A method and system for coordinated and optimized operation of power distribution network resources and energy storage

Abstract

This invention discloses an active distribution network source-storage coordinated optimization operation method and system. It obtains basic system technical information from the distribution network operator; sets the system's operating reference voltage and reference capacity; and executes the active distribution network source-storage coordinated optimization operation method considering second-order cone power flow. The relaxed second-order cone power flow is restored to its original state. The equality of the relaxed second-order cone power flow constraints during optimization operation is verified. If the equality is not satisfied, the output power flow cannot be restored; if it is satisfied, the power flow calculation results, including node voltage amplitude, branch active power, branch reactive power, and branch current amplitude, are output. The power flow phase angle is restored using the calculation results to obtain the scheduling result of the active distribution network source-storage coordinated optimization operation method considering second-order cone power flow, thus realizing the active distribution network source-storage coordinated optimization operation. It features high computational efficiency and is simple to use.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization operation technology, specifically relating to an active distribution network source-storage coordinated optimization operation method and system. Background Technology

[0002] With the high proportion and large scale of distributed photovoltaic and other renewable energy sources, as well as emerging loads such as energy storage, being connected to the grid on the distribution network side, the distribution network, which was originally a single electricity consumer, can also act as an energy producer, feeding back electricity to the high-voltage network. In this context, load fluctuations will be further amplified by the volatility and randomness of distributed power output, exacerbating the difficulty of source-load power balance and placing greater pressure on the safe operation of the grid and reliable power supply. Traditional DC power flow models are approximate estimates of active power distribution in high-voltage transmission networks, but they cannot be used at the distribution network level, mainly for the following reasons:

[0003] 1) The resistance and reactance of the lines in the distribution network are often very close, resulting in a high degree of coupling between active power and reactive power. Therefore, it is impossible to analyze only the active power without considering the impact of reactive power.

[0004] 2) Distribution networks are generally designed in a closed loop but operate in an open loop. Therefore, as the line length increases, the voltage drop will be more significant, and it is impossible to assume that the voltage amplitude is 1.

[0005] 3) In active distribution networks, the existence of bidirectional power flow makes the power flow distribution more complex, requiring a more complete power flow model to characterize it.

[0006] However, directly applying traditional power flow solution methods is difficult because the power flow equations are a system of nonlinear equations, leading to undesirable non-convex properties in the optimization problem. Linearized power flow calculation methods also introduce significant errors when the operating point voltage deviates from the rated voltage. Furthermore, the reactive power voltage support capability of distributed photovoltaic and other new energy sources is relatively weak, and their output is highly random, making them more prone to voltage exceeding limits and power flow exceeding boundaries when connected to the system.

[0007] Therefore, considering the output characteristics of renewable energy, the operation characteristics of energy storage, and the power flow description methods of distribution networks, there is an urgent need to propose an optimization model suitable for describing the bidirectional power flow distribution of active distribution networks to solve the power balance and optimization operation problems under the access of renewable energy sources such as distributed photovoltaics. Summary of the Invention

[0008] The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing a method and system for coordinated and optimized operation of active distribution networks, which is used to solve the technical problem of difficult bidirectional power flow safety operation caused by the high proportion of renewable energy access. It is used to accurately characterize the power flow distribution in the active distribution network in the source-load economic dispatch, and ensure the safe and economical operation of the system under the premise that the voltage does not exceed the limit and the power flow does not exceed the limit.

[0009] The present invention adopts the following technical solution:

[0010] An active distribution network source-storage coordinated optimization operation method includes the following steps:

[0011] S1. Based on the basic technical information of the system, set the reference voltage and reference capacity for system operation, and implement the active distribution network source-storage coordinated optimization operation method that considers second-order cone power flow;

[0012] S2. Restore the second-order cone relaxed power flow obtained from the active distribution network source-storage coordination optimization operation solution in step S1 to the original power flow. Verify whether the equality of the second-order cone power flow constraint after relaxation in step S1 optimization operation is valid. If it is not valid, the output power flow cannot be restored. If it is valid, output the power flow calculation results including node voltage amplitude, branch active power, branch reactive power and branch current amplitude.

[0013] S3. Using the power flow calculation results obtained in step S2, the power flow phase angle is restored to obtain the scheduling results of the active distribution network source-storage coordinated optimization operation method considering the second-order cone power flow, thereby realizing the active distribution network source-storage coordinated optimization operation.

[0014] Specifically, in step S1, the basic technical information of the system includes: the predicted power information of distributed renewable energy and load, the technical parameters and initial state of charge of the energy storage device in the system, and the existing transmission grid and network parameters.

[0015] Specifically, step S1 is as follows:

[0016] S101. Establish mathematical models for distributed renewable energy, energy storage devices, and second-order cones of transmission lines respectively. Couple these mathematical models to form a mathematical model for safe system operation through a system node power balance model.

[0017] S102. Establish the objective function of the optimization model for the coordinated operation of the active distribution network source and storage considering the second-order cone power flow, as well as the mathematical model of the whole system. Solve the system to obtain the scheduling results for the next 24 hours with 15-minute intervals, with the lowest economic operating cost of the system.

[0018] Furthermore, in step S101, the mathematical model for distributed renewable energy is as follows:

[0019]

[0020] Among them, T STC G STC These represent the battery temperature and solar radiation intensity under standard test conditions, respectively, G. c P represents the solar radiation intensity at the actual working point. STC T represents the rated output power under standard test conditions. c Here, k represents the battery temperature at the operating point, and k is the power-temperature coefficient.

