A multi-scale method for distributed energy consumption in an active power distribution network

Abstract

The application discloses a kind of active distribution network distributed energy multi-scale accommodation method, comprising: using multi-time scale, construct active distribution network distributed energy accommodation model;Based on distributed energy accommodation rate and system comprehensive operation cost, determine the objective function of day-ahead accommodation model, establish optimal day-ahead accommodation model;Utilize opportunity constraint form expression the day-ahead accommodation model objective function, convert the optimal day-ahead accommodation model into the maximization of optimistic value;Real-time power adjustment model is carried out day-ahead unbalanced power deviation adjustment, obtains optimal real-time power adjustment model.The model proposed in the application can improve the accommodation rate of power distribution network to new energy generation, reduce system operation cost, improve comprehensive satisfaction index;Compared with traditional day-ahead accommodation model, multi-time scale accommodation model can solve the adjustment problem of real-time operation unbalanced power, effectively reduce system operation cost and improve accommodation rate.

Description

Technical Field

[0001] This invention relates to the field of distributed energy technology, and in particular to a multi-scale absorption method for distributed energy in an active distribution network. Background Technology

[0002] Distributed energy resources (DERs), as an important form of power supply in active distribution networks, have seen a continuous increase in penetration rates in distribution networks in recent years. Connecting distributed generation to the distribution network and supplementing it with monitoring systems, energy management systems, voltage and frequency control systems to form an active distribution network is one of the important future development forms of distribution networks. Uncontrollable distributed power sources in distributed generation include distributed photovoltaic (PV) and distributed wind power; the output uncertainty of both directly affects the system's energy consumption plan and operational economics.

[0003] However, most current literature establishes distributed renewable energy consumption models using a single time scale, and many are based solely on day-ahead optimization theory. Due to the inherent biases in distributed renewable energy output prediction, day-ahead consumption plans will no longer be entirely applicable in actual operation. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that current distributed renewable energy consumption models all use a single time scale, ignoring the bias in the forecast of distributed renewable energy output, which makes the day-ahead consumption plan no longer fully applicable in actual operation.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: Collecting the distributed energy output and load of the previous day and predicting the distributed energy output and load of the next day; based on the predicted distributed energy output and load of the next day and the real-time distributed energy output and load, constructing an active distribution network distributed energy absorption model using multiple time scales; the distributed energy absorption model includes a day-ahead absorption model and a real-time power adjustment model; determining the objective function of the day-ahead absorption model based on the distributed energy absorption rate and the overall system operating cost, and establishing an optimal day-ahead absorption model; expressing the objective function of the day-ahead absorption model using chance constraints, transforming the optimal day-ahead absorption model into maximizing the optimistic value; adjusting the day-ahead unbalanced power deviation of the real-time power adjustment model to obtain the optimal real-time power adjustment model, and verifying the effectiveness of the active distribution network distributed energy absorption model.

[0008] As a preferred embodiment of the multi-scale absorption method for distributed energy resources in active distribution networks described in this invention, the day-ahead absorption model is constructed using a multi-objective function approach, and its opportunity constraints include:

[0009]

[0010] in, F Denotes the objective function, max F This means maximizing the objective function. This indicates the distributed energy consumption rate of Target 1. This represents the overall operating cost of the second objective system. Indicates the weighting coefficients. Indicates the weighting coefficients. This represents the optimal objective that can be achieved when sub-objective one is used as the objective function alone. This represents the optimal objective that can be achieved when sub-objective two is used as the objective function alone.

[0011] As a preferred embodiment of the multi-scale absorption method for distributed energy resources in active distribution networks described in this invention, the opportunity constraints for maximizing the objective function of the day-ahead absorption model, constructed in the form of a multi-objective function, include:

[0012]

[0013] in, F Describe the objective function. Indicates the model confidence level. This indicates that the objective function, given a confidence level of , Optimism at that time min This indicates that the objective function, given a confidence level of , The minimum optimistic value at that time.

[0014] As a preferred embodiment of the multi-scale absorption method for distributed energy resources in active distribution networks described in this invention, the objective function max is calculated based on the actual output of distributed energy resources and the maximum available output of distributed energy resources. f 1 includes,

[0015]

[0016] in, T This indicates the scheduling period of a power distribution network during a day. express t Actual output of distributed energy resources during specific time periods express t Maximum available output of distributed energy resources during specific time periods.

