Distributed resource optimal configuration method and system of power distribution system

Abstract

The invention discloses a distributed resource optimal configuration method and system of a power distribution system. The method comprises the following steps: establishing a double-layer optimization model of distributed resources in the power distribution system; wherein the double-layer optimization model comprises an upper-layer resource configuration model and a lower-layer operation regulation and control model; the upper-layer resource configuration model is constructed by taking annual comprehensive cost minimization of distributed resources as a target; the lower-layer operation regulation and control model is constructed by taking the annual comprehensive operation cost minimization of the distributed resources and the flexibility insufficiency rate minimization of the power distribution system as targets; and solving the double-layer optimization model to obtain an optimal configuration result of the distributed resources. The economical efficiency of the power distribution system can be improved, and meanwhile the flexibility and stability of power grid dispatching are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution system optimization configuration, and in particular to a method and system for optimizing configuration of distributed resources in a power distribution system. Background Art

[0002] With the growing global demand for clean energy and the increasing awareness of environmental protection, the application of distributed resources such as distributed renewable energy (DRE), flexible load (FL) and distributed energy storage (DES) systems in distribution systems has increased rapidly. Traditional distribution systems mainly rely on the "source follows load" power supply mode, that is, the power supply is adjusted as the load demand changes. However, with the rapid development of distributed resources, the distribution system is gradually shifting to a new power supply mode of "source, load and storage synergistic interaction". Clean distributed renewable energy is becoming the main source of power supply in the new distribution system and gradually replacing traditional distributed generation units (DG).

[0003] Although distributed renewable energy plays a key role in the transformation of distribution systems, its introduction also brings new challenges. Compared with traditional energy, distributed renewable energy has higher volatility, which is mainly due to the uncontrollability of natural conditions. For example, solar photovoltaic power generation is greatly affected by weather and day and night changes, while wind power generation is significantly affected by wind speed and wind direction. At the same time, there is uncertainty in load demand, which is difficult to accurately predict. The high volatility of distributed renewable energy and the uncertainty of load have brought huge challenges to the supply and demand balance of the distribution system. Frequent supply and demand imbalance accidents have caused grid instability and affected the reliability of power supply. In order to effectively respond to these challenges and achieve the stable operation of the new distribution system, it is urgent to study and optimize the coordinated configuration strategy of various resources such as source, load and storage to improve the flexibility of the distribution system. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a method and system for optimizing the configuration of distributed resources in a power distribution system, which can improve the economy of the power distribution system and enhance the flexibility and stability of power grid scheduling.

[0005] A first aspect of the present invention provides a method for optimizing the configuration of distributed resources in a power distribution system, comprising:

[0006] Establish a two-layer optimization model for distributed resources in a power distribution system; wherein the two-layer optimization model includes: an upper-layer resource configuration model and a lower-layer operation and control model; the upper-layer resource configuration model is constructed with the goal of minimizing the annual comprehensive cost of the distributed resources; the lower-layer operation and control model is constructed with the goal of minimizing the annual comprehensive operation cost of the distributed resources and minimizing the flexibility deficiency rate of the power distribution system;

[0007] The two-layer optimization model is solved to obtain the optimal configuration result of the distributed resources.

[0008] Optionally, the construction of the upper-layer resource configuration model includes:

[0009] An upper objective function is established with the goal of minimizing the annual comprehensive cost of the distributed resources; wherein the annual comprehensive cost includes the annual investment cost and the annual comprehensive operation and maintenance cost; the annual investment cost includes at least one or more of the following: the equivalent annual investment cost of the distributed energy storage system, the equivalent annual investment cost of the distributed generator set, the equivalent annual investment cost of the distributed renewable energy and the cost of signing a demand response contract for flexible load participation; the annual comprehensive operation and maintenance cost includes at least one or more of the following: annual fixed operation and maintenance cost, annual variable operation cost, annual network loss cost, annual voltage deviation penalty cost and annual carbon emission cost;

[0010] A first set of constraints is configured for the upper-level objective function to obtain the upper-level resource configuration model; wherein the first set of constraints includes at least one or more of the following: installation quantity constraints, capacity constraints, demand response contract capacity constraints, and new energy penetration constraints.

[0011] Optionally, the construction of the lower layer operation control model includes:

[0012] The lower-level economic objective function is established with the goal of minimizing the annual comprehensive operating cost of the distributed resources; wherein the annual comprehensive operating cost includes at least one or more of the following: annual variable operating cost, annual network loss cost, annual voltage deviation cost and annual carbon emission cost; the annual variable operating cost includes at least one or more of the following: operating cost of distributed generator sets, operating cost of distributed energy storage systems, operating cost of flexible loads, cost of purchasing and selling electricity to the superior power grid and penalty cost for wind and solar power abandonment;

[0013] Taking minimizing the flexibility deficiency rate of the distribution system as the goal, establishing a lower-level flexibility objective function;

[0014] A second set of constraints is configured for both the lower-level economic objective function and the lower-level flexibility objective function to obtain the lower-level operation and control model; wherein the second set of constraints includes at least one or more of the following: source-load-storage resource operation constraints, distribution system power balance constraints, three-phase power flow constraints and voltage constraints.

[0015] Furthermore, the upper layer objective function is specifically:

[0016] f up =C inv +C ope ;

[0017] Among them, F up is the annual comprehensive cost, C inv is the annual investment cost, C ope is the annual comprehensive operation and maintenance cost;

[0018] The annual investment cost is calculated as follows:

[0019]

[0020] in, is the equivalent annual investment cost of the distributed energy storage system, is the equivalent annual investment cost of distributed generator sets, is the equivalent annual investment cost of distributed renewable energy, is the contract cost of flexible load participating in demand response; i is load node i; and They are respectively a set of load nodes connected to distributed energy storage systems, distributed generators, distributed renewable energy and flexible loads in the medium voltage system; and are the load node sets connected to distributed energy storage systems, distributed generators, distributed renewable energy and flexible loads in the low-voltage area; λ is the discount rate, and y is the configuration period; and are the unit capacity investment cost, unit power investment cost, configured capacity and configured power of the distributed energy storage system in load node i respectively; and are the unit power investment cost and configuration capacity of the distributed generator set in load node i respectively; and are the unit power investment cost and configuration capacity of distributed renewable energy in load node i respectively; The unit price of demand response contract costs for flexible loads; Contracted capacity for demand response of flexible loads, with an annual metering period;

[0021] The calculation formula for the annual comprehensive operation and maintenance cost is as follows:

[0022] C ope =C main +C vope +C loss +C v,dev +C carbon ,

[0023]

[0024] Among them, C main is the annual fixed operation and maintenance cost, C vope is the annual variable operating cost, C loss is the annual network loss cost, C v,dev is the annual voltage deviation penalty cost, C carbon is the annual carbon emission cost; and are the annual operation and maintenance costs of distributed generators and distributed renewable energy respectively; η DG and η DRE are the annual operation and maintenance cost coefficients of distributed generators and distributed renewable energy respectively; D is the annual operating days.

