A new collaborative planning and operation method for distribution systems

Abstract

The present invention relates to a novel distribution system collaborative planning and operation method, comprising: constructing a novel distribution system planning and operation joint optimization model for HESS-SOP node-network flexibility, the optimization model being divided into a planning layer and an operation layer, wherein the planning layer's constraints include flexibility supply and demand balance constraints, and the operation layer's constraints include HESS-SOP operation constraints; solving the joint optimization model using a hybrid optimization algorithm to obtain an optimal planning configuration and operation strategy, wherein the optimal planning configuration includes an optimal SOP capacity and an optimal DC-DC converter capacity. Compared with existing technologies, the present invention has the advantages of alleviating flexibility supply and demand imbalances at both temporal and spatial levels.

Description

Technical Field

[0001] The present invention relates to the technical field of novel power distribution systems, and in particular to a novel collaborative planning and operation method for power distribution systems. Background Art

[0002] As the penetration rate of distributed renewable energy (DRG) in new distribution systems gradually increases, the distribution systems are faced with the dual flexibility challenges of insufficient node power support and branch transmission blockage due to their inherent random output defects, which can easily lead to major safety problems such as wind and solar power abandonment and load loss. It is urgent to scientifically plan and build sufficient flexible adjustment resources at key nodes and terminals of the power grid to improve the flexible adjustment capability and stable operation level of the power grid.

[0003] The Hybrid Energy Storage System (HESS) combines the dual advantages of Energy-Based Storage (EBS) and Power-Based Storage (PBS), becoming a key development trend in meeting the system's multi-timescale regulation needs. Low-pass filtering, wavelet packet analysis, and empirical mode decomposition can be used to perform spectral analysis of pulsating power, enabling EBS and PBS to implement graded, smoothed, and refined control of power in different frequency bands. Furthermore, as a key carrier of flexible resources, the rationality of the grid structure of the new distribution system is crucial for the regulation of node flexibility resources and the balanced flow of branch power. Intelligent soft open points (SOPs), based on fully controlled power electronic devices, are not limited by the number of traditional mechanical switch operations and possess precise power flow control capabilities, making them an important network-based flexible resource. Faced with the problem of insufficient dual flexibility of "node-network" in the new distribution system, the coordinated "planning-operation" of heterogeneous flexible resources such as node type and network type has become an important technical means to improve the flexible adjustment capability of system nodes and network transmission capacity margin, making the joint introduction of SOP and HESS in the new distribution system and participation in the flexible adjustment of system nodes and networks a research hotspot.

[0004] In addition, with the dramatic increase in the uncertainty factors of "source and load" in the system, higher requirements are placed on flexibility regulation. In essence, it can be regarded as a problem of supply and demand balance of flexibility resources at multiple spatiotemporal scales. Its reasonable quantitative analysis is the theoretical basis for supporting flexibility assessment, scheduling, and optimization. However, most current studies focus on the supply and demand balance at the node level, ignoring the analysis of the flexibility resource transmission process, and breaking the "node-network" flexibility interaction correlation. This makes it a new problem to comprehensively consider the coordinated role of node flexibility resources and network flexibility resources, supplemented by precise analysis and control, to achieve the "node-network" two-dimensional flexibility supply and demand balance of the system operating in multiple spatiotemporal domains. Summary of the Invention

[0005] The purpose of this invention is to provide a new distribution system collaborative planning and operation method to alleviate the imbalance between flexibility supply and demand at the dual levels of time and space.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A novel distribution system collaborative planning and operation method, comprising:

[0008] Construct a new distribution system planning and operation joint optimization model for HESS-SOP node-network flexibility. The optimization model is divided into a planning layer model and an operation layer model. The constraints of the planning layer model include flexibility supply and demand balance constraints, and the constraints of the operation layer model include HESS-SOP operation constraints.

[0009] The joint optimization model is solved based on a hybrid optimization algorithm to obtain an optimal planning configuration and an operation strategy result, wherein the optimal planning configuration includes an optimal SOP capacity and an optimal DCDC converter capacity.

[0010] Furthermore, the new power distribution system includes an intelligent soft switch SOP and a hybrid energy storage system HESS. The intelligent soft switch SOP includes a back-to-back voltage source converter VSC1 and a back-to-back voltage source converter VSC2. The back-to-back voltage source converter VSC1 is a converter ACDC1, and the back-to-back voltage source converter VSC2 is a converter ACDC2. The converter ACDC1 and the converter ACDC2 are connected to each other and a capacitor in parallel. The hybrid energy storage system HESS adopts an active topology structure, including a converter DCDC1 and a converter DCDC2. The converter DCDC1 and the converter DCDC2 are both connected to the converter ACDC1 and the converter ACDC2. Lithium-ion batteries LIB are connected in parallel on both sides of the converter DCDC1, and supercapacitors SC are connected in parallel on both sides of the converter ACDC2.

[0011] Furthermore, the HESS-SOP operation constraints include charge and discharge characteristic constraints, power loss constraints, power balance constraints, state of charge constraints and SOP regulation constraints.

[0012] Furthermore, the charge and discharge characteristics are constrained as follows:

[0013]

[0014]

[0015] in, are the charge and discharge powers of LIB and SC at time t, are the charge and discharge flags of LIB and SC at time t, which are 0-1 variables, and η LIB ,η SC are the charge-discharge conversion efficiency of LIB and SC, are the total capacity of the two DC / DC converters respectively.

