Power distribution network operation control method and device considering micro-storage-load cooperative support

Abstract

The invention relates to the technical field of power system optimization and control systems, and particularly provides a power distribution network operation control method and device considering micro-energy storage-load cooperative support, and the method comprises the following steps: considering the influence of a random event on a distributed power supply and a load in a power distribution network; establishing a power distribution network operation control optimization model containing uncertain variables; establishing constraint conditions of the power distribution network operation control optimization model; the change range of uncertain variables is considered, and a Monte Carlo method is adopted to construct an extremely unfavorable scene; and solving the power distribution network operation control optimization model on the basis of extremely adverse scenes and constraint conditions to obtain a coordination control scheme of the micro-grid, the energy storage and the controllable load in the power distribution network. Therefore, the load, energy storage and micro-grid coordinated control in the power distribution network is realized by the influence of the distributed power supply and the load power uncertainty equivalent random event, the risk resisting capability of the power distribution network is improved to the greatest extent, and the safe and stable operation of the power distribution network is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and control, and in particular to a distribution network operation control method considering micro-storage-load collaborative support and a distribution network operation control device considering micro-storage-load collaborative support. Background Art

[0002] Compared to traditional distribution networks, modern distribution networks have made significant strides in power source composition, intelligent dispatching, and load flexibility, and are therefore often referred to as active distribution networks. For example, active distribution networks incorporate a variety of power sources, including distributed photovoltaics, distributed wind power, small gas turbines, and energy storage. Furthermore, in addition to typical power sources participating in power regulation within the distribution network, flexible loads and microgrids within active distribution networks can also participate in grid operation and regulation, significantly increasing the flexibility of distribution network operation and regulation.

[0003] Distributed photovoltaic and wind power, among other renewable energy sources, are widely integrated into distribution networks and are susceptible to weather fluctuations, resulting in significant randomness in their operation. Conventional multi-source coordinated control methods for distribution networks primarily consider day-ahead operational information and optimize solutions based on typical scenarios. However, they fail to adequately consider extremely adverse scenarios, resulting in insufficient resilience in distribution network operation control.

[0004] Therefore, it is necessary to consider the impact of uncertainty in the multi-source coordinated control optimization model of the distribution network to maximize the ability of the distribution network to resist disturbances and ensure the safe and efficient operation of the distribution network. Summary of the Invention

[0005] In order to solve one of the above technical problems, the present invention proposes the following technical solution.

[0006] The first aspect of the present invention proposes a distribution network operation control method considering micro-storage-load collaborative support, and the method includes the following steps: considering the impact of random events on distributed power sources and loads in the distribution network, establishing a distribution network operation control optimization model containing uncertain variables; establishing constraints for the distribution network operation control optimization model; considering the range of variation of the uncertain variables, using the Monte Carlo method to construct an extremely unfavorable scenario; based on the extremely unfavorable scenario and constraints, solving the distribution network operation control optimization model to obtain a coordinated control scheme for microgrids, energy storage and controllable loads in the distribution network.

[0007] In addition, the distribution network operation control method considering micro-storage-load collaborative support according to the above embodiment of the present invention can also have the following additional technical features.

[0008] According to one embodiment of the present invention, the mathematical expression of the distribution network operation control optimization model is:

[0009]

[0010] Among them, N T is the total number of control time windows, N B is the total number of distribution network nodes, K i is the load shedding price, c D , c G , c UP and c ST They are electricity sales price, microgrid electricity purchase price, main grid electricity purchase price and energy storage electricity purchase price. is the load power cut at node i in the distribution network at time t, and are the user power, microgrid power generation and energy storage power generation of node i in the distribution network at time t, and are the power purchased from the main grid and the power sold to the main grid at time t, respectively.

[0011] According to one embodiment of the present invention, the constraint conditions include distribution network flow constraints, microgrid operation constraints, main grid power regulation constraints, and energy storage battery operation constraints.

[0012] According to one embodiment of the present invention, the distribution network flow constraint describes the impact of random events on the distribution network state in the form of distributed photovoltaic, distributed wind power and load uncertainty, and has the following form:

[0013]

[0014] in, and are the user power and microgrid generation power of node i in the distribution network at time t, and are the power generation of distributed wind power and distributed photovoltaic power at node i in the distribution network at time t, is the total uncertainty power of distributed wind power, distributed photovoltaic power and load at distribution network node i at time t under the influence of random events, and are the discharge power and charging power of the energy storage at node i in the distribution network at time t, N B is the total number of distribution network nodes, is the active power of line ij in the distribution network at time t.

