Campus integrated energy optimization scheduling method and system

Abstract

The application provides a park comprehensive energy optimization scheduling method and system, and relates to the technical field of energy scheduling. The method comprises the following steps: obtaining state information of a park comprehensive energy system; establishing an energy scheduling model according to the state information; determining constraint conditions and an objective function of the energy scheduling model; under the constraint of the constraint conditions, solving the energy scheduling model by an epsilon-constraint algorithm to generate a scheduling solution set, processing the scheduling solution set by a fuzzy decision method to generate an optimal scheduling solution, and taking the optimal scheduling solution as a scheduling scheme to optimize the park comprehensive energy system. The application can improve energy utilization efficiency and enhance park energy security and flexibility.

Description

Technical Field

[0001] This invention relates to the field of energy dispatching technology, and in particular to a method and system for comprehensive energy optimization dispatching in industrial parks. Background Technology

[0002] A "park" refers to a relatively independent area, such as an industrial park, science and technology park, or university park, which has multiple energy demands (electricity, heat, gas, etc.). Integrated energy management refers to the unified consideration and management of multiple energy sources (electricity, heat, cooling, gas, renewable energy, etc.) rather than optimizing a single energy source. A comprehensive energy optimization and scheduling method for a park refers to the rational scheduling of the production, storage, and use of different energy sources through mathematical models and algorithms, taking into account factors such as the operation of energy equipment in the park, external energy prices, and carbon emission policies, thereby achieving the effects of "energy saving, cost reduction, and emission reduction".

[0003] By promoting the green and low-carbon transformation of the park's energy consumption structure through demand response and carbon trading mechanisms, carbon emissions and energy waste can be effectively reduced. This will not only improve the park's economic efficiency and energy utilization rate, but also enhance its adaptability and resilience to energy fluctuations and policy adjustments, thereby driving the park's sustainable development and intelligent upgrading.

[0004] However, existing integrated energy optimization and dispatching methods for industrial parks tend to focus too much on single objectives, such as reducing operating costs or improving energy efficiency, often neglecting the uncertainties brought about by carbon emissions, environmental constraints, and fluctuations in renewable energy, making it difficult to balance economic efficiency and environmental protection. Secondly, existing dispatching models generally adopt idealized assumptions and fail to adequately characterize the complex multi-energy flow coupling relationships in industrial parks (such as the dynamic interaction between electricity, heat, gas, and energy storage). They cannot fully reflect the diversity of equipment operating status and user needs, making it difficult to adapt to the real-time requirements of industrial park energy systems. This results in a lack of flexible response to policy changes and market price fluctuations, thereby limiting the overall optimization effect and long-term sustainable development of integrated energy systems in industrial parks. Summary of the Invention

[0005] To address the technical problems of existing integrated energy optimization scheduling methods in industrial parks that overemphasize a single objective, making it difficult to balance economic efficiency and environmental protection, and the fact that existing scheduling models generally adopt idealized assumptions and cannot fully reflect the diversity of equipment operating status and user needs, making it difficult to adapt to the real-time requirements of industrial park energy systems and resulting in a lack of flexible response to policy changes and market price fluctuations, thus limiting the overall optimization effect and long-term sustainable development of industrial park integrated energy systems, this invention provides an integrated energy optimization scheduling method and system for industrial parks.

[0006] The technical solutions provided by the embodiments of the present invention are as follows:

[0007] First aspect:

[0008] This invention provides a method for optimizing energy scheduling in a park, comprising:

[0009] S1: Obtain the status information of the park's integrated energy system;

[0010] S2: Establish an energy dispatch model based on the state information;

[0011] S3: Determine the constraints and objective function of the energy dispatch model;

[0012] S4: Under the constraints of the constraints, with the goal of minimizing the objective function, the energy scheduling model is solved using the ε-constraint algorithm to generate a scheduling solution set;

[0013] S5: Process the scheduling solution set through fuzzy decision-making to generate the optimal scheduling solution;

[0014] S6: Use the optimal scheduling solution as the scheduling scheme to optimize the scheduling of the park's integrated energy system.

[0015] The second aspect:

[0016] An embodiment of the present invention provides a comprehensive energy optimization and scheduling system for industrial parks, comprising:

[0017] processor;

[0018] The memory stores computer-readable instructions, which, when executed by the processor, implement the integrated energy optimization scheduling method for the park as described in the first aspect.

[0019] Third aspect:

[0020] The present invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the integrated energy optimization scheduling method for industrial parks as described in the first aspect.

[0021] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following:

[0022] In this embodiment of the invention, by acquiring multi-dimensional state information of the park's integrated energy system and establishing a refined scheduling model, the energy supply and demand and equipment operation characteristics of the park can be comprehensively reflected. By setting reasonable constraints and objective functions, the scheduling process can satisfy both energy security and economic and environmental benefits. The ε-constraint algorithm is used to perform multi-objective optimization of the model, seeking a balance between cost, efficiency, and carbon emissions and forming multiple feasible scheduling schemes. Finally, the fuzzy decision-making method is combined to comprehensively evaluate and screen the solution set, effectively avoiding the one-sidedness caused by single-objective optimization, and ultimately obtaining the optimal scheduling solution. Overall, by combining state perception, intelligent optimization, and multi-objective decision-making, the low cost, high efficiency, and low carbon emissions of the park's energy system operation are achieved, significantly improving the scientific nature and sustainability of energy management. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating a comprehensive energy optimization scheduling method for industrial parks, provided as an embodiment of the present invention;

[0025] Figure 2 This is a schematic diagram of a comprehensive energy optimization and scheduling system for a park, provided as an embodiment of the present invention. Detailed Implementation

[0026] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0027] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0028] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0029] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0030] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0031] Reference manual attached Figure 1 The diagram shows a flowchart of a comprehensive energy optimization scheduling method for industrial parks provided by an embodiment of the present invention.

