Distributed power supply planning method and device for power distribution network, terminal equipment and storage medium

Abstract

The invention discloses a distributed power supply planning method and device for a power distribution network, terminal equipment and a storage medium, and the method comprises the steps: building a first target function with the minimization of the regional overheating risks of all power distribution regions as a target through considering the regional overheating risks caused by the power failure of the power distribution regions; and solving the first objective function and the second objective function under a first constraint condition that the regional overheating risk of each power distribution region cannot exceed a preset risk threshold value and a second constraint condition used for constraining normal operation of the power distribution network by using the first objective function and the second objective function with the minimization of the distributed power supply planning cost as a target. According to the generated planning scheme, the power utilization experience of users in each power distribution area in extreme hot wave weather is considered, and the installation cost and the power failure cost of the distributed power supply and the operation stability of the power distribution network are also considered.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network planning, and particularly to a distributed power source planning method, device, terminal device and storage medium for a distribution network. Background Art

[0002] Extreme weather events, such as heatwaves, are becoming more frequent and intense around the world, leading to a large amount of use of heating, ventilation and air conditioning (HVAC) systems by consumers to maintain the safety and comfort of the internal temperature of buildings. This large-scale use of HVAC has led to overloading of the load on the power distribution side, coupled with insufficient power generation and limited power transfer capacity on the transmission side (also affected by excessive heat), which has disrupted the balance between power generation and demand and increased the risk of large-scale power outages.

[0003] When the power system is at risk of widespread power outages during extreme weather, operators urge power companies to take measures to relieve the overload. These measures include the ultimate strategy of sequentially disconnecting power distribution circuits by rotating power outages during peak demand periods to avoid large-scale power outages, and the consequences of controlled rotating power outages implemented during extreme heat can be mitigated by equipping distributed energy resources (such as solar power generation or energy storage) in the power distribution circuits.

[0004] However, the investment decision of distributed energy in the distribution network is only based on technical economy and infrastructure standards, aiming to reduce the overall power outage cost. But this approach often ignores the power consumption experience of people in disadvantaged areas, and in extreme weather, long-term power outages may cause physical harm to users. Although it is currently proposed to set distributed power sources in different distribution areas to relieve the pressure on the distribution network and at the same time shorten the power outage time of the distribution area, if distributed power sources are installed on a large scale, the cost is very expensive, and for distribution network nodes with low carrying capacity, it may also cause line damage. Therefore, how to plan the newly added distributed power sources in each distribution area and allocate the power outage duration of each distribution area has become an urgent problem to be solved. Summary of the Invention

[0005] The embodiments of the present invention provide a distributed power source planning method, device, terminal device and storage medium for a distribution network, which can generate a planning scheme that takes into account both the power consumption experience of users in each distribution area under extreme heatwave weather and the installation cost, power outage cost, and operation stability of the distribution network. An embodiment of the present invention provides a distributed power source planning method for a distribution network, which is characterized by including: Obtain the planning cost of distributed power sources in each distribution area of the distribution network, the power outage cost of each distribution area per unit time, the indoor air temperature of several buildings in each distribution area during rotating power outages, and the power consumption of the air conditioning system. According to the indoor air temperature, construct a regional overheating risk model for calculating the regional overheating risk caused by power outages in all power distribution areas, and then determine the correlation between the unit power consumption and the regional overheating risk in the power distribution area per unit time according to the regional overheating risk model and the power consumption; According to the correlation, construct a first objective function aiming at minimizing the regional overheating risk of all power distribution areas, and construct a second objective function aiming at minimizing the cost according to the planning cost and the power outage cost of each power distribution area; According to a number of preset constraint conditions, use the power outage time, the power outage duration, and the newly added distributed power sources in each power distribution area as decision variables, and solve the first objective function and the second objective function simultaneously to generate a planning scheme; Among them, the constraint conditions include: a first constraint condition for restricting the regional overheating risk of each power distribution area not to exceed a preset risk threshold, and a second constraint condition for restricting the safe operation of the distribution network; The planning scheme includes: the newly added distributed power sources required for each power distribution area, and the target power outage time and target power outage duration of each power distribution area during the rotating power outage.

[0006] Further, obtaining the indoor air temperature of several buildings and the power consumption of the air conditioning system in each power distribution area during the rotating power outage includes: Obtain the outdoor air temperature during the rotating power outage, the performance parameters of the air conditioning system used in each building in each power distribution area, and the structural parameters of each building; Calculate the power consumption of the air conditioning system of each building according to the performance parameters and the outdoor air temperature; Calculate the first indoor air temperature of each building that has experienced a power outage according to the structural parameters and the outdoor air temperature; Calculate the second indoor air temperature of each building that has not experienced a power outage according to the power consumption, the outdoor air temperature, and the structural parameters.

[0007] Further, the constructing a regional overheating risk model for calculating the indoor overheating risk caused by power outages in all power distribution areas according to the indoor air temperature and the power consumption includes: Obtain the power outage period of the power distribution area during the rotating power outage; Determine the indoor overheating risk caused by power outages for each building according to the power outage period and the indoor air temperature of the building in each power distribution area; Construct a building overheating risk sub-model for characterizing the relationship between indoor overheating risk and power outage time according to the indoor overheating risk of each building; Integrate the indoor overheating risks of all buildings in all power distribution areas according to the building overheating risk sub-model to generate a regional overheating risk model for calculating the indoor overheating risk caused by power outages in all power distribution areas.

[0008] Further, the regional overheating risk model is as follows: Wherein, is the regional overheating risk model, representing the regional overheating risk caused by power outage in distribution area a; N i，c represents the number of buildings c connected to the i-th node in distribution area a; is the building overheating risk sub-model, representing the indoor overheating risk of building c when the power is out at time t and the power outage duration is D; B a represents the set of nodes belonging to distribution area a; C represents the set of buildings c; A represents the set of distribution areas.

