A near-zero carbon park cold and hot electricity hybrid energy combined optimization scheduling system and method

Abstract

The application provides a near-zero carbon park cold-heat-power hybrid energy combined optimization scheduling system and method, relates to the technical field of energy scheduling, and comprises the following modules: a constraint system construction module; a load time sequence prediction module, which is used for constructing a multi-time scale load demand sequence; a power supply boundary construction module, which is used for constructing a multi-source power supply boundary set; a scheduling model construction module, which is used for constructing a cold-heat-power combined optimization scheduling model with the minimum system comprehensive operation cost as the target; a solving module, which is used for obtaining an optimal output scheme of each energy equipment and an energy storage charging and discharging strategy; and a correction scheduling module, which is used for rolling correction scheduling according to actual operation deviation. The application solves the technical problem that the prior art mainly adopts a single energy or a weakly coupled optimization model, the energy conversion relationship of the cold-heat-power hybrid energy cannot be fully described, the scheduling result cannot reflect the real multi-energy system operation mechanism, and the energy utilization efficiency is low.

Description

Technical Field

[0001] This invention relates to the field of energy dispatching technology, specifically to a near-zero carbon industrial park hybrid energy joint optimization dispatching system and method. Background Technology

[0002] With the continuous advancement of dual-carbon goals, industrial parks, as the core carriers of energy consumption and carbon emissions, are gradually transforming their energy systems from traditional single-energy supply models to integrated energy systems that combine multiple energy sources and coordinate cooling, heating, and power. Near-zero carbon parks typically integrate distributed photovoltaic, wind power, combined cooling, heating, and power (CCHP), energy storage, and various types of energy loads, forming a complex multi-energy flow coupled system. However, such systems exhibit significant randomness, uncertainty, and strong coupling characteristics in actual operation, making the scheduling problem exhibit high-dimensionality, strong constraints, and nonlinear features.

[0003] In existing technologies, energy dispatching in industrial parks is typically based on day-ahead or near-real-time optimization models, focusing on economic optimization or single-objective optimization, and incorporating load forecasting results for planned dispatching. Although existing technologies have introduced multi-energy complementarity and energy storage regulation mechanisms, the overall approach remains primarily "static optimization plus local correction," making it difficult to adapt to dynamic changes in complex environments. Specifically, existing technologies often employ single-energy or weakly coupled optimization models, failing to fully characterize the energy conversion relationships between cooling, heating, and electricity. This results in dispatching results that do not reflect the actual operating mechanisms of multi-energy systems, leading to low energy utilization efficiency and significant energy redundancy or waste. Summary of the Invention

[0004] This application provides a near-zero carbon industrial park integrated optimization scheduling system and method for cooling, heating and power hybrid energy, which aims to solve the technical problem that existing technologies mostly use single energy or weakly coupled optimization models, which cannot fully characterize the energy conversion relationship of cooling, heating and power hybrid energy, and the scheduling results cannot reflect the actual operation mechanism of multi-energy systems, thus leading to low energy utilization efficiency.

[0005] The first aspect disclosed in this application provides a near-zero carbon park hybrid energy joint optimization scheduling system, which includes: a constraint system construction module, used to construct a multi-level integrated energy twin model based on energy topology on a digital model of the target park's energy system, and establish a power balance constraint system and an equipment operation constraint system; a load time series prediction module, used to perform time series prediction of the park's load based on historical meteorological data and historical operating data, and construct a multi-time scale load demand sequence; and an energy supply boundary construction module, used to combine distributed photovoltaic, distributed wind power and gas... Based on the operating characteristics of the combined cooling, heating, and power (CCHP) units, a multi-source energy supply boundary set is constructed. A scheduling model construction module is used to construct a combined cooling, heating, and power optimization scheduling model with the objective of minimizing the overall system operating cost, based on the multi-timescale load demand sequence, the multi-source energy supply boundary set, the power balance constraint system, and the equipment operation constraint system. A solution module is used to solve the combined cooling, heating, and power optimization scheduling model using a mixed integer optimization algorithm to obtain the optimal output scheme and energy storage charging and discharging strategy for each energy device. A correction scheduling module is used to distribute the optimal output scheme and energy storage charging and discharging strategy to each energy device for execution and to perform rolling correction scheduling based on actual operating deviations.

[0006] The second aspect of this application discloses a method for the joint optimization scheduling of hybrid energy (cooling, heating, and power) in near-zero carbon industrial parks. This method is implemented through the aforementioned joint optimization scheduling system for hybrid energy in near-zero carbon industrial parks. The method includes: constructing a multi-level integrated energy twin model based on energy topology relationships on a digital model of the target park's energy system, establishing a power balance constraint system and an equipment operation constraint system; performing time-series load forecasting of the park based on historical meteorological and operational data, constructing a multi-timescale load demand sequence; constructing a multi-source energy supply boundary set by combining the operating characteristics of distributed photovoltaic, distributed wind power, and gas-fired combined heat and power units; constructing a joint optimization scheduling model for cooling, heating, and power with the objective of minimizing the overall system operating cost based on the multi-timescale load demand sequence, the multi-source energy supply boundary set, the power balance constraint system, and the equipment operation constraint system; solving the joint optimization scheduling model for cooling, heating, and power using a mixed integer optimization algorithm to obtain the optimal output scheme and energy storage charging / discharging strategy for each energy device; distributing the optimal output scheme and energy storage charging / discharging strategy to each energy device for execution, and performing rolling correction scheduling based on actual operating deviations.

