Distribution network-microgrid multi-agent distributed collaborative optimization method and equipment

Abstract

The invention provides a distribution network-micro-grid multi-agent distributed collaborative optimization method and equipment, and the method comprises the steps: constructing a safe operation model of a distribution network-micro-grid system, and building a multi-stage optimization model with the minimization of the operation cost as a target based on the model, a multi-stage optimization model is decomposed into an active power distribution network operator optimization sub-problem and a plurality of micro-grid operator optimization sub-problems, and a distributed optimization algorithm is designed based on an augmented Lagrange function and a consistent alternating direction multiplier method, so that each main body iteratively updates a local decision variable, a shared variable copy and a Lagrange multiplier, and the optimal power distribution network operator optimization sub-problem is obtained. Collaborative optimization solution is realized; based on a distributed optimization algorithm, a rolling time domain optimization framework is adopted to realize real-time cooperative operation and reactive power support regulation and control of the distribution network and the microgrid. Based on the method, the invention further provides distribution network-microgrid multi-agent distributed collaborative optimization equipment. According to the invention, safe operation of the distribution network and the micro-grid is guaranteed, the reactive power support capability of the system is improved, and the operation cost is reduced.

Description

Technical Field

[0001] This invention belongs to the field of power system operation and control technology, and specifically relates to a method and equipment for multi-entity distributed collaborative optimization of distribution network-microgrid. Background Technology

[0002] The increasing proportion of renewable energy in the power system and the gradual phasing out of traditional centralized thermal power plants have led to a significant reduction in reactive power sources in the transmission network, posing a severe challenge to system voltage stability. Although distribution system operators (DSOs) deploy equipment such as static var compensators, stepper voltage regulators, and on-load tap-changing transformers, they face challenges such as high initial investment and high operation and maintenance costs.

[0003] As distribution networks transform into active distribution networks, a large number of renewable energy sources, energy storage, and local loads are connected in the form of microgrids, necessitating the solution of the distribution network-microgrid coordinated operation problem. Existing technologies for solving the distribution-microgrid coordinated optimization problem fall into two categories: heuristic algorithms and traditional operations research algorithms. Heuristic algorithms suffer from local convergence and low computational efficiency, making them difficult to adapt to complex distribution network topologies. Traditional operations research algorithms (such as integer programming and interior-point methods), while possessing mathematical rigor, are mostly centralized control systems, easily limited by communication bandwidth, and do not fully utilize the reactive power support potential of microgrids. Therefore, existing distributed algorithms (such as distributed particle swarm optimization) cannot guarantee the global optimality of the solution, and do not quantitatively analyze the errors between centralized and distributed solutions. Furthermore, they lack a precise characterization of the feasible region for active-reactive power exchange between the distribution network and the upper-level grid, easily leading to system operation in the reactive power penalty region and increasing operating costs.

[0004] Therefore, existing technologies suffer from several technical challenges, including insufficient utilization of reactive power support capabilities, inadequate accuracy and convergence in distributed optimization, and imprecise characterization of power exchange constraints. There is an urgent need for an optimized operation method that balances safety constraints, reactive power support, and distributed collaboration. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a multi-entity distributed collaborative optimization method and device for distribution networks and microgrids. By constructing a distributed collaborative optimization framework with precise reactive power support domain constraints, the system's reactive power support capability is significantly improved, operating costs are reduced, and the challenges of voltage stability and optimized scheduling under high-proportion renewable energy access are effectively solved while ensuring the safe operation of both the distribution network and the microgrid.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A multi-stakeholder distributed collaborative optimization method for distribution network-microgrid includes the following steps: A safe operation model for the distribution network-microgrid system is constructed. The safe operation model includes distribution network power flow constraints based on a linearized distributed power flow model, reactive power support domain constraints based on mixed integer linear programming, and microgrid inverter-energy storage collaborative operation constraints. Based on the aforementioned safe operation model, a multi-stage optimization model is established with the goal of minimizing operating costs. The objective function includes electricity purchase cost, reactive power support violation penalty cost, grid loss cost, energy storage operation cost, and load reduction cost. The multi-stage optimization model is decomposed into an active distribution network operator optimization sub-problem and multiple microgrid operator optimization sub-problems. Based on the augmented Lagrangian function and the consistent alternating direction multiplier method, a distributed optimization algorithm is designed so that each entity can achieve collaborative optimization by iteratively updating local decision variables, shared variable copies and Lagrangian multipliers. Based on the aforementioned distributed optimization algorithm, a rolling time-domain optimization framework is adopted to achieve real-time coordinated operation and reactive power support control between the distribution network and the microgrid.

[0007] The present invention also proposes a distribution network-microgrid multi-entity distributed collaborative optimization device, including at least one processor and a memory, wherein the memory stores a computer program, and the computer program implements the distribution network-microgrid multi-entity distributed collaborative optimization method when executed by the at least one processor.

