Optimal Scheduling Method and System for Virtual Power Plant Model Based on Distributed Energy

Abstract

The present invention relates to the field of power plant energy scheduling, and particularly relates to an optimized scheduling method and system for a virtual power plant model based on distributed energy. The method includes the following steps: establishing the deviation between the theoretical value of the electricity quantity required by the virtual power plant and the actual electricity quantity obtained by the virtual power plant as the deviation value; establishing an optimization model, and taking the deviation value as the target value of the objective function of the optimization model; constraining the target value of the optimization model through the parameters of the virtual power plant; maximizing the deviation value through the upper-layer objective function of the optimization model, and minimizing the deviation value through the lower-layer objective function of the optimization model; determining whether the deviation value is a predetermined value. If the deviation value is the predetermined value, the virtual power plant can perform deviation-free scheduling based on the given scheduling scheme. The present invention has the effect of optimizing the power plant scheduling scheme.

Description

Technical Field

[0001] The present invention belongs to the technical field of power plant energy scheduling, and particularly relates to an optimized scheduling method and system for a virtual power plant model based on distributed energy. Background Art

[0002] In recent years, many countries have been exploring an energy development model that combines Internet technology with renewable energy technology. With the smart grid as the resource allocation center, a multi-energy coupling system with horizontal multi-source complementarity and vertical source-network-load-storage coordination is constructed. Under the emerging energy Internet architecture, the energy supply and demand system is a "source-network-load-storage" complex, and various energy sources such as electricity, heat, cold, and gas are coupled and interconnected in each link from the demand side to the supply side.

[0003] Under this background, distributed energy not only includes distributed power generation resources such as wind, light, and biomass, but also includes distribution-side and demand-side resources such as energy storage, electric vehicles, and controllable loads, as well as various energy coupling devices. With the rapid development of the energy Internet, the power system faces new challenges and opportunities. On the one hand, with the increase in the proportion of renewable energy injected into the grid and the increase in the peak-valley difference of the load, it is necessary to quickly and reliably schedule more resources with regulation capacity and regulation rate; on the other hand, due to the characteristics of distributed resources such as small capacity, multiple types, large quantity, and strong interactivity, it is difficult for system operators to directly control these distributed resources through centralized scheduling methods, which in turn weakens the power system's scheduling ability for distributed resources.

[0004] As an effective means of aggregating distributed energy, the virtual power plant realizes energy aggregation, energy storage, energy supply, and energy consumption without changing the grid connection mode and geographical location of each resource, effectively connecting distributed energy with the power system and realizing resource integration and distribution. In addition, as an aggregation entity, the virtual power plant represents its internal distributed resources to participate in the power market, which is an important way for the smart grid to achieve interaction and intelligence on the energy supply and demand side. However, the operating characteristics of distributed energy bring challenges to the resource aggregation and optimized scheduling of the virtual power plant in terms of energy scale, space scale, and time scale.

[0005] However, the operating characteristics of distributed energy bring challenges to the resource aggregation cluster and optimized scheduling of the virtual power plant in terms of energy scale, space scale, and time scale. Specifically, how to achieve the complementary coupling of multi-energy systems such as power supply, heat, cold, and gas puts higher requirements on the virtual power plant to coordinate the scheduling among multiple energies; distributed energy is scattered and operates independently in each regional power system, increasing the complexity of the space scale; different types of distributed energy have great differences in terms of regulation speed, regulation range, and regulation duration, and provide auxiliary services for the power grid across multiple time scales, increasing the complexity of the time scale.

[0006] Generally speaking, the large-scale grid connection of distributed renewable energy makes the power system present extremely high complexity and uncertainty. To meet the flexibility requirements of the power grid, virtual power plants urgently need to reasonably manage and optimally dispatch distributed energy sources. Summary of the Invention

[0007] In view of the above problems, the present invention provides an optimized scheduling method and system for a virtual power plant model based on distributed energy sources.

[0008] In a first aspect, the present invention provides an optimized scheduling method for a virtual power plant model based on distributed energy sources, including the following technical solutions.

