Method and system for compliance checking and correction of regional integrated energy planning

Abstract

The application discloses a compliance checking and correction method and system for regional comprehensive energy planning and belongs to the technical field of regional comprehensive energy planning. The method comprises the following steps: based on a scenario parameter, calling a comprehensive energy scenario simulation model to perform simulation calculation, generating a simulation output index set; obtaining a calculable rule expression and establishing a mapping relationship between the simulation output index set and the calculable rule expression; based on the mapping relationship, executing the calculable rule expression, performing compliance checking on the simulation output index set, and generating a rule satisfaction judgment result; when the judgment result is not satisfied, calculating a gap amount between a simulation output value of a not satisfied item and a rule threshold value, and generating a correction suggestion for guiding adjustment of the scenario parameter based on the gap amount. Through the closed loop of "simulation-checking-quantitative gap-correction suggestion", the application realizes automatic iteration optimization of a planning scheme, and significantly improves the efficiency, compliance and feasibility of planning.

Description

Technical Field

[0001] This invention relates to the field of regional integrated energy planning technology, and in particular to a method and system for compliance verification and correction of regional integrated energy planning. Background Technology

[0002] Regional integrated energy system (IES) planning is a key step in achieving energy structure transformation and "dual carbon" goals. In planning practice, integrated energy simulation models are typically used to simulate energy flows, cost-effectiveness, and environmental impacts under different development scenarios in order to evaluate and compare planning options.

[0003] However, existing technological practices have significant shortcomings: First, there is a disconnect between simulation results and policies, regulations, and technical standards. Planning schemes need to meet numerous policy constraints (such as the proportion of renewable energy storage and carbon emission intensity) and technical specifications (such as equipment energy efficiency standards) that exist in the form of natural language text. Existing methods typically rely on planners manually comparing simulation results with rule clauses, a cumbersome and inefficient process prone to oversights, making it difficult to achieve comprehensive and rapid compliance verification of complex planning schemes.

[0004] Secondly, existing technologies typically only output review reports or identify non-compliant items after discovering deviations between the planning scheme and the rules. The system does not provide specific guidance on how to revise the original planning scheme to meet compliance requirements. Planners still need to rely on their own experience and repeatedly adjust the input parameters of the simulation model through trial and error, which greatly reduces the efficiency of iterative optimization of the planning scheme and fails to form an effective "problem discovery-problem solving" closed loop.

[0005] Therefore, there is an urgent need in this field for a systematic method that can automatically link simulation output with rule verification and quantify the gap and provide specific corrective suggestions after non-compliance items are found. Summary of the Invention

[0006] To address the technical problems in the background art, such as the difficulty in automatically connecting the simulation results of regional integrated energy system planning with policies and technical rules for compliance verification, and the lack of quantitative gap calculation and closed-loop correction suggestions after non-compliance items are discovered, resulting in low efficiency of planning scheme iteration, this invention aims to provide a closed-loop planning verification method and system that can achieve automated compliance verification, quantify gaps, and generate correction suggestions.

[0007] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for compliance verification and correction of regional integrated energy planning, including: Based on the comprehensive energy scenario parameter set describing the characteristics of the energy system in the target area, the comprehensive energy scenario parameter set is input into the comprehensive energy scenario simulation model for simulation calculation, and a simulation output index set is generated. Obtain the field mapping relationship between the computable rule set and the simulation output indicator set; the computable rule set is formed by extracting information from the policy text through a rule verification model. Based on the field mapping relationship, the computable rule set is executed to perform compliance verification on the simulation output index set and generate rule satisfaction determination results; When the rule satisfaction determination result is not satisfied, the difference between the specific value of the unsatisfied field in the simulation output index set and the rule threshold is calculated, and a correction suggestion is generated based on the difference to guide the adjustment of the integrated energy scenario parameter set.

[0008] Secondly, this application provides a compliance verification and correction system for regional integrated energy planning, including: The simulation module is used to input the comprehensive energy scenario parameter set, which describes the characteristics of the energy system in the target area, into the comprehensive energy scenario simulation model for simulation calculation, and generate a set of simulation output indicators. The rule processing module is used to obtain the field mapping relationship between the computable rule set and the simulation output indicator set. The computable rule set is formed by extracting information from the policy text through the rule verification model. The verification module is used to perform compliance verification on the simulation output index set based on the field mapping relationship and execute the computable rule set, and generate rule satisfaction judgment results. The suggestion generation module is used to calculate the difference between the specific value of the unmet field in the simulation output index set and the rule threshold when the rule satisfaction determination result is unsatisfied, and to generate correction suggestions based on the difference to guide the adjustment of the integrated energy scenario parameter set.

[0009] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the compliance verification and correction method for regional integrated energy planning as described above.

[0010] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the compliance verification and correction method for regional integrated energy planning as described above.

[0011] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the compliance verification and correction method for regional integrated energy planning as described above.

[0012] According to the specific embodiments provided in this application, the following technical effects are disclosed: 1. Achieving Automated Closed-Loop Optimization: This invention constructs a complete closed loop of "simulation-verification-gap quantification-correction suggestions," transforming the original trial-and-error process relying on manual experience into an automated, clearly guided iterative optimization process. By automatically calculating the gaps in unmet requirements and generating specific correction suggestions, it guides planners to directly adjust scenario parameters, significantly improving the efficiency and quality of regional integrated energy planning.

