Search

Abstract Intersecting socio-demographic transformations and warming climates portend increasing worldwide heat exposures and health sequelae. Cooling adaptation via air conditioning (AC) is effective, but energy-intensive and constrained by household-level differences in income and adaptive capacity. Using statistical models trained on a large multi-country household survey dataset (n = 673,215), we project AC adoption and energy use to mid-century at fine spatial resolution worldwide. Globally, the share of households with residential AC could grow from 27% to 41% (range of scenarios assessed: 33-48%), implying up to a doubling of residential cooling electricity consumption, from 1220 to 1940 (scenarios range: 1590-2377) terawatt-hours yr.−1, emitting between 590 and 1,365 million tons of carbon dioxide equivalent (MtCO2e). AC access and utilization will remain highly unequal within and across countries and income groups, with significant regressive impacts. Up to 4 billion people may lack air-conditioning in 2050. Our global gridded projections facilitate incorporation of AC’s vulnerability, health, and decarbonization effects into integrated assessments of climate change.

Abstract The global population is aging at the same time as heat exposures are increasing due to climate change. Age structure, and its biological and socio-economic drivers, determine populations’ vulnerability to high temperatures. Here we combine age-stratified demographic projections with downscaled temperature projections to mid-century and find that chronic exposure to heat doubles across all warming scenarios. Moreover, >23% of the global population aged 69+ will inhabit climates whose 95th percentile of daily maximum temperature exceeds the critical threshold of 37.5 °C, compared with 14% today, exposing an additional 177–246 million older adults to dangerous acute heat. Effects are most severe in Asia and Africa, which also have the lowest adaptive capacity. Our results facilitate regional heat risk assessments and inform public health decision-making.

Abstract Studies show the role of various electrification technologies in providing electricity access to households in Sub-Saharan Africa, with a focus on electricity demand for end-use services such as lighting, cooking, heating, cooling and other appliance use. The demand for productive use of electricity, which is important to enhance income generation opportunities and labour productivity, is usually not considered. Using the IMAGE-TIMER integrated assessment model framework, we present a methodology to project the impact of productive activities on the electricity system of the region. We show that growing productive activities increase household electricity demand by half, which has important consequences for determining the cost-optimal electrification technologies. We argue that planning of electricity systems should accommodate this increase in electricity demand for productive uses. In addition, while productive uses of electricity have a positive impact on the financial viability of electrification systems, they also increase the electricity sector investment requirements considerably.

In Nigeria, 86 million people lack electricity access, the highest number worldwide, predominantly in rural areas. Despite government efforts, constrained budgets necessitate private investors, who, without adequate incentives, are hesitant to commit capital due to perceived high risks. This study identifies three existing incentive policies—concessionary loans, capital subsidy, and financing productive use equipment—aimed at promoting rural electrification in Nigeria. Employing geospatial and regulatory analyses, we evaluate their impact on electrification planning across 22,696 population clusters. While all incentives encourage mini-grids and stand-alone systems, results show varied impacts, predominantly favouring mini-grids. Under the baseline, grid extension is optimal for 66% of clusters, followed by mini-grids (27%) and stand-alone systems (7%). Concessionary loans boost mini-grid and Stand-Alone Systems shares by 10% and 5%, respectively. Capital subsidies increase the mini-grid share to 41%, surpassing concessional loans (37%). Financing productive equipment enhances Stand-Alone Systems and mini-grid shares to 15% and 43%. Incentives impact LCOE, CAPEX, and OPEX, with average LCOE reducing to 0.31 EUR/kWh (concessionary loans), 0.30 EUR/kWh (capital subsidy), and 0.27 EUR/kWh (financing productive use). Financing productive uses proves decisively more effective in lowering costs for mini-grids and stand-alone systems than loans or capital subsidies. The important policy implications of this study reinforce the need for tailored incentives for distinct electrification options.

In this work, we explore how locally led innovations can advance sustainable energy access in low-income regions of Africa. Given the emphasis of the Sustainable Development Goals on the importance of affordable, reliable, and sustainable energy, we critically assess how local innovations can address the unique challenges of regions characterized by informality and low purchasing power and where existing grid-based electricity supply is weak. Through case studies, we highlight successful initiatives in expanding energy services access, including product bundling, pay-as-you-go financial models, leveraging existing supply chains, and securing strong funding support. Our findings underscore the importance of local ownership, contextual knowledge, and the adaptation of innovations to specific socio-economic and cultural conditions. By comparing the case studies, we identify common strategies that can contribute to the effectiveness of local innovations in improving energy access and sustainability that can inform efforts to replicate and scale up such initiatives. We conclude by identifying common elements contributing to the success of these cases and their significance, focusing on those crucial for achieving speed, scale, and impact.