[0009] Further, determining the correlation relationship between the unit power consumption and the regional overheating risk of the distribution area per unit time according to the regional overheating risk model and the power consumption includes: Calculating according to the following formula the regional overheating risk caused by each unit of power consumption during the rotation power outage of each distribution area, and taking the regional overheating risk caused by each unit of power consumption as the correlation relationship: Wherein, represents the regional overheating risk caused by each unit of power consumption during the rotation power outage of distribution area a; N i，c represents the number of buildings c connected to the i-th node in distribution area a; is the building overheating risk sub-model, representing the indoor overheating risk of building c when the power is out at time t and the power outage duration is D; B a represents the set of nodes belonging to distribution area a; C represents the set of buildings c; A represents the set of distribution areas; represents the total reduced power of the i-th node in distribution area a at the power outage time t; T′ represents the time interval during the rotation power outage.

[0010] Further, solving the first objective function and the second objective function simultaneously with the power outage time, the power outage duration, and the newly added distributed power sources in each distribution area as decision variables according to a plurality of preset constraint conditions, and generating a planning scheme, includes: Obtaining the actual structure model of the distribution network, and randomly setting the newly added distributed power sources in each distribution area according to the actual structure model under the second constraint condition to generate the initial structure model of the distribution network; Repeatedly performing the planning scheme optimization operation according to the first objective function, the second objective function, and the initial structure model until the planning scheme is generated; The optimization operation of the planning scheme includes: Obtain the structure model to be evaluated. Initially, the structure model to be evaluated is the initial structure model. According to the structure model to be evaluated and the first constraint condition, calculate the power outage time to be evaluated and the power outage duration to be evaluated for each distribution area. According to the first objective function, the power outage time to be evaluated for each distribution area, and the power outage duration to be evaluated, calculate the regional overheating risk score of the distribution network. According to the second objective function and the structure model to be evaluated, calculate the cost to be evaluated of the newly added distributed power sources. According to the regional overheating risk score and the evaluation cost, calculate the scheme score of the scheme to be evaluated. Determine whether the scheme score converges. If so, use the scheme to be evaluated as the planning scheme and output it. If not, under the second constraint condition, according to the scheme score, re-plan the newly added distributed power sources in each distribution area to generate an optimized structure model, and use the optimized structure model as the structure model to be evaluated required for the next round of planning scheme optimization operation.

[0011] Another embodiment of the present invention provides a distributed power source planning device for a distribution network, which is characterized by including: a data acquisition module for acquiring the planning cost of the distributed power sources in each distribution area of the distribution network, the power outage cost of each distribution area per unit time, the indoor air temperature of several buildings in each distribution area during the rotating power outage, and the power consumption of the air conditioning system. A model construction module for constructing a regional overheating risk model for calculating the regional overheating risk caused by power outages in all distribution areas according to the indoor air temperature, and further determining the correlation between the unit power consumption and the regional overheating risk in each distribution area per unit time according to the regional overheating risk model and the power consumption. A function construction module for constructing a first objective function with the goal of minimizing the overheating risk of all distribution areas according to the correlation, and constructing a second objective function with the goal of minimizing the cost according to the planning cost and the power outage cost of each distribution area. A solution generation module, configured to simultaneously solve the first objective function and the second objective function by taking the power outage time, the power outage duration, and the newly added distributed power sources in each distribution area as decision variables according to a plurality of preset constraint conditions, and generate a planning solution; wherein, the constraint conditions include: a first constraint condition for constraining that the regional overheating risk of each distribution area does not exceed a preset risk threshold, and a second constraint condition for constraining the normal operation of the distribution network; the planning solution includes: the newly added distributed power sources required for each distribution area, and the target power outage time and the target power outage duration of each distribution area during the rotating power outage period.

[0012] Further, the data acquisition module acquires the indoor air temperatures of several buildings in each distribution area and the power consumption of the air conditioning systems during the rotating power outage period, including: Acquire the outdoor air temperature during the rotating power outage period, the performance parameters of the air conditioning system used in each building in each distribution area, and the structural parameters of each building. Calculate the power consumption of the air conditioning systems of each building according to the performance parameters and the outdoor air temperature. Calculate the first indoor air temperature of each building that has had a power outage according to the structural parameters and the outdoor air temperature. Calculate the second indoor air temperature of each building that has not had a power outage according to the power consumption, the outdoor air temperature, and the structural parameters.

[0013] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a distributed power source planning method for a distribution network as described in any one of the above embodiments.

[0014] Another embodiment of the present invention provides a storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the storage medium is located to execute a distributed power source planning method for a distribution network as described in any one of the above embodiments.

[0015] By implementing the present invention, the following beneficial effects are achieved: The present invention discloses a distributed power source planning method, device, terminal device and storage medium for a distribution network. By considering the regional overheating risk caused by power outages in the distribution area, the method constructs a first objective function aiming at minimizing the regional overheating risk of all distribution areas, and a second objective function aiming at minimizing the distributed power source planning cost. Under the first constraint condition that the regional overheating risk of each distribution area cannot exceed a preset risk threshold, and the second constraint condition for restricting the normal operation of the distribution network, the first objective function and the second objective function are solved, so that the generated planning scheme takes into account both the power consumption experience of each distribution area in extreme heat wave weather and the installation cost, power outage cost of the distributed power source, and the stability of the distribution network operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 FIG. is a schematic flow chart of a distributed power source planning method for a distribution network provided by an embodiment of the present invention.

[0017] Figure 2 FIG. is a schematic structural diagram of a distributed power source planning device for a distribution network provided by an embodiment of the present invention.

[0018] Figure 3 FIG. is a schematic diagram of the actual structural model of a distribution network provided by an embodiment of the present invention.

[0019] Figure 4 FIG. is a schematic diagram of the distribution network model after adding distributed power sources based on the planning scheme provided by an embodiment of the present invention.

[0020] Figure 5 FIG. is a schematic diagram of the overheating risk caused by power outages in each distribution area provided by an embodiment of the present invention.

[0021] Figure 6 FIG. is a schematic diagram of the influence of different planning schemes on the available power supply of different buildings provided by an embodiment of the present invention.

[0022] Figure 7 FIG. is a schematic diagram of the overheating risk caused by power outages during rotating power outages provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present application belong to the scope of protection of the present application.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs; the terms used herein are for the purpose of describing specific embodiments only and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above drawings are intended to cover non-exclusive inclusion.