[0017] As a preferred embodiment of the multi-scale absorption method for distributed energy resources in active distribution networks described in this invention, wherein: the objective binary function min is minimized. f The acquisition of 2 includes,

[0018]

[0019] in, express The power supply of the time-period system to the external network express Time-of-use electricity pricing levels This indicates the government's subsidy price for distributed photovoltaic energy. This indicates the government's subsidy price for wind power distributed energy. express Solar distributed energy output during specific time periods express Wind power distributed energy output during certain periods This is the energy storage operation and maintenance cost coefficient. for Time-of-use energy storage charging and discharging power, This represents the operation and maintenance cost coefficient of photovoltaic power generation. This represents the operation and maintenance cost coefficient for wind power.

[0020] As a preferred embodiment of the multi-scale absorption method for distributed energy in active distribution networks described in this invention, the power supply of the distribution network is classified according to the imbalance deviation, and the power supply capacity is adjusted. The power supply of the distribution network includes power shortage and power surplus.

[0021] As a preferred embodiment of the active distribution network distributed energy multi-scale absorption method described in this invention, if the distribution network lacks power supply and the current distributed energy is not fully absorbed, the power supply capacity is increased in a limited order of distributed energy, energy storage, and common coupling point.

[0022] As a preferred embodiment of the active distribution network distributed energy multi-scale absorption method described in this invention, if the distribution network lacks power supply and the current distributed energy has been fully absorbed, the power supply capacity is increased in a limited order of energy storage and common coupling point.

[0023] As a preferred embodiment of the multi-scale absorption method for distributed energy in active distribution networks described in this invention, if there is a power surplus in the distribution network, the output is reduced through a finite sequence of energy storage, common coupling points, and distributed energy.

[0024] The beneficial effects of this invention are as follows: The model proposed in this invention can improve the absorption rate of new energy power generation in the distribution network, reduce system operating costs, and improve the overall satisfaction index. Compared with the traditional day-ahead absorption model, the multi-time-scale absorption model can solve the problem of adjusting unbalanced power in real-time operation and effectively reduce system operating costs and improve absorption rate. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0026] Figure 1 This is a schematic diagram of the principle architecture of an active distribution network distributed energy multi-time scale absorption method provided in an embodiment of the present invention;

[0027] Figure 2 A flowchart illustrating the solution process for a distributed renewable energy consumption model in an active distribution network based on a chaotic particle swarm optimization algorithm, provided in one embodiment of the present invention.

[0028] Figure 3 A diagram of a distribution network structure with distributed energy connected to the grid, based on an IEEE 33 standard node system, is provided as an embodiment of the present invention for a multi-scale active distribution network distributed energy consumption method.

[0029] Figure 4 The following is a typical operating day diagram of the distributed photovoltaic output and distributed wind power output of the distribution network under a multi-scale active distribution network energy multi-scale consumption method provided in an embodiment of the present invention;

[0030] Figure 5 A typical operating day load curve of a multi-scale active distribution network energy consumption method provided in an embodiment of the present invention;

[0031] Figure 6 This is a typical intraday distributed energy consumption strategy diagram of a multi-scale active distribution network distributed energy consumption method provided in an embodiment of the present invention;

[0032] Figure 7 This is a graph showing the distribution relationship between the comprehensive satisfaction index of the multi-objective function and the confidence level under different confidence parameters for a multi-scale absorption method of distributed energy in an active distribution network, provided as an embodiment of the present invention. Detailed Implementation

[0033] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0034] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0035] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0036] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0037] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0038] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0039] Example 1

[0040] Reference Figures 1 to 2 As an embodiment of the present invention, a multi-scale absorption method for distributed energy resources in an active distribution network is provided, comprising:

[0041] S1: Collect the distributed energy output and load of the previous day, and predict the distributed energy output and load of the next day. Based on the predicted distributed energy output and load of the next day and the real-time distributed energy output and load, construct an active distribution network distributed energy consumption model using multiple time scales. It should be noted that:

[0042] The distributed energy consumption model includes a day-ahead consumption model and a real-time power adjustment model. This invention adopts multi-timescale modeling, dividing the distributed energy consumption model into a day-ahead consumption model and a real-time power adjustment model. The day-ahead consumption model is formulated on the previous day based on the forecast of the distributed energy output and load for the next day, thus determining the overall distributed energy consumption plan. The real-time power adjustment model further adjusts the consumption plan based on the power deviation generated by the real-time operation of the day-ahead plan. The two time scales complement each other and together constitute the distributed energy consumption model.