[0007] One or more technical solutions provided in this application have at least the following beneficial effects: By constructing a multi-level integrated energy twin model based on energy topology and introducing power balance constraints and equipment operation constraints, the originally dispersed energy system is transformed into a computable and optimizable unified network model, providing a reliable foundation for subsequent optimization. By introducing historical meteorological and operational data to establish a load-meteorological mapping relationship and constructing multi-timescale load demand sequences, the impact of meteorological changes on the park's overall load can be effectively captured, improving the temporal consistency and accuracy of load forecasting. By constructing the maximum available output boundaries of photovoltaic and wind power and the adjustable output range of CCHP units, and combining this with the grid interaction boundary to form a unified multi-source energy supply boundary set, the system's overall performance is effectively improved. The system adapts to the intermittency and uncertainty of renewable energy; based on load demand and energy supply boundaries, a combined cooling, heating, and power optimization model is constructed with the goal of minimizing the overall system operating cost. This model is combined with power balance and equipment constraints to form a complete optimization system, achieving coordinated scheduling and unified economic optimization among multiple energy sources, significantly reducing the overall operating cost of the park; a mixed-integer optimization algorithm is used to solve the model, achieving the optimal configuration of the output of each energy device and energy storage strategy, ensuring the engineering feasibility of the scheduling scheme; by collecting operating data in real time and performing deviation analysis with the prediction results, a rolling correction scheduling mechanism is constructed, significantly improving the robustness and stability of the system under uncertain environments.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of a near-zero carbon industrial park hybrid energy joint optimization scheduling system provided in an embodiment of this application.

[0010] Figure 2 This is a schematic diagram of a near-zero carbon industrial park hybrid energy joint optimization scheduling method provided in an embodiment of this application.

[0011] Figure labeling: Constraint system construction module 10, load time series prediction module 20, energy supply boundary construction module 30, scheduling model construction module 40, solution module 50, and corrected scheduling module 60. Detailed Implementation

[0012] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0013] Example 1, as Figure 1As shown in the figure, this application provides a near-zero carbon park hybrid energy joint optimization scheduling system, which includes: The constraint system construction module 10 is used to construct a multi-level integrated energy twin model based on the energy topology relationship on the digital model of the target park's energy system, and establish a power balance constraint system and an equipment operation constraint system; the load time series prediction module 20 is used to predict the park's load time series based on historical meteorological data and historical operating data, and construct a multi-time scale load demand sequence; the energy supply boundary construction module 30 is used to construct a multi-source energy supply boundary set by combining the operating characteristics of distributed photovoltaic, distributed wind power and gas combined heat and power units; the scheduling model construction module 40 is used to construct a combined cooling, heating and power optimization scheduling model with the goal of minimizing the overall system operating cost based on the multi-time scale load demand sequence, the multi-source energy supply boundary set, the power balance constraint system and the equipment operation constraint system; the solution module 50 is used to solve the combined cooling, heating and power optimization scheduling model using a mixed integer optimization algorithm to obtain the optimal output scheme and energy storage charging and discharging strategy for each energy device; the correction scheduling module 60 is used to distribute the optimal output scheme and energy storage charging and discharging strategy to each energy device for execution, and to perform rolling correction scheduling based on actual operating deviations.

[0014] Furthermore, the constraint system construction module 10 includes: The first unit partitioning node is used to divide distributed photovoltaic power generation units, distributed wind power generation units, gas-fired combined heat and power (CHP) units, and external grid interface units into energy production layer component sets based on the system structure of the target park's energy system. The second unit partitioning node is used to divide electric chiller units, absorption chiller units, heat pump units, and energy storage units, thermal storage units, and cold storage units into energy conversion and storage layer component sets. The load partitioning node is used to divide the park's electrical load, heat load, and cold load into energy consumption layer component sets. The energy topology relationship construction node is used to construct the energy topology relationship based on the energy flow direction between the energy production layer component sets, energy conversion and storage layer component sets, and energy consumption layer component sets. The twin model construction node is used to inject equipment thermoelectric conversion characteristic parameters and capacity parameters into the energy topology relationship to generate the multi-level integrated energy twin model.

[0015] Furthermore, the load time-series prediction module 20 includes: The mapping relationship establishment node is used to extract historical electricity load data, historical heat load data, and historical cold load data of the target park based on the historical operation data, and establish a mapping relationship between load and meteorological variables by combining the historical meteorological data; the input calculation node is used to input and calculate the meteorological forecast data for the future scheduling cycle, and output the electricity load forecast sequence, heat load forecast sequence, and cold load forecast sequence respectively; the resampling processing node is used to perform time-scale synchronization and resampling processing on the electricity load forecast sequence, heat load forecast sequence, and cold load forecast sequence to obtain a multi-time-scale load demand sequence with a predetermined time step.