[0032] This invention provides a method for integrated energy optimization scheduling in a park. This method can be implemented by an integrated energy optimization scheduling device, which can be a terminal or a server. The processing flow of the integrated energy optimization scheduling method may include the following steps:

[0033] S1: Obtain the status information of the park's integrated energy system.

[0034] The integrated energy system of the park refers to the overall system within the park that involves multiple energy sources, including electricity, heat, gas, energy storage, and renewable energy, as well as their coupled networks and operating equipment. Status information refers to dynamic data that can characterize the system's operating status at a certain moment, including environmental parameters (such as temperature, humidity, and light intensity), energy equipment operating status (such as power output and efficiency), energy storage device status (such as battery SOC), and external energy market parameters (such as electricity price and carbon price).

[0035] It should be noted that by acquiring and dynamically sensing the status information of the park's integrated energy system in real time, a comprehensive understanding of the park's energy supply and demand relationship and equipment operating status can be achieved, providing accurate data support for subsequent optimization modeling and scheduling. This not only ensures the authenticity and reliability of the scheduling model, but also enables the system to respond flexibly to fluctuations in electricity prices, adjustments in carbon prices, or changes in environmental conditions.

[0036] In one possible implementation, the status information specifically includes: outdoor temperature and humidity of the park, light intensity, indoor environmental parameters of the park, status of energy equipment in the park, energy storage SOC, real-time electricity price, and carbon price.

[0037] It should be noted that the complete acquisition of status information helps to coordinate and optimize the coupling of multiple energy flows, avoids increased energy consumption and carbon emissions caused by information lag or lack, and thus significantly improves the intelligence level and operating efficiency of the energy system.

[0038] S2: Establish an energy dispatch model based on the state information.

[0039] Among them, the energy dispatch model is a mathematical or optimization model established based on the acquired state information, in accordance with the law of energy conservation and the operating characteristics of equipment. It is used to describe the production, conversion, storage and consumption relationships of various energy sources such as electricity, heat, gas and energy storage in the park, and to provide a basis for subsequent optimization calculations.

[0040] It should be noted that by transforming state information into a systematic and computable energy dispatch model, the coupling relationships of multiple energy flows and the dynamic characteristics of energy supply and demand within the park can be comprehensively depicted, transforming complex energy management problems into a quantifiable and optimizable form. This model not only reflects the conversion efficiency, constraints, and operational boundaries between different energy sources but also incorporates external factors such as carbon emissions and price fluctuations into a unified framework, providing a scientific basis for subsequent optimization solutions. Compared to traditional empirical dispatching, the model-based approach significantly improves the accuracy and scalability of the dispatching process, enabling the park to maintain high flexibility and economy even in complex environments, thus laying a solid foundation for achieving low-cost, high-efficiency, and low-carbon operation.

[0041] In one possible implementation, S2 specifically includes:

[0042] S201: Determine the boundary range of the park's integrated energy system.

[0043] The boundary range refers to the spatial and energy range studied by the scheduling model, including the energy input, output, conversion equipment and their coupling relationships within the park.

[0044] S202: Determine the flue gas treatment model based on boundary range and state information:

[0045] P FG (t)=P FG,pur (t)+P FG,sep (t)

[0046] P FG,pur (t)=ω FG,pur .V FG (t)

[0047] P FG,sep (t)=ω FG,sep .V FG,sep (t)

[0048] V FG,sep (t)=α FG .β FG .V FG (t)

[0049] Among them, P FG (t) represents the total energy consumed in processing the flue gas at time t, P FG,pur (t) represents the power consumed in purifying the flue gas at time t, PFG,sep (t) represents the power consumed in separating CO2 from the flue gas at time t, ω FG,pur V represents the energy consumption coefficient for unit flue gas purification. FG (t) represents the volume of flue gas produced at time t, ω FG,sep V represents the energy consumption coefficient for separation. FG,sep (t) represents the volume of CO2 separated from the flue gas at time t, α FG β represents the separation rate. FG This indicates the proportion of CO2 in the flue gas.

[0050] Among them, the flue gas treatment model is used to describe the energy consumption relationship of flue gas generated during the combustion of gas in the purification and CO2 separation stages.