[0008] The effects described in the invention are merely those of the embodiments, and not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects: This invention proposes a multi-entity distributed collaborative optimization method and device for distribution network-microgrid systems. The method includes the following steps: constructing a safe operation model for the distribution network-microgrid system, which includes distribution network power flow constraints based on a linearized distributed power flow model, reactive power support domain constraints based on mixed-integer linear programming, and microgrid inverter-energy storage collaborative operation constraints; based on the safe operation model, establishing a multi-stage optimization model with the goal of minimizing operating costs, wherein the objective function includes electricity purchase cost, reactive power support violation penalty cost, network loss cost, energy storage operation cost, and load reduction cost; decomposing the multi-stage optimization model into an active distribution network operator optimization sub-problem and multiple microgrid operator optimization sub-problems, and designing a distributed optimization algorithm based on the augmented Lagrangian function and the consistent alternating direction multiplier method, so that each entity achieves collaborative optimization by iteratively updating local decision variables, shared variable copies, and Lagrangian multipliers; based on the distributed optimization algorithm, adopting a rolling time-domain optimization framework to achieve real-time collaborative operation and reactive power support control of the distribution network and microgrid. Based on a multi-entity distributed collaborative optimization method for distribution networks and microgrids, a multi-entity distributed collaborative optimization device for distribution networks and microgrids is also proposed. This invention constructs a distributed collaborative optimization framework with precise reactive power support domain constraints, which significantly improves the reactive power support capability of the system, reduces operating costs, and effectively solves the voltage stability and optimized scheduling challenges under high-proportion renewable energy access, while ensuring the safe operation of the distribution network and microgrid.

[0009] This invention achieves reactive power coordination support for the upper-level power grid by accurately modeling the reactive power regulation capability of microgrid inverters and the flexible support characteristics of energy storage systems, effectively alleviating voltage stability problems caused by the high proportion of renewable energy access.

[0010] The uniform alternating direction multiplier method based on augmented Lagrange functions designed in this invention has a fast convergence speed and good scalability while ensuring global optimality, and can adapt to the distributed optimization needs of large-scale distribution network-microgrid systems.

[0011] This invention uses a mixed-integer linear programming method to accurately characterize the feasible region of active-reactive power exchange between the distribution network and the upper-level power grid, thereby avoiding the system operating in the reactive power penalty region and reducing operating costs.

[0012] Under the distributed optimization architecture, the present invention only requires each entity to exchange boundary interaction power information without disclosing internal operating details, thus protecting business privacy and operational security.

[0013] This invention comprehensively considers electricity purchase costs, grid loss costs, energy storage operation costs, load reduction costs, and reactive power support penalty costs, achieving a balance between economic operation and reactive power support while ensuring the safe operation of the system.

[0014] This invention, through a rolling time-domain optimization framework, can effectively address the uncertainties of load and renewable energy output, thereby improving the robustness and adaptability of the system. Attached Figure Description

[0015] Figure 1 This is a flowchart of a multi-entity distributed collaborative optimization method for distribution network-microgrid proposed in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the branch power flow proposed in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the reactive power support domain of the active distribution network proposed in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of the microgrid structure proposed in Embodiment 1 of the present invention; Figure 5 This is a schematic diagram of a multi-entity distributed collaborative optimization device for distribution network-microgrid proposed in Embodiment 1 of the present invention. Detailed Implementation

[0016] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure of the invention, components and arrangements of specific examples are described below. Furthermore, reference numerals and / or letters may be repeated in different examples. This repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. Descriptions of well-known components, processing techniques, and processes are omitted in this invention to avoid unnecessarily limiting the invention.

[0017] Example 1 Embodiment 1 of this invention proposes a multi-entity distributed collaborative optimization method for distribution networks and microgrids, which is used to solve the technical problems in the prior art, such as insufficient utilization of reactive power support capacity, insufficient accuracy and convergence of distributed optimization, and inaccurate characterization of power exchange constraints.

[0018] This invention achieves multi-entity distributed collaborative optimization of distribution network-microgrid through four aspects: constructing a safe operation model, establishing a multi-stage optimization model, designing a distributed optimization algorithm, and realizing coordinated control.

[0019] Figure 1 This is a flowchart of a multi-entity distributed collaborative optimization method for distribution network-microgrid proposed in Embodiment 1 of the present invention; In step S1, the process begins.