In this study, we introduce an integrated modeling framework that combines a hydrologic model, a biophysical crop model, and a techno-economic model to assess solar irrigation potential in Sub-Saharan Africa (SSA) based on seven commonly grown food crops-maize, wheat, sorghum, potato, cassava, tomato, and onion. The study involves determining the irrigation requirements, location-specific capital investment costs, crop-specific profitability, and the cropland area under various cost scenarios (low and high) and soil fertility (low, moderate, near-optimal, and optimal) scenarios. Our research reveals considerable potential for solar irrigation, with profitability and viable cropland areas that vary according to crop type, irrigation system cost scenarios, and soil fertility levels. Our assessment shows that approximately 9.34 million ha of SSA’s current rainfed cropland are hydrologically and economically feasible for solar irrigation. Specifically, maize and onion display the lowest and highest viability, spanning 1–4 million ha and 29–33 million ha, respectively, under optimal soil fertility conditions. In terms of profitability, maize and onion rank as the least and most economically viable crops for solar irrigation, yielding average annual returns of $50-$125/ha and $933-$1450/ha, respectively, under optimal soil fertility conditions. The lower and upper bounds of profitability and cropland range correspond to high-cost and low-cost scenarios, respectively. Furthermore, our study reveals distinct regional differences in the economic feasibility of solar irrigation. Eastern Africa is more economically favorable for maize, sorghum, tomato, and cassava. Central Africa stands out for onion cultivation, whereas West and Southern Africa are more profitable for potato and wheat, respectively. To realize the irrigation benefits highlighted, an energy input of 940-2,168 kWh/ha/yr is necessary, varying by crop and geographic sub-region of the SSA sub-continent. Our model and its results highlights the importance of selecting the right crops, applying fertilizers at the appropriate rates, and considering regional factors to maximize the benefits of solar irrigation in SSA. These insights are crucial for strategic planning and investment in the region’s agricultural sector.

Shared pooled mobility has been hailed as a sustainable mobility solution that uses digital innovation to efficiently bundle rides. Multiple disciplines have started investigating and analyzing shared pooled mobility systems. However, there is a lack of cross-community communication making it hard to build upon knowledge from other fields or know which open questions may be of interest to other fields. Here, we identify and review 9 perspectives: transdisciplinary social sciences, social physics, transport simulations, urban and energy economics, psychology, climate change solutions, and the Global South research and provide a common terminology. We identify more than 25 000 papers, with more than 100 fold variation in terms of literature count between research perspectives. Our review demonstrates the intellectual attractivity of this as a novel perceived mode of transportation, but also highlights that real world economics may limit its viability, if not supported with concordant incentives and regulation. We then sketch out cross-disciplinary open questions centered around (1) optimal configuration of ride-pooling systems, (2) empirical studies, and (3) market drivers and implications for the economics of ride-pooling. We call for researchers of different disciplines to actively exchange results and views to advance a transdisciplinary research agenda.

This paper presents a comparative study of the linguistic data collected during fieldwork in three Arabic-speaking towns in Morocco: Larache, in the north; Temara, on the central Atlantic coast; and Berkane, in the east. The goal of the article is twofold: first of all, it aims at highlighting the new dialectological findings made by analysing the three corpora separately; secondly, it attempts to clarify, by means of a comparative analysis, if there is evidence of a modern koine spreading from the urban areas in Central Morocco to other regions of the country. To these purposes, seven variables pertaining to the levels of phonetics, verbal and nominal morphology are selected for cross-analysis among the three corpora. Previous dialectological data on the same and other Moroccan areas are then taken into account to advance hypotheses on the spread and the ancientness of the identified phenomena of linguistic variation, and their more or less likely connection to the modern koine. The results show that, while there is indeed a certain degree of convergence among the three towns (with Berkane being the least involved in the converging trend), some of the phenomena identified could be explained as accommodation to other varieties having common features with the inter-urban koine, while others could be the result of mutual rather than one-way convergence. More sociolinguistic data are needed to clarify the doubts raised by the analysis.

