A power planning method and device considering thermal power decommissioning and demand response optimization

Abstract

This invention discloses a power planning method and apparatus that considers thermal power plant decommissioning and demand response optimization, belonging to the field of power system planning technology. First, a dynamic techno-economic model of the thermal power unit decommissioning process is established, quantifying the relationship between decommissioning costs and net asset value recovery, and setting continuity constraints. Then, a multi-type demand response bimodal model, including peak-shifting and peak-shaving models, is constructed to form a correlation constraint between response quantity and construction scale. Next, the thermal power decommissioning and demand response models are integrated to establish a multi-stage planning model that includes constraints on renewable energy penetration rate, carbon emission limits, and system inertia. Safety, economy, and green goals are comprehensively optimized through energy storage virtual inertia and carbon intensity constraints. Finally, the Big M method is used to linearize the model's bilinear terms, and a typical scenario clustering algorithm is used to compress the time scale of wind and solar load data, thereby improving the model's solution efficiency and the synergy and economy of the planning scheme.

Description

Technical Field

[0001] This invention relates to the field of power system planning technology, and in particular to a power planning method, apparatus, equipment and storage medium that takes into account the decommissioning of thermal power plants and demand response optimization. Background Technology

[0002] Under the "dual carbon" target, the proportion of new energy sources is gradually increasing, and the number of power electronic devices in the power system is also constantly increasing, leading to a continuous decline in the rotational inertia of the power system. Traditional large-capacity rotating equipment, mainly coal-fired power plants, is becoming increasingly scarce, exacerbating issues such as system safety and stability. If a large number of traditional thermal power units are decommissioned in a short period, the stable operating range of the power grid may change significantly. Coupled with the uncertainty of new energy output, the power grid will face system stability problems such as power shortages and frequency anomalies. Currently, some overseas power grids have already experienced frequency problems caused by insufficient inertia, such as the "8.9" blackout in the UK in 2019, which was mainly caused by excessive frequency disturbances (0.125Hz / s) due to wind power failures. This also indicates that frequency safety and stability issues caused by excessively low inertia are a key concern after large-scale grid integration of new energy sources.

[0003] Some scholars have proposed a strategic concept of "preserving capacity and reducing capacity" for coal-fired power plants during the peak carbon era, and "retiring without dismantling" during the carbon neutrality period, highlighting the important role of coal-fired power in the construction of new power systems. Therefore, how to rationally plan the transition path of traditional thermal power units after they reach the end of their lifespan, while ensuring system safety and reliability, is an emerging and important research area. It is also one of the key considerations for power system planning departments in various countries when carrying out long-term power system planning and investment. Where should the path of coal phasing out take under the low-carbon transformation, and how to promote the construction of new power systems more economically while ensuring system safety and stability, remain questions worthy of scientific consideration. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] To address this, this invention proposes a power planning method that considers both thermal power plant decommissioning and demand response optimization. By establishing a dynamic techno-economic model of the thermal power unit decommissioning process and constructing a bimodal response model for multiple types of demand response, the two are integrated to form a multi-stage planning model that includes renewable energy penetration rate, carbon emission limits, and system inertia constraints. Furthermore, the Big M method is used for linearization and typical scenario clustering algorithms for data compression to improve model solution efficiency and achieve a holistic optimization of safety, economy, and green goals.

[0006] Another objective of this invention is to provide a power planning device that takes into account the decommissioning of thermal power plants and demand response optimization.

[0007] The third objective of this invention is to provide a computer device.

[0008] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0009] To achieve the above objectives, this invention proposes a power planning method that considers the decommissioning of thermal power plants and demand response optimization, comprising: S1. Establish a dynamic techno-economic model for the decommissioning process of thermal power units, quantify the difference between decommissioning costs and equipment net value recovery, and set continuity constraints for decommissioning decisions. S2, construct a bimodal response model for multiple types of demand response, and perform technical and economic modeling for peak-shifting load demand response and peak-shaving load demand response respectively, forming a correlation constraint between response quantity and construction scale. S3 integrates thermal power decommissioning models and demand response models to establish a multi-stage planning model that includes new energy penetration rate, carbon emission limits and system inertia constraints. It achieves the overall optimization of safety, economy and green goals through virtual inertia calculation of energy storage equipment and carbon emission intensity constraints. S4 employs the Big M method to linearize the bilinear terms in the mixed-integer programming model and uses a typical scenario clustering algorithm to compress the time scale of wind and solar load data to improve the model's solution efficiency.

[0010] An embodiment of the power planning method of the present invention that considers the decommissioning of thermal power plants and demand response optimization may also have the following additional technical features: In one embodiment of the present invention, the step of establishing a dynamic techno-economic model for the decommissioning process of thermal power units, quantifying the difference between decommissioning costs and the recovery of net equipment value, and setting continuity constraints for decommissioning decisions includes: S11, through formula Calculate the total cost of decommissioning a thermal power unit; among which, This represents the difference between the cost of dismantling the equipment and the net asset value recovery. This indicates the retained refurbishment cost of thermal power units; S12, through formula Establish continuity constraints for decommissioning decisions to ensure that the decommissioning process of thermal power units is irreversible.

[0011] In one embodiment of the present invention, the construction of a bimodal response model for multiple types of demand response involves technical and economic modeling of peak-shifting load demand response and peak-shaving load demand response, respectively, to form a constraint on the relationship between response quantity and construction scale, including: S21, through formula The negative response of peak-shifting demand response is limited to not exceeding its construction scale, using the formula... Limit the negative response volume of peak-shaving demand response to not exceed its construction scale; S22, through formula Ensure that the total response volume for peak-shifting demand is zero during the participation period.

[0012] In one embodiment of the present invention, the integrated thermal power decommissioning model and demand response model establish a multi-stage planning model that includes renewable energy penetration rate, carbon emission limits, and system inertia constraints. This model achieves comprehensive optimization of safety, economy, and green goals through virtual inertia calculation of energy storage devices and carbon emission intensity constraints, including: S31, through formula Establish system inertia constraints, where This represents the virtual inertia provided by the energy storage device; S32, through formula Establish carbon emission quota constraints and quantify the total carbon emissions generated by the operation of thermal power units.

[0013] In one embodiment of the present invention, it further includes: S5, Establish seasonal power constraints for hydropower units, using the formula The total output of hydropower units in a given scenario is limited to the product of their planned capacity and the upper limit of electricity generation, and is determined by the formula... Ensure that the minimum output meets the lower limit of forced operation.

