Intelligent soft switching and multilayer electro-hydrogen hybrid energy storage two-stage space-time decoupling configuration method and system

Abstract

The invention discloses an intelligent soft switching and multilayer electro-hydrogen hybrid energy storage two-stage space-time decoupling configuration method and system. The method comprises the following steps: inputting basic data and initializing network basic parameters; in the first stage of constructing the two-stage MEH-SOP model, the power / capacity of each device of the MEH is optimally configured on the basis of annual 8760h time sequence operation simulation considering time-of-use electricity price; constructing a second stage of the two-stage MEH-SOP model, extracting typical scenes of MEH full-year high-power charging / discharging and power distribution network full-year voltage serious out-of-limit, and optimally configuring SOP multi-port capacity; and solving the constructed two-stage MEH-SOP model, and outputting an optimal configuration result. According to the invention, a multi-level energy storage system covering from the day to the week is constructed, and the multi-port SOP direct current side is connected in a modularized manner, so that the structural flexibility and the operation flexibility of the power distribution network are remarkably enhanced.

Description

Technical Field

[0001] This application relates to the field of power distribution network planning technology, specifically to a two-stage spatiotemporal decoupling configuration method and system for intelligent soft switching and multi-layer electric-hydrogen hybrid energy storage. Background Technology

[0002] Under the global trend of energy structure transformation towards clean and low-carbon, new energy power generation has become a core force driving energy system reform. Among them, distributed photovoltaic (PV) power, with its advantages of wide resource distribution, flexible installation, and clean and pollution-free operation, has become an important development direction in the new energy field. Because the power generation scale and grid connection location of distributed PV are highly compatible with user-side electricity demand, it is mainly connected to medium and low-voltage distribution networks. Through the "local production and local consumption" model, it effectively reduces dependence on long-distance transmission networks, providing crucial support for the efficient operation of the energy system.

[0003] However, with the continuous growth of distributed photovoltaic installed capacity, large-scale grid-connected operation is gradually bringing severe challenges to the safe, stable, economical, and efficient operation of medium- and low-voltage distribution networks, specifically in the following two core aspects:

[0004] On the one hand, the risks to the safe operation of the distribution network have significantly increased. Traditional medium and low voltage distribution networks are designed with a radial unidirectional power flow structure, where electricity is typically transmitted unidirectionally from the upstream substation to the downstream user. When distributed photovoltaic (PV) systems are connected on a large scale, especially during peak PV output periods such as midday, the PV power generation in some areas may far exceed the local electricity load, leading to a backflow phenomenon in the distribution network, where electricity is transmitted in reverse to the upstream grid. This reverse flow not only disrupts the original operating mode of the distribution network but also further triggers voltage over-limit problems—the voltage at PV access nodes and along the lines will continuously rise due to reverse power injection; at the same time, the reverse load rate of the lines increases significantly, with some lines operating in an overloaded or near-overloaded state for extended periods, seriously threatening the equipment safety and power supply reliability of the distribution network. In areas with high PV penetration, the aforementioned node voltage deviation and reverse load rate problems are further amplified, and the operational risks increase exponentially.

[0005] On the other hand, existing distribution network planning methods are ill-suited to meet the high-proportion distributed photovoltaic (PV) grid integration needs. Current planning approaches for distribution networks containing PV generally suffer from a single regulation method, relying heavily on traditional hardware upgrades such as adding new lines and transforming transformers, lacking a mechanism for the coordinated utilization of various flexible resources across the distribution network, flexible loads, and energy storage. This single planning model has two major limitations: First, it cannot cope with the multi-timescale fluctuations in distributed PV output—with short-term fluctuations at the minute and hourly levels within a day and long-term fluctuations between seasons, relying solely on grid hardware regulation is insufficient to achieve power balance, easily leading to PV power abandonment; second, it is difficult to balance the economics of planning with PV integration needs—over-reliance on hardware upgrades significantly increases planning investment costs, while reducing hardware investment to control costs leads to insufficient regulation capacity and lower PV integration rates, creating a "cost-integration" contradiction.

[0006] In summary, with the large-scale integration of distributed photovoltaic (PV) power, traditional distribution networks face the dual challenges of increased safety risks and insufficient adaptability of planning methods. There is an urgent need for a grid-load-storage collaborative planning method that integrates regional grid reconfiguration, flexible load dispatching, and optimized energy storage configuration. This method would systematically improve the distributed PV absorption capacity while ensuring the safe and stable operation of the distribution network, and also take into account the economic efficiency of the planning scheme, thus providing technical support for the clean transformation of medium- and low-voltage distribution networks. Summary of the Invention

[0007] In view of the technical problems mentioned in the background, the purpose of this invention is to provide a two-stage spatiotemporal decoupling configuration method and system for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage.

[0008] To achieve the objectives of this invention, the technical solution provided by this invention is as follows:

[0009] First aspect

[0010] This application provides a two-stage spatiotemporal decoupling configuration method for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage, including the following steps:

[0011] Step S1: Input basic data and initialize basic network parameters;

[0012] Step S2: The first stage of constructing the two-stage MEH-SOP model is based on the annual 8760h time-series operation simulation taking into account time-of-use pricing, to optimize the power / capacity of each MEH device;

[0013] Step S3: Construct the second stage of the two-stage MEH-SOP model, extract typical scenarios of high-power charging / discharging of MEH throughout the year and severe voltage overruns in the distribution network throughout the year, and optimize the configuration of SOP multi-port capacity;

[0014] Step S4: Solve the constructed two-stage MEH-SOP model and output the optimal configuration result.

[0015] Second aspect

[0016] Corresponding to the above method, this application provides a two-stage spatiotemporal decoupling configuration system for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage, including the following units: data input and parameter initialization unit, model first stage construction unit, model second stage construction unit, and solution output unit;

[0017] The data input and parameter initialization unit is used to input basic data and initialize basic network parameters.

[0018] The first-stage construction unit of the model is used to construct the first stage of the two-stage MEH-SOP model, based on the annual 8760h time-series operation simulation taking into account time-of-use pricing, to optimize the power / capacity of each MEH device.

[0019] The second-stage construction unit of the model is used to construct the second stage of the two-stage MEH-SOP model, extract typical scenarios of MEH high-power charging / discharging throughout the year and severe voltage overruns in the distribution network throughout the year, and optimize the configuration of SOP multi-port capacity.

[0020] The solution output unit is used to solve the constructed two-stage MEH-SOP model and output the optimal configuration result.

[0021] Compared with existing technologies, the two-stage spatiotemporal decoupling configuration method and system for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage provided by this invention has the following beneficial effects:

[0022] This application proposes a multi-temporal-scale hybrid electric-hydrogen energy storage-intelligent soft-switching (MEH-SOP) system architecture with multi-temporal-scale regulation capabilities and modular integration. By constructing a multi-level energy storage system covering intraday to weekly periods and modularly connecting it to the DC side of a multi-port SOP, the structural flexibility and operational flexibility of the distribution network are significantly enhanced.