[0025] Furthermore, the demand response contract capacity constraint is specifically:

[0026]

[0027] in, is the contracted capacity of load node i participating in demand response on day d, is the maximum load of load node i on day d, δ is the ratio of the maximum contractable capacity to the maximum load;

[0028] The new energy penetration rate constraints are specifically:

[0029]

[0030] in, is the active power of distributed renewable energy at load node i at time t, Q d is the annual electricity consumption, ε is the penetration rate of new energy; T is the time set, D is the annual operating days, and They are respectively the load node sets connected to distributed renewable energy in the medium voltage system and low voltage area.

[0031] Furthermore, the lower-level economic objective function is specifically:

[0032] f 1 down =Cvope +C loss +C v,dev +C carbon ;

[0033] Among them, F 1 down is the annual comprehensive operating cost, C vope is the annual variable operating cost, C loss is the annual network loss cost, C v,dev is the annual voltage deviation penalty cost, C carbon is the annual carbon emission cost;

[0034] The calculation formula for the annual variable operating cost is as follows:

[0035] C vope =C DG +C DES +C FL +C M +C DRE,cut ,

[0036]

[0037] Among them, C DG is the operating cost of distributed generators, C DES is the operating cost of the distributed energy storage system, C FL is the flexible load operation cost, C M is the cost of purchasing and selling electricity to the upper grid, C DRE,cut is the penalty cost for wind and solar curtailment; s is a typical day scene, φ is a typical day scene set, p s is the probability of a typical day scenario s, D is the number of operating days per year, and T is the time set; is the active power of the distributed generator set at load node i at time t, is the cost coefficient of the distributed generator set at load node i; and are the charging active power and discharging active power of the distributed energy storage system at load node i at time t, and are the charging cost coefficient and discharging cost coefficient of the distributed energy storage system at load node i respectively; is the regulated active power of the flexible charge at load node i at time t, is the utility function coefficient of the flexible charge of load node i participating in demand response; is the amount of wind and solar power abandoned at load node i at time t, P is the cost coefficient of the penalty for wind and solar power abandonment at time t; t Mis the power purchased and sold by the upper grid at time t, is the cost coefficient of electricity purchase and sales by the superior power grid at time t; and They are respectively a set of load nodes connected to distributed energy storage systems, distributed generators, distributed renewable energy and flexible loads in the medium voltage system; and They are the load node sets connected to the distributed energy storage system, distributed generator sets, distributed renewable energy and flexible loads in the low-voltage area;

[0038] The annual network loss cost is calculated as follows:

[0039]

[0040] Among them, c loss is the unit active network loss coefficient, r ij is the resistance of branch ij, l is the branch set; I ij,t is the current flowing into branch ij at time t;

[0041] The annual voltage deviation penalty cost is calculated as follows:

[0042]

[0043] Among them, c v,dev is the voltage deviation penalty price, U i,t is the square of the voltage amplitude of load node i at time t, is the square of the rated voltage amplitude of load node i at time t;

[0044] The annual carbon emission cost is calculated as follows:

[0045]

[0046] Among them, E c is the carbon quota of the distribution system, E lim is the length of the carbon emission interval division, q is the number of step divisions, and α is the carbon price per step c co2 Growth rate, E CE is the carbon emissions of the distribution system, χ DG is the carbon emission intensity per unit of electricity generated by distributed generators, χ M is the carbon emission intensity per unit of power generation when the upper power grid purchases and sells electricity, P i DG is the active power of the distributed generator set at load node i, is the power purchased and sold by the upper power grid of load node i at time t.

[0047] Furthermore, the lower layer flexibility objective function is specifically:

[0048]

[0049]

[0050] in, is the flexibility deficiency rate of the distribution system, f flex,up is the upward flexibility deficiency rate of the distribution system, f flex,dn is the downward flexibility deficiency rate of the distribution system, and ω is the weight coefficient.

[0051] Furthermore, the upward flexibility deficiency rate is specifically:

[0052]

[0053] Among them, F t de,up is the upward flexibility demand of the distribution system at time t, F t su,up is the upward flexibility adjustment capability provided by the source, load and storage resources of the distribution system at time t; T is the time set;

[0054] The downward flexibility deficiency rate is specifically:

[0055]

[0056] Among them, F t de,dn is the downward flexibility demand of the distribution system at time t, F t su,dn The downward flexibility adjustment capability provided by the source, load and storage resources of the distribution system at time t.

[0057] Optionally, solving the two-layer optimization model to obtain an optimal configuration result of the distributed resources includes:

[0058] Constructing a state vector of a universe individual; wherein the state vector includes at least one or more of the following configuration parameters: the active power of the distributed generator set of each load node at time t, the charging active power of the distributed energy storage system of each load node at time t, the discharging active power of the distributed energy storage system of each load node at time t, and the regulating active power of the flexible charge of each load node at time t;

[0059] Determining a fitness function based on the annual comprehensive cost in the upper-level resource allocation model;

[0060] Based on the state vector and the fitness function, the two-layer optimization model is solved using a metaverse optimization algorithm, and the universe individual with the lowest fitness is selected as the optimal configuration result of the distributed resources.

[0061] A second aspect of the present invention provides a system for optimizing the configuration of distributed resources in a power distribution system, which is used to implement the method for optimizing the configuration of distributed resources in a power distribution system as described in any one of the first aspects above. The system includes:

[0062] A model building module, used to establish a two-layer optimization model of distributed resources in a power distribution system; wherein the two-layer optimization model includes: an upper-layer resource configuration model and a lower-layer operation and regulation model; the upper-layer resource configuration model is constructed with the goal of minimizing the annual comprehensive cost of the distributed resources; the lower-layer operation and regulation model is constructed with the goal of minimizing the annual comprehensive operation cost of the distributed resources and minimizing the flexibility deficiency rate of the power distribution system;

[0063] The model solving module is used to solve the two-layer optimization model to obtain the optimal configuration result of the distributed resources.

[0064] Compared with the prior art, the embodiment of the present invention provides a method and system for optimizing the configuration of distributed resources in a power distribution system, the method comprising: establishing a two-layer optimization model of distributed resources in the power distribution system; wherein the two-layer optimization model comprises: an upper-layer resource configuration model and a lower-layer operation and control model; the upper-layer resource configuration model is constructed with the goal of minimizing the annual comprehensive cost of distributed resources; the lower-layer operation and control model is constructed with the goal of minimizing the annual comprehensive operation cost of distributed resources and minimizing the flexibility deficiency rate of the power distribution system; the two-layer optimization model is solved to obtain the optimal configuration result of the distributed resources. The present invention can improve the economy of the power distribution system while enhancing the flexibility and stability of power grid dispatching. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is an exemplary diagram of an embodiment of a medium and low voltage power distribution system provided by the present invention;

[0066] Figure 2 It is a flow chart of an embodiment of the method for optimizing configuration of distributed resources in a power distribution system provided by the present invention;

[0067] Figure 3 This is an example diagram comparing the total capacity of distributed resource access in the system under different solutions provided by the present invention;

[0068] Figure 4 It is a structural diagram of an embodiment of a distributed resource optimization configuration system for a power distribution system provided by the present invention. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this technical field without creative work are within the scope of protection of the present invention.