[0025] In the description of the embodiments of this application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity, specific order, or primary-secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "a plurality of" is more than two, unless otherwise specifically defined.

[0026] Referring to "embodiment" herein means that the specific features, structures, or characteristics described in connection with the embodiment may be included in at least one embodiment of this application. The phrase appearing in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0027] In the description of the embodiments of this application, the term "and / or" is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0028] In the description of the embodiments of this application, the term "a plurality of" refers to more than two (including two). Similarly, "a plurality of groups" refers to more than two groups (including two groups), and "a plurality of pieces" refers to more than two pieces (including two pieces).

[0029] In the description of the embodiments of this application, unless otherwise clearly specified and limited, technical terms such as "installation", "connection", "connection", and "fixation" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can also be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of this application can be understood according to specific circumstances.

[0030] See Figure 1 , which is a schematic flowchart of a distributed power source planning method for a distribution network provided by an embodiment of the present invention, including: S1. Obtain the planning cost of distributed power sources in each distribution area of the distribution network, the power outage cost of each distribution area per unit time, the indoor air temperature of several buildings in each distribution area during rotating power outages, and the power consumption of the air conditioning system. Preferably, obtaining the indoor air temperature of several buildings in each distribution area and the power consumption of the air conditioning system during rotating power outages includes: S11. Obtain the outdoor air temperature during rotating power outages, the performance parameters of the air conditioning system used in each building in each distribution area, and the structural parameters of each building. S12. Calculate the power consumption of the air conditioning system of each building according to the performance parameters and the outdoor air temperature. S13. Calculate the first indoor air temperature of the buildings with power outages according to the structural parameters and the outdoor air temperature. S14. Calculate the second indoor air temperature of the buildings without power outages according to the power consumption, the outdoor air temperature, and the structural parameters.

[0031] In a preferred embodiment of the present invention, first calculate according to the following formula the indoor air temperature of building c at time t: Where, θ c，t represents the indoor air temperature of building c at time t; represents the power consumption of the air conditioning system of building c at time t; COP c represents the performance coefficient of the air conditioning system installed in building c; represents the outdoor air temperature; a c , b c , d c represent coefficients related to the building envelope structure; calculated based on the thermal resistance R c and heat capacity C c of the building.

[0032] The maximum power consumption required by the air conditioning system of building c at time t is calculated using the following formula: Where, represents the power consumption of the air conditioning system of building c at time t; represents the outdoor air temperature; represents the indoor temperature set value of building c at time t; COP c represents the performance coefficient of the air conditioning system installed in building c; R c represents the thermal resistance of building c; represents the maximum power capacity of the air conditioning system installed in building c.

[0033] S2. Based on the indoor air temperature, construct a regional overheating risk model for calculating the regional overheating risk caused by power outages in all distribution areas, and then determine the correlation between the unit power consumption and the regional overheating risk per unit time in the distribution area according to the regional overheating risk model and the power consumption; Preferably, the constructing a regional overheating risk model for calculating the indoor overheating risk caused by power outages in all distribution areas based on the indoor air temperature and the power consumption includes: S21. Obtain the power outage periods during the rotating power outages in the distribution area; S22. Determine the indoor overheating risk caused by power outages in each building according to the power outage periods and the indoor air temperature in the distribution areas where the buildings are located; S23. Construct a building overheating risk sub-model for characterizing the relationship between the indoor overheating risk and the power outage time according to the indoor overheating risks of the buildings; S24. Integrate the indoor overheating risks of all buildings in all distribution areas according to the building overheating risk sub-model to generate a regional overheating risk model for calculating the indoor overheating risk caused by power outages in all distribution areas.

[0034] Preferably, the regional overheating risk model is: Wherein, is the regional overheating risk model, indicating the regional overheating risk caused by power outages in distribution area a; N i，c represents the number of buildings c connected to the i-th node in distribution area a; is the building overheating risk sub-model, indicating the indoor overheating risk of building c when power is out at time t and the power outage duration is D; B a represents the set of nodes belonging to distribution area a; C represents the set of buildings c; A represents the set of distribution areas.

[0035] In a preferred embodiment of the present invention, first, after determining the indoor air temperature, construct a building overheating risk sub-model for calculating the overheating risk caused by power interruption in the building: Wherein, represents the overheating risk caused by power interruption of building c when power is out at time t and the power outage duration is D; represents the total overheating risk of building c when power is out at time t and the power outage duration is D; represents the overheating risk not caused by power interruption of building c when power is out at time t and the power outage duration is D.

[0036] The following formula is used to calculate the total overheating risk of the building during the rotating power outages: Among them, represents the total overheating risk of building c under a power outage at time t and with a continuous power outage duration D; θ c，t represents the indoor air temperature of building c at time t; θ th represents the overheating warning temperature threshold; Δt represents the time interval; T′ represents the time interval starting from the occurrence of the power interruption.

[0037] Furthermore, through the following formula, calculate the overheating risk of the building and calculate the overheating risk unrelated to the power interruption: Among them, represents the overheating risk caused by non-power interruption of building c under a power outage at time t and with a continuous power outage duration D; represents the indoor air temperature of building c at time t without a power interruption; θ th represents the overheating warning temperature threshold; Δt represents the time interval; T′ represents the time interval starting from the occurrence of the power interruption.

[0038] Furthermore, according to the following formula, and the building overheating risk sub-model Integrate the indoor overheating risks of all buildings in all distribution areas: Among them, represents the total overheating risk during the rotating power outage in distribution area a; N i,c represents the number of buildings c connected to the i-th node in distribution area a; represents the total overheating risk of building c under a power outage at time t and with a continuous power outage duration D; B a represents the set of nodes belonging to distribution area a; C represents the set of buildings c; A represents the set of distribution areas.