[0013] 2. Enhancing the compliance and feasibility of planning outcomes: By formalizing rules derived from policy texts and technical specifications into computable expressions and automating the verification of simulation results, it can be ensured that planning schemes meet various rigid constraints (such as storage ratios and carbon emission intensity) from the proposal stage. This avoids significant adjustments or abandonment due to non-compliance in later stages, enhancing the engineering credibility and feasibility of planning conclusions.

[0014] 3. Enhanced traceability and comparability of solutions: By establishing a clear mapping relationship between the simulation output index set and the computable rule expressions, each verification result can be traced back to specific simulation data and rule clauses. This standardized processing method ensures that the verification process and results of different scenario solutions have a unified caliber and standard, facilitating horizontal comparative analysis of multiple solutions and improving the scientific rigor and precision of the planning process. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the compliance verification and correction method for regional integrated energy planning in one embodiment of this application; Figure 2 This is a reference schematic diagram of computable rules and their constraints in one embodiment of this application; Figure 3 This is a schematic diagram of the architecture of a compliance verification and correction method device for regional integrated energy planning in one embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] The compliance verification and correction method for regional integrated energy planning in this embodiment is executed by a computer device, which can be a terminal computing device or a server. The terminal computing device can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, etc. The server can be a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0020] This application provides a compliance verification and correction method for regional integrated energy planning, aiming to address the technical problems in the prior art where there is a disconnect between simulation results of energy planning and policies, regulations, and technical standards, as well as the lack of quantitative guidance and closed-loop correction mechanisms after non-compliance items are discovered. This method significantly improves the efficiency, accuracy, and scientific rigor of regional integrated energy planning by constructing an automated closed-loop process of "simulation – verification – quantitative gap – correction suggestions."

[0021] In one embodiment of the present invention, the method for compliance verification and correction of regional integrated energy planning specifically includes: Step S110: Based on the comprehensive energy scenario parameter set describing the characteristics of the energy system in the target area, the comprehensive energy scenario parameter set is input into the comprehensive energy scenario simulation model for simulation calculation, and a simulation output index set is generated.

[0022] This step forms the data foundation of the entire methodology, transforming abstract planning schemes into quantifiable and analyzable data. Specifically, planners first define a comprehensive energy scenario parameter set describing the characteristics of the target region's energy system, such as projected economic growth rate, industrial structure, energy demand forecasts, and technology options. Subsequently, a comprehensive energy scenario simulation model (e.g., LEAP or GenX models) uses these parameters to perform comprehensive simulation calculations of energy flow, cost-effectiveness, and environmental impact under the planning scenario. This step unifies the previously scattered and heterogeneous planning information, generating a standardized and structured set of simulation output indicators, including energy flow maps, energy balance sheets, carbon emission results, and equipment configuration lists. This resolves the issue of incomparable results due to inconsistent definitions in previous techniques, providing reliable and consistent data input for subsequent automated verification.

[0023] For example, basic data related to electricity, heat, gas and hydrogen energy in the target area can be collected, and the basic data can be cleaned, standardized and updated to form a resource dataset. Based on the resource dataset, a comprehensive energy scenario parameter set can be constructed. The scenario parameter set is used to describe the values ​​of key parameters such as energy demand, supply structure, cross-energy conversion efficiency, energy storage configuration, emission factor and hydrogen energy route under different scenarios.

[0024] For example, the integrated energy scenario simulation model can be composed of an integrated energy scenario simulation sub-model and a power system planning sub-model: the integrated energy scenario simulation sub-model is used to conduct energy flow and emission simulations under the boundary of multiple energy products such as electricity, heat, gas and hydrogen, and output energy flow diagrams, energy balance sheets and carbon emission results; the power system planning sub-model is used to plan and evaluate the power subsystem's power structure, energy storage configuration and operation constraints under multiple scenario conditions.

[0025] The integrated energy scenario simulation sub-model is the LEAP model or its equivalent integrated energy scenario simulation model; the power system planning sub-model is the GenX model or its equivalent power system planning model. Step S120: Obtain the field mapping relationship between the computable rule set and the simulation output indicator set. The computable rule set is formed by extracting information from policy text and technical boundary text through the rule verification model.

[0026] The core objective of this step is to transform unstructured compliance requirements, expressed in natural language, into instructions that computers can understand and execute. In existing technologies, planners need to manually interpret policy documents and technical standards, a method that is inefficient and prone to errors. This step extracts rule elements from policy texts and technical boundary texts, automatically identifying key information such as applicable objects, indicator definitions, comparison symbols (greater than, less than, belonging to an interval, etc.), and threshold parameters, forming structured rule metadata. This metadata is then translated into formalized, computable rule expressions. Simultaneously, the system establishes a mapping relationship between the input variables required for these expressions and the corresponding data fields in the simulation output indicator set generated in the previous step. This automated connection process successfully solves the pain point of the difficulty in automatically connecting simulation results with policy constraints, paving the way for automated verification.

