V ˙ Lamax: determining the optimal test duration for maximal lactate formation rate during all-out sprint cycle ergometry.

PURPOSE: This study aimed to ascertain the optimal test duration to elicit the highest maximal lactate formation rate ( V ˙ Lamax), whilst exploring the underpinning energetics, and identifying the optimal blood lactate sampling period. METHODS: Fifteen trained to well-trained males (age 27 ± 6 years; peak power: 1134 ± 174 W) participated in a randomised cross-over design completing three all-out sprint cycling tests of differing test durations (10, 15, and 30 s). Peak and mean power output (W and W.kg-1), oxygen uptake, and blood lactate concentrations were measured. V ˙ Lamax and energetic contributions (phosphagen, glycolytic, and oxidative) were determined using these parameters. RESULTS: The shortest test duration of 10 s elicited a significantly (p = 0.003; p < 0.001) higher V ˙ Lamax (0.86 ± 0.17 mmol.L-1.s-1; 95% CI 0.802-0.974) compared with both 15 s (0.68 ± 0.18 mmol.L-1.s-1; 95% CI 0.596-0.794) and 30 s (0.45 ± 0.07 mmol.L-1.s-1; 95% CI 0.410-0.487). Differences in V ˙ Lamax were associated with large effect sizes (d = 1.07, d = 3.15). We observed 81% of the PCr and 53% of the glycolytic work completed over the 30 s sprint duration was attained after 10 s. BLamaxpost were achieved at 5 ± 2 min (ttest 10 s), 6 ± 2 min (ttest 15 s), and 7 ± 2 min (ttest 30 s), respectively. CONCLUSION: Our findings demonstrated a 10 s test duration elicited the highest V ˙ Lamax. Furthermore, the 10 s test duration mitigated the influence of the oxidative metabolism during all-out cycling. The optimal sample time to determine peak blood lactate concentration following 10 s was 5 ± 2 min.

Strategic management of CO2: A scalable model for CCS in decarbonised societies.

In future decarbonised energy systems, residual carbon emissions require strategic planning and management. In environmental management, an evaluation of carbon removal considering local geographic frameworks is needed. This paper introduces a scalable and adaptable model for evaluating the economics and geography of future carbon capture and storage (CCS) configurations across geographical scales, covering capture, transport, and storage of carbon. The model is applied to the North Denmark Region, showing that future energy production carbon sources will be concentrated in Thisted and Jammerbugt, while industrial sources remain in Aalborg and Rebild municipalities. Carbon transport configurations, including truck, pipeline, and shipping are assessed, for the carbon to be stored in onshore and offshore geological storages. The regional scale findings suggest that pipelines and onshore storage provide the most economical configuration. However, a sensitivity study using a smaller geographical scope indicates potential for optimising carbon transport by evaluating both carbon volume and distance. The paper discusses how the model's flexibility and scalability enable the integration of alternate cost components, thereby supporting the calculation of the carbon repurposing potentials, including carbon capture, utilisation, and storage (CCUS) configurations.

Towards CO2 valorization in a multi remote renewable energy hub framework with uncertainty quantification.

In this paper, we propose a multi-RREH (Remote Renewable Energy Hub) based optimization framework. This framework allows a valorization of CO2 using carbon capture technologies. This valorization is grounded on the idea that CO2 gathered from the atmosphere or post combustion can be combined with hydrogen to produce synthetic methane. The hydrogen is obtained from water electrolysis using renewable energy. Such renewable energy is generated in RREH, which are locations where RE is cheap and abundant (e.g., solar PV in the Sahara Desert, or wind in Greenland). We instantiate our framework on a case study focusing on Belgium and 2 RREH, and we conduct a techno-economic analysis under uncertainty. This analysis highlights, among others, the interest in capturing CO2 via Post Combustion Carbon Capture (PCCC) rather than only through Direct Air Capture (DAC) for methane synthesis in RREH. By doing so, a notable reduction of 10% is observed in the total cost of the system under our reference scenario. In addition, we use our framework to derive a carbon price threshold above which carbon capture technologies may start playing a pivotal role in the decarbonation process of our industries.

Dynamic Pluronic F127 Crosslinking Enhancement of Biopolymeric Nanocomposites for Piezo-Triboelectric Single-Hybrid Nanogenerators and Self-Powered Sensors.

