Abstract

In recent years, floating photovoltaic (FPV) systems have emerged as a promising technology for generating renewable energy using the surface of water bodies such as reservoirs, lakes, and oceans. FPV systems offer several advantages over traditional land-based solar arrays, including increased land-use efficiency, reduced water evaporation, and improved cooling and maintenance. However, like all solar power systems, FPVs are subject to variability and intermittency due to changes in weather, seasons, and time of day. The environmental impact is discussed along with the deployment consideration and the feasibility for a better understanding of the system. Challenges associated with this are addressed by progressed research suggesting the integration of FPVs with various energy storage and hybrid systems. The most promising areas researched in this paper look at hybrid FPV hydropower plants (HPP), in parts of the world experiencing droughts HPP is not working to its optimum capacity. A review of available literature has been conducted on the topic of offshore and onshore floating solar electricity generation using floating solar photovoltaics to identify the challenges and opportunities presented. This work looks at a variety of other hybrid FPV energy sources with varying technology readiness levels. This paper concludes with the possibility of integrating different renewable technologies with existing FPVs and highlights the boons of doing so with some examples. Ultimately, current as well as future perspectives have been provided which consolidate the current research being done and give recommendations for future research work.

Introduction

One of the biggest challenges that is faced by the world is global warming due to which both humans and the planet are suffering as a whole, this is directly linked to the burning of the fossil fuels that we rely on to facilitate our daily life. The Intergovernmental Panel on Climate Change (IPCC) set out the impacts of global warming 1.5 °C above preindustrial levels [1]. We are not currently on course to limit 21st-century global warming to under 1.5 °C or even 2 °C [2]. Another key issue is the lack of clean electrification for a vast population which is hampering the development of the planet in tackling the climate change issue as many rely on the burning of fossil fuel for various daily requirements from generating electricity to cooking. It has been estimated that about 675 million people are still forced to live in the dark most of them belong to sub-Saharan Africa according to 2021 data. Though there has been an increase in the rate of access to electricity from 87% in 2015 to 91% in 2019, this has provided electricity to nearly 800 million people [3]. This is where solar PV can play a substantial role, solar PV has the benefit of being a renewable energy source, producing electricity from solar irradiance without any greenhouse emission [4].

However, there are challenges that must be addressed in order to fully realize the potential of solar energy and traditional photovoltaics [5]. These challenges include land usage, intermittency, storage, and integration into existing energy grids. One promising and upcoming alternative to traditional land-based photovoltaics is Floating Photovoltaics (FPV) or flotavoltaics [6]. The majority of renewable energy sources, such as biomass, solar, and others, take considerable footprint areas to generate electricity on a larger scale, which restricts the use of land for agriculture [[7], [8], [9]]. This sparked the discussion over whether land should be used for food production or energy production [10,11], encouraging research into offshore renewable technologies [12], and led to the creation of the floating photovoltaic (PV) array concept for the production of commercial electricity [13]. FPV technology is a concept in which solar panels are placed on platforms that float on water bodies such as natural lakes, man-made reservoirs, and the seas and oceans [14]. Fig. 1 shows a typical standalone floating photovoltaic system with all the components including an inverter, pontoons, solar panels, and cables connected to the grid.

Conclusion

This review article has examined the current state of research on the integration of floating photovoltaics with different storage and hybrid systems, including batteries, pumped hydro storage, compressed air energy storage, hydrogen storage and mixed energy storage options as well as the hybrid systems of FPV wind, FPV aquaculture, and FPV hydrogen production. The findings suggest that such systems have the potential to significantly increase the efficiency and reliability of renewable energy generation, as well as provide additional flexibility in managing electricity supply and demand.

FPV has many benefits over ground-mounted such as reduced land costs, increased efficiency, reduced soiling, reduced shading, reduced evaporation, and lower visual impact. Hybrid FPV has all of these benefits while being able to reduce its lifetime costs when paired with another technology a reduction in O&M costs, grid infrastructure and cable pooling. Overall, hybrid FPV has all the benefits of FPV plus reduced costs, while enhancing another technology. While FPV is set to rise this will initially be in land first with offshore following, the priority should be covering HPP first due to its complementary nature and benefits for both technologies. Countries, which have HPP reservoirs, should be looking into all the benefits of combining FPV with it. As FPV becomes more common it will be seen moving offshore, as the offshore wind increases it creates the opportunity for FPV to become a hybrid system.

