Optimal scheduling method, device and equipment of combined cooling heating and power microgrid and medium

Abstract

Embodiments of the present application provide a kind of optimization scheduling method, device, equipment and medium of cold heat and power microgrid.It first obtains the operation data and energy scheduling constraint condition of multiple energy equipment in cold heat and power microgrid, then based on operation data, determine power imbalance risk data and cost deviation risk data, then according to operation data, power imbalance risk data and cost deviation risk data, determine multi-objective optimization function, finally based on energy scheduling constraint condition to multi-objective optimization function is solved, determine energy scheduling strategy, wherein, energy scheduling strategy is used to indicate to multiple energy equipment in cold heat and power microgrid carries out energy optimization scheduling.The technical solution of the application is applied to the operation risk of cold heat and power microgrid is quantified, improve the collaborative optimization ability of different energy, accurately realize the multi-objective energy scheduling of cold heat and power microgrid.

Description

Technical Field

[0001] This application relates to the field of microgrid optimization scheduling technology, and in particular to an optimization scheduling method, device, equipment and medium for a combined cooling, heating and power microgrid. Background Technology

[0002] With the advancement of energy structure transformation and clean energy development, distributed energy systems are playing an increasingly important role in regional energy management and microgrid operation. Combined Cooling, Heating, and Power (CCHP) microgrids, as a new type of integrated energy system, can simultaneously provide electricity, heat, and cooling within a local area, improving energy utilization and providing users with highly reliable energy services. However, CCHP microgrid operation faces multiple challenges, requiring optimized scheduling.

[0003] Existing optimal scheduling methods for CCHP microgrids mainly rely on traditional mathematical programming algorithms (such as linear programming, mixed integer programming, etc.) and heuristic algorithms (such as genetic algorithms, particle swarm optimization, etc.) to solve for the optimal solution by establishing a deterministic model.

[0004] However, existing optimization scheduling methods are insufficient in their ability to coordinate different energy sources, resulting in low accuracy in multi-objective optimization scheduling. Summary of the Invention

[0005] This application provides an optimized scheduling method, apparatus, equipment, and medium for combined cooling, heating, and power (CCHP) microgrids, which aims to improve the accuracy of multi-objective optimized scheduling of CCHP microgrids.

[0006] In a first aspect, embodiments of this application provide an optimized scheduling method for a combined cooling, heating and power (CCHP) microgrid, comprising:

[0007] To obtain operational data and energy dispatch constraints of multiple energy devices in a combined cooling, heating and power microgrid;

[0008] Based on the aforementioned operational data, power imbalance risk data and cost deviation risk data are determined;

[0009] Based on the operating data, the power imbalance risk data, and the cost deviation risk data, a multi-objective optimization function is determined. The multi-objective optimization function is a function that characterizes the operating cost, environmental pollution cost, and energy supply reliability index of the multiple energy devices.

[0010] The multi-objective optimization function is solved based on the energy dispatch constraints to determine the energy dispatch strategy, which is used to instruct the energy optimization dispatch of multiple energy devices in the combined cooling, heating and power microgrid.

[0011] In one or more embodiments, the operational data includes: the expected power of curtailment risk and the expected power of power shortage risk under multiple risk levels, as well as the actual operating cost and planned operating cost under multiple uncertain scenarios;

[0012] Accordingly, determining the power imbalance risk data and cost deviation risk data based on the operational data includes:

[0013] The expected power of the energy curtailment risk and the expected power of the power shortage risk under the multiple risk levels are weighted and summed according to the preset penalty coefficient to obtain the power imbalance risk data.

[0014] The cost deviation risk data is obtained by taking the ratio of the total number of uncertain scenarios in which the difference between the actual operating cost and the planned operating cost exists under the multiple uncertain scenarios.

[0015] In one or more embodiments, the multi-objective optimization function includes: an operating cost objective function, an environmental pollution cost objective function, and a comprehensive risk objective function;

[0016] Accordingly, determining the multi-objective optimization function based on the operating data, the power imbalance risk data, and the cost deviation risk data includes:

[0017] The objective function for operating costs is determined based on the power data in the operating data, the maintenance costs of the multiple energy devices, and the electricity purchase costs.

[0018] The environmental pollution cost objective function is determined based on the power data, electricity purchase amount, and preset pollutant emission coefficient in the operating data.

[0019] The comprehensive risk objective function is determined based on the power imbalance risk data, the cost deviation risk data, and the preset weighting coefficients.

[0020] In one or more embodiments, solving the multi-objective optimization function based on energy scheduling constraints to determine the energy scheduling strategy includes:

[0021] Based on the energy scheduling constraints, the controllable scheduling parameters in the multi-objective optimization function are mapped using the chaotic mapping in the preset gray wolf algorithm to obtain multiple candidate energy scheduling strategies. Each candidate energy scheduling strategy includes the value of the controllable scheduling parameters.

[0022] Determine the values ​​of the multi-objective optimization functions corresponding to the multiple candidate energy scheduling strategies;

[0023] The candidate energy scheduling strategy corresponding to the optimal value among the values ​​of the multi-objective optimization function is taken as the target energy scheduling strategy;

[0024] Based on the target energy scheduling strategy, other candidate energy scheduling strategies are iterated repeatedly until a preset number of iterations is reached to obtain the energy scheduling strategy. The other candidate energy scheduling strategies are candidate energy scheduling strategies other than the target energy scheduling strategy among the multiple candidate energy scheduling strategies.

[0025] In one or more embodiments, the step of repeatedly iterating over other candidate energy scheduling strategies based on the target energy scheduling strategy until a preset number of iterations is reached to obtain the energy scheduling strategy includes:

[0026] S1, based on the control parameters in the preset gray wolf algorithm and the target energy scheduling strategy, iteratively update other candidate energy scheduling strategies to obtain the updated candidate energy scheduling strategy;

[0027] S2, update the control parameters according to the preset number of iterations to obtain the updated control parameters;

[0028] S3. Based on the updated control parameters and the target energy scheduling strategy, iteratively update the updated candidate energy scheduling strategy, repeating steps S2-S3 until the preset number of iterations is reached, to obtain the energy scheduling strategy.

[0029] In one or more embodiments, based on the energy scheduling constraints, the controllable scheduling parameters in the multi-objective optimization function are mapped using the chaotic mapping in the preset Grey Wolf algorithm to obtain multiple candidate energy scheduling strategies, including:

[0030] Based on the energy scheduling constraints, the controllable scheduling parameters in the multi-objective optimization function are sequentially mapped using the chaotic mapping in the preset gray wolf algorithm to obtain multiple chaotic sequences, each of which includes the normalized value of the controllable scheduling parameters.

[0031] Power mapping is performed on the multiple chaotic sequences to obtain multiple candidate energy scheduling strategies. Each candidate energy scheduling strategy includes power information of multiple energy devices in the combined cooling, heating and power microgrid.

[0032] In one or more embodiments, the energy dispatch constraints include: energy balance constraints, equipment operation constraints, energy storage constraints, and interactive power constraints;

[0033] The energy balance constraints include: electrical load constraints, thermal load constraints, and cooling load constraints. The electrical load constraints include electrical load demand values, the thermal load constraints include thermal load demand values, and the cooling load constraints include cooling load demand values.

[0034] The equipment operation constraints include the operating power thresholds corresponding to the plurality of energy devices;

[0035] The energy storage constraints include the energy storage thresholds corresponding to the energy storage devices among the plurality of energy devices;

[0036] The interactive power constraint includes the power threshold for the interaction between the combined cooling, heating and power microgrid and the upper-level power grid.

[0037] Secondly, embodiments of this application provide an optimized scheduling device for a combined cooling, heating and power microgrid, comprising:

[0038] The acquisition module is used to acquire the operating data and energy dispatch constraints of multiple energy devices in a combined cooling, heating and power microgrid;

[0039] The first determining module is used to determine power imbalance risk data and cost deviation risk data based on the operating data;

[0040] The second determining module is used to determine a multi-objective optimization function based on the operating data, the power imbalance risk data, and the cost deviation risk data. The multi-objective optimization function is a function that characterizes the operating cost, environmental pollution cost, and energy supply reliability index of the multiple energy devices.

[0041] The third determining module is used to solve the multi-objective optimization function based on the energy dispatch constraints and determine the energy dispatch strategy. The energy dispatch strategy is used to instruct the energy optimization dispatch of multiple energy devices in the combined cooling, heating and power microgrid.

[0042] In one or more embodiments, the operational data includes: the expected power of curtailment risk and the expected power of power shortage risk under multiple risk levels, as well as the actual operating cost and planned operating cost under multiple uncertain scenarios;

[0043] Accordingly, the first determining module is specifically used for:

[0044] The expected power of the energy curtailment risk and the expected power of the power shortage risk under the multiple risk levels are weighted and summed according to the preset penalty coefficient to obtain the power imbalance risk data.

[0045] The cost deviation risk data is obtained by taking the ratio of the total number of uncertain scenarios in which the difference between the actual operating cost and the planned operating cost exists under the multiple uncertain scenarios.

[0046] In one or more embodiments, the multi-objective optimization function includes: an operating cost objective function, an environmental pollution cost objective function, and a comprehensive risk objective function;

[0047] Accordingly, the second determining module is specifically used for:

[0048] The objective function for operating costs is determined based on the power data in the operating data, the maintenance costs of the multiple energy devices, and the electricity purchase costs.

[0049] The environmental pollution cost objective function is determined based on the power data, electricity purchase amount, and preset pollutant emission coefficient in the operating data.

[0050] The comprehensive risk objective function is determined based on the power imbalance risk data, the cost deviation risk data, and the preset weighting coefficients.

[0051] In one or more embodiments, the third determining module is specifically used for:

[0052] Based on the energy scheduling constraints, the controllable scheduling parameters in the multi-objective optimization function are mapped using the chaotic mapping in the preset gray wolf algorithm to obtain multiple candidate energy scheduling strategies. Each candidate energy scheduling strategy includes the value of the controllable scheduling parameters.

