Virtual power plant power frequency modulation method and system

Abstract

The present application relates to the technical field of power system energy scheduling, and discloses a virtual power plant electric energy frequency modulation method and system. In the joint 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, can reasonably allocate the bidding capacity in the two types of markets, establishes the basis for the optimal bidding scheme of the virtual power plant, and at the same time provides framework support for the virtual power plant to participate in multi-market coupling transaction. At the moment when wind and light are in full swing, the virtual power plant can obtain more bidding opportunities in the electric energy market with lower bidding; 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.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system energy scheduling, and particularly relates to a virtual power plant electric energy frequency modulation method and system. BACKGROUND

[0002] With the rapid development of economy, the living standards of residents are continuously improved, and the demand for electricity is increasing. As an independent subject participating in the joint clearing of the electric energy market and the frequency modulation market, the virtual power plant is a benefit-oriented aggregation mechanism on the one hand, and the electric energy market and the frequency modulation market are correlated on the other hand. Therefore, the virtual power plant needs to coordinate its bidding capacity in the two markets. In the electric energy market, the virtual power plant reports the electric quantity and the electric price, while in the frequency modulation auxiliary service market, the virtual power plant reports the frequency modulation capacity, the frequency modulation mileage, the frequency modulation capacity price and the frequency modulation mileage price, so as to maximize its own benefit. The power dispatching institution carries out the joint clearing of the electric energy market and the frequency modulation market according to the reporting information of each participant and the load demand and the frequency modulation demand of the system, so as to meet the power constraints, the winning amount constraints and the network security constraints and the like. For the frequency modulation signal, the traditional power generator inevitably has the shortcomings of long response time, low unit climbing rate, adjustment delay and inability to accurately track the frequency modulation instruction issued by the power grid. SUMMARY

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

[0004] To solve the above technical problems, the technical solution provided by the application is:

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

[0006] S1: constructing a target function of a virtual power plant decision model and constructing constraint conditions of each type of cost;

[0007] S2: solving the target function based on the constraint conditions by using a whale optimization algorithm method with adaptive weights;

[0008] S3: executing frequency modulation according to the solving result.

[0009] In a second aspect, the application provides 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, and the processor implements the steps of the method of the first aspect when executing the computer program.

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

[0011] The virtual power plant electric energy frequency modulation method provided by the application can reasonably allocate the bidding capacity in the two types of markets, establish a foundation for the optimal bidding scheme of the virtual power plant, and provide framework support for the participation of the virtual power plant in multi-market coupling transactions. In the case of large wind and light generation, the virtual power plant can obtain more winning opportunities in the electric energy market at a lower price. By aggregating high-quality frequency modulation resources such as energy storage, the virtual power plant can improve the comprehensive frequency modulation performance and competitiveness in the frequency modulation market.

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 is a flowchart of a virtual power plant electric energy frequency modulation method provided by the application;

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

[0015] In order to facilitate the understanding of the present application, the present application will be described in more detail and in a more comprehensive and detailed manner in combination with the accompanying drawings and preferred embodiments. However, the protection scope of the present application is not limited to the following specific embodiments.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The technical terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the protection scope of the present application.

[0017] Unless otherwise defined, the technical terms or scientific terms used in the present application should be understood as the usual meaning by those skilled in the art in the field to which the present application belongs. The terms "first", "second" and similar words used in the present application do not represent any order, quantity or importance, but are only used to distinguish different components. Similarly, "one" or "a" and similar words do not represent a quantity limitation, but represent the existence of at least one. The words "connected" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to represent relative positional relationships, and when the absolute position of the described object changes, the relative positional relationship also changes accordingly.

[0018] Please refer to Figure 1 The virtual power plant power frequency modulation method provided by the application comprises:

[0019] S1: constructing a target function of a virtual power plant decision model, and constructing constraint conditions of each type of cost;

[0020] S2: solving the target function based on the constraint conditions by using a whale algorithm method with adaptive weights;

[0021] S3: performing frequency modulation according to the solving result.

