Distributed resource aggregation operation boundary construction method, system, equipment and medium

Abstract

The invention belongs to the technical field of resource aggregation, and discloses a distributed resource aggregation operation boundary construction method, system, device and medium, and the method comprises the steps: constructing a distributed resource aggregation operation feasible region model based on an aggregation operation range, and integrating the model with a balance unit day-ahead scheduling power baseline model to obtain a distributed resource deterministic aggregation model; analyzing the influence of the new energy unit output and the source load uncertainty of the load power utilization power on the distributed resource determinacy aggregation model, and constructing a balance unit baseline operation model; and solving the distributed resource aggregation operation feasible region model, and determining a distributed resource aggregation operation boundary considering uncertainty. According to the invention, the IGDT theory is introduced to characterize the uncertainty of the output at the two sides of the source load, DER day-ahead scheduling baseline models with different risk preferences are constructed, and on the basis, the solution of the DER operation feasible region boundary considering the uncertainty of the source load is realized.

Description

Technical Field

[0001] This invention relates to the field of resource aggregation technology, and in particular to a method, system, device and medium for constructing the operational boundary of distributed resource aggregation. Background Technology

[0002] Against the backdrop of dual-carbon goals and the construction of new power systems, distributed energy resources (DERs), primarily wind and solar power, are developing rapidly. Traditional generating units are facing significant challenges, with their regulation capabilities declining and the peak-to-valley difference in the power grid widening, leading to a substantial increase in the demand for flexible grid regulation. While the vast amount of DERs on the distribution network possesses abundant regulation potential, limitations in capacity, quantity, and geographical location prevent them from participating independently in grid dispatch or the electricity market, thus their regulation capabilities have not been fully explored and utilized. Therefore, it is urgent to construct an aggregation model for DERs to promote their participation in power system dispatch and regulation ancillary services.

[0003] Currently, existing technologies for DER aggregation management mainly include aggregation entities such as microgrids, load aggregators, and virtual power plants. The balancing unit is a basic regulation unit built for regional power grids, aiming to achieve local power balance and regulation functions. It can promote the decentralization of the main grid's balancing responsibility and incorporate prefecture / county-level dispatching into the active power balance system. The balancing unit can achieve self-balancing of power generation and consumption, which can effectively alleviate the pressure of centralized control, reduce the risk of system imbalance, improve the reliability of power supply and the capacity for renewable energy consumption, and support the stable operation of new power systems. Some current research on DER aggregation focuses on the multi-dimensional quantification of the aggregation response capability of distributed resources, including indicators such as regulation direction, amplitude, response time, and duration, but does not consider the impact of topology on the model. Another part uses mathematical tools such as Chino polyhedra and Minkowski summation to characterize the equivalent model of the feasible domain space of DER clusters, but this is prone to the problem of the curse of dimensionality when applied to large-scale DER clusters.

[0004] The uncertainty in the power consumption of distributed wind power, photovoltaic power, and flexible loads poses a serious challenge to the reliability of the DER aggregation model. Traditional stochastic optimization methods rely on the probability distribution of random variables or historical data, but the distribution information of actual data or random variables is difficult to obtain. Fuzzy optimization methods can transform some fuzzy information that is difficult to quantify precisely (such as users' willingness to respond) into variables that can be handled by mathematical methods through membership functions, but they are highly subjective and their physical meaning is not very clear. Robust optimization methods use intervals or sets to describe the uncertainty of random variables. Although they can ensure that the optimization results meet the requirements in all cases, they are too conservative, may sacrifice economic efficiency, and are computationally complex. Therefore, it is urgent to study a DER aggregation model that takes into account both risk aversion and economic potential.

[0005] Information gap decision theory (IGDT) offers a novel approach to addressing the aforementioned problems. This method characterizes the uncertainty of variables using non-probability intervals, independent of historical data or probability distributions of the uncertainties. IGDT solves for the range of uncertainties while satisfying expected costs or profits, thus ensuring model robustness while maintaining economic efficiency, or minimizing model uncertainty while pursuing economic efficiency. Currently, this method has some applications in power system optimal scheduling, unit combination, and bidding decisions; however, research on DER convergence models remains insufficient.

[0006] Therefore, how to provide methods, systems, devices, and media for constructing distributed resource aggregation operation boundaries is an urgent problem to be solved. Summary of the Invention

[0007] This invention provides a method, system, device, and medium for constructing distributed resource aggregation operation boundaries to solve the problems mentioned above in the prior art.

[0008] According to a first aspect of the present invention, a method for constructing a distributed resource aggregation runtime boundary is provided.

[0009] In one embodiment, a method for constructing the runtime boundary of distributed resource aggregation includes:

[0010] The day-ahead scheduling power baseline model of the balancing unit is used to determine the aggregated operation range of the balancing unit. Based on the aggregated operation range, a feasible region model for the aggregated operation of distributed resources is constructed and integrated with the day-ahead scheduling power baseline model of the balancing unit to obtain a deterministic aggregated model for distributed resources.

[0011] Based on the information gap decision theory, the impact of the source-load uncertainty of the output of new energy units and the power consumption of loads on the deterministic aggregation model of distributed resources is analyzed, and a baseline operation model of the balance unit is constructed based on the impact results.

[0012] The feasible region model for distributed resource aggregation is solved by using the baseline operation model of the balanced unit, the adjustable range of the balanced unit aggregation under different risk attitudes is obtained, and the boundary of distributed resource aggregation operation considering uncertainty is determined.

[0013] In one embodiment, the day-ahead scheduling power baseline model of the balancing unit is used to determine the aggregated operating range of the balancing unit. Based on the aggregated operating range, a feasible region model for distributed resource aggregation is constructed and integrated with the day-ahead scheduling power baseline model of the balancing unit to obtain a deterministic aggregation model for distributed resources, including:

[0014] Controllable distributed power sources, new energy units, energy storage and flexible loads are regarded as distributed resources, and output constraints of controllable distributed power sources are constructed based on the upper and lower limits of active power output of controllable distributed power sources.

[0015] Obtain the actual active power output and predicted output power of the new energy units, construct the actual active power output constraints of the new energy units, and generate energy storage operation constraints by combining the charging and discharging power and total capacity of energy storage.

[0016] The power consumption of flexible loads generates load constraints, and the power flow constraints of the distribution network are constructed based on the actual active power and reactive power values ​​transmitted through the tie lines between the balancing unit and the upper-level power grid.

[0017] The day-ahead dispatch power baseline model of the balancing unit is constructed by combining load constraints, distribution network power flow constraints, output constraints, actual active power output constraints and energy storage operation constraints.

[0018] Based on the day-ahead scheduling power baseline model of the balancing unit, the active power of the tie line is set, and the aggregate operation boundary of the distributed resources is calculated using the active power of the tie line, thus constructing a feasible domain model for the aggregate operation of the distributed resources.

[0019] By integrating the feasible domain model of distributed resource aggregation operation with the day-ahead scheduling power baseline model of the balancing unit, a deterministic aggregation model of distributed resources is obtained.

[0020] In one embodiment, the active power of tie lines is set based on the day-ahead scheduling power baseline model of the balancing unit, and the aggregated operation boundary of distributed resources is calculated using the active power of tie lines to construct a feasible domain model for the aggregated operation of distributed resources, including:

[0021] Based on the day-ahead scheduling power baseline model of the balancing unit, the maximum and minimum values ​​of active power of the tie line are used as the optimization objective function, and the optimization objective function is solved to determine the power adjustable boundary of the distribution network at the grid connection point for each time period.

[0022] Based on the power adjustable boundary analysis, the adjustable power that the controllable distributed power source can provide and the active power output of the new energy unit in the baseline state are analyzed, and the unit adjustment amount constraint is determined by combining the state of charge after energy storage participates in the regulation.

[0023] The upper limit coefficients of the power consumption and allowable adjustment power of flexible loads when they do not participate in demand response are obtained to generate constraints on the adjustment characteristics of flexible loads. At the same time, a feasible domain model for distributed resource aggregation operation is generated by combining the power flow constraints of the distribution network.

[0024] In one embodiment, the impact of source-load uncertainty in the output of new energy generating units and the power consumption of loads on the deterministic aggregation model of distributed resources is analyzed based on information gap decision theory, and a baseline operation model of the balancing unit is constructed based on the impact results, including:

[0025] Based on the power output fluctuation of new energy units and the power consumption fluctuation of flexible loads in the distributed resource deterministic aggregation model, an information gap decision envelope constraint model is constructed.