Biomechanical and nanomechanical energy harvesting systems have gained a wealth of interest, resulting in a plethora of research into the development of biopolymeric-based devices as sustainable alternatives. Piezoelectric, triboelectric, and hybrid nanogenerator devices for electrical applications are engineered and fabricated using innovative, sustainable, facile-approach flexible composite films with high performance based on bacterial cellulose and BaTiO3 , intrinsically and structurally enhanced by Pluronic F127, a micellar cross-linker. The voltage and current outputs of the modified versions with multiwalled carbon nanotube as a conductivity enhancer and post-poling effect are 38 V and 2.8 µA cm-2 , respectively. The multiconnective devices' power density can approach 10 µW cm-2 . The rectified output power is capable of charging capacitors, driving light-emitting diode lights, powering a digital watch and interfacing with a commercial microcontroller board to operate as a piezoresistive force sensor switch as a proof of concept. Magnetoelectric studies show that the composites have the potential to be incorporated into magnetoelectric systems. The biopolymeric composites prove to be desirable candidates for multifunctional energy harvesters and electronic devices.

Role of reinforcement learning for risk-based robust control of cyber-physical energy systems.

Critical infrastructures such as cyber-physical energy systems (CPS-E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS-E is critical to ensure societal well-being and economic prosperity. Automated control is key for real-time operations and may be mathematically cast as a sequential decision-making problem under uncertainty. Emergence of data-driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision-making problems for risk-based robust CPS-E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS-E applications, addressing the effect of risk preferences across multiple RL methods, and development of open-source domain-aware simulation environments for RL experimentation within a CPS-E context. This article systematically analyzes the applicability of four types of RL methods (model-free, model-based, hybrid model-free and model-based, and hierarchical) for risk-based robust CPS-E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk-averse, risk-neutral, and risk-seeking) through the development and application of an open-source simulation environment. Motivating numerical simulation examples include representative single-zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.

Unpacking the modeling process for energy policy making.

This article explores how the modeling of energy systems may lead to an undue closure of alternatives by generating an excess of certainty around some of the possible policy options. We retrospectively exemplify the problem with the case of the International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s. We discuss different methodologies for quality assessment that may help mitigate this issue, which include Numeral Unit Spread Assessment Pedigree (NUSAP), diagnostic diagrams, and sensitivity auditing (SAUD). We illustrate the potential of these reflexive modeling practices in energy policy-making with three additional cases: (i) the case of the energy system modeling environment (ESME) for the creation of UK energy policy; (ii) the negative emission technologies (NETs) uptake in integrated assessment models (IAMs); and (iii) the ecological footprint indicator. We encourage modelers to adopt these approaches to achieve more robust, defensible, and inclusive modeling activities in the field of energy research.

A Parameter Estimation of Photovoltaic Models Using a Boosting Flower Pollination Algorithm.

An accurate and reliable estimation of photovoltaic models holds immense significance within the realm of energy systems. In pursuit of this objective, a Boosting Flower Pollination Algorithm (BFPA) was introduced to facilitate the robust identification of photovoltaic model parameters and enhance the conversion efficiency of solar energy into electrical energy. The incorporation of a Gaussian distribution within the BFPA serves the dual purpose of conserving computational resources and ensuring solution stability. A population clustering strategy is implemented to steer individuals in the direction of favorable population evolution. Moreover, adaptive boundary handling strategies are deployed to mitigate the adverse effects of multiple individuals clustering near problem boundaries. To demonstrate the reliability and effectiveness of the BFPA, it is initially employed to extract unknown parameters from well-established single-diode, double-diode, and photovoltaic module models. In rigorous benchmarking against eight control methods, statistical tests affirm the substantial superiority of the BFPA over these controls. Furthermore, the BFPA successfully extracts model parameters from three distinct commercial photovoltaic cells operating under varying temperatures and light irradiances. A meticulous statistical analysis of the data underscores a high degree of consistency between simulated data generated by the BFPA and observed data. These successful outcomes underscore the potential of the BFPA as a promising approach in the field of photovoltaic modeling, offering substantial enhancements in both accuracy and reliability.

Climate crisis and recent developments in bio-based restoration of ecosystems.