[0016] Furthermore, the SOP control constraints are:

[0017]

[0018] Among them, P sop,i,t 、P sop,j,t and are the active power output and the corresponding active power loss of SOP from ports i and j at time t, respectively, where ports i and j represent the two ports of ACDC1 and ACDC2 respectively; sop,i 、A sop,j are the active power loss coefficients at ports i and j of the SOP respectively; Q sop,i,t , Q sop,j,t are the reactive power outputs at ports i and j of the SOP respectively; Q sop,i,min , Q sop,j,min and Q sop,i,max , Q sop,j,max They are the upper and lower limits of reactive power output at ports i and j of the SOP respectively; are the rated capacities of converters at ports i and j of SOP respectively; is the total active power loss of the HESS-SOP device at time t.

[0019] Furthermore, the flexibility supply and demand balance constraint is:

[0020]

[0021] Among them, θ max is the maximum probability of insufficient flexibility of a given allowed line; E maxFor a given maximum expected value of the allowed line flexibility deficiency, E up 、E down are the expected values ​​of the flexibility deficit for upward and downward adjustments, θ up ,θ down Adjust the expected value of the probability of insufficient flexibility upward or downward respectively.

[0022] Furthermore, the expected values ​​of the upward and downward adjustment of the insufficient flexibility are specifically:

[0023]

[0024] Among them, Z1 and Z2 respectively increase and decrease the number of intervals with insufficient flexibility; k1 and k2 respectively increase and decrease the sequence number of intervals with insufficient flexibility; They are the left and right time boundary points of the k1th interval of insufficient upward flexibility; are the left and right time boundary points of the k2th interval of insufficient downward flexibility, M is the number of lines in the system, is the required transmission volume of up and down adjustment flexibility of line j at time t.

[0025] Furthermore, the expected probability of insufficient upward and downward flexibility is:

[0026]

[0027] in, are the total duration of the upward and downward insufficient flexibility intervals in a scheduling cycle; T is the total scheduling cycle.

[0028] Furthermore, the objective function of the planning layer model is:

[0029]

[0030] in, They are the annual investment cost and maintenance cost of SOP, HESS, etc.;

[0031] The objective function of the operational layer model is:

[0032] minF R =C L +C A +C EP +C fle

[0033]

[0034] Among them, c LIB 、c SC 、c L are the unit power operation cost and unit active power network loss cost of LIB and SC respectively; c EPThe price of electricity purchased from the higher-level power grid unit; The power support provided by the upper power grid at time t in scenario s; is the total network loss of the distribution system at time t in scenario s; is the internal loss of HESS-SOP at time t in scenario s, N s is the number of scenes, P(s) is the scene probability; are the charge and discharge power of LIB and SC at time t in scenario s; c up 、c d It is the penalty factor for the flexibility exceeding the upper and lower limits.

[0035] Furthermore, the specific steps of solving the joint optimization model based on the hybrid optimization algorithm to obtain the optimal planning configuration and operation strategy results are as follows:

[0036] Initialize the parameters and use Latin hypercube sampling to generate scenes;

[0037] Enter the planning layer model to obtain scenario parameters and HESS initial configuration plan;

[0038] generating an initial configuration scheme population, the population including SOP capacity and DCDC converter capacity, performing roulette wheel selection, multi-point crossover and multi-point mutation, and merging the parent and child populations;

[0039] The annual investment and maintenance cost of the HESS-SOP device is used as the individual fitness to determine whether it meets the constraints of the planning layer. If not, the chromosome is regenerated. If so, the fitness is calculated, and then it is determined whether it meets the convergence conditions of the planning layer. If so, the configuration results obtained by the planning layer are input into the operation layer model. Otherwise, roulette wheel selection, multi-point crossover and multi-point mutation are repeated.

[0040] After the configuration results obtained by the planning layer model are input into the operation layer, the scenario s is set, the operation layer model is cone processed, the constraints of the operation layer are linearized, the solver is called to solve the operation layer model, and then the scenario is switched until all scenarios are traversed to determine whether the convergence conditions of the operation layer model are met. If not, the planning layer model is returned to be solved again. If so, it is determined whether the reproduction generation is reached. If so, the optimal planning configuration and operation strategy results are output. If not, the roulette wheel selection, multi-point crossover and multi-point mutation are repeated.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention constructs a new distribution system planning-operation joint optimization model for HESS-SOP node-network flexibility, wherein the constraints of the planning layer include flexibility supply and demand balance constraints, and flexibility supply and demand balance conditions and evaluation indicators are established in the balance constraints, which can effectively quantify and characterize flexibility resources, analyze the "node-network" two-dimensional supply and demand balance mechanism, and meet the actual needs of the new distribution system; at the same time, the constraints of the operation layer include HESS-SOP operation constraints, and the HESS-SOP operation constraints include constraints obtained by analyzing the HESS-SOP regulation characteristics. The charge and discharge states of two different energy storages of the HESS and the HESS-SOP power balance constraints are determined by the charge and discharge characteristics. The two VSCs of the SOP can control the bidirectional flow of power. Multiple operating states can be switched through these two parts, while optimizing the system flow distribution, and the flexibility supply and demand imbalance problem can be alleviated at the dual levels of time and space. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is the grid-connected HESS-SOP topology diagram of the present invention;

[0044] Figure 2 is the feasible domain for the HESS operation of the present invention;

[0045] Figure 3 A flowchart for solving the problem of the present invention;

[0046] Figure 4 These are five typical scenarios and their occurrence probability diagrams of the present invention, where Figure 4 (a) is the output curve of scenario 1, Figure 4 (b) is the output curve of scenario 2. Figure 4 (c) is the output curve of scenario 3, Figure 4 (d) is the output curve of scenario 4, Figure 4 (e) is the output curve of scenario 5, Figure 4 (f) is the probability of occurrence of each scene;

[0047] Figure 5 The topology diagram of the IEEE33 node system connected to HESS-SOP;

[0048] Figure 6 is the fluctuation range of the net load of the system nodes;

[0049] Figure 7 Comparison of supply and demand balance of reference line flexibility under four scenarios;

[0050] Figure 8 The different energy storage power curves and SOC changes in scheme 4;

[0051] Figure 9 Comparison of the average voltage of each node under four schemes. DETAILED DESCRIPTION

[0052] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0053] The present invention proposes a novel coordinated planning and operation method for a power distribution system, which comprises the following steps:

[0054] Construct a new distribution system planning and operation joint optimization model for HESS-SOP node-network flexibility. The optimization model is divided into a planning layer model and an operation layer model. The constraints of the planning layer model include flexibility supply and demand balance constraints, and the constraints of the operation layer model include HESS-SOP operation constraints.

