Levelized Research Articles

Offshore wind power plant site selection in the Baltic Sea

The selection of suitable sites for offshore wind power plants is an important marine spatial planning problem in the Baltic Sea. The application of multiple and different decision support tools (DSTs) can facilitate site selection. This study employs a geographical information system (GIS) integrated with the levelized cost of energy (LCOE) model, and GIS with multi-criteria decision analysis (MCDA) using an analytical hierarch process (AHP), fuzzy logic, weighted linear combination (WLC), and the technique for order of preference by similarity to ideal solution (TOPSIS) as an extension of MCDA. The core novelty of the study is the use of several concurrent state-of-the art techniques to quantify the detailed spatial pattern of suitability of different sea areas for offshore wind power plant installations for the entire Baltic Sea at an unprecedented, for such a large sea area, resolution. Important parameters were identified and data from the Global Wind Atlas, HELCOM, and EMODnet portals were used. The most appropriate areas exhibit LCOE values between 42 and 58 €/MWh, MCDA values in the range of 0.78–0.92, and capacity factor values from 0.52 to 0.55. The areas identified as the least appropriate were characterized by LCOE values ranging from 107 to 210 €/MWh, MCDA values between 0.34 and 0.59, and capacity factor values within the range of 0.30–0.43. The outcomes using both methodologies were similar. The decision preferred are mostly locations close to shore in shallow water and with a high capacity factor. The largest suitable areas are in the south-west Baltic Sea whereas some suitable locations are identified further to the north and east.

Feasibility study: Economic and technical analysis of optimal configuration and operation of a hybrid CSP/PV/wind power cogeneration system with energy storage

In this study, a hybrid photovoltaic-wind-concentrated solar power renewable energy system and two cogeneration models are proposed. Evaluation criteria are employed, including the levelized cost of energy (LCOE) and the loss of power supply probability (LPSP). The optimal configuration and dynamic dispatch strategy of the hybrid system are determined through multi-objective optimization using the natural resources of Xining City. The economic feasibility of the system is assessed through comparison and analysis. The findings reveal that both cogeneration modes of the system effectively meet the power output requirements and dynamic thermal load. In thermal energy storage tanks' heat production mode without a battery storage system, the system achieves a minimum LCOE of 0.0526$/kWh and a maximum LPSP of 6.86%. With a battery storage system in this mode, the system attains a minimum LPSP of 4.83% and a maximum LCOE of 0.160$/kWh. Additionally, in the turbine spent vapor recovery heat supply mode, the system exhibits an LCOE of 0.127$/kWh and an LPSP of 4.96%, the system has better flexibility and wind-solar complementarity in this mode. This study also analyzed the importance of affordable electric energy storage for stable energy supply in hybrid energy systems.

Comparative techno-economic analyses and optimization of standalone and grid-tied renewable energy systems for South Asia and Sub-Saharan Africa

This study evaluates the economic, technical, and environmental performance of stand-alone and grid-tied HRES in Khiriya Bharka, India; Thayet Township, Myanmar; Lower Manya Krobo, Ghana; and Mamfe, Cameroon, considering different regional solar radiation, wind speed diversity, and climate. The HRES are designed and modeled using the Hybrid Optimization of Multiple Energy Resources software (HOMER PRO) to meet consumers' daily loads in the selected places. The analysis results are compared considering the levelized cost of energy (LCOE), net present cost (NPC), greenhouse gas (GHG) emission, renewable fraction (RF), and optimum system configuration. The optimal system configurations are the combination of PV-WT-DG-BAT-CON for grid-tied and stand-alone HRES in India, Ghana, and Cameroon, respectively, and the best configuration for Thayet Township, Myanmar, is WT-DG-BAT-CON. The LCOE of the considered places without grid connection is $0.127/kWh, $0.145/kWh, $0.174/kWh, and $0.143/kWh, respectively. The LCOE of grid-tied HRES is 0.026$/kWh, 0.0286$/kWh, 0.01$/kWh, and 0.0281$/kWh for Khiriya Bharka, Thayet Township, Lower Manya Krobo, and Mamfe, respectively. This research can be useful for planning grid-tied and stand-alone HRES between Asia and African countries by comparing grid-tied and stand-alone HRES to determine the optimum system configuration and finding the best optimization results under different climate conditions. These results are further validated by finding loss of power supply probability (LPSP) and LCOE using particle swarm optimization (PSO). To better inform policymakers and stakeholders in various regions, this research aims to assess and compare renewable energy solutions in terms of their feasibility, cost-effectiveness, environmental impact, and sustainability across a range of geographical, social, and economic contexts.

Wind load analysis and cost assessment of a dual-axis stand-alone photovoltaic structure

This study investigated the effect of wind load on the structural stability of a dual axis tracking system developed at the Botswana International University of Science and Technology (BIUST). Through simulations, the study explored wind speed variations, from 8 m s−1 to 28 m s−1, while simultaneously adjusting the azimuth and elevation angles of the structure. Utilizing computational methods and numerical simulations, the study models wind flow interactions with the dual-axis tracker system. This knowledge can be used to improve the system’s design, ensure safety, and optimize energy production and efficiency. The findings of this study contribute to the field of renewable energy, specifically in the context of solar power generation using dual-axis tracker systems. The results offer valuable insights into the wind load behavior, structural response, and energy consumption, facilitating the development of more robust and efficient tracker designs. Furthermore, the study used the levelized cost of electricity (LCOE) to investigate the cost effectiveness of the solar system. The aim was to establish how the energy production of the system, the installation, operation, and maintenance costs compare to other systems using the local electricity tariffs.

Technical-economic evaluation of various photovoltaic tracking systems considering carbon emission trading

Renewable energy generation plays a crucial role in mitigating global climate change and achieving carbon peak and carbon neutrality targets. Photovoltaic (PV) power is an important component of renewable energy generation. With China's commitment to meet its dual carbon targets on time, the application of PV tracking systems, which significantly increase the amount of PV power generated, has become a trend for the future. However, the suitability of PV tracking systems varies significantly between regions. This paper aims to assess and compare the technical and economic performance of PV systems using different PV tracking systems in six different latitude regions in China under the consideration of carbon trading. By utilizing the PVsyst software, the monthly/yearly unit electricity generation of different tracking systems in each region is calculated. Furthermore, the technical and economic performance of these systems is assessed by computing the levelized cost of electricity (LCOE), net present value (NPV), and discounted payback period (DPP) with and without carbon benefits, taking into account sensitivity analyses. The results indicate that the dual-axis tracking (DAT) system achieves the highest electricity generation, providing a gain of 17.94 %–31.52 % compared to the fixed-tilt (FT) system. The horizontal axis tracking North-South (HATNS) system and the tilted-axis tracking (TAT) system are probably the most economically viable systems in areas with high radiation levels, with NPV values 2.02 %–25.55 % and 6.44 %–19.45 % higher, respectively, compared to the FT system. The introduction of carbon benefits significantly increases the economic viability of all tracking systems.

Development of a prediction-based scheduling control strategy with V2B mode for PV-building-EV integrated systems

Extending the energy boundary from the building-integrated photovoltaic to E-mobility is an effective alternative to further improve the integrated energy system performance. In this study, a new prediction-based scheduling control (PSC) strategy was developed considering vehicle-to-building mode for PV-building-EV integrated systems. The control strategies consisted of three components, model predictive control (MPC), simple operation mode, and schedule control. First, the MPC was based on the weather forecast, while the calculated daily renewable power generation and building energy consumption were used for mode selections. Second, the simple operation mode was based on system operation using conventional methods. Third, the developed scheduling control strategy was activated based on the periods of the day, involving different operating modes (e.g., V2B mode, renewable-to-building mode, etc.). Overall, the developed PSC can effectively improve energy performance and achieve cost savings. Validation tests were conducted for an office building, whose results showed higher renewable energy penetration and renewable power use efficiency at 9.56% and 30.48%, respectively. The levelized cost of energy (LCOE) values for building energy consumption and EV charging decreased to 0.4561 CNY/kWh and 0.6304 CNY/kWh, respectively. It indicated that the annual cost savings of building energy consumption and EV charging decreased by 16.5% and 42.7%, respectively.

Optimal sizing and energy management of a microgrid: A joint MILP approach for minimization of energy cost and carbon emission

The optimal design and operation of microgrids involves complex trade-offs between technical, economic, and environmental factors. This research addresses these challenges by proposing a comprehensive approach that combines the sizing and energy management problems of a microgrid into a single decision-making framework. The study introduces a joint multi-objective mixed-integer linear programming algorithm that minimizes the levelized cost of energy (LCOE) and life cycle emissions (LCE) of the microgrid formed by photovoltaic system, battery storage, and with/without grid connections. By considering 25-year project lifetime horizon and hourly energy management, the algorithm ensures an optimal solution that accounts for long-term planning and real-time operation. The research conducts an analysis of French electricity grid as a case study where a Pareto-front is created using the trade-off constraint approach. Furthermore, the impact of peak shaving is assessed on Pareto-front for various subscription power to utility grid. Lastly, the research examines energy sources in the French electricity grid, revealing insights into the energy mix’s implications for microgrid design. The results showed that LCOE and LCE can vary up to 28% and 17%, respectively, while the local renewable energy utilization can be increased to 54% by limiting the grid subscription power at 36 kVA.

Configuration Strategy and Performance Analysis of Combined Heat and Power System Integrated with Biomass Gasification, Solid Oxide Fuel Cell, and Steam Power System

Renewable energy integration is a crucial approach for achieving a low-carbon energy supply in industrial utility systems. However, the uncertainty of user demand often leads to a mismatch between the system’s real operating conditions and the optimal operating points, resulting in energy wastage and high emissions. This study presents a multi-source heat and power system that integrates biomass gasification, solar collecting, solid oxide fuel cell (SOFC), gas turbine, and steam power systems. A scheduling strategy that varies the heat-to-power ratio is proposed to accommodate changes in user requirements. A simulation model of this multi-source system is established and validated. The influence of three key parameters on system performance under different configurations is explored. Energy and economic evaluations are conducted for three different configurations, and the system’s energy production and adjustable range are determined. The analysis reveals that, under the optimal configuration, the system can achieve an energy efficiency of 64.51%, and it is economically feasible with the levelized cost of electricity (LCOE) of USD 0.16/kWh. The system is capable of producing an output power ranging from 11.52 to 355.53 MW by implementing different configuration strategies. The heat-to-power ratio can be adjusted from 0.91 to 28.09.

Optimal social distancing in epidemic control: cost prioritization, adherence and insights into preparedness principles

The COVID-19 pandemic experience has highlighted the importance of developing general control principles to inform future pandemic preparedness based on the tension between the different control options, ranging from elimination to mitigation, and related costs. Similarly, during the COVID-19 pandemic, social distancing has been confirmed to be the critical response tool until vaccines become available. Open-loop optimal control of a transmission model for COVID-19 in one of its most aggressive outbreaks is used to identify the best social distancing policies aimed at balancing the direct epidemiological costs of a threatening epidemic with its indirect (i.e., societal level) costs arising from enduring control measures. In particular, we analyse how optimal social distancing varies according to three key policy factors, namely, the degree of prioritization of indirect costs, the adherence to control measures, and the timeliness of intervention. As the prioritization of indirect costs increases, (i) the corresponding optimal distancing policy suddenly switches from elimination to suppression and, finally, to mitigation; (ii) the “effective” mitigation region—where hospitals’ overwhelming is prevented—is dramatically narrow and shows multiple control waves; and (iii) a delicate balance emerges, whereby low adherence and lack of timeliness inevitably force ineffective mitigation as the only accessible policy option. The present results show the importance of open-loop optimal control, which is traditionally absent in public health preparedness, for studying the suppression–mitigation trade-off and supplying robust preparedness guidelines.

Optimize and analyze a large-scale grid-tied solar PV-powered SWRO system for sustainable water-energy nexus

The lack of clean water and access to energy are two major obstacles to global sustainable systems. The development of solar photovoltaic (PV) technology gained the attention of water desalination projects due to its cost decline, carbon-free emission, and the on-site energy production. However, studies on in-depth analysis of the performance of these systems remain rare. This study performs a comprehensive a design optimization and seven-factor analysis (energy, exergy, economic, environmental, energoeconomic, exergoeconomic, enviroeconomic - 7E) of a grid-tied PV system powering a large-scale seawater reverse osmosis (SWRO) plant. First, an optimal design model is developed based on real hourly measured data for the SWRO load demand and renewable resources, while considering net energy metering mechanism. The objective function is delineated to diminish the energy cost of the system, concomitant with the imposition of operational constraints. Second, an analytical model is established to perform exergetic calculations for the optimal design. Third, a sensitivity analysis is conducted to show how the model parameters affect the system design and costs. The results show that the optimal system relies on a 4.25 MW solar PV array and a 3.125 MW grid-tied inverter. This configuration reduces the levelized cost of energy by 48.3 % and carbon emissions by 50 % compared to a grid-only system. The implementation of net-metering has also maximized the designed system's revenues to 263,834 $/year. Further, the optimally designed system achieves a lower levelized cost of water (0.2217 $/m3), minimal capacity shortage, and negligible unmet load ratios (0.048 %) despite grid outages. From the exergetic assessment, the proposed system has a high exergy efficiency (9.3 %) which can be achieved despite the high value of the exergy loss. Moreover, the energoeconomic and exergoeconomic indicators have recorded tiny values (3.35E-5 kWh/$ and 0.0311 kWh/$, respectively), which are considered a desired outcome. Notably, a profit of 4285.4$ is achieved through the sale of carbon emissions reduction. The sensitivity analysis revealed an increase in the system's total cost of 4.91 % and 9.36 % and the system's energy cost of 4.91 % and 8.7 % if the energy purchase price raised by 10 % and 20 %, respectively. Also, it indicates that a fall in the electricity sellback rate of 10 % would remarkably raise the system's energy cost by 47.3 %. Finally, reducing the PV-Inverter costs by 15 % would drop the system's net present and energy costs by 51.4 % and 60 %, respectively. Overall, the insights from this study have the potential to expedite the realization of sustainable development goals 6, 7, and 13, particularly in developing nations abundant in solar energy resources.

Optimal design and techno-economic analysis of hybrid renewable energy systems: A case study of Thala city, Tunisia

ABSTRACT This study explores the techno-economic feasibility of, both off-grid and on-grid, hybrid renewable energy systems for remote rural electrification in Thala City, located in the highest region of Tunisia, using wind and biomass resources. Employing Hybrid Optimization of Multiple Energy Resources based on different scenarios includes grid-connected and stand-alone configurations with pumped storage hydropower and lead acid battery storage while minimizing the levelized cost of energy, the net present cost, and greenhouse gas emissions. The optimal configuration wind/biomass/pumped-hydro storage/Converter grid-connected, minimizes the levelized cost of energy (LCOE) and net present cost (NPC), resulting in a cost of 501,540 US$ and LCOE of 0.042 US$/kWh. Notably, 7% of electricity is generated from olive mill waste, 69% from wind turbines, and 24% is purchased from the grid. This hybrid system emits 342 tons/year of CO2, 76% less than a grid-alone system, contributing to an annual CO2 reduction of 1000 tons.

Generation and application of typical meteorological year data for PV system potential assessment: A case study in China

The potential assessment of photovoltaic(PV) systems is of great significance for installing and laying out PV. Short-term meteorological data cannot reflect the local climate characteristics, so it is not suitable for the potential assessment of PV. A typical meteorological year(TMY) can reflect a region's climatic characteristics, and therefore, it is appropriate for the potential assessment of PV. Although the generation methods of TMY for building energy consumption simulation are mature, there are still limited studies on TMY for PV. Given this, this study employs a method to generate TMY data suitable for the Chinese region, while redefining the influencing factors and their respective weights. A TMY dataset was built for 90 cities based on 38 years of measured data in China and applied to BAPV potential assessment. The study assesses annual generation, CO2 reduction, levelized cost of electricity (LCOE), and net returns(NR). The results reveal the highest annual electricity generation in the Northwest region, with Shiquanhe, Golmud, and Ejina sites reaching 215.65 MWh, 201.2 MWh, and 197.5 MWh, respectively. Conversely, southern and coastal regions, notably Chongqing, exhibit the lowest generation at 75.75 MWh. BAPV in Class I tariff areas, along with some Class II and III areas, can achieve demand-side parity (DSP) in full on-grid mode. Through operational adjustments, DSP can be implemented in 23 currently ineligible areas. This study provides dataset support for calculating PV system power generation and analyzes PV potential across diverse cities. The results contribute to policy formulation and future solar energy research, facilitating precise calculations for efficient green power planning in pursuit of carbon neutrality.

The Potential of Renewable Energy to Replace Diesel Power Plants in Supporting Energy Transition in Indonesia

The Indonesian government is committed to reducing greenhouse gas emissions under the Paris Agreement. One form of this commitment is the program to replace diesel power plants with renewable energy plants in the electricity sector. Fossil-based diesel power plant operations have disadvantages, such as relatively expensive generation costs and greenhouse gas emissions. There are three renewable energy technology options as potential substitutes for diesel power plants: a solar photovoltaic (PV) power plant integrated with a battery energy storage system (BESS), a small-scale geothermal power plant, and a micro-hydropower plant. This paper will analyze the prospects of each technology option based on its economic and environmental impact. Various technical parameters, including renewable energy potential and intermittent factors, as well as economic parameters such as investment and generation costs, are important factors for selecting prospective renewable energy generators. The analysis was carried out by calculating the levelized cost of electricity (LCOE), CO2 emissions, and oil fuel consumption for four different scenarios. The results of the analysis show that replacing the diesel power plants with micro-hydropower plants can reduce LCOE by 0.24 USD/kWh to 0.12 USD/kWh. The use of a solar PV power plant integrated with a BESS has the most potential to reduce CO2 emissions, which account for 54% of emissions from diesel power plants. Meanwhile, the potential for oil fuel reduction from this program can reach an annual average of 0.85 million kl.

Performance analysis of two generations of heaving point absorber WECs in farms of hexagon-shaped array layouts

ABSTRACT Numerical analyses are presented for two generations of a floating heaving point absorber wave energy converter (WEC) installed with different farm array layouts. The wave farm configurations are based on WECs developed by Waves4Power. The numerical models are developed in the DNV software package, Sesam. Parametric studies of the isolated WEC configurations and farm array layouts are conducted under typical environmental conditions and various incident wave directions to understand the hydrodynamic power performance and the levelised cost of energy (LCoE). Hexagonal layouts are proposed for deploying the WEC units and compared with a 10-unit layout termed StarBuoy, which has been reported in previous work. The results of the present study confirm that the interactions between arrayed units in a farm can have either positive or negative effects on the LCoE, which is dependent on the array layout and environmental conditions. The hexagonal array layouts lead to lower LCoE owing to constructive interaction effects.

Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns.

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.

Sustainable solar energy in urban areas: A novel framework for uncertainty, reliability, and 7E analysis

Nowadays, access to clean, reliable, and sustainable energy is essential for urban cities. Solar energy, a widely available renewable energy source (RES), is present in abundance throughout the world. Nonetheless, using this energy has challenges such as uncertainty and reliability. The present study proposes a novel two phase framework based on a multi-criteria analysis of solar plants in urban cities in terms of uncertainty and reliability to address this issue. In phase-1, solar plants were evaluated using multi criteria decision-making from technical, economic, environmental, social, political, and risk aspects based on Z-number theory. Different methods such as Z-EDAS, Z-TOPSIS, Z-ARAS, Z-COPRAS, Z-SAW, and Z-MABAC were used for robust assessment. In phase-2, a 10 MW solar plant was examined from seven indicators (energy, exergy, economic, environmental, energoeconomic, exergoeconomic, and enviroeconomic as 7E analysis). The 7E analysis showed the solar photovoltaic plant can generate annually 12.3 GWh of electricity, with an energy and exergy efficiency of 13.85 % and 14.9 %, respectively. Additionally, the Levelized cost of electricity (LCOE) was 57$/MWh. Furthermore, the proposed framework based on Z-number theory effectively consider uncertainty and reliability in solar site assessment in urban cities and can be used in future studies for RES and sustainable urban planning.

A Numerical Method for the Dynamics Analysis of Blade Fracture Faults in Wind Turbines Using Geometrically Exact Beam Theory and Its Validation

In pursuit of China’s goals for carbon peak and carbon neutrality, wind turbines are continually evolving to achieve a lower levelized cost of energy. The primary technological focus in the wind power industry is on large-scale, lightweight designs for entire turbines to enhance cost competitiveness. However, this advancement has led to an increased risk of blade fractures under extreme operating conditions. This paper addresses this challenging issue by using geometrically exact beam theory to develop a nonlinear simulation model for long, flexible blades. The model accounts for sudden changes in blade properties at the moment of failure, covering both the extensive motions and deformations of the fractured blade. The validation of the proposed model is carried out by comparing the results from power production cases with bladed simulations and further validating the simulations of blade fracture load cases against measurement data. The methodologies and findings presented in this study offer valuable insights for diagnosing faults in wind turbines.

Optimal Design of PV/WT/Battery Based Microgrid for Rural Areas in Leh Using Dragonfly Algorithm

This study proposes an optimal microgrid design for rural electrification in India’s Leh and Ladakh regions, using wind energy, solar, energy, and battery energy storage system. The Dragonfly Algorithm (DA) is used to calculate the optimal number of microgrid units, and results are compared with popular optimization algorithms such as Grey Wolf optimization (GWO), Differential Evolution (DE), and Discrete Harmony Search (DHS). The optimal design is based on an objective function to minimize the Levelized cost of energy (LCOE) while keeping the loss of power supply probability (LOPSP) as a reliability constraint. Three configuration studies are carried out, with three cases, each with a different maximum permissible LOPSP (LOPSPmax) value. The results show that optimal design and efficient energy management reliably meet the load demand. The energy generated from the proposed microgrid is clean compared to the grid supply, and the amount of greenhouse gas (GHG) emissions is reduced by 91.2% from Configuration-I, Case-I, which is the most economical configuration. The LCOE obtained from Configuration-I, Case-I is 0.129 $/kWh, the lowest among similar systems available in the literature. To determine the parameter cost with supply, the LCOE and Total life cycle cost (TLCC) sensitivity to LOPSPmax are considered. Furthermore, statistical analysis shows that DA outperforms GWO, DE, and DHS in terms of accuracy and convergence rate.

Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States

Abstract. A major issue in quantifying potential power generation from prospective wind energy sites is the lack of observations from heights relevant to modern wind turbines, particularly for offshore where blade tip heights are projected to increase beyond 250 m. We present analyses of uniquely detailed data sets from lidar (light detection and ranging) deployments in New York State and on two buoys in the adjacent New York Bight to examine the relative power generation potential and power quality at these on- and offshore locations. Time series of 10 min wind power production are computed from these wind speeds using the power curve from the International Energy Agency 15 MW reference wind turbine. Given the relatively close proximity of these lidar deployments, they share a common synoptic-scale meteorology and seasonal variability with lowest wind speeds in July and August. Time series of power production from the on- and offshore location are highly spatially correlated with the Spearman rank correlation coefficient dropping below 0.4 for separation distances of approximately 350 km. Hence careful planning of on- and offshore wind farms (i.e., separation of major plants by > 350 km) can be used reduce the system-wide probability of low wind energy power production. Energy density at 150 m height at the offshore buoys is more than 40 % higher, and the Weibull scale parameter is 2 m s−1 higher than at all but one of the land sites. Analyses of power production time series indicate annual energy production is almost twice as high for the two offshore locations. Further, electrical power production quality is higher from the offshore sites that exhibit a lower amplitude of diurnal variability, plus a lower probability of wind speeds below the cut-in and of ramp events of any magnitude. Despite this and the higher resource, the estimated levelized cost of energy (LCoE) is higher from the offshore sites mainly due to the higher infrastructure costs. Nonetheless, the projected LCoE is highly competitive from all sites considered.

الحجم الأمثل للنظام الهجين المستقل والمتجدد على أساس التكاليف باستخدام تقنية Gray Wolf Optimization

Ensuring low-cost and highly reliable electricity generation while meeting the demand load poses a significant challenge. Power plants operate continuously to produce power, with the Programmable Logic Controller (PLC) control system playing a crucial role in managing all the equipment. Power deficiencies in the PLC rooms can lead to equipment disturbances and potential damage to the plant. To address this problem and utilize the abundant solar energy in Libya, this study introduces the optimal sizing of an autonomous hybrid storage system using an optimization method. The Net Present Costs (NPC), Loss Power Supply Probability (LPSP), and Levelized Cost of Energy (LCOE) are employed to extract a suitable function database. The proposed system generates 7 kW of power to meet demand and is implemented in MATLAB. The Grey Wolf Optimization (GWO) method is applied to determine the optimal number of PV panels and BESS capacity for the autonomous hybrid system. The optimal solution obtained through GWO is determined to be 40 PV panels and a 60Ah BESS capacity.