Power market simulation and benefit evaluation method based on new energy electrothermal coupling system

Abstract

The invention discloses a method for a friendly power station of a new energy electrothermal coupling system to participate in electric power market simulation and benefit evaluation, and belongs to the technical field of electrical engineering. And a simulation model of the friendly power station of the new energy electrothermal coupling system is constructed, the simulation model is comprehensively incorporated into electricity market electricity price signals, system peak power supply function requirements and detailed operation constraints of all equipment, and accurate simulation of the operation process of the power station is achieved. On the basis, the benefit evaluation method is provided, the investment and operation and maintenance cost of various devices of the power station, the income of participating in the spot market and the auxiliary service market and the peak capacity compensation income are comprehensively considered, and an evaluation system covering the whole life cycle of the power station is constructed. The method provides key technical support for planning, operation strategy making and economic benefit evaluation of the new energy electrothermal coupling system friendly power station in a power market environment.

Description

Technical Field

[0001] This invention belongs to the field of electrical engineering technology, specifically relating to a power system simulation and economic benefit evaluation method, and more particularly to a simulation and benefit evaluation method applicable to the participation of renewable energy electrothermal coupling system power plants in the electricity market. Background Technology

[0002] With the rapid increase in the installed capacity of new energy sources, the randomness, volatility, and intermittency of their output pose significant challenges to the safe and stable operation of the power system. Meanwhile, the deepening of power market reforms has made electricity price signals a key factor guiding the behavior of power supply providers. Against this backdrop, new energy electrothermal coupling systems—friendly power plants integrating wind power, photovoltaics, electric boilers, steam turbines, thermal storage, and electrochemical energy storage—have become an important solution for promoting new energy consumption and improving system reliability due to their flexible adjustment capabilities and peak-load support potential.

[0003] However, the planning, construction, and operation of such complex systems face two major challenges: First, the accuracy of operational simulations is insufficient. Existing technologies mostly focus on simulation modeling of single or a few types of power sources, lacking complete and refined simulation models for the coupled operation of multiple devices such as wind power, photovoltaics, electric boilers, steam turbines, thermal storage, and electrochemical energy storage. Existing models often simplify or fail to simultaneously incorporate dynamic electricity market price signals, the system's rigid requirements for the power station's peak capacity, and the strict physical operational constraints of various devices. This results in simulation results that fail to accurately reflect the actual operating status of the power station in complex market environments, and cannot provide effective support for optimizing operational strategies. Second, the benefit evaluation system is incomplete. Existing benefit evaluation methods mostly focus on a single market or a single technical and economic indicator, failing to construct a comprehensive benefit evaluation system covering the entire life cycle of the power station. Specifically, cost accounting does not fully cover the investment and operation and maintenance costs of all types of equipment; revenue calculation does not fully include the revenue from participating in the spot market, various ancillary service markets, and the capacity compensation revenue obtained from providing reliable peak capacity. Such a one-sided assessment cannot scientifically and accurately measure the true economic viability of power plants in the electricity market environment, resulting in a lack of reliable basis for investment decisions and affecting the accurate formulation of relevant incentive policies.

[0004] Therefore, it is necessary to design a method that can accurately simulate the operation behavior of a new energy electrothermal coupling system-friendly power plant in the electricity market and scientifically evaluate its comprehensive benefits throughout its entire life cycle, so as to support the healthy development of such power plants. Summary of the Invention

[0005] This invention aims to address the problems of insufficient simulation accuracy and incomplete benefit evaluation system of new energy electrothermal coupling system power plants in the power market environment in existing technologies, and provides an evaluation method that integrates accurate simulation of the collaborative operation of multiple devices and scientific evaluation of comprehensive benefits throughout the entire life cycle.