[0053] Determine the values ​​of the multi-objective optimization functions corresponding to the multiple candidate energy scheduling strategies;

[0054] The candidate energy scheduling strategy corresponding to the optimal value among the values ​​of the multi-objective optimization function is taken as the target energy scheduling strategy;

[0055] Based on the target energy scheduling strategy, other candidate energy scheduling strategies are iterated repeatedly until a preset number of iterations is reached to obtain the energy scheduling strategy. The other candidate energy scheduling strategies are candidate energy scheduling strategies other than the target energy scheduling strategy among the multiple candidate energy scheduling strategies.

[0056] In one or more embodiments, the third determining module, based on the target energy scheduling strategy, iterates over other candidate energy scheduling strategies until a preset number of iterations is reached to obtain the energy scheduling strategy, specifically for:

[0057] S1, based on the control parameters in the preset gray wolf algorithm and the target energy scheduling strategy, iteratively update other candidate energy scheduling strategies to obtain the updated candidate energy scheduling strategy;

[0058] S2, update the control parameters according to the preset number of iterations to obtain the updated control parameters;

[0059] S3. Based on the updated control parameters and the target energy scheduling strategy, iteratively update the updated candidate energy scheduling strategy, repeating steps S2-S3 until the preset number of iterations is reached, to obtain the energy scheduling strategy.

[0060] In one or more embodiments, the third determining module, based on the energy scheduling constraints, uses the chaotic mapping in the preset Grey Wolf algorithm to map the controllable scheduling parameters in the multi-objective optimization function to obtain multiple candidate energy scheduling strategies, specifically for:

[0061] Based on the energy scheduling constraints, the controllable scheduling parameters in the multi-objective optimization function are sequentially mapped using the chaotic mapping in the preset gray wolf algorithm to obtain multiple chaotic sequences, each of which includes the normalized value of the controllable scheduling parameters.

[0062] Power mapping is performed on the multiple chaotic sequences to obtain multiple candidate energy scheduling strategies. Each candidate energy scheduling strategy includes power information of multiple energy devices in the combined cooling, heating and power microgrid.

[0063] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0064] The memory stores computer-executed instructions;

[0065] The processor executes computer execution instructions stored in the memory, such that the processor, when executed, is used to implement the method described in the first aspect and any of the embodiments above.

[0066] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods described in the first aspect and any of the embodiments above.

[0067] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, is used to implement the optimized scheduling method for a combined cooling, heating and power microgrid as described in the first aspect and various possible implementations of the first aspect.

[0068] This application provides an optimized scheduling method, apparatus, equipment, and medium for combined cooling, heating, and power (CCHP) microgrids. The method first acquires operating data and energy scheduling constraints for multiple energy devices in the CCHP microgrid. Then, based on the operating data, it determines power imbalance risk data and cost deviation risk data. Next, based on the operating data, power imbalance risk data, and cost deviation risk data, it determines a multi-objective optimization function. Finally, based on the energy scheduling constraints, it solves the multi-objective optimization function to determine an energy scheduling strategy. This energy scheduling strategy is used to instruct the optimized energy scheduling of multiple energy devices in the CCHP microgrid. The above method acquires operational data from multiple energy devices in a combined cooling, heating, and power (CCHP) microgrid. Based on this data, the uncertainties in the operation of these devices are quantified into power imbalance risk data and cost deviation risk data. Then, by combining the operational data, power imbalance risk data, and cost deviation risk data, a multi-objective optimization function is constructed to characterize the operating costs, environmental pollution costs, and energy supply reliability indicators of the multiple energy devices. This transforms the multi-objective optimization scheduling problem of the CCHP microgrid under complex load fluctuations and random renewable energy output into a computable and iterative decision-making process. Finally, the multi-objective optimization function is solved based on the acquired energy scheduling constraints. This approach enables the determination of a more reasonable energy scheduling strategy by considering uncertainties and the synergistic relationship between multiple energy devices, thereby effectively improving the accuracy, operational reliability, and multi-device synergy of the CCHP microgrid. Attached Figure Description

[0069] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0070] Figure 1 A schematic diagram of the structure of a combined cooling, heating and power microgrid provided in an embodiment of this application;

[0071] Figure 2 A flowchart illustrating the optimized scheduling method for a combined cooling, heating, and power microgrid provided in this application embodiment. Figure 1 ;

[0072] Figure 3 A flowchart illustrating the optimized scheduling method for a combined cooling, heating, and power microgrid provided in this application embodiment. Figure 2 ;

[0073] Figure 4 A flowchart illustrating the optimized scheduling method for a combined cooling, heating, and power microgrid provided in this application embodiment. Figure 3 ;

[0074] Figure 5 A schematic diagram of load forecasting for a combined cooling, heating and power (CCHP) simulated microgrid provided in an embodiment of this application;

[0075] Figure 6 A schematic diagram illustrating the output prediction of wind turbines and photovoltaic cells in a combined cooling, heating and power (CCHP) simulated microgrid provided in this application embodiment;

[0076] Figure 7 This is a schematic diagram of power dispatching of power supply equipment in a combined cooling, heating and power simulated microgrid provided in an embodiment of this application;

[0077] Figure 8 This is a schematic diagram of the operating curve of a battery in a combined cooling, heating and power simulated microgrid provided in the embodiments of this application;

[0078] Figure 9 This is a schematic diagram of the operating curve of a battery in a combined cooling, heating and power simulated microgrid provided in an embodiment of this application.

[0079] Figure 10 This is a schematic diagram illustrating the risk probability of a simulated microgrid with combined cooling, heating and power (CCHP) provided in an embodiment of this application before scheduling optimization.

[0080] Figure 11 This is a schematic diagram illustrating the risk probability of a simulated microgrid with combined cooling, heating and power (CCHP) provided in an embodiment of this application after scheduling optimization.

[0081] Figure 12 A schematic diagram illustrating the optimized scheduling of a combined cooling, heating and power (CCHP) simulated microgrid using the standard grey wolf algorithm, provided in an embodiment of this application.

[0082] Figure 13 A schematic diagram illustrating the optimized scheduling of a combined cooling, heating and power (CCHP) simulated microgrid using a preset gray wolf algorithm, provided in an embodiment of this application.

[0083] Figure 14 A schematic diagram of the structure of the optimized scheduling device for a combined cooling, heating and power microgrid provided in an embodiment of this application;

[0084] Figure 15 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0085] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0086] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0087] Before introducing the embodiments of this application, the application background of the embodiments of this application will be explained first:

[0088] Combined Cooling, Heating and Power (CCHP) microgrids are a new type of integrated energy system, mainly targeting scenarios with multiple types of load demands, such as industrial parks, commercial complexes, public building clusters, and residential communities, where there are multiple types of load demands, including electricity, heat, and cooling.

[0089] Figure 1 This is a schematic diagram of the structure of a combined cooling, heating and power microgrid provided in an embodiment of this application. Figure 1 As shown, a combined cooling, heating, and power (CCHP) microgrid includes multiple energy devices such as micro gas turbines, waste heat recovery units, absorption chillers, photovoltaic cells, batteries, wind turbines, thermal storage tanks, electric heating equipment, and split-type air conditioners, and maintains an interactive relationship with the upstream power grid. A CCHP microgrid can generate cooling loads, heating loads, and electrical loads to meet users' comprehensive needs for electricity, heat, and cooling energy at different times and under different climatic conditions.

[0090] Existing optimization scheduling methods for combined cooling, heating, and power (CCHP) microgrids typically employ mathematical programming models or heuristic optimization algorithms to jointly solve for the power allocation of multiple energy devices within a scheduling cycle. This aims to reduce operating costs and environmental pollution emissions while meeting cooling, heating, and power load demands. For example, firstly, a balance relationship between electricity, heat, and cooling loads is established based on historical or predicted data. Then, considering factors such as equipment capacity limits, ramping constraints, energy storage state-of-charge constraints, and energy conversion efficiency, an optimization model is constructed, prioritizing economic efficiency or environmental friendliness. Subsequently, scheduling schemes are solved using methods such as linear programming, mixed-integer programming, genetic algorithms, or particle swarm optimization.

[0091] However, existing methods treat uncertainties in the operation of combined cooling, heating, and power (CCHP) microgrids merely as static disturbances, resulting in insufficient risk management. Furthermore, under multi-objective processing and complex constraint scenarios, they lack the ability to coordinate and optimize different energy sources, leading to low accuracy in multi-objective optimization scheduling. Consequently, existing CCHP microgrid scheduling schemes often fail to simultaneously meet the requirements of economy, environmental protection, and reliability when facing real-world operating environments.

[0092] The optimized scheduling method for combined cooling, heating, and power (CCHP) microgrids provided in this application aims to solve the aforementioned technical problems of existing technologies. The technical concept of this application is as follows: Existing technical solutions only treat uncertainties in the operation of CCHP microgrids as static disturbances. Under multi-objective optimization and complex constraint operating scenarios, the accuracy of multi-objective optimized scheduling is low. Optimized scheduling of CCHP microgrids mainly considers simultaneously achieving multi-objective collaborative optimization under complex constraint conditions. If the uncertainties in the operation of CCHP microgrids can be quantified, and a multi-objective optimization function considering risk assessment can be constructed and solved, the accuracy of multi-objective optimized scheduling can be effectively improved. Therefore, the embodiments of this application consider obtaining operating data and energy scheduling constraints of multiple energy devices, then quantifying the uncertainties in the operation process based on the operating data to determine power imbalance risk data and cost deviation risk data. Then, a multi-objective optimization function is constructed based on the operating data, power imbalance risk data, and cost deviation risk data, and solved under energy scheduling constraints to obtain a scheduling strategy for instructing multiple energy devices to perform energy optimization scheduling. By combining operational data, risk data, and energy dispatch constraints, more accurate dispatch strategies can be provided for combined cooling, heating, and power microgrids under complex and uncertain conditions, thereby improving the coordination of energy allocation, the executability of dispatch results, and the overall reliability of operation.

[0093] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0094] Figure 2 A flowchart illustrating the optimized scheduling method for a combined cooling, heating, and power microgrid provided in this application embodiment. Figure 1 .like Figure 2 As shown, the optimized scheduling method for this combined cooling, heating and power microgrid includes the following steps:

[0095] S201. Obtain the operating data and energy dispatch constraints of multiple energy devices in the combined cooling, heating and power microgrid.

