Distribution network-microgrid planning optimization method and system based on distribution-microgrid collaboration mode

Abstract

A distribution network-microgrid planning optimization method and system based on a distribution-microgrid collaborative model uses the operating costs of each power source within the microgrid and the penalty for abandoning renewable energy as objective functions to determine the interaction power between each microgrid and the distribution network. The location of multiple microgrids connected to the distribution network system is determined by minimizing the combined weighted cost of the distribution network operating cost and voltage deviation as the objective function. A two-layer collaborative planning model for active and reactive power sources is established for the distribution network-microgrid. The upper-layer planning targets the minimum annual comprehensive cost and plans the energy storage and reactive device capacity connected to the distribution network, as well as the energy storage capacity within the microgrid. The lower-layer planning targets the minimum operating costs of each microgrid and the distribution network and plans the location and capacity of the access nodes for energy storage and reactive power in the distribution network, as well as the interaction power between each microgrid. This invention has important theoretical value and engineering application significance for improving the economic, safe, and stable operation of distribution networks, increasing the absorption rate of renewable energy, and promoting the construction of new power systems.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system planning and optimization, and specifically relates to a distribution network-microgrid planning optimization method and system based on a distribution-microgrid collaborative mode. Background Art

[0002] With the continuous advancement of environmental protection and sustainable development goals, the characteristics of distribution networks are gradually shifting from passive to active, unidirectional to bidirectional, and deterministic to random. The penetration rate of distributed generation (DGs) is increasing year by year, and load demand continues to grow. Their intermittent and random characteristics can impact the safe and economic operation of distribution networks, leading to issues such as power backflow, increased system losses, node voltage violations, difficulty in localizing renewable energy output, and insufficient system flexibility. Consequently, traditional distribution network planning and operation are no longer adaptable to these new requirements. Their objectives have evolved from improving power supply security to promoting multi-energy integration and interaction. Furthermore, the introduction of microgrids has introduced new factors and challenges to distribution network planning and operation. Microgrids are systems that integrate multiple DGs, energy storage devices, loads, and monitoring and protection devices within a single area. As a new technology for integrating source-grid-load-storage resources on the distribution network side, microgrids are an effective means of promoting the localization of large-scale distributed renewable energy and enabling the local integration and interaction of diverse new elements. The distribution-microgrid collaborative model refers to a power system in which the distribution network and microgrids achieve efficient, reliable, economical, and low-carbon power supply through coordinated operation and complementary support. This model is of great significance in the context of a high proportion of distributed generation (DGs) and is a crucial component of future power systems. However, with the large number of microgrids connected to the distribution network, their disorderly access can lead to overloads at key nodes, irrational power flow, increased line losses, and reduced power quality, all impacting the safe and stable operation of the distribution network. Therefore, it is crucial to comprehensively consider the impact of the distribution-microgrid collaborative model, DGs, and the planning and access of multiple microgrids on the system, and to develop effective collaborative planning solutions to address the challenges currently faced by distribution networks containing multiple microgrids.

[0003] Current research on power planning for distribution network-microgrid systems, such as the patent application with publication number CN119742759A, uses historical operating data from distribution networks and microgrids to obtain typical scenario characteristics; based on the typical scenario characteristics and preset grid collaborative operation requirements, constraints and objective functions are established to obtain a collaborative planning and operation model for the distribution network and microgrid; the collaborative planning and operation model includes a grid planning layer and a grid operation layer; the linearized collaborative planning and operation model is solved to obtain the configuration and scheduling strategy. Another example is the patent application with publication number CN108197766A, which proposes a two-layer optimization scheduling model suitable for active distribution networks containing microgrid groups. The upper-layer model takes the distribution network as the research object, with the optimization objectives of improving power quality and reducing line losses, while the lower-layer model takes the microgrid as the research object, with the optimization objective of minimizing cost.

[0004] The consideration of the distribution-microgrid collaborative model is relatively simple. The generation of its interactive power does not comprehensively consider the economic efficiency of the distribution-microgrid system operation and the maximization of new energy consumption. In addition, it also ignores the impact of time-of-use electricity prices on interactive power. For the access location planning of multiple microgrids, most of them do not consider the impact of microgrids on the distribution system before the power planning stage. For the distribution-microgrid system after photovoltaic vision planning within the microgrid, there is little research on the collaborative planning of active and reactive power sources between the microgrid and the distribution network.

[0005] Therefore, in order to solve the above problems, it is urgent to study a distribution network-microgrid planning optimization method that considers the distribution-microgrid collaborative mode. Summary of the Invention

[0006] To address the shortcomings of the existing technology, the present invention provides a distribution network-microgrid planning optimization method and system based on a distribution-microgrid collaborative model. This method comprehensively considers the distribution-microgrid collaborative model, optimizes the generation of distribution-microgrid interaction power, and selects multiple microgrid sites. It establishes a two-layer distribution network-microgrid collaborative planning model. This model is adaptable to distribution network systems with varying renewable energy penetration rates and multiple microgrids, supports multi-power planning, and enables more comprehensive and accurate planning of distribution network systems with multiple microgrids. The implementation of this invention will provide important technical support for improving active and reactive power planning for distribution networks with multiple microgrids, while also providing a scientific basis for the construction of new power systems and the development of distribution network microgrids.

[0007] The present invention proposes a distribution network-microgrid planning optimization method based on a distribution-microgrid collaborative model, comprising:

[0008] Step 1: Considering the distribution-microgrid collaboration model, based on the time-of-use electricity price and taking the operating cost of each power source within the microgrid as the objective function, determine the interaction power between each microgrid and the distribution network;

[0009] Step 2: Taking the comprehensive weighted minimization of distribution network operation cost and voltage deviation as the objective function, determine the location where multiple microgrids are connected to the distribution network system;

[0010] Step 3: Consider resource conditions and actual site area, determine the photovoltaic capacity planned to be connected to each microgrid, and set the number of distribution network partitions based on the photovoltaic capacity;

[0011] Step 4: Based on the interaction power between each microgrid and the distribution network, the locations where multiple microgrids are connected to the distribution network system, the photovoltaic capacity and the number of zones planned to be connected to each microgrid, a two-layer collaborative planning model for active and reactive power sources of the distribution network-microgrid is established, and an iterative solution is performed to obtain the distribution network-microgrid planning result.