[0021] The mathematical model of the energy storage device is as follows:

[0022]

[0023]

[0024] Among them, Ω ESS A collection of energy storage devices in an active distribution network. Let T be the maximum charging and discharging power of the i-th energy storage device in the active distribution network, and T be the number of time periods within the system's scheduling cycle.

[0025] The specific mathematical model of a second-order cone transmission line is as follows:

[0026]

[0027] in, v is the square of the magnitude of the current in branch ij. i P is the square of the node voltage magnitude. ij Q ij Let l represent the active power and reactive power flowing through a branch with endpoint i and endpoint j in a power network. ij Let E be the set of branches in the power network;

[0028] The specific system node power balance model is as follows:

[0029]

[0030] Among them, y i Let s be the ground susceptance of node i in the power network. i Let be the complex vector of injected power of node i in the power network, and N be the set of nodes in the power network.

[0031] Furthermore, in step S102, the mathematical model minf for the entire system is:

[0032] minf = minf ESS+f Grid +C PV

[0033] Among them, f ESS The daily operating cost of the energy storage device, f Grid C is the cost of the power exchange for the tie line throughout the day. PV This is a penalty for curtailment of solar power generation units.

[0034] Furthermore, the daily operating cost of the energy storage device f ESS The total daily power exchange cost of the tie line f Grid The curtailment penalty for photovoltaic power generation units, item C PV They are respectively:

[0035]

[0036]

[0037]

[0038] Where, n ESS The number of energy storage devices in an active distribution network. Here, denoted as the charging and discharging cost coefficients of the i-th energy storage device in the active distribution network, and Δt is the time interval for optimal scheduling. These represent the power purchased and sold by the active distribution network interconnection line during time period t. These represent the electricity purchase and sale prices for the active distribution network interconnection line during time period t, and n. PV c represents the number of distributed photovoltaic (PV) systems in an active distribution network. PV This represents the curtailment penalty coefficient in an active distribution network.

[0039] Specifically, step S3 is as follows:

[0040] S301. For all transmission circuits in the existing transmission network, generate the adjacency matrix of the system and eliminate the row represented by the node connected to the high-voltage network. Then transpose the adjacency matrix.

[0041] S302. The phase angle of each node is obtained by solving a linear equation system of the phase angle difference between the two ends of a branch and the phase angle of the node. Then, the branch current vector and branch power vector are obtained by Ohm's law, and the optimized scheduling results of each power source, energy storage, load and network loss are output.

[0042] Furthermore, in step S301, the transposed adjacency matrix is ​​as follows:

[0043]

[0044] Furthermore, in step S302, the branch current vector I ij and branch complex power vector S ij Specifically:

[0045]

[0046] Among them, V i Let z be the nodal voltage phasor of node i in the power network. ij Let I be the branch reactance of a power network with i as the starting point and j as the ending point. ij Let S be the current vector flowing through a branch in a power network with endpoint i and endpoint j. ij Let be the power vector flowing through the branch with i as the starting point and j as the ending point in the power network. Let be the conjugate phasor of the voltage phasor at node j. V is the conjugate of the complex impedance of branch ij. j Let θ be the nodal voltage phasor of node j in the power network. ij The voltage phase angle difference between the two ends of the line. The square of the magnitude of the current in branch ij. Let be the conjugate of the complex power flowing through branch ij.

[0047] Secondly, embodiments of the present invention provide an active distribution network source-storage coordinated optimization operation system, comprising:

[0048] The data module obtains basic technical information about the system from the distribution network operator;

[0049] The execution module, based on the basic technical information of the system obtained from the data module, sets the reference voltage and reference capacity for system operation, and executes the active distribution network source-storage coordinated optimization operation method that considers second-order cone power flow.

[0050] The verification module restores the second-order cone relaxed power flow to the original power flow and verifies whether the equality of the second-order cone power flow constraint is valid during the optimization operation of the execution module. If it is not valid, the output power flow cannot be restored. If it is valid, the output includes the power flow calculation results containing the node voltage amplitude, branch active power, branch reactive power and branch current amplitude.

[0051] The optimization module uses the power flow calculation results obtained from the verification module to restore the power flow phase angle, and obtains the scheduling results of the active distribution network source-storage coordinated optimization operation method considering the second-order cone power flow, thereby realizing the active distribution network source-storage coordinated optimization operation.

[0052] Compared with the prior art, the present invention has at least the following beneficial effects:

[0053] An optimized operation method for coordinated operation of active distribution networks (ADNs and energy storage) improves the safety and economy of ADNs in daily operation. This method establishes a single-layer deterministic model that can be directly solved using common commercial solvers, offering high computational efficiency and ease of use. By obtaining the node voltage amplitude, branch active power, branch reactive power, and branch current amplitude obtained from the optimized model, the equality of the second-order cone inequality is determined to assess whether the power flow can be restored. Compared to methods that do not consider the network structure or only consider DC / linearized power flow constraints, this invention employs a network constraint that accurately characterizes the bidirectional power flow in active distribution networks. It comprehensively considers the high coupling between active and reactive power and the magnitude of node voltage drops, ensuring the arrangement of operation plans and the safety of transmission, and better reflects the complex power flow distribution in active distribution networks. Since various distributed renewable energy sources have weak voltage support capabilities and strong non-randomness in output, this method is suitable for systems with a high proportion of distributed renewable energy, especially for active distribution networks where voltage and power flow are prone to exceed limits, simultaneously improving both safety and economy.