[0027] Step S130: Based on the field mapping relationship, execute the computable rule set, perform compliance verification on the simulation output index set, and generate rule satisfaction judgment results.

[0028] This step is the core of automated compliance verification. The system utilizes the mapping relationship established in the previous step to extract relevant data from the simulation output indicator set and feeds it as input to the corresponding computable rule expressions. For example, the simulation-derived values ​​of "energy storage system power" and "newly installed photovoltaic capacity" are substituted into the "energy storage ratio" rule expression for calculation and comparison. This process is automated and can quickly and accurately verify hundreds or thousands of rules simultaneously. After verification, the system generates a clear "satisfied" or "unsatisfied" result for each rule. This significantly improves the efficiency and coverage of compliance assessments, avoiding potential omissions and errors that may occur with manual verification.

[0029] Step S140: When the rule satisfaction determination result is not satisfied, calculate the difference between the specific value of the unsatisfied field in the simulation output index set and the rule threshold, and generate correction suggestions based on the difference to guide the adjustment of the integrated energy scenario parameter set.

[0030] This step is crucial for achieving "closed-loop correction" in this invention, directly addressing the significant shortcoming of existing technologies that can only identify problems but cannot provide solutions. Once a non-compliant rule is detected, the system doesn't simply issue an alert but performs in-depth quantitative analysis. It precisely calculates the specific gap between the simulated value and the rule threshold, i.e., the "gap amount." Furthermore, the system intelligently generates specific and actionable correction suggestions by combining this gap amount, the rule type of the non-compliant item (e.g., efficiency constraint, emission constraint, or policy constraint), and the original scenario parameters associated with that rule. For example, it might suggest "increasing the energy storage system power by at least 3 megawatts," rather than vaguely indicating "insufficient energy storage." By providing quantitative correction instructions, this method transforms the iterative optimization process of planning schemes from "blind trial and error" to "precise guidance," significantly shortening the planning cycle and improving the final quality of the scheme.

[0031] In summary, this invention transforms the original trial-and-error process of adjusting solutions, which relied on manual experience, into an automated and clearly guided iterative optimization process by constructing a complete closed loop of "simulation-verification-gap quantification-correction suggestions." By automatically calculating the gaps in non-compliant items and generating specific correction suggestions, it guides planners to directly adjust scenario parameters, significantly improving the efficiency and quality of regional integrated energy planning. Specifically, by formalizing rules derived from policy texts and technical specifications into computable expressions and automating the verification of simulation results, it ensures that planning schemes meet all rigid constraints (such as storage-allocation ratios and carbon emission intensity) at the proposal stage. This avoids significant adjustments or abandonment due to non-compliance later on, enhancing the engineering credibility and feasibility of the planning conclusions. Furthermore, by establishing a clear mapping relationship between the simulation output indicator set and computable rule expressions, each verification result can be traced back to specific simulation data and rule clauses. This standardized approach ensures that the verification process and results of different scenario schemes have a unified caliber and standard, facilitating horizontal comparative analysis of multiple schemes and improving the scientific rigor and precision of the planning process.

[0032] In one specific implementation, compliance verification includes verifying at least one of the following: energy conservation closed-loop verification, technical efficiency range verification, resource utilization verification, emission verification, policy constraint verification, safety assurance verification, economic verification, or scale constraint verification.

[0033] This design aims to construct a comprehensive, multi-dimensional verification system to ensure the rigor and feasibility of the planning scheme. By expanding the verification scope from a single dimension to cover multiple key aspects such as the physical laws of the energy system, technical and economic performance, environmental impact, policies and regulations, and supply security, a comprehensive evaluation of the planning scheme can be conducted. This systematically identifies various potential risks and irrationalities, greatly improving the overall quality and practical operability of the planning results.

[0034] Furthermore, the technical efficiency range verification includes verifying the thermal conversion efficiency or electrified heating parameters. This refined limitation aims to ensure that the energy conversion technologies adopted in the planning scheme are advanced and efficient. Energy conversion efficiency is a core indicator for measuring the performance of an energy system, directly related to energy utilization and operating costs. By rigorously verifying the thermal conversion efficiency of equipment such as combined heat and power plants and industrial boilers, as well as the electrified heating parameters of equipment such as electric boilers and heat pumps, it is possible to effectively avoid selecting outdated or unreasonable technologies and equipment in the planning process, thereby ensuring the efficient operation of the energy system from the source and promoting the application of energy-saving technologies.

[0035] Among them, the verification of heat conversion efficiency includes: verifying whether the efficiency of industrial boilers, cogeneration boilers or district heating boilers falls within their respective preset efficiency ranges.

[0036] This is a concretization and instantiation of technical efficiency verification. For example, by setting the thermal conversion efficiency range of industrial boilers to [0.80, 0.90] and the comprehensive energy utilization rate range of combined heat and power (CHP) to [0.70, 0.90], advanced technical standards and best practices in the industry can be solidified into executable verification rules. When simulation results show that the thermal conversion efficiency of the industrial boiler selected in a certain planning scheme is only 0.75, the system can immediately identify that it does not meet the requirements of technological advancement and prompt the planner to make adjustments. In this way, it is ensured that every key technology in the planning scheme meets recognized engineering standards, avoiding the infeasibility or low efficiency of the scheme in actual construction due to improper technology selection.