[0096] In this step, the operating data of multiple energy devices in the combined cooling, heating and power (CCHP) microgrid are obtained to provide data basis for the subsequent optimized scheduling of the CCHP microgrid, and energy scheduling constraints are obtained to limit the optimized scheduling of the CCHP microgrid.

[0097] For example, multiple energy devices may include micro gas turbines, waste heat recovery devices, absorption chillers, photovoltaic cells, batteries, wind turbines, thermal storage tanks, electric heating equipment, and split air conditioners; wherein, the micro gas turbine is driven by fuels such as natural gas and outputs electrical energy and recoverable waste heat; the waste heat recovery device converts the heat energy in high-temperature exhaust gas into usable heat; and the absorption chiller drives a refrigeration cycle with heat energy and outputs cooling capacity.

[0098] Operational data refers to a set of data that can characterize the current operating status of various energy devices. Specifically, it can include the power generation of micro gas turbines, the power generation of waste heat recovery devices, the output power of wind turbines, the output power of photovoltaic cells, the charging / discharging power of batteries, the driving heat power, the actual light intensity of photovoltaic cells, the current remaining power of batteries, the remaining heat energy in the heat storage tank, active power, reactive power, heating power, cooling power, equipment start-up and shutdown status, temperature, pressure, fuel consumption, and purchased electricity, etc.

[0099] In one possible implementation, the dispatch center of a combined cooling, heating, and power (CCHP) microgrid can establish communication connections with sensors, smart meters, protection and control devices, programmable logic controllers (PLCs), and energy management terminals within the CCHP microgrid via fieldbus, industrial Ethernet, private wireless networks, or distribution communication networks. This allows the center to receive real-time operating data from each energy device according to a preset sampling period. Examples include the power generation, exhaust temperature, and gas flow rate of a micro gas turbine; the state of charge, terminal voltage, and charging / discharging power of a battery; the heat exchange and outlet temperature of a waste heat recovery device; and the cooling capacity and circulation flow rate of an absorption chiller.

[0100] In addition, to ensure the consistency of data from different sources in terms of time sequence, the scheduling center can perform standardized processing on the collected real-time operation data, such as timestamp alignment, outlier removal, missing value imputation, and unit unification.

[0101] In one possible implementation, the mathematical description equations for the CCHP microgrid are constructed for each energy device in the CCHP microgrid:

[0102] (1) For micro gas turbines, both their power generation efficiency and heating efficiency are related to the load factor, which can be expressed as follows:

[0103]

[0104]

[0105] in, and These represent power generation efficiency and heating efficiency, respectively. Indicates the rated power of the equipment. This indicates the load factor.

[0106] (2) For waste heat recovery devices, such as the Organic Rankine Cycle (ORC) waste heat power generation-heating combined unit, the waste heat of the micro gas turbine can be recovered simultaneously for power generation and heating. Its power generation and heating power are expressed as follows:

[0107]

[0108]

[0109] in, Indicates the power generation capacity of the ORC device. Indicates the heating capacity of the ORC device. Indicates the power generation efficiency of the ORC device. This represents the heat loss coefficient of the ORC device. This indicates the waste heat from a micro gas turbine.

[0110] (3) For absorption chillers, such as lithium bromide absorption chillers, their cooling power is expressed as follows:

[0111]

[0112] in, This indicates the cooling capacity of a lithium bromide absorption chiller. This indicates that the waste heat recovery device provides the driving heat power for the lithium bromide absorption chiller. This represents the coefficient of performance (COP) of a lithium bromide absorption chiller.

[0113] (4) For photovoltaic cells, their output power is expressed as follows:

[0114]

[0115] in, Indicates the output power of the photovoltaic cell. This indicates the rated output power under standard test conditions. This indicates the actual light intensity of the photovoltaic cell. Indicates the light intensity under standard test conditions. Indicates the power temperature coefficient of a photovoltaic cell. Indicates ambient temperature. Indicates the rated temperature. This indicates the surface temperature under standard test conditions.

[0116] (5) For storage batteries, storage batteries can store excess electricity generated in the combined cooling, heating and power microgrid and provide auxiliary power supply during peak electricity consumption periods.

[0117] The state of charge (SOC) of a battery is defined as the ratio of its current remaining charge to its rated maximum charge.

[0118]

[0119] in, This indicates the current remaining battery power. This indicates the battery's rated maximum capacity.

[0120] when This indicates that the battery is fully charged. This indicates that the battery is completely discharged.

[0121] The state of charge of the battery for each time period can be calculated as follows:

[0122]

[0123] in, express The state of charge of the battery at all times. express The state of charge of the battery at all times. This indicates the battery power (positive when discharging). Indicates the time span.

[0124] (6) For wind turbines, their output power is expressed as follows:

[0125]

[0126] in, Indicates the output power of the wind turbine. Indicates the cut-in wind speed. Indicates the cut-out wind speed. Indicates the current wind speed. Indicates the rated wind speed. This indicates the rated output power of the wind turbine.

[0127] (7) For heat storage tanks, heat storage tanks can store and utilize the surplus heat generated by the combined cooling, heating and power microgrid, but heat energy will be dissipated during the heat storage process.

[0128] The remaining thermal energy in the thermal storage tank is represented as follows:

[0129]

[0130] in, express The residual heat energy in the heat storage tank at all times, express The residual heat energy in the heat storage tank at all times, This represents the heat loss coefficient of the thermal storage tank. express The constant charging / discharging power of the heat storage tank, with heat storage being positive and heat dissipation being negative.

[0131] (8) For electric heating equipment, its heating power is expressed as follows:

[0132]

[0133] in, This indicates the heating capacity of the electric heating equipment. This indicates the electrical power consumed by the electric heating equipment. This indicates the coefficient of performance (COP) of electric heating equipment.

[0134] (9) For split-type air conditioners, their cooling capacity is expressed as follows:

[0135]

[0136] in, This indicates the cooling capacity of a split-type air conditioner. This indicates the electrical power consumed by a split-type air conditioner. This indicates the coefficient of performance (COP) of a split-type air conditioner.

[0137] In one possible implementation, based on the mathematical description equations of the CCHP microgrid for each of the above-mentioned energy devices, a comprehensive physical model of the CCHP microgrid is constructed, which integrates wind, solar, energy storage, electricity, heat and cooling energy devices into the same CCHP microgrid coupling framework.

[0138] This comprehensive physical model includes: electrical power balance equations, thermal power balance equations, cold power balance equations, and energy conversion relationships between heat-to-cold and electrical-to-cold conversions, as shown below:

[0139]

[0140] The first line represents the power balance equation. express The power generation capacity of the micro gas turbine at any given time. express The power generation capacity of the ORC device at any given time. express The power generation capacity of wind turbines at all times. express The power generation capacity of photovoltaic cells at all times. express The discharge power of the battery at all times. express The power purchased from the upper-level power grid at all times. express The electrical load power on the user side at any given time. express The charging power of the battery at all times. express The electrical power consumed by the electric heating equipment at all times. express The power consumption of a split-type air conditioner at all times. express The power output that is constantly sold to the upper-level power grid.

[0141] The second line represents the heat power balance equation. express The heating capacity of the micro gas turbine at any given time. express The heating power of the ORC device at any given time. express The heat dissipation power of the heat storage tank at all times. express The heating capacity of electric heating equipment at all times. express The user-side heat load power at any given time express The heat storage capacity of the heat storage tank at all times. express The input driving heat power of the absorption chiller.

[0142] The third line represents the cold power balance equation. express The cooling capacity of a constant-time absorption chiller express The cooling capacity of a split-type air conditioner at all times. express The cooling load power on the user side at any given time.

[0143] The fourth line represents the energy conversion relationship from hot to cold. express The coefficient of performance (COP) of a constant-time absorption chiller.

[0144] The fifth line represents the energy conversion relationship from cold to hot. express The coefficient of performance (COP) of a split-type air conditioner.

[0145] It is understandable that the physical quantities in the above integrated physical model can be obtained by conversion or equivalent transformation based on the physical quantities in the mathematical description equations of the CCHP microgrid for each energy device (1)-(9).

[0146] S202. Based on operational data, determine power imbalance risk data and cost deviation risk data.

[0147] In this step, based on operational data, we analyze the uncertainties that may cause power imbalance or cost deviation during the operation of multiple energy devices in a combined cooling, heating and power (CCHP) microgrid, and quantify them to obtain power imbalance risk data and cost deviation risk data.

[0148] For example, power imbalance risk data is used to quantify the probability and severity of supply and demand mismatch in the three dimensions of electricity, heat and cooling energy in a combined cooling, heating and power microgrid within a scheduling cycle or multiple rolling periods; cost deviation risk data is used to quantify the degree of deviation of actual operating costs from planned costs, predicted costs or benchmark costs.

[0149] In one possible implementation, a prediction baseline can be established first based on the operating data and historical operating data of multiple energy devices obtained in step S101. For example, time series models, regression models or machine learning models can be used to predict the electricity load, heat load, cooling load, photovoltaic output, wind power output, electricity price and gas price in the next scheduling cycle. Then, the real-time monitoring values ​​in the operating data are compared with the predicted values ​​in the prediction baseline to obtain the load deviation, output deviation and price deviation.

[0150] Next, a multi-scenario risk assessment is conducted on the combined cooling, heating, and power (CCHP) microgrid. Load deviations and output deviations are mapped to power imbalance risk data, and price deviations are mapped to cost deviation risk data. For example, multiple random operating scenarios are generated to address renewable energy fluctuations and load randomness. Each random operating scenario includes different photovoltaic output trajectories, load change trajectories, and price change trajectories, with different probabilities assigned to different trajectory data. Under each random operating scenario, the power-heat-cold balance relationship is calculated based on the availability of the actual operating energy equipment to determine the probability of power imbalance. Cost deviation risk can be quantified by the difference between the actual total cost and the planned total cost under the same random operating scenario.

[0151] In this embodiment, uncertainties are quantified into calculable and constrainable power imbalance risk data and cost deviation risk data, which effectively solves the problem that existing methods are insufficient in reflecting the fluctuations in renewable energy output, sudden load changes and price deviations, improves the risk perception capability, and thus enhances the robustness and disturbance resistance of the optimization results in the real operating environment.

