Intelligent energy storage soft switch configuration method considering refined photovoltaic power generation potential evaluation

Abstract

The invention relates to an intelligent energy storage soft switching configuration method considering refined photovoltaic power generation potential evaluation. The method comprises the following steps: constructing a photovoltaic power generation potential evaluation model based on a physical model chain; constructing an intelligent energy storage soft switch model; and configuring the obtained intelligent energy storage soft switch model. An energy storage device is integrated to a direct current port of an SOP, an intelligent energy storage soft switching device model is constructed, a power distribution network has space-time flexible adjustment potential, an intelligent energy storage soft switching configuration method considering photovoltaic power generation potential evaluation is provided, a space power transfer-time energy scheduling two-dimensional cooperation mechanism of an E-SOP is utilized, and the power distribution network is optimized. And the energy production and elimination among the regions are balanced, so that the random fluctuation of the source and load sides of the power distribution network can be better relieved.

Description

Technical Field

[0001] This invention belongs to the field of power system technology, and in particular to a smart energy storage soft-switching configuration method that takes into account the refined assessment of photovoltaic power generation potential. Background Technology

[0002] As a strategic emerging industry promoted by the state to facilitate the clean and low-carbon transformation of energy and expand domestic demand, distributed photovoltaic (PV) has maintained rapid growth in my country in recent years, with its development speed and scale far exceeding expectations. Distributed PV has become a major power source, profoundly changing the basic energy structure and exhibiting the characteristics of "more than half in three categories and one main body," namely: more than half of the installed power capacity is from renewable energy sources, more than half of the installed renewable energy capacity is from PV, more than half of the installed PV capacity is from distributed PV, and distributed PV is the main body.

[0003] Currently, traditional planning methods such as building new substations and expanding line capacity lack flexibility and are insufficient to address the challenges of photovoltaic (PV) absorption caused by the high proportion of PV grids connected to multiple points, as well as issues like voltage exceeding limits, line overload, and other safety problems in the distribution network. With the advancement of diversified energy sources, new business models, and various energy utilization methods, the structure and function of future distribution networks will undergo significant changes. Distribution network planning and operation are no longer limited to local optimization of single feeders but require coordinated operation among multiple feeders. With the rapid development of flexible interconnection technology in distribution networks, smart soft switches (SOPs) can achieve real-time, precise power control at the feeder level, thereby coordinating various flexible source-load resources on a larger spatial scale. However, most existing research couples the SOP's converter to the AC side of the distribution network, only leveraging the SOP's continuous power flow control capabilities in a spatial dimension, failing to utilize the SOP's internal DC-side resource reuse effect. There is an urgent need to fully utilize the advantages of the DC port on the basis of the traditional SOP structure, explore a new type of power electronic device, and study its planning methods to achieve spatiotemporal coordinated operation and resource sharing among multiple feeders. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a smart energy storage soft-switching configuration method that takes into account the refined assessment of photovoltaic power generation potential. By integrating the energy storage device into the DC port of the SOP, a smart energy storage soft-switching device model is constructed, enabling the distribution network to have the potential for flexible spatiotemporal adjustment. Furthermore, a smart energy storage integrated soft-switching (E-SOP) configuration method that takes into account the assessment of photovoltaic power generation potential is proposed. By utilizing the dual-dimensional collaborative mechanism of "spatial power transfer - temporal energy scheduling" of E-SOP, the energy production and consumption between different regions can be balanced, thereby better mitigating the random fluctuations on both the source and load sides of the distribution network.

[0005] The technical problem solved by this invention is achieved through the following technical solution:

[0006] A method for configuring smart energy storage soft switches that takes into account refined photovoltaic power generation potential assessment includes the following steps:

[0007] Step 1: Construct a photovoltaic power generation potential assessment model based on a physical model chain;

[0008] Step 2: Integrate the energy storage device into the DC port of the smart soft switch SOP to build a smart energy storage soft switch model;

[0009] Step 3: Generate a refined photovoltaic output curve based on the photovoltaic power generation potential assessment model obtained in Step 1, establish a typical scenario based on the obtained photovoltaic output curve, and configure the intelligent energy storage soft-switching model obtained in Step 2.