[0015] According to one embodiment of the present invention, the total uncertainty power of the distribution network under the influence of the random event is The Monte Carlo sampling method can be used for simulation calculations using the following formula:

[0016]

[0017] in, and They represent the offset values ​​caused by the uncertainty of the load, distributed wind power and distributed photovoltaic at node i at time t, and their values ​​are all in the interval [0,1]; and They represent the lower and upper bounds of the load growth, distributed wind power output reduction, and distributed photovoltaic output reduction at node i at time t, respectively.

[0018] According to one embodiment of the present invention, when solving the distribution network operation control optimization model, the search space of the target parameters includes: reduced load power, microgrid power generation, power purchased from the main grid and energy storage power generation.

[0019] According to one embodiment of the present invention, a distribution network operation control method considering micro-storage-load collaborative support is characterized by further comprising: evaluating the resilience index of the distribution network under the coordinated control scheme.

[0020] According to one embodiment of the present invention, the toughness index is calculated using the following formula:

[0021]

[0022] in, is the user power of node i in the distribution network at time t, is the load power reduced at node i in the distribution network at time t, RI i.t is the resilience index of node i in the distribution network at time t, RI i is the resilience index of node i in the distribution network during the entire period, t end This is the last optimization period.

[0023] The second aspect of the present invention proposes an embodiment of a distribution network operation control device that considers micro-storage-load collaborative support, including: a first establishment module, used to consider the impact of random events on distributed power sources and loads in the distribution network, and establish a distribution network operation control optimization model containing uncertain variables; a second establishment module, used to establish constraints of the distribution network operation control optimization model; a construction module, used to consider the variation range of the uncertain variables, and use the Monte Carlo method to construct an extremely unfavorable scenario; a solution module, used to solve the distribution network operation control optimization model based on the extremely unfavorable scenario and constraints, and obtain a coordinated control scheme for the microgrid, energy storage and controllable load in the distribution network.

[0024] The technical solution of the embodiment of the present invention, on the one hand, describes the impact of random events with the uncertainty of distributed power sources and loads in the distribution network operation control optimization model, and realizes coordination among the distribution network microgrid, energy storage and controllable loads based on extremely unfavorable scenarios, so as to maximize the distribution network's ability to resist risks and ensure the safe and stable operation of the distribution network; on the other hand, based on the multi-source coordinated configuration scheme of the distribution network, a distribution network resilience evaluation index is proposed to facilitate the analysis of the effectiveness of the distribution network scheme configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a flow chart of a distribution network operation control method considering micro-storage-load collaborative support according to an embodiment of the present invention.

[0026] Figure 2 This is a structural diagram of a modified IEEE 33-node system according to a specific example of the present invention.

[0027] Figure 3 The daily load curve of the distribution network and the main network power purchase price are a specific example of the present invention.

[0028] Figure 4 The daily power curves of distributed wind power and photovoltaic power in a distribution network are a specific example of the present invention.

[0029] Figure 5 This is a specific example of a distribution network load reduction control scheme of the present invention.

[0030] Figure 6 This is a specific example of a control scheme for energy storage equipment in a distribution network of the present invention.

[0031] Figure 7 This is a structural block diagram of a distribution network operation control device considering micro-storage-load collaborative support according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] The present invention provides a distribution network operation control method considering micro-storage-load collaborative support. In the distribution network multi-source coordinated control optimization model, the influence of uncertainty is taken into account to maximize the distribution network's ability to resist disturbances and ensure safe and efficient operation of the distribution network.

[0034] Figure 1This is a flow chart of a distribution network operation control method considering micro-storage-load collaborative support according to an embodiment of the present invention.

[0035] like Figure 1 As shown, the distribution network operation control method considering micro-storage-load collaborative support includes the following steps S1 to S4.

[0036] S1, considering the impact of random events on distributed power sources and loads in the distribution network, establish a distribution network operation control optimization model containing uncertain variables.

[0037] Among them, the uncertain variables include the reduced load power, user power, microgrid power generation, energy storage power generation, power purchased from the main grid, and power sold to the main grid.

[0038] Specifically, first, based on the operation plan information of the distribution network, the impact of random events such as weather changes and load fluctuations on the distribution network is described by distributed power sources and load uncertainties, and a distribution network operation control optimization model containing uncertain variables is established. The optimization goal of this model is to minimize the cost during micro-storage-load collaborative control.

