of the gas turbine in the i-th segment, r CGT,down is the gas turbine ramp down rate, r CGT,up is the gas turbine ramp-up rate, and Δt represents the time change.

[0034] Energy storage costs and constraints are as follows:

[0035]

[0036]

[0037] Among them, x discharge is the marginal cost of energy storage discharge, x charge The marginal cost of charging the energy storage, is the charging capacity during period t, is the discharge capacity during period t, P charge,max is the maximum charging power of energy storage, P discharge,max is the maximum discharge power of energy storage, is whether the energy storage is charged during period t, is whether the energy storage is discharged during period t, is the amount of energy stored in the energy storage system during period t, For energy storage charging efficiency, The energy storage discharge efficiency, The minimum storage capacity, The maximum storage capacity.

[0038] The costs and constraints of wind power generation are as follows:

[0039]

[0040] Among them, x WIND is the wind abandonment penalty coefficient, Forecast output of wind power in period t, It contributes to the wind power generation capacity during period t.

[0041] The costs and constraints of photovoltaic power generation are as follows:

[0042]

[0043] Among them, x SUN is the light abandonment penalty coefficient, Forecast output of photovoltaic power generation in period t, Contribute to the photovoltaic power generation capacity during period t.

[0044] The transferable user demand response costs and constraints are as follows:

[0045]

[0046]

[0047] in is the actual power consumption of user n in period t, G n,DR,real is the total power required by user n during the operation cycle, is the lower limit of power consumption of user n in period t, is the upper limit of power consumption of user n in period t, is the unit compensation price for user n participating in demand response, is the user demand response fee that can be shifted during period t, The demand response amount provided by the load that can be shifted during period t, J i The set of loads that can be shifted to participate in demand response. is the original power consumption of user n in period t.

[0048] Market Clearing Model:

[0049] Total benefits of a virtual power plant:

[0050]

[0051] in, is the power that the virtual power plant won in the t period, is the clearing electricity price for the period t, is the price of the frequency regulation capacity in period t, is the price of the down-regulation capacity in period t, is the frequency regulation capacity of the virtual power plant in period t, is the frequency regulation capacity of the virtual power plant in period t, is the frequency adjustment mileage price during period t, is the frequency adjustment mileage price in period t, The frequency regulation mileage that the virtual power plant won the bid for in period t, The frequency regulation mileage that the virtual power plant wins the bid for in period t.

[0052] Virtual power plant bidding constraints:

[0053]

[0054] in, is the frequency regulation capacity of the virtual power plant in period t, is the frequency regulation capacity of the virtual power plant in period t, Apply for a lower limit on frequency regulation capacity for virtual power plants, Apply for an upper limit on frequency regulation capacity for virtual power plants, Lower the lower limit of frequency regulation capacity for virtual power plants Apply for a lower frequency regulation capacity cap for virtual power plants. Bidding for power floors for virtual power plants, Bidding for power caps for virtual power plants, is the actual wind power output during period t, is the actual photovoltaic power generation output during period t, is the total demand response amount in period t, The demand response amount provided for the load that can be shifted during period t, is the internal load power consumption of the virtual power plant in period t, is the bidding electricity of the virtual power plant in period t.

[0055] Further, such as Figure 2 As shown, the whale algorithm based on adaptive weights is used to solve:

[0056] The Whale Optimization Algorithm (WOA) is a heuristic optimization algorithm that simulates the search strategy and hunting mechanism of humpback whales to find the optimal solution to the problem. There are three important stages: shrinking and encircling, spiraling, and random search. The synergy of these stages makes the Whale Optimization Algorithm an effective optimization algorithm that can be applied to various problems that require optimal solutions.

[0057] The shrinking and encircling process is as follows:

[0058] The search scope of the WOA algorithm is the entire solution space, and the location of the prey needs to be determined in order to surround it. Since the optimal solution is not known a priori in the algorithm, the WOA algorithm assumes that the current best candidate solution is the target prey. After the prey is determined, other whales will try to update their positions towards the prey. The formula for this behavior is as follows:

[0059] P(t+1)=P best (t)-A·|B·P best(t)-P t |

[0060] A=2α·r1-α

[0061] B=2r2

[0062] Among them, P t Represents the current location of the whale, P best (t) represents the location of the prey, t is the number of current iterations; A and B are coefficient vectors, α represents the linear weight, and as the number of iterations increases, α decreases linearly from the initial value 2 to 0, r1 and r2 are random vectors between the interval [0,1].

[0063] The spiral process is as follows:

[0064] In the process of foraging, the two mechanisms of contraction and spiral rise are carried out simultaneously. In the optimization process, when the coefficient vector is, the whale group will choose contraction or spiral rise with equal probability, as shown in the following formula:

[0065]

[0066] Where b is a constant, l is a random number in the interval [-1,1], and ρ is a random number in the interval [0,1].

[0067] The random search process is as follows:

[0068] In addition to the two position update mechanisms of shrinking and spiraling, whales can also swim randomly to search for prey. When the coefficient vector |A|≥1, the whales will randomly search for prey based on the position of the entire group, as shown in the following formula:

[0069] P(t+1)=P tand (t)-A·|B·P rand (t)-P t |

[0070] Among them, P rand (t) is a whale individual randomly selected from the population.

[0071] In this embodiment, an adaptive inertia weight is introduced, and the specific process is as follows:

[0072] It is worth pointing out that the weights in the standard WOA algorithm are linear weights, and α will decrease linearly from 2 to 0 as the number of iterations increases, which to a certain extent causes the problem of incomplete search for prey and slow encirclement of prey. Therefore, a nonlinear convergence method is designed to overcome the adverse effects of linear convergence on the algorithm. The formula for the adaptive inertia weight is as follows:

[0073]

[0074] Where D represents the number of current iterations, n represents the maximum number of iterations, and rand represents a random number in the interval [0.5, 1].