[0070] The distributed resources involved in the present invention include but are not limited to distributed renewable energy, flexible loads, distributed energy storage systems and distributed generator sets (such as units that use fossil energy as power generation fuel), etc., which are determined when the embodiments of the present invention are applied to specific products or technologies.

[0071] The adjustable power domain of the above-mentioned distributed renewable energy (such as wind power resources and photovoltaic resources) is its active power domain; the adjustable power domain of distributed generator sets includes the unit active power domain and the unit climbing rate domain; the adjustable power domain of flexible loads includes the flexible load adjustment domain and the flexible load climbing rate domain; the adjustable power domain of distributed energy storage systems is the energy storage charging and discharging power domain, the energy storage energy domain and the energy storage climbing rate domain.

[0072] When the distribution system accepts distributed resources, it not only involves whether the above distributed resources can be physically connected, but also needs to consider the stability and flexibility of the distribution system during operation. The following is an in-depth analysis of the acceptance capacity of distributed resources in medium and low voltage distribution systems and its impact on the flexibility, voltage distribution and network loss of the distribution system conducted by the inventors of the present invention during the research and development process. Based on the above analysis, we can better understand the reasons for optimizing the configuration of distributed resources and the necessity of the technical solutions in the embodiments of the present invention. Among them, the acceptance capacity of distributed resources in the distribution system refers to the maximum capacity that the distribution system can accommodate under the premise of meeting the system flexibility and system operation constraints. The acceptance capacity is mainly affected by factors such as DRE volatility, system network topology, and operation constraints. The following is the specific analysis process:

[0073] From the perspective of flexibility, in order to measure the flexibility of the distribution system under the coordination of source, load and storage, the present invention proposes the flexibility deficiency rate as an indicator for evaluating the flexibility of the distribution system; wherein, the calculation formulas for the upward and downward flexibility deficiency rates of the distribution system are as follows:

[0074]

[0075] Among them, f flex,up is the upward flexibility deficiency rate of the distribution system, f flex,dn F is the downward flexibility deficiency rate of the distribution system; t de,up is the upward flexibility demand of the distribution system at time t, Ft de,dn is the downward flexibility demand of the distribution system at time t; F t su,up The upward flexibility adjustment capability provided by the source, load and storage resources of the distribution system at time t, F t su,dn is the downward flexibility adjustment capability provided by the source, load and storage resources of the distribution system at time t; T is the time set. In addition, It is used to focus only on the part where supply is less than demand (i.e. the part where flexibility is insufficient), while the part where supply is greater than demand is set to 0 (i.e. "discarded"). Therefore, the upward and downward flexibility deficiency rates measure the proportion of the distribution system that fails to meet the flexibility demand in a certain period of time; if the proportion is lower, it means that the stronger the adjustment ability of the distribution system in the upward (or downward) direction, the higher the stability of the distribution system currently connected to the distributed resources.

[0076] It can be understood that upward flexibility refers to the ability of the system to increase power output. When electricity demand increases, the distribution system needs to be able to quickly increase power generation or reduce load to maintain a balance between supply and demand. For example, if you need to increase power generation, you can check which power generation resources are currently available to increase output immediately (such as increasing solar power generation on sunny days); if you need to reduce load, you can identify which loads are "flexible loads" that can be temporarily shut down or postponed, or shut down the energy storage system for charging. Downward flexibility: refers to the system's ability to reduce power output. When electricity demand decreases or there is excess power generation, the system needs to be able to reduce power generation or increase load to avoid excessive voltage or equipment overload. For example, if you need to reduce power generation, check which power generation resources are currently available to reduce output, such as reducing wind power output or stopping non-essential generators; if you need to increase load, check whether there are any devices or systems that can be enabled immediately to consume excess power, such as starting the energy storage system to charge.

[0077] From the perspective of voltage distribution, we analyze how the change of node voltage in the distribution system is affected by the access of distributed resources. Taking the access of load node i to DRE as an example, the voltage amplitude of load node j is as follows:

[0078]

[0079] Among them, U j is the voltage amplitude of load node j, U 0 is the terminal voltage amplitude, P Li and Q Li are the active load and reactive load of load node i respectively; i=1,2,…,n; P i DRE is the active output of DRE at load node i; R k and Xk are the resistance and reactance between load node k-1 and load node k respectively. Li and Q Li are greater than zero, U i-1 ≤U i ≤0. If the output of the connected distributed resources (such as photovoltaic power generation) is less than the node load demand, the node voltage will increase to a certain extent; if the output of the connected distributed resources is greater than the node load demand and there is spare power to provide power to other node loads, the node voltage will continue to increase or even exceed the limit. It can be seen that when the distribution system is connected to distributed resources, the voltage range of each load node in the distribution system must be considered to ensure safe power operation.

[0080] See also Figure 1 , is an exemplary diagram of an embodiment of the medium and low voltage power distribution system provided by the present invention.

[0081] From the perspective of network loss, the network topology of the medium and low voltage distribution system is determined based on the node data and branch data of the medium and low voltage distribution system, and its voltage loss is analyzed. Figure 1 shown.

[0082] The calculation formula for total active network loss of medium and low voltage distribution system is:

[0083]

[0084] in, is the total active power network loss; It is the active power loss generated on the equivalent impedance of the medium voltage system; It is the active power loss generated on the equivalent impedance of the low voltage station area; Active power loss generated by transformers connecting medium and low voltage to the upper power grid; and All of them are active power losses generated by medium and low voltage distribution transformers.

[0085] The corresponding calculation formula is as follows:

[0086]

[0087] in, and are the equivalent load active power of the medium voltage and low voltage sections respectively; and are the equivalent distributed wind and solar active powers connected to the medium voltage and low voltage areas respectively; R MV and R LV are the equivalent resistances of the medium voltage and low voltage sections respectively; G 1 and G 2are the equivalent conductances of the medium and low voltage distribution transformers respectively; R T1 and R T2 All are transformer equivalent resistance; P 1 , P 2 and P 3 They are Figure 1 Active power injected into the middle nodes 1, 2 and 3; U 1 , U 2 and U 3 They are Figure 1 The voltage magnitudes of nodes 1, 2, and 3 in the figure. As can be seen from the above, when power is transmitted from one node to another, power loss will occur due to factors such as resistance. Therefore, when accessing distributed resources, the economic efficiency of the distribution system needs to be considered.