[0006] The present invention provides a method for power market simulation and benefit evaluation based on a new energy electrothermal coupling system, comprising the following steps: S1, System architecture and basic physical model construction step: Integrating wind power generation units, photovoltaic power generation units, electric heating units, thermal power generation units, thermal storage units, and electrochemical energy storage units, establishing input-output characteristic equations and key performance parameter models for each unit, and defining the electrical and thermal energy coupling and transfer relationship network between each unit, thereby forming the system architecture and basic physical model of a new energy electrothermal coupling system-friendly power plant; S2, Market and operational constraint integrated modeling step: Integrating the electricity price time-series prediction signal provided by the electricity market for the future target period, and the power grid dispatching agency's model based on system reliability requirements... The mandatory peak power supply command and the detailed operational constraints of each unit derived from the equipment manufacturer's technical specifications and operating procedures are integrated into the system architecture and basic physical model as boundary conditions and constraint equations. This forms a mixed-integer linear programming or nonlinear programming mathematical model that includes market incentives, scheduling requirements, and physical constraints to simulate the power plant's participation in the electricity market. S3, Multi-timescale collaborative optimization simulation steps: Based on historical data and weather forecasts, the predicted output sequences of wind power and photovoltaic power generation within the target time period are generated, combined with the electricity price time-series prediction signal, with the objective function being to maximize the expected total economic benefit of the power plant within the target time period or minimize its total operating cost. The mathematical model is solved. Under the premise of satisfying all operational constraints and peak commands, the solution process optimizes and determines the optimal operating state of each unit at each simulation step within the target time period. This includes the start-stop commands and output plans of each generator unit, the charging and discharging power and heat storage capacity of the thermal storage unit, the charging and discharging power and state of charge of the electrochemical energy storage unit, and the net exchange power plan between the power station and the grid interconnection line. Finally, a complete power station collaborative operation simulation dataset is output. S4, Refined Calculation Steps for Full-Cycle Operating Revenue: Based on the simulation dataset, and according to the time-of-use settlement rules of the electricity spot market, the net electricity sold by the power station to the grid in each time period is compared with the corresponding nodal marginal price or system marginal price. Multiply and accumulate the electrical energy revenue over time periods; calculate the revenue from ancillary services such as peak shaving, frequency regulation, or reserve based on the winning service type, service provision volume, and corresponding market clearing price or compensation standard of the power station in the ancillary service market according to the simulation data; calculate the capacity compensation revenue based on the fulfillment of the peak power supply instruction, i.e., the actual peak capacity provided by the power station and its duration, according to the preset capacity market compensation rate or capacity electricity price mechanism; S5, full life cycle cost structured accounting steps: based on the detailed equipment list and engineering quantity of the power station, obtain the initial investment cost of all constituent units, and amortize it to each year of the entire project evaluation cycle using the equal annuity method or straight depreciation method;Simultaneously, based on equipment maintenance manuals and operation and maintenance experience data, the fixed operation and maintenance costs incurred each year during the project cycle, as well as the variable operation and maintenance costs related to the actual power generation, charging and discharging volume, or operating time of the equipment, are estimated and summarized to form a structured annual cost cash flow sequence; S6, Comprehensive Economic Indicator Generation and Evaluation Step: The annual operating revenue cash flow sequences calculated in step S4 are merged with the annual cost cash flow sequences obtained in step S5 to generate an annual net cash flow statement for the entire life cycle of the power plant; Based on this net cash flow statement, a financial evaluation model is applied to calculate core economic evaluation indicators, including the project's total investment financial internal rate of return, financial net present value, and dynamic investment payback period, which serve as a quantitative basis for evaluating the feasibility of power plant investment and comparing the economic benefits of different technical solutions or market strategies.

[0007] This invention, by constructing a complete technology chain, is the first to achieve end-to-end closed-loop analysis of a new energy electrothermal coupling system-friendly power plant in the power market environment, from physical operation to economic value. It can systematically and meticulously simulate the complex behavior of the power plant in response to market signals and dispatch instructions, and output scientific and economic evaluations that can be directly used for investment decisions. It provides a comprehensive and quantitative decision support tool for the planning, design, operation and policy formulation of this type of new power plant, from technical feasibility to economic feasibility.

[0008] Based on the optimization of the above evaluation method, the electric heating unit is an electric boiler or an electrode boiler; the thermal power generation unit is a condensing steam turbine, a back-pressure steam turbine, or a screw expander; the thermal storage unit is a sensible heat storage system or a phase change thermal storage system using molten salt, thermal oil, or water; and the electrochemical energy storage unit is a lithium-ion battery, a flow battery, or a sodium-sulfur battery energy storage system. This solution, by clearly defining the specific selectable equipment types for the electric heating, thermal power generation, thermal storage, and electrochemical energy storage units, closely integrates the method of this invention with mainstream, technologically mature industrial equipment solutions. This significantly enhances the direct applicability and operability of the method in engineering practice, ensures that the input parameters of the simulation model have a clear real-world source, and makes the simulation results closer to the technical and economic performance of actual engineering projects. Therefore, it improves the practical value and credibility of the method of this invention as a tool for early-stage project evaluation and scheme comparison.

[0009] Based on the optimization of the above evaluation method, the detailed operational constraints in step S2 include: upper and lower limits of output and power ramp-up rate constraints for the wind power generation unit and photovoltaic power generation unit; minimum technical output constraints, rated output constraints, minimum continuous start-up and shutdown time constraints, and efficiency characteristic curves for the electric heating unit and thermal power generation unit; upper and lower limits of thermal storage capacity constraints, maximum charge and discharge power constraints, and safe operating temperature range constraints for the thermal storage unit; and state-of-charge (SOC) operating window constraints, maximum charge and discharge power constraints, charge and discharge efficiency constraints, and a capacity decay model based on cycle number for the electrochemical energy storage unit. Refining the physical limits and operating procedures of the six core units into specific mathematical constraints is key to achieving high-fidelity simulation in this method. By incorporating refined constraints such as unit start-up and shutdown time, thermal storage temperature window, and SOC operating range, the simulation results strictly avoid situations such as equipment exceeding limits or violating operating procedures, thereby improving the engineering feasibility and safety of the operating strategy output by the simulation model and providing a reliable data foundation for subsequent benefit evaluation.

