Coal-to-electricity heating power grid supply and demand flexibility quantification method, optimal dispatch method and medium

Abstract

The present application discloses a kind of coal to electricity heating power grid supply and demand flexibility quantification method, optimization scheduling method and medium, quantification method includes: obtaining the basic data of power system of high-altitude coal to electricity heating area;Supply and demand flexibility quantification optimization model is constructed based on basic data, and supply and demand flexibility quantification optimization model includes supply side quantification module and demand side quantification module;Supply and demand flexibility quantification optimization model is solved based on multiple constraint conditions, and supply and demand flexibility margin is output.Optimization scheduling method is based on the construction of multiple time scale operation simulation model quantification method, with the multiple basic data of high-altitude coal to electricity scene as input, fusion extreme weather correction coefficient and multiple constraint conditions, can accurately output supply and demand flexibility margin;Collaborative optimization scheduling method is based on the construction of multiple time scale operation simulation model quantification result, solves the technical problems that supply and demand flexibility is difficult to accurately quantify in high-altitude coal to electricity heating scene, multiple time scale scheduling collaborative failure.

Description

Technical Field

[0001] This invention belongs to the field of coal-to-electricity heating power grid technology, specifically involving a method for quantifying the supply and demand flexibility of coal-to-electricity heating power grids, an optimized scheduling method, and a medium. Background Technology

[0002] High-altitude regions generally experience extreme climate characteristics such as severe winters, low air pressure, and large diurnal temperature variations. Furthermore, with the promotion of clean coal resource utilization policies, coal-to-electricity heating has become a core transformation direction for regional winter heating. However, in high-altitude coal-to-electricity heating scenarios, the power grid faces multiple prominent contradictions, leading to difficulties in accurately quantifying supply and demand flexibility and insufficient grid dispatch adaptability. This severely restricts the large-scale promotion of coal-to-electricity projects and the safe and stable operation of the power grid.

[0003] On the one hand, heating load exhibits strong seasonality and volatility: under extreme low temperatures at high altitudes, the heating load from coal-to-electricity conversion surges exponentially. Coupled with environmental factors such as low air pressure and high wind speeds, the output of traditional power sources such as thermal power units and hydropower units decreases significantly, and the output of new energy power generation (wind power and photovoltaic) fluctuates even more due to the influence of high-altitude climate, resulting in a significant limitation on the grid's supply-side regulation capacity. At the same time, the heating load from coal-to-electricity conversion is mainly for residential and industrial centralized heating, and the electricity consumption period is highly coupled with the peak and valley periods of the power grid, resulting in a prominent supply-demand mismatch problem.

[0004] On the other hand, demand-side flexibility resources have not been effectively quantified and utilized.

[0005] Currently, there is still a lack of technology for quantifying the power grid supply and demand flexibility in high-altitude coal-to-electricity heating scenarios. It is difficult to accurately assess the power grid supply and demand flexibility margin, and it cannot provide a scientific basis for power grid planning and dispatching in coal-to-electricity heating areas. This has become a key technical bottleneck restricting the transformation of coal-to-electricity clean heating in high-altitude areas and ensuring the safe and stable operation of the power grid. Summary of the Invention

[0006] The purpose of this invention is to achieve precise quantification of the flexibility of the supply side and the demand side, thereby accurately outputting the supply and demand flexibility margin, and realizing the quantification method, optimization scheduling method and medium for the supply and demand flexibility of the coal-to-electricity heating power grid based on the supply and demand flexibility margin.

[0007] To achieve the above objectives, this invention proposes a method for quantifying the supply and demand flexibility of a coal-to-electricity heating power grid. The method includes: acquiring basic power system data for high-altitude coal-to-electricity heating areas, including load data, thermal power unit data, hydropower station data, flexibility resource information, extreme weather characteristic data, thermal comfort thresholds, and power transmission contract parameters; constructing a supply and demand flexibility quantification optimization model based on the basic data, including a supply-side quantification module and a demand-side quantification module; and solving the supply and demand flexibility quantification optimization model based on multiple constraints, outputting the supply and demand flexibility margin. These multiple constraints include risk constraints, thermal comfort constraints, extreme weather adaptation constraints, grid security constraints, energy storage operation constraints, and power transmission contract constraints.

[0008] In one optional implementation, a quantitative optimization model for supply and demand flexibility is constructed based on the aforementioned basic data, specifically including: calculating a high-altitude correction factor based on extreme weather characteristic data and equipment or fuel characteristic data of the high-altitude coal-to-electricity heating area; calculating the supply-side flexible response capability based on the high-altitude correction factor, wherein the supply-side flexible response capability includes the supply-side upward adjustment capability and the supply-side downward adjustment capability; calculating the demand-side flexible adjustment demand based on the high-altitude correction factor, wherein the demand-side adjustment demand includes the demand-side upward adjustment capability and the demand-side downward adjustment capability; and obtaining the quantitative optimization model for supply and demand flexibility based on the supply-side flexible response capability, the demand-side adjustment demand, and the high-altitude correction factor.

[0009] In one optional implementation, the supply-side flexible response capability is calculated based on the high-altitude correction factor, specifically including: dividing the supply-side flexible resources into at least thermal power units, hydropower stations, energy storage devices, wind and solar power stations, and V2G resources; calculating the upward and downward adjustment capabilities of the supply-side flexible resources based on the high-altitude correction factor; and summing the upward and downward adjustment capabilities of all supply-side resources to obtain the supply-side flexible response capability.

[0010] In one optional implementation, the demand-side flexible adjustment demand is calculated based on the high-altitude correction factor, specifically including: constructing the demand-side quantification module with the thermal comfort threshold as the core constraint and in combination with the high-altitude correction factor; the demand-side quantification module includes the upward and downward adjustment capabilities of transferable loads and interruptible loads; based on the high-altitude correction factor, summarizing the upward adjustment capabilities of transferable loads and interruptible loads, and summarizing the downward adjustment capabilities of transferable loads and interruptible loads, to obtain the demand-side flexible adjustment demand.

[0011] In one optional implementation, a high-altitude correction factor is calculated based on extreme weather characteristic data and equipment or fuel characteristic data of the high-altitude coal-to-electricity heating area. Specifically, this includes: calculating an environmental characteristic correction factor, which characterizes the fundamental correction impact of high-altitude low temperature and low air pressure on the operating environment of thermal power units, hydropower units, new energy sources, and energy storage equipment; determining equipment or fuel characteristic loss coefficients, which characterize the assignment of values ​​for high-proportion high-alkali coal, physical constraints of the boiler-circulation system, and fuel combustion stability characteristics; and calculating the high-altitude correction factor based on the environmental characteristic correction factor and the equipment or fuel characteristic loss coefficients, whereby the high-altitude correction factor characterizes the comprehensive correction impact of quantifying high-altitude geographical factors and extreme weather on the output and load characteristics of power equipment.

[0012] In one optional implementation, solving the supply and demand flexibility quantification optimization model based on multiple constraints and outputting the supply and demand flexibility margin includes: solving the supply-side quantification module and the demand-side quantification module based on the multiple constraints to obtain basic adjustment capacity parameters, which include the total upward adjustment capacity and the total downward adjustment capacity of the supply side, and the total upward adjustment demand and the total downward adjustment demand of the demand side; constructing a supply and demand flexibility objective function by combining the multiple constraints and the basic adjustment capacity parameters with the goal of maximizing the supply and demand flexibility margin; solving the supply and demand flexibility objective function to obtain the upward flexibility margin and the downward flexibility margin; and correcting the upward flexibility margin and the downward flexibility margin based on the high-altitude correction factor to obtain the supply and demand flexibility margin.

[0013] On the other hand, this invention also proposes a method for coordinated optimization and scheduling of supply and demand flexibility in coal-to-electricity heating power grids, comprising: constructing a multi-time-scale operation simulation model based on any of the aforementioned methods for quantifying the supply and demand flexibility of coal-to-electricity heating power grids, wherein the multi-time-scale operation simulation model includes a weekly-scale operation simulation model, a day-ahead-scale operation simulation model, and an intraday-scale operation simulation model; running the weekly-scale operation simulation model based on power system basic data, new energy output forecasts, and high-altitude correction factors, performing overall energy balance and regulation resource allocation simulation at the weekly scale, and outputting weekly-scale constraints, wherein the weekly-scale constraints include boundary constraint parameters, regulation resource allocation schemes, and scenario-based optimization results; and running the day-ahead-scale operation simulation model based on the weekly-scale constraints, performing supply and demand balance and scheduling scheme simulation based on general scenarios at the day-ahead scale, and obtaining a day-ahead scheduling scheme.

