A multi-microgrid electric carbon collaborative scheduling method and system

Abstract

The application provides a multi-microgrid electricity-carbon collaborative scheduling method and system, which comprises the following steps: establishing a distributed power generation and energy storage mathematical model according to a multi-microgrid system, and defining parameters according to the distributed power generation and energy storage mathematical model; constructing an endogenous thermal feedback model according to a thermal feedback coefficient and a load thermal sensitivity coefficient; designing an intraday rolling optimization strategy according to the endogenous thermal feedback model; and generating a comprehensive operation cost function and a power balance constraint according to the distributed power generation and energy storage mathematical model, the endogenous thermal feedback model and the intraday rolling optimization strategy, so as to perform multi-microgrid electricity-carbon collaborative scheduling. The endogenous thermal feedback model is constructed, the two-way interaction between the multi-microgrid system and the ambient temperature is quantified, the intraday rolling optimization strategy is integrated, the operation cost, the carbon emission and the power supply guarantee are optimized under complex constraints, the source-load fluctuation under the extreme heat wave environment is effectively responded, and the carbon emission reduction capacity and the operation economy of the multi-microgrid system are improved.

Description

Technical Field

[0001] This invention relates to the field of energy management and low-carbon scheduling technology for multi-microgrid systems, and particularly to a method and system for coordinated scheduling of electricity and carbon emissions in multi-microgrid systems. Background Technology

[0002] With the high proportion of renewable energy integration and the large-scale deployment of distributed power sources, microgrid generators (MMGs) have attracted much attention due to their ability to significantly improve the flexibility, reliability, and low-carbon performance of power systems. However, the increasingly frequent extreme heat waves pose a severe challenge to the safe operation of MMGs. High-temperature environments not only lead to a sharp increase in cooling loads but also reduce the power generation efficiency of renewable energy sources such as photovoltaics, exacerbating the supply-demand imbalance between the source and load sides. Especially at the microgrid scale, the waste heat generated by the system operation further intensifies the local heat island effect, forming a positive feedback loop between ambient temperature and electricity demand. Against this backdrop, the power balance within the microgrid is disrupted. In order to maintain stability, the system is forced to significantly increase the power generation of conventional units such as gas turbines. This not only leads to highly time-varying and volatile internal power interactions but also pushes up the system's carbon emission intensity, causing a sharp increase in total carbon emissions.

[0003] To address the complex physical evolution and high carbon emission crisis brought about by extreme heat waves, microgrids typically employ a dispatch architecture that combines day-ahead economic dispatch with intraday rolling optimization. However, traditional distributed collaborative optimization and existing dispatch strategies face severe challenges in such extreme scenarios. On the one hand, although intraday rolling optimization based on Model Predictive Control (MPC) can effectively mitigate rapid fluctuations on both the source and load sides, it often treats temperature as a static exogenous variable independent of the system, failing to fully account for endogenous heat feedback effects and making it difficult to capture the dynamic evolution of temperature, which can easily lead to inaccurate dispatch decisions. On the other hand, most existing carbon-sensing dispatch models are designed for conventional meteorological conditions and fail to deeply integrate the thermodynamic evolution under extreme heat waves with carbon emission mechanisms.

[0004] Therefore, how to effectively respond to source-load fluctuations under extreme heat wave conditions and improve the carbon emission reduction capacity and operational economy of multi-microgrid systems is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] To address the aforementioned technical challenges, this invention provides a multi-microgrid power-carbon coordinated scheduling method and system. By constructing an environmental temperature evolution model incorporating endogenous heat feedback, the bidirectional interaction between the multi-microgrid system and environmental temperature is quantified. Simultaneously, a Stepped Carbon Trading Mechanism (SCTM) and an intraday rolling optimization strategy (MPC) are integrated to optimize operating costs, carbon emissions, and power supply reliability under complex constraints. Compared to existing technologies, this method effectively responds to source-load fluctuations under extreme heat waves, enhancing the carbon reduction capacity and operational economics of the multi-microgrid system.

[0006] The first objective of this invention is to provide a method for coordinated scheduling of electricity and carbon emissions from multiple microgrids; The technical solution provided by this invention is as follows: A multi-microgrid power carbon coordinated scheduling method includes the following steps: A mathematical model for distributed generation and energy storage is established based on the multi-microgrid system, and parameters are defined based on the mathematical model for distributed generation and energy storage. An endogenous heat feedback model is constructed based on the heat feedback coefficient and the load heat sensitivity coefficient. Design an intraday rolling optimization strategy based on the endogenous heat feedback model; Based on the distributed generation and energy storage mathematical model, the endogenous heat feedback model, and the intraday rolling optimization strategy, a comprehensive operating cost function and power balance constraints are generated to carry out multi-microgrid power carbon collaborative scheduling.