ObjectivesTo propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs).MethodsA combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake.ResultsA novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AI-enabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS-assisted allocation of vaccines at the local level, considering context-specific constraints and objectives.ConclusionsWe showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines.

IntroductionGhana is the first country in sub-Saharan Africa (SSA) to aim for universal health coverage (UHC). Based on Ghana's UHC system, the accessibility and distribution of healthcare facilities were evaluated for 2020. Projecting into 2030, this study aimed at providing geographical information data for guiding future policies on siting required healthcare facilities. Ghana as a precedent for SSA was evaluated and proposed to "leave no disease behind" in the surveillance of infectious diseases (IDs). This is to reinforce the sustainable development goals (SDG) 3 agenda on health that underpins monitoring equity in "leaving no one behind."MethodsGeospatial accessibility, travel time data, and algorithms were employed to evaluate the universality and accessibility of healthcare facilities, and their future projections to meet UHC by 2030. Healthcare facilities as surveillance sites were compared to community-based surveillance to identify which would be more applicable as a surveillance system to leave no disease behind in Ghana.FindingsGhana has 93.8%, 6.1% and 0.1% as primary, secondary and tertiary healthcare facilities respectively. It has 26.1% of healthcare facilities remaining to meet the SDG 3 health target by 2030. In terms of providing quality healthcare, 29.3% and 67.2% of the additional required healthcare facilities for optimal allocation and achieving the UHC target need to be secondary and tertiary respectively. In assessing the broad spectrum of IDs studied from 2000 to 2020, an average of 226 IDs were endemic or potentially endemic to Ghana. The majority of the studies carried out to identify these IDs were done through community-based surveillance.ConclusionEstablishing community-based surveillance sites to leave no disease behind and also providing the required healthcare facilities to reinforce leaving no one behind will enhance the universal health security of Ghana as a precedent for SSA.

Transitioning to a sustainable energy system poses a massive challenge to communities, nations, and the global economy in the next decade and beyond. A growing portfolio of satellite data products is available to support this transition. Satellite data complement other information sources to provide a more complete picture of the global energy system, often with continuous spatial coverage over targeted areas or even the entire Earth. We find that satellite data are already being applied to a wide range of energy issues with varying information needs, from planning and operation of renewable energy projects, to tracking changing patterns in energy access and use, to monitoring environmental impacts and verifying the effectiveness of emissions reduction efforts. While satellite data could play a larger role throughout the policy and planning lifecycle, there are technical, social, and structural barriers to their increased use. We conclude with a discussion of opportunities for satellite data applications to energy and recommendations for research to maximize the value of satellite data for sustainable energy transitions.

In rural sub-Saharan Africa – the global poverty hotspot – the vast majority of cropland is rainfed only, resulting in reduced and unstable yields. Smallholder farmers account for 80% of agricultural production but they have limited access to relevant services to support both commercial operations and their livelihoods: more than two-thirds of rural dwellers have no access to electricity (crucial for crop irrigation, processing, and storage) and about 40% have no access to clean water. Previous research has analysed integrated technological and resource management approaches to tackle these overlapping development gaps. To finance and implement such transformations in resource-constrained settings, it is now crucial to understand the business and investment implications, also considering the strong regional population growth and the increasing frequency and intensity of climate extremes. Here, we lay out a research agenda that promotes the integration of multi-scale modelling excellence along the climate-water-renewable energy-agriculture-development Nexus and the creation of robust business models for private companies that can sustainably support private smallholder farmers of SSAin their effort to eradicate poverty and inequality. The proposed agenda is a cornerstone of the EC-H2020 project LEAP-RE RE4AFAGRI (“Renewable Energy for African Agriculture: Integrating Modelling Excellence and Robust Business Models”). In proposing the agenda, we highlight the importance of integrating energy access into the Nexus framework from both research and investment perspectives.