[0051] S203: Determine the biogas treatment model based on biogas purification and biogas separation:

[0052] P BG (t)=P BG,pur (t)+P BG,sep (t)

[0053] P BG,pur (t)=ω BG,pur .V BG (t)

[0054] P BG,sep (t)=ω BG,sep .V BG,sep (t)

[0055] V BG,sep (t)=α BG .β BG .V BG (t)

[0056] Among them, P BG (t) represents the total energy consumed in biogas treatment at time t, P BG,pur (t) represents the power consumed in biogas purification at time t, P BG,sep (t) represents the energy consumed in separating CO2 from biogas at time t, ω BG,pur V represents the energy consumption coefficient for biogas purification per unit. BG (t) represents the volume of biogas produced by the biogas digester in the park at time t, ω BG,sep V represents the energy consumption coefficient. BG,sep (t) represents the volume of CO2 separated from the biogas at time t, α BG β represents the biogas separation rate. BG This indicates the ratio of CO2 in biogas.

[0057] Among them, the biogas treatment model is used to describe the energy consumption relationship between biogas purification and CO2 separation.

[0058] S204: Determine the power-to-gas conversion model based on CO2 obtained from processing flue gas and biogas:

[0059]

[0060] V P2G,co2 (t)=α co2 .η P2G .P P2G (t)

[0061] Among them, V P2G,CH4 (t) represents the volume of CH4 produced by the P2G electro-gas converter at time t, H g η represents the calorific value of synthetic natural gas (SNG). P2G P represents the electrical conversion factor of the P2G (Power-to-Gas) equipment. P2G (t) represents the electrical power input to the P2G device at time t, V. P2G,co2 (t) represents the volumetric flow rate of carbon dioxide processed by the P2G electro-gas converter at time t, α co2 This represents the carbon dioxide conversion coefficient.

[0062] Among them, the electro-gas model describes the process of converting electricity into methane (CH4) and utilizing CO2 through electrolysis or catalysis.

[0063] S205: With peak shaving and valley filling as the goal, determine the electrical energy storage model and thermal energy storage model.

[0064] Among them, the electrical energy storage model and the thermal energy storage model describe the storage and release processes of electrical energy and thermal energy, respectively, with the goal of peak shaving and valley filling.

[0065] S206: Based on the energy conservation relationship and calorific value conversion principle of methane supply, and combined with the coupled gas supply of methane produced by the power-to-gas unit and the methane content of biogas, the micro-turbine model is determined:

[0066]

[0067] Among them, P MT (t) represents the output power of the micro turbine MT device at time t, η MT V represents the power generation efficiency of a micro turbine (MT) device. CH4 (t) represents the volumetric flow rate of methane entering the gas turbine at time t, V P2G,CH4 (t) represents the volume of CH4 produced by the P2G electro-gas converter at time t, α CH4 This indicates the volume fraction of methane in biomass fuel gas.

[0068] Among them, the micro turbine model generates electricity by burning methane, with energy sources including CH4 produced by electricity-to-gas conversion and methane from biogas.

[0069] S207: Based on the energy use characteristics of the park, determine the comprehensive demand response model of the park, which includes: a reduceable power load model, a tiered power load model, and a transferable power load model.

[0070] The park's integrated demand response model describes the adjustability of user loads, including loads that can be reduced, loads that can be shifted (stepped), and loads that can be transferred.

[0071] The specific model for reducing electricity load is as follows:

[0072]

[0073] Among them, P cut (t) represents the load power after the power is reduced at time t, P load,cut (t) represents the amount of power load reduced at time t, Δ represents the increment operator, and α cut Indicates the maximum allowable load reduction ratio, 0≤ΔP load,cut (t)≤α cut P load,cut (t) represents the reduction constraint.

[0074] The stepped power load model is as follows:

[0075]

[0076] Among them, t sb+ and t sb- P represents the start and end times of the time window for the transferable load. sbift (t) represents the stepped load power at time t, P load,sbift (t) represents the load power before the transfer at time t.

[0077] The transferable power load model is as follows:

[0078]

[0079] Among them, t tr- and t tr+ P represents the start and end times of the time window for transferable load. trans (t) represents the power of the load that can be transferred at time t, P load,trans (t) represents the transferable load power demand at time t.

[0080] S208: Based on the total carbon emissions of the park, determine the carbon emission accounting model for the park:

[0081]

[0082] Among them, F CO2,S V represents the total input of all CO2 within the park. P2G,CO2 F represents the volumetric flow rate of carbon dioxide processed by the P2G (Power to Gas) equipment. CO2,plant A represents the CO2 emission mass flow rate within the park. j λ represents the activity level of the j-th production stage in the industrial park. plant,i This represents the carbon emission factor of the i-th production stage in the industrial park.

[0083] The carbon emission accounting model is used to calculate the total carbon emissions within the park, including CO2 from P2G treatment and emissions from the park's production processes.

[0084] S209: Combine flue gas treatment model, biogas treatment model, electricity-to-gas model, electricity storage model, thermal energy storage model, micro turbine model, park comprehensive demand response model, and park carbon emission accounting model to determine the energy dispatch model.

[0085] It should be noted that this multi-model coupling approach not only improves the accuracy and completeness of the scheduling model, but also reflects the multi-energy complementarity and carbon emission reduction characteristics in actual operation, providing a solid data and theoretical foundation for subsequent optimization algorithms. Compared with traditional single-energy scheduling methods, this scheme is more comprehensive and forward-looking, effectively balancing economic efficiency, energy efficiency, and carbon emissions, and supporting the park's development towards low-carbon, intelligent, and sustainable directions.

[0086] S3: Determine the constraints and objective function of the energy dispatch model.