[0012] Furthermore, in step 1, in the distribution network and microgrid collaborative mode where the distribution network and microgrid interact through bidirectional power flow, the microgrid operation cost is The minimum is the objective function, which includes the sum of the internal micro gas turbine operating cost, the wind and solar curtailment cost, the energy storage transaction cost and the transaction cost with the distribution network power. It also takes into account the impact of time-of-use electricity prices during microgrid transactions, and optimizes the interaction power between each microgrid and the distribution network.

[0013] Furthermore, in step 1, the power transaction cost between the microgrid and the distribution network is It can be expressed as:

[0014] ;

[0015] Where, 、 They are The electricity price for the microgrid to purchase and sell electricity from the distribution network is based on the time-of-use electricity price; 、 are the power purchase and sales of the microgrid and the distribution network at time t; The scheduling period.

[0016] Furthermore, in step 1, the microgrid operating cost The constraints of the microgrid consider the operation constraints of each power source within the microgrid and the upper and lower limits of the interactive power of the distribution microgrid; the power sources include micro gas turbines, photovoltaics, wind power and energy storage;

[0017] Combined with the constraints, the optimization solution is obtained to obtain the interactive power between each microgrid and the distribution network under the distribution-micro collaborative mode. .

[0018] Furthermore, in step 2, the distribution network system operation cost and voltage offset The planning goal is to minimize the weighted sum of the multiple microgrid access points in the distribution network.

[0019] Furthermore, in step 2, the distribution network system operating cost Cost of generating electricity for the system , the cost of curtailing wind and solar power , network loss cost sum;

[0020] Voltage offset It is the sum of the absolute values ​​of the deviations between the per-unit voltage values ​​of each node and the standard voltage value 1 during the entire operating period.

[0021] Furthermore, in step 3, the photovoltaic planning capacity The calculation formula is:

[0022] ;

[0023] Where, is the total area; is the ratio of building roof area to floor area; is the roof utilization coefficient; is the photovoltaic installed density per unit area.

[0024] Furthermore, in step 4, the distribution network-microgrid active and reactive power source two-layer collaborative planning model includes an upper-layer planning model and a lower-layer planning model;

[0025] In the upper-level planning model, the planning objective is the annual comprehensive cost of the distribution network; the decision variables are the total energy storage and reactive power capacity allocated to the distribution network, and the energy storage capacity of the microgrid; the constraints include the investment capacity constraints of each power source;

[0026] In the lower-level planning model, the planning objectives are the distribution network operating costs and the microgrid operating costs; the decision variables are the access locations and access capacities of each energy storage and reactive power in the distribution network, and the interaction power between the microgrid and the distribution network; the constraints include distribution network flow constraints, energy storage and reactive power planning capacity constraints, microgrid operation constraints, system safety operation constraints, and unit operation constraints.

[0027] Furthermore, in step 4, in the upper-level planning model, the planning target is the minimum annual comprehensive cost of the distribution network, including the equivalent annual investment cost of energy storage and reactive power devices, and the annual operation and maintenance cost of the system;

[0028] Among them, the equivalent annual investment cost Including the equivalent annual investment cost of distribution network and microgrid and ;

[0029] Annual operation and maintenance costs Including the annual operation and maintenance costs of distribution networks and microgrids and ;

[0030] The constraints of the objective function in the upper-level planning model include the installed capacity constraints of energy storage and reactive power in the distribution network, and the installed capacity constraints of energy storage in the microgrid.

[0031] Furthermore, in step 4, in the lower-level planning model, the distribution network takes the minimum operating cost as the objective function, including the system power generation cost , the cost of curtailing wind and solar power , network loss cost and planned installed energy storage and reactive power operation and maintenance costs .

[0032] Furthermore, in step 4, the constraints of the objective function in the lower-level planning model include the total capacity constraints of energy storage and reactive power connected to each node of the distribution network, the distribution network flow constraints, the microgrid operation constraints, the system operation safety constraints, and the operation constraints of each unit;

[0033] Among them, the total capacity of energy storage and reactive power connected to each node of the distribution network is constrained by the decision variables in the upper-level planning model.

[0034] Furthermore, in step 4, a perturbation number uniformly distributed between [0, 1] is generated:

[0035] ;

[0036] Where, ; For the The number of disturbances generated by the iterations is used as the disturbance term in the optimization process of the two-level collaborative planning model; is the maximum number of iterations; is a constant between [0,1];

[0037] for dimensional particle space, based on the The number of perturbations generated by the iteration is The decision variables of the dimension are weighted and the The upper and lower limits of the value of the dimensional variable and the weighted The decision variables of the dimension are interpolated to obtain the opposite point;

[0038] According to the opposite point and The upper and lower limits of the dimensional variables are taken, and the quasi-opposite points are obtained by random value selection. A new population is obtained based on the quasi-opposite points and mixed with the original population. The mixed population is solved to obtain the global optimal solution, which is used as the distribution network-microgrid planning result.

[0039] The present invention also proposes a distribution network-microgrid planning and optimization system based on the distribution-microgrid collaborative mode, including an interactive power calculation module, a multi-microgrid access location determination module, a photovoltaic capacity calculation module for each microgrid planning access, and a distribution network-microgrid active and reactive power collaborative planning module:

[0040] The interactive power calculation module considers the distribution-microgrid collaborative mode, based on time-of-use electricity prices and taking the operating costs of each power source within the microgrid as the objective function, to determine the interactive power between each microgrid and the distribution network.

[0041] The multi-microgrid access location determination module determines the locations where multiple microgrids are connected to the distribution network system, taking the comprehensive weighted minimization of distribution network operation cost and voltage deviation as the objective function.

[0042] Each microgrid is planned to be connected to the photovoltaic capacity calculation module, taking into account resource conditions and actual site area, to determine the photovoltaic capacity planned to be connected to each microgrid, and set the number of distribution network partitions based on the photovoltaic capacity.

[0043] The distribution network-microgrid active and reactive power collaborative planning module establishes a two-layer distribution network-microgrid active and reactive power collaborative planning model based on the interaction power between each microgrid and the distribution network, the locations where multiple microgrids are connected to the distribution network system, the photovoltaic capacity planned to be connected to each microgrid, and the number of zones. It then performs an iterative solution to obtain the distribution network-microgrid planning results.