[0022] The virtual power plant power frequency modulation method described above, in the joint clearing mode of the electric energy market and the frequency modulation auxiliary service market, enables the virtual power plant to participate in the electric energy frequency modulation market transaction model, can reasonably allocate the bidding capacity in the two types of markets, establishes a foundation for the optimal bidding scheme of the virtual power plant, and at the same time provides framework support for the virtual power plant to participate in multi-market coupling transactions. At the moment when wind and light are in full swing, the virtual power plant can obtain more bid-winning opportunities in the electric energy market at a lower price; by aggregating high-quality frequency modulation resources such as energy storage, the virtual power plant can improve the comprehensive frequency modulation performance and competitiveness in the frequency modulation market.

[0023] Next, the steps of the virtual power plant power frequency modulation method described above are described in detail with a complete example:

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

[0025] The virtual power plant takes the maximization of its own interests as the target, and the target function is the opposite number of the original target function, that is, the cost minus the income, to find the minimum value, and the target function is as follows:

[0026]

[0027] Among them, is the operation cost of the virtual power plant in period t, is the total income of the virtual power plant in period t.

[0028] The operation cost of the virtual power plant is as follows:

[0029]

[0030] Among them, is the power generation cost of the gas turbine unit in period t, is the operation cost of the energy storage in period t, is the wind curtailment cost in period t, is the light curtailment cost in period t, is the demand response fee of the time-shiftable user in period t.

[0031] The gas turbine cost and constraints are as follows:

[0032]

[0033] where x is the gas turbine fixed cost, c start is the gas turbine start-up cost, c end is the gas turbine shut-down cost, denotes whether the gas turbine is in operation at time period t, denotes whether the gas turbine is started up at time period t, denotes whether the gas turbine is shut down at time period t, N i is the set of gas turbine output segments, y i is the gas turbine generation cost slope at the i-th segment, is the output of the gas turbine at the k-th segment at time period t, is the sum of the gas turbine output at each segment at time period t, denotes the maximum output of the gas turbine at the i-th segment, r CGT,down is the gas turbine down-ramp rate, r CGT,up is the gas turbine up-ramp rate, and Δt denotes the time change.

[0034] The energy storage cost and constraints are as follows:

[0035]

[0036] where x discharge is the marginal cost of energy storage discharging, x charge is the marginal cost of energy storage charging, is the charging capacity at time period t, is the discharging capacity at time period t, P charge,max is the maximum charging power of the energy storage, P discharge,max is the maximum discharging power of the energy storage, is whether the energy storage is charging at time period t, is whether the energy storage is discharging at time period t, is the energy storage state of charge at time period t, is the energy storage charging efficiency, is the energy storage discharging efficiency, is the minimum energy storage state of charge, is the maximum energy storage state of charge.

[0037] The wind power cost and constraints are as follows:

[0038]

[0039] where x WIND is the wind curtailment penalty coefficient, Forecasted wind power output for period t, Actual wind power output for period t.

[0040] Photovoltaic power generation cost and constraints are as follows:

[0041]

[0042] Wherein, x SUN Penalty coefficient for light abandonment, Forecasted photovoltaic power output for period t, Actual photovoltaic power output for period t.

[0043] Shiftable user demand response cost and constraints are as follows:

[0044]

[0045] Wherein Actual power consumption of user n in period t, G n,DR,real Total power consumption of user n in the operating cycle, Lower limit of power consumption of user n in period t, Upper limit of power consumption of user n in period t, Unit compensation price of user n participating in demand response, Shiftable user demand response cost for period t, Demand response amount provided by shiftable load in period t, J i Set of shiftable loads participating in demand response, Original power consumption of user n in period t.

[0046] Market clearing model:

[0047] Total revenue of virtual power plant:

[0048]

[0049] Wherein, Power won by virtual power plant in period t, Day-ahead clearing price for period t, Up-regulation frequency capacity price for period t, Down-regulation frequency capacity price for period t, Up-regulation frequency capacity of virtual power plant in period t, Down-regulation frequency capacity of virtual power plant in period t, Up-regulation frequency mileage price for period t, Down-regulation frequency mileage price for period t, Up-regulation frequency mileage won by virtual power plant in period t, is the frequency regulation capacity of the virtual power plant in the t period.