[0020] In step S2, a safe operation model of the distribution network-microgrid system is constructed. The safe operation model includes distribution network power flow constraints based on a linearized distributed power flow model, reactive power support domain constraints based on mixed integer linear programming, and microgrid inverter-energy storage collaborative operation constraints. Define commonly used variables for the set of nodes in an Active Distribution Network (ADN). For example: This indicates the number of elements in the node set, i.e., the number of nodes; This represents the set of ADN branches in an active distribution network; This represents the set of nodes connected to the microgrid. That is, the number of microgrids; The optimized time window contains In the period, at the The decision variables for each time period are: (1) in, express Active power flow of ADN branch during the time period; express Reactive power flow in ADN branches during the time period; This represents the active power injected into a node of the ADN. This represents the reactive power injection of a node in the ADN. Indicates voltage amplitude. Indicates the current amplitude; This represents the active power interacting between the ADN and the upstream power grid. This represents the reactive power exchanged between the ADN and the upstream power grid. When their values ​​are greater than zero, it indicates that power is purchased from the upstream power grid, and when their values ​​are less than zero, it indicates that power is sent to the upstream power grid. Indicates the power of energy storage, when The time indicates the injection of power into the microgrid. This indicates the active power load that has been reduced. This represents the reactive power generated or absorbed by the inverter in the microgrid. This represents the available electrical energy in the energy storage, and the value is determined by... The initial charge level is determined here, so we assume that the initial charge level is determined here. It is known. It is the cost of violating reactive power support constraints.

[0021] In this invention, the process of constructing power flow constraints for a distribution network based on a linearized distributed power flow model includes: The Distflow model is used to describe the radial topology of the distribution network, and a physical constraint system including branch power conservation equations and node voltage equations is established. For the nonlinear current-voltage relationship in the Distflow model, a first-order Taylor series expansion is applied at the set initial operating reference point to achieve linearization.

[0022] Specifically: the power flow constraint model uses a directed graph. Describes the distribution network topology, where, For the set of nodes in the distribution network, Let be the set of branches in the distribution network; assume that any node has only one parent node, but can have any number of child nodes. Let the node... The parent node is ,node The set of child nodes is ; Figure 2 This is a schematic diagram of the branch power flow proposed in Embodiment 1 of the present invention.

[0023] Defining the positive direction of power flow as from parent node to child node, and using the standard Distflow model for power flow constraints, the power flow constraints of the distribution network are expressed as follows: (2) (3) (4) (5) in, Indicates the parent node Flow to Node The active power; Represents a node child nodes; Represents a node Flow out to all its child nodes All of them have made contributions; Represents a node Flowing to each child node The total active power loss generated on the line; Indicates the injected node Net active power; Indicates the parent node Flow to Node reactive power; Represents a node Flow out to all its child nodes Total no-efficiency; Represents a node Flowing to each child node The total reactive power loss generated on the line; Indicates the injected node Net reactive power; Represents a node The voltage amplitude; Represents a node The voltage amplitude; Indicates a branch The resistance; Indicates a branch The reactance; Indicates a branch The active power; Indicates a branch reactive power; Indicates a branch The current amplitude; In the Distflow model above, equations (2)-(4) are linear constraints, while equation (5) is a nonlinear and non-convex constraint.

[0024] To ensure the feasibility of solving large-scale systems, at points The branch current equations are linearized using a first-order Taylor series: ; in, This represents the amplitude of the branch current at the linearized reference point; This represents the active power of the branch at the linearized reference point; This represents the partial derivative of the current with respect to the active power at the reference point; This represents the reactive power of the branch at the linearized reference point; This represents the partial derivative of the current with respect to reactive power at the reference point; Nodes representing the linearization reference point The voltage amplitude; This represents the partial derivative of the current with respect to the voltage amplitude at the reference point; The branch power of each line needs to be limited within the thermal safety range, and the voltage amplitude of each node is also not allowed to exceed the limit. These constraints can be expressed as the following inequalities: (7) (8) (9) in, Indicates a branch The maximum active power; Indicates a branch The maximum value of reactive power; Represents a node The minimum value of the voltage amplitude; Represents a node The maximum value of the voltage amplitude.

[0025] The process of constructing reactive power support domain constraints based on mixed integer linear programming in this invention includes: geometrically dividing the feasible plane of active-reactive power exchange between the active distribution network and the upper-level power grid by introducing multiple binary decision variables, and defining the support domain and non-support domain that meet the operation requirements of the upper-level power grid; then introducing a penalty cost term related to the location of the power exchange point. When the optimized operating point falls into the non-support domain, the penalty cost term is activated and quantified according to the degree of exceeding the limit.

[0026] Specifically, in order to ensure that the ADN provides a certain amount of reactive power support to the main grid, or at least ensure that it does not consume a large amount of reactive power and put excessive pressure on the upstream main grid, the upstream main grid dispatch requires the power exchanged between the ADN and the upstream power grid. Stable within the preset safe range, Figure 3 This is a schematic diagram of the reactive power support domain of the active distribution network proposed in Embodiment 1 of the present invention; when At that time, the ADN, acting as a load, purchases power from the upstream main grid. Support domain Z1 refers to the domain where... At that time, power factor The area, and when At that time, by and The rectangular area formed. When When power exchange occurs, the ADN is considered as a power source providing active power to the upstream grid, so the left half-plane belongs to the support domain. The non-support domain Z2 is all the region outside the support domain. When the domain is not supported, the ADN operator should be penalized. It is the cost of violating the reactive power support constraint, and its mathematical expression is as follows: (10) in, This represents the cost of violating reactive power support constraints; Indicates the penalty factor; This indicates the reactive power of the ADN that interacts with the upstream power grid; Indicates the upper limit of reactive power interaction; Indicates a non-supporting domain; This indicates the supporting domain.

