Cooperative control method for park-level multi-microgrid participating in power grid interaction

Abstract

The invention provides a cooperative control method for park-level multi-microgrid participation in power grid interaction, and the method comprises the steps: obtaining power grid real-time data of a power grid platform and multiple microgrids, and each microgrid comprises a distributed power supply, an energy storage unit and a load; obtaining a power grid operation state according to the power grid real-time data, wherein the power grid operation state comprises a grid-connected mode and an off-grid mode; performing energy management analysis based on the power grid operation state and the power grid real-time data, and generating a control instruction; based on the control instruction, carrying out frequency voltage stability control on bottom layer equipment of the multiple microgrids; and making an electric energy transaction strategy based on the control instruction and the multiple microgrids. According to the invention, stable control in the micro-grid is realized, and the economy, stability and operation efficiency of the multi-micro-grid system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-microgrid control, and in particular relates to a collaborative control method for park-level multi-microgrids participating in power grid interaction. Background Art

[0002] The control of multi-microgrid systems is an operational management strategy centered around loads such as photovoltaic power generation, wind power generation, energy storage systems, and charging stations. Research focuses on both cost-effectiveness and system stability.

[0003] Most of these studies consider the coordinated control and stable control of multi-microgrid systems at the park level from the perspective of the microgrid's internal structure, which cannot guarantee the economy of the microgrid and the stability of the large power grid. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, the present invention proposes a collaborative control method for park-level multi-microgrids participating in power grid interaction.

[0005] Provided is a collaborative control method for park-level multi-microgrids participating in grid interaction, comprising: acquiring real-time grid data from a grid platform and multiple microgrids, each microgrid including distributed power sources, energy storage, and loads; acquiring a grid operating status based on the real-time grid data, the grid operating status including a grid-connected mode and an off-grid mode; performing energy management analysis based on the grid operating status and the real-time grid data to generate control instructions; performing frequency and voltage stabilization control on underlying equipment of the multiple microgrids based on the control instructions; and formulating an energy trading strategy based on the control instructions and the multiple microgrids.

[0006] In an optional embodiment, energy management analysis is performed based on the grid operation status and the grid real-time data to generate control instructions, specifically including: obtaining power generation prediction results and load power prediction results based on grid historical data and the grid real-time data; formulating a scheduling strategy based on the power generation prediction results and the load power prediction results; formulating a control strategy based on the grid operation status, the grid real-time data, the power generation prediction results and the load power prediction results; and generating the control instructions based on the scheduling strategy and the control strategy.

[0007] In an optional embodiment, a scheduling strategy is formulated based on the power generation prediction results and the load power prediction results, specifically including: formulating an internal optimization scheduling strategy for each microgrid based on the power generation prediction results and the load power prediction results; obtaining the microgrid's buying and selling electric power based on the internal optimization scheduling strategy; and constructing an external optimization scheduling model based on the optimal power flow using the buying and selling electric power as the interconnection line power.

[0008] In an optional embodiment, an internal optimization scheduling strategy for each microgrid is formulated based on the power generation prediction results and the load power prediction results, specifically including: constructing a scheduling constraint function with the safe operation restrictions of the grid equipment as constraints; constructing an internal scheduling optimization model based on the scheduling constraint function, the power generation prediction results and the load power prediction results; constructing a scheduling optimization objective function based on the internal scheduling optimization model and the scheduling constraint function; and solving the scheduling optimization objective function to obtain the internal optimization scheduling strategy.

[0009] In an optional embodiment, an internal scheduling optimization model is constructed based on the scheduling constraint function, the power generation prediction result, and the load power prediction result, specifically including: establishing a microgrid multi-objective dynamic optimization scheduling model with the goal of minimizing economic cost and environmental cost: F(X)=min([f c (X),f e (X)] T ); where f c represents the economic goal, f e represents the environmental target, X represents the optimization variable of the microgrid optimization scheduling model; the economic cost is expressed as: Where C Elec is the cost of purchasing electricity from the grid, C ONM is the equipment operation and maintenance cost, C Fuel is the fuel cost, C bw is the unit discharge depreciation cost of the battery, P bat,i is the average charge and discharge power of the battery in period i, where a positive sign indicates discharge and a negative sign indicates charge. H represents the total number of periods in the scheduling cycle. The environmental cost is expressed as: Where V e,i is the environmental value of the i-th pollutant, n is the type of pollutant, Q i (X) is the emission of the i-th pollutant, V i The fine for emitting the i-th pollutant; constructing the internal scheduling optimization model based on the multi-objective dynamic optimization scheduling model.

[0010] In an optional embodiment, the buying and selling electric power is used as the interconnection line power to construct an external optimization scheduling model based on the optimal power flow, specifically including: constructing an optimal power flow model with the goal of minimizing system network loss; performing model conversion on the optimal power flow model based on second-order cone constraints to obtain a conversion model; using a Gurobi solver to solve the conversion model, outputting decision variables, and obtaining the external optimization scheduling model.

[0011] In an optional embodiment, formulating an electric energy trading strategy based on the control instruction and the multi-microgrid specifically includes: formulating an electric energy trading objective function based on the multi-microgrid and the control instruction: Where, T is the total dispatch hours, N is the number of microgrids, C dr,i,t is the demand-side response cost of the i-th microgrid in period t, C es,i,t is the depreciation cost of the energy storage battery charging and discharging of the i-th microgrid in period t, C b,i,t The power purchase cost or power sales revenue of each microgrid from the distribution network during time period t is constructed. Based on the multi-microgrid, power trading constraints are established. The power trading constraints include power balance constraints for each microgrid and power upper and lower limits for the regulation unit. The power trading strategy is constructed based on the power trading constraints and the power trading objective function.

[0012] In an optional embodiment, the interactive control system includes: a multi-microgrid system, including multiple microgrids, each of the microgrids, each microgrid including distributed power sources, energy storage and loads; a power grid platform, communicatively connected to the multi-microgrid system, the power grid platform is suitable for obtaining basic electrical parameters, cold and heat data, environmental data, power generation data and energy storage data; a collaborative control system, communicatively connected to the power grid platform, the collaborative control system is suitable for obtaining real-time power grid data of the power grid platform and the multi-microgrid system, and obtaining the power grid operating status based on the real-time power grid data, performing energy management analysis based on the power grid operating status and the real-time power grid data, generating control instructions, and performing frequency and voltage stability control of the underlying equipment of the multi-microgrid based on the control instructions, and formulating an energy trading strategy based on the control instructions and the multi-microgrid.

[0013] In an optional embodiment, the collaborative control system includes: a master station control center; a coordination controller, which is in communication with the master station control center; an autonomous control module, which is in communication with the coordination controller and the multi-microgrid system respectively; an optimization scheduling module, which is in communication with the multi-microgrid system; and a market transaction module, which is in communication with the multi-microgrid system.

