Power distribution network expansion planning method considering load space-time transfer and multi-energy flow cooperation

Abstract

The invention discloses a power distribution network expansion planning method considering load space-time transfer and multi-energy flow cooperation. The method comprises the following steps: (1) carrying out flexible interconnection transformation on a network frame by utilizing SOP; (2) establishing a DHM-PCS operation model; (3) configuring DHM-PCS in the power distribution network according to the time sequence voltage sensitivity index; and (IV) constructing a planning model. According to the photovoltaic consumption method provided by the invention, consumption is carried out from three dimensions of energy translation in time, energy transfer in space and energy conversion in energy categories; the dimensionality of absorption is improved from optimization of points to coordination between networks; and considering coordination characteristics between feeder lines, constructing a DHM-PCS configuration model of limited key nodes, and realizing global consumption benefit maximization.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network expansion planning, and specifically relates to a distribution network expansion planning method taking into account load time-space transfer and multi-energy flow coordination. Background Art

[0002] Distributed photovoltaics are mainly connected at the distribution network level. The reasons for their difficulty in absorption can be divided into three aspects: 1) Insufficient development of source-side resources: insufficient integration of diversified demands of other energy systems such as cooling and heating with distribution networks; 2) The grid topology restricts the coordinated absorption capacity between feeders: energy interaction cannot be achieved between feeders; 3) Limited regulation resources: lack of various heterogeneous resources such as energy storage.

[0003] The future consumption model of photovoltaic energy is no longer a simple scale grid connection, but requires deep integration with other industries. At present, research is mainly focused on the configuration of multiple types of heterogeneous resources. The rapid development of multiple types of energy storage technologies has also become the core link of multi-energy complementarity. For example, in essence, they all establish a cold, hot and electric multi-energy flow coupling system to meet the demand for energy. Traditional multi-energy flow coupling systems mostly use natural gas for coupling, and their safety and economic operation are still challenging. In addition, areas lacking natural gas resources face more restrictions, and the combustion of natural gas still emits greenhouse gases.

[0004] Hydrogen energy, as a clean energy source, is currently attracting much attention. Existing research mainly focuses on proposing planning methods for distributed hydrogen-based microgrids. In particular, when it is configured in most feeders, although the proposed absorption strategy is effective, it is highly dependent on land. In fact, the scarcity and high cost of land jointly limit its widespread application. Therefore, it is necessary to consider the photovoltaic absorption under the energy synergy between multiple feeders, rather than configuring the absorption device at the level of a single feeder, that is, it is necessary to accurately configure resources at limited key nodes to alleviate the absorption problems faced by multiple feeders. The focus is on how to link the grid structure between feeders.

[0005] The flexible interconnection transformation of the grid using AC-DC technology has been widely studied. Existing research combines electric energy storage for consumption under the premise of coordinated operation between feeders. However, due to the lack of multiple types of heterogeneous regulation devices, its consumption means and operation flexibility are limited. In fact, the traditional distribution network is transforming into a new distribution network with massive resource access and increasingly complex time-space characteristics. The new distribution network requires both resource coupling within a single feeder and resource coordination between multiple feeders. Therefore, the planning challenge facing consumption has shifted from local optimization of a single feeder and a single regulation device to global coordinated configuration of multiple heterogeneous resources considering the energy coordination characteristics between multiple feeders. However, systematic research on this transformation is very limited. Summary of the invention

[0006] The present invention is proposed to overcome the shortcomings existing in the prior art, and its purpose is to provide a distribution network expansion planning method taking into account load time-space transfer and multi-energy flow coordination.

[0007] The present invention is achieved through the following technical solutions:

[0008] A distribution network expansion planning method taking into account load time-space transfer and multi-energy flow coordination includes the following steps:

[0009] (I) Transform the location of the tie switch in the distribution network into a SOP device and establish a SOP operation model;

[0010] Use SOP to transform the grid into a flexible interconnection: transform the location of the tie switch in the distribution network into an SOP (soft open point) device, and establish an SOP operation model to achieve two-way power transmission between feeders. The SOP access medium-voltage distribution network transformation method is as follows: Figure 1 As shown;

[0011] After the SOP equipment is installed, the conversion and interconnection between AC and DC are realized through the back-to-back voltage source converter (B2B VSC), thereby decoupling the electrical connections on both sides of the feeder and achieving independent control and regulation; the SOP equipment can realize continuous bidirectional power flow regulation between different feeders, improving the operating efficiency and system stability of the distribution network.

[0012] The SOP operation model includes active power constraints and capacity constraints;

[0013] The SOP operation model includes active power constraints and capacity constraints;

[0014] The active power and capacity constraint expressions of the SOP are as follows:

[0015]

[0016] In the formula: In the formula: is the three-phase power of node i connected through the sth SOP at time t, in kVar; is the three-phase power of node j connected through the sth SOP at time t, in kVar; and Both are equal; Ω SOP is a collection of SOP devices; φ represents three phase sequences; is the reactive power of the sth SOP at node i at time t, in kVar; It is the capacity corresponding to SOP, in kVA.

[0017] (II) Select the distributed hydrogen-based multi-energy photovoltaic consumption system (DHM-PCS) as the photovoltaic energy consumption device, link the distribution network with the cold and hot network, distribute the cold and hot loads of each node according to the proportion of electric load, and establish the cold, heat, electricity and hydrogen multi-energy flow conversion relationship in the distributed hydrogen-based multi-energy photovoltaic consumption system (DHM-PCS) ( Figure 2 );

[0018] The equipment includes: Alkaline Electrolysis Cell (AEC), Lithium Bromide Absorption Chiller (LBAC), Electric Heat Pump (EHP), Thermal Storage Tank TST, Hydrogen Storage Tank (HST), Solid Oxide Fuel Cell (SOFC), Electric Chiller (EC).

[0019] The energy balance equation of the cold, heat, electricity and hydrogen multi-energy flow conversion relationship includes an electric energy balance equation, a heat energy balance equation and a cold energy balance equation;

[0020] The power of DHM-PCS comes from the distribution network (including PV (Photovoltaic)) and SOFC. The power load includes AEC, EHP, EC, so the power balance equation is:

[0021] P PDN (t) = P SOFC,E (t)-P EHP,in (t)-P AEC,in (t)-P EC,in (t) (3)

[0022] Where: P PDN (t) represents the power consumption of the distributed hydrogen-based multi-energy flow photovoltaic consumption system (DHM-PCS) at time t, in kW; P SOFC,E (t), P EHP,in (t), P AEC,in (t), P EC,in (t) represents the power generated or absorbed by the fuel cell SOFC, electric heat pump EHP, electrolytic water equipment AEC, and electric refrigerator EC at time t, in kW;

[0023] The output heat energy is provided by EHP, TST and SOFC, so the heat balance equation is:

[0024] P h (t) = P EHP,out (t)+βP TST,out (t)+P SOFC,H (t)

[0025] -P LBAC,in (t)-(1-β)P TST,in (t) (4)

[0026] Where: P h (t) is the thermal power generated by the distributed hydrogen-based multi-energy flow photovoltaic consumption system at time t, in kW; P EHP,out (t), P TST,out (t), P SOFC,H (t) represents the thermal power generated by EHP, TST and SOFC at time t, in kW; P LBAC,in (t), P TST,in (t) represents the thermal power absorbed by LBAC and TST at time t, in kW; β is a 0 / 1 variable, representing the operating state of TST;

[0027] Cold energy comes from EC and LBAC, so the cold energy balance equation is:

[0028] P c (t) = P EC,out (t)+P LBAC,out (t) (5)

[0029] Where: P c (t) is the cooling power generated by the distributed hydrogen-based multi-energy flow photovoltaic consumption system at time t, in kW; P EC,out (t), P LBAC,out (t) is the cooling power generated by EC and LBAC at time t, both in kW.