[0087] Among them, constraints refer to the basic rules and boundary conditions that the model must satisfy during optimization calculations. The objective function is a mathematical expression used to measure the quality of a scheduling scheme and is the core basis for optimization.

[0088] It should be noted that the constraints ensure that the supply and demand of various energy sources, such as electricity, heat, and gas, are always balanced and that equipment operating limits are met during the scheduling process, avoiding unrealistic or physically incompatible results. The objective function design provides a clear optimization direction for the scheduling, allowing for flexible trade-offs between minimizing economic costs, maximizing energy efficiency, and minimizing carbon emissions. Compared to traditional single-objective scheduling methods, this multi-objective and multi-constraint approach more comprehensively reflects the actual operational needs of the park, improves the operability and practical value of the optimization results, and thus provides solid theoretical support for subsequent intelligent optimization solutions.

[0089] In one possible implementation, the objective function includes a first objective function and a second objective function.

[0090] To minimize the overall cost of the park's integrated energy system, the first objective function is constructed as follows:

[0091]

[0092] Where min represents minimization, f1 represents the first objective function, and T represents the total scheduling period. This represents the total cost of purchasing electricity at time t. This represents the total revenue from electricity sales at time t. This represents the total cost of purchasing gas at time t. This represents the total revenue from gas sales at time t. This represents the cost of compensating for electrical loss load at time t. This represents the cost of compensating for heat loss load at time t. This represents the cost of DR interrupted power load compensation at time t. This represents the amount of electricity purchased at time t. This represents the unit electricity purchase price at time t. This represents the unit electricity price at time t. This represents the electricity sold at time t. This represents the unit gas purchase price at time t. This represents the amount of gas purchased at time t. This represents the unit gas price at time t. This represents the gas sales volume at time t. This represents the unit heat loss compensation price at time t. This represents the heat loss load at time t. This represents the unit power loss compensation price at time t. This represents the electrical load loss at time t. This represents the unit DR interruption compensation price at time t. This represents the amount of power load interrupted by DR at time t.

[0093] Using the dynamic carbon footprint tracking factor as a penalty term, combined with carbon trading costs, and aiming to minimize the synergy between the economy, efficiency, and environment, a second objective function is constructed:

[0094]

[0095] Where f2 represents the second objective function, This represents the cost of wind curtailment penalty at time t. This represents the cost of the light-wasting penalty at time t. This represents the carbon trading cost at time t. This represents the unit wind curtailment penalty price at time t. This represents the amount of wind curtailment at time t. This represents the unit wastelight penalty price at time t. ω represents the amount of curtailed solar power at time t, which is the solar power output minus the actual solar power consumption; T represents the scheduling cycle; and m represents the total number of energy devices participating in the scheduling within the park.

[0096] Among them, the second objective function, taking into account carbon trading and penalties for wind and solar curtailment, achieves comprehensive optimization of economy, efficiency and environment.

[0097] It should be noted that by simultaneously constructing a first objective function and a second objective function, a comprehensive optimization of the park's energy system is achieved, balancing economic efficiency and environmental impact. The first objective function focuses on minimizing operating costs, directly improving the park's economic benefits. The second objective function, while considering costs, incorporates carbon trading mechanisms and renewable energy utilization into its optimization objectives, helping to reduce carbon emissions and wind and solar power curtailment, and promoting the park's green and low-carbon development. This dual-objective design not only enhances the flexibility and scientific rigor of the scheduling model but also enables it to balance short-term economic benefits with long-term environmental benefits, avoiding energy waste and increased carbon emissions resulting from singular cost optimization, thereby realizing the overall value of sustainable development.

[0098] In one possible implementation, the calculation process for carbon trading costs specifically includes:

[0099] Calculate the direct carbon emission costs of major carbon emission sources:

[0100]

[0101] in, c represents the direct carbon emission cost of the gas turbine GT at time t. c Represents carbon valence, α GT β represents the carbon emission coefficient per unit output of a gas turbine GT. GT This represents the carbon emission deduction factor for a gas turbine GT. This represents the output of the gas turbine GT at time t. α represents the direct carbon emission cost of a combined heat and power (CHP) unit at time t. CHP β represents the carbon emission coefficient per unit output of a combined heat and power (CHP) unit. CHP This represents the carbon emission deduction factor for CHP (Concentrated Heat and Power) units. This represents the output of the combined heat and power unit CHP at time t.

[0102] This refers to the cost that needs to be paid based on the carbon market price (carbon price) for carbon dioxide emissions from the direct combustion of fossil fuels by energy equipment (such as gas turbines GT and combined heat and power units CHP) within the park during operation.

[0103] Calculate the carbon reduction benefits of carbon reduction facilities:

[0104]

[0105] in, This represents the carbon emission reduction benefit of the P2G (Power to Gas) equipment at time t. This represents the output of the P2G electro-gas converter at time t, i.e., the throughput.

[0106] This includes the amount of electricity converted or the amount of gas produced.

[0107] Among them, carbon emission reduction revenue refers to the economic benefits obtained in the carbon trading market from the low-carbon or negative-carbon facilities (such as P2G power-to-gas conversion units) in the park that can reduce or absorb carbon dioxide during operation.

[0108] The carbon trading cost is determined based on the direct carbon emission costs and carbon emission reduction benefits:

[0109]

[0110] in, This represents the carbon trading cost at time t.