[0014] The present invention achieves the following beneficial effects: through optimal control of a microgrid system including wind, solar, and storage, stable control within the microgrid, coordinated control between multiple microgrids, and friendly interaction with the power grid, it achieves coordinated and stable control of a campus-level multi-microgrid system. Specifically, based on energy management, optimized scheduling, and real-time control of multiple microgrids, friendly interaction with the power grid and stable control within the microgrid are achieved, improving the economic efficiency, stability, and operational efficiency of the multi-microgrid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0016] Figure 1 A block diagram of an interactive control system for a collaborative control method for park-level multi-microgrid participation in grid interaction provided by one embodiment of the present invention;

[0017] Figure 2 A control block diagram of a collaborative control system for a collaborative control method for park-level multi-microgrids participating in grid interaction provided by one embodiment of the present invention;

[0018] Figure 3 This is a diagram of the collaborative control system interface for the collaborative control method of park-level multi-microgrids participating in grid interaction provided by the example of the present invention;

[0019] Figure 4 A flow chart of a collaborative control method for park-level multi-microgrids participating in grid interaction provided by one embodiment of the present invention;

[0020] Figure 5 A multi-microgrid system power regulation flow chart of a collaborative control method for park-level multi-microgrids participating in grid interaction provided by one embodiment of the present invention;

[0021] Figure 6 A photovoltaic output prediction effect diagram of an example of a collaborative control method for campus-level multi-microgrids participating in grid interaction provided by an embodiment of the present invention;

[0022] Figure 7 A demand-side response compensation price diagram illustrating an example of a collaborative control method for campus-level multi-microgrids participating in grid interaction provided by an embodiment of the present invention;

[0023] Figure 8 A demand-side response power diagram of an example of a collaborative control method for campus-level multi-microgrids participating in grid interaction provided by an embodiment of the present invention;

[0024] Figure 9 A power purchasing and selling power diagram of an example of a collaborative control method for park-level multi-microgrids participating in grid interaction provided by an embodiment of the present invention;

[0025] Figure 10 The energy storage charging and discharging power of an example of a collaborative control method for park-level multi-microgrids participating in grid interaction provided by an embodiment of the present invention;

[0026] Figure 11 A diagram of microgrid group regulation costs and benefits, illustrating an example of a collaborative control method for park-level multi-microgrid participation in grid interaction provided by an embodiment of the present invention;

[0027] Figure 12A load power diagram after MG1 implements demand-side response in an example of a collaborative control method for campus-level multi-microgrids participating in grid interaction provided by an embodiment of the present invention;

[0028] Figure 13 A load power diagram after MG2 implements demand-side response in an example of a collaborative control method for campus-level multi-microgrids participating in grid interaction provided by an embodiment of the present invention;

[0029] Figure 14 A load power diagram after MG3 implements demand-side response in an example of a collaborative control method for campus-level multi-microgrids participating in grid interaction provided by an embodiment of the present invention;

[0030] Figure 15 A diagram showing the output of distributed power sources in a distribution network, illustrating an example of a collaborative control method for park-level multi-microgrids participating in grid interaction provided by an embodiment of the present invention;

[0031] Figure 16 A system network loss diagram illustrating an example of a collaborative control method for campus-level multi-microgrids participating in grid interaction provided by an embodiment of the present invention;

[0032] Figure 17 This is a system power generation cost diagram of an example of a collaborative control method for campus-level multi-microgrids participating in grid interaction provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0033] The following is combined with Figures 1 to 17 The present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the relevant invention and are not intended to limit the invention. It should also be noted that for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0034] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0035] Please refer to Figure 1 and Figure 2, an embodiment of the present invention provides a campus-level multi-microgrid collaborative control system, including: a multi-microgrid system, including multiple microgrids, each microgrid, each microgrid includes distributed power sources, energy storage and loads; a power grid platform, which is communicatively connected to the multi-microgrid system, and the power grid platform is suitable for obtaining basic electrical parameters, cold and hot data, environmental data, power generation data and energy storage data; a collaborative control system, which is communicatively connected to the power grid platform, and the collaborative control system is suitable for obtaining real-time power grid data of the power grid platform and the multi-microgrid system, and obtaining the power grid operation status based on the real-time power grid data, performing energy management analysis based on the power grid operation status and the real-time power grid data, generating control instructions, and performing frequency and voltage stability control of the underlying equipment of the multi-microgrid based on the control instructions, and formulating an energy trading strategy based on the control instructions and the multi-microgrid.

[0036] In this embodiment, the park-level multi-microgrid collaborative control system includes a multi-microgrid system, a power grid platform and a collaborative control system. It can focus on realizing intelligent energy management of industrial parks, with industrial enterprises, commercial complexes, various supporting buildings and roads in industrial parks as the main application scenarios, and connect energy generation, energy storage, distribution networks, and energy consumption ends. Through a multi-energy coordinated localized control platform, it can achieve multi-level control goals of cold, heat and electricity from cloud analysis to local execution, and create a smart microgrid for the park.

[0037] The collaborative control system, equipped with collaborative control strategies, can generate demand instructions for load, ancillary services, and grid maintenance. Based on the generated collaborative control strategies, control instructions are issued to each microgrid. By interconnecting energy systems, the collaborative control system improves the spatiotemporal imbalances between different energy sources under varying supply and demand scenarios, thereby reducing system energy costs, improving energy efficiency, and enhancing system energy supply reliability. Through the collaborative collaboration of multiple systems, the economic and energy efficiency goals of the regional system are achieved, and the large-scale consumption of regional renewable energy is promoted. By constructing an optimal scheduling model for multiple microgrids, the system optimizes their scheduling and captures the amount of energy exchange between each microgrid and the main grid. This not only enables the sharing of excess energy among microgrids, improving energy utilization, but also allows each microgrid to participate in the energy exchange process with the main grid, increasing their initiative in interacting with the main grid.

[0038] Furthermore, the collaborative control system includes: a master station control center; a coordination controller, which is in communication with the master station control center; an autonomous control module, which is in communication with the coordination controller and the multi-microgrid system respectively; an optimization scheduling module, which is in communication with the multi-microgrid system; and a market transaction module, which is in communication with the multi-microgrid system.

[0039] The master control center optimizes the scheduling and control of multi-microgrid systems. The coordination controller, connected to the master control center, coordinates energy exchange and scheduling between microgrids. The coordination controller monitors the voltage and frequency of the microgrid and employs appropriate control strategies to ensure stable voltage and frequency. Collaborative optimization and scheduling based on multi-energy complementarity are the foundation of collaborative control system planning and market interaction. The autonomous control module, connected to the coordination controller and the multi-microgrid system, executes control commands issued by the coordination controller and makes real-time adjustments based on the microgrid's internal operating status. The market transaction module adjusts power demand and supply within the microgrid based on the power regulation and allocation plan provided by the optimization scheduling module, as well as electricity price signals or incentives, to maximize economic benefits.