[0030] The above electric energy balance equation, thermal energy balance equation and cold energy balance equation constitute the heat-cold balance. The calculation method of each variable involved in the balance formula is as follows:

[0031] P AEC,out (t) = α AEC P AEC,in (t) (6)

[0032] P SOFC,E (t) = α SOFC P HST,out (t) (7)

[0033] P EHP,out (t) = α EHP P EHP,in (t) (8)

[0034] P SOFC,H (t) = α SOFC,H θ SOFC P SOFC,E (t) (9)

[0035] P EC,out (t) = α EC P EC,in (t) (10)

[0036] P LBAC,out (t) = α LBAC P LBAC,in (11)

[0037]

[0038] E HST (0) = E HST (23) (13)

[0039] P HST,out (t)<=P HST,max (14)

[0040] P HST,in (t)<=P HST,max (15)

[0041]

[0042] E TST (0) = E TST (23) (17)

[0043] P TST,out (t)≤P TST,max (18)

[0044] P TST,in (t)≤P TST,max (19)

[0045] Where: α AEC , α EHP , α SOFC , α EC , α LBAC They are the energy conversion efficiency of electrolytic water equipment AEC, electric heat pump EHP, fuel cell SOFC, electric refrigerator EC, and absorption refrigerator LBAC; α SOFC,H is the thermal-electric ratio of the fuel cell SOFC; P AEC,in (t), P HST,out (t), P EHP,in (t), P SOFC,E (t), P EC,in (t), P LBAC,in(t) is the power emitted or absorbed by the water electrolysis equipment AEC, hydrogen storage tank HST, electric heat pump EHP, fuel cell SOFC, electric refrigerator EC, and absorption refrigerator LBACt at the moment, in kW; θ SOFC is the waste heat utilization rate of the fuel cell; E HST (t), E TST (t) is the energy stored in the heat storage tank HST and the hydrogen storage tank TST at time t, both in kWh; η HST , η TST is the charging and discharging efficiency of the heat storage tank HST and the hydrogen storage tank TST; β HST , β TST is a 0 / 1 variable reflecting the working status of the heat storage tank HST and the hydrogen storage tank TST; P TST,in , P HST,out , P HST,in , P TST,out P is the charging and discharging energy of the heat storage tank HST and the hydrogen storage tank TST, both in kW; HST,max , P TST,max The maximum charging energy per unit time of the heat storage tank HST and the hydrogen storage tank TST, both in kW;

[0046] (III) Select a typical day through the K-means clustering method, obtain the three cold, hot, electric and photovoltaic curves of the typical day, and apply the cold, hot, electric and photovoltaic curve data of the typical day to steps (IV) and (V); the typical day represents the typical electricity consumption scenarios of the transition season, summer and winter respectively, and this data can be used to simulate the operation of last year;

[0047] Typical daily data are used in step (IV) to calculate the sensitivity and in step (V) to solve the distribution network expansion planning model.

[0048] (IV) Determine the configuration location of the distributed hydrogen-based multi-energy flow photovoltaic absorption system DHM-PCS in the distribution network based on the time-series voltage sensitivity index, reduce the land dependence and absorption device configuration redundancy of the traditional single feeder absorption method, and the specific method includes the following steps:

[0049] (1) Calculation of voltage sensitivity

[0050] Use voltage sensitivity analysis methods to calculate the sensitivity of each node to voltage changes and identify the nodes where voltage fluctuations have the greatest impact on the system;

[0051] The method for calculating the sensitivity of the node to voltage changes is specifically as follows:

[0052] The voltage offset weight factor is introduced into the traditional sensitivity, and this factor can reflect the differentiated upward and downward voltage regulation requirements of different nodes. The sensitivity S of the active power change injected by node n to the voltage of node m at time t is mn,t It can be expressed as:

[0053] S mn,t =λ mn,t ×ΔV m,t (20)

[0054] ΔV m,t =V m,t -V eu,m,t (twenty one)

[0055] Where: S mn,t is the sensitivity of the active power change injected into node n to the voltage of node m at time t; mn,t It is the traditional voltage sensitivity index; ΔV m,t is the deviation between the node voltage and the expected voltage, in kV; V m,t is the current node voltage in kV; V eu,m,t is the expected voltage of the node, in kV;

[0056] The comprehensive sensitivity S of node m to the feeder at time t m,t for:

[0057]

[0058] Among them, FL is the set of current feeder load nodes;

[0059] The voltage deviation weight factor in the above formula belongs to the weight allocation to different nodes in the same time section. However, in the time dimension, different weights should be allocated to different time periods according to the voltage crossing conditions in different time sections, and the comprehensive sensitivity S of the time weight is introduced. op,m,t as follows:

[0060]

[0061] Where: S op,m,t is the comprehensive sensitivity; S m,t is the comprehensive sensitivity of node m to the feeder at time t; N exceed,t is the number of nodes with voltage exceeding the limit at time t, V exceed,t The node voltage over-limit value at time t, in kV;

[0062] According to the number of days under different operating conditions (such as different seasons), different weights are assigned to different operating conditions, and the comprehensive sensitivity S of the operating state weight is introduced. opw,m as follows:

[0063]

[0064] Where: S opw,m is the comprehensive sensitivity of the operating state weight; S op,m,t is the comprehensive sensitivity; Days opis the number of days in different operating states, in days; op represents different operating states, and M is the type of operating state.

[0065] For a distribution network with R coordination units, the internal nodes of each coordination unit are sorted, and the index I after sorting is r As follows:

[0066]

[0067] Among them, Ω Lnode is the set of load nodes in all feeders, and r is a coordination unit.

[0068] (2) Select key nodes

[0069] According to step (1), the sensitivity of voltage change is calculated and obtained, and several key nodes with high voltage sensitivity are selected as the configuration locations of the distributed hydrogen-based multi-energy flow photovoltaic absorption system;

[0070] In case of large-scale coordination units, according to the principle of non-aggregated deployment, multiple nodes with high sensitivity and non-aggregated (adjacent) are selected to deploy DHM-PCS in a dispersed manner;

[0071] Specifically: when the number of nodes in the coordination unit is less than A, the node with the highest sensitivity in the coordination unit is selected as the configuration location of the distributed hydrogen-based multi-energy flow photovoltaic consumption system;

[0072] When the number of nodes in the coordination unit is greater than A, the node with the highest sensitivity within each geographical range in the coordination unit is selected as the configuration location of the distributed hydrogen-based multi-energy flow photovoltaic consumption system.