[0050] Virtual power plant bidding constraints:

[0051]

[0052]

[0053] wherein, is the frequency regulation capacity of the virtual power plant in the t period, is the frequency regulation capacity of the virtual power plant in the t period, is the lower limit of the frequency regulation capacity of the virtual power plant, is the upper limit of the frequency regulation capacity of the virtual power plant, is the lower limit of the frequency regulation capacity of the virtual power plant, is the upper limit of the frequency regulation capacity of the virtual power plant, is the lower limit of the virtual power plant bidding power, is the upper limit of the virtual power plant bidding power, is the actual output of wind power in the t period, is the actual output of photovoltaic power in the t period, is the total demand response amount in the t period, is the demand response amount provided by the movable load in the t period, is the internal load power consumption of the virtual power plant in the t period, is the bidding power of the virtual power plant in the t period.

[0054] Further, as shown in Figure 2 , an adaptive weight-based whale optimization algorithm is used for solving:

[0055] 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 of a problem. There are three important stages: shrinkage, spiral ascent and random search. The synergistic effect of these stages makes the whale optimization algorithm an effective optimization algorithm that can be applied to various problems of optimal solution.

[0056] The shrinkage process is as follows:

[0057] The search range of the WOA algorithm is the entire solution space, and the position of the prey needs to be determined for the enclosure. 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 own positions towards the prey, and the formula for this behavior is as follows:

[0058] P(t+1)=P best (t)-A·|B·P best(t) - P t

[0059] A = 2a - r1

[0060] B = 2r2

[0061] where P t represents the current position of the whale, P best (t) represents the position of the prey, t is the number of current iterations; A and B are coefficient vectors, a represents the linear weight, which linearly decreases from the initial value 2 to 0 as the number of iterations increases, and r1 and r2 are random vectors in the interval [0, 1].

[0062] The spiral rising process is as follows:

[0063] In the foraging process, the two mechanisms of contraction and spiral rising are carried out at the same time. In the optimization process, when the coefficient vector is |A| < 1, the whale group will select the contraction or spiral rising with equal probability, as shown in the following formula:

[0064]

[0065] where b is a constant, l is a random number in the interval [-1, 1], and p is a random number in the interval [0, 1].

[0066] The random search process is as follows:

[0067] In addition to the two position updating mechanisms of contraction and spiral rising, the whale can also randomly move to search for prey. When the coefficient vector |A| ≥ 1, the whale will randomly search for prey according to the position of the entire group, as shown in the following formula:

[0068] P(t+1) = P rand (t) - A - |B - P rand (t) - P t

[0069] where P rand (t) is a whale individual randomly selected in the population.

[0070] In this embodiment, an adaptive inertia weight is introduced, and the specific process is as follows:

[0071] It is worth noting that the weight in the standard WOA algorithm is a linear weight, and a will linearly decrease from 2 to 0 as the number of iterations increases, which to some extent causes the problem of incomplete search for prey and slow speed of surrounding prey. Therefore, a nonlinear convergence method is designed to overcome the adverse effects of linear convergence on the algorithm, and the formula of the adaptive inertia weight is as follows:

[0072] ​​

[0073] wherein D represents the number of current iteration, n represents the maximum number of iterations, and rand represents a random number between the interval [0.5, 1].

[0074] The application further provides 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, and 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 each embodiment of the above virtual power plant electric energy frequency modulation method and achieve the same beneficial effects, and thus will not be described here in detail.

[0075] The preferred embodiments of the application are described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations without creative labor based on the concept of the application. Therefore, any technical solution obtained by logical analysis, reasoning or limited experiment based on the existing technology according to the concept of the application shall be within the protection scope defined by the claims.