[0111] Among them, carbon trading costs refer to the costs that the park needs to pay for emitting carbon dioxide or the benefits obtained from emission reduction under the carbon emission trading mechanism.

[0112] It should be noted that by introducing refined calculations of carbon trading costs, and comprehensively accounting for the direct carbon emission costs of major emission sources such as gas turbines and combined heat and power units, along with the benefits of carbon reduction facilities such as electricity-to-gas conversion, the actual economic burden of the industrial park under the carbon market mechanism can be accurately reflected. Compared to traditional methods that only consider energy operating costs, this mechanism not only strengthens the constraint of carbon emissions on energy dispatch but also guides the park to actively utilize low-carbon and carbon-negative technologies to achieve carbon emission reduction, thereby balancing economic and environmental benefits in the process of optimizing dispatch. This design can promote the green and low-carbon transformation of the park's energy system and enhance its competitiveness and adaptability under the carbon trading policy environment.

[0113] In one possible implementation, the constraints specifically include: power supply and demand balance constraints, thermal power balance constraints, gas balance constraints, power-to-gas conversion equipment constraints, external network constraints, and thermal demand response constraints.

[0114] Among them, the power supply and demand balance constraint means that at any given time, the total power generation, energy storage discharge, and external power purchase in the park must equal the total power consumption, energy storage charging, and power sales in the park, to ensure the stable operation of the power system. The thermal power balance constraint means that the heating power of heating equipment (such as boilers, CHP, and thermal storage devices) in the park must be consistent with the heating demand of users in the park, to avoid excess or insufficient heat energy.

[0115] Among them, the gas balance constraint means that the gas source (purchased gas volume, self-produced gas volume) and gas consumption (usage of gas turbines, CHP, P2G, etc.) in the park must be equal to ensure a stable gas supply.

[0116] Among them, the constraints on P2G (Power to Gas) equipment refer to the fact that the operating power, efficiency and input / output range of P2G equipment must meet the physical characteristics and equipment capacity limitations and cannot exceed the technically permissible range.

[0117] Among them, external network constraints refer to the fact that when the park exchanges energy with external power grids, gas networks, heating networks and other energy networks, the exchange power and flow must be within the limits allowed by the contract or technology to avoid system overload.

[0118] Among them, thermal demand response constraints refer to the requirement that the thermal load of users in the park must meet comfort and process limitations when it can be reduced, transferred or adjusted, and cannot be adjusted without restrictions.

[0119] It should be noted that the feasibility and safety of the energy dispatch model are ensured by comprehensively constraining electricity, heat, gas, equipment operation, and external networks. Electricity, heat, and gas balance constraints ensure that system supply and demand are always matched, avoiding energy shortages or waste. P2G equipment and external network constraints ensure that equipment operation complies with technical limitations and that the interaction between the park and external energy systems is stable and reliable. Heat demand response constraints balance user experience with flexible dispatch. Overall, these constraints ensure that the optimization results are not only mathematically sound but also practically implementable, thereby significantly improving the scientific rigor, safety, and operability of the park's energy system dispatch.

[0120] S4: Under the constraints, with the objective function as the goal, the energy scheduling model is solved using the ε-constraint algorithm to generate a scheduling solution set.

[0121] The ε-constraint algorithm is a common multi-objective optimization algorithm. Its basic idea is to use one objective function as the primary optimization objective, and transform other objective functions into constraints (by setting the ε value, i.e., the allowable deviation range), thereby obtaining a series of solutions that take into account different objectives. The scheduling solution set refers to a set of possible scheduling schemes obtained by solving the optimization model, typically containing non-dominated solutions after weighing the trade-offs between different objectives.

[0122] It should be noted that by introducing the ε-constraint algorithm, the complex multi-objective optimization problem is transformed into a main objective and several constraints, making the problem easier to solve while ensuring a balance between different objectives. This ensures that the park's energy scheduling minimizes overall costs while meeting supply and demand balance and equipment limitations. Furthermore, by setting the ε value, multiple scheduling schemes that balance economy, efficiency, and environmental protection can be generated, forming a complete solution set. Compared to traditional single-objective optimization, this method better reflects the idea of ​​multi-objective trade-offs, avoids one-sided results, enhances the flexibility and selectivity of scheduling schemes, and provides more scientific and diversified support for subsequent decision-making.

[0123] In one possible implementation, S4 specifically includes:

[0124] S401: The second objective function is transformed into a constraint objective through the ε-constraint algorithm, and the first objective function is taken as the main optimization objective.

[0125] Among these, the constraint objective refers to introducing the synergistic optimization goal of economy, efficiency, and environment through constraints, rather than directly using it as the primary optimization goal, to ensure that the main goal (cost minimization) takes priority. The main optimization goal refers to minimizing the overall system operating cost as the core objective of scheduling optimization.

[0126] S402: Determine the feasible range of the constrained objective by calculating the ideal value and Nadal value of the constrained objective.

[0127] The ideal value refers to the theoretically optimal value of the constraint objective (such as carbon emission costs) without considering other limitations. The Nadal value is the worst-case value of the constraint objective among all possible solutions. The ideal value and the Nadal value constitute the feasible interval of the constraint objective.