[0010] Based on the optimization of the above evaluation method, the peak power supply instruction in step S2 is specifically defined as follows: during the preset peak load period of the power grid or the period of system reliability shortage, the output power of the thermal power generation unit is required to maintain no less than a specified percentage of its rated capacity, and the duration of this output state is no less than a specified threshold. This scheme quantifies the core functional requirements of the system for friendly power plants into explicit instructions on the specific power level and duration of the thermal power generation unit, and embeds them as hard constraints into the model. This ensures that the simulation process must prioritize meeting the power grid's supply needs, so that the final operation strategy and benefit evaluation results are based on fulfilling this social responsibility. This helps to accurately assess the true economic viability of the power plant when undertaking the supply guarantee obligation and provides a key analysis scenario for designing a reasonable capacity compensation mechanism.

[0011] Based on the optimization of the above evaluation method, the objective function of the optimization algorithm in step S3 is to maximize the total operating revenue of the power plant or minimize its total operating cost during the simulation period. The decision variables of the optimization algorithm include the output plan, start-up and shutdown status, and interactive power plan with the main grid for each unit in each simulation period. This scheme explicitly sets maximizing the total operating revenue or minimizing the total cost as the objective function, and uses the detailed operation plan of each unit as the decision variable. It directly simulates the rational decision-making process of the power plant as an economic agent in the market, enabling the simulation results to reflect its optimal business behavior under market incentives. This overcomes the limitations of traditional simulation methods that are guided by optimal technical indicators, and makes the analysis conclusions more revealing of the power plant's market viability and profit potential.

[0012] Based on the optimization of the above evaluation method, the ancillary service market in step S4 includes at least one of the peak-shaving market, frequency regulation market, and reserve market. The service compensation revenue is calculated based on the service provision volume and market clearing price in the simulation dataset. This scheme explicitly includes major ancillary service markets such as peak-shaving, frequency regulation, and reserve in the revenue accounting scope, fully exploring and quantifying the important value creation capabilities of power plants outside the energy market, and comprehensively reflecting their multiple market identities as a flexibility resource. This helps investors and policymakers to more fully understand the comprehensive value of power plants, avoid underestimating their economic feasibility by ignoring ancillary service revenue, and incentivize power plants to participate more actively in system regulation.

[0013] Based on the optimization of the above evaluation method, the financial model in step S5 is either the equal annuity method or the straight-line depreciation method. The operation and maintenance costs include fixed operation and maintenance costs related to equipment operating time and variable operation and maintenance costs related to equipment power generation or energy conversion. This solution uses standard financial methods to allocate investment costs and distinguishes between fixed and variable operation and maintenance costs for structured accounting. This ensures that the cost analysis conforms to common project financial evaluation standards, guarantees the matching and accuracy of cost data with subsequent financial indicator calculations, and facilitates more refined break-even analysis and cost sensitivity analysis, providing a basis for cost control and pricing strategies.

[0014] Based on the optimization of the above evaluation method, the comprehensive benefit evaluation index in step S6 includes at least one of the following: internal rate of return on total investment, net present value, dynamic payback period, and levelized cost of electricity. This transforms complex technical and market simulation results into economic language that is generally understood and trusted by the investment community and decision-makers, enhancing the persuasiveness and comparability of the conclusions output by the method of this invention. This directly serves the investment and financing decisions of projects, enabling a direct comparison of the merits of technical solutions through clear economic return data.

[0015] Based on the optimization of the above evaluation method, the method further includes step S7: sensitivity analysis and strategy optimization. By changing key input parameters including the new energy forecast output curve, market electricity price curve, equipment investment cost, and ancillary service price, steps S1 to S6 are repeated to analyze the sensitivity of the comprehensive benefit evaluation index to each parameter and compare the economic differences under different equipment configuration schemes or market participation strategies, providing a quantitative basis for power plant planning and operation decisions. This invention, by adding sensitivity analysis and strategy optimization steps, constitutes a complete feedback loop of the methodology, thereby enabling a systematic assessment of the project's economic vulnerability to various key uncertainties and identifying major sources of risk. Simultaneously, by comparing different configurations and strategies, it can proactively seek optimization directions to improve project economics, making this method not only an evaluation tool but also a strategy exploration platform guiding project optimization design and flexible operation.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention is the first to construct a unified simulation framework model integrating six types of heterogeneous equipment: wind power, photovoltaic, electric boilers, steam turbines, thermal energy storage, and electrochemical energy storage. It synchronously incorporates dynamically changing electricity market price signals, mandatory peak-load requirements issued by the grid dispatching system, and the strict physical and operational constraints of each type of equipment into the optimization calculation. This design fundamentally overcomes the simulation distortion problems caused by incomplete consideration of equipment coupling, missing key constraints, or simplification of market factors in existing models. The simulation framework model can simulate the multi-timescale collaborative optimization operation of power plants under the random fluctuations of renewable energy output and the dynamic guidance of electricity price signals with high fidelity. It provides a highly reliable testing environment for power plants to formulate day-ahead plans, make real-time adjustments, and participate in multi-level market bidding, significantly improving the scientific nature and robustness of operational decisions.