[0014] To achieve the above objectives, another aspect of the present invention proposes a power planning device that considers the decommissioning of thermal power plants and demand response optimization, comprising: The dynamic modeling module for decommissioning thermal power units is used to establish a dynamic techno-economic model of the decommissioning process of thermal power units, quantify the difference between decommissioning costs and equipment net value recovery, and set continuity constraints for decommissioning decisions. The multi-type demand response bimodal modeling module is used to construct bimodal response models for multiple types of demand responses. It performs technical and economic modeling for peak-shifting load demand response and peak-shaving load demand response respectively, forming a correlation constraint between response quantity and construction scale. The multi-stage planning model fusion module is used to integrate the thermal power decommissioning model and the demand response model to establish a multi-stage planning model that includes new energy penetration rate, carbon emission limit and system inertia constraint. Through the virtual inertia calculation of energy storage equipment and carbon emission intensity constraint, the overall optimization of safety-economy-green goals is achieved. The mixed-integer model linearization and time compression module is used to linearize the bilinear terms in the mixed-integer programming model using the Big M method, and to compress the time scale of wind and solar load data based on a typical scenario clustering algorithm to improve the model solution efficiency.

[0015] In one embodiment of the present invention, it further includes: The seasonal power constraint module for hydropower units is used to establish seasonal power constraints for hydropower units through formulas. The total output of hydropower units in a given scenario is limited to the product of their planned capacity and the upper limit of electricity generation, and is determined by the formula... Ensure that the minimum output meets the lower limit of forced operation.

[0016] This invention discloses a power planning method and apparatus that considers thermal power plant decommissioning and demand response optimization. By establishing a dynamic economic model for thermal power plant decommissioning and a multi-type bimodal demand response model, and integrating these with a multi-stage planning model incorporating constraints such as renewable energy penetration rate, carbon emissions, and system inertia, it effectively addresses the limitations of traditional planning methods, including isolated elements, conflicting objectives, and difficulty in solving problems. It achieves comprehensive optimization of orderly thermal power plant decommissioning, coordinated demand-side resource allocation, and system safety and stability constraints across multiple time scales. Furthermore, through model linearization and data time compression techniques, it significantly improves the solution efficiency of large-scale mixed-integer programming problems, thereby significantly enhancing the economy and low-carbon nature of the planning scheme while ensuring the safe and stable operation of the power system.

[0017] To achieve the above objectives, a third aspect of this application provides a computer device comprising a processor and a memory; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, for implementing a power planning method considering thermal power decommissioning and demand response optimization as described in the first aspect embodiment.

[0018] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements a power planning method considering thermal power plant decommissioning and demand response optimization as described in the first aspect embodiment.

[0019] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0020] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a power planning method that considers thermal power plant decommissioning and demand response optimization according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the specific steps of a power planning method that considers thermal power plant decommissioning and demand response optimization according to an embodiment of the present invention. Figure 3This is a schematic diagram of a power planning device that considers the decommissioning of thermal power plants and demand response optimization according to an embodiment of the present invention; Figure 4 It is a computer device according to an embodiment of the present invention. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] The following description, with reference to the accompanying drawings, describes a power planning method, apparatus, equipment, and storage medium that considers thermal power plant decommissioning and demand response optimization according to an embodiment of the present invention.

[0024] The core idea of ​​this invention is to propose a multi-stage collaborative planning framework for power systems. This framework organically integrates the orderly retirement process of thermal power plants, various types of demand response resources, and multi-dimensional system security constraints, placing techno-economic modeling, operational simulation analysis, and multi-objective optimization decision-making within a unified time-series planning system. First, a dynamic techno-economic model for the retirement of thermal power units is established to quantify the relationship between retirement costs and asset recovery. Then, a bi-peak demand response model covering peak shifting and peak shaving types is constructed to characterize the intrinsic correlation between its regulation capacity and construction scale. Furthermore, the above models are deeply integrated to establish a mathematical programming model that simultaneously includes renewable energy penetration rate, carbon emission limits, and system inertia constraints. By introducing virtual inertia support from energy storage and carbon emission intensity constraints, a comprehensive and optimized solution is achieved for safe operation, economic costs, and green and low-carbon goals. Finally, the Big M method is used to linearize the bilinear terms of the model, and a typical scenario clustering algorithm is applied to compress the time scale of wind and solar load data. This transforms the traditionally relatively independent, phased planning problem into a collaborative optimization problem that can be solved efficiently and systematically balances long-term transformation paths and short-term operational adaptability. This significantly improved the overall economy, reliability, and low-carbon nature of the planning scheme.

[0025] Example 1 To achieve the above invention, embodiments of the present invention provide a power planning method that considers the decommissioning of thermal power plants and demand response optimization, such as... Figure 1 As shown, it includes: S1. Establish a dynamic techno-economic model for the decommissioning process of thermal power units, quantify the relationship between the difference between decommissioning costs and the recovery of net equipment value, and set continuity constraints for decommissioning decisions.

[0026] Specifically, this model, starting from the comprehensive benefits of power system planning, meticulously depicts the decommissioning behavior of thermal power units at different stages, providing a scientific basis for the low-carbon transformation of the power system.

[0027] Furthermore, the decommissioning cost of a thermal power unit is determined by the difference between the equipment dismantling cost and the net asset value recovery benefit, specifically expressed as follows: ,in For equipment dismantling costs, For the net value recovery of equipment, For retirement decisions, 0-1 variables This refers to the retirement rate. Additionally, if the unit is chosen to be retained and refurbished, retention and refurbishment costs are introduced. ,in For renovation cost coefficient, This represents the maximum retention rate. Using the above formula, the model can dynamically reflect the economic differences in the decommissioning and retention of thermal power units in different years.

[0028] Specifically, the key parameters involved in the model include: retirement rate. Maximum and minimum retirement rates and Equipment dismantling costs Net asset value recovery income Refurbishment costs Unit lifespan Retirement decision variables These parameters need to be set reasonably based on the type of unit, its operating years, market environment, and policy guidance to ensure the accuracy and practicality of the model.

[0029] Specifically, this model is applicable to multi-stage decommissioning planning of thermal power units in power systems, and is particularly suitable for the context of a gradual increase in the proportion of renewable energy under the "dual carbon" target, addressing how to achieve the economical decommissioning of thermal power units while ensuring system inertia and frequency stability. The model can be embedded into power system optimization dispatch and investment planning platforms, supporting decision-makers in assessing total system cost, carbon emission levels, and operational risks under different decommissioning strategies.

[0030] Specifically, this step, by introducing the difference between decommissioning costs and net asset value recovery, can more realistically reflect the economic impact of decommissioning thermal power units, avoiding the simplification bias of traditional models that only consider fixed decommissioning costs. Simultaneously, it sets continuity constraints for decommissioning decisions (such as...). This model ensures that once units begin to be decommissioned, no further capacity expansion will be implemented in subsequent years, thereby avoiding unreasonable investment and improving the model's logical consistency and solution stability. The model provides a quantitative basis for the orderly decommissioning of thermal power units, enhancing the scientific and economic feasibility of the power system's low-carbon transformation path.