[0007] Preferably, the step of establishing a distributed generation and energy storage mathematical model based on the multi-microgrid system, and defining parameters based on the distributed generation and energy storage mathematical model, specifically includes: A distributed generation and energy storage mathematical model is established based on the multi-microgrid system, and constraints are imposed on the gas turbine output, gas turbine ramping, energy storage system charging and discharging power and state, and state-of-charge safety boundary and endpoint based on the distributed generation and energy storage mathematical model. The parameters include: the output of the gas turbine, the ramp rate of the gas turbine, the charging and discharging power and status of the energy storage system, and the state-of-charge safety boundary and endpoint.

[0008] Preferably, the step of constructing an endogenous heat feedback model based on the heat feedback coefficient and the load heat sensitivity coefficient specifically includes: A local ambient temperature rise model is constructed based on the aforementioned thermal feedback coefficient; An endogenous heat feedback model is constructed based on the local ambient temperature rise model and the load thermal sensitivity coefficient.

[0009] Preferably, the step of constructing a local ambient temperature rise model based on the thermal feedback coefficient specifically includes: The model for the rise in local ambient temperature is as follows: ; in, This is a model for localized ambient temperature rise, representing the time period. The local equivalent ambient temperature of microgrids and thermal feedback; To predict baseline temperature; This is for the active power output of the gas turbine during the previous scheduling period; The thermal feedback coefficient represents the degree of local temperature rise caused by a unit output force.

[0010] Preferably, the step of constructing an endogenous heat feedback model based on the local ambient temperature rise model and the load thermal sensitivity coefficient specifically includes: The endogenous heat feedback model is as follows: ; in, This is an endogenous heat feedback model used to represent the actual cooling power corrected for temperature rise; Predict power for the original baseline; The load thermal sensitivity coefficient represents the linear increment of the cooling load for every 1°C increase in temperature above a threshold. It is a continuous auxiliary variable used to rigorously quantify the effective temperature rise beyond the comfort zone.

[0011] Preferably, the continuous auxiliary variable Specifically: ; ; in, This is the reference temperature at which the cooling load begins to surge and the photovoltaic efficiency begins to decline.

[0012] Preferably, the step of designing an intraday rolling optimization strategy based on the endogenous heat feedback model specifically includes: Calculate the total carbon trading cost based on preset conditions. and total load reduction costs .

[0013] Preferably, the step of generating a comprehensive operating cost function and power balance constraints based on the distributed generation and energy storage mathematical model, the endogenous heat feedback model, and the intraday rolling optimization strategy for multi-microgrid power carbon coordinated scheduling specifically includes: Based on the total carbon trading cost The total load reduction cost Economic costs and system penalty costs Obtain the comprehensive operating cost function; Define microgrid During the period Net active power interaction with external entities and interact based on the net active power. Building microgrids The power balance equation.

[0014] Preferably, it also includes: using a tiered carbon trading mechanism to transfer microgrids The overall assessment of carbon emissions is divided into multiple tiers.

[0015] The second objective of this invention is to provide a multi-microgrid electricity-carbon coordinated scheduling system; The technical solution provided by this invention is as follows: A multi-microgrid carbon emission coordinated scheduling system includes: a parameter definition module, a construction module, a design module, and a generation module; The parameter definition module is used to establish a distributed generation and energy storage mathematical model based on the multi-microgrid system, and to define parameters based on the distributed generation and energy storage mathematical model. The construction module is used to construct an endogenous heat feedback model based on the heat feedback coefficient and the load heat sensitivity coefficient. The design module is used to design an intraday rolling optimization strategy based on the endogenous heat feedback model; The generation module is used to generate a comprehensive operating cost function and power balance constraints based on the distributed generation and energy storage mathematical model, the endogenous heat feedback model and the intraday rolling optimization strategy, so as to carry out multi-microgrid power carbon collaborative scheduling.

[0016] Compared with existing technologies, this invention provides a multi-microgrid electricity-carbon coordinated scheduling method, comprising the following steps: establishing a distributed generation and energy storage mathematical model based on the multi-microgrid system, and defining parameters based on the distributed generation and energy storage mathematical model; constructing an endogenous heat feedback model based on the heat feedback coefficient and load heat sensitivity coefficient; designing an intraday rolling optimization strategy based on the endogenous heat feedback model; generating a comprehensive operating cost function and power balance constraints based on the distributed generation and energy storage mathematical model, the endogenous heat feedback model, and the intraday rolling optimization strategy, for multi-microgrid electricity-carbon coordinated scheduling; this method quantifies the bidirectional interaction between the multi-microgrid system and ambient temperature by constructing an environmental temperature evolution model that includes endogenous heat feedback; simultaneously, it integrates a Stepped Carbon Trading Mechanism (SCTM) and an MPC intraday rolling optimization strategy to optimize operating costs, carbon emissions, and power supply security under complex constraints. Compared with existing technologies, this method effectively responds to source-load fluctuations under extreme heat wave environments, improving the carbon emission reduction capacity and operational economy of the multi-microgrid system.