[0055] The joint optimization model is solved based on a hybrid optimization algorithm to obtain an optimal planning configuration and an operation strategy result, wherein the optimal planning configuration includes an optimal SOP capacity and an optimal DCDC converter capacity.

[0056] First, the new power distribution system includes an intelligent soft switch SOP and a hybrid energy storage system HESS. The intelligent soft switch SOP includes a back-to-back voltage source converter VSC1 and a back-to-back voltage source converter VSC2. The back-to-back voltage source converter VSC1 is a converter ACDC1, and the back-to-back voltage source converter VSC2 is a converter ACDC2. The converter ACDC1 and the converter ACDC2 are interconnected and connected in parallel with a capacitor. The hybrid energy storage system HESS adopts an active topology structure, including a converter DCDC1 and a converter DCDC2. The converter DCDC1 and the converter DCDC2 are both connected to the converter ACDC1 and the converter ACDC2. The lithium-ion battery LIB is connected in parallel on both sides of the converter DCDC1, and the supercapacitor SC is connected in parallel on both sides of the converter ACDC2. The structural diagram of the new power distribution system is shown in FIG. Figure 1 shown.

[0057] The method of constructing a new distribution system planning-operation joint optimization model for HESS-SOP node-network flexibility in the present invention is as follows:

[0058] 1. Analyze the HESS-SOP topology. The present invention uses a back-to-back voltage source converter (B2B VSC) as the basic structure of the SOP and adopts a PQ-VdcQ control mode. Its controllable variables are active and reactive power output. The HESS adopts an active topology, with lithium-ion batteries (LIBs) as energy storage and supercapacitors (SCs) as power storage. Power electronic converters at both ends of the SOP are used to control the charge and discharge mode of the energy storage element. The energy storage function is integrated into its power transmission control function, making the HESS-SOP a highly integrated and flexible control device.

[0059] 2. Analysis of HESS-SOP regulatory characteristics:

[0060] (1) HESS control characteristics

[0061] Charge and discharge characteristics: The HESS charge and discharge power consists of two parts, and at time t, each type of energy storage is only in the charging or discharging state. Its model is as follows:

[0062]

[0063] Formula (1) is the HESS charging and discharging power model, where are the charge and discharge power of LIB and SC at time t, respectively. The power value is set to be negative in the charging state and positive in the discharging state. Formula (2) is the charge and discharge sign model. are the charge and discharge flags of LIB and SC at time t, which are 0-1 variables; η LIB ,η SC are the charge-discharge conversion efficiencies of LIB and SC respectively; Equation (3) is the charge-discharge power constraint, are the total capacity of the two DC / DC converters respectively.

[0064] The power loss during HESS operation mainly consists of two parts: LIB and SC charging and discharging power loss:

[0065]

[0066] Where: are the total loss and charging and discharging losses of HESS at time t, respectively.

[0067] Power balance constraints:

[0068]

[0069] Where: E LIB,t 、E SC,tare the power of LIB and SC at time t, and Δt is the scheduling time interval.

[0070] State of charge constraints:

[0071]

[0072] Where: SOC L IB,t , SOC S C,t and are the state of charge and upper and lower limits of operation of LIB and SC at time t; SOC L IB,t0 , SOC S C,t0 and SOC L IB,T , SOC L IB,T are the charge states of LIB and SC at the beginning and end of the scheduling cycle respectively; T is the duration of the scheduling cycle.

[0073] (2) SOP control characteristics

[0074]

[0075] Where: P sop,i,t 、P sop,j,t and are the active power output of SOP from ports i and j at time t and their corresponding active power loss; A sop,i 、A sop,j are the active power loss coefficients at ports i and j of the SOP respectively; Q sop,i,t , Q sop,j,t are the reactive power outputs at ports i and j of the SOP respectively; Q sop,i,min , Q sop,j,min and Q sop,i,max , Q sop,j,max They are the upper and lower limits of reactive power output at ports i and j of the SOP respectively; are the rated capacities of converters at ports i and j of SOP respectively; is the total active power loss of the HESS-SOP device at time t.

[0076] Formula (8) is the HESS-SOP power balance constraint, which means that the sum of the active power output from both ends of the SOP, the active power input from the HESS to the SOP, and the active power loss of the SOP is 0. In addition, it is stipulated that the power flows out of the SOP in a positive direction and flows into the SOP in a negative direction. The HESS power flows into the SOP in a positive direction and flows from the SOP to the HESS in a negative direction. Formula (9) is the expression for the active power loss of the SOP. Formula (10) is the upper and lower limit constraint of the reactive power output of the SOP. Formula (11) is the capacity constraint of the SOP. The rated capacity of the two ends is usually set to be equal, that is, Equation (12) represents the total loss of the HESS-SOP device.

[0077] Different from the primary SOP, the SOP with HESS adds energy storage devices. The sum of active power injected into the network by VSC1 and VSC2 is not always equal to 0, which greatly increases the operational flexibility. Its operational feasible domain is as follows: Figure 2 shown.