[0010] Furthermore, the photovoltaic power generation potential assessment model in step 1 includes an input side, a physical model chain, and an output side. The input side includes latitude and longitude, time, total horizontal irradiance (GHI), temperature, and wind speed. The physical model chain includes a solar positioning model, an irradiance separation model, an irradiance conversion model, an irradiance loss model, a battery temperature model, a DC output power model, an inverter model, and a power loss model. The output side includes the actual AC output power of the photovoltaic system.

[0011] The latitude, longitude, and time are input into the solar positioning model, the total horizontal irradiance (GHI) is input into the irradiance separation model and the irradiance conversion model, respectively, and the temperature and wind speed are input into the battery temperature model. The solar positioning model, irradiance separation model, irradiance conversion model, irradiance loss model, and battery temperature model are connected in sequence. The irradiance loss model and battery temperature model are connected into the DC output power model, respectively. The DC output power model, inverter model, and power loss model are connected in sequence. The power loss model outputs the actual output AC power of the photovoltaic system.

[0012] Furthermore, the intelligent energy storage soft-switching model in step 2 includes an AC-DC converter, a DC-DC converter, and an energy storage device.

[0013] Furthermore, step 2 also includes operational constraints on the SOP device:

[0014]

[0015] in, and These represent the active and reactive power injected into the distribution network by the converter at node j, which is numbered i and has a smart energy storage soft switch installed at node j, respectively, in kW. and These represent the charging and discharging power of the energy storage device in the smart energy storage soft switch numbered i at time t; and These are the upper limits of reactive power and active power for the State of Operation (SOP), respectively, and both satisfy... This indicates the converter capacity of the smart energy storage soft switch numbered i installed at node j.

[0016] Furthermore, step 2 also includes energy balance constraints, power limitation constraints, and state constraints:

[0017]

[0018] In the formula, This represents the total electrical charge of the energy storage device numbered i at time t; These represent the charging and discharging efficiencies of the energy storage device; This indicates the rated capacity of the energy storage device numbered i; This indicates the rated charging and discharging power of the energy storage device numbered i; and This represents the charging and discharging state of the energy storage device; and These represent the maximum and minimum state of charge of the energy storage device numbered i, respectively.

[0019] Furthermore, the specific implementation method of step 3 is as follows:

[0020] Step 3.1: Construct the objective function;

[0021] Step 3.2: Establish constraints based on the objective function;

[0022] Step 3.3: Solve using the CPLEX solver to obtain the configuration results of the smart energy storage soft-switching model.

[0023] Furthermore, the objective function of step 3.1 is the total cost C, which includes the following components: annualized investment cost C of smart energy storage soft switching. inv Annual maintenance cost C for intelligent energy storage soft switches opm Network loss fee C loss Waste light cost C apv :

[0024] C = min(C inv +C opm +C loss +C apv ).

[0025] Furthermore, the constraints in step 3.2 include distribution network operation security constraints, distribution network operation power flow constraints, and E-SOP operation constraints.

[0026] The advantages and positive effects of this invention are:

[0027] This invention constructs a photovoltaic power generation potential assessment model based on a physical model chain; constructs an intelligent energy storage soft-switching model; and configures the resulting intelligent energy storage soft-switching model. By integrating energy storage devices into the DC port of the Smart Open Point (SOP), this invention constructs an intelligent energy storage soft-switching device model, enabling the distribution network to possess the potential for flexible spatiotemporal adjustment. Furthermore, it proposes a configuration method for the intelligent energy storage soft-switching (E-SOP) that considers photovoltaic power generation potential assessment. Utilizing the E-SOP's dual-dimensional collaborative mechanism of "spatial power transfer - temporal energy scheduling," it balances energy production and consumption between different regions, thereby better mitigating random fluctuations on both the source and load sides of the distribution network. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the chain structure of the physical model of the present invention;

[0029] Figure 2 This is a schematic diagram of the E-SOP model of the intelligent energy storage soft switch of the present invention;

[0030] Figure 3 This is a schematic diagram of a newly connected photovoltaic power distribution system in a certain area of ​​central China, as improved by the present invention.

[0031] Figure 4 This is a schematic diagram illustrating the operation of eight typical intraday smart energy storage soft switches according to an embodiment of the present invention.

