Energy Power Plants Research Articles

Simulating the Effects of Ammonia Crossover and Alternative Ligands in Thermally Regenerative Flow Batteries

There is a significant amount of low-temperature heat (< 100 °C) available globally from various sources such as geothermal energy, industrial processes, and thermal power plants. Electrochemical and membrane-based methods for harnessing this heat are becoming of greater interest due to their ability to utilize the low temperature energy sources and because they can be sized modularly to fit the power, energy, and spatial requirements of the application. The all-aqueous thermally regenerative ammonia battery (Cuaq-TRAB) is a leading technology in the waste heat to electrical power production space due to recent results showing operational power densities of 25 mW cm-2 with an estimated thermal energy efficiency of 7%, outperforming most technologies in this space. Previous research on the Cuaq-TRAB has shown that the battery suffers from the crossover of ammonia through the membrane which adversely impacts battery performance.To investigate these impacts, we developed a simple numerical model that simulates discharge curves using one fitting parameter to account for parasitic crossover instead of modeling species transport through the membrane. The model was able to replicate experimental data from Cuaq-TRABs with different membrane materials and for multiple applied current densities with < 5% error for average power and energy capacity. Results showed that there is a 20% loss in Cuaq-TRAB energy capacity due to ammonia crossover when compared to a discharge with no parasitic crossover losses. The model was then extended to demonstrate the impact of different changes to the electrolyte chemistry on the resultant power and energy capacities for Cuaq-TRABs. Using this model, we estimate that if chloride was the ligand for the positive electrolyte instead of bromide, it would decrease the power and energy capacity of the battery by 30%. However, considering that an economic analysis showed that chloride is 80% cheaper than bromide, it may be beneficial to consider the use of chloride ligands in this technology despite its worse electrochemical performance.

Towards Automated Model Selection for Wind Speed and Solar Irradiance Forecasting.

Given the recent increase in demand for electricity, it is necessary for renewable energy sources (RESs) to be widely integrated into power networks, with the two most commonly adopted alternatives being solar and wind power. Nonetheless, there is a significant amount of variation in wind speed and solar irradiance, on both a seasonal and a daily basis, an issue that, in turn, causes a large degree of variation in the amount of solar and wind energy produced. Therefore, RES technology integration into electricity networks is challenging. Accurate forecasting of solar irradiance and wind speed is crucial for the efficient operation of renewable energy power plants, guaranteeing the electricity supply at the most competitive price and preserving the dependability and security of electrical networks. In this research, a variety of different models were evaluated to predict medium-term (24 h ahead) wind speed and solar irradiance based on real-time measurement data relevant to the island of Crete, Greece. Illustrating several preprocessing steps and exploring a collection of "classical" and deep learning algorithms, this analysis highlights their conceptual design and rationale as time series predictors. Concluding the analysis, it discusses the importance of the "features" (intended as "time steps"), showing how it is possible to pinpoint the specific time of the day that most influences the forecast. Aside from producing the most accurate model for the case under examination, the necessity of performing extensive model searches in similar studies is highlighted by the current work.

Reliability enhancement through optimal placement of photovoltaic power plant and battery energy storage in distribution system

Integration of distributed generation (DG) units could reduce the duration of power outage for a certain number of consumers in distribution networks after a network failure. Prerequisites are that i) the DG unit has the technical characteristics that enable its islanded operation, the degree of automation of the distribution network allows it and ii) it is in accordance with the current technical regulations of the country. The extent to which integration of distributed sources can improve reliability depends on the DG size, type, and the point of common coupling. Besides, an energy storage system could be installed along with DG unit, so that energy supply availability during fault periods can be secured. This paper proposes an algorithm based on mixed-integer linear programming (MILP) approach for optimal placement of photovoltaic power plant (PVPP) and battery energy storage system (BESS). The optimal location is determined so that expected non supplied energy is minimized. The uncertainties in the estimation of production of PVPP, load and BESS state of charge (SoC) were taken into account by Monte Carlo simulations (MCS).

Virtual power plant optimization with demand response and multi-complementary energy planning

Abstract To effectively address the increasing complexity of system operation optimization and resource allocation caused by the continuous enrichment of supply-side resources and the flexible variability of demand-side loads in the energy system, a robust optimization model for virtual power plants considering demand response and multi-energy complementarity is proposed. Handling renewable energy and load uncertainty in virtual power plants by building a two-stage robust optimisation model. The introduction of robust coefficients allows for flexible adjustment of the conservatism of the optimized scheduling. Results indicate that considering demand response and multi-energy complementarity can effectively enhance the system’s robustness and flexibility.