[0014] Run the intraday-scale operation simulation model to perform short-term operation status prediction and scheme verification on an intraday scale, obtain short-term prediction results, and perform intraday rolling optimization scheduling based on real-time operation data to generate real-time scheduling instructions;

[0015] Based on real-time operational data and the short-term forecast results, the deviation between the current operational status and the day-ahead scheduling scheme is calculated. The day-ahead scheduling scheme is verified and corrected under the current operational conditions, and scheduling deviation and prediction error information containing operational error information are extracted. The scheduling deviation and prediction error information containing operational error information are fed back to the day-ahead scale to update the day-ahead scheduling scheme for the next scheduling cycle. Contract execution deviation information, equipment operating status information, and the scheduling deviation and prediction error information containing operational error information are fed back to the weekly-scale operational simulation model to obtain feedback information. Based on the feedback information, the weekly-scale constraints are recalculated and updated, and full-week energy balance and inter-day flexibility resource relay optimization simulation is performed. At least one of the SOC boundary or external power transmission fulfillment boundary of inter-day energy storage and V2G is verified, and the multi-time-scale operational simulation model is updated with the updated weekly-scale constraints.

[0016] In one optional implementation, the weekly-scale operation simulation model employs a two-stage operation simulation logic, specifically including: The first stage aims to minimize overall cost by solving for the inter-day flexibility boundary, which includes the daily energy boundary, energy storage power boundary, V2G regulation capacity quota, thermal power reserve or ramp-up boundary, and external power transmission target; The second stage uses the inter-day flexibility boundary as a rigid constraint to quantify the impact of intraday net energy deviation on the inter-day state, and verifies the adaptability of the inter-day flexibility boundary based on robust compensatory constraints and the intraday net energy deviation. If the verification fails, the inter-day flexibility boundary is iteratively corrected to ensure that the SOC of energy storage or V2G always falls within the preset inter-day bandwidth throughout the week, avoiding the cumulative out-of-control deviation across days. The intraday net energy deviation includes wind and solar power output deviation and heating load fluctuations caused by extreme weather; the robust compensatory constraints at least include inter-day SOC bandwidth constraints and extreme weather adaptation constraints.

[0017] In one optional implementation, the intraday rolling optimization scheduling adopts the MPC rolling optimization model, and the objective function of the MPC rolling optimization model is: In the formula, These represent the control actions calculated by MPC at the current time t (where t is a future time), the baseline scheduling plan given before the current time, and the weight matrix, respectively. Indicates the degree of deviation from the previous day's level within the day; This indicates the temporary balancing power that must be deployed in the event of an emergency. The penalty coefficient indicates that system security takes precedence; This indicates that in extreme cases, various resources may be exhausted, necessitating the sacrifice of some comfort to ensure safety.

[0018] On the other hand, the present invention also proposes a medium storing a computer program, which, when executed by a processor, implements the method for quantifying the supply and demand flexibility of the coal-to-electricity heating power grid as described in any one of the claims and / or implements the method for coordinated optimization scheduling of the supply and demand flexibility of the coal-to-electricity heating power grid as described in any one of the claims.

[0019] The beneficial effects of this invention are as follows: The quantification method uses diverse basic data from high-altitude coal-to-electricity conversion scenarios as input, integrates extreme weather correction coefficients and various constraints, accurately quantifies the flexibility capabilities of the supply and demand sides, and outputs the supply and demand flexibility margin, filling the technical gap in flexibility quantification for high-altitude coal-to-electricity conversion scenarios; the collaborative optimization scheduling method constructs multi-timescale operation simulation models based on the quantification results (weekly, day-ahead, and intraday), and achieves cross-scale collaborative allocation of flexible resources through deviation feedback, parameter updates, and rolling optimization scheduling, ensuring stable heating load supply, power transmission contract fulfillment, and safe grid operation, while also considering scheduling economy and operational robustness, providing core technical support for refined scheduling and flexible operation of high-altitude coal-to-electricity heating grids. Through three core designs—adaptation to high-altitude extreme climates, multi-scale resource collaborative scheduling, and closed-loop coupling of constraints—the invention solves the technical pain points of difficulty in accurately quantifying supply and demand flexibility and the failure of multi-timescale scheduling collaboration in high-altitude coal-to-electricity heating scenarios. Attached Figure Description

[0020] Figure 1 A flowchart illustrating the method for quantifying the supply and demand flexibility of the coal-to-electricity heating power grid provided in this embodiment of the invention;

[0021] Figure 2 A flowchart of the coal-to-electricity heating power grid supply and demand flexibility optimization scheduling method provided in the embodiments of the present invention. Detailed Implementation

[0022] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] like Figure 1 As shown in the embodiments of the present invention, in one aspect, a method for quantifying the supply and demand flexibility of a coal-to-electricity heating power grid is provided, comprising the following steps:

[0024] Step S101: Obtain basic power system data for high-altitude coal-to-electricity heating areas. The basic data includes load data, thermal power unit data, hydropower station data, flexibility resource information, extreme weather characteristic data, thermal comfort thresholds, and power transmission contract parameters. Flexibility resource information includes V2G equipment parameters, energy storage equipment parameters, and new energy power generation equipment parameters. Extreme weather characteristic data includes extreme low temperatures, low air pressure parameters, wind speed thresholds, and snowfall statistics for high-altitude areas.

[0025] Step S103: Construct a quantitative optimization model for supply and demand flexibility based on basic data. The quantitative optimization model for supply and demand flexibility includes a supply-side quantitative module and a demand-side quantitative module.

[0026] Among them, the supply-side quantification module uses the peak-shaving capacity of thermal power units, the regulating capacity of hydropower, the capacity of new energy consumption, the charging and discharging capacity of energy storage, and the adjustable capacity of V2G as core variables to quantify the flexible supply capacity of the supply side; the demand-side quantification module uses the thermal comfort threshold as a constraint to quantify the transferable / reducible response capacity of coal-to-electricity heating load; at the same time, extreme weather characteristic data are transformed into extreme weather load correction coefficients and equipment output attenuation coefficients, which are integrated into the input layer of the supply and demand flexibility quantification optimization model.

[0027] Step S105: Solve the supply and demand flexibility quantitative optimization model based on multiple constraints, and output the supply and demand flexibility margin. The multiple constraints include risk constraints, thermal comfort constraints, extreme weather adaptation constraints, grid security constraints, energy storage operation constraints, and power transmission contract constraints.

[0028] In this embodiment, by acquiring basic data from the power system, such as load data, thermal power unit data, hydropower station data, and flexibility resource information, a V2G control model and a new energy output prediction model are constructed. A power system supply and demand flexibility quantification model is established, quantifying both the flexibility demand side and supply side separately. Considering the impact of extreme weather, and based on grid security and energy storage operation constraints, thermal comfort and power transmission contracts are set as conditional boundaries to optimize the supply and demand balance criteria.

[0029] Step S101 aims to acquire comprehensive basic data on the operation of the power system in high-altitude coal-to-electricity heating areas, providing accurate input support for subsequent quantitative model construction. Specifically, the basic data covers three main categories: core parameters of power system operation, flexibility resource characteristic parameters, and environmental and constraint boundary parameters.

[0030] Load data includes daily / weekly / quarterly fluctuation curves of residential heating load and industrial and commercial heating load in the region, peak and valley period distribution, historical load change trends, and records of load abrupt changes under extreme weather conditions.

[0031] Data for thermal power units includes peak-shaving-related parameters such as rated capacity, minimum technical output, ramp rate, peak-shaving depth, start-up and shutdown costs, and fuel consumption characteristics; data for hydropower stations covers reservoir capacity, runoff forecast data, power generation efficiency curves, output regulation range, and water curtailment constraints.

[0032] The flexibility resource information is further refined into the rated charging and discharging power, battery capacity, SOC (state of charge) operating range, response delay time, and user participation willingness coefficient of V2G equipment; the charging and discharging efficiency, rated power, capacity limit, cycle life, and operation and maintenance cost of energy storage equipment; and the installed capacity, output prediction curve, power fluctuation range, and power generation efficiency attenuation coefficient in high-altitude environments of new energy power generation equipment (wind power, photovoltaic).

[0033] Extreme weather characteristic data are specifically designed for high-altitude regions. Data such as historical winter low temperature extremes, daily average low pressure values, instantaneous maximum wind speed thresholds, cumulative snowfall, and duration of snowfall are collected, and the probability of occurrence under different weather scenarios is obtained through statistical analysis.

[0034] The thermal comfort threshold is based on the human body's heating comfort needs and combined with the indoor and outdoor temperature difference characteristics in high-altitude areas to determine a reasonable range of indoor temperature, which serves as a rigid boundary for demand-side load adjustment.