Over the years, due to the climate crisis, sustainable economic growth and biodiversity protection have been increasingly promoted. Scientists, researchers, and experts in the field of sustainable development highlighted that bio-based restoration of ecosystems and responsible management of existing resources are needed to meet the needs of future generations. This paper discusses some of the latest developments in three main areas of sustainability, i.e., energy, water and environment, that emerged from the "16th Sustainable Development of Energy, Water and Environment Systems Conference - SDEWES 2021". The purpose of this introduction article is to briefly review the articles included in this Virtual Special Issue. As such, it acts as an editorial paper for the virtual special issue of the Journal of Environmental Management, dedicated to the SDEWES 2021 conference.

Designing for Care.

This article introduces Designing for Care (D4C), a distinctive approach to project management and technological design informed by Care Ethics. We propose to conceptualize "care" as both the foundational value of D4C and as its guiding mid-level principle. As a value, care provides moral grounding. As a principle, it equips D4C with moral guidance to enact a caring process. The latter is made of a set of concrete, and often recursive, caring practices. One of the key assumption of D4C is a relational ontology of individual and group identities, which fosters the actualization of caring practices as essentially relational and (often) reciprocal. Moreover, D4C adopts the "ecological turn" in CE and stresses the ecological situatedness and impact of concrete projects, envisioning an extension of caring from intra-species to inter-species relations. We argue that care and caring can influence directly some of the phases and practices within the management of (energy) projects and the design of sociotechnical (energy) artefacts and systems. When issues related to "value change" emerge as problematic (e.g., values trade-offs, conflicts), the mid-level guiding principle of care helps evaluate and prioritize different values at stake within specific projects. Although there may be several actors and stakeholders involved in project management and technological design, here we will focus on the professionals in charge of imagining, designing, and carrying out these processes (i.e., project managers, designers, engineers). We suggest that adopting D4C would improve their ability to capture and assess stakeholders' values, critically reflect on and evaluate their own values, and judge which values prioritize. Although D4C may be adaptable to different fields and design contexts, we recommend its use especially within small and medium-scale (energy) projects. To show the benefits of adopting it, we envisage the application of D4C within the project management and the technological design of a community battery. The adoption of D4C can have multiple positive effects: transforming the mentality and practice of managing a project and designing technologies; enhancing caring relationships between managers, designers, and users as well as among users; achieving better communication, more inclusive participation, and more just decision-making. This is an initial attempt to articulate the structure and the procedural character of D4C. The application of D4C in a concrete project is needed to assess its actual impact, benefits, and limitations.

Design, modeling and control of a hybrid grid-connected photovoltaic-wind system for the region of Adrar, Algeria.

The use of fossil energy for electricity production is an evident source of pollution, global warming and climate change. Consequently, researchers have been working to shift toward sustainable and clean energy by exploiting renewable an environmentally friendly resources such as wind and solar energies. On the other hand, energy security can only be achieved by considering multiple resources. Large-scale renewable energy power plants are a key solution for diversifying the total energy mix and ensuring energy security. This paper presents a contribution to diversify the energy mix in Algeria and help mitigate power shortages and improve grid performance. In particular, the paper aims at designing and modeling a large-scale hybrid photovoltaic-wind system that is grid connected. An innovative control approach using improved particle swarm optimized PI controllers is proposed to control the hybrid system and generate the maximum power from the available wind and solar energy resources. Furthermore, economic, environmental and feasibility studies of the project were conducted using HOMER software to assess the viability of the system and its contribution to reduce greenhouse gas emissions.

How Well Do Emission Factors Approximate Emission Changes from Electricity System Models?

Multiple forms of marginal and average emission factors have been developed to estimate the carbon emissions of adding technologies, such as electric vehicles or solar panels, to the electricity grid. Different methods can produce very different results and conclusions, indicating that choosing between methods is not trivial. Researchers would therefore like to know how well these emission factors can approximate emission changes in the actual power grid. This question remains unanswered because of the difficulty in characterizing the accuracy of these methods. Ideally, estimates would be compared to measured emission changes, but it is implausible to measure these changes on an actual grid. Instead, we propose testing these emission factor methods in a controlled environment, using an electricity system dispatch model as a reference for comparison. We find that average emission factors have lower accuracy when estimating emissions from demand shifts and observe the same for demand-based marginal emission factors at an hourly resolution. In contrast, incremental and thermal marginal emission factors can reproduce the emission changes of a power grid model under many testing conditions and scenarios. We also find that easier-to-use annual time averages offer similar results to finer time resolutions for marginal and average factors, except demand-based.