[0039] Finally, generate a regional overheating risk model for calculating the indoor overheating risk caused by power outages in all distribution areas: Among them, is the regional overheating risk model, representing the regional overheating risk caused by power outages in distribution area a; N i,c represents the number of buildings c connected to the i-th node in distribution area a; is the building overheating risk sub-model, representing the indoor overheating risk of building c under a power outage at time t and with a continuous power outage duration D; B a represents the set of nodes belonging to distribution area a; C represents the set of buildings c; A represents the set of distribution areas.

[0040] Preferably, determining the correlation between the unit power consumption and the regional overheating risk per unit time of the distribution area according to the regional overheating risk model and the power consumption includes: S25. Calculate, according to the following formula, the regional overheating risk that can be caused by each unit of power during the rotating power outage period of each distribution area, and use the regional overheating risk caused by each unit of power as the correlation: Wherein, represents the regional overheating risk caused by each unit of power (kilowatt-hour) during the rotating power outage period of distribution area a; N i,c represents the number of buildings c connected to the i-th node in distribution area a; is the building overheating risk sub-model, representing the indoor overheating risk of building c when power is cut off at time t and the power outage duration is D; B a represents the set of nodes belonging to distribution area a; C represents the set of buildings c; A represents the set of distribution areas; represents the total power reduction of the i-th node in distribution area a at the power outage time t; T′ represents the time interval during the rotating power outage period.

[0041] In a preferred embodiment of the present invention, the overheating risk caused by the power outage of each user in the distribution area is also calculated by the following formula: Wherein, represents the overheating risk of each building in distribution area a during the rotating power outage period; N i，c represents the number of buildings c connected to node i; represents the overheating risk caused by the power interruption of building c when power is cut off at time t and the power outage duration is D; B a represents the set of nodes belonging to distribution area a; C represents the set of buildings c; A represents the set of distribution areas a that can still be normally powered during the heat wave; represents the power reduction amount of node i at time t; T′ represents the time interval from the start of the rotating power outage.

[0042] S3. According to the correlation, construct a first objective function with the goal of minimizing the regional overheating risk of all distribution areas, and construct a second objective function with the goal of minimizing the cost according to the planning cost and the power outage cost of each distribution area; In a preferred embodiment of the present invention, in this embodiment, when the distributed power source is set as a solar power source and energy storage, the second objective function is as follows: Among them, the annualized fixed investment for installing solar energy and energy storage is: Among them, B′ represents the set of nodes installed with solar energy or energy storage; A pv and A es respectively represent the annualized investment costs of the solar energy unit and the energy storage; cf g and cf e respectively represent the fixed installation costs of the solar energy equipment and the energy storage; and are binary variables representing whether the solar energy unit and the energy storage are installed at node i respectively. If installed, the binary variable is 1, otherwise it is 0.

[0043] The annualized variable investment related to the increase in the capacity of the solar power generation and energy storage system is: Among them, B′ represents the set of nodes installed with solar energy or energy storage; A pv and A es respectively represent the annualized investment costs of the solar energy unit and the energy storage; cv g 、cv e 、cv s respectively represent the cost unit prices of the newly added solar energy capacity, the newly added charge / discharge capacity of the energy storage, and the newly added energy storage capacity; are the newly added solar energy capacity and the newly added charge / discharge capacity of the energy storage respectively; is the newly added energy storage capacity.

[0044] The fixed maintenance cost of the solar energy and the energy storage is: Among them, B′ represents the set of nodes installed with solar energy or energy storage; of g 、of e 、of s are the annual fixed maintenance costs per kilowatt of solar energy, per kilowatt of energy storage, and per kilowatt-hour of energy storage respectively.

[0045] The variable operating cost related to the power output of the solar energy and the energy storage is: Among them, T is the total time step of power supply; B′ represents the set of nodes installed with solar energy or energy storage; represents the solar power injected into node i at time t; ov g represents its unit cost; and represent the charge / discharge power of the energy storage at time t; ov e represents its unit cost.

[0046] The electricity purchase cost for purchasing electricity from the power transmission system is: where T represents the total time steps of power supply; B represents the set of distribution network nodes; represents the power injected into node i by the power transmission system at time t; represents the electricity price of the power transmission system at time t.

[0047] The outage cost of rolling blackouts during a heatwave: where represents the set of time steps of rolling blackouts; B represents the set of distribution network nodes; is included as a weight, representing the number of rolling blackouts occurring during a heatwave each year; c i represents the outage cost of node i; e i，t is a binary variable, representing whether rolling blackouts occur at node i at time t; represents the active load connected to node i at time t; Δt represents the duration of rolling blackouts.

[0048] Furthermore, the first objective function is as follows: where represents the maximum value of the overheating risk per unit of electricity, such as per kilowatt-hour, in distribution area a during rolling blackouts.

[0049] S4. According to a number of preset constraint conditions, with the outage time, outage duration, and newly added distributed power sources in each distribution area as decision variables, solve the first objective function and the second objective function simultaneously, and generate a planning scheme; where the constraint conditions include: a first constraint condition for restricting the area overheating risk of each distribution area not to exceed a preset risk threshold, and a second constraint condition for restricting the safe operation of the distribution network; the planning scheme includes: the newly added distributed power sources required for each distribution area, and the target outage time and target outage duration of each distribution area during rolling blackouts.

[0050] In a preferred embodiment of the present invention, the first constraint condition is: where represents the global maximum overheating risk; represents the overheating risk metric of distribution area a when there is an outage at time t and the outage duration is D.

[0051] Calculate the overheating risk when the system is not powered during each time period during rolling blackouts: Among them, represents the overheating risk metric of power distribution area a during power outage at time t and with a continuous power outage duration D; is a binary variable indicating whether power is supplied at time t. If power distribution area a is supplied with power at time t, it is 1; otherwise, it is 0; represents the overheating risk per kilowatt-hour of power distribution area a during rotational power outages.

[0052] Furthermore, to ensure that the power supply status of all nodes i in power distribution area a is consistent, this embodiment also sets a third constraint condition: Among them, is a binary variable indicating whether power is supplied to power distribution area a at time t; e i，t represents the power supply status of node i at time t; B a represents the set of nodes belonging to power distribution area a.