[0009] An optimized scheduling method for a virtual power plant model based on distributed energy sources includes the following steps: establishing the deviation between the theoretical value of the electricity quantity required by the virtual power plant and the actual electricity quantity obtained by the virtual power plant as the deviation value Δp t ; establishing an optimization model, taking the deviation value as the target value of the objective function of the optimization model; constraining the target value of the optimization model through the parameters of the virtual power plant; maximizing the deviation value through the upper-layer objective function of the optimization model and minimizing the deviation value through the lower-layer objective function of the optimization model; determining whether the deviation value is a predetermined value. If the deviation value is a predetermined value, the virtual power plant can perform bias-free scheduling based on the given scheduling plan.

[0010] Furthermore, the objective function of the optimization model is as follows:

[0011]

[0012] where and represent the scheduling decision variables at time t.

[0013] Furthermore, the constraint of the target value of the optimization model through the parameters of the virtual power plant specifically includes constraining the target value through the following formula:

[0014]

[0015] where represents the energy interaction between the virtual power plant and the main power grid at time t, represents the output of distributed power source i at time t, represents the total power generation of the power generation equipment, represents the charging amount of the energy storage system at time t, represents the total reserve energy of the energy storage system, represents the load value of the virtual power plant at time t.

[0016] Furthermore, the method further includes constraining the output of the distributed power source i at time t, specifically including constraining it through the following formula:

[0017]

[0018]

[0019] where P g_i_min represents the minimum output power of the distributed power source i, P g_i_max represents the maximum output power of the distributed power source i, ramp g_i_down represents the minimum ramp constraint speed of the distributed power source i, ramp g_i_up represents the maximum ramp constraint speed of the distributed power source i, and t represents the scheduling time.

[0020] Furthermore, the method further includes constraining the charging amount of the energy storage system at time t specifically including constraining it through the following formula:

[0021]

[0022]

[0023]

[0024] where represents the charging amount of the energy storage system i at time t, E disc_i_max represents the maximum value of the charging power of the energy storage system, E c_i_max represents the maximum value of the discharging power of the energy storage system, W ess_i_min and W ess_i_max represent the state of charge constraints of the energy storage system.

[0025] Furthermore, the method further includes constraining the load value of the virtual power plant at time t, specifically including constraining it through the following formula:

[0026]

[0027]

[0028] where represents the load value of the virtual power plant at time t, respectively represent the lower limit and the upper limit of the load floating value.

[0029] Furthermore, the predetermined value is 0.

[0030] On the other hand, the present invention provides an optimized scheduling system for a virtual power plant model based on distributed energy, and the system includes: an input unit and a processing unit; the input unit is configured to establish a deviation between the theoretical value of the power required by the virtual power plant and the actual power obtained by the virtual power plant as a deviation value Δp t , and input the deviation value into the processing unit; the processing unit is configured to establish an optimization model and use the deviation value as the target value of the objective function of the optimization model; the processing unit is further configured to constrain the target value of the optimization model according to the parameters of the virtual power plant; the processing unit is further configured to maximize the deviation value through the upper-level objective function of the optimization model and minimize the deviation value through the lower-level objective function of the optimization model; the processing unit is further configured to determine whether the deviation value is a predetermined value, and if the deviation value is a predetermined value, the virtual power plant can perform bias-free scheduling based on a given scheduling scheme.

[0031] Furthermore, the optimization model constructed by the processing unit is as follows

[0032]

[0033] where and represent the scheduling decision variables at time t.

[0034] Furthermore, the processing unit constraining the target value of the optimization model according to the parameters of the virtual power plant specifically includes constraining the target value through the following formula

[0035]

[0036] represents the energy interaction between the virtual power plant and the large power grid at time t represents the output of distributed power source i at time t represents the total power generation of the power generation equipment represents the charging amount of the energy storage system at time t represents the total reserve energy of the energy storage system represents the load value of the virtual power plant at time t.