[0032] Figure 5 This is a schematic diagram showing the configuration results of the intelligent energy storage soft switch in embodiments Case 1 and Case 3 of the present invention;

[0033] Figure 6 This is a schematic diagram of the configuration result of the multi-port SOP in Embodiment 2 of the present invention. Detailed Implementation

[0034] The present invention will be further described in detail below with reference to the accompanying drawings.

[0035] A method for configuring smart energy storage soft switches that takes into account refined photovoltaic power generation potential assessment includes the following steps:

[0036] Step 1: Construct a photovoltaic power generation potential assessment model based on a physical model chain.

[0037] Photovoltaic power generation potential assessment based on physical model chains can more accurately and realistically characterize photovoltaic power generation output, and is a crucial step in realizing the optimized configuration of smart energy storage soft switches in distribution networks. A schematic diagram of the physical model chain structure is shown below. Figure 1As shown, firstly, using latitude and longitude, time, and Global Horizontal Irradiance (GHI) as inputs, the effective irradiance is obtained through a solar positioning model, irradiance separation model, irradiance conversion model, and irradiance loss model. Secondly, using effective irradiance, ambient temperature, and wind speed as inputs, the battery temperature is obtained through a battery temperature model. Finally, based on the obtained effective irradiance and battery temperature, the actual AC power output of the distributed photovoltaic system after physical model chain modeling is obtained through a DC output power model, an inverter model, and a power loss model.

[0038] Step 2: Based on the photovoltaic power generation potential assessment model obtained in Step 1, construct a smart energy storage soft-switching model.

[0039] In recent years, key equipment and control technologies for flexible interconnection have developed rapidly. Smart soft switches (SOPs), as important power regulation components, enable flexible interconnection of multiple feeders, extending the source-load balance of a single feeder to resource sharing among multiple feeders. However, SOPs themselves lack energy storage capacity and must rely on the energy supply of the connected feeders, making it difficult to address issues such as midday photovoltaic power generation surplus and local energy supply-demand imbalances. This indicates limitations in their time-dimensional regulation capabilities. Energy storage devices, as energy components, can significantly enhance the flexibility of distribution networks in the time dimension, further promoting the efficient consumption of new energy sources. To address the difficulty of simultaneously meeting the spatiotemporal balance of source and load requirements with energy storage devices and SOPs, energy storage devices are integrated into the DC port of the SOP, forming a smart energy storage soft switch (E-SOP). This device combines the power regulation capability of the SOP with the energy balancing capability of energy storage, achieving complementary advantages in space and time, power and energy dimensions. This, in turn, can promote photovoltaic consumption in both time and space dimensions. Its model structure is as follows: Figure 2 As shown.

[0040] Appendix Figure 2 The model introduces a multi-port smart energy storage soft switch model using the example of an energy storage device connected to a three-port smart soft switch SOP. This is a specific model. For other numbers of ports, it can be extended to a general model simply by increasing the number of AC-DC converters. The method of this invention does not lose its generality.

[0041] In the E-SOP model, the SOP is connected via DC circuits of three back-to-back voltage source converters, and PQ control is used under steady-state operation. The operational modeling of the SOP mainly includes active power constraints and capacity constraints.

[0042] Suppose that in the intelligent energy storage soft switch numbered i, the set of nodes connected to each port is . Unitless. Assuming the injected network is in the positive direction of power flow, the expressions for the operating constraints of the SOP device are shown in equations (1)-(3):

[0043]

[0044] In the formula, and These represent the active and reactive power injected into the distribution network by the converter at node j, which is numbered i and has a smart energy storage soft switch installed at node j, respectively, in kW. and These represent the charging and discharging power of the energy storage device in the smart energy storage soft switch numbered i at time t, in kW; and These are the reactive power and active power upper limit coefficients of SOP, respectively, without units, and both satisfy... This indicates the converter capacity of the smart energy storage soft switch numbered i, installed at node j, in kVA.

[0045] In the intelligent energy storage soft switch, the energy storage device is connected to the DC port of the SOP through a DC-DC converter. Its operation model includes energy balance constraints, power limitation constraints and state constraints, which are expressed as shown in equations (4) to (9):

[0046]

[0047]

[0048] In the formula, This represents the total electrical energy of the energy storage device numbered i at time t, expressed in kW.h.