[0075] The present application also provides a virtual power plant electric energy frequency modulation system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program. The virtual power plant electric energy frequency modulation system can implement various embodiments of the above virtual power plant electric energy frequency modulation method and achieve the same beneficial effects, which will not be described in detail here.

[0076] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for frequency modulation of electric energy in a virtual power plant, characterized in that: include: S1: Construct the objective function of the virtual power plant decision model and construct the constraints of various types of costs; S2: Solving the objective function using the whale algorithm method with adaptive weights based on the constraint conditions; S3: Execute frequency modulation according to the solution result.

2. The method for frequency modulation of electric energy in a virtual power plant according to claim 1, characterized in that: The S1 includes: The objective function of the virtual power plant decision model is constructed based on the operating cost of the virtual power plant and the total revenue of the virtual power plant.

3. The method for frequency modulation of electric energy in a virtual power plant according to any one of claims 1 or 2, characterized in that: The objective function satisfies the following relationship: in, is the operating cost of the virtual power plant in period t, is the total revenue of the virtual power plant in period t.

4. The method for frequency modulation of electric energy in a virtual power plant according to claim 3, characterized in that: The operating costs of a virtual power plant include: the power generation cost of gas-fired units, the operating cost of the energy storage system, the wind curtailment cost, the solar curtailment cost, and the cost of responding to shiftable user demand.

5. The method for frequency modulation of electric energy in a virtual power plant according to claim 4, characterized in that: The power generation cost of the gas unit and the corresponding constraints satisfy the following relationship: in, is the power generation cost of the gas unit in period t, x is the fixed cost of the gas turbine, c start is the gas turbine startup cost, c end is the gas turbine downtime cost, Indicates whether the gas turbine is in working state during period t. Indicates whether the gas turbine is in working state during the period t-1. Indicates whether the gas turbine is started during period t. Indicates whether the gas turbine stops during period t, N i is the gas turbine output segment set, y i is the power generation cost slope of the gas turbine in the i-th segment, is the output of the gas turbine at the kth stage during period t, is the sum of the output of each section of the gas turbine during period t, represents the maximum output of the gas turbine in the i-th segment, r CGT,down is the gas turbine ramp down rate, r CGT,up is the gas turbine upward ramp rate, Δt represents the time variation; The operating cost of the energy storage system and the corresponding constraints satisfy the following relationship: in, represents the operating cost of the energy storage system, x discharge is the marginal cost of energy storage discharge, x charge The marginal cost of charging the energy storage, is the charging capacity during period t, is the discharge capacity during period t, P charge,max is the maximum charging power of energy storage, P discharge,max is the maximum discharge power of energy storage, is whether the energy storage is charged during period t, is whether the energy storage is discharged during period t, is the amount of energy stored in the energy storage system during period t, For energy storage charging efficiency, is the energy storage discharge efficiency, is the minimum storage capacity, The maximum storage capacity of energy storage; The shiftable user demand response costs and corresponding constraints satisfy the following relationship: in, represents the cost of shiftable user demand response, is the actual power consumption of user n in period t, G n,DR,real is the total power required by user n during the operation cycle, is the lower limit of power consumption of user n in period t, is the upper limit of power consumption of user n in period t, is the unit compensation price for user n participating in demand response, is the user demand response fee that can be shifted during period t, The demand response amount provided by the load that can be shifted during period t, J i The set of loads that can be shifted to participate in demand response, is the original power consumption of user n in period t.

6. The method for frequency modulation of electric energy in a virtual power plant according to claim 3, characterized in that: The total revenue of the virtual power plant satisfies the following relationship: in, is the power that the virtual power plant won the bid for in period t, is the day-ahead clearing electricity price for period t, is the price of the frequency regulation capacity in period t, is the price of down regulation capacity in period t, is the frequency regulation capacity of the virtual power plant in period t, is the frequency regulation capacity of the virtual power plant in period t, is the frequency adjustment mileage price during period t, is the frequency adjustment mileage price in period t, The frequency regulation mileage that the virtual power plant won the bid for in period t, The frequency regulation mileage that the virtual power plant wins the bid for in period t.

7. The method for frequency modulation of electric energy in a virtual power plant according to claim 6, characterized in that: The virtual power plant bidding constraints corresponding to the total revenue of the virtual power plant are as follows: in, is the frequency regulation capacity of the virtual power plant in period t, is the frequency regulation capacity of the virtual power plant in period t, The lower limit of frequency regulation capacity for virtual power plants is reported. Apply for an upper limit on frequency regulation capacity for virtual power plants. The lower limit of frequency regulation capacity for virtual power plant applications is lowered. Apply for a lower frequency regulation capacity cap for virtual power plants. Bidding for power floors for virtual power plants, Bidding for power caps for virtual power plants, is the actual wind power output during period t, is the actual photovoltaic power generation output during period t, is the total demand response amount in period t, The demand response amount provided for the load that can be shifted during period t, is the internal load power consumption of the virtual power plant in period t, is the bidding electricity of the virtual power plant in period t.

8. The method for frequency modulation of electric energy in a virtual power plant according to claim 1, characterized in that: The S2 includes: Inputting the objective function and the constraint condition into the whale algorithm based on adaptive weights to obtain an output result of the whale algorithm based on adaptive weights; Among them, the adaptive inertia weight α satisfies the following relationship: Where D represents the number of current iterations, n represents the maximum number of iterations, and rand represents a random number in the interval [0.5, 1].

9. A virtual power plant electric energy frequency modulation system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method described in any one of claims 1 to 8 are implemented.

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