[0044] The beneficial effects of the present invention are that, compared with the prior art, the distribution network-microgrid planning optimization method of the present invention considering the distribution-microgrid collaborative mode has the following significant advantages over the traditional distribution network planning method:

[0045] 1. The present invention comprehensively considers the impact of the distribution-microgrid collaborative model on planning: Traditional distribution network planning is mostly based on its own total load, and does not fully consider the impact of microgrids in distribution network planning.

[0046] 2. The present invention comprehensively considers the impact of the microgrid access location on the distribution network. When there are multiple microgrids to be planned for access in the distribution network system, it is necessary to consider the impact of random and disorderly access of the microgrid on the system, such as power fluctuations at the access point, which may increase the network loss of the distribution system, and cause voltage fluctuations in the entire distribution system. In addition, in order to cope with the access of microgrids and prevent line overload, some access nodes also need to upgrade and transform the lines, thereby increasing the investment cost. Therefore, it is necessary to ensure that microgrids can be accessed in an orderly manner through reasonable planning, reduce the adverse effects on system operation, and ensure the reliability of distribution network operation and high power quality.

[0047] 3. The present invention establishes a two-layer collaborative planning model of distribution network and microgrid, taking into account the photovoltaic long-term planning of the microgrid. By planning the active and reactive power sources of the distribution network and the energy storage planning of the microgrid, it effectively improves the economic, safe and stable operation of the distribution network and the new energy absorption rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of the distribution network-microgrid planning optimization method based on the distribution-microgrid collaborative mode in the present invention;

[0049] Figure 2 This is a system diagram of a distribution network example in the present invention;

[0050] Figure 3 It is a typical daily load power curve diagram of multiple microgrids in the present invention;

[0051] Figure 4 It is a graph of the interaction power between each microgrid and the distribution network in the distribution-micro collaborative mode of the present invention;

[0052] Figure 5 It is a node location diagram of multiple microgrids connected to the distribution network in the present invention;

[0053] Figure 6 It is a diagram of energy storage and reactive power location accessed in the distribution network planning of the present invention;

[0054] Figure 7 This is a power curve diagram of each microgrid and distribution network interaction when no energy storage is planned after the photovoltaic long-term planning of the present invention;

[0055] Figure 8 It is a power curve diagram of the interaction between each microgrid and the distribution network after the planned energy storage after the photovoltaic vision planning of the present invention. DETAILED DESCRIPTION

[0056] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making creative efforts are all within the scope of protection of the present invention.

[0057] Example 1

[0058] The first embodiment of the present invention provides a distribution network-microgrid planning optimization method based on the distribution-microgrid collaboration mode, such as Figure 1 As shown, the specific steps include the following steps.

[0059] Step 1: Considering the distribution-microgrid collaborative model, based on the time-of-use electricity price, the operating cost of each power source within the microgrid and the penalty for abandoning new energy are used as the objective function to determine the interaction power between each microgrid and the distribution network.

[0060] Specifically, the distribution-microgrid collaboration model uses technical management methods to achieve two-way power interaction, source-load-storage resource complementarity, and collaborative planning between the distribution and microgrids. This is specifically manifested in:

[0061] (1) Based on the source, load and storage elements within the microgrid, the economic performance of the microgrid system and the new energy absorption rate are taken into consideration, and the interaction with the distribution network is considered. The output of each unit and the interactive power are optimized with the lowest operating cost (including the penalty cost of wind and solar power abandonment) as the objective function;

[0062] (2) The power exchange between the distribution and microgrids is carried out through the interconnection line. The microgrid operates in the grid-connected mode. The power between the distribution and microgrids is bidirectional, that is, the microgrid can purchase electricity from the distribution network and sell electricity to the distribution network, and there are certain upper and lower limits on the interactive power.

[0063] (3) Considering time-of-use electricity prices, the interaction between the distribution network and the microgrid is influenced and guided by the dynamic changes of electricity price signals, so that the interactive power is further turned to "active optimization";

[0064] (4) Considering the impact of the widespread access of multiple microgrids on the planning of the distribution network system, the distribution network needs to plan complementary power sources (such as energy storage and reactive devices) for the distributed power sources within the microgrid to balance the volatility and intermittency of the interactive power between the distribution and microgrids. In addition, the planning objectives are no longer single, and collaborative optimization objectives are required to consider multiple objectives such as the safety and reliability of the distribution network's own operation, the autonomy requirements of the microgrid, and the penetration rate of new energy.

[0065] According to the components of each microgrid, the operating cost of the microgrid is The minimum is the objective function, which includes the sum of the internal micro gas turbine operating cost, the wind and solar curtailment cost, the energy storage transaction cost and the transaction cost with the distribution network power. It also takes into account the impact of time-of-use electricity prices during microgrid transactions, and optimizes the interaction power between each microgrid and the distribution network.

[0066] The specific formula of the objective function is as follows:

[0067] ;

[0068] Where, The operating cost of the microgrid; is the cost of power generation from microturbine; Penalty costs for curtailing wind and solar power; The operating cost of energy storage; is the transaction cost of power with the distribution network.

[0069] Cost of power generation from microturbines It can be expressed as a linear function of its output power:

[0070] ;

[0071] Where, 、 is the cost coefficient; is the scheduling period, usually =24h; for The output power of the micro gas turbine at any moment.

[0072] Cost of curtailing wind and solar power It can be expressed as:

[0073] ;

[0074] Where, 、 are the unit abandonment penalty coefficients for photovoltaic and wind power, respectively; 、 Photovoltaic and wind power Predicted power generation at the moment; 、 Photovoltaic and wind power The actual power generation at the moment; is the scheduling period, usually =24h.

[0075] Operating costs of energy storage Mainly consider its one-time investment cost and operation and maintenance cost, during the investment recovery period The average charging and discharging cost of a period can be expressed as:

[0076] ;

[0077] Where, 、 are the converted unit charging cost coefficient and discharge benefit coefficient respectively; 、 They are Charging and discharging power of energy storage at all times; 、 are the charging and discharging efficiency of energy storage, respectively; is the scheduling period, usually =24h.

[0078] Power transaction costs between microgrids and distribution networks It can be expressed as:

[0079] ;

[0080] Where, 、 They are The electricity price for the microgrid to purchase and sell electricity from the distribution network is based on the time-of-use electricity price; 、 are the power purchase and sales of the microgrid and the distribution network at time t; is the scheduling period, usually =24h.