[0088] See also Figure 2 , is a flow chart of an embodiment of a method for optimizing configuration of distributed resources in a power distribution system provided by the present invention.

[0089] Based on the above analysis, in order to obtain a distributed resource configuration result (configuration scheme) with high economy and flexibility in the power distribution system, the first aspect of the present invention provides a distributed resource optimization configuration method for the power distribution system, which is performed by a distributed resource configuration device for controlling the distributed resource optimization configuration work of the power distribution system. The method includes steps S1 to S2, which are specifically as follows:

[0090] Step S1: Establishing a two-layer optimization model of distributed resources in a power distribution system; wherein the two-layer optimization model comprises: an upper-layer resource configuration model and a lower-layer operation and control model; the upper-layer resource configuration model is constructed with the goal of minimizing the annual comprehensive cost of the distributed resources; the lower-layer operation and control model is constructed with the goal of minimizing the annual comprehensive operation cost of the distributed resources and minimizing the flexibility deficiency rate of the power distribution system;

[0091] Step S2: Solve the two-layer optimization model to obtain the optimal configuration result of the distributed resources.

[0092] It is worth noting that the power distribution system involved in the embodiments of the present invention includes but is not limited to a medium and low voltage power distribution system, which is determined when the embodiments of the present invention are applied to specific products or technologies.

[0093] The embodiment of the present invention constructs a two-layer optimization model for distributed resources, focusing on different optimization objectives at each level, that is, in the upper-layer resource configuration model, the goal is to minimize the annual comprehensive cost; in the lower-layer operation and control model, the goal is to minimize the annual comprehensive operation cost of distributed resources and minimize the flexibility deficiency rate of the distribution system. In this two-layer optimization model, the optimization objectives of the upper and lower layers affect each other; the resource configuration results of the upper layer directly affect the annual comprehensive operation cost and flexibility of the lower layer, while the annual comprehensive operation cost and flexibility requirements of the lower layer will react on the configuration decisions of the upper layer to ensure that the optimal configuration plan of distributed resources can simultaneously meet the economic and flexibility requirements of power grid operation.

[0094] In an optional embodiment, the construction of the upper-layer resource configuration model includes:

[0095] Taking the minimization of the annual comprehensive cost of the distributed resources as the goal, an upper-level objective function is established; wherein,

[0096] The annual comprehensive cost includes the annual investment cost and the annual comprehensive operation and maintenance cost; the annual investment cost includes at least one or more of the following: the equivalent annual investment cost of the distributed energy storage system, the equivalent annual investment cost of the distributed generator set, the equivalent annual investment cost of the distributed renewable energy and the cost of signing a demand response contract for flexible load participation; the annual comprehensive operation and maintenance cost includes at least one or more of the following: annual fixed operation and maintenance cost, annual variable operation cost, annual network loss cost, annual voltage deviation penalty cost and annual carbon emission cost;

[0097] A first set of constraints is configured for the upper-level objective function to obtain the upper-level resource configuration model; wherein the first set of constraints includes at least one or more of the following: installation quantity constraints, capacity constraints, demand response contract capacity constraints, and new energy penetration constraints.

[0098] Furthermore, the upper layer objective function is specifically:

[0099] f up =C inv +C ope ;

[0100] Among them, F up is the annual comprehensive cost, C inv is the annual investment cost, C ope is the annual comprehensive operation and maintenance cost;

[0101] The annual investment cost is calculated as follows:

[0102]

[0103] in, is the equivalent annual investment cost of the distributed energy storage system, is the equivalent annual investment cost of distributed generator sets, is the equivalent annual investment cost of distributed renewable energy, is the contract cost of flexible load participating in demand response; i is load node i, and i=1,2,…,n; and They are respectively a set of load nodes connected to distributed energy storage systems, distributed generators, distributed renewable energy and flexible loads in the medium voltage system; and are the load node sets connected to distributed energy storage systems, distributed generators, distributed renewable energy and flexible loads in the low-voltage area; λ is the discount rate, and y is the configuration period; and are the unit capacity investment cost, unit power investment cost, configured capacity and configured power of the distributed energy storage system in load node i respectively; and are the unit power investment cost and configuration capacity of the distributed generator set in load node i respectively; and are the unit power investment cost and configuration capacity of distributed renewable energy in load node i respectively; The unit price of demand response contract costs for flexible loads; Contracted capacity for demand response of flexible loads, with an annual metering period;

[0104] The calculation formula for the annual comprehensive operation and maintenance cost is as follows:

[0105] C ope =C main +C vope +C loss +C v,dev +C carbon ,

[0106]

[0107] Among them, C main is the annual fixed operation and maintenance cost, C vope is the annual variable operating cost, C loss is the annual network loss cost, C v,dev is the annual voltage deviation penalty cost, C carbon is the annual carbon emission cost; and are the annual operation and maintenance costs of distributed generators and distributed renewable energy respectively; η DG and η DREare the annual operation and maintenance cost coefficients of distributed generators and distributed renewable energy respectively; D is the annual operating days.

[0108] It is worth noting that the upper-level resource allocation model calculates the annual comprehensive cost of the entire distribution system (medium and low voltage distribution system) by allocating the initial investment cost to each year and combining it with the annual operation and maintenance costs. In particular, the annual comprehensive cost also takes into account the annual carbon emission cost, so that the optimal allocation plan of distributed resources corresponding to the upper-level objective function is not only economical, but also can effectively reduce environmental impact.

[0109] Furthermore, the first constraint condition set configured in the upper-layer resource configuration model includes at least one or more of the following:

[0110] The installation quantity constraint is specifically:

[0111] 0≤N m,i ≤N m,max ;

[0112] Among them, N m,i is the number of installed resources of type m in load node i, N m,max is the maximum allowed number of installed resources of the mth category, 1≤m≤M; these M categories of resources include: distributed generation units, distributed renewable energy and distributed energy storage systems;

[0113] The capacity constraints are specifically:

[0114] 0≤S m,i ≤S m,max ;

[0115] Among them, S m,i is the configuration capacity of the mth resource in load node i, S m,max is the maximum allowable configuration capacity of the mth type of resources, 1≤m≤M; these M types of resources include: distributed generation units, distributed renewable energy and distributed energy storage systems;

[0116] The demand response contract capacity constraint is specifically:

[0117]

[0118] in, is the contracted capacity of load node i participating in demand response on day d, is the maximum load of load node i on day d, δ is the ratio of the maximum contractable capacity to the maximum load;

[0119] The new energy penetration rate constraints are specifically:

[0120]

[0121] in, is the active power of distributed renewable energy at load node i at time t, Q d is the annual electricity consumption, ε is the penetration rate of new energy; T is the time set, D is the annual operating days (i.e. the number of days in a year), and They are respectively the load node sets connected to distributed renewable energy in the medium voltage system and low voltage area.

