Coordinated optimization scheduling method and system of power distribution network-multi-microgrid

Abstract

The application discloses a kind of power distribution network-multiple microgrid collaborative optimization scheduling method, including obtaining the data information of target power distribution network and target microgrid;The basic constraint model of power distribution network side is constructed;Based on the constraint model constructed, the first stage optimization of target power distribution network is carried out with the operation optimization of power distribution network as goal;With the operation optimization of each microgrid as goal, the response optimization of each microgrid is carried out based on the first stage optimization result of target power distribution network obtained;According to the response optimization result of each microgrid obtained, the second stage optimization of target power distribution network is carried out again with the operation optimization of power distribution network as goal;According to the optimization result obtained, the collaborative optimization scheduling of power distribution network-multiple microgrid is completed.The application further discloses a kind of system for realizing the collaborative optimization scheduling of power distribution network-multiple microgrid.The application not only realizes the collaborative optimization scheduling of power distribution network-multiple microgrid, and the scheme of the application is relatively more simple, and the reliability is higher, and the accuracy is better.

Description

Technical Field

[0001] This invention belongs to the field of electrical automation, specifically relating to a collaborative optimization scheduling method and system for distribution networks and multiple microgrids. Background Technology

[0002] With economic and technological development and the improvement of people's living standards, electricity has become an indispensable secondary energy source in people's production and daily life, bringing endless convenience. Therefore, ensuring a stable and reliable supply of electricity has become one of the most important tasks of the power system.

[0003] Currently, an increasing number of new energy power generation systems are being integrated into the power grid and generating electricity. In the distribution network, flexible resources such as distributed photovoltaics, electrochemical energy storage, and adjustable loads are being connected on a large scale and are gradually operating independently in the form of microgrids. Microgrids typically have autonomous energy management capabilities, ensuring reliable power supply and economical and efficient operation for their own loads while engaging in power exchange and energy complementarity with the distribution network through a point of common coupling.

[0004] In the current operational scenario, the distribution network not only needs to ensure its own energy supply and demand balance, but also needs to undertake corresponding peak shaving, valley shaving, and power regulation tasks according to the dispatch instructions of the superior power grid. Therefore, how to effectively coordinate multiple microgrids with independent operating objectives to participate in the regulation of the power system has become the key to the safe and reliable operation of the current distribution network.

[0005] Currently, collaborative optimization scheduling schemes for distribution networks and multiple microgrids mainly employ centralized optimization schemes, game theory-based schemes, or distributed algorithm schemes. However, centralized optimization schemes typically require access to the complete operating model and private data of the microgrids, which has significant limitations and poor applicability in practical engineering applications. While game theory-based schemes or distributed algorithm schemes can theoretically achieve collaborative coordination among multiple stakeholders, these schemes often require multiple rounds of information exchange and iterative calculations, resulting in high complexity. Summary of the Invention

[0006] One of the objectives of this invention is to provide a highly reliable and accurate collaborative optimization scheduling method for distribution networks and multiple microgrids.

[0007] The second objective of this invention is to provide a system for implementing the collaborative optimization scheduling method of the distribution network and multiple microgrids.

[0008] The collaborative optimization scheduling method for distribution networks and multiple microgrids provided by this invention includes the following steps:

[0009] S1. Obtain data information about the target distribution network and the target microgrid;

[0010] S2. Based on the data obtained in step S1, construct the basic constraint model for the distribution network side;

[0011] S3. Based on the constraint model constructed in step S2, the first stage of optimization of the target distribution network is carried out with the goal of optimizing the operation of the distribution network;

[0012] S4. Based on the first-stage optimization results of the target distribution network obtained in step S3, optimize the response of each microgrid with the goal of optimizing the operation of each microgrid;

[0013] S5. Based on the response optimization results of each microgrid obtained in step S4, the second stage optimization of the target distribution network is carried out again with the goal of optimizing the operation of the distribution network.

[0014] S6. Based on the optimization results obtained in steps S4 and S5, complete the coordinated optimization scheduling of the distribution network and multiple microgrids.

[0015] Step S1, which involves acquiring data information about the target distribution network and the target microgrid, specifically includes the following steps:

[0016] Acquire data information from the target distribution network and the target microgrid;

[0017] The data information includes energy storage system data, load reduction data, photovoltaic data, load data, and power limitation data between the distribution network and the upstream power grid.

[0018] Step S2, which involves constructing a basic constraint model for the distribution network side based on the data information obtained in step S1, specifically includes the following steps:

[0019] The following formula is used as the charging power constraint for the energy storage system:

[0020] In the formula Let t be the charging power of the distribution network-side energy storage system at time t; This represents the maximum charging and discharging power of the energy storage system.

[0021] The following formula is used as the discharge power constraint for the energy storage system:

[0022] In the formula Let t be the discharge power of the distribution network-side energy storage system at time t;

[0023] The following formula is used as a dynamic constraint on the remaining power of the energy storage system:

[0024] In the formula Let t be the remaining power of the energy storage system in the distribution network at time t; The set scheduling time step; The charging efficiency of energy storage systems in the power distribution network; The discharge efficiency of the energy storage system in the power distribution network;

[0025] The following formula is used as the boundary constraint for the remaining power of the energy storage system:

[0026] In the formula This refers to the minimum allowable remaining power of the energy storage system in the power distribution network. This refers to the maximum allowable remaining power of the energy storage system in the power distribution network.

