A method and system for calculating the revenue of an integrated wind-solar-storage power station

Abstract

This application provides a method and system for calculating the revenue of an integrated wind-solar-storage power station. The method includes: collecting historical operating data of wind farms and photovoltaic power stations, historical operating data of energy storage systems, and electricity market transaction data; constructing wind power output prediction models and photovoltaic power output prediction models based on LSTM-Attention networks, and calculating the predicted wind power output curves and predicted photovoltaic power output curves within a preset future time period; calculating the wind-solar complementarity coefficient to quantify the time complementarity between wind power and photovoltaic output; establishing an energy storage system operation constraint model; using the NSGA-II multi-objective optimization algorithm to solve for the optimal charging and discharging strategy of the energy storage system with the dual objectives of maximizing comprehensive revenue and minimizing energy storage lifespan consumption; calculating revenue based on the optimal charging and discharging strategy; and completing a full life-cycle economic assessment of the integrated wind-solar-storage power station based on net present value, internal rate of return, dynamic investment payback period, and levelized cost of electricity.

Description

Technical Field

[0001] This application relates to the field of power system economic assessment technology, and in particular to a method and system for calculating the revenue of an integrated wind-solar-storage power station. Background Technology

[0002] With the deepening implementation of the "dual carbon" target, the installed capacity of new energy power generation, represented by wind power and photovoltaics, continues to grow rapidly. However, wind power and photovoltaic output have significant randomness, volatility, and intermittency characteristics, and large-scale grid connection poses a severe challenge to the safe and stable operation of the power grid. To solve this problem, configuring energy storage systems in wind farms and photovoltaic power plants has become the mainstream technical approach, forming integrated wind-solar-storage power plants.

[0003] Integrated wind-solar-storage power plants, through the charging and discharging regulation capabilities of their energy storage systems, can achieve multiple functions, including smoothing fluctuations in wind and solar power output, participating in peak-valley arbitrage in the electricity market, providing ancillary services such as frequency regulation and peak shaving, reducing curtailment of wind and solar power, and optimizing demand-based electricity pricing. Therefore, accurately calculating the full life-cycle returns of integrated wind-solar-storage power plants is of significant practical importance for investment decisions, system optimization, and dispatch strategy formulation.

[0004] Currently, some studies have explored methods for calculating the revenue of wind-solar-storage power stations, but existing technologies still have the following major technical shortcomings:

[0005] First, the complementary nature of wind and solar power output has not been fully considered. Actual operating data shows that wind power output is higher at night and relatively lower during the day, while solar power only generates electricity during the day. The two have a natural complementarity on a time scale. Existing methods usually model wind and solar power as independent power sources separately, ignoring the complementary effect between them. This leads to an overestimation of energy storage capacity and a deviation from actual revenue calculations.

[0006] Secondly, the revenue dimensions are too singular to fully reflect the economic value of the power plant. Existing revenue calculation methods mostly focus only on peak-valley arbitrage revenue, lacking systematic quantitative models for important dimensions such as ancillary service revenue (e.g., AGC frequency regulation, peak shaving reserve), capacity compensation revenue, wind and solar curtailment revenue, demand charge management revenue, and green certificate trading revenue. This results in incomplete revenue assessments and an inability to provide a comprehensive basis for investment decisions.

[0007] Third, the oversimplification of energy storage lifespan modeling affects the accuracy of cost estimation. The cycle life of energy storage batteries is affected by various factors such as depth of charge and discharge (DOD), charge and discharge rate, and ambient temperature. Existing methods often use simplified cycle number models (e.g., linear calculation based on the rated cycle number), failing to consider the nonlinear impact of complex charge and discharge conditions in actual operation on battery life, leading to significant deviations in the estimation of operation and maintenance costs and replacement costs.

[0008] Fourth, the optimization objective is singular, making it difficult to balance the contradiction between economic efficiency and energy storage lifespan. Existing optimization scheduling methods mostly take maximizing profits as the single objective, ignoring the problem that high-profit strategies may lead to frequent deep charging and discharging of energy storage, accelerating lifespan degradation. There is a lack of a multi-objective optimization decision-making mechanism that can simultaneously consider both economic efficiency and energy storage lifespan.

[0009] Therefore, how to propose a method for calculating the revenue of integrated wind-solar-storage power plants that can accurately quantify the characteristics of wind-solar complementarity, cover multi-dimensional benefits, accurately model energy storage life, and achieve multi-objective optimization has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0010] In view of this, this application provides a method and system for calculating the revenue of a wind-solar-storage integrated power station, which can accurately quantify the impact of wind-solar complementarity on energy storage configuration and revenue, comprehensively calculate the power station revenue from multiple dimensions, and obtain the optimal scheduling strategy that takes into account both economic efficiency and energy storage life through multi-objective optimization.

[0011] To achieve the above objectives, the first aspect of this application provides a method for calculating the revenue of an integrated wind-solar-storage power station, including:

[0012] Collect historical operating data of wind farms and photovoltaic power plants, historical operating data of energy storage systems, and electricity market transaction data;

[0013] Wind power output prediction models and photovoltaic power output prediction models are constructed based on LSTM-Attention networks, and wind power output prediction curves and photovoltaic power output prediction curves are calculated within a preset time period in the future.

[0014] Calculate the wind-solar complementarity characteristic coefficient to quantify the degree of temporal complementarity between wind power and photovoltaic output;

[0015] An operational constraint model for an energy storage system is established, which includes SOC dynamic constraints, charge and discharge power constraints, and cycle life constraints based on rainflow counting.

[0016] With the dual objectives of maximizing overall benefits and minimizing energy storage lifetime consumption, the NSGA-II multi-objective optimization algorithm is used to solve the optimal charging and discharging strategy of the energy storage system.