[0026] The comprehensive uncertainty of the information gap decision envelope constraint model is constructed using linear weighting techniques, and a two-level optimization model including the information gap decision robust model and the information gap decision chance model is constructed by maximizing and minimizing the comprehensive uncertainty.

[0027] The two-layer optimization model is transformed into a single-layer model through equivalent transformation, and constraints are added by generating aggregate operation boundaries using different risk scheduling schemes, thus constructing a baseline operation model of the balanced unit based on information gap decision-making.

[0028] In one embodiment, the comprehensive uncertainty of the information gap decision envelope constraint model is constructed using a linear weighting technique, and a two-level optimization model comprising an information gap decision robust model and an information gap decision chance model is constructed by maximizing and minimizing the comprehensive uncertainty.

[0029] Based on the weighting coefficients of the uncertainty of the output of new energy units and the uncertainty of the power consumption of flexible loads, the comprehensive uncertainty of the information gap decision envelope constraint model is calculated, and a two-level optimization model is constructed in combination with the deviation factor formulated by the balancing unit operator.

[0030] The decision value of the two-level optimization model is optimized by maximizing the comprehensive uncertainty, ensuring that the expected cost does not exceed the target threshold under any disturbance, and a robust information gap decision model with conservative equilibrium unit aggregator is obtained based on the maximization result.

[0031] The decision value of the bi-level optimization model is optimized by minimizing the comprehensive uncertainty, and the decision scheme with at least one perturbation that makes the expected cost reach the target is found. Based on the minimization result, an information gap decision opportunity model with an aggressive equilibrium unit aggregator is obtained.

[0032] In one embodiment, the feasible region model for distributed resource aggregation is solved using the baseline operation model of the balancing unit to obtain the adjustable range of balancing unit aggregation under different risk attitudes, and the operational boundary of distributed resource aggregation considering uncertainties is determined as follows:

[0033] Based on the baseline operation model of the balance unit, the operating power baseline of the balance unit under different risk preferences and the output allocation results of different distributed resources are analyzed, and the feasible domain model of distributed resource aggregation operation is re-solved based on the analysis results.

[0034] Based on the solution results of the feasible region model for distributed resource aggregation operation, a model of adjustable range of aggregation power of balancing unit is constructed. The adjustable range of aggregation power of balancing unit is obtained under different risk attitudes using the model of adjustable range of aggregation power of balancing unit.

[0035] Based on the adjustable range of aggregation and the baseline operation model of the balanced unit, the feasible boundary of the power of robust aggregation of distributed resources and the feasible boundary of the power of opportunistic aggregation are output.

[0036] In one embodiment, based on the aggregate adjustable range and the baseline operating model of the balancing unit, the output feasible domain boundary of the distributed resource robust aggregate operating power and the feasible domain boundary of the opportunity aggregate operating power include:

[0037] Based on operational experience, cost robustness deviation parameters are obtained, the baseline operation model of the balance unit is solved, the prediction error and the total cost value of each distributed resource scheduling scheme are obtained, and the robustness condition value is output.

[0038] By combining the robust condition value with the adjustable range of aggregation, the boundary of the feasible region for distributed resource aggregation operation corresponding to the decision-maker bias factor is obtained based on the feasible region model for distributed resource aggregation operation.

[0039] Based on operational experience, cost opportunity deviation parameters are obtained, the information gap decision opportunity model is solved, the prediction error and the total cost value of each distributed resource scheduling scheme are obtained, and the opportunity condition value is output.

[0040] By combining the opportunity condition value with the adjustable range of aggregation, the boundary of the feasible region for distributed resource opportunity aggregation operation corresponding to the decision-maker bias factor is obtained based on the feasible region model for distributed resource aggregation operation.

[0041] According to a second aspect of the present invention, a distributed resource aggregation runtime boundary construction system is provided.

[0042] In one embodiment, a distributed resource aggregation runtime boundary construction system includes:

[0043] The deterministic aggregation model construction unit is used to determine the aggregation operation range of the balancing unit using the day-ahead scheduling power baseline model of the balancing unit, construct the feasible region model of distributed resource aggregation operation based on the aggregation operation range, and integrate it with the day-ahead scheduling power baseline model of the balancing unit to obtain the distributed resource deterministic aggregation model.

[0044] The baseline operation model construction unit is used to analyze the impact of the source-load uncertainty of the output of new energy units and the power consumption of load on the deterministic aggregation model of distributed resources based on the information gap decision theory, and to construct the baseline operation model of the balance unit based on the impact results.

[0045] The aggregation operation boundary construction unit is used to solve the feasible domain model of distributed resource aggregation operation using the baseline operation model of the balance unit, obtain the adjustable range of the balance unit aggregation under different risk attitudes, and determine the distributed resource aggregation operation boundary considering uncertainty.

[0046] In one embodiment, the deterministic aggregation model building unit includes:

[0047] The output constraint construction module is used to treat controllable distributed power sources, new energy units, energy storage and flexible loads as distributed resources, and to construct output constraints for controllable distributed power sources based on the upper and lower limits of their active power output.

[0048] The energy storage constraint construction module is used to obtain the actual active power output and power output prediction values ​​of the new energy unit, construct the actual active power output constraints of the new energy unit, and generate energy storage operation constraints by combining the charging and discharging power and total capacity of the energy storage.

[0049] The power flow constraint module of the distribution network is used to generate load constraint conditions based on the power consumption of flexible loads, and to construct power flow constraints of the distribution network based on the actual active power value and reactive power value transmitted by the tie line between the balancing unit and the upper-level power grid.

[0050] The power baseline model construction module is used to combine load constraints, distribution network power flow constraints, output constraints, actual active power output constraints, and energy storage operation constraints to construct the day-ahead dispatch power baseline model of the balance unit.

[0051] The feasible region model construction module is used to set the active power of tie lines based on the day-ahead scheduling power baseline model of the balancing unit, and to use the active power of tie lines to calculate the aggregate operation boundary of distributed resources and construct the feasible region model of distributed resource aggregate operation.

[0052] The aggregation model integration module is used to integrate the feasible domain model of distributed resource aggregation operation with the day-ahead scheduling power baseline model of the balancing unit to obtain a deterministic aggregation model of distributed resources.

[0053] In one embodiment, the active power of tie lines is set based on the day-ahead scheduling power baseline model of the balancing unit, and the aggregated operation boundary of distributed resources is calculated using the active power of tie lines to construct a feasible domain model for the aggregated operation of distributed resources, including:

[0054] Based on the day-ahead scheduling power baseline model of the balancing unit, the maximum and minimum values ​​of active power of the tie line are used as the optimization objective function, and the optimization objective function is solved to determine the power adjustable boundary of the distribution network at the grid connection point for each time period.

[0055] Based on the power adjustable boundary analysis, the adjustable power that the controllable distributed power source can provide and the active power output of the new energy unit in the baseline state are analyzed, and the unit adjustment amount constraint is determined by combining the state of charge after energy storage participates in the regulation.

[0056] The upper limit coefficients of the power consumption and allowable adjustment power of flexible loads when they do not participate in demand response are obtained to generate constraints on the adjustment characteristics of flexible loads. At the same time, a feasible domain model for distributed resource aggregation operation is generated by combining the power flow constraints of the distribution network.

[0057] In one embodiment, the baseline running model building unit includes:

[0058] The envelope constraint model construction module is used to construct an information gap decision envelope constraint model based on the power output fluctuation range of new energy units and the power consumption fluctuation range of flexible loads in the distributed resource deterministic aggregation model.

[0059] The two-level optimization model construction module is used to construct the comprehensive uncertainty of the information gap decision envelope constraint model using linear weighting techniques, and to construct a two-level optimization model containing the information gap decision robust model and the information gap decision opportunity model by maximizing and minimizing the comprehensive uncertainty.

[0060] The baseline operation model construction module is used to transform the two-layer optimization model into a single-layer model through equivalent transformation, and to generate aggregate operation boundaries by using different risk scheduling schemes to increase constraints, thereby constructing a baseline operation model of the equilibrium unit based on information gap decision-making.

[0061] According to a third aspect of the present invention, a computer device is provided.

[0062] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0063] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0064] In one embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the above method.

[0065] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0066] Starting from the operation and regulation characteristics of multiple types of DERs, this invention constructs a deterministic DER aggregation model and introduces IGDT theory to characterize the uncertainty of output on both sides of the source load. It also constructs a day-ahead scheduling baseline model for DERs with different risk preferences and, based on this model, solves the boundary of the DER operational feasibility domain that takes into account the uncertainty of the source load.