[0037] Furthermore, the processing unit is further configured to constrain the output of distributed power source i at time t, specifically including constraining through the following formula

[0038]

[0039]

[0040] where P g_i_minrepresents the minimum output power of distributed power source i, P g_i_max represents the maximum output power of distributed power source i, ramp g_i_down represents the minimum ramp constraint speed of distributed power source i, ramp g_i_up represents the maximum ramp constraint speed of distributed power source i, and t represents the scheduling time.

[0041] Furthermore, the processing unit is also used to constrain the charging amount of the energy storage system at time t Specifically, the constraint is carried out through the following formula

[0042]

[0043]

[0044]

[0045] Among them, represents the charging amount of energy storage system i at time t, E disc_i_max represents the maximum value of the charging power of the energy storage system, E c_i_max represents the maximum value of the discharging power of the energy storage system, W ess_i_min and W ess_i_max represent the charge state constraint of the energy storage system.

[0046] Furthermore, the processing unit is also used to constrain the load value of the virtual power plant at time t. Specifically, the constraint is carried out through the following formula

[0047]

[0048]

[0049]

[0050] Among them, represents the load value of the virtual power plant at time t, respectively represent the lower limit and the upper limit of the load floating value.

[0051] The present invention has at least the following effects

[0052] The above technical solution constructs a virtual power plant model integrating multiple energy sources. This model serves as an energy aggregator to evaluate the adjustability of distributed resources, and presents itself as a whole to the system operator by obtaining the parameters of its external equivalent model.

[0053] Aiming at the complex multi - energy supply network and the internal and external uncertainty factors of the virtual power plant, an optimized scheduling method for the virtual power plant model based on distributed energy is proposed. A two - layer structure is established and a robust method is adopted to ensure the robustness and feasibility of the virtual power plant model. The complex original problem is decomposed into multiple easily - solved sub - problems for solution. Finally, the modeling and calculation of the multi - energy flow system of large - scale distributed energy are realized.

[0054] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structure pointed out in the specification and claims. Detailed implementation manners

[0055] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0056] First, an embodiment of the present application discloses a virtual power plant model based on distributed energy, where the distributed energy includes a generator model, an energy storage model, and a curve model. The union of the constraints of the generator model, the energy storage model, and the curve model is the equivalent model of the multi - energy flow virtual power plant, and the power of the only connection line between the virtual power plant and the large power grid is used as the decision variable. The constraints of the multi - energy flow virtual equivalent model include: decision variable constraints, decision variable ramp power constraints, and decision variable capacity index constraints. To maintain the consistency of the multi - energy flow virtual power plant model, it is necessary to expand these three types of general models of distributed energy.

[0057] 1. For the generator model, the upper and lower limit constraints of the power accumulation value are combined, so that the feasible region of the extended generator is represented by the following constraint conditions, that is, the power and other parameters are controlled within the following ranges,

[0058]

[0059]

[0060] Among them, represents the output power of distributed power source i at time t, P g_i_min and P g_i_max respectively represent the minimum output power and the maximum output power of distributed power source i, ramp g_i_down and ramp g_i_uprespectively represent the minimum and maximum ramping rates of distributed power source i, and t represents the scheduling time.

[0061] II. For the energy storage model, the feasible region in the energy storage model can be constrained by the following constraint conditions, that is, the charging amount in the energy storage system is constrained.

[0062]

[0063]

[0064]

[0065] Among them, represents the charging amount of energy storage system i at time t, and E disc_i_max represents the maximum value of the charging power of the energy storage system, and E c_i_max represents the maximum value of the discharging power of the energy storage system, and W ess_i_min and W ess_i_max represent the state of charge constraints of the energy storage system. Inequality (6) limits the charging and discharging rate of energy storage system i at time t, and W ess_i_min and W ess_i_max represent the state of charge constraints of the energy storage system, that is, W ess_i_min and W ess_i_max are the minimum and maximum intervals of the state of charge of energy storage system i. Inequality (7) is the constraint on the energy storage value of the energy storage system, which limits the size of the energy storage value. The energy storage value of the energy storage system is always within the range of the minimum and maximum intervals.

[0066] III. For the curve model, its equivalent generator model is expressed as:

[0067]

[0068]

[0069] Among them, represents the load value of the virtual power plant at time t. and respectively represent the lower limit and upper limit of the load floating value.