[0017] The present invention also provides a multi-microgrid electricity-carbon coordinated scheduling system. Since this system and the multi-microgrid electricity-carbon coordinated scheduling method solve the same technical problem and belong to the same technical concept, they should have the same beneficial effects, and will not be described in detail here. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of a multi-microgrid carbon collaborative scheduling method provided in this application embodiment; Figure 2 This is a schematic diagram of the MMG system structure provided in an embodiment of this application; Figure 3 A graph showing the change of ambient temperature over time in the MMG system provided in this application embodiment; Figure 4 The power generation and load curves of the microgrid 1 provided in this application embodiment; Figure 5 The power generation and load curves of the microgrid 2 provided in this embodiment of the application; Figure 6 The power generation and load curves of the microgrid 3 provided in the embodiments of this application; Figure 7 The temperature prediction and actual deviation analysis diagrams for Examples 2 and 3 provided in the embodiments of this application consider thermal feedback; Figure 8 Comparison charts of carbon emissions and load reductions in Examples 2 and 3 provided for embodiments of this application; Figure 9 Daily carbon emission graphs for Examples 3 and 4 provided in the embodiments of this application; Figure 10 Comparison of local ambient temperature between Examples 3 and 4 provided for embodiments of this application; Figure 11 This application provides an actual cooling and air conditioning load diagram for a microgrid 2 as an embodiment of the present application. Figure 12 A comparison chart of total load reduction for Examples 3 and 4 provided in this application embodiment; Figure 13 Carbon quota transfer diagram between multiple microgrid systems provided in this application embodiment; Figure 14A comparison chart of carbon emissions of microgrids under independent operation and coordinated dispatch provided in the embodiments of this application; Figure 15 The load guarantee rate and load reduction curves of the multi-microgrid system provided in the embodiments of this application; Figure 16 The charge state curves of the microgrid under independent and cooperative scheduling provided in the embodiments of this application; Figure 17 A schematic diagram of the structure of a multi-microgrid electricity carbon collaborative scheduling system provided in this application embodiment; Figure 18 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] like Figure 1 As shown, this embodiment of the invention provides a multi-microgrid electricity carbon collaborative scheduling method, including the following steps: S1. Establish a mathematical model for distributed generation and energy storage based on the multi-microgrid system, and define parameters based on the mathematical model for distributed generation and energy storage; S2. Construct an endogenous heat feedback model based on the heat feedback coefficient and the load heat sensitivity coefficient; S3. Design an intraday rolling optimization strategy based on the endogenous heat feedback model; S4. Generate a comprehensive operating cost function and power balance constraints based on the distributed generation and energy storage mathematical model, the endogenous heat feedback model, and the intraday rolling optimization strategy, so as to carry out multi-microgrid power carbon collaborative scheduling.

[0022] Steps S1 to S4 have a progressive logical relationship. First, step S1 establishes a mathematical model of distributed generation and energy storage to determine the basic operating boundaries of gas turbines, energy storage systems, renewable energy sources, and the load side in the multi-microgrid system, providing equipment constraints and state variables for subsequent optimized scheduling. Second, step S2, based on the above equipment operation model, introduces a thermal feedback coefficient and a load thermal sensitivity coefficient to establish the coupling relationship between ambient temperature, gas turbine output, cooling load, and renewable energy output, thereby incorporating the impact of temperature changes under extreme heat wave conditions into the scheduling model. Third, step S3, based on the endogenous thermal feedback model obtained in step S2, constructs an intraday rolling optimization strategy, enabling the system to dynamically update the operating plan for the future prediction time window based on the latest ambient temperature, load forecast, energy storage status, and unit output status during each scheduling period. Finally, step S4 incorporates the equipment constraints obtained in step S1, the thermal feedback constraints obtained in step S2, and the rolling optimization recursive relationship obtained in step S3 into the optimization framework, generating a comprehensive operating cost function and power balance constraints, thereby achieving coordinated optimization of power scheduling, carbon emission control, and power supply guarantee in the multi-microgrid system.

[0023] Preferably, the step of establishing a distributed generation and energy storage mathematical model based on the multi-microgrid system, and defining parameters based on the distributed generation and energy storage mathematical model, specifically includes: A distributed generation and energy storage mathematical model is established based on the multi-microgrid system, and constraints are imposed on the gas turbine output, gas turbine ramping, energy storage system charging and discharging power and state, and state-of-charge safety boundary and endpoint based on the distributed generation and energy storage mathematical model. The parameters include: the output of the gas turbine, the ramp rate of the gas turbine, the charging and discharging power and status of the energy storage system, and the state-of-charge safety boundary and endpoint.