[0031] Furthermore, S1 includes: S11, through formula Calculate the total cost of decommissioning a thermal power unit; among which, This represents the difference between the cost of dismantling the equipment and the net asset value recovery. This indicates the retained refurbishment cost of thermal power units.

[0032] Specifically, the core of this step lies in making reasonable decisions about the decommissioning process of thermal power units through refined economic assessments, thereby optimizing the overall economy of the power system while meeting constraints such as system inertia and carbon emissions.

[0033] Furthermore, in the formula Indicates the first Taiwan thermal power unit in the The annual decommissioning cost is calculated based on the equipment dismantling cost. With the net value recovery income of equipment The difference, multiplied by the retirement decision variable and retirement rate .in, This is a 0-1 binary variable, indicating whether the unit was decommissioned in that year. This represents the retirement ratio, typically set based on the unit's operating years, equipment aging level, and retirement strategy, ranging from [0,1]. This model further considers the possibility of the unit being refurbished and retained after reaching its service life. This indicates that refurbishment costs are retained, thereby enabling a comprehensive assessment of the decommissioning and retention paths.

[0034] Specifically, It is usually estimated based on market quotations or historical data for equipment dismantling projects, with the unit being RMB 10,000 per megawatt; The value of the assets that can be recovered after retirement is calculated based on the current net value of the equipment and the residual value recovery rate. and The combined use of these technologies allows the model to flexibly adapt to different decommissioning strategies, such as one-time decommissioning or phased decommissioning. Furthermore, This indicates the lifespan of the generating unit and is used to determine whether it has entered the retirement decision-making stage.

[0035] Specifically, this step applies to multi-stage decommissioning planning for thermal power units in power systems, especially under the "dual carbon" objective, which requires the orderly withdrawal of thermal power units while ensuring system inertia and frequency stability. Through this model, planners can conduct comparative analysis of decommissioning costs across different years and different units, thereby determining the optimal decommissioning sequence and scale.

[0036] Specifically, by introducing refined modeling of decommissioning costs, the economic impact of thermal power unit decommissioning can be effectively reflected, providing key cost inputs for multi-stage planning models. Simultaneously, by combining the constraints of decommissioning decision variables and decommissioning rates, the model ensures good convergence and computational efficiency during the solution process, providing a solid foundation for achieving coordinated optimization of low-carbon, safe, and economical power systems.

[0037] S12, through formula Establish continuity constraints for decommissioning decisions to ensure that the decommissioning process of thermal power units is irreversible.

[0038] Specifically, this step ensures, from a technical perspective, that once a thermal power unit enters a decommissioning state, its decommissioning decision is irreversible in subsequent stages, thereby avoiding system instability caused by repeated start-ups and shutdowns or reactivation of decommissioned thermal power units.

[0039] Specifically, the formula introduces binary variables. , used to indicate the first Taiwan thermal power unit in the Whether retirement will occur in that year. This indicates that the crew has been decommissioned. This indicates that the unit has not been decommissioned or can still be expanded. (This is achieved through settings.) To ensure that if a certain thermal power unit is in the [missing information] He retired in [year], so he was in [year]. The system must remain in a state of decommissioning from 2010 onwards to ensure the continuity and irreversibility of the decommissioning process. Meanwhile, This ensures the binary properties of the variables.

[0040] Specifically, this constraint applies to all thermal power units. And only in It will take effect in the year specified, that is, starting from the second phase. Initial year. hour, This indicates that all thermal power units were not decommissioned during the initial phase. Furthermore, this constraint is related to the lifespan of the thermal power units. This is related to ensuring that newly built units are not decommissioned before reaching their design life, thereby avoiding resource waste and a sharp drop in system inertia.

[0041] Furthermore, this constraint is embedded in the multi-stage planning model of the power system, working in conjunction with conditions such as the expansion and decommissioning capacity of thermal power units, inertia constraints, and carbon emission limits to optimize the decommissioning sequence of thermal power units. Through this constraint, system planners can formulate scientific and reasonable strategies for the withdrawal of thermal power units while meeting safety, economic, and low-carbon objectives, avoiding problems such as system frequency instability and insufficient reserve caused by large-scale and disorderly decommissioning of thermal power units.

[0042] Specifically, by introducing continuity constraints in decommissioning decisions, we can effectively prevent "leapfrog" changes in the decommissioning decisions of thermal power units, ensure a smooth transition in the decommissioning process of thermal power units, and thus improve the operational reliability and economy of the power system during the low-carbon transformation process.

[0043] S2. Construct a bimodal response model for multiple types of demand response, and perform technical and economic modeling for peak-shifting load demand response and peak-shaving load demand response respectively, forming a correlation constraint between response quantity and construction scale.

[0044] Specifically, the model quantifies the responsiveness of demand-side resources under different operating scenarios, providing flexible adjustment means for the power system during its low-carbon transformation, thereby improving the economy and security of system operation.

[0045] Specifically, Load Shifting Demand Response (LS-DR) is primarily achieved through load shifting, i.e., reducing load during peak electricity demand periods and increasing load during off-peak periods, thereby optimizing the load curve and reducing system operating costs. Peak Shaving Demand Response (PS-DR), on the other hand, focuses on reducing load during peak periods to alleviate system pressure and avoid overload or frequency instability. Modeling both types of demand responses requires considering the upper and lower limits of their response power, response rate limitations, and the balance constraints of the total response. For example, the total response power of a peak-shifting response should meet certain conditions during the participating period. This ensures that the total load remains unchanged.

[0046] Furthermore, key variables were introduced into the model. and Let and represent the power reduction and power increase of the peak-shifting load at time t, respectively, and their response quantities must satisfy . and Meanwhile, the absolute change in response power must be limited by the response rate. ,Right now This is to avoid the impact of sudden load changes on system stability.

[0047] Specifically, this model is applicable to the optimal allocation of demand-side resources in power systems, especially in the context of high renewable energy penetration and the gradual retirement of thermal power units. By rationally arranging peak-shaving and peak-shifting response capabilities, it can improve system flexibility and economy. For example, during peak electricity consumption in summer, peak-shaving response can effectively reduce grid load pressure, while peak-shifting response can be used to optimize load curves and improve the utilization rate of energy storage and renewable energy.

[0048] Specifically, this step establishes a bimodal demand response model, enabling dynamic matching of demand response resources with system operating status, providing adjustable and flexible support for the low-carbon transformation of the power system. Simultaneously, by introducing constraints on response quantity and construction scale, it ensures that the planning of demand response resources matches actual operational capacity, avoiding resource waste or insufficient response, thereby improving the overall operational efficiency and economy of the system.

[0049] Furthermore, S2 includes: S21, through formula The negative response of peak-shifting demand response is limited to not exceeding its construction scale, using the formula... The negative response volume of peak-shaving demand response shall be limited to not exceed its construction scale.