[0073] The standard A for dividing the number of nodes within the coordination unit is mainly based on voltage sensitivity analysis, geographical environment and load density, system scale and complexity, as well as historical operation data and experience. By calculating the voltage sensitivity, the nodes that are most sensitive to voltage changes are determined, and the nodes with the highest sensitivity are usually selected for preliminary evaluation. According to the geographical environment and load density of the region, the setting of the number of nodes needs to be flexibly adjusted. For example, the settings in urban areas, suburbs and rural areas may be different. For large distribution network systems and small and medium-sized distribution network systems, the standards for the number of nodes will also vary. In the case of high system complexity, the number of nodes can be appropriately increased. Through the analysis of historical operation data and actual project experience, the determination of the number of nodes needs to be combined with the actual geographical environment, and each place needs to be discussed.

[0074] The number of nodes in the coordination unit must be determined on a local basis, taking into account "several aspects such as geographical environment and load density, system size and complexity, and historical operating data and experience."

[0075] The coordination unit can be directly derived from the connection relationship between the distribution network lines. For example, line a is connected to line b through the SOP device, then ab is a coordination unit;

[0076] The selection of the key nodes is specifically as follows:

[0077] S1. Screening candidate nodes

[0078] Based on the calculated sequential voltage sensitivity of each node, the nodes with the top 5% sequential voltage sensitivity are selected as candidate nodes;

[0079] S2. Determine the number of key nodes based on geographical environment and load density

[0080] The geographical environment includes urban, suburban and rural areas; the load density includes three levels: high, medium and low;

[0081] When the geographical environment is a city, the number of key nodes is set to 5 to 10;

[0082] When the geographical environment is suburban, the number of key nodes is set to 3 to 7;

[0083] When the geographical environment is rural, the number of key nodes is set to 1 to 5;

[0084] If the geographical environment is suburban but the load density is high, the number of key nodes can be appropriately adjusted within the above range according to the actual situation. The specific adjustment method can be as follows:

[0085] Due to the high load density, the number of key nodes can be close to the city, that is, close to the upper limit, and can be considered to be set to 7;

[0086] If the load density is extremely high and close to the urban level, the number of key nodes can be further increased to 8-10 to ensure the stability and efficiency of the system;

[0087] S3. Adjust the number of key nodes based on historical operation data and actual project experience

[0088] The historical operation data includes failure frequency and maintenance records;

[0089] According to historical data, the number of key nodes in areas with frequent failures increased by 10% to 20%, while the number of energy hub nodes in areas with good maintenance records decreased by 10% to 15%.

[0090] The definition of frequent failures can be quantitatively determined by the following criteria: If the number of failures or the duration of failures of a node per year exceeds 1.5 times the average value of the entire network, it can be considered that the area where the node is located has frequent failures.

[0091] A good maintenance record is defined as: the number of maintenance times of a node per year is less than 0.5 times the average maintenance times of the entire network.

[0092] In practical applications, the above indicators need to be evaluated comprehensively; the preliminary assessment determines the candidate nodes based on voltage sensitivity analysis, and then refines the number and location of energy hub nodes based on the geographical environment and load density, and then determines the specific number based on the system scale and complexity. Finally, it is verified and adjusted through historical operation data to ensure rationality and practicality;

[0093] (V) Constructing and solving the distribution network expansion planning model

[0094] The constructed distribution network expansion planning model includes network losses, abandoned solar power, investment, maintenance costs, and the DHM-PCS profit maximization operation strategy based on time-of-use electricity prices;

[0095] The distribution network expansion planning model takes into account both economic efficiency and consumption conditions. The objective function is divided into the following components: annual investment cost of each equipment, annual maintenance cost of each equipment, network loss cost, operating income and abandoned light cost.

[0096] The objective function of the distribution network expansion planning model includes the annualized investment cost C of each equipment. inv 、Annual maintenance cost of each equipment C opm 、Network loss cost C loss 、Operating income C m And the abandonment cost C m ;

[0097] The objective function expression of the distribution network expansion planning model is:

[0098] C=min(C inv +C opm +C loss +C m +C apv ) (26)

[0099] The expression of the annualized investment cost of each equipment is as follows:

[0100]

[0101] Where: C inv is the annualized investment cost of each equipment, in yuan; is the AEC investment cost, in yuan; is the LBAC investment cost, in yuan; is the EHP investment cost, in yuan; is the TST investment cost, in yuan; is the TST investment cost, in yuan; is the investment cost of SOFC, in yuan; is the EC investment cost, in yuan; is the SOP investment cost, in yuan; is the AEC rated power or capacity, in kW; is the rated power or capacity of LBAC, in kW; is the rated power or capacity of the EHP, in kW; is the TST rated power or capacity, in kW; is the rated power or capacity of the HST, in kW; is the rated power or capacity of SOFC, in kW; is the rated power or capacity of the EC, in kW; is the rated power or capacity of SOP, in kW; τ AEC is the capital recovery factor of AEC; τ LBAC is the capital recovery factor of LBAC; τ EHP is the capital recovery factor of EHP; τ TST is the capital recovery factor of TST; τ HST is the capital recovery factor of HST; τ SOFC is the capital recovery factor of SOFC; τ EC is the capital recovery factor of EC; τ SOP is the capital recovery factor of SOP; Ψ For each device collection;

[0102] τ AEC , τ LBAC , τ EHP , τ TST , τ HST , τ SOFC , τ EC and τ SOP The calculation method is the same, and the following formula is used for calculation:

[0103]

[0104] Where: τ is τ AEC , τ LBAC , τ EHP , τ TST , τ HST , τ SOFC , τ EC or τ SOP ; r is the interest rate; LT is the life of AEC, LBAC, EHP, TST, HST, SOFC, EC or SOP, in years;

[0105] The expression of the annual maintenance cost of each equipment is as follows:

[0106]

[0107] Where: C opm is the annual maintenance cost of each equipment, in yuan; is the maintenance cost of AEC unit capacity, in Yuan; is the maintenance cost per unit capacity of LBAC, in Yuan; is the maintenance cost per unit capacity of EHP, in yuan; is the maintenance cost per unit capacity of TST, in Yuan; is the maintenance cost per unit capacity of HST, in Yuan; is the maintenance cost per unit capacity of SOFC, in Yuan; is the maintenance cost per unit capacity of EC, in Yuan; is the maintenance cost per unit capacity of SOP, in Yuan; is the AEC rated power or capacity, in kW; is the rated power or capacity of LBAC, in kW; is the rated power or capacity of the EHP, in kW; is the TST rated power or capacity, in kW; is the rated power or capacity of the HST, in kW; is the rated power or capacity of SOFC, in kW; is the rated power or capacity of the EC, in kW; is the SOP rated power or capacity, in kW;

[0108] The expression of the network loss cost is as follows:

[0109]

[0110] Where: C loss is the network loss cost, in RMB; Days op is the number of days under different operating conditions, in days; f loss,t is the network loss cost at time t, in yuan; r mn is the line resistance, in Ω; is the square of the line current at time t in the op-th scenario, in A 2 ;

[0111] The expression of the operating income is as follows:

[0112]

[0113] Where: C mis the operating income, in yuan; EH represents the total number of distributed hydrogen-based multi-energy flow photovoltaic consumption systems; Represents the energy purchase cost of the eh-th distributed hydrogen-based multi-energy flow photovoltaic absorption system at time t in the op-th scenario, in yuan; Represents the energy sales cost of the eh-th distributed hydrogen-based multi-energy flow photovoltaic consumption system at time t in the op-th scenario, in yuan;

[0114] The objective function of the abandoned light cost is as follows:

[0115]

[0116] Where: C apv is the abandoned light cost, in yuan; f pv is the cost of abandoned light, in RMB; Days op is the number of days in different operating states, in days; is the abandoned light power of distributed photovoltaic pv at time t in the op-th scenario, in kW; PV is the collection of distributed photovoltaics.