Abstract

     Economic development and the comfort-loving nature of human beings in recent years have resulted in increased energy demand. Since energy resources are scarce and should be preserved for future generations, optimizing energy systems is ideal. Still, due to the complexity of integrated energy systems, such a feat is by no means easy. Here is where computer-aided decision-making can be very game-changing in determining the optimum point for supply and demand. The concept of artificial intelligence (AI) and machine learning (ML) was born in the twentieth century to enable computers to simulate humans’ learning and decision-making capabilities. Since then, data mining and artificial intelligence have become increasingly essential areas in many different research fields. Naturally, the energy section is one area where artificial intelligence and machine learning can be very beneficial. This paper uses the VOSviewer software to investigate and review the usage of artificial intelligence and machine learning in the energy field and proposes promising yet neglected or unexplored areas in which these concepts can be used. To achieve this, the 2000 most recent papers in addition to the 2000 most cited ones in different energy-related keywords were studied and their relationship to AI- and ML-related keywords was visualized. The results revealed different research trends in recent years from the basic to more cutting-edge topics and revealed many promising areas that are yet to be explored. Results also showed that from the commercial aspect, patents submitted for artificial intelligence and machine learning in energy-related areas had a sharp increase.

Introduction

     Economic development and increasing welfare are always entangled with the rising consumption of energy resources. Increasing energy generation as the default answer to how to cope with this additive energy consumption may not be the best answer. From an energy justice perspective, it’s not acceptable to deplete energy reservoirs that belong to the next generations [1]. Although retrofitting existing equipment and minimizing energy usage by combining (or cascading) multiple systems for increased efficiency may be an answer to this challenge, increasing efficiency may not be the ideal solution. It’s valid that efficiency leads to lower consumption, but it should be noted that many of these systems might also be able to be turned off or operate on lower loads. So, a more dynamic approach may be a better solution.

     Artificial intelligence and machine learning are relatively new concepts in energy that can be promising tools to operate systems by implementing past and predicted futures to increase the effectiveness of systems.

Conclusion

     The concept of artificial intelligence (AI) and machine learning (ML) is for computers to simulate humans’ learning and decision-making capabilities. With advances in computing systems, AI and ML have become increasingly important areas in many different branches of science and industry. The energy section is also one of the areas that can benefit from AI and ML. To investigate the current standing point of AI and ML in energy-related areas, we used VOSviewer software to investigate and review the relatively new usage of AI and ML in the energy field and propose promising or neglected areas in which these concepts can be used.

     The results showed that from 2000 AI gains an increasing focus, especially after 2014 when the number of articles in ML skyrocketed. And in 6 years become 10 times more compared to 2014. Although there have been many papers in different fields of energy that introduced new usages of AI and ML in that section, due to the vastness of energy usage and respected fields of it, obviously, in no way the available articles could cover all these areas.

Abstract

     Significant amounts of data are collected in buildings. While these data have great potential for maximizing the energy efficiency of buildings in general, only a small portion of the data are accessible to researchers, government, and industry for analyses. Concerns about privacy are one of the major barriers prohibiting access to these data. Privacy preservation techniques are generally applied to this problem not only to preserve underlying privacy but also to improve the usefulness of data. Among various privacy preserving techniques, differential privacy has become one of the more popular solutions since its introduction in 2006. Differential privacy is a mathematical measure for protecting privacy so that one’s privacy cannot be incurred by participating in a database. Although significant research improvements have been made for more than a decade, applying differential privacy to data collected in buildings is still an immature field of study. Because implementing differential privacy on a certain use case is not straightforward and can be achieved with various configurations, it is important to understand variation of configurations with different use cases around data collected from buildings. This literature review aims to introduce what has been done to implement differential privacy in data collected in buildings, and to discuss associated challenges and potential future research opportunities.

Introduction

     Background: The residential and commercial buildings sector accounted for 20% of global energy consumption in 2018 [1], and a much higher 39% in the United States in 2019 [2]. Many research efforts are focused on reducing the energy consumption, increasing the energy efficiency of buildings, and reducing carbon emission. Buildings can also provide services to utilities to enable deeper penetration of renewable energy on the grid. One of the major pathways to achieve these goals is to first understand the operational performance of buildings with collected data. The performance reflected in these data either informs the reality of existing buildings, reflects the effects of short- or long-term events and improvements in buildings, or aids the development of innovative approaches for maximizing building energy efficiency. The focus of this literature review aligns with the last use case, where extracting insights from the data greatly benefits further research.