[0023] This application proposes a multi-temporal and spatial coordinated control mechanism for MEH-SOP with an embedded distribution network autonomous operation strategy. It clarifies the scheduling priorities and collaborative paths of various MEH and SOP devices at different time scales, constructs a multi-temporal and spatial scale operation mechanism with orderly response and clear coupling, and improves the efficiency of resource coordination and utilization.

[0024] This application constructs a two-stage spatiotemporally decoupled configuration model for MEH-SOP, effectively alleviating the problems of high model dimensionality and computational complexity caused by strong spatiotemporal coupling. The first stage focuses on the time dimension, optimizing the power / capacity configuration of each MEH device based on a full-year 8760-hour time-series operation simulation that takes into account time-of-use pricing. The second stage focuses on the spatial dimension, extracting typical scenarios of high-power charging / discharging of MEH throughout the year (from the results of the first stage) and severe voltage exceedance of the distribution network throughout the year (based on power flow calculations), optimizing the configuration of SOP multi-port capacity. Attached Figure Description

[0025] Figure 1 A schematic diagram of the two-stage spatiotemporal decoupling configuration method for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage provided in the embodiments of this application;

[0026] Figure 2 This is a schematic diagram illustrating an application example. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0028] like Figure 1 As shown, this embodiment provides a two-stage spatiotemporal decoupling configuration method for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage, including the following steps:

[0029] Step S1: Input basic data and initialize basic network parameters; the basic data includes existing load parameters, load curves, distributed renewable energy output curves, and energy storage device parameters; the basic network parameters include the topology of the distribution network and the location information of renewable energy power generation equipment.

[0030] Step S2: The first stage of constructing the two-stage MEH-SOP model is based on the annual 8760h time-series operation simulation taking into account time-of-use pricing, to optimize the power / capacity of each MEH device;

[0031] Step S2 specifically includes the following:

[0032] Step S2.1: Structural Characteristics and Spatiotemporal Regulation Mechanism of MEH-SOP

[0033] Due to the influence of production activities and meteorological fluctuations, in distribution networks with a very high proportion of distributed generation (DG), the supply of DG and load demand often exhibit mismatches at multiple time scales, including intraday, interday, and interweekal, leading to severe wind / solar curtailment and serious voltage limit exceedance issues. To address this, this paper proposes a MEH-SOP system architecture.

[0034] The MEH in the MEH-SOP system consists of an energy conversion device and a multi-level energy storage unit; in terms of operation mechanism, MEH-SOP has the ability to coordinate and regulate in both time and space dimensions.

[0035] The MEH in the MEH-SOP system consists of an energy conversion device and a multi-level energy storage unit:

[0036] a) Energy conversion devices: Alkaline electrolyzer (AEC), seasonal alkaline electrolyzer (SAEC), solid oxide fuel cell (SOFC), seasonal solid oxide fuel cell (SSOFC).

[0037] b) Multi-level energy storage unit: Composed of bioelectrochemical energy storage (BESS), hydrogen energy storage (HS), and SHS. Additionally, the MEH is connected to the DC side of the multi-port SOP. The MEH-SOP is highly modular, allowing for flexible configuration of port and device capacity according to actual needs, meeting deployment and adaptation requirements in various scenarios. In terms of operation, the MEH-SOP possesses coordinated adjustment capabilities in both time and space dimensions.

[0038] Table 1. Internal MEH Operating Characteristics

[0039]

[0040] a. Time Dimension: The BESS (Balanced Energy Storage System) addresses hourly power fluctuations, the HS (High-Speed ​​Regulator) adjusts daytime power consumption within the week, and the SHS (Supply-Speed ​​Regulator) handles longer-term weektime energy balance. When daytime energy imbalances exceed the adjustment range of the BESS and HS, the SHS can shift surplus or deficit energy to the following week or several weeks, achieving seasonal cross-cycle regulation. Table 1 lists the balance cycle and charge / discharge characteristics of the three types of energy storage. Figure 2 This further demonstrates its typical operating mechanism across multiple time scales.

[0041] b. Spatial dimension: As a flexible interconnection device between feeders, the SOP further endows the MEH connected to its DC side with active power regulation capability in a wide area; in addition, the SOP itself has reactive power regulation capability, further realizing power flow optimization between feeders.

[0042] Step S2.2: Multi-port SOP modeling in MEH-SOP

[0043] (1) Power balance constraint for multi-port SOP, the formula is as follows:

[0044]

[0045] Among them, P t,i,SOP Ω represents the power at port i of SOP at time t. k For SOP port set; P t,MEH Let t be the exchange power between the MEH and the DC side of the SOP;

[0046] (2) Multi-port capacity constraint, the formula is as follows:

[0047]

[0048] Among them, Q t,i,sop Let S be the reactive power at port i at time t. i,SOP Capacity of port i;

[0049] Step S2.3: MEH System Modeling in MEH-SOP

[0050] (1) Power balance constraint

[0051]

[0052] Among them, P t,BESS+ With P t,BESS- P represents the charge / discharge power of BEES at time t. t,SOFC With P t,SSOFC P represents the discharge power of SOFC and SSOFC at time t. t,AEC With P t,SAEC The hydrogen production power of AEC and SAEC at time t; The collection is for 8760 hours throughout the year;

[0053] (2) BEES runtime constraints

[0054]

[0055] Among them, I t,BEES The 0 / 1 variable represents the charge / discharge state of BEES; γ BEES E represents the BEES power-to-capacity ratio. 0,BESS E 1,BESS E t,BEES Let E represent the charged states of BEES at the initial time, time 1, and time t. BEES Rated capacity for BEES; η BEES For BEES charge / discharge efficiency; P 1,BESS P t,BESS Let be the power of BESS at time 1 and time t; Δt be the time interval; and D be the set of days throughout the year.

[0056] Formulas (4)-(5) constrain the charging and discharging power of BEES; Formulas (6)-(7) describe the load state of BEES; Formula (8) constrains the capacity of BEES to not exceed the rated capacity; Formula (9) is the BEES state of charge balance constraint, indicating that BEES returns to the initial state of charge in the last hour of each day.

[0057] (3) HS operating constraints

[0058]

[0059]

[0060] Among them, H 0,HS With H 1,HS The load states at the initial time of HS and at time 1; η AEC With η SOFC For AEC and SOFC efficiency; P 1,AEC With P 1,SOFC The power of AEC and SOFC at time 1; H t,HS The HS load state at time t; H HS H is the rated capacity of HS; 0,HS With H 168,HS HS capacity is the initial time and time 168; W is the set of all weeks throughout the year; I t,HS The variable 0 / 1 represents the hydrogen charging / discharging state of HS, indicating that HS only exists in one charging / discharging state on that day;

[0061] Formulas (10)-(11) describe the HS load state; Formula (12) is the HS state of charge balance constraint, indicating that the HS returns to the initial state of charge in the last hour of each week; Formula (13) restricts the HS capacity from exceeding the rated capacity; Formulas (14)-(15) restrict the AEC and SOFC power from exceeding the rated value; Formula (16) indicates that the HS has only one charge and discharge state each day.