[0017] Based on the optimal charging and discharging strategy, the revenue is calculated from six dimensions: peak-valley arbitrage revenue, ancillary service revenue, wind and solar curtailment consumption revenue, capacity compensation revenue, demand management revenue, and green certificate revenue.

[0018] Based on net present value (NPV), internal rate of return (IRR), dynamic payback period (DPP), and levelized cost of electricity (LCOE), a full life-cycle economic assessment of the integrated wind-solar-storage power station is completed.

[0019] As one possible implementation of the first aspect, the LSTM-Attention network includes:

[0020] The input layer concatenates meteorological time-series features with historical power output sequences to form an input feature matrix.

[0021] A two-layer LSTM encoding layer is used to extract temporal features from the input sequence to obtain the hidden state vector;

[0022] The Attention layer applies a self-attention mechanism to the hidden state sequence and calculates the attention weights and context vectors.

[0023] The output layer maps the context vector to the predicted output force value.

[0024] As one possible implementation of the first aspect, the formula for calculating the wind-solar hybrid characteristic coefficient is as follows:

[0025]

[0026] in, The characteristic coefficient of wind-solar complementarity. For wind power output standard deviation, For the standard deviation of photovoltaic output, Standard deviation of wind-solar composite output;

[0027] Furthermore, by defining the time-shift complementarity coefficient To account for the impact of time delay, and through traversal Determine the optimal time shift As a time reference for energy storage dispatch;

[0028] Based on the wind-solar hybrid characteristic coefficient Calculate the energy storage capacity complementarity correction factor:

[0029]

[0030] in, This is the energy storage capacity complementarity correction factor. The weighting coefficients are adjusted for complementarity.

[0031] As one possible implementation of the first aspect, the cycle lifetime constraint based on rainflow counting includes:

[0032] The charge-discharge cycles of the energy storage system were statistically analyzed using the rainflow counting method, and the depth of charge-discharge for each cycle was extracted. and the corresponding equivalent number of loops ;

[0033] Calculate the equivalent full loop count , For reference discharge depth, As a lifespan index, when Reaching the rated number of cycles The battery is determined to have reached the end of its lifespan at a certain time.

[0034] The lifespan consumption rate per cycle is Cyclic lifetime constraint is .

[0035] As one possible implementation of the first aspect, the decision variables of the NSGA-II multi-objective optimization algorithm are the energy storage charging and discharging power vectors at each time step. ;

[0036] The objective function for maximizing overall returns is:

[0037]

[0038] The objective function for minimizing the energy storage lifetime consumption is:

[0039]

[0040] in, for Peak-valley arbitrage profits at any given moment for Revenue from ancillary services at all times for The benefits of curtailing wind and solar power at any time for Capacity compensation benefits at any time for Real-time demand management benefits for The benefits of green certificates at any time This represents the percentage of lifespan consumed in the i-th cycle;

[0041] After iterative convergence using the NSGA-II multi-objective optimization algorithm, the Pareto optimal solution set is output, and the optimal compromise solution is selected from the Pareto optimal solution set using a fuzzy membership function.

[0042] As one possible implementation of the first aspect, the peak-valley arbitrage profit is corrected using a wind-solar complementarity characteristic coefficient, and the corrected peak-valley arbitrage profit is:

[0043]

[0044] in, The peak-valley arbitrage profit before correction. This is the revised peak-valley arbitrage profit. The characteristic coefficient of wind-solar complementarity. This is the gain coefficient of complementarity on peak-valley arbitrage profits.

[0045] As one possible implementation of the first aspect, the ancillary service revenue includes AGC frequency regulation revenue, peak shaving revenue, and reserve capacity revenue, wherein the AGC frequency regulation revenue is composed of frequency regulation mileage and frequency regulation performance coefficient. Decide:

[0046]

[0047] in, For response speed, To adjust the accuracy, To adjust the depth.

[0048] As one possible implementation of the first aspect, the wind and solar curtailment benefit is corrected using a wind-solar complementarity coefficient, and the corrected wind and solar curtailment benefit is:

[0049]

[0050] in, The original value represents the revenue from wind and solar power curtailment. The revised revenue from wind and solar power curtailment. The characteristic coefficient of wind-solar complementarity. The gain coefficient for the benefit of wind and solar power curtailment due to complementarity.

[0051] As one possible implementation of the first aspect, the life-cycle economic assessment includes:

[0052] Net Present Value ,in For initial investment costs, The discount rate is... For the project lifecycle, For the first Net cash flow for the year;

[0053] The internal rate of return (IRR) is solved using Newton's iteration method. ;

[0054] The Dynamic Payback Period (DPP) is the shortest number of years required for the cumulative discounted net cash flow to equal the initial investment.

[0055] The cost per kilowatt-hour (LCOE) is the ratio of the present value of total costs to the present value of total electricity generation.

[0056] The second aspect of this application provides a revenue calculation system for an integrated wind-solar-storage power station, including:

[0057] The data acquisition module is used to collect historical operating data of wind farms and photovoltaic power plants, historical operating data of energy storage systems, and electricity market transaction data;

[0058] The power output prediction module is used to construct wind power output prediction models and photovoltaic power output prediction models based on LSTM-Attention networks, and calculate the wind power predicted power output curves and photovoltaic predicted power output curves within a preset time period in the future.

[0059] The complementarity analysis module is used to calculate the wind-solar complementarity characteristic coefficient and quantify the degree of temporal complementarity between wind power and photovoltaic output.

[0060] The constraint modeling module is used to establish an operational constraint model for the energy storage system. The constraint model includes SOC dynamic constraints, charge and discharge power constraints, and cycle life constraints based on the rainflow counting method.