[0016] Furthermore, the energy supply boundary construction module 30 includes: The output boundary acquisition node is used to calculate the maximum available output boundary of the distributed photovoltaic power generation unit and the distributed wind power generation unit based on the meteorological forecast data; the output range acquisition node is used to determine the adjustable output range based on the rated capacity and operating efficiency of the gas-fired combined heat and power (CHP) unit; the interaction boundary acquisition node is used to form the external power interaction boundary by combining the power purchase and sale capabilities of the external power grid interface unit; and the constraint integration node is used to constrain and integrate the maximum available output boundary, the adjustable output range, and the external power interaction boundary to generate the multi-source energy supply boundary set.

[0017] Furthermore, the overall operating cost of the system includes at least energy purchase costs, carbon emission costs, equipment operation and maintenance costs, and renewable energy curtailment penalty costs.

[0018] Furthermore, the formula for calculating the energy purchase cost is as follows:

[0019] in, For energy purchase costs, The total number of time periods within the scheduling period. The duration of a single time period. , They are respectively Electricity purchase price and electricity sales price for different time periods , They are respectively The time period is determined by the power purchased and sold from the external power grid. This refers to the unit price of natural gas. for Natural gas consumption during a given period.

[0020] Furthermore, the formula for calculating the carbon emission cost is as follows:

[0021] in, For carbon emission costs, For carbon trading prices, This represents the carbon emission coefficient of natural gas. The carbon emission coefficient of electricity from the external power grid. for Natural gas consumption during a given period for Power purchased during specific time periods.

[0022] Furthermore, the power balance constraint system includes electrical power balance constraints and thermal power balance constraints; The power balance constraint is:

[0023] in, , They are respectively Solar and wind power output during different time periods for CCHP unit power generation during the period , They are respectively The charging and discharging power of the time-limited energy storage unit. for The park's electrical load during certain periods for Power consumption of the time-limited electric chiller for Power consumption of heat pump during certain time periods; The thermal power balance constraint is:

[0024] in, for The waste heat recovery power of the CCHP unit during the time period for Heat output of gas-fired boilers during specific time periods for Heat pump power output during specific time periods , They are respectively The heat storage capacity and heat release capacity of the time-segmented thermal storage unit. for Park heat load during the period for Heat consumption of time-period absorption chillers.

[0025] Furthermore, the revised scheduling module 60 includes: The re-solution node is used to collect the actual operation data of the park in real time and calculate the deviation with the predicted scheduling results. When the deviation exceeds the preset threshold, the current actual operation status is used as the initial condition for optimization. The cold, heat and power joint optimization scheduling model is reconstructed and updated for the remaining scheduling cycle, and the solution is re-solved. The updated scheduling results are then sent to each energy device for execution.

[0026] Through the detailed description of the combined optimal scheduling method of cooling, heating and power hybrid energy in a near-zero carbon industrial park, those skilled in the art will clearly understand the combined optimal scheduling system of cooling, heating and power hybrid energy in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to in the method section.

[0027] Example 2, based on the same inventive concept as the near-zero carbon park hybrid energy joint optimization scheduling system for cooling, heating and power in the aforementioned examples, such as... Figure 2 As shown in the embodiment of this application, a method for joint optimization scheduling of hybrid energy (cooling, heating, and power) in near-zero carbon industrial parks is provided. The method includes: Based on the digital model of the target park's energy system, a multi-level integrated energy twin model based on energy topology is constructed, and a power balance constraint system and an equipment operation constraint system are established.

[0028] Based on the digital model of the target park's energy system, a multi-level integrated energy twin model based on energy topology is first constructed. This energy twin model is a virtual system that digitally maps the park's real energy system, synchronously reflecting equipment status and energy flow relationships. Specifically, distributed photovoltaic, wind power, combined heat and power (CCHP) units within the park and their external grid interfaces are classified as energy production layer components; electric chillers, absorption chillers, heat pumps, and energy, heat, and cold storage units are classified as energy conversion and storage layer components; and electrical load, heat load, and cold load are classified as energy consumption layer components. Based on this, a topological connection structure is constructed according to energy flow relationships, embedding equipment capacity parameters, energy conversion efficiency, and thermoelectric coupling coefficients into the topological structure. Simultaneously, a power balance constraint system is established to ensure consistent supply and demand across different time periods; and an equipment operation constraint system is established, including upper and lower limits for output, efficiency variations, and energy storage charging and discharging limits, providing a physical constraint basis for subsequent optimization.

[0029] Based on historical meteorological and operational data, load time series forecasts are made for the park, and a multi-time scale load demand sequence is constructed.

[0030] Based on historical meteorological and operational data, time-series forecasts of the park's cooling, heating, and electrical loads are conducted to construct multi-timescale load demand sequences. Multi-timescale refers to simultaneously considering hourly, intraday, and even cross-day variation characteristics to improve dispatch adaptability. First, historical electrical, heating, and cooling load data are extracted and combined with meteorological variables such as temperature, irradiance, and wind speed to establish a mapping model between load and meteorological conditions. This can be achieved using regression or time series models. Meteorological forecast data for future dispatch cycles is input into the models to obtain electrical, heating, and cooling load forecast sequences, respectively. Finally, the different energy load sequences undergo time-scale synchronization processing and resampling to unify them to the same dispatch time step, such as 1 hour, forming a structurally consistent multi-timescale load demand sequence, providing a unified demand input for optimized dispatch.