     Definition of privacy: The big data era saw a massive increase in data collection as well as unprecedented privacy threats. It is worth noting what privacy means in this field of study because the definition of privacy has evolved over the years. An early definition of privacy started with “the right to be left alone” [3]; however, social situations have evolved since then, as mentioned by Hayashi [4] who notes “self-determination such as sexual orientation or contraception is admitted as one of the privacy elements.” Similar evolution also occurred and continues to occur in data collected from buildings. Granular data, such as smart meter data collected from advanced metering infrastructure (AMI) in sub-hourly (e.g., down to 15 min) intervals, has become available (owned by many utility companies around the world), but research has also shown that these data include various types of private information given the strong correlation of occupants (or building operators) with building energy consumption. Thus, there is a need to define privacy, especially for data collected in buildings. While Warren and Brandeis [3], Prosser [5], and Clark [6] defined privacy in general, Hayashi [4], Begum and Nausheen [7], and Jain et al. [8] considered privacy in terms of big data, and Pillitteri and Brewer [9] specifically focused on the context of smart grids. Pillitteri and Brewer [9] translated the privacy classifications defined by Clark [6] in the context of the smart grid application as shown in Table 1.This literature review follows the definition of privacy depicted by Pillitteri and Brewer [9].

Conclusions

     This article reviewed previous literature to provide building researchers with the basics of differential privacy implementation around data collected in buildings. Although the specific scope of this article is still in an early research stage, fast-growing data collection in buildings along with the breadth and scale of data available necessitated an examination of privacy preserving research for differential privacy. Because the topic is also not common in the representative building research journals, this study aimed to provide a greater level of detail for readers who might never have heard of differential privacy. To provide relevant context around privacy preservation in data, this literature review presented 1) privacy risks associated with data collected in buildings, 2) use cases that could be supported by analyses from these data, and 3) reviews of differential privacy implementation. The findings from the literature emphasize not only technical development but also engagement from stakeholders and policymakers in properly configuring differential privacy and protecting underlying privacy in data collected in buildings.

China is the largest power producer and consumer and has the largest installed capacity of wind turbines (WTs) worldwide. In the last two decades, China’s installed capacity of WTs has exploded, significantly impacting its wind power (WP) industry and promoting the development of the industry globally. This paper examines China as one part of the world and elucidates its role, status, and impact on global WP development from a global perspective. Research indicates that China has become the global leader in WP, its WP development has greatly promoted the growth rate of global WP, and it has become an indispensable force in global WP development. However, the comprehensive capability of China’s WP generation is not only lower than that of other major WP countries but also lower than the global average. China’s WTs have low global recognition and are difficult to export. In addition, the proportion of WP in China’s power structure is lower than that of most other major WP countries. Fortunately, these gaps between China and other major WP countries are gradually narrowing. The goal of this paper is to help researchers and investors better understand China’s situation and to provide a reference for the rational investment in and development of China’s WP.

Introduction

Many countries have signed agreements to address global climate change, such as the Kyoto Protocol and the Paris Agreement, and these countries are striving to fulfill their commitments [1,2]. There is consensus on the need to reduce fossil energy and to accelerate the development and utilization of renewable energy among most countries, and they are engaging in concerted action [3,4]. As a source of clean energy with high storage, no pollution, and using mature technology, many countries are seeking to utilize wind energy [5] and consider wind power (WP) to be a promising energy [6]. China, a major energy-consuming carbon emission country, is one of many countries that have installed wind turbines (WTs) (as shown in Fig. 1). The Chinese government is actively supporting the development of clean energy through laws, regulations, finance, and taxation. With the strong support of the Chinese government, the country’s WP has experienced explosive growth [7,8]. In 2018, the cumulative capacity of WP installed in China accounted for a record high of 35.7% of the total globally, ranking first. China’s WP development has attracted worldwide attention. To date, a high volume of literature has reported on country’s WP status, development and policies.

Graphical abstract

1. Introduction

2. Energy efficiency evaluation methods

3. Discussion

4. Development in the future

5. Conclusion

Declaration of interest statement

Acknowledgement

References

Abstract

As the most effective indicator for energy saving and emission reduction, energy efficiency evaluation is widely used in complex petrochemical industries. It is nowadays common to combine traditional mechanism methods based on momentum transport, energy transport, quality transport (TT) and reaction engineering (RG) (TT-RG), with data-driven artificial intelligence methods. Using the combined method to achieve production optimization and energy saving by analyzing the evaluation indicator of energy efficiency has gradually become an important part in complex petrochemical industries. Therefore, this paper introduced the main methods and the latest research results of energy efficiency evaluation of complex petrochemical industries. These methods are mainly divided into three parts, including the mechanism methods based on TT-RG, the data-driven artificial intelligence methods, and the hybrid methods combining the mechanism and the data-driven. Then, different methods are compared and described in detail. Moreover, the best method for evaluating the energy efficiency can be found to provide theoretical guidance for energy saving and emission reduction of complex petrochemical industries. Finally, the future development direction for energy efficiency evaluation in complex petrochemical industries is given.