[0017] 2. The benefit evaluation method proposed in this invention breaks through the limitations of traditional assessments that only focus on a single market or a few technical and economic indicators. It pioneers an economic analysis system covering the entire life cycle of a power plant. It not only systematically calculates the initial investment and long-term operation and maintenance costs of all core equipment, avoiding inflated benefits due to cost omissions, but also comprehensively quantifies the power plant's revenue from participating in the electricity spot market, compensation revenue from participating in ancillary services such as peak shaving and frequency regulation, and capacity mechanism revenue obtained from providing reliable peak capacity. This dual panoramic perspective can objectively and accurately depict the true profit model and cash flow status of a power plant in the complex electricity market ecosystem, providing irreplaceable quantitative evidence for project investment feasibility analysis, financial risk assessment, and the exploration of differentiated business models.

[0018] 3. This invention achieves deep data linkage and functional closed loop between two major modules: physical operation simulation and economic value assessment. The simulation model provides accurate and real-time operational data input for benefit assessment, ensuring the authenticity of the source of revenue and cost accounting. Conversely, the benefit assessment results can verify the economic optimality of the operating strategies in the simulation model and guide parameter optimization for equipment configuration and control strategies. This integrated process establishes a complete analytical chain from technical solution design to the realization of final economic value. It not only provides project developers and investors with decision-making tools for the entire process from front-end planning to later operation, but also provides policymakers with a policy simulation sandbox based on detailed data for designing incentive-compatible market rules and capacity compensation mechanisms, possessing significant industrial application value and promising prospects for promotion. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the architecture of the new energy electrothermal coupling system power plant in this invention; Figure 2This is a flowchart of the power market simulation and benefit evaluation method based on a new energy electrothermal coupling system according to the present invention; Figure 3 This is a block diagram for optimizing the modeling and solving of the simulation model in this invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0021] like Figures 1-3 As shown, the present invention provides a method for power market simulation and benefit evaluation based on a new energy electrothermal coupling system, comprising the following steps: S1, System architecture and basic physical model construction step: Integrating wind power generation units, photovoltaic power generation units, electric heating units, thermal power generation units, thermal storage units, and electrochemical energy storage units, establishing input-output dynamic characteristic equations and key performance parameter models for each unit based on physical principles. The key performance parameter models include, but are not limited to, conversion efficiency curves, thermodynamic characteristic parameters, and capacity decay characteristic parameters; simultaneously, based on the first law of thermodynamics and the principle of conservation of electrical energy, defining the electrical and thermal energy coupling and transfer relationship network between each unit through connecting pipes and power grid lines, and establishing a set of energy flow balance equations, thereby forming a complete system architecture and a basic physical model of multi-energy flow coupling for a new energy electrothermal coupling system power plant.

[0022] S2. Market and Operational Constraint Integrated Modeling Steps: Collect the day-ahead and real-time market price time-series forecast signals provided by the electricity market for the future target time period; the mandatory peak power supply command issued by the grid dispatching agency based on system reliability requirements, containing specific power requirements and duration windows; and the detailed operational constraints of each unit derived from equipment manufacturer technical specifications and operating procedures. Use the price time-series forecast signals as input variables for the economic objective function; transform the peak power supply command into power lower limit constraints and duration constraints for the thermal power generation unit; transform the detailed operational constraints of each unit into a set of mathematical constraint equations including power limit constraints, start-up and shutdown time constraints, ramp rate constraints, and state continuity constraints; integrate all the above boundary conditions and constraint equations into the system architecture and basic physical model through linearization or piecewise linearization methods, constructing a mixed-integer linear programming mathematical model that includes market incentives, dispatching requirements, and physical constraints, capable of accurately simulating the power plant's participation in day-ahead market bidding and real-time market balancing behavior.

[0023] S3. Multi-timescale collaborative optimization simulation steps: Based on historical data and the predicted probability distribution of wind and photovoltaic power output within the target time period generated by a high-precision meteorological forecast system, and combined with the time-series prediction signals of electricity prices in the day-ahead and real-time markets, a two-stage or robust optimization model considering prediction uncertainties is established; with the objective function of maximizing the expected total economic benefit of the power station participating in the multi-level electricity market or minimizing its total operating cost within the target time period, the mixed-integer linear programming mathematical model is solved accurately using the branch-and-bound method or the interior-point method; the solution process strictly satisfies the physical operation requirements of all equipment. Under the constraints of safety operation and peak command, the optimal operating state of each unit is determined at each 15-minute or 1-hour simulation step within the target time period. This includes the output curves of wind turbines and photovoltaic arrays, the start-stop commands and refined output plans of thermal power generators, the time-sharing charging and discharging power and real-time dynamic change curves of thermal storage units, the charging and discharging power plans and state of charge change trajectories of electrochemical energy storage units, and the time-sharing net exchange power plans between the power plant and the grid interconnection line. Finally, a complete multi-timescale collaborative operation simulation dataset of the power plant containing time series data and state transition data is output.