[0117] ② The constraints of the distribution network expansion planning model include distribution network operation flow constraints, DHM-PCS equipment constraints and SOP operation constraints;

[0118] The power flow constraints of the distribution network are as follows:

[0119]

[0120] Where: is the square of the line current, in A 2 ; The square of the maximum current that the line can withstand, in A 2 ;Ω Line is the line set; Ω node is a node set;

[0121] is the square of the node voltage upper limit, in kV 2 ; is the square of the lower limit of the node voltage, in kV 2 ; is the square of the current node voltage, in kV 2 ;

[0122] P ij,op,t is the active power flowing through branch ij at time t, in kw; Q ij,op,t is the reactive power flowing through branch ij at time t, in kVar; π(:,j) is the set whose end node is node j; δ(j,:) is the set whose head node is node j; is the node net load, in kw;

[0123] r ij is the line resistance, in Ω; x ij is the line reactance, in Ω;

[0124] is the node net load, in kw;

[0125] η EH The variable indicating whether the node is connected to the distributed hydrogen-based multi-energy flow photovoltaic absorption system;

[0126] η SOP A variable indicating whether the node is connected to SOP;

[0127] is the EH input or absorbed power, in kw; It is the active power transmitted by SOP, in kw; is the net reactive power of the grid, in kVar; is the SOP transmitted reactive power, in kVar; P jk,op,t and Q jk,op,t The active and reactive power flowing from the current node to the child node, in kw; and is the square of the voltage amplitude between node j and node i, in kV 2 ; and The square of the active and reactive power of the line, in kw 2 .

[0128] The DHM-PCS equipment constraints are: The power of each DHM-PCS equipment must be less than the rated power. The DHM-PCS equipment operating conditions are given by formulas 6 to 9;

[0129] SOP operation constraints:

[0130] The SOP constraints are given by Equation 1 to Equation 2;

[0131] ③Model solving method:

[0132] This application uses CPLEX for solving, but CPLEX cannot solve nonlinear equations, so Formula 2 and Formula 37 are cone processed.

[0133] Design principle of the method of the present invention:

[0134] (1) Grid topology transformation considering energy coordination between feeders

[0135] The medium-voltage distribution area covers a large area, and with the access of massive resources, the planning and operation of the distribution network is no longer a local optimization of a single feeder, but requires coordination and cooperation among multiple feeders. However, traditional medium-voltage feeders cannot be interconnected, and the return to the upper power grid will affect the safe operation of the upper power grid. Therefore, studying how to promote energy interaction between multiple feeders without hindering the stability of the upper power grid will become a key path to improve the absorption capacity in the future.

[0136] In the past development process, the construction of distribution network benefited from the rapid advancement of urbanization. However, with the slowdown of urbanization, it is no longer realistic to rebuild or transform the distribution network on a large scale to adapt to the continuous development of source and load. The progress of power electronics technology has provided a new solution for this, among which AC and DC distribution technology has become an effective strategy. At present, the AC and DC transformation schemes that are widely used are SOP interconnection, AC-DC interconnection, AC-DC-AC interconnection and "two AC and one DC" interconnection. Table 1 shows the transformation methods of various methods.

[0137] Table 1 Four types of AC and DC distribution network transformation methods

[0138]

[0139] From the comparison in Table 1, it can be seen that the SOP interconnection type is simple to transform and the scheduling is more flexible. Therefore, this application adopts the SOP interconnection type to carry out flexible interconnection transformation of the grid.

[0140] Through the SOP technology, different feeders are flexibly interconnected, and multiple feeders together form a coordination unit, which changes the source-load balance of a single feeder into the balance within the coordination unit, and changes the source-load balance of a single feeder into the mutual assistance of resources within the coordination unit. Figure 1 As shown. The source-load balance of a single feeder is changed to the balance within the coordination unit. Different from the interconnection mode of traditional tie switches (which cannot accurately control the power flow in real time), the distribution network based on SOP interconnection can start from the synergy characteristics between multiple feeders and can accurately control the power flow between feeders in real time, thereby optimizing the operating efficiency of the entire distribution network.

[0141] The types of SOP are back-to-back voltage source converter (B2B VSC), static synchronous series compensator (SSSC) and unified power flow controller (UPFC). Among them, B2B VSC is widely used due to its excellent performance. The device is connected through a DC circuit between two converters, and the AC feeder parts on both sides will be decoupled. Under steady-state operation, the SOP device operates in PQ control mode, and the variables involve active and reactive power of the two ports. In addition, the transmission efficiency of this type of SOP device is 98%, so the transmission loss is ignored.

[0142] (2) SOP operation characteristics modeling

[0143] It is crucial to model the operating characteristics of SOPs, because SOPs have the ability to accurately control power flow in real time, while traditional tie switches can only perform simple opening and closing operations and cannot regulate power flow. Therefore, SOP operating characteristic modeling can describe in detail the active and reactive power control and capacity limitations of SOPs, which are the basis for achieving refined power management. Compared with traditional tie switches, SOPs can not only connect feeders, but also achieve energy mutual assistance between multiple feeders through fine control, greatly improving the operating efficiency and overall performance of the distribution network.

[0144] (3) Energy coupling strategy and site selection of DHM-PCS

[0145] In addition to the flexible interconnection transformation of the grid, the configuration of the consumption device is also crucial. This application selects the distributed hydrogen-based multi-energy flow photovoltaic consumption system (DHM-PCS) as the photovoltaic energy consumption device, which covers the supply and demand of multiple energy media such as cold, heat, electricity and hydrogen.

[0146] The distributed hydrogen-based multi-energy flow photovoltaic consumption system (DHM-PCS) includes alkaline electrolysis cell (AEC), lithium bromide absorption chiller (LBAC), electric heat pump (EHP), thermal storage tank (TST), hydrogen storage tank (HST), solid oxide fuel cell (SOFC), and electric chiller (EC).

[0147] Figure 2 The conversion relationship of cold, heat, electricity and hydrogen multi-energy flows in DHM-PCS is demonstrated, and its energy balance equation is as described above;

[0148] (4) DHM-PCS site selection

[0149] In an AC / DC hybrid distribution network that considers the flexible mutual assistance of energy between multiple feeders, the system will be automatically subdivided into multiple coordination units according to the interconnection between feeders. In these coordination units, any node can exchange energy with other nodes in the same coordination unit through SOP equipment (not through the upper power grid). Therefore, when deploying DHM-PCS, it is only necessary to configure it according to the coordination unit, without configuring all or most feeders, reducing the redundancy of the consumption system and the degree of dependence on land resources.

[0150] This application uses sequential voltage sensitivity as the basis for site selection, simplifies its formula, and reduces the computational complexity. At the same time, the single feeder site selection corresponding to its formula is extended to power grid scenarios involving flexible interconnection of multiple feeders.