[0027] Because the formula describes a complex region, it cannot be directly used to construct the cost function and constraints for optimization problems. Therefore, it is necessary to transform it into a simpler and more explicit mathematical expression. Here, three binary variables are introduced. And use mixed integer linear constraints to represent the above discontinuous cost function. Indicates the first binary variable; Indicates the second binary variable; This represents the third binary variable.

[0028] when hour, The ADN provides active power to the upper-level power grid; when hour, ADN purchases active power from the upper-level power grid.

[0029] (11) (12) in, M represents the active power of the ADN that interacts with the upstream power grid; M is a sufficiently large positive constant. Constrained and between.

[0030] when hour, ADN operates in the reactive power support domain; when hour, ADN operates in the reactive non-support domain.

[0031] (13) (14) (15) in, This represents the cost of violating reactive power support constraints; Indicates the penalty factor; This represents the upper limit of reactive power interaction; M is a sufficiently large positive integer. This indicates the reactive power of the ADN that interacts with the upstream power grid.

[0032] Formula (13) will penalize costs when the run point is in Z2. Limited to Within this. Formulas (14)-(15) ensure Limited to the range .

[0033] when hour, ;when hour, The resulting inequality is as follows: (16) (17) (18) (19) Where M is a sufficiently large positive constant; It is a very small positive number; an auxiliary variable is introduced here. ,when hour, ,on the contrary, ; Indicates power factor The corresponding reactive-active boundary slope; This indicates the active power of the ADN that interacts with the upstream power grid; This represents the minimum value of active interaction.

[0034] The power factor value protected by this invention is not limited to the values ​​listed in Example 1, and those skilled in the art can make reasonable selections based on actual circumstances.

[0035] The dividing boundary between Z1 and Z2 is determined by the following formula: ; (20) when hour, .

[0036] when hour, Follow Linear growth.

[0037] Similarly, the cost of violating reactive power support constraints It can also be expressed using the system of inequalities (21)-(23): ;(twenty one) ;(twenty two) ;(twenty three) When running point When in the non-supported domain Z2, the system of inequalities (21)-(23) will Limited to the range Among them.

[0038] When running point When in the support domain Z1, Thus far, formulas (11)-(23) characterize the support domains Z1 and Z2, and the corresponding penalty cost calculation formula (10).

[0039] The process of constructing constraints for the coordinated operation of microgrid inverters and energy storage in this invention includes: Based on the physical characteristics of energy storage devices, a dynamic evolution equation for their state of charge is established, and upper and lower limits for energy storage and charging / discharging power are set to establish state of charge constraints based on the energy storage state equation. To address the capacity constraints of inverters, a polygonal linearization method is adopted to approximate the original quadratic rotating cone constraint as a set of linear inequalities to describe the joint feasible region of active and reactive power, and to establish an apparent power polygonal linearization approximation constraint based on inverter capacity constraints. Based on the law of conservation of energy, a balance constraint between active and reactive power is established on the AC bus inside the microgrid.

[0040] Inverters are installed at the interface between each microgrid and the active distribution network to enable energy exchange between the AC component (AC loads and the active distribution network) and the DC component (energy storage devices, DC loads, and photovoltaic modules). Figure 4 This is a schematic diagram of the microgrid structure proposed in Embodiment 1 of the present invention.

[0041] Inverters can provide pure reactive power, thus replacing additional reactive power compensation devices. This invention adopts this operating mode and imposes safety constraints on the active and reactive power at the inverter ports.

[0042] ;(twenty four) in, Represents a node The inverter on the device is at all times The active power is the active component of AC / DC energy conversion within the microgrid; Represents a node The inverter on the device is at all times The reactive power is provided by the inverter. Represents a node The inverter on the device is at all times Apparent power; This represents the set of nodes connected to the microgrid.

[0043] Linearizing equation (24) yields: (25) (26) (27) (28) in, Indicates the coefficients of the first piecewise linearization; Indicates the coefficients of the second piecewise linearization; The coefficients representing the linearization of the third segment are used to construct linear inequalities through trigonometric functions to approximate the power constraints of the inverter. Indicates a segmented index, with a range of values. ; This indicates the total number of segments, used to convert the inverter's secondary power constraints into linear constraints. Represents a node The apparent power of the inverter.