[0040] Through precise energy management analysis and real-time control, frequency and voltage stability control and the development of power trading strategies for multi-microgrid systems are achieved. This system not only improves energy efficiency and enhances grid stability, but also provides optimized trading strategies for microgrids participating in the electricity market.

[0041] The key participants in the energy trading and commercial operations of a multi-energy complementary system include integrated energy management platforms, integrated energy service providers, various user groups, electric vehicles, renewable energy power generation systems, energy storage facilities, and combined heat and power units. Based on their electricity usage habits, risk tolerance, and regulatory potential, users can adjust their electricity plans based on price fluctuations and service interruption notices issued by the management center, achieving flexible interaction with the power system.

[0042] like Figure 4 As shown, the present invention also proposes a collaborative control method for park-level multi-microgrids participating in power grid interaction, comprising the following steps:

[0043] Step S101: Acquire real-time grid data of a grid platform and multiple microgrids, where each microgrid includes distributed power sources, energy storage, and loads.

[0044] Step S103: acquiring the grid operation status according to the real-time grid data, where the grid operation status includes a grid-connected mode and an off-grid mode.

[0045] Step S105: Perform energy management analysis based on the grid operation status and real-time grid data, and generate control instructions.

[0046] Step S107: performing frequency and voltage stabilization control on the underlying devices of the multi-microgrid based on the control instructions;

[0047] Step S109: Develop an energy trading strategy based on the control instructions and the multi-microgrid.

[0048] In grid-connected mode, the microgrid is connected to the main grid, enabling the purchase and sale of electricity and interactive interaction with the grid. In off-grid mode, the microgrid is disconnected from the main grid and operates independently. An energy management system ensures a balanced supply and demand, maintaining system stability. Grid operating status data includes flow, voltage, load, power generation, and equipment status.

[0049] The goal of energy management is to achieve the optimal balance between economy, environmental protection and reliability under the premise of satisfying the constraints. In this method, energy management adopts a method that combines day-ahead scheduling and real-time scheduling.

[0050] like Figure 5 As shown, frequency and voltage stability control utilizes the coordinated collaboration of local equipment control, stability control systems, AGC / AVC, and a multi-energy integrated coordinated control system to implement a multi-timescale control system spanning milliseconds, seconds, and minutes, providing favorable support for grid frequency and voltage stability. Based on constraints such as the operating status, adjustable capacity, response rate, and ramp rate of adjustable devices like wind, solar, and storage, primary frequency regulation power allocation considers online gaming and automatic transfer. The power allocation priorities and control values ​​are determined based on the weights of multiple power source constraints, achieving optimal allocation of node power regulation values ​​across multiple heterogeneous controlled resources. The overall control strategy prioritizes regulation, replacing shedding with regulation. This maximizes renewable energy generation while implementing millisecond-level frequency stability control for auxiliary system voltage sources.

[0051] Based on constraints such as the operating status, adjustable capacity, response rate, and ramp rate of adjustable devices like wind, solar, and storage, the system considers online gaming and automatic transfer of primary frequency regulation power allocation. The system prioritizes power allocation and controls the value of power distribution based on the weights of multiple power source constraints, achieving optimal allocation of active power regulation across multiple heterogeneous controlled resources. The overall control strategy prioritizes regulation, replacing switching with regulation, ensuring maximum renewable energy generation while implementing millisecond-level frequency stability control for auxiliary system voltage sources.

[0052] For the purpose of controlling voltage over-limit, the adjustable energy storage nodes and output range are determined according to the number of adjustable wind, solar and storage systems and the rated charge and discharge power limits, and the photovoltaic inverter or wind turbine inverter is adjusted to perform voltage control.

[0053] Users adjust their electricity usage plans based on electricity price information and system notifications, providing basic data and demand information for subsequent frequency and voltage stability control and energy trading. These adjustments directly impact the load demand of the power system, providing a prerequisite for its stable operation. Energy trading is guided by marginal electricity prices, with profit distribution based on cooperative game theory, enabling collaboration among multiple entities.

[0054] Furthermore, step S105, performing energy management analysis based on the grid operation status and real-time grid data to generate control instructions, specifically includes the following steps:

[0055] Step S1051: obtaining generation power prediction results and load power prediction results based on power grid historical data and power grid real-time data.

[0056] Step S1053: Develop a scheduling strategy based on the power generation prediction results and the load power prediction results.

[0057] Step S1055: Develop a control strategy based on the grid operation status, grid real-time data, power generation power forecast results, and load power forecast results.

[0058] Step S1057: Generate control instructions based on the scheduling strategy and the control strategy.

[0059] To achieve precise energy management and optimized scheduling, it is first necessary to predict future power generation and load power. Historical data, including past power generation and load records, and weather data, is collected to monitor the current grid's operating status, including current power generation, load, and environmental conditions, in real time. Statistical methods, machine learning algorithms, or time series analysis techniques are applied, combining historical and real-time data to predict future power generation and load power. Considering the uncertainty of renewable energy sources, such as the influence of weather on photovoltaic power generation and wind power generation on wind speed, probabilistic or interval forecasting methods are used to quantify forecast uncertainty.

[0060] Renewable energy power forecasting based on spatiotemporal data fusion combines prediction methods based on time series data and spatial distribution data. By integrating multiple data sources, such as meteorological data, geographic information, and historical power data, it can better capture the spatiotemporal variations in renewable energy power generation. Spatiotemporal data fusion is performed based on a graph neural network (GNN), using a hybrid model, a graph convolutional network (GCN)-LSTM, to predict renewable energy power using spatiotemporal data fusion. Predictive results are obtained through model training and optimization.

[0061] Load power forecasting uses a similar day + neural network approach to predict power load. Samples with similar feature vectors to the predicted day are extracted from historical data to serve as the basis for neural network training and prediction. Using similar day samples, the neural network is trained and predictions are made for the prediction object.

[0062] Based on forecasted power generation and load data, day-ahead and intraday dispatch plans are developed to ensure a balanced power supply and demand. This includes determining the generation schedule for each distributed generation unit, the charging and discharging schedule for energy storage devices, and the load power consumption schedule. Taking into account the grid's real-time operating status and forecasted data, control strategies are developed to achieve stable frequency and voltage control.

[0063] Based on forecasted renewable energy generation and load power data, and subject to grid equipment safety constraints, we develop day-ahead and intraday dispatch plans for renewable energy, as well as energy storage charging and discharging plans. Maintaining stability, we prioritize renewable energy absorption and economic performance, continuously updating grid operation risk information. This strategy, characterized by sequential progress, rolling optimization, and incremental revisions, encompasses a dynamic "day-ahead, intraday, and real-time" contingency plan development strategy, enhancing microgrid system stability.