[0054] Furthermore, obtaining basic system technical information from distribution network operators is a necessary condition for realizing the coordinated and optimized operation of active distribution network sources and storage.

[0055] Furthermore, by setting a reference voltage and reference capacity for system operation and converting nominal values ​​into per-unit values, the computational complexity can be reduced, while also enhancing the scalability of the method. By integrating mathematical models of renewable energy and energy storage devices, optimal operational economy can be achieved without violating the safe operating conditions of the equipment.

[0056] Furthermore, establishing a source-load coordination optimization operation model for an active distribution network under second-order conical power flow constraints is the core of this invention. Under given baseline values, by establishing and solving this model, a relaxed solution can be obtained that ensures voltage and power flow do not exceed limits. Because it comprehensively considers the influence of active and reactive power in the network, it is more accurate and the results are more feasible compared to a DC power flow model. Simultaneously, since the model is a single-layer deterministic model, it can be directly calculated using commercial solvers such as Gurobi, which offer high solution efficiency and ease of use.

[0057] Furthermore, establishing the objective function of the optimization model and the mathematical model of the entire system reflects the subjective intentions of the distribution network operator, which helps to set friendly operating goals for the distribution network operator.

[0058] Furthermore, by setting the daily operating cost of the energy storage device, the configuration cost of energy storage is allocated over the entire life cycle, making it easier for system operators to make reasonable use of energy storage to extend its service life; the daily switching power cost of the tie line reflects the economic situation of the active distribution network throughout the day, which is the most important indicator for system operators; finally, setting a curtailment penalty for photovoltaic units can improve the utilization efficiency of new energy sources in the system and promote the consumption of new energy sources.

[0059] Furthermore, the relationship between the phase angle difference, voltage, current, and power phasors at both ends of the branch is analyzed to obtain the mapping relationship between the power flow relaxation solution and the actual solution. Under the influence of this mapping relationship, the second-order cone relaxation power flow obtained from the source-storage coordinated optimization operation solution of the active distribution network is restored to the actual power flow of the active distribution network. Then, the equality sign is checked to determine whether the restoration operation was successful.

[0060] Furthermore, the row represented by the node connected to the high-voltage grid is eliminated, and then the adjacency matrix is ​​transposed. The newly obtained matrix will be used as a key factor in the next step to restore the phase angle of each node.

[0061] Furthermore, by solving the equations relating the phase angle difference between the two ends of the branch and the phase angle difference between the nodes, the accurate solution of the power flow of the active distribution network is obtained, thus yielding the scheduling results of the source-storage coordinated optimization operation of the active distribution network considering the second-order cone power flow.

[0062] It is understandable that the beneficial effects of the second aspect mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0063] In summary, this invention is used to accurately characterize the power flow distribution in the source-load economic dispatch of active distribution networks, ensuring the safe and economical operation of the system under the premise that voltage and power flow do not exceed limits. Furthermore, it can be solved directly using commercial solvers, offering high efficiency and ease of use.

[0064] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the process of the present invention;

[0066] Figure 2 This is a network diagram of a 33-node system according to embodiment 3 of the present invention;

[0067] Figure 3 This is a load curve diagram of each node in the 33-node system of this invention;

[0068] Figure 4 This is a price chart of the interconnection nodes in the 33-node system of the present invention;

[0069] Figure 5 This is a diagram showing the optimized scheduling results of the 33-node system in Embodiment 3 of the present invention. Detailed Implementation

[0070] 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0072] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0073] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this document generally indicates that the preceding and following objects have an "or" relationship.

[0074] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0075] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0076] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0077] Active distribution network source-storage coordinated optimization operation method is an important prerequisite for distribution network energy management. It uses second-order cone power flow constraints to characterize the power flow distribution of the distribution network within the optimization period, reflecting the safe operation status of each branch. Combined with operational constraints from distributed photovoltaic and other new energy sources and energy storage, it jointly completes the task of energy supply and optimal allocation within the region, improving system security and economy. Furthermore, since the second-order cone power flow originates from traditional power flow equations, satisfying nodal power balance is sufficient to satisfy the global power balance of the system, while also including the overall network loss component, providing a reference for operators.

[0078] This invention provides a method for coordinated optimization of source and storage operation in active distribution networks. It accurately characterizes the power flow distribution within the network during source-load economic dispatch, ensuring safe and economical system operation under the premise of voltage and power flow not exceeding limits. The network transmission constraint set, constructed based on an accurate second-order cone AC power flow model, comprehensively considers the impact of active and reactive power components on system power generation planning and transmission security. By rationally allocating the charging and discharging periods of energy storage, the system's operating cost is minimized under given operating characteristics of distributed photovoltaic and other renewable energy sources. Furthermore, the proposed method establishes a single-layer deterministic model, which can be directly solved using common commercial solvers, resulting in high computational efficiency and ease of use. Compared to methods that do not consider the network structure or only consider DC / linearized power flow constraints, the proposed method employs network constraints that accurately characterize bidirectional power flow in active distribution networks. It comprehensively considers the high coupling between active and reactive power and the magnitude of node voltage drops, thus ensuring the arrangement of operation plans and transmission security, and better reflects the complex power flow distribution in active distribution networks. Since various types of distributed renewable energy have weak voltage support capabilities and strong randomness in power output, the method proposed in this invention is very suitable for systems with a high proportion of distributed renewable energy access, especially for active distribution networks where voltage is prone to exceed limits and power flow is prone to exceed boundaries, thereby improving both safety and economy.