Low‐carbon economic schedule of the H2DRI‐EAF steel plant integrated with a power‐to‐hydrogen system driven by blue hydrogen and green hydrogen

AbstractHydrogen direct reduction iron coupled with electronic arc furnace (H2DRI‐EAF) technology, as an important technology for decarbonisation in the iron and steel industry, has the advantages of high electrification and low carbon emissions. However, the large demand for hydrogen in this technology relies significantly on the production of electrolytic hydrogen, leading to a substantial increase in power consumption in the steel production process. Moreover, the use of an unclean power source in electrolytic hydrogen production leads to increases in indirect carbon emissions, reducing the low‐carbon attributes of the technology. This study investigates the integrated flexible operation mode of a steel plant. An illustrating method is utilised for modelling the entire steel production process and power to hydrogen (PtH2) process in detail for the H2DRI‐EAF steel plant, which includes natural gas, photovoltaic, wind power self‐provided power plants, and carbon capture and storage (CCS) systems. A mixed integer linear programming (MILP) model is developed for the comprehensive scheduling of the steel mill. The results of the case studies indicate that by reliably integrating the production of renewable energy and natural gas power plants, the PtH2 system can fully consume the renewable energy output while ensuring the smooth progress of steel production and maximising the reduction of carbon emissions from hydrogen production and the total cost of steel production.

Design and analysis of a concentrated solar power-based system with hydrogen production for a resilient community

This paper investigates how to improve the utilization of solar energy in concentrated solar power plants for multi-tasked operation, including hydrogen production. In this regard, an integrated energy system with multiple power generation stages is developed to utilize heat from concentrated solar collector with molten salt thermal energy storage system. The two-stage steam Rankine cycle and organic Rankine cycle are used as power generation cycles in order to utilize heat from the molten salt at different temperature levels, which improves source utilization for power generation. Continuous energy supply is considered a crucial challenge for renewables, which can be achieved by diversifying the source or adding energy storage. The current study uses two different energy storage methods to ensure the continuous energy supply to achieve self-sufficiency as well for the community. Hydrogen energy subsystem is the second energy storage within the integrated system, which is the secondary energy storage that is used if heat is not sufficient to drive the power generation cycles. In order to make decisions for a better source utilization, ensure continuous power supply, and facilitate the operations of the components and energy storage subsystems according to the loads and source availability, an operational decision-making strategy is developed and applied within the spreadsheet model. In order to test the proposed system and the developed algorithm, a time-dependent analysis is carried out where hourly community load and hourly source data are employed. The analyses are carried out using the first and second laws of thermodynamics, primarily through energy and exergy aspects. The power cycle can generate power from heat within the temperature range of 160°C and 500°C, in between 6.12% and 37.2% of heat to power energy conversion efficiency. The overall energy and exergy efficiencies of the integrated system are found to be 31.29% and19.71% by considering the average meteorological data.

P2P Optimization Operation Strategy for Photovoltaic Virtual Power Plant Based on Asymmetric Nash Negotiation

Under the guidance of the “dual-carbon” target, the utilization of and demand for renewable energy have been growing rapidly. In order to achieve the complementary advantages of renewable energy in virtual power plants with different load characteristics and improve the rate of consumption, an interactive operation strategy for virtual power plants based on asymmetric Nash negotiation is proposed. Firstly, the photovoltaic virtual power plant is proposed to establish the optimal scheduling model for the operation of the virtual power plant, and then the asymmetric Nash negotiation method is adopted to achieve the fair distribution of benefits. Finally, the ADMM distribution is used to solve the proposed model in the solution algorithm. The simulation results show that the revenue enhancement rates are 28.27%, 1.09%, and 12.37%, respectively. The participating subjects’ revenues are effectively enhanced through P2P power sharing. Each subject can obtain a fair distribution of benefits according to the size of its power contribution, which effectively improves the enthusiasm of the PV virtual power plant to participate in P2P interactions and thus promotes the development and consumption of renewable energy.