[0027] The following formula is used as the power supply constraint from the microgrid to the upper-level distribution network:

[0028] In the formula Let be the power output of the j-th microgrid to the distribution network at time t; This represents the maximum power limit value for the j-th microgrid to supply power to the distribution network.

[0029] The following formula is used as the power receiving constraint of the microgrid from the distribution network:

[0030] In the formula Let be the power input from the distribution network to the j-th microgrid at time t; This represents the maximum power limit value that the j-th microgrid can receive from the distribution network;

[0031] The following formula is used as the upward adjustment power constraint for the microgrid:

[0032] In the formula Let be the upward adjustment power of the j-th microgrid at time t; This represents the upper limit of the upward regulation power of the j-th microgrid;

[0033] The following formula is used as the downward adjustment power constraint for the microgrid:

[0034] In the formula Let be the downward regulation power of the j-th microgrid at time t; Let be the upper limit of the downward regulation power of the j-th microgrid;

[0035] The following formula is used as the energy balance constraint for microgrid regulation:

[0036] The following formula is used as the power balance constraint within the microgrid:

[0037] In the formula Let be the predicted baseline load of the j-th microgrid at time t; Let the photovoltaic output of the j-th microgrid be at time t.

[0038] The following formula is used as the power constraint for the distribution network-upper-level grid interaction:

[0039] In the formula Let t be the power input from the upstream power grid to the distribution network at time t; Let t be the power output from the distribution network to the upper-level grid at time t;

[0040] The following formula is used as the power receiving constraint of the distribution network from the upper-level power grid:

[0041] In the formula This is the maximum power limit that the distribution network can receive from the upstream power grid;

[0042] The following formula is used as the power supply constraint from the distribution network to the upper-level power grid:

[0043] In the formula The maximum power limit for the distribution network to send power to the upper-level power grid.

[0044] Step S3, based on the constraint model constructed in step S2, aims to optimize the operation of the distribution network and performs the first stage of optimization of the target distribution network, specifically including the following steps:

[0045] The following formula is used as the objective function for the first stage optimization of the target distribution network:

[0046] In the formula The objective function value; Let t be the electricity price at which the distribution network purchases electricity from the upstream power grid. The price at which the distribution network sells electricity to the upper-level grid at time t; The levelized cost of energy for the entire lifecycle of an energy storage system;

[0047] The basic constraint model of the distribution network side constructed in step S2 is used as the constraint condition;

[0048] The first-stage optimization of the target distribution network is performed to obtain the first-stage optimization parameters of the target distribution network; the first-stage optimization parameters include... , , , , and .

[0049] Step S4, based on the first-stage optimization results of the target distribution network obtained in step S3, optimizes the response of each microgrid with the goal of optimizing the operation of each microgrid. Specifically, it includes the following steps:

[0050] The following formula is used as the objective function for the response optimization of each microgrid:

[0051] In the formula Let the objective function value be the response optimization value of the j-th microgrid; Let be the penalty value of the j-th microgrid at time t, and , To adjust the direction coefficient, , The reference power receiving capacity of the j-th microgrid after the first stage optimization of the target distribution network. The reference power supply point for the j-th microgrid after the first stage of optimization of the target distribution network; The set directional penalty coefficient; The scheduling cost of transferable load; This represents the maximum power that the load transferable by the j-th microgrid can adjust downwards.

[0052] Construct the constraints for response optimization of each microgrid:

[0053] The following formula is used as the constraint for upward adjustment of transferable load:

[0054] In the formula Let be the upward adjustment power of the transferable load of the j-th microgrid at time t; This is an adjustable time-counting parameter; if the transferable load at time t can be adjusted downwards, then... If the transferable load at time t can be adjusted upwards, then ; This represents the maximum power that the load that can be transferred to the j-th microgrid can be adjusted upwards.

[0055] The following formula is used as the downward adjustment constraint for transferable loads:

[0056] In the formula Let be the downward adjustment power of the j-th microgrid's transferable load at time t; This represents the maximum power that the load transferable by the j-th microgrid can adjust downwards.

[0057] The following formula is used as the constraint for the adjustment duration of transferable load:

[0058] In the formula The maximum daily adjustment duration for the set transferable load;

[0059] The following formula is used to extend the power balance constraint of the microgrid:

[0060] In the formula Let be the charging power of the energy storage system inside the j-th microgrid at time t; Let be the discharge power of the energy storage system inside the j-th microgrid at time t;

[0061] Response optimization is performed on each microgrid to obtain response optimization parameters for each microgrid; the response optimization parameters for each microgrid include... , , , , and .

[0062] Step S5, based on the response optimization results of each microgrid obtained in step S4, again aims at optimizing the operation of the distribution network and performs a second-stage optimization of the target distribution network. This specifically includes the following steps:

[0063] The following formula is used as the objective function for the second stage optimization of the target distribution network:

[0064] In the formula The objective function value for the second stage optimization of the target distribution network;

[0065] Constraints for the second stage optimization of the target distribution network:

[0066] The following formula is used as the charging power constraint:

[0067] The following formula is used as the discharge power constraint:

[0068] The following formula is used as a dynamic constraint on the remaining energy storage capacity:

[0069] The following formula is used as the boundary constraint for the remaining power:

[0070] The following formula is used as the global power balance constraint for the distribution network:

[0071] The following formula is used as the constraint for the distribution network to receive power from the upper-level power grid:

[0072] The following formula is used as the constraint for the power transmission from the distribution network to the upper-level power grid:

[0073] When performing the second phase of optimization, and The result obtained in step S4 is used and is no longer used in the optimization process;

[0074] A second-stage optimization of the target distribution network is performed to obtain the second-stage optimization parameters of the target distribution network; the second-stage optimization parameters include... , , , , and .