[0079] Please see Figure 1 The present invention provides a method for coordinated and optimized operation of power distribution network sources and storage, comprising the following steps:

[0080] S1. Obtain basic technical information about the system from the distribution network operator;

[0081] The basic technical information of the system includes: the predicted power information of distributed renewable energy and load, the technical parameters and initial state of charge of the energy storage devices in the system, and the existing transmission grid and network parameters.

[0082] S2. Set the reference voltage and reference capacity for system operation, such as a reference voltage of 10kV and a reference capacity of 1000kVA. Execute the active distribution network source-storage coordinated optimization operation method considering second-order cone power flow, specifically including the following steps:

[0083] S201. Establish mathematical models for distributed renewable energy, energy storage devices, second-order cone transmission lines, and system node power balance models, respectively.

[0084] (1) Constructing second-order cone power flow constraints for active distribution networks

[0085] For any transmission line, it is equivalent to the connection of one branch inductance and two capacitances to ground. For the branch inductance, applying Ohm's law, the following equation holds:

[0086]

[0087]

[0088] Among them, V i Let z be the node voltage vector of node i in the power network. ij Let I be the branch reactance of a power network with i as the starting point and j as the ending point. ij Let S be the current vector flowing through a branch in a power network with endpoint i and endpoint j. ij Let E be the power vector flowing through a branch in the power network with i as the starting point and j as the ending point, and let E be the set of branches in the power network.

[0089] For any node in a power network, according to Kirchhoff's current law, the following node power balance equation exists:

[0090]

[0091] Among them, y i Let s be the ground susceptance of node i in the power network. i Let be the complex vector of injected power of node i in the power network, and N be the set of nodes in the power network.

[0092] The branch power flow model is formed by equations (1) to (3). Since the distribution network is generally designed as a closed loop and operated in an open loop, this invention considers the active distribution network to be a radial network.

[0093] If the system has n+1 nodes (nodes connected to the main network are added as relaxed nodes), then there will be n branches. The set of parameters to be solved for the above problem is:

[0094] X(s)={x=(S,I,V,s0)|x solves (1)~(3) given s} (4)

[0095] Where S represents the complex power flowing in the branch, I represents the complex current flowing in the branch, V represents the complex voltage of each node, s represents the injected complex power of each node, and s0 represents the injected complex power of the relaxation node.

[0096] Given the complex vectors of injected power at each node, there are a total of 2n+n+1 nonlinear complex equations and n+n+n+1 complex variables in the power flow of the system. The number of complex equations equals the number of unknown complex variables; therefore, from a mathematical perspective, this problem is a well-posed problem. Furthermore, since the power grid often operates near per-unit values, the equations should have a unique solution, namely the power flow solution.

[0097] Considering that complex variables are not conducive to calculation, the real and imaginary parts of the above power flow equations are separated. Therefore, this invention substitutes equation (2) into equation (1) and denotes v. i =|V i | 2 , We obtain the following formula:

[0098]

[0099] Then, by separating the active power equation and the reactive power equation in equation (3), the power flow equation in the real domain after phase angle relaxation can be obtained:

[0100]

[0101]

[0102]

[0103] Simultaneously, supplement the equation relating the square of voltage to the square of current:

[0104]

[0105] Where, p i ,q i Injecting active and reactive power into node i in the power network, P ij Q ij Let r be the active power and reactive power flowing through a branch with endpoint i and endpoint j in the power network. ij ,xij Let i and j be the branch resistance and reactance of a power network, respectively, with i as the starting point and j as the ending point. i Let be the ground susceptance of node i in the power network.

[0106] At this point, after deformation, the above model has omitted the phase angle information of voltage and current, and only the amplitude information exists.

[0107] However, since equation (9) is still a nonlinear equality constraint, the entire problem remains a nonconvex problem, which is difficult to solve. Therefore, equation (9) is further relaxed:

[0108]

[0109] When the inequality sign in the above equation takes the equal sign, the solution to this relaxation step is equivalent to the solution after phase angle relaxation. Research indicates that when the upper bound power of the load is infinite, or the objective function is an increasing function of current and a non-decreasing function of load power, the optimal solution to this problem is always the same as the optimal solution after phase angle relaxation. Verification through numerous numerical examples also shows that the equality holds in most cases.

[0110] Further transformation of equation (10) yields the standard second-order cone constraint form, as shown below. Therefore, the original problem is transformed from a non-convex problem into a second-order cone problem (SOCP), which can be solved directly using a commercial solver, as follows:

[0111]

[0112] (2) Constructing distributed photovoltaic constraints for active distribution networks

[0113] Distributed photovoltaic (PV) power generation primarily utilizes the photovoltaic effect of semiconductors to convert light energy into electrical energy, with solar cells being its core component. However, solar cells can only generate direct current (DC), so inverters are often needed to convert the DC power before it can be connected to the AC power grid.