Introduction

With the continuous development of economy, China has become the second largest economy nation in the world. As a pillar of modern economy, energy has greatly promoted the economy development. However, problems of environmental pollution and high energy consumption become more and more serious. The comparison of carbon dioxide (CO2) emissions and energy consumption between China and other countries (or regions) in 2018 are shown in Fig. 1 and Fig. 2, respectively, where the data are from the World Energy Statistics Yearbook. It can be seen from Figs. 1 and 2 that improving the energy efficiency and reducing pollutants emission have been in a highly strategic position for the sustainable development of all countries in the world [1]. In the process of energy utilization, the ratio of the working energy to the actual consumed energy is the energy efficiency. How to improve the energy efficiency is the main goal of the energy development. The energy efficiency evaluation can provide the most effective indicator for this goal, which explores ways to improve the energy efficiency by establishing mathematical models of production processes, or to reach the same or more output with less energy input [2]. The complex petrochemical industry in China is a high energy consumption industry with large scales. Therefore, by evaluating the energy efficiency, finding efficient ways of energy utilization, and then reducing pollutants emission such as CO2 are hot research topics for energy saving and emission reduction [3]. At the same time, it has far-reaching significance for the sustainable development of economy and society [4].

Wind energy as one of the renewable energies is serving as an indispensable role in generating new electric power. The worldwide installation of wind farms has considerably increased recently. To extract more wind resources, multi-megawatt wind turbines are usually designed and constructed with large rotors and slender tower. These flexible structures are susceptible to external dynamic excitations such as wind, wave and seismic loads. The excessive vibrations can compromise the wind energy conversion, lead to the structural fatigue damage and even result in the catastrophic failure of wind turbines in harsh environmental conditions. Various control devices have been proposed and used to mitigate the unwanted vibrations of wind turbines to enhance their safety and serviceability. This paper aims to provide a state-of-the-art review of the current vibration control techniques and their applications to wind turbines. Firstly, the widely used control strategies in engineering structures are briefly introduced. Their applications to suppress the adverse vibrations of the structural components of wind turbines, mainly the tower and blades, are then reviewed and discussed in detail. It can be concluded that the vibration mitigation of wind turbines is very challenging due to the fact that the dynamic behaviours of wind turbines are very complicated, which are associated with the aerodynamics, rotation of the blades, interaction between the tower and rotating blades, and soil-structure interaction, etc. Moreover, it is a challenge to straightforwardly use many of the conventional control devices because of the limited spaces in the tower and blades.

Introduction

Wind energy as one of the renewable energies plays an attractive means to generate new electric power. Wind farms have experienced rapid growth and expansion recently especially in the last decade. As reported by the Global Wind Energy Council, the worldwide installations of wind turbines reached about 539 GW at the end of 2017, with an increase of 2155% compared to that in 2011 [1]. Moreover, numbers of wind turbines are erected far away from coastlines to more efficiently extract the huge wind resources. The offshore wind power reached a historical record in 2017 with 4334 MW new installations, and the cumulative capacity was 18,814 MW [1]. Fig. 1 shows the global annual and cumulative installations of wind turbines from 2001 to 2017, and the total installations of offshore wind farms in 2011–2017 are shown in Fig. 2. It can be seen that though the offshore wind capacity in 2017 was only about 3.5% of the total wind capacity, it is growing very quickly and with great prospects. Wind turbines can be broadly grouped into the horizontal and vertical axis categories depending on the orientation of the rotation axis of the blades. Fig. 3 shows the typical geometrical configurations of the horizontal and vertical axis wind turbines (HAWTs and VAWTs) [2]. As implied by the name, the blades of HAWTs rotate about the horizontal axis, and they are perpendicular to the direction of wind flow, while the blades of VAWTs rotate vertically and they are not required to face the wind. As the HAWTs dominate the utility-scale wind turbine market currently, only the research works on the vibration control of HAWTs are reviewed and discussed herein.

Wind conditions in the built environment are complex in nature and characterized by lower wind speeds and higher turbulence due to the presence of obstructions. A growing body of literature and research/testing activities related to performance evaluation of small wind turbines (SWTs) in urban wind conditions have inferred that urban installed SWTs are subjected to higher level of turbulence and face dynamic loading that impedes their performance, and reduces fatigue life. This paper reviews the diverse studies conducted on the application of SWT technology in the built environment to understand the characteristics of inflowing wind, their performance and identify the gaps in the knowledge. This review paper also investigates the extent to which the international design standard for SWTs, IEC 41400-2, is valid for urban installations. The findings from this review show that the wind models incorporated in IEC 61400-2 is not suitable for installation of SWTs in the built environment. The authors recommend a thorough study through measured data and characterization of urban wind to make current standard inclusive of wind classes that characterize urban wind conditions. Thus, SWT design can be made more consistent with urban wind conditions and their performance and reliability can be assured.