[0081] Furthermore, the operating cost of the microgrid The constraints generally consider the operating constraints of each power source within the microgrid and the upper and lower limits of the power interaction between the power supply and the microgrid. Power sources include micro gas turbines, photovoltaics, wind power, and energy storage. The operating constraints of micro gas turbines, photovoltaics, and wind power are relatively simple, generally only considering that their output power does not exceed their maximum allowable power, which will not be elaborated here. The operating constraints of energy storage generally consider power coupling constraints, maximum charge and discharge power constraints, battery remaining capacity constraints, and capacity balance constraints, which can be specifically expressed as:

[0082] ;

[0083] Where, for The amount of energy stored at any time; 、 They are Charging and discharging power of energy storage at all times; 、 They are The charge and discharge status of the energy storage at any moment, which is a 0-1 variable; 、 are the charging and discharging efficiency of energy storage, respectively; The initial power before the energy storage scheduling cycle begins; 、 are the maximum charging and discharging power of energy storage respectively; 、 are the minimum and maximum capacities allowed for energy storage respectively; is the scheduling period, usually =24h.

[0084] The upper and lower limits of the interactive power of the distribution microgrid can be expressed as:

[0085] ;

[0086] Where, 、 are the power purchase and sales of the microgrid and the distribution network at time t; 、 They are the upper limits of electricity purchase and sales from the microgrid to the distribution network.

[0087] Therefore, the interaction power between each microgrid and distribution network under the distribution-micro collaborative mode can be optimized and solved. :

[0088] ;

[0089] in, 、 are the power purchase and sales of the microgrid and the distribution network at time t; >0 means purchasing electricity from the distribution network. <0 means selling electricity to the distribution network.

[0090] The application of the distribution-micro collaborative model optimizes the interaction power between the microgrid and the distribution network, reducing the operating costs of the microgrid. By guiding time-of-use electricity prices, the economic viability of the microgrid is ensured, while maximizing the utilization of renewable energy, effectively avoiding wind and solar power curtailment, and improving energy efficiency.

[0091] Step 2: Taking the comprehensive weighted minimization of distribution network operation cost and voltage deviation as the objective function, determine the locations where multiple microgrids are connected to the distribution network system.

[0092] Specifically, the site selection for the planning and access of multiple microgrids to the distribution network requires reasonable planning to ensure that the microgrids can be connected in an orderly manner, reduce the adverse effects on the system operation, and ensure the reliability of the distribution network operation and high power quality. Therefore, the multiple microgrid access points in the distribution network are mainly based on the distribution network system operation cost. and voltage offset The planning goal is to select the site with the minimum weighted sum.

[0093] ;

[0094] Where, The objective function for site selection and planning of microgrid access points; 、 are the weighting coefficients of the corresponding target components, which convert multiple targets of different dimensions into a single objective function of unified unit (element).

[0095] Distribution network system operating costs Cost of generating electricity for the system , the cost of curtailing wind and solar power , network loss cost sum.

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] Where, is the operation scheduling cycle, generally =24h; is the power generation cost coefficient of the generator; For the generator Power generation at the moment; is the distribution network node; 、 are the penalty coefficients for curtailing solar power and wind power, respectively; 、 They are photovoltaic and wind turbine access nodes respectively. A collection of 、 They are Always access the node The output power of photovoltaic and wind turbines; 、 Photovoltaic and wind power Inject nodes at all times The actual power; is the network loss cost coefficient; for The amplitude of the current in line (i, j) at time instant; For the line The resistance value; It is the collection of all lines in the distribution network.

[0101] Voltage offset It is the sum of the absolute values ​​of the deviations between the per-unit voltage values ​​of each node and the standard voltage value 1 during the entire operating period, which can be expressed as:

[0102] ;

[0103] Where, is the set of all nodes in the distribution network; for Node in The voltage per unit value at the moment; For the operation scheduling cycle, generally take =24h.

[0104] By precisely determining the connection locations of multiple microgrids, we can maximize the efficiency of the distribution network and reduce the operating costs of the power system. Optimizing the connection locations can reduce voltage deviations, ensure stable system operation, and reduce overall losses in the distribution network.

[0105] Step 3: Conduct photovoltaic long-term planning for the microgrid, determine the photovoltaic capacity to be connected to each microgrid, and set the number of distribution network partitions based on the photovoltaic capacity. .

[0106] Specifically, the calculation of PV planning capacity generally requires comprehensive consideration of resource conditions, technical parameters, economic targets, and grid constraints. The formula is:

[0107] ;

[0108] Where, The target power generation capacity needs to be determined based on load demand or grid connection target; is the local annual equivalent utilization hours, which depends on the sunlight resources; is the total efficiency of the photovoltaic system.

[0109] Generally, considering resource conditions and actual site area, it is necessary to consider the correction of land / roof constraints, which can be expressed as:

[0110] ;

[0111] Where, is the total area; is the ratio of building roof area to floor area; is the roof utilization coefficient; is the photovoltaic installed density per unit area.

[0112] By scientifically planning the access of photovoltaic capacity, combined with actual resource conditions and site constraints, the overall efficiency of photovoltaic power generation can be improved. Reasonable photovoltaic capacity allocation ensures maximum energy utilization and lays the foundation for future sustainable development.

[0113] Step 4: Based on the interaction power between each microgrid and the distribution network, the locations where multiple microgrids are connected to the distribution network system, the photovoltaic capacity planned to be connected to each microgrid, and the number of partitions, a two-layer collaborative planning model of active and reactive power sources for the distribution network and microgrid is established, and the chaotic quasi-opposition algorithm is used to solve it.

[0114] Specifically, the distribution network-microgrid active and reactive power two-layer collaborative planning model includes an upper-layer planning model and a lower-layer planning model.

[0115] In the upper-level planning model, the planning objectives are the annual investment costs of energy storage and reactive power in the distribution network, the annual investment costs of energy storage in the microgrid, and the annual system operation and maintenance costs. The decision variables are the total energy storage and reactive power capacity allocated to the distribution network and the energy storage capacity of the microgrid. Constraints include the investment capacity constraints of each power source, and an improved particle swarm algorithm is used to solve the problem.

[0116] In the lower-level planning model, the planning objectives are the distribution network operating costs (including system losses, renewable energy decommissioning costs, power generation costs, and the operating costs of planned energy storage) and the microgrid operating costs. The decision variables are the connection locations and capacities of each energy storage and reactive power component in the distribution network, and the interaction power between the microgrid and the distribution network. Constraints include distribution network flow constraints, energy storage and reactive power planning capacity constraints, microgrid operation constraints, system safety operation constraints, and individual unit operation constraints. An algorithm combining an improved particle swarm optimization and MOSEK solver is employed.