[0062] (4) SHS Operational Constraints

[0063]

[0064] H 8760,SHS =H 0,SHS (19)

[0065] H t,SHS ≤H SHS (20)

[0066]

[0067] Among them, H 0,SHS With H 1,SHS The load states at the initial time of HS and at time 1; ηSAEC With η SSOFC For SAEC and SSOFC efficiency; P 1,SAEC With P 1,SSOFC The power of SAEC and SSOFC at time 1; H t,SHS The SHS load state at time t; H SHS For SHS rated capacity; H 0,SHS With H 168,SHS HS capacity at initial time and time 168; I t,SHS The 0 / 1 variable describes the hydrogen charging / discharging state of the SHS, indicating that the SHS has only one charging / discharging state each week.

[0068] Formulas (17)-(18) describe the SHS load state; Formula (19) is the SHS state of charge balance constraint, indicating that the SHS returns to its initial state of charge in the last hour of the year; Formula (20) restricts the SHS capacity from exceeding the rated capacity; Formulas (21)-(22) limit the power of SAEC and SSOFC; Formula (23) indicates that the SHS has only one charge / discharge state per week.

[0069] Step S2.4: Establishment of MEH Optimal Configuration Model

[0070] The objective function for the first stage considers the equivalent annual investment cost C of various equipment in MEH. inv,1 Maintenance costs of various equipment (C) main,1 The cost of abandoning light penalty C apv,1 And MEH electricity sales revenue C based on time-of-use pricing m,1 The objective function is as follows:

[0071] (1) Objective function of the first stage

[0072] C1 = min(C inv,1 +C main,1 +C apv,1 -C m,1 )(twenty four)

[0073] The calculation methods for each sub-function of the objective function C1 are explained in detail by formulas (25)-(29);

[0074] (2) Investment Costs

[0075] Considering the time value of investment, the equivalent annual investment cost is calculated as follows:

[0076]

[0077]

[0078] Where Ψ represents the collection of various types of equipment; τ iLet i be the capital recovery factor for equipment, r be the interest rate, and LT-SOP be the standard operating procedure (SOP). i For the service life of equipment i; C i,inv,1 For the investment cost of equipment i, P e,i The rated power / capacity of device i;

[0079] (3) Maintenance costs

[0080]

[0081] Among them, C i,main,1 The maintenance cost of device i;

[0082] (4) Cost of curtailing wind / solar energy

[0083]

[0084] Where abs() is the absolute value function, min(0,X) means only retaining the part where X<0 (that is, when the MEH absorbed power Pt,MEH is less than the net load of the entire DN network and is a negative power, the difference is considered as wind / solar power curtailment); P t,net The net load power of the entire network before MEH connection; f DG Cost of DG for forfeiting light / wind; P t,MEH The absorbed power is negative, therefore formula (29) P t,net With P t,MEH make a mistake;

[0085] (5) MEH operating revenue

[0086] By guiding MEH to release electricity during peak hours through time-of-use pricing signals, the system's economy and power flow optimization effects can be improved while ensuring energy balance across multiple time scales.

[0087]

[0088] Among them, P t,MEH,dis λ represents the power discharged by MEH to the DC side of SOP at time t; t,grid The MEH system is mainly designed to improve the utilization of DG energy in DN under a very high proportion of renewable energy penetration. Therefore, the MEH only charges when the net load of the entire network is negative. At the same time, one of the assumptions of this application is that DG energy that could not be utilized before can be stored through the MEH without the cost of purchasing electricity. Therefore, formula (30) has no cost of purchasing electricity.

[0089] (6) Constraints

[0090]

[0091] Formula (31) constrains the charging and discharging of MEH, that is, when P in DNt,net When P > 0, t,MEH Discharge to DN, and conversely charge it.

[0092] Step S3: Construct the second stage of the two-stage MEH-SOP model, extract typical scenarios of MEH high-power charging / discharging throughout the year (from the results of the first stage) and severe voltage overruns in the distribution network throughout the year (based on power flow calculation), and optimize the configuration of SOP multi-port capacity;

[0093] Step S3 specifically includes the following:

[0094] Step S3.1: SOP Optimal Capacity Configuration Model

[0095] The optimal SOP configuration model needs to consider the annual investment cost C of SOP. inv,2 Maintenance cost C main,2 Active power loss cost C loss The objective function is as follows:

[0096] (1) Objective function of the second stage

[0097] C2=min(C inv,2 +C main,2 +C loss (32)

[0098] The calculation methods for each sub-function of the objective function C2 are explained in detail by formulas (33)-(42);

[0099] (2) Investment Costs

[0100]

[0101] Ψ SOP ={SOP1,...,SOP n}(34)

[0102]

[0103] Among them, Ψ SOP For each port of an SOP; LT-SOP represents the lifespan of the SOP; C i,inv,2 The investment cost for SOP port i; S i,SOP For port i capacity; τ SOP This indicates the capital recovery rate of SOP; LT-SOP represents the service life of the SOP equipment.

[0104] (3) Maintenance costs

[0105]

[0106] Among them, C i,main,2 Maintenance cost for SOP port i;

[0107] (4) Network loss cost

[0108]

[0109] Where, op is the op-th scenario among the typical scenarios of M; Ω (Line) is the set of lines; λ t,grid The time-of-use electricity price at time t; Let be the square of the line current at time t; op represents the th scenario in M, r mn For resistance;

[0110] Step S3.2: Second-stage SOP multi-port capacity optimal configuration constraints

[0111] (1) DN power flow and safe operation constraints

[0112]

[0113] Where π(:,j) represents the set of nodes ending at node j, and δ(j,:) represents the set of nodes starting at node j; P ij,op,t and Q ij,op,t Let represent the active power and reactive power of branch ij at time t, respectively; represent the net load of the node; η represents the net load of the node. sop Indicates whether a node is connected to the SOP variable; P jk,op,t and Q jk,op,t These represent the active power and reactive power transmitted from the current node to the child node, respectively.

[0114]

[0115] in, It is the square of the voltage. It is the square of the current. The squares of the active and reactive power of line ij are:

[0116]

[0117] in, This represents the maximum current value of line ij. These represent the upper and lower limits of the voltage at node i. The square of the voltage at node i;

[0118] (2) DC-side power interaction constraints between MEH and SOP

[0119] The MEH is a DC side integrated within the SOP. Therefore, when establishing the SOP operation model, the interaction power between each MEH device and the SOP DC side, which was solved in the first stage, should be used as a constraint.