Irrespective of water resource abundance, agriculture in sub-Saharan Africa (SSA) is predominantly rainfed. Along with fertilization, irrigation could support smallholder farmers with stabilizing crop yields, increasing incomes, and achieving food security. A key barrier to irrigation uptake is inadequate rural electricity supply for pumping and distributing water, besides other infrastructure deficits. Here we devise a spatially explicit integrated modelling framework to show that over one third of unmet crop water requirements of 19 major crops in smallholder cropland of SSA could be supplied with standalone solar photovoltaic (PV) irrigation systems that can be paid back by farmers within 20 years. This accounts for 60 km ^3 yr ^−1 of blue irrigation water requirements distributed over 55 million ha of currently rainfed harvested area (about 40% of the total). Crucially, we identify 10 million ha with a profit potential >$100 ha ^−1 yr ^−1 . To finance such distributed small-scale infrastructure deployment and operation, we estimate an average discounted investment requirement of $3 billion yr ^−1 , generating potential profits of over $5 billion yr ^−1 from increased yields to the smallholder farmers, as well as significant food security and energy access co-benefits. We demonstrate the critical importance of business models and investment incentives, crop prices, and PV & battery costs in shaping the economic feasibility and profitability of solar irrigation. Yet, we find that without strong land and water resources management infrastructure and governance, a widespread deployment of solar pumps may drive an unsustainable exploitation of water sources and reduce environmental flows. Our analysis supports public and private stakeholders seeking to target investments along the water–energy–food–economy–sustainable development nexus.

In the framework of the European Optimised Microturbine Solar Power system (OMSoP) project, a novel energy system for solar electricity production was developed, based on the integration of the solar dish technology with Micro Gas Turbines (MGT). A pilot plant with a capacity of 5–7 kWe was realized and installed at the ENEA Casaccia site (Rome) and went under testing to validate the feasibility of the technology and improve the current design. The present work deals with the development of a quasi-state system model, built in the Engineering Equation Solver environment, composed of different modules that correspond to the main system components. The system model was used to define the optimal system parameters, to help the elaboration on an operational strategy to maximize the overall plant efficiency, and to guide the improvement of the single components in view of their optimised design. From the analysis it emerged that the system in design conditions is able to generate, in nominal conditions, 4.5 kWe instead of the expected 5 kWe due to the limitation of the stator current to 13 A, while maximum levels of 5.6 kW could be achieved by “overcharging” the high-speed generator up to 15 A and operating the MGT at the very high speed of 150 krpm. From the transient simulation of the demo system on an annual basis, the maximum average output power is 3.58 kWe. Regarding the cycle efficiency, the annual averaged value is about 17%, whereas the target value is 21%. The improvement of the generator only does not seem to significantly increase the power output on the annual basis (3.75 kWe vs. 3.58 kWe). Differently, the improvement of the solar dish, with the upgrade of the other system components, would significantly increase the system power output to around ~10 kWe.

Globally about 800 million people live without electricity at home, over two thirds of which are in sub-Saharan Africa. Planning electricity access infrastructure and allocating resources efficiently requires a careful assessment of the diverse energy needs across space, time, and sectors. Because of data scarcity, most country or regional-scale electrification planning studies have however assumed a spatio-temporally homogeneous (top-down) potential electricity demand. Poorly representing the heterogeneity in the potential electricity demand across space, time, and energy sectors can lead to inappropriate energy planning, inaccurate energy system sizing, and misleading cost assessments. Here we introduce M-LED, a Multi-sectoral Latent Electricity Demand geospatial data processing platform to estimate electricity demand in communities that live in energy poverty. The platform shows how big data and bottom-up energy modelling can be leveraged together to represent the potential electricity demand with high spatio-temporal and sectoral granularity. We apply the methodology to Kenya as a country-study and devote specific attention to the implications for water-energy-agriculture-development interlinkages. A more detailed representation of the demand-side in large-scale electrification planning tools bears a potential for improving energy planning and policy.

Hydro-climatic extremes can affect the reliability of electricity supply, in particular in countries that depend greatly on hydropower or cooling water and have a limited adaptive capacity. Assessments of the vulnerability of the power sector and of the impact of extreme events are thus crucial for decision-makers, and yet often they are severely constrained by data scarcity. Here, we introduce and validate an energy-climate-water framework linking remotely-sensed data from multiple satellite missions and instruments (TOPEX/POSEIDON. OSTM/Jason, VIIRS, MODIS, TMPA, AMSR-E) and field observations. The platform exploits random forests regression algorithms to mitigate data scarcity and predict river discharge variability when ungauged. The validated predictions are used to assess the impact of hydroclimatic extremes on hydropower reliability and on the final use of electricity in urban areas proxied by nighttime light radiance variation. We apply the framework to the case of Malawi for the periods 2000–2018 and 2012–2018 for hydrology and power, respectively. Our results highlight the significant impact of hydro-climatic variability and dry extremes on both the supply of electricity and its final use. We thus show that a modelling framework based on open-access data from satellites, machine learning algorithms, and regression analysis can mitigate data scarcity and improve the understanding of vulnerabilities. The proposed approach can support long-term infrastructure development monitoring and identify vulnerable populations, in particular under a changing climate.