[0114] Photovoltaic power generation has many advantages, such as abundant reserves, convenient maintenance, and clean and low-carbon operation. However, it also has significant disadvantages, including low energy density, discontinuity, and instability. Its output is closely related to natural conditions, including solar radiation intensity and ambient temperature. The output power of photovoltaic solar cells is positively correlated with solar radiation intensity. Its actual output is generally referenced to a standard test environment (solar radiation intensity of 1000 W / m²). 2 The output power (T) is calculated based on the battery temperature of 25℃. STC G STC These represent the battery temperature and solar radiation intensity under standard test conditions, respectively, G. c P represents the solar radiation intensity at the actual working point. STCT represents the rated output power under standard test conditions. c Let be the battery temperature at the operating point, and k be the power-temperature coefficient. Then, under standard test conditions, the output of the photovoltaic unit can be expressed as:

[0115]

[0116] The photovoltaic output is calculated by a prediction module. In this invention, the predicted value is the maximum output value, and the actual output should be between 0 and the predicted value, depending on the specific circumstances (whether curtailment is allowed).

[0117]

[0118] (3) Constraints on energy storage devices for constructing active distribution networks

[0119] Energy storage devices play a role in energy buffering, peak shaving and valley filling, and smoothing power fluctuations and absorbing new energy sources in active distribution network systems.

[0120] At night, when users consume less electricity and it is a low-demand period, electricity prices are cheaper. At this time, energy storage often increases the system's electricity consumption by charging, storing the excess energy. This plays a role in filling the valley and absorbing new energy sources.

[0121] At noon, users' electricity consumption surges. The exchange capacity between the distribution network and the high-voltage transmission network is limited, and there may be a power shortage. At this time, the electricity price is also high. At this time, the energy storage device discharges to reduce the operating cost of the system.

[0122] The charging and discharging states of energy storage are flexible and controllable, but it should not be possible to charge and discharge at the same time. Therefore, in order to ensure that the model has a certain degree of practical applicability and meets its safe operation conditions, the limitations of its state of charge (SOC) and charging and discharging power should be considered.

[0123] Considering that energy storage cannot charge and discharge simultaneously in real-world scenarios, it is necessary to use a 0 / 1 variable plus mutual exclusion constraints to correct the output of the energy storage:

[0124]

[0125]

[0126]

[0127] Among them, Ω ESS A collection of energy storage devices in an active distribution network. Let r be the maximum charging and discharging power (kW) of the i-th energy storage device in the active distribution network. ij ,xij Let b be the branch resistance and branch reactance of a power network with i as the starting point and j as the ending point. i Let be the ground susceptance of node i in the power network.

[0128] Considering the rapid dynamic adjustment characteristics of microgrids connected via power electronics, ramp-up constraints are not considered, but the charging continuity rules of the energy storage device must be satisfied. Furthermore, considering the lifespan of the energy storage device, it is generally not allowed to be fully charged or discharged; therefore, the following two constraints hold:

[0129]

[0130]

[0131] in, represents the upper and lower limits of the state of charge of the i-th energy storage device, respectively.

[0132] In fact, considering the requirements for solution time, equations (14) to (16) are still replaced by equations (19) and (20).

[0133]

[0134]

[0135] Because the optimization process will not result in simultaneous charging and discharging power values. The following proof will demonstrate this: Note that the cost coefficients for energy storage are all positive. For a given output power, there are two scenarios that achieve this: either only one of the charging or discharging power is non-zero, or both are non-zero. Considering the second scenario, if the net energy storage power is discharging, it means the actual discharging power is greater than the net power. This portion of the cost is already higher than the cost of only discharging, not even including the cost of charging power, and vice versa. Therefore, simultaneous charging and discharging will not occur during the optimization process.

[0136] S202. Establish the objective function of the optimization model and the mathematical model of the whole system. Solve for the system's economic operating cost to obtain the scheduling results for the next 24 hours (96 time periods) with 15-minute intervals.

[0137] The optimization model minimizes the daily operating cost of the system while ensuring that the technical limitations of each component in the distribution network, the power flow of each branch does not exceed the limits, and the voltage of each node does not exceed the limits. Therefore, the mathematical model of the entire system can be written as follows:

[0138] minf = minf ESS +f Grid +C PV (twenty one)

[0139] Satisfying constraints (6)~(8), (10)~(11), (13), (17)~(20), and including tie-line switching power limits and node power balance constraints:

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146] Among them, f ESS The total daily operating cost of the energy storage device is determined by equation (25); n ESS The number of energy storage devices in an active distribution network. Let f be the charging and discharging cost coefficient of the i-th energy storage device in the active distribution network, Δt be the time interval for optimal scheduling, and f be the value of f. Grid The total power exchange cost for the tie line throughout the day is determined by equation (26). The power purchased and sold by the active distribution network interconnection line during time period t. C represents the electricity purchase and sale price of the active distribution network interconnection line during time period t. PV The curtailment penalty term for photovoltaic power generation is determined by equation (27), n PV c represents the number of distributed photovoltaic (PV) systems in an active distribution network. PV This represents the curtailment penalty coefficient in an active distribution network.

[0147] S3. Verify that the second-order cone relaxation power flow has been restored to the original power flow.

[0148] By verifying equation (10) at each solution period, check whether the relaxed second-order cone power flow constraint is valid. If it is not valid, the output power flow cannot be restored, and the distribution network operator is reminded to prepare for power flow over-limit. If it is valid, proceed to step S4.