[0075] Step S6, which involves completing the coordinated optimization scheduling of the distribution network and multiple microgrids based on the optimization results obtained in steps S4 and S5, specifically includes the following steps:

[0076] According to the obtained and Determine the interaction power between the target distribution network and the upstream power grid to ensure power balance between the distribution network and the upstream power grid;

[0077] According to the obtained and Determine the charging and discharging plan of the energy storage system in the target distribution network and optimize the operating status of the energy storage system on the distribution network side;

[0078] According to the obtained and The interaction power between each microgrid and the target distribution network is determined, serving as the basis for the operation of each microgrid.

[0079] Complete the coordinated and optimized scheduling of the distribution network and multiple microgrids.

[0080] This invention also provides a system for implementing the cooperative optimization scheduling method of the distribution network-multiple microgrids, comprising a data acquisition module, a constraint construction module, a first optimization module, a response optimization module, a second optimization module, and an optimization scheduling module; the data acquisition module, constraint construction module, first optimization module, response optimization module, second optimization module, and optimization scheduling module are connected in series; the data acquisition module is used to acquire data information of the target distribution network and the target microgrid, and upload the data information to the constraint construction module; the constraint construction module is used to construct a basic constraint model on the distribution network side based on the received data information and the acquired data information, and upload the data information to the first optimization module; the first optimization module is used to optimize the scheduling method based on the received data information and the constructed constraint model, in order to optimize the distribution network-multiple microgrids. With the goal of optimizing the operation of the power grid, the first stage of optimization of the target distribution network is performed, and the data information is uploaded to the response optimization module. The response optimization module is used to optimize the response of each microgrid based on the received data information and the first stage optimization results of the target distribution network, with the goal of optimizing the operation of each microgrid, and uploads the data information to the second optimization module. The second optimization module is used to optimize the target distribution network again based on the received data information and the response optimization results of each microgrid, with the goal of optimizing the operation of the distribution network, and uploads the data information to the optimization scheduling module. The optimization scheduling module is used to complete the coordinated optimization scheduling of the distribution network and multiple microgrids based on the received data information and the optimization results.

[0081] The method and system for coordinated optimization scheduling of distribution network and multiple microgrids provided by this invention acquires data from the target distribution network and each microgrid, and based on a scheme of primary optimization of distribution network - microgrid response optimization - secondary optimization of distribution network, not only realizes coordinated optimization scheduling of distribution network and multiple microgrids, but also the scheme of this invention is relatively simpler, more reliable and more accurate. Attached Figure Description

[0082] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0083] Figure 2 This is a schematic diagram of the functional modules of the system of the present invention. Detailed Implementation

[0084] like Figure 1 The diagram shown is a flowchart of the method of the present invention: The collaborative optimization scheduling method for distribution networks and multiple microgrids disclosed in this invention includes the following steps:

[0085] S1. Obtain data information of the target distribution network and the target microgrid; specifically including the following steps:

[0086] Acquire data information from the target distribution network and the target microgrid;

[0087] The data information includes energy storage system data, load reduction data, photovoltaic data, load data, and power limitation data between the distribution network and the upstream power grid.

[0088] S2. Based on the data obtained in step S1, construct the basic constraint model for the distribution network side; specifically including the following steps:

[0089] As the upper-level decision-making body, the core responsibility of the distribution network is to formulate global dispatch strategies and guide the microgrid response. Before carrying out specific optimizations, it is necessary to clarify the key equipment (energy storage system) of the distribution network itself, its interaction with the microgrid, the internal operating rules of the microgrid, and the connection boundary with the upper-level power grid. These constraints are the premise for subsequent optimizations, ensuring that the dispatch scheme is feasible within the framework of equipment safety and system stability.

[0090] Energy storage system operating constraints:

[0091] Energy storage systems are core flexible regulation resources on the distribution network side, undertaking key tasks such as peak shaving and valley filling, and power balance regulation. Their operation must strictly follow charging and discharging power limits, energy conservation and power boundary requirements to avoid overcharging and over-discharging, which could lead to equipment damage or performance degradation.

[0092] The following formula is used as the charging power constraint for the energy storage system:

[0093] In the formula Let t be the charging power of the distribution network-side energy storage system at time t (a core variable in dispatch optimization). The maximum charging and discharging power of the energy storage system; the charging power of the energy storage system cannot be negative (i.e., it cannot discharge in reverse), and it cannot exceed the maximum charging power allowed by the inverter and other electrical equipment, so as to ensure electrical safety and normal operation of the equipment during the charging process;

[0094] The following formula is used as the discharge power constraint for the energy storage system:

[0095] In the formula Let t be the discharge power of the energy storage system on the distribution network side. This variable is mutually exclusive with the charging power (it cannot charge and discharge at the same time). The discharge power cannot be negative (i.e., it cannot charge in reverse) and cannot exceed the maximum discharge power of electrical equipment such as inverters, so as to avoid voltage and frequency fluctuations caused by excessive discharge power and ensure the power quality of the distribution network.

[0096] The following formula is used as a dynamic constraint on the remaining power of the energy storage system:

[0097] In the formula Let t be the remaining power of the energy storage system in the distribution network at time t. This variable reflects the energy state of the energy storage system and is a key state quantity that needs to be tracked in real time during scheduling. The set scheduling time step; The charging efficiency of energy storage systems in the power distribution network; This is the discharge efficiency of the energy storage system in the distribution network. This formula reflects the energy conservation characteristics of the energy storage system. The remaining power at the next moment is determined by the remaining power at the current moment, the effective charging power during the charging process (minus charging losses), and the effective discharging power during the discharging process (considering discharge losses), ensuring that the energy state of the energy storage system can be accurately predicted and controlled.