[0037] In addition, the verification of electrified heating parameters includes: verifying whether the efficiency of the electric boiler is higher than the preset efficiency threshold or whether the energy efficiency ratio of the heat pump falls within the preset energy efficiency ratio range.

[0038] This is also a further refinement of the technical efficiency verification branch, with a particular focus on electrified heating technologies. For example, the rules could stipulate that the electrothermal conversion efficiency of electric boilers must be higher than 0.95, or the energy efficiency ratio of air source heat pumps must be within a reasonable range of 2 to 4. By verifying these key parameters, it can be ensured that the planning scheme promotes the electrification of energy consumption while employing efficient electrification technologies, avoiding the phenomenon of "inferior electrification" that replaces traditional energy with "high-energy-consuming" electricity. This ensures the high efficiency of the entire energy system while achieving cleaner heating. Specifically, the heat conversion verification model used for heat conversion efficiency verification is as follows: ;in, For the first Input energy of the thermodynamic conversion unit For the first The output heat of the heat conversion unit This refers to thermal efficiency or overall energy utilization rate.

[0039] For example, the heat conversion unit can be an industrial boiler, an industrial kiln, a combined heat and power (CHP) unit, a district heating boiler, a solar thermal utilization unit, and a geothermal direct utilization unit. The regulations for the thermal efficiency or overall energy utilization rate of these devices are as follows: [Specific requirements for industrial boilers are missing from the original text]. Industrial kilns Combined heat and power equipment District heating boilers Solar thermal utilization equipment Geothermal direct utilization equipment This means that verifying the thermal conversion efficiency requires determining whether the thermal efficiency or overall energy utilization rate of these devices meets the corresponding rule requirements.

[0040] Specifically, the verification of electrified heating parameters should at least use an electric boiler verification model and a heat pump verification model. For example, the electric boiler verification model is as follows: The formula for the heat pump verification model is: ;in, This is for generating heat in electric boilers. Electricity consumption of electric boilers For the electrothermal conversion efficiency of electric boilers, For the power consumption of heat pumps, To generate heat for the heat pump, This refers to the energy efficiency ratio of the heat pump.

[0041] By using the above-mentioned electric boiler verification model and heat pump verification model, we can verify whether the electrothermal conversion efficiency of the electric boiler and the energy efficiency ratio of the heat pump meet the requirements of the rules.

[0042] In one specific implementation, emissions verification includes verifying the fuel emission factor or the carbon emission intensity of gas-fired power plants.

[0043] This constraint directly serves the "dual carbon" objective, aiming to control carbon emissions from energy activities at their source. By standardizing and verifying the emission factors of different fuels (such as coal and natural gas), or setting clear upper limits for carbon emission intensity in key processes such as gas-fired power generation, macro-level emission reduction targets can be broken down and implemented into specific planning parameters. This allows planning schemes to accurately assess and constrain their carbon emission performance during the design phase, ensuring they meet regional carbon peaking and carbon neutrality pathway requirements, thus making energy planning a powerful tool for achieving a green and low-carbon transition.

[0044] Furthermore, the verification of carbon emission intensity for gas-fired power generation includes verifying whether the carbon emission intensity of combined cycle gas turbines or internal combustion engines falls within their respective preset intensity ranges.

[0045] This represents a refined management of emissions verification. For example, regulations can require that the carbon emission intensity of combined cycle gas turbines must be within the range of [350, 400] gCO2 / kWh. If a planning scheme selects a certain type of gas turbine, and its simulated carbon emission intensity is 410 gCO2 / kWh, the system will determine that it does not meet environmental protection requirements. By classifying and limiting the carbon emission intensity of different types of power generation equipment, planning schemes can be guided to choose cleaner and more efficient power generation technologies, thereby ensuring the achievement of the low-carbon goals of the entire regional energy system in detail.

[0046] Furthermore, the verification of fuel emission factors should include at least a verification of standard coal emissions and a verification of relative emission reductions from natural gas. The verification of standard coal emissions is as follows: The emission factor of natural gas relative to coal is reduced to: In the formula, This refers to the consumption of standard coal. This represents the carbon dioxide emissions corresponding to standard coal. and These are emission factors for coal and gas, respectively.

[0047] In one specific implementation, the policy constraint verification includes verifying the allocation ratio or duration of new energy storage.

[0048] The purpose of this restriction is to ensure that the planning scheme strictly complies with national and local mandatory policies regarding the development of new energy. With the large-scale grid connection of fluctuating renewable energy sources such as wind power and photovoltaics, configuring energy storage systems has become a key policy requirement for ensuring the stable operation of the power grid. By formalizing policy clauses such as "energy storage ratio not less than 15%" and "energy storage duration not less than 2 hours" into calculable rules, this method can automatically verify whether the planned new energy projects meet these mandatory requirements. This solves the pain point of the disconnect between simulation results and policy requirements in the background technology, ensuring the compliance and feasibility of the planning scheme.