[0064] Analyze forecast results to determine the balance point between power supply and demand, identifying potential power surpluses or shortages. Develop a day-ahead dispatch plan, including determining the generation schedule for each distributed generation unit, the charging and discharging schedule for energy storage devices, and the load power consumption schedule. Consider grid operational constraints, such as equipment operating limits and grid stability requirements, to ensure the feasibility of the dispatch plan. Optimize dispatch strategies to minimize operating costs, improve energy efficiency, or meet other specific dispatch objectives.

[0065] By combining real-time grid data, power generation forecasts, and load power forecasts, the current state and future trends of the grid are assessed. Control strategies are developed, including adjusting the output of power generation units, controlling the charging and discharging of energy storage devices, and managing load power consumption. Adaptive or robust control methods are employed, taking into account the dynamic characteristics and uncertainties of the grid, such as the volatility of renewable energy and the randomness of loads. Effective execution of the control strategy under different grid operating modes is ensured to achieve stable frequency and voltage control of the grid. Through these steps, the collaborative control system enables precise control and optimized scheduling of multi-microgrid systems, improving energy efficiency, reducing operating costs, and enhancing grid stability and reliability.

[0066] Step S1053, formulating a dispatching strategy based on the power generation forecast results and the load power forecast results, specifically includes the following steps:

[0067] Step S10531: Formulate an internal optimization scheduling strategy for each microgrid based on the power generation prediction results and the load power prediction results.

[0068] Step S10533: Obtain the microgrid's purchased and sold electric power based on the internal optimization scheduling strategy.

[0069] Step S10535: Using the bought and sold electric power as the tie line power, an external optimization scheduling model based on the optimal power flow is constructed.

[0070] In this embodiment, an optimized scheduling strategy is developed for the distributed power sources, energy storage, and loads within each microgrid to achieve efficient energy utilization. Based on this internal optimized scheduling strategy, the power surplus or deficit of each microgrid at different time periods is calculated, and the amount of power the microgrid needs to purchase from or can sell to the upper-level grid is determined. The resulting microgrid's purchased and sold power is used to determine the amount of power traded between each microgrid and the upper-level grid, i.e., the microgrid's net power demand or supply.

[0071] Through these steps, the collaborative control system can achieve precise control and optimized scheduling of multi-microgrid systems, improve energy utilization efficiency, reduce operating costs, and enhance the stability and reliability of the power grid.

[0072] Step S10531: formulating an internal optimization scheduling strategy for each microgrid based on the power generation prediction results and the load power prediction results, specifically including the following steps:

[0073] Step S105311: Construct a dispatch constraint function based on the safe operation restrictions of power grid equipment.

[0074] Step S105313: Construct an internal scheduling optimization model based on the scheduling constraint function, the power generation prediction results and the load power prediction results.

[0075] Step S105315: Construct a scheduling optimization objective function based on the internal scheduling optimization model and the scheduling constraint function.

[0076] Step S105317: Solve the scheduling optimization objective function to obtain the internal optimization scheduling strategy.

[0077] By solving the scheduling optimization objective function, we obtain the optimal solution that satisfies all constraints. This solution is the internal optimization scheduling strategy, which will guide the operation of distributed power sources, energy storage devices, and loads within the microgrid to achieve frequency and voltage stability control of the grid and optimize energy use.

[0078] A wind, solar, storage, and charging operation optimization model, also known as an internal dispatch optimization model, was established. This model aims to minimize the sum of storage dispatch costs, electricity purchase and sales costs, and network loss costs. It considers constraints such as network operation and equipment capacity, and solves the model using a particle swarm algorithm to determine the daily dispatch output of the wind, solar, storage, and charging system.

[0079] With the goal of minimizing economic and environmental costs, a general model for multi-objective dynamic optimization scheduling of microgrids is established and solved using an improved NSGA-II algorithm. The improved algorithm uses an initial value guidance technique during the initialization phase. This technique first uses a single-objective genetic algorithm to obtain a subset of individuals from the NSGA-II initial population to improve the algorithm's convergence performance.

[0080] Furthermore, step S105313 constructs an internal scheduling optimization model based on the scheduling constraint function, the generation power prediction result, and the load power prediction result, specifically including the following steps:

[0081] Step S1053131: With the goal of minimizing economic and environmental costs, a multi-objective dynamic optimization scheduling model for the microgrid is established:

[0082] F(X)=min([f c (X),f e (X)] T ).

[0083] Where, f c represents the economic goal, f e represents the environmental target, and X represents the optimization variable of the microgrid optimization scheduling model. For example, in the dynamic optimization scheduling model, the dispatchable power output power, energy storage charging and discharging power, and grid exchange power in each period within the scheduling cycle can be selected as optimization variables.

[0084] For a grid-connected microgrid consisting of photovoltaics, wind turbines, batteries, and generators, the main considerations are electricity purchase costs, equipment operation and maintenance costs, fuel costs, and battery depreciation costs. The economic cost is expressed as:

[0085]

[0086] Where C Elec is the cost of purchasing electricity from the grid, C ONM is the equipment operation and maintenance cost, C Fuel is the fuel cost, C bw is the unit discharge depreciation cost of the battery, P bat,i is the average charge and discharge power of the battery in time period i, a positive sign indicates discharge, a negative sign indicates charge, and H represents the total number of time periods in the scheduling cycle.

[0087] Environmental costs mainly consider the impact of carbon emissions and other polluting gases and particulate matter emissions on the environment. To reflect the differences in environmental impacts of different polluting emissions, environmental costs are expressed as:

[0088]

[0089] Where V e,i is the environmental value of the i-th pollutant, n is the type of pollutant, Q i (X) is the emission of the i-th pollutant, V i The fine for emitting item i of the pollutant.

[0090] Step S1053133: Construct an internal scheduling optimization model based on the multi-objective dynamic optimization scheduling model.

[0091] Constraint handling methods

[0092] 1) Component Model Constraint Handling Method. Component model constraints in simulation models, such as battery charge and discharge power limits and generator power constraints, can be handled using hard constraints. This is because these constraints often represent the physical limits of equipment operation and must be met. Furthermore, these constraints are typically only affected by the component's current operating variables and can be satisfied by adjusting the corresponding operating variables. Therefore, during the simulation process, the operating variables of the components that violate the constraints are forced to the boundary values ​​that satisfy the constraints.

[0093] In particular, if the system includes constraints such as generator ramp rate and minimum generator on-time, the correlation between different time periods needs to be considered. The first time period can be used as a benchmark, and the variable values ​​of subsequent time periods can be adjusted based on the boundary values ​​of the constraints.

[0094] 2) System Operation Constraint Handling Methods. System operation constraints in simulation models are often related to the operating variables of multiple components or the same component over multiple time periods, resulting in strong coupling. Therefore, when operational constraints are violated, it is difficult to determine how to adjust the related variables. Therefore, flexible constraints or dimensionality reduction can be used.