[0035] The parameters of the power transmission contract include the upper and lower limits of the power to be transmitted, the period of performance, the penalty coefficient for breach of contract, and the monthly / quarterly performance volume requirements.

[0036] After data collection, various types of data are preprocessed in a standardized manner to remove outliers, fill in missing data, and store them according to time granularity (hourly, daily) and data type to ensure that the data quality meets the requirements of model calculation.

[0037] This method specifically collects extreme weather characteristic data for high-altitude areas and incorporates it into the model by converting it into load correction coefficients and equipment output attenuation coefficients. This solves the problem of quantification bias caused by traditional quantification methods neglecting the environmental impacts of high-altitude low temperatures, low air pressure, and heavy snowfall, achieving deep adaptation to high-altitude coal-to-electricity conversion scenarios. Simultaneously, it quantifies demand-side potential using thermal comfort thresholds as rigid constraints, balancing the essential needs of residential heating with flexible adjustment requirements. This fills the technical gap in integrated quantification of supply and demand flexibility in high-altitude coal-to-electricity conversion scenarios, providing a dedicated and precise quantification tool for such special areas.

[0038] Step S103: Based on the preprocessed basic data, construct a quantitative optimization model for supply and demand flexibility adapted to the high-altitude coal-to-electricity conversion scenario. This model achieves accurate quantification of flexibility resources across all dimensions through the collaborative design of the supply-side quantification module and the demand-side quantification module.

[0039] The supply-side quantification module uses the peak-shaving capacity of thermal power units, the regulating capacity of hydropower, the capacity for renewable energy consumption, the charging and discharging capacity of energy storage, and the adjustable capacity of V2G as core decision variables to construct a multi-resource collaborative quantification equation. Specifically, the quantification of the peak-shaving capacity of thermal power units considers the output attenuation caused by high altitude and low air pressure, correcting the actual peak-shaving range through equipment output attenuation coefficients; the regulating capacity of hydropower combines runoff forecasting and water curtailment constraints to quantify the adjustable output range; the renewable energy consumption capacity is based on the output forecast curve and fluctuation characteristics, combined with the grid's acceptance capacity to determine the maximum consumption scale; and the charging and discharging capacity of energy storage and the adjustable capacity of V2G comprehensively consider equipment parameters, operational constraints, and user response characteristics to quantify their flexible adjustment potential at different times, ultimately achieving a systematic quantification of the supply-side flexible supply capacity.

[0040] The demand-side quantification module uses thermal comfort thresholds as rigid constraints and combines building thermal inertia models and heating load characteristics to construct a load transfer / reduction quantification model. By analyzing the response sensitivity of heating load to temperature changes, it determines the time window for load transfer and the maximum proportion that can be reduced without exceeding thermal comfort boundaries, accurately quantifying the demand-side flexible response capacity. This ensures both the user's heating experience and fully taps the load-side adjustment potential.

[0041] Meanwhile, in response to the impact of extreme weather at high altitudes on system operation, extreme weather characteristic data are transformed into extreme weather load correction coefficients (quantifying the surge in heating load caused by low temperature and snowfall) and equipment output attenuation coefficients (quantifying the degree of power output reduction caused by low air pressure and high wind speed) through statistical modeling. These coefficients are then integrated into the input layer of the supply and demand flexibility quantification optimization model to achieve deep model adaptation to high-altitude environments and solve the quantification bias problem caused by traditional models ignoring the impact of special environments.

[0042] By constructing a dual-module architecture for both the supply and demand sides, the supply side comprehensively covers diverse flexible resources such as thermal power units, hydropower, new energy, energy storage, and V2G, while the demand side focuses on the characteristics of coal-to-electricity heating loads. This breaks through the limitations of traditional methods that only focus on a single power source or load, achieving coordinated quantification of flexible resources across all dimensions. Simultaneously, by integrating multiple operational boundaries such as risk constraints, grid security constraints, and power transmission contract constraints, it ensures that the quantification results not only conform to technical specifications but also align with actual dispatch needs. This makes the output of supply and demand flexibility margins more comprehensive and scientific, providing precise data support for the optimal allocation of grid flexibility resources.

[0043] Step S105 solves the supply and demand flexibility quantification optimization model based on multiple constraints to ensure that the quantification results conform to the actual boundary of power grid operation and accurately output the supply and demand flexibility margin.

[0044] Multiple constraints form a comprehensive operational boundary system. Risk constraints, through opportunity constraints and CVaR (Conditional Value at Risk) constraints, quantify the supply-demand imbalance risks caused by extreme weather and renewable energy fluctuations, ensuring sufficient robustness in flexibility margins. Thermal comfort constraints strictly limit the temperature boundaries of demand-side load adjustment, preventing a decline in heating experience due to load reduction. Extreme weather adaptability constraints, through equipment output attenuation coefficients and load correction coefficients, constrain the upper limit of power output and the range of load adjustment, adapting to the system operating characteristics in high-altitude environments. Grid security constraints cover power balance constraints, power flow constraints, and line transmission capacity constraints, ensuring grid operational stability. Energy storage operation constraints limit the charging and discharging power, SOC range, and cycle life of energy storage equipment, preventing equipment overload operation. Power transmission contract constraints limit the scope of external power transmission based on contract parameters, reducing the risk of default.

[0045] The model solution employs a hierarchical optimization strategy. First, the quantitative sub-models for the supply side and demand side are solved separately to obtain the basic adjustment capabilities of each resource. Then, multiple constraints are integrated to construct the main optimization model, which is solved using a mixed-integer linear programming algorithm. Finally, the upward flexibility margin (the degree to which the upward adjustment capability of the supply side covers the upward adjustment demand of the demand side) and the downward flexibility margin (the degree to which the downward adjustment capability of the supply side covers the downward adjustment demand of the demand side) are output, providing accurate quantitative basis for subsequent power grid dispatching.

[0046] In this embodiment, by acquiring basic data of the power system, such as load data, thermal power unit data, hydropower station data, and flexibility resource information, a V2G control model and a new energy output prediction model are constructed to accurately characterize the operational characteristics of diverse flexibility resources. A quantitative model of power system supply and demand flexibility is established to quantify both the demand and supply sides of flexibility, covering the adjustment potential of diverse power sources on the supply side and exploring the response capability of heating loads on the demand side. At the same time, the impact of extreme weather is fully considered, and based on grid security and energy storage operation constraints, thermal comfort and power transmission contracts are set as conditional boundaries to optimize the supply and demand balance criteria, ensuring that the quantitative results are scientific, practical, and compliant.

[0047] By incorporating multiple constraints, including thermal comfort, extreme weather adaptability, and grid security, into the model, a comprehensive operational boundary system is formed. Risk constraints ensure the robustness of the quantitative results, preventing supply-demand imbalances in extreme scenarios; thermal comfort constraints guarantee a comfortable heating experience for users, meeting public needs; power transmission contract constraints reduce operational default risks and improve compliance; and grid security and energy storage operation constraints ensure stable operation of equipment and the grid. This effectively addresses the problem of insufficient practicality caused by the single constraint in traditional quantitative methods, ensuring that the quantitative results can directly guide engineering practice.

[0048] The output supply and demand flexibility margin accurately reflects the power grid's supply and demand regulation capacity, flexibility gap, and redundancy capacity in high-altitude coal-to-electricity heating areas, providing a scientific basis for multi-timescale power grid scheduling, flexible resource allocation, and coal-to-electricity project layout. Based on this quantitative result, the power grid can rationally formulate scheduling plans, optimize the coordinated operation of diverse flexible resources, improve the absorption capacity of new energy sources, reduce peak-shaving costs, and reduce energy waste. Simultaneously, it avoids heating interruptions or power grid safety accidents caused by supply and demand imbalances, significantly improving the operational economy, stability, and reliability of high-altitude coal-to-electricity heating power grids, and promoting the large-scale and sustainable development of regional clean heating projects.

[0049] To construct flexible resources, we seek upward and downward adjustment capabilities, and optimize the supply and demand balance criteria based on grid constraints, thermal comfort, and the impact of extreme weather, while also considering grid power transmission contracts, wind-solar-storage collaborative operation, and large-scale V2G participation in flexibility adjustment, in order to meet user needs.

[0050] Further, step S103 involves constructing a quantitative optimization model for supply and demand flexibility based on the basic data, specifically including the following steps:

[0051] Step S1031: Calculate the high-altitude correction factor based on extreme weather characteristic data and equipment or fuel characteristic data of high-altitude coal-to-electricity heating areas.

[0052] Step S1033: Calculate the supply-side flexible response capability based on the high altitude correction factor. The supply-side flexible response capability includes the supply-side upward adjustment capability and the supply-side downward adjustment capability.

[0053] Step S1035: Calculate the demand-side flexible adjustment demand based on the high altitude correction factor. The demand-side adjustment demand includes the demand-side upward adjustment capability and the demand-side downward adjustment capability.