Optimal planning of renewable energy park for green hydrogen production using detailed cost and efficiency curves of PEM electrolyzer

Installing multi-renewable energy (RE) power plants at designated locations, known as RE parks, is a promising solution to address their intermittent power. This research focuses on optimizing RE parks for three scenarios: photovoltaic (PV)-only, wind-only, and hybrid PV-wind, with the aim of generating green hydrogen in locations with different RE potentials. To ensure rapid response to RE fluctuations, a Proton Exchange Membrane (PEM) electrolyzer is employed. Furthermore, this research proposes detailed models for manufacturer-provided wind power curves, electrolyzer efficiency against its operating power, and electrolyzer cost towards its capacity. Two optimization cases are conducted in MATLAB, evaluating the optimum sizes of the plants in minimizing levelized cost of hydrogen (LCOH) using classical discrete combinatorial method and determining the ideal PV-to-wind capacity ratio for operating PEM electrolyzer within hybrid PV-wind parks using particle swarm optimization. Numerical simulations show that wind power-based hydrogen production is more cost-effective than PV-only RE parks. The lowest LCOH, $4.26/kg H2, and the highest LCOH, $14.378/kg H2, are obtained from wind-only and PV-only configurations, respectively. Both occurred in Adum-Kirkeby, Denmark, as it has highest average wind speed and lowest irradiance level. Notably, LCOH is reduced with the hybrid PV-wind configuration. The results suggest the optimum PV-to-wind capacity ratio is 65:35 on average and indicate that LCOH is more sensitive to electrolyzer's cost than to electricity tariff variation. This study highlights two important factors, i.e., selecting the suitable location based on the available RE resources and determining the optimum size ratio between the plants within the RE park.

Power Trading and Access Control Scheme Based on IPFS and Blockchain

In recent years, a large number of renewable energy power plants have been built all over the country, which has led to a sharp increase in the pressure on the current centralized electricity trading platform. There are data storage bottlenecks and data privacy problems in the current blockchain-based power transaction and access control system. In this paper, the interstellar file system is proposed to store the data of the new distributed power trading platform in a distributed way, so as to improve the storage capacity of the physical nodes; An improved ciphertext strategy attribute encryption scheme, which is lightweight, traceable and supports outsourced decryption combined with state secret algorithm, is adopted to integrate the user’s unique identity into the user’s private key and part of the expensive decryption calculation is outsourced to the cloud server to realize fine-grained data access control in the electricity market. The experimental results show that compared to the basic CP-ABE scheme, when the encryption attribute is 100, the decryption time of the proposed scheme is reduced from 556[Formula: see text]ms to 1.1[Formula: see text]ms; Compared to blockchain-based solutions alone, when storing data of 15[Formula: see text]MB, GAS consumption decreases from 2968738133 to 89360. The scheme can greatly improve the storage capacity of the system, improve the operational efficiency and meet the actual performance requirements.

Management of sustainable investments: A comprehensive financial evaluation of wind energy facilities in Kastamonu

The rich renewable sources of nature, such as water, wind, solar, geothermal, and biomass, are converted into energy, revitalized into the economy without causing pollution, and ensure sustainability in energy management. Wind power plants have been among the most preferred renewable energy sources in recent years since they are harmless to the environment and easy to install in places with consistent and sufficient wind speed. This study focuses on conducting a feasibility analysis of a Wind Power Plant (WPP) in Kastamonu, a region known for its persistent wind patterns. The analysis aims to contribute to the existing literature by examining the potential of a WPP investment project within the region. Utilizing wind speed data collected from 23 observation stations for six years, starting from 2017 to 2022, a total of 1,200,600 h, a statistical evaluation method known as the Weibull probability density function is employed to indicate the wind energy potential. The parameters are determined by applying the maximum likelihood method. The findings indicate that it is feasible to establish Wind Energy Power Plants in specific locations, namely İnebolu, İnebolu Kar, Küre, and Tosya stations. The cost estimation for establishing a 10 MW power plant with three different turbine powers across these four locations ranges from 10 million USD to 20,8 million USD. Furthermore, the analysis indicates that the net profit for producing 1 kW of electrical energy falls from 9.39 to 50.65 USD. The projected payback period for the investment ranges from 2 to 25 years, depending on factors such as the exact site of the facility and the size of the turbines. Some investments are more feasible than others, with economic viability and potential return on investment varying significantly within the specified locations.

A Bi-Level Optimized Dispatching Method for Grids with High Penetration of New Energy Considering Ancillary Service Costs

To address the issue of high auxiliary service costs caused by the grid connection of new energy generation, a dual layer optimization scheduling method for high penetration new energy power grids is proposed, taking into account the auxiliary service costs of grid connection. The correlation between the output fluctuations and load fluctuations of new energy generation is analyzed, and a calculation model for auxiliary service costs caused by grid connection of new energy generation is established. Taking into account the timescale division requirements for grid energy trading and auxiliary service market transactions, a new high penetration energy grid two-level optimization scheduling model is established. The upper level scheduling model aims to minimize the sum of grid energy procurement costs and auxiliary service costs caused by new energy generation and completes the optimization scheduling of the grid for several hours. The lower level scheduling model aims to closely follow the upper level scheduling plan for new energy power plants/groups. Optimization and scheduling of the power grid for tens of minutes is completed, taking into account the balanced allocation of wind and solar losses among various power plants within the new energy power plant group. The simulation results show that the method proposed in this paper can reduce the energy transaction costs and auxiliary service transaction costs of the power grid during low and peak load periods.