[0044] Assuming each microgrid consists of batteries, photovoltaics, and local loads, such as Figure 4 As shown, the operating status of the battery is represented as follows: (29) (30) (31) in, Represents a node The battery on the device is always The energy state; Represents a node The battery on the device is always The energy state; This represents the product of the battery charging / discharging efficiency and the sampling time interval of the optimization problem, where it is assumed that the charging / discharging efficiency is infinitely close to 1; Represents a node The battery on the device is always The power; Represents a node The minimum energy of the battery on the device; Represents a node The maximum energy of the battery on the device; and They are set to 0.2 times and 0.9 times the battery energy, respectively. Represents a node The absolute value of the maximum charging and discharging power of the battery is used to constrain the battery power within a safe range.

[0045] (32) (33) in, Represents a node At any moment The injected active power; Represents a node The battery on the device is always The power; Represents a node The photovoltaic modules in the microgrid are constantly Those who have made meritorious contributions; Represents a node At any moment Reduced active power load; Represents a node The total active power load of DC and AC; Represents a node At any moment Injected reactive power; Represents nodes in a micronet The battery on the device is always reactive power; The angle representing the power factor of the microgrid; Represents a node Total reactive load in the microgrid.

[0046] In step S3, based on the safe operation model, a multi-stage optimization model is established with the goal of minimizing operating costs. The objective function includes electricity purchase cost, reactive power support violation penalty cost, grid loss cost, energy storage operation cost, and load reduction cost. The primary goal of ADN is to minimize operating costs while ensuring security. The cost function proposed in this invention is specifically as follows: The formula for calculating the cost of purchasing electricity from the upper-level power grid is as follows: ; Penalty costs arising from violation of reactive power support constraints ; The active transmission loss cost is calculated using the following formula: ; The operating cost of a storage battery is calculated using the following formula: ; The formula for calculating load reduction costs is as follows: ; Therefore, the multi-stage optimization model is as follows: ; in, These are decision variables defined by formula (1); This indicates the electricity price from the higher-level power grid; This indicates the active power of an active distribution network that interacts with the upstream power grid. This indicates the penalty cost incurred due to violation of reactive power support constraints. This represents the set of branches in the active distribution network; The penalty coefficient representing active power transmission loss; Indicates a branch At any moment The current amplitude; Indicates a branch The resistance; This represents the set of nodes connected to the microgrid. This represents the operating cost coefficient of the battery. Indicates storage battery At any moment The power; This represents the penalty coefficient for load reduction costs; Represents a node At any moment The load reduces active power.

[0047] By setting the penalty vector appropriately Equation (34) can be rewritten in a compact form The optimized time window contains Each time period. Similarly, the compact form of the multi-stage optimization model is: (35) st (36) (37) (38) (39) in, It is a vector composed of binary variables. These are the coefficient matrices corresponding to the respective constraints. These are the coefficient vectors corresponding to the constraints. Equations (36)-(37) contain constraints directly related to the ADN, and equations (38)-(39) contain constraints directly related to the microgrid. Specifically, the constraints corresponding to equation (36) include power flow capacity constraints (7)-(8), voltage amplitude constraints (9), and reactive power support domain constraints (11)-(23). The constraints corresponding to equation (37) include power flow calculation equations (2)-(3) and current linear approximation equations (6). The constraints corresponding to equation (38) include inverter power constraints (25), energy storage capacity constraints (30), and energy storage power constraints (31). Equation (39) includes the energy storage charge state equation (29) and power balance equations (32)-(33).

[0048] In step S4, the multi-stage optimization model is decomposed into an active distribution network operator optimization sub-problem and multiple microgrid operator optimization sub-problems. Based on the augmented Lagrangian function and the consistent alternating direction multiplier method, a distributed optimization algorithm is designed so that each entity can achieve collaborative optimization by iteratively updating local decision variables, shared variable copies and Lagrangian multipliers. The multi-stage optimization model is decomposed into an active distribution network operator optimization sub-problem and multiple microgrid operator optimization sub-problems. Specifically, the decision variables are decoupled and analyzed to identify and separate the first type of variables directly related to the active distribution network operator and the second type of variables directly related to each microgrid operator. The second type of variables includes the control commands of the internal equipment of the microgrid. Shared variables are defined to characterize the interactive power at the connection boundary between the distribution network and each microgrid. Through variable classification, the multi-stage optimization model is decomposed into an active distribution network operator optimization sub-problem and multiple microgrid operator optimization sub-problems, and each sub-problem is coupled through shared variables.

[0049] Suppose a fully connected graph The above multi-stage optimization model (35)-(39) represents the communication topology. Describes the set of communication subjects, defined Refers to the ADN system operator, remaining This refers to the microgrid operator. This represents a set of communication links. Representation and subject A set of entities with communication connections. (The variable...) It is decomposed into two parts, the first part being the decision variables that are directly related only to ADN. ,Right now (40) in, express Active power flow of ADN branch during the time period; express Reactive power flow in ADN branches during the time period; Indicates voltage amplitude; Indicates the current amplitude; The active power of an ADN that interacts with the upstream power grid; The reactive power of an ADN is the reactive power that interacts with the upstream power grid. This represents the cost of violating reactive power support constraints; Indicates a non-micronet sub-node Injecting active power; Indicates a non-micronet sub-node Inject reactive power; This indicates the active power load that has been reduced. Represents a binary variable.