[0054] Step S1037: Based on the supply-side flexible response capability, demand-side demand adjustment and high-altitude correction factor, obtain the quantitative optimization model of supply and demand flexibility.

[0055] Based on the high-altitude correction factor, which characterizes the upward / downward adjustment capacity of the supply side and the upward / downward adjustment capacity of the demand side, a two-way matching quantitative system of "supply-side capacity - demand-side demand" is constructed. This breaks through the limitation of traditional models that only assess the flexibility of the supply side or the demand side, and can achieve accurate quantification of the flexibility of both supply and demand sides, providing a scientific basis for scheduling decisions.

[0056] By integrating supply-side response capabilities, demand-side adjustment needs, and high-altitude correction factors, a complete quantitative optimization model for supply and demand flexibility is constructed. This model enables the global optimization and allocation of flexible resources in high-altitude coal-to-electricity conversion scenarios, accurately supporting engineering applications such as multi-timescale scheduling, new energy consumption optimization, and load-side resource allocation, thereby significantly improving the accuracy and reliability of scheduling decisions.

[0057] The application of high-altitude correction factors enables the model to fully consider the impact of heating load fluctuations and equipment output attenuation under extreme weather conditions. While accurately quantifying the flexibility of supply and demand, it also takes into account the basic heating needs of the people and the requirements for safe and stable operation of the power grid, avoiding scheduling decision deviations caused by the lack of environmental correction, and ensuring stable heating supply and safe and reliable operation of the power system in high-altitude coal-to-electricity conversion areas.

[0058] Further, step S1031, calculating the high-altitude correction factor based on extreme weather characteristic data and equipment or fuel characteristic data of high-altitude coal-to-electricity heating areas, specifically includes the following steps:

[0059] Step S10311: Calculate the environmental characteristic correction factor. The environmental characteristic correction factor characterizes the basic correction impact of high altitude, low temperature and low air pressure on the operating environment of thermal power units, hydropower units, new energy and energy storage equipment.

[0060] Step S10313: Determine the equipment or fuel characteristic loss coefficient. The equipment or fuel characteristic loss coefficient characterizes the assigned values ​​for high proportion of high-alkali coal, physical constraints of the boiler-circulation system, and fuel combustion stability characteristics.

[0061] Step S10315: Calculate the high-altitude correction factor based on the environmental characteristic correction factor and the equipment or fuel characteristic loss coefficient. The high-altitude correction factor characterizes and quantifies the comprehensive correction impact of high-altitude geographical factors and extreme weather on the output and load characteristics of power equipment.

[0062] Taking into account geographical factors in high-altitude areas, the general high-altitude correction factor for each unit is:

[0063] ;

[0064] In the formula, As a comprehensive correction factor, As an environmental characteristic correction factor, This is the characteristic loss coefficient of the equipment or fuel.

[0065] This invention addresses the complex geographical and climatic characteristics of high-altitude regions, characterized by low temperatures, low air pressure, strong winds, and heavy snowfall. It innovatively separates environmental characteristic correction factors from equipment / fuel characteristic loss coefficients, quantifying the dual impacts of high altitude on the operating environment of power equipment, the combustion characteristics of high-proportion, high-alkali coal, and the physical constraints of the boiler-circulation system. Compared to traditional single-altitude correction methods, this layered calculation approach accurately characterizes the comprehensive correction effects of high-altitude geographical factors and extreme weather on power equipment output and load characteristics. It completely solves the problems of existing technologies neglecting the complex environmental impact of high altitudes, leading to quantitative deviations in equipment output and inaccurate load forecasts, and is adapted to the specific operating characteristics of high-altitude coal-to-electricity heating scenarios.

[0066] Addressing the unique operating conditions of high-proportion, high-alkali coal combustion in high-altitude coal-to-electricity conversion areas, this invention clarifies equipment / fuel characteristic loss coefficients, accurately characterizes the impact of high-alkali coal combustion on the physical constraints of the boiler-circulation system and fuel combustion stability, and corrects problems such as output attenuation and combustion efficiency reduction caused by high-alkali coal combustion in high-altitude environments. This design fills the gap in quantitative correction of power output in high-altitude high-alkali coal co-firing scenarios, making the quantitative results of regulation capacity of thermal power units and converted units more consistent with actual operating conditions, and laying a core foundation for the precise quantification of supply-side flexible supply capacity.

[0067] By employing a product-based universal correction factor formula, a standardized comprehensive correction system for high-altitude geographical factors and extreme weather impacts is established, achieving unified correction and adaptation for all types of power equipment, including thermal power units, hydropower units, new energy sources, and energy storage. This system ensures the standardization and replicability of correction factor calculations while systematically integrating the environmental impacts of high altitudes into the input layer of the supply-demand flexibility quantitative optimization model. This significantly improves the model's robustness to high-altitude coal-to-electricity conversion scenarios, ensuring that the quantitative results are unaffected by environmental factors and possessing engineering application value.

[0068] High-altitude correction factors accurately quantify the impact of extreme weather on heating load. Combined with thermal comfort threshold constraints, the model simultaneously adjusts equipment output and adapts to changes in load characteristics, enabling the supply-demand flexibility quantification model to consider both power source output correction and load-side response adaptation. On the one hand, this ensures the accuracy of output quantification for thermal power units and new energy equipment, avoiding deviations in supply-side capacity assessment due to insufficient environmental correction. On the other hand, it accurately depicts the fluctuation range of heating load under extreme weather conditions, providing precise input for quantifying demand-side transferable / reducible capacity. Ultimately, this ensures that the output of supply-demand flexibility margin aligns with the actual operation scenario of coal-to-electricity conversion at high altitudes, providing a reliable basis for subsequent multi-timescale coordinated scheduling and flexible resource optimization, ensuring stable winter heating supply and safe and stable grid operation.

[0069] Furthermore, step S1033, calculating the supply-side flexible response capability based on the high-altitude correction factor, specifically includes the following steps:

[0070] Step S10331: Divide the supply-side flexibility resources into at least thermal power units, hydropower stations, energy storage devices, wind and solar power stations, and V2G resources.

[0071] Step S10333: Based on the high altitude correction factor, calculate the upward and downward adjustment capabilities of supply-side flexibility resources respectively;

[0072] Step S1035: Summarize the upward and downward adjustment capabilities of all supply-side resources to obtain the flexible response capability of the supply side.

[0073] Supply-side (thermal power units, wind and solar power, energy storage) and demand-side (electric heating, flexible loads) are respectively adapted to high-altitude adjustments to ensure that factors align with equipment operating characteristics. Considering the quantification of flexible supply across multiple time scales, thermal power units are divided into conventional units and modified units that are high-altitude, high-proportion supercritical coal-fired power units burning high-alkali coal. The upward and downward adjustment capabilities of conventional units are:

[0074] ;

[0075] These are the maximum and minimum instantaneous power of the thermal power unit, respectively. These refer to the unit's uphill and downhill ramp rates. To adjust the time interval, the unit was modified as follows:

[0076] ;

[0077] This is expressed as a corrected value for high-altitude and high-alkali coal. This refers to the corrected uphill and downhill speeds. This indicates that the instantaneous physical constraints of the boiler-circulation system, such as steam pressure / temperature, sliding pressure control, or superheater temperature rise rate limits, are converted into power limits (MW). This indicates additional constraints (MW) related to fuel characteristics (high alkalinity, slagging, combustion stability).

[0078] The hydropower station's upward and downward regulation capabilities are:

[0079] ;

[0080] Upward and downward adjustment capabilities of energy storage devices:

[0081] ;

[0082] These represent the energy output at time t and the maximum charging / discharging power, respectively. These represent the stored energy amount and its upper and lower limits for energy storage during time period t. These are the charge and discharge efficiencies, respectively.

[0083] Energy storage can work in conjunction with wind and solar power. Firstly, the upscaling and downscaling capabilities of solar power are:

[0084] ;

[0085] For photovoltaic predictions, given a confidence level... Select the lower error -Quantities; Upward and downward regulation capabilities of wind power (considering only farm-scale wind turbines): First, a single turbine: ;

[0086] Then, field level: ;

[0087] Considering the synergistic cooperation of wind, solar, and energy storage, and incorporating priority regulation, with wind and solar resources prioritized during normal times and energy storage used for temporary emergency regulation: the total upward and downward adjustment capacity is then:

[0088] ;

[0089] By incorporating priority control, the required up-adjustment capability at a given time t can be specified. In other words, when the upscaling capacity of wind and solar power meets the required flexibility for upscaling, wind and solar power will be used first; when this is not the case, energy storage will be added for dispatching. .