[0024] S4. Refined Calculation Steps for Full-Cycle Operational Revenue: Based on the simulation dataset, a two-tier settlement mechanism is adopted according to the day-ahead and real-time market time-of-use settlement rules of the electricity spot market: the winning bid volume of the power plant in the day-ahead market for each time period is multiplied by the day-ahead market clearing price for the corresponding time period. At the same time, the deviation between the actual operation and the day-ahead plan is settled according to the real-time market clearing price. The two are accumulated time-by-time to obtain accurate energy revenue. Based on the winning service type, frequency regulation mileage service volume and corresponding frequency regulation mileage clearing price of the power plant in the frequency regulation ancillary service market in the simulation dataset, and the peak regulation depth and duration and corresponding peak regulation compensation standard in the peak regulation ancillary service market, the frequency regulation service revenue and peak regulation service revenue are calculated respectively. Based on the fulfillment of the peak power supply instruction, that is, the actual peak capacity and duration provided by the thermal power generation unit in the simulation dataset during the specified peak period, the accurate annual capacity compensation revenue is calculated according to the capacity compensation rate or capacity price mechanism published by the provincial or regional capacity market, combined with the capacity performance assessment results.

[0025] S5. Structured Accounting Steps for Full Life Cycle Costs: Based on the detailed equipment list, bill of quantities, and procurement contracts of the power plant, obtain the initial fixed asset investment cost of all constituent units, and use the equal annuity method or the straight-line depreciation method that considers the time value of money, combined with the expected lifespan of the equipment and technological iteration factors, to reasonably allocate it to each year of the entire project evaluation cycle; at the same time, based on the maintenance manuals provided by the equipment manufacturers, historical operation and maintenance databases, and industry operation and maintenance experience data, use a reliability-based operation and maintenance cost model to estimate and summarize the fixed operation and maintenance costs incurred each year of the project cycle, including regular maintenance costs, preventive maintenance costs, insurance premiums, and labor costs, as well as the variable operation and maintenance costs, including fuel consumption costs, incremental maintenance costs caused by equipment performance degradation, and variable operation and maintenance costs that are positively correlated with the actual power generation, charging and discharging capacity, or operating time of the equipment, to form a structured annual cost cash flow sequence with time dimension and cost category.

[0026] S6. Comprehensive Economic Indicator Generation and Evaluation Steps: The annual operating revenue cash flow series calculated in step S4 and the annual cost cash flow series obtained in step S5 are merged according to the same time benchmark to generate a complete annual net cash flow statement covering the construction and operation periods throughout the power plant's entire life cycle. Based on this net cash flow statement, a standard financial evaluation model is applied, considering income tax and inflation factors, to calculate core economic evaluation indicators, including the project's total investment internal rate of return, net present value, dynamic investment payback period, and levelized cost of electricity. Furthermore, a comprehensive evaluation matrix is ​​established, combined with technical reliability indicators and environmental benefit indicators, to serve as a multi-dimensional quantitative decision-making basis for assessing the feasibility of power plant investment and comparing the economic benefits of different technical solutions or market strategies.

[0027] In this invention, the electric heating unit is an electric boiler using resistance heating or electromagnetic induction heating principles, or an electrode boiler using direct water-electricity contact heating; the thermal power generation unit is a condensing steam turbine using a Rankine cycle, a back-pressure steam turbine using a combined heat and power (CHP) mode, or a screw expander suitable for medium- and low-temperature heat source recovery; the thermal storage unit is a sensible heat storage system using molten salt, thermal oil, or water as the heat storage medium, or a latent heat storage system using phase change materials, including but not limited to single-tank inclined-temperature layer thermal storage systems, dual-tank thermal storage systems, and packed-bed thermal storage systems; the electrochemical energy storage unit is a lithium-ion battery energy storage system using lithium iron phosphate or ternary lithium materials, a flow battery energy storage system using vanadium or other electrolyte systems, or a sodium-sulfur battery energy storage system using a solid electrolyte.

[0028] The detailed operational constraints in step S2 include: The operational constraints set for the wind power generation unit and the photovoltaic power generation unit include: the active power output must not exceed the upper limit of its rated installed capacity, and must not be lower than zero or the minimum output allowed by technology; and the power ramp-up rate constraint to ensure grid frequency stability and limit the variation of output in adjacent time periods.

[0029] The operational constraints set for the electric heating unit and the thermal power generation unit include: minimum technical output constraints necessary to ensure stable operation of the equipment, and rated output constraints to ensure that the output does not exceed the equipment's design capacity; start-up and shutdown time constraints to prevent damage to the equipment due to frequent operation; and piecewise linearized efficiency characteristic curve constraints describing the changes in energy conversion efficiency of the equipment under different load rates.