[0050] Specifically, the technical implementation principle of this step is based on the collaborative modeling of the physical boundaries of the schedulable capacity of multi-type demand response resources and the planned investment scale. Specifically, the formula set... This is used to constrain the power adjustment range of peak-shifting demand response (DR-LS) and peak-shaving demand response (DR-PS) during operation, respectively. Indicates the year ,area Time period Scene and typical days The load power reduced through peak shifting; This represents the load power directly reduced through peak shaving under the same spatiotemporal conditions. Both are continuous decision variables, characterizing the actual demand response level of the system. Correspondingly... and This refers to the maximum schedulable capacity of the corresponding demand response resources that have been built or are permitted by planning in the corresponding planning year in the relevant region. It is a fixed parameter determined during the system planning stage.

[0051] Furthermore, this constraint system technically clarifies that the power limit of demand response resources during runtime must not exceed their preset capacity, thus mathematically representing the physical reality that "response capability is limited by infrastructure scale." This is achieved by using runtime variables... With planning parameters With direct coupling, the model achieves cross-timescale connection from long-term planning to short-term scheduling, ensuring that scheduling instructions are within the actual capacity of resources.

[0052] Specifically, this combined constraint is widely applied in multi-stage collaborative planning and production simulation of high-proportion renewable energy power systems. With the increasing penetration rate of fluctuating power sources such as wind and solar power, the system's demand for rapid and flexible resource adjustment is growing. Through the aforementioned constraints, the model can reasonably assess the actual available capacity of demand response resources during optimization, avoiding overly optimistic estimates of their adjustment potential. This improves the system's operational reliability when facing severe net load fluctuations, reduces deep reliance on conventional thermal power units for peak shaving, and facilitates the synergy between the system's low-carbon transformation and safe, economical operation.

[0053] Specifically, this set of constraints mitigates the risk of infeasible scheduling instructions due to "over-utilization" of demand response resources at the system level, while providing clear capacity boundaries for the economic assessment of demand response investments. Together with system power flow constraints, unit combination constraints, and energy storage operation constraints, it constitutes a complete and solvable planning and operation model, ensuring that demand response plays a practical and controllable role in improving system flexibility, reducing operating costs, and promoting the integration of new energy sources.

[0054] S22, through formula Ensure that the total response volume for peak-shifting demand is zero during the participation period.

[0055] Specifically, this formula is one of the core constraints for achieving the goal of "zero net load change" in peak-shifting demand response in the power system.

[0056] Specifically, this formula introduces a response quantity balancing mechanism in the time dimension into the model to address the positive response quantity of peak-shifting demand. With reverse response quantity Apply summation constraints to ensure that the set of responses falls within the specified response time period. Within this design, the sum of the positive and negative responses is zero. This design ensures that the overall system load level remains unchanged, optimizing system operation solely through time-based load shifts, thereby avoiding system imbalances caused by load reduction or increase.

[0057] Furthermore, the parameters involved in the formula include the year. ,node Time period Scene and demand response type .in, This indicates that in a specific scenario, the node During the period The positive response power; This represents the reverse response power. Represents a node In demand response type The set of responsive time periods is typically composed of typical daily data selected by clustering algorithms to represent the annual operating characteristics.

[0058] Specifically, this formula applies to scenarios in power systems where system operation optimization needs to be achieved through load shifting without changing the total load. For example, in systems with high renewable energy penetration, the fluctuating output of wind and solar power may lead to oversupply or undersupply at certain times. In such cases, peak-shifting demand response can smooth load fluctuations and improve the economic efficiency and stability of system operation by reducing electricity consumption during peak load periods and increasing electricity consumption during off-peak periods.

[0059] Specifically, this formula ensures that peak-shifting demand response achieves "zero net response" during the participation period, meaning the total load remains unchanged, and system operation is optimized only through time-based adjustments. This design helps improve system flexibility and reduce wind and solar curtailment without additional investment, while also lowering the start-up, shutdown, and operating costs of thermal power units. Furthermore, the formula, in conjunction with system node power balance constraints, enhances the feasibility and robustness of the model in multi-stage planning, providing crucial support for achieving low-carbon, safe, and economically synergistic optimization of the power system.

[0060] S3 integrates thermal power plant retirement models and demand response models to establish a multi-stage planning model that includes new energy penetration rate, carbon emission limits and system inertia constraints. It achieves the overall optimization of safety, economy and green goals through virtual inertia calculation of energy storage equipment and carbon emission intensity constraints.

[0061] Specifically, the model effectively addresses the challenges of system inertia reduction and carbon emission control caused by the large-scale integration of new energy sources by introducing a virtual inertia calculation mechanism for energy storage devices and carbon emission intensity constraints.

[0062] Specifically, this step first establishes a techno-economic model based on the decommissioning process of thermal power units, where the decommissioning cost is determined by the difference between the equipment dismantling cost and the net asset recovery benefit, as expressed in the following expression: Applicable to the year of retirement At the same time, retain renovation costs. This is used to assess whether thermal power units should be refurbished and retained after reaching the end of their service life. Secondly, constraint models for response quantity and response rate are established for peak-shaving and peak-shifting load demand responses to ensure that the demand response is reasonably configured within an adjustable range.

[0063] Furthermore, key parameters involved in the model include the decommissioning rate of thermal power units. Inertia coefficient Carbon emission intensity Typical scenario probability Annualized investment cost of energy storage equipment These parameters need to be set reasonably based on historical operating data, equipment characteristics, and policy guidance; for example, the inertia coefficient. The value is usually taken between 0.5 and 2.0 s, while the carbon emission intensity is estimated based on the fuel type and efficiency level of the thermal power unit.

[0064] Specifically, this model is applicable to scenarios such as power system long-term planning, low-carbon transition path design, and multi-energy coordinated optimization scheduling. Through multi-stage rolling optimization, it can provide planning departments with suggestions on the retirement sequence of thermal power units and the capacity configuration of new energy and energy storage equipment, while ensuring that the system inertia is not lower than the safety threshold. and meet carbon emission limits. .

[0065] Specifically, this step, by introducing virtual inertia and carbon emission intensity constraints, effectively improves the system's frequency stability and low-carbon operation capability under a high proportion of renewable energy integration. Simultaneously, through linearization processing and time-scale clustering acceleration methods, the model's solution efficiency is significantly improved, making it applicable to large-scale power system planning problems. Under the premise of ensuring system safety, this model achieves coordinated decision-making between thermal power unit decommissioning and demand response resource optimization, providing scientific support for the low-carbon transformation of the power system.

[0066] Furthermore, S3 includes: S31, through formula Establish system inertia constraints, where This represents the virtual inertia provided by the energy storage device.

[0067] Specifically, this step uses the formula The core purpose of establishing system inertia constraints in a multi-stage power system planning model is to ensure that the system remains stable in any planning year. Typical scenarios ,time The inertia level is not lower than the set minimum inertia requirement, thereby ensuring system frequency stability. Indicates the first Taiwan energy storage device in the first Year, Scene ,time The provided virtual inertia The inertia coefficient of the energy storage device. This represents the operating capacity of the energy storage device at that moment.