[0053] Prevent the inability to restore power supply once power distribution area a loses power during rotational power outages: Among them, e i，t represents the power supply status of node i at time t; represents the start time of power interruption, represents the time of power restoration.

[0054] To ensure that all investments do not exceed the budget limit, this embodiment also sets a fourth constraint condition: Among them, cf g and cf e represent the fixed installation costs per kilowatt of solar energy and energy storage; and are binary variables indicating whether a solar unit or an energy storage system is installed at node i; are the newly added solar capacity and the newly added energy storage charge and discharge capacity respectively; is the newly added energy storage capacity; cv g cv e cv s represent the cost unit prices of the newly added solar capacity, the newly added energy storage charge and discharge capacity, and the newly added energy storage capacity respectively; is the budget limit.

[0055] Furthermore, the second constraint condition specifically includes: grid operation constraints, solar energy and energy storage constraints, and linearized charge and discharge constraints; In the grid operation constraints, the specific expression for the active power balance of node i at time t is as follows: Among them, represents the active power injected into node i by the transmission system at time t; represents the active power provided by the solar unit installed at node i at time t; represents the discharge power from the energy storage at node i at time t; represents the charging power from the energy storage at node i at time t; represents the active load of node i; e i，t is a binary variable indicating whether node i is in the power supply state at time t. If it is in the power supply state, the value is 1; otherwise, it is 0; p ij,t is the actual active power flow from node i to node j.

[0056] In the grid operation constraints, the specific expression for the reactive power balance of node i at time t is as follows: Among them, represents the reactive power injected into node i by the transmission system at time t; represents the reactive power provided by the solar unit installed at node i at time t; represents the reactive power from the energy storage at node i at time t; represents the reactive load of node i; e i,t is a binary variable indicating whether node i is in the power supply state at time t. If it is in the power supply state, the value is 1; otherwise, it is 0; q ij,t is the actual reactive power flow from node i to node j.

[0057] In the grid operation constraints, the specific expression for restricting the active power flow between nodes is as follows: Among them, represents the maximum allowable active power flow from node i to node j; s ij,t is a binary variable indicating the state of the automatic switch. If the switch is closed, the value is 1; otherwise, it is 0.

[0058] In the grid operation constraints, the specific expression for restricting the reactive power flow between nodes is as follows: Among them, represents the maximum allowable reactive power flow from node i to node j; s ij,tis a binary variable representing the status of the automatic switch. If the switch is closed, the value is 1; otherwise, it is 0.

[0059] In the grid operation constraints, the specific expression indicating that nodes connected by a line or a closed switch must share the same energized state is as follows: where, e i represents the energized condition of the node; s ij，t is a binary variable representing the status of the automatic switch. If the switch is closed, the value is 1; otherwise, it is 0.

[0060] In the grid operation constraints, the specific expression indicating the voltage drop limit between nodes is as follows: where, v j，t represents the voltage of node j; r i，j and x i，j represent the resistance and reactance of the line between nodes i and j; M is a large number; s ij，t is a binary variable representing the status of the automatic switch. If the switch is closed, the value is 1; otherwise, it is 0.

[0061] In the grid operation constraints, the specific expression indicating the constraint voltage limit is as follows: where, v and represent the lower and upper voltage limits respectively; v i，t represents the voltage of node i.

[0062] In the solar and energy storage constraints, the specific expression for constraining the maximum solar capacity of each node is as follows: where, represents the capacity of the newly installed solar unit; represents the maximum allowable solar capacity for each node; is a binary variable indicating whether a solar unit is installed at node i.

[0063] In the solar and energy storage constraints, the specific expression for constraining the energy storage power output and limiting its maximum power is as follows: where, represents the charge and discharge power of the newly installed energy storage; represents the maximum power output of the energy storage; is a binary variable indicating whether an energy storage is installed at node i.

[0064] In the solar energy and energy storage constraints, the specific expression for limiting the installed energy storage capacity is as follows: Wherein, represents the capacity of the newly installed energy storage at node i; E i represents the lower limit of the energy storage capacity at node i; represents the upper limit of the energy storage capacity; is a binary variable indicating whether to install energy storage at node i.

[0065] In the solar energy and energy storage constraints, the specific expression for limiting the active power of solar energy is as follows: Wherein, represents the active power generated by the solar energy unit at node i at time t; represents the maximum active power of the newly installed solar energy unit at node i; represents the normalized solar radiation intensity, indicating the solar radiation level at a certain time, and its value range is from 0 to 1, where 0 represents no solar radiation and 1 represents the maximum solar radiation.

[0066] In the solar energy and energy storage constraints, the specific expression for limiting the reactive power of solar energy is as follows: Wherein, represents the active power generated by the solar energy unit at node i at time t; represents the maximum active power of the newly installed solar energy unit at node i; represents the normalized solar radiation intensity, indicating the solar radiation level at a certain time, and its value range is from 0 to 1, where 0 represents no solar radiation and 1 represents the maximum solar radiation.

[0067] In the solar energy and energy storage constraints, the specific expression for calculating the stored energy of the energy storage is as follows: Wherein, E i,t represents the stored energy at node i at time t; represents the charging efficiency of the energy storage; represents the discharging efficiency of the energy storage; represents the charging power at node i at time t; represents the discharging power at node i at time t; Δt represents the duration time step; T represents the total time step of power supply.

[0068] In the solar energy and energy storage constraints, the specific expression for the charging and discharging limits of the energy storage is as follows: Wherein, Denote the energy storage charging power of node \(i\) at time \(t\); Denote the energy storage discharging power of node \(i\) at time \(t\); Denote the charge and discharge limit of the energy storage of node \(i\); \(\beta\) i，t Is a binary variable, representing the charge and discharge state of the energy storage of node \(i\). If it is in the charging state, the value is 1, otherwise it is 0.

[0069] In the solar and energy storage constraints, the specific expression for restricting the available energy of the energy storage is as follows: Among them, \(E\) i，t Denote the available energy of the energy storage at node \(i\) at time \(t\); E i Denote the lower limit of the available energy of the energy storage; Denote the newly installed energy storage capacity of node \(i\).