[0049] For example, the allocation ratio of new energy to energy storage / Or storage duration The verification is as follows: or market-oriented projects In the formula, For newly built wind power / solar power capacity, For energy storage power, This refers to the continuous operating time of the energy storage system.

[0050] In one specific implementation, the safety assurance verification includes verifying the number of days of coal supply inventory.

[0051] This constraint aims to integrate the national strategic goal of energy security into the planning verification process. By setting minimum thresholds for the number of days of coal mine inventory or the number of days of available coal inventory at power plants (e.g., power plant inventory must meet 30 days of daily coal consumption) and verifying inventory levels in simulated scenarios, the resilience of planning schemes in the face of extreme situations such as supply chain disruptions or sudden increases in demand can be effectively assessed. This ensures that energy planning not only focuses on economics and environmental protection but also considers the reliability and security of supply as important factors, thereby enhancing the strategic value of planning schemes.

[0052] Furthermore, verifying the coal supply guarantee inventory days can include checking whether the coal mine inventory days or the power plant available coal inventory days meet their respective preset thresholds. This is a concrete implementation of safety assurance verification. For example, the rules can stipulate that the coal mine inventory days should not be less than 15 days, and the power plant inventory days should not be less than 30 days. During verification, the system will calculate the values ​​of these two key indicators based on the coal production, consumption, and inventory data output from the simulation, and compare them with the preset thresholds. If it is found that under a high-consumption scenario, the power plant inventory days drop to 20 days, the system will issue an early warning and suggest increasing coal reserves or signing more stable long-term contracts. Through this refined safety verification, the resilience of the energy system can be significantly enhanced.

[0053] For example, the verification of coal supply inventory days includes at least: In the formula, This refers to the coal mine inventory. For the daily production capacity of coal mines, This refers to the amount of coal available for the power plant. Daily coal consumption of power plants In one specific implementation, the resource utilization rate verification includes waste heat recovery rate verification, the economic efficiency verification includes consistency verification between new energy power generation efficiency and annual power generation, and the scale constraint verification includes cost-emission-scale constraint verification of hydrogen production route.

[0054] For example, the recovery accounting model used for waste heat recovery rate verification is as follows: In the formula, Based on recoverable waste heat, To recover usable heat, This refers to the waste heat recovery rate.

[0055] For example, the consistency verification of new energy power generation efficiency and annual power generation includes at least photovoltaic power generation verification. Photovoltaic power generation verification includes at least constructing a photovoltaic power generation calculation model based on irradiance, module area, energy conversion efficiency, and performance ratio; calculating the time-series output power of the photovoltaic system; and integrating or accumulating the output power within a statistical period to obtain the annual power generation. The photovoltaic system at time... Output power With annual power generation They respectively satisfy the following relations: in, time The output power (kW) of the photovoltaic AC side or DC side is defined by the system boundary; The effective energy conversion efficiency of photovoltaic modules (or systems); time The solar irradiance incident on the component plane; The effective light-receiving area of ​​the component; This is the performance ratio, used to characterize the combined impact of system losses such as temperature loss, inverter loss, line loss, and shading / dust accumulation; it is usually taken as 0–1. Annual power generation (kWh); It is the time step; It is a time index; and uses a reasonable range of annual power generation per unit area of ​​monocrystalline silicon modules as a consistency criterion to verify the simulation output.

[0056] For example, the cost-emissions-scale constraint verification of hydrogen production routes includes at least hydrogen production cost accounting, carbon emission accounting, and scale constraints, wherein: In the formula, Number the hydrogen production route. For the first Hydrogen production or supply from the route. For the unit cost of hydrogen production, Carbon emission intensity per unit This represents the upper limit of the scale.

[0057] Furthermore, the energy conservation closure check satisfies the energy balance residual constraint for each energy category: in, This represents an energy category index used to distinguish different energy carriers such as electricity, heat, gas, and hydrogen. Energy categories Energy balance residual, The smaller the value, the better the energy conservation principle is maintained; It is an energy product category The One source of supply (excluding inventory); It is an energy product category The The destination of the demand; It is an energy product category The sum of losses or unusable losses incurred during the transmission, distribution, storage, and conversion processes; It is an energy product category The change in inventory is positive, indicating an increase in inventory, meaning that some supply has not been consumed and has entered the inventory; negative indicates a decrease in inventory, meaning that some demand has been met by the release of inventory. Energy categories In the The net conversion amount in a cross-energy conversion process is symbolized by the energy class. The net consumption is positive, used to uniformly characterize the energy flow relationship of different energy categories in inter-energy conversion processes such as power generation, heating, cooling, hydrogen production, energy storage charging and discharging, and waste heat recovery. At that time, it indicates the impact of the energy conversion process on energy categories. This results in net consumption, meaning the conversion process involves energy categories. As an input energy carrier, when At that time, it indicates the impact of the energy conversion process on energy categories. Net output is generated, meaning the conversion process results in a shift towards energy products. Output energy; Energy categories The allowable error threshold.

[0058] In one specific implementation, the step of obtaining a computable rule expression includes: extracting rule elements from policy text and technology boundary text to obtain structured rule metadata; and translating the rule metadata into a computable rule expression.