[0098] The following formula is used as the boundary constraint for the remaining power of the energy storage system:

[0099] In the formula This refers to the minimum allowable remaining power of the energy storage system in the power distribution network. This is the maximum allowable remaining power of the energy storage system in the distribution network. This limit keeps the remaining power of the energy storage system within a safe range, avoiding safety risks such as battery bulging and fire caused by overcharging, while also preventing irreversible capacity decay caused by over-discharging, thus extending the service life of the equipment.

[0100] Distribution network-microgrid interaction power constraints:

[0101] Microgrids exchange power with the distribution network through the point of common coupling (PCC). The power exchanged must simultaneously meet the limitations of its own grid connection capacity and the dispatch requirements of the distribution network, and the regulation capability of the microgrid must maintain global balance.

[0102] The following formula is used as the power supply constraint from the microgrid to the upper-level distribution network:

[0103] In the formula Let be the power output of the j-th microgrid to the distribution network at time t; This is the maximum power limit value for the j-th microgrid to supply power to the distribution network; the power supplied by the microgrid to the distribution network cannot be negative (i.e., it cannot receive power in reverse) and cannot exceed the maximum allowable power supply, so as to avoid overload of the distribution network lines or voltage exceeding the limit due to excessive power supply.

[0104] The following formula is used as the power receiving constraint of the microgrid from the distribution network:

[0105] In the formula Let be the power input from the distribution network to the j-th microgrid at time t; This is the maximum power limit value for the j-th microgrid to receive power from the distribution network; the power received by the microgrid from the distribution network cannot be negative (i.e., it cannot send power in reverse) and cannot exceed the maximum allowable power to receive, so as to avoid local voltage drop or insufficient power supply in the distribution network due to excessive power received;

[0106] The following formula is used as the upward adjustment power constraint for the microgrid:

[0107] In the formula Let be the upward adjustment power of the j-th microgrid at time t; This is the upper limit of the upward regulation power of the j-th microgrid; the upward regulation power is a non-negative value and does not exceed its maximum regulation capacity, ensuring that the regulation behavior of the microgrid is within the allowable range of its own adjustable resources;

[0108] The following formula is used as the downward adjustment power constraint for the microgrid:

[0109] In the formula Let be the downward regulation power of the j-th microgrid at time t; This is the upper limit of the downward adjustment power of the j-th microgrid; the downward adjustment power is a non-negative value and does not exceed its maximum adjustment capacity to avoid over-adjustment leading to power imbalance within the microgrid;

[0110] The following formula is used as the energy balance constraint for microgrid regulation:

[0111] Throughout the entire scheduling cycle, the total energy of upward regulation by the j-th microgrid must be equal to the total energy of downward regulation. This is because the microgrid's regulation resources, such as transferable loads, can only realize the transfer of energy at different times and cannot create or consume additional energy. This constraint ensures the global energy conservation of regulation behavior and avoids the occurrence of energy regulation that is created out of thin air.

[0112] Power balance constraints within a microgrid:

[0113] Each microgrid, as an independent energy unit, must satisfy its internal real-time power balance at every scheduling moment, that is, the sum of all input power equals the sum of all output power, to ensure the stable operation of the microgrid itself;

[0114] The following formula is used as the power balance constraint within the microgrid:

[0115] In the formula Let be the predicted baseline load of the j-th microgrid at time t; The photovoltaic output of the j-th microgrid at time t; the constraint equalization on both sides ensures the real-time power balance of the microgrid at time t, avoiding problems such as frequency fluctuations and power outages caused by power surplus or shortage;

[0116] Distribution network-upper-level grid interaction power constraints:

[0117] As an intermediate link connecting the upper-level power grid and the microgrid, the power exchange between the distribution network and the upper-level power grid must meet the requirements of the global power balance of the system and the grid connection capacity limit to ensure the stable operation of the entire power system.

[0118] The following formula is used as the power constraint for the distribution network-upper-level grid interaction:

[0119] In the formula Let t be the power input from the upstream power grid to the distribution network at time t; Let t be the power output from the distribution network to the upper-level grid at time t;

[0120] The following formula is used as the power receiving constraint of the distribution network from the upper-level power grid:

[0121] In the formula This is the maximum power limit for the distribution network to receive power from the upstream power grid; the power received by the distribution network from the upstream power grid cannot be negative (i.e., reverse power transmission is not allowed), and it cannot exceed the maximum allowable power to receive, so as to avoid overloading of the connecting lines or tight power supply from the upstream power grid due to excessive power received;

[0122] The following formula is used as the power supply constraint from the distribution network to the upper-level power grid:

[0123] In the formula This is the maximum power limit for the distribution network to send power to the upper-level power grid; the power sent from the distribution network to the upper-level power grid cannot be negative (i.e., it cannot receive power in reverse), and cannot exceed the maximum allowable power to send, so as to avoid line overload or frequency fluctuations in the upper-level power grid due to excessive power sending.