[0039] S2, establish the constraints of the distribution network operation control optimization model.

[0040] Among them, the constraint conditions include distribution network flow constraints, microgrid operation constraints, main grid power regulation constraints and energy storage battery operation constraints.

[0041] S3, considering the range of changes in uncertain variables, uses the Monte Carlo method to construct extremely adverse scenarios.

[0042] S4, based on extremely unfavorable scenarios and constraints, solves the distribution network operation control optimization model to obtain a coordinated control scheme for microgrids, energy storage, and controllable loads in the distribution network.

[0043] Specifically, based on the calculation of unfavorable scenarios and constraints, an intelligent optimization algorithm is used to solve the distribution network operation control optimization model, and finally a coordinated control scheme for microgrids, energy storage and controllable loads in the distribution network is obtained.

[0044] Therefore, the distribution network operation control method considering the collaborative support of micro-storage-load in the embodiment of the present invention realizes the coordinated control of loads, energy storage and microgrids in the distribution network by taking into account the influence of random events equivalent to distributed power sources and load power uncertainty, thereby maximizing the distribution network's ability to resist risks and ensuring safe and stable operation of the distribution network.

[0045] In one embodiment, the distribution network operation control optimization model includes load shedding cost, microgrid regulation cost, main grid power support cost, and battery regulation cost, and its mathematical expression is:

[0046]

[0047] Among them, N T is the total number of control time windows, N B is the total number of distribution network nodes, K i is the load shedding price (the economic loss to be paid for shedding the load), C D , C G , C UP and C ST They are electricity sales price, microgrid electricity purchase price, main grid electricity purchase price and energy storage electricity purchase price. is the load power cut at node i in the distribution network at time t, and are the user power, microgrid power generation and energy storage power generation of node i in the distribution network at time t, and are the power purchased from the main grid and the power sold to the main grid at time t, respectively.

[0048] In one embodiment, the distribution network flow constraint describes the impact of random events on the distribution network state in the form of distributed photovoltaic, distributed wind power and load uncertainty, and has the following form:

[0049]

[0050] in, and are the user power and microgrid generation power of node i in the distribution network at time t, and are the power generation of distributed wind power and distributed photovoltaic power at node i in the distribution network at time t, is the total uncertainty power of distributed wind power, distributed photovoltaic power and load at distribution network node i at time t under the influence of random events, and are the discharge power and charging power of the energy storage at node i in the distribution network at time t, N B is the total number of distribution network nodes, is the active power of line ij in the distribution network at time t.

[0051] Furthermore, the total uncertainty power of the distribution network under the influence of random events The Monte Carlo sampling method can be used for simulation calculations using the following formula:

[0052]

[0053] in, and They represent the offset values ​​caused by the uncertainty of load, distributed wind power and distributed photovoltaic at node i at time t, and their values ​​are all in the interval [0,1]; and They represent the lower and upper bounds of the load growth, distributed wind power output reduction, and distributed photovoltaic output reduction of node i at time t, respectively, and can be given through experience or historical statistical data.

[0054] In one embodiment, when solving the distribution network operation control optimization model, the search space of the target parameters includes: the reduced load power p shed , microgrid power generation power p G , Power p purchased from the main network UPB and the energy storage power p ST .

[0055] In one embodiment, a distribution network operation control method considering micro-storage-load collaborative support is characterized in that it can also include: evaluating the resilience index of the distribution network under the coordinated control scheme.

[0056] Specifically, the load shedding amount is used as an indicator to evaluate the resilience of the distribution network under the proposed scheme, which is calculated using the following formula:

[0057]

[0058]

[0059] in, is the user power of node i in the distribution network at time t, is the load power reduced at node i in the distribution network at time t, RI i.t is the resilience index of node i in the distribution network at time t, RI i is the resilience index of node i in the distribution network during the entire period, t end This is the last optimization period.

[0060] In general, the present invention aims to improve the resilience of a multi-power distribution network. Based on the distribution network's operational plan information, and taking into account the uncertainty of distributed power sources and loads in the distribution network under the influence of random events, a distribution network operation control optimization model is established. Furthermore, based on the uncertainty of distributed power sources and loads, the Monte Carlo method is used to screen out extremely unfavorable scenarios. The distribution network operation control optimization model is then calculated using an intelligent optimization algorithm to obtain a distribution network operation control solution under random event scenarios. Finally, the load shedding amount is used as an evaluation metric to assess the resilience of the distribution network under these random event scenarios.