[0078] In the above process, formulas (1) and (2) represent the HESS control characteristics, which determine the charge and discharge states of the two different HESS energy storages. Formula (8) represents the HESS-SOP power balance constraint, where the two VSCs of the SOP can control the bidirectional flow of power. These two parts are used to define multiple operating states.

[0079] 3. HESS-SOP supports the dual flexibility analysis of the "node-network" of the new power distribution system:

[0080] (1) Node flexibility resource supply and demand model

[0081] The demand for node flexibility in the new distribution system mainly includes net load fluctuation and its corresponding prediction error, as shown in Equations (13) and (14):

[0082]

[0083] Where: are the actual value and predicted value of net load at node i at time t respectively; are the actual value and predicted value of load respectively; are the actual values ​​of power support from wind and solar nodes to node i; are the predicted power support values ​​of wind and solar nodes to node i; Ω WT ,Ω PV are wind and solar generator node sets respectively; ω i 、ω load,i 、ω WT,i 、ω PV,i They are respectively the system net load prediction error, load prediction error, wind turbine power support prediction error, and photovoltaic unit power support prediction error, all of which obey the normal distribution with an expectation of 0.

[0084] To calculate the net load fluctuation range, the standard deviation of the net load forecast error is defined as:

[0085]

[0086] In the formula are the standard deviation of net load, wind and solar power units, and load forecast error, respectively; are the installed capacities of wind and solar turbines respectively.

[0087] The present invention assumes a prediction confidence level of 95%. By comparing the Z-score table, the corresponding confidence level critical value z is 1.96. Therefore, the net load fluctuation range at time t can be expressed as:

[0088]

[0089] The node upward and downward flexibility requirement boundaries are as follows:

[0090]

[0091] As an important node-type flexibility resource, HESS can support bidirectional flexibility and safety regulation by determining the scope of multiple types of energy storage, while extending battery life. Its upper and lower flexibility regulation capabilities are shown in formula (19):

[0092]

[0093] Where: They are the upward and downward adjustment flexibility capabilities of the hybrid energy storage system at time t; are the maximum active charge and discharge limits of lithium batteries and supercapacitors at time t respectively.

[0094] In addition, the upper-level main network is the most basic flexibility resource supporting the distribution network. Its control characteristics are as follows:

[0095]

[0096] Where: are the upward and downward flexibility capabilities of the main grid system at time t; ΔP MG,max is the maximum value of the upper main grid climbing power; P MG,max Provides maximum power support for the upper power grid; is the power transmission from the upper power grid to the distribution network at time t.

[0097] (2) Grid flexibility resource supply and demand model

[0098] The node resource flexibility response capability fully releases the flexible transmission capability that is highly dependent on network resources. The connection between the two is established by introducing the injection shift distribution factor (ISDF), which reflects the impact of the node active power change on the line flow, as shown in formula (21):

[0099] ΔP Branch,j =I SDF,i:j ΔP i (twenty one)

[0100] Where: ΔP i The change in active power injected into node i; ΔP Branch,j is the change in active power flow on line j; I SDF,i～j Inject the transfer distribution factor between node i and line j.

[0101] In the entire power distribution system, the node and line flexibility are highly coupled. The supply of node flexibility power can be allocated to each line through ISDF mapping, as shown in Equations (22) and (23):

[0102]

[0103] Where: The transmission capacity is provided for the flexibility of increasing and decreasing the line j at time t; I SDF,MG～j Inject transfer distribution factor between upper grid connection node and line j; Ω HESS is the node where the HESS is located; M is the number of lines in the system. Similarly, the node flexibility power demand is obtained from each line through ISDF allocation:

[0104]

[0105] Where: is the required transmission volume of up and down adjustment flexibility of line j at time t; N is the number of system nodes.

[0106] b03) Establishment of flexible supply and demand balance conditions and evaluation indicators

[0107] When the supply of flexibility resources on all lines in the system meets the demand envelope at any point in time and is within the transmission carrying capacity of the line, it means that the system has sufficient flexibility resources. However, in new distribution systems with high penetration of renewable energy, there are many types of flexibility resources and their regulation characteristics vary greatly. It is extremely difficult and uneconomical to fully cover the flexibility demand. It is necessary to establish reasonable flexibility indicators to reflect the system's ability to cope with emergencies. Taking the line supply and demand balance as the starting point, two types of flexibility indicators are established: the expected probability of insufficient flexibility (EPIF) and the expected value of insufficient flexibility (EIF).

[0108] EPIF describes the expected value of the ratio of the line inflexibility time period to the scheduling cycle duration, reflecting the probability of the inflexibility event, as shown in formula (24):

[0109]

[0110] Where: θ up ,θ down They are respectively adjusted upward and downward for the expected value of the probability of insufficient flexibility; are the total duration of the upward and downward insufficient flexibility intervals in a scheduling cycle; T is the total scheduling cycle.

[0111] EIF describes the expected value of line flexibility deficiency and reflects the degree of inflexibility deficiency, as shown in formula (25):

[0112]

[0113] Where: E up 、E down are the expected values ​​of the amount of inflexibility increased or decreased respectively; Z1 and Z2 are the number of inflexibility intervals increased or decreased respectively; k1 and k2 are the sequential numbers of inflexibility intervals increased or decreased respectively; They are the left and right time boundary points of the k1th interval of insufficient upward flexibility; They are respectively the left and right time boundary points of the k2th interval of insufficient downward flexibility.

[0114] By reasonably relaxing the supply and demand balance conditions, the flexibility supply and demand balance criteria can be described as follows: 1) The expected probability of insufficient flexibility on the system lines is lower than a given threshold; 2) The expected value of insufficient flexibility on the system lines is lower than their respective maximum values. This can be described using the following formula:

[0115]

[0116] Where: θ max is the maximum probability of insufficient flexibility of a given allowed line; E max The system achieves flexibility supply and demand balance when all lines in the system meet the above two supply and demand balance criteria during the entire scheduling.