[0120] Step S3.3: Typical daily selection of SOP multi-port capacity optimal configuration model

[0121] The selection of a typical day for the second-phase SOP multi-port capacity configuration model is based on two considerations:

[0122] (1) High-power scenario on the DC side of SOP: Considering that MEH is integrated on the DC side of SOP, when it needs to quickly charge and discharge a large amount of electrical energy to the DC side of SOP in a short period of time, SOP should have the corresponding power generation / absorption capability; therefore, from the MEH annual operation data obtained in the first stage, several typical days in which MEH exchanges high-power energy with the DC side of SOP are selected as key inputs for configuring the rated capacity of SOP.

[0123] (2) Severe overvoltage scenario in distribution network: The SOP has multi-terminal active / reactive power coordination control capability, which can be used to alleviate the problem of voltage exceeding the limit at the end of the feeder. In order to demonstrate its voltage regulation efficiency, it is also necessary to select a typical day with severe node voltage exceeding the limit throughout the year before the MEH is configured; such scenarios can be used to guide the SOP to take into account the voltage support capability in the second phase of capacity configuration.

[0124] Step S4: Solve the constructed two-stage MEH-SOP model and output the optimal configuration result.

[0125] Application example:

[0126] like Figure 2 As shown, the verification was conducted on an improved distribution system. This system comprises three 11.4kV medium-voltage feeders, with tie switches located at nodes 8, 21, and 27. The configured MEH-SOP modification location is at the tie switch location. The source-load baseline data comes from a Chinese distribution network (8760 hours), with a DG penetration rate of approximately 102%. Model-related parameters and time-of-use pricing are shown in Tables 2 and 3.

[0127] Table 2 Equipment Parameters

[0128] parameter Numerical values ​​and units AEC / SAEC ]]> ​ 0.7 SOFC / SSOFC ]]> ​ 0.5 <![CDATA[η BEES ]]> 0.9 <![CDATA[C inv,BESS ]]> 1200 (¥ / kW) <![CDATA[C inv,AEC / SAEC ]]> 4000 (¥ / kW) <![CDATA[C inv,SOFC / SSOFC ]]> 5000 (¥ / kW) <![CDATA[C inv,SOP ]]> 1000 (￥ / kVA) <![CDATA[C inv,HS / SHS ]]> 63.45 (¥ / kW)

[0129] Table 3 Time-of-use Electricity Prices

[0130]

[0131] The calculated optimal capacity configuration results for MEH and SOP are shown in Tables 4 and 5.

[0132] Table 4 Optimal MEH Configuration

[0133]

[0134]

[0135] Table 5 SOP Optimal Configuration

[0136] SOP parameters numerical values SOP-8 (kVA) 4.22 SOP-21 (kVA) 0.96 SOP-27 (kVA) 3.52 <![CDATA[C inv,2 +C main,2 (10 4 ￥)]]> 97.31

[0137] Corresponding to the above method, this embodiment provides a two-stage spatiotemporal decoupling configuration system for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage, including the following units: data input and parameter initialization unit, model first stage construction unit, model second stage construction unit, and solution output unit;

[0138] The data input and parameter initialization unit is used to input basic data and initialize basic network parameters.

[0139] The first-stage construction unit of the model is used to construct the first stage of the two-stage MEH-SOP model, based on the annual 8760h time-series operation simulation taking into account time-of-use pricing, to optimize the power / capacity of each MEH device.

[0140] The second-stage construction unit of the model is used to construct the second stage of the two-stage MEH-SOP model, extract typical scenarios of MEH high-power charging / discharging throughout the year and severe voltage overruns in the distribution network throughout the year, and optimize the configuration of SOP multi-port capacity.

[0141] The solution output unit is used to solve the constructed two-stage MEH-SOP model and output the optimal configuration result.

[0142] The basic data includes existing load parameters, load curves, distributed renewable energy output curves, and energy storage device parameters; the basic network parameters include the topology of the distribution network and the location information of renewable energy power generation equipment.

[0143] The first-stage construction unit of the model is specifically used to perform the following:

[0144] Step S2.1: Structural Characteristics and Spatiotemporal Regulation Mechanism of MEH-SOP

[0145] The MEH in the MEH-SOP system consists of an energy conversion device and a multi-level energy storage unit; in terms of operation mechanism, MEH-SOP has the ability to coordinate and regulate in both time and space dimensions.

[0146] Step S2.2: Multi-port SOP modeling in MEH-SOP

[0147] (1) Power balance constraint for multi-port SOP, the formula is as follows:

[0148]

[0149] Among them, P t,i,SOP Ω represents the power at port i of SOP at time t. kFor SOP port set; P t,MEH Let t be the exchange power between the MEH and the DC side of the SOP;

[0150] (2) Multi-port capacity constraint, the formula is as follows:

[0151]

[0152] Among them, Q t,i,sop Let S be the reactive power at port i at time t. i,SOP Capacity of port i;

[0153] Step S2.3: MEH System Modeling in MEH-SOP

[0154] (1) Power balance constraint

[0155]

[0156] Among them, P t,BESS+ With P t,BESS- P represents the charge / discharge power of BEES at time t. t,SOFC With P t,SSOFC P represents the discharge power of SOFC and SSOFC at time t. t,AEC With P t,SAEC The hydrogen production power of AEC and SAEC at time t; The collection is for 8760 hours throughout the year;

[0157] (2) BEES runtime constraints

[0158]

[0159] Among them, I t,BEES The 0 / 1 variable represents the charge / discharge state of BEES; γ BEES E represents the BEES power-to-capacity ratio. 0,BESS E 1,BESS E t,BEES Let E represent the charged states of BEES at the initial time, time 1, and time t. BEES Rated capacity for BEES; η BEES For BEES charge / discharge efficiency; P 1,BESS P t,BESS Let be the power of BESS at time 1 and time t; Δt be the time interval; and D be the set of days throughout the year.

[0160] (3) HS operating constraints

[0161]

[0162]

[0163] Among them, H 0,HS With H 1,HS The load states at the initial time of HS and at time 1; η AEC With η SOFC For AEC and SOFC efficiency; P 1,AEC With P 1,SOFC The power of AEC and SOFC at time 1; H t,HS The HS load state at time t; H HS H is the rated capacity of HS; 0,HS With H 168,HS HS capacity is the initial time and time 168; W is the set of all weeks throughout the year; I t,HS The variable 0 / 1 represents the hydrogen charging / discharging state of HS, indicating that HS only exists in one charging / discharging state on that day;

[0164] (4) SHS Operational Constraints

[0165]

[0166] H 8760,SHS =H 0,SHS (19)

[0167] H t,SHS ≤H SHS (20)

[0168]

[0169] Among them, H 0,SHS With H 1,SHS The load states at the initial time of HS and at time 1; η SAEC With η SSOFC For SAEC and SSOFC efficiency; P 1,SAEC With P 1,SSOFC The power of SAEC and SSOFC at time 1; H t,SHS The SHS load state at time t; H SHS For SHS rated capacity; H 0,SHS With H 168,SHS HS capacity at initial time and time 168; I t,SHS The 0 / 1 variable describes the hydrogen charging / discharging state of the SHS, indicating that the SHS has only one charging / discharging state each week.