[0050] The second part is only related to micro-networks. Directly related decision variables ,Right now (41) in, This represents the active power of energy storage in the microgrid; This represents the active power load that has been reduced in the microgrid; This represents the reactive power of the inverter in the microgrid; This represents the available electrical energy stored in the microgrid.

[0051] However, according to formulas (32)-(33), the variables Includes the interaction power between the ADN and the microgrid, therefore defined These are shared variables, and all communicating entities have copies of these shared variables. It is necessary to ensure the consistency of these shared variables when the algorithm converges.

[0052] The multi-stage optimization model (35)-(39) can be equivalently transformed into the following form: (42) st (43) (44) (45) (46) (47) in, This is the first coefficient matrix of the constraints; This is the second coefficient matrix of the constraints; This is the third coefficient matrix of the constraints; This is the fourth coefficient matrix of the constraints; This is the fifth coefficient matrix of the constraints; The first right-hand constant vector of the constraint; The second right-hand constant vector of the constraint; The third right-hand constant vector of the constraint; Auxiliary variables introduced; as the main body Shared variables; as the main body Shared variables; The constraints in equation 36) are included in equation 43), equation 37) corresponds to equation 44), and equations 38)-39) are written into equation 45). When introducing auxiliary variables... Based on this, constraints 46)-47) can ensure that the shared variables between two entities that are communicating remain consistent.

[0053] The original problem can be decomposed into an ADN optimization subproblem and This is a sub-problem of micro-network optimization. Let... The Lagrange multipliers representing the equality constraints (46)-(47) are used to obtain the ADN optimization subproblem using the augmented Lagrange function as follows: (48) st formulas (43)-(44); (49) in, Represents the direct cost of ADN private variables; Indicates that ADN is in the first Private decision variables for different time periods; Represents the direct cost of shared variables in ADN; Indicates ADN in the 1st A shared copy of the variable for a given time period; This represents a Lagrange multiplier term (first-order penalty); This indicates an augmented Lagrange penalty term (second-order penalty). Indicates ADN in the 1st Lagrange multiplier vectors for the time period; This represents the set of shared variable copies of the ADN neighbor entities; Indicates the penalty coefficient; Indicates the dimension of ADN private variables; Indicates the shared variable dimension of ADN; This represents the Lagrange multiplier vector corresponding to ADN.

[0054] ADN operators use data from their neighbors. Received shared variables and the shared variables obtained in the previous iteration To ensure that the new shared variable remains consistent with its neighbors. Penalty coefficient. Used to control the relative weights of norm and cost terms. Lagrange multipliers. Iterative updates can be performed based on the received neighbor information: (50) Similarly, using the augmented Lagrangian function, the optimization subproblems for each micronet are obtained as follows: (51) st (52) in, Indicates the first In the nth iteration, ADN is at the... Lagrange multiplier vectors for the time period; Indicates the first Multiplier vector at the next iteration; This represents the set of neighbors that have direct communication connections with the ADN; Indicates ADN in the 1st The iteration, the... A shared copy of the variable for a given time period; Indicates neighbors In the ; Indicates consistency deviation; The optimization subproblem of each micronet is essentially a quadratic programming problem, with its Lagrange multipliers... The update is similar to (50). Furthermore, the dual variable... Local computation can be performed based on the received information, without requiring global sharing, which increases the privacy of the algorithm to some extent. The variable that needs to be interacted with in the method proposed in this invention is the node injection power. From formulas (32)-(33), we can see that Essentially, it is a composite variable that includes addition and subtraction operations for active and reactive power such as photovoltaics and load, which also protects privacy to some extent.

[0055] In step S5, based on the distributed optimization algorithm, a rolling time-domain optimization framework is adopted to realize the real-time coordinated operation and reactive power support control of the distribution network and microgrid.

[0056] The execution process of the rolling time-domain optimization framework is as follows: the prediction time domain is set to include multiple continuous scheduling periods, and the optimization problem is updated on a rolling basis with the latest measured data at fixed time intervals, triggering the distributed optimization algorithm to solve iteratively.

[0057] Real-time collaborative operation and reactive power support regulation include: after the distributed optimization algorithm converges iteratively, the active distribution network operator sends interactive power commands to each microgrid, and each microgrid adjusts its local energy storage charging and discharging power and inverter reactive power output according to the commands.

[0058] This invention employs a rolling time-domain optimization method to achieve real-time collaborative control. The prediction time domain is... The sampling interval is 15 minutes.