[0117] Based on the above analysis, the model proposed by the present invention is as follows:

[0118] (1) Planning layer model:

[0119] (1) Objective function

[0120] The planning layer model takes the minimum annual comprehensive operating cost of HESS-SOP as the optimization goal and sets the objective function F p It mainly includes the annual investment and operation and maintenance costs of the HESS-SOP devices to be planned for the power distribution system, as shown below:

[0121]

[0122] Where: They are the annual investment cost and maintenance cost of SOP, HESS, etc.; their specific expressions are as follows:

[0123] SOP and other annual investment costs and maintenance costs:

[0124]

[0125] Where: d sop is the SOP discount rate; y sop is the service life of SOP; c sop is the unit capacity investment cost; η sop is the annual operation and maintenance cost coefficient.

[0126] HESS annual investment cost and maintenance cost:

[0127]

[0128] Where: y LIB 、y sc are the service life of LIB and SC respectively; d H is the HESS discount rate; are the rated power and rated capacity of LIB and SC respectively; c LIB,P 、c LIB,E 、c SC,P 、c SC,E The unit power and capacity installation costs of LIB and SC are respectively;

[0129] cDCDC is the unit DC-DC converter capacity cost, ξ LIB ,ξ SC are the maintenance costs per unit capacity of LIB and SC, Y T For working hours, take 8760.

[0130] (2) Constraints

[0131] DC / AC converter configuration constraints

[0132]

[0133] Where: The upper limit of the installation capacity allowed by SOP at the planned location; sop Unit installation capacity; m sop The number of SOP unit capacity configurations must be a non-negative integer.

[0134] DC / DC Converter Configuration Constraints

[0135]

[0136] Where: The upper limit of the DC / DC converter capacity allowed for installation in HESS-SOP; DCDC is the unit installation capacity of the DC / DC converter; m DCDC Configure the quantity per unit capacity of the DC / DC converter.

[0137] In addition, the line flexibility supply and demand balance constraint must be met at the planning level, as shown in formula (26).

[0138] (2) Operational layer model:

[0139] Given that node flexibility resources and grid flexibility resources are mutually coupled in the actual operation of the distribution system, the injection transfer distribution factor (ISDF) is introduced to shift the supply and demand balance problem from the conventional node perspective to the line supply and demand balance problem, and a flexibility over-limit evaluation index is established to simultaneously reflect the supply and demand balance of flexibility in both node and grid dimensions. A flexibility over-limit penalty cost factor is introduced into the operation layer model to map the system flexibility quality to an economic indicator, and the optimization goal is to minimize the total cost of the system under multiple scenarios. The objective function F R Mainly includes system network loss cost C L , HESS-SOP action cost C A , Annual cost of purchasing electricity from the upper grid C EP , and the flexibility penalty cost C fle , the specific formula is as follows:

[0140] (1) Objective function

[0141] minF R =C L +C A +C EP +C fle (36)

[0142]

[0143] Where: c LIB 、c SC 、c L are the unit power operation cost and unit active power network loss cost of LIB and SC respectively; c EP The price of electricity purchased from the higher-level power grid unit; The power support provided by the upper power grid at time t in scenario s; is the total network loss of the distribution system at time t in scenario s; is the internal loss of HESS-SOP at time t in scenario s, and its specific expression is shown in formula (12); N s is the number of scenes, P(s) is the scene probability; are the charge and discharge power of LIB and SC at time t in scenario s; c up 、c d It is the penalty factor for the flexibility exceeding the upper and lower limits.

[0144] (2) Constraints

[0145] The operation layer model must satisfy the HESS-SOP operation constraints, as shown in formulas (1)-(12); in addition, it must also satisfy the distribution system operation flow constraints, safety constraints, power balance constraints, etc. Considering that formulas (9), (11) and flow constraints are nonlinear models, the convex relaxation method is introduced to transform them into a second-order cone relaxation form.

[0146] The joint optimization model established above is difficult to solve using a single solution method or directly using a solver. Therefore, a hybrid optimization algorithm based on the combination of genetic algorithm and cone programming is proposed. The basic idea is to use the genetic algorithm as the overall framework of the optimization algorithm to determine the configuration variables of the planning layer and pass them into the operation layer as constraints. In each iteration of the genetic algorithm, the Gurobi solver is called to solve the cone programming model of the operation layer in each scenario. The specific solution process is as follows: Figure 3 shown.

[0147] The beneficial effects of the present invention are:

[0148] 1) The established HESS-SOP topology and control characteristic model has significant practical value. By switching between multiple operating states, it optimizes the system power flow distribution and alleviates the imbalance between flexibility supply and demand at both the temporal and spatial levels.

[0149] 2) The established flexibility supply and demand balance model and indicators based on transfer distribution factors can effectively quantify and characterize flexibility resources and analyze the "node-network" dual-dimensional supply and demand balance mechanism, which meets the actual needs of the new distribution system;

[0150] 3) A new distribution system "planning-operation" joint optimization model is constructed for the HESS-SOP "node-network" dual-dimensional flexibility, which combines the reasonable configuration and deployment of early flexibility resources with the optimal operation of multiple time-series scenarios, and significantly improves the system economy and flexibility through "planning-operation" joint optimization.