[0151] The beneficial effects of the present invention are:

[0152] The present invention provides a distribution network expansion planning method taking into account the temporal and spatial transfer of loads and the coordination of multiple energy flows, and proposes a photovoltaic absorption distribution network planning model taking into account the integration of energy temporal and spatial mutual assistance and the coupling of hydrogen-based multiple energy flows. The proposed photovoltaic absorption method absorbs energy from three dimensions: energy translation in time, energy transfer in space, and energy conversion in energy categories. An operation strategy considering energy coordination among multiple feeders is proposed to break the limitations of conventional "source-storage-load" on distributed power consumption, and the dimension of consumption is improved from "point" optimization to "network" coordination. An operation mode of a distributed hydrogen-based multiple energy coupling photovoltaic absorption system (DHM-PCS) is proposed, taking into account the coordination characteristics among feeders, and constructing a DHM-PCS configuration model with limited key nodes to maximize the global absorption benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0153] Figure 1 It is a schematic diagram of the location of the SOP connected to the medium voltage distribution network in the present invention;

[0154] Figure 2 It is a schematic diagram of DHM-PCS energy conversion in the present invention;

[0155] Figure 3 It is the Portuguese 54-node system improved by Embodiment 1 of the present invention;

[0156] Figure 4 is a timing voltage sensitivity diagram of each node in Example 1 of the present invention;

[0157] Figure 5 is a heat balance diagram of coordination unit 1 in Case 1 of Example 1 of the present invention;

[0158] Figure 6 is a cold balance diagram of coordination unit 1 in Case 1 of Example 1 of the present invention;

[0159] Figure 7 It is a HST state diagram of coordination unit 1 in Case 1 of Example 1 of the present invention;

[0160] Figure 8 This is a state diagram of the coordination unit 1BESS in Case 2 of Example 1 of the present invention.

[0161] For ordinary technicians in this field, other relevant drawings can be obtained based on the above drawings without any creative work. DETAILED DESCRIPTION

[0162] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0163] Example 1

[0164] This embodiment applies the method of the present application to Figure 3 The improved Portuguese 54-node system shown is verified, and the Portuguese 54-node system has 4 substations (S1~S4), 10 feeders, and 5 tie switches.

[0165] The basic data sources of this embodiment are as follows: the load comes from a distribution network in a city in China. The photovoltaic data is generated by the refined physical model chain PVlib method, and the required temperature, wind speed, GHI, DNI, and DHI data locations are the cities corresponding to the loads.

[0166] The distribution network expansion planning method considering load spatiotemporal transfer and multi-energy flow coordination specifically includes the following steps:

[0167] (I) Using SOP to transform the grid into a flexible interconnection

[0168] The five tie switches in the improved Portuguese 54-node system were transformed into SOP devices. After configuring the SOP, the 10 feeders were divided into three coordination units based on the electrical connection relationship.

[0169] (II) Select DHM-PCS as the photovoltaic energy consumption device and establish the DHM-PCS operation model;

[0170] This embodiment only plans the distribution network, adopts DHM-PCS as a multi-energy flow coupling device, associates the distribution network with the cold and hot network, and distributes the cold and hot loads of each node according to the proportion of the electric load; Table 2 and Table 3 show the equipment parameters and time-of-use electricity prices (purchase and sale of electricity), and the planning period is 20 years; the unit calorific value price is 0.223 yuan / kWh.

[0171] Table 2 Parameters of each device

[0172]

[0173] Table 3 Time-of-use electricity prices

[0174]

[0175] In the study of planning problems, models are often extremely complex, and their computational time costs need to be considered. Therefore, most studies use various technical means to reduce the countless possible scenarios to a limited number. This application selects typical days through the K-means clustering method. Table 4 determines that the most suitable number of clusters is 3 by analyzing the SC coefficient and the CH index. In essence, the scenarios are divided according to the operating characteristics of the seasons, and typical scenarios are selected under different operating characteristics. Furthermore, from each cluster, the day with the highest correlation with other days in the cluster is selected as the typical day. In addition, the model proposed in this application shows scalability, that is, the typical day can be flexibly replaced without making a lot of changes to the model.

[0176] Table 4 Determination of cluster number

[0177]

[0178] (III) Determine the DHM-PCS configuration location in the distribution network based on the time-series voltage sensitivity index

[0179] according to Figure 4 The sequential voltage sensitivity index of different nodes at different times is displayed, and their mean values ​​are connected. Sequential voltage sensitivity is an index to measure the sensitivity of voltage to power changes. The increase in the index value indicates that the voltage stability of the corresponding node is reduced. According to the DHM-PCS non-clustering configuration principle, nodes 8, 13, 25, 44, and 50 are selected as the configuration points of DHM-PCS.

[0180] pass Figure 3 It can be seen that it is divided into 3 coordination units in total.

[0181] In the coordination unit 1, due to its small size (including few nodes), only the node 25 with the highest sensitivity is selected as the configuration node.

[0182] In coordination unit 2, due to its large scale (many nodes), it may be difficult to cover the heating and cooling energy needs of enough nodes by configuring only one DHM-PCS. According to the ranking results of node sensitivity, it can be seen that nodes 4-8, 12, 13, and 38 have relatively high sensitivity. However, the function of DHM-PCS is to meet the heating and cooling energy needs within a certain range, so if it is configured in a cluster, it does not meet its function. By observation, it can be seen that nodes 4-8 are in a geographical range, 12-13 are in a geographical range, and 38 itself is in a geographical range. Therefore, we select the nodes with the highest sensitivity in each range as the configuration nodes, namely 8, 13, and 38.

[0183] In the coordination unit 3, due to its small size (few nodes included), only the node 50 with the highest sensitivity is selected as the configuration node.

[0184] (IV) Planning results

[0185] The traditional single-feeder level absorption device configuration has been effectively proven in a large number of studies, but it has the characteristics of high dependence on land resources and inability to achieve energy interaction between feeders. Therefore, this application focuses on the use of multiple heterogeneous resources for photovoltaic absorption under the premise of synergistic interaction of energy between feeders.

[0186] This embodiment sets up 3 cases to study the impact of different configurations on photovoltaic absorption capacity.

[0187] Case 1: Consider the energy coordination interaction among multiple feeders, and configure DHM-PCS containing multiple heterogeneous resources for consumption.

[0188] Case 2: Consider the coordinated interaction of energy among multiple feeders and configure a single energy storage device for absorption.

[0189] Case 3: Only consider the energy coordination interaction among multiple feeders and configure only SOP devices.

[0190] 1) Comparative analysis of planning results

[0191] Table 5 Indicators of the three cases

[0192]

[0193] Table 5 presents in detail the investment and maintenance costs, network loss costs, operating income, annual total costs, and photovoltaic energy consumption in three cases (Case 1, Case 2, and Case 3). Specifically, Case 1 and Case 2 both achieved 100% photovoltaic consumption rate, proving that considering energy coordination between multiple feeders and installing consumption devices at key nodes is an effective strategy to improve photovoltaic energy consumption rate. However, due to its diversified energy sales (electricity, cooling, and heat) income, coupled with lower investment and maintenance costs, Case 1's annual total income exceeds Case 2. However, the DHM-PCS equipment involved in Case 1 needs to couple electricity, cooling, heat, and hydrogen energy, and its demand for electricity is larger than that of Case 2, resulting in higher network losses than Case 2.