[0149] S4. Using the power flow calculation results obtained from step S3, including node voltage magnitude, branch active power, branch reactive power, and branch current magnitude, the power flow phase angle is restored according to the following steps:

[0150] S401. For all transmission circuits in the existing transmission network, generate the system's adjacency matrix, eliminate the row represented by the node connected to the high-voltage network, and then transpose this adjacency matrix.

[0151] The phase angle restoration problem of power flow involves network topology. Therefore, some auxiliary quantities are defined to characterize the network connectivity. C is the adjacency matrix of the system, defined as follows: according to the specified current reference direction, for the current in branch i-->j, if the current flows out of the node, the corresponding position is 1; if the current flows into the node, the corresponding position is -1. Its mathematical form is as follows:

[0152]

[0153] Matrix B is defined by removing the first row of matrix C (i.e., removing the connection to the reference node) and then transposing it. Its mathematical form is as follows:

[0154]

[0155] S402. Then, the phase angle of each node is obtained by solving a system of linear equations relating the phase angle difference between the two ends of a branch to the phase angle of the node. The branch current vector and branch power vector are then obtained by Ohm's law, and the optimized scheduling results of each power source, energy storage, load and network loss are output.

[0156] First, consider Ohm's law on one branch, as shown in equation (1). Multiplying both sides by the conjugate of the voltage at node j, we get:

[0157]

[0158] The phase angle difference between the voltages at both ends of the line is obtained as follows:

[0159]

[0160] Let α be the set of phase angle differences for each line, then its relationship with the phase angle differences at both ends of the line is:

[0161] Bθ=α+2kπ, k∈Z (32)

[0162] Since the distribution network is a radial network, matrix B must be invertible. Therefore, there exists a unique phase angle corresponding to the node voltage. Thus, for a radial network, the solution after phase angle relaxation can be reduced to the solution of the original problem.

[0163] Finally, the accurate power flow solution of the active distribution network and the output power of each power source are returned to obtain the scheduling results of the source-storage coordinated optimization operation method of the active distribution network considering the second-order cone power flow.

[0164] In another embodiment of the present invention, an active distribution network source-storage coordinated optimization operation system is provided. This system can be used to implement the above-mentioned active distribution network source-storage coordinated optimization operation method. Specifically, the active distribution network source-storage coordinated optimization operation system includes a data module, an execution module, a verification module, and an optimization module.

[0165] The data module obtains basic technical information about the system from the distribution network operator.

[0166] The execution module, based on the basic technical information of the system obtained from the data module, sets the reference voltage and reference capacity for system operation, and executes the active distribution network source-storage coordinated optimization operation method that considers second-order cone power flow.

[0167] The verification module restores the second-order cone relaxed power flow to the original power flow and verifies whether the equality of the second-order cone power flow constraint is valid during the optimization operation of the execution module. If it is not valid, the output power flow cannot be restored. If it is valid, the output includes the power flow calculation results containing the node voltage amplitude, branch active power, branch reactive power and branch current amplitude.

[0168] The optimization module uses the power flow calculation results obtained from the verification module to restore the power flow phase angle, and obtains the scheduling results of the active distribution network source-storage coordinated optimization operation method considering the second-order cone power flow, thereby realizing the active distribution network source-storage coordinated optimization operation.

[0169] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment of the present invention can be used in the operation of an active distribution network source-storage coordinated optimization operation method, including:

[0170] Based on the system's basic technical information, the reference voltage and reference capacity for system operation are set, and an active distribution network source-storage coordinated optimization operation method considering second-order cone power flow is executed. The second-order cone relaxed power flow obtained from the active distribution network source-storage coordinated optimization operation is restored to the original power flow. The equality of the second-order cone power flow constraint after optimization operation is checked. If it is not valid, the output power flow cannot be restored. If it is valid, the power flow calculation results including node voltage amplitude, branch active power, branch reactive power, and branch current amplitude are output. The power flow calculation results are used to restore the power flow phase angle, and the scheduling result of the active distribution network source-storage coordinated optimization operation method considering second-order cone power flow is obtained, realizing the active distribution network source-storage coordinated optimization operation.

[0171] In another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (memory). This computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0172] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the active distribution network source-storage coordinated optimization operation method in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps:

[0173] Based on the system's basic technical information, the reference voltage and reference capacity for system operation are set, and an active distribution network source-storage coordinated optimization operation method considering second-order cone power flow is executed. The second-order cone relaxed power flow obtained from the active distribution network source-storage coordinated optimization operation is restored to the original power flow. The equality of the second-order cone power flow constraint after optimization operation is checked. If it is not valid, the output power flow cannot be restored. If it is valid, the power flow calculation results including node voltage amplitude, branch active power, branch reactive power, and branch current amplitude are output. The power flow calculation results are used to restore the power flow phase angle, and the scheduling result of the active distribution network source-storage coordinated optimization operation method considering second-order cone power flow is obtained, realizing the active distribution network source-storage coordinated optimization operation.

[0174] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0175] To verify the effectiveness of the method proposed in this invention, a typical 33-node active distribution network system was selected for calculation and analysis.