[0152] In one possible implementation, the operational data includes: the expected power of curtailment risk and the expected power of power shortage risk under multiple risk levels, as well as the actual operating cost and planned operating cost under multiple uncertain scenarios;

[0153] Accordingly, one possible implementation of step S202 above includes the following steps:

[0154] Step 1: Based on the preset penalty coefficient, the expected power of the curtailment risk and the expected power of the power shortage risk under multiple risk levels are weighted and summed to obtain the power imbalance risk data.

[0155] For example, risk level is used to characterize the operational deviation state under different degrees of uncertainty, expected power of curtailment risk is used to characterize the average power of renewable energy that is not utilized due to constraints, and expected power of power shortage risk is used to characterize the average power of load demand that cannot be met.

[0156] In one possible implementation, the expected power of curtailment risk, the expected power of power shortage risk, and the pre-set penalty coefficients for each of the multiple risk levels are first determined. Then, for the same risk level, the power of curtailment risk and the power of power shortage risk corresponding to that risk level are weighted and summed using the pre-set penalty coefficients for that risk level, and the power imbalance risk data is output.

[0157] Power imbalance risk data The calculation formula is expressed as follows:

[0158]

[0159] in, express The moment occurred Expected power (kW) for level curtailment risk. express The moment occurred Expected power (kW) for the risk of power outage (load shedding). Indicates the total number of risk levels. and They represent the first Level 1 energy curtailment risk and The preset penalty coefficient for the risk of power shortage is set. The higher the risk level, the greater the penalty coefficient.

[0160] Step 2: The cost deviation risk data is obtained by taking the ratio of the difference between the actual operating cost and the planned operating cost under multiple uncertain scenarios to the total number of uncertain scenarios.

[0161] For example, the actual operating cost and the planned operating cost correspond to the actual expenditure and the estimated scheduling expenditure after each uncertain scenario is executed, respectively.

[0162] In one possible implementation, multiple uncertain scenarios can be generated through Monte Carlo simulation, including but not limited to those based on historical disturbance samples, probability discretization results, or random sampling results. By calculating the difference between the actual operating cost and the planned operating cost in each scenario separately, and normalizing all scenario differences by the total number of scenarios, cost deviation risk data is obtained, thereby quantifying the deviation between the actual operating cost and the planned cost caused by uncertainties such as market electricity price fluctuations.

[0163] Cost deviation risk data The calculation formula is expressed as follows:

[0164]

[0165] in, Indicates the first In an uncertain scenario The actual operating cost at any given time Indicates the first In an uncertain scenario The cost of planning at any given moment This represents the total number of uncertain scenarios.

[0166] In addition, when When the risk level of a time period reaches a preset threshold and the user load is transferable, economic compensation for that time period can also be calculated. The calculation formula is as follows:

[0167]

[0168] in, express The economic compensation available to users during the specified time period. This represents the electricity price guidance coefficient corresponding to each electricity consumption period, with a total of 3 electricity consumption periods ( Take 1, 2, and 3, which correspond to peak hours, normal hours, and valley hours, respectively. Indicates the type of risk (including CCHP waste risk and load shedding risk). express Time period Such risks in the first The occurrence / existence indicator under the risk level 1 This indicates that the two types of risks are in the first... A uniform compensation coefficient under each risk level. and The larger the value, the greater the harm to the combined cooling, heating and power microgrid when a risk occurs. Indicates that the user side is The load power that can be transferred during a certain period of time.

[0169] Within a preset scheduling period Within this period, the total economic compensation available to the user is The calculation is as follows:

[0170]

[0171] In this embodiment, power imbalance risk data can constrain the output of energy equipment in subsequent scheduling optimization, avoiding the disruption of the balance of the combined cooling, heating and power microgrid due to local energy curtailment or insufficient supply; cost deviation risk data can reflect the average level of economic deviation under different uncertain scenarios.

[0172] S203. Based on operating data, power imbalance risk data, and cost deviation risk data, determine the multi-objective optimization function;

[0173] Among them, the multi-objective optimization function is a function that characterizes the operating costs, environmental pollution costs, and energy supply reliability indicators of multiple energy devices.

[0174] In this step, functions characterizing operating costs and environmental pollution costs are constructed based on the operating data of multiple energy devices, and functions characterizing energy supply reliability indicators are constructed based on power imbalance risk data and cost deviation risk data.

[0175] For example, a multi-objective optimization function is used to express multiple objectives in the scheduling problem of combined cooling, heating and power microgrids as a solvable functional relationship model.

[0176] Operating costs may include fuel costs, electricity purchase costs, energy storage loss costs, equipment maintenance costs, and energy curtailment loss costs;

[0177] Environmental pollution costs are used to reflect the amount or cost of pollutant emissions, and can include the cost of carbon dioxide emissions, the cost of nitrogen oxide emissions, and other converted environmental costs.

[0178] Power supply reliability indicators are used to reflect the risk resistance capability of combined cooling, heating and power microgrids under the condition of meeting users' energy needs.

[0179] In one possible implementation, the power generation of the micro gas turbine, the charging and discharging power of the energy storage, the charging and discharging heat power of the thermal energy storage device, the waste heat distribution, the input heat of the absorption chiller, the cooling power, the power purchased and sold, and other load transfer quantities can be set as decision variables.

[0180] The multi-objective optimization function can simultaneously retain three independent objective optimization functions: operating cost, environmental pollution cost, and power supply reliability. The optimization objectives for the three independent objective optimization functions are: minimizing operating cost, minimizing environmental pollution cost, and maximizing power supply reliability (i.e., minimizing risk). The solution is then obtained by a pre-defined solver or algorithm model.

[0181] S204. Solve the multi-objective optimization function based on energy dispatch constraints to determine the energy dispatch strategy;

[0182] Among them, the energy dispatch strategy is used to instruct the energy optimization dispatch of multiple energy devices in a combined cooling, heating and power microgrid.

[0183] In this step, the multi-objective optimization function is solved in combination with energy dispatch constraints. Under the condition that the energy dispatch constraints are met, an energy dispatch strategy that can indicate the energy optimization dispatch of multiple energy devices in the combined cooling, heating and power microgrid is determined.

[0184] For example, an energy dispatch strategy is a data scheme that arranges the operation mode and output allocation of various energy devices in different dispatch periods. It may include start-stop commands and output values ​​of micro gas turbines in each period, charging and discharging power values ​​of photovoltaic cells or batteries, heat storage and release values ​​of heat storage tanks, distribution values ​​of recovered heat from waste heat recovery devices, and cooling capacity distribution ratio values ​​of absorption chillers, etc.

[0185] In one possible implementation, the Grey Wolf algorithm or a modified Grey Wolf algorithm can be used to solve the objective optimization function under energy dispatch constraints. During the solution process, initial candidate solutions or an initial population are first generated based on the energy dispatch constraints. Each candidate solution or initial population corresponds to a complete set of equipment time-series output schemes, i.e., the energy dispatch strategy. Subsequently, each candidate solution undergoes constraint verification, including whether equipment output exceeds upper and lower limits, whether changes between adjacent time periods exceed ramp-up limits, whether the energy storage state of charge exceeds limits, whether the electrical, thermal, and cold balance is satisfied, and whether the grid-connected power exchange exceeds the permissible range. Candidate solutions that do not meet the constraints can be repaired, penalized, or directly eliminated. For candidate solutions that meet the constraints, the values ​​of their multi-objective functions (including operating cost values, environmental pollution cost values, and energy supply reliability index values) are calculated. After multiple iterations, a non-dominated solution set or a comprehensive optimal solution is obtained.

[0186] When the output is a non-dominated solution set (i.e., a set of solutions that cannot be simultaneously outperformed by other solutions on all objectives in multi-objective optimization), further decision screening can be performed. For example, during periods of high supply demand, the solution with the best power supply reliability index and the second-best operating cost can be selected; in scenarios with high requirements for renewable energy consumption, the solution with a good balance between operating cost and environmental pollution cost can be selected; in scenarios with high cost control pressure, the solution with the lowest total cost within an acceptable risk range can be selected.

[0187] In one possible implementation, energy dispatch constraints include: energy balance constraints, equipment operation constraints, energy storage constraints, and interactive power constraints.

[0188] (1) Energy balance constraints include: electrical load constraints, heat load constraints and cooling load constraints. Electrical load constraints include electrical load demand values, heat load constraints include heat load demand values ​​and cooling load constraints include cooling load demand values.

[0189] For example, energy balance constraints are used to characterize the correspondence between energy supply and demand within a scheduling cycle. The electrical load demand value, heat load demand value, and cooling load demand value correspond to the electrical energy, heat energy, and cooling energy demand of the combined cooling, heating, and power microgrid in the same time period, respectively.

[0190] In one possible implementation, the electrical load constraint means that the sum of the electrical power output from all energy devices in the combined cooling, heating, and power (CCHP) microgrid must meet the park's electrical load demand:

[0191] 1) For storage batteries:

[0192] When the battery is discharging, the following constraints must be met:

[0193]

[0194] When the battery is charging, the following constraints must be met:

[0195]

[0196] in, This indicates the power generation capacity of the micro gas turbine. Indicates the power generation capacity of the ORC device. This indicates the power generation capacity of the wind turbine. This indicates the power generation capacity of the photovoltaic cell. This indicates the battery's power (positive when discharging). This indicates the power purchased from the upper-level power grid. Indicates discharge efficiency. Indicates charging efficiency. This indicates the electrical load of the industrial park.

[0197] 2) For ORC devices:

[0198] The power generation capacity of an ORC unit is determined by the waste heat from the micro gas turbine and the unit's own power generation efficiency, and must meet the following requirements:

[0199]

[0200]

[0201] in, Indicates the power generation capacity of the ORC device. Indicates the power generation efficiency of the ORC device. This indicates the waste heat from a micro gas turbine. This indicates the minimum power generation limit of the ORC device. This indicates the maximum power output limit of the ORC device.