[0068] Specifically, this step compensates for the decrease in system rotational inertia after the decommissioning of thermal power units by incorporating the virtual inertia of the energy storage device into the system inertia calculation. Since energy storage devices do not possess the mechanical inertia of traditional rotating equipment, their inertia contribution needs to be simulated through control strategies. Virtual inertia control technology is typically used, which simulates the system's inertia characteristics by adjusting the power response speed of the energy storage device. In this formula, This indicates the total number of units in the system that can provide inertia, including thermal power, hydropower, wind power, photovoltaic, and energy storage equipment. This indicates the actual load demand of the system at that moment. To establish a baseline value for the system's inertia requirements, This is the minimum acceptable inertia coefficient for the system, used to characterize the system's requirement for inertia safety margin.

[0069] Furthermore, The value needs to be calibrated according to the type of energy storage device and the control strategy. For example, lithium battery energy storage systems can typically be configured with... Flywheel energy storage systems, due to their rapid response characteristics, Reachable . This is determined by the capacity configuration and operating status of the energy storage equipment, and needs to be modeled in conjunction with its scheduling strategy.

[0070] Specifically, this technology is applicable to power system planning scenarios with a high proportion of renewable energy integration. Particularly in the context of the gradual retirement of thermal power units and the continuous decline in system inertia, the introduction of virtual inertia from energy storage devices can effectively improve system frequency stability and prevent frequency instability incidents caused by insufficient inertia, such as the 2019 UK blackout. Its technological value lies in constructing a constraint mechanism that balances system safety and low-carbon transformation, providing a quantitative basis for the orderly retirement of thermal power units and the coordinated configuration of energy storage.

[0071] S32, through formula Establish carbon emission quota constraints and quantify the total carbon emissions generated by the operation of thermal power units.

[0072] Specifically, the formula in this step models the carbon emissions of thermal power units from multiple dimensions such as carbon emission intensity, unit output, and scenario probability, to ensure that the total carbon emissions during system operation do not exceed the preset annual carbon emission limit.

[0073] Specifically, the formula uses a triple summation structure to calculate the thermal power units in different years. Different scenarios Different times Operating output Corresponding carbon emission intensity Perform a weighted summation, where the scenario probabilities This is used to reflect the frequency of occurrence of different operating scenarios, thereby improving the robustness and practical adaptability of the model. In the formula, Indicates the total number of scenes. This represents the number of time steps in a single scene. This indicates the total number of thermal power units. For the first Annual carbon emission limits are typically set by national or regional carbon emission policies, such as annual total carbon emission control targets under the goals of "carbon peaking" or "carbon neutrality".

[0074] Furthermore, For thermal power units In the Annual carbon intensity, usually measured in units of 1 Its value depends on the type of unit (such as coal-fired power, gas-fired power, etc.) and operating efficiency. Indicates thermal power unit In the Year, No. The first scenario, the first The active power output at each time step, in units of Scenario probability Weights are typically obtained by processing historical load, wind and solar power output, and other data through clustering algorithms (such as K-means) and are used to characterize different operating states.

[0075] Specifically, this formula is embedded in a multi-stage planning model for the power system as one of the constraints, and is used in conjunction with constraints such as the retirement decision of thermal power units, the penetration rate of new energy sources, and system inertia for coordinated optimization. Through this constraint, the planning model can rationally arrange the retirement sequence of thermal power units and the configuration scale of new energy sources and energy storage while meeting carbon emission limits, thereby achieving a holistic optimization of the power system in terms of safety, economy, and greenness.

[0076] Specifically, this step effectively quantifies the total carbon emissions of thermal power units, providing a calculable and controllable constraint mechanism for the low-carbon transformation of the power system. By using carbon emission limits as hard constraints, it can prevent thermal power units from exceeding carbon emission standards due to over-operation during the decommissioning process, thereby promoting the evolution of the system towards a cleaner and lower-carbon direction and improving the environmental friendliness and policy compliance of the overall planning scheme.

[0077] S4 employs the Big M method to linearize the bilinear terms in the mixed-integer programming model and uses a typical scenario clustering algorithm to compress the time scale of wind and solar load data to improve the model's solution efficiency.

[0078] Specifically, in this invention, the Big M method is used to linearize the bilinear terms appearing in the mixed-integer programming model, thereby improving the solution efficiency of the model. Specifically, the binary variables involved in the model... With continuous variables The product term (e.g.) This can lead to nonlinear constraints, thus increasing the difficulty of solving the problem. To address this issue, this invention introduces three nonnegative continuous variables. , and Linearization equivalent: (1) (2) (3) (4) (5) (6) (7) They are used to replace the three types of bilinear terms mentioned above.

[0079] Furthermore, the Big M method introduces a sufficiently large constant. This transforms the original bilinear terms into linear constraints, where... The value of must satisfy This ensures that the constraints are valid across all possible variable values. In this way, the nonlinear terms in the model are transformed into linear constraints, thereby significantly reducing the solution complexity and improving computational efficiency.

[0080] Furthermore, this invention also performs time-scale compression on wind and solar load data based on a typical scenario clustering algorithm. Specifically, clustering algorithms (such as K-means or fuzzy C-means) are used to process hourly wind and solar output and load data throughout the year, extracting typical days for each month as representatives to reflect the system's operational characteristics under different seasons and weather conditions. To avoid the local optimum problem caused by the initial cluster center selection, this invention uses a full sample traversal to select the optimal initial center point, thereby improving the stability and representativeness of the clustering results.

[0081] Specifically, this method can significantly reduce the time-series variable dimension of the model, decrease the computational scale, and is suitable for solving multi-stage planning problems in large-scale power systems. Its technical advantages are twofold: firstly, the linearization process using the Big M method makes the model easier for standard solvers (such as CPLEX and Gurobi) to solve efficiently; secondly, the typical scenario clustering algorithm effectively compresses the time dimension while ensuring that data features are not distorted, thus improving the model's operating efficiency. In the context of the low-carbon transformation of the power system, this method provides an efficient and feasible modeling and solution path for the orderly decommissioning of thermal power units and the coordinated optimization of demand response.

[0082] S5, Establish seasonal power constraints for hydropower units, using the formula The total output of hydropower units in a given scenario is limited to the product of their planned capacity and the upper limit of electricity generation, and is determined by the formula... Ensure that the minimum output meets the lower limit of forced operation.

[0083] Specifically, in some implementations, this constraint is expressed through a formula. The total output of hydropower units in a specific scenario is limited to the product of their planned capacity and the upper limit of electricity generation, and is also determined by the formula... Ensure that the hydropower units meet the minimum output requirements during operation to meet the system stability requirements under forced operation.