[0117] The upper and lower optimization problems in a bi-level planning model each have their own objective functions, decision variables, and constraints. However, the optimization processes of the upper and lower levels are interdependent, requiring information exchange between the layers through parameter passing. The upper level passes the decision variables as parameters to the lower level, serving as initial conditions and constraints for optimizing the lower level's decision variables. The lower level model then optimizes the problem based on these parameters. The resulting sequential output of each planned power source is then fed back into the upper level's constraints and objective function, enabling information exchange and completing the iterative optimization process.

[0118] In the upper-level planning model, the objective function is expressed as:

[0119] ;

[0120] Where, is the annual comprehensive cost of the distribution network, including the equivalent annual investment cost of energy storage and reactive power devices , Annual operation and maintenance cost of the system .

[0121] Specifically, the equivalent annual investment cost The specific calculation formula is as follows:

[0122] ;

[0123] ;

[0124] ;

[0125] Where, 、 are the equivalent annual investment costs of the distribution network and microgrid respectively; 、 are the service life of energy storage and reactive devices respectively; is the discount rate; 、 are the unit capacity investment costs of energy storage and reactive power devices, respectively; 、 The energy storage and reactive capacity to be installed for the distribution network are planned and installed respectively; The energy storage capacity planned to be installed for microgrid k; 、 are the number of connected microgrids and the number of partitions, respectively.

[0126] Annual operation and maintenance costs The specific calculation formula is as follows:

[0127] ;

[0128] ;

[0129] ;

[0130] Where, 、 are the annual operation and maintenance costs of the distribution network and microgrid respectively; 、 are the unit cost coefficients of energy storage charging and discharging, The unit cost coefficient for reactive power generated by the reactive device; 、 、 Respectively in The energy storage charging and discharging power and reactive power generated by the reactive device installed in the distribution network at any given moment; >0 means inductive reactive power is emitted. <0 means capacitive reactive power is emitted; 、 are the energy storage charging and discharging powers planned to be installed in microgrid k at time t; is the scheduling period, usually =24h.

[0131] Furthermore, the constraints of the objective function in the upper-level planning model include the installed capacity constraints of energy storage and reactive power in the distribution network, and the installed capacity constraints of energy storage in the microgrid. The specific constraints are expressed as follows:

[0132] Constraints on the maximum planned installed capacity of energy storage and reactive power devices in distribution networks:

[0133] ;

[0134] Where, 、 The energy storage and reactive capacity to be installed for the distribution network are planned and installed respectively; 、 They are the maximum installed capacities of energy storage and reactive devices allowed to be planned and installed in the distribution network.

[0135] The maximum installed capacity constraint of energy storage allowed by microgrid k is:

[0136] ;

[0137] Where, The energy storage capacity planned to be installed for microgrid k; The maximum installed capacity of energy storage allowed to be planned and installed in microgrid k.

[0138] The lower-level planning model, based on distribution network nodes and microgrids, solves the energy storage and reactive power location and sizing problems for the distribution network, as determined by the upper-level model, as well as the interaction power between the microgrid and the distribution network. The distribution network optimizes the location and capacity of energy storage and reactive power devices with the goal of minimizing operating costs; the microgrid optimizes its interaction power with the goal of minimizing operating costs.

[0139] In the lower-level planning model, the distribution network is based on the operating cost Minimum is the objective function, including the system power generation cost , the cost of curtailing wind and solar power , network loss cost and planned installed energy storage and reactive power operation and maintenance costs , the specific formula of the objective function is:

[0140] ;

[0141] ;

[0142] Where, 、 are the unit cost coefficients of energy storage charging and discharging, The unit cost coefficient for reactive power generated by the reactive device; 、 are the charging and discharging efficiency of energy storage, respectively; 、 Plan access nodes for energy storage and reactive devices respectively A collection of 、 、 They are Always access the node The energy storage charging and discharging power and the reactive power generated by the reactive device.

[0143] The microgrid is based on the planning decision variables passed from the upper layer. , set the objective function as the microgrid k operation cost Minimum, to solve the interaction power between it and the distribution network . To use the original micro gas turbine power generation cost of the system , the cost of curtailing wind and solar power , energy storage transaction costs , and the cost of power interaction with the distribution network and planned installed energy storage operation and maintenance costs .

[0144] ;

[0145] Furthermore, the constraints of the objective function in the lower-level planning model include the total capacity constraints of energy storage and reactive power connected to each node in the distribution network, distribution network flow constraints, microgrid operation constraints, system operation safety constraints, and operation constraints of each unit.

[0146] Specifically, the total energy storage and reactive power capacity connected to each node in the distribution network is constrained by the upper-level decision variables (the planned total energy storage and reactive power capacity), as shown in the following formula:

[0147] ;

[0148] Where, 、 Plan access nodes for energy storage and reactive devices in the distribution network respectively A collection of 、 Plan access nodes Energy storage and reactive device capacity.

[0149] The DistFlow branch flow method is used to establish the flow constraints of the distribution network system. The formula is as follows:

[0150] ;

[0151] Where, 、 Node Injected active and reactive power; 、 、 They are Energy storage, photovoltaic and wind power at the node Injected active power; 、 They are The reactive power device and the original reactive power device are planned at the node Injected reactive power; 、 Node Active and reactive loads; 、 They are Time flow through the line and Active power; 、 They are Time flow through the line and Reactive power; ∑ For slave nodes Flow out to downstream nodes The total power (with the node There may be more than one connected downstream branch, so the summation is required);∑ For slave nodes Injection Node The total power (with the node There may be more than one upstream branch connected, so a sum is required); is the reactance value of line (i, j); 、 They are Node of moment Voltage amplitude, line The current amplitude; 、 are the nodes after phase angle relaxation Voltage amplitude square, line square of the current amplitude; 、 Don't be here Time Node The charging and discharging power of ESS.

[0152] The operation constraints of the microgrid mainly consider its internal power balance constraints, which can be expressed as:

[0153] ;

[0154] The system operation safety constraints are specifically expressed as:

[0155] ;

[0156] Where, 、 They are Node of moment Voltage amplitude, line The current amplitude; 、 are the lowest and highest node voltages allowed for system operation respectively; For the line The maximum current allowed during operation.