[0151] The following is an actual experimental analysis:

[0152] An example analysis is conducted based on the improved IEEE33-node power distribution system. Figure 5 As shown in Figure 1, wind turbines are connected at nodes 11 and 32, and photovoltaic turbines are connected at nodes 6 and 16. The specific parameter settings are shown in Table 1. Based on historical data, Latin hypercube sampling (LHS) is used to generate scenarios and the backward subtraction method is used to reduce the generated scenarios to 5 classic scenarios. The specific output curves are shown in Figure 1. Figure 3 As shown, in addition, the system structure parameters refer to the IEEE33 standard node system in the pandapower library, Table 2 is the hybrid energy storage parameter table, Table 3 is the intelligent soft switch parameter table, and Table 4 is other parameters.

[0153] Table 1 Wind and solar access location and capacity

[0154]

[0155] Table 2 Hybrid energy storage parameters

[0156]

[0157] Table 3 Intelligent soft switch parameters

[0158]

[0159] Table 4 Other related parameters

[0160]

[0161]

[0162] The analysis steps include:

[0163] a) Analysis on improving system economic efficiency;

[0164] b) Analysis on improving system flexibility;

[0165] c) Comparison of voltage fluctuations.

[0166] The analysis of improving the economic efficiency of the system is as follows:

[0167] a01) Comparison scheme settings:

[0168] In order to compare and analyze the effect of connecting the HESS-SOP device to the distribution network on improving its economy and flexibility, four comparison schemes are set up here:

[0169] Solution 1: No power flow regulation device is connected, and calculation and analysis are performed only on the original distribution network structure.

[0170] Option 2: Access the general SOP for planning and operation analysis.

[0171] Option 3: Connect to the E-SOP device for planning and operation analysis.

[0172] Option 4: Connect the HESS-SOP device proposed in this article to perform planning-operation analysis.

[0173] a02) Cost Analysis:

[0174] Table 5 Comparison of configuration results and costs of Schemes 2, 3, and 4

[0175]

[0176] Note: Configuration cost includes annual investment cost and operation and maintenance cost

[0177] Table 6 Comparison of annual operating costs under four schemes

[0178]

[0179] Note: The time scale for calculating each cost is one year; the total cost is the sum of the configuration cost and each operating cost.

[0180] Table 5 compares the configuration results and costs of Schemes 2, 3, and 4. As shown in the table, compared to Scheme 2, which installs a standard SOP, Schemes 3 and 4 configure larger ACDC converter capacities (i.e., SOP capacities). This is because the ACDC converter capacities in ESOP and HESS-SOP must accommodate both branch transmission power and energy storage system transmission power. Similarly, the DC / DC converter capacities in Schemes 3 and 4 must accommodate the transmission power of each energy storage system. Regarding energy storage system parameter configuration, given the different output characteristics and mission attributes of LIBs and SCs, SCs are typically configured with higher power and lower capacity than LIBs to accommodate different operating strategies.

[0181] Table 6 compares the annual operating costs under the four schemes. Combining Tables 5 and 6, we can see that although Scheme 4 has higher distribution system configuration costs after configuring the regulating device, the total annual operating costs are reduced by 50.01%, 47.83%, and 0.82% compared to Schemes 1, 2, and 3, respectively. The annual network loss costs are reduced by 60.40%, 57.89%, and 15.77%, respectively; the flexibility costs are reduced by 83.89%, 75.68%, and 52.48%, respectively; and the annual electricity purchase costs are reduced by 89.39%, 85.85%, and 52.25%, respectively. Comparing Option 4 with Option 1, total costs decreased significantly after installing the HESS-SOP. The most significant improvements were seen in flexibility costs and electricity purchase costs, which decreased by 6.4562 million yuan and 7.7701 million yuan, respectively. This is due to the definition of system flexibility supply-demand balance: when the system has sufficient flexibility to adjust resource supply up and down, the corresponding probability and cost of load shedding and wind and solar curtailment decreases, and vice versa. The proposed HESS-SOP device combines the spatial flow optimization capabilities of the SOP with the temporal power scaling capabilities of the HESS, significantly enhancing system flexibility while reducing reliance on external networks. Comparing Option 4 with Option 3, the HESS consisting of LIBs and SCs achieves more refined power distribution, balances response speed and energy storage, and offers superior overall economic benefits compared to installing a single energy storage ESS.

[0182] The analysis of improving the economic efficiency of the system is as follows:

[0183] b01) Analysis of the original system node net load fluctuation

[0184] Figure 6 This is a bar chart of the net load fluctuation range of nodes in the distribution system, taking into account prediction errors. Combined with Table 1 and from a spatial perspective, we can see that compared to other nodes, the net load fluctuation range of nodes connected to wind and solar power (such as nodes 6, 11, 16, and 32) is significantly larger, indicating that these nodes have a large flexibility deficit during operation. Combined with the wind, solar, and load output curves and from a temporal perspective, compared to other times, when wind, solar, and load fluctuate sharply (such as at 9:00, 1:00 PM, and 3:00 PM), the net load fluctuation range is also large, and similarly, these nodes have a large flexibility deficit.

[0185] b02) Comparison of flexibility supply and demand balance

[0186] Conventional research methods based on the node perspective lack the analysis of the flexibility resource transmission process. The introduction of transfer factors transforms it into a study of the supply and demand balance of lines, which can simultaneously reflect the node flexibility resource regulation capability and the line flexibility resource transmission and distribution capability. Figure 3The analysis shows that there is a large flexibility shortage in node 11. During the scheduling process, the lines connected to it need to bear a heavier task of delivering flexibility resources. Therefore, the reference line (see Figure 2 Marked) to conduct comparative analysis on the balance of supply and demand of line flexibility under four schemes, such as Figure 7 shown.