[0059] This design reveals the core technological path to automating rule-making. It utilizes Natural Language Processing (NLP) and information extraction technologies to automatically identify and extract key rule elements, such as "allocation ratio," "not less than," and "15%," from massive amounts of unstructured policy documents and technical manuals, storing them in a standardized database (rule metadata). Then, a translation engine automatically converts this structured metadata into computer-executable logical expressions. This process fundamentally solves the bottleneck of manual rule interpretation and transcription, and is a crucial prerequisite for automating and intelligentizing the entire verification process.

[0060] For example, this step can identify keywords, core clauses, scope of application, comparison symbols and threshold parameters, and standardize their storage according to dimensions such as policy type, issuing agency, timeliness and applicable objects to form a computable rule set; the rule set can include fields such as rule ID, indicator name, threshold / range, unit caliber, statistical period, scope of application, effective time, priority and conflict handling strategy, thereby supporting the traceability review and version management of rules.

[0061] In one specific implementation, the compliance verification and correction method for regional integrated energy planning further includes: outputting a verification report containing rule details and corresponding simulation output values, rule thresholds, satisfaction results, gap amounts, and correction suggestions.

[0062] This step aims to provide planners with a comprehensive, clear, and actionable decision support tool. The report goes beyond simply stating "pass" or "fail," detailing the verification process and results for each rule in a highly structured manner. Planners can clearly see the specific requirements of each rule (rule details and thresholds), the performance of the current solution (simulation output values), existing problems (unmet criteria and quantifiable gaps), and how to resolve these problems (specific corrective suggestions). This comprehensive report greatly enhances the transparency and traceability of the planning process, providing solid data support for scientific decision-making and solution optimization, forming a complete closed loop for business applications.

[0063] It is important to note that the key aspect of this invention lies in constructing a complete closed loop of "simulation-verification-gap quantification-correction recommendations," transforming the original trial-and-error process of adjusting solutions, which relied on manual experience, into an automated and clearly guided iterative optimization process. By automatically calculating the gaps in non-compliant items and generating specific correction recommendations, planners can be guided to directly adjust scenario parameters, significantly improving the efficiency and quality of regional integrated energy planning. Specifically, by formalizing rules derived from policy texts and technical specifications into computable expressions and automating the verification of simulation results, it ensures that planning schemes meet various rigid constraints (such as storage-to-allocation ratios and carbon emission intensity) at the proposal stage. This avoids significant adjustments or abandonment due to non-compliance in later stages, enhancing the engineering credibility and feasibility of the planning conclusions. Furthermore, by establishing a clear mapping relationship between the simulation output indicator set and computable rule expressions, each verification result can be traced back to specific simulation data and rule clauses. This standardized approach ensures that the verification process and results for different scenario solutions have a unified caliber and standard, facilitating cross-sectional comparative analysis of multiple solutions and enhancing the scientific rigor and precision of the planning process. The verification items and the computable expressions used in the verification can change dynamically and are usually updated as policy texts and technical boundary texts are updated. Therefore, the above specific descriptions of each verification item should not be construed as specific limitations of this invention.

[0064] For example, Figure 2 The document provides a series of specific computable expressions and their corresponding verification constraints.

[0065] This application also provides a compliance verification and correction system for regional integrated energy planning. This system aims to implement the aforementioned method through a modular approach, with each module working collaboratively to form a complete automated verification and correction system. The device includes a simulation module, a rule processing module, a verification module, and a suggestion generation module.

[0066] Specifically, the simulation module is the core of data generation. It receives scenario parameters from external input and, based on these parameters, calls internal or external integrated energy scenario simulation models to perform simulation calculations to generate a set of simulation output indicators. For example, after planners input scenario parameters such as the photovoltaic installation target and electricity price forecast for a region over the next five years, the simulation module runs the simulation and outputs detailed data on power generation, cost, carbon emissions, etc., under that scenario, forming a structured set of simulation output indicators, which is then provided to the rule processing module and the verification module.

[0067] The rule processing module is the rule management and translation center. It is used to acquire computable rule expressions and establish a mapping relationship between computable rule expressions and simulation output indicator sets. This module is responsible for extracting or receiving formalized computable rule expressions from policy libraries and standard libraries. Furthermore, it analyzes the input data required by these rule expressions and intelligently searches for and matches corresponding fields in the simulation output indicator set output by the simulation module, thereby establishing a dynamic link between rules and data. This mapping relationship forms the basis for subsequent automated verification.

[0068] The compliance verification module is the executor of compliance determination. Based on the mapping relationship established by the rule processing module, it executes computable rule expressions to perform compliance verification on the simulation output indicator set, generating a rule satisfaction determination result. It iterates through all rules, extracts data from the simulation output indicator set using the mapping relationship, substitutes it into the rule expression for calculation and judgment, and finally outputs a "satisfied" or "not satisfied" result for each rule. This result is then sent to the suggestion generation module.