[0070] Combining the three rising models (generator model, energy storage model, and curve model), the output power (i.e., tie line power) of the multi-energy flow virtual power plant can be expressed by the following equation.

[0071]

[0072] At the same time, the equivalent constraints of the multi-energy flow virtual power plant can be expressed by the following inequalities (11)-(13).

[0073]

[0074]

[0075]

[0076] In the above formula, represents the energy interaction between the virtual power plant and the large power grid at time t. Its specific value is the electricity quantity of the internal generator minus the energy storage quantity in the energy storage system and the load quantity of the virtual power plant, as shown in Equation (10).

[0077] is the upper limit of the output power, P t is the lower limit of the output power, is the upper limit of the ramp constraint, ΔP t is the lower limit of the ramp constraint, is the upper limit of the cumulative power, is the lower limit of the cumulative power. Different from traditional generators and energy storage devices, the value of each parameter in the above model changes at different times, so it is necessary to calculate the equivalent parameters for each time. When calculating the parameters of the above model, the value of each type of parameter at a certain time is taken as a reference when reaching the extreme value. At this time, the generator power and ramp power are used as strong constraint conditions, that is, it is necessary to ensure that the values on both sides of the inequality constraint are equal. At the same time, the cumulative power of the generator is a weak constraint condition, that is, the inequality still holds when the values at both ends are not equal.

[0078] For the charging power and cumulative charging power constraints of the energy storage system (ESS), strong constraints are adopted, while for the ramp charging power of the energy storage system (ESS), weak constraints are adopted. Therefore, the equivalent parameters of the virtual power plant directly obtained by solving belong to weak constraint conditions, which may cause the virtual power plant to be unable to execute the dispatching commands issued by the independent system operator (ISO). Therefore, in this embodiment, the above model parameters are corrected according to the actual situation of the virtual power plant.

[0079] In the dispatching problem, considering that the uncertainty faced by the virtual power plant is mainly the uncertainty of its internal load. For the uncertainty of the internal load, at time t, the virtual power plant can only obtain the actual load and electricity price information of the current time period, and obtains the load and electricity price information of future time periods through prediction. Within the dispatching interval T, the uncertainty information of the internal load of the virtual power plant is represented by the following equation:

[0080]

[0081] Among them, is the load prediction information for the t + i time period, ε i is the prediction error, which is a normal distribution random variable Ν(u, σ2 ), (u, σ 2 ) is the mean and standard deviation of the state distribution; among which, the variance of price prediction increases linearly with the time interval of the prediction interval.

[0082] The calculation of the equivalent parameters of the output power of the multi-energy flow virtual power plant in inequalities (11)-(13) is described below:

[0083] First of all, the upper limit and the lower limit of the output power can be calculated respectively through the following formulas.

[0084]

[0085]

[0086] In the above formula, represents the virtual power plant maximizing the equivalent external output, represents the virtual power plant minimizing the equivalent external output, and the upper and lower limits of the output power are calculated through the above formula.

[0087] Secondly, the upper limit and the lower limit of the ramp power are calculated respectively through the following formulas.

[0088]

[0089]

[0090] represents the virtual power plant maximizing the ramp power at time t, represents the virtual power plant minimizing the ramp power at time t, and the upper and lower limits of the ramp power are calculated through the above formulas.

[0091] Thirdly, for the upper and lower limits of the cumulative power, they are calculated through the following formulas.

[0092]

[0093]

[0094] Among which, is the virtual power plant maximizing the cumulative power, is the virtual power plant minimizing the cumulative power, and equations (19) and (20) respectively calculate the upper and lower limits by maximizing the cumulative power and minimizing the cumulative power.

[0095] In addition, for which represents the energy interaction between the virtual power plant and the large power grid at time t, for it can also be constrained by the following formula.

[0096]

[0097] Among them, is the total power generation of the power generation equipment, is the total energy storage capacity of the energy storage equipment, is the load value of the virtual power plant.