Claims

1. A virtual power plant power frequency modulation method, characterized in that, The method comprises the following steps: S1: constructing a target function of a virtual power plant decision model, and constructing constraint conditions of various types of costs; S2: solving the target function based on the constraint conditions by using a whale optimization algorithm method with adaptive weights; S3: performing frequency adjustment according to a solution result; The target function satisfies the following relationship: wherein, is the operating cost of the virtual power plant at time period t, is the total revenue of the virtual power plant at time period t, and Δt is the time change. The operation cost of the virtual power plant comprises: a power generation cost of a gas unit, an operation cost of an energy storage system, a wind curtailment cost, a light curtailment cost, and a demand response fee of a translatable user; The power generation cost of the gas unit and the corresponding constraint condition satisfy the following relationship: wherein, is the generation cost of the gas turbine at time period t, x is the fixed cost of the gas turbine, c start is the start-up cost of the gas turbine, c end is the shut-down cost of the gas turbine, denotes whether the gas turbine is in operation at time period t, denotes whether the gas turbine is in operation at time period t-1, denotes whether the gas turbine is started at time period t, denotes whether the gas turbine is stopped at time period t, N i is the set of output segments of the gas turbine, y i is the generation cost slope of the gas turbine at the i-th segment, is the output of the gas turbine at the i-th segment at time period t, is the sum of the outputs of the gas turbine at all segments at time period t, denotes the maximum output of the gas turbine at the i-th segment, r CGT,down is the down ramp rate of the gas turbine, r CGT,up is the up ramp rate of the gas turbine, Δt denotes the change in time; The operation cost of the energy storage system and the corresponding constraint condition satisfy the following relationship: wherein, represents the operating cost of the energy storage system, x discharge is the marginal cost of discharging the energy storage, x charge is the marginal cost of charging the energy storage, is the charging capacity at time period t, is the discharging capacity at time period t, P charge,max is the maximum charging power of the energy storage, P discharge,max is the maximum discharging power of the energy storage, is whether the energy storage is charging at time period t, is whether the energy storage is discharging at time period t, is the state of charge of the energy storage at time period t, is the charging efficiency of the energy storage, is the discharging efficiency of the energy storage, is the minimum state of charge of the energy storage, is the maximum state of charge of the energy storage; The demand response fee of the translatable user and the corresponding constraint condition satisfy the following relationship: wherein, denotes the shiftable user demand response cost, denotes the actual power consumption of user n at time period t, G n,DR,real denotes the total power consumption of user n during the operating period, denotes the lower limit of power consumption of user n at time period t, denotes the upper limit of power consumption of user n at time period t, denotes the unit compensation price of user n participating in demand response, denotes the shiftable user demand response cost at time period t, denotes the demand response amount provided by the shiftable load at time period t, J i denotes the set of shiftable loads participating in demand response, denotes the original power consumption of user n at time period t.

2. The virtual power plant power frequency modulation method according to claim 1, wherein, The total income of the virtual power plant satisfies the following relationship: wherein, is the target power of the virtual power plant in the t period, is the day-ahead clearing price of the t period, is the upward frequency capacity price of the t period, is the downward frequency capacity price of the t period, is the upward frequency capacity of the virtual power plant in the t period, is the downward frequency capacity of the virtual power plant in the t period, is the upward frequency mileage price of the t period, is the downward frequency mileage price of the t period, is the upward frequency mileage of the virtual power plant in the t period, is the downward frequency mileage of the virtual power plant in the t period.

3. The virtual power plant power frequency modulation method according to claim 2, wherein, The virtual power plant bidding constraint condition corresponding to the total income of the virtual power plant is as follows: wherein, is the up-regulation frequency capacity of the virtual power plant at time period t, is the down-regulation frequency capacity of the virtual power plant at time period t, is the lower limit of the up-regulation frequency capacity declared by the virtual power plant, is the upper limit of the up-regulation frequency capacity declared by the virtual power plant, is the lower limit of the down-regulation frequency capacity declared by the virtual power plant, is the upper limit of the down-regulation frequency capacity declared by the virtual power plant, is the lower limit of the bid power of the virtual power plant, is the upper limit of the bid power of the virtual power plant, is the actual wind power output at time period t, is the actual photovoltaic power output at time period t, is the total demand response amount at time period t, is the demand response amount provided by the shiftable load at time period t, is the internal load power consumption of the virtual power plant at time period t, is the bid power of the virtual power plant at time period t.

4. The virtual power plant power frequency modulation method according to claim 1, wherein, The S2 comprises: The target function and the constraint condition are input into the whale optimization algorithm with adaptive weights, and an output result of the whale optimization algorithm with adaptive weights is obtained; Wherein, the adaptive inertia weight α satisfies the following relationship: Wherein, 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].

5. A virtual power plant power frequency modulation system comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, The processor executes the computer program to realize the steps of the method in any one of claims 1 to 4.

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