[0031] By combining the operating characteristics of distributed photovoltaic, distributed wind power and gas-fired combined heat and power units, a multi-source energy supply boundary set is constructed.

[0032] Combining the operational characteristics of distributed photovoltaic (PV), distributed wind power (CHP), and combined heat and power (CHP) units, a multi-source energy supply boundary set is constructed to describe the feasible supply range of each energy source. The energy supply boundary set refers to the maximum and minimum output range achievable by each energy source at different time periods under various external conditions and equipment constraints. First, the maximum available output boundaries of PV and wind power are calculated based on meteorological forecast data. PV is affected by irradiance, and wind power by wind speed distribution, forming an uncertain but predictable upper bound. Based on the rated capacity, heat-to-power ratio, and operating efficiency of the CHP units, their adjustable output range is determined. Next, the power purchase and sale capacity constraints of the external grid interface are combined to form a power interaction boundary. Finally, the above three types of boundaries are unified and integrated to form a complete multi-source energy supply boundary set, which is used to limit the feasible solution space in subsequent optimization models and improve the engineering feasibility of scheduling results.

[0033] Based on the multi-timescale load demand sequence, multi-source energy supply boundary set, power balance constraint system and equipment operation constraint system, a combined cooling, heating and power optimization scheduling model is constructed with the goal of minimizing the overall system operating cost.

[0034] Based on multi-timescale load demand sequences, multi-source energy supply boundary sets, and power balance and equipment operation constraints, a combined cooling, heating, and power (CHP) optimization scheduling model is constructed. The model's objective function is to minimize the overall system operating cost, which includes energy purchase costs, carbon emission costs, equipment maintenance costs, and wind / solar curtailment penalties. The overall operating cost is used to uniformly measure economic efficiency and low-carbon performance. Carbon emission costs are converted into economic benefits through carbon trading prices, thereby guiding low-carbon scheduling. Constraints include electrical and thermal power balance constraints (ensuring real-time supply-demand balance), upper and lower limits for equipment output constraints (ensuring safe equipment operation), energy storage charging and discharging constraints (avoiding simultaneous charging and discharging), and mutual exclusion constraints for power purchase and sale. Through the joint modeling of the above objective function and constraints, the multi-energy flow system of the industrial park is transformed into a standard mixed-integer optimization problem, providing a mathematical foundation for subsequent solutions.

[0035] The combined cooling, heating, and power optimization scheduling model is solved using a mixed integer optimization algorithm to obtain the optimal output scheme of each energy device and the energy storage charging and discharging strategy.

[0036] A mixed-integer optimization algorithm is used to solve the combined cooling, heating, and power (CHP) scheduling model to obtain the optimal output schemes for each energy device and the energy storage charging and discharging strategies. Mixed-integer optimization refers to a model that simultaneously includes continuous variables (such as device output power) and 0-1 integer variables (such as energy storage charging and discharging status and electricity purchase and sale status), which is a typical MILP problem. The solution process first initializes the model parameters, including device capacity parameters, efficiency parameters, meteorological forecast data, and market information such as electricity prices, gas prices, and carbon prices. The objective function and constraints are then transformed into standard mathematical expressions. Next, a MILP solver or an improved particle swarm optimization algorithm is called for iterative optimization, ultimately outputting a global or near-optimal solution. The output results include the output plans of photovoltaic, wind power, and CCHP units for each time period, as well as the interaction power between the power, heating, and cooling energy storage charging and discharging strategies and the grid, achieving optimal coordinated operation of the entire system.

[0037] The optimal output scheme and energy storage charging and discharging strategy are distributed to each energy device for execution, and rolling adjustments are made based on actual operating deviations.

[0038] The optimal power output scheme and energy storage charging and discharging strategy obtained from the solution are distributed to each energy device for execution, and dynamic monitoring and deviation correction are carried out during actual operation. Rolling correction scheduling refers to a mechanism that updates the optimization problem in real time at fixed time intervals to address load forecasting errors and renewable energy fluctuations. The data acquisition module acquires the operating status and actual load data of each device in real time and compares the deviation with the forecast results. When the deviation exceeds a preset threshold (e.g., an electricity load deviation exceeding 10%), the current actual operating status is used as a new initial condition to reconstruct the optimization model for the remaining scheduling cycle and solve it again. The updated scheduling results are then re-issued for execution, thus forming a closed-loop control mechanism to ensure that the park can still achieve its low-carbon and economical operation goals under uncertain environments.

[0039] Furthermore, constructing a multi-level integrated energy twin model based on energy topology relationships includes: Based on the system structure of the target park's energy system, distributed photovoltaic power generation units, distributed wind power generation units, gas-fired combined heat and power (CHP) units, and external grid interface units are divided into energy production layer component sets; electric chillers, absorption chillers, heat pump units, and energy storage units, thermal storage units, and cold storage units are divided into energy conversion and storage layer component sets; the park's electrical load, heat load, and cold load are divided into energy consumption layer component sets; the energy topology is constructed based on the energy flow direction between the energy production layer component sets, energy conversion and storage layer component sets, and energy consumption layer component sets; equipment thermoelectric conversion characteristic parameters and capacity parameters are injected into the energy topology to generate the multi-level integrated energy twin model.