[0095] For example, considering the time coupling characteristics of the battery SOC, the present invention uses a flexible constraint processing method, adds it as a penalty term to the total operating cost, and obtains the objective function taking into account the SOC constraint:

[0096] F′(X)=F(X)+β|S OCend -S OC0 |

[0097] Where β represents the SOC constraint penalty factor.

[0098] Because the operating constraints of the power balance equation include the operating variables of multiple components, the present invention adopts a dimensionality reduction method for decoupling, that is, it assumes that each variable is an independent variable that is not restricted by the equation constraints, and only one of the variables is used as the dependent variable, and its value is determined by the other variables and the equation constraints. If the dependent variable cannot meet the constraints of its component model itself at this time, the dependent variable is set to the boundary value that meets the constraints. The unbalanced component in the equation constraint caused by this is manifested as excess power or insufficient power. When excess power occurs, the power generation cost is invisibly increased; when insufficient power occurs, the corresponding power shortage penalty cost needs to be added to the objective function. However, the present invention focuses on the grid-connected microgrid system, and believes that the power supply capacity of the power grid is sufficient and the power shortage rate is 0, so the power shortage cost is not taken into account in the objective function.

[0099] The optimization module uses the multi-objective genetic algorithm NSGA-II, which selects individuals based on the Pareto hierarchical sorting principle. Combining the simulation module and the optimization module, the general process of multi-objective dynamic optimization scheduling of microgrids is obtained.

[0100] The detailed operation process is as follows:

[0101] 1) Data initialization: Input the microgrid system composition and structure parameters, device model parameters, and NSGA-II algorithm parameters.

[0102] 2) Population initialization. Each individual in the initial population corresponds to a scheduling solution within a scheduling cycle. Floating-point encoding is used here, and the individual can be directly represented by the optimization variable X.

[0103] X=[x 1,1 ,x 1,2 ,...,x 1,H ,x 2,1 ,...,x 2,H ,...,x i,j ,...,x N,H ].

[0104] Where xi,j represents the dispatch variable of device i in time period j of the dispatch cycle, N represents the total number of dispatchable power generation devices, and H represents the total number of time periods in the dispatch cycle.

[0105] 3) Input the population individuals into the simulation module as system variables, correct the variables that violate the constraints, and calculate the economic and environmental cost target values ​​and penalty items of the system.

[0106] 4) The target value of the simulation module and the penalty item expectation are returned to the optimization module as output to calculate the value of the individual fitness. Here, the target value considering the penalty item is taken as the individual fitness, that is,

[0107] Fitness = F'(X).

[0108] 5) Take individual fitness as the input of the optimization model, and obtain the offspring population through tournament selection, single-point crossover, and uniform mutation operations.

[0109] 6) Repeat steps 3) and 4).

[0110] 7) Perform Pareto stratified sorting on the parent population and the offspring population as a whole to obtain new population individuals.

[0111] The Pareto hierarchical sorting method retains non-dominated solutions based on the dominance relationship and clustering distance of individuals. To prevent premature maturation during the iteration of the NSGA-II algorithm, the sorted non-dominated solutions are deduplicated, that is, duplicate individuals in the solution set are removed. After deduplication, several individuals with low levels and large clustering distances are sequentially selected from the dominated solution set and added to the new population to maintain the population size unchanged.

[0112] 8) Return to step 6) until the termination condition is met. The present invention takes the termination condition as the maximum genetic generation, outputs the final optimization scheduling result, otherwise returns to 4).

[0113] Based on step S10535, an external optimization scheduling model based on optimal power flow is constructed using the purchased and sold power as the tie line power. Specifically, the model includes the following steps:

[0114] Step S105351: Build an optimal power flow model with the goal of minimizing system network loss;

[0115] Step S105353: performing model conversion on the optimal power flow model based on the second-order cone constraint to obtain a conversion model;

[0116] Step S105355: Use the Gurobi solver to solve the conversion model, output the decision variables, and obtain the external optimization scheduling model.

[0117] The optimal power flow model aims to minimize the system network loss and calculates the system power generation cost when the network loss is minimized. The final power flow model expression is:

[0118] F=minP loss , P loss Indicates the network power loss.

[0119] Constraints

[0120] (1) Power balance constraints:

[0121]

[0122] Where, P Gi , Q Gi Represent the active power and reactive power of the i-th group of generators, P Li , Q Li Represents the active power and reactive power consumed by the load at node i. ij represents the phase angle difference between the voltages at node i and node j; G ij is the line conductance between node i and node j, B ij Represents the line susceptance between node i and node j. When the PCC power value is negative, the microgrid acts as a load node in the system and its absolute value is substituted into P in the above formula.Li , the node acts as a power source, bringing in P Gi .

[0123] (2) Upper and lower limits of power and voltage constraints:

[0124]

[0125] where v i,max , v i,min are the upper and lower limits of voltage amplitude of node i; P Gi,max , Q Gi,max are the upper and lower limits of active power output of generator i; Q Gi,max , Q Gi,min are the upper and lower limits of reactive power output of generator i.

[0126] (3) Second-order cone constraints:

[0127]

[0128] Model solution

[0129] (1) Multi-microgrid cluster model solution

[0130] A discrete first-order consensus algorithm is used to solve the optimization scheduling model of the lower multi-microgrid system.

[0131]

[0132] where C i represents the total regulation cost of the i-th microgrid, is the i-th row and j-th column element of the communication matrix H at the k-th iteration, μ is the error adjustment step size of the consensus algorithm. Δd represents the deviation between the current state and the target state of the system, and the current state refers to the total power of all regulation units in the microgrid; the purpose of consensus power regulation is to achieve power supply-demand balance in each microgrid, so the expected state is the overall power supply-demand balance of the system.

[0133] The convergence condition judged by the consensus algorithm is:

[0134] |Δd|<θ

[0135] where θ represents the convergence error, and the convergence speed and solution accuracy are considered, and 0.001 kW is taken here.

[0136] The flow chart of power regulation of the multi-microgrid system is shown in Figure 2 .

[0137] The solution to the optimization scheduling model is to bring the buying and selling power obtained from the lower-level optimization into the distribution network line parameters as the tie-line power, read the initialization data, use the second-order cone technology to replace the variables, convert the upper-level optimization model into a solvable form, solve the model with the Gurobi solver, and output the decision variables, namely the DG power generation value of each node and the objective function value.

[0138] Collaborative control considers demand-side response for a two-tiered, optimized dispatching system for interconnected, multi-microgrid campuses. The lower-tier multi-microgrid system participates in grid-interactive optimization based on demand-side response. A consensus algorithm is used to optimize power regulation and allocation schemes based on the varying load characteristics and demand-side response methods of commercial, residential, and office microgrids. The upper-tier model uses the microgrid buy and sell power from the lower-tier model solution as the tie-line power, establishing an optimal power flow-based coordinated control system for optimal dispatching. Second-order cone relaxation techniques are used for model transformation, and the Gurobi solver is employed for the model solution.