Exploring chemical disposal options for non-condensable gasses in geothermal power plants: A case study of Kızıldere geothermal field (Türkiye)

Geothermal power plants are among the most important renewable energy power plants owing to their high-capacity factors and integrated utilization possibilities. Currently, these power plants utilize geothermal fluid to generate electricity. Although their emissions are lower than those of conventional power plants, gasses such as CO2 and H2S are released into the air from the cooling towers, particularly in flash-type geothermal power plantsTo reduce the emission of CO2 gas released from geothermal power plants, reinjection studies have mainly been carried out around the world. These types of studies require extensive analysis of underground fracture systems, detailed geosciences, and the reservoir studies. However, these studies are considered risky and expensive for most plant operators because possible changes in underground fracture systems may affect the productivity of geothermal production zones. In terms of the environmental impact, hydrogen sulfide is a more harmful gas than CO2. Effective H2S removal methods cannot be widely used, except in areas with extremely high concentrations, because they commonly incur significant costs for plant operators. Effective H2S removal methods are not widely available except for geothermal sites with high concentrations. The fact that local limit values can be exceeded in geothermal power plants with relatively low H2S concentrations, such as geothermal power plants in Türkiye, pushes plant operators to find new low-cost solutions due to high operation costs. For this reason, a treatment method that can be applied at every site and whose cost is not too high has not yet been put forward. However, NaOH is used for this purpose in geothermal fields such as steam-dominated Geyser field to increase the pH values in geothermal wells, which has been producing for a long time.In this study, field tests were carried out with five different chemicals and pure water to examine the reduction of non-condensable gasses in a geothermal power plant located in the Kızıldere (Denizli, Türkiye) geothermal field, one of the most important geothermal fields in the world. According to this, the capture of these gasses is technically possible using chemical methods, with a performance of up to 70 % observed in CO2 gas capture.However, although it is possible to capture 70 % of non-condensable gasses with such chemical methods, the consumable cost of the operation is quite high.

Sequential Data-Based Fault Location for Single-Line-to-Ground Fault in a T-Connection Power Line

Due to the demand for temporary rapid grid connection in renewable energy power plants, the topology structure of T-connected power lines has been widely used in the power grid. In this three-terminal system, fault localization is difficult because of traditional impedance-based or traveling wave-based fault localization methods; the three-terminal data should be synchronized and communicated. Since different terminal assets belong to different enterprises, it is actually difficult to maintain good synchronization between them. Therefore, in practical applications, the fault location of T-connected power lines often fails. This article proposes a single terminal fault location method for a T-connection power line to address this issue. It is based on the fact that the local topology of the T-connected power line in the healthy phase remains unchanged during the fault-clearing process. It utilizes the sequential current and voltage data changes generated by the sequential tripping ping emitted by the circuit breaker from different terminals to describe the constant topology of the healthy phase as an equation and calculates the accurate fault location after solving the equation. The Levenberg–Marquardt algorithm was used to calculate fault distance and transition resistance, and the effectiveness of this method was verified through simulation.

Research on deep peaking cost allocation mechanism considering peaking demand subject and thermal power unit

The current peak-shaving auxiliary service cost allocation mechanism balances the revenue and expenditure of the power plant side, the peak-valley difference of the power grid is mainly caused by the peak-valley characteristics of the load and renewable energy. It is not reasonable that not reasonable the peak-shaving cost allocation mechanism does not consider user allocation. Therefore, this paper establishes a peak-shaving cost allocation mechanism that considers the peak-shaving demand subject, this article incorporates renewable energy power plants and loads into the peak-shaving demand subject. Firstly, this paper constructs an alternative scenario without peak-shaving demand through the mean line of load and available energy output and establishes an optimal scheduling model with deep peak-shaving and pumped storage power stations, this article takes the difference of system peak-shaving cost between two scenarios with and without peak-shaving demand as the marginal peak-shaving cost. Then, the Shapley value method in cooperative game is used to establish the peak-shaving cost allocation mechanism considering the main body of peak-shaving demand. In addition, It is used that the equal-kWh following load method constructed the equivalent output curve of renewable energy, following load method improves the existing allocation mechanism by comprehensive similarity. Finally, this paper analyzes and compares three different peaking cost allocation mechanisms. The example analysis shows that the peaking cost allocation mechanism considering peaking demand subjects. The example can reflect the peaking cost caused by different subjects and reduce the peaking pressure of thermal power units.