[0069] The suggestion generation module acts as the decision-making brain for closed-loop optimization. It receives scenario parameters and, when the rule satisfaction determination result generated by the verification module indicates a non-compliance, calculates the difference between the simulation output value of the non-compliant item and the threshold required by the rule. Based on this difference, it generates correction suggestions to guide the adjustment of scenario parameters. This module is activated upon receiving a "non-compliance" signal. It not only calculates the specific difference but also traces back to the original scenario parameter causing the non-compliance and, combined with the rule type, generates precise correction instructions such as "It is recommended to adjust parameter A from value X to no less than Y." These correction suggestions provide planners with direct and actionable optimization paths.

[0070] In summary, this application fully considers the shortcomings in regional integrated energy planning practice, such as "the difficulty in verifying the computable consistency of multi-scenario simulation results with policy provisions and key technology boundaries, the incomparability of results due to inconsistent definitions of different scenarios, and the lack of identification and rectification loop for non-compliance items," and proposes a compliance verification method for regional integrated energy system planning results. This method, based on the governance and standardization of multi-source fundamental data (electricity, heat, gas, hydrogen, etc.), constructs a unified set of scenario parameters to drive a comprehensive energy scenario simulation model to conduct multi-scenario extrapolation, obtaining outputs such as energy flow maps, energy balance sheets, and carbon emissions. It formalizes policy thresholds, technical boundary parameters, and emission accounting standards into a set of computable rules, performing rule checks on the simulation output. These checks include energy conservation closure, thermodynamic conversion efficiency, electrification heating parameters, waste heat recovery rate, fuel emission factors and substitution emission reduction, gas-electricity carbon intensity range, new energy allocation and storage ratio and duration, coal supply and inventory, and cost-emission-scale constraints of hydrogen production routes. If these constraints are not met, the method further calculates the discrepancy and generates parameter correction suggestions, thus forming a closed-loop mechanism of "simulation output—rule check—discrepancy—correction suggestions." Through this closed-loop mechanism, this application can achieve unified result comparison and traceable verification in multi-scenario planning analysis, improving the automation and quantitative rectification support capabilities of compliance assessment, and enhancing the engineering credibility and feasibility of planning conclusions.

[0071] In a specific application scenario, a municipal government planning department plans to construct a large-scale photovoltaic power generation base in a new industrial park to respond to the national "dual-carbon" initiative and increase the proportion of green energy in the park. According to the city's energy development plan, it is explicitly required that "newly constructed centralized photovoltaic projects should be equipped with energy storage systems at a rate of no less than 15% of the installed capacity, and the energy storage systems must have a continuous discharge capacity of no less than 2 hours." The regional integrated energy planning compliance verification and correction method and system of this invention plays a crucial role in this scenario.

[0072] First, planners activate the regional integrated energy planning compliance verification and correction system of this invention. The rule element extraction function in the system's rule processing module is activated. This function automatically scans and parses the city's energy development planning documents (i.e., policy text) using natural language processing technology. It successfully identifies keywords such as "centralized photovoltaic," "energy storage ratio," "not less than," "15%," "energy storage duration," and "2 hours," and extracts these structured rule metadata. Subsequently, the translator within the module automatically converts this metadata into two computable rule expressions: and This step demonstrates the ability to automatically translate unstructured text into computable rules.

[0073] Next, planners input the preliminary planning scheme for the photovoltaic base as scenario parameters into the system interface. For example, they set a "high proportion of renewable energy" scenario, in which the newly installed photovoltaic capacity... The capacity is 500 megawatts (MW). Due to initial cost control considerations, the power of the accompanying energy storage system is [not specified]. The initial plan is for 60 megawatts, with a storage duration of [missing information]. The duration is 2 hours. These scenario parameters are sent to the simulation module.

[0074] After receiving the scenario parameters, the simulation module calls its internally integrated comprehensive energy scenario simulation model (such as GenX) to perform simulation calculations on the planning scheme. The simulation process not only verifies the grid acceptance capacity under this configuration, but also generates a set of simulation output indicators containing a detailed equipment configuration list. This indicator set explicitly includes the fields "new photovoltaic installed capacity = 500MW", "energy storage system power = 60MW", and "energy storage duration = 2h".

[0075] After the simulation is complete, the verification module starts immediately. Based on the mapping relationship established by the rule processing module, it obtains the simulation output. and The value is calculated and substituted into the expression of the first rule for verification. The calculation process is as follows: The minimum energy storage power required by the rule is 0.15. 500MW = 75MW. The verification module compares the simulated energy storage power of 60 MW with the required 75 MW and determines that it is "not met". For the second rule, the simulated energy storage duration is 2 hours, which meets the requirement of "not less than 2 hours", and the determination result is "met".

[0076] Because a "non-compliance" item was found, the suggestion generation module was triggered. It first calculated the discrepancy of 75 MW - 60 MW = 15 MW for the non-compliance of the "reservoir allocation ratio" rule. Then, based on this discrepancy, the rule type (policy constraint), and associated scenario parameters... This resulted in a clear and quantifiable recommendation for correction: "In order to meet the policy requirement that the energy storage ratio be no less than 15%, it is recommended to increase the power of the energy storage system by at least 15 MW, that is, from 60 MW to no less than 75 MW."