[0202] In one possible implementation, the heat load constraint means that the heating power of the thermal energy equipment in the combined cooling, heating and power microgrid must meet both energy conservation and heat load requirements:

[0203]

[0204] in, Indicates the heating capacity of the ORC device. This indicates the heating capacity of the electric heating equipment. This indicates the park's heat load.

[0205] For ORC devices, the heating power must meet the following requirements:

[0206]

[0207]

[0208] in, Indicates the heating capacity of the ORC device. Indicates the power generation efficiency of the ORC device. This represents the heat loss coefficient of the ORC device. This indicates the minimum heating capacity of the ORC unit. This indicates the maximum heating power limit of the ORC device.

[0209] In one possible implementation, the cooling load constraint means that the sum of the cooling power outputs of the lithium bromide absorption chiller and the split air conditioners must meet the cooling load requirements of the park:

[0210]

[0211] in, This indicates the cooling capacity of a lithium bromide absorption chiller. This indicates the cooling capacity of a split-type air conditioner. This indicates the park's cooling load.

[0212] (2) Equipment operation constraints include operating power thresholds for multiple energy devices;

[0213] For example, equipment operation constraints are used to define the operating boundaries of multiple energy devices such as micro gas turbines, waste heat recovery devices, and absorption chillers, and their operating power thresholds are used to prevent the equipment from being in a state of over-rated output or low load and low efficiency for a long time.

[0214] In one possible implementation, the operating power of each energy device must be within the upper and lower limits of the operating power threshold:

[0215]

[0216] in, Indicates energy equipment Actual operating power Indicates energy equipment The lower limit of operating power, Indicates energy equipment The upper limit of operating power.

[0217] (3) Energy storage constraints include the energy storage thresholds corresponding to energy storage devices among multiple energy devices;

[0218] For example, energy storage constraints are used to limit the range of energy storage state changes of electrical energy storage devices or thermal energy storage devices. The energy storage threshold corresponds to the upper and lower limits of the range of energy storage state changes to prevent overcharging, over-discharging, and lifespan degradation.

[0219] In one possible implementation, for an energy storage device, namely a battery, its state of charge should be within the upper and lower limits of the corresponding energy storage threshold:

[0220]

[0221] in, and These represent the lower and upper limits of the battery's state of charge, respectively.

[0222] For thermal energy storage equipment, i.e., thermal storage tanks, the amount of heat that can be stored should be within the upper and lower limits of the corresponding thermal energy storage threshold:

[0223]

[0224] in, and These represent the lower and upper limits of the heat storage capacity of the heat storage tank, respectively.

[0225] (4) Interactive power constraints include the power threshold for interaction between the combined cooling, heating and power microgrid and the upper-level power grid.

[0226] For example, interactive power constraints are used to limit the range of interactive power between a combined cooling, heating and power microgrid and the upper-level grid, thereby meeting grid connection security and external dispatch requirements.

[0227] In one possible implementation, the interaction power between the combined cooling, heating and power microgrid and the upper-level grid... Must meet:

[0228]

[0229] in, and These represent the lower and upper limits of the interaction power, respectively.

[0230] In addition, for micro gas turbines, ramping constraints must also be met:

[0231]

[0232] in, and They represent Time and The power generation capacity of the micro gas turbine at any given time. and These represent the maximum downhill climbing rate and the maximum uphill climbing rate, respectively.

[0233] The optimized scheduling method for a combined cooling, heating, and power (CCHP) microgrid provided in this application first obtains the operating data and energy scheduling constraints of multiple energy devices in the CCHP microgrid. Then, based on the operating data, it determines the power imbalance risk data and cost deviation risk data. Next, based on the operating data, power imbalance risk data, and cost deviation risk data, it determines a multi-objective optimization function. Finally, it solves the multi-objective optimization function based on the energy scheduling constraints to determine the energy scheduling strategy. The energy scheduling strategy is used to instruct the optimized energy scheduling of multiple energy devices in the CCHP microgrid. In this embodiment, by acquiring the operational data of multiple energy devices in a combined cooling, heating, and power (CCHP) microgrid, and quantifying the uncertain risks during the operation of these devices into power imbalance risk data and cost deviation risk data, a multi-objective optimization function is constructed by combining the operational data, power imbalance risk data, and cost deviation risk data to characterize the operating costs, environmental pollution costs, and energy supply reliability indicators of the multiple energy devices. This transforms the multi-objective optimization scheduling problem of the CCHP microgrid under complex load fluctuations and random renewable energy output into a computable and iterative decision-making process. Finally, the multi-objective optimization function is solved based on energy scheduling constraints. This approach enables the determination of a more reasonable energy scheduling strategy by considering uncertain risks and the synergistic relationship between multiple energy devices, thereby effectively improving the accuracy, operational reliability, and multi-device synergy of the CCHP microgrid.

[0234] Based on the above embodiments, Figure 3 A flowchart illustrating the optimized scheduling method for a combined cooling, heating, and power microgrid provided in this application embodiment. Figure 2 .like Figure 3 As shown, the multi-objective optimization functions include: the operating cost objective function, the environmental pollution cost objective function, and the comprehensive risk objective function;

[0235] Accordingly, one possible implementation of step S203 above includes the following steps:

[0236] S301. Determine the objective function for operating costs based on the power data in the operating data, the maintenance costs of multiple energy devices, and the electricity purchase cost.

[0237] In this step, different energy devices generate different power and have different maintenance costs. Based on the power data and corresponding maintenance costs of each energy device in the operation data, and combined with the purchased power and purchase cost in the power data, an objective function for operation cost is constructed.

[0238] For example, power data includes power generation, heating power, and cooling power; maintenance cost is used to characterize the expenses incurred by the equipment during the current operating period due to maintenance, consumable wear and tear, and lifespan reduction; and electricity purchase cost is used to characterize the electricity expenditure corresponding to the purchase of electricity from the upper-level power grid.

[0239] The objective function for operating costs can be obtained by summing up the fuel consumption costs corresponding to the normal operation of multiple energy devices in each time period, the maintenance costs of multiple energy devices, the cost of wind curtailment, the cost of solar curtailment, the cost of purchasing electricity from the upstream grid, and the economic compensation available to users.

[0240] In one possible implementation, the runtime cost objective function is... It is expressed as follows:

[0241]

[0242] in, This represents the operating cost of a combined cooling, heating, and power (CCHP) microgrid over 24 hours. This indicates the unit price of natural gas (i.e., the cost of fuel per unit of electricity). This indicates the power generation capacity of the micro gas turbine. This indicates the power generation efficiency of a micro gas turbine. This indicates the number of conventional energy equipment (excluding ORC devices). Indicates conventional energy equipment single-time power, Indicates conventional energy equipment The unit power maintenance cost This indicates the power of the ORC device during a single time period. This indicates the unit power maintenance cost of the ORC device. This represents the cost coefficient for wind curtailment. Indicates the wind curtailment power in a single time period. This represents the cost coefficient for wasted solar power. Indicates the power of light discarded in a single time period. This indicates the unit price of electricity purchased from the superior power grid. This indicates the unit price of electricity sold by the superior power grid. This indicates the interaction power (i.e., the power purchased or the power sold) between the combined cooling, heating and power microgrid and the upper-level power grid. This indicates the economic compensation that the user can receive.

[0243] In one possible implementation, minimizing operating costs is used as the optimization objective of the operating cost objective function. The power generation of the micro gas turbine, the single-period power of conventional energy equipment, the single-period power of the ORC device, the curtailed solar power, the curtailed wind power, and the interaction power between the combined cooling, heating and power microgrid and the upper-level grid are taken as decision variables of the operating cost objective function. That is, the values ​​of the above decision variables are optimized to minimize the operating costs.

[0244] S302. Determine the environmental pollution cost objective function based on the power data, purchased electricity, and preset pollutant emission coefficients in the operating data.

[0245] In this step, the environmental pollution generated by the combined cooling, heating and power microgrid during operation mainly comes from the combustion of natural gas and indirect emissions from purchasing electricity from the upper-level grid. The power data and purchased electricity in the operation data are multiplied by the preset pollutant emission coefficients to construct the environmental pollution cost objective function.

[0246] For example, pollutant emission coefficients are used to characterize the level of carbon dioxide, nitrogen oxides, or sulfur oxide emissions per unit of electricity generated or per unit of electricity purchased, thereby quantifying the external costs of carbon emissions into optimizable target quantities. Specifically, these include: the pollutant emission coefficient of micro gas turbines, the pollutant emission coefficient of the upstream power grid, and the equivalent pollutant emission reduction coefficient per unit of power generated by ORC devices.

[0247] In one possible implementation, the environmental pollution cost objective function It is expressed as follows:

[0248]

[0249] in, This represents the environmental remediation cost of a combined cooling, heating, and power (CCHP) microgrid within 24 hours. Indicates the types and quantities of pollutants. This indicates that micro gas turbines reduce pollutants. The emission coefficient, This indicates that the upper-level power grid is responsible for pollutants. The emission coefficient, This indicates the electrical power purchased from the upstream power grid. This represents the equivalent pollutant emission reduction factor per unit power generated by the ORC device. Indicates the power generation capacity of the ORC device. Indicates pollutants The unit governance cost.

[0250] In one possible implementation, minimizing environmental pollution costs is taken as the optimization objective of the environmental pollution cost objective function. The power generation of the micro gas turbine, the power purchased from the upstream power grid, and the power generation of the ORC device in the above expression are taken as the decision variables of the environmental pollution cost objective function. That is, the values ​​of the above decision variables are optimized to minimize the environmental pollution costs.

[0251] S303. Determine the comprehensive risk objective function based on power imbalance risk data, cost deviation risk data, and preset weighting coefficients.

[0252] In this step, the power imbalance risk data and cost deviation risk data are weighted and summed according to the preset weighting coefficients to construct a comprehensive risk objective function.

[0253] In one possible implementation, the comprehensive risk objective function It is expressed as follows:

[0254]

[0255] in, and These represent power imbalance risk data. and cost deviation risk data The weighting coefficients can be set according to the importance attached to the safety, reliability, and economy of the combined cooling, heating, and power microgrid in actual applications, and .

[0256] In one possible implementation, minimizing the overall risk is used as the optimization objective of the overall risk objective function. The power imbalance risk data and cost deviation risk data in the above expression are used as decision variables. That is, the values ​​of the above decision variables are optimized to minimize the overall risk cost.