[0194] Considering the energy coordination between multiple feeders (i.e., configuring SOP devices), it is often necessary to configure them together with energy storage devices to achieve the maximum effect. The configuration of SOP alone often has the problem of low economy. In Case 3, due to the lack of absorption devices, its photovoltaic energy absorption capacity is completely dependent on the feeders with absorption functions and their load conditions. In addition, on the basis of taking into account the absorption rate and configuration cost, the objective function of Case 3 is more inclined to minimize network loss when setting, so as to facilitate the reconfiguration of the absorption device in practice. Case 3 only uses SOP devices to flexibly interconnect the power grid architecture, thus showing a relatively low absorption capacity. In addition, due to the lack of regulation of energy storage facilities, the network loss of Case 3 is significantly higher than that of Case 2, but less than that of Case 1. Tables 6 and 7 show the optimization results of the equipment capacity of each coordination unit in Case 1.

[0195] Table 6 Equipment configuration of each coordination unit

[0196]

[0197]

[0198] Table 7 SOP device configuration in Case 1

[0199]

[0200] 2) Operation results analysis

[0201] Figures 5 to 8They are the operating status of each device in Case 1. In the transition season, in order to reduce the operating cost of DHM-PCS, the photovoltaic power is mainly consumed by EHP and ER devices during the photovoltaic power generation period. In the rest of the period, the heating power is mainly provided by the thermal storage tank. At the same time, considering the time-of-use electricity price factor, the EHP device no longer outputs during the high electricity price period in the evening. However, due to the constraint that the capacity of the thermal storage tank is consistent at the beginning and end, it needs to be supplemented with heat energy after releasing heat. Therefore, EHP chooses to charge it at the end of the low electricity price. In the refrigeration system, the absorption chiller is the main control equipment. The electric chiller is used as a cold source supplement to the absorption chiller, and only outputs cold energy when the photovoltaic power is consumed by the photovoltaic power generation. In winter, due to the existence of centralized heating, the demand for heat load surges. In the early morning low electricity price period, the EHP is used for heating and the waste heat is stored in the thermal storage tank. In the high electricity price period and not belonging to the photovoltaic power generation period, the heat load is mainly provided by the thermal storage tank. In the cold balance, the cold source is mainly provided by the absorption chiller, and the electric chiller only provides cooling at the end. This is because during the evening period with high electricity prices, the EHP stops working, and the heat storage tank releases a large amount of heat energy to meet the heat demand. The heat storage tank needs to ensure that the energy storage is consistent at the end. Therefore, at the end of the low electricity price, the EHP charges the heat storage tank. In the summer, the electricity and cooling loads surge (especially the electricity). During the period of large photovoltaic power generation, the electricity load surges, and some photovoltaic power generation moments are greater than the photovoltaic output, and the problem of consumption is effectively alleviated. The operation of EHP is similar to that in winter, mainly avoiding the purchase of electricity during high electricity price periods. Due to the surge in cooling load, there is a supply bottleneck mainly relying on LBAC. Therefore, the cooling power is mainly provided by ER and LBAC.

[0202] From the operating characteristics, it can be seen that: considering the operating costs, EHP, as the main power-consuming equipment, mainly works during the low electricity price and the peak photovoltaic power generation period. As energy storage devices, heat storage tanks and hydrogen storage tanks store energy during low electricity price periods or photovoltaic power generation periods, and supply energy during high electricity price periods, converting electric energy across time. SOFC, as a hydrogen-electric-thermal coupling device, mainly works during high electricity price periods and non-peak photovoltaic power generation periods. Due to economic and efficiency factors, LBAC serves as the main cooling equipment. Multiple devices work together to regulate photovoltaic energy across time and space and convert energy types through DHM-PCS and SOP devices, which can greatly promote the consumption of distributed photovoltaics.

[0203] 3) Comparison between DHM-PCS and energy storage

[0204] The DHM-PCS is compared with conventional electrochemical energy storage systems in terms of operating characteristics, cost, efficiency, and environmental friendliness.

[0205] Operating characteristics, Figure 7 and Figure 8The capacity of the two energy storage devices in coordination unit 1 at each time is shown. The similarity is that they charge at the time of high photovoltaic power generation and sell energy at the time of high electricity price. In terms of internal energy fluctuation, the coefficient of variation (CV) can be compared.

[0206] Table 8 Coordination unit 1 HST and BESS energy CV indicators

[0207]

[0208] In the process of photovoltaic energy consumption, hydrogen energy storage exhibits lower energy volatility than electrochemical energy storage systems due to the integration of other consumption and regulation devices. This advantage has been quantitatively demonstrated by the comparison of coefficient of variation (CV), see Table 8. Due to the coupled interactive operation of multiple types of devices in Case 1, the lower CV value of its hydrogen energy storage system proves that it can effectively reduce the risk of fluctuations caused by excess or insufficient energy, and further enhance the overall resilience of the consumption system. As the only consumption device, the electric energy storage in Case 2 lacks the coupled interaction of multiple types of devices, and its internal energy fluctuations are large, and its operating characteristics are relatively simple.

[0209] In terms of cost, Table 5 summarizes its economic cost in detail, and DHM-PCS is more economical. Affected by electrochemical characteristics, the life of BESS equipment is generally shorter than that of equipment in DHM-PCS. In addition, the unit capacity investment cost of HST and TST energy storage equipment is much lower than that of BESS. Only SOFC is a high-investment equipment. Therefore, in terms of cost, DHM-PCS is lower than traditional BESS.

[0210] In terms of energy density and application flexibility, hydrogen energy storage is significantly superior to electrochemical energy storage. Its high volumetric energy density enables it to store more energy while occupying less space, making it particularly suitable for large-scale energy storage needs where land resources are tight. In addition, in the event of a power shortage, hydrogen energy can be used to make up for the power shortage through transportation. In contrast, electrochemical storage systems have limitations in flexibility and energy storage density.

[0211] In terms of environment and stability, hydrogen energy storage, especially hydrogen produced by renewable energy, has low greenhouse gas emissions during its life cycle and is the key to clean energy transformation. At the same time, although it is flammable, appropriate technology can ensure safety. Electric energy storage systems, especially lithium batteries, may affect the environment during production and recycling, and pose safety risks when damaged, although safety technology continues to advance.

[0212] In terms of energy coupling, traditional BESS can only interact from the perspective of electric energy, but hydrogen energy storage can couple multiple energy flows with cold, heat and electricity, improve the system operation flexibility from the perspective of energy medium conversion, and achieve effective docking and integration with other energy systems.

[0213] In summary, the coordinated operation of hydrogen storage and multi-type energy coupling devices can greatly enhance the flexibility of energy storage devices and the overall resilience of the consumption system. Under the influence of the policy of rapid development of clean energy, the generalized energy storage and consumption system with hydrogen-based multi-energy flow coupling will become the focus of future development.