Policy and political perceptions of risk: the challenges to building resilient energy systems.

Decisions relating to energy systems resilience must take into account a range of environmental and societal transitions together with an array of future threats. These must be assessed broadly and systematically but also must consider how risks and vulnerabilities are linked, and that small events can cascade across a system and between systems to escalate into large-scale collapse. Developing resilience involves not only identifying such threats and potential points of failure but acting upon them with an appropriate level of future planning. Decision-making in complex systems is often affected by cognitive biases and runs into contestation and obstacles of politics, policy complexities and differing views of potential futures. This paper discusses the challenges to building resilient systems for the future, given the inevitable biases and differing risk perspectives of decision-makers at all levels that often confound expert analysis. This article is part of the theme issue 'Developing resilient energy systems'.

Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria.

Complex energy monitoring and control systems have been widely studied as the related topics include different approaches, advanced sensors, and technologies applied to a strongly varying amount of application fields. This paper is a systematic review of what has been done regarding energy metering system issues about (i) sensors, (ii) the choice of their technology and their characterization depending on the application fields, (iii) advanced measurement approaches and methodologies, and (iv) the setup of energy Key Performance Indicators (KPIs). The paper provides models about KPI estimation, by highlighting design criteria of complex energy networks. The proposed study is carried out to give useful elements to build models and to simulate in detail energy systems for performance prediction purposes. Some examples of energy complex KPIs based on the integration of the Artificial Intelligence (AI) concept and on basic KPIs or variables are provided in order to define innovative formulation criteria depending on the application field. The proposed examples highlight how modeling a complex KPI as a function of basic variables or KPIs is possible, by means of graph models of architectures.

A multi-disciplinary approach to estimate the medium-term impact of COVID-19 on transport and energy: A case study for Italy.

The aim of this paper is to estimate the potential impacts of different COVID-19 scenarios on the Italian energy sector through 2030, with a specific focus on transport and industry. The analysis takes a multi-disciplinary approach to properly consider the complex interactions of sectors across Italy. This approach includes the assessment of economic conditions using macroeconomic and input-output models, modelling the evolution of the energy system using an energy and transport model, and forecasting the reaction of travel demand and modal choice using econometric models and expert interviews. Results show that the effect of COVID-19 pandemic may lead to mid-term effects on energy consumption. The medium scenario, which assumes a stop of the emergency by the end of 2021, shows that energy-related emissions remain 10% lower than the baseline in the industry sector and 6% lower in the transport sector by 2030, when compared with a pre-COVID trend. Policy recommendations to support a green recovery are discussed in light of the results.

Sustainable development in period of climate crisis.

The ongoing process of climate change has shown that sustainable development of humankind is a necessity. Existing resources need to be used in a form of a circular economy, and no more in a linear economy as has been the case until now. Resources need to be better managed to meet the needs of future generations. Therefore, energy, water and environment systems need to be integrated in order to slow down their overexploitation. This paper discusses some of the latest developments in three main areas of sustainability, i.e., energy, water and environment, that emerged from the four "Sustainable Development of Energy, Water and Environment Systems" (SDEWES) Conferences that took place in 2020. The purpose of this review introduction article is to provide a brief introduction to the field and the articles included in this Virtual Special Issue. As such, it acts as an editorial paper for the virtual special issue of the Journal of Environmental Management, dedicated to the SDEWES 2020 conferences.

Parity and the Resolution of Value Conflicts in Design.

Recent developments in theories for responsible innovation have focused on the importance of actively accounting for values in our technological designs. Leading among these theories is that of Value Sensitive Design (VSD) which attempts to guide the design process on the basis of evaluative analysis. However, values often come into conflict and VSD has been criticized for not providing a proper method to resolve such inevitable conflicts. This paper examines three such methods and argues that although each has its merits, they all fail to account for a common source of value conflicts known as value incommensurability. Drawing on literature from the field of axiology, this paper argues that by incorporating the evaluative relation of 'parity' each of these three methods, and the VSD framework in general, will be able to properly understand the relation which holds between conflicting design options stemming from the incommensurable of values and be able to guide designers in making rational decision in the face of such conflicts.