[0139] The microgrid control is equipped with energy management to support microgrid autonomy, while being able to coordinate and coordinate the overall instructions issued by the system to the underlying device layer.

[0140] In a campus-level multi-microgrid system, each microgrid can trade electricity through power market mechanisms, guided by marginal electricity prices to develop optimal trading strategies. Furthermore, cooperative game theory has been applied to multi-microgrid systems, distributing profits based on microgrid decision-making information to achieve collaboration among multiple entities, thereby improving the efficiency and reliability of microgrids in a distributed renewable energy environment.

[0141] Step S109, formulating an energy trading strategy based on the control instructions and the multi-microgrid, specifically includes the following steps:

[0142] Step S1091: Based on the multi-microgrid and control instructions, formulate the electric energy trading objective function:

[0143]

[0144] Where, T is the total dispatch hours, N is the number of microgrids, C dr,i,t is the demand-side response cost of the i-th microgrid in period t, C es,i,t is the depreciation cost of the energy storage battery charging and discharging of the i-th microgrid in period t, C b,i,t is the electricity purchase cost or electricity sales revenue of each microgrid from the distribution network during period t.

[0145] Step S1093: constructing power transaction constraints based on multiple microgrids, where the power transaction constraints include: power balance constraints based on each microgrid and upper and lower power limit constraints of the regulation unit.

[0146] Step S1095: Constructing an electric energy trading strategy based on the electric energy trading constraints and the electric energy trading objective function.

[0147] In this embodiment, the goal of the campus-level multi-microgrid system is to optimize scheduling while maximizing electricity sales revenue and minimizing regulation costs. The electricity trading objective function can be expressed as the inverse of the sum of maximizing electricity sales revenue and minimizing regulation costs, that is, minimizing the sum of the loss of electricity sales revenue and regulation costs.

[0148] The constraints for multi-microgrid optimal scheduling mainly include the power balance constraints of each microgrid and the upper and lower power constraints of the regulation unit.

[0149] (1) Power balance constraints:

[0150]

[0151] Where p Pv,i,t is the photovoltaic output power of microgrid i in period t, p Pw,i,t is the wind power output of microgrid i in period t, P b，t represents the transaction power between the microgrid and the distribution network during period t, i.e., the PCC tie line power; p e,i,t is the electric load demand of microgrid i in period t; p dr,i,,t is the demand-side response power of microgrid i in period t, including interrupted load and transferred load. The demand-side response power of each microgrid in period t is the sum of the two demand-side response powers mentioned above, and the relationship is as follows:

[0152]

[0153] Where p tr,i,,t is the transferred load power of microgrid i in period t, p ir,i,,t is the load power interrupted by microgrid i during period t. In demand-side response regulation, load transfer has a higher priority than interrupted load. That is, if the period requiring demand-side response is within the time constraint of the load transfer, the load transfer is given priority.

[0154] (2) Tie line power constraints:

[0155]

[0156] Where, P b,t The power purchased or sold by the microgrid from the distribution network obtained by the lower layer optimization is used as the tie line power. PCC,min , P PCC,max are the minimum and maximum values ​​of the PCC tie line power respectively.

[0157] (3) Energy storage system power constraints:

[0158]

[0159] Where, They represent the maximum charging and discharging power of energy storage respectively.

[0160] (4) Battery ramp rate constraint:

[0161]

[0162] Where, They are the upper and lower limits of the battery's ramp rate respectively.

[0163] (5) Demand-side response constraints:

[0164]

[0165] Where p tr,i,max represents the maximum transfer load power of microgrid i, p ir,i,max represents the maximum interruptible load power of microgrid i.

[0166]

[0167] p tr,i,t =p tr,i,t d i,t ;

[0168] Where, P tr,i,t d i,t , represents the transfer load power and transfer state variable of the i-th microgrid in period t, P tr,i,t Negative indicates the output load power, P tr,i,t A positive value indicates that the load is being transferred. The transfer time interval of the i-th transferable load; The transfer-out time interval of the i-th transferable load; the transferable load is added to the total load demand power of the period during the transfer-in period.

[0169] The underlying equipment is controlled in real time to collect data, execute control commands, and make autonomous decisions in emergencies.

[0170] Based on control commands issued by the microgrid control system, the underlying system performs real-time control of equipment. The real-time control objectives are to optimize wind turbine power, photovoltaic power, adjustable load power, and energy storage output to meet control command requirements, while also taking into account system stability. If the adjusted power difference is less than the power regulation threshold Plimit, no adjustment is required.

[0171] The present invention considers a collaborative control method for multiple microgrids participating in grid interaction at the park level, applying a control method that combines a master station, substations, and underlying equipment. The master station is a collaborative control system, and the substations are multi-microgrid systems. This method enables demand control, peak shaving and valley filling, power control, wind / solar storage and charging coordination, grid-friendly interaction, and stable microgrid operation in grid-connected operation mode. This method improves the utilization rate of wind and solar power generation while ensuring the stability and reliability of the microgrid's power supply. This prevents wind and solar curtailment, achieves peak shaving and valley filling, and peak-valley arbitrage, participates in load regulation, and maximizes the economic viability of the microgrid and the stability of the larger grid.

[0172] The present invention also provides a storage medium storing a computer program, which, when executed by a processor, implements any one of the collaborative control methods for park-level multi-microgrids participating in power grid interaction.

[0173] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM). The various embodiments in this specification are described in a progressive manner, and similar parts between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the apparatus, device, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referenced to the partial description of the method embodiments.

[0174] Examples of the Invention

[0175] This invention is applied to three parks in a certain province, where the installed capacity of new energy vehicles participating in the demonstration is greater than 30 MW, the load of new materials and equipment manufacturing is greater than 10 MW, and the energy storage system capacity is greater than 1 MW. Based on the scale and complexity of the demonstration park, the implementation site has been fully planned and prepared, including the adaptability of the site environment, the installation and commissioning of system equipment, and the training of operators. The data architecture of the demonstration site can be found at Figure 1 .

[0176] The interface diagram of the demonstration site collaborative control master station system is shown in Figure 3 The system has six major functions: system overview, real-time monitoring, collaborative control, data analysis, event alarms, and historical data. The collaborative control function, equipped with the method of the present invention, achieves multi-energy complementary coordination and stable control within the microgrid, ensuring the stability, safety, and economy of the multi-microgrid system, and contributing to energy conservation, emission reduction, and low-carbon goals.