[0040] Based on the actual structure of the target park's energy system, the various energy devices within the system are functionally layered. Distributed photovoltaic power generation units, distributed wind power generation units, combined gas-fired power generation (CCHP) units, and external grid interface units are uniformly categorized into an energy production layer component set. Here, a component set refers to a combination of devices with the same functional attributes and energy output characteristics, used for abstract modeling. Electric chillers, absorption chillers, heat pump units, and energy storage, thermal storage, and cold storage units are classified into an energy conversion and storage layer component set. This layer is used to realize the energy form conversion between electricity, heat, and cold, and the energy storage regulation over time. Finally, the park's electrical load, heat load, and cooling load are uniformly classified into an energy consumption layer component set to characterize the end-user energy demand-side structure.

[0041] After dividing the components into three categories, an energy topology is constructed based on the energy flow relationships between the components. This involves establishing a network structure composed of nodes and edges, with the flow of energy from production to conversion or storage, and then to consumption, as the main line. Nodes represent equipment or load units, and edges represent energy transfer paths and conversion relationships. Furthermore, equipment thermoelectric conversion characteristic parameters, such as CCHP (comparative heat-to-power ratio) and heat pump efficiency ratio, as well as equipment capacity parameters, such as rated power and energy storage capacity, are injected into the topology. This imbues the abstract topology with physical meaning and engineering constraints, ultimately generating a multi-level integrated energy twin model that can be used for optimization calculations. This provides the foundation for structured digital mapping and behavioral simulation of the park's energy system.

[0042] Furthermore, based on historical meteorological and operational data, time-series load forecasting for the industrial park is conducted to construct a multi-timescale load demand sequence, including: Based on the historical operational data, historical electricity load data, historical heat load data, and historical cold load data of the target park are extracted. Combined with the historical meteorological data, a mapping relationship between load and meteorological variables is established. The meteorological forecast data for the future scheduling cycle is input for calculation, and electricity load forecast sequence, heat load forecast sequence, and cold load forecast sequence are output respectively. The electricity load forecast sequence, heat load forecast sequence, and cold load forecast sequence are subjected to time scale synchronization and resampling processing to obtain a multi-time scale load demand sequence with a predetermined time step.

[0043] Based on historical operational data, features of energy consumption behavior in the target industrial park are extracted, including historical electricity load data, historical heat load data, and historical cooling load data. These are then time-series aligned with historical meteorological data, including temperature, humidity, solar irradiance, and wind speed. On this basis, a mapping model between load and meteorological variables is established. This mapping relationship refers to the functional or statistical correlation between changes in meteorological factors and changes in various energy demands. This can be achieved using regression models, time series models, or machine learning models, thus forming a load response model for prediction.

[0044] By inputting meteorological forecast data for future scheduling cycles into the aforementioned mapping model, the corresponding time-period electrical load forecast sequences, heat load forecast sequences, and cooling load forecast sequences are calculated, thereby achieving time-sharing forecast output of future multi-energy demand. The forecast sequence refers to a set of load change data arranged in chronological order, used to reflect the dynamic trend of load changes within the scheduling cycle.

[0045] The three types of load forecast sequences undergo time-scale synchronization and resampling processing. This involves mapping data with different sampling frequencies or forecast step sizes to the same time resolution, such as 1 hour or 15 minutes, forming a structurally consistent multi-time-scale load demand sequence. This processing ensures that the three types of loads—electricity, heat, and cooling—can be compared and jointly optimized under the same time base, providing a unified input data foundation for subsequent multi-energy flow collaborative scheduling models.

[0046] Furthermore, combining the operational characteristics of distributed photovoltaic, distributed wind power, and gas-fired combined heat and power (CHP) units, a multi-source energy supply boundary set is constructed, including: Based on the meteorological forecast data, the maximum available output boundaries of distributed photovoltaic power generation units and distributed wind power generation units are calculated; the adjustable output range is determined according to the rated capacity and operating efficiency of the gas-fired combined heat and power (CHP) unit; the external power interaction boundary is formed by combining the power purchase and sales capabilities of the external power grid interface unit; the maximum available output boundary, the adjustable output range, and the external power interaction boundary are constrained and integrated to generate the multi-source energy supply boundary set.

[0047] Based on meteorological forecast data, the maximum available output boundaries of distributed photovoltaic (PV) and distributed wind power generation units are calculated. The maximum available output boundary refers to the theoretical maximum output power limit that each renewable energy device can achieve under ideal conditions, without considering curtailment, under current meteorological constraints. Specifically, PV output is determined by solar irradiance, module efficiency, and temperature correction coefficient, while wind power output is determined by wind speed distribution and wind turbine power curves, thus forming a time-varying upper bound constraint sequence.

[0048] Based on the rated capacity and operating efficiency characteristics of the gas-fired combined heat and power (CCHP) unit, its adjustable output range is determined. The adjustable output range refers to the minimum and maximum output range that the CCHP unit can achieve at different times, while meeting the constraints of combined heat and power and fuel consumption. This range is also limited by the heat-to-power ratio and the equipment's ramp-up capability, and is used to characterize its flexible adjustment capability.