On incorporating forecasts into linear state space model Markov decision processes.

Weather forecast information will very likely find increasing application in the control of future energy systems. In this paper, we introduce an augmented state space model formulation with linear dynamics, within which one can incorporate forecast information that is dynamically revealed alongside the evolution of the underlying state variable. We use the martingale model for forecast evolution (MMFE) to enforce the necessary consistency properties that must govern the joint evolution of forecasts with the underlying state. The formulation also generates jointly Markovian dynamics that give rise to Markov decision processes (MDPs) that remain computationally tractable. This paper is the first to enforce MMFE consistency requirements within an MDP formulation that preserves tractability. This article is part of the theme issue 'The mathematics of energy systems'.

Near-term deployment of carbon capture and sequestration from biorefineries in the United States.

Capture and permanent geologic sequestration of biogenic CO2 emissions may provide critical flexibility in ambitious climate change mitigation. However, most bioenergy with carbon capture and sequestration (BECCS) technologies are technically immature or commercially unavailable. Here, we evaluate low-cost, commercially ready CO2 capture opportunities for existing ethanol biorefineries in the United States. The analysis combines process engineering, spatial optimization, and lifecycle assessment to consider the technical, economic, and institutional feasibility of near-term carbon capture and sequestration (CCS). Our modeling framework evaluates least cost source-sink relationships and aggregation opportunities for pipeline transport, which can cost-effectively transport small CO2 volumes to suitable sequestration sites; 216 existing US biorefineries emit 45 Mt CO2 annually from fermentation, of which 60% could be captured and compressed for pipeline transport for under $25/tCO2 A sequestration credit, analogous to existing CCS tax credits, of $60/tCO2 could incent 30 Mt of sequestration and 6,900 km of pipeline infrastructure across the United States. Similarly, a carbon abatement credit, analogous to existing tradeable CO2 credits, of $90/tCO2 can incent 38 Mt of abatement. Aggregation of CO2 sources enables cost-effective long-distance pipeline transport to distant sequestration sites. Financial incentives under the low-carbon fuel standard in California and recent revisions to existing federal tax credits suggest a substantial near-term opportunity to permanently sequester biogenic CO2 This financial opportunity could catalyze the growth of carbon capture, transport, and sequestration; improve the lifecycle impacts of conventional biofuels; support development of carbon-negative fuels; and help fulfill the mandates of low-carbon fuel policies across the United States.

Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review.

This paper summarises a literature review on the applications of Internet of Things (IoT) with the aim of enhancing building energy use and reducing greenhouse gas emissions (GHGs). A detailed assessment of contemporary practical reviews and works was conducted to understand how different IoT systems and technologies are being developed to increase energy efficiencies in both residential and commercial buildings. Most of the reviewed works were invariably related to the dilemma of efficient heating systems in buildings. Several features of the central components of IoT, namely, the hardware and software needed for building controls, are analysed. Common design factors across the many IoT systems comprise the selection of sensors and actuators and their powering techniques, control strategies for collecting information and activating appliances, monitoring of actual data to forecast prospect energy consumption and communication methods amongst IoT components. Some building energy applications using IoT are provided. It was found that each application presented has the potential for significant energy reduction and user comfort improvement. This is confirmed in two case studies summarised, which report the energy savings resulting from implementing IoT systems. Results revealed that a few elements are user-specific that need to be considered in the decision processes. Last, based on the studies reviewed, a few aspects of prospective research were recommended.

Green development challenges within the environmental management framework.

Green development of energy, water and environment systems is essential as these three systems represent the basic life needs of humankind. Therefore, environmental problems arising from each of these three systems need to be carefully addressed to preserve the energy, water and environment resources for future generations. This paper discusses some of the latest developments in three main areas of sustainability themes, namely energy, water and environment, that emerged from the 14th Sustainable Development of Energy, Water and Environment Systems (SDEWES) Conference held in 2019. As such, it acts as an editorial paper for the virtual special issue of the Journal of Environmental Management, dedicated to the SDEWES 2019 conference.