[0098] Combined with the content above, in order to verify whether the model can execute all scheduling schemes in this embodiment, an optimized scheduling method is disclosed in this embodiment, specifically:

[0099] An optimized scheduling method for a virtual power plant model includes the following steps:

[0100] S1. Establish the deviation between the theoretical value of the electricity required by the virtual power plant and the actual electricity obtained by the virtual power plant as the deviation value Δp t ;

[0101] S2. Establish an optimization model and use the deviation value as the target value of the objective function of the optimization model;

[0102] S3. Constrain the target value of the optimization model through the parameters of the virtual power plant;

[0103] S4. Maximize the deviation value through the upper-layer objective function of the optimization model and minimize the deviation value through the lower-layer objective function of the optimization model;

[0104] S5. Determine whether the deviation value is a predetermined value. If the deviation value is a predetermined value, the virtual power plant can perform deviation-free scheduling based on the given scheduling scheme. In this embodiment, the predetermined value is preferably 0.

[0105] In the method of this embodiment, the established optimization model is as follows. Equations (1) and (2) are respectively the objective function and constraint conditions of the optimization model. That is, in step S2, the established optimization model is as shown in Equation (1), and in step S3, the target value of the optimization model is constrained, specifically as shown in Equation (2).

[0106]

[0107]

[0108] represents the output of distributed power source i at time t, represents the total power generation of the power generation equipment, represents the charging amount of the energy storage system at time t, represents the total reserve energy of the energy storage system, represents the load value of the virtual power plant at time t. Δp tIt is a slack variable, representing the deviation between the theoretical value of the electricity required by the virtual power plant and the actual electricity obtained by the virtual power plant. The above optimization model is a typical two-layer robust model; the upper-layer objective function of the model is to maximize the deviation value under the worst case, and the lower-layer objective function is to minimize the internal scheduling deviation of the virtual power plant. In S3, the target value is constrained by the parameters of the virtual power plant, specifically through Equation (2).

[0109] Specifically, for S3, when constraining the target value, it also includes constraining the parameters in Equation (2), specifically including

[0110] constraining the output of the distributed power source i at time t which is specifically constrained by Equation (3) and Equation (4);

[0111] Pg_i_min represents the minimum output power of the distributed power source i, Pg_i_max represents the maximum output power of the distributed power source i, and ramp g_i_down represents the minimum ramp constraint speed of the distributed power source i, and ramp g_i_up represents the maximum ramp constraint speed of the distributed power source i, and t represents the scheduling time. The output of the distributed power source i at time t is constrained by Equation (3) and Equation (4).

[0112] When constraining the target value, it is also necessary to constrain the charging amount of the energy storage system at time t which is specifically constrained by Equation (5)-(7);

[0113] In Equation (5)-(7), represents the charging amount of the energy storage system i at time t, and E disc_i_max represents the maximum value of the charging power of the energy storage system, and E c_i_max represents the maximum value of the discharging power of the energy storage system, and W ess_i_min and W ess_i_max represent the charge state constraint of the energy storage system. The constraint on the charging amount of the energy storage system at time t is realized.

[0114] In addition, this method also constrains the load value of the virtual power plant at time t, specifically through Equation (8) and Equation (9). represents the load value of the virtual power plant at time t, respectively represent the lower limit and the upper limit of the load floating value. The constraint on the load value at time t is realized through Equation (8) and Equation (9).

[0115] When calculating the equivalent parameters of the above model, the values of various types of parameters at a certain moment are taken as references when they reach extreme values. At this time, the generator power and ramp power are tight constraint conditions, that is, it is necessary to ensure that the values on both sides of this inequality constraint are equal; for the accumulated power of the generator, it is a loose constraint condition, that is, the inequality still holds when the values at both ends are not equal. For the charging power and cumulative charging power constraints of the energy storage system (ESS), tight constraints are adopted, while for the ramp charging power of the energy storage system ESS, loose constraints are adopted.