[0067] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0068] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0069] Figure 1 This is a flowchart illustrating a method for constructing a distributed resource aggregation runtime boundary according to an exemplary embodiment;

[0070] Figure 2 This is a principle block diagram illustrating a distributed resource aggregation operation boundary construction system according to an exemplary embodiment;

[0071] Figure 3 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment;

[0072] Figure 4 This is a flowchart illustrating a distributed resource aggregation runtime boundary construction method in a specific application, according to an exemplary embodiment.

[0073] Figure 5 This is a schematic diagram of a radial power distribution system network structure in a distributed resource aggregation operation boundary construction method according to an exemplary embodiment;

[0074] Figure 6 This is a schematic diagram of an improved IEEE 33-node power distribution system network topology in a distributed resource aggregation operation boundary construction method according to an exemplary embodiment.

[0075] Figure 7This is a current time-of-use electricity price curve in a distributed resource aggregation operation boundary construction method illustrated according to an exemplary embodiment;

[0076] Figure 8 This is a diagram showing the wind and solar power output prediction curves and flexible load power consumption curves in a distributed resource aggregation operation boundary construction method according to an exemplary embodiment.

[0077] Figure 9 This is a diagram illustrating the baseline output status of generating units and the baseline operating power of the balancing unit in a distributed resource aggregation operation boundary construction method according to an exemplary embodiment.

[0078] Figure 10 This is a diagram illustrating the adjustable power range of the deterministic aggregation of the balancing unit in a distributed resource aggregation operation boundary construction method according to an exemplary embodiment;

[0079] Figure 11 This is a schematic diagram illustrating the changes in the balancing unit scheduling operation cost and model uncertainty under the IGDT robust model in the distributed resource aggregation operation boundary construction method according to an exemplary embodiment;

[0080] Figure 12 This is a schematic diagram illustrating the changes in the balancing unit scheduling operation cost and model uncertainty under the IGDT opportunity model in a distributed resource aggregation operation boundary construction method according to an exemplary embodiment.

[0081] Figure 13 This is a schematic diagram illustrating the changes in the operating baseline of the balancing unit based on IGDT in a distributed resource aggregation operation boundary construction method according to an exemplary embodiment.

[0082] Figure 14 This is a schematic diagram illustrating the change in the adjustable range of the balancing unit based on IGDT in a distributed resource aggregation operation boundary construction method according to an exemplary embodiment;

[0083] Figure 15 This is a schematic diagram of the operational framework of the balancing unit in a distributed resource aggregation operation boundary construction method according to an exemplary embodiment. Detailed Implementation

[0084] Figure 1 An embodiment of the distributed resource aggregation runtime boundary construction method of the present invention is shown.

[0085] In this optional embodiment, the method for constructing the distributed resource aggregation runtime boundary includes:

[0086] Step S101: Determine the aggregated operating range of the balancing unit using the day-ahead scheduling power baseline model of the balancing unit, construct a feasible region model for distributed resource aggregated operation based on the aggregated operating range, and integrate it with the day-ahead scheduling power baseline model of the balancing unit to obtain a deterministic aggregated model for distributed resources.

[0087] Step S102: Based on the information gap decision theory, analyze the impact of the source-load uncertainty of the output of new energy units and the power consumption of loads on the deterministic aggregation model of distributed resources, and construct the baseline operation model of the balance unit based on the impact results.

[0088] Step S103: Solve the feasible region model of distributed resource aggregation operation using the baseline operation model of the balanced unit, obtain the adjustable range of balanced unit aggregation under different risk attitudes, and determine the boundary of distributed resource aggregation operation considering uncertainty.

[0089] In this optional embodiment, the day-ahead scheduling power baseline model of the balancing unit is used to determine the aggregated operating range of the balancing unit. Based on the aggregated operating range, a feasible region model for the aggregated operation of distributed resources is constructed and integrated with the day-ahead scheduling power baseline model of the balancing unit to obtain a deterministic aggregated model for distributed resources. This model includes: taking controllable distributed power sources, new energy units, energy storage, and flexible loads as distributed resources, and constructing output constraints for controllable distributed power sources based on the upper and lower limits of their active power output; obtaining the actual active power output and predicted output power values ​​of new energy units to construct actual active power output constraints for new energy units, and generating energy storage operation constraints by combining the charging and discharging power and total capacity of energy storage; based on The power consumption of flexible loads generates load constraints, and distribution network power flow constraints are constructed based on the actual active and reactive power values ​​transmitted through the tie lines between the balancing unit and the upper-level power grid. The load constraints, distribution network power flow constraints, output constraints, actual active power output constraints, and energy storage operation constraints are combined to construct the day-ahead dispatch power baseline model of the balancing unit. Based on the day-ahead dispatch power baseline model of the balancing unit, the active power of the tie lines is set, and the aggregation operation boundary of distributed resources is calculated using the active power of the tie lines to construct the feasible region model of distributed resource aggregation operation. The feasible region model of distributed resource aggregation operation and the day-ahead dispatch power baseline model of the balancing unit are integrated to obtain the deterministic aggregation model of distributed resources.

[0090] In this optional embodiment, the active power of tie lines is set based on the day-ahead scheduling power baseline model of the balancing unit, and the aggregated operation boundary of distributed resources is calculated using the active power of tie lines. The construction of the feasible domain model for aggregated operation of distributed resources includes: based on the day-ahead scheduling power baseline model of the balancing unit, the maximum and minimum values ​​of the active power of tie lines are used as the optimization objective function, and the optimization objective function is solved to determine the power adjustable boundary of the distribution network at each time period at the grid connection point; based on the power adjustable boundary, the adjustable power that controllable distributed power sources can provide and the baseline active power output of new energy units are analyzed, and the unit adjustment amount constraint is determined by combining the state of charge after energy storage participates in the adjustment; the upper limit coefficient of the power consumption and allowable adjustment power when flexible loads do not participate in demand response is obtained to generate the flexible load adjustment characteristic constraint, and the distributed resource aggregated operation feasible domain model is generated by combining the power flow constraint of the distribution network.

[0091] In this optional embodiment, the impact of the source-load uncertainty of the output of new energy units and the power consumption of loads on the distributed resource deterministic aggregation model is analyzed based on the information gap decision theory. The baseline operation model of the balancing unit is constructed based on the following: an information gap decision envelope constraint model is constructed based on the output fluctuation amplitude of new energy units and the fluctuation amplitude of flexible load power consumption in the distributed resource deterministic aggregation model; the comprehensive uncertainty of the information gap decision envelope constraint model is constructed using linear weighting techniques, and a two-level optimization model including an information gap decision robust model and an information gap decision opportunity model is constructed by maximizing and minimizing the comprehensive uncertainty; the two-level optimization model is transformed into a single-level model through equivalent transformation, and constraints are added by generating aggregation operation boundaries using different risk scheduling schemes, thus constructing the baseline operation model of the balancing unit based on information gap decision.

[0092] In this optional embodiment, the comprehensive uncertainty of the information gap decision envelope constraint model is constructed using linear weighting techniques. A two-level optimization model, comprising an information gap decision robust model and an information gap decision opportunity model, is constructed by maximizing and minimizing the comprehensive uncertainty. This includes: calculating the comprehensive uncertainty of the information gap decision envelope constraint model based on the weighting coefficients of the output uncertainty of the new energy unit and the power consumption uncertainty of the flexible load; constructing a two-level optimization model by combining the deviation factor specified by the balancing unit operator; maximizing the comprehensive uncertainty to optimize the decision value of the two-level optimization model, ensuring that the expected cost does not exceed the target threshold under any disturbance; and obtaining a conservative information gap decision robust model based on the maximization result; and minimizing the comprehensive uncertainty to optimize the decision value of the two-level optimization model, finding a decision scheme where at least one disturbance allows the expected cost to meet the target; and obtaining an aggressive information gap decision opportunity model based on the minimization result.

[0093] In this optional embodiment, the feasible region model for distributed resource aggregation operation is solved using the baseline operation model of the balancing unit to obtain the adjustable range of balancing unit aggregation under different risk attitudes. The determination of the operational boundary of distributed resource aggregation considering uncertainty includes: analyzing the baseline of balancing unit operation power and the output allocation results of different distributed resources under different risk preferences based on the baseline operation model of the balancing unit, and resolving the feasible region model of distributed resource aggregation operation based on the analysis results; constructing the adjustable range model of balancing unit aggregation power based on the solution results of the feasible region model of distributed resource aggregation operation, and obtaining the adjustable range of balancing unit aggregation under different risk attitudes using the adjustable range model of balancing unit aggregation power; and outputting the feasible region boundary of distributed resource robust aggregation operation power and the feasible region boundary of opportunity aggregation operation power based on the adjustable range of aggregation and the baseline operation model of the balancing unit.