[0257] The optimized scheduling method for a combined cooling, heating, and power (CCHP) microgrid provided in this application first determines an operating cost objective function based on power data, maintenance costs of multiple energy devices, and electricity purchase costs from the operating data. Then, it determines an environmental pollution cost objective function based on power data, electricity purchase costs, and preset pollutant emission coefficients from the operating data. Finally, it determines a comprehensive risk objective function based on power imbalance risk data, cost deviation risk data, and preset weighting coefficients. In this embodiment, based on power data, electricity purchase costs, electricity purchase costs, and other data from the operating data, an operating cost objective function representing the operating costs of multiple energy devices, an environmental pollution cost objective function representing environmental pollution costs, and a comprehensive risk objective function representing energy supply reliability indicators are constructed respectively. These are then combined to obtain a multi-objective optimization function, which can effectively improve the adaptability of the optimized scheduling results to the actual operating state, reduce the probability of scheduling failures caused by power imbalances or cost deviations, improve risk resistance, and reduce cost expenditures and pollution emissions while ensuring energy supply reliability.

[0258] Based on the above embodiments, Figure 4 A flowchart illustrating the optimized scheduling method for a combined cooling, heating, and power microgrid provided in this application embodiment. Figure 3 .like Figure 4 As shown, one possible implementation of step S204 above includes the following steps:

[0259] S401. Based on energy scheduling constraints, the controllable scheduling parameters in the multi-objective optimization function are mapped using the chaotic mapping in the pre-set gray wolf algorithm to obtain multiple candidate energy scheduling strategies.

[0260] Each candidate energy dispatch strategy includes the values ​​of controllable dispatch parameters.

[0261] In this step, under the constraints of energy scheduling, the preset Grey Wolf algorithm is used to solve the objective optimization function. Based on the chaotic mapping, the controllable scheduling parameters in the multi-objective optimization function are mapped to obtain multiple candidate energy scheduling strategies including the values ​​of the controllable scheduling parameters.

[0262] For example, chaotic mapping is used to initialize controllable scheduling parameters that meet energy scheduling constraints to the candidate solution space, so that the candidate energy scheduling strategies maintain strong dispersion within the value range, thereby enhancing the global search capability.

[0263] Controllable scheduling parameters refer to the decision variables involved in scheduling solutions. Their numerical range is limited by energy scheduling constraints and mainly includes: micro gas turbine power generation, energy storage charging and discharging power, power purchase, heating power, and cooling power.

[0264] Candidate strategies represent a set of combined values ​​for controllable scheduling parameters within the same scheduling period.

[0265] In one possible implementation, compared to the standard Grey Wolf algorithm which uses a random method to generate the initial population (i.e. a set of initial candidate energy scheduling strategies), the preset Grey Wolf algorithm introduces Tent chaotic mapping on the basis of the standard Grey Wolf algorithm, which effectively improves the diversity and ergodicity of the initial population. By replacing the population position initialization process in the preset Grey Wolf algorithm with a chaotic sequence generation process, multiple initial candidate energy scheduling strategies can be obtained.

[0266] In one possible implementation, step S401 may include the following steps:

[0267] Step 1: Based on energy scheduling constraints, the controllable scheduling parameters in the multi-objective optimization function are sequentially mapped using the chaotic mapping in the pre-set Grey Wolf algorithm to obtain multiple chaotic sequences;

[0268] Each chaotic sequence includes a normalized value of a controllable scheduling parameter.

[0269] For example, chaotic mapping is used to transform controllable scheduling parameters in a multi-objective optimization function into a normalized chaotic sequence with ergodicity and randomness.

[0270] Chaotic sequences can be generated by Tent mapping, or by Logistic mapping, Sine mapping, or Chebyshev mapping, and the mapping results are constrained to between zero and one, thereby establishing a unified correspondence with scheduling variables of different dimensions.

[0271] In one possible implementation, a chaotic sequence in the [0,1] interval is generated using the Tent mapping. The sequence length is consistent with the encoding dimension of the individual gray wolf (i.e., the energy dispatch strategy of a complete CCHP microgrid). The Tent mapping formula is as follows:

[0272]

[0273] in, and This represents the adjacent chaotic sequence generated by the Tent mapping, with values ​​ranging from [0,1]. This represents the normalized value of the controllable scheduling parameters generated by the Tent mapping (not directly representing the power of the energy equipment).

[0274] Step 2: Perform power mapping on multiple chaotic sequences to obtain multiple candidate energy scheduling strategies;

[0275] Each candidate energy dispatch strategy includes power information for multiple energy devices in a combined cooling, heating and power microgrid.

[0276] For example, power mapping refers to linearly or piecewise scaling the normalized values ​​of each chaotic sequence, including controllable scheduling parameters, according to the rated capacity range of each energy device, thereby obtaining a power setting value that satisfies the scheduling boundary.

[0277] In one possible implementation, multiple energy devices include micro gas turbines, photovoltaic power generation equipment, energy storage equipment, absorption chillers, waste heat recovery devices, and equipment for interacting with the upper-level power grid.

[0278] Each dimension of the chaotic sequence corresponds to the active power, charging and discharging power, power purchased and sold, heating power, or cooling power of each energy device during the scheduling cycle.

[0279] In addition, to improve the feasibility of candidate strategies, the mapped power values ​​can be trimmed and corrected in conjunction with constraint boundaries to avoid equipment exceeding limits or imbalance between heating, cooling, and electricity.

[0280] In this embodiment, by first converting the controllable scheduling parameters into a chaotic sequence and then further converting them into specific power information, the scheduling solution can quickly form multiple feasible candidate energy scheduling strategies within a large search space. This can fully reflect the collaborative relationship between various energy devices in a combined cooling, heating and power microgrid, thereby improving the executability of the energy scheduling strategy and the global optimization effect.

[0281] S402. Determine the values ​​of the multi-objective optimization functions corresponding to the multiple candidate energy dispatch strategies.

[0282] In this step, the values ​​of the controllable scheduling parameters in each candidate energy scheduling strategy are substituted into the relational expression of the multi-objective optimization function to obtain the values ​​of the multi-objective optimization function corresponding to each candidate energy scheduling strategy.

[0283] For example, the values ​​corresponding to the multi-objective optimization function include: operating cost, environmental pollution cost, and comprehensive risk data, which are used to characterize the comprehensive suitability of candidate energy dispatch strategies in terms of operating cost, environmental pollution cost, and energy supply reliability indicators.

[0284] S403. The candidate energy scheduling strategy corresponding to the optimal value among the values ​​of the multi-objective optimization function shall be taken as the target energy scheduling strategy.

[0285] In this step, based on the optimization objective of the multi-objective optimization function, the optimal value among the possible values ​​of the multi-objective optimization function is determined, and the candidate energy scheduling strategy corresponding to the optimal value is taken as the target energy scheduling strategy.

[0286] In one possible implementation, the value of the multi-objective optimization function can be a fitness value. The fitness is calculated by substituting the values ​​of the controllable scheduling parameters in each candidate energy scheduling strategy into the relation of the multi-objective optimization function, and the target energy scheduling strategy is determined based on the size of the fitness value.

[0287] S404. Based on the target energy dispatch strategy, repeatedly iterate over other candidate energy dispatch strategies until the preset number of iterations is reached to obtain the energy dispatch strategy.

[0288] Among them, other candidate energy dispatch strategies are candidate energy dispatch strategies other than the target energy dispatch strategy among multiple candidate energy dispatch strategies.

[0289] In this step, the target energy scheduling strategy is used as the guiding position to update the positions of the remaining candidate energy scheduling strategies, so that the updated candidate energy scheduling strategies continue to satisfy the constraints. After each round of updates, the value of the multi-objective optimization function corresponding to the updated candidate energy scheduling strategy is recalculated until the preset number of iterations is reached, and the final energy scheduling strategy is output.

[0290] In one possible implementation, step S404 described above may include the following steps:

[0291] Step 1: Based on the control parameters and target energy scheduling strategy in the preset gray wolf algorithm, iteratively update other candidate energy scheduling strategies to obtain the updated candidate energy scheduling strategies.

[0292] For example, the target energy scheduling strategy is used to characterize the candidate energy scheduling strategy that achieves the optimal value of the multi-objective optimization function under energy scheduling constraints; other candidate energy scheduling strategies are used to characterize alternative energy scheduling strategies that are searched and corrected based on the target energy scheduling strategy.

[0293] The control parameters in the pre-defined Grey Wolf algorithm are used to adjust the search amplitude and convergence strength when the candidate scheduling results approach the target energy scheduling strategy. Their values ​​can be dynamically changed with the iteration process to take into account both global search and local convergence.

[0294] In one possible implementation, the baseline values ​​of each controllable scheduling parameter are first extracted based on the target energy scheduling strategy, and the position of other candidate energy scheduling strategies is corrected (i.e. iteratively updated) using the baseline values, so that the updated candidate energy scheduling strategies gradually approach the target energy scheduling strategy in terms of electricity, heat and cooling power allocation.

[0295] For example, the target energy scheduling strategy corresponds to the global leader in the pre-defined gray wolf algorithm. , ,as well as If we take this as the guiding energy scheduling strategy used to guide the population search in the current iteration, then the formula for calculating the candidate positions guided by the global leader is as follows:

[0296]

[0297] in, , ,as well as These represent the global leader in the preset Grey Wolf algorithm. , ,as well as These represent the candidate positions guided by the global leader. This indicates the current position of the individual gray wolf (i.e., the current energy scheduling strategy). , as well as These represent the position update coefficients, , ,as well as These represent random coefficients, used to increase the randomness of the search.

[0298] Location update coefficient , as well as Control parameters at the same time With random numbers (The value range is [0,1]) It is obtained through dynamic calculation, and the calculation formula is: .