[0176] Please see Figure 2 For the system's grid structure Figure 1 The system comprises 33 nodes, 32 transmission lines, 2 distributed photovoltaic (PV) generators, 2 distributed energy storage devices, and 32 node loads. All power sources within the system are renewable energy sources, while load demand is met by power transmitted from the high-voltage grid, making it a typical active distribution network structure. The load conditions of each node are as follows: Figure 3 As shown, the node electricity price for node 1 to purchase electricity from the main network is as follows: Figure 4 As shown in the figure. The parameters of the photovoltaic and energy storage devices in the system are shown in Table 1 and Table 2, respectively.

[0177] Table 1 Parameters of photovoltaic units within 33 nodes

[0178]

[0179] Table 2 Parameters of energy storage devices within 33 nodes

[0180]

[0181] By establishing the mathematical model proposed in this invention and performing solution analysis, the following results are obtained: Figure 5 The optimized scheduling results are shown.

[0182] The graph shows that, due to precise network constraints, simply looking at the source-load ratio in the system cannot achieve real-time power balance, and there is a situation where power generation exceeds load. In reality, considering the influence of line resistance, there is a certain amount of active power loss during power transmission; therefore, the excess power generation is used to balance this network loss. Further analysis of the changes in network losses reveals that, since distributed photovoltaic (PV) units are not generating power at night, the system's energy supply comes entirely from power exchange between the active distribution network and the high-voltage main grid. Due to the long reach of the distribution network, network losses are significant. As the PV units operate, the equivalent electrical distance between the power generation system and the load shortens, resulting in a decrease in network losses at midday. The nighttime energy storage discharge phase can also be analyzed.

[0183] From an economic perspective, since nighttime is a period of low electricity consumption, and considering the electricity price curve, it can be seen that the electricity price is lower at this time. Therefore, energy storage can utilize cheap electricity to store power during this period and release it during peak consumption periods, achieving the effect of peak-hour utilization and reducing the system's operating costs. Regarding power flow distribution, since the number of time periods solved is 96, Table 3 only lists the power flow solution results for the 48th time period (reference voltage is 10kV, reference capacity is 1000kVA).

[0184] Table 3 shows the power flow solution results for the system in time period 48.

[0185]

[0186]

[0187]

[0188] The power flow solution results clearly show that, due to the consideration of a refined power flow model, all power flow results include both real and imaginary parts. At the same time, the power of each branch does not exceed the limit and the voltage does not exceed the limit, which effectively ensures the safety of the active distribution network operation.

[0189] In summary, this invention presents an active distribution network source-storage coordinated optimization operation method and system. Based on a precise second-order cone AC power flow model, the network transmission constraint set comprehensively considers the impact of active and reactive power components on system power generation planning and transmission security. By rationally allocating the charging and discharging periods of energy storage, the system's operating cost is minimized under the given operating characteristics of distributed photovoltaic and other renewable energy sources. Due to the consideration of a refined model, the solution results can fully guarantee the safe and economical operation of the system throughout all time periods. Furthermore, the model used in this invention is a one-layer deterministic model, which can be directly calculated using commercial solvers, resulting in high solution efficiency and a low barrier to entry.

[0190] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0191] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0192] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0193] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0194] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0195] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0196] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0197] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0198] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0199] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0200] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for coordinated and optimized operation of power generation and storage in an active distribution network, characterized in that, Includes the following steps: S1. Based on the system's basic technical information, set the system's reference voltage and reference capacity, and implement an active distribution network source-storage coordinated optimization operation method considering second-order cone power flow, specifically: S101. Establish mathematical models for distributed renewable energy, energy storage devices, and second-order cone transmission lines respectively. Couple these mathematical models to form a mathematical model for safe system operation through a system node power balance model. The specific mathematical model for distributed renewable energy is as follows: in, , These represent the battery temperature and solar radiation irradiance under standard test conditions, respectively. This represents the solar radiation intensity at the actual working point. Rated output power under standard test conditions. The battery temperature at the operating point. The power-temperature coefficient; The mathematical model of the energy storage device is as follows: in, A collection of energy storage devices in an active distribution network. For the first in an active distribution network i The maximum charging and discharging power of the energy storage device in Taiwan This represents the number of time periods within the system's scheduling cycle. The specific mathematical model of a second-order cone transmission line is as follows: in, branch road ij The square of the current magnitude, It is the square of the node voltage magnitude. In the power grid i As the beginning, j The active and reactive power flowing through the terminal branch are the active and reactive power, respectively. A set of branches in a power network; The specific system node power balance model is as follows: in, Nodes in a power network i The susceptibility to ground, Nodes in a power network i The injection power complex vector, It is the set of nodes in a power network. For the power grid For the beginning, Let be the power vector flowing through the terminal branch. For the power grid i As the beginning, j Let be the power vector flowing through the terminal branch. For the power grid i As the beginning, j For the branch reactance at the end, For the power grid i As the beginning, j Let be the vector of the current flowing through the branch at the end. Nodes in a power network j The conjugate complex number of the ground admittance, Nodes in a power network j The node voltage vector; S102. Establish the objective function of the optimization model for the coordinated operation of the active distribution network source and storage considering the second-order cone power flow, as well as the mathematical model of the whole system. Solve the system to obtain the scheduling results for the next 24 hours with 15-minute intervals, with the lowest economic operating cost of the system. S2. Restore the second-order cone relaxed power flow obtained from the active distribution network source-storage coordination optimization operation solution in step S1 to the original power flow. Verify whether the equality of the second-order cone power flow constraint after the optimization operation relaxation in step S1 is valid. If it is not valid, the output power flow cannot be restored. If it is valid, output the power flow calculation results including node voltage amplitude, branch active power, branch reactive power and branch current amplitude. S3. Using the power flow calculation results obtained in step S2, the power flow phase angle is restored to obtain the scheduling results of the active distribution network source-storage coordinated optimization operation method considering the second-order cone power flow, thereby realizing the active distribution network source-storage coordinated optimization operation.