[0124] S3. Based on the constraint model constructed in step S2, and with the goal of optimizing the operation of the distribution network, perform the first stage optimization of the target distribution network; specifically including the following steps:

[0125] After completing the construction of the basic constraint model on the distribution network side, the first stage of the distribution network optimization is entered. At this time, the distribution network has not yet obtained the actual response information of each microgrid. Therefore, based on the electricity price signal of the upper-level grid, its own flexible resources (energy storage) and the maximum regulation capacity of the microgrid, with the goal of maximizing its own operating economy, the initial scheduling scheme and directional regulation requirements are formulated to provide a reference benchmark for the subsequent microgrid response.

[0126] The following formula is used as the objective function for the first stage optimization of the target distribution network:

[0127] In the formula The objective function value; Let t be the electricity price at which the distribution network purchases electricity from the upstream power grid. The price at which the distribution network sells electricity to the upper-level grid at time t; The objective function is the levelized cost of energy throughout the entire lifecycle of the energy storage system. Through this objective function, the distribution network will rationally arrange its power purchase and sale behavior and energy storage charging and discharging strategies under the guidance of electricity price signals, so as to achieve optimal operating economy.

[0128] The basic constraint model of the distribution network side constructed in step S2 is used as the constraint condition; since the actual response of the microgrid has not yet been obtained at this stage, the constraints on the microgrid only consider its maximum regulation capacity. and Instead of actual power regulation, this ensures that the optimization model can still obtain a feasible solution even in the absence of microgrid response information;

[0129] The first-stage optimization of the target distribution network is performed to obtain the first-stage optimization parameters of the target distribution network; the first-stage optimization parameters include... , , , , and ;in, Forehead This ensures power balance between the distribution network and the upstream power grid. and The final charging and discharging plan for energy storage on the distribution network side has been determined. and The reference interaction power between the microgrid and the distribution network was determined;

[0130] S4. Based on the first-stage optimization results of the target distribution network obtained in step S3, and with the operational optimization of each microgrid as the objective, perform response optimization for each microgrid; specifically including the following steps:

[0131] Each microgrid, as an independent decision-making entity, after receiving the adjustment signal from the first-stage optimization output of the distribution network, will combine its own equipment constraints, load demand, photovoltaic output and directional penalty mechanism, with the goal of minimizing its own operating costs, to autonomously optimize the power interaction scheme with the distribution network and the internal resource scheduling strategy.

[0132] The following formula is used as the objective function for the response optimization of each microgrid:

[0133] In the formula Let the objective function value be the response optimization value of the j-th microgrid; Let be the penalty value of the j-th microgrid at time t, and , To adjust the direction coefficient, , The reference power receiving capacity of the j-th microgrid after the first stage optimization of the target distribution network. The reference power supply point for the j-th microgrid after the first stage of optimization of the target distribution network; The set directional penalty coefficient; The scheduling cost of transferable load; Let be the maximum power that the transferable load of the j-th microgrid can adjust downwards. This objective function requires that while pursuing its own economic optimization, the microgrid must also consider the directional penalty cost and actively adjust the direction of the interactive power to meet the global dispatch requirements of the distribution network, thereby achieving a balance between individual optimization and global optimization.

[0134] In practice, and This is to facilitate the calculation of the intermediate variables introduced, and numerically compare them with the output results of the first optimization of the distribution network. and Consistent;

[0135] To guide microgrids to proactively meet the regulation needs of the distribution network while ensuring their decision-making autonomy, a penalty mechanism is introduced into the objective function; the regulation direction coefficient... The calculation, by imposing additional costs on interactive behaviors opposite to the distribution network's regulation direction, incentivizes the microgrid to adjust its own interactive power direction, thereby achieving coordination with the distribution network; when At that time, molecules A positive value indicates that the total power received by all microgrids from the distribution network exceeds the total power transmitted, indicating a power shortage in the distribution network as a whole. This necessitates supplementing power from the upstream grid, requiring microgrids to reduce power transmission to the distribution network or increase power reception from it. When... At that time, molecules A negative value indicates that the total power supplied by all microgrids to the distribution network exceeds the total power received, resulting in a surplus in the distribution network. This requires the microgrids to send more power to the higher-level grid to absorb the surplus. In this case, the microgrids need to increase their power supply to the distribution network or decrease their power receipts from the distribution network; adjusting the direction coefficient... The power supply and demand status and regulation direction requirements of the distribution network are directly reflected by the net interaction power between all microgrids and the distribution network, i.e., the difference between the power received and the power transmitted.

[0136] Then, by constructing the penalty value of the microgrid To achieve: when the actual net interactive power of the microgrid Reference net interaction power with distribution network When the directions are consistent, At this point, the penalty value There is no additional penalty cost; when the actual net interaction power of the microgrid is in the opposite direction to the reference net interaction power, At this point, the penalty value The result of this product being negative, when substituted into the objective function, will increase the operating cost of the microgrid, thereby incentivizing the microgrid to adjust the direction of the interactive power to meet the regulation needs of the distribution network.

[0137] Construct the constraints for response optimization of each microgrid:

[0138] Microgrid load transfer model:

[0139] Transferable loads are the core flexible regulation resources within a microgrid. They are characterized by the ability to transfer electricity consumption time (such as industrial production loads and electric vehicle charging loads) at different times within the dispatch cycle, while the total electricity consumption remains unchanged. To avoid excessive transfer affecting users' normal electricity consumption, constraints must be placed on their regulation power and regulation duration.

[0140] The following formula is used as the constraint for upward adjustment of transferable load:

[0141] In the formula Let be the upward adjustment power of the transferable load of the j-th microgrid at time t; This is an adjustable time-counting parameter; if the transferable load at time t can be adjusted downwards, then... If the transferable load at time t can be adjusted upwards, then ; This represents the maximum power that the load that can be transferred to the j-th microgrid can be adjusted upwards.