[0049] These represent the charge and discharge efficiencies of the energy storage device, without units. This indicates the rated capacity of the energy storage device numbered i, in kW.h. This indicates the rated charging and discharging power of the energy storage device numbered i, in kW; and

[0050] This represents the charging and discharging state of the energy storage device; it has no unit and is represented by 0 or 1. and These represent the maximum and minimum state of charge values ​​for energy storage device numbered i, respectively. They are unitless and are taken as 0.2 and 0.8.

[0051] Step 3: Configure the smart energy storage soft-switching model obtained in Step 2.

[0052] To ensure that the power distribution network can fully absorb renewable energy generation such as photovoltaic power under extreme weather conditions, while taking into account the economy and security of the power distribution network, this invention constructs an intelligent energy storage soft-switching configuration model based on the high-precision photovoltaic output value provided by the physical model chain.

[0053] Step 3.1: Construct the objective function

[0054] Taking into account both the economics of smart energy storage soft switching and the capacity for renewable energy absorption, the objective function is the total cost C, which includes the following components: annualized investment cost C of smart energy storage soft switching. inv Annual maintenance cost C for intelligent energy storage soft switches opm Network loss fee C loss Waste light cost C apv .

[0055] C = min(C inv +C opm +C loss +C apv (10)

[0056] ①The annualized investment cost of intelligent energy storage soft switches is as follows:

[0057]

[0058] In the formula, τ E-SOP Capital recovery factor for smart energy storage soft switch E-SOP, unitless; The unit cost per unit capacity and unit cost per unit power of the energy storage device in the E-SOP are given, in yuan. The unit capacity investment cost of the SOP converter in E-SOP is expressed in yuan. These represent the rated capacity and power of the energy storage device in E-SOP numbered k, respectively, in kW.h and kW. The rated capacity of the i-th SOP converter in the E-SOP numbered k is represented in kVA; r is the interest rate, which has no unit; LT is the equipment service life, which has no unit; Ψ(E-SOP) and Ψ(SOP) represent the sets of E-SOP and SOP respectively, both of which have no unit.

[0059] ②The annual maintenance cost of the intelligent energy storage soft switch is as follows:

[0060]

[0061] In the formula, and These are the unit power maintenance cost of the energy storage device in the E-SOP and the unit capacity maintenance cost of the SOP converter, respectively, in yuan / kW and yuan / kVA.

[0062] ③ Network loss costs are as follows:

[0063]

[0064] In the formula, f loss,t The cost of network loss at time t is expressed in yuan / W; r mn The resistance of line mn is expressed in Ω; Ω Line This is a collection of routes, without units. The square of the line current at time t in the op-th scenario is expressed in amperes (A). 2 .

[0065] ④ Cost of curtailment:

[0066]

[0067] In the formula, f pv The cost of curtailment is expressed in yuan / kW; Ψ(PV) represents the collection of distributed photovoltaic power, which has no unit. Let represent the curtailment power of distributed photovoltaic power at time t in the op-th scenario, in kW.

[0068] Step 3.2 Set constraints based on the objective function:

[0069] Model constraints include distribution network operation security constraints, distribution network operation power flow constraints, and E-SOP operation constraints:

[0070] ① Distribution network operation safety constraints:

[0071]

[0072] In the formula, The maximum current that line ij can withstand is the square of its maximum capacity, expressed in amperes (A). 2 ; This is the square of the current at node i at time t in the OP scenario, in amperes (A). 2 ; These are the squares of the upper and lower limits of the voltage at node i, respectively. The voltage squared at node i at time t in the OP scenario, in V. 2 Ω Line Ω node These represent the set of routes and the set of nodes, respectively, neither of which has a unit.

[0073] ② Distribution network operation power flow constraints:

[0074]

[0075] In the formula, π(:,j) is the set of nodes whose terminal node is j, and it has no unit; δ(j,:) is the set of nodes whose head node is j, and it has no unit; r ij xij P represents the resistance and reactance of line ij, both in Ω; ij,op (t), Q ij,op (t) represents the active power and reactive power flowing through branch ij at time t in the op scenario, with units of kW and kVar, respectively; η represents the net load at the node, in kW. SOP This is a variable indicating whether a node is connected to the SOP converter in the E-SOP; it is unitless and takes the value 0 or 1. P jk,op (t), Q jk,op (t) represents the power flowing from the current node to the child node in the op scenario, with units of kW and kVar respectively; The squares of the voltage magnitudes at nodes i and j, in volts (V). 2 ; The square of the line current ij, in amperes (A). 2 ; and The square of the line power ij is given by the following units: W. 2 and Var 2

[0076] ③E-SOP operating constraints:

[0077] The E-SOP operating constraints have been given in equations (1)-(9).