[0214] 4) Analysis of the effect of energy coordination among multiple feeders

[0215] In traditional consumption methods, consumption devices are often deployed on each feeder. This method can effectively alleviate the consumption difficulties faced, but it has the problems of low efficiency and huge demand for land resources. In practice, the demand for a large amount of land is often difficult to meet, especially in areas with tight land resources. However, the transformation from the grid level, by considering the energy coordination between multiple feeders, and using SOP devices to interconnect the feeders. The problem of tight land resources faced by traditional methods can be alleviated. For example, the traditional distributed consumption method requires the configuration of 8 consumption devices, while this method only requires the configuration of 5 consumption devices. However, only considering the energy coordination between multiple feeders, the lack of consumption devices, there is a problem of limited consumption capacity. For example, in Case 3, its consumption rate is only 69.29%. In Case 1 and Case 2, on the basis of configuring SOP, the configuration of consumption devices can maximize the utilization rate of renewable energy. Although Case 3 has the problem of insufficient consumption capacity, the power between feeders can be controlled by SOP devices, which can play a role in voltage stabilization. In this respect, Case 3 is the same as Case 1-2. In Case 1-3, all node voltages are stable between 0.95 and 1.05.

[0216] The method of the present invention utilizes SOP to carry out flexible interconnection transformation of the grid, so that the feeders have the ability of two-way continuous power flow regulation; according to the difference in timing voltage sensitivity, a limited number of key nodes in the distribution network are selected to arrange a distributed hydrogen-based multi-energy flow photovoltaic absorption system (DHM-PCS); in the constructed planning model, network loss, abandoned light, investment, maintenance cost and DHM-PCS profit maximization operation strategy based on time-of-use electricity price are covered.

[0217] The applicant declares that the above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention shall fall within the protection scope and disclosure scope of the present invention.

Claims

1. A distribution network expansion planning method taking into account load spatiotemporal transfer and multi-energy flow coordination, characterized in that: The following steps are involved: (I) Transform the location of the tie switch in the distribution network into a SOP device and establish a SOP operation model; (II) Select the distributed hydrogen-based multi-energy flow photovoltaic consumption system as the photovoltaic energy consumption device, link the distribution network with the cold and hot network, distribute the cold and hot loads of each node according to the proportion of electric load, and establish the cold, heat, electricity and hydrogen multi-energy flow conversion relationship in the distributed hydrogen-based multi-energy flow photovoltaic consumption system; (III) Selecting a typical day by using a K-means clustering method to obtain the cold, heat, electricity, and photovoltaic curves of the typical day, and applying the cold, heat, electricity, and photovoltaic curve data of the typical day to steps (IV) and (V); (IV) Determine the configuration location of the distributed hydrogen-based multi-energy flow photovoltaic absorption system in the distribution network based on the time-series voltage sensitivity index; (V) Construct and solve the distribution network expansion planning model.

2. The distribution network expansion planning method considering load time-space transfer and multi-energy flow coordination according to claim 1 is characterized in that: The SOP operation model includes active power constraints and capacity constraints; The active power and capacity constraint expressions of the SOP are as follows: In the formula: In the formula: is the three-phase power of node i connected through the sth SOP at time t, in kVar; is the three-phase power of node j connected through the sth SOP at time t, in kVar; and Both are equal; Ω SOP is a collection of SOP devices; φ represents three phase sequences; is the reactive power of the sth SOP at node i at time t, in kVar; It is the capacity corresponding to SOP, in kVA.

3. The distribution network expansion planning method considering load time-space transfer and multi-energy flow coordination according to claim 1 is characterized in that: The energy balance equation of the cold, heat, electricity and hydrogen multi-energy flow conversion relationship includes an electric energy balance equation, a heat energy balance equation and a cold energy balance equation; The electric energy balance equation is: P PDN (t)=P SOFC,E (t)-P EHP,in (t)-P AEC,in (t)-P EC,in (t) Where: P PDN (t) represents the power consumption of the distributed hydrogen-based multi-energy flow photovoltaic consumption system at time t, in kW; P SOFC,E (t) represents the power generated by SOFC at time t, in kW; P EHP,in (t), P AEC,in (t), P EC,in (t) represents the power absorbed by EHP, AEC and EC at time t, respectively, in kW; The thermal energy balance equation is: P h (t)=P EHP,out (t)+βP TST,out (t)+P SOFC,H (t)-P LBAC,in (t)-(1-β)P TST,in (t) Where: P h (t) is the thermal power generated by the distributed hydrogen-based multi-energy flow photovoltaic consumption system at time t, in kW; P EHP,out (t), P TST,out (t), P SOFC,H (t) represents the thermal power generated by EHP, TST and SOFC at time t, in kW; P LBAC,in (t), P TST,in (t) represents the thermal power absorbed by LBAC and TST at time t, in kW; β is a 0 / 1 variable, representing the operating state of TST; The cold energy balance equation is: P c (t)=P EC,out (t)+P LBAC,out (t) Where: P c (t) is the cooling power generated by the distributed hydrogen-based multi-energy flow photovoltaic consumption system at time t, in kW; P EC,out (t), P LBAC,out (t) is the cooling power generated by EC and LBAC at time t, both in kW.

4. The distribution network expansion planning method considering load time-space transfer and multi-energy flow coordination according to claim 1 is characterized in that: The method for determining the configuration location of the distributed hydrogen-based multi-energy flow photovoltaic consumption system in the distribution network in step (IV) comprises the following steps: (1) Calculation of voltage sensitivity Use the voltage sensitivity analysis method to calculate the sensitivity of each node to voltage changes, that is, the timing voltage sensitivity of each node; (2) Select key nodes The time-sequence voltage sensitivity is calculated according to step (1), and key nodes with high time-sequence voltage sensitivity are selected as the configuration locations of the distributed hydrogen-based multi-energy flow photovoltaic absorption system.

5. The distribution network expansion planning method taking into account load time-space transfer and multi-energy flow coordination according to claim 4 is characterized in that: The selection of the key nodes is specifically as follows: S1. Screening candidate nodes Based on the calculated sequential voltage sensitivity of each node, the nodes with the top 5% sequential voltage sensitivity are selected as candidate nodes; S2. Determine the number of key nodes based on geographical environment and load density The geographical environment includes urban, suburban and rural areas; the load density includes three levels: high, medium and low; When the geographical environment is a city, the number of key nodes is set to 5 to 10; When the geographical environment is suburban, the number of key nodes is set to 3 to 7; When the geographical environment is rural, the number of key nodes is set to 1 to 5; S3. Adjust the number of key nodes based on historical operation data and actual project experience The historical operation data includes failure frequency and maintenance records; According to historical data, the number of key nodes in areas with frequent failures increased by 10% to 20%, while the number of energy hub nodes in areas with good maintenance records decreased by 10% to 15%.