[0061] The present invention will be further described in detail below with reference to the accompanying drawings and in conjunction with embodiments, but the present invention is not limited to the given examples.

[0062] Based on the IEEE 33-node system, 4 MTs (microgrids), 3 WTs (distributed wind power), 1 PV (distributed photovoltaic) and 2 ESs (distributed energy storage) are added to the distribution network. The structure is as shown in the attached figure. Figure 2 shown.

[0063] The interactive power between the system and the main grid is 3MW. The total load of the system and the main grid power purchase price are as shown in the attached Figure 3 The distributed wind power capacity is 0.8MW, the distributed photovoltaic capacity is 0.6MW, and the microgrid capacity is 1.0MW. The daily power curves of distributed wind power and photovoltaic are shown in Figure 4 As shown in the figure, the active power regulation range of the microgrid is uniformly set to 0-2MW, with a price of 20 yuan / MWh. The capacity of the distributed energy storage system is uniformly set to 1.5MWh, with a price of 20 yuan / MWh. The price of load control on buses 26-33 is 300 yuan / MW, and the price of load control on other buses is 150 yuan / MW.

[0064] The optimization calculation of the distribution network multi-source control scheme is performed based on the random event scenario where the distribution network load changes satisfy a uniform distribution (0, 2) and the distributed photovoltaic and wind power changes in the distribution network satisfy a uniform distribution (0, 2). Considering that the microgrid does not participate in power regulation, the objectives of the distribution network multi-source control are only load reduction and energy storage charging and discharging control.

[0065] In this scenario, the Monte Carlo method is used to calculate the uncertain power in the most unfavorable scenario caused by random events to reach 4MW. The intelligent optimization algorithm is used to calculate the distribution network multi-source coordinated control optimization model. Figures 5 and 6 In this scenario, the control schemes for distribution network load reduction control and energy storage charging and discharging control in different time periods are given.

[0066] Furthermore, based on the proposed multi-source coordinated control scheme for the distribution network, the distribution network resilience index under this scheme was calculated to reach 97.7589%. Compared with the case without the multi-source coordinated control scheme, the present invention has better performance, enhances the resilience of the distribution network operation, and has better risk resistance capabilities.

[0067] In summary, the present invention takes into account the impact of random events such as weather fluctuations and load changes on distributed power sources and loads in the distribution network, and establishes a distribution network operation control optimization model that includes uncertain variables. And based on extremely unfavorable scenarios, an intelligent optimization algorithm is used to solve the distribution network operation control optimization model. Thus, a coordinated control scheme for microgrids, energy storage and controllable loads in the distribution network is finally obtained, and the load shedding amount is used as an indicator to evaluate the resilience of the distribution network under the proposed scheme. Therefore, the present invention can provide guidance for the production mode of each power source in the distribution network, thereby ensuring that the distribution network as a whole has lower operating costs and higher resilience during operation.

[0068] Corresponding to the distribution network operation control method considering micro-storage-load collaborative support in the above embodiment, the present invention also proposes a distribution network operation control device considering micro-storage-load collaborative support.

[0069] Figure 7 This is a structural block diagram of a distribution network operation control device considering micro-storage-load collaborative support according to an embodiment of the present invention.

[0070] like Figure 7 As shown, the distribution network operation control device considering micro-storage-load collaborative support includes: a determination module 10 for determining the different physical fields involved in the electric, hydrogen and heat integrated energy system for low-carbon buildings and the mechanism models under each physical field, and determining the model interface of each mechanism model; a first establishment module 20 for establishing the dynamic model of each component equipment of the electric, hydrogen and heat integrated energy system according to the mechanism models under the physical fields; a second establishment module 30 for establishing each subsystem model of the electric, hydrogen and heat integrated energy system based on the dynamic model of each component equipment; a combination module 40 for combining the subsystem models according to the model interface to obtain the electric, hydrogen and heat integrated energy system model for low-carbon buildings.

[0071] It should be noted that the specific implementation of the charging pile can refer to the specific implementation of the distribution network operation control method considering the micro-storage-load collaborative support mentioned above. To avoid redundancy, it will not be described in detail here.