Introduction

With rapid growth in population, the global energy consumption is projected to increase by 56% between 2010 and 2040 [1]. In 2015, fossil fuel (coal, petroleum and natural gas) accounted for 78.4% of global final energy consumption, with the share of renewables (modern and traditional) and nuclear power at 19.3% and 2.3% respectively[2]. Worldwide, the share of renewable energy will increase to address global climate change by 2030. Modern renewable energy is being used increasingly in four distinct markets: power generation, heating and cooling, transport fuels, and rural/off-grid energy services [3]. In recent years, progress has been made in increasing the renewable energy share in the power 103 sector particularly in the wind, solar photovoltaic (PV) and hydropower sectors [4]. In 2014, 104 the total renewable power capacity was 712 GW out of which 370 GW was from wind energy through utility scale wind turbines [5]. The total global capacity of wind energy reached 432 106 GW at the end of 2015, representing a cumulative growth of 17%. Although large-scale generation using wind energy has taken shape, there are some problems for their sustainable development. For example, the main barriers of large scale on-shore wind farm are available sites, impact of grid power quality, public acceptability and losses in transmission and distribution of electricity to the consumers [6]. One alternative that reduces some of these barriers is the application of small wind turbine (SWT) technology.

1- Introduction

2- MPPT selection criteria

3- MPPT techniques

4- Conclusion

References

Abstract

This paper presents a comprehensive overview on various maximum power point tracking (MPPT) techniques, which have been recently designed, simulated and/or experimentally validated in the PV literature. The primary goal of each MPPT technique is to optimize the output of shaded/unshaded photovoltaic (PV) array under static and dynamic weather conditions. Though each MPPT technique has its own pros and cons, an optimized MPPT technique is characterized in many aspects like hardware and software simplicity, implementation, cost effectiveness, sensors required, popularity, accuracy and convergence speed. In this paper the rating of various MPPT methods has been done based on the benchmark P&O method. The rating criteria is separately calculated for the techniques that are capable to work in full-sun and partial shading conditions. A rule based table is set to evaluate the MPPT against the algorithm’s complexity, hardware implementation, tracking speed, and steady state accuracy or detection of global maximum. Moreover, special consideration has been given to bio-inspired MPPT algorithms. The bio-inspired algorithms are compared side by side with their specific application in PV system. A tree diagram is also designed to see the emergence of partial shading algorithms over a period of time. The traits presented in this paper are novel and provide bottom-line for the researchers to select and implement an appropriate MPPT technique.

Introduction

The worldwide energy demand is increasing on a faster pace than the energy generation because of enhanced industrialization, growing population and improved living standards. According to international energy agency (IEA) reports, the energy consumption of world will increase by 44% from 2006 to 2030 [1]. In the present scenario, the fossil fuels such as oil, gas and coal are depleting with every passing day, which are the primary energy drivers and pollution contributors [2–4]. Therefore, the world is moving to new energy resources, which are promising, socially compatible, renewable, and sustainable. These sources are solar, wind, biomass, small hydro, geothermal, and ocean tidal/thermal. Photovoltaic (PV) is a prominent renewable energy source due to its sustainability, local availability, environmental friendly nature, simple technology, increasing cost effectiveness and less balance of systems. In photovoltaic, the sunlight is converted into direct current using semiconductor materials without any moving parts and carbon emissions. For the past decades, the power generation from PV systems have showed extensive penetration in commercial and domestic facilities. PV has now turned into the third most essential renewable energy source after hydro and wind. At present, more than 100 nations are utilizing ground mounted or building integrated PV systems for domestic, commercial and grid applications. According to IEA-PVPS (international energy agency for photovoltaic power systems), the worldwide installed PV capacity has expanded to 227 GW in March 2015 and is expected to reach up to 500 GW by the end of 2020 [5,6]. The current-voltage (I-V) curve of PV array relies on irradiance and temperature conditions. When the irradiance increases with constant temperature, the photovoltaic current also increases in direct proportion with negligible effect on PV voltage. Similarly, if the temperature increases with constant irradiance, the PV voltage decreases substantially while the PV current increases slightly. Under uniform condition, PV array exhibits unique maximum power point (MPP) on its I-V curve. The tracking mechanism in PV system, known as maximum power point tracking (MPPT) technique, is inevitable to search the MPP as innate characteristic of PV varies non-linearly with irradiance, temperature, and load.