[0094] In this optional embodiment, the output of the feasible domain boundaries of robust aggregated operation power and opportunity aggregated operation power based on the aggregated adjustable range and the baseline operation model of the balancing unit includes: obtaining cost robustness deviation parameters based on operational experience, solving the baseline operation model of the balancing unit to obtain the prediction error and the total cost value of each distributed resource scheduling scheme, and outputting robustness condition values; combining the robustness condition values ​​with the aggregated adjustable range, and calculating the feasible domain boundary of the distributed resource robust aggregated operation corresponding to the decision-maker bias factor based on the feasible domain model of distributed resource aggregated operation; obtaining cost opportunity deviation parameters based on operational experience, solving the information gap decision opportunity model to obtain the prediction error and the total cost value of each distributed resource scheduling scheme, and outputting opportunity condition values; combining the opportunity condition values ​​with the aggregated adjustable range, and calculating the feasible domain boundary of the distributed resource opportunity aggregated operation corresponding to the decision-maker bias factor based on the feasible domain model of distributed resource aggregated operation.

[0095] Figure 2 An embodiment of the distributed resource aggregation runtime boundary construction system of the present invention is shown.

[0096] In this optional embodiment, the distributed resource aggregation runtime boundary construction system includes:

[0097] The deterministic aggregation model construction unit 201 is used to determine the aggregation operation range of the balancing unit using the day-ahead scheduling power baseline model of the balancing unit, construct the feasible region model of distributed resource aggregation operation based on the aggregation operation range, and integrate it with the day-ahead scheduling power baseline model of the balancing unit to obtain the distributed resource deterministic aggregation model.

[0098] Baseline operation model construction unit 202 is used to analyze the impact of the source-load uncertainty of the output of new energy units and the power consumption of load on the deterministic aggregation model of distributed resources based on the information gap decision theory, and to construct the baseline operation model of the balance unit based on the impact results.

[0099] The aggregation operation boundary construction unit 203 is used to solve the feasible domain model of distributed resource aggregation operation using the baseline operation model of the balance unit, obtain the adjustable range of the balance unit aggregation under different risk attitudes, and determine the distributed resource aggregation operation boundary considering uncertainty.

[0100] In this optional embodiment, the deterministic aggregation model construction unit 201 includes:

[0101] The output constraint construction module is used to treat controllable distributed power sources, new energy units, energy storage and flexible loads as distributed resources, and to construct output constraints for controllable distributed power sources based on the upper and lower limits of their active power output.

[0102] The energy storage constraint construction module is used to obtain the actual active power output and power output prediction values ​​of the new energy unit, construct the actual active power output constraints of the new energy unit, and generate energy storage operation constraints by combining the charging and discharging power and total capacity of the energy storage.

[0103] The power flow constraint module of the distribution network is used to generate load constraint conditions based on the power consumption of flexible loads, and to construct power flow constraints of the distribution network based on the actual active power value and reactive power value transmitted by the tie line between the balancing unit and the upper-level power grid.

[0104] The power baseline model construction module is used to combine load constraints, distribution network power flow constraints, output constraints, actual active power output constraints, and energy storage operation constraints to construct the day-ahead dispatch power baseline model of the balance unit.

[0105] The feasible region model construction module is used to set the active power of tie lines based on the day-ahead scheduling power baseline model of the balancing unit, and to use the active power of tie lines to calculate the aggregate operation boundary of distributed resources and construct the feasible region model of distributed resource aggregate operation.

[0106] The aggregation model integration module is used to integrate the feasible domain model of distributed resource aggregation operation with the day-ahead scheduling power baseline model of the balancing unit to obtain a deterministic aggregation model of distributed resources.

[0107] In this optional embodiment, the active power of tie lines is set based on the day-ahead scheduling power baseline model of the balancing unit, and the aggregated operation boundary of distributed resources is calculated using the active power of tie lines. The construction of the feasible domain model for aggregated operation of distributed resources includes:

[0108] Based on the day-ahead scheduling power baseline model of the balancing unit, the maximum and minimum values ​​of active power of the tie line are used as the optimization objective function, and the optimization objective function is solved to determine the power adjustable boundary of the distribution network at the grid connection point for each time period.

[0109] Based on the power adjustable boundary analysis, the adjustable power that the controllable distributed power source can provide and the active power output of the new energy unit in the baseline state are analyzed, and the unit adjustment amount constraint is determined by combining the state of charge after energy storage participates in the regulation.

[0110] The upper limit coefficients of the power consumption and allowable adjustment power of flexible loads when they do not participate in demand response are obtained to generate constraints on the adjustment characteristics of flexible loads. At the same time, a feasible domain model for distributed resource aggregation operation is generated by combining the power flow constraints of the distribution network.

[0111] In this optional embodiment, the baseline running model construction unit 202 includes:

[0112] The envelope constraint model construction module is used to construct an information gap decision envelope constraint model based on the power output fluctuation range of new energy units and the power consumption fluctuation range of flexible loads in the distributed resource deterministic aggregation model.

[0113] The two-level optimization model construction module is used to construct the comprehensive uncertainty of the information gap decision envelope constraint model using linear weighting techniques, and to construct a two-level optimization model containing the information gap decision robust model and the information gap decision opportunity model by maximizing and minimizing the comprehensive uncertainty.

[0114] The baseline operation model construction module is used to transform the two-layer optimization model into a single-layer model through equivalent transformation, and to generate aggregate operation boundaries by using different risk scheduling schemes to increase constraints, thereby constructing a baseline operation model of the equilibrium unit based on information gap decision-making.

[0115] To facilitate understanding of the above technical solutions of the present invention, the following further explains the above technical solutions of the present invention from the perspectives of architecture and principle, such as... Figure 4 As shown, the specific implementation process is as follows:

[0116] I. Problem Statement and Research Framework Construction:

[0117] Because distributed energy resources (DERs) are limited by their own capacity and market entry barriers, they cannot independently participate in grid dispatch and market transactions. The flexibility of distribution and consumption side resources is not included in the controllable range, and their adjustability is not fully utilized. There is an urgent need to utilize balancing units (such as...) Figure 15 (As shown) A distributed resource aggregation model for power grid dispatch is constructed to mine and aggregate the active power regulation capabilities of flexible resources. Meanwhile, wind and solar power output and flexible load power consumption have uncertainties. This embodiment focuses on the impact of these uncertainties on the ground-state output of each DER (Decentralized Resource Allocation) and, based on this, solves the aggregated adjustable range of DERs under different uncertainty levels, such as... Figure 1 As shown.

[0118] This embodiment mainly studies the active power and its boundaries of the tie-line nodes between the distribution network and the upper-level power grid. The process of distributed resource aggregation connected to the distribution network can be regarded as the process of solving the feasible domain boundary of the tie-line power at its root node. After the distributed resource aggregator obtains its tie-line feasible domain through aggregation, it further considers the impact of the uncertainty of wind, solar power output and load power consumption on the ground state output of DER, and solves its DER aggregation operation feasible domain considering uncertainty. Finally, all the aggregation data is uploaded to the upper-level power grid. The upper-level power grid dispatchers issue dispatch instructions to the lower-level distribution network based on the aggregation results. Based on this model framework, this embodiment intends to construct a corresponding DER aggregation model.

[0119] II. Construction of a Deterministic Aggregation Model for Distributed Resources:

[0120] (1) Baseline model of day-ahead scheduling power of distributed resources;

[0121] The operating power baseline of various DERs is solved by the day-ahead optimization scheduling method. This baseline serves as the power curve of the tie line of the balancing unit without external intervention, providing a reference benchmark for the calculation of day-ahead scheduling and subsequent aggregation adjustment costs of the upper-level power grid. The objective function of the model considers the generation cost of each controllable distributed source and the power purchase cost of the balancing unit, as shown in Equation 1 below:

[0122]

[0123] In the formula, T represents the set of scheduling time periods, which in this embodiment is set to 24 time periods before the current day; N DG It represents the set of all controllable distributed power sources in the balancing unit; This represents the generation cost of the controllable distributed power source n1 during time period t; This represents the cost for the balancing unit to purchase electricity from the upper-level power grid during time period t.

[0124] The individual costs in the objective function are represented by Equations 2 and 3, respectively:

[0125]

[0126] In the formula, a n1 b n1 c n1 c represents the power generation cost coefficient of the n1th controllable distributed power source. tie,t This represents the time-of-use electricity price for the power grid during period t.