[0030] For the aforementioned thermal storage unit, the operational constraints include: upper and lower limits of thermal storage capacity to prevent overflow or depletion of the thermal storage medium; maximum charging power and maximum releasing power to limit the rate of heat energy access per unit time; and maximum and minimum safe operating temperatures of the thermal storage medium to ensure that the thermal storage medium is always maintained within a safe phase range.

[0031] The operational constraints set for the electrochemical energy storage unit include: a state-of-charge operating range constraint to prevent overcharging and over-discharging and ensure battery life and safety; a maximum allowable charging power and discharging power constraint set by the battery management system; a charging efficiency and discharging efficiency constraint considering energy loss during charging and discharging; and a battery capacity decay model that dynamically corrects the battery's usable capacity based on the cumulative equivalent cycle number to assess battery life decay.

[0032] In this invention, the peak power supply instruction in step S2 is specifically defined as: a mandatory operation requirement issued to the new energy electrothermal coupling system friendly power station during a pre-set peak load period or a specific period when the system power supply reliability is in a state of shortage, based on the power balance warning or demand response requirements issued by the power grid dispatching agency. This instruction explicitly requires that the thermal power generation unit must be put into operation, and its output power must be continuously maintained at a certain proportion not lower than the rated power capacity specified on its nameplate. At the same time, this power output state must be maintained for a continuous period of not less than a preset minimum duration threshold, so as to ensure that the power station provides stable, reliable and sufficient peak capacity support to the power grid when the system needs it most.

[0033] In this invention, the objective function of the optimization algorithm in step S3 is set to optimize the economic benefits of the power plant during the entire simulation cycle. Specifically, it is chosen to maximize the total economic revenue obtained from participating in multiple electricity markets, or to minimize the total operating expenditure, including fuel costs, operation and maintenance costs, and market transaction costs. The decision variables of the optimization algorithm cover the operating strategies of all controllable units within the power plant during the entire simulation period, specifically including: the maximum transmittance plan of the wind power generation unit and photovoltaic power generation unit under grid connection constraints; the time-by-time electrothermal conversion power plan of the electric heating unit; the unit status and time-by-time power generation plan of the thermal power generation unit under start-up and shutdown constraints; the time-by-time thermal storage / heat release power plan and thermal storage status of the thermal storage unit; the time-by-time charging / discharging power plan and its state of charge change trajectory of the electrochemical energy storage unit; and the net exchange power plan at the connection point between the entire power plant and the main grid for each time period.

[0034] The ancillary service market in step S4 encompasses the paid service trading mechanism established by the power system to maintain safe and stable operation and power quality. Specifically, it includes, but is not limited to: 1) for the peak-shaving market where the power system experiences intraday supply-demand imbalances, power plants provide upward and downward adjustment services by adjusting their output plans; 2) for the frequency regulation market to maintain system frequency stability, power plants provide regulation capacity and regulation mileage services through rapid response commands; and 3) and for the backup market to ensure the system can cope with sudden failures, power plants provide backup services by reserving and maintaining readily available generation capacity. The ancillary service revenue refers to the economic return accurately calculated by the power plant based on the simulated service volume or actual provision volume in various ancillary service markets, combined with the service clearing price, mileage price, or capacity compensation price officially announced by the relevant market or formed through bidding, according to the established market settlement rules.

[0035] In this invention, the financial model in step S5 adopts a standardized asset value allocation method, specifically including: the equal annuity method, which converts the initial investment cost into an equal annual annuity within the project evaluation period based on the time value of money; or the straight-line depreciation method, which allocates the initial investment cost over the expected service life of the equipment on an average basis over the years without considering the time value of money.

[0036] The aforementioned operation and maintenance costs cover the ongoing expenditures incurred by the power plant throughout its entire life cycle to maintain the normal operation of all equipment. Specifically, they are divided into: fixed operation and maintenance costs, which are annual or periodic costs that are unrelated to whether the equipment is in operation and are mainly based on calendar time and the existence of the equipment, including equipment insurance premiums, basic labor costs, annual routine maintenance costs, software licensing fees, and management allocation fees; and variable operation and maintenance costs, which are costs directly related to the actual operating intensity of the equipment. Their amount varies linearly or non-linearly with the equipment's power generation, energy conversion, charging and discharging capacity, or equivalent operating time. They mainly include fuel consumption costs, replacement costs of worn parts, incremental maintenance costs due to performance degradation, and specific labor and material consumption costs that are positively correlated with operating time.

[0037] In this invention, the comprehensive benefit evaluation indicators in step S6 include: the internal rate of return on total investment, which measures the discount rate at which the cumulative present value of net cash flows in each year of the project's entire calculation period equals zero, and is a key dynamic indicator for assessing the project's profitability; the net present value (NPV), which refers to the sum of the present values ​​of net cash flows in each year of the project's calculation period discounted to the beginning of the construction period according to a set industry benchmark rate of return or cost of capital, and is used to directly reflect the absolute profit value of the project; the dynamic payback period, which refers to the time required for the present value of the project's net income to offset the present value of all investments, considering the time value of money, and is used to assess the project's investment recovery speed and risk; and the levelized cost of electricity (LCOE), which refers to the unit power generation cost obtained by dividing the present value of total costs by the present value of total power generation over the entire life cycle of the project, and is a core indicator for horizontally comparing the economic efficiency of different power generation technologies; the method quantifies the economic benefits of the power plant based on at least one of the above indicators.