[0070] In the solar and energy storage constraints, the specific expression for restricting whether a node installs solar energy or energy storage is as follows: Among them, and Are binary variables, representing whether a solar energy unit and an energy storage are installed at node \(i\); \(B'\) is the set where solar energy or energy storage can be installed.

[0071] In the linearized charge and discharge constraints, the charge and discharge constraints of the energy storage involve the product of the continuous variable and the binary variable \(\beta\) i，t to form a mixed-integer non-linear optimization model. In this model, the McCormick envelope is used to replace the non-linear constraints. Let \(z\) i，t be and \(\beta\) i，t 's product. Considering that and 's maximum value is The resulting constraint is: \(z\) i，t \(\geq0\) Among them, Denote the energy storage charging power of node \(i\) at time \(t\); Denote the energy storage discharging power of node \(i\) at time \(t\); \(z\) i，t is an auxiliary variable, whose value is and \(\beta\) i，t 's product; Denote the energy storage discharging power of node \(i\) at time \(t\); Indicates the charge and discharge limit of the energy storage at node i; β i,t Is a binary variable, indicating whether node i is allowed to charge at time t. If charging is allowed, its value is 1, otherwise 0; Represents the maximum charging power of the energy storage.

[0072] Preferably, according to a number of preset constraint conditions, with the power outage time, power outage duration, and newly added distributed power sources in each distribution area as decision variables, the first objective function and the second objective function are solved simultaneously, and a planning scheme is generated, including: S41. Obtain the actual structure model of the distribution network, and randomly set the newly added distributed power sources in each distribution area according to the actual structure model under the second constraint condition to generate the initial structure model of the distribution network; S42. According to the first objective function, the second objective function, and the initial structure model, repeatedly perform the planning scheme optimization operation until the planning scheme is generated; Among them, the planning scheme optimization operation includes: S421. Obtain the structure model to be evaluated. Initially, the structure model to be evaluated is the initial structure model; S422. According to the structure model to be evaluated, calculate the power outage time to be evaluated and the power outage duration to be evaluated in each distribution area under the first constraint condition; S423. According to the first objective function, the power outage time to be evaluated in each distribution area, and the power outage duration to be evaluated, calculate the regional overheating risk score of the distribution network; S424. According to the second objective function and the structure model to be evaluated, calculate the cost to be evaluated of the newly added distributed power sources; S425. According to the regional overheating risk score and the evaluation cost, calculate the scheme score of the scheme to be evaluated; S426. Determine whether the scheme score converges; S427. If so, use the scheme to be evaluated as the planning scheme and output it; S428. If not, under the second constraint condition, re-plan the newly added distributed power sources in each distribution area according to the scheme score to generate an optimized structure model, and use the optimized structure model as the structure model to be evaluated required for the next round of planning scheme optimization operation.

[0073] This embodiment provides a distributed power source planning method for a distribution network. By considering the risk of regional overheating caused by power outages in the distribution area, a first objective function aiming to minimize the regional overheating risk of all distribution areas and a second objective function aiming to minimize the distributed power source planning cost are constructed. Under the first constraint condition that the regional overheating risk of each distribution area cannot exceed the preset risk threshold and the second constraint condition for restricting the normal operation of the distribution network, the first objective function and the second objective function are solved, so that the generated planning scheme takes into account both the power consumption experience of each distribution area in extreme heatwave weather and the installation cost, power outage cost, and operation stability of the distributed power source. Experimental verification is carried out on a distributed power source planning method for a distribution network based on the above embodiment. By constructing an overheating risk scenario under a heatwave disaster based on the IEEE-33 node system, the method of the present invention is verified. Its topological structure is as Figure 3 shown. It is assumed that manual switches are installed on all lines in the distribution network, and 6 lines are equipped with normally closed automatic switches. The 33 nodes are divided into six parts, and nodes 5, 8, 11, 14, 17, 20, 23, 26, 29, and 32 are set as candidate nodes for distributed resources.

[0074] The data used in the planning considers a one-year planning period, in which one working day and one weekend day are selected for each month. These data are from CIMIS data in 2022 and are used to run the model. In addition, it includes the hourly outdoor temperature data during two days of heatwave from 00:00 on September 5, 2022 to 00:00 on September 7, 2022 at Durham Station. At the same time, the nodal marginal prices (LMPs) at the corresponding time points come from the California Independent System Operator (CAISO).

[0075] The coefficients are calculated by simulating the indoor air temperature under different power outage durations in 15-minute steps. Four types of residential building types are considered in the study, and their parameters are listed in Table 1, which are used to represent the sensitivity of different building types to temperature changes during power outages. The distribution of these building types in each distribution network area is summarized in Table 2. The investment and operation costs are listed in Table 3.

[0076] Table 1 Residential building parameter information: Table 2 Number of building types in each part of the distribution network: Table 3 Investment and operation costs of solar energy and energy storage: As described in the previous content, the cost-based optimization method first evaluates the optimal locations of distributed resources to minimize the costs considered in the objective function, and then uses the investment cost obtained from the solution as the upper limit of the budget constraint. The optimal budget after the cost-based optimization solution is $3,512,318. The design results of the two optimization models are as Figure 4 shown, and the power output and capacity are shown in Table 4.

[0077] Table 4 Distribution of Distributed Resources: Each planning model performs differently in reducing the overheating risk. As Figure 5 shown, the figure shows the overheating risk per kilowatt-hour for each section. The cost-based planning ensures that during the rotating power outage, the areas where distributed energy resources (DERs) are installed can be fully powered, resulting in this indicator being zero and no changes in Sections 1, 2, and 5. Feeder Section 2 is a critical section because it contains the most consumers and has a high concentration of overheating risk. In this case, the cost-based planning exacerbates the thermal differences between sections. On the other hand, the fairness-based planning leads to a situation where the avoidable overheating risk is relatively balanced between sections. This indicator is reduced by more than 50% in Sections 2 and 5, while there is a moderate reduction in Sections 3 and 6.