[0116] The goal in this embodiment is to minimize the deviation. This process generally uses a common optimization scheduling parser (such as Gurobi software) to solve the model proposed in this project to obtain the minimum value (i.e., the optimal value); in this embodiment, only the modeling and solving process is involved, and no specific optimization algorithm is involved. The optimization algorithm is provided inside the optimization scheduling parser. In the most ideal case, if the optimal value of the optimization model is 0 (i.e., the deviation is 0), it indicates that the virtual power plant can perform unbiased scheduling for the scheduling curve given by the ISO.

[0117] Taking the deviation value as the objective value of the objective function, the deviation value is maximized through the upper-layer objective function of the optimization model, and the deviation value is minimized through the lower-layer objective function of the optimization model. In this embodiment, the preset value is set to 0, and the given deviation value is 0, indicating that the virtual power plant can perform unbiased scheduling based on the given scheduling curve, which is convenient for optimizing and guiding the scheduling process.

[0118] The problems in the above stage can be solved by the column constraint generation (C&CG) method to ensure the convergence of solving the original problem. The core of the C&CG method is to decompose the original optimization problem into a master problem and a sub-problem. After each sub-problem is solved, new constraints are added to the master problem, and through the iterative solution of the master problem and the sub-problem, the solution result of the original problem is obtained.

[0119] In summary, this application considers the actual operation mode of distributed resources and specifically constructs a virtual power plant model for multi-energy aggregation. This model serves as an energy aggregator to evaluate the adjustability of distributed resources and presents it as a whole to the system operator by obtaining the parameters of its external equivalent model. Aiming at the complex multi-energy supply network and the internal and external uncertainty factors of the virtual power plant, an optimization scheduling method for the virtual power plant model based on distributed energy is proposed to provide guidance for the optimization scheduling of the virtual power plant.

[0120] The embodiment of the present application also discloses an optimized scheduling system for a virtual power plant model based on distributed energy. The system includes an input unit and a processing unit. The input unit is used to establish the deviation between the theoretical value of the power required by the virtual power plant and the actual power obtained by the virtual power plant as the deviation value Δp t , and input the deviation value into the processing unit; the processing unit is used to establish an optimization model and use the deviation value as the target value of the objective function of the optimization model; the processing unit is also used to constrain the target value of the optimization model according to the parameters of the virtual power plant; the processing unit is also used to maximize the deviation value through the upper-level objective function of the optimization model and minimize the deviation value through the lower-level objective function of the optimization model; the processing unit is also used to determine whether the deviation value is a predetermined value. If the deviation value is a predetermined value, the virtual power plant can perform bias-free scheduling based on the given scheduling scheme.

[0121] For the processing unit, when constraining the target value of the optimization model according to the parameters of the virtual power plant, it is necessary to constrain the output of distributed power source i at time t; it is necessary to constrain the charging amount of the energy storage system at time t, and it is also necessary to constrain the load value of the virtual power plant at time t. The above three are respectively constrained by formulas (3) and (4), formulas (5)-(7), and formulas (8) and (9). The above formulas have been introduced in the previous text.

[0122] The scheduling method and scheduling system disclosed in the present invention ensure the robustness and feasibility of the virtual power plant model by establishing a two-layer structure and adopting a robust method, and decompose the complex original problem into multiple easily solved sub-problems for solution. Finally, the modeling and calculation of the multi-energy flow system of large-scale distributed energy are realized.

[0123] Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An optimal scheduling method for a virtual power plant model based on distributed energy, characterized in that, Including the following steps: The deviation between the theoretical value of the required electricity of the virtual power plant and the actual electricity obtained by the virtual power plant is defined as the deviation value Δp t ; Establish an optimization model, and use the deviation value as the objective value of the objective function of the optimization model, where the objective function of the optimization model is Constrain the objective value of the optimization model through the parameters of the virtual power plant, where the objective value is specifically constrained by the following formula In formulas (1) and (2), represents the energy interaction between the virtual power plant and the large power grid at time t, represents the output of distributed power source i at time t, represents the total power generation of the power generation equipment, represents the charging amount of the energy storage system at time t, represents the total reserve energy of the energy storage system, represents the load value of the virtual power plant at time t; Maximize the deviation value through the upper-level objective function of the optimization model, and minimize the deviation value through the lower-level objective function of the optimization model; Determine whether the deviation value is a predetermined value. If the deviation value is the predetermined value, the virtual power plant can perform non-deviation dispatching based on the given dispatching scheme where the non-deviation dispatching is carried out, and the predetermined value is 0.