[0142] The following formula is used as the downward adjustment constraint for transferable loads:

[0143] In the formula Let be the downward adjustment power of the j-th microgrid's transferable load at time t; This represents the maximum power that the load transferable by the j-th microgrid can adjust downwards.

[0144] The following formula is used as the constraint for the adjustment duration of transferable load:

[0145] In the formula The maximum daily adjustment duration for transferable loads is set; limiting the total adjustment duration of transferable loads within a day can prevent excessive adjustment from affecting users' normal production and life, and ensure the feasibility of adjustment behavior;

[0146] Power balance constraints within a microgrid:

[0147] Considering the regulation behavior of transferable loads and the operation of the microgrid's own energy storage system, the power balance constraints within the microgrid need to be extended to ensure real-time power balance including all flexible resources.

[0148] The following formula is used to extend the power balance constraint of the microgrid:

[0149] In the formula Let be the charging power of the energy storage system inside the j-th microgrid at time t; Let t be the discharge power of the energy storage system inside the j-th microgrid at time t. This constraint adds the transferable load regulation power and the charging and discharging power of the microgrid's own energy storage to the original constraint, ensuring the real-time power balance of the microgrid after considering all flexible resource regulation behaviors, and ensuring the stable operation of the microgrid.

[0150] Response optimization is performed on each microgrid to obtain response optimization parameters for each microgrid; the response optimization parameters for each microgrid include... , , , , and ;

[0151] in, and Determine the actual interactive power of the optimized microgrid; and Determine the regulating power of the transferable load in the microgrid; and Determine the microgrid's own energy storage charging and discharging plan;

[0152] S5. Based on the response optimization results of each microgrid obtained in step S4, the second stage optimization of the target distribution network is carried out again with the goal of optimizing the operation of the distribution network; specifically, the following steps are included:

[0153] After the distribution network obtains the response results of all microgrids, it clarifies the actual interaction power with the microgrids, and this power remains fixed in this stage. At this time, the distribution network needs to combine its own flexible resources (such as energy storage systems) and the interaction constraints with the upper-level grid to perform a second optimization, revise the initial scheduling scheme, and form the final collaborative scheduling strategy to ensure global power balance and optimal economy.

[0154] The following formula is used as the objective function for the second stage optimization of the target distribution network:

[0155] In the formula The objective function value for the second stage optimization of the target distribution network; the second stage optimization is to modify the initial scheme with known actual microgrid response, rather than changing the optimization objective.

[0156] Constraints for the second stage optimization of the target distribution network:

[0157] The following formula is used as the charging power constraint:

[0158] The following formula is used as the discharge power constraint:

[0159] The following formula is used as a dynamic constraint on the remaining energy storage capacity:

[0160] The following formula is used as the boundary constraint for the remaining power:

[0161] The following formula is used as the global power balance constraint for the distribution network:

[0162] The following formula is used as the constraint for the distribution network to receive power from the upper-level power grid:

[0163] The following formula is used as the constraint for the power transmission from the distribution network to the upper-level power grid:

[0164] When performing the second phase of optimization, and The result obtained in step S4 is adopted and is no longer used in the optimization solution process; the actual response of the power grid may deviate from the reference value in the first stage. Fixing the interaction power after the deviation can make the second optimization of the distribution network more in line with the actual operation and ensure the feasibility of the dispatching scheme.

[0165] A second-stage optimization of the target distribution network is performed to obtain the second-stage optimization parameters of the target distribution network; the second-stage optimization parameters include... , , , , and ;

[0166] S6. Based on the optimization results obtained in steps S4 and S5, complete the coordinated optimization scheduling of the distribution network and multiple microgrids; specifically including the following steps:

[0167] According to the obtained and Determine the interaction power between the target distribution network and the upstream power grid to ensure power balance between the distribution network and the upstream power grid;

[0168] According to the obtained and Determine the charging and discharging plan of the energy storage system in the target distribution network and optimize the operating status of the energy storage system on the distribution network side;

[0169] According to the obtained and The interaction power between each microgrid and the target distribution network is determined, serving as the basis for the operation of each microgrid.

[0170] Complete the coordinated and optimized scheduling of the distribution network and multiple microgrids.

[0171] In the above model solving process, existing commercial mathematical programming solvers such as CPLEX and Gurobi can be used to achieve efficient solutions.

[0172] This invention addresses the challenges of traditional centralized optimization methods, which require access to microgrid private data and are difficult to implement in engineering projects. It also overcomes the drawbacks of traditional distributed algorithms and game theory methods, such as multiple iterations, high computational complexity, poor real-time performance, and incompatibility with existing scheduling systems. The hierarchical architecture is highly compatible with existing distribution network scheduling models, ensuring strong engineering feasibility. The combination of two-stage distribution network optimization and parallel microgrid response guarantees both overall system economy and improved scheduling accuracy and real-time performance. This invention enables coordinated control of flexible resources (energy storage, transferable loads, and photovoltaics) within the microgrid, avoiding system fluctuations caused by disordered adjustments of transferable loads. For example, when facing high photovoltaic capacity in agricultural microgrids or high load demands in industrial microgrids, the model can accurately match the characteristic differences of different types of microgrids (commercial, industrial, and agricultural), resulting in stronger adaptability.