[0128] S403: Based on the feasible interval, generate the ε value sequence using the equal interval method.

[0129] The ε-value sequence refers to a set of ε-values ​​generated at equal intervals within the feasible interval, used to control the deviation range of secondary objectives.

[0130] S404: Based on the ε value sequence, iteratively solve the main optimization objective to generate an optimized solution set.

[0131] The optimal solution set is a series of optimized solutions obtained by iteratively solving for different ε values.

[0132] S405: Filter the non-dominated solutions in the optimization solution set to obtain the non-dominated solution set, i.e. the scheduling solution set, where each non-dominated solution in the non-dominated solution set corresponds to a different scheduling solution.

[0133] In multi-objective optimization, a non-dominated solution refers to a solution set in which no single solution is superior to another in all objectives; that is, solutions are mutually exclusive. A scheduling solution set refers to the set of non-dominated solutions obtained through selection, where each solution represents a feasible and highly optimized scheduling scheme.

[0134] It's important to note that, firstly, transforming the second objective function into a constraint objective avoids conflicts in multi-objective optimization, highlighting the core objective of cost minimization. Secondly, defining the feasible interval using ideal and Nadal values ​​ensures the rationality and operability of the constraints. Then, iteratively solving using the ε-value sequence not only covers multiple solutions under different trade-offs but also yields a high-quality scheduling solution set by filtering non-dominated solutions. This approach not only enhances the flexibility and robustness of the optimization process but also provides decision-makers with diverse candidate solutions, facilitating the optimal choice between economy, environment, and efficiency, significantly increasing the practical value and implementability of the model results.

[0135] S5: Process the scheduling solution set through fuzzy decision-making to generate the optimal scheduling solution.

[0136] Among them, fuzzy decision-making is a multi-attribute decision-making method based on fuzzy mathematics. It can handle the problems of "difficulty in precise measurement" and "uncertainty" in multi-objective optimization. It comprehensively evaluates different solutions through fuzzy membership functions and weight vectors, thereby selecting the optimal solution. The optimal scheduling solution refers to the best scheme selected after fuzzy decision-making processing. It is the result of a weighted evaluation considering multiple objectives such as economy, energy efficiency, and carbon emission reduction, and is also the final scheduling scheme used for the operation of the park's energy system.

[0137] It should be noted that further screening and evaluation of the scheduling solution set using fuzzy decision-making methods can effectively address the problem of "many solutions, but none of them being optimal" in multi-objective optimization. Fuzzy membership functions can quantify the merits of each solution across different objectives, and weight vectors can be combined to reflect the decision-maker's preferences for different objectives (such as cost, efficiency, and carbon emissions) to calculate a comprehensive score. This not only avoids the one-sidedness of relying on a single objective to select a solution but also enhances the flexibility and scientific rigor of the decision-making process. The resulting optimal scheduling solution better balances economic efficiency and low carbon emissions, and meets actual operational needs, making the park's energy scheduling results both theoretically sound and practically applicable.

[0138] In one possible implementation, S5 specifically includes:

[0139] S501: Determine the fuzzy membership function of the objective function based on the scheduling solution set and the objective function.

[0140] The fuzzy membership function is used to measure how close a scheduling solution is to the ideal value for a certain objective (such as the lowest cost or the lowest carbon emissions). Its value is usually between 0 and 1, and the larger the value, the closer it is to the optimal value.

[0141] S502: Determine the weight vector of the objective function through expert consultation or fuzzy hierarchical analysis.

[0142] Among these methods, the expert consultation method involves collecting opinions from experts in the field to comprehensively assess the importance of different objectives. The fuzzy hierarchical analysis method involves establishing a multi-level structure and pairwise comparison matrices, combined with fuzzy numbers, to obtain the weights of each objective. The weight vector represents the relative importance of each objective function in the decision-making process; for example, cost might have a weight of 0.5, carbon emissions 0.3, and energy efficiency 0.2.

[0143] S503: Calculate the comprehensive fuzzy score of each scheduling solution in the scheduling solution set by using the weighted average method, combined with the fuzzy membership function and weight vector.

[0144] The weighted average method is a comprehensive evaluation method that combines fuzzy membership functions with target weights to calculate the comprehensive fuzzy score for each scheduling solution. The comprehensive fuzzy score represents the overall performance of the scheduling solution under all objectives and is used to compare the merits of different scheduling solutions.

[0145] S504: Sort the comprehensive fuzzy scores corresponding to each scheduling solution in descending order.

[0146] S505: Determine the optimal scheduling solution based on the descending sorting results.

[0147] Among them, the optimal scheduling solution refers to the scheduling solution with the highest score, which is the final scheduling scheme applied to the operation of the park's energy system.

[0148] It should be noted that fuzzy mathematics methods are used to transform multi-objective optimization results into comparable comprehensive scores, thus providing a scientific basis for the final scheme selection. Fuzzy membership functions quantify the performance of each scheduling solution across different objectives, while weight vectors, combined with expert experience or analytic hierarchy process (AHP), reflect the decision-maker's preferences for different objectives. The comprehensive fuzzy score is then calculated using a weighted average method, avoiding the bias inherent in single-objective selection and making the scheme selection more rational. Descending order sorting allows for the rapid identification of the optimal scheduling solution, achieving a balance between economy, energy efficiency, and environmental friendliness. Overall, this method not only improves the scientific rigor and transparency of decision-making but also enhances the practicality and operability of the scheduling results.