[0177] Demonstration site data analysis:

[0178] (1) Basic parameters of multi-microgrid system

[0179] The multi-microgrid system can directly realize the interconnection system of energy mutual assistance, regardless of the specific wiring method between microgrids, and the geographical proximity of each microgrid, without considering the power transmission loss when the microgrids are mutually beneficial. The equipment and equipment capacity of each microgrid are shown in the following table:

[0180] Table 1 Equipment capacity of multi-microgrid system

[0181]

[0182] In the calculation example, the initial state of charge of the energy storage battery is set to 10%, the charge and discharge efficiency is 90%, and the charge and discharge depreciation coefficient is 0.05 yuan / kW. It is assumed that the photovoltaic / wind power absorption rate is 100%, and the wind / solar output power is fully absorbed. Without considering the loss of the multi-microgrid interconnection line connected to the distribution network, a 24-hour scheduling cycle is set. The photovoltaic output forecast within a scheduling cycle is as follows: Figure 6 As shown in the figure, the load curve for each microgrid represents the sum of its fixed load, interruptible load, and transferable load. Users of different microgrid types have different peak and valley periods for electricity demand, resulting in different load trends for each of the three microgrids within a scheduling cycle. MG1 is a shopping mall microgrid, with peak load periods between 16:00 and 22:00; MG2 is an office building microgrid, with peak load periods between 9:00 and 16:00; and MG3 is a residential microgrid, with peak load periods between 19:00 and 22:00, with a smaller peak between 8:00 and 10:00.

[0183] The transaction electricity price adopts the peak electricity price. The electricity price for each period of a dispatch cycle is as follows:

[0184] Table 2 Peak electricity prices traded between multiple microgrids and distribution networks

[0185]

[0186] The characteristic of incentive-based demand-side response is that it provides certain financial subsidies based on the user's response time and response volume. If the user fails to respond in accordance with the contract, a certain penalty may be imposed. In the demonstration, the user fully complies with the pre-determined dispatch plan and responds without incurring any penalty fees. The unit compensation price is set according to the user's willingness to respond and the electricity price in different time periods. The unit compensation price paid to the user is as follows: Figure 7 As shown in r1, r2 and r3.

[0187] Depend on Figure 7 As shown in Table 2, unit compensation prices are highest during peak electricity price periods. Providing higher compensation to participating users during peak load or electricity price periods is beneficial for mobilizing user response enthusiasm. Commercial microgrid MG1, with its largest total load and large adjustable load power, receives a relatively low unit compensation price. From 7:00 PM to 9:00 PM, residential microgrid MG3 experiences peak electricity demand, while MG2 experiences a low load period, resulting in a higher unit compensation price for MG3. For other periods, unit compensation prices are determined based on the load characteristics and user types of each microgrid.

[0188] (2) Characteristic parameters of each unit in the distribution network system

[0189] The characteristic parameters and power generation capacity of the generator sets in the distribution network system are shown in Table 3 below:

[0190] Table 3 Characteristic parameters and power generation range of each unit in the distribution network system

[0191]

[0192]

[0193] (3) Lower-level scheduling results and analysis

[0194] The dispatch results of each microgrid in the multi-microgrid system include: demand-side response power, electric power traded with the distribution network, and energy storage charging and discharging power, such as Figure 8 As shown in the figure, the photovoltaic output of each microgrid is sufficient from 7 to 17 o'clock in the daytime, and the demand-side response power is zero, so there is no need to adjust the power balance through demand-side response.

[0195] Figure 9=( ...

[0196] Figure 10 The figure shows the energy storage charging and discharging status of each microgrid during a scheduling cycle. A positive value indicates that the energy storage system is charging, while a negative value indicates that it is discharging. During the day, when photovoltaic power generation is sufficient, each microgrid uses this opportunity to charge the energy storage system. At night, when photovoltaic power generation is reduced, each microgrid meets power demand during its peak load period by discharging the energy storage system. In particular, commercial microgrid MG1, due to its large energy storage capacity, has relatively high energy storage charging and discharging power.

[0197] Figure 11 The curve represents the sum of each microgrid's power regulation costs and electricity sales revenue. Positive values ​​indicate microgrid electricity sales revenue, while negative values ​​indicate the microgrid's power regulation costs. During periods 1-5 and 19-24, even after mutual regulation, the microgrids still cannot meet electricity demand. Cost-consistent power regulation is required through demand-side response and power purchases from the distribution network. After adjustment, costs are consistent, resulting in the same regulation costs for each microgrid in the figure. During periods 7-9 and 16-17, each microgrid's photovoltaic power generation meets its own electricity demand, then charges the energy storage. After charging, there is no surplus energy, and the costs and benefits are zero. If power trading with the distribution network is not conducted during peak distributed energy generation periods, the total amount of wind and solar power curtailed will reach 631 kilowatts, resulting in a significant waste of natural resources. Furthermore, relying solely on external power purchases to regulate power during peak load periods will increase regulation costs. Therefore, the two-tiered optimal scheduling scheme proposed in this paper can improve economic and environmental benefits while ensuring power reliability.

[0198] The flexible load types of commercial microgrid MG1 and residential microgrid MG3 include transferable load and interruptible load, while office building microgrid MG2 only includes interruptible load. Figure 12 、 13, 14. The "load after DR" in the figure represents the total load after demand-side response. The white portion represents the load transferred to other time periods, and the shaded "load transferred in" portion represents the load transferred from other time periods to the current time period. As can be seen from the figure, load transfer and reduction primarily occur at night. This is because photovoltaic power generation is not effective at night, resulting in insufficient power supply to the microgrid, necessitating load interruption and transfer. The time period from 20 to 22 in MG1 and MG3 is a peak load period with higher electricity prices. The optimized demand-side response solution shifts the transferable load during this period to daytime use, which helps achieve the effect of peak shaving and valley filling. By comparing the photovoltaic output curves in the figure, we can see that by coordinating the two flexible load response methods in the demand-side response (i.e., transfer and reduction), the load curve can be made as close as possible to the power curve of photovoltaic power generation, thereby improving the utilization rate of photovoltaic power and optimizing the operating efficiency of the power grid.

[0199] (4) Upper-layer optimization scheduling results

[0200] The output of distributed power sources dispatched by the distribution network 24 hours a day, such as Figure 15 As shown in the figure, the values ​​of PCC power in periods 1 to 6 and 17 to 24 are negative, indicating that the microgrid purchases electricity from the distribution network and is therefore connected to the distribution network system as a load.

[0201] Figure 16 The figure shows that during the 10-15 period, the distribution network system's network losses decreased. The CC curve indicates that the multi-microgrid system sells electricity to the distribution network, acting as a virtual power source to reduce network losses. During the 1-6 and 18-24 periods, the multi-microgrid system's power purchases increase system load, leading to increased network losses.

[0202] Combine Figure 17 and Figure 15 The PCC curve in Figure 2 shows that during the period when the multi-microgrid system sells electricity to the distribution network, it is used as a power source in the distribution network, allowing other power sources to reduce their output, thereby reducing the system's power generation costs. However, during the period when the multi-microgrid system purchases electricity from the distribution network, the system's units increase their output, resulting in an increase in power generation costs.