[0157] The operating constraints of each unit are consistent with the constraints considered when generating the interactive power of each microgrid in the above-mentioned distribution microgrid coordination mode, and will not be repeated here.

[0158] Furthermore, the traditional particle swarm optimization algorithm has the following problems: when the population is initialized and updated, it is easy to deviate from the optimal solution or fall into the local optimal solution too early; the algorithm is affected by the inertia weight of the core parameter. and learning factors 、 The influence of traditional inertia weight Using a linear decrement approach can cause the algorithm's weight to decrease too quickly, leading to premature convergence. Furthermore, a fixed learning factor is unsuitable for dynamically changing problems. To address this issue, an improved particle swarm optimization algorithm (PSO) is proposed to enable the two-level collaborative planning model to converge quickly to the global optimal solution under multiple constraints. In this improved PSO, a chaotic quasi-opposition strategy is designed to update the population.

[0159] Specifically, the principle of the present invention is to generate a quasi-opposite population through perturbation mapping when the population is updated. Perturbation mapping can ensure the diversity and randomness of the quasi-opposite population. At the same time, the generated quasi-opposite points are closer to the global optimal solution, thereby improving the search efficiency and accuracy of the algorithm. The specific implementation is as follows: the number of perturbations uniformly distributed between [0,1]:

[0160] ;

[0161] Where, ; For the The number of disturbances generated by the iterations is used as the disturbance term in the optimization process of the two-level collaborative planning model; is the maximum number of iterations; is a constant between [0, 1] and is set to 0.68 in this embodiment.

[0162] Furthermore, an opposite point is generated. The opposite point is a mirror image point of a solution in the feasible domain about the center of the search space. In a particle space with a dimension length, the opposite points are generated using the following formula:

[0163] ;

[0164] Where, For particle The decision variables of the dimension are ; 、 Respectively The upper and lower limits of the value of the dimensional variable; for The opposite point of the particle.

[0165] Furthermore, quasi-opposite points are generated , the quasi-opposite point is closer to the optimal solution than the opposite point, and its expression is:

[0166]

[0167] Using this method, a new population is obtained and combined with the initial population to form a mixed population. Based on the final particle fitness value, the first half of the population with high fitness is selected to enter the next iteration to achieve the purpose of improving the population, so that the optimization result is close to the global optimal solution.

[0168] When solving optimization problems in upper and lower planning models, the method of the present invention can provide more diverse search paths, avoid the trap of local optimal solutions, and enhance global optimization capabilities. In a two-layer collaborative planning model, especially in the optimization process of the interactive power between microgrids and distribution networks, this method can be used to generate multiple possible interactive power combinations, and evaluate them based on the cost and constraints of each combination, and further achieve better interactive power scheduling through optimization algorithms.

[0169] Furthermore, the traditional inertia weight usually adopts a linear or exponential decreasing method according to the number of iterations. Although it improves the ability of finding the global optimal solution to a certain extent, it cannot refine the search locally. Therefore, the present invention proposes a new nonlinear decreasing method for updating the inertia weight:

[0170] ;

[0171] Where, is the inertia weight; For the The inertia weight obtained in the iteration is used to adjust the weight of each parameter in the objective function of the two-level collaborative planning model; 、 are the upper and lower limits of the inertia weight respectively.

[0172] Dynamically updating the inertia weights ensures that the optimization of upper and lower level decision variables can be carried out within a reasonable search range.

[0173] Furthermore, the learning factor 、 Adjustments are made to make particles pay more attention to the position information of the particle population while maintaining the convergence speed and search effect. Learning factor 、 The update formula is:

[0174] ;

[0175] Where, is the number of iterations; is the maximum number of iterations; 、 are the upper and lower limits of the learning factor respectively.

[0176] The chaotic quasi-opposition strategy, inertia weight updates, and learning factor adjustments are not only important parameters in the particle swarm optimization algorithm but also play a vital role in the distribution network-microgrid two-layer collaborative planning model. By introducing these strategies, the efficiency of the algorithm can be improved during the multi-level, multi-objective optimization process, local optimal solutions can be avoided, and the global nature of the optimized solution can be ensured. This allows for better coordinated operation and optimized scheduling between the microgrid and distribution network, ultimately achieving an optimal balance between system economy, security, and stability.

[0177] Example 2

[0178] In order to verify the effectiveness of the proposed distribution network-microgrid planning optimization method considering the distribution-microgrid collaborative mode, the second embodiment of the present invention adopts the following Figure 2 The distribution network system and four load characteristic curves shown are as follows Figure 3 The microgrid configuration example shown is analyzed.

[0179] The four microgrids are set up as follows: one residential microgrid with a load capacity of 1000KW, two industrial microgrids with a load capacity of 1500KW, one for continuous production and one for intermittent production, and one for commercial microgrid with a load capacity of 1200KW. The typical daily load power curves of the four microgrids are as follows: Figure 3 As shown in the figure, each microgrid is equipped with a micro gas turbine, energy storage, and photovoltaics. The improved distribution network system includes two photovoltaic nodes, three wind power nodes, two energy storage nodes, one reactive power compensation device node, and four nodes to be connected to the microgrid.

[0180] According to the internal components of the four microgrids and the generation method of the interaction power of each microgrid in the proposed distribution microgrid collaborative mode, the optimized operation cost of each microgrid is shown in the following table. The interaction power with the distribution network is shown in the following table. Figure 4 shown.

[0181] Table 1 Operating costs of each microgrid under the distribution-microgrid collaboration model

[0182]

[0183] According to the proposed site selection method for multi-microgrid planning and access to the distribution network, a particle swarm optimization algorithm is used for optimization. Considering that there is a set of nodes to be planned at the location of each microgrid when it is connected to the distribution network in reality, the candidate nodes of the residential microgrid are set to [1 2 3 4 18 19 20 21], the candidate nodes of the continuous production microgrid are set to [5 6 22 23 24 25 26 27], the candidate nodes of the intermittent production microgrid are set to [7 8 9 28 29 30 31 32], and the candidate nodes of the commercial microgrid are set to [10 11 12 13 14 15 16 17].

[0184] The final optimization result of the algorithm is as follows: Figure 5 As shown in the figure, the access locations of residential microgrid, continuous production industrial microgrid, intermittent production industrial microgrid and commercial microgrid are [18 22 28 10] in order.

[0185] The planning results are compared with the operation of the distribution network with random access to four locations [21 27 9 15]. The operating costs and voltage fluctuations are shown in the following table.