[0059] Day-ahead phase: ADN operators acquire next day's load and photovoltaic output forecast data, and initialize algorithm parameters (penalty coefficient). Value, error tolerance ); Intraday rolling phase: Real-time data is updated every 15 minutes, triggering distributed algorithm iteration, ADN optimizes branch power flow and upstream power exchange, and microgrid autonomously optimizes energy storage charging and discharging and inverter reactive power; Control command issuance: After the algorithm converges, the ADN issues shared variables to the microgrid. (Interactive power command) The microgrid adjusts the operating status of local equipment according to the command to ensure that the distribution network-microgrid operates in the reactive power support domain Z1 and avoids penalty costs.

[0060] In step S6, the process ends.

[0061] The multi-entity distributed collaborative optimization method for distribution network and microgrid proposed in Embodiment 1 of this invention constructs a distributed collaborative optimization framework with precise reactive power support domain constraints. While ensuring the safe operation of distribution network and microgrid, it significantly improves the reactive power support capability of the system, reduces operating costs, and effectively solves the problems of voltage stability and optimized scheduling under high proportion of renewable energy access.

[0062] Example 2 The present invention also proposes a device, Figure 5 This is a schematic diagram of a multi-entity distributed collaborative optimization device for distribution networks and microgrids proposed in Embodiment 1 of the present invention, comprising: Memory, used to store computer programs; When a processor executes the computer program, the method steps are as follows: In step S1, the process begins.

[0063] In step S2, a safe operation model of the distribution network-microgrid system is constructed. The safe operation model includes distribution network power flow constraints based on a linearized distributed power flow model, reactive power support domain constraints based on mixed integer linear programming, and microgrid inverter-energy storage collaborative operation constraints. In step S3, based on the safe operation model, a multi-stage optimization model is established with the goal of minimizing operating costs. The objective function includes electricity purchase cost, reactive power support violation penalty cost, grid loss cost, energy storage operation cost, and load reduction cost. In step S4, the multi-stage optimization model is decomposed into an active distribution network operator optimization sub-problem and multiple microgrid operator optimization sub-problems. Based on the augmented Lagrangian function and the consistent alternating direction multiplier method, a distributed optimization algorithm is designed so that each entity can achieve collaborative optimization by iteratively updating local decision variables, shared variable copies and Lagrangian multipliers. In step S5, based on the distributed optimization algorithm, a rolling time-domain optimization framework is adopted to realize the real-time coordinated operation and reactive power support control of the distribution network and microgrid.

[0064] In step S6, the process ends.

[0065] The present invention 2 proposes a multi-entity distributed collaborative optimization device for distribution networks and microgrids. By constructing a distributed collaborative optimization framework with precise reactive power support domain constraints, it significantly improves the reactive power support capability of the system, reduces operating costs, and effectively solves the problem of voltage stability and optimized scheduling under high proportion of renewable energy access while ensuring the safe operation of distribution networks and microgrids.

[0066] It should be noted that the present invention also provides an electronic device, including: a communication interface capable of interacting with other devices such as network devices; and a processor connected to the communication interface to enable information interaction with other devices, used to execute a multi-subject distributed collaborative optimization device for a distribution network-microgrid provided by one or more of the above technical solutions when running a computer program, wherein the computer program is stored in a memory. Of course, in practical applications, the various components in the electronic device are coupled together through a bus system. It is understood that the bus system is used to realize the connection and communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus. The memory in the embodiments of this application is used to store various types of data to support the operation of the electronic device. Examples of this data include any computer program used to operate on the electronic device. It is understood that the memory can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory, flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache.By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM). The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable types of memory. The methods disclosed in the embodiments of this application can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by integrated logic circuits in the processor hardware or by instructions in software form. The processor can be a general-purpose processor, a DSP (Digital Signal Processing, i.e., a chip capable of implementing digital signal processing technology), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules can be located in a storage medium, which is located in memory. The processor reads the program from the memory and, in conjunction with its hardware, completes the steps of the aforementioned method. When the processor executes the program, it implements the corresponding processes in the various methods of the embodiments of this application; for simplicity, these will not be elaborated further here.

[0067] The description of the relevant parts of the distribution network-microgrid multi-entity distributed collaborative optimization device provided in Embodiment 2 of this application can be found in the detailed description of the corresponding parts of the distribution network-microgrid multi-entity distributed collaborative optimization method provided in Embodiment 1 of this application, and will not be repeated here.