[0299] The update formula for the candidate energy scheduling strategy based on candidate positions guided by the global leader is as follows:

[0300]

[0301] in, This indicates the updated candidate energy dispatch strategy. and These represent social learning factors and cognitive learning factors, respectively, used to balance the influence of global optimum and individual optimum. , ,as well as These represent the inertia weights that are dynamically adjusted based on the candidate positions guided by the global leader, and are calculated using the following formulas:

[0302]

[0303] Step 2: Update the control parameters according to the preset number of iterations to obtain the updated control parameters;

[0304] For example, the control parameters are updated in a nonlinear decreasing manner to improve the optimization efficiency of the pre-set Grey Wolf algorithm under complex coupled constraints.

[0305] In one possible implementation, control parameters The update formula is as follows:

[0306]

[0307] in, Indicates the current iteration number. This indicates the maximum number of iterations (i.e., the preset number of iterations). and These represent the control parameters. The initial and final values ​​(2 and 0 in this embodiment) make the control parameters The slow decrease in the early stages of iteration ensures that the algorithm has enough time to explore the whole system; the faster decrease in the later stages of iteration helps the algorithm converge to the optimal solution region quickly, thereby improving the overall optimization performance of the algorithm.

[0308] Step 3: Based on the updated control parameters and target energy dispatch strategy, iteratively update the updated candidate energy dispatch strategy. Repeat steps 2 and 3 until the preset number of iterations is reached to obtain the energy dispatch strategy.

[0309] For example, the updated candidate energy dispatch strategy is revised again using the updated control parameters, and the feasibility of the candidate energy dispatch strategy is verified after each round of revision to ensure that it meets the energy dispatch constraints. After repeated iterations, the final output energy dispatch strategy can be directly used to instruct the power allocation of micro gas turbines, energy storage devices, and cooling and heating conversion devices in the CCHP microgrid.

[0310] In this embodiment, by continuously correcting the candidate solution deviation through the above iterative update method and reducing local optimum stagnation, the adaptability of the final energy dispatch strategy to load fluctuations and equipment constraint changes can be improved, thereby enhancing the executability, stability and overall optimization effect of the energy dispatch strategy.

[0311] In addition, the performance of the pre-set Grey Wolf algorithm for solving multi-objective optimization functions was compared and verified. For example, a simulated microgrid with combined cooling, heating and power was constructed. The simulated microgrid included: wind turbines (rated power 80kW), photovoltaic cells (rated power 80kW), gas turbines (rated power generation 100kW), batteries (rated charging / discharging power 40kW), ORC devices (rated power generation 180kW), energy storage devices and other energy equipment, and the operating cycle of the simulated microgrid was set to 24 hours.

[0312] Under the above settings, the Pareto front solution set (corresponding to multiple candidate energy scheduling strategies) generated based on the preset gray wolf algorithm is evenly distributed, converges quickly, and achieves a good balance between operating costs and environmental pollution cost objectives. Furthermore, the risk probability decreases significantly after optimization, with the probability of each risk period being less than 20%, thus achieving synergistic optimization of economy, environmental protection, and operational safety.

[0313] Figure 5 This is a schematic diagram of load forecasting for a combined cooling, heating, and power (CCHP) simulated microgrid provided in an embodiment of this application. For example... Figure 5 As shown, load forecasting for a combined cooling, heating and power (CCHP) simulated microgrid includes cooling load forecasting, heating load forecasting, and electrical load forecasting.

[0314] Figure 6 This is a schematic diagram illustrating the output prediction of wind turbines and photovoltaic cells in a combined cooling, heating, and power (CCHP) simulated microgrid provided in this application embodiment. (See attached diagram.) Figure 6 As shown, during the 24-hour simulated microgrid operation cycle, the output power of the wind turbine fluctuates significantly, while the output power of the photovoltaic cell shows a trend of first increasing and then decreasing.

[0315] Figure 7 This is a schematic diagram illustrating power dispatching of power supply equipment in a combined cooling, heating, and power (CCHP) simulated microgrid, provided as an embodiment of this application. For example... Figure 7 As shown, the power supply equipment includes: a higher-level power grid, energy storage devices, and micro gas turbines. Scheduling optimization is performed with the goal of minimizing operating costs. During peak electricity price periods (12-15 and 18-21), the micro gas turbines maintain full-load operation, as their self-generation cost is lower than the purchased electricity price, and full-load operation offers higher energy efficiency. During off-peak periods (such as 1-6 and 23-24), the simulated microgrid prioritizes power supply from the higher-level power grid, thereby achieving optimal economic efficiency. It should be noted that... Figure 7 The output power of wind turbines and photovoltaic cells is not included in the calculation; it is directly assigned by the corresponding value of the output prediction.

[0316] Figure 8 This is a schematic diagram of the operating curve of a battery in a combined cooling, heating, and power (CCHP) simulated microgrid provided in an embodiment of this application. Figure 8 As shown, scheduling optimization is performed with the goal of minimizing operating costs. Batteries are charged during periods of lower electricity prices (periods 1-9 and 16-17) and discharged during periods of higher electricity prices (periods 11-15 and 18-22). This "peak shaving and valley filling" strategy effectively improves the overall economic efficiency of the simulated microgrid. Furthermore, during periods of high electricity prices, prioritizing the use of micro gas turbines and batteries for power supply and selling excess energy to the upstream grid achieves dual cost savings and efficiency gains.

[0317] Figure 9This is a schematic diagram of the operating curve of a battery in a combined cooling, heating, and power (CCHP) simulated microgrid provided in an embodiment of this application. Figure 9 As shown, scheduling optimization with the goal of minimizing environmental pollution costs was carried out. In order to minimize pollutant emissions and increase the usage frequency of micro gas turbines, the total power generation reached 1508kWh, which is significantly higher than 1303kWh under the goal of minimizing operating costs. Furthermore, it effectively avoided pollution emissions generated during the process of purchasing electricity from and selling electricity to the upper-level grid.

[0318] Figure 10 This is a schematic diagram illustrating the risk probability of a simulated microgrid with combined cooling, heating and power (CCHP) provided in an embodiment of this application before scheduling optimization. Figure 11 This is a schematic diagram illustrating the risk probability of a simulated microgrid with combined cooling, heating, and power (CCHP) provided in an embodiment of this application after scheduling optimization. For example... Figure 10 and Figure 11 As shown, the introduction of user economic compensation significantly reduces the overall risk probability of the microgrid after users adjust their load power consumption, with all risk probabilities below 20% throughout the entire period, thus improving system stability.

[0319] Figure 12 This is a schematic diagram illustrating the optimized scheduling of a combined cooling, heating and power (CCHP) simulated microgrid using the standard grey wolf algorithm, as provided in an embodiment of this application. Figure 13 This is a schematic diagram illustrating the optimized scheduling of a combined cooling, heating, and power (CCHP) simulated microgrid using a preset gray wolf algorithm, as provided in an embodiment of this application. Figure 12 and Figure 13 As shown, under the optimization objectives of minimizing operating cost and minimizing environmental pollution cost, the pre-defined Grey Wolf algorithm exhibits a wider overall distribution of box-and-whisker graph results, indicating stronger search space coverage and higher solution set diversity. Regarding the operating cost objective, the pre-defined Grey Wolf algorithm can find lower optimal values, but the results fluctuate more significantly, showing slightly weaker stability than the standard Grey Wolf algorithm. For the environmental pollution cost objective, the pre-defined Grey Wolf algorithm's box-and-whisker graph shows a downward shift in the overall size of the boxes, and a significant decrease in the median (the value corresponding to the horizontal line in the middle of the box in the graph), indicating superior optimization performance on this objective. In summary, the main advantages of the pre-defined Grey Wolf algorithm lie in its enhanced global search capability and the breadth of the Pareto solution set distribution, rather than exhibiting absolute mean or median optimality on all single objectives. Furthermore, using the pre-defined Grey Wolf algorithm improves the efficiency of generating feasible solutions and significantly shortens the convergence time.

[0320] The optimized scheduling method for combined cooling, heating, and power (CCHP) microgrids provided in this application firstly maps the controllable scheduling parameters in the multi-objective optimization function using chaotic mapping in the pre-defined Grey Wolf algorithm, based on energy scheduling constraints, to obtain multiple candidate energy scheduling strategies. Next, the values ​​of the multi-objective optimization function corresponding to each candidate energy scheduling strategy are determined. Then, the candidate energy scheduling strategy corresponding to the optimal value among these values ​​is taken as the target energy scheduling strategy. Finally, based on the target energy scheduling strategy, other candidate energy scheduling strategies are iterated repeatedly until a pre-defined number of iterations is reached to obtain the final energy scheduling strategy. In this embodiment, chaotic mapping enhances the diversity of candidate energy scheduling strategies, reduces the probability of getting trapped in local optima during the solution process, and utilizes an iterative update mechanism to continuously approach a better energy scheduling strategy. Since the target energy scheduling strategy guides other candidate strategies to search collaboratively under constraints, it improves the stability and convergence of the multi-objective optimization function solution, thereby obtaining an energy scheduling strategy that better meets the operational needs of the CCHP microgrid.

[0321] Based on the above embodiments, the following are embodiments of the apparatus involved in this application:

[0322] Figure 14 A schematic diagram of the structure of the optimized scheduling device for a combined cooling, heating, and power microgrid provided in this application embodiment. (See attached diagram.) Figure 14 As shown, the optimized dispatching device 1400 for the combined cooling, heating and power microgrid includes:

[0323] The acquisition module 1401 is used to acquire the operating data and energy dispatch constraints of multiple energy devices in the combined cooling, heating and power microgrid;

[0324] The first determining module 1402 is used to determine power imbalance risk data and cost deviation risk data based on operating data;

[0325] The second determining module 1403 is used to determine a multi-objective optimization function based on operating data, power imbalance risk data, and cost deviation risk data. The multi-objective optimization function is a function that characterizes the operating cost, environmental pollution cost, and energy supply reliability index of multiple energy devices.

[0326] The third determining module 1404 is used to solve the multi-objective optimization function based on energy dispatch constraints and determine the energy dispatch strategy. The energy dispatch strategy is used to instruct the energy optimization dispatch of multiple energy devices in the combined cooling, heating and power microgrid.