[0170] Step S2.4: Establishment of MEH Optimal Configuration Model

[0171] The objective function for the first stage considers the equivalent annual investment cost C of various equipment in MEH. inv,1 Maintenance costs of various equipment (C) main,1 The cost of abandoning light penalty C apv,1 And MEH electricity sales revenue C based on time-of-use pricingm,1 The objective function is as follows:

[0172] (1) Objective function of the first stage

[0173] C1×min(C inv,1 +C main,1 +C apv,1 -C m,1 )(twenty four)

[0174] The calculation methods for each sub-function of the objective function C1 are explained in detail by formulas (25)-(29);

[0175] (2) Investment Costs

[0176] Considering the time value of investment, the equivalent annual investment cost is calculated as follows:

[0177]

[0178] Ψ MEH ={BEES,HS,SHS,AEC,SAEC,SOFC,SSOFC}(26)

[0179]

[0180] Where Ψ represents the collection of various types of equipment; τ i Let i be the capital recovery factor for equipment, r be the interest rate, and LT-SOP be the standard operating procedure (SOP). i For the service life of equipment i; C i,inv,1 For the investment cost of equipment i, P e,i The rated power / capacity of device i;

[0181] (3) Maintenance costs

[0182]

[0183] Among them, C i,main,1 The maintenance cost of device i;

[0184] (4) Cost of curtailing wind / solar energy

[0185]

[0186] Where abs() is the absolute value function, and min(0,X) means keeping only the part where X<0; P t,net The net load power of the entire network before MEH connection; f DG Cost of DG for forfeiting light / wind; P t,MEH The absorbed power is negative, therefore formula (29) P t,net With P t,MEH make a mistake;

[0187] (5) MEH operating revenue

[0188] By guiding MEH to release electricity during peak hours through time-of-use pricing signals, the system's economy and power flow optimization effects can be improved while ensuring energy balance across multiple time scales.

[0189]

[0190] Among them, P t,MEH,dis λ represents the power discharged by MEH to the DC side of SOP at time t; t,grid The electricity price is based on time-of-use pricing; there is no electricity purchase cost in formula (30);

[0191] (6) Constraints

[0192]

[0193] Formula (31) constrains the charging and discharging of MEH, that is, when P in DN t,net When P > 0, t,MEH Discharge to DN, and conversely charge it.

[0194] In step S2.1, the energy conversion device includes: an alkaline electrolyzer (AEC), a seasonal alkaline electrolyzer (SAEC), a solid oxide fuel cell (SOFC), and a seasonal solid oxide fuel cell (SSOFC); the multi-level energy storage unit includes: electrochemical energy storage (BESS), hydrogen energy storage (HS), and SHS.

[0195] The time dimension is as follows: BESS is used to deal with hourly power fluctuations, HS regulates daytime power consumption within the week, and SHS undertakes the energy balance of a longer period of week. When the daytime energy imbalance exceeds the adjustment range of BESS and HS, SHS can transfer the surplus or deficit energy to the next week or several weeks in the future to achieve seasonal cross-cycle adjustment.

[0196] The spatial dimension: As a flexible interconnection device between feeders, the SOP further endows the MEH connected to its DC side with active power regulation capability in a wide area.

[0197] The second-stage construction unit of the model is specifically used to perform the following:

[0198] Step S3.1: SOP Optimal Capacity Configuration Model

[0199] The optimal SOP configuration model needs to consider the annual investment cost C of SOP. inv,2 Maintenance cost C main,2 Active power loss cost C loss The objective function is as follows:

[0200] (1) Objective function of the second stage

[0201] C2×min(C inv,2 +C main,2 +C loss (32)

[0202] The calculation methods for each sub-function of the objective function C2 are explained in detail by formulas (33)-(42);

[0203] (2) Investment Costs

[0204]

[0205] Ψ SOP ={SOP1,...,SOP n}(34)

[0206]

[0207] Among them, Ψ SOP For each port of an SOP; LT-SOP represents the lifespan of the SOP; C i,inv,2 The investment cost for SOP port i; S i,SOP For port i capacity; τ SOP This indicates the capital recovery rate of SOP; LT-SOP represents the service life of the SOP equipment.

[0208] (3) Maintenance costs

[0209]

[0210] Among them, C i,main,2 Maintenance cost for SOP port i;

[0211] (4) Network loss cost

[0212]

[0213] Where, op is the op-th scenario among the typical scenarios of M; Ω (Line) is the set of lines; λ t,grid The time-of-use electricity price at time t; Let be the square of the line current at time t; op represents the th scenario in M, r mn For resistance;

[0214] Step S3.2: Second-stage SOP multi-port capacity optimal configuration constraints

[0215] (1) DN power flow and safe operation constraints

[0216]

[0217]

[0218] Where π(:,j) represents the set of nodes ending at node j, and δ(j,:) represents the set of nodes starting at node j; P ij,op,t and Q ij,op,t Let represent the active power and reactive power of branch ij at time t, respectively; represent the net load of the node; η represents the net load of the node. sop Indicates whether a node is connected to the SOP variable; P jk,op,t and Q jk,op,t These represent the active power and reactive power transmitted from the current node to the child node, respectively.

[0219]

[0220] in, It is the square of the voltage. It is the square of the current. The squares of the active and reactive power of line ij are:

[0221]

[0222] in, This represents the maximum current value of line ij. These represent the upper and lower limits of the voltage at node i. The square of the voltage at node i;

[0223] (2) DC-side power interaction constraints between MEH and SOP

[0224] The MEH is a DC side integrated within the SOP. Therefore, when establishing the SOP operation model, the interaction power between each MEH device and the SOP DC side, which was solved in the first stage, should be used as a constraint.

[0225] Step S3.3: Typical daily selection of SOP multi-port capacity optimal configuration model

[0226] The selection of a typical day for the second-phase SOP multi-port capacity configuration model is based on two considerations:

[0227] (1) High-power scenario on the DC side of SOP: Considering that MEH is integrated on the DC side of SOP, when it needs to quickly charge and discharge a large amount of electrical energy to the DC side of SOP in a short period of time, SOP should have the corresponding power generation / absorption capability; therefore, from the MEH annual operation data obtained in the first stage, several typical days in which MEH exchanges high-power energy with the DC side of SOP are selected as key inputs for configuring the rated capacity of SOP.

[0228] (2) Severe overvoltage scenario in distribution network: The SOP has multi-terminal active / reactive power coordinated control capabilities, which can be used to alleviate the problem of voltage exceeding limits at the feeder end. To demonstrate its voltage regulation efficiency, it is also necessary to select a typical day with severe node voltage exceeding limits throughout the year before the MEH is configured; this type of scenario can be used to guide the SOP to take voltage support capabilities into account in the second-stage capacity configuration.