[0078] Step 3.3, Solution Method:

[0079] This invention utilizes the CPLEX solver for solving. However, the model constructed in this invention contains nonlinear power flow constraints, mixed integer running constraints, and a large number of other linear constraints, belonging to a nonlinear, nonconvex mixed integer programming model. CPLEX cannot solve the nonlinear equations, therefore, equations (3) and (20) need to be subjected to cone relaxation, as follows:

[0080]

[0081] Based on the above-mentioned intelligent energy storage soft-switching configuration method that takes into account refined photovoltaic power generation potential assessment, the effectiveness of the invention has been verified through validation in a newly connected photovoltaic power distribution system in a certain region of central China. Figure 3 As shown, the system has 4 substations, 11 feeders, and 62 nodes, making it a typical multi-station, multi-feeder power distribution system.

[0082] The load data was taken from an actual power distribution network in a region of central China, and the temperature, wind speed, and GHI data required in the photovoltaic physical model chain were all based on actual historical data from that region. The k-means clustering method was used to cluster the generated annual photovoltaic output data and the annual historical load data into eight typical scenarios. Network losses were calculated using time-of-use pricing, and the candidate nodes for the intelligent energy storage soft switch E-SOP are shown in Table 1.

[0083] Table 1. Alternative Nodes for E-SOP

[0084]

[0085] Based on the aforementioned intelligent energy storage soft-switching configuration method that takes into account refined photovoltaic power generation potential assessment, three case studies are conducted to investigate the impact of different configurations on photovoltaic absorption capacity:

[0086] Case 1: Use a physical model chain to assess the photovoltaic power generation potential and configure smart energy storage soft switching.

[0087] Case 2: Use a physical model chain to assess the photovoltaic power generation potential and configure a multi-port SOP.

[0088] Case 3: Using traditional methods to assess the potential of photovoltaic power generation and configure smart energy storage soft switches.

[0089] 1) E-SOP Operation Analysis

[0090] For Case 1, the operation of the intelligent energy storage soft switch over 8 typical days is as follows: Figure 4 As shown, by connecting energy storage to the DC port of the SOP to form a smart energy storage soft switch, it is possible to not only realize load transfer and new energy consumption of multiple feeders at the spatial level, but also improve the energy balance capability of the distribution network at the temporal level, and have good flexibility.

[0091] 2) Comparative analysis of planning results

[0092] Figure 5 , 6 The configuration results for three cases are shown.

[0093] Table 2. Economic Analysis of the Case Study

[0094]

[0095] Table 2 details the investment and maintenance costs, network loss costs, operating revenue, total cost, and photovoltaic energy consumption status under three scenarios (Case 1, Case 2, and Case 3). Specifically, Case 1 achieved a photovoltaic energy consumption rate of 78.19%, demonstrating that considering energy coordination among multiple feeders and installing energy consumption devices at key nodes is an effective strategy to improve photovoltaic energy consumption. Furthermore, the physical model chain provides more accurate photovoltaic modeling and generates more precise photovoltaic output compared to the traditional single-equation transformation photovoltaic output model; therefore, Case 1 has lower investment and maintenance costs and lower total cost compared to Case 3.

[0096] In Case 2, because only SOP (Standard Operating Procedure) devices are used for flexible interconnection of the grid architecture, there is a lack of absorption capacity on a time scale. Its photovoltaic energy absorption capacity depends entirely on the feeders with absorption capabilities and their load conditions, thus exhibiting a relatively low photovoltaic absorption capacity. Furthermore, due to the lack of energy storage facilities for regulation, the network losses in Case 2 are significantly higher than in Cases 1 and 3. Therefore, absorption methods considering energy coordination among multiple feeders often need to be combined with energy storage devices to achieve maximum effectiveness. Configuring SOPs alone often results in low economic efficiency and unsatisfactory photovoltaic absorption rates.

[0097] It should be emphasized that the embodiments described in this invention are illustrative rather than limiting. Therefore, this invention includes, but is not limited to, the embodiments described in the specific implementation. Any other implementations derived by those skilled in the art based on the technical solutions of this invention are also within the scope of protection of this invention.