[0187] Depend on Figure 7 As shown in the figure, the bar chart shows the range of system flexibility demand for upward and downward adjustments, while the line chart shows the flexibility supply for each solution. Affected by fluctuations in net load at system nodes and forecast errors, the reference line experiences both upward and downward flexibility demands at various time points. Combining the load fluctuation curve with the wind and solar output curves, upward flexibility demand peaks during peak load periods (6:00-11:00 and 1:00-3:00 PM), while downward flexibility demand peaks during peak wind and solar output periods (6:00-19:00 PM).

[0188] Compared with Option 1, Options 2, 3, and 4 are configured with SOP, which significantly improves the supply and demand balance of flexibility resources for upward adjustments on the line; and compared with Options 1 and 2, Options 3 and 4 configure energy storage systems to increase system node flexibility resources, which significantly improves the supply and demand of flexibility resources for the line. From the comparison of the above two options, it can be seen that the system configuration SOP can better improve the balance of upward flexibility, while the configuration of energy storage systems tends to improve the downward flexibility. This is because, due to economic costs, the configuration capacity of the energy storage system is much smaller than the total system load, and due to Figure 4 From the demand bar chart, we can see that the reference line has a much larger deficit in upward flexibility than downward flexibility, so the energy storage system cannot provide sufficient upward flexibility support. SOP can effectively optimize the system flow distribution, significantly improving the balance between upward flexibility supply and demand. However, SOP does not generate or consume system power in essence. When the system has a deficit in downward flexibility, the system without energy storage has a weak ability to "accommodate" excess flexibility resources. Table 7 shows the comparison of the overall system flexibility indicators, and its numerical information is consistent with Figure 7 The conclusions are basically consistent with those of , verifying the rationality of the reference line selection. In summary, configuring SOP and energy storage systems simultaneously can effectively take into account the flexibility requirements of both upward and downward adjustments of the system.

[0189] Table 7 Comparison of system flexibility indicators under four schemes

[0190]

[0191] b03) Output analysis:

[0192] SCs, with their rapid charge and discharge characteristics, are suitable for smoothing high-frequency components in the net load curve. LIBs, with their large capacity and long charge and discharge duration, are suitable for smoothing low-frequency components in the net load curve. However, low-frequency components are in short supply, and deploying LIBs of the same capacity would undoubtedly significantly increase investment and operating and maintenance costs. Therefore, consideration is being given to cooperating with the upper-level power grid to smooth low-frequency components, under the premise of time-of-use electricity prices. Figure 8 The figure shows the active power output curves and state of charge (SOC) of the LIB and SC in Scenario 1 for Scheme 4. The sampling interval is 15 minutes, and the initial SOC of both the LIB and SC is set to 0.5, which remains consistent throughout the scheduling cycle. The figure shows that the SC's active power charging and discharging states switch rapidly, effectively smoothing out the high-frequency component shortfall. During off-peak hours (0:00-7:00, 21:00-24:00, off-peak electricity price: 0.32 yuan / kWh), the upper grid provides power support for the low-frequency component and charges the LIB, causing its SOC to continue to rise but not exceed the upper limit of 0.8. During peak hours (8:00-11:00, 17:00-20:00, peak electricity price: 0.58 yuan / kWh), the LIB responds quickly and assumes most of the power output. When it reaches the upper limit of 0.7MW, the remainder is supplemented by the upper grid. Similarly, during LIB discharge, the SOC should not fall below the lower limit of 0.2. Therefore, the SOC value should be increased during off-peak hours and normal hours (12:00-16:00, normal electricity price: 0.42 yuan / kWh) to "accumulate power" for LIB discharge during the peak period. The power combination strategy of LIB and the upper-level power grid can not only effectively smooth the low-frequency component of the net load, but also LIB can achieve arbitrage through "low storage and high generation".

[0193] Compared to Option 3, which only uses a single energy storage system, Option 4 leverages the technical characteristics of different energy storage media and coordinates their use. This effectively avoids the risk of SOC overruns in actual operation, maintains the SC's ability to respond to high-frequency power shortages, and reduces the frequency of LIB charge and discharge state transitions, extending their service life. Overall, Option 4's introduction of a HESS enriches the system's flexible resource resources and enhances its flexibility and adaptability to various scenarios.

[0194] The voltage fluctuation comparative analysis is specifically as follows:

[0195] Under the same scenario, the average voltage of each node in the four schemes during the entire scheduling period is compared, as shown in the following example: Figure 9 shown. Figure 9The figure shows that some nodes in Scheme 1 experience voltage over-limits, while Schemes 2, 3, and 4 do not. This is because Schemes 2, 3, and 4 incorporate SOPs, which not only optimize the system's active power flow but also provide continuous reactive power support, significantly improving system voltage levels. The four curves show that compared to Schemes 1 and 2, Schemes 3 and 4 incorporate energy storage systems, which can shift the allocation of active flexibility resources over time, resulting in smoother overall voltage fluctuations. In summary, the spatiotemporal dual regulation characteristics of SOPs and energy storage systems can effectively address the dual challenges of weak system flexibility regulation and line power transmission congestion.

[0196] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A novel distribution system collaborative planning and operation method, characterized in that: Methods include: Construct a new distribution system planning and operation joint optimization model for HESS-SOP node-network flexibility. The optimization model is divided into a planning layer model and an operation layer model. The constraints of the planning layer model include flexibility supply and demand balance constraints, and the constraints of the operation layer model include HESS-SOP operation constraints. Solving the joint optimization model based on a hybrid optimization algorithm to obtain an optimal planning configuration and an operation strategy result, wherein the optimal planning configuration includes an optimal SOP capacity and an optimal DCDC converter capacity; The new power distribution system includes an intelligent soft switch SOP and a hybrid energy storage system HESS. The intelligent soft switch SOP includes a back-to-back voltage source converter VSC1 and a back-to-back voltage source converter VSC2. The back-to-back voltage source converter VSC1 is a converter ACDC1, and the back-to-back voltage source converter VSC2 is a converter ACDC2. The converter ACDC1 and the converter ACDC2 are connected to each other and a capacitor is connected in parallel. The hybrid energy storage system HESS adopts an active topology structure, including a converter DCDC1 and a converter DCDC2. The converter DCDC1 and the converter DCDC2 are both connected to the converter ACDC1 and the converter ACDC2. Lithium-ion batteries LIB are connected in parallel on both sides of the converter DCDC1, and supercapacitors SC are connected in parallel on both sides of the converter ACDC2.