[0090] Wind and solar resources are given priority in regulation. When the regulation capacity of wind and solar is insufficient, it is supplemented by energy storage, V2G and transferable loads. At the same time, the strategy of "energy storage takes priority in absorption and wind and solar power output is reduced" is clearly defined when regulating downwards. This maximizes the absorption of new energy sources and ensures the system's regulation margin in emergency scenarios, thereby improving the overall utilization efficiency of flexible resources.

[0091] Downward regulation capability involves energy storage first charging to absorb power, then wind and solar power reducing output power. Alternatively, it can be used as part of a power transmission plan. After the energy storage has absorbed the power, wind power does not need to be reduced, and the power transmission plan can proceed, with energy storage taking priority in absorption. ,in The system has excess power; the remaining excess power is then: When the surplus energy absorbed by the energy storage can be used for power transmission plans: ; This refers to the capacity of the external transmission channel.

[0092] The upward and downward adjustment capabilities of V2G in regulation are:

[0093] ;

[0094] in This is a dispatchable indicator variable for whether a vehicle is connected to the grid. A value of 0 indicates the vehicle is offline, while a value of 1 indicates it can participate in V2G. These represent charge and discharge efficiencies, These represent the maximum charging and discharging power, respectively.

[0095] The upward and downward adjustment capabilities of transferable and interruptible loads in factories, commerce, and other sectors are as follows:

[0096] ;

[0097] in These are the maximum and minimum values ​​of the transferable load, respectively. Maximum interruptible load.

[0098] Supply-side flexible response capability refers to the total supply-side capacity, which is the total amount of flexible resources that the system can provide. It focuses only on the supply-side capacity of adjustable resources on the power supply side, energy storage, V2G, and load side.

[0099] Optimizing the supply-demand balance criteria to meet user needs requires establishing constraints to ensure user thermal comfort remains within a reasonable range. This necessitates considering the power generation required by the power transmission contract, with the following constraints: The cost of default is: ;

[0100] in These are the actual power delivery volume and the contracted power delivery volume, respectively. Penalties are imposed for unit deviations. Power balance must be considered first in power grid safety constraints.

[0101] ;

[0102] Current and line constraints:

[0103] ; For the first The power flow of the line at time t.

[0104] Thermal comfort constraints, setting dynamic indoor temperature: The thermal comfort range is: The penalty for allowing soft constraints is: ;

[0105] Energy storage constraints are: ;

[0106] V2G scheduling constraints: ;

[0107] Taking into account multiple constraints such as power balance, line flow, thermal comfort, energy storage SOC and V2G dispatch, the system deeply integrates user thermal comfort, external transmission contract performance and grid safety operation requirements into flexibility quantification and dispatch decision-making. While meeting heating reliability requirements, it effectively avoids risks such as line overload and energy storage exceeding limits, achieving a multi-objective balance of "safety-economy-comfort".

[0108] By accurately quantifying the overall flexible response capability of the supply side, reliable boundary conditions and parameter inputs are provided for the multi-timescale scheduling model, making the decisions on weekly-scale resource allocation, day-ahead scenario optimization, and intraday rolling correction more targeted and operable. This significantly reduces the deviation between the scheduling scheme and actual operation and enhances the system's ability to cope with uncertainty.

[0109] Furthermore, step S1035, calculating the flexible adjustment demand on the demand side based on the high-altitude correction factor, specifically includes the following steps:

[0110] Step S10351: Using the thermal comfort threshold as the core constraint and combining it with a high-altitude correction factor, a demand-side quantification module is constructed. This module includes the upward and downward adjustment capabilities of transferable load and interruptible load. Using the thermal comfort threshold as the core constraint and combining it with a high-altitude correction factor, a quantitative model for flexible demand adjustment on the demand side is constructed. This model accurately quantifies the upward demand caused by factors such as extreme weather, increased heating at high altitudes, and uncertainties in wind and solar power, as well as the downward demand caused by factors such as renewable energy consumption and external transmission contract constraints. This makes the demand-side flexibility assessment more closely aligned with the actual operational patterns of high-altitude coal-to-electricity conversion scenarios.

[0111] Step S10353: Based on the high altitude correction factor, summarize the upward adjustment capacity of transferable load and the upward adjustment capacity of interruptible load, and summarize the downward adjustment capacity of transferable load and the downward adjustment capacity of interruptible load to obtain the flexible adjustment demand on the demand side.

[0112] The upward and downward adjustment capabilities of the demand side mainly consider several aspects. Upward adjustment is due to additional heating demand caused by extreme weather and high-altitude areas, system reserve capacity, uncertainties in wind and solar power, and risks to thermal comfort. Downward adjustment is due to the absorption of surplus renewable energy or forecast deviations, and downward adjustment of demand caused by power transmission contract constraints.

[0113] .

[0114] By quantifying the flexible response capability of the supply side and the flexible adjustment of demand on the demand side respectively, a clear supply and demand balance criterion is provided for the multi-time-scale scheduling model. This effectively avoids the bias of "emphasizing supply and neglecting demand" in traditional scheduling, enabling scheduling decisions to maximize the use of demand-side resources to smooth system fluctuations and improve overall adjustment efficiency while ensuring user thermal comfort.

[0115] By incorporating key uncertainties such as thermal comfort risk, wind and solar power uncertainty risk, and external power transmission contract constraints into the quantitative framework of demand-side regulation, the scheduling plan can anticipate and respond to various risks in advance. Even in scenarios such as extreme weather and new energy forecast deviations, demand-side regulation can still ensure the safe and stable operation of the system and the heating experience for users.

[0116] The precise quantification of demand-side adjustment capacity provides a reliable basis for priority regulation. When there is a surplus of new energy, priority is given to consumption through demand-side downward adjustment, and when there is a system shortage, priority is given to supplementing through demand-side upward adjustment. This effectively reduces the dependence on traditional thermal power regulation and reduces system operating costs and the risk of default on transmission contracts.

[0117] Step S103, based on multiple constraints, solve the supply and demand flexibility quantification optimization model and output the supply and demand flexibility margin, including the following steps:

[0118] Step S1031: Based on multiple constraints, solve the supply-side quantification module and the demand-side quantification module respectively to obtain the basic adjustment capacity parameters. The basic adjustment capacity parameters include the total upward adjustment capacity and the total downward adjustment capacity of the supply side, and the total upward adjustment demand and the total downward adjustment demand of the demand side.

[0119] Step S1033: With the goal of maximizing the supply and demand flexibility margin, construct the supply and demand flexibility objective function by combining multiple constraints and basic adjustment capability parameters.

[0120] Step S1035: Solve the supply and demand flexibility objective function to obtain the upward flexibility margin and the downward flexibility margin.

[0121] Step S1037: Correct the upward and downward flexibility margins and supply and demand flexibility margins based on the high altitude correction factor to obtain the supply and demand flexibility margins.

[0122] By leveraging the flexibility of demand and supply, upward and downward adjustment capabilities are achieved, resulting in an adjustable margin. Combined with constraints and the influence of high-altitude regions, the supply-demand balance criteria are optimized.

[0123] The total upward and downward adjustment capabilities are: ;

[0124] Its adjustment margin is:

[0125] ;

[0126] Based on the supply-demand balance criterion, opportunity constraints and conditional value at risk (CVaR) are introduced to characterize the risks of wind and solar uncertainty, extreme weather, and residents' thermal comfort, thus constructing a multi-period supply-demand balance criterion with controllable risks:

[0127] Suppose a day, k, is divided into five time periods, i.e. Introducing uncertain random variables These are respectively wind and solar forecast errors, heating demand deviations caused by extreme weather, and comfort constraint default risks, collectively referred to as... Then the supply and demand margin becomes a random variable:

[0128] ;

[0129] Constructing opportunity constraint criteria:

[0130] ;

[0131] in With a confidence level of [0,1], the system can achieve supply and demand balance within this confidence level. A CVaR risk-measured supply and demand balance criterion is introduced, defining a supply and demand imbalance risk variable. The imbalance measure is defined for the upward adjustment direction as: Its confidence level is: ,in The risk aversion level is typically set to 0.95. yes Auxiliary variables. The optimized supply-demand balance criterion is then:

[0132] ;

[0133] The maximum permissible level of imbalance is set smaller during extreme weather conditions and high load periods, particularly during critical heating seasons.

[0134] By solving the quantitative modules on the supply and demand sides separately, the basic adjustment capacity parameters are obtained. The objective function is constructed with the goal of maximizing the supply and demand flexibility margin, which accurately quantifies the upward / downward flexibility margin of the system. This provides a clear supply and demand balance criterion for scheduling decisions and effectively avoids the bias of "emphasizing supply and neglecting demand" in traditional methods.