[0043] S2: Based on the distributed energy consumption rate and the overall system operating cost, determine the objective function of the day-ahead consumption model and establish the optimal day-ahead consumption model. It should be noted that:

[0044] Based on the actual output of distributed energy resources and the maximum available output of distributed energy resources, the objective function max is calculated. f 1 includes,

[0045]

[0046] in, T This indicates the scheduling period of a power distribution network during a day. express t Actual output of distributed energy resources during specific time periods express t Maximum available output of distributed energy resources during specific time periods.

[0047] Minimize the objective function min f The acquisition of 2 includes,

[0048]

[0049] in, express The power supply of the time-period system to the external network express Time-of-use electricity pricing levels This indicates the government's subsidy price for distributed photovoltaic energy. This indicates the government's subsidy price for wind power distributed energy. express Solar distributed energy output during specific time periods express Wind power distributed energy output during certain periods This is the energy storage operation and maintenance cost coefficient. for Time-of-use energy storage charging and discharging power, This represents the operation and maintenance cost coefficient of photovoltaic power generation. This represents the operation and maintenance cost coefficient for wind power.

[0050] S3: Express the objective function of the day-ahead absorption model using opportunity constraints, transforming the optimal day-ahead absorption model into maximizing the optimistic value. It should be noted that:

[0051] The maximization objective function and its chance constraints for constructing the day-ahead absorption model using a multi-objective function approach include:

[0052]

[0053] in, F Denotes the objective function, max F This means maximizing the objective function. This indicates the distributed energy consumption rate of Target 1. This represents the overall operating cost of the second objective system. Indicates the weighting coefficients. Indicates the weighting coefficients. This represents the optimal objective that can be achieved when sub-objective one is used as the objective function alone. This represents the optimal objective that can be achieved when sub-objective two is used as the objective function alone.

[0054] The current elimination model's chance constraints for maximizing the objective function include,

[0055]

[0056] in, F Describe the objective function. Indicates the model confidence level. This indicates that the objective function, given a confidence level of , Optimism at that time min This indicates that the objective function, given a confidence level of , The minimum optimistic value at that time.

[0057] The current absorption model needs to meet the following constraints:

[0058] ① Node voltage constraints

[0059]

[0060] in, Indicates the first power grid Voltage amplitude at each load node; and They represent the first Minimum and maximum voltage amplitudes at each load node. This indicates the confidence level that the node voltage constraint is valid;

[0061] In the above formula, the node voltage Obtained from the power flow calculation equations;

[0062]

[0063]

[0064] in, Indicates the number of system nodes; and express Time period The active and reactive power injected by each node is determined by the decisions of the lower-level decision-makers. , and They respectively represent the connection of the first The node and the first The conductance, susceptance, and voltage phase angle difference of each node branch. and They represent Time period The node and the first Voltage amplitude at each node;

[0065] ②Distributed energy consumption constraints

[0066] The actual power consumed by distributed energy resources in each time period must not exceed the predicted power.

[0067]

[0068] in, express The maximum output of photovoltaic power available during a given period. express The maximum output of wind power that can be mobilized during a given period;

[0069] ③ Operational constraints of energy storage module equipment

[0070] The operating conditions of energy storage are limited by the operating constraints of energy storage module equipment.

[0071]

[0072] in, This indicates the maximum charging power of the energy storage system, and is a negative value. This represents the maximum discharge power of the energy storage, and is a positive value. The energy storage is defined as being in a discharge state when its operating power is greater than zero. and Indicates the minimum and maximum allowable remaining energy storage capacity;

[0073] ④ Operating power constraints at common coupling points

[0074] Power exchange between an active distribution network and its upstream external network is limited by the capacity of the point of common coupling, including:

[0075]

[0076] in, This represents the maximum power exchanged with the external network.