[0229] Finally, it should be noted that the above embodiments are merely illustrative and explanatory of the present invention, and are not intended to limit the present invention to the scope of the described embodiments. Furthermore, those skilled in the art will understand that the present invention is not limited to the above embodiments, and many more variations and modifications can be made based on the teachings of the present invention, all of which fall within the scope of protection claimed by the present invention.

Claims

1. A two-stage spatiotemporal decoupling configuration method for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage, characterized in that, Includes the following steps: Step S1: Input basic data and initialize basic network parameters; Step S2: The first stage of constructing the two-stage MEH-SOP model is based on the annual 8760h time-series operation simulation taking into account time-of-use pricing, to optimize the power / capacity of each MEH device; Step S3: Construct the second stage of the two-stage MEH-SOP model, extract typical scenarios of high-power charging / discharging of MEH throughout the year and severe voltage overruns in the distribution network throughout the year, and optimize the configuration of SOP multi-port capacity; Step S4: Solve the constructed two-stage MEH-SOP model and output the optimal configuration result.

2. The two-stage spatiotemporal decoupling configuration method for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage according to claim 1, characterized in that, The basic data includes existing load parameters, load curves, distributed renewable energy output curves, and energy storage device parameters; the basic network parameters include the topology of the distribution network and the location information of renewable energy power generation equipment.

3. The two-stage spatiotemporal decoupling configuration method for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage according to claim 1, characterized in that, Step S2 specifically includes the following: Step S2.1: Structural Characteristics and Spatiotemporal Regulation Mechanism of MEH-SOP The MEH in the MEH-SOP system consists of an energy conversion device and a multi-level energy storage unit; in terms of operation mechanism, MEH-SOP has the ability to coordinate and regulate in both time and space dimensions. Step S2.2: Multi-port SOP modeling in MEH-SOP (1) Power balance constraint for multi-port SOP, the formula is as follows: Among them, P t,i,SOP Ω represents the power at port i of SOP at time t. k For SOP port set; P t,MEH Let t be the exchange power between the MEH and the DC side of the SOP; (2) Multi-port capacity constraint, the formula is as follows: Among them, Q t,i,sop Let S be the reactive power at port i at time t. i,SOP Capacity of port i; Step S2.3: MEH System Modeling in MEH-SOP (1) Power balance constraint Among them, P t,BESS+ With P t,BESS- P represents the charge / discharge power of BEES at time t. t,SOFC With P t,SSOFC P represents the discharge power of SOFC and SSOFC at time t. t,AEC With P t,SAEC Let AEC and SAEC represent the hydrogen production power at time t; τ is the total annual hydrogen production of 8760 hours. (2) BEES runtime constraints Among them, I t,BEES The 0 / 1 variable represents the charge / discharge state of BEES; γ BEES E represents the BEES power-to-capacity ratio. 0,BESS E 1,BESS E t,BEES Let E represent the charged states of BEES at the initial time, time 1, and time t. BEES Rated capacity for BEES; η BEES For BEES charge / discharge efficiency; P 1,BESS P t,BESS Let be the power of BESS at time 1 and time t; Δt be the time interval; and D be the set of days throughout the year. (3) HS operating constraints Among them, H 0,HS With H 1,HS The load states at the initial time of HS and at time 1; η AEC With η SOFC For AEC and SOFC efficiency; P 1,AEC With P 1,SOFC The power of AEC and SOFC at time 1; H t,HS The HS load state at time t; H HS H is the rated capacity of HS; 0,HS With H 168,HS HS capacity is the initial time and time 168; W is the set of all weeks throughout the year; I t,HS The variable 0 / 1 represents the hydrogen charging / discharging state of HS, indicating that HS only exists in one charging / discharging state on that day; (4) SHS Operational Constraints H 8760,SHS =H 0,SHS (19) H t,SHS ≤H SHS (20) Among them, H 0,SHS With H 1,SHS The load states at the initial time of HS and at time 1; η SAEC With η SSOFC For SAEC and SSOFC efficiency; P 1,SAEC With P 1,SSOFC The power of SAEC and SSOFC at time 1; H t,SHS The SHS load state at time t; H SHS For SHS rated capacity; H 0,SHS With H 168,SHS HS capacity at initial time and time 168; I t,SHS The 0 / 1 variable describes the hydrogen charging / discharging state of the SHS, indicating that the SHS has only one charging / discharging state each week. Step S2.4: Establishment of MEH Optimal Configuration Model The objective function for the first stage considers the equivalent annual investment cost C of various equipment in MEH. inv,1 Maintenance costs of various equipment (C) main,1 The cost of abandoning light penalty C apv,1 And MEH electricity sales revenue C based on time-of-use pricing m,1 The objective function is as follows: (1) Objective function of the first stage C1=min(C inv,1 +C main,1 +C apv,1 -C m,1 )(24) The calculation methods for each sub-function of the objective function C1 are explained in detail by formulas (25)-(29); (2) Investment Costs Considering the time value of investment, the equivalent annual investment cost is calculated as follows: Ψ MEH ={BEES,HS,SHS,AEC,SAEC,SOFC,SSOFC}(26) Where Ψ represents the collection of various types of equipment; τ i Let i be the capital recovery factor for equipment, r be the interest rate, and LT-SOP be the standard operating procedure (SOP). i For the service life of equipment i; C i,inv,1 For the investment cost of equipment i, P e,i The rated power / capacity of device i; (3) Maintenance costs Among them, C i,main,1 The maintenance cost of device i; (4) Cost of curtailing wind / solar energy Where abs() is the absolute value function, and min(0,X) means keeping only the part where X<0; P t,net The net load power of the entire network before MEH connection; f DG For DG's cost of forfeiting light / wind; P t,MEH The absorbed power is negative, therefore formula (29) P t,net With P t,MEH make a mistake; (5) MEH operating revenue By guiding MEH to release electricity during peak hours through time-of-use pricing signals, the system's economy and power flow optimization effects can be improved while ensuring energy balance across multiple time scales. Among them, P t,MEH,dis λ represents the power discharged by MEH to the DC side of SOP at time t; t,grid The electricity price is based on time-of-use pricing; there is no electricity purchase cost in formula (30); (6) Constraints Formula (31) constrains the charging and discharging of MEH, that is, when P in DN t,net When P > 0, t,MEH Discharge to DN, and conversely charge it.

4. The two-stage spatiotemporal decoupling configuration method for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage according to claim 3, characterized in that, In step S2.1, the energy conversion device includes: an alkaline electrolyzer (AEC), a seasonal alkaline electrolyzer (SAEC), a solid oxide fuel cell (SOFC), and a seasonal solid oxide fuel cell (SSOFC); the multi-level energy storage unit includes: electrochemical energy storage (BESS), hydrogen energy storage (HS), and SHS. The time dimension is as follows: BESS is used to deal with hourly power fluctuations, HS regulates daytime power consumption within the week, and SHS undertakes the energy balance of a longer period of week. When the daytime energy imbalance exceeds the adjustment range of BESS and HS, SHS can transfer the surplus or deficit energy to the next week or several weeks in the future to achieve seasonal cross-cycle adjustment. The spatial dimension: As a flexible interconnection device between feeders, the SOP further endows the MEH connected to its DC side with active power regulation capability in a wide area.