[0078] Figure 6 Shows the impact of the location of distributed energy resources (DERs) on the available power supply for different buildings under the cost-based and fairness-based models. Figure 6 -(a) shows the power supply to households under the cost-based planning, which lasts throughout the rotating power outage from start to end. However, as Figure 6 shown in -(a), nearly 40% of the low- and medium-risk household consumers received power supply, while approximately 20% of the high-overheating-risk consumers had power supply. On the other hand, the available energy resources allocated by the fairness-based approach can supply power continuously for up to 2.5 hours, after which the system will remain powered off until the power supply is restored, as Figure 6 shown in -(b). Figure 6 -(b) shows that within the first hour, more than 92% of the high-risk consumers received power supply, and within the second hour, more than 67% of the high-risk consumers received power supply. Therefore, the power distribution is based on the criticality of households in terms of overheating risk, which meets the expectations of fairness considerations.

[0079] Table 5 summarizes the results of different quantification metrics to compare the impacts of cost-based and fairness-considered planning decisions. The introduction of distributed energy resources (DER) reduces the outage cost in both scenarios, with a 32.47% reduction in cost-based investment and a 24.75% reduction in fairness-considered investment. Different from cost-based investment that prioritizes areas with higher loads and higher outage costs, fairness-considered investment prioritizes areas with higher overheating risks. In terms of operation and maintenance, cost-based investment has lower operation costs but higher maintenance costs, manifested as wider use of installed DER throughout the year to reduce the cost of purchasing electricity from the bulk transmission system. On the other hand, fairness-considered investment preferentially uses DER during outages to reduce the imbalance of overheating risks; Table 5 Price Comparison See Figure 2 , which is a schematic structural diagram of a distributed power planning device for a distribution network provided by an embodiment of the present invention, including: a data acquisition module, configured to acquire the planning cost of distributed power sources in each distribution area of the distribution network, the outage cost of each distribution area per unit time, the indoor air temperature of several buildings in each distribution area during rotating outages, and the power consumption of the air conditioning system; A model construction module, configured to construct a regional overheating risk model for calculating the regional overheating risk caused by outages in all distribution areas based on the indoor air temperature, and then determine the correlation between the unit power consumption and the regional overheating risk in each distribution area per unit time according to the regional overheating risk model and the power consumption; A function construction module, configured to construct a first objective function with the goal of minimizing the overheating risk of all distribution areas based on the correlation, and construct a second objective function with the goal of minimizing the cost based on the planning cost and the outage cost of each distribution area; A solution generation module, configured to simultaneously solve the first objective function and the second objective function with the outage time, outage duration, and newly added distributed power sources in each distribution area as decision variables according to a preset number of constraint conditions, and generate a planning solution; wherein, the constraint conditions include: a first constraint condition for constraining the regional overheating risk of each distribution area not to exceed a preset risk threshold, and a second constraint condition for constraining the normal operation of the distribution network; the planning solution includes: the newly added distributed power sources required for each distribution area, and the target outage time and target outage duration of each distribution area during rotating outages.

[0080] Further, the data acquisition module acquires the indoor air temperature of several buildings in each distribution area and the power consumption of the air conditioning system during rotating outages, including: Obtain the outdoor temperature during the rotating power outage, the performance parameters of the air conditioning systems used in each building in each distribution area, and the structural parameters of each building; Calculate the power consumption of the air conditioning systems of each building according to the performance parameters and the outdoor temperature; Calculate the first indoor temperature of each building where power has been cut off according to the structural parameters and the outdoor temperature; Calculate the second indoor temperature of each building where power has not been cut off according to the power consumption, the outdoor temperature, and the structural parameters.

[0081] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationships between the modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement without creative efforts.

[0082] Those skilled in the art can clearly understand that for the sake of convenience and conciseness, the specific working process of the device described above can refer to the corresponding process in the foregoing method embodiment, and will not be elaborated here.

[0083] Another preferred embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a distributed power source planning method for a distribution network as described in any one of the above embodiments.

[0084] The terminal device can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory.

[0085] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the terminal device, connecting all parts of the entire terminal device through various interfaces and lines.

[0086] The memory can be used to store the computer program. By running or executing the computer program stored in the memory and invoking the data stored in the memory, the processor realizes various functions of the terminal device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store the operating system, application programs required for at least one function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, memory, plug-in hard disks, Smart Media Cards (SMCs), Secure Digital (SD) cards, Flash Cards, at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices.

[0087] Another preferred embodiment of the present invention provides a storage medium, which is a computer-readable storage medium. The computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be realized. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0088] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A distributed power planning method for a distribution network, characterized in that: include: Obtain the planning cost of distributed power sources in each distribution area in the distribution network, the power outage cost per unit time in each distribution area, the indoor temperature of several buildings in each distribution area during the rotating power outage, and the power consumption of the air conditioning system; According to the indoor temperature, a regional overheating risk model is constructed for calculating the risk of regional overheating caused by power outages in all power distribution areas, and then, according to the regional overheating risk model and the power consumption, a correlation between the unit power consumption and the regional overheating risk in the power distribution area per unit time is determined; According to the association, a first objective function is constructed with the goal of minimizing the regional overheating risk of all distribution areas, and according to the planning cost and the power outage cost of each distribution area, a second objective function is constructed with the goal of minimizing the cost; according to a number of preset constraints, the power outage time, the power outage duration, and the newly added distributed power sources in each distribution area are used as decision variables, the first objective function and the second objective function are solved simultaneously, and a planning scheme is generated; wherein the constraints include: a first constraint for constraining that the regional overheating risk of each distribution area shall not exceed a preset risk threshold, and a second constraint for constraining the safe operation of the distribution network; the planning scheme includes: the newly added distributed power sources required for each distribution area, and the target power outage time and target power outage duration of each distribution area during the rotating power outage.

2. A distributed power planning method for a distribution network according to claim 1, characterized in that: Obtain the indoor temperature of several buildings in each distribution area and the power consumption of the air conditioning system during the rolling blackout period, including: Obtain the outdoor temperature during the rotating blackout period, the performance parameters of the air conditioning system used by each building in each distribution area, and the structural parameters of each building; Calculate the power consumption of the air conditioning system of each building based on the performance parameters and the outdoor temperature; Calculating a first indoor temperature of each building that has been out of power based on the structural parameters and the outdoor temperature; The second indoor air temperature of each building that has not experienced a power outage is calculated based on the power consumption, the outdoor air temperature, and the structural parameters.