[0077] Finally, the system automatically generates and outputs a detailed verification report. This report contains all the information from the entire verification process: details of the verified rule ("storage ratio ≥ 15%), the corresponding simulation output value ("energy storage power = 60MW"), the threshold required by the rule ("75MW"), a clear "non-compliance" judgment, the precise discrepancy amount ("15MW"), and the aforementioned operational correction suggestion. After seeing this report, planners no longer need to perform any guessing or complex calculations; they can directly adjust the energy storage power in the planning scheme to 75 MW based on the suggestion, and then resubmit for a new round of rapid verification until all rules are met.

[0078] This complete embodiment demonstrates how the present invention seamlessly integrates multiple stages, including automatic policy text parsing, multi-scenario simulation, automated compliance verification, quantitative gap calculation, and intelligent correction suggestion generation, forming a highly efficient, intelligent, and closed-loop planning support decision-making system. It not only addresses many pain points in the background technology but also significantly enhances the scientific rigor, compliance, and feasibility of regional integrated energy planning by providing precise quantitative guidance, showcasing its practical application value.

[0079] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0080] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0081] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0082] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0083] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0084] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0085] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0086] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for compliance verification and correction of regional integrated energy planning, characterized in that, include: Based on the comprehensive energy scenario parameter set describing the characteristics of the energy system in the target area, the comprehensive energy scenario parameter set is input into the comprehensive energy scenario simulation model for simulation calculation, and a simulation output index set is generated. Obtain the field mapping relationship between the computable rule set and the simulation output indicator set; the computable rule set is formed by extracting information from the policy text through a rule verification model. Based on the field mapping relationship, the computable rule set is executed to perform compliance verification on the simulation output index set and generate rule satisfaction determination results; When the rule satisfaction determination result is not satisfied, the difference between the specific value of the unsatisfied field in the simulation output index set and the rule threshold is calculated, and a correction suggestion is generated based on the difference to guide the adjustment of the integrated energy scenario parameter set.

2. The method for compliance verification and correction of regional integrated energy planning according to claim 1, characterized in that, The compliance verification includes verifying at least one of the following: Energy conservation closed-loop check, technical efficiency range check, resource utilization rate check, emission check, policy constraint check, safety assurance check, economic check or scale constraint check.

3. The method for compliance verification and correction of regional integrated energy planning according to claim 2, characterized in that, The technical efficiency range verification includes verification of thermal conversion efficiency or electrified heating parameters; And / or, the emissions verification includes verifying the fuel emission factor or the carbon emission intensity of gas-fired power plants; And / or, the policy constraint verification includes verifying the allocation ratio or duration of new energy storage; And / or, the security assurance verification includes verifying the number of days of coal supply inventory.

4. The compliance verification and correction method for regional integrated energy planning according to claim 3, characterized in that, The verification of thermal conversion efficiency includes: verifying whether the efficiency of industrial boilers, cogeneration boilers or district heating boilers falls within their respective preset efficiency ranges. The verification of electrified heating parameters includes: verifying whether the efficiency of the electric boiler is higher than the preset efficiency threshold or whether the energy efficiency ratio of the heat pump falls within the preset energy efficiency ratio range.

5. The method for compliance verification and correction of regional integrated energy planning according to claim 3, characterized in that, The verification of carbon emission intensity of gas-fired power plants includes: verifying whether the carbon emission intensity of combined cycle gas turbines or internal combustion engines falls within their respective preset intensity ranges.

6. The method for compliance verification and correction of regional integrated energy planning according to claim 3, characterized in that, The verification of coal supply inventory days includes: verifying whether the coal mine inventory days or the power plant available coal inventory days meet their respective preset day thresholds.

7. The method for compliance verification and correction of regional integrated energy planning according to claim 2, characterized in that, The resource utilization rate verification includes waste heat recovery rate verification; the economic efficiency verification includes consistency verification between new energy power generation efficiency and annual power generation; and the scale constraint verification includes hydrogen production route cost-emission-scale constraint verification.

8. The method for compliance verification and correction of regional integrated energy planning according to any one of claims 1-7, characterized in that, The steps for obtaining the computable rule expression include: Rule elements are extracted from the policy text and the technology boundary text to obtain structured rule metadata; The rule metadata is translated into the computable rule expression.

9. The method for compliance verification and correction of regional integrated energy planning according to any one of claims 1-7, characterized in that, Also includes: The output includes a verification report containing rule details, the corresponding simulation output value, rule threshold, satisfaction result, gap amount, and correction suggestions.

10. A compliance verification and correction system for regional integrated energy planning, characterized in that, include: The simulation module is used to input the comprehensive energy scenario parameter set, which describes the characteristics of the energy system in the target area, into the comprehensive energy scenario simulation model for simulation calculation, and generate a set of simulation output indicators. The rule processing module is used to obtain the field mapping relationship between the computable rule set and the simulation output indicator set. The computable rule set is formed by extracting information from the policy text through the rule verification model. The verification module is used to perform compliance verification on the simulation output index set based on the field mapping relationship and execute the computable rule set, and generate rule satisfaction judgment results. The suggestion generation module is used to calculate the difference between the specific value of the unmet field in the simulation output index set and the rule threshold when the rule satisfaction determination result is unsatisfied, and to generate correction suggestions based on the difference to guide the adjustment of the integrated energy scenario parameter set.

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