[0024] In practical applications, a multi-microgrid interconnection system consists of a main power grid and three microgrids, with the system structure as follows: Figure 2 As shown in the diagram. Microgrid 1's main power supply comes from a large-capacity wind turbine, with its load side primarily consisting of core production loads and factory lighting loads. Microgrid 2's power supply is provided by distributed photovoltaic (PV) systems, and its load includes commercial information equipment loads and cooling / air conditioning loads, the latter of which are significantly affected by ambient temperature. Microgrid 3 is equipped with wind turbines, distributed PV systems, gas turbines, and energy storage systems, and its power consumption side mainly includes electric vehicle charging, bulk industrial loads, and flexible loads such as landscape lighting. Furthermore, assuming this multi-microgrid interconnected system is located within the same local geographical area, each microgrid shares the same local microclimate environment; therefore, the waste heat from a node's generator will affect the overall local ambient temperature of the system. The system's ambient temperature changes are as follows: Figure 3 As shown, the power generation and load curves of microgrid 1 are as follows: Figure 4As shown, the power generation and load curves of microgrid 2 are as follows: Figure 5 As shown, the power generation and load curves of microgrid 3 are as follows: Figure 6 As shown.

[0025] A mathematical model for distributed generation and energy storage is established, including the following constraints: Gas turbine output constraints: ; in, For time period The output power of the gas turbine and These are its minimum and maximum power output limits, respectively.

[0026] To ensure the safe and stable operation of the unit, the power change between two adjacent scheduling periods must not exceed the unit's maximum ramping capability.

[0027] Gas turbine ramping constraints: ; in, Indicates the scheduling time step Maximum climbing power within the range.

[0028] Modeling an energy storage system must take into account the mutual exclusivity of charge and discharge states, the time evolution of the state of charge, and capacity limitations. At any given time, the energy storage system cannot be in both charge and discharge states simultaneously.

[0029] Energy storage system charge / discharge power and state constraints: ; ; ; in, , Indicates time period The charging and discharging power, This refers to the upper limit of the charging and discharging power of the energy storage system. To indicate time period A binary variable representing the charging status.

[0030] Time period The state of charge (SPC) depends on the SPC of the previous time period, as well as the current charge / discharge power and conversion efficiency. The SPC evolution equation is: ; in, For time period The state of charge, For charging and discharging efficiency, Determine the capacity for the total energy storage amount. This is the scheduling time step.

[0031] State of charge safety boundary and endpoint constraints: ; ; in, For the minimum and maximum allowable states of charge, The initial and final charged states of the scheduling cycle.

[0032] Preferably, the step of constructing an endogenous heat feedback model based on the heat feedback coefficient and the load heat sensitivity coefficient specifically includes: A local ambient temperature rise model is constructed based on the aforementioned thermal feedback coefficient; An endogenous heat feedback model is constructed based on the local ambient temperature rise model and the load thermal sensitivity coefficient.

[0033] In practical applications, firstly, a thermal feedback coefficient is introduced. The unit output is linearly mapped to the local ambient temperature rise. Considering the thermal inertia of the environment, the actual ambient temperature in the current period is affected by the unit output in the previous period. The local ambient temperature rise model is as follows: ; in, For time period The local equivalent ambient temperature of microgrids and thermal feedback. To predict baseline temperature, For the active power output of the gas turbine in the previous scheduling period, The thermal feedback coefficient represents the degree of local temperature rise caused by a unit output force.

[0034] Secondly, to avoid miscalculating the natural cooling process below the critical temperature as load reduction, a continuous auxiliary variable was introduced. To rigorously quantify the effective temperature rise beyond the comfort zone: ; ; in This is the reference temperature at which the cooling load begins to surge and the photovoltaic efficiency begins to decline.

[0035] The refrigeration and air conditioning load increases linearly with temperature rise, introducing a load thermal sensitivity coefficient. The model is as follows: ; in, and These are the actual cooling power corrected for temperature rise and the original baseline predicted power, respectively. This represents the linear increment of the cooling load for every 1°C increase in temperature above the threshold.

[0036] Photovoltaic modules experience efficiency loss at high temperatures, introducing a power-temperature coefficient. This represents the linear rate of decrease in efficiency. The actual usable power limit of microgrid 2 is defined as: ; in, For photovoltaic power prediction under standard test conditions, This represents the high-temperature thermal attenuation coefficient. The final actual grid-connected power of the system will be determined jointly by this sensing upper limit and the curtailment control variable.

[0037] Preferably, the step of designing an intraday rolling optimization strategy based on the endogenous heat feedback model specifically includes: Calculate the total carbon trading cost based on preset conditions. and total load reduction costs .

[0038] In practical applications, a generalized predecessor state variable is defined. To unify initial boundary constraints with continuous evolution within the prediction time window: ; in, These represent the state of charge and active power output, respectively. The actual execution value recorded in the previous scheduling period. For at any time Internal forecasts for future periods For the step size index within the MPC prediction time window, , The total number of steps for the MPC prediction time window.