[0084] Furthermore, the output of hydropower units is significantly affected by seasonal water inflows; therefore, a seasonal upper limit for electricity generation is introduced into the model. and minimum output ratio These are used to constrain the upper limit of total hydropower output in different seasons and the lower limit of minimum output in a single time period, respectively. This represents the total number of time periods in scenario s. This represents the planned capacity of hydropower unit h in year y. This represents the actual output of the hydropower unit in year y, scenario s, and time period t. By combining hydropower output with seasonal parameters, the model can more realistically simulate the operating capacity of hydropower in different seasons, avoiding planning deviations caused by ignoring seasonal factors.

[0085] Specifically, It is usually set based on historical water inflow data and reservoir scheduling rules, and the value range is generally between 0.8 and 1.0, representing the proportion of the maximum available hours of hydropower in a specific season; This parameter is used to set the minimum output ratio under forced operation, typically ranging from 0.1 to 0.3, to ensure that hydropower has basic regulation capabilities during critical periods. The settings of these parameters must comply with power system operation standards, such as the requirements for hydropower regulation capabilities in the "Guidelines for Power System Safety and Stability".

[0086] Furthermore, this constraint is applicable to regional power system planning that includes hydropower resources. Particularly in the context of high-proportion renewable energy integration, hydropower, as an important regulating resource, has a significant impact on system flexibility and economy due to its seasonal operating characteristics. Through this constraint, the model can rationally arrange hydropower operation strategies in different seasons, avoiding over-reliance on hydropower during the dry season, thereby improving the robustness of system operation.

[0087] Specifically, this step effectively improves the model's accuracy in characterizing hydropower operation characteristics, enhances the system's seasonal adaptability in multi-stage planning, and helps to achieve more economical power structure optimization and operation scheduling arrangements while meeting system inertia and carbon emission constraints.

[0088] This invention presents a power planning method that considers thermal power plant decommissioning and demand response optimization. By integrating a dynamic decommissioning model of thermal power units with multiple types of bimodal demand response models, and systematically incorporating multiple constraints such as renewable energy penetration rate, carbon emission limits, and system inertia, it effectively solves the core problems of fragmented elements, conflicting objectives, and difficult solutions in traditional planning. This method achieves coordinated decision-making for the orderly withdrawal of thermal power, precise allocation of demand-side resources, and safe and stable system operation during long-term transformation. Furthermore, through model linearization and data time-scale compression techniques, it significantly improves the solution efficiency and feasibility of large-scale complex planning problems, thereby effectively promoting the coordinated optimization of system operation economy and decarbonization level while ensuring the safety and reliability of the power system.

[0089] Example 2 To achieve the above invention, embodiments of the present invention also provide specific steps for a power planning method that considers thermal power plant decommissioning and demand response optimization, such as... Figure 2 As shown, it includes: Specifically, the technical and economic model of the decommissioning process of thermal power units is developed by coordinating the expansion and decommissioning capacity decisions of thermal power units at different stages and in different regions. It also considers multiple types of demand response, developing technical and economic models for peak-shaving and peak-shifting load demand responses. Based on the decommissioning process of thermal power units, a technical and economic model is developed, and through constraints such as renewable energy penetration rate, carbon emission limits, and system inertia, a multi-stage planning model of the power system's source-grid-storage system is formed. The efficiency of model solving is improved by designing linearization of the model and time-scale clustering acceleration methods. According to a preferred implementation, the technical and economic model of the thermal power unit decommissioning process includes: establishing a thermal power unit decommissioning model and establishing a set of constraints for the thermal power units.

[0090] According to a preferred embodiment, the decommissioning cost incurred by the thermal power unit during the decommissioning process is determined by the difference between the equipment dismantling cost calculated at each stage of the decommissioning process and the current net value of the equipment.

[0091] According to a preferred embodiment, considering the possibility of continued growth in thermal power units, the constraints on thermal power units in this invention consider both direct decommissioning and expansion followed by decommissioning. The specific formula is as follows: (8) (9) (10) (11) (12) (13) (14) (15) (16) Specifically, in the above formula: These represent the maximum and minimum decommissioning rates of the g-th thermal power unit in year y, respectively. This represents the upper limit of the expansion capacity of the g-th thermal power unit in year y; and represent the expansion capacity and decommissioning capacity of the g-th thermal power unit in year y, respectively; equations (8) and (10) represent the expansion constraints of thermal power units, when Equations (9) and (11) limit the upper and lower limits of the decommissioning capacity of existing units. Equation (12) restricts the continuity of decommissioning; if a unit begins to be decommissioned, it cannot be expanded after the year it begins to be decommissioned. Equation (13) restricts the initial year from participating in the decommissioning decision. Since the model in this part also considers the case where new units can be built, Equation (14) indicates that new units can only be decommissioned after they have reached the end of their service life. denoted by , g represents the lifespan of the g-th thermal power unit; Equations (15) and (16) represent the relationship between the capacity of thermal power units and the capacity of thermal power expansion and decommissioning. According to a preferred embodiment, the rapid operation simulation constraint set includes demand response operation constraints, system node power balance constraints, transmission network constraints, thermal power unit operation constraints, wind farm operation constraints, photovoltaic power station operation constraints, and energy storage operation constraints.

[0092] Furthermore, according to a preferred implementation, the demand response constraint set is established as follows: (17) (18) (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) Specifically, equations (17) and (19) indicate that the response amount of peak-shifting load demand response and peak-shaving load demand response should be within the specified maximum demand response potential during the responsive period, while equations (18) and (20) indicate that the response amount should be less than the construction scale of the demand response. Equation (21) indicates that the total power of peak-shifting load demand response during the participating load demand response period is zero. Equations (22) to (25) indicate that the response rate of peak-shifting load demand response and peak-shaving load demand response should be within a certain range, respectively.

[0093] Specifically, according to a preferred implementation method, a multi-stage planning model for the power system considering the orderly retirement of thermal power plants and the optimization and coordination of multiple types of demand response is established, and its objective function is: (26) In the above formula, The total cost of the system; The total investment cost of the system's generating units, transmission lines, and demand-side flexibility resources; Total maintenance cost of system units and lines; The total operating cost of the system; Costs associated with the decommissioning of thermal power units.

[0094] Specifically, investment costs include investment in generating units, transmission lines, and demand-side flexibility resources.

[0095] Specifically, the unit types include five categories: traditional thermal power, hydropower, wind power, photovoltaic, and energy storage equipment. The total fixed operation and maintenance cost can be expressed similarly; the system's operating cost includes the start-up and shutdown costs, variable operating costs, and load shedding costs of traditional thermal power units under various scenarios over many years. The decommissioning costs incurred during the decommissioning process of thermal power units are determined by the difference between the equipment dismantling costs calculated at each stage of decommissioning and the current net value of the equipment.