1- Introduction

2- Empirical strategy

3- Data and methodology

4- Results and discussion

5- Conclusions and policy implications

References

Abstract

Gross domestic product (GDP) has been inappropriately used as the main indicator for assessing the sustainability of economic development for a long time. Inclusive wealth (IW) offers a new approach to assess sustainability by comprehensively measuring the productive base of the economy that involves three types of capital assets of nations (produced, human and natural capital), and aggregates them into a single measure of wealth. This study proposes an alternative to the literature on the conventional energy – growth nexus that widely uses GDP as a proxy of the growth. This study aims to investigate the impact of energy consumption on wealth in the IW framework and forecast the growth of IW over the next three decades. For this purpose, this study uses both parametric and non-parametric analyses on 104 countries for 1993–2014. Our results indicate that there is a negative and significant impact of energy consumption on IW growth, suggesting an unsustainable pattern of world energy consumption. Using a machine learning technique, it is forecasted that increasing the efficiency of energy consumption leads to a higher growth in average per capita IW. This study also suggests that a shift to renewables is a precondition for sustainable development.

Introduction

Access to reliable and affordable energy is essential not only for supporting basic human needs but also for creating human well-being. The central role of energy and its sustainability has also been recognized in the United Nation’s sustainable development goals (SDGs). However, the pattern of world energy consumption in the past tends to be unsustainable since it is highly associated with the rapid exhaustion of natural resources and environmental pollution. Energy consumption has been a predominant source of climate change, accounting for more than 60% of total global greenhouse gasses (GHG) emission. This trend is expected to increase along with the growing population and increasing economic activity, particularly for developing countries [1]. The negative impact of energy consumption and/or economic development on environmental quality has led to a quandary over whether to boost economic growth as high as possible by encouraging higher energy consumption, or giving precedence to environmental sustainability by curbing energy consumption which might result in lower economic growth [2]. The environmental Kuznets curve (EKC) hypothesis, on the other hand, argues that environmental sustainability can be achieved without restraining economic development. The EKC hypothesis suggests the existence of a turning point in the economy subsequent to which the increasing trend in environmental degradation will be reversed (see Grossman and Krueger [3] for rationale behind the EKC hypothesis). The composition and technical effects of the economy will decouple economic growth from GHG emissions through the introduction of renewable energy sources in the energy mix, investment in new and cleaner energy technologies, and adoption of more stringent environmental regulations. As a result, environmental damages that occurred in the earlier stages of development will be ameliorated, and further economic growth will lead to a better environmental quality. Although the EKC hypothesis proposes a promising concept for sustainability, it has some caveats worth mentioning. For instance, the estimated turning point of the EKC might exist at very high levels of income per capita, which are difficult or even impossible to achieve [4–6]. Additionally, De Bruyn et al. [7] and Sugiawan et al. [8], among others, argue that over the long term, new pollutants and environmental problems might appear, creating a secondary turning point in the economy so that the declining trend in the income-environmental quality relationship will revert back to its former trend.

1- Introduction

2- Requirements for sensors to determine the state of an anaerobic digester

3- Algorithm-based monitoring

4- Utilisation of colourimetric sensors in anaerobic digestion

5- Concluding remarks

References

Abstract

Although biogas is not a new approach to producing renewable fuel, it could further be developed to improve its potential as an alternative energy source. To achieve this, vast improvements in the efficiency and cost of biogas production are essential. These enhancements require detailed systematic monitoring to attain a near-optimal biogas production process. To date, there is a striking imbalance between the inherent biological complexity of anaerobic digestion, and the minimal information currently measured on-line. The objective of this review is to discuss how improvements in availability and cost of sensor technology used for determining the key compounds and their dynamics within the biogas processing plant will facilitate the further understanding of the biogas production process, preventing the biological process failure. In particular, colourimetric assays (sensor assays based on coloured dyes) for variable detection in anaerobic digestion provide a stable, multivariate system for the detection of Volatile Fatty Acids (VFAs), but also provide a much deeper insight into the process by assessing other parameters, which, to date have never been measured on-line. These sensor improvements will allow the biogas production, even on a small scale, to be guided in the optimum direction, avoiding the biological process from collapsing. This will result in improved efficiency, at a reduced operational cost. The potential of colourimetric assay methods for use in anaerobic digestion as a sensor technology with associated data analysis methodologies has not previously been observed. Here, a 23-dye colourimetric sensor array was experimentally assessed to exhibit the differentiation of 10 mM acetic acid, 5 mM propionic acid and 0.3 mM butyric acid. The feasibility of on-line, cost-effective, rapid, and efficient detection of VFAs together with other key parameters by these colourimetric sensor arrays is intended to be assessed to advocate their usage in AD.