5. The two-stage spatiotemporal decoupling configuration method for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage according to claim 1, characterized in that, Step S3 specifically includes the following: Step S3.1: SOP Optimal Capacity Configuration Model The optimal SOP configuration model needs to consider the annual investment cost C of SOP. inv,2 Maintenance cost C main,2 Active power loss cost C loss The objective function is as follows: (1) Objective function of the second stage C2=min(C inv,2 +C main,2 +C loss )(32) The calculation methods for each sub-function of the objective function C2 are explained in detail by formulas (33)-(42); (2) Investment Costs P SOP ={SOP1,...,SOP n }(34) Among them, Ψ SOP For each port of an SOP; LT-SOP represents the lifespan of the SOP; C i,inv,2 The investment cost for SOP port i; S i,SOP For port i capacity; τ SOP This indicates the capital recovery rate of SOP; LT-SOP represents the service life of the SOP equipment. (3) Maintenance costs Among them, C i,main,2 Maintenance cost for SOP port i; (4) Network loss cost Where, op is the op-th scenario among the typical scenarios of M; Ω (Line) is the set of lines; λ t,grid The time-of-use electricity price at time t; Let be the square of the line current at time t; op represents the th scenario in M, r mn For resistance; Step S3.2: Second-stage SOP multi-port capacity optimal configuration constraints (1) DN power flow and safe operation constraints Where π(:,j) represents the set of nodes ending at node j, and δ(j,:) represents the set of nodes starting at node j; P ij,op,t and Q ij,op,t Let represent the active power and reactive power of branch ij at time t, respectively; represent the net load of the node; η represents the net load of the node. sop Indicates whether a node is connected to the SOP variable; P jk,op,t and Q jk,op,t These represent the active power and reactive power transmitted from the current node to the child node, respectively. in, It is the square of the voltage. It is the square of the current. The squares of the active and reactive power of line ij are: in, This represents the maximum current value of line ij. These represent the upper and lower limits of the voltage at node i. The square of the voltage at node i; (2) DC-side power interaction constraints between MEH and SOP The MEH is a DC side integrated within the SOP. Therefore, when establishing the SOP operation model, the interaction power between each MEH device and the SOP DC side, which was solved in the first stage, should be used as a constraint. Step S3.3: Typical daily selection of SOP multi-port capacity optimal configuration model The selection of a typical day for the second-phase SOP multi-port capacity configuration model is based on two considerations: (1) High-power scenario on the DC side of SOP: Considering that MEH is integrated on the DC side of SOP, when it needs to quickly charge and discharge a large amount of electrical energy to the DC side of SOP in a short period of time, SOP should have the corresponding power generation / absorption capability; therefore, from the MEH annual operation data obtained in the first stage, several typical days in which MEH exchanges high-power energy with the DC side of SOP are selected as key inputs for configuring the rated capacity of SOP. (2) Severe overvoltage scenario in distribution network: The SOP has multi-terminal active / reactive power coordination control capability, which can be used to alleviate the problem of voltage exceeding the limit at the end of the feeder. In order to demonstrate its voltage regulation efficiency, it is also necessary to select a typical day with severe node voltage exceeding the limit throughout the year before the MEH is configured; such scenarios can be used to guide the SOP to take into account the voltage support capability in the second phase of capacity configuration.

6. A two-stage spatiotemporal decoupling configuration system for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage, characterized in that, It includes the following units: data input and parameter initialization unit, model first-stage construction unit, model second-stage construction unit, and solution output unit; The data input and parameter initialization unit is used to input basic data and initialize basic network parameters. The first-stage construction unit of the model is used to construct the first stage of the two-stage MEH-SOP model, based on the annual 8760h time-series operation simulation taking into account time-of-use pricing, to optimize the power / capacity of each MEH device. The second-stage construction unit of the model is used to construct the second stage of the two-stage MEH-SOP model, extract typical scenarios of MEH high-power charging / discharging throughout the year and severe voltage overruns in the distribution network throughout the year, and optimize the configuration of SOP multi-port capacity. The solution output unit is used to solve the constructed two-stage MEH-SOP model and output the optimal configuration result.

7. The two-stage spatiotemporal decoupling configuration system for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage according to claim 1, characterized in that, The basic data includes existing load parameters, load curves, distributed renewable energy output curves, and energy storage device parameters; the basic network parameters include the topology of the distribution network and the location information of renewable energy power generation equipment.