Using remotely-sensed Suomi National Polar-orbiting Partnership (NPP)-VIIRS (Visible Infrared Imagery Radiometer Suite) night-time light (NTL) imagery between 2012 and 2016 and electricity consumption data from the IEA World Energy Balance database, we assemble a five-year panel dataset to evaluate if and to what extent NTL data are able to capture interannual changes in electricity consumption within different countries worldwide. We analyze the strength of the relationship both across World Bank income categories and between regional clusters, and we evaluate the heterogeneity of the link for different sectors of consumption. Our results show that interannual variation in nighttime light radiance is an effective proxy for predicting within-country changes in power consumption across all sectors, but only in lower-middle income countries. The result is robust to different econometric specifications. We discuss the key reasons behind this finding. The regions of Sub-Saharan Africa, Middle-East and North Africa, Latin America and the Caribbeans, and East Asia and the Pacific render a significant outcome, while changes in Europe, North America and South Asia are not successfully predicted by NTL. The designed methodological steps to process the raw data and the findings of the analysis improve the design and application of predictive models for electricity consumption based on NTL at different spatio-temporal scales.

No abstract

This paper intends to provide an epistemological reflection on the problem of the representability of space. The author's core argument is that the aporia of the representation of space is mainly revealed by geographical maps. More precisely, a territory cannot be described exhaustively by a map. Nevertheless, the author argues that the process of hominization starts precisely when human beings attempt to describe their ambient in order to identify dangers and opportunities. From a philosophical viewpoint, such an original aporia of space develops throughout the different perspectives on the concepts of topos and chora.

Multi-dimensional and overlapping barriers to wellbeing severely affect many areas in rural sub-Saharan Africa. In the region, more than 90% of cropland is rainfed, less than one third of households have electricity, almost 60% of the population reports food insecurity, and more than 35% of the population lives below the international poverty line. Climate change impacts on vulnerable systems with limited adaptive capacity and strong population growth are increasing the magnitude of these challenges, slowing and potentially reversing development. Thus, there is a strong need for multi-sector interventions across multiple levels, from national policies, to regional and river catchment-scale planning, to local planning and investment. To implement such actions, it is key not only to assess technological solutions and their investment needs, but also to appraise their feasibility and implementation potential (from both a policy and a financial point of view). Here, we implement a modelling platform (RE4AFAGRI platform), which soft-links bottom-up process-based water and energy demand and techno-economic infrastructure assessment models (WaterCROP, M-LED, OnSSET) into a multi-node, national Nexus-extended Integrated Assessment Model (MESSAGEix-Nexus) for supply and investment assessment. The results of our analysis shed light on the role of water and energy demand in the agricultural sector for jointly affecting infrastructure and investment requirements for achieving rural sustainable development objectives. We find that scenarios with increased ambition in expanding irrigation and agricultural productivity result in improved diffusion and economic feasibility of infrastructure to provide universal energy access while supporting productive uses of energy. Moreover, we conduct business model analysis to appraise the framework conditions and micro and macro determinants that can ensure feasibility of investment and uptake of small-scale infrastructure, crucial for rural development. Altogether, our research demonstrates how integrated modelling with an explicit focus on Nexus interlinkages can represent the enabling role and the business conditions for renewable energy input in agriculture to become a leverage of rural sustainable development. In turn, important policy and investment-relevant insights can be derived. [ABSTRACT FROM AUTHOR]