[0127] The model's constraints take into account the operational characteristics of each distributed resource and the power flow constraints of the distribution network:

[0128] 1) Constraints on the operational characteristics of distributed resources:

[0129] The DERs in the balance unit can be divided into the following four categories: controllable distributed power sources (diesel generators / gas turbines), new energy units (distributed wind power / photovoltaics), energy storage (batteries), and flexible loads (loads that can be reduced).

[0130] A. Controllable Distributed Power Sources: Due to their flexible power regulation capabilities, controllable distributed power sources can quickly respond to the scheduling commands of the balancing unit, significantly improving the system's power balance capability and suppressing fluctuations in renewable energy output. In the modeling process of the balancing unit, at least one such controllable power source is usually required to ensure the reliability of system operation. Its output constraints are as follows: Equations 4 to 5:

[0131]

[0132] In the formula, P DG,n1,t Let represent the active power output of the n1th controllable distributed power source during time period t. and These represent the upper and lower limits of the active power output of the n1th controllable distributed power source, respectively. Since this balancing unit mainly participates in power system regulation and ancillary services, each unit has a certain regulation margin during day-ahead optimization scheduling. and These represent the upward and downward adjustment margin coefficients reserved by the n1th controllable distributed power source, respectively. Δt represents the upper limit of the ramp rate of the n1th controllable distributed power source, and Δt represents the time interval from time period t-1 to time period t.

[0133] B. New Energy Units: The power output of new energy units is constrained by natural conditions such as wind speed and sunlight, and their output is uncertain, requiring prediction. Therefore, when constructing a deterministic aggregation model, it is set that the actual active power output of new energy units must not exceed the predicted value of their power, as shown in Equation 6 below:

[0134]

[0135] In the formula, P NE,n2,t This represents the actual active power output of the n2th renewable energy unit at time t. This represents the predicted power output of the n2th renewable energy unit during time period t. These represent the upward and downward adjustment margin coefficients retained by the n2th new energy unit, respectively.

[0136] C. Energy Storage: Energy storage is a special type of DER with both charging and discharging operating modes. Its operating constraints are expressed in Equations 7 to 9 below:

[0137]

[0138]

[0139] In the formula, SOC n3,t E represents the state of charge of the n3rd energy storage unit during time period t; N n3 represents the total capacity of the n3rd energy storage unit, P ES ,n3,t represents the charging / discharging power of the n3rd energy storage unit during time period t. A positive value indicates that the energy storage is discharging, and a negative value indicates that it is charging. η n3,cha η n3,dis Represent the charging and discharging efficiencies of the n3rd energy storage unit, and SOC, respectively. max SOC min These represent the upper and lower limits of the SOC of the n3rd energy storage unit, respectively. This represents the maximum charging / discharging power of the n3rd energy storage unit.

[0140] 2) Distribution network distflow constraints:

[0141] Compared to traditional nodal power AC power flow models with highly non-convex and nonlinear characteristics, the Distflow model, which establishes AC power flow equations based on branch power, is more suitable for radial distribution systems. Therefore, this embodiment uses this model as the power flow constraint for the distribution network, such as... Figure 5 The diagram shows a typical radial power distribution system network structure. The specific mathematical model of the Distflow power flow model is shown in Equations 10 to 19 below:

[0142]

[0143] In the formula, i, j, and k represent node numbers, and u(j) and v(j) represent the downstream and upstream nodes connected to node j, respectively; P ij,t and Q ij,t R represents the active and reactive power flowing through line ij during time period t, respectively; ij and X ij These represent the resistance and reactance values ​​of line ij, respectively; I ij,t P represents the current flowing through line ij during time period t; j,t and Q j,t P represents the equivalent active power injection and equivalent reactive power injection at node j during time period t, respectively. tie,t Q tie,t This represents the actual active and reactive power transmitted via the tie line between the balancing unit and the upstream power grid during time period t. These are the upper and lower limits of its active power, respectively. Distinguish between the upper and lower limits of its reactive power; N (j) For the DER set that connects to node j, λ represents the active power output of the m-th DER at node j during time period t. λ is a flag bit; if node j is the root node of the tie line, its value is 1, otherwise it is 0. These represent the active and reactive loads of node j during time period t, U j,t and U i,t U represents the voltage amplitude at node j and node i during time period t, respectively. max U min These are the upper and lower limits of the voltage at distribution network nodes; Ω E Ω represents the set of lines in a power distribution network. N This represents the set of nodes in a power distribution network.

[0144] (2) Feasible domain model for distributed resource aggregation operation;

[0145] Based on the calculated baseline of the day-ahead dispatching power of the DER, the next step in building the aggregation model is to calculate the aggregation operation boundary of various flexible DERs within it. This can further explore the adjustability potential of the distribution network-side DERs, realize the effective aggregation of DERs, and enable them to participate in the dispatching of the upper-level power grid.

[0146] The aggregated power operating boundary of the balancing unit access to flexible resources is mainly determined by the remaining flexible adjustment capacity of the DER in the day-ahead scheduling plan. Based on the baseline of the balancing unit's day-ahead scheduling plan, the remaining adjustable margin of each unit is calculated. The maximum and minimum values ​​of the active power of the tie line are set as the optimization objective functions as shown in Equations 20 and 21 below. By solving the single-time period optimization problem and traversing all time periods, the power adjustable boundary of the distribution network at the grid connection point for each time period can be obtained.

[0147] max P tie,t ;

[0148] min P tie,t ;

[0149] In the formula, P tie,t This represents the actual active power transmitted between the balancing unit and the upstream power grid during time period t.

[0150] The constraints of the DER pooled power operating boundary model include constraints on each DER regulation amount and power flow constraints:

[0151] 1) Constraints on the adjustment amounts of each DER;

[0152] A. Unit regulation constraints (Equations 22 to 26):

[0153]

[0154] In the formula, ΔP DG,n1,t This represents the adjustable power that the n1th controllable distributed power source can provide during time period t. This represents its active power output at the baseline state during time period t; ΔP NE,n2,t ω represents the regulating power that the n2th renewable energy unit can provide during time period t. NE,n2 This indicates its maximum permissible wind / solar curtailment rate. The baseline active power output of the n2th renewable energy unit during time period t; This indicates the state of charge of the n3rd energy storage unit after it participates in the regulation service during time period t. ΔP represents its state of charge at the baseline state. NE,n3,t This represents the regulating power that the n3rd energy storage unit can provide during time period t. This indicates its baseline charge / discharge power.

[0155] B. Flexible Load Regulation Constraints: To alleviate power supply difficulties or solve the problem of renewable energy consumption, the power grid will incentivize flexible loads to participate in demand response. The flexible load regulation characteristics constraint must satisfy the following equation (Equation 27):

[0156]

[0157] In the formula, ΔP represents the power consumption of flexible loads during time period t when they do not participate in demand response. FE,t This indicates the adjustable power it can provide. With ω FL This represents the upper and lower limits of the allowable adjustable power of the flexible load. This represents the predicted power point value of the flexible load during time period t.

[0158] 2) Distflow power flow constraints;

[0159] The following constraint (Equation 28) needs to be added to the Distflow power flow constraints (10) to (19) of the DER day-ahead scheduling power model:

[0160]

[0161] In the formula, ΔP represents the baseline active power output of the m-th DER at node j during time period t. DER,m,t This indicates the amount of active power that can be adjusted.

[0162] III. Distributed resource aggregation model based on information gap decision theory:

[0163] Based on the constructed DER deterministic aggregation model, this embodiment introduces IGDT theory to study the impact of source-load uncertainty of new energy unit output and load power consumption on the DER aggregation model.

[0164] (1) Overview of Information Gap Decision Theory:

[0165] IGDT is a mathematical optimization method for non-probabilistic uncertainty. Its purpose is to study the various impacts that uncertain parameters may cause under the premise of meeting a preset objective. Based on the difference in the preset objective, IGDT can characterize two different risk preferences of decision-makers. The corresponding models are called IGDT robust models and chance models, which correspond to two completely different attitudes of decision-makers when facing risks: pessimistic decision-makers believe that the existence of uncertain parameters will have a negative impact on the expected objective and hope to reduce the uncertainty of the model by increasing costs; optimistic decision-makers believe that the uncertainty will develop in a direction that is beneficial to the system, thereby helping to reduce costs or increase profits.