[0173] like Figure 2 The diagram shows the functional modules of the system of the present invention: The system for implementing the collaborative optimization scheduling method of the distribution network-multiple microgrids disclosed in this invention includes a data acquisition module, a constraint construction module, a first optimization module, a response optimization module, a second optimization module, and an optimization scheduling module; the data acquisition module, constraint construction module, first optimization module, response optimization module, second optimization module, and optimization scheduling module are connected in series; the data acquisition module is used to acquire data information of the target distribution network and the target microgrid, and upload the data information to the constraint construction module; the constraint construction module is used to construct a basic constraint model on the distribution network side based on the received data information and the acquired data information, and upload the data information to the first optimization module; the first optimization module is used to construct a basic constraint model on the distribution network side based on the received data information. The constraint model aims to optimize the operation of the distribution network. It performs a first-stage optimization of the target distribution network and uploads the data to the response optimization module. The response optimization module, based on the received data and the first-stage optimization results of the target distribution network, optimizes the response of each microgrid, aiming at optimizing its operation, and uploads the data to the second optimization module. The second optimization module, based on the received data and the response optimization results of each microgrid, performs a second-stage optimization of the target distribution network, again aiming at optimizing its operation, and uploads the data to the optimization scheduling module. The optimization scheduling module, based on the received data and the optimization results, completes the coordinated optimization scheduling of the distribution network and multiple microgrids.

Claims

1. A method for coordinated optimization scheduling of a distribution network and multiple microgrids, comprising the following steps: S1. Obtain data information about the target distribution network and the target microgrid; S2. Based on the data obtained in step S1, construct the basic constraint model for the distribution network side; S3. Based on the constraint model constructed in step S2, the first stage of optimization of the target distribution network is carried out with the goal of optimizing the operation of the distribution network; S4. Based on the first-stage optimization results of the target distribution network obtained in step S3, optimize the response of each microgrid with the goal of optimizing the operation of each microgrid; S5. Based on the response optimization results of each microgrid obtained in step S4, the second stage optimization of the target distribution network is carried out again with the goal of optimizing the operation of the distribution network. S6. Based on the optimization results obtained in steps S4 and S5, complete the coordinated optimization scheduling of the distribution network and multiple microgrids.

2. The method for coordinated optimization scheduling of distribution network and multiple microgrids according to claim 1, characterized in that... Step S1, which involves acquiring data information about the target distribution network and the target microgrid, specifically includes the following steps: Acquire data information from the target distribution network and the target microgrid; The data information includes energy storage system data, load reduction data, photovoltaic data, load data, and power limitation data between the distribution network and the upstream power grid.

3. The method for coordinated optimization scheduling of distribution network and multiple microgrids according to claim 2, characterized in that... Step S2, which involves constructing a basic constraint model for the distribution network side based on the data information obtained in step S1, specifically includes the following steps: The following formula is used as the charging power constraint for the energy storage system: In the formula Let t be the charging power of the distribution network-side energy storage system at time t; This represents the maximum charging and discharging power of the energy storage system. The following formula is used as the discharge power constraint for the energy storage system: In the formula Let t be the discharge power of the distribution network-side energy storage system at time t; The following formula is used as a dynamic constraint on the remaining power of the energy storage system: In the formula Let t be the remaining power of the energy storage system in the distribution network at time t; The set scheduling time step; The charging efficiency of energy storage systems in the power distribution network; The discharge efficiency of the energy storage system in the power distribution network; The following formula is used as the boundary constraint for the remaining power of the energy storage system: In the formula This refers to the minimum allowable remaining power of the energy storage system in the power distribution network. This refers to the maximum allowable remaining power of the energy storage system in the power distribution network. The following formula is used as the power supply constraint from the microgrid to the upper-level distribution network: In the formula Let be the power output of the j-th microgrid to the distribution network at time t; This represents the maximum power limit value for the j-th microgrid to supply power to the distribution network. The following formula is used as the power receiving constraint of the microgrid from the distribution network: In the formula Let be the power input from the distribution network to the j-th microgrid at time t; This represents the maximum power limit value that the j-th microgrid can receive from the distribution network; The following formula is used as the upward adjustment power constraint for the microgrid: In the formula Let be the upward adjustment power of the j-th microgrid at time t; This represents the upper limit of the upward regulation power of the j-th microgrid; The following formula is used as the downward adjustment power constraint for the microgrid: In the formula Let be the downward regulation power of the j-th microgrid at time t; Let be the upper limit of the downward regulation power of the j-th microgrid; The following formula is used as the energy balance constraint for microgrid regulation: The following formula is used as the power balance constraint within the microgrid: In the formula Let be the predicted baseline load of the j-th microgrid at time t; Let the photovoltaic output of the j-th microgrid be at time t. The following formula is used as the power constraint for the distribution network-upper-level grid interaction: In the formula Let t be the power input from the upstream power grid to the distribution network at time t; Let t be the power output from the distribution network to the upper-level grid at time t; The following formula is used as the power receiving constraint of the distribution network from the upper-level power grid: In the formula This is the maximum power limit that the distribution network can receive from the upstream power grid; The following formula is used as the power supply constraint from the distribution network to the upper-level power grid: In the formula The maximum power limit for the distribution network to send power to the upper-level power grid.

4. The method for coordinated optimization scheduling of distribution network and multiple microgrids according to claim 3, characterized in that... Step S3, based on the constraint model constructed in step S2, aims to optimize the operation of the distribution network and performs the first stage of optimization of the target distribution network, specifically including the following steps: The following formula is used as the objective function for the first stage optimization of the target distribution network: In the formula The objective function value; Let t be the electricity price at which the distribution network purchases electricity from the upstream power grid. The price at which the distribution network sells electricity to the upper-level grid at time t; The levelized cost of energy for the entire lifecycle of an energy storage system; The basic constraint model of the distribution network side constructed in step S2 is used as the constraint condition; The first-stage optimization of the target distribution network is performed to obtain the first-stage optimization parameters of the target distribution network; the first-stage optimization parameters include... , , , , and .