[0149] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following:

[0150] In this embodiment of the invention, by acquiring multi-dimensional state information of the park's integrated energy system and establishing a refined scheduling model, the energy supply and demand and equipment operation characteristics of the park can be comprehensively reflected. By setting reasonable constraints and objective functions, the scheduling process can satisfy both energy security and economic and environmental benefits. The ε-constraint algorithm is used to perform multi-objective optimization of the model, seeking a balance between cost, efficiency, and carbon emissions and forming multiple feasible scheduling schemes. Finally, the fuzzy decision-making method is combined to comprehensively evaluate and screen the solution set, effectively avoiding the one-sidedness caused by single-objective optimization, and ultimately obtaining the optimal scheduling solution. Overall, by combining state perception, intelligent optimization, and multi-objective decision-making, the low cost, high efficiency, and low carbon emissions of the park's energy system operation are achieved, significantly improving the scientific nature and sustainability of energy management.

[0151] Reference manual attached Figure 2 The diagram shows a structural schematic of a comprehensive energy optimization and scheduling system for industrial parks provided by the present invention.

[0152] The present invention also provides a park integrated energy optimization scheduling system 20, applied to the above-mentioned park integrated energy optimization scheduling method, comprising:

[0153] Processor 201.

[0154] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, the integrated energy optimization scheduling method for the park as described in the method embodiment is implemented.

[0155] The integrated energy optimization scheduling system 20 for parks provided by the present invention can execute the above-mentioned integrated energy optimization scheduling method for parks and achieve the same or similar technical effects. To avoid duplication, the present invention will not elaborate further.

[0156] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following:

[0157] In this embodiment of the invention, by acquiring multi-dimensional state information of the park's integrated energy system and establishing a refined scheduling model, the energy supply and demand and equipment operation characteristics of the park can be comprehensively reflected. By setting reasonable constraints and objective functions, the scheduling process can satisfy both energy security and economic and environmental benefits. The ε-constraint algorithm is used to perform multi-objective optimization of the model, seeking a balance between cost, efficiency, and carbon emissions and forming multiple feasible scheduling schemes. Finally, the fuzzy decision-making method is combined to comprehensively evaluate and screen the solution set, effectively avoiding the one-sidedness caused by single-objective optimization, and ultimately obtaining the optimal scheduling solution. Overall, by combining state perception, intelligent optimization, and multi-objective decision-making, the low cost, high efficiency, and low carbon emissions of the park's energy system operation are achieved, significantly improving the scientific nature and sustainability of energy management.

[0158] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0159] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0160] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0161] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0162] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0163] It should be understood that, in various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0164] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0165] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0166] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0167] 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.

[0168] 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.

[0169] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present 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 described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0170] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the integrated energy optimization scheduling method for industrial parks as described in the method embodiments.

[0171] The present invention provides a computer-readable storage medium that can implement the steps and effects of the park integrated energy optimization scheduling method of the above-described method embodiments. To avoid repetition, the present invention will not repeat them.

[0172] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following:

[0173] In this embodiment of the invention, by acquiring multi-dimensional state information of the park's integrated energy system and establishing a refined scheduling model, the energy supply and demand and equipment operation characteristics of the park can be comprehensively reflected. By setting reasonable constraints and objective functions, the scheduling process can satisfy both energy security and economic and environmental benefits. The ε-constraint algorithm is used to perform multi-objective optimization of the model, seeking a balance between cost, efficiency, and carbon emissions and forming multiple feasible scheduling schemes. Finally, the fuzzy decision-making method is combined to comprehensively evaluate and screen the solution set, effectively avoiding the one-sidedness caused by single-objective optimization, and ultimately obtaining the optimal scheduling solution. Overall, by combining state perception, intelligent optimization, and multi-objective decision-making, the low cost, high efficiency, and low carbon emissions of the park's energy system operation are achieved, significantly improving the scientific nature and sustainability of energy management.

[0174] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0175] The following points need to be explained:

[0176] (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0177] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the present invention; that is, these drawings are not drawn to actual scale. It is understood that when an element such as a layer, film, region, or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element, or there may be intermediate elements.