[0077] This active distribution network can not only obtain power from the external grid, but also transmit surplus power to the external grid to generate revenue.

[0078] S4: Adjust the day-ahead unbalanced power deviation of the real-time power adjustment model to obtain the optimal real-time power adjustment model. It should be noted that:

[0079] Due to the existence of distributed energy output forecasting errors, the plan will inevitably generate unbalanced power deviations during real-time operation. Therefore, it is necessary to make real-time adjustments to address these deviations and further improve the distributed energy absorption rate while meeting power balance requirements. The intraday real-time unbalanced power adjustment is carried out in 10-minute scheduling periods. For each period, ultra-short-term power forecasting is performed to obtain the output trend of distributed energy and formulate adjustment plans.

[0080] The power supply of the distribution network is classified according to the imbalance deviation, and the power supply capacity is adjusted accordingly. The power supply of the distribution network includes power shortage and power surplus.

[0081] If the power supply of the distribution network is insufficient and the current distributed energy is not fully utilized, the power supply capacity shall be increased in a limited order of distributed energy, energy storage, and common coupling points.

[0082] If the power distribution network lacks power and the current distributed energy resources have been fully absorbed, the power supply capacity should be increased in a limited order of energy storage and common coupling points.

[0083] If the power distribution network has an excess supply, the output can be reduced in a limited sequence through energy storage, common coupling points, and distributed energy sources.

[0084] The model proposed in this invention can improve the absorption rate of new energy power generation in the distribution network, reduce system operating costs, and improve the overall satisfaction index. Compared with the traditional day-ahead absorption model, the multi-time-scale absorption model can solve the problem of adjusting unbalanced power in real-time operation and effectively reduce system operating costs and improve absorption rate.

[0085] Example 2

[0086] Reference Figures 3 to 7 This is the second embodiment of the present invention. Unlike the first embodiment, this embodiment provides a verification test of a multi-scale absorption method for distributed energy in an active distribution network, which verifies and explains the technical effects used in this method.

[0087] For example Figure 3 The diagram shows an active distribution network built on the IEEE 33 standard node system, which includes distributed energy grid connection. The network includes two distributed wind turbines (located at grid connection node 6 and grid connection node 11), two distributed photovoltaic module clusters (located at grid connection node 29 and grid connection node 32), and two mobile energy storage stations (located at grid connection node 13 and grid connection node 16). The grid connection status of each device is shown in Table 1. In addition, to ensure more reliable power supply, a 200kW capacity of controllable distributed generation, including micro-turbines and fuel cells, is also configured in the distribution network.

[0088] Table 1: Grid connection status of distributed energy resources in active distribution networks.

[0089]

[0090] The current optimized operation model has a scheduling period of 1 hour, and the energy storage discharge efficiency is... and energy storage charging efficiency The energy storage self-discharge coefficient is 0.98. The overall energy system load capacity is 1755kW, the capacity of the common coupling point connected to the upper-level external grid is 600kW, and the comprehensive penetration rate of distributed wind and solar power is 0.76. A new energy consumption plan for the system is formulated with a one-day scheduling period, with a scheduling period length of 1 hour. In the multi-objective function, the weight coefficients of sub-objective one and sub-objective two are set to 0.6 and 0.4 respectively. In the stochastic chance constrained programming model, the confidence level of the objective function is set to 0.9, and the confidence level of the constraint conditions is set to 0.95. The daily distributed solar power output, distributed wind power output, and load curves of the system on a typical operating day are shown below. Figure 4 , Figure 5 As shown.

[0091] By running the established distributed energy consumption model, the consumption strategy of the system under this typical operating day can be obtained, such as... Figure 6 As shown; the absorption strategies mainly include the power exchange curve with the external grid, the adjustment curve of controllable distributed generation, and the energy storage charging and discharging curve.

[0092] Specifically, from Figure 6 As can be seen from the first to the tenth time period, the load is at a low point, but the distributed renewable energy generation is also at a low point. The system load is mainly met by the external grid, while the energy storage will appropriately charge the excess power. From the eleventh to the sixteenth time period, the distributed generation reaches its peak, but the increase in the system load level is relatively low. Therefore, the system makes full use of the distributed generation to supply power to the load and stores the power in the energy storage to prepare for subsequent discharge. From the seventeenth time period to the end of the day, the load reaches its peak, but the output of the distributed renewable energy generation drops sharply. At this time, the distribution network fully utilizes the energy storage power to supply power, but even so, it still cannot meet the power supply demand. At the same time, due to the high time-of-use electricity price of the external grid, the distribution network has to activate the controllable distributed generation to meet the power balance.