[0038] In this invention, the method further includes step S7: multi-dimensional sensitivity analysis and operation strategy optimization. This step systematically changes key uncertainty input parameters by constructing a set of parameter disturbance scenarios—including new energy power generation output prediction curves based on different meteorological scenarios, multi-scenario electricity price curves reflecting future market supply and demand changes, equipment investment costs considering technological progress and supply chain fluctuations, and various ancillary service market prices that change with policy adjustments—and re-executes steps S1 to S6 completely under each set of parameter settings, thereby quantitatively analyzing the sensitivity and change patterns of various comprehensive benefit evaluation indicators to the above-mentioned key parameters. Furthermore, by setting and comparing different equipment capacity configuration schemes, equipment technology selection schemes, and differentiated bidding strategy combinations targeting the day-ahead market, real-time market, and ancillary service market, the economic differences of various planning schemes and operation strategies are evaluated, ultimately providing an optimized decision-making basis based on a large number of scenario simulations and quantitative comparisons for the initial investment planning, mid-term technical transformation, and daily market-oriented operation of the power plant.

[0039] In summary, this invention provides a complete solution for simulation and benefit evaluation of new energy electrothermal coupling system-friendly power plants. This method establishes a refined multi-energy flow coupling physical model and innovatively integrates dynamic market electricity prices, mandatory peak dispatch instructions, and detailed operational constraints of various equipment into a unified optimization framework. This achieves high-fidelity simulation of the power plant's multi-timescale collaborative operation behavior under complex electricity market conditions. Based on simulation data, this method further constructs a comprehensive economic evaluation system covering the entire life cycle cost and multiple benefits, capable of outputting industry-standard financial indicators as a basis for investment decisions. Finally, by introducing multi-parameter sensitivity analysis and multi-strategy comparison optimization modules, this method becomes not only an evaluation tool but also a decision support system supporting the dynamic optimization of power plant planning, design, and operation strategies, providing crucial technical support for enhancing the competitiveness and sustainable development capabilities of such power plants in the market environment.

[0040] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit the technical solutions. Although the applicant has described the present invention in detail with reference to preferred embodiments, those skilled in the art should understand that any modifications or equivalent substitutions made to the technical solutions of the present invention cannot depart from the spirit and scope of the present invention and should be covered within the scope of the claims of the present invention.

Claims

1. A method for power market simulation and benefit evaluation based on a new energy electrothermal coupling system, characterized in that, Includes the following steps: S1. System Architecture and Basic Physical Model Construction Steps: Integrate wind power generation unit, photovoltaic power generation unit, electric heating unit, thermal power generation unit, thermal storage unit and electrochemical energy storage unit, establish input-output characteristic equations and key performance parameter models for each unit, and define the electrical energy and thermal energy coupling and transfer relationship network between each unit, thereby forming the system architecture and basic physical model of the new energy electric-thermal coupling system friendly power station. S2. Market and Operational Constraint Integrated Modeling Steps: The electricity price time-series forecast signal for the future target period provided by the electricity market, the mandatory peak power supply command issued by the grid dispatching agency based on system reliability requirements, and the detailed operational constraints of each unit derived from the technical specifications and operating procedures of equipment manufacturers are integrated into the system architecture and basic physical model as boundary conditions and constraint equations. This constructs a mixed-integer linear programming or nonlinear programming mathematical model that includes market incentives, dispatching requirements, and physical constraints to simulate the behavior of power plants participating in the electricity market. S3. Multi-Time-Scale Collaborative Optimization Simulation Steps: Based on the wind power and photovoltaic power output forecast sequences generated from historical data and weather forecasts for the target period, combined with the electricity price time-series forecast signal, the mathematical model is solved with the objective function of maximizing the expected total economic benefits of the power plant in the target period or minimizing its total operating costs. The solution process, under the premise of satisfying all operational constraints and peak command, optimizes and determines the optimal operating state of each unit at each simulation step within the target time period, including the start-stop commands and output plans of each generator unit, the charging and discharging power and heat storage capacity of the thermal storage unit, the charging and discharging power and state of charge of the electrochemical energy storage unit, and the net exchange power plan between the power station and the grid interconnection line, and finally outputs a complete power station collaborative operation simulation dataset; S4, Refined calculation steps of full-cycle operation revenue: Based on the simulation dataset, according to the time-of-use settlement rules of the electricity spot market, the net electricity sold by the power station to the grid in each time period is multiplied by the corresponding nodal marginal price or system marginal price, and the electricity revenue is accumulated for each time period; Based on the winning service type, service provision volume and corresponding market clearing price or compensation standard of the power station in the ancillary service market in the simulation dataset, the revenue of ancillary services such as peak shaving, frequency regulation or reserve is calculated; Based on the fulfillment of the peak power supply command, that is, the actual peak capacity provided by the power station and its duration, the revenue is calculated according to the preset capacity market compensation rate or capacity price mechanism. The process involves several steps: S5, Structural cost accounting for the entire life cycle: Based on the detailed equipment list and engineering quantities of the power plant, the initial investment cost of all constituent units is obtained and allocated to each year of the entire project evaluation cycle using the equal annuity method or straight-line depreciation method. Simultaneously, based on the equipment maintenance manual and operation and maintenance experience data, the fixed operation and maintenance costs incurred each year during the project cycle, as well as the variable operation and maintenance costs related to the actual power generation, charging and discharging capacity, or operating time of the equipment, are estimated and summarized to form a structured annual cost cash flow sequence. S6, Simplified economic indicator generation and evaluation: The annual operating revenue cash flow sequence calculated in step S4 is merged with the annual cost cash flow sequence obtained in step S5 to generate an annual net cash flow statement for the entire life cycle of the power plant. Based on this net cash flow statement, a financial evaluation model is applied to calculate core economic evaluation indicators, including the project's total investment internal rate of return, net present value, and dynamic investment payback period. These indicators serve as a quantitative basis for assessing the feasibility of power plant investment and comparing the economic benefits of different technical solutions or market strategies.