[0049] By combining the power purchase and sales capabilities of the external power grid interface unit, an external power interaction boundary is formed. This boundary is used to constrain the maximum exchange power between the park and the power grid, including the upper limit of power purchase and the upper limit of power sales, thereby reflecting the grid access capacity and market constraints.

[0050] By unifying and integrating the maximum output boundary of renewable energy, the adjustable output range of CCHP, and the grid interaction boundary, a set of multi-source energy supply boundaries is constructed. This set is used to describe the global energy supply feasible domain of the park at different time scales, providing a strict physical feasibility constraint basis for subsequent optimization scheduling models.

[0051] Furthermore, the overall operating cost of the system includes at least energy purchase costs, carbon emission costs, equipment operation and maintenance costs, and renewable energy curtailment penalty costs.

[0052] The overall system operating cost is used to quantify the park's comprehensive economic and low-carbon performance throughout the entire scheduling cycle, forming the core objective function of the optimization model. This cost system comprises at least four components: energy purchase cost, carbon emission cost, equipment operation and maintenance cost, and renewable energy curtailment penalty cost. Specifically, energy purchase cost reflects the park's direct economic expenditure on obtaining energy from the external power grid and natural gas market; carbon emission cost converts CO2 emissions into economic costs, thus endogenizing low-carbon constraints during the optimization process; equipment operation and maintenance cost characterizes the wear, maintenance, and depreciation costs incurred by various energy equipment during operation; and curtailment penalty cost constrains the underutilization of renewable energy sources such as photovoltaic and wind power. Through this multi-dimensional cost structure, a unified model of economic and environmental constraints is achieved, making the optimization results more aligned with the near-zero carbon park operation goals.

[0053] Furthermore, the formula for calculating the energy purchase cost is as follows:

[0054] in, For energy purchase costs, The total number of time periods within the scheduling period. The duration of a single time period. , They are respectively Electricity purchase price and electricity sales price for different time periods , They are respectively The time period is determined by the power purchased and sold from the external power grid. This refers to the unit price of natural gas. for Natural gas consumption during a given period.

[0055] The formula for calculating energy purchase costs is:

[0056] This formula represents the net economic cost incurred by the park in obtaining energy from the external energy market during the entire scheduling cycle T. The first term... This represents the cost of purchasing electricity from an external power grid during time period t, reflecting the cost of purchasing electricity from external sources; the second item... This indicates that revenue from selling electricity to the grid is deducted from total expenses as a negative cost; Item 3 This represents the fuel cost generated by natural gas consumption, of which This represents the amount of gas consumed. The time step is used to convert power into an energy scale. The core significance of this expression lies in unifying the market pricing methods for electricity and gas, enabling the system to achieve an optimal economic balance between purchasing and selling electricity and using gas, thereby achieving cost-minimizing scheduling under multi-energy market conditions.

[0057] Furthermore, the formula for calculating the carbon emission cost is as follows:

[0058] in, For carbon emission costs, For carbon trading prices, This represents the carbon emission coefficient of natural gas. The carbon emission coefficient of electricity from the external power grid. for Natural gas consumption during a given period for Power purchased during specific time periods.

[0059] The formula for calculating carbon emission costs is:

[0060] This formula is used to convert the carbon emissions generated during the operation of the industrial park into economic costs. Among them, It represents the price in the carbon trading market and is used to measure the intensity of the economic penalty per unit of carbon emissions; This is the carbon emission factor for natural gas, representing the CO2 emissions per unit of natural gas consumption. This is the carbon emission factor for electricity from the external power grid, used to reflect the indirect carbon emission intensity implied by electricity purchases. In the expression... This represents the natural gas consumption during time period t. Indicates the power purchased, expressed in terms of time step. Converted to an energy scale, the core function of this cost function is to monetize carbon emissions, enabling the optimization model to automatically favor low-carbon energy structure configurations while achieving economic optimality. This guides the system to prioritize the use of renewable energy and high-efficiency equipment, thereby achieving the goal of near-zero carbon operation.

[0061] Furthermore, the power balance constraint system includes electrical power balance constraints and thermal power balance constraints; the electrical power balance constraints are as follows:

[0062] in, , They are respectively Solar and wind power output during different time periods for CCHP unit power generation during the period , They are respectively The charging and discharging power of the time-limited energy storage unit. for The park's electrical load during certain periods for Power consumption of the time-limited electric chiller for Power consumption of heat pump during different time periods.

[0063] The thermal power balance constraint is:

[0064] in, for The waste heat recovery power of the CCHP unit during the time period for Heat output of gas-fired boilers during specific time periods for Heat pump power output during specific time periods , They are respectively The heat storage capacity and heat release capacity of the time-segmented thermal storage unit. for Park heat load during the period for Heat consumption of time-period absorption chillers.