Research of the regulator of an expander-generator unit

The relevance of research is determined by the tasks of increasing the efficiency of alternative and renewable energy power plants. The purpose of the work is to develop a method of coordinating the settings of the automatic excitation regulators and the speed of rotation of the rotor of the generator from the vortex installation of the expander-generator unit, which is part of the autonomous power consumption network. To achieve the goal, the methods of mathematical modelling in the MATLAB environment, physical modelling, processing of experimental data, theory of automatic control, theory of magnetic field, theory of electric machines, programming of microprocessor devices were used. Considering the need to expand the base of alternative energy sources, the task of controlling the energy plant for the utilization of excess pressure of gas transportation networks was considered. The study of the regulator of the expander-generator unit was based on a model reflecting the interaction of mechanical and electrical processes. A set of nonlinear differential equations corresponded to the description of these processes. Accurate consideration of nonlinearities of the considered control object complicates the implementation of the corresponding microprocessor control systems. Modelling is aimed at achieving a compromise between the required accuracy of object control and the possibilities of simplifying the object control structure. The use of experimental and datasheet data of the devices made it possible to evaluate the dynamics of turbine rotation and voltage generation, forming the factors for the untying of the control circuits. The use of MATLAB with the Lookup Table option made it possible to perform a multi-point linearization of the nonlinear description of the process, speeding up the modelling and adjustment of the regulator. This is important to incoordinate the settings of the frequency control loops and the output voltage, given the trade-off between the speed of the regulator and the minimization of oscillations in the generation frequency. Based on the obtained results, a regulator of the expander-generator unit was developed, which ensures the stabilization of the initial parameters of the unit under the conditions of electrical load disturbances. In practice, the obtained data can be used in the integration of renewable energy sources into the general energy system through the optimization of the operation of generators

Long-term performance analysis of operational efficiency of a grid-connected solar power plant under Mauritania climate

This work examines a solar power plant connected to the Nouakchott electricity grid in Mauritania. Operating since 2013, the 15 MWp plant's reliability and energy yield have been evaluated using a performance index. The assessment involves three phases. First, the plant's meteorological environment and technical indicators are presented. In the second phase, mathematical performance models specified by the International Energy Agency (IEA) are applied to calculate performance indices using data from the data acquisition system (SCADA). The third phase compares actual production data for 2015, 2017, and 2020 with results simulated for PVsyst for the same years. The obtained results are thoroughly analyzed to highlight relevant physical phenomena. The analysis focuses on the plant's 7-year operating period and its impact on performance indicators for electricity production fed into the grid. This study provides insights into the solar power plant's reliability and energy yield, aiding future operational enhancements. It underscores the importance of performance monitoring and assessment in optimizing solar power generation systems.

Modeling of Wind Power Plants and Their Impact on Economic and Environmental Development

Abstract This research addresses the environmental dynamics of wind power plants and their impact on the economy and environment. A system dynamics framework is used as a tool for model development since it accommodates relationships between complex and nonlinear variables affecting the wind power plants and their impact on the economy and environment. The scientific contribution of this research is the creation of scenario modeling that describes the interrelationships of variables and parameters affecting wind power plants and their impact. By changing the structure of the model, projections on the future of wind power plant generation can be estimated. Several scenarios being developed include adding turbines in the Sidrap and Jeneponto regencies to increase the fulfillment ratio of wind energy and scenarios to reduce CO2 emissions using solid direct air capture (S-DAC). The data and information used in this research come from the Central Statistics Agency, articles on wind energy power plants, and data from related previous studies. These models and scenarios can be applied in other regions by adjusting the parameter values of the case study model. Total wind energy depends on density, wind speed, blade cross-sectional area, and the efficiency of the Betz limit. With the addition of 25 turbines in Sidrap and 20 turbines in Jeneponto, the average fulfillment ratio is estimated to increase by around 7% due to increased production. Meanwhile, total CO2 emissions are estimated to decrease by approximately 45% due to solid direct air capture.