[0039] Based on generalized predecessor state variables Based on this definition, the dynamic recursive model for predicting the evolution of the state of charge, ramp-up limitations, and thermal feedback within the prediction time window is expressed as follows:

[0040] in, For the predicted state of charge with step size k, Indicates the state of charge; This represents the generalized precursor state of the gas turbine output, with other variables as described above.

[0041] Considering the end-of-pipe carbon emission penalties in the forecast time domain to mitigate the short-sightedness of MPC, the total carbon trading cost The calculation is as follows: ; in, The total cost of carbon trading. Steps Carbon penalty prices and cross-day emissions prices, The predicted emissions for the following day are used to mitigate the short-sightedness of the MPC.

[0042] To reflect different power supply priorities, a load shedding penalty cost method is used to assign different penalty weights to different load types. Total load reduction cost. The model is as follows: ; in, For time period microgrids The Middle Reduced power for similar loads This represents the corresponding unit penalty cost.

[0043] Preferably, the step of generating a comprehensive operating cost function and power balance constraints based on the distributed generation and energy storage mathematical model, the endogenous heat feedback model, and the intraday rolling optimization strategy for multi-microgrid power carbon coordinated scheduling specifically includes: Based on the total carbon trading cost The total load reduction cost Economic costs and system penalty costs Obtain the comprehensive operating cost function; Define microgrid During the period Net active power interaction with external entities and interact based on the net active power. Building microgrids The power balance equation.

[0044] In practical applications, the comprehensive operating cost function Aggregated economic costs System penalty costs Carbon trading costs and load reduction costs : ; Among them, economic costs and system penalty costs They are expressed as follows: ; ; Define microgrid During the period Net active power interaction with external entities : ; Subsequently, a microgrid was constructed. The power balance equation. The left side represents total demand, and the right side represents total supply:

[0045] in, This indicates the additional load induced by environmental factors such as extreme heat waves. For microgrids During the period Total load reduction Contributing to theoretical predictions of renewable energy, minus the variable of curtailed electricity. Then it becomes an actual renewable energy source.

[0046] To ensure the physical justification of load shedding operations, the power reduction for any load type must not exceed the intraday forecast demand for that load during that period. Assume... For microgrids The Middle The reduction amount of this type of load must meet the following requirements: ; The carbon emission accounting formula is as follows: ; in, This represents the indirect carbon emissions from purchasing electricity from the grid. This represents the direct carbon emissions of the local generating units. It is worth noting that since microgrid 1 and microgrid 2 do not contain gas turbines, their corresponding... and The value is always 0, and the formula automatically simplifies to electricity purchase accounting. Microgrid 3 involves the dual carbon emissions from electricity purchase and gas turbines.

[0047] Preferably, it also includes: using a tiered carbon trading mechanism to transfer microgrids The overall assessment of carbon emissions is divided into multiple tiers.

[0048] In practical applications, a tiered carbon trading mechanism has been introduced to curb the disorderly emissions from high-carbon microgrids. Total assessment of carbon emissions Divided into 5 steps : ; Each tier is subject to its capacity limit, with tier 0 representing the free quota. The width of the subsequent intermediate steps is Logically, the tiers must be activated sequentially: carbon emissions can only be allocated to a tier after tier j reaches its upper limit. .

[0049] Furthermore, the effectiveness of this application is illustrated by the following embodiments: To evaluate the effectiveness of the proposed strategy, five simulation cases were designed as shown in Table 1:

[0050] By comparing and analyzing system carbon emissions, load reduction, and operating costs in various cases, the effectiveness of the proposed method in comprehensively improving the power supply reliability, economy, and low-carbon characteristics of multi-microgrid systems under extreme heat waves is verified. The specific analysis is as follows: A. Validation of the effectiveness of the MPC-based scheduling strategy: Figure 7 Typical intraday temperature profiles during extreme heat waves are shown. In Case 2, the predicted peak temperature was 41.5°C, but due to endogenous heat feedback, the actual temperature rose to 43°C, with the maximum deviation occurring during the peak load period of 14:00-15:00, reaching 1.5°C. This deviation is directly attributed to the waste heat generated by the large output of the gas turbine during the peak period. The results indicate that ignoring this heat feedback leads to a significant underestimation of the actual system temperature.

[0051] Figure 8 Carbon emissions and load reduction response under two different scheduling strategies were compared. In Case 2, which lacked MPC rolling optimization, the system could not anticipate the thermal-induced unit efficiency decline and had to implement large-scale protective load reduction, with a peak of approximately 340 kW and a peak carbon emission of 0.23 tons. In contrast, Case 3, which integrated an intraday MPC rolling optimization mechanism, reduced the peak carbon emission to 0.1 tons, a 56% reduction compared to Case 2.