2. The optimized scheduling method of a virtual power plant model based on distributed energy according to claim 1, characterized in that The method further includes constraining the output of the distributed power source i at time t Specifically, the constraint is carried out by the following formula Among them, Pg_i_min represents the minimum output power of distributed power source i, Pg_i_max represents the maximum output power of distributed power source i, ramp g_i_down represents the minimum ramp constraint speed of distributed power source i, ramp g_i_up represents the maximum ramp constraint speed of distributed power source i, and t represents the scheduling time.

3. An optimized scheduling method for a virtual power plant model based on distributed energy according to claim 1, characterized in that, The method further includes constraining the charging amount of the energy storage system at time t Specifically, the constraint is performed by the following formula Among them, represents the charging amount of energy storage system i at time t, E disc_i_max represents the maximum value of the charging power of the energy storage system, E c_i_max represents the maximum value of the discharging power of the energy storage system, W ess_i_min and W ess_i_max represent the state of charge constraint of the energy storage system.

4. An optimized scheduling method for a virtual power plant model based on distributed energy according to claim 1, characterized in that The method further includes constraining the load value of the virtual power plant at time t, specifically, by the following formula for the constraint Among them, represents the load value of the virtual power plant at time t, respectively represent the lower limit and the upper limit of the load fluctuation value.

5. An optimized scheduling system for a virtual power plant model based on distributed energy, characterized in that, The system includes: an input unit and a processing unit; An input unit is used to establish that the deviation between the theoretical value of the power required by the virtual power plant and the actual power obtained by the virtual power plant is the deviation value Δp t and input the deviation value into the processing unit; The processing unit is used to establish an optimization model, and use the deviation value as the objective value of the objective function of the optimization model. The optimization model constructed by the processing unit is shown as follows The processing unit is further used to constrain the objective value of the optimization model according to the parameters of the virtual power plant, where the objective value is constrained by the following formula In formulas (1) and (2), represents the energy interaction between the virtual power plant and the large power grid at time t, represents the output of distributed power source i at time t, represents the total power generation of the power generation equipment, represents the charging amount of the energy storage system at time t, represents the total reserve energy of the energy storage system, represents the load value of the virtual power plant at time t; The processing unit is further used to maximize the deviation value through the upper-level objective function of the optimization model, and minimize the deviation value through the lower-level objective function of the optimization model; The processing unit is further configured to determine whether the deviation value is a predetermined value. If the deviation value is the predetermined value, the virtual power plant may perform deviation-free scheduling based on a given scheduling scheme wherein the predetermined value is 0.

6. The optimized scheduling system of a virtual power plant model based on distributed energy according to claim 5, characterized in that, The processing unit is further configured to constrain the output power of the distributed power source i at time t Specifically, the constraint is performed by the following formula Among them, P g_i_min represents the minimum output power of distributed power source i, P g_i_max represents the maximum output power of distributed power source i, ramp g_i_down represents the minimum ramp constraint speed of distributed power source i, ramp g_i_up represents the maximum ramp constraint speed of distributed power source i, and t represents the scheduling time.

7. An optimized scheduling system for a virtual power plant model based on distributed energy according to claim 5, characterized in that, The processing unit is further configured to constrain the charging amount of the energy storage system at time t Specifically, the constraint is performed by the following formula Among them, represents the charging amount of energy storage system i at time t, E disc_i_max represents the maximum value of the charging power of the energy storage system, E c_i_max represents the maximum value of the discharging power of the energy storage system, W ess_i_min and W ess_i_max represent the state of charge constraint of the energy storage system.

8. The optimized scheduling system of a virtual power plant model based on distributed energy according to claim 5, characterized in that, The processing unit is further used to constrain the load value of the virtual power plant at time t, specifically, by the following formula for the constraint Among them, represents the load value of the virtual power plant at time t, respectively represent the lower limit and the upper limit of the load fluctuation value.

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