[0068] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that the elements inherent in a process, method, article, or apparatus that includes a list of elements are included. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Additionally, portions of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0069] While specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art can make other modifications or variations based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A multi-entity distributed collaborative optimization method for distribution network-microgrid, characterized in that, Includes the following steps: A safe operation model for the distribution network-microgrid system is constructed. The safe operation model includes distribution network power flow constraints based on a linearized distributed power flow model, reactive power support domain constraints based on mixed integer linear programming, and microgrid inverter-energy storage collaborative operation constraints. Based on the aforementioned safe operation model, a multi-stage optimization model is established with the goal of minimizing operating costs. The objective function includes electricity purchase cost, reactive power support violation penalty cost, grid loss cost, energy storage operation cost, and load reduction cost. The multi-stage optimization model is decomposed into an active distribution network operator optimization sub-problem and multiple microgrid operator optimization sub-problems. Based on the augmented Lagrangian function and the consistent alternating direction multiplier method, a distributed optimization algorithm is designed so that each entity can achieve collaborative optimization by iteratively updating local decision variables, shared variable copies and Lagrangian multipliers. Based on the aforementioned distributed optimization algorithm, a rolling time-domain optimization framework is adopted to achieve real-time coordinated operation and reactive power support control between the distribution network and the microgrid.

2. The method according to claim 1, characterized in that, The process of constructing power flow constraints for distribution networks based on linearized distributed power flow models includes: The Distflow model is used to describe the radial topology of the distribution network, and a physical constraint system including branch power conservation equations and node voltage equations is established. For the nonlinear current-voltage relationship in the Distflow model, a first-order Taylor series expansion is applied at the set initial operating reference point to achieve linearization.

3. The method according to claim 1, characterized in that, The process of constructing reactive power support domain constraints based on mixed-integer linear programming includes: By introducing multiple binary decision variables, the feasible plane for active-reactive power exchange between the active distribution network and the upper-level power grid is geometrically divided, defining the support domain and non-support domain that meet the operation requirements of the upper-level power grid. Then, a penalty cost term related to the location of the power exchange point is introduced. When the optimized operating point falls into the non-supported domain, the penalty cost term is activated and the penalty is quantified according to the degree of exceeding the limit.

4. The method according to claim 1, characterized in that, The process of constructing constraints for the coordinated operation of microgrid inverters and energy storage includes: Based on the physical characteristics of energy storage devices, a dynamic evolution equation for their state of charge is established, and upper and lower limits for energy storage and charging / discharging power are set to establish state of charge constraints based on the energy storage state equation. To address the capacity constraints of inverters, a polygonal linearization method is adopted to approximate the original quadratic rotating cone constraint as a set of linear inequalities to describe the joint feasible region of active and reactive power, and to establish an apparent power polygonal linearization approximation constraint based on inverter capacity constraints. Based on the law of conservation of energy, a balance constraint between active and reactive power is established on the AC bus inside the microgrid.

5. The method according to claim 1, characterized in that, The multi-stage optimization model is as follows: ； in, Represent decision variables; This indicates the electricity price from the higher-level power grid; This indicates the active power of an active distribution network that interacts with the upstream power grid. This indicates the penalty cost incurred due to violation of reactive power support constraints. This represents the set of branches in the active distribution network; The penalty coefficient representing active power transmission loss; Indicates a branch At any moment The current amplitude; Indicates a branch The resistance; This represents the set of nodes connected to the microgrid. This represents the operating cost coefficient of the battery. Indicates storage battery At any moment The power; This represents the penalty coefficient for load reduction costs; Represents a node At any moment The load reduces active power.

6. The method according to claim 1, characterized in that, The multi-stage optimization model is decomposed into an active distribution network operator optimization sub-problem and multiple microgrid operator optimization sub-problems, specifically: Decoupling analysis was performed on the decision variables to identify and separate the first type of variables directly related to the active distribution network operator and the second type of variables directly related to each microgrid operator; the second type of variables includes the control commands of the internal equipment of the microgrid. A shared variable is defined to characterize the interactive power at the connection boundary between the distribution network and each microgrid. By classifying the variables, the multi-stage optimization model is decomposed into an active distribution network operator optimization sub-problem and multiple microgrid operator optimization sub-problems, and each sub-problem is coupled through the shared variable.

7. The method according to claim 6, characterized in that, The distributed optimization algorithm incorporates the consistency constraints of the shared variables into the objective functions of each agent by constructing an augmented Lagrange function, and uses the alternating direction multiplier method to alternately update the local decision variables, copies of shared variables, and Lagrange multipliers of each agent.

8. The method according to claim 1, characterized in that, The execution process of the rolling time-domain optimization framework is as follows: the prediction time domain is set to include multiple continuous scheduling periods, and the optimization problem is updated on a rolling basis with the latest measured data at fixed time intervals, triggering the distributed optimization algorithm to solve iteratively.

9. The method according to claim 8, characterized in that, Real-time collaborative operation and reactive power support regulation include: after the distributed optimization algorithm converges iteratively, the active distribution network operator sends interactive power commands to each microgrid, and each microgrid adjusts its local energy storage charging and discharging power and inverter reactive power output according to the commands.

10. A multi-entity distributed collaborative optimization device for a distribution network-microgrid, comprising at least one processor and a memory, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the at least one processor, it implements a multi-subject distributed collaborative optimization method for distribution network-microgrid as described in any one of claims 1 to 9.

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