[0052] B. Verification of the effectiveness of the tiered carbon trading mechanism: Figure 9 The daily carbon emissions of Case 3 and Case 4 were compared. During the peak load period from 10:00 to 15:00, the emissions in Case 4 were lower than those in Case 3. This indicates that the tiered carbon trading mechanism primarily functions when carbon emissions exceed a specific threshold. By imposing higher carbon costs during these critical periods, the mechanism forces the system to reduce the output of high-carbon-intensity units, effectively reducing peak emissions.

[0053] like Figure 11 , 12As shown, in Case 4, the power consumption of HVAC decreased during the same period, achieving a maximum peak reduction of 4.52kW. Compared with Case 3, the total load reduction in Case 4 decreased by 2.53kW, with the difference mainly concentrated in the peak pressure window from 14:00 to 16:00.

[0054] C. Validation of the effectiveness of carbon quota flow in multi-microgrid interconnection systems: like Figure 13 As shown, during the low-load period from 0:00 to 7:00, microgrid 3, with lower environmental pressure, exhibited a significant quota surplus, transferring approximately 0.09 tons of emission quotas to microgrids 1 and 2. This transfer logic is directly reflected in... Figure 13 Although Case 5 showed a slight increase in instantaneous emissions at its peak at 16:00, the overall carbon emissions decreased significantly on a global scale.

[0055] Furthermore, Figure 13 and Figure 14 Together, they reflect the resource allocation capacity and emission reduction effect of combining the tiered carbon trading mechanism with coordinated scheduling. The shift from Case 4 to Case 5 shows that under extreme high temperature and endogenous heat feedback conditions, a single microgrid is easily constrained by both power supply capacity and carbon emission constraints. However, coordinated scheduling of multiple microgrids can enable carbon quotas to flow between different microgrids, transforming carbon quotas from rigid constraints into dispatchable resources.

[0056] Under the constraint that the transmission capacity of the tie line is limited to 50kW, such as Figure 13 As shown, during the low-load period from 0:00 to 7:00, microgrid 3 has a significant carbon allowance surplus and transfers approximately 0.09 tons of emission allowances to microgrids 1 and 2. This transfer process is further reflected in... Figure 14 The carbon emission trajectory. Although Case 5 showed a slight increase in instantaneous carbon emissions during the peak period around 16:00, the overall total carbon emissions of the system decreased throughout the day, indicating that coordinated scheduling can achieve global emission reduction while meeting power supply constraints.

[0057] like Figure 15 As shown, Case 5 achieved a near 100% load guarantee rate for most of the day, a significant improvement compared to the stand-alone operation mode. Although the system still allows for a small amount of load reduction to maintain stability under extreme thermal stress, the reduction amount is significantly reduced, indicating that power sharing and carbon quota coordination among multiple microgrids can improve the system's power supply reliability.

[0058] like Figure 16As shown, under the coordinated dispatch mode, the state of charge (SOC) of the energy storage system is more forward-looking. When the MPC's intraday rolling optimization anticipates subsequent cross-grid support needs within the forecast time window, microgrid 3 will actively charge during low-load periods to release energy and provide support during peak periods. Therefore, this invention can improve the low-carbon nature, reliability, and operational stability of multi-microgrid systems under extreme heat wave conditions through carbon quota flow, power interaction, and energy storage coordination.

[0059] D. Economic Analysis: Table 2 details the cost breakdown of the system under five operating scenarios.

[0060] In Case 1, the total system cost remained low during the normal baseline scenario. However, when the system encountered extreme heat waves and endogenous heat feedback, the load-cutting penalty cost for microgrid 2 in Case 2 surged to 47099.1, severely deteriorating the overall system economics. Case 3 introduced intraday MPC rolling optimization, significantly reducing the penalty cost for microgrid 2. In Case 4, after activating the tiered carbon trading mechanism, although the carbon emission cost for microgrid 2 increased to 2231.22, the economic pressure effectively suppressed peak-hour electricity demand. In Case 5, after implementing coordinated dispatch and carbon quota sharing, the load-cutting penalty cost for microgrid 2 decreased to 100.7, and the total system cost decreased to 8662.79.

[0061]

[0062] The above results indicate that combining the tiered carbon mechanism with the multi-microgrid interconnection architecture can reduce the total operating cost of the system through the redistribution of local costs, thus meeting the requirements for power supply security and low-carbon operation under extreme conditions.

[0063] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention quantifies the bidirectional interaction between multi-microgrid systems and ambient temperature by constructing an environmental temperature evolution model that includes endogenous heat feedback. This overcomes the limitations of treating ambient temperature as an exogenous variable and achieves real-time quantification and dynamic compensation of the source-load dual-side coupling effect caused by high temperatures. Computational results show that, under a coordinated scheduling scenario, the total operating cost and carbon emission cost are further reduced by 25.47% and 18.88%, respectively, significantly enhancing the adaptability and low-carbon economy of multi-microgrid systems under extreme heat waves.