[0065] The power balance constraint system is used to ensure a strict match between supply and demand in the park's integrated energy system during any scheduling period, and is one of the fundamental physical constraints of the entire optimization model. This system mainly consists of two parts: electrical power balance constraints and thermal power balance constraints. Electrical power balance describes the conservation relationship of electrical energy throughout the entire process of "generation-conversion-storage-consumption-interaction" within the park, while thermal power balance describes the dynamic balance relationship between multiple source supply and multiple load consumption of thermal energy. The core function of this constraint system is to avoid virtual power surpluses or deficits, ensuring that the optimization results always conform to actual physical realizability, and providing a stable feasible region boundary for subsequent mixed-integer optimization solutions.

[0066] The power balance constraint is:

[0067] This formula indicates that within any time period t, the total power supply in the park must equal the total power demand. This balance reflects the real-time conservation characteristics of power in the system, ensuring that the scheduling results will not result in power mismatch or physical unavailability.

[0068] The thermal power balance constraint is:

[0069] This formula is used to describe the supply and demand balance of the thermal energy system in the park within any time period t. This constraint ensures that the thermal energy maintains a dynamic balance in the multi-source coupled system, while also reflecting the impact of the electro-thermal coupling conversion relationship on the overall system scheduling.

[0070] Furthermore, rolling adjustment scheduling is performed based on actual operational deviations, including: The system collects real-time operational data of the park and calculates the deviation between the data and the predicted scheduling results. When the deviation exceeds a preset threshold, the system uses the current actual operating status as the initial condition for optimization, reconstructs and updates the combined cooling, heating, and power optimization scheduling model for the remaining scheduling cycle, and solves the problem again. The updated scheduling results are then sent to each energy device for execution.

[0071] The system collects real-time data on the park's operational status, including output data from photovoltaic, wind, and CCHP units, actual electricity, heat, and cooling load consumption data, and operational information such as the state of charge and charging / discharging power of the energy storage system. Simultaneously, it acquires external grid interaction power and natural gas consumption data. This process generates an actual operational status vector, used to characterize the system's current true operating point.

[0072] The actual operating data is compared and analyzed with the prediction results in the optimized scheduling model on a time-by-time basis to calculate the operating deviations of various key variables, such as load deviation, renewable energy output deviation, and energy storage status deviation. When the deviation exceeds a preset threshold, such as an electricity load deviation exceeding 10% or a significant fluctuation in renewable energy output deviation, a rolling correction mechanism is triggered.

[0073] After the correction is triggered, the current actual operating state is used as the new initial condition for optimization, and the combined cooling, heating, and power optimization scheduling model for the remaining scheduling period is reconstructed. The update modeling refers to dynamically refreshing the load demand sequence, equipment status, and boundary constraints for the remaining future time periods to ensure the model reflects the latest system state.

[0074] The mixed integer optimization algorithm is re-invoked to solve the updated model, resulting in a new optimal power output scheme and energy storage charging and discharging strategy. The updated results are then immediately sent to each energy device for execution, thus forming a rolling closed-loop control mechanism to ensure that the system can maintain near-optimal operating conditions and stable energy supply capacity even under uncertain environments.

[0075] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A near-zero carbon industrial park hybrid energy joint optimization scheduling system, characterized in that, The system includes: The constraint system construction module is used to build a multi-level integrated energy twin model based on the energy topology relationship on the digital model of the target park's energy system, and to establish a power balance constraint system and an equipment operation constraint system. The load time series forecasting module is used to forecast the park's load time series based on historical meteorological data and historical operational data, and to construct a multi-time scale load demand sequence. The energy supply boundary construction module is used to construct a set of multi-source energy supply boundaries by combining the operating characteristics of distributed photovoltaic, distributed wind power and gas combined heat and power units; The scheduling model construction module is used to construct a combined cooling, heating and power optimization scheduling model with the goal of minimizing the overall system operating cost, based on the multi-timescale load demand sequence, multi-source energy supply boundary set, power balance constraint system and equipment operation constraint system. The solution module is used to solve the combined cooling, heating and power optimization scheduling model using a mixed integer optimization algorithm to obtain the optimal output scheme of each energy device and the energy storage charging and discharging strategy. The correction scheduling module is used to distribute the optimal output scheme and energy storage charging and discharging strategy to each energy device for execution, and to perform rolling correction scheduling based on actual operating deviations.

2. The near-zero carbon industrial park hybrid energy joint optimization scheduling system for cooling, heating, and power as described in claim 1, characterized in that, The constraint system construction module includes: The first unit is a node division, which is used to divide the distributed photovoltaic power generation unit, distributed wind power generation unit, gas combined heat and power unit and external power grid interface unit into a set of energy production layer components based on the system structure of the target park's energy system. The second unit is divided into nodes, which are used to divide the electric refrigeration unit, absorption refrigeration unit, heat pump unit, and energy storage unit, thermal storage unit, and cold storage unit into a set of energy conversion and storage layer components. Load partitioning nodes are used to divide the park's electrical load, heat load, and cooling load into sets of energy consumption layer components; An energy topology construction node is used to construct the energy topology relationship based on the energy flow direction between the energy production layer component set, the energy conversion and storage layer component set, and the energy consumption layer component set. Twin model construction nodes are used to inject equipment thermoelectric conversion characteristic parameters and capacity parameters into the energy topology to generate the multi-level integrated energy twin model.