[0166] (2) Construction of DER operational baseline model based on IGDT:

[0167] When the probability density functions or historical data of wind power, solar power output, and load power consumption in a deterministic aggregation model are unknown, their actual power... The fluctuations around the predicted values ​​can be described by the IGDT envelope constraint model as shown in Equation 29 below:

[0168]

[0169] In the formula, α NE,n2 α represents the fluctuation range of the output of the n2th new energy unit. FL,n4 This represents the fluctuation range of the power consumption of the n4th flexible load. These represent the actual output of the n2th renewable energy unit and the actual power consumption of the n4th flexible load during time period t, respectively. This indicates that the actual output of the n2th renewable energy unit and the power consumption of the n4th flexible load deviate from the predicted values ​​by no more than [a certain range]. and

[0170] Furthermore, this embodiment uses a linear weighting method to construct the overall uncertainty α of the model, as shown in Equation 30 below:

[0171]

[0172] In the formula, κ NE n2、κ FL ,n4 are the weighting coefficients for the uncertainty of the power output of the n2th new energy unit and the power consumption of the n4th flexible load, respectively.

[0173] When faced with uncertainties, conservative operators typically adopt worst-case assumptions to ensure the achievement of minimum expected targets, maximizing the adverse effects of uncertain parameters. Conversely, aggressive operators tend to pursue the additional benefits that uncertainty may bring, as illustrated in Equations 31 and 32 below:

[0174]

[0175] In the formula, F0 represents the objective function value of equation (1) obtained by optimizing the output of new energy units and the power consumption of flexible loads under the deterministic aggregation model, taking their predicted values; β RM β OM This represents the deviation factor set by the balancing unit operator, indicating the degree of deviation between expected costs and certain costs F0.

[0176] Equation 31 corresponds to two different risk preferences of the aggregator in the equilibrium unit: The robust model of Equation 31 ensures that the expected cost of the decision value does not exceed the threshold F under any perturbation by maximizing the uncertainty radius α. RM Furthermore, the larger α is, the stronger the model's ability to withstand fluctuations in uncertainties; Equation 32, the opportunity model, seeks a decision scheme where at least one disturbance exists to achieve the expected cost by minimizing α. In this case, the smaller α is, the higher the probability of achieving the goal. Both models achieve a trade-off between risk and return by adjusting the deviation factor parameter β, respectively reflecting the difference in risk attitude between conservative and aggressive balance unit operators.

[0177] (3) Equivalent simplification of the model:

[0178] Since Equations 31 and 32 are two-layer optimization models, they cannot be directly solved by the solver and need to be simplified to a single-layer model. Taking the IGDT robust model as an example, in the day-ahead dispatch baseline model of the balancing unit (Equations 1 to 19), when the wind and solar power output is lower than the predicted value or the flexible load power consumption is higher than the predicted value, the system power deficit will be compensated by increasing the power generation of conventional units or by purchasing electricity from the grid, resulting in a synchronous increase in the day-ahead dispatch cost of the balancing unit. Since cost and power deficit are positively correlated, when the wind and solar power output is significantly lower than the planned value and the load power consumption is higher than the predicted value, i.e., when the output is as shown in Equations 33 and 34, the total cost will reach a maximum value.

[0179]

[0180]

[0181] If the optimal objective value obtained from the original deterministic model is F0, then based on the optimization model and assuming that the decision-maker, based on operational experience, gives the maximum acceptable pessimistic cost under adverse perturbations as (1+β). RM )F0, where β RMIf ≥0, then the optimization model needs to add the following constraint as shown in Equation 35:

[0182] F≤(1+β RM )F0;

[0183] In summary, the baseline model for day-ahead scheduling power of DER based on IGDT is shown in Equation 36 below:

[0184]

[0185] The decision solution obtained by this model is robust to uncertainties in wind and solar power output and load power consumption. Similarly, the chance model can be simplified to the following equation (Equation 37):

[0186]

[0187] (4) Adjustable range model of aggregated power of balanced unit based on IGDT:

[0188] After solving the IGDT-based baseline operation model of the balance unit in equations (36) to (37) respectively, the baseline operation power of the balance unit and the output allocation results of different DERs corresponding to different risk preferences can be obtained. Then, after resolving the feasible domain model of DER aggregation operation in equations (20) to (28), the balance unit power operation boundary considering uncertainty is obtained. The following model is constructed to solve the aggregate adjustable range of the balance unit under different risk attitudes.

[0189]

[0190] In the formula, These represent the upward and downward adjustment ranges of the balancing unit during time period t, respectively. The operating boundary values ​​of the equilibrium unit obtained by solving equations (20) and (21) for time period t are expressed as follows. This represents the operating baseline power value of the balancing unit during time period t.

[0191] IV. Construction and Solution Process of Distributed Resource Aggregation Model Based on Information Gap Decision Theory:

[0192] Based on the above, this embodiment presents the solution process for the DER aggregation model based on IGDT as follows: Figure 4 As shown, based on Figure 4 The solution process for the DER aggregation model based on IGDT proposed in this embodiment is explained as follows:

[0193] 1. Replace the output of the new energy units and the power consumption of the flexible load with point prediction values ​​respectively, solve the day-ahead scheduling power baseline model equations (1) to (19) of the balancing unit, obtain the deterministic day-ahead scheduling total cost value F0, and determine the aggregated operation range of the balancing unit based on the output status of each unit at the baseline.

[0194] 2. Based on operational experience, the cost robustness deviation parameter β is given. RM Solve the IGDT robust power baseline model (36) to obtain the prediction error α and the total cost of each DER scheduling scheme;

[0195] 3. Based on the results obtained in steps 1 and 2, run the feasible region model according to the DER deterministic aggregation method to calculate the decision-maker bias factor β. RM The corresponding DER robust aggregation operation feasible region boundary;

[0196] 4. Solve the DER opportunistic aggregation model according to the methods in steps 2 and 3, and then the process ends.

[0197] Fifth, through simulation analysis of an improved 33-node power distribution system, the effectiveness and superiority of the proposed distributed resource aggregation model based on information gap decision theory in DER aggregation control were verified.

[0198] This embodiment uses an improved IEEE 33-node distribution system for verification. Distributed wind power, decentralized photovoltaics, diesel generators, energy storage, and flexible loads are used as four types of DERs, which are aggregated through a balancing unit to provide regulation services to the upper-level grid. The access methods and network topology of each DER are as follows: Figure 6 As shown.

[0199] Node 1 is the interconnection node with the upstream power grid; nodes 11 and 22 are connected to 2MW and 1.2MW gas turbines respectively; node 31 is connected to a 1.8MW diesel generator; node 33 is connected to a 1.5MW wind turbine; node 17 is connected to a 1.8MW photovoltaic unit; node 16 is connected to a 2MW energy storage unit; and nodes 13 and 25 are connected to flexible loads with capacities of 0.5MW and 0.7MW respectively. The parameters of the controllable distributed power sources are shown in Table 1.

[0200] Table 1: Output and Cost Parameters of Controllable Distributed Power Sources

[0201]

[0202] Current time-of-use electricity pricing in a certain area, such as Figure 7 As shown, the predicted power output curves for wind power and photovoltaic power, as well as the predicted power consumption curves for flexible loads, are as follows: Figure 8 As shown, the specific steps in this embodiment are as follows:

[0203] Step 1: Obtain the deterministic aggregation result based on the distributed resource deterministic aggregation model;

[0204] The deterministic baseline output of each DER, obtained through the recently optimized scheduling model, is as follows: Figure 9 As shown, the output that can reduce load is positive, indicating that it is reducing the power consumption of the load. Diesel generators and gas turbines are the main regulating power generation resources, and their output increases significantly during peak load periods (10-18 hours). The output of wind and solar power exhibits typical renewable energy characteristics. Solar power output is larger during the day (6-18 hours), while wind power output is present at all times but fluctuates greatly. Flexible loads consume electricity according to their predicted power consumption. Energy storage is used to match the imbalance between power generation and load. During periods of high electricity prices (8-11 hours), the balancing unit reduces the power purchased and sends electricity out. During periods of 15-17 hours, due to the high output of the units and the low power consumption of the load, the balancing unit sends electricity out. The baseline power is approximately between -1.76 and 0.93 MW.

[0205] The results of the adjustable range of polymerization power of the balanced unit are as follows: Figure 10 As shown, the upward adjustment range is higher during periods 1-5 and 22-24, while the upward adjustment range for other periods is between 2.0 and 2.5 MW. This is because the load is at its lowest point during periods 1-5 and 22-24, and the unit output is at the lower limit, allowing for greater upward adjustment. The downward adjustment range fluctuates between 3 and 3.5 MW for most periods; the adjustable power value is larger during periods 6-18 because the photovoltaic unit output is higher, allowing for greater downward adjustment.