[0064] 2. This invention integrates a tiered carbon trading mechanism with an intraday rolling optimization strategy (MPC), which dynamically adjusts the scheduling strategy based on the real-time system status and carbon intensity, significantly reducing carbon emissions and operating costs while ensuring power supply reliability. The case study results show that Case 5 achieved near 100% load protection, significantly improving system resilience compared to the standalone mode.

[0065] 3. This invention combines a tiered carbon mechanism with a multi-microgrid interconnection architecture to enable the spatial flow of carbon allowances across power grids, transforming carbon allowances from rigid constraints into schedulable resources. The example results show that during the low-load period from 0:00 to 7:00, microgrid 3, with lower environmental pressure, transferred approximately 0.06 tons of emission allowances to microgrids 1 and 2. This redistribution of local costs reduced the overall system operating cost, meeting the requirements for power supply security and low-carbon operation under extreme conditions.

[0066] like Figure 17 As shown, this embodiment of the invention provides a multi-microgrid carbon emission collaborative scheduling system, including: a parameter definition module, a construction module, a design module, and a generation module; The parameter definition module is used to establish a distributed generation and energy storage mathematical model based on the multi-microgrid system, and to define parameters based on the distributed generation and energy storage mathematical model. The construction module is used to construct an endogenous heat feedback model based on the heat feedback coefficient and the load heat sensitivity coefficient. The design module is used to design an intraday rolling optimization strategy based on the endogenous heat feedback model; The generation module is used to generate a comprehensive operating cost function and power balance constraints based on the distributed generation and energy storage mathematical model, the endogenous heat feedback model and the intraday rolling optimization strategy, so as to carry out multi-microgrid power carbon collaborative scheduling.

[0067] In practical applications, the multi-microgrid electricity-carbon coordinated dispatch system includes a parameter definition module, a construction module, a design module, and a generation module. The parameter definition module is connected to the generation module; the construction module is connected to the generation module; and the design module is connected to both the construction module and the generation module. The parameter definition module establishes a distributed generation and energy storage mathematical model based on the multi-microgrid system, defines parameters according to the model, and then transmits the data to the generation module. The construction module constructs an endogenous heat feedback model based on the heat feedback coefficient and the load heat sensitivity coefficient, and then transmits the endogenous heat feedback model to both the design module and the generation module. The design module designs an intraday rolling optimization strategy based on the endogenous heat feedback model and then transmits this strategy to the generation module. The generation module then generates a comprehensive operating cost function and power balance constraints based on the distributed generation and energy storage mathematical model, the endogenous heat feedback model, and the intraday rolling optimization strategy for multi-microgrid power-carbon coordinated scheduling. This system, through the coordinated operation of the parameter definition module, construction module, design module, and generation module, quantifies the bidirectional interaction between the multi-microgrid system and ambient temperature by constructing an environmental temperature evolution model incorporating endogenous heat feedback. Simultaneously, it integrates the Stepped Carbon Trading Mechanism (SCTM) and the MPC intraday rolling optimization strategy to optimize operating costs, carbon emissions, and power supply security under complex constraints. Compared to existing technologies, this method effectively responds to source-load fluctuations under extreme heat waves, improving the carbon reduction capacity and operational economy of the multi-microgrid system.

[0068] Furthermore, embodiments of this application also disclose an electronic device, Figure 18 This is a structural diagram of an electronic device according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0069] Figure 18 This is a schematic diagram of an electronic device provided in an embodiment of this application. The electronic device 20 specifically includes: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the multi-microgrid carbon collaborative scheduling method disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment can specifically be an electronic computer.

[0070] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a multi-microgrid power carbon collaborative scheduling channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0071] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222 and data 223, etc., and the storage method can be temporary storage or permanent storage.

[0072] The operating system 221 manages and controls the various hardware devices on the electronic device 20 and the computer program 222 to enable the processor 21 to perform calculations and processing on the data 223 in the memory 22. It can be Windows Server, Netware, Unix, Linux, etc. The computer program 222, in addition to including a computer program capable of performing the multi-microgrid carbon emission coordination scheduling method disclosed in any of the foregoing embodiments, may further include computer programs capable of performing other specific tasks. The data 223 may include data received by the multi-microgrid carbon emission coordination scheduling device from external devices, as well as data collected by its own input / output interface 25.

[0073] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0074] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned multi-microgrid carbon collaborative scheduling method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0075] It should be understood that the use of terms such as "method," "apparatus," "unit," and / or "module" in this application is merely to distinguish one method of different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0076] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "a," and / or "the" are not specifically singular and may include the plural. Generally, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements. An element defined by the phrase "comprising an..." does not exclude the presence of other identical elements in the process, method, product, or apparatus that includes the element.