2. The method for power market simulation and benefit evaluation based on a new energy electrothermal coupling system according to claim 1, characterized in that, The electric heating unit is an electric boiler or an electrode boiler; the thermal power generation unit is a condensing steam turbine, a back-pressure steam turbine, or a screw expander; the thermal storage unit is a sensible heat storage system or a phase change thermal storage system using molten salt, thermal oil, or water; and the electrochemical energy storage unit is a lithium-ion battery, a flow battery, or a sodium-sulfur battery energy storage system.

3. The method for power market simulation and benefit evaluation based on a new energy electrothermal coupling system according to claim 1, characterized in that, The detailed operational constraints in step S2 include: upper and lower limits of output and power ramp-up rate constraints for the wind power generation unit and photovoltaic power generation unit; minimum technical output constraints, rated output constraints, minimum continuous start-stop time constraints, and efficiency characteristic curves for the electric heating unit and thermal power generation unit; upper and lower limits of thermal storage capacity constraints, maximum charge and discharge power constraints, and safe operating temperature range constraints for the thermal storage unit; and state-of-charge operating window constraints, maximum charge and discharge power constraints, charge and discharge efficiency constraints, and capacity decay model based on cycle number constraints for the electrochemical energy storage unit.

4. The method for power market simulation and benefit evaluation based on a new energy electrothermal coupling system according to claim 1, characterized in that, The peak power supply command in step S2 is specifically defined as follows: during the preset peak period of grid load or the period of system reliability shortage, the output power of the thermal power generation unit is required to be maintained at no less than a specified percentage of its rated capacity, and the duration of this output state is no less than a specified threshold.

5. The method for power market simulation and benefit evaluation based on a new energy electrothermal coupling system according to claim 1, characterized in that, The objective function of the optimization algorithm in step S3 is to maximize the total operating revenue of the power plant or minimize its total operating cost during the simulation period. The decision variables of the optimization algorithm include the output plan, start-up and shutdown status, and power interaction plan with the main grid for each unit in each simulation period.

6. The method for power market simulation and benefit evaluation based on a new energy electrothermal coupling system according to claim 1, characterized in that, The ancillary service market in step S4 includes at least one of the peak shaving market, frequency regulation market, and reserve market, and the service compensation revenue is calculated based on the service provision volume and market clearing price in the simulation dataset.

7. The method for power market simulation and benefit evaluation based on a new energy electrothermal coupling system according to claim 1, characterized in that, The financial model in step S5 is the equal annuity method or the straight-line depreciation method. The operation and maintenance costs include fixed operation and maintenance costs related to equipment operating time and variable operation and maintenance costs related to equipment power generation or energy conversion.

8. The method for power market simulation and benefit evaluation based on a new energy electrothermal coupling system according to claim 1, characterized in that, The comprehensive benefit evaluation indicators in step S6 include at least one of the following: internal rate of return on total investment, net present value, dynamic payback period, and levelized cost of electricity.

9. The method for power market simulation and benefit evaluation based on a new energy electrothermal coupling system according to claim 1, characterized in that, The method further includes step S7: sensitivity analysis and strategy optimization step, which involves changing key input parameters including the new energy forecast output curve, market electricity price curve, equipment investment cost and ancillary service price, and repeating steps S1 to S6 to analyze the sensitivity of the comprehensive benefit evaluation index to each parameter, and compare the economic differences under different equipment configuration schemes or market participation strategies, so as to provide a quantitative basis for power plant planning and operation decisions.

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