3. A distributed power planning method for a distribution network as claimed in claim 2, characterized in that: The constructing, according to the indoor temperature and the power consumption, a regional overheating risk model for calculating the indoor overheating risk caused by power outage in all power distribution areas includes: Obtain the power outage period of the distribution area during the rotating power outage; Determine the indoor overheating risk of each building caused by power outages based on the power outage period and indoor temperature of the power distribution area where each building is located; According to the indoor overheating risk of each building, a building overheating risk sub-model is constructed to characterize the relationship between indoor overheating risk and power outage time; According to the building overheating risk sub-model, the indoor overheating risks of all buildings in all power distribution areas are integrated to generate a regional overheating risk model for calculating the indoor overheating risks caused by power outages in all power distribution areas.

4. A distributed power planning method for a distribution network as claimed in claim 3, characterized in that: The regional overheating risk model is: in, is the regional overheating risk model, which represents the regional overheating risk caused by power outage in distribution area a; N i，c represents the number of buildings c connected to the i-th node in distribution area a; B is the building overheating risk sub-model, which represents the indoor overheating risk of building c when the power outage occurs at time t and the power outage lasts for D; a represents the set of nodes belonging to the distribution area a; C represents the set of buildings c; A represents the set of distribution areas.

5. A distributed power planning method for a distribution network as claimed in claim 4, characterized in that: The determining, according to the regional overheating risk model and the power consumption, a correlation relationship between unit power and regional overheating risk in the power distribution area per unit time includes: The regional overheating risk that can be caused by each unit of electricity in each distribution area during the rotational power outage is calculated according to the following formula, and the regional overheating risk caused by each unit of electricity is used as the association relationship: in, N represents the regional overheating risk caused by each unit of electricity in distribution area a during the rolling blackout period; i，c represents the number of buildings c connected to the i-th node in distribution area a; B is the building overheating risk sub-model, which represents the indoor overheating risk of building c when the power outage occurs at time t and the power outage lasts for D; a represents the set of nodes belonging to the distribution area a; C represents the set of buildings c; A represents the set of distribution areas; represents the total power reduction of the i-th node in the distribution area a at time t; T′ represents the time interval during the rotating power outage.

6. A distributed power planning method for a distribution network as claimed in claim 5, characterized in that: According to the preset constraints, the first objective function and the second objective function are solved simultaneously with the power outage time, the power outage duration, and the newly added distributed power sources in each distribution area as decision variables, and a planning scheme is generated, including: Acquire the actual structural model of the distribution network, and under the second constraint condition, randomly set the newly added distributed power sources in each distribution area according to the actual structural model to generate an initial structural model of the distribution network; Repeating the planning scheme optimization operation according to the first objective function, the second objective function, and the initial structural model until the planning scheme is generated; The planning scheme optimization operation includes: Acquire a structural model to be evaluated, wherein, initially, the structural model to be evaluated is the initial structural model; According to the structure model to be evaluated, under the first constraint condition, calculating the power outage time to be evaluated and the power outage duration to be evaluated of each power distribution area; Calculating a regional overheating risk score of the distribution network according to the first objective function, the to-be-assessed power outage time of each distribution area, and the to-be-assessed power outage duration; Calculating the cost to be evaluated of the newly added distributed power source according to the second objective function and the structural model to be evaluated; Calculating a scheme score of the scheme to be evaluated according to the regional overheating risk score and the evaluation cost; Determine whether the solution score converges; If yes, the scheme to be evaluated is used as the planning scheme and outputted; If not, then under the second constraint condition, according to the scheme score, the newly added distributed power sources in each distribution area are replanned to generate an optimized structural model, and the optimized structural model is used as the structural model to be evaluated for the next round of planning scheme optimization operations.

7. A distributed power planning device for a distribution network, characterized in that: include: A data acquisition module is used to obtain the planning cost of distributed power sources in each distribution area in the distribution network, the power outage cost per unit time in each distribution area, the indoor temperature of several buildings in each distribution area during the rotating power outage, and the power consumption of the air conditioning system; A model building module, for building a regional overheating risk model for calculating the risk of regional overheating caused by power outages in all power distribution areas according to the indoor temperature, and then determining the correlation between unit power consumption and regional overheating risk in the power distribution area per unit time according to the regional overheating risk model and the power consumption; A function construction module, configured to construct a first objective function with the goal of minimizing the overheating risk of all distribution areas according to the association relationship, and to construct a second objective function with the goal of minimizing the cost according to the planning cost and the power outage cost of each distribution area; A scheme generation module is used to solve the first objective function and the second objective function simultaneously and generate a planning scheme according to a number of preset constraints, with the power outage time, power outage duration, and newly added distributed power sources in each distribution area as decision variables; wherein the constraints include: a first constraint for constraining that the regional overheating risk of each distribution area must not exceed a preset risk threshold, and a second constraint for constraining the normal operation of the distribution network; the planning scheme includes: the newly added distributed power sources required in each distribution area, and the target power outage time and target power outage duration of each distribution area during the rotating power outage.

8. A distributed power planning device for a distribution network as claimed in claim 7, characterized in that: The data acquisition module acquires the indoor temperature of several buildings in each power distribution area and the power consumption of the air conditioning system during the rotating power outage, including: Obtain the outdoor temperature during the rotating blackout period, the performance parameters of the air conditioning system used by each building in each distribution area, and the structural parameters of each building; Calculate the power consumption of the air conditioning system of each building based on the performance parameters and the outdoor temperature; Calculating a first indoor temperature of each building that has been out of power based on the structural parameters and the outdoor temperature; The second indoor air temperature of each building that has not experienced a power outage is calculated based on the power consumption, the outdoor air temperature, and the structural parameters.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a distributed power source planning method for a distribution network as described in any one of claims 1 to 6 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a distributed power source planning method for a distribution network as described in any one of claims 1 to 6.

Images