[0077] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0078] If a flowchart is used in this application, it is used to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0079] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-microgrid electricity carbon collaborative scheduling method, characterized in that, Includes the following steps: A mathematical model for distributed generation and energy storage is established based on the multi-microgrid system, and parameters are defined based on the mathematical model for distributed generation and energy storage. An endogenous heat feedback model is constructed based on the heat feedback coefficient and the load heat sensitivity coefficient. Design an intraday rolling optimization strategy based on the endogenous heat feedback model; Based on the distributed generation and energy storage mathematical model, the endogenous heat feedback model, and the intraday rolling optimization strategy, a comprehensive operating cost function and power balance constraints are generated to carry out multi-microgrid power carbon collaborative scheduling.

2. The multi-microgrid electricity carbon coordinated scheduling method according to claim 1, characterized in that, The establishment of a distributed generation and energy storage mathematical model based on the multi-microgrid system, and the definition of parameters based on the distributed generation and energy storage mathematical model, specifically includes: A distributed generation and energy storage mathematical model is established based on the multi-microgrid system, and constraints are imposed on the gas turbine output, gas turbine ramping, energy storage system charging and discharging power and state, and state-of-charge safety boundary and endpoint based on the distributed generation and energy storage mathematical model. The parameters include: the output of the gas turbine, the ramp rate of the gas turbine, the charging and discharging power and status of the energy storage system, and the state-of-charge safety boundary and endpoint.

3. The multi-microgrid electricity carbon coordinated scheduling method according to claim 1, characterized in that, The construction of the endogenous heat feedback model based on the heat feedback coefficient and the load heat sensitivity coefficient specifically includes: A local ambient temperature rise model is constructed based on the aforementioned thermal feedback coefficient; An endogenous heat feedback model is constructed based on the local ambient temperature rise model and the load thermal sensitivity coefficient.

4. The multi-microgrid electricity carbon coordinated scheduling method according to claim 3, characterized in that, The construction of the local ambient temperature rise model based on the thermal feedback coefficient specifically includes: The model for the rise in local ambient temperature is as follows: ； in, This is a model for localized ambient temperature rise, representing the time period. The local equivalent ambient temperature of microgrids and thermal feedback; To predict baseline temperature; This is for the active power output of the gas turbine during the previous scheduling period; The thermal feedback coefficient represents the degree of local temperature rise caused by a unit output force.

5. The multi-microgrid electricity carbon coordinated scheduling method according to claim 3, characterized in that, The construction of the endogenous heat feedback model based on the local ambient temperature rise model and the load thermal sensitivity coefficient specifically includes: The endogenous heat feedback model is as follows: ； in, This is an endogenous heat feedback model used to represent the actual cooling power corrected for temperature rise; Predict power for the original baseline; The load thermal sensitivity coefficient represents the linear increment of the cooling load for every 1°C increase in temperature above a threshold. It is a continuous auxiliary variable used to rigorously quantify the effective temperature rise beyond the comfort zone.

6. The multi-microgrid electricity carbon coordinated scheduling method according to claim 5, characterized in that, The continuous auxiliary variable Specifically: ； ； in, This is the reference temperature at which the cooling load begins to surge and the photovoltaic efficiency begins to decline.

7. The multi-microgrid electricity carbon coordinated scheduling method according to claim 1, characterized in that, The design of the intraday rolling optimization strategy based on the endogenous heat feedback model specifically includes: Calculate the total carbon trading cost based on preset conditions. and total load reduction costs .

8. The multi-microgrid electricity carbon coordinated scheduling method according to claim 7, characterized in that, The process of generating a comprehensive operating cost function and power balance constraints based on the distributed generation and energy storage mathematical model, the endogenous heat feedback model, and the intraday rolling optimization strategy for multi-microgrid power carbon coordinated scheduling specifically includes: Based on the total carbon trading cost The total load reduction cost Economic costs and system penalty costs Obtain the comprehensive operating cost function; Define microgrid During the period Net active power interaction with external entities and interact based on the net active power. Building microgrids The power balance equation.

9. The multi-microgrid electricity carbon coordinated scheduling method according to claim 1, characterized in that, Also includes: Microgrids will be integrated through a tiered carbon trading mechanism. The overall assessment of carbon emissions is divided into multiple tiers.

10. A multi-microgrid electricity carbon collaborative scheduling system, characterized in that, include: Parameter definition module, construction module, design module, and generation module; The parameter definition module is used to establish a distributed generation and energy storage mathematical model based on the multi-microgrid system, and to define parameters based on the distributed generation and energy storage mathematical model. The construction module is used to construct an endogenous heat feedback model based on the heat feedback coefficient and the load heat sensitivity coefficient. The design module is used to design an intraday rolling optimization strategy based on the endogenous heat feedback model; The generation module is used to generate a comprehensive operating cost function and power balance constraints based on the distributed generation and energy storage mathematical model, the endogenous heat feedback model and the intraday rolling optimization strategy, so as to carry out multi-microgrid power carbon collaborative scheduling.

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