[0327] In one or more embodiments, the operational data includes: the expected power of curtailment risk and the expected power of power shortage risk under multiple risk levels, as well as the actual operating cost and planned operating cost under multiple uncertain scenarios;

[0328] Accordingly, the first determining module 1402 is specifically used for:

[0329] Based on the preset penalty coefficient, the expected power of the curtailment risk and the expected power of the power shortage risk under multiple risk levels are weighted and summed to obtain the power imbalance risk data.

[0330] Cost deviation risk data is obtained by taking the ratio of the difference between the actual operating cost and the planned operating cost under multiple uncertain scenarios to the total number of uncertain scenarios.

[0331] In one or more embodiments, the multi-objective optimization function includes: an operating cost objective function, an environmental pollution cost objective function, and a comprehensive risk objective function;

[0332] Accordingly, the second determining module 1403 is specifically used for:

[0333] Based on the power data in the operating data, the maintenance costs of multiple energy devices, and the electricity purchase cost, determine the objective function for operating costs;

[0334] Based on the power data, electricity purchase, and preset pollutant emission coefficients in the operating data, determine the objective function for environmental pollution costs;

[0335] Based on power imbalance risk data, cost deviation risk data, and preset weighting coefficients, a comprehensive risk objective function is determined.

[0336] In one or more embodiments, the third determining module 1404 is specifically used for:

[0337] Based on energy scheduling constraints, the chaotic mapping in the pre-defined Grey Wolf algorithm is used to map the controllable scheduling parameters in the multi-objective optimization function to obtain multiple candidate energy scheduling strategies. Each candidate energy scheduling strategy includes the value of the controllable scheduling parameters.

[0338] Determine the values ​​of the multi-objective optimization functions corresponding to multiple candidate energy dispatch strategies;

[0339] The candidate energy scheduling strategy corresponding to the optimal value among the values ​​of the multi-objective optimization function is taken as the target energy scheduling strategy.

[0340] Based on the target energy dispatch strategy, other candidate energy dispatch strategies are iterated repeatedly until a preset number of iterations is reached to obtain the energy dispatch strategy. Among them, other candidate energy dispatch strategies are candidate energy dispatch strategies other than the target energy dispatch strategy among multiple candidate energy dispatch strategies.

[0341] In one or more embodiments, the third determining module 1404, based on the target energy scheduling strategy, iterates over other candidate energy scheduling strategies until a preset number of iterations is reached to obtain the energy scheduling strategy, specifically for:

[0342] S1. Based on the control parameters and target energy scheduling strategy in the preset gray wolf algorithm, other candidate energy scheduling strategies are iteratively updated to obtain the updated candidate energy scheduling strategy.

[0343] S2, update the control parameters according to the preset number of iterations to obtain the updated control parameters;

[0344] S3. Based on the updated control parameters and target energy dispatch strategy, iteratively update the updated candidate energy dispatch strategy, repeating steps S2-S3 until the preset number of iterations is reached to obtain the energy dispatch strategy.

[0345] In one or more embodiments, the third determining module 1404, based on energy scheduling constraints, uses the chaotic mapping in the preset Grey Wolf algorithm to map the controllable scheduling parameters in the multi-objective optimization function to obtain multiple candidate energy scheduling strategies, specifically used for:

[0346] Based on energy scheduling constraints, the chaotic mapping in the pre-defined Grey Wolf algorithm is used to perform sequence mapping on the controllable scheduling parameters in the multi-objective optimization function, resulting in multiple chaotic sequences. Each chaotic sequence includes the normalized values ​​of the controllable scheduling parameters.

[0347] Power mapping is performed on multiple chaotic sequences to obtain multiple candidate energy dispatch strategies. Each candidate energy dispatch strategy includes power information of multiple energy devices in a combined cooling, heating and power microgrid.

[0348] The optimized scheduling device for combined cooling, heating and power microgrids provided in this embodiment can execute the methods provided in the above-described method embodiments. Its implementation principle and technical effects are similar, and will not be described in detail here.

[0349] Figure 15 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 15 As shown, the electronic device 1500 includes: a processor 1501, a memory 1502, and a bus 1503;

[0350] The memory 1502 is used to store the computer-executed instructions of the processor 1501;

[0351] The processor 1501 is configured to execute the technical solutions of any of the foregoing method embodiments by executing computer execution instructions.

[0352] The specific implementation process of processor 1501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0353] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0354] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0355] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0356] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0357] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0358] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0359] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0360] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0361] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0362] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0363] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory (RAM), magnetic disks, or optical disks.

[0364] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0365] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. An optimized scheduling method for a combined cooling, heating and power (CCHP) microgrid, characterized in that, include: To obtain operational data and energy dispatch constraints of multiple energy devices in a combined cooling, heating and power microgrid; Based on the aforementioned operational data, power imbalance risk data and cost deviation risk data are determined; Based on the operating data, the power imbalance risk data, and the cost deviation risk data, a multi-objective optimization function is determined. The multi-objective optimization function is a function that characterizes the operating cost, environmental pollution cost, and energy supply reliability index of the multiple energy devices. The multi-objective optimization function is solved based on the energy dispatch constraints to determine the energy dispatch strategy, which is used to instruct the energy optimization dispatch of multiple energy devices in the combined cooling, heating and power microgrid.

2. The method according to claim 1, characterized in that, The operational data includes: the expected power of energy curtailment risk and the expected power of power shortage risk under multiple risk levels, as well as the actual operating cost and planned operating cost under multiple uncertain scenarios; Accordingly, determining the power imbalance risk data and cost deviation risk data based on the operational data includes: The expected power of the energy curtailment risk and the expected power of the power shortage risk under the multiple risk levels are weighted and summed according to the preset penalty coefficient to obtain the power imbalance risk data. The cost deviation risk data is obtained by taking the ratio of the total number of uncertain scenarios in which the difference between the actual operating cost and the planned operating cost exists under the multiple uncertain scenarios.

3. The method according to claim 2, characterized in that, The multi-objective optimization function includes: an operating cost objective function, an environmental pollution cost objective function, and a comprehensive risk objective function; Accordingly, determining the multi-objective optimization function based on the operating data, the power imbalance risk data, and the cost deviation risk data includes: The objective function for operating costs is determined based on the power data in the operating data, the maintenance costs of the multiple energy devices, and the electricity purchase costs. The environmental pollution cost objective function is determined based on the power data, electricity purchase amount, and preset pollutant emission coefficient in the operating data. The comprehensive risk objective function is determined based on the power imbalance risk data, the cost deviation risk data, and the preset weighting coefficients.

4. The method according to any one of claims 1-3, characterized in that, The process of solving the multi-objective optimization function based on energy scheduling constraints to determine the energy scheduling strategy includes: Based on the energy scheduling constraints, the controllable scheduling parameters in the multi-objective optimization function are mapped using the chaotic mapping in the preset gray wolf algorithm to obtain multiple candidate energy scheduling strategies. Each candidate energy scheduling strategy includes the value of the controllable scheduling parameters. Determine the values ​​of the multi-objective optimization functions corresponding to the multiple candidate energy scheduling strategies; The candidate energy scheduling strategy corresponding to the optimal value among the values ​​of the multi-objective optimization function is taken as the target energy scheduling strategy; Based on the target energy scheduling strategy, other candidate energy scheduling strategies are iterated repeatedly until a preset number of iterations is reached to obtain the energy scheduling strategy. The other candidate energy scheduling strategies are candidate energy scheduling strategies other than the target energy scheduling strategy among the multiple candidate energy scheduling strategies.

5. The method according to claim 4, characterized in that, The step of iteratively iterating over other candidate energy scheduling strategies based on the target energy scheduling strategy until a preset number of iterations is reached to obtain the energy scheduling strategy includes: S1, based on the control parameters in the preset gray wolf algorithm and the target energy scheduling strategy, iteratively update other candidate energy scheduling strategies to obtain the updated candidate energy scheduling strategy; S2, update the control parameters according to the preset number of iterations to obtain the updated control parameters; S3. Based on the updated control parameters and the target energy scheduling strategy, iteratively update the updated candidate energy scheduling strategy, repeating steps S2-S3 until the preset number of iterations is reached, to obtain the energy scheduling strategy.

6. The method according to claim 4, characterized in that, Based on the energy scheduling constraints, the controllable scheduling parameters in the multi-objective optimization function are mapped using the chaotic mapping in the preset Grey Wolf algorithm to obtain multiple candidate energy scheduling strategies, including: Based on the energy scheduling constraints, the controllable scheduling parameters in the multi-objective optimization function are sequentially mapped using the chaotic mapping in the preset gray wolf algorithm to obtain multiple chaotic sequences, each of which includes the normalized value of the controllable scheduling parameters. Power mapping is performed on the multiple chaotic sequences to obtain multiple candidate energy scheduling strategies. Each candidate energy scheduling strategy includes power information of multiple energy devices in the combined cooling, heating and power microgrid.

7. The method according to any one of claims 1-3, characterized in that, The energy dispatch constraints include: energy balance constraints, equipment operation constraints, energy storage constraints, and interactive power constraints. The energy balance constraints include: electrical load constraints, thermal load constraints, and cooling load constraints. The electrical load constraints include electrical load demand values, the thermal load constraints include thermal load demand values, and the cooling load constraints include cooling load demand values. The equipment operation constraints include the operating power thresholds corresponding to the plurality of energy devices; The energy storage constraints include the energy storage thresholds corresponding to the energy storage devices among the plurality of energy devices; The interactive power constraint includes the power threshold for the interaction between the combined cooling, heating and power microgrid and the upper-level power grid.

8. An optimized dispatching device for a combined cooling, heating and power microgrid, characterized in that, include: The acquisition module is used to acquire the operating data and energy dispatch constraints of multiple energy devices in a combined cooling, heating and power microgrid; The first determining module is used to determine power imbalance risk data and cost deviation risk data based on the operating data; The second determining module is used to determine a multi-objective optimization function based on the operating data, the power imbalance risk data, and the cost deviation risk data. The multi-objective optimization function is a function that characterizes the operating cost, environmental pollution cost, and energy supply reliability index of the multiple energy devices. The third determining module is used to solve the multi-objective optimization function based on the energy dispatch constraints and determine the energy dispatch strategy. The energy dispatch strategy is used to instruct the energy optimization dispatch of multiple energy devices in the combined cooling, heating and power microgrid.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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