Introduction

One route to production of renewable, clean energy is through biogas production, where biogas is derived from biological waste materials. Biogas is produced through anaerobic digestion (AD) of organic waste materials by a variety of microorganisms [1]. The inherent complexity of this ecosystem resulting from the dynamical interaction of several hundreds of bacterial and archaeal species [2], developing on a mixture of substrates, results in significant difficulty in determining the state of the digestion process [1]. The most straightforward information is provided by monitoring of gases (H2, CH4 and CO2) measured in the gas-phase of the anaerobic digester. Their fluctuations can provide information about the digesters’ productivity [3]. Measurement of pH is also important. A pH outside the range of 6–8 often indicates process deterioration, limiting methane production and eventually collapse of the entire biological process [4]. Concentrations of volatile fatty acids (VFAs; mainly acetic, butyric and propionic acid) have been suggested as useful control parameters, as these acids are indirectly indicative of the activity of the methanogenic consortia [3,4]. VFA accumulation can be interpreted as organic overload or inhibition of the methanogenic microbial communities [5]. Acidogenic microorganisms transform hydrolysis products into VFAs, while acetogenic microorganisms then convert VFAs into acetate, H2 and CO2. Methane is then produced by the methanogens [6], with the majority being produced by acetotrophic methanogens, transforming acetate into methane and CO2 [7]. VFA concentrations are a required control parameter for biogas production monitoring [8,9], and it is essential that VFAs are monitored to understand the biological process. Additionally, the ratio of VFAs over total alkalinity is often suggested as an indicator of the processes stability, with the objective of this ratio remaining below 0.3 [10].

1- Introduction

2- Electrode materials

3- Mechanisms of electron transport

4- Field standardisation and comparison techniques

5- Conclusions

References

Abstract

Research into alternative renewable energy generation is a priority, due to the ever-increasing concern of climate change. Microbial fuel cells (MFCs) are one potential avenue to be explored, as a partial solution towards combating the over-reliance on fossil fuel based electricity. Limitations have slowed the advancement of MFC development, including low power generation, expensive electrode materials and the inability to scale up MFCs to industrially relevant capacities. However, utilisation of new advanced electrode-materials (i.e. 2D nanomaterials), has promise to advance the field of electromicrobiology. New electrode materials coupled with a more thorough understanding of the mechanisms in which electrogenic bacteria partake in electron transfer could dramatically increase power outputs, potentially reaching the upper extremities of theoretical limits. Continued research into both the electrochemistry and microbiology is of paramount importance in order to achieve industrial-scale development of MFCs. This review gives an overview of the current field and knowledge in regards to MFCs and discusses the known mechanisms underpinning MFC technology, which allows bacteria to facilitate in electron transfer processes. This review focusses specifically on enhancing the performance of MFCs, with the key intrinsic factor currently limiting power output from MFCs being the rate of electron transfer to/from the anode; the use of advanced carbon-based materials as electrode surfaces is discussed.

Introduction

Energy generation, storage and consumption are topics that are increasingly prevalent within modern research fields and are of global interest and importance [1,2]. Research into alternative renewable energy generation sources are increasing exponentially, with vast research showing promising results, in an abundance of areas including: solar [3], wind [4], tidal [5], geothermal [6] and biomass energy generation (Fig. 1) [7,8]. Currently no individual renewable energy source has the ability to compete with and replace the conventional fossil-fuel based energy generation approach, however, combining renewable energy sources such as, solar-wind hybrids and/or solar-hydrogen fuel cells may be alternative routes to be explored [9,10]. One potential alternative energy source is the use of microbial fuel cells (MFCs). MFCs follow a similar concept to traditional fuel cells (Fig. 2). However, MFCs utilise the bio-catalytic capabilities of viable microorganisms and are capable of using a range of organic fuel sources, by converting the energy stored in the chemical bonds, to generate an electrical current instead of relying for example, on the use of metal catalysts [1]. Microorganisms, such as bacteria, can generate electricity by utilising organic matter and biodegradable substrates such as wastewater, whilst also accomplishing biodegradation/treatment of biodegradable products, such as municipal wastewater [1,11]. Table 1 provides an overview of the current literature of MFCs. Clearly, significant attention has been given to MFCs cells due to their ambient operating conditions (e.g. utilisation at low temperatures) and a variety of biodegradable substrates as fuel. This review aims to highlight the current understanding of MFCs, whilst giving a thorough overview of the field. Particular emphasis is placed upon the fundamentals of MFC technologies, electrode materials, mechanism of electron transport and field standardisation.