[0122] It is worth noting that demand response is an incentive mechanism to reduce users' electricity consumption during peak hours, and the demand response contract capacity constraint can ensure that when users participate in demand response, the contracted load reduction will not exceed the reasonable range of the user's actual electricity consumption capacity; this not only protects the rights and interests of users, but also benefits the stability of the power grid. In addition, the new energy penetration constraint ensures that a certain proportion of electricity in the entire distribution system comes from renewable energy, which helps promote the use of green energy, reduce dependence on fossil fuels, and thus reduce carbon emissions.

[0123] In an optional embodiment, the construction of the lower layer operation control model includes:

[0124] The lower-level economic objective function is established with the goal of minimizing the annual comprehensive operating cost of the distributed resources; wherein the annual comprehensive operating cost includes at least one or more of the following: annual variable operating cost, annual network loss cost, annual voltage deviation cost and annual carbon emission cost; the annual variable operating cost includes at least one or more of the following: operating cost of distributed generator sets, operating cost of distributed energy storage systems, operating cost of flexible loads, cost of purchasing and selling electricity to the superior power grid and penalty cost for wind and solar power abandonment;

[0125] Taking minimizing the flexibility deficiency rate of the distribution system as the goal, establishing a lower-level flexibility objective function;

[0126] A second set of constraints is configured for both the lower-level economic objective function and the lower-level flexibility objective function to obtain the lower-level operation and control model; wherein the second set of constraints includes at least one or more of the following: source-load-storage resource operation constraints, distribution system power balance constraints, three-phase power flow constraints and voltage constraints.

[0127] Furthermore, the lower-level economic objective function is specifically:

[0128] f 1 down =C vope +C loss +C v,dev +C carbon ;

[0129] Among them, F 1 down is the annual comprehensive operating cost, C vope is the annual variable operating cost, C loss is the annual network loss cost, C v,dev is the annual voltage deviation penalty cost, C carbon is the annual carbon emission cost;

[0130] The calculation formula for the annual variable operating cost is as follows:

[0131] C vope =C DG +C DES +C FL +C M +C DRE,cut ,

[0132]

[0133] Among them, C DG is the operating cost of distributed generators, C DES is the operating cost of the distributed energy storage system, C FL is the flexible load operation cost, C M is the cost of purchasing and selling electricity to the upper grid, C DRE,cut is the penalty cost for wind and solar curtailment; s is a typical day scene, φ is a typical day scene set, p s is the probability of a typical day scenario s, D is the number of operating days per year, and T is the time set; is the active power of the distributed generator set at load node i at time t, is the cost coefficient of the distributed generator set at load node i; and are the charging active power and discharging active power of the distributed energy storage system at load node i at time t, and are the charging cost coefficient and discharging cost coefficient of the distributed energy storage system at load node i respectively; is the regulated active power of the flexible charge at load node i at time t, is the utility function coefficient of the flexible charge of load node i participating in demand response; is the amount of wind and solar power abandoned at load node i at time t, P is the cost coefficient of the penalty for wind and solar power abandonment at time t; t M is the power purchased and sold by the upper grid at time t, is the cost coefficient of electricity purchase and sales by the superior power grid at time t; and They are respectively a set of load nodes connected to distributed energy storage systems, distributed generators, distributed renewable energy and flexible loads in the medium voltage system; and They are the load node sets connected to the distributed energy storage system, distributed generator sets, distributed renewable energy and flexible loads in the low-voltage area;

[0134] The annual network loss cost is calculated as follows:

[0135]

[0136] Among them, c loss is the unit active network loss coefficient, r ij is the resistance of branch ij, l is the branch set; I ij,t is the current flowing into branch ij at time t;

[0137] The annual voltage deviation penalty cost is calculated as follows:

[0138]

[0139] Among them, c v,dev is the voltage deviation penalty price, U i,t is the square of the voltage amplitude of load node i at time t, is the square of the rated voltage amplitude of load node i at time t;

[0140] The annual carbon emission cost is calculated as follows:

[0141]

[0142] Among them, E c is the carbon quota of the distribution system, E lim is the length of the carbon emission interval division, q is the number of step divisions, and α is the carbon price per step c co2 Growth rate, E CE is the carbon emissions of the distribution system, χ DG is the carbon emission intensity per unit of electricity generated by distributed generators, χ M is the carbon emission intensity per unit of power generation when the upper power grid purchases and sells electricity, P i DG is the active power of the distributed generator set at load node i, is the power purchased and sold by the upper power grid of load node i at time t.

[0143] It is worth noting that in the lower-level operation and control model, various costs during the operation of the distribution system can be obtained from the lower-level economic objective function. These operating costs include but are not limited to power generation costs, energy storage costs, demand response costs, penalty costs for wind and solar power abandonment, and electricity trading costs with the upper-level power grid. By solving the lower-level economic objective function, the optimal configuration scheme of distributed resources obtained can significantly reduce the amount of wind and solar power abandonment, operating costs and carbon emissions of the distribution system, thereby making the distribution system both economical and environmentally friendly. In addition, the embodiment of the present invention also comprehensively considers the synergistic characteristics of distributed resources across voltage levels, reduces the operating network loss and voltage deviation costs of the entire system, and realizes the rationalization of the distributed resource configuration scheme.

[0144] Furthermore, the lower layer flexibility objective function is specifically:

[0145]

[0146]

[0147] in, is the flexibility deficiency rate of the distribution system, f flex,up is the upward flexibility deficiency rate of the distribution system, f flex,dn is the downward flexibility deficiency rate of the distribution system, and ω is the weight coefficient.

[0148] Specifically, the upward flexibility deficiency rate is:

[0149]

[0150] Among them, F t de,up is the upward flexibility demand of the distribution system at time t, F t su,up is the upward flexibility adjustment capability provided by the source, load and storage resources of the distribution system at time t; T is the time set;

[0151] The downward flexibility deficiency rate is specifically:

[0152]

[0153] Among them, F t de,dn is the downward flexibility demand of the distribution system at time t, F t su,dn The downward flexibility adjustment capability provided by the source, load and storage resources of the distribution system at time t.

[0154] Furthermore, the second constraint condition set configured in the lower-layer operation control model includes at least one or more of the following:

[0155] (1) Conventional constraints such as source, load and storage resource operation constraints, distribution system power balance constraints, and three-phase power flow constraints;

[0156] (2) Voltage constraints of medium and low voltage distribution systems, specifically:

[0157] U i,min ≤U i,t ≤U i,max ;

[0158] Among them, U i,t is the voltage amplitude of load node i at time t, U i,min and U i,max The voltage of each load node in the medium voltage system is usually limited to ±7% of the rated voltage, while the voltage of each load node in the low voltage area is usually limited to -10% to +7% of the rated voltage.