2. A novel power distribution system collaborative planning and operation method according to claim 1, characterized in that: The HESS-SOP operation constraints include charge and discharge characteristic constraints, power loss constraints, power balance constraints, state of charge constraints and SOP regulation constraints.

3. A novel power distribution system collaborative planning and operation method according to claim 2, characterized in that: The charge and discharge characteristic constraints are: in, are the charge and discharge powers of LIB and SC at time t, are the charge and discharge flags of LIB and SC at time t, which are 0-1 variables, and η LIB ,η SC are the charge-discharge conversion efficiency of LIB and SC, are the total capacity of the two DC / DC converters respectively.

4. A novel power distribution system collaborative planning and operation method according to claim 3, characterized in that: The SOP control constraints are: Among them, P sop,i,t 、P sop,j,t and are the active power output and the corresponding active power loss of SOP from ports i and j at time t, respectively, where ports i and j represent the two ports of ACDC1 and ACDC2 respectively; sop,i 、A sop,j are the active power loss coefficients at ports i and j of the SOP respectively; Q sop,i,t , Q sop,j,t are the reactive power outputs at ports i and j of the SOP respectively; Q sop,i,min , Q sop,j,min and Q sop,i,max , Q sop,j,max They are the upper and lower limits of reactive power output at ports i and j of the SOP respectively; are the rated capacities of converters at ports i and j of SOP respectively; is the total active power loss of the HESS-SOP device at time t.

5. A novel power distribution system collaborative planning and operation method according to claim 1, characterized in that: The flexibility supply and demand balance constraint is: Among them, θ max is the maximum probability of insufficient flexibility of a given allowed line; E max For a given maximum expected value of the allowed line flexibility deficiency, E up 、E down are the expected values ​​of the flexibility deficit for upward and downward adjustments, θ up ,θ down Adjust the expected value of the probability of insufficient flexibility upward or downward respectively.

6. A novel power distribution system collaborative planning and operation method according to claim 5, characterized in that: The expected values ​​of the upward and downward adjustment flexibility deficiency are specifically: Among them, Z1 and Z2 respectively increase and decrease the number of intervals with insufficient flexibility; k1 and k2 respectively increase and decrease the sequence number of intervals with insufficient flexibility; They are the left and right time boundary points of the k1th interval of insufficient upward flexibility; are the left and right time boundary points of the k2th interval of insufficient downward flexibility, M is the number of lines in the system, is the required transmission volume of up and down adjustment flexibility of line j at time t, Provides throughput for the flexibility of up- and down-regulating line j at time t.

7. A novel power distribution system collaborative planning and operation method according to claim 6, characterized in that: The expected probability of insufficient upward and downward flexibility is: in, are the total duration of the upward and downward insufficient flexibility intervals in a scheduling cycle; T is the total scheduling cycle.

8. A novel power distribution system collaborative planning and operation method according to claim 5, characterized in that: The objective function of the planning layer model is: in, They are the annual investment cost and maintenance cost of SOP, HESS, etc.; The objective function of the operational layer model is: minF R =C L +C A +C EP +C fle Among them, c LIB 、c SC 、c L are the unit power operation cost and unit active power network loss cost of LIB and SC respectively; c EP The price of electricity purchased from the higher-level power grid unit; The power support provided by the upper power grid at time t in scenario s; is the total network loss of the distribution system at time t in scenario s; is the internal loss of HESS-SOP at time t in scenario s, N s is the number of scenes, P(s) is the scene probability; are the charge and discharge power of LIB and SC at time t in scenario s; c up 、c d It is the penalty factor for the flexibility exceeding the upper and lower limits.

9. A novel power distribution system collaborative planning and operation method according to claim 1, characterized in that: The specific steps for solving the joint optimization model based on the hybrid optimization algorithm to obtain the optimal planning configuration and operation strategy results are as follows: Initialize the parameters and use Latin hypercube sampling to generate scenes; Enter the planning layer model to obtain scenario parameters and HESS initial configuration plan; generating an initial configuration scheme population, the population including SOP capacity and DCDC converter capacity, performing roulette wheel selection, multi-point crossover and multi-point mutation, and merging the parent and child populations; The annual investment and maintenance cost of the HESS-SOP device is used as the individual fitness to determine whether it meets the constraints of the planning layer. If not, the chromosome is regenerated. If so, the fitness is calculated, and then it is determined whether it meets the convergence conditions of the planning layer. If so, the configuration results obtained by the planning layer are input into the operation layer model. Otherwise, roulette wheel selection, multi-point crossover and multi-point mutation are repeated. After the configuration results obtained by the planning layer model are input into the operation layer, the scenario s is set, the operation layer model is cone processed, the constraints of the operation layer are linearized, the solver is called to solve the operation layer model, and then the scenario is switched until all scenarios are traversed to determine whether the convergence conditions of the operation layer model are met. If not, the planning layer model is returned to be solved again. If so, it is determined whether the reproduction generation is reached. If so, the optimal planning configuration and operation strategy results are output. If not, the roulette wheel selection, multi-point crossover and multi-point mutation are repeated.

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