GIS-based multi-criteria analysis for solar, wind, and biomass energy potential: A case study of Iraq with implications for climate goals

The study utilizes a GIS-Based Multi-Criteria Analysis to evaluate the viability of solar, wind, and biomass energy in Iraq, focusing on enhancing the nation's energy independence and meeting international climate objectives. Detailed spatial analysis revealed specific zones suited for the efficient installation of energy power plants. Zones with an energy surplus factor of less than 0.2 are deemed not viable for development, whereas regions with factors less than 0.4 are ideal for harnessing solar, wind, and biomass resources. Furthermore, zones with surplus factors greater than 0.6 are highly recommended for energy projects. Notably, the results show that strategic development of these zones can cater to between 45 % and 68 % of the country total energy demand, potentially reducing the CO2 footprint by 0.5. Moreover, with an increase in the construction threshold, the CO2 reduction potential has shown a decrease to 1.29E+08 tones, particularly evident in areas with rigorous construction standards. A plateau in reduction figures is observed once the construction threshold surpasses 3.5, stabilizing between 2.5E+07 tones. As the threshold exceeds 5, a further stabilization is noted, consistently around 2.17E+07 tones, persisting even when the threshold approaches 8. The outcomes underscores the critical role of strategic zoning in maximizing renewable energy potential, highlighting pathways for the country to achieve energy self-sufficiency and make significant strides in climate goals.

China's Fossil Fuel CO2 Emissions Estimated Using Surface Observations of Coemitted NO2.

Accurate estimates of fossil fuel CO2 (FFCO2) emissions are of great importance for climate prediction and mitigation regulations but remain a significant challenge for accounting methods relying on economic statistics and emission factors. In this study, we employed a regional data assimilation framework to assimilate in situ NO2 observations, allowing us to combine observation-constrained NOx emissions coemitted with FFCO2 and grid-specific CO2-to-NOx emission ratios to infer the daily FFCO2 emissions over China. The estimated national total for 2016 was 11.4 PgCO2·yr-1, with an uncertainty (1σ) of 1.5 PgCO2·yr-1 that accounted for errors associated with atmospheric transport, inversion framework parameters, and CO2-to-NOx emission ratios. Our findings indicated that widely used "bottom-up" emission inventories generally ignore numerous activity level statistics of FFCO2 related to energy industries and power plants in western China, whereas the inventories are significantly overestimated in developed regions and key urban areas owing to exaggerated emission factors and inexact spatial disaggregation. The optimized FFCO2 estimate exhibited more distinct seasonality with a significant increase in emissions in winter. These findings advance our understanding of the spatiotemporal regime of FFCO2 emissions in China.

A spherical fuzzy-based DIBR II-AROMAN model for sustainability performance benchmarking of wind energy power plants

Wind energy power plants (WEPPs) are renewable energy generation facilities that are increasingly used today. The performance evaluations of these energy plants are generally based on technical performance. Although there are studies in the literature focusing on determining the technical performance of WEPPs, there is a lack of research on evaluating their sustainable performance. The primary motivation of this study is to develop a decision model for identifying the sustainable performance of WEPPs. The model incorporates fuzzy logic and multi-criteria decision-making (MCDM) approaches, incorporating expert opinions and both qualitative and quantitative criteria. To determine the influence levels of experts, spherical fuzzy (SF) sets are utilized. The weighting of criteria is achieved using the SF-defining interrelationships between ranked criteria II (DIBR II) method, which is a novel extension of the DIBR II method based on SF sets. Additionally, an SF-alternative ranking order method accounting for two-step normalization (AROMAN) method is developed for ranking the sustainable performance of WEPPs, building upon the AROMAN method, and adapting it to SF sets. This hybrid model is named the SF-DIBR II-AROMAN hybrid model. The step-by-step procedures of this model are presented in the research, and an algorithm for these procedures is developed. To demonstrate its practicality, a case study is conducted, focusing on WEPPs with a capacity ranging from 25 to 150 megawatts in Çanakkale, Turkey. According to the research results, among the fifteen WEPPs in the Çanakkale region, “Saros WEPP” is identified as having the best one. The robustness of the obtained results is ensured through sensitivity analyses. Based on the results of two sensitivity analysis scenarios, “Saros WEPP” is found to have the highest sustainable performance. The SF-DIBR II-AROMAN hybrid model results are compared with different alternative ranking method results, highlighting its superior aspects. Overall, SF-DIBR II-AROMAN is consistent and robust. The research also provides insights and managerial implications, as well as explains the benefits of the proposed model for evaluating WEPPs’ sustainable performance.