Achieving universal electricity access in sub-Saharan Africa – a milestone of SDG 7 – requires about $30bn annually until 2030 on the top of baseline investment. The private sector plays a key role in supplying these investment flows, given the governmental budgetary constraints. Yet, private players face numerous sources of risk in their infrastructure investment decisions. This risk is usually factored in using a discount rate. To allow for a more realistic evaluation of the role of the investment environment in financing energy access, here we introduce the Electricity Access Governance Index (EAGI), a composite index of energy sector regulatory quality, energy sector governance, and market risk. The index is implemented through a discount rate conversion into a bottom-up integrated electricity planning model (IMAGE-TIMER) to evaluate the role of different sources of risk for electrification investment dynamics. Our results show that the adoption of decentralised systems for achieving universal energy access requires governance and institutional reform to lower discount rates faced by companies and households and mobilise private finance. Failure to reform investment environments will likely hamper the uptake of decentralised systems even in areas where they would be the techno-economically least-cost electrification option, and thus likely leave many without electricity. • Private investment key for universal electricity access in sub-Saharan Africa • Regulatory, market and governance risks limit private energy investment. • Electricity Access Governance Index calculated to estimate private discount rates • Bottom-up electricity access modelling in SSA with IMAGE-TIMER model • Mass uptake of decentralised systems requires reducing private discount rates. [ABSTRACT FROM AUTHOR]

The study of elevation change using pre- and post-event raster grids obtained by airborne laser scans is central to many scientific areas, including geomorphology, landscape science and environmental science. Separating real elevation change from noise is a nontrivial task due to complex sources of measurement uncertainty causing errors to be spatially variable. For high-resolution grids, an additional hurdle is the excessive computational effort required by common statistical models for the spatial error. This paper introduces a screening methodology for change detection in high-resolution elevation difference grids using a composite likelihood regularization framework. We propose to model the spatial error by a sparse spatial autoregressive conditional heteroscedasticity process and obtain simultaneous detection of change location and change size estimation by maximizing a penalized composite likelihood objective. The methodology is implemented through a fast coordinate-wise maximization algorithm with computational complexity growing linearly with the grid size. Numerical studies on simulated and real data show satisfactory accuracy for both change detection and parameter estimation. [ABSTRACT FROM AUTHOR]

Solar power generation has been gaining worldwide increasing interest by virtue of its ability to meet both the growing energy needs and the increasing concerns on the carbon dioxide emissions. One of the most promising Concentrated Solar Power (CSP) technologies under development uses a parabolic dish to concentrate solar power into a focal point, raising thetemperature of a working fluid which is then used in a thermodynamic cycle to generate electricity. In the OMSoP project, funded by the European Commission, it is proposed to use a Brayton cycle in the form of a micro-gas turbine (MGT), which replaces the more conventional Stirling engine,with the aim of increasing the ratio of the electric power generated to the solar energy collected and improving the operability in relation to solar energy short time fluctuations. To achieve these objectives, research and development will be conducted in all aspects of the system leading to a full scale demonstrative plant to be located at the ENEA Casaccia Research Centre.The present work deals with the activities carried out so far by ENEA, which is principally involved in the development and experimental characterization of the dish component, and in the integration of the complete system, both in terms of modelling and realization. [ABSTRACT FROM AUTHOR]

Abstract: The dynamic simulation of a molten salt operated parabolic trough plant is presented. The simulated plant is a typical 9 MWe CSP unit comprising 10 loops with 8 collectors in series (in turn this can be a “module” of a larger CSP solar field) a Two Tanks TES and a molten salt Steam Generator. This type of plant represents a challenge due to the large extension and relative complication of piping network operating with molten salt, in comparison to e.g. a molten salt Tower. The simulation model, implemented in Isaac Dynamics environment, is able to represent the normal operation of the plant with circulating HTF at variable mass flow depending on input DNI, and night circulation at reduced mass flow. In addition, in molten salt operated CSP plants draining operations are of prominent importance, given that these operations should be operated within a due time (depending on boundary conditions) avoiding the possible freezing of the salt mixture. Moreover also emergency draining (e.g. as a consequence of faults in the pump and/or in heat trace equipment) should be analyzed. At this purpose, the model has been modified and utilized to represent also circuit draining, involving flow reversal, that normally requires use of specialized thermo-hydraulic codes like Relap (Reactor Loss of coolant Analysis Program). In conclusion, the paper shows how the simulation environment is able to represent most of the dynamics that affect the operation of a molten salt parabolic trough plant. [Copyright &y& Elsevier]

Copyright of Historical Review / Revue Historique is the property of Institute for Neohellenic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)