[0135] By introducing opportunity constraints and conditional value at risk (CVaR), the system quantifies the risks of wind and solar forecast errors, heating demand deviations caused by extreme weather, and thermal comfort defaults. It transforms supply and demand margins into random variables and ensures that the system can achieve supply and demand balance under high confidence by setting confidence levels and risk aversion coefficients. At the same time, it controls the maximum imbalance level under key scenarios such as extreme weather and high load within an acceptable range, significantly improving the robustness and safety of the system operation.

[0136] On the other hand, such as Figure 2 As shown, this invention also proposes a method for coordinated optimization scheduling of supply and demand flexibility in coal-to-electricity heating power grids, comprising the following steps:

[0137] Step S201: Based on any one of the methods for quantifying the supply and demand flexibility of the coal-to-electricity heating power grid, construct a multi-time-scale operation simulation model, which includes a weekly-scale operation simulation model, a day-ahead-scale operation simulation model, and an intraday-scale operation simulation model.

[0138] Step S203: Based on the power system basic data, new energy output forecast, and high altitude correction factor, run the weekly-scale operation simulation model, perform overall energy balance and regulation resource allocation simulation on the weekly scale, and output the weekly-scale constraints, which include boundary constraint parameters, regulation resource allocation schemes, and scenario-based optimization results.

[0139] Step S205: Based on the weekly scale constraints, run the day-ahead scale simulation model to simulate the supply and demand balance and scheduling scheme based on the general scenario at the day-ahead scale, and obtain the day-ahead scheduling scheme.

[0140] Step S207: Run the intraday-scale operation simulation model to perform short-term operation status prediction and scheme verification on the intraday scale, obtain short-term prediction results, and perform intraday rolling optimization scheduling based on real-time operation data to generate real-time scheduling instructions.

[0141] Step S209: Based on real-time operating data and short-term forecast results, calculate the deviation between the current operating status and the day-ahead scheduling plan, verify and correct the day-ahead scheduling plan under the current operating conditions, and extract the scheduling deviation and forecast error information containing operating error information.

[0142] Step S2011: Feed back the scheduling deviation and prediction error information, which include operational error information, to the day-ahead scale to update the day-ahead scheduling scheme for the next scheduling cycle.

[0143] Step S2013: Feed back the contract execution deviation information, equipment operation status information, and scheduling deviation and prediction error information containing operation error information to the weekly-scale operation simulation model to obtain feedback information.

[0144] Step S2015: Based on the feedback information, recalculate and update the weekly-scale constraints, perform full-week energy balance and inter-day flexibility resource relay optimization simulation, verify at least one of the SOC boundary or external power transmission fulfillment boundary of inter-day energy storage and V2G, and update the multi-time-scale operation simulation model with the updated weekly-scale constraints.

[0145] In this embodiment, comprehensive basic data on high-altitude coal-to-electricity heating scenarios are collected, covering three categories: supply side, demand side, and environment and constraints.

[0146] Supply side: Parameters such as installed capacity, upper and lower limits of output, ramp rate, charge and discharge efficiency, SOC constraints, and fuel characteristics of thermal power units (conventional / modified units), hydropower stations, wind and solar new energy sources, energy storage devices, and V2G resources;

[0147] Demand side: power limits, response speed, thermal comfort constraints, and user preferences for electric heating loads, transferable / interruptible loads, and industrial / commercial flexible loads;

[0148] Environment and constraints: altitude, atmospheric pressure, ambient temperature, extreme weather parameters, contracted power transmission volume, grid power flow / line capacity, thermal comfort range, and wind and solar forecast error distribution.

[0149] Data preprocessing: removing outliers, filling in missing time series data, standardizing units (power MW, energy MWh, altitude m), and aligning time scales (day-ahead / day-intraday / real-time).

[0150] A multi-timescale operation simulation model is established, encompassing weekly, day-ahead, and intraday simulations. At the weekly scale, overall energy balance and resource allocation simulations are performed, clearly defining the operational boundaries of flexible resources such as inter-day energy storage and V2G. At the day-ahead scale, supply and demand balance and scheduling schemes based on general scenarios are simulated, achieving optimal combinations and pre-scheduling of resources such as thermal power, energy storage, and heat pumps, significantly improving energy utilization efficiency and reducing system operating costs. At the intraday scale, short-term operating status prediction and scheme verification are conducted, with rolling corrections based on model predictive control (MPC). This enables rapid response to emergencies based on real-time operating data, dynamically adjusting day-ahead scheduling schemes to ensure a high degree of matching between scheduling commands and actual operating conditions, enhancing the system's adaptability to load and renewable energy fluctuations.

[0151] At the intraday scale, based on real-time operational data and short-term forecast results, the scheduling scheme obtained at the day-ahead scale is verified and corrected under the current operating conditions, and scheduling deviation and prediction error information are extracted. The error information is fed back to the day-ahead scale to update the day-ahead scheduling scheme for the next scheduling cycle. At the same time, the operational error information, contract execution deviation information, and equipment operating status information are fed back to the weekly scale to correct the scene weights in the multi-time-scale operation simulation model and adjust the resource allocation boundaries or flexibility parameters.

[0152] By feeding back intraday operational errors, contract execution deviations, and equipment status information to the day-ahead and week-ahead scales, a complete closed-loop optimization chain is formed. This continuously corrects key parameters such as scenario weights and adjusts resource boundaries, enabling the scheduling scheme to iteratively optimize throughout the entire cycle and gradually approach the optimal solution. Fully considering the impact of high-altitude areas on equipment output and grid transmission efficiency, targeted adjustments to geographical factors and constraint settings are made to ensure the scheduling scheme better meets the actual needs of high-altitude coal-to-electricity heating scenarios, guaranteeing a dual improvement in heating reliability and grid operating efficiency.

[0153] Specifically, continue to combine Figure 2 As shown, the weekly-scale simulation model adopts a two-stage simulation logic, which specifically includes the following steps:

[0154] Step S301: The first stage aims to minimize the overall cost and solves the inter-day flexibility boundary. The inter-day flexibility boundary includes the daily energy boundary, energy storage power boundary, V2G regulation capacity quota, thermal power reserve or ramp-up boundary, and external power transmission target.

[0155] Step S303: In the second stage, the impact of intraday net energy deviation on the interday state is quantified using the interday flexibility boundary as a rigid constraint. Based on the robust compensatory constraint and intraday net energy deviation, the adaptability of the interday flexibility boundary is verified. If the verification fails, the interday flexibility boundary is iteratively corrected to ensure that the SOC of energy storage or V2G always falls within the preset interday bandwidth throughout the week, avoiding the loss of control due to the cumulative interday deviation. Intraday net energy deviation includes wind and solar power output deviation and heating load fluctuations caused by extreme weather.

[0156] In the weekly-scale boundary optimization phase, two-stage stochastic optimization and robust boundary optimization simulations are first conducted based on renewable energy output forecasts, basic power data, and corrections for geographical factors in high-altitude areas. Subsequently, a capacity assessment is performed: if the assessment results indicate insufficient system capacity, reserve capacity is increased, priority control is introduced, or flexibility margins are left for re-optimization; if capacity is sufficient, weekly-scale constraints are output, including boundary constraint parameters, adjustment resource allocation schemes, and scenario-based optimization results, serving as constraints for day-ahead-scale scheduling.

[0157] By employing two-stage stochastic optimization and robust boundary simulation at the weekly scale, combined with corrections for high-altitude geographical factors, the system can anticipate and reserve sufficient flexibility margins in advance, effectively addressing uncertainties such as fluctuations in new energy output and extreme weather, and ensuring the safe and stable operation of the power grid throughout the entire week.

[0158] During the day-ahead planning phase, under weekly constraints, a day-ahead operational simulation model is run to perform multi-scenario stochastic optimization and scenario decomposition simulation, making scenario scheduling decisions based on the coal-to-electricity conversion background. Simultaneously, pre-scheduling actions such as thermal power rescheduling, energy storage scheduling, V2G load scheduling, and early heat pump storage at low loads are executed to obtain the day-ahead scheduling scheme, providing a basic plan for intraday rolling adjustments.

[0159] During the intraday rolling correction phase, an intraday operational simulation model is run, and short-term operational status predictions and scheme verifications are performed based on Model Predictive Control (MPC) to obtain short-term prediction results. The feasibility of scenario decisions is then assessed: if infeasible, the prediction error information is transmitted back to the day-ahead scale for rescheduling; if feasible, intraday rolling optimization scheduling is performed based on real-time operational data, and the scheduling scheme is revised and refined in real-time according to unforeseen circumstances, generating real-time scheduling instructions.