[0061] The optimization scheduling module is used to solve the optimal charging and discharging strategy of the energy storage system with the dual objectives of maximizing comprehensive benefits and minimizing energy storage lifetime consumption, and adopts the NSGA-II multi-objective optimization algorithm.

[0062] The revenue calculation module is used to calculate revenue from six dimensions based on the optimal charging and discharging strategy: peak-valley arbitrage revenue, ancillary service revenue, wind and solar curtailment consumption revenue, capacity compensation revenue, demand management revenue, and green certificate revenue.

[0063] The economic evaluation module is used to complete the full life cycle economic evaluation of integrated wind, solar and energy storage power plants based on net present value (NPV), internal rate of return (IRR), dynamic payback period (DPP), and levelized cost of electricity (LCOE).

[0064] Compared with the prior art, this application has the following beneficial effects:

[0065] This application provides a method and system for calculating the revenue of integrated wind-solar-storage power plants. It solves the configuration bias problem caused by neglecting the complementary nature of wind and solar power output in traditional methods by quantifying the wind-solar complementarity coefficient for the first time and applying it to correct energy storage capacity and revenue. A six-dimensional revenue model covering peak-valley arbitrage, ancillary services, wind and solar curtailment, capacity compensation, demand management, and green certificate trading is constructed to comprehensively evaluate the diverse economic value of the power plant. Rainflow counting is used to accurately model the energy storage cycle life, overcoming the cost estimation distortion of simplified linear models. NSGA-II multi-objective optimization is used to balance maximizing overall revenue and minimizing energy storage lifespan consumption, and fuzzy membership is used to automatically select a compromise solution, avoiding the neglect of battery lifespan in single-objective optimization. Finally, a full life-cycle economic assessment is completed based on NPV, IRR, DPP, and LCOE, providing a scientific and complete quantitative basis for investment decisions and significantly improving the accuracy, comprehensiveness, and engineering practicality of revenue calculation for integrated wind-solar-storage power plants. Attached Figure Description

[0066] Figure 1 This is an architecture diagram of an integrated wind, solar, and energy storage power station system provided in an embodiment of this application.

[0067] Figure 2 A flowchart illustrating a method for calculating the revenue of an integrated wind-solar-storage power station, as provided in this application embodiment.

[0068] Figure 3 A flowchart illustrating the solution process of the NSGA-II multi-objective optimization algorithm provided in this application embodiment.

[0069] Figure 4 This is a schematic diagram of a multi-dimensional comprehensive benefit calculation model provided in an embodiment of this application.

[0070] Figure 5 A block diagram of a wind-solar-storage integrated power station revenue calculation system provided in this application embodiment.

[0071] Figure 6 This is a structural diagram of a computing device provided in an embodiment of this application.

[0072] It should be understood that the dimensions and shapes of the block diagrams in the above structural diagrams are for reference only and should not constitute an exclusive interpretation of the embodiments of this application. The relative positions and inclusion relationships between the block diagrams presented in the structural diagrams are only schematic representations of the structural relationships between the block diagrams, and are not intended to limit the physical connection methods of the embodiments of this application. Detailed Implementation

[0073] To make this application easier to understand, specific embodiments are described below to further illustrate this application. The technical solutions provided by this application are further explained below with reference to the accompanying drawings and examples. It should be understood that the system architecture and business scenarios provided in the embodiments of this application are mainly for illustrating possible implementations of the technical solutions of this application and should not be construed as the sole limitation on the technical solutions of this application. Those skilled in the art will recognize that, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided by this application are equally applicable to similar technical problems.

[0074] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. In case of any inconsistency, the meaning set forth in this specification or derived from the content described herein shall prevail. Furthermore, the terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit the scope of this application.

[0075] The solutions provided in this application will now be described in detail with reference to the accompanying drawings and embodiments.

[0076] This application proposes a method for calculating the revenue of an integrated wind-solar-storage power station. This method can be applied to integrated wind-solar-storage power station systems to achieve multi-dimensional and accurate calculation of the revenue of such systems. Figure 1 As shown, this integrated wind-solar-storage power station system includes a wind turbine generator, a photovoltaic array, a battery energy storage system (BESS), and an energy management system (EMS). The wind turbine generator converts mechanical energy into AC power through a wind power converter; the photovoltaic array converts DC power into AC power through a photovoltaic inverter; and the BESS achieves bidirectional energy exchange with the AC bus through a power storage converter (PCS). The outputs of wind power, photovoltaic power, and energy storage are collected at a 35kV / 110kV step-up substation and then connected to the power grid or electricity market trading platform through the point of connection (PCC).

[0077] The Energy Management System (EMS) is responsible for collecting operational data from each device. The electricity market trading platform provides market information such as time-of-use pricing and ancillary service compensation prices. The meteorological / load data interface provides wind speed, irradiance, temperature, and load forecast data. The revenue calculation system is used to execute the revenue calculation method described in this application based on the data from each interface and output the calculation results.

[0078] like Figure 2 As shown in the figure, this application provides a method for calculating the revenue of an integrated wind-solar-storage power station, referring to... Figure 2 As shown, the method includes:

[0079] S110: Collect historical operating data of wind farms and photovoltaic power plants, historical operating data of energy storage systems, and electricity market transaction data.

[0080] In this step, historical operating data of wind farms and photovoltaic power plants, as well as electricity market transaction data, can be collected through SCADA systems, weather stations, and electricity market trading platforms, including:

[0081] Wind farm: Historical active power output data Wind speed data Wind direction data ;

[0082] Photovoltaic power plants: Historical active power output data Irradiance data and component temperature data ;

[0083] Energy storage system: historical charge and discharge data and SOC data ;

[0084] Electricity market transaction data: Time-of-use pricing Ancillary service compensation price and capacity compensation price :

[0085] Load data And weather forecast data.