[0178] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0179] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for comprehensive energy optimization scheduling in a park, characterized in that, include: S1: Obtain the status information of the park's integrated energy system; S2: Determine the energy dispatch model based on the aforementioned state information; S3: Combining the dynamic carbon footprint tracking factor, construct the constraints and objective function of the energy dispatch model; S4: Under the constraints of the above constraints, with the objective function as the goal, the energy scheduling model is solved using the ε-constraint algorithm to generate a candidate scheduling solution set; S5: The candidate scheduling solution set is processed by fuzzy decision-making to generate the optimal scheduling solution; S6: Use the optimal scheduling solution as the scheduling scheme to optimize the scheduling of the park's integrated energy system; Following S2, the following is also included: Based on the energy dispatch model, calculate the demand flexibility index: ； in, express t The flexibility index of time-to-time demand. express t Reduce load at all times. express t Constantly shifting load amount, express t Constantly shifting load, express t Total load power at any given time; The objective function includes a first objective function and a second objective function; The first objective function is constructed with the goal of minimizing the overall cost of the integrated energy system of the park. The dynamic carbon footprint tracking factor is used as a penalty term, combined with carbon trading costs, to construct the second objective function with the goal of minimizing the synergy between economy, efficiency and environment; Specifically, S4 includes: S401: Using the ε-constraint algorithm, the second objective function is transformed into a constraint objective, and the first objective function is used as the main optimization objective; S402: Determine the feasible range of the constraint target by calculating the ideal value and Nadal value of the constraint target; S403: Based on the feasible interval, generate an ε value sequence using the equal interval method; S404: Based on the ε value sequence, iteratively solve the main optimization objective to generate an optimized solution set; S405: Filter the non-dominated solutions in the optimization solution set to obtain the non-dominated solution set, i.e. the scheduling solution set, wherein each non-dominated solution in the non-dominated solution set corresponds to a different scheduling solution; S406: According to the demand flexibility index, the scheduling solution set is classified and processed: if the demand flexibility index is greater than or equal to the first demand flexibility threshold, the peak shaving and valley filling efforts are increased to expand the demand response scheduling range. If the demand flexibility index is greater than or equal to the second demand flexibility threshold and less than the first demand flexibility threshold, a demand response scheduling strategy based on load shifting and load reduction is executed. When the demand flexibility index is less than the second demand flexibility threshold, energy is supplemented to the park's integrated energy system through the external power grid or energy storage equipment to maintain the system's supply and demand balance. S407: Based on the classification results, a candidate scheduling solution set is formed.

2. The integrated energy optimization scheduling method for industrial parks according to claim 1, characterized in that, The status information specifically includes: outdoor temperature and humidity, light intensity, indoor environmental parameters, energy equipment status, energy storage SOC, real-time electricity price, and carbon price.

3. The integrated energy optimization scheduling method for industrial parks according to claim 1, characterized in that, S2 specifically includes: S201: Determine the boundary range of the integrated energy system of the park; S202: Determine the flue gas treatment model based on the boundary range and the state information; S203: Determine the biogas treatment model based on biogas purification and biogas separation; S204: Determine the power-to-gas conversion model based on the CO2 obtained from processing flue gas and biogas; S205: With peak shaving and valley filling as the goal, determine the electrical energy storage model and thermal energy storage model; S206: Based on the energy conservation relationship and calorific value conversion principle of methane supply, and combined with the coupled gas supply of methane produced by the power-to-gas unit and the methane content of biogas, the micro turbine model is determined. S207: Based on the energy use characteristics of the park, determine the comprehensive demand response model of the park, wherein the comprehensive demand response model of the park includes: a power load model that can be reduced, a tiered power load model, and a power load model that can be transferred. S208: Determine the carbon emission accounting model for the park based on the total carbon emissions of the park; S209: By combining the flue gas treatment model, the biogas treatment model, the electricity-to-gas model, the electricity storage model, the thermal energy storage model, the micro turbine model, the park comprehensive demand response model, and the park carbon emission accounting model, the energy dispatch model is determined.

4. The integrated energy optimization scheduling method for industrial parks according to claim 1, characterized in that, The dynamic carbon footprint tracking factor is calculated as follows: Based on the aforementioned status information and cross-system auxiliary data, the dynamic carbon footprint tracking factor is calculated, wherein the cross-system auxiliary data includes transportation and logistics carbon emission factors, building-implied carbon emission intensity, and regional power grid cleanliness index. ； in, Indicates the first k One energy device in t Dynamic carbon footprint tracking factor at any given time Indicates the first k One energy device in t The first time used i The amount of energy input, Indicates the first i Upstream carbon emission factors of this energy source Indicates the first k Carbon emission coefficient per unit of energy equipment Indicates the first k One energy device in t Actual output at any moment Indicates the first k The maximum output of each energy device Indicates the first k The correlation coefficient between energy equipment and transportation logistics express t Carbon emission factors of transportation and logistics at any time Indicates the first k The correlation coefficient between energy equipment and building facilities express t The carbon emission intensity of buildings at any given time. n This indicates the type and quantity of energy used by the equipment.

5. The integrated energy optimization scheduling method for industrial parks according to claim 1, characterized in that, The constraints specifically include: power supply and demand balance constraints, heat power balance constraints, gas balance constraints, power-to-gas conversion equipment constraints, external network constraints, heat demand response constraints, and real-time carbon footprint constraints.

6. The integrated energy optimization scheduling method for industrial parks according to claim 1, characterized in that, S5 specifically includes: S501: Determine the fuzzy membership function of the objective function based on the candidate scheduling solution set and the objective function; S502: Determine the weight vector of the objective function through expert consultation or fuzzy hierarchical analysis. S503: Calculate the comprehensive fuzzy score of each candidate scheduling in the candidate scheduling solution set by using the weighted average method, combining the fuzzy membership function and the weight vector; S504: Sort the comprehensive fuzzy scores corresponding to each of the candidate schedulings in descending order; S505: Determine the optimal scheduling solution based on the descending sorting results.

7. A comprehensive energy optimization and dispatching system for industrial parks, characterized in that, include: processor; A memory storing computer-readable instructions, which, when executed by the processor, implement the integrated energy optimization scheduling method for the park as described in any one of claims 1 to 6.

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