3. The near-zero carbon industrial park hybrid energy joint optimization scheduling system for cooling, heating, and power as described in claim 1, characterized in that, The load time-series forecasting module includes: The mapping relationship establishment node is used to extract historical electricity load data, historical heat load data and historical cooling load data of the target park based on the historical operation data, and to establish a mapping relationship between load and meteorological variables in combination with the historical meteorological data; The input computing node is used to perform input calculations on meteorological forecast data for future scheduling cycles and output electrical load forecast sequences, heat load forecast sequences, and cooling load forecast sequences, respectively. The resampling processing node is used to perform time-scale synchronization and resampling processing on the electrical load prediction sequence, heat load prediction sequence and cooling load prediction sequence to obtain a multi-time-scale load demand sequence with a predetermined time step.

4. The near-zero carbon industrial park hybrid energy joint optimization scheduling system for cooling, heating, and power as described in claim 3, characterized in that, The power supply boundary construction module includes: The output boundary acquisition node is used to calculate the maximum available output boundary of the distributed photovoltaic power generation unit and the distributed wind power generation unit based on the meteorological forecast data. The output range acquisition node is used to determine the adjustable output range based on the rated capacity and operating efficiency of the gas-fired combined heat and power (CHP) unit. Interaction boundary acquisition node, used to combine the power purchase and sale capabilities of external power grid interface units to form an external power interaction boundary; The constraint integration node is used to constrain and integrate the maximum available output boundary, the adjustable output range, and the external power interaction boundary to generate the multi-source power supply boundary set.

5. The near-zero carbon industrial park hybrid energy joint optimization scheduling system for cooling, heating, and power as described in claim 1, characterized in that, The overall operating cost of the system includes at least the energy purchase cost, carbon emission cost, equipment operation and maintenance cost, and renewable energy curtailment penalty cost.

6. The near-zero carbon industrial park hybrid energy joint optimization scheduling system for cooling, heating, and power as described in claim 5, characterized in that, The formula for calculating the energy purchase cost is as follows: in, For energy purchase costs, The total number of time periods within the scheduling period. The duration of a single time period. , They are respectively Electricity purchase price and electricity sales price for different time periods , They are respectively The time period is determined by the power purchased and sold from the external power grid. This refers to the unit price of natural gas. for Natural gas consumption during a given period.

7. The near-zero carbon industrial park hybrid energy joint optimization scheduling system for cooling, heating, and power as described in claim 5, characterized in that, The formula for calculating the carbon emission cost is as follows: in, For carbon emission costs, For carbon trading prices, This represents the carbon emission coefficient of natural gas. The carbon emission coefficient of electricity from the external power grid. for Natural gas consumption during a given period for Power purchased during specific time periods.

8. The near-zero carbon industrial park hybrid energy joint optimization scheduling system for cooling, heating, and power as described in claim 1, characterized in that, The power balance constraint system includes electrical power balance constraints and thermal power balance constraints. The power balance constraint is: in, , They are respectively Solar and wind power output during different time periods for CCHP unit power generation during the period , They are respectively The charging and discharging power of the time-limited energy storage unit. for The park's electrical load during certain periods for Power consumption of the time-limited electric chiller for Power consumption of heat pump during certain time periods; The thermal power balance constraint is: in, for The waste heat recovery power of the CCHP unit during the time period for Heat output of gas-fired boilers during specific time periods for Heat pump power output during specific time periods , They are respectively The heat storage capacity and heat release capacity of the time-segmented thermal storage unit. for Park heat load during the period for Heat consumption of time-period absorption chillers.

9. A near-zero carbon industrial park hybrid energy joint optimization scheduling system for cooling, heating, and power as described in claim 1, characterized in that, The scheduling module has been revised, including: The re-solution node is used to collect the actual operation data of the park in real time and calculate the deviation with the predicted scheduling results. When the deviation exceeds the preset threshold, the current actual operation status is used as the initial condition for optimization. The cold, heat and power joint optimization scheduling model is reconstructed and updated for the remaining scheduling cycle, and the solution is re-solved. The updated scheduling results are then sent to each energy device for execution.

10. A method for joint optimization scheduling of hybrid energy (cooling, heating, and power) in near-zero carbon industrial parks, characterized in that, Based on the implementation of the near-zero carbon industrial park hybrid energy joint optimization scheduling system according to any one of claims 1-9, the method includes: Based on the digital model of the target park's energy system, a multi-level integrated energy twin model based on energy topology is constructed, and a power balance constraint system and an equipment operation constraint system are established. Based on historical meteorological and operational data, load time series forecasts for the park are conducted to construct a multi-time-scale load demand sequence. By combining the operating characteristics of distributed photovoltaic, distributed wind power and gas-fired combined heat and power units, a multi-source energy supply boundary set is constructed; Based on the multi-timescale load demand sequence, multi-source energy supply boundary set, power balance constraint system and equipment operation constraint system, a combined cooling, heating and power optimization scheduling model is constructed with the goal of minimizing the overall system operating cost. The combined cooling, heating, and power optimization scheduling model is solved using a mixed integer optimization algorithm to obtain the optimal output scheme of each energy device and the energy storage charging and discharging strategy. The optimal output scheme and energy storage charging and discharging strategy are distributed to each energy device for execution, and rolling adjustments are made based on actual operating deviations.

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