[0096] Furthermore, according to a preferred embodiment, the investment cost considers the investment in generating units, transmission lines, and demand-side flexibility resources. The generating unit types include five categories: traditional thermal power, hydropower, wind power, photovoltaic power, and energy storage equipment. The specific mathematical model is as follows: (27) (28) Specifically, in the above formula: set G , H , W , PV , B These correspond to the collections of traditional thermal power, hydropower, wind power, photovoltaic, and energy storage equipment units, respectively. CL This is a collection of lines to be built. N K The quantity of each set is represented by ; The planned capacity of the k-th type of generating unit / line in year y; the annualized investment cost of each type of generating unit in year y. To express. The annualized investment cost of peak-shaving demand response resources, Positive incentive costs for resources to respond to peak-shaving demand. The reverse incentive cost for peak-shaving demand response resources For the first The annual installed capacity limit for peak-shaving demand response resources.

[0097] Furthermore, according to a preferred embodiment, the total fixed maintenance cost can be expressed in a similar manner, as shown in the following formula: (29) In the above formula, This represents the fixed operation and maintenance cost of the k-th type of unit / line in year y.

[0098] Furthermore, according to a preferred embodiment, the multi-stage planning model for the power system considering the orderly retirement of thermal power plants and the optimization and coordination of multiple types of demand response mainly includes investment decision constraints and operational simulation constraints. Investment decision constraints mainly include construction capacity constraints for various types of generating units, upper limits of investment budgets for power sources, energy storage equipment, and demand response, constraints on the proportion of renewable energy generation, carbon emission constraints, and retirement decision constraints, etc.; operational simulation constraints mainly include node power balance constraints, network power flow constraints, operational output constraints for various types of generating units, intermittent renewable energy output constraints, operational constraints for energy storage equipment, and operational constraints for demand response, etc.

[0100] Specifically, in addition to the above constraints, this invention introduces constraints on the multi-year continuity of different types of equipment construction capacity, thermal power unit decommissioning constraints, system inertia constraints, and carbon emission quota constraints from the perspectives of system safety and decarbonization. At the same time, considering the seasonal variation of water volume throughout the year, seasonal power generation constraints for hydropower units are established based on the available power output characteristics of hydropower.

[0101] Furthermore, according to a preferred embodiment, considering that other types of generating units, energy storage, and lines besides thermal power units need to remain unchanged or continue to expand to meet the growing demand for electricity load, the expression is as follows: (31) Furthermore, according to a preferred embodiment, considering the rapid grid connection capability of energy storage devices, it is assumed that energy storage can provide a certain virtual inertia, which is equal to the product of the energy storage installed capacity and its inertia constant. The specific expression is as follows: (32) (33) (34) In the above formula: N I This refers to the total number of units of all types that can provide inertia; D y,s,t Let be the actual load demand of the system at time t in scenario s during year y, i.e., the difference between the demand load and the load shedding. z 0 represents the planned system inertia requirement level; This represents the minimum acceptable inertia coefficient of the system; z k Indicates the inertia coefficient of each type of unit; Let t be the operating capacity of the kth thermal power plant in scenario s at time t in year y.

[0102] Furthermore, according to a preferred embodiment, considering the constraints on total carbon emissions under the "dual carbon" target, carbon emission limits are established: (35) Specifically, in the formula: This represents the carbon emission intensity of the g-th thermal power unit in year y. This represents the carbon emission limit for year y.

[0103] According to a preferred embodiment, the system node power balance constraint is as follows: (36) (37) Equation (36) indicates that the active power of each node in the system needs to be balanced at each time under each scenario; Equation (37) indicates that the load shedding power should not be greater than the load demand of the node.

[0104] Furthermore, according to a preferred embodiment, considering the constraints of hydropower operation simulation, the original model equates the hydropower unit to a thermal power unit capable of climbing slopes and performs corresponding operation simulations, but does not consider the monthly output limits of hydropower. Therefore, this invention adds monthly power generation constraints and minimum output constraints for hydropower, thereby better reflecting the actual operation of hydropower. The specific mathematical expression is as follows: (38) (39) In the above formula: The planned output of the hydropower units at each moment; Ts is the number of moments in the corresponding scenario. The upper limit ratio of electricity consumption for each hydropower unit in each scenario; This represents the minimum forced output ratio for each hydropower unit in each scenario.

[0105] Furthermore, according to a preferred embodiment, considering the introduction of binary variables in the unit decommissioning decision, the existence of product terms between binary and continuous variables in the calculation of unit decommissioning costs and post-decision capacity constraints can cause the mixed-integer programming model to diverge. Therefore, it is necessary to linearize the bilinear terms during the solution process. Simplifying the constraints reveals that the main goal is to simplify... , and There are three types of product term relationships, therefore this invention utilizes the Big M method by introducing non-negative continuous variables. , and Linearization is performed, as shown in equations (1) to (7), where, Let be a sufficiently large constant. Therefore, it can be equivalent to: (40) Equations (8)-(11) are equivalent to: (41) (42) (43) (44) Specifically, according to a preferred embodiment, a typical day of each month is selected to represent the system's operation within a single year. A clustering algorithm is used to select typical scenarios for each month based on the wind and solar power and load output data. To prevent suboptimal problems caused by random initial points, this invention performs a full traversal of the entire sample to select the optimal center point.

[0106] This invention discloses specific steps of a power planning method that considers thermal power plant decommissioning and demand response optimization. By constructing a collaborative planning framework that integrates orderly thermal power plant decommissioning decisions with multi-type demand response adjustments, and systematically incorporating multiple safety and low-carbon constraints such as system inertia, carbon emission limits, and seasonal hydropower generation, it effectively overcomes the core limitations of traditional planning, such as isolated optimization of power generation, grid, load, and storage elements, and the disconnect between long-term transformation and short-term operation. It achieves overall collaborative optimization of thermal power plant decommissioning timing, flexibility resource investment, and system operation modes while ensuring system frequency stability and carbon emission compliance. Furthermore, by introducing linearization processing and typical scenario clustering techniques, it significantly improves the solution efficiency and engineering practicality of complex mixed-integer programming models, thereby providing scientific and efficient decision support for the economic, safe, and low-carbon transformation of the power system under the "dual carbon" objective.

[0107] Example 3 To achieve the above invention, such as Figure 3 As shown, this embodiment also provides a power planning device 10 that considers thermal power plant decommissioning and demand response optimization. The device 10 includes: The thermal power unit decommissioning dynamic modeling module 100 is used to establish a dynamic techno-economic model of the thermal power unit decommissioning process, quantify the difference between decommissioning costs and equipment net value recovery, and set continuity constraints for decommissioning decisions.

[0108] The Multi-Type Demand Response Bimodal Modeling Module 200 is used to construct bimodal response models for multiple types of demand responses. It performs technical and economic modeling for peak-shifting load demand response and peak-shaving load demand response respectively, forming a correlation constraint between response quantity and construction scale.