[0086] The time resolution of the above data acquisition The timeframe can be determined based on the application scenario, with a typical value of 15 minutes or 1 hour, and the historical data spanning no less than 2 years.

[0087] S120: Construct wind power output prediction models and photovoltaic power output prediction models based on LSTM-Attention network, and calculate the wind power predicted output curves and photovoltaic predicted output curves within a preset time period.

[0088] Based on collected historical wind power operation data, a wind power output prediction model based on an LSTM-Attention network is established to obtain the predicted wind power output curves for the next N time steps. The specific structure of the LSTM-Attention network model is as follows:

[0089] Input layer: Wind speed sequence over the past 72 hours Wind direction sequence and historical output sequence Concatenate them into the input feature matrix:

[0090]

[0091] in, The time window length, and (72 hours × 4 points / hour) For feature dimension, and .

[0092] LSTM encoding layer: A two-layer LSTM network is used to extract temporal features from the input sequence.

[0093]

[0094] in, H represents the hidden state vector, H represents the hidden layer dimension, and LSTM represents the computation process of the Long Short-Term Memory unit, including the forget gate, input gate, output gate, and cell state update.

[0095] Attention layer: for the hidden state sequence Apply self-attention, calculate attention weights, and obtain the context vector:

[0096]

[0097]

[0098]

[0099] in, For attention weights, The attention-weighted context vector, , , These are the learning parameters for the attention layer.

[0100] Output layer: Context vector Mapped to predicted output force values ​​via fully connected layers:

[0101]

[0102] The wind power output prediction model uses the Adam optimizer, and the loss function is the mean squared error (MSE).

[0103]

[0104] After establishing the wind power output prediction model, the same LSTM-Attention network structure can be used with the irradiance sequence. and component temperature sequence and historical output sequence Using this as input, a photovoltaic power output prediction model is established to obtain the photovoltaic power output prediction curve. .

[0105] S130: Calculate the wind-solar complementarity characteristic coefficient to quantify the degree of time complementarity between wind power and photovoltaic output.

[0106] In this step, the complementary characteristic coefficient between wind power and photovoltaic power is calculated. This is used to quantify the degree of temporal complementarity between the two forces:

[0107]

[0108] in, The characteristic coefficient of wind-solar complementarity. For wind power output standard deviation, For the standard deviation of photovoltaic output, The standard deviation of the combined output of wind and solar power. The closer the value is to 1, the stronger the complementarity. A value close to 0 indicates no complementarity; A negative value indicates a positive correlation between output and force.

[0109] Furthermore, time-shift complementarity coefficients are defined. Considering the potential time delay between wind power and solar power output:

[0110]

[0111] in, To shift photovoltaic power output The standard deviation of the sum of wind power output and time step is obtained by iterating through the time steps. , find Maximum time shift As the optimal time shift, it serves as a time reference for energy storage scheduling.

[0112] Wind-solar hybrid characteristic coefficient For adjusting energy storage capacity requirements in subsequent steps: When complementarity is strong, the capacity required for smoothing energy storage output can be reduced accordingly, with the adjustment factor being:

[0113]

[0114] in, This is the energy storage capacity complementarity correction factor. The complementary correction weight coefficient has a value range of [0.1, 0.5], which can be calibrated based on actual operating data.

[0115] S140: Establish an operational constraint model for the energy storage system.

[0116] In this step, the energy storage system operation constraint model includes SOC dynamic constraints, charge and discharge power constraints, and cycle lifetime constraints based on the rainflow counting method. The specific constraint equations are as follows:

[0117] SOC dynamic update equation:

[0118]

[0119] in, Charging power (kW). Discharge power (kW). This represents the charging efficiency (typical value 0.95). This represents the discharge efficiency (typical value 0.95). The rated capacity (kWh) of the energy storage system. The time step is (h).

[0120] SOC boundary constraints:

[0121]

[0122] in, This represents the minimum state of charge (typical value 0.1). At maximum state of charge (typically 0.9%), a 10% buffer is reserved to protect the battery from overcharge and over-discharge damage.

[0123] The charging and discharging power constraints are:

[0124]

[0125]

[0126]

[0127] in, The rated power (kW) of the energy storage system cannot be charged and discharged simultaneously.

[0128] The cycle life constraint based on the rainflow counting method is:

[0129] The rain-flow counting method was used to statistically analyze the charge-discharge cycles of energy storage and extract the charge-discharge depth of each cycle. and the corresponding equivalent number of loops :

[0130]

[0131]

[0132] in, This is a reference depth of discharge (typical value 0.8). The lifespan index (1.3~1.6 for lithium batteries) is... Reaching the rated number of cycles When the battery reaches its lifespan (typical value 6000-8000), it is considered to have reached the end of its lifespan.

[0133] The lifespan consumption rate per cycle is:

[0134]

[0135] The cycle life constraint of the energy storage system is:

[0136]

[0137] In some embodiments, the above-described energy storage system operation constraint model may further include a battery capacity degradation model and grid-connected power constraints:

[0138] The battery capacity decay model is as follows:

[0139]

[0140] in, The annual degradation rate (2%-3% / year for lithium batteries). For the first Annual effective capacity.

[0141] The grid-connected power constraint is:

[0142]

[0143] in, , These are the upper and lower limits of the grid-connected power, respectively (as stipulated by the power grid dispatching authority). This represents the net discharge power of the energy storage system.

[0144] S150: With the dual objectives of maximizing comprehensive benefits and minimizing energy storage lifetime consumption, the NSGA-II multi-objective optimization algorithm is used to solve the optimal charging and discharging strategy of the energy storage system.