5. The collaborative optimization scheduling method for distribution network-multiple microgrids according to claim 4, characterized in that... Step S4, based on the first-stage optimization results of the target distribution network obtained in step S3, optimizes the response of each microgrid with the goal of optimizing the operation of each microgrid. Specifically, it includes the following steps: The following formula is used as the objective function for the response optimization of each microgrid: In the formula Let the objective function value be the response optimization value of the j-th microgrid; Let be the penalty value of the j-th microgrid at time t, and , To adjust the direction coefficient, , The reference power receiving capacity of the j-th microgrid after the first stage optimization of the target distribution network. The reference power supply point for the j-th microgrid after the first stage of optimization of the target distribution network; The set directional penalty coefficient; The scheduling cost of transferable load; This represents the maximum power that the load transferable by the j-th microgrid can adjust downwards. Construct the constraints for response optimization of each microgrid: The following formula is used as the constraint for upward adjustment of transferable load: In the formula Let be the upward adjustment power of the transferable load of the j-th microgrid at time t; This is an adjustable time-counting parameter; if the transferable load at time t can be adjusted downwards, then... If the transferable load at time t can be adjusted upwards, then ; This represents the maximum power that the load that can be transferred to the j-th microgrid can be adjusted upwards. The following formula is used as the downward adjustment constraint for transferable loads: In the formula Let be the downward adjustment power of the j-th microgrid's transferable load at time t; This represents the maximum power that the load transferable by the j-th microgrid can adjust downwards. The following formula is used as the constraint for the adjustment duration of transferable load: In the formula The maximum daily adjustment duration for the set transferable load; The following formula is used to extend the power balance constraint of the microgrid: In the formula Let be the charging power of the energy storage system inside the j-th microgrid at time t; Let be the discharge power of the energy storage system inside the j-th microgrid at time t; Response optimization is performed on each microgrid to obtain response optimization parameters for each microgrid; the response optimization parameters for each microgrid include... , , , , and .

6. The method for coordinated optimization scheduling of distribution network and multiple microgrids according to claim 5, characterized in that... Step S5, based on the response optimization results of each microgrid obtained in step S4, again aims at optimizing the operation of the distribution network and performs a second-stage optimization of the target distribution network. This specifically includes the following steps: The following formula is used as the objective function for the second stage optimization of the target distribution network: In the formula The objective function value for the second stage optimization of the target distribution network; Constraints for the second stage optimization of the target distribution network: The following formula is used as the charging power constraint: The following formula is used as the discharge power constraint: The following formula is used as a dynamic constraint on the remaining energy storage capacity: The following formula is used as the boundary constraint for the remaining power: The following formula is used as the global power balance constraint for the distribution network: The following formula is used as the constraint for the distribution network to receive power from the upper-level power grid: The following formula is used as the constraint for the power transmission from the distribution network to the upper-level power grid: When performing the second phase of optimization, and The result obtained in step S4 is used and is no longer used in the optimization process; The second-stage optimization of the target distribution network is performed to obtain the second-stage optimization parameters of the target distribution network. The second-stage optimization parameters include , , , , and .

7. The method for coordinated optimization scheduling of distribution network and multiple microgrids according to claim 6, characterized in that... Step S6, which involves completing the coordinated optimization scheduling of the distribution network and multiple microgrids based on the optimization results obtained in steps S4 and S5, specifically includes the following steps: According to the obtained and Determine the interaction power between the target distribution network and the upstream power grid to ensure power balance between the distribution network and the upstream power grid; According to the obtained and Determine the charging and discharging plan of the energy storage system in the target distribution network and optimize the operating status of the energy storage system on the distribution network side; According to the obtained and The interaction power between each microgrid and the target distribution network is determined, serving as the basis for the operation of each microgrid. Complete the coordinated and optimized scheduling of the distribution network and multiple microgrids.

8. A system for implementing the cooperative optimization scheduling method for distribution network-multiple microgrids as described in any one of claims 1 to 7, characterized in that... It includes a data acquisition module, a constraint construction module, a first optimization module, a response optimization module, a second optimization module, and an optimization scheduling module; the data acquisition module, constraint construction module, first optimization module, response optimization module, second optimization module, and optimization scheduling module are connected in series; the data acquisition module is used to acquire data information of the target distribution network and the target microgrid, and upload the data information to the constraint construction module; the constraint construction module is used to construct the basic constraint model of the distribution network side based on the received data information and the acquired data information, and upload the data information to the first optimization module; The first optimization module is used to perform the first stage optimization of the target distribution network based on the received data information and the constructed constraint model, with the goal of optimizing the operation of the distribution network, and upload the data information to the response optimization module. The response optimization module is used to optimize the response of each microgrid based on the received data information and the first-stage optimization results of the target distribution network, with the goal of optimizing the operation of each microgrid, and then upload the data information to the second optimization module. The second optimization module is used to perform a second-stage optimization of the target distribution network based on the received data information and the optimization results of each microgrid response, with the goal of optimizing the operation of the distribution network, and upload the data information to the optimization scheduling module. The optimization scheduling module is used to complete the coordinated optimization scheduling of the distribution network and multiple microgrids based on the received data and the optimization results.

Images