[0109] The multi-stage planning model fusion module 300 is used to integrate the thermal power decommissioning model and the demand response model to establish a multi-stage planning model that includes new energy penetration rate, carbon emission limit and system inertia constraint. It achieves overall optimization of safety-economy-green goals through virtual inertia calculation of energy storage equipment and carbon emission intensity constraint.

[0110] The mixed-integer model linearization and time compression module 400 is used to linearize the bilinear terms in the mixed-integer programming model using the Big M method, and to compress the time scale of wind and solar load data based on a typical scenario clustering algorithm to improve the model solution efficiency.

[0111] In one embodiment of the present invention, it further includes: a seasonal power constraint module for hydropower units, used to establish seasonal power constraints for hydropower units through formulas. The total output of hydropower units in a given scenario is limited to the product of their planned capacity and the upper limit of electricity generation, and is determined by the formula... Ensure that the minimum output meets the lower limit of forced operation.

[0112] This invention provides a power planning device that considers thermal power plant decommissioning and demand response optimization. By organically integrating core modules such as dynamic modeling of thermal power plant decommissioning, bimodal demand response modeling, multi-constraint fusion optimization, and efficient model solving, it systematically solves the prominent problems in traditional power system planning, such as insufficient source-load interaction, difficulty in coordinating multiple objectives, and difficulty in solving long-term time-series models. This device achieves end-to-end optimization from technical and economic modeling and operational simulation to multi-stage decision-making, significantly improving the planning scheme's ability to coordinate economic, safety, and low-carbon objectives and its computational feasibility. It provides a precise and efficient decision support tool for the transformation path of high-proportion renewable energy power systems.

[0113] To implement the methods of the above embodiments, the present invention also provides a computer device, such as... Figure 4 As shown, the computer device 600 includes a memory 601 and a processor 602; wherein, the processor 602 reads the executable program code stored in the memory 601 to run a program corresponding to the executable program code, so as to implement the various steps of the power planning method that considers thermal power decommissioning and demand response optimization described above.

[0114] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a power planning method that considers thermal power plant decommissioning and demand response optimization as described in the foregoing embodiments.

[0115] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0116] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A power planning method considering thermal power decommissioning and demand response optimization, characterized in that, include: S1. Establish a dynamic techno-economic model for the decommissioning process of thermal power units, quantify the difference between decommissioning costs and equipment net value recovery, and set continuity constraints for decommissioning decisions. S2, construct a bimodal response model for multiple types of demand response, and perform technical and economic modeling for peak-shifting load demand response and peak-shaving load demand response respectively, forming a correlation constraint between response quantity and construction scale. S3 integrates thermal power decommissioning models and demand response models to establish a multi-stage planning model that includes new energy penetration rate, carbon emission limits and system inertia constraints. It achieves the overall optimization of safety, economy and green goals through virtual inertia calculation of energy storage equipment and carbon emission intensity constraints. S4 employs the Big M method to linearize the bilinear terms in the mixed-integer programming model and uses a typical scenario clustering algorithm to compress the time scale of wind and solar load data to improve the model's solution efficiency.

2. The method of claim 1, wherein, The establishment of a dynamic techno-economic model for the decommissioning process of thermal power units quantifies the relationship between the difference between decommissioning costs and the recovery of the net value of the equipment, and sets continuity constraints for decommissioning decisions, including: S11, through formula Calculate the total cost of decommissioning a thermal power unit; among which, This represents the difference between the cost of dismantling the equipment and the net asset value recovery. This indicates the retained refurbishment cost of thermal power units; S12, through formula Establish continuity constraints for decommissioning decisions to ensure that the decommissioning process of thermal power units is irreversible.

3. The multi-stage collaborative planning method for power systems as described in claim 1, characterized in that, The aforementioned bimodal response model for constructing multiple types of demand response performs techno-economic modeling for peak-shifting load demand response and peak-shaving load demand response respectively, forming a correlation constraint between response quantity and construction scale, including: S21, through formula The negative response of peak-shifting demand response is limited to not exceeding its construction scale, using the formula... The negative response volume of peak-shaving demand response shall be limited to no more than its construction scale; S22, through formula Ensure that the total response volume for peak-shifting demand is zero during the participation period.

4. The multi-stage collaborative planning method for power systems as described in claim 1, characterized in that, The integrated thermal power plant decommissioning model and demand response model establish a multi-stage planning model that includes renewable energy penetration rate, carbon emission limits, and system inertia constraints. Through virtual inertia calculation of energy storage devices and carbon emission intensity constraints, it achieves comprehensive optimization of safety, economy, and green goals, including: S31, through formula Establish system inertia constraints, where This represents the virtual inertia provided by the energy storage device; S32, through formula Establish carbon emission quota constraints and quantify the total carbon emissions generated by the operation of thermal power units.

5. The multi-stage collaborative planning method for power systems as described in claim 1, characterized in that, Also includes: S5, Establish seasonal power constraints for hydropower units, using the formula The total output of hydropower units in a given scenario is limited to the product of their planned capacity and the upper limit of electricity generation, and is determined by the formula... Ensure that the minimum output meets the lower limit of forced operation.

6. A power planning device that considers the decommissioning of thermal power plants and demand response optimization, characterized in that, include: The dynamic modeling module for decommissioning thermal power units is used to establish a dynamic techno-economic model of the decommissioning process of thermal power units, quantify the difference between decommissioning costs and equipment net value recovery, and set continuity constraints for decommissioning decisions. The multi-type demand response bimodal modeling module is used to construct bimodal response models for multiple types of demand responses. It performs technical and economic modeling for peak-shifting load demand response and peak-shaving load demand response respectively, forming a correlation constraint between response quantity and construction scale. The multi-stage planning model fusion module is used to integrate the thermal power decommissioning model and the demand response model to establish a multi-stage planning model that includes new energy penetration rate, carbon emission limit and system inertia constraint. It achieves overall optimization of safety-economy-green goals through virtual inertia calculation of energy storage equipment and carbon emission intensity constraint. The mixed-integer model linearization and time compression module is used to linearize the bilinear terms in the mixed-integer programming model using the Big M method, and to compress the time scale of wind and solar load data based on a typical scenario clustering algorithm to improve the model solution efficiency.

7. The apparatus as claimed in claim 6, characterized in that, Also includes: The seasonal power constraint module for hydropower units is used to establish seasonal power constraints for hydropower units through formulas. The total output of hydropower units in a given scenario is limited to the product of their planned capacity and the upper limit of electricity generation, and is determined by the formula... Ensure that the minimum output meets the lower limit of forced operation.

8. An electronic device, comprising: processor; The memory stores executable instructions; when the processor executes the instructions, it implements a power planning method that considers the decommissioning of thermal power plants and demand response optimization as described in any one of claims 1-5.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, implements a power planning method considering thermal power decommissioning and demand response optimization as described in any one of claims 1-5.

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