[0159] To sum up, the distributed resource optimization configuration method of the distribution system provided by the embodiment of the present invention, by constructing an upper-level resource configuration model and a lower-level operation and control model, determines the optimal distributed resource configuration scheme under the objective functions of minimizing the annual comprehensive cost, minimizing the annual operating cost, and minimizing the flexibility deficiency rate, so as to achieve the optimal balance between the economy, low carbon and flexibility of the power grid.

[0160] In the prior art, the source-load-storage resource configuration model of the multi-stage distribution system and the source-load-storage resource configuration model based on the optimal network constraint mainly focus on reducing the investment cost and environmental penalty cost of the distribution system. However, these studies mainly focus on the distribution system of the same voltage level (single voltage level), ignoring the flexible and low-carbon collaborative characteristics of the source-load-storage resources distributed at medium and low voltage levels. In fact, the distribution system usually includes multiple voltage levels, and the distributed resources will be connected to different voltage levels according to their capacity. Based on this, the distributed resource optimization configuration method of the distribution system proposed in the embodiment of the present invention can not only realize the coordinated work of source, load and storage, but also can be applied in medium and low voltage distribution systems (across voltage levels), so as to effectively cope with the complex requirements of multi-voltage level coupling in future distribution systems.

[0161] In an optional embodiment, solving the two-layer optimization model to obtain the optimal configuration result of the distributed resources includes:

[0162] Constructing a state vector of a universe individual; wherein the state vector includes at least one or more of the following configuration parameters: the active power of the distributed generator set of each load node at time t, the charging active power of the distributed energy storage system of each load node at time t, the discharging active power of the distributed energy storage system of each load node at time t, and the regulating active power of the flexible charge of each load node at time t;

[0163] Determining a fitness function based on the annual comprehensive cost in the upper-level resource allocation model;

[0164] Based on the state vector and the fitness function, the two-layer optimization model is solved using a metaverse optimization algorithm, and the universe individual with the lowest fitness is selected as the optimal configuration result of the distributed resources.

[0165] It is worth noting that after establishing the two-layer optimization model, the multiverse optimization algorithm (MVO) is used to solve the model to achieve the optimal configuration of distributed resources. The multiverse optimization algorithm simulates cosmic phenomena such as the inflation rate of the universe, white holes, black holes, and wormholes to efficiently explore the solution space and find the global optimal solution. The specific implementation process is as follows:

[0166] The first step is to initialize the universe:

[0167] The initial universe set consists of a series of randomly generated solutions, each of which represents a possible distributed resource configuration scheme. That is, the state vector of each universe individual contains the configuration parameters of the distributed resources (such as installation location, capacity, etc.), specifically:

[0168]

[0169] Among them, U l is the lth universe individual, i is the load node i, i=1,2,…,n; t∈T, T is the time set; is the active power of the distributed generator set at load node i at time t; and are respectively the charging active power and discharging active power of the distributed energy storage system at load node i at time t; is the regulated active power of the flexible charge at load node i at time t.

[0170] In the second step, calculate the fitness and inflation rate:

[0171] Calculate the fitness value F(U l ), which is determined by the upper optimization objective function F upThe lower the fitness value, the lower the cost and the better the optimization effect. An inflation rate is assigned to each universe based on the fitness value, and a universe with a lower inflation rate is considered to be a better solution.

[0172] The third step is to use the material exchange and wormhole crossing mechanism to select and update the universe:

[0173] Material exchange: Through the white hole and black hole mechanism, matter (part of the solution parameters) is exchanged between universes. Matter is transferred from a universe with higher fitness (poorer solution) to a universe with lower fitness (better solution), promoting the diversity of solutions and global search capabilities.

[0174] Wormhole crossing: Utilize the wormhole mechanism for local optimization, randomly select certain parameters in the universe for fine-tuning, and enhance the algorithm's local search capabilities and the sophistication of the solution.

[0175] Repeat the steps of material exchange and wormhole crossing until the predetermined number of iterations is met or other convergence conditions are reached (such as the fitness change is less than the threshold). During the iteration process, the state of the universe is continuously updated to find a better solution.

[0176] Step 4: Output the optimal configuration solution:

[0177] After the iteration, the universe individual with the lowest fitness is selected as the final optimization configuration scheme. The state vector of this universe individual represents the optimal configuration parameters of distributed resources.

[0178] In the application process of the above MVO algorithm, the key formulas and constraints involved include:

[0179] Fitness calculation formula:

[0180] F(U l )=C inv (U l )+C ope (U l );

[0181] Inflation rate calculation formula:

[0182]

[0183] Among them, IR l is the inflation rate of the lth universe, fit l is the fitness value of the lth universe, fit best and fit worst are the best and worst fitness values ​​in the current universe set, respectively.

[0184] Material exchange mechanism:

[0185] xnew =x current +TDR(x Best -x Worst );

[0186] Among them, x new represents the updated universe position (solution); x current Indicates the current cosmic position; TDR travel distance rate, controls the intensity of matter exchange; x Best and x Worst Represent the positions of the best and worst universes respectively.

[0187] Wormhole crossing mechanism:

[0188] x new =x current +rand(-1,1)WEP(x Best -x Worst );

[0189] Among them, x new and x current Represent the updated and current universe positions respectively; rand(-1,1) is a random number generated in the range [-1,1]; WEP is the probability of wormhole existence, which controls the possibility of wormhole crossing; represent the current optimal and worst universe positions respectively. In particular, adjusting the wormhole existence probability WEP or the travel distance rate TDR can better explore the solution space of the two-level optimization model.

[0190] Constraint processing: By introducing the penalty function method, it is ensured that each solution meets the constraints of power system operation, such as:

[0191] Penalty=α·max(0,constraint_violation);

[0192] Among them, constraint_violation is the degree of constraint violation, and α is the coefficient of the penalty function.

[0193] It is worth noting that when the embodiment of the present invention adopts the multiverse optimization algorithm to solve the two-layer optimization model, the algorithm parameters, such as the wormhole existence probability (WEP) and the travel distance rate (TDR), are further dynamically adjusted to improve the algorithm's search capability and convergence speed, ensuring that the optimal configuration scheme that meets the multi-objective requirements of the power grid can be quickly found, so that the optimal configuration scheme can take into account multiple objectives, optimize resource utilization, and enhance the economy and environmental friendliness of the power grid.

[0194] For example, a typical medium and low voltage distribution system case was selected for simulation experiments to verify the performance of the adjusted and optimized MVO algorithm on the two-layer optimization model. The algorithm was further refined and optimized by comparing the impact of different parameter settings and strategies. The optimal parameter settings, including the number of universes, the maximum number of iterations, the material exchange parameters, and the wormhole crossing parameters, were determined through experiments and sensitivity analysis.

[0195] In order to verify the effectiveness of the two-layer optimization model proposed in this invention, the following schemes are set for comparison. The total access capacity of the four schemes is as follows: Figure 3 The comparison of the configuration results of each solution is shown in Table 1.