[0186] Table 2 Comparison of the operation of microgrids with planned and random access to the distribution network

[0187]

[0188] By comparison, it can be seen that the planned access of multiple microgrids has a significant improvement on network loss and voltage fluctuation compared with unplanned random access. The effect of reducing network loss and voltage fluctuation is significant, and the economic, safe and stable operation of the distribution system is improved.

[0189] After the site selection and access of each microgrid, long-term photovoltaic planning was carried out. According to the actual situation of each microgrid, it was calculated that 800KW, 500KW, 500KW and 500KW of photovoltaic power were planned to be connected in the four microgrids.

[0190] The proposed distribution network-microgrid active and reactive two-layer collaborative planning model is used to carry out the collaborative planning of distribution network-microgrid. The distribution network is divided into four areas, and the access location and capacity of energy storage and reactive power are planned in the four partitions respectively. The upper limit of energy storage installation is set at 20 units in each partition (the capacity of a single unit is 80KW), the upper limit of reactive power installation is set at 2 units (the capacity of a single reactive device is [-50KW 100KW]), and the upper limit of energy storage installation in each microgrid is 20 units (the capacity of a single unit is 50KW). A two-layer particle swarm algorithm is used to solve the model. The upper layer particles are set as the energy storage capacity of 4 partitions, the reactive capacity of 4 partitions and the energy storage capacity of 4 microgrids; the lower layer particles are set as the energy storage access location and capacity of 4 partitions, and the reactive power access location and capacity of 4 partitions. The upper and lower layers solve according to their respective objective functions and constraints, and exchange information to complete the iterative optimization solution. The optimal planning result is as follows. Figure 6 shown.

[0191] The planning results are as follows: Energy storage installation in Zone 1: 11 units at node 21 and 9 units at node 27; Reactive power installation in Zone 1: 1 unit at node 28 and 0 units at node 21; Energy storage installation in Zone 2: 9 units at node 23 and 11 units at node 22; Reactive power installation in Zone 2: 1 unit at node 22 and 0 units at node 23; Energy storage installation in Zone 3: 10 units at node 11 and 10 units at node 10; Reactive power installation in Zone 3: 1 unit at node 7 and 1 unit at node 11; Energy storage installation in Zone 4: 10 units at node 14 and 10 units at node 15; Reactive power installation in Zone 4: 0 units at node 14 and 0 units at node 13. Residential microgrids will have 15 units of energy storage installed, continuous industrial microgrids will have 17 units, intermittent industrial microgrids will have 9 units, and commercial microgrids will have 8 units.

[0192] The following table compares the operating conditions of each microgrid planned by Envision PV before and after energy storage planning.

[0193] Table 3 Comparison of operating costs before and after residential microgrid planning

[0194]

[0195] Table 4 Comparison of operating costs before and after continuous production microgrid planning

[0196]

[0197] Table 5 Comparison of operating costs before and after planning of intermittent production microgrids

[0198]

[0199] Table 6 Comparison of operating costs before and after commercial microgrid planning

[0200]

[0201] Comparison of interactive power between microgrid and distribution network before and after PV long-term planning Figure 7 、 Figure 8 shown.

[0202] Comparing the operational performance of various microgrids reveals that the total operating cost of microgrids has decreased after energy storage planning, reducing the output and operating costs of micro gas turbines. Furthermore, in microgrids with significant curtailment of solar power following PV vision planning, energy storage significantly increased the system's renewable energy absorption rate. Consequently, this significantly improves both the economical and low-carbon operation of microgrids and their renewable energy absorption. A comparison of the power exchange with the distribution network before and after energy storage planning reveals a significant reduction in the volatility of this power exchange, contributing to the safe and stable operation of the distribution microgrid system.

[0203] The comparison results before and after planning for energy storage and reactive power in the distribution network are shown in the following table.

[0204] Table 7 Comparison of operating costs before and after energy storage and reactive power planning for distribution networks

[0205]

[0206] It can be seen that after the planning, the operating cost of the distribution network has been greatly reduced, the power generation cost and network loss cost have dropped significantly, and the new energy absorption rate has been increased. At the same time, the voltage fluctuation has been greatly reduced, and the economic security and stable operation of the distribution network system have been improved.

[0207] This method is suitable for the distribution network planning scenario of future multi-microgrid planning and access, and has important theoretical value and engineering application significance for improving the economic, safe and stable operation of the distribution network, increasing the new energy absorption rate and promoting the construction of new power systems.

[0208] Example 3

[0209] The present invention also proposes a distribution network-microgrid planning and optimization system based on the distribution-microgrid collaborative mode, including an interactive power calculation module, a multi-microgrid access location determination module, a photovoltaic capacity calculation module for each microgrid planning access, and a distribution network-microgrid active and reactive power collaborative planning module:

[0210] The interactive power calculation module considers the distribution-microgrid collaborative mode, based on time-of-use electricity prices and taking the operating costs of each power source within the microgrid as the objective function, to determine the interactive power between each microgrid and the distribution network.

[0211] The multi-microgrid access location determination module determines the locations where multiple microgrids are connected to the distribution network system, taking the comprehensive weighted minimization of distribution network operation cost and voltage deviation as the objective function.

[0212] Each microgrid is planned to be connected to the photovoltaic capacity calculation module, to carry out photovoltaic long-term planning of the microgrid, to determine the photovoltaic capacity planned to be connected to each microgrid, and to set the distribution network partition.

[0213] The distribution network-microgrid active and reactive power collaborative planning module establishes a two-layer active and reactive power collaborative planning model for the distribution network-microgrid based on the interaction power between each microgrid and the distribution network, the locations where multiple microgrids are connected to the distribution network system, and the photovoltaic capacity planned to be connected to each microgrid, and performs iterative solution.