[0206] Step 2: Results and analysis of the ground-state output power of distributed resources considering uncertainties;

[0207] The deterministic baseline optimization cost of the equilibrium unit obtained in step 1 is 22941.52 yuan. This cost value is used as the benchmark value for the subsequent IGDT robust baseline scheduling model and opportunistic baseline scheduling model, β. RM This parameter represents the extent to which the baseline operating cost of DER exceeds the deterministic cost under risk-averse conditions. It reflects the degree of risk aversion among the balance unit decision-makers. Different β values ​​are selected... RM The value can be used to solve for different robust scheduling costs and the overall uncertainty of the model, such as... Figure 11 As shown in the figure, when β RMAs the expected scheduling baseline cost for DER gradually increases from 0 to 0.2 in increments of 0.02, the uncertainty α that the model can withstand also gradually increases. When the expected baseline scheduling operating cost rises from RMB 22,941.52 to RMB 27,529.82, the uncertainty that the model can withstand increases by 15.27%. This indicates that decision-makers believe that the uncertainty of the generating units will develop in a way that is unfavorable to the system, and they hope to mitigate certain risks brought about by uncertainty by increasing the expected scheduling cost.

[0208] β OM This parameter represents the degree to which opportunistic decision-makers believe the actual baseline scheduling cost of DER is lower than the deterministic cost. It reflects the decision-maker's optimism about the risk. Different β values ​​can be selected to indicate this. OM The value can be used to solve for different opportunity costs and model uncertainties, such as... Figure 12 As shown in the figure, with the opportunity cost deviation parameter β... OM As the uncertainty α increases, the total cost of DER baseline scheduling gradually decreases, while the uncertainty α of the model gradually increases. This indicates that optimistic balance unit decision-makers believe that uncertain units can bring certain benefits, and the greater the uncertainty, the greater the cost reduction.

[0209] Step 3: Results and analysis of changes in the operating baseline and adjustment range of the balancing unit considering uncertainties;

[0210] By solving the DER scheduling baseline model considering uncertainties under different cost deviation factors, we can obtain the changes in the cost deviation parameter with different balancing unit operating baselines, as follows: Figure 13 As shown, when the model uncertainty increases, the actual output of wind and solar power decreases, the actual power consumption of the load increases, the balancing unit increases the amount of electricity purchased to maintain local power self-balance, and the operating baseline of the balancing unit gradually decreases.

[0211] After obtaining the operating baseline and DER ground-state operating plan of the balance unit under different cost deviation factors, the feasible domain boundary model of the balance unit operation can be further solved by equations (20) to (28) to obtain the operating boundary of the balance unit under different risk attitudes. Finally, the adjustable range of the power aggregation power of the balance unit under different risk attitudes can be obtained by solving the model of equation (38). The results are as follows: Figure 14 As shown in the figure, the shaded area represents the adjustable range of the equilibrium unit obtained under the deterministic model.

[0212] Since this invention does not take into account the upward adjustment potential of wind power and photovoltaic power, the change in the adjustment capability of the balancing unit is mainly caused by the change in the adjustment potential of flexible loads. In the IGDT robust model, as the power consumption of flexible loads increases, their upward adjustment potential decreases while their downward adjustment potential increases. Therefore, the upward adjustment range of the balancing unit increases while the downward adjustment range decreases. Similarly, in the IGDT opportunistic model, as the output of wind power and photovoltaic power increases, the power consumption of flexible loads decreases. Therefore, the downward adjustment range of the balancing unit increases while the upward adjustment range decreases.

[0213] The numerical examples verify the effectiveness and superiority of the distributed resource aggregation model proposed in this embodiment in the aggregation and management of distributed resources by the balancing unit. This embodiment starts from the operation and adjustment characteristics of multiple types of DERs, constructs a deterministic DER aggregation model, and introduces IGDT theory to characterize the uncertainty of output on both sides of the source load. It constructs DER day-ahead scheduling baseline models with different risk preferences, and solves the balancing unit operation baseline and the adjustable range of DER aggregation that take into account the uncertainty of the source load. Finally, the feasibility of the constructed model is verified by numerical examples.

[0214] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0215] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0216] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0217] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0218] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0219] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A method for constructing a distributed resource aggregation operation boundary, characterized in that, The method comprises: determining the aggregated operation range of the balancing unit by using the balancing unit day-ahead scheduling power baseline model, constructing a distributed resource aggregated operation feasible region model based on the aggregated operation range, and integrating the distributed resource aggregated operation feasible region model with the balancing unit day-ahead scheduling power baseline model to obtain a distributed resource deterministic aggregation model; analyzing the influence of the source-load uncertainty of the new energy unit output and the load power on the distributed resource deterministic aggregation model based on the information gap decision theory, and constructing a balancing unit baseline operation model according to the influence result; solving the distributed resource aggregated operation feasible region model by using the balancing unit baseline operation model to obtain the balancing unit aggregated adjustable range under different risk attitudes, and determining the distributed resource aggregated operation boundary considering the uncertainty.

2. The method of claim 1, wherein, The method comprises: controllable distributed power, new energy units, energy storage and flexible loads are taken as distributed resources, and the output constraint condition of the controllable distributed power is constructed according to the upper and lower limits of the active power output of the controllable distributed power; the actual active power output and the predicted value of the output power of the new energy unit are obtained, the actual active power output constraint of the new energy unit is constructed, and the energy storage operation constraint condition is generated in combination with the charging and discharging power and the total capacity of the energy storage; the load constraint condition is generated based on the power of the flexible load, and the power flow constraint of the distribution network is constructed according to the actual transmission active power value and the reactive power value of the tie line between the balancing unit and the upper-level power grid; the balancing unit day-ahead scheduling power baseline model is constructed by combining the load constraint condition, the power flow constraint of the distribution network, the output constraint condition, the actual active power output constraint and the energy storage operation constraint condition; the active power of the tie line is set based on the balancing unit day-ahead scheduling power baseline model, the aggregated operation boundary of the distributed resource is calculated by using the active power of the tie line, and the distributed resource aggregated operation feasible region model is constructed; the distributed resource deterministic aggregation model is obtained by integrating the distributed resource aggregated operation feasible region model with the balancing unit day-ahead scheduling power baseline model.

3. The method of claim 2, wherein, The method comprises: the maximum and minimum values of the active power of the tie line are taken as the optimization objective function based on the balancing unit day-ahead scheduling power baseline model, and the power adjustable boundary of the distribution network at the point of common coupling in each period is determined by solving the optimization objective function; the adjustable power provided by the controllable distributed power and the baseline state active power output of the new energy unit are analyzed according to the power adjustable boundary, and the unit adjustment amount constraint is determined in combination with the state of charge after the energy storage participates in adjustment; the power of the flexible load when the flexible load does not participate in demand response and the upper limit coefficient of the allowable adjustment power are obtained to generate the flexible load adjustment characteristic constraint, and the distributed resource aggregated operation feasible region model is generated in combination with the power flow constraint of the distribution network.

4. The method of claim 3, wherein, The calculation formula of the state of charge after the energy storage participation adjustment is: In the formula, denotes the state of charge of the nth3 energy storage after participating in regulation service in the t period, ΔP ES,n3,t denotes the regulation power that the nth3 energy storage can provide in the t period, η n3,cha denotes the charging efficiency of the nth3 energy storage, η n3,dis denotes the discharging efficiency of the nth3 energy storage, P ES,n3,t denotes the charging / discharging power of the nth3 energy storage in the t period, E N,n3 denotes the total capacity of the nth3 energy storage, denotes the state of charge of the baseline state of the nth3 energy storage in the t period, denotes the baseline charging / discharging power of the nth3 energy storage in the t period.

5. The method of claim 1, wherein, The information gap decision theory is used to analyze the influence of the source-load uncertainty of the new energy unit output and the load power on the distributed resource deterministic aggregation model, and a balanced unit baseline operation model is constructed according to the influence result, including: Based on the output fluctuation amplitude of the new energy unit in the distributed resource deterministic aggregation model and the fluctuation amplitude of the flexible load power, an information gap decision envelope constraint model is constructed; A comprehensive uncertainty of the information gap decision envelope constraint model is constructed by using a linear weighting technique, and a double-layer optimization model containing an information gap decision robust model and an information gap decision opportunity model is constructed by maximizing and minimizing the comprehensive uncertainty; The double-layer optimization model is converted into a single-layer model through equivalent conversion, and an aggregated operation boundary is generated by using different risk scheduling schemes to generate an increase constraint, and a balanced unit baseline operation model based on the information gap decision is constructed.