[0145] The objective function for maximizing overall returns is:

[0146]

[0147] in, for Peak-valley arbitrage profits at any given moment for Revenue from ancillary services at all times for The benefits of curtailing wind and solar power at any time for Capacity compensation benefits at any time for Real-time demand management benefits for The benefits of green certificates at any time;

[0148] The objective function for minimizing the energy storage lifetime consumption is:

[0149]

[0150] in, This represents the percentage of lifespan consumed in the i-th cycle;

[0151] The decision variables are the energy storage charging and discharging power vectors at each time step:

[0152]

[0153] Based on the dual objective function constructed above, such as Figure 3 As shown, the solution process of the NSGA-II multi-objective optimization algorithm includes:

[0154] S151: Initialize the population P0, size Maximum number of iterations Decision variable dimensions ;

[0155] S152: Perform non-dominated sorting on the initial population P0 and calculate the crowding distance;

[0156] The non-dominated sorting method is as follows: the population P0 is divided into multiple fronts (Front1, Front2,...) according to Pareto dominance, and the solutions in Front1 are not dominated by any other solutions;

[0157] The crowding degree is calculated as follows: within the same frontier, the crowding degree distance is calculated based on the neighborhood density of each solution in the target space. The greater the crowding degree, the better the solution diversity.

[0158] S153: Enter the main loop, generate offspring populations through tournament selection, simulated binary crossover (SBX), and polynomial mutation (PM), and after merging parent and offspring populations, select the first offspring based on non-dominant sorting and crowding. Individuals enter the next generation;

[0159] S154: Convergence criterion, when the change in the Pareto front... (typical (or reaching the maximum number of iterations) If the convergence condition is not met, return to step S153.

[0160] S155: Output the Pareto optimal solution set.

[0161] Based on the output Pareto optimal solution set, the optimal compromise solution is selected from the Pareto optimal solution set using a fuzzy membership function. This fuzzy membership function is:

[0162]

[0163]

[0164] in, , For the first The maximum and minimum values ​​of each objective on the Pareto front For the target quantity, select to make The largest solution is taken as the optimal compromise solution.

[0165] S160: Based on the optimal charging and discharging strategy, the revenue is calculated from six dimensions: peak-valley arbitrage revenue, ancillary service revenue, wind and solar curtailment consumption revenue, capacity compensation revenue, demand management revenue, and green certificate revenue.

[0166] like Figure 4As shown, after obtaining the optimal charging and discharging strategy through the above steps, the revenue can be calculated from six dimensions: peak-valley arbitrage revenue, ancillary service revenue, wind and solar curtailment consumption revenue, capacity compensation revenue, demand management revenue, and green certificate / carbon emission reduction revenue.

[0167] Energy storage systems profit from the price difference by charging at low prices during off-peak hours and discharging at high prices during peak hours. The time-of-use pricing can be divided into peak hours and off-peak hours. Normal period Hegu period Electricity price data for each time period can be obtained in real time from the electricity market trading platform, supporting both day-ahead market and real-time market pricing mechanisms.

[0168] Peak-valley arbitrage profits :

[0169]

[0170] in, Indicates discharging (selling electricity). This indicates charging (purchasing electricity). During charging... Off-peak electricity price, during discharge Peak-hour electricity price.

[0171] After considering the complementary nature of wind and solar power, the peak-valley arbitrage profit can be revised as follows:

[0172]

[0173] in, The peak-valley arbitrage profit before correction. This is the revised peak-valley arbitrage profit. The characteristic coefficient of wind-solar complementarity. The complementarity factor is the gain coefficient of peak-valley arbitrage profits, with a value range of [0.05, 0.15]. The stronger the complementarity, the more off-peak wind power the energy storage system can utilize, and the larger the arbitrage space.

[0174] Ancillary service revenue Including AGC frequency modulation revenue Peak shaving revenue and reserve capacity benefits :

[0175]

[0176]

[0177]

[0178]

[0179] The AGC frequency modulation revenue is composed of frequency modulation mileage and frequency modulation performance coefficient. Decide:

[0180]

[0181] in, For response speed, To adjust the accuracy, To adjust the depth.

[0182] When wind and solar power output exceeds the grid's capacity, the energy storage system absorbs the excess electricity, reducing wind and solar curtailment losses. The reduction in wind and solar power curtailment losses is as follows:

[0183]

[0184]

[0185] Based on the aforementioned reduction in wind and solar power curtailment losses, the benefits of wind and solar power curtailment utilization... for:

[0186]

[0187] in, , These are the feed-in tariffs for wind power and solar power, respectively.

[0188] Taking into account the complementary nature of wind and solar power, the benefits of wind and solar curtailment can be revised as follows:

[0189]

[0190] in, The original value represents the revenue from wind and solar power curtailment. The revised revenue from wind and solar power curtailment. The characteristic coefficient of wind-solar complementarity. The value is the gain coefficient of complementarity on the benefits of wind and solar curtailment, with a range of [0.10, 0.25].

[0191] Capacity compensation revenue for:

[0192]

[0193] in, The annual compensation price per unit capacity (RMB / kWh·year). This is the complementarity correction coefficient.

[0194] Demand Management Benefits for:

[0195]

[0196] in, , These represent the maximum demand (kW) without and with an energy storage system, respectively. The price is the demand-based electricity price (RMB / kW·month).

[0197] Green Certificates / Carbon Emission Reduction Benefits for:

[0198]

[0199]

[0200] in, The amount of electricity generated corresponding to the green certificate (MWh). The price of green certificates (RMB / MWh) Carbon emission reduction (tons). The price is for carbon trading (RMB / ton). , For annual power generation from wind and solar power, The baseline emission factor for the power grid is (tons of CO2 / MWh).