[0196] Option 1: Install DG, considering flexibility goals;

[0197] Option 2: Install DG and DES, taking flexibility objectives into consideration;

[0198] Option 3: Install DG and DES and consider demand response;

[0199] Option 4: Install DG and DES to take into account both demand response and flexibility goals.

[0200] Table 1 Comparison of distribution system optimization configuration results under different schemes

[0201]

[0202]

[0203] As can be seen from the above table, Scheme 1 only considers the configuration of DG, and its total investment cost is relatively low among the four schemes, but its wind and solar power abandonment is the largest. This is because DG provides sufficient upward flexibility to the distribution system, but its ability to provide downward flexibility is poor, especially during the period of high wind power output at low load. Therefore, Scheme 1 has the lowest DRE capacity and the highest flexibility deficiency rate.

[0204] In Scheme 2, the DRE access capacity is increased, but due to the high investment and construction cost of DES, the annual investment cost and operating cost increase, but the carbon emissions and wind and solar power abandonment are reduced. This is because DES can coordinate with DRE to absorb excess electricity during periods of high DRE output, and release the energy stored in DES to reduce DG output when the output of new energy is insufficient, thereby reducing dependence on the power of the upper main grid, significantly reducing the system's operating costs and carbon emissions. Although DES has fast and flexible response capabilities, it is limited by its own capacity and state of charge, so the system's lack of flexibility has not been well improved.

[0205] In Scheme 3, DG and DES are configured in coordination with the flexibility adjustment capability of FL without considering the flexibility index. The amount of wind and solar power abandoned and the flexibility deficiency rate are reduced compared with Scenarios 1 and 2, which shows that the coordinated interaction of source, load and storage in the two-layer model can effectively solve the problems of large volatility and strong intermittency of DRE.

[0206] Scheme 4 takes into account the flexibility index on the basis of the economic index of Scheme 3. Whether in the medium voltage system or in the low voltage area, the capacity of DRE is reduced, and DG and DES with strong flexibility adjustment capabilities are more inclined to be configured, but the investment cost and total operating cost are lower than Scheme 3. This is because the configured DES can significantly improve the absorption level of DRE in the distribution system, so that the entire system can reserve a certain flexibility margin to adapt to the net load fluctuation problem, and the probability of insufficient flexibility is reduced.

[0207] See also Figure 4 , is a structural diagram of an embodiment of a distributed resource optimization configuration system for a power distribution system provided by the present invention.

[0208] The second aspect of the present invention provides a system for optimizing the configuration of distributed resources in a power distribution system, which is used to implement the method for optimizing the configuration of distributed resources in a power distribution system described in the first aspect of the present invention. The system includes:

[0209] The model building module 11 is used to establish a two-layer optimization model of distributed resources in the power distribution system; wherein the two-layer optimization model includes: an upper-layer resource configuration model and a lower-layer operation and regulation model; the upper-layer resource configuration model is constructed with the goal of minimizing the annual comprehensive cost of the distributed resources; the lower-layer operation and regulation model is constructed with the goal of minimizing the annual comprehensive operation cost of the distributed resources and minimizing the flexibility deficiency rate of the power distribution system;

[0210] The model solving module 12 is used to solve the two-layer optimization model to obtain the optimal configuration result of the distributed resources.

[0211] It should be noted that the distributed resource optimization configuration system for a distribution system provided in the second aspect of the embodiment of the present invention can implement all the processes of the distributed resource optimization configuration method for a distribution system described in the first aspect of the embodiment. The functions of each module in the system and the technical effects achieved are respectively the same as the functions of the distributed resource optimization configuration method for a distribution system described in the first aspect of the embodiment, and will not be repeated here.

[0212] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing the configuration of distributed resources in a power distribution system, characterized in that: include: Establish a two-layer optimization model for distributed resources in a power distribution system; wherein the two-layer optimization model includes: an upper-layer resource configuration model and a lower-layer operation and control model; the upper-layer resource configuration model is constructed with the goal of minimizing the annual comprehensive cost of the distributed resources; the lower-layer operation and control model is constructed with the goal of minimizing the annual comprehensive operation cost of the distributed resources and minimizing the flexibility deficiency rate of the power distribution system; The two-layer optimization model is solved to obtain the optimal configuration result of the distributed resources.

2. The method for optimizing the configuration of distributed resources in a power distribution system according to claim 1, characterized in that: The construction of the upper-level resource configuration model includes: An upper objective function is established with the goal of minimizing the annual comprehensive cost of the distributed resources; wherein the annual comprehensive cost includes the annual investment cost and the annual comprehensive operation and maintenance cost; the annual investment cost includes at least one or more of the following: the equivalent annual investment cost of the distributed energy storage system, the equivalent annual investment cost of the distributed generator set, the equivalent annual investment cost of the distributed renewable energy and the cost of signing a demand response contract for flexible load participation; the annual comprehensive operation and maintenance cost includes at least one or more of the following: annual fixed operation and maintenance cost, annual variable operation cost, annual network loss cost, annual voltage deviation penalty cost and annual carbon emission cost; A first set of constraints is configured for the upper-level objective function to obtain the upper-level resource configuration model; wherein the first set of constraints includes at least one or more of the following: installation quantity constraints, capacity constraints, demand response contract capacity constraints, and new energy penetration constraints.

3. The method for optimizing the configuration of distributed resources in a power distribution system according to claim 1, characterized in that: The construction of the lower-level operation control model includes: The lower-level economic objective function is established with the goal of minimizing the annual comprehensive operating cost of the distributed resources; wherein the annual comprehensive operating cost includes at least one or more of the following: annual variable operating cost, annual network loss cost, annual voltage deviation cost and annual carbon emission cost; the annual variable operating cost includes at least one or more of the following: operating cost of distributed generator sets, operating cost of distributed energy storage systems, operating cost of flexible loads, cost of purchasing and selling electricity to the superior power grid and penalty cost for wind and solar power abandonment; Taking minimizing the flexibility deficiency rate of the distribution system as the goal, establishing a lower-level flexibility objective function; A second set of constraints is configured for both the lower-level economic objective function and the lower-level flexibility objective function to obtain the lower-level operation and control model; wherein the second set of constraints includes at least one or more of the following: source-load-storage resource operation constraints, distribution system power balance constraints, three-phase power flow constraints and voltage constraints.