Claims

1. A method for configuring intelligent energy storage soft switches that takes into account refined photovoltaic power generation potential assessment, characterized in that: Includes the following steps: Step 1: Construct a photovoltaic power generation potential assessment model based on a physical model chain; Step 2: Integrate the energy storage device into the DC port of the smart soft switch SOP to build a smart energy storage soft switch model; Step 3: Generate a refined photovoltaic output curve based on the photovoltaic power generation potential assessment model obtained in Step 1, establish a typical scenario based on the obtained photovoltaic output curve, and configure the intelligent energy storage soft-switching model obtained in Step 2.

2. The intelligent energy storage soft-switching configuration method considering refined photovoltaic power generation potential assessment according to claim 1, characterized in that: The photovoltaic power generation potential assessment model in step 1 includes an input side, a physical model chain, and an output side. The input side includes latitude and longitude, time, total horizontal irradiance (GHI), temperature, and wind speed. The physical model chain includes a solar positioning model, an irradiance separation model, an irradiance conversion model, an irradiance loss model, a battery temperature model, a DC output power model, an inverter model, and a power loss model. The output side includes the actual AC output power of the photovoltaic system. The latitude, longitude, and time are input into the solar positioning model, the total horizontal irradiance (GHI) is input into the irradiance separation model and the irradiance conversion model, respectively, and the temperature and wind speed are input into the battery temperature model. The solar positioning model, irradiance separation model, irradiance conversion model, irradiance loss model, and battery temperature model are connected in sequence. The irradiance loss model and battery temperature model are connected into the DC output power model, respectively. The DC output power model, inverter model, and power loss model are connected in sequence. The power loss model outputs the actual output AC power of the photovoltaic system.

3. The intelligent energy storage soft-switching configuration method considering refined photovoltaic power generation potential assessment according to claim 1, characterized in that: The intelligent energy storage soft-switching model in step 2 includes an AC-DC converter, a DC-DC converter, and an energy storage device.

4. The intelligent energy storage soft-switching configuration method considering refined photovoltaic power generation potential assessment according to claim 3, characterized in that: Step 2 also includes operational constraints on the SOP device: in, and These represent the active and reactive power injected into the distribution network by the converter at node j, which is numbered i and has a smart energy storage soft switch installed at node j, respectively, in kW. and These represent the charging and discharging power of the energy storage device in the smart energy storage soft switch numbered i at time t; and These are the upper limits of reactive power and active power for the State of Operation (SOP), respectively, and both satisfy... This indicates the converter capacity of the smart energy storage soft switch numbered i installed at node j.

5. The intelligent energy storage soft-switching configuration method considering refined photovoltaic power generation potential assessment according to claim 4, characterized in that: Step 2 also includes energy balance constraints, power limitation constraints, and state constraints: Where, This represents the total electrical charge of the energy storage device numbered i at time t; These represent the charging and discharging efficiencies of the energy storage device; This indicates the rated capacity of the energy storage device numbered i; This indicates the rated charging and discharging power of the energy storage device numbered i; and This represents the charging and discharging state of the energy storage device; and These represent the maximum and minimum state of charge of the energy storage device numbered i, respectively.

6. The intelligent energy storage soft-switching configuration method considering refined photovoltaic power generation potential assessment according to claim 1, characterized in that: The specific implementation method of step 3 is as follows: Step 3.1: Construct the objective function; Step 3.2: Establish constraints based on the objective function; Step 3.3: Solve using the CPLEX solver to obtain the configuration results of the smart energy storage soft-switching model.

7. The intelligent energy storage soft-switching configuration method considering refined photovoltaic power generation potential assessment according to claim 6, characterized in that: The objective function of step 3.1 is the total cost C, which includes the following components: annualized investment cost of smart energy storage soft switching C. inv Annual maintenance cost C for intelligent energy storage soft switches opm Network loss fee C loss Waste light cost C apv : C=min(C inv +C opm +C loss +C apv )。 8. The intelligent energy storage soft-switching configuration method considering refined photovoltaic power generation potential assessment according to claim 6, characterized in that: The constraints in step 3.2 include distribution network operation security constraints, distribution network operation power flow constraints, and E-SOP operation constraints.

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