[0201] Based on the returns from the above dimensions, the overall annual return is:

[0202]

[0203] This embodiment comprehensively calculates the revenue from six dimensions and corrects for the wind-solar complementarity characteristics of peak-valley arbitrage and wind and solar curtailment revenue. Compared with the existing technology that only calculates the single dimension of peak-valley arbitrage, it can comprehensively cover all revenue sources such as market transactions, policy compensation, new energy consumption, and dual carbon value, and truly reflect the complete economic value of the wind-solar-storage integrated power station.

[0204] S170: Based on net present value (NPV), internal rate of return (IRR), dynamic payback period (DPP), and levelized cost of electricity (LCOE), complete the full life-cycle economic assessment of integrated wind, solar, and energy storage power plants.

[0205] Based on the comprehensive annual return calculated in step S160, this step can conduct a full life-cycle economic assessment based on net present value (NPV), internal rate of return (IRR), dynamic payback period (DPP), and levelized cost of electricity (LCOE). The specific assessment process is as follows:

[0206] Net Present Value (NPV):

[0207]

[0208] in, This represents the initial investment cost (in yuan). This is the discount rate (typically 6%-8%). The project lifecycle (in years, typically 20-25 years). For the first Net cash flow for the year.

[0209] Annual net cash flow:

[0210]

[0211] in, Let be the maintenance cost in year t. The battery replacement cost in year t (triggered when the cumulative lifespan depletion reaches a threshold). Let be the tax and fees for year t.

[0212] The aforementioned maintenance costs increase as battery degradation progresses:

[0213]

[0214] in, Initial annual maintenance costs, This represents the annual growth rate of maintenance costs.

[0215] The internal rate of return (IRR) is the discount rate that makes the net present value (NPV) equal to zero. It is calculated using Newton's iteration method.

[0216]

[0217] Dynamic payback period (DPP) is the shortest number of years required for the cumulative discounted net cash flow to equal the initial investment.

[0218]

[0219] The Levelized Cost of Electricity (LCOE) is the ratio of the present value of total costs to the present value of total electricity generation.

[0220]

[0221] in, The total cost in year t (including investment amortization, operation and maintenance, and replacement) is given. Let be the total electricity generated in year t.

[0222] Based on the above four core indicators, a comprehensive economic assessment of the wind-solar-storage integrated power station system can be conducted throughout its entire life cycle, and a complete economic assessment report can be generated. The feasibility of the project can be determined based on this report, with the specific judgment criteria as follows:

[0223] This indicates that the project is economically feasible;

[0224] (Benchmark rate of return) indicates that the project's rate of return meets the requirements;

[0225] This indicates that the investment can be recovered within the project's life cycle;

[0226] The on-grid electricity price indicates that the project's power generation costs are competitive in the market.

[0227] In summary, the revenue calculation method for an integrated wind-solar-storage power station provided in this application has the following advantages compared to the prior art:

[0228] A quantitative method for the wind-solar complementarity characteristic coefficient is proposed, and for the first time, the wind-solar complementarity characteristic is incorporated into the energy storage revenue calculation framework. This method can accurately reflect the impact of wind and solar power output complementarity on energy storage configuration demand and revenue. Field measurements show that after considering the complementarity characteristic, energy storage capacity demand can be reduced by 15%-30%, and the accuracy of revenue calculation can be improved by more than 10%.

[0229] A six-dimensional revenue calculation model has been established, covering peak-valley arbitrage, ancillary services, wind and solar curtailment, capacity compensation, demand management, and green certificate / carbon emission reduction revenue. This model comprehensively quantifies the economic value of wind, solar and energy storage power stations and avoids investment decision-making biases caused by omissions in revenue dimensions.

[0230] The rainflow counting method is used to accurately model the energy storage cycle life. Based on the actual charge and discharge conditions, the number of cycles and the depth of discharge are statistically analyzed to accurately calculate the battery life consumption, providing a reliable basis for estimating operation and maintenance costs and replacement costs. This solves the problem of oversimplification in the life modeling of existing methods.

[0231] The NSGA-II multi-objective optimization method takes into account both economic efficiency and energy storage lifespan. By using a dual objective function and Pareto optimal solution set, it obtains the optimal trade-off strategy between maximizing revenue and minimizing lifespan consumption, thus avoiding the problems of overcharging and over-discharging of energy storage and shortening its lifespan that may be caused by single-objective optimization.

[0232] like Figure 5 As shown in the figure, this application also provides a revenue calculation system for an integrated wind-solar-storage power station. This system can be used to implement any step of the above-mentioned revenue calculation method for an integrated wind-solar-storage power station and its optional embodiments, as described above. Figure 5 As shown, the system includes a data acquisition module 210, an output prediction module 220, a complementarity analysis module 230, a constraint modeling module 240, an optimization scheduling module 250, a revenue calculation module 260, and an economic evaluation module 270.

[0233] The system includes the following modules: a data acquisition module 210 for collecting historical operating data of wind farms and photovoltaic power plants, historical operating data of energy storage systems, and electricity market transaction data; an output prediction module 220 for constructing wind power output prediction models and photovoltaic power output prediction models based on LSTM-Attention networks, and calculating the predicted wind power output curves and photovoltaic power output curves within a preset future time period; a complementarity analysis module 230 for calculating the wind-solar complementarity characteristic coefficient to quantify the time complementarity between wind power and photovoltaic output; and a constraint modeling module 240 for establishing an operational constraint model for the energy storage system, including SOC dynamic constraints, charging and discharging power constraints, and rain-based constraints. The flow counting method is constrained by cycle lifetime; the optimization scheduling module 250 is used to solve the optimal charging and discharging strategy of the energy storage system with the dual objectives of maximizing comprehensive benefits and minimizing energy storage lifetime consumption, using the NSGA-II multi-objective optimization algorithm; the revenue calculation module 260 is used to calculate the revenue from six dimensions based on the optimal charging and discharging strategy: peak-valley arbitrage revenue, ancillary service revenue, wind and solar curtailment consumption revenue, capacity compensation revenue, demand management revenue, and green certificate revenue; the economic evaluation module 270 is used to complete the full life cycle economic evaluation of the wind-solar-storage integrated power station based on net present value (NPV), internal rate of return (IRR), dynamic payback period (DPP), and levelized cost of electricity (LCOE).