1- Introduction

2- Structural databases

3- Data mining techniques

4- Examples of data mining energy materials from structure databases

5- Suggestions and outlook

6- Conclusions

References

Abstract

New energy materials that act as clean power sources and data science are developing rapidly in the past decades and the advancement of the two research areas have significantly benefited the development of each other. At the meantime, structural information of materials have been obtained and stored in various structure databases, such as the Cambridge Structure Database (CSD) and the Inorganic Crystal Structure Database (ICSD). Researchers have developed various structure-property relationships of the energy materials, which could be applied to screen the potential suitable materials from structure databases; this has become an efficient route to explore and design new energy materials. In this article, we review recent progresses on the data mining study of new energy materials based on structure databases such as CSD and ICSD, in the context of dye-sensitized solar cells and perovskite solar cells, and also include other energy systems such as water splitting systems, lithium batteries, thermoelectric devices and gas adsorbent materials. The structure descriptors that are more fundamental in the data mining procedure employing the structure-properties relationships are focused; the structural descriptors are complementary to the quantum descriptors and are efficient in the materials design process. We believe that with the successful formulation of more advanced and case-by-case structure-property relationships of energy materials, many new energy materials could be efficiently identified with much lower cost and shorter design period via the data mining process.

Introduction

There has been global energy crisis and environmental issues in the past decades due to the overuse of fossil fuels. New energy materials should be identified and developed to provide clean energy [1–5]. For example, efficient solar cell materials should be explored to harness the clean energy from the non-exhaustible solar system to provide the electricity; materials incorporated in water-splitting systems should be screened to provide solar fuels; lithium-based materials could be exploited to be tailored to undergo charge/discharge process reversibly and store electrical energies for portable devices and vehicles; metalorganic frameworks could be discovered to capture the carbon and gas molecules effectively; thermoelectric materials and piezoelectric materials should be identified to covert the thermal and mechanical energy into the electric current. At the current stage, the discovery of these energy materials relies predominantly on the experimental serendipity and the try-and-error experimental process that are inefficient and time-consuming. Nevertheless, many evidences have shown that the discovery process of new energy materials could be greatly accelerated by the data mining process, which have already shown their excellence in pharmaceutical drug discovery, finance, medicine, and marketing [6,7]. A large amount of structure information of new materials have been determined and stored in structure databases, which are mainly prepared by the crystallization processes and solved by the X-ray diffraction techniques and other crystallographic techniques [8]. Most of these materials have their structures accurately determined within the resolution of 0.01 Å and serve as outstanding platforms for the structural analysis and more advanced structure-property analysis. Two outstanding databases of materials structures have emerged: the Cambridge Structure Database (CSD) and the Inorganic Crystal Structure Database (ICSD), which focus on the accurate crystal structures of organic and inorganic materials, respectively [9,10]. These databases formed a good foundation for the data mining process of new energy materials. These databases store structures of materials that have already been synthesized in laboratory and exhibit significant advantages to expedite the process towards achieving promising candidates [11,12].

1- Introduction

2- Methodology

3- Results and discussion

4- Conclusions

References

Abstract

Despite the considerable contribution of hydropower in driving the American economy for over a century, the rationale for hydropower in the U.S. energy mix needs to be reassessed in the context of advanced science and technology. Other alternative-yet-cheaper energy resources have been identified and hazards associated with aging hydro-dams have escalated in recent years. Furthermore, research has shown more negative environmental consequences associated with hydro-dams—and dams in general. To compare the contribution of hydro-electricity to the total energy production in the U.S., and to identify its regional distribution and contemporary patterns, we conducted a systematic analysis of large-scale multi-year data from U.S. federal agencies and tallied the nameplate capacities of major hydro-dams against their existing energy production values. We found that despite continuous efforts at upgrading hydro-facilities, since 2000 the mean contribution of hydroelectricity has remained less than 10% of the total generated energy in the U.S. and has been declining since then. Based on our results, we conclude that reservoir- and dam-based hydroelectricity may not be an efficient energy resource—at least from the American perspective, and perhaps it is timely to consider promoting other non-conventional renewable resources for energy production.

Introduction

Hydropower has been an integral component of American electricity production for nearly 130 years and has played a substantial role in the nation’s industrial revolution [1]. Differences in regional precipitation and runoff, river morphologies, local settlement patterns and concordant energy needs and alternatives, and the nature of hydropower facilities has varied widely across the conterminous U.S. However, