Levelized Cost Of Energy Reduction Research Articles

Assessment of PV excess generation savings in Ghana considering peer-to-peer energy trading

ABSTRACT Solar is one of the Renewable (RE) sources with a high electricity generation potential. However, the challenge with using individual stand-alone Photovoltaic (PV) systems is the mismatch between load demands and electricity generated such that surplus and insufficient energy are experienced by prosumers. This study used the Net Present Value (NPV), and levelized cost of energy (LCOE) economic models to assess the impact of modern (peer-to-peer, P2P) trading and applicable PV system cost reduction strategy in Ghana, considering a cluster of stand-alone systems. The energy demand and generations of a total of 7 prosumer and consumer buildings in the Don Bosco Technical Institute, Ashaiman, were used. The results, considering a mini-grid connection between the buildings, showed an LCOE of GH₵ 1.358 over 10 years and NPV of about GH₵ 5 million for 25 years when excess generation is traded as compared to GH₵ 2.423 for LCOE and about GH₵ 180,000 NPV when excess generation is disregarded. A proposed shared investment community-owned system design showed further LCOE reduction to GH₵ 1.122 because the capital cost decreased. This shows that P2P energy trading can make PV generation attractive amidst other benefits like job creation, revenue generation and emissions reduction if RE is used. Lastly, a suitable energy trading market mechanism was proposed for implementing P2P in the context of Ghana. Though the research revealed that more benefits can be obtained from PV systems in Ghana, future technical analysis regarding transmission losses, the Internet of Things (IoTs), and blockchain need to be done.

Machine learning-based diagnosis in wave power plants for cost reduction using real measured experimental data: Mutriku Wave Power Plant

In comparison to wind farms, the relative scarcity of actual operational data from wave power plants has contributed to a significant research gap in the areas of wave farm forecasting and cost reduction. In this context, this manuscript presents a new Machine Learning-based Power Take-Off (PTO) diagnosis for wave energy generation farms which has the potential to serve as an extensive reference for other wave energy farms and offer substantial benefits to both investors and policymakers involved in the advancement of the emerging wave technologies. The suggested method has been employed at the Mutriku Wave Power Plant (WWP) to facilitate the implementation of predictive maintenance strategies and reduce the Levelized Cost of Energy (LCoE). Hence, the research study considers two main extraction methods, namely, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), used to select the most relevant features for OWC diagnosis. In addition, two classification methods have been considered: Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN). The obtained data show that, although both methods allow to achieve an effective performance with an excellent degree of accuracy, the ANN-based method presents better results with 98% accuracy against 81% for the SVM when using PCA extraction method. Then, the developed classification-based OWC diagnosis has been used for the development of a predictive maintenance strategy at the Mutriku WPP, analyzing its impact on the economic indicators. The results indicate that, using the proposed predictive maintenance strategy, the OpEx may be decreased down to 17%, downtime may be decreased down to 55% and plant availability may be better up to 95%, leading to a 5% LCoE reduction.

Navigating the Cost-Efficiency Frontier: Exploring the viability of Grid-Connected energy storage systems in meeting district load demand

In this investigation, we explored the cost-effectiveness and operational efficiency of grid-connected Energy Storage System (ESS) technologies—specifically, Proton Exchange Membrane-Reversible Fuel Cell (PEM-RFC) and Li-ion Battery (LIB)—with the goal of meeting load demand. Our conclusion reveals that integrating ESS isn't the optimal solution due to impractical implementation costs, making reliance solely on grid electricity a more pragmatic choice. Despite this, a secondary optimum value emerged, suggesting economic viability for ESS integration and introducing a dynamic trade-off between cost considerations and energy storage utilization. Furthermore, our study conducted a sensitivity analysis, anchored in the target costs set by the US Department of Energy, to identify conditions under which ESS systems could transition from impractical to feasible. The results outlined the potential to reduce the levelized cost of energy (LCOE) but also emphasized the prospect of increasing the storage factor by optimizing various system characteristics. Crucial factors like capital cost, efficiency, and lifetime were identified as pivotal in shaping the economic landscape of ESS integration. The study showcased tangible outcomes upon sensitivity analysis, including a 12% reduction in PEM-RFC LCOE and 58.5% reduction in LCOE for LIB, indicating improved cost efficiency and feasibility of ESS integration to the grid. To address a critical research gap, this study provides a novel comparison between PEM-RFC and LIB technologies as ESS, considering dynamic grid electricity prices. It sheds light on intricate dynamics that must be considered in ESS deployment decisions, emphasizing the delicate balance between cost-effectiveness and operational efficiency. Focused on the electricity load demand of commercial buildings in the Santa Barbara district, our techno-economic assessment contributes valuable insights for informed decision-making in ESS integration.

Energy, economic, and environmental evaluation of a solar-assisted heat pump integrated with photovoltaic thermal modules using low-GWP refrigerants

Solar-assisted heat pumps integrated with photovoltaic thermal modules (PVT-HPs) have been extensively studied owing to their superior heating performance. However, the investigation of PVT-HPs using low global warming potential (GWP) refrigerants to replace R134a is required to comply with environmental regulations. This study aimed to investigate the substitutability of R1234yf, R1234ze(E), and R152a in PVT-HPs based on energy, economic, and environmental analyses. The heating performance of heat pumps using these low-GWP refrigerants was measured. Furthermore, transient simulations were performed using transient system simulation software to determine the annual performance of the PVT-HPs using low-GWP refrigerants. Based on the simulation results, the levelized cost of energy (LCOE) and Specific Life Cycle Climate Performance (SLCCP) were evaluated. R152a was the most economical refrigerant, showing a 50% reduction in LCOE compared to R134a because of its lower electricity purchase cost and higher sale cost. Furthermore, the SLCCPs of PVT-HPs using R1234yf, R1234ze(E), and R152a decreased by 16.8%, 17.5%, and 20.8%, respectively, compared to those using R134a. Accordingly, the refrigerant substitutability index of R152a was the highest at 1.431, owing to its superior economic and environmental performance. In conclusion, only R152a was deemed appropriate for replacing R134a in PVT-HPs.

Blade Design and Aerodynamic Performance Analysis of a 20 MW Wind Turbine for LCoE Reduction

The aim of this study is to develop a blade mass model that incorporates a low-induction rotor (LIR) and a low-specific power concept to reduce aerodynamic loads and lower the Levelized Cost of Energy (LCoE). This blade mass model replaces the traditional simple scaling rule and incorporates the concept of LCoE reduction, presenting not only the mass distribution in the blade span direction but also the stiffness distribution. In order to achieve the desired reduction in LCoE, we developed a mathematical model that expresses blade mass as a function of the axial induction factor, which influences the aerodynamic load on the blade. We used this model to determine geometries of various low-induction rotors for 20 MW class horizontal axis wind turbine, and to identify the axial induction factor that correlates with the lowest blade mass. The chord length and twist angle in the spanwise direction of the blade were determined using PROPID’s reverse design process, based on the specified axial induction factor. Since the low-induction concept is not aerodynamically optimal, a low-specific power design approach was also adopted. This involved increasing the blade length and shifting the power curve to the left. By doing so, the AEP is increased, directly contributing to a reduction in the LCoE. Mass per unit length of the blade was presented, reflecting the distribution of airfoil type, blade geometry, and shapes of internal structures such as spars and webs.

Assessing the influence of lightweight generators on substructure cost and levelized cost of energy: a techno-economic model for large offshore wind turbines

It is expected that lightweight drive train systems for large wind turbines can reduce overall levelized cost of energy (LCOE) compared to conventional designs, particularly through a less robust and therefore lower-cost turbine substructure. Representing a major share of drive train mass, the focus of mass reduction efforts concentrate on the generator. In order to assess the influence of specific lightweight generator designs on overall LCOE, a techno-economic software tool was created which feeds generator mass into the LCOE calculation through the design and capital expenditure (CAPEX) calculation of an appropriate wind turbine substructure. A case study was carried out, comparing a direct drive design featuring a permanent magnet generator with ring architecture and large diameter—resulting in significantly low mass and low CAPEX—to a reference turbine with conventional drive train design. The results show that a relevant LCOE reduction could be achieved, but generator mass reduction and substructure mass and consequently CAPEX reduction are highly disproportionate due to technical substructure design constraints. It is indicated that the assessed lightweight design has a high potential to economize on lightweight design decisions to reduce its CAPEX and in consequence overall LCOE even further.

A Tri-Port Current-Source Soft-Switching Medium-Voltage String Inverter for Large-Scale Solar-Plus-Storage Farms

This article presents a tri-port current-source soft-switching medium-voltage string inverter (TMVSI) to reduce the Levelized cost of energy (LCOE) of large-scale solar-plus-storage (SPS) farms. Throughout this work, the topology, operating principle, circuit simulation, control, and hardware design of the TMVSI are introduced. In addition, three key challenges compromising the performance of the TMVSI are addressed in this article. First, a new feed-forward compensation for model predictive control of the TMVSI is proposed to compensate for sampling and computational delay and high dc-link ripple with low computational cost and high scalability. Second, the use of laminated permanent magnets is proposed to reduce the losses of the MV medium-frequency transformer (MFT) in the TMVSI while increasing its saturation current. Finally, an improved U-shape winding pattern is proposed to decrease the leakage inductance of the MV MFT and reduce the voltage stress across semiconductor switches without adding cost or complexity. The effectiveness of the TMVSI is validated by experiments at up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20\,\mathrm{kW/500\,V}$</tex-math></inline-formula> in different test cases with custom-built prototypes. The contributions of this work make the TMVSI a viable solution to SPS farms and unleash its potential for LCOE reduction.

Technoeconomic Analysis of Changing PV Array Convective Cooling Through Changing Array Spacing

Accuracy in photovoltaic (PV) module temperature modeling is crucial to achieving precision in energy performance yield calculations and subsequent economic evaluations of PV projects. While there have been numerous approaches to PV temperature modeling based on both the steady-state and transient thermal assumptions, there have been few attempts to account for changing convective cooling on PV module surfaces resulting from changes in the PV system layout. Changes in system row spacing, in particular, can have a meaningful impact on module electrical efficiency and subsequent economic performance, even when considering additional costs from the changes in row spacing. Using a heat transfer approach based on the spatial definition of a PV array, technoeconomic analyses of different plant configurations are presented here that show an improved system levelized cost of energy (LCOE) for fixed-tilt PV systems when increasing system row spacing. These LCOE improvements have been found to be as high as 2.15% in climates characterized by low ambient temperatures and higher average annual wind speeds in U.S. climates. While the LCOE improvements are primarily driven by incident irradiance changes for altered row spacing, the waterfall analysis of the different components of changing system LCOE show that modifications in the heat transfer dynamics have a 0.5% contribution to the LCOE reduction for the largest LCOE, compared with a 3.3% reduction from irradiance changes.

Wind power generation in Brazil: An overview about investment and scale analysis in 758 projects using the Levelized Cost of Energy

This paper studied 758 Brazilian wind power projects on the basis of energy auctions between 2009 and 2019 and analyzed investment and scale. It also provided an overview of the expected performance of these projects, especially in terms of costs and economic feasibility, using the Levelized Cost of Energy (LCOE). Our intention is to present results that allow us to question if the policies for this renewable energy have been effective in Brazil. The evolvement of these projects became clear in the findings, namely in the increase of capacity factors, and in the reduction in total investment and LCOE. The scale of majority of the projects are under the scale of 31 MW of installed capacity, and the projects above this threshold have lower LCOE, resulting in an inconclusive discussion if it might be seen as evidence of scale gains. The LCOE values were calculated using the two main taxation methods in Brazil, confirming the market expectations that the presumed profit method provides lower expenses than the actual profit method. The projects are expected to be in a critical economic situation, with LCOE averages being far higher than the auction price averages, raising concerns about the Brazilian renewable energy policies.

Utilization of technology and digitalization to bring equality energy access for remote areas

More electricity infrastructures to serve off-grid or mini-grid solutions have been installed to become the best choice for rural areas to increase the electrification ratio. The Indonesian government uses this ratio as a primary indicator for measuring the range of energy supply. The electrification ratio of Indonesia reached 99.2% in 2020. However, this seemingly bright achievement must be followed by improving the quality of access to electricity. The solution to this challenge is digital transformation to modernize data and perform analysis in the network ecosystem to create an intelligent utility system that will improve the quality of access to electricity. The simulation of the utilization of SEMons shows a significant decrease in the LCOE (Levelized Cost of Energy) of the solar PV power plant of around 14.52% compared to the existing condition with the manual monitoring method. For the units built-in 2017, there is a reduction of LCOE from 0.1034 USD/kWh to 0.0866 USD/kWh. This decrease in the LCOE value indicates a reduction in fixed Operation & Maintenance costs due to the optimized predictive maintenance to improve the availability of units.

Assessment of current developments and future prospects of wind energy in Canada

The negative consequences of global warming and climate change demonstrate that conversion to carbon-free or low-carbon, sustainable, and eco-friendly energy sources is essential. Wind energy substitution, as a renewable and widely available energy source, can be an appropriate, effective, and practical contribution to reducing fossil fuel use worldwide. To show the importance of wind energy in the future global energy supply, the current status and future prospects of wind power in Canada are statistically analyzed, as a case study, in terms of installed capacity, generation, capacity factor, and insights are provided on the levelized cost of energy (LCOE). The originality of this study is to present a comprehensive basis for assessing future growth and the potential development of wind power in Canada’s electricity supply mix to the year 2040 by using different predictive methods, including historical compound annual growth rate (CAGR), linear forecasting, and CAGR of 5, 10 and 15%, selected based on a presumed policy interjection at the provincial- or territorial-level. The comparative assessment of wind energy generated by various predictive methods up to 2040 shows that the historical CAGR and the CAGR of 15% respectively have the lowest and highest growth rates in meeting the total annual end-use electricity demand for Canadian provinces and territories, except for Alberta, Saskatchewan, Yukon, and Nunavut, where the lowest growth rate belongs to the linear forecasting approach. However, these predictive scenarios strongly depend on the energy policies of the provincial or territorial governments. In addition, the influence of increasing installed wind power capacity in Canada on the LCOE is examined using a linear forecasting model with various decline rates for costs. The results indicate that the reduction in LCOE robustly depends on a higher installed wind power capacity. Moreover, the relationship between the wind power capacity factor and LCOE is evaluated based on different forecasting scenarios, where it is observed that the LCOE is strongly inversely associated with capacity factor. As a general result, many regions of Canada are identified as having a highly promising wind energy potential, especially in the Gulf of St. Lawrence, either as a stand-alone source or integrated with other energy sources, along with energy storage capabilities, to meet future electricity requirements with a low carbon footprint.

High-temperature latent thermal storage system for solar power: Materials, concepts, and challenges

Limited reserves of fossil fuels, increased world energy demand and swelling of environment pollution drive a transition towards renewable energy resources which can deliver 3E objectives (Economic, environmental and energy security). However, the inherent intermittency necessitates efficient energy storage systems to harness the true potential of renewable resources. In this context, high-temperature latent heat storage (LHS) using phase change medium (PCM) can be a promising alternative to address the challenges of the variable renewable energy generation with respect to time and space. Currently, central receiver-based 3rd Gen concentrated solar thermal (CST) plant operating at high-temperatures (800-1000 °C) is the most attractive technology to convert solar energy to heat. Moreover, advanced power-generating cycles such as supercritical CO2 (sCO2) Brayton cycle operating at high-temperature can reduce the Levelized cost of Energy (LCoE) by achieving higher cycle efficiency. Hence, coupling a high-temperature LHS between the central receiver system and sCO2 cycle can have dual benefits: increase in dispatchability of energy and reduction in LCoE. Furthermore, high-temperature LHS can be devised to store high-grade energy such as spillage of energy from Photovoltaic (PV), wind power plant, and waste heat from energy-intensive industries (e.g. glass melting furnace). This article reports a holistic approach to review different components and design aspects of high-temperature LHS with techno-economic challenges to be overcome. A preliminary numerical study has been performed to predict the melting behavior of high-temperature silicon using COMSOL Multiphysics. Based on the review, two configurations of high-temperature LHS have been illustrated to produce continuous and cost-effective electricity. The first layout is high-temperature LHS coupled with 3rd generation (Gen) CST and the second one is a standalone high-temperature LHS device with Thermionic-photovoltaic (TIPV) diode.

AQUADA: Automated quantification of damages in composite wind turbine blades for LCOE reduction

AbstractReliability and cost are two important driving factors in the development of wind energy. Automation and digitalization of operation and maintenance (O&amp;M) procedures help to increase turbine reliability and reduce the levelized cost of energy (LCOE). Here, we demonstrate a novel method, coined as AQUADA, which may change the current labor‐intensive and operation‐interfering blade inspection by using thermography and computer vision. We experimentally show that structural damages below the surfaces can be detected and quantified remotely when wind turbine blades are subject to fatigue loads. The data acquisition and analysis are automatically done in one single step, which may shift the current inspection paradigm through more automated O&amp;M procedures. The cost analysis shows that the AQUADA method has a potential to at least half the total inspection cost and reduce the LCOE by 1%–2% when applied to a baseline land‐based wind farm consisting of twenty 2.45‐MW turbines. All data and source codes are published for researchers to reproduce our results and facilitate further development of AQUADA towards more mature industrial applications.

Quantifying the Importance of Solar Soft Costs: A New Method to Apply Sensitivity Analysis to a Value Function

Abstract This paper presents a new approach to build a decision model for government funding agencies, such as the US Department of Energy (DOE) solar office, to evaluate solar research funding strategies. High solar project costs—including technology costs, such as modules, and soft costs, such as permitting—currently hinder many installations; project cost reduction could lead to a lower project levelized cost of energy (LCOE) and, in turn, higher installation rates. Government research funding is a crucial driver to solar industry growth and potential cost reduction; however, DOE solar funding has not historically aligned with the industry priorities for LCOE reduction. Solar technology has received significantly higher research funding from the DOE compared to soft costs. Increased research funding to soft cost programs could spur needed innovation and accelerate cost reduction for the industry. To this end, we build a cost model to calculate the LCOE of a utility-scale solar development using technology and soft costs and conduct a sensitivity analysis to quantify how the inputs influence the LCOE. Using these results, we develop a multi-attribute value function and evaluate six funding strategies as possible alternatives. We find the strategy based on current DOE allocations results in the lowest calculated value and the strategy that prioritizes soft cost results in the highest calculated value, suggesting alternative ways for the DOE solar office to prioritize research funding and potentially spur future cost reduction.

A multi-scenario approach for a robust design of solar-based ORC systems

The application of Organic Rankine cycle (ORC) units in concentrating solar power systems is a very promising solution. However, fluctuations in the available solar energy often force solar-based ORC systems to operate at off-design conditions. An innovative methodology for finding robust design solutions of such ORC systems, based on the minimization of the expected Levelized Cost of Energy (LCOE), is therefore proposed. The expected variations in the ORC heat source and heat sink are considered during the design stage by adopting a multi-scenario approach. The proposed methodology has been tested by referring to a medium-scale ORC unit and by considering different working fluids. As case studies, the direct coupling of the ORC unit with a solar field and the integration of a thermal energy storage system have been investigated. The comparison of the results obtained by using a multi-scenario and a single-scenario approach highlights a reduction of the actual LCOE. The ORC configuration obtained by adopting a multi-scenario approach is characterized by lower performance under design conditions, but it is less sensitive to variations in the main inputs during off-design operating periods. This fact is particularly noteworthy for the case with the direct coupling of the solar field.

AN ENHANCED TECHNO-ECONOMIC ANALYSIS OF LCOE: PUBLIC INCENTIVES VS PRIVATE INVESTMENT

This paper offers a new approach for the estimation of levelized cost of energy (LCOE) by considering the shareholder internal rate of return (IRR) as an unexplored measure in this kind of analysis. The study relies on a comprehensive techno-economic evaluation based on interactions among a set of factors. This mathematical model is then empirically tested for a CSP power plant in Extremadura (Spain) due to their dominant market position and also for being the most developed renewable system at the present. A sensitivity analysis is also performed to establish the influence that market conditions have on the determination of LCOE for different scenarios under the maintenance of a given shareholder IRR for investors. This last assumption makes investment decisions indifferent among several projects in order to focus solely on the minimization of the LCOE. Results reveal that while the annual net electricity production contributes to the reduction of LCOE, total investments, equity percentage and operation and maintenance (O&amp;M) costs help to increase their value by a high percentage. This study gives important scientific basis for investment decision making and also becomes a standpoint to design suitable public incentives that may enhance future technological developments in the CSP generation industry.

Layout Optimization Process to Minimize the Cost of Energy of an Offshore Floating Hybrid Wind–Wave Farm

Offshore floating hybrid wind and wave energy is a young technology yet to be scaled up. A way to reduce the total costs of the energy production process in order to ensure competitiveness in the sustainable energy market is to maximize the farm’s efficiency. To do so, an energy generation and costs calculation model was developed with the objective of minimizing the technology’s Levelized Cost of Energy (LCOE) of the P80 hybrid wind-wave concept, designed by the company Floating Power Plant A/S. A Particle Swarm Optimization (PSO) algorithm was then implemented on top of other technical and decision-making processes, taking as decision variables the layout, the offshore substation position, and the export cable choice. The process was applied off the west coast of Ireland in a site of interest for the company, and after a quantitative and qualitative optimization process, a minimized LCOE was obtained. It was then found that lower costs of ~73% can be reached in the short-term, and the room for improvement in the structure’s design and materials was highlighted, with an LCOE reduction potential of up to 32%. The model serves usefully as a preliminary analysis. However, the uncertainty estimate of 11% indicates that further site-specific studies and measurements are essential.

Cost-benefit evolution for concentrated solar power in China

Abstract The prospective cost-benefit of CSP (concentrated solar power) is the attention focus for policy-making and investment decisions. In order to analyze cost-benefit evolution of CSP, the paper adopted the net present value and discounted cash flows techniques to develop a mathematical model, and calculated LCOE (levelized cost of energy) of CSP between 2018 and 2050 by taking the CSP industry of China as an example. The results show that: Firstly, CSP has a large potential in the LCOE reduction, and the values of parabolic trough and solar tower CSP systems reduce by 46%–57% and 47%–56% from 2018 to 2050, respectively. Secondly, predicted evolution for the LCOE of CSP strongly depends, not only on the cumulative installed capacity determined by the specific growth paths of Blue Map and Roadmap Scenarios, but also on the learning curves impacted by technical progress and experience accumulation. Thirdly, to select the sites with higher direct normal irradiation and land cost exemption is crucial to improve cost-benefit evolution of CSP. Lastly, the expected returns (i.e. discount rate) have important effect on the LCOE evolution of CSP, high expected returns lead to high the LCOE, and vice versa.

Molten salt power towers operating at 600–650 °C: Salt selection and cost benefits

This analysis examines the potential benefit of adopting the supercritical carbon dioxide (sCO2) Brayton cycle at 600–650 °C compared to the current state-of-the-art power tower operating a steam-Rankine cycle with solar salt at approximately 574 °C. The analysis compares a molten-salt power tower configuration using direct storage of solar salt (60:40 wt% sodium nitrate: potassium nitrate) or single-component nitrate salts at 600 °C or alternative carbonate- or chloride-based salts at 650 °C.The increase in power cycle efficiency offered by the sCO2 Brayton cycle is expected to reduce the size and cost of the solar field required for a given thermal energy input. Power cycle capital cost is expected to decrease compared to the superheated steam-Rankine cycle, based on projections from sCO2 cycle developers. Maximizing the ΔT of the storage system is required for viable deployment of sensible-salt TES. In this regard, the partial-cooling sCO2 cycle is noted as a better option than the recompression sCO2 cycle. In the current analysis it is assumed that a ΔT = 180 K can be achieved with the partial-cooling cycle. Even with ΔT = 180 K, the potential benefits of the sCO2 Brayton cycle are partially or completely offset by increased thermal storage cost, albeit for reasons that differ for the different salts. An approximate 5% reduction in levelized cost of energy (LCOE) is achieved with either solar salt at 600 °C or ternary magnesium chloride salt at 650 °C.The potential of using pure sodium nitrate or potassium nitrate is considered because the cold tank temperature for the sCO2 power cycle is estimated at 420 °C, which would allow use of a salt with a higher melting point than solar salt. Sodium nitrate is the most cost effective, resulting in an overall LCOE reduction of 8.5%; however, sodium nitrate is known to have lower thermal stability than potassium nitrate.The strong influence of salt cost and hot-tank cost on overall economics led to the analysis of single-tank thermocline options. The thermocline design significantly reduces salt inventory (by 50% or more) and in many cases also reduces the tank size versus the hot salt tank of the 2-tank system. It is speculated that integration of encapsulated phase-change material (PCM) in the thermocline could further increase the thermal-storage energy density and reduce storage tank volume. The thermocline cases led to three scenarios with relative LCOE reductions of approximately 10%; however, this must be tempered by possible operational inefficiencies of the thermocline temperature profile.

Improving investigation of wind turbine optimal site matching through the self-organizing maps

A wind turbine (WT) site optimization procedure was developed and applied on two different onshore and one offshore sites, supplied with tall met masts and belonging to exemplary wind climates. In addition to detecting the most suitable WT for each site, Kohonen's self-organizing maps (SOMs) were employed to improve investigation of those parameters mostly influencing site optimization. Three years (2013–2015) of 1-h vertical observations from local met masts and a database of 377 onshore and 23 offshore commercial WTs were used.As a result, maximizing capacity factor (CF) was confirmed as a good objective function, though not the best, which was minimizing levelized cost of energy (LCoE). In general, these two conditions do not necessarily match: variously setting WT parameters may either result in an LCoE reduction or CF increase, but both conditions do not occur concurrently. A key finding was that minimum LCoE cannot be achieved by indefinitely increasing the WT hub height, but rather through detection of an optimum value obtained as a unique solution of the optimization procedure. Furthermore, the capability of SOM to recognise the cluster structure of all parameters influencing WT site optimization shed further light on their mutual relationship, thus proving to be an ideal tool to address the non-convex nature of this issue.