[0093] In fact, under this consumption strategy, the comprehensive consumption rate of distributed renewable energy reached 96.4%, with wind and solar curtailment mainly occurring during the peak output period of distributed renewable energy in the middle of the day. The system's daily comprehensive operating cost was 8462.68 yuan, and the optimistic value of the comprehensive satisfaction index of the multi-objective function reached 0.646. Considering the impact of power prediction errors during real-time operation, the real-time unbalanced power adjustment strategy formulated in this invention yields an unbalanced power adjustment cost of 562.45 yuan, meeting the system's comprehensive balance requirements. The actual daily comprehensive operating cost of the system is 9025.13 yuan.

[0094] The simulation results above were obtained with the confidence levels set at predetermined levels. To verify the results of the stochastic chance constrained programming model under different confidence levels, different confidence levels were set for the objective function and constraints, and the elimination model was run. The distribution relationship between the comprehensive satisfaction index of the multi-objective function and the confidence level was obtained as follows: Figure 7 As shown.

[0095] from Figure 7 As can be seen, regardless of the confidence level of the objective function or the constraints, the optimistic value obtainable by the objective function decreases as the confidence level increases. This is because as the confidence level increases, the conditions for the system to satisfy the objective function and constraints become more stringent, thus forcing it to sacrifice the superiority of the absorption strategy to meet conservatism. Therefore, stochastic chance constrained programming models can provide different optimistic values ​​for decision-makers with different risk preferences.

[0096] To compare the advantages of the multi-timescale model of this invention with the traditional absorption model, the pure day-ahead absorption model in the literature is used as a control group for comparison with the model of this invention. In the day-ahead absorption model, if there is an unbalanced power deviation in actual operation, it is stipulated that a common coupling point must be used to smooth it out, which will incur costs. The absorption indexes of the two methods are compared in Table 2.

[0097] Table 2: Comparison of distribution network absorption indicators for distributed renewable energy under different absorption models.

[0098]

[0099] As shown in Table 2, compared to the traditional day-ahead absorption model, the multi-time-scale absorption model established in this invention can adapt to the adjustment needs of unbalanced power at different time scales. The comprehensive absorption rate of Method 2 reaches 96.4%, significantly higher than the 93.5% of Method 1. Regarding distribution network operating costs, the difference in day-ahead operating costs between the two absorption methods is not significant. However, Method 2 significantly reduces the cost of unbalanced power adjustment compared to Method 1. Combined with the comparison of absorption rates, the comprehensive satisfaction rate of Method 2 reaches 0.646, significantly higher than the 0.612 of Method 1, verifying the effectiveness of the established model.

[0100] The absorption capacity of active distribution networks for distributed energy generation also depends on the penetration rate. The penetration rate refers to the ratio of electricity generated by distributed energy to the electricity consumed by the entire distribution system. As the penetration rate of distributed energy increases, the absorption capacity of the system for distributed energy will decrease. In order to study the absorption capacity of active distribution networks under different penetration rates, distributed energy with different penetration rates were connected to the grid. The absorption rate comparison was obtained by running the absorption model under the same operating conditions, as shown in Table 3.

[0101] Table 3: Comparison of the impact of different penetration rates on the energy absorption rate of the distribution network.

[0102]

[0103] As shown in Table 3, when the penetration rate is higher than 0.77, the active distribution network cannot fully absorb distributed renewable energy. Furthermore, as the penetration rate increases further, the system's absorption capacity decreases sharply. When the penetration rate is lower than 0.77, the system can basically absorb all distributed renewable energy. This indicates that when configuring the installed capacity of distributed renewable energy in the distribution network, a reasonable penetration rate should be considered to improve the system's absorption capacity.