2. The active distribution network source-storage coordinated optimization operation method according to claim 1, characterized in that, In step S1, the basic technical information of the system includes: the predicted power information of distributed renewable energy and load, the technical parameters and initial state of charge of the energy storage device in the system, and the existing transmission grid and network parameters.

3. The active distribution network source-storage coordinated optimization operation method according to claim 1, characterized in that, In step S102, the mathematical model of the whole system for: in, The total daily operating cost of the energy storage device. The cost of power switching for the tie line throughout the day. This is a penalty for curtailment of solar power generation units.

4. The active distribution network source-storage coordinated optimization operation method according to claim 3, characterized in that, The daily operating cost of an energy storage device The cost of power exchange for the entire day on the interconnecting line Curtailment penalty for photovoltaic power generation units They are respectively: in, The number of energy storage devices in an active distribution network. These are the first in the active distribution network i The charging and discharging cost coefficient of Taiwan's energy storage devices To optimize the scheduling time interval, These are the active distribution network tie lines in different time periods. t Purchase and sale power These are the active distribution network tie lines in different time periods. t Electricity purchase and sale price, This refers to the number of distributed photovoltaic (PV) systems in an active distribution network. This represents the curtailment penalty coefficient in an active distribution network.

5. The active distribution network source-storage coordinated optimization operation method according to claim 1, characterized in that, Step S3 is as follows: S301. For all transmission circuits in the existing transmission network, generate the adjacency matrix of the system and eliminate the row represented by the node connected to the high-voltage network. Then transpose the adjacency matrix. S302. The phase angle of each node is obtained by solving a linear equation system of the phase angle difference between the two ends of a branch and the phase angle of the node. Then, the branch current vector and branch power vector are obtained by Ohm's law, and the optimized scheduling results of each power source, energy storage, load and network loss are output.

6. The active distribution network source-storage coordinated optimization operation method according to claim 5, characterized in that, In step S301, the transposed adjacency matrix is ​​as follows: 。 7. The active distribution network source-storage coordinated optimization operation method according to claim 5, characterized in that, In step S302, the branch current vector and branch complex power vector Specifically: in, Nodes in a power network i The node voltage phasor For the power grid i As the beginning, j For the branch reactance at the end, For the power grid i As the beginning, j Let be the vector of the current flowing through the branch at the end. For the power grid i As the beginning, j Let be the power vector flowing through the terminal branch. For nodes j The conjugate phasor of the voltage phasor. branch road ij Conjugate of complex impedance, Nodes in a power network j The node voltage phasor The voltage phase angle difference between the two ends of the line. branch road ij The square of the current magnitude, branch road ij The conjugate of the complex power flowing through.

8. An active distribution network source-storage coordinated optimization operation system, characterized in that, include: The execution module, based on the system's basic technical information, sets the system's reference voltage and reference capacity, and executes an active distribution network source-storage coordinated optimization operation method considering second-order cone power flow, specifically: Mathematical models for distributed renewable energy, energy storage devices, and second-order cone transmission lines are established separately. These models are then coupled using a system node power balance model to form a mathematical model for safe system operation. Specifically, the mathematical model for distributed renewable energy is as follows: in, , These represent the battery temperature and solar radiation irradiance under standard test conditions, respectively. This represents the solar radiation intensity at the actual working point. Rated output power under standard test conditions. The battery temperature at the operating point. The power-temperature coefficient; The mathematical model of the energy storage device is as follows: in, A collection of energy storage devices in an active distribution network. For the first in an active distribution network i The maximum charging and discharging power of the energy storage device in Taiwan This represents the number of time periods within the system's scheduling cycle. The specific mathematical model of a second-order cone transmission line is as follows: in, branch road ij The square of the current magnitude, It is the square of the node voltage magnitude. In the power grid i As the beginning, j The active and reactive power flowing through the terminal branch are the active and reactive power, respectively. for, A set of branches in a power network; The specific system node power balance model is as follows: in, Nodes in a power network i The susceptibility to ground, Nodes in a power network i The injection power complex vector, It is the set of nodes in a power network; An objective function and a mathematical model of the entire system are established for an optimization model of active distribution network source-storage coordinated operation considering second-order cone power flow. The system's economic operating cost is minimized to obtain the scheduling results for the next 24 hours at 15-minute intervals. The verification module restores the second-order cone relaxed power flow obtained from the active distribution network source-storage coordinated optimization operation to the original power flow. It verifies whether the equality of the relaxed second-order cone power flow constraint is valid during the optimization operation of the execution module. If it is not valid, the output power flow cannot be restored. If it is valid, the output includes the power flow calculation results containing the node voltage amplitude, branch active power, branch reactive power and branch current amplitude. The optimization module uses the power flow calculation results obtained from the verification module to restore the power flow phase angle, and obtains the scheduling results of the active distribution network source-storage coordinated optimization operation method considering the second-order cone power flow, thereby realizing the active distribution network source-storage coordinated optimization operation.

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