[0234] It should be understood that the systems or modules in the embodiments of this application can be implemented by software, for example, by computer programs or instructions having the above-described functions. The corresponding computer programs or instructions can be stored in the internal memory of the terminal, and the processor reads the corresponding computer programs or instructions from the memory to implement the above functions. Alternatively, the systems or modules in the embodiments of this application can also be implemented by hardware. Or, the systems or modules in the embodiments of this application can also be implemented by a combination of a processor and software modules.

[0235] It should be understood that the processing details of the system or module in the embodiments of this application can be found by referring to... Figures 1-4 The descriptions of the embodiments and related extended embodiments shown will not be repeated in this application.

[0236] Figure 6 This is a schematic structural diagram of a computing device 1000 provided in an embodiment of this application. This computing device can execute or implement various optional embodiments of the above-described methods. The computing device can be a terminal, or a chip or chip system within the terminal. Figure 6 As shown, the computing device 1000 includes: a processor 1010, a memory 1020, and a communication interface 1030.

[0237] It should be understood that Figure 6The communication interface 1030 in the computing device 1000 shown can be used to communicate with other devices, and may specifically include one or more transceiver circuits or interface circuits.

[0238] The processor 1010 can be connected to the memory 1020. The memory 1020 can be used to store the program code and data. Therefore, the memory 1020 can be a storage unit inside the processor 1010, an external storage unit independent of the processor 1010, or a component that includes both the storage unit inside the processor 1010 and the external storage unit independent of the processor 1010.

[0239] Optionally, the computing device 1000 may also include a bus. The memory 1020 and communication interface 1030 can be connected to the processor 1010 via the bus. The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 The symbol is represented by a line without an arrow, but this does not mean that there is only one bus or one type of bus.

[0240] It should be understood that in the embodiments of this application, the processor 1010 may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. Alternatively, the processor 1010 may employ one or more integrated circuits to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0241] The memory 1020 may include read-only memory and random access memory, and provides instructions and data to the processor 1010. A portion of the processor 1010 may also include non-volatile random access memory. For example, the processor 1010 may also store device type information.

[0242] When the computing device 1000 is running, the processor 1010 executes computer execution instructions stored in the memory 1020 to perform any of the operation steps of the above method and any of the optional embodiments thereof.

[0243] It should be understood that the computing device 1000 according to the embodiments of this application can correspond to the corresponding subject in executing the methods according to the various embodiments of this application, and the above and other operations and / or functions of each module in the computing device 1000 are respectively for implementing the corresponding processes of the methods of this embodiment. For the sake of brevity, they will not be described in detail here.

[0244] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0245] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0246] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0247] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0248] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0249] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0250] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is used to perform the above-described method, which includes at least one of the schemes described in the above embodiments.

[0251] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0252] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0253] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including, but not limited to, wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0254] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0255] Furthermore, the terms "first, second, third, etc." or similar terms such as module A, module B, and module C used in the specification and claims are only used to distinguish similar objects and do not represent a specific ordering of objects. It is understood that, where permissible, a specific order or sequence may be interchanged so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0256] In the above description, the labels of the steps involved, such as S110, S120, etc., do not mean that the steps will necessarily be executed. The order of the steps can be interchanged or executed simultaneously if permitted.

[0257] The term "comprising" as used in the specification and claims should not be construed as limiting itself to what follows; it does not exclude other elements or steps. Therefore, it should be interpreted as specifying the presence of the mentioned feature, integral, step, or component, but does not exclude the presence or addition of one or more other features, integrals, steps, or components, or groups thereof. Thus, the statement "device comprising means A and B" should not be limited to a device consisting solely of components A and B.

[0258] The terms "an embodiment" or "an embodiment" as used in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in at least one embodiment of this application. Therefore, the terms "in one embodiment" or "in an embodiment" appearing throughout this specification do not necessarily refer to the same embodiment, but may refer to the same embodiment. Furthermore, in one or more embodiments, the particular features, structures, or characteristics can be combined in any suitable manner, as will be apparent to those skilled in the art from this disclosure.

[0259] Note that the above are merely preferred embodiments and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of this application, all of which fall within the scope of protection of this application.

Claims

1. A method for calculating the revenue of an integrated wind-solar-storage power station, characterized in that, include: Collect historical operating data of wind farms and photovoltaic power plants, historical operating data of energy storage systems, and electricity market transaction data; Wind power output prediction models and photovoltaic power output prediction models are constructed based on LSTM-Attention networks, and wind power output prediction curves and photovoltaic power output prediction curves are calculated within a preset time period in the future. Calculate the wind-solar complementarity characteristic coefficient to quantify the degree of temporal complementarity between wind power and photovoltaic output; An operational constraint model for an energy storage system is established, which includes SOC dynamic constraints, charge and discharge power constraints, and cycle life constraints based on rainflow counting. With the dual objectives of maximizing overall benefits and minimizing energy storage lifetime consumption, the NSGA-II multi-objective optimization algorithm is used to solve the optimal charging and discharging strategy of the energy storage system. Based on the optimal charging and discharging strategy, the revenue is calculated from six dimensions: peak-valley arbitrage revenue, ancillary service revenue, wind and solar curtailment consumption revenue, capacity compensation revenue, demand management revenue, and green certificate revenue. Based on net present value (NPV), internal rate of return (IRR), dynamic payback period (DPP), and levelized cost of electricity (LCOE), a full life-cycle economic assessment of the integrated wind-solar-storage power station is completed.