8. The two-stage spatiotemporal decoupling configuration method for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage according to claim 1, characterized in that, The first-stage construction unit of the model is specifically used to perform the following: Step S2.1: Structural Characteristics and Spatiotemporal Regulation Mechanism of MEH-SOP The MEH in the MEH-SOP system consists of an energy conversion device and a multi-level energy storage unit; in terms of operation mechanism, MEH-SOP has the ability to coordinate and regulate in both time and space dimensions. Step S2.2: Multi-port SOP modeling in MEH-SOP (1) Power balance constraint for multi-port SOP, the formula is as follows: Among them, P t,i,SOP Ω represents the power at port i of SOP at time t. k For SOP port set; P t,MEH Let t be the exchange power between the MEH and the DC side of the SOP; (2) Multi-port capacity constraint, the formula is as follows: Among them, Q t,i,sop Let S be the reactive power at port i at time t. i,SOP Capacity of port i; Step S2.3: MEH System Modeling in MEH-SOP (1) Power balance constraint Among them, P t,BESS+ With P t,BESS- P represents the charge / discharge power of BEES at time t. t,SOFC With P t,SSOFC P represents the discharge power of SOFC and SSOFC at time t. t,AEC With P t,SAEC The hydrogen production power of AEC and SAEC at time t; The collection is for 8760 hours throughout the year; (2) BEES runtime constraints Among them, I t,BEES The 0 / 1 variable represents the charge / discharge state of BEES; γ BEES E represents the BEES power-to-capacity ratio. 0,BESS E 1,BESS E t,BEES Let E represent the charged states of BEES at the initial time, time 1, and time t. BEES Rated capacity for BEES; η BEES For BEES charge / discharge efficiency; P 1,BESS P t,BESS Let be the power of BESS at time 1 and time t; Δt be the time interval; and D be the set of days throughout the year. (3) HS operating constraints Among them, H 0,HS With H 1,HS The load states at the initial time of HS and at time 1; η AEC With η SOFC For AEC and SOFC efficiency; P 1,AEC With P 1,SOFC The power of AEC and SOFC at time 1; H t,HS The HS load state at time t; H HS H is the rated capacity of HS; 0,HS With H 168,HS HS capacity is the initial time and time 168; W is the set of all weeks throughout the year; I t,HS The variable 0 / 1 represents the hydrogen charging / discharging state of HS, indicating that HS only exists in one charging / discharging state on that day; (4) SHS Operational Constraints H 8760,SHS =H 0,SHS (19) H t,SHS ≤H SHS (20) Among them, H 0,SHS With H 1,SHS The load states at the initial time of HS and at time 1; η SAEC With η SSOFC For SAEC and SSOFC efficiency; P 1,SAEC With P 1,SSOFC The power of SAEC and SSOFC at time 1; H t,SHS The SHS load state at time t; H SHS For SHS rated capacity; H 0,SHS With H 168,SHS HS capacity at initial time and time 168; I t,SHS The 0 / 1 variable describes the hydrogen charging / discharging state of the SHS, indicating that the SHS has only one charging / discharging state each week. Step S2.4: Establishment of MEH Optimal Configuration Model The objective function for the first stage considers the equivalent annual investment cost C of various equipment in MEH. inv,1 Maintenance costs of various equipment (C) main,1 The cost of abandoning light penalty C apv,1 And MEH electricity sales revenue C based on time-of-use pricing m,1 The objective function is as follows: (1) Objective function of the first stage C1=min(C inv,1 +C main,1 +C apv,1 -C m,1 )(24) The calculation methods for each sub-function of the objective function C1 are explained in detail by formulas (25)-(29); (2) Investment Costs Considering the time value of investment, the equivalent annual investment cost is calculated as follows: Ψ MEH ={BEES,HS,SHS,AEC,SAEC,SOFC,SSOFC}(26) Where Ψ represents the collection of various types of equipment; τ i Let i be the capital recovery factor for equipment, r be the interest rate, and LT-SOP be the standard operating procedure (SOP). i For the service life of equipment i; C i,inv,1 For the investment cost of equipment i, P e,i The rated power / capacity of device i; (3) Maintenance costs Among them, C i,main,1 The maintenance cost of device i; (4) Cost of curtailing wind / solar energy Where abs() is the absolute value function, and min(0,X) means keeping only the part where X<0; P t,net The net load power of the entire network before MEH connection; f DG For DG's cost of forfeiting light / wind; P t,MEH The absorbed power is negative, therefore formula (29) P t,net With P t,MEH make a mistake; (5) MEH operating revenue By guiding MEH to release electricity during peak hours through time-of-use pricing signals, the system's economy and power flow optimization effects can be improved while ensuring energy balance across multiple time scales. Among them, P t,MEH,dis λ represents the power discharged by MEH to the DC side of SOP at time t; t,grid The electricity price is based on time-of-use pricing; there is no electricity purchase cost in formula (30); (6) Constraints Formula (31) constrains the charging and discharging of MEH, that is, when P in DN t,net When P > 0, t,MEH Discharge to DN, and conversely charge it.

9. The two-stage spatiotemporal decoupling configuration system for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage according to claim 8, characterized in that, In step S2.1, the energy conversion device includes: an alkaline electrolyzer (AEC), a seasonal alkaline electrolyzer (SAEC), a solid oxide fuel cell (SOFC), and a seasonal solid oxide fuel cell (SSOFC); the multi-level energy storage unit includes: electrochemical energy storage (BESS), hydrogen energy storage (HS), and SHS. The time dimension is as follows: BESS is used to deal with hourly power fluctuations, HS regulates daytime power consumption within the week, and SHS undertakes the energy balance of a longer period of week. When the daytime energy imbalance exceeds the adjustment range of BESS and HS, SHS can transfer the surplus or deficit energy to the next week or several weeks in the future to achieve seasonal cross-cycle adjustment. The spatial dimension: As a flexible interconnection device between feeders, the SOP further endows the MEH connected to its DC side with active power regulation capability in a wide area.

10. The two-stage spatiotemporal decoupling configuration system for intelligent soft switching and multilayer electro-hydrogen hybrid energy storage according to claim 6, characterized in that, The second-stage construction unit of the model is specifically used to perform the following: Step S3.1: SOP Optimal Capacity Configuration Model The optimal SOP configuration model needs to consider the annual investment cost C of SOP. inv,2 Maintenance cost C main,2 Active power loss cost C loss The objective function is as follows: (1) Objective function of the second stage C2=min(C inv,2 +C main,2 +C loss )(32) The calculation methods for each sub-function of the objective function C2 are explained in detail by formulas (33)-(42); (2) Investment Costs P SOP ={SOP1,...,SOP n }(34) Among them, Ψ SOP For each port of an SOP; LT-SOP represents the lifespan of the SOP; C i,inv,2 The investment cost for SOP port i; S i,SOP For port i capacity; τ SOP This indicates the capital recovery rate of SOP; LT-SOP represents the service life of the SOP equipment. (3) Maintenance costs Among them, C i,main,2 Maintenance cost for SOP port i; (4) Network loss cost Where, op is the op-th scenario among the typical scenarios of M; Ω (Line) is the set of lines; λ t,grid The time-of-use electricity price at time t; Let be the square of the line current at time t; op represents the th scenario in M, r mn For resistance; Step S3.2: Second-stage SOP multi-port capacity optimal configuration constraints (1) DN power flow and safe operation constraints Where π(:,j) represents the set of nodes ending at node j, and δ(j,:) represents the set of nodes starting at node j; P ij,op,t and Q ij,op,t Let represent the active power and reactive power of branch ij at time t, respectively; represent the net load of the node; η represents the net load of the node. sop Indicates whether a node is connected to the SOP variable; P jk,op,t and Q jk,op,t These represent the active power and reactive power transmitted from the current node to the child node, respectively. in, It is the square of the voltage. It is the square of the current. The squares of the active and reactive power of line ij are: in, This represents the maximum current value of line ij. These represent the upper and lower limits of the voltage at node i. The square of the voltage at node i; (2) DC-side power interaction constraints between MEH and SOP The MEH is a DC side integrated within the SOP. Therefore, when establishing the SOP operation model, the interaction power between each MEH device and the SOP DC side, which was solved in the first stage, should be used as a constraint. Step S3.3: Typical daily selection of SOP multi-port capacity optimal configuration model The selection of a typical day for the second-phase SOP multi-port capacity configuration model is based on two considerations: (1) High-power scenario on the DC side of SOP: Considering that MEH is integrated on the DC side of SOP, when it needs to quickly charge and discharge a large amount of electrical energy to the DC side of SOP in a short period of time, SOP should have the corresponding power generation / absorption capability; therefore, from the MEH annual operation data obtained in the first stage, several typical days in which MEH exchanges high-power energy with the DC side of SOP are selected as key inputs for configuring the rated capacity of SOP. (2) Severe overvoltage scenario in distribution network: The SOP has multi-terminal active / reactive power coordination control capability, which can be used to alleviate the problem of voltage exceeding the limit at the end of the feeder. In order to demonstrate its voltage regulation efficiency, it is also necessary to select a typical day with severe node voltage exceeding the limit throughout the year before the MEH is configured; such scenarios can be used to guide the SOP to take into account the voltage support capability in the second phase of capacity configuration.

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