[0104] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multi-scale absorption method for distributed energy resources in an active distribution network, characterized in that, include: The system collects the distributed energy output and load of the previous day and predicts the distributed energy output and load of the next day. Based on the predicted distributed energy output and load of the next day, the real-time distributed energy output and load, and using multiple time scales, an active distribution network distributed energy consumption model is constructed. The distributed energy consumption model includes a day-ahead consumption model and a real-time power adjustment model; Based on the distributed energy consumption rate and the overall system operating cost, the objective function of the day-ahead consumption model is determined, and the optimal day-ahead consumption model is established. The objective function of the day-ahead absorption model is expressed in the form of opportunity constraints, and the optimal day-ahead absorption model is transformed into maximizing the optimistic value. Adjust the day-ahead unbalanced power deviation of the real-time power adjustment model to obtain the optimal real-time power adjustment model, and verify the effectiveness of the active distribution network distributed energy consumption model.

2. The method for multi-scale absorption of distributed energy resources in an active distribution network as described in claim 1, characterized in that: The maximization objective function and its chance constraints for constructing the day-ahead absorption model using a multi-objective function approach include: in, F Denotes the objective function, max F This means maximizing the objective function. This indicates the distributed energy consumption rate of Target 1. This represents the overall operating cost of the second objective system. Indicates the weighting coefficients. Indicates the weighting coefficients. This represents the optimal objective that can be achieved when sub-objective one is used as the objective function alone. This represents the optimal objective that can be achieved when sub-objective two is used as the objective function alone.

3. The method for multi-scale absorption of distributed energy resources in an active distribution network as described in claim 1 or 2, characterized in that: The opportunity constraints for maximizing the objective function of the day-ahead absorption model, constructed using a multi-objective function approach, include: in, F Describe the objective function. Indicates the model confidence level. This indicates that the objective function, given a confidence level of , Optimism at that time min This indicates that the objective function, given a confidence level of , The minimum optimistic value at that time.

4. The method for multi-scale absorption of distributed energy resources in an active distribution network as described in claim 3, characterized in that: Based on the actual output of distributed energy resources and the maximum available output of distributed energy resources, the objective function max is calculated. f 1 includes, in, T This indicates the scheduling period of a power distribution network during a day. express t Actual output of distributed energy resources during specific time periods express t Maximum available output of distributed energy resources during specific time periods.

5. The method for multi-scale absorption of distributed energy resources in an active distribution network as described in claim 4, characterized in that: Minimize the objective function min f The acquisition of 2 includes, in, express The power supply of the time-period system to the external network express Time-of-use electricity pricing levels This indicates the government's subsidy price for distributed photovoltaic energy. This indicates the government's subsidy price for wind power distributed energy. express Solar distributed energy output during specific time periods express Wind power distributed energy output during certain periods This is the energy storage operation and maintenance cost coefficient. for Time-of-use energy storage charging and discharging power, This represents the operation and maintenance cost coefficient of photovoltaic power generation. This represents the operation and maintenance cost coefficient for wind power.

6. The method for multi-scale absorption of distributed energy resources in an active distribution network as described in claim 5, characterized in that: The power supply of the distribution network is classified according to the imbalance deviation, and the power supply capacity is adjusted accordingly. The power supply of the distribution network includes power shortage and power surplus.

7. The method for multi-scale absorption of distributed energy resources in an active distribution network as described in claim 6, characterized in that: If the power supply of the distribution network is insufficient and the current distributed energy is not fully utilized, the power supply capacity shall be increased in a limited order of distributed energy, energy storage, and common coupling points.

8. The method for multi-scale absorption of distributed energy resources in an active distribution network as described in claim 7, characterized in that: If the power distribution network lacks power and the current distributed energy resources have been fully absorbed, the power supply capacity should be increased in a limited order of energy storage and common coupling points.

9. The method for multi-scale absorption of distributed energy resources in an active distribution network as described in any one of claims 6 to 8, characterized in that: If the power distribution network has an excess supply, the output can be reduced in a limited sequence through energy storage, common coupling points, and distributed energy sources.

10. The method for multi-scale absorption of distributed energy resources in an active distribution network as described in claim 9, characterized in that: include, The chaotic particle swarm optimization algorithm was used to design and solve the established active distribution network distributed energy consumption model, and the effectiveness of the active distribution network distributed energy consumption model was verified.

Images