2. The method according to claim 1, characterized in that, The LSTM-Attention network includes: The input layer concatenates meteorological time-series features with historical power output sequences to form an input feature matrix. A two-layer LSTM encoding layer is used to extract temporal features from the input sequence to obtain the hidden state vector; The Attention layer applies a self-attention mechanism to the hidden state sequence and calculates the attention weights and context vectors. The output layer maps the context vector to the predicted output force value.

3. The method according to claim 1, characterized in that, The formula for calculating the wind-solar hybrid characteristic coefficient is as follows: ； in, The characteristic coefficient of wind-solar complementarity. For wind power output standard deviation, For the standard deviation of photovoltaic output, Standard deviation of wind-solar composite output; Furthermore, by defining the time-shift complementarity coefficient To account for the impact of time delay, and through traversal Determine the optimal time shift As a time reference for energy storage dispatch; Based on the wind-solar hybrid characteristic coefficient Calculate the energy storage capacity complementarity correction factor: ； in, This is the energy storage capacity complementarity correction factor. The weighting coefficients are adjusted for complementarity.

4. The method according to claim 1, characterized in that, The cycle life constraint based on rainflow counting includes: The charge-discharge cycles of the energy storage system were statistically analyzed using the rainflow counting method, and the depth of charge-discharge for each cycle was extracted. and the corresponding equivalent number of loops ; Calculate the equivalent full loop count , For reference discharge depth, As a lifespan index, when Reaching the rated number of cycles The battery is determined to have reached the end of its lifespan at a certain time. The lifespan consumption rate per cycle is Cyclic lifetime constraint is .

5. The method according to claim 1, characterized in that, The decision variables of the NSGA-II multi-objective optimization algorithm are the energy storage charging and discharging power vectors at each time step. ; The objective function for maximizing overall returns is: ； The objective function for minimizing the energy storage lifetime consumption is: ； in, for Peak-valley arbitrage profits at any given moment for Revenue from ancillary services at all times for The benefits of curtailing wind and solar power at any time for Capacity compensation benefits at any time for Real-time demand management benefits for The benefits of green certificates at any time This represents the percentage of lifespan consumed in the i-th cycle; After iterative convergence using the NSGA-II multi-objective optimization algorithm, the Pareto optimal solution set is output, and the optimal compromise solution is selected from the Pareto optimal solution set using a fuzzy membership function.

6. The method according to claim 1, characterized in that, The peak-valley arbitrage profit is corrected using a wind-solar complementarity coefficient. The corrected peak-valley arbitrage profit is: ； in, The peak-valley arbitrage profit before correction. This is the revised peak-valley arbitrage profit. The characteristic coefficient of wind-solar complementarity. This is the gain coefficient of complementarity on peak-valley arbitrage profits.

7. The method according to claim 1, characterized in that, The ancillary service revenue includes AGC frequency regulation revenue, peak shaving revenue, and reserve capacity revenue, wherein AGC frequency regulation revenue is composed of frequency regulation mileage and frequency regulation performance coefficient. Decide: ； in, For response speed, To adjust the accuracy, To adjust the depth.

8. The method according to claim 1, characterized in that, The wind and solar curtailment benefit is corrected using a wind-solar complementarity coefficient. The corrected wind and solar curtailment benefit is: ； in, The original value represents the revenue from wind and solar power curtailment. The revised revenue from wind and solar power curtailment. The characteristic coefficient of wind-solar complementarity. The gain coefficient for the benefit of wind and solar power curtailment due to complementarity.

9. The method according to claim 1, characterized in that, The life-cycle economic assessment includes: Net Present Value ,in For initial investment costs, The discount rate is... For the project lifecycle, For the first Net cash flow for the year; The internal rate of return (IRR) is solved using Newton's iteration method. ; The Dynamic Payback Period (DPP) is the shortest number of years required for the cumulative discounted net cash flow to equal the initial investment. The cost per kilowatt-hour (LCOE) is the ratio of the present value of total costs to the present value of total electricity generation.

10. A revenue calculation system for an integrated wind-solar-storage power station, characterized in that, include: The data acquisition module is used to collect historical operating data of wind farms and photovoltaic power plants, historical operating data of energy storage systems, and electricity market transaction data; The power output prediction module is used to construct wind power output prediction models and photovoltaic power output prediction models based on LSTM-Attention networks, and calculate the wind power predicted power output curves and photovoltaic predicted power output curves within a preset time period in the future. The complementarity analysis module is used to calculate the wind-solar complementarity characteristic coefficient and quantify the degree of temporal complementarity between wind power and photovoltaic output. The constraint modeling module is used to establish an operational constraint model for the energy storage system. The constraint model includes SOC dynamic constraints, charge and discharge power constraints, and cycle life constraints based on the rainflow counting method. The optimization scheduling module is used to solve the optimal charging and discharging strategy of the energy storage system with the dual objectives of maximizing comprehensive benefits and minimizing energy storage lifetime consumption, and adopts the NSGA-II multi-objective optimization algorithm. The revenue calculation module is used to calculate revenue from six dimensions based on the optimal charging and discharging strategy: peak-valley arbitrage revenue, ancillary service revenue, wind and solar curtailment consumption revenue, capacity compensation revenue, demand management revenue, and green certificate revenue. The economic evaluation module is used to complete the full life cycle economic evaluation of integrated wind, solar and energy storage power plants based on net present value (NPV), internal rate of return (IRR), dynamic payback period (DPP), and levelized cost of electricity (LCOE).

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