[0203] This invention proposes a collaborative control method for park-level multi-microgrid grid interaction. Based on photovoltaic power generation forecasts, wind power generation forecasts, load power consumption forecasts, energy storage battery power monitoring, and real-time measurement data from various devices, it incorporates an energy management strategy that integrates demand control, peak shaving and valley filling, power control, and wind, solar, and storage-charging coordination. It employs cutting-edge scheduling algorithms combined with intelligent hardware to achieve refined regulation of energy resources. While ensuring the stability and reliability of the microgrid's power supply, it improves the utilization rate of wind and solar power generation, prevents wind and solar curtailment, and maximizes the economic viability of the microgrid and the stability of the larger power grid.

[0204] The optimization decision generation time of the method of the present invention is less than 2 minutes, and the execution cycle supports 1 minute, 5 minutes and 15 minutes.

[0205] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention herein is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A collaborative control method for multi-microgrids participating in power grid interaction at the park level, based on an interactive control system, characterized in that: The method comprises: Obtain real-time grid data from the grid platform and multiple microgrids, each including distributed generation, energy storage, and loads; Acquire a grid operation state according to the real-time grid data, wherein the grid operation state includes a grid-connected mode and an off-grid mode; Performing energy management analysis based on the grid operation status and the grid real-time data to generate control instructions; Performing frequency and voltage stabilization control on the underlying devices of the multi-microgrid based on the control instructions; An electric energy trading strategy is formulated based on the control instructions and the multi-microgrid.

2. The collaborative control method for park-level multi-microgrids participating in power grid interaction according to claim 1 is characterized in that: Performing energy management analysis based on the grid operation status and the grid real-time data and generating control instructions specifically includes: Obtaining a power generation prediction result and a load power prediction result based on the historical data of the power grid and the real-time data of the power grid; Formulate a scheduling strategy based on the generated power prediction result and the load power prediction result; Formulate a control strategy based on the grid operation status, the grid real-time data, the generated power prediction result, and the load power prediction result; The control instruction is generated based on the scheduling strategy and the control strategy.

3. The collaborative control method for park-level multi-microgrids participating in power grid interaction according to claim 2 is characterized in that: Formulate a scheduling strategy based on the generated power prediction result and the load power prediction result, specifically including: Formulate an internal optimization scheduling strategy for each microgrid based on the generated power prediction result and the load power prediction result; Obtaining microgrid buying and selling power based on the internal optimization scheduling strategy; The buying and selling electric power is used as the tie line power, and an external optimization scheduling model based on the optimal power flow is constructed.

4. The collaborative control method for park-level multi-microgrids participating in grid interaction according to claim 3 is characterized in that: Formulate an internal optimization scheduling strategy for each microgrid based on the power generation prediction result and the load power prediction result, specifically including: Taking the safe operation limit of power grid equipment as the constraint, a dispatch constraint function is constructed; Constructing an internal scheduling optimization model based on the scheduling constraint function, the generated power prediction result and the load power prediction result; Constructing a scheduling optimization objective function based on the internal scheduling optimization model and the scheduling constraint function; Solve the scheduling optimization objective function to obtain the internal optimization scheduling strategy.

5. The collaborative control method for park-level multi-microgrids participating in grid interaction according to claim 4 is characterized in that: Constructing an internal scheduling optimization model based on the scheduling constraint function, the generated power prediction result, and the load power prediction result specifically includes: With the goal of minimizing economic and environmental costs, a multi-objective dynamic optimization scheduling model for microgrids is established: F(X)=min([f c (X),f e (X)] T ); Where, f c represents the economic goal, f e represents the environmental target, X represents the optimization variable of the microgrid optimization scheduling model; The economic cost is expressed as: Where C Elec is the cost of purchasing electricity from the grid, C ONM is the equipment operation and maintenance cost, C Fuel is the fuel cost, C bw is the unit discharge depreciation cost of the battery, P bat,i is the average charge and discharge power of the battery in time period i, a positive sign indicates discharge, a negative sign indicates charge, and H represents the total number of time periods in the scheduling cycle; Environmental costs are expressed as: Where V e,i is the environmental value of the i-th pollutant, n is the type of pollutant, Q i (X) is the emission of the i-th pollutant, V i Fines incurred for the discharge of pollutant No. i; The internal scheduling optimization model is constructed based on the multi-objective dynamic optimization scheduling model.

6. The collaborative control method for park-level multi-microgrids participating in grid interaction according to claim 3 is characterized in that: Taking the purchased and sold electric power as the tie-line power, an external optimization scheduling model based on optimal power flow is constructed, specifically including: Build an optimal power flow model with the goal of minimizing system network loss; Performing model transformation on the optimal power flow model based on the second-order cone constraint to obtain a transformation model; The conversion model is solved using the Gurobi solver, and decision variables are output to obtain the external optimization scheduling model.

7. The collaborative control method for park-level multi-microgrids participating in grid interaction according to any one of claims 1 to 6, characterized in that: Formulate an energy trading strategy based on the control instructions and the multi-microgrid, specifically including: Based on the multi-microgrid and the control instructions, an electric energy trading objective function is formulated: Where, T is the total dispatch hours, N is the number of microgrids, C dr,i,t is the demand-side response cost of the i-th microgrid in period t, C es,i,t is the depreciation cost of the energy storage battery charging and discharging of the i-th microgrid in period t, C b,i,t is the electricity purchase cost or electricity sales revenue of each microgrid from the distribution network during period t; Based on the multi-microgrid, electric energy transaction constraints are constructed, and the electric energy transaction constraints include: power balance constraints based on each microgrid and upper and lower limit constraints of the power of the regulation unit. The electric energy trading strategy is constructed based on the electric energy trading constraints and the electric energy trading objective function.

8. The collaborative control method for park-level multi-microgrids participating in power grid interaction according to any one of claims 1 to 6, characterized in that: The interactive control system comprises: A multi-microgrid system comprising a plurality of microgrids, each of said microgrids including a distributed power source, energy storage and loads; a power grid platform, communicatively connected to the multi-microgrid system, the power grid platform being adapted to acquire basic electrical parameters, cooling and heating data, environmental data, power generation data, and energy storage data; A collaborative control system is communicatively connected to the power grid platform, and is suitable for obtaining real-time power grid data of the power grid platform and the multi-microgrid system, and obtaining the power grid operation status based on the real-time power grid data, performing energy management analysis based on the power grid operation status and the real-time power grid data, generating control instructions, and performing frequency and voltage stabilization control of the underlying equipment of the multi-microgrid based on the control instructions, and formulating an electric energy trading strategy based on the control instructions and the multi-microgrid.

9. The collaborative control method for park-level multi-microgrids participating in grid interaction according to claim 8, characterized in that: The collaborative control system includes: Master station control center; A coordination controller, communicatively connected to the master station control center; an autonomous control module, communicatively connected to the coordination controller and the multi-microgrid system; an optimization scheduling module, communicatively connected to the multi-microgrid system; The market transaction module is communicatively connected with the multi-microgrid system.

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