[0072] The distribution network operation control device considering micro-storage-load collaborative support in the embodiment of the present invention is based on a modular modeling approach and divides the modeling models according to different fields. It can accurately reflect the coupling relationship and dynamic characteristics within the electric, hydrogen and thermal integrated energy system, and help predict the dynamic changes of the system under different load conditions.

[0073] In the description of this specification, the reference terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" and the like are intended to mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are exemplary and are not to be construed as limiting the present invention. Those skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A distribution network operation control method considering micro-storage-load collaborative support, characterized in that: The following steps are involved: Considering the impact of random events on distributed power sources and loads in the distribution network, a distribution network operation control optimization model containing uncertain variables is established; Establishing constraint conditions for the distribution network operation control optimization model; Taking into account the variation range of the uncertain variables, the Monte Carlo method is used to construct extremely adverse scenarios; Based on extremely unfavorable scenarios and constraints, the distribution network operation control optimization model is solved to obtain a coordinated control scheme for microgrids, energy storage and controllable loads in the distribution network.

2. The distribution network operation control method considering micro-storage-load collaborative support according to claim 1 is characterized in that: The mathematical expression of the distribution network operation control optimization model is: Among them, N T is the total number of control time windows, N B is the total number of distribution network nodes, K i is the load shedding price, c D , c G , c UP and c ST They are electricity sales price, microgrid electricity purchase price, main grid electricity purchase price and energy storage electricity purchase price. is the load power cut at node i in the distribution network at time t, and are the user power, microgrid power generation and energy storage power generation of node i in the distribution network at time t, and are the power purchased from the main grid and the power sold to the main grid at time t, respectively.

3. The distribution network operation control method considering micro-storage-load collaborative support according to claim 1 is characterized in that: The constraint conditions include distribution network flow constraints, microgrid operation constraints, main grid power regulation constraints, and energy storage battery operation constraints.

4. The distribution network operation control method considering micro-storage-load collaborative support according to claim 3 is characterized in that: The distribution network flow constraint describes the impact of random events on the distribution network state in the form of distributed photovoltaic, distributed wind power and load uncertainty, and has the following form: in, and are the user power and microgrid generation power of node i in the distribution network at time t, and are the power generation of distributed wind power and distributed photovoltaic power at node i in the distribution network at time t, is the total uncertainty power of distributed wind power, distributed photovoltaic power and load at distribution network node i at time t under the influence of random events, and are the discharge power and charging power of the energy storage at node i in the distribution network at time t, N B is the total number of distribution network nodes, is the active power of line ij in the distribution network at time t.

5. The distribution network operation control method considering micro-storage-load collaborative support according to claim 4 is characterized in that: The total uncertainty power of the distribution network under the influence of the random event The Monte Carlo sampling method can be used for simulation calculations using the following formula: in, and They represent the offset values ​​caused by the uncertainty of the load, distributed wind power and distributed photovoltaic at node i at time t, and their values ​​are all in the interval [0,1]; and They represent the lower and upper bounds of the load growth, distributed wind power output reduction, and distributed photovoltaic output reduction at node i at time t, respectively.

6. The distribution network operation control method considering micro-storage-load collaborative support according to claim 1 is characterized in that: When solving the distribution network operation control optimization model, the search space of the target parameters includes: the reduced load power, the microgrid generation power, the power purchased from the main grid and the energy storage generation power.

7. The distribution network operation control method considering micro-storage-load collaborative support according to any one of claims 1 to 6, characterized in that: Also includes: Evaluate the resilience indicators of the distribution network under the coordinated control scheme.

8. The distribution network operation control method considering micro-storage-load collaborative support according to claim 7 is characterized in that: The toughness index is calculated using the following formula: in, is the user power of node i in the distribution network at time t, is the load power reduced at node i in the distribution network at time t, RI i.t is the resilience index of node i in the distribution network at time t, RI i is the resilience index of node i in the distribution network during the entire period, t end This is the last optimization period.

9. A distribution network operation control device considering micro-storage-load collaborative support, characterized in that: include: The first establishment module is used to consider the impact of random events on distributed power sources and loads in the distribution network and establish a distribution network operation control optimization model containing uncertain variables; A second establishing module is used to establish the constraint conditions of the distribution network operation control optimization model; A construction module, for considering the variation range of the uncertain variables and constructing an extremely adverse scenario using a Monte Carlo method; The solution module is used to solve the distribution network operation control optimization model based on extremely unfavorable scenarios and constraints, and obtain the coordinated control scheme of microgrids, energy storage and controllable loads in the distribution network.

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