4. The method for optimizing the configuration of distributed resources in a power distribution system according to claim 2, characterized in that: The upper layer objective function is specifically: minF up =C inv +C ope ; Among them, F up is the annual comprehensive cost, C inv is the annual investment cost, C ope is the annual comprehensive operation and maintenance cost; The annual investment cost is calculated as follows: in, is the equivalent annual investment cost of the distributed energy storage system, is the equivalent annual investment cost of distributed generator sets, is the equivalent annual investment cost of distributed renewable energy, is the contract cost of flexible load participating in demand response; i is load node i; and They are respectively a set of load nodes connected to distributed energy storage systems, distributed generators, distributed renewable energy and flexible loads in the medium voltage system; and are the load node sets connected to distributed energy storage systems, distributed generators, distributed renewable energy and flexible loads in the low-voltage area; λ is the discount rate, and y is the configuration period; and P i DES are the unit capacity investment cost, unit power investment cost, configured capacity and configured power of the distributed energy storage system in load node i respectively; and are the unit power investment cost and configuration capacity of the distributed generator set in load node i respectively; and are the unit power investment cost and configuration capacity of distributed renewable energy in load node i respectively; The unit price of demand response contract costs for flexible loads; Contracted capacity for demand response of flexible loads, with an annual metering period; The calculation formula for the annual comprehensive operation and maintenance cost is as follows: C ope =C main +C vope +C loss +C v,dev +C carbon , Among them, C main is the annual fixed operation and maintenance cost, C vope is the annual variable operating cost, C loss is the annual network loss cost, C v ,dev is the annual voltage deviation penalty cost, C carbon is the annual carbon emission cost; and are the annual operation and maintenance costs of distributed generators and distributed renewable energy respectively; η DG and η DRE are the annual operation and maintenance cost coefficients of distributed generators and distributed renewable energy respectively; D is the annual operating days.

5. The method for optimizing the configuration of distributed resources in a power distribution system according to claim 2, characterized in that: The demand response contract capacity constraint is specifically: in, is the contracted capacity of load node i participating in demand response on day d, is the maximum load of load node i on day d, δ is the ratio of the maximum contractable capacity to the maximum load; The new energy penetration rate constraints are specifically: in, is the active power of distributed renewable energy at load node i at time t, Q d is the annual electricity consumption, ε is the penetration rate of new energy; T is the time set, D is the annual operating days, and They are respectively the load node sets connected to distributed renewable energy in the medium voltage system and low voltage area.

6. The method for optimizing the configuration of distributed resources in a power distribution system according to claim 3, characterized in that: The lower-level economic objective function is specifically: minF1 down =C vope +C loss +C v,dev +C carbon ; Among them, F1 down is the annual comprehensive operating cost, C vope is the annual variable operating cost, C loss is the annual network loss cost, C v,dev is the annual voltage deviation penalty cost, C carbon is the annual carbon emission cost; The calculation formula for the annual variable operating cost is as follows: C vope =C DG +C DES +C FL +C M +C DRE,cut , Among them, C DG is the operating cost of distributed generators, C DES is the operating cost of the distributed energy storage system, C FL is the flexible load operation cost, C M is the cost of purchasing and selling electricity to the upper grid, C DRE,cut is the penalty cost for wind and solar curtailment; s is a typical day scene, φ is a typical day scene set, p s is the probability of a typical day scenario s, D is the number of operating days per year, and T is the time set; is the active power of the distributed generator set at load node i at time t, is the cost coefficient of the distributed generator set at load node i; and are the charging active power and discharging active power of the distributed energy storage system at load node i at time t, and are the charging cost coefficient and discharging cost coefficient of the distributed energy storage system at load node i respectively; is the regulated active power of the flexible charge at load node i at time t, is the utility function coefficient of the flexible charge of load node i participating in demand response; is the amount of wind and solar power abandoned at load node i at time t, P is the cost coefficient of the penalty for wind and solar power abandonment at time t; t M is the power purchased and sold by the upper grid at time t, is the cost coefficient of electricity purchase and sales by the superior power grid at time t; and They are respectively a set of load nodes connected to distributed energy storage systems, distributed generators, distributed renewable energy and flexible loads in the medium voltage system; and They are the load node sets connected to the distributed energy storage system, distributed generator sets, distributed renewable energy and flexible loads in the low-voltage area; The annual network loss cost is calculated as follows: Among them, c loss is the unit active network loss coefficient, r ij is the resistance of branch ij, l is the branch set; I ij,t is the current flowing into branch ij at time t; The annual voltage deviation penalty cost is calculated as follows: Among them, c v,dev is the voltage deviation penalty price, U i,t is the square of the voltage amplitude of load node i at time t, is the square of the rated voltage amplitude of load node i at time t; The annual carbon emission cost is calculated as follows: Among them, E c is the carbon quota of the distribution system, E lim is the length of the carbon emission interval, q is the number of steps, and α is the carbon price per step. Growth rate, E CE is the carbon emissions of the distribution system, χ DG is the carbon emission intensity per unit of electricity generated by distributed generators, χ M is the carbon emission intensity per unit of power generation when the upper power grid purchases and sells electricity, P i DG is the active power of the distributed generator set at load node i, is the power purchased and sold by the upper power grid of load node i at time t.

7. The method for optimizing the configuration of distributed resources in a power distribution system according to claim 3, characterized in that: The lower layer flexibility objective function is specifically: in, is the flexibility deficiency rate of the distribution system, f flex,up is the upward flexibility deficiency rate of the distribution system, f flex,dn is the downward flexibility deficiency rate of the distribution system, and ω is the weight coefficient.

8. The method for optimizing the configuration of distributed resources in a power distribution system according to claim 7, characterized in that: The upward flexibility deficiency rate is specifically: Among them, F t de,up is the upward flexibility demand of the distribution system at time t, F t su,up is the upward flexibility adjustment capability provided by the source, load and storage resources of the distribution system at time t; T is the time set; The downward flexibility deficiency rate is specifically: Among them, F t de,dn is the downward flexibility demand of the distribution system at time t, F t su,dn The downward flexibility adjustment capability provided by the source, load and storage resources of the distribution system at time t.

9. The method for optimizing the configuration of distributed resources in a power distribution system according to claim 1, characterized in that: Solving the two-layer optimization model to obtain the optimal configuration result of the distributed resources includes: Constructing a state vector of a universe individual; wherein the state vector includes at least one or more of the following configuration parameters: the active power of the distributed generator set of each load node at time t, the charging active power of the distributed energy storage system of each load node at time t, the discharging active power of the distributed energy storage system of each load node at time t, and the regulating active power of the flexible charge of each load node at time t; Determining a fitness function based on the annual comprehensive cost in the upper-level resource allocation model; Based on the state vector and the fitness function, the two-layer optimization model is solved using a metaverse optimization algorithm, and the universe individual with the lowest fitness is selected as the optimal configuration result of the distributed resources.

10. A distributed resource optimization configuration system for a power distribution system, characterized in that: include: A model building module, used to establish a two-layer optimization model of distributed resources in a power distribution system; wherein the two-layer optimization model includes: an upper-layer resource configuration model and a lower-layer operation and regulation model; the upper-layer resource configuration model is constructed with the goal of minimizing the annual comprehensive cost of the distributed resources; the lower-layer operation and regulation model is constructed with the goal of minimizing the annual comprehensive operation cost of the distributed resources and minimizing the flexibility deficiency rate of the power distribution system; The model solving module is used to solve the two-layer optimization model to obtain the optimal configuration result of the distributed resources.

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