[0160] After the intraday scheduling is completed, error summarization, contract deviation settlement, and equipment status collection are performed. Contract execution deviation information, equipment operating status information, and operating error information are fed back to the weekly-scale operation simulation model to obtain feedback information. Based on the feedback information, the weekly-scale constraints are recalculated and updated. A full-week energy balance and inter-day flexibility resource relay optimization simulation is performed to verify at least one of the following: the SOC boundary of inter-day energy storage and V2G, or the external power transmission fulfillment boundary. The multi-time-scale operation simulation model is then updated with the updated weekly-scale constraints before entering the next scheduling cycle.

[0161] Robust compensatory constraints include at least cross-day SOC bandwidth constraints and extreme weather adaptation constraints.

[0162] Intraday rolling optimization scheduling adopts the MPC rolling optimization model, and the objective function of the MPC rolling optimization model is:

[0163] ;

[0164] In the formula, These represent the control actions calculated by MPC at the current time t (where t is a future time), the baseline scheduling plan given before the current time, and the weight matrix, respectively. Indicates the degree of deviation from the previous day's level within the day; This indicates the temporary balancing power that must be deployed in the event of an emergency. The penalty coefficient indicates that system security takes precedence; This indicates that in extreme cases, various resources may be exhausted, necessitating the sacrifice of some comfort to ensure safety.

[0165] A weekly-scale operational simulation model is established to evaluate and make decisions regarding the overall energy balance and regulation resource allocation of the system over a longer time period. By analyzing the state evolution process of flexible resources with trans-day characteristics, such as energy storage systems and electric vehicles, under continuous operational deviation conditions, it determines whether the configured regulation resource boundaries can achieve trans-day relay and avoid irreversible cumulative shifts in the system's operating state. A two-stage operational simulation logic is adopted.

[0166] To minimize overall costs, a weekly power generation plan is developed, with fixed costs incorporated into the optimization objectives in the first phase. ;

[0167] in As a fixed cost, it includes the opportunity cost of reserving backup capacity, the flexibility loss caused by energy storage maintaining a high SOC, the fuel / efficiency loss of allowing thermal power to maintain a greater ramp-up capability, and the availability constraint cost caused by reserving capacity for V2G. These are costs that must be paid before the scheduling decision itself.

[0168] The first phase, held a week prior, will primarily determine daily energy boundaries, energy storage power boundaries, V2G regulation capacity quotas, thermal power reserve or ramp-up boundaries, and external power transmission targets. , , , , Configure the cross-day flexibility boundary x.

[0169] The second stage defines the impact of intraday net energy deviation on cross-day status, taking energy storage as an example: Suppose that after the pre-day / intraday scheduling is executed on day d, the daily net change in energy storage is:

[0170] It depends on the uncertainty of the day. Extreme weather, wind and solar deviations, heating load deviations, V2G availability, etc. The cross-day relay evolution state is as follows:

[0171] ;

[0172] Considering robust compensability constraints, ensure that for all intraweek uncertain sequences, as long as... Within this range, the SOC consistently falls within the configured cross-day bandwidth:

[0173] To prevent deviations from accumulating and spiraling out of control over multiple days, the weekly-scale output of the daily boundary x is passed to the day before.

[0174] The weekly scale incorporates fixed costs (such as the opportunity cost of reserve capacity and the loss of energy storage flexibility) into the optimization objective to achieve the optimal configuration of the weekly power generation plan and the cross-day flexibility boundary.

[0175] A day-ahead timescale simulation model is constructed. After the day-ahead layer accepts the week-ahead boundary, an original set of multiple scenes is generated. K-means clustering is used to reduce the original scenes to obtain a set of classic scenes. , This represents the probability of scenario s occurring, such as wind and solar power output, extreme weather / temperature, coal-to-electricity load, and V2G availability. The day-ahead layer, through K-means scenario reduction and Benders decomposition, significantly reduces the complexity of multi-scenario optimization while ensuring robustness, thereby improving the economy and feasibility of the scheduling scheme.

[0176] Based on this scenario set, general scenario decision variables and scenario-related compensation variables are introduced to generate a unified scheduling scheme that is feasible for multiple typical scenarios under uncertain operating conditions:

[0177] ;

[0178] These represent general scenario decision-making and scenario-related compensation / relaxation / bias variables, respectively. , These respectively represent the planned output of thermal power units, the planned output of hydropower, the planned charging and discharging power of energy storage, the energy status trajectory of energy storage, the power of V2G aggregation and dispatch, and the power transmission contract. , These represent the power balance compensation, energy storage deviation correction, thermal comfort relaxation variable, and transmission contract deviation, respectively. They comply with the boundary conditions given by the weekly scale and the established set of constraints.

[0179] The objective function for the current day layer is then constructed based on the lowest average performance and risk cost:

[0180] ;

[0181] These represent the unit's power generation cost, energy storage usage and depreciation cost, V2G call cost, power balance compensation cost, thermal comfort deviation penalty, and external transmission contract deviation cost, respectively. The Benders decomposition solution is used to derive the decision, which is then passed to the intraday execution layer.

[0182] Constructing an intraday timescale operational simulation model requires measuring the system's state and calculating deviations, including new energy prediction errors, load heating prediction errors, V2G practical deviation rate, and execution deviations, denoted as... Deviations in the execution of power transmission contracts are recorded as follows: This includes deviations in transmitted power volume, deviations in power purchase and sale contracts, and temporary adjustments. Changes in equipment status are recorded as follows: This includes the actual end-of-day state of battery SOC, battery aging status, changes in unit availability, and long-term V2G participation rate.

[0183] The intraday layer employs an MPC rolling optimization model, which dynamically corrects scheduling instructions by tracking prediction errors, contract deviations, and equipment status changes in real time, quickly mitigating sudden disturbances. Simultaneously, intraday deviations are marked and attributed online, and the day-ahead scenario weights and distribution parameters are continuously updated, enabling scheduling decisions to converge toward the optimal solution.

[0184] Short-term forecasting is performed, using the latest data to predict the next H steps, and the prediction error is labeled and attributed online. MPC rolling optimization is then performed.

[0185] ;

[0186] These represent the control actions calculated by MPC at the current time t (where t is a future time), the baseline scheduling plan given a day ago, and the weight matrix, respectively. The difference between the two represents the degree of deviation from the day ago. This refers to the temporary balancing power that must be deployed in the event of an emergency. This is the penalty coefficient, indicating that system security takes precedence; In extreme cases, when various resources are exhausted, some comfort must be sacrificed to ensure safety.

[0187] Perform the first step of control and predict error online: , This represents the control quantity actually executed at time t. This represents the control quantity calculated for time t, given the information at time t. Record. , This indicates the MPC tension level. At the end of the day, the daily deviation set is summarized: This is used to update the day-ahead scheduling decision for the next cycle. It also updates the scene distribution parameters at the day-ahead layer. Update and reduce scene weights: After normalization, we can obtain Update the initial state of the day before the next update: .

[0188] Use the updated version Solve the day-ahead model for the next day to obtain a new general decision. .

[0189] Summary of intraday forecast deviations over the past week: Contract Deviation Summary: Equipment status summary: Used to correct the weekly-scale input and output boundaries for the next week: .

[0190] By specifically introducing corrections based on high-altitude geographical factors, thermal comfort constraints, and external power transmission fulfillment boundaries, the characteristics of scenarios such as heating load and V2G participation rate are deeply integrated into the optimization objectives and constraints at various time scales. This makes the dispatching scheme more in line with the actual needs of coal-to-electricity heating in high-altitude areas, while taking into account both heating reliability and grid operation efficiency.

[0191] On the other hand, the present invention also proposes an electronic device, comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute any one of the methods for quantifying the supply and demand flexibility of the coal-to-electricity heating grid and / or to implement any one of the methods for collaborative optimization scheduling of the supply and demand flexibility of the coal-to-electricity heating grid.

[0192] On the other hand, the present invention also proposes a medium storing a computer program, which, when executed by a processor, implements either a method for quantifying the supply and demand flexibility of a coal-to-electricity heating power grid or a method for collaborative optimization scheduling of the supply and demand flexibility of a coal-to-electricity heating power grid.

[0193] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Dual Data SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus Direct RAM (RDRAM), Direct Memory Bus Dynamic RAM (DRDRAM), and Memory Bus Dynamic RAM (RDRAM). The various embodiments described in this specification are presented in a progressive manner, and similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, for embodiments of apparatus, devices, and non-volatile computer storage media, since they are substantially similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments.

[0194] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for quantifying the supply and demand flexibility of a coal-to-electricity heating power grid, characterized in that, include: Acquire basic power system data for high-altitude coal-to-electricity heating areas. The basic data includes load data, thermal power unit data, hydropower station data, flexibility resource information, extreme weather characteristic data, thermal comfort thresholds, and power transmission contract parameters. Based on the aforementioned basic data, a quantitative optimization model for supply and demand flexibility is constructed. The quantitative optimization model for supply and demand flexibility includes a supply-side quantitative module and a demand-side quantitative module. The supply and demand flexibility quantitative optimization model is solved based on multiple constraints, and the supply and demand flexibility margin is output. The multiple constraints include risk constraints, thermal comfort constraints, extreme weather adaptation constraints, grid security constraints, energy storage operation constraints, and power transmission contract constraints.

2. The method for quantifying the supply and demand flexibility of the coal-to-electricity heating power grid according to claim 1, characterized in that, Based on the aforementioned fundamental data, a quantitative optimization model for supply and demand flexibility is constructed, specifically including: Calculate the high-altitude correction factor based on extreme weather characteristic data and equipment or fuel characteristic data of the high-altitude coal-to-electricity heating area. The supply-side flexible response capability is calculated based on the high-altitude correction factor, which includes the supply-side upward adjustment capability and the supply-side downward adjustment capability. The demand-side flexible adjustment demand is calculated based on the high-altitude correction factor, and the demand-side adjustment demand includes the demand-side upward adjustment capability and the demand-side downward adjustment capability. Based on the supply-side flexible response capability, the demand-side demand adjustment capability, and the high-altitude correction factor, a quantitative optimization model for supply and demand flexibility is obtained.

3. The method for quantifying the supply and demand flexibility of the coal-to-electricity heating power grid according to claim 2, characterized in that, The supply-side flexible response capability is calculated based on the high-altitude correction factor, specifically including: Supply-side flexibility resources should be divided into at least thermal power units, hydropower stations, energy storage devices, wind and solar power stations, and V2G resources. Based on the high-altitude correction factor, the upward and downward adjustment capabilities of the supply-side flexibility resources are respectively obtained; The supply-side flexible response capability is obtained by summing up the upward and downward adjustment capabilities of all supply-side resources.

4. The method for quantifying the supply and demand flexibility of the coal-to-electricity heating power grid according to claim 2, characterized in that, Based on the high-altitude correction factor, the demand-side flexible adjustment demand is calculated, specifically including: Using the thermal comfort threshold as the core constraint and combining it with the high altitude correction factor, the demand-side quantification module is constructed. The demand-side quantification module includes the ability to adjust transferable loads upward and downward, and the ability to adjust interruptible loads upward and downward. Based on the high-altitude correction factor, the upward adjustment capacity of transferable load and the upward adjustment capacity of interruptible load are summarized, and the downward adjustment capacity of transferable load and the downward adjustment capacity of interruptible load are summarized to obtain the flexible adjustment demand on the demand side.

5. The method for quantifying the supply and demand flexibility of the coal-to-electricity heating power grid according to any one of claims 2 to 4, characterized in that, The high-altitude correction factor is calculated based on extreme weather characteristic data and equipment or fuel characteristic data of the high-altitude coal-to-electricity heating area, specifically including: Calculate the environmental characteristic correction factor, which characterizes the basic correction impact of high altitude, low temperature and low air pressure on the operating environment of thermal power units, hydropower units, new energy and energy storage equipment. Determine the equipment or fuel characteristic loss coefficient, which characterizes the assigned values ​​for high-proportion high-alkali coal, physical constraints of the boiler-circulation system, and fuel combustion stability characteristics. The high-altitude correction factor is calculated based on the environmental characteristic correction factor and the equipment or fuel characteristic loss coefficient. The high-altitude correction factor characterizes and quantifies the comprehensive correction effect of high-altitude geographical factors and extreme weather on the output and load characteristics of power equipment.

6. The method for quantifying the supply and demand flexibility of the coal-to-electricity heating power grid according to any one of claims 2 to 4, characterized in that, The supply and demand flexibility quantification optimization model is solved based on multiple constraints, and the supply and demand flexibility margin is output, including: Based on the aforementioned multiple constraints, the supply-side quantification module and the demand-side quantification module are solved respectively to obtain basic adjustment capacity parameters. The basic adjustment capacity parameters include the total upward adjustment capacity and the total downward adjustment capacity of the supply side, and the total upward adjustment demand and the total downward adjustment demand of the demand side. With the goal of maximizing the supply and demand flexibility margin, a supply and demand flexibility objective function is constructed by combining the multiple constraints and the basic adjustment capability parameters. Solving the supply and demand flexibility objective function yields the upward and downward flexibility margins, respectively. The supply and demand flexibility margin is obtained by correcting the upward and downward flexibility margins based on the high altitude correction factor.

7. A method for coordinated optimization scheduling of supply and demand flexibility in coal-to-electricity heating power grids, characterized in that, include: Based on the method for quantifying the supply and demand flexibility of the coal-to-electricity heating grid as described in any one of claims 1 to 6, a multi-time-scale operation simulation model is constructed, which includes a weekly-scale operation simulation model, a day-ahead-scale operation simulation model, and an intraday-scale operation simulation model. Based on power system basic data, new energy output forecasts, and high-altitude correction factors, the weekly-scale operation simulation model is run to simulate the overall energy balance and regulation resource allocation on a weekly scale, and outputs weekly-scale constraints, which include boundary constraint parameters, regulation resource allocation schemes, and scenario-based optimization results. Based on the weekly-scale constraints, the day-ahead-scale operation simulation model is run to simulate the supply and demand balance and scheduling scheme based on a general scenario at the day-ahead scale, and the day-ahead scheduling scheme is obtained. Run the intraday-scale operation simulation model to perform short-term operation status prediction and scheme verification on an intraday scale, obtain short-term prediction results, and perform intraday rolling optimization scheduling based on real-time operation data to generate real-time scheduling instructions; Based on real-time operational data and the short-term forecast results, the deviation between the current operational status and the day-ahead scheduling scheme is calculated. The day-ahead scheduling scheme is then verified and corrected under the current operational conditions, and scheduling deviation and forecast error information containing operational error information are extracted. The scheduling deviation and prediction error information, which include operational error information, are fed back to the day-ahead scale to update the day-ahead scheduling scheme for the next scheduling cycle. The contract execution deviation information, equipment operation status information, and scheduling deviation and prediction error information containing operation error information are fed back to the weekly-scale operation simulation model to obtain feedback information. Based on the feedback information, the weekly-scale constraints are recalculated and updated, and the full-week energy balance and inter-day flexibility resource relay optimization simulation is performed. At least one of the SOC boundary of inter-day energy storage, V2G, or the external power transmission fulfillment boundary is verified, and the multi-time-scale operation simulation model is updated with the updated weekly-scale constraints.

8. The method for coordinated optimization scheduling of supply and demand flexibility in coal-to-electricity heating power grids according to claim 7, characterized in that, The weekly-scale operation simulation model adopts a two-stage operation simulation logic, specifically including: The first stage aims to minimize the overall cost and solves for the inter-day flexibility boundary, which includes the daily energy boundary, energy storage power boundary, V2G regulation capacity quota, thermal power reserve or ramp-up boundary and external power transmission target. The second stage uses the inter-day flexibility boundary as a rigid constraint to quantify the impact of intraday net energy deviation on inter-day status. Based on robust compensatory constraints and intraday net energy deviation, the adaptability of the inter-day flexibility boundary is verified. If the verification fails, the inter-day flexibility boundary is iteratively corrected to ensure that the SOC of energy storage or V2G always falls within the preset inter-day bandwidth throughout the week, avoiding the loss of control due to the cumulative inter-day deviation. The intraday net energy deviation includes wind and solar power output deviation and heating load fluctuations caused by extreme weather. The robust compensatory constraints include at least the cross-day SOC bandwidth constraint and the extreme weather adaptation constraint.

9. The method for coordinated optimization scheduling of supply and demand flexibility in coal-to-electricity heating power grids according to claim 7, characterized in that, The intraday rolling optimization scheduling adopts the MPC rolling optimization model, and the objective function of the MPC rolling optimization model is: ； In the formula, These represent the control actions calculated by MPC at the current time t (where t is a future time), the baseline scheduling plan given before the current time, and the weight matrix, respectively. Indicates the degree of deviation from the previous day's level within the day; This indicates the temporary balancing power that must be deployed in the event of an emergency. The penalty coefficient indicates that system security takes precedence; This indicates that in extreme cases, various resources may be exhausted, necessitating the sacrifice of some comfort to ensure safety.

10. A medium, characterized in that, The system stores a computer program, which, when executed by a processor, implements the method for quantifying the supply and demand flexibility of the coal-to-electricity heating grid as described in any one of claims 1 to 6 and / or the method for collaborative optimization scheduling of the supply and demand flexibility of the coal-to-electricity heating grid as described in any one of claims 7 to 9.

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