6. The distribution network expansion planning method taking into account load time-space transfer and multi-energy flow coordination according to claim 4 is characterized in that: The method for calculating the sensitivity of the node to voltage changes is specifically as follows: The voltage offset weight factor is introduced into the traditional sensitivity, and the calculation formula of the sensitivity of the active power change injected into node n at time t to the voltage of node m is: S mn,t =λ mn,t ×ΔV m,t ΔV m,t =V m,t -V eu,m,t Where: S mn,t is the sensitivity of the active power change injected into node n to the voltage of node m at time t; mn,t It is the traditional voltage sensitivity index; ΔV m,t is the deviation between the node voltage and the expected voltage, in kV; V m,t is the current node voltage in kV; V eu,m,t is the expected voltage of the node, in kV; The calculation formula of the comprehensive sensitivity of node m to the feeder at time t is: Where: S m,t is the comprehensive sensitivity of node m to the feeder at time t; FL is the set of current feeder load nodes; λ mn,t It is the traditional voltage sensitivity index; ΔV m,t is the deviation between the node voltage and the expected voltage, in kV; The comprehensive sensitivity of the time weight is introduced, and the calculation formula of the comprehensive sensitivity is: Where: S op,m,t is the comprehensive sensitivity; S m,t is the comprehensive sensitivity of node m to the feeder at time t; N exceed,t is the number of nodes with voltage exceeding the limit at time t, V exceed,t The node voltage over-limit value at time t, in kV; According to the number of days under different operating conditions, different weights are assigned to different operating conditions, and the comprehensive sensitivity of the operating status weight is introduced. The calculation formula of the comprehensive sensitivity of the operating status weight is as follows: Where: S opw,m is the comprehensive sensitivity of the operating state weight; S op,m,t is the comprehensive sensitivity; Days op is the number of days in different operating states, in days; op represents different operating states, and M represents the type of operating state.

7. The distribution network expansion planning method considering load time-space transfer and multi-energy flow coordination according to claim 1 is characterized by: The objective function expression of the distribution network expansion planning model is: C=min(C inv +C opm +C loss +C m +C apv ) Where: C is the objective function of the distribution network expansion planning model; C inv Annualized investment cost of each equipment; C opm Annual maintenance cost of each equipment; C loss Network loss cost; C m Operating income; C apv Cost of abandoned light; The expression of the annualized investment cost of each equipment is as follows: Where: C inv is the annualized investment cost of each equipment, in yuan; is the AEC investment cost, in yuan; is the LBAC investment cost, in yuan; is the EHP investment cost, in yuan; is the TST investment cost, in yuan; is the TST investment cost, in yuan; is the SOFC investment cost, in yuan; is the EC investment cost, in yuan; is the SOP investment cost, in yuan; is the AEC rated power or capacity, in kW; is the rated power or capacity of LBAC, in kW; is the rated power or capacity of the EHP, in kW; is the TST rated power or capacity, in kW; is the rated power or capacity of the HST, in kW; is the rated power or capacity of SOFC, in kW; is the rated power or capacity of the EC, in kW; is the rated power or capacity of SOP, in kW; τ AEC is the capital recovery factor of AEC; τ LBAC is the capital recovery factor of LBAC; τ EHP is the capital recovery factor of EHP; τ TST is the capital recovery factor of TST; τ HST is the capital recovery factor of HST; τ SOFC is the capital recovery factor of SOFC; τ EC is the capital recovery factor of EC; τ SOP is the capital recovery factor of SOP; Ψ is the set of equipment; τ AEC , τ LBAC , τ EHP , τ TST , τ HST , τ SOFC , τ EC and τ SOP The calculation method is the same, and the following formula is used for calculation: Where: τ is τ AEC , τ LBAC , τ EHP , τ TST , τ HST , τ SOFC , τ EC or τ SOP ; r is the interest rate; LT is the life of AEC, LBAC, EHP, TST, HST, SOFC, EC or SOP, in years; The expression of the annual maintenance cost of each equipment is as follows: Where: C opm is the annual maintenance cost of each equipment, in yuan; is the maintenance cost per unit capacity of AEC, in Yuan; is the maintenance cost per unit capacity of LBAC, in Yuan; is the maintenance cost per unit capacity of EHP, in yuan; is the maintenance cost per unit capacity of TST, in Yuan; is the maintenance cost per unit capacity of HST, in Yuan; is the maintenance cost per unit capacity of SOFC, in Yuan; is the maintenance cost per unit capacity of EC, in Yuan; is the maintenance cost per unit capacity of SOP, in Yuan; is the AEC rated power or capacity, in kW; is the rated power or capacity of LBAC, in kW; is the rated power or capacity of the EHP, in kW; is the TST rated power or capacity, in kW; is the rated power or capacity of the HST, in kW; is the rated power or capacity of SOFC, in kW; is the rated power or capacity of the EC, in kW; is the SOP rated power or capacity, in kW; The expression of the network loss cost is as follows: Where: C loss is the network loss cost, in RMB; Days op is the number of days in different operating states, in days; f loss,t is the network loss cost at time t, in yuan; r mn is the line resistance, in Ω; is the square of the line current at time t in the op-th scenario, in A 2 ; The expression of the operating income is as follows: Where: C m is the operating income, in yuan; EH represents the total number of distributed hydrogen-based multi-energy flow photovoltaic consumption systems; Represents the energy purchase cost of the eh-th distributed hydrogen-based multi-energy flow photovoltaic absorption system at time t in the op-th scenario, in yuan; Represents the energy sales cost of the eh-th distributed hydrogen-based multi-energy flow photovoltaic consumption system at time t in the op-th scenario, in yuan; The objective function of the abandoned light cost is as follows: Where: C apv is the abandoned light cost, in yuan; f pv is the cost of abandoned light, in RMB; Days op is the number of days in different operating states, in days; is the abandoned light power of distributed photovoltaic pv at time t in the op-th scenario, in kW; PV is the collection of distributed photovoltaics.

8. The distribution network expansion planning method considering load time-space transfer and multi-energy flow coordination according to claim 1 is characterized by: The constraints of the distribution network expansion planning model include distribution network operation flow constraints, distributed hydrogen-based multi-energy flow photovoltaic absorption system constraints and SOP operation constraints.

9. The distribution network expansion planning method taking into account load time-space transfer and multi-energy flow coordination according to claim 8 is characterized in that: The expression of the distribution network operation flow constraint is: Where: is the square of the line current, in A 2 ; The square of the maximum current that the line can withstand, in A 2 ;Ω Line is the line set; Ω node is a node set; is the square of the node voltage upper limit, in kV 2 ; is the square of the lower limit of the node voltage, in kV 2 ; is the square of the current node voltage, in kV 2 ;P ij,op,t is the active power flowing through branch ij at time t, in kw; Q ij,op,t is the reactive power flowing through branch ij at time t, in kVar; π(:,j) is the set whose end node is node j; δ(j,:) is the set whose head node is node j; is the node net load, in kw; r ij is the line resistance, in Ω; x ij is the line reactance, in Ω; is the node net load, in kw; η EH The variable indicating whether the node is connected to the distributed hydrogen-based multi-energy flow photovoltaic absorption system; η SOP A variable indicating whether the node is connected to SOP; is the EH input or absorbed power, in kw; It is the active power transmitted by SOP, in kw; is the net reactive power of the grid, in kVar; is the SOP transmitted reactive power, in kVar; P jk,op,t and Q jk,op,t The active and reactive power flowing from the current node to the child node, in kw; and is the square of the voltage amplitude between node j and node i, in kV 2 ; and The square of the active and reactive power of the line, in kw 2 .

10. The distribution network expansion planning method considering load time-space transfer and multi-energy flow coordination according to claim 1 is characterized in that: The distribution network expansion planning model is solved by using CPLEX, and the capacity constraint formula of SOP and the distribution network operation flow constraint formula of the distribution network expansion planning model are cone processed.

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