[0214] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A distribution network-microgrid planning optimization method based on a distribution-microgrid collaborative model is characterized by: include: Step 1: Considering the distribution-microgrid collaboration model, based on the time-of-use electricity price and taking the operating cost of each power source within the microgrid as the objective function, determine the interaction power between each microgrid and the distribution network; Step 2: Taking the comprehensive weighted minimization of distribution network operation cost and voltage deviation as the objective function, determine the locations where multiple microgrids are connected to the distribution network system; Step 3: Consider resource conditions and actual site area, determine the photovoltaic capacity planned to be connected to each microgrid, and set the number of distribution network partitions based on the photovoltaic capacity; Step 4: Based on the interaction power between each microgrid and the distribution network, the locations where multiple microgrids are connected to the distribution network system, the planned photovoltaic capacity and the number of zones for each microgrid, a two-layer collaborative planning model for active and reactive power sources of the distribution network and microgrid is established. The model is iteratively solved to obtain the distribution network and microgrid planning results. In step 1, under the distribution network and microgrid collaborative mode where the distribution network and microgrid interact through bidirectional power flow, the microgrid operation cost is The minimum is the objective function, which includes the sum of the internal micro-turbine operating cost, the cost of curtailed wind and solar power, the transaction cost of energy storage, and the transaction cost with the distribution network power. It also takes into account the impact of time-of-use electricity prices during micro-grid transactions, and optimizes the interaction power between each microgrid and the distribution network. In step 1, the power transaction cost between the microgrid and the distribution network is It can be expressed as: ； Where, 、 They are The electricity price for the microgrid to purchase and sell electricity from the distribution network is based on the time-of-use electricity price; 、 are the power purchase and sales of the microgrid and the distribution network at time t; is the scheduling period; In step 2, the operating cost of the distribution network system is and voltage offset The planning goal is to minimize the weighted sum of the multiple microgrid access points in the distribution network; In step 2, the distribution network system operating cost Cost of generating electricity for the system , the cost of curtailing wind and solar power , network loss cost sum; Voltage offset The sum of the absolute values ​​of the deviations between the per-unit voltage values ​​of each node and the standard voltage value 1 during the entire operating period; In step 3, PV planning capacity The calculation formula is: ； Where, is the total area; is the ratio of building roof area to floor area; is the roof utilization coefficient; is the photovoltaic installed density per unit area; In step 4, the distribution network-microgrid active and reactive power two-layer collaborative planning model includes an upper-layer planning model and a lower-layer planning model; In the upper-level planning model, the planning objective is the annual comprehensive cost of the distribution network; the decision variables are the total energy storage and reactive power capacity allocated to the distribution network, and the energy storage capacity of the microgrid; the constraints include the investment capacity constraints of each power source; In the lower-level planning model, the planning objectives are the distribution network operating costs and the microgrid operating costs; the decision variables are the access locations and access capacities of each energy storage and reactive power in the distribution network, and the interaction power between the microgrid and the distribution network; the constraints include distribution network flow constraints, energy storage and reactive power planning capacity constraints, microgrid operation constraints, system safety operation constraints, and unit operation constraints. In step 4, in the upper-level planning model, the planning target is to minimize the annual comprehensive cost of the distribution network, including the equivalent annual investment cost of energy storage and reactive power devices, and the annual operation and maintenance cost of the system; Among them, the equivalent annual investment cost Including the equivalent annual investment cost of distribution network and microgrid and ; Annual operation and maintenance costs Including the annual operation and maintenance costs of distribution networks and microgrids and ; The constraints of the objective function in the upper-level planning model include the installed capacity constraints of energy storage and reactive power in the distribution network, and the installed capacity constraints of energy storage in the microgrid; In step 4, in the lower-level planning model, the distribution network takes the minimum operating cost as the objective function, including the system power generation cost , the cost of curtailing wind and solar power , network loss cost and planned installed energy storage and reactive power operation and maintenance costs ; In step 4, the constraints of the objective function in the lower-level planning model include the total capacity constraints of energy storage and reactive power connected to each node of the distribution network, distribution network power flow constraints, microgrid operation constraints, system operation safety constraints, and each unit operation constraints; Among them, the total capacity of energy storage and reactive power connected to each node of the distribution network is constrained by the decision variables in the upper-level planning model.

2. The distribution network-microgrid planning optimization method based on the distribution-microgrid collaborative mode according to claim 1 is characterized by: In step 1, the microgrid operating cost The constraints of the microgrid consider the operation constraints of each power source within the microgrid and the upper and lower limits of the interactive power of the distribution microgrid; the power sources include micro gas turbines, photovoltaics, wind power and energy storage; Combined with the constraints, the optimization solution is obtained to obtain the interactive power between each microgrid and the distribution network under the distribution-micro collaborative mode. .

3. The distribution network-microgrid planning optimization method based on the distribution-microgrid collaborative mode according to claim 1 is characterized by: In step 4, generate the perturbation number uniformly distributed between [0,1]: ； Where, ; For the The number of disturbances generated by the iterations is used as the disturbance term in the optimization process of the two-level collaborative planning model; is the maximum number of iterations; is a constant between [0,1]; for dimensional particle space, based on the The number of perturbations generated by the iteration is The decision variables of the dimension are weighted and the The upper and lower limits of the value of the dimensional variable and the weighted The decision variables of the dimension are interpolated to obtain the opposite point; According to the opposite point and The upper and lower limits of the dimensional variables are used to obtain quasi-opposite points by random selection; a new population is obtained based on the quasi-opposite points and mixed with the original population; The mixed population is solved to obtain the global optimal solution, which is used as the distribution network-microgrid planning result.

4. A distribution network-microgrid planning and optimization system based on a distribution-microgrid collaborative model, the system utilizing the method according to any one of claims 1-3, comprising an interactive power calculation module, a multi-microgrid access location determination module, a photovoltaic capacity calculation module for each microgrid planned access, and a distribution network-microgrid active and reactive power collaborative planning module, characterized in that: The interactive power calculation module considers the distribution-microgrid collaboration model, based on time-of-use electricity prices and taking the operating costs of each power source within the microgrid as the objective function, to determine the interactive power between each microgrid and the distribution network; The multi-microgrid access location determination module determines the locations where multiple microgrids are connected to the distribution network system, taking the comprehensive weighted minimization of distribution network operation cost and voltage deviation as the objective function; Each microgrid is planned to be connected to the photovoltaic capacity calculation module, taking into account resource conditions and actual site area, to determine the photovoltaic capacity planned to be connected to each microgrid, and to set the number of distribution network partitions based on the photovoltaic capacity; The distribution network-microgrid active and reactive power collaborative planning module establishes a two-layer distribution network-microgrid active and reactive power collaborative planning model based on the interaction power between each microgrid and the distribution network, the locations where multiple microgrids are connected to the distribution network system, the photovoltaic capacity planned to be connected to each microgrid, and the number of zones. It then performs an iterative solution to obtain the distribution network-microgrid planning results.

Images