6. The method of claim 5, wherein, The comprehensive uncertainty of the information gap decision envelope constraint model is calculated based on the weight coefficient values of the new energy unit output uncertainty and the flexible load power uncertainty, and a double-layer optimization model is constructed in combination with the deviation factor formulated by the balanced unit operator; The decision value of the double-layer optimization model is optimized by maximizing the comprehensive uncertainty, which ensures that the expected cost does not exceed the target threshold under any disturbance, and the information gap decision robust model of the balanced unit aggregator is obtained based on the maximization result; The decision value of the double-layer optimization model is optimized by minimizing the comprehensive uncertainty, which finds a decision scheme that makes the expected cost meet the standard under at least one disturbance, and the information gap decision opportunity model of the balanced unit aggregator is obtained based on the minimization result. The calculation formula of the comprehensive uncertainty is:

7. The method of claim 6, wherein, The distributed resource aggregation operation feasible region model is solved by using the balanced unit baseline operation model to obtain the adjustable range of the balanced unit aggregator under different risk attitudes, and the distributed resource aggregation operation boundary considering uncertainty is determined, including: In the formula, α represents the comprehensive uncertainty, κ NE,n2 represents the weight coefficient value of the output uncertainty of the nth2 new energy unit, κ FL,n4 represents the weight coefficient value of the power uncertainty of the nth4 flexible load, N4 represents the number of flexible loads, N2 represents the number of new energy units, α NE,n2 represents the fluctuation amplitude of the output of the nth2 new energy unit, α FL,n4 represents the fluctuation amplitude of the power of the nth4 flexible load, represents the actual power of the nth2 new energy unit, represents the actual power of the nth4 flexible load.

8. The method of claim 1, wherein, Based on the balanced unit baseline operation model, the operation power baseline of the balanced unit of different risk preference types and the output distribution result of different distributed resources are analyzed, and the distributed resource aggregation operation feasible region model is re-solved according to the analysis result; A balanced unit aggregation power adjustable range model is constructed according to the solving result of the distributed resource aggregation operation feasible region model, and the aggregation adjustable range of the balanced unit under different risk attitudes is obtained by using the balanced unit aggregation power adjustable range model; Based on the aggregation adjustable range and the balanced unit baseline operation model, the robust aggregation operation power feasible region boundary and the opportunity aggregation operation power feasible region boundary of the distributed resource are output. The robust aggregation operation power feasible region boundary and the opportunity aggregation operation power feasible region boundary of the distributed resource are output based on the aggregation adjustable range and the balanced unit baseline operation model, including:

9. The method of claim 8, wherein, ​ According to the operation experience, the cost robust deviation parameter is obtained, the balance unit baseline operation model is solved, the prediction error and the total cost value of each distributed resource scheduling scheme are obtained, and the robust condition value is output; The robust condition value is combined with the aggregated adjustable range, and the distributed resource robust aggregated operation feasible region boundary corresponding to the decision maker deviation factor is solved according to the distributed resource aggregated operation feasible region model; According to the operation experience, the cost opportunity deviation parameter is obtained, the information gap decision opportunity model is solved, the prediction error and the total cost value of each distributed resource scheduling scheme are obtained, and the opportunity condition value is output; The opportunity condition value is combined with the aggregated adjustable range, and the distributed resource opportunity aggregated operation feasible region boundary corresponding to the decision maker deviation factor is solved according to the distributed resource aggregated operation feasible region model.

10. A distributed resource aggregation run boundary construction system, characterized by, The system comprises: A deterministic aggregation model construction unit is configured to determine an aggregated operation range of a balance unit by using a balance unit day-ahead scheduling power baseline model, construct a distributed resource aggregated operation feasible region model based on the aggregated operation range, and integrate the balance unit day-ahead scheduling power baseline model to obtain a distributed resource deterministic aggregation model; A baseline operation model construction unit is configured to analyze the influence of source-load uncertainty of new energy unit output and load power on the distributed resource deterministic aggregation model based on information gap decision theory, and construct a balance unit baseline operation model according to the influence result; An aggregated operation boundary construction unit is configured to solve the distributed resource aggregated operation feasible region model by using the balance unit baseline operation model, obtain the balance unit aggregated adjustable range under different risk attitudes, and determine the distributed resource aggregated operation boundary considering uncertainty.

11. The distributed resource aggregation run boundary construction system of claim 10, wherein, The deterministic aggregation model construction unit comprises: An output constraint condition construction module is configured to take controllable distributed power, new energy unit, energy storage and flexible load as distributed resources, and construct output constraint conditions of controllable distributed power according to upper and lower limits of active power output of the controllable distributed power; An energy storage constraint condition construction module is configured to obtain actual active power output and output power prediction value of the new energy unit, construct actual active power output constraints of the new energy unit, and generate energy storage operation constraints in combination with total capacity and charge-discharge power of the energy storage; A power distribution network power flow constraint module is configured to generate load constraints based on power consumption of the flexible load, and construct power distribution network power flow constraints according to actual transmission active power value and reactive power value of a tie line between the balance unit and an upper-level power grid; A power baseline model construction module is configured to combine the load constraints, the power distribution network power flow constraints, the output constraint conditions, the actual active power output constraints and the energy storage operation constraints to construct a balance unit day-ahead scheduling power baseline model; A feasible region model construction module is configured to set tie line active power based on the balance unit day-ahead scheduling power baseline model, calculate aggregated operation boundaries of the distributed resources by using the tie line active power, and construct a distributed resource aggregated operation feasible region model; An aggregation model integration module is configured to integrate the distributed resource aggregated operation feasible region model and the balance unit day-ahead scheduling power baseline model to obtain a distributed resource deterministic aggregation model.

12. The distributed resource aggregation run boundary construction system of claim 11, wherein, The active power of the tie line is set based on the balancing unit day-ahead dispatch power baseline model, and the aggregated operation boundary of the distributed resources is calculated using the active power of the tie line, and a distributed resource aggregated operation feasible region model is constructed, which includes: Based on the balancing unit day-ahead dispatch power baseline model, the maximum and minimum values of the active power of the tie line are taken as the optimization objective function, and the optimization objective function is solved to determine the power adjustable boundary of the distribution network at each time period at the grid connection point; According to the power adjustable boundary, the adjustable power provided by the controllable distributed power supply and the baseline state active power output of the new energy unit are analyzed, and the state of charge of the energy storage after participating in adjustment is combined to determine the unit adjustment amount constraint; The upper limit coefficient of the power consumption of the flexible load when not participating in demand response is obtained, the flexible load adjustment characteristic constraint is generated, and the distributed resource aggregated operation feasible region model is generated in combination with the power flow constraint of the distribution network.

13. The distributed resource aggregation run boundary construction system of claim 12, wherein, The calculation formula of the state of charge of the energy storage after participating in adjustment is: In the formula, denotes the state of charge of the nth3 energy storage after participating in regulation service in the t period, ΔP ES,n3,t denotes the regulation power that the nth3 energy storage can provide in the t period, η n3,cha denotes the charging efficiency of the nth3 energy storage, η n3,dis denotes the discharging efficiency of the nth3 energy storage, P ES,n3,t denotes the charging / discharging power of the nth3 energy storage in the t period, E N,n3 denotes the total capacity of the nth3 energy storage, denotes the state of charge of the baseline state of the nth3 energy storage in the t period, denotes the baseline charging / discharging power of the nth3 energy storage in the t period.

14. The distributed resource aggregation run boundary construction system of claim 10, wherein, The baseline operation model construction unit includes: An envelope constraint model construction module is configured to determine the output fluctuation amplitude of the new energy unit in the distributed resource deterministic aggregation model and the fluctuation amplitude of the power consumption of the flexible load, and construct an information gap decision envelope constraint model; A double-layer optimization model construction module is configured to use linear weighting technology to construct the comprehensive uncertainty of the information gap decision envelope constraint model, and construct a double-layer optimization model containing an information gap decision robust model and an information gap decision opportunity model by maximizing and minimizing the comprehensive uncertainty; A baseline operation model construction module is configured to convert the double-layer optimization model into a single-layer model through equivalent conversion, generate an aggregated operation boundary generation constraint using different risk scheduling schemes, and construct a balancing unit baseline operation model based on information gap decision.

15. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to realize the steps of the method of any one of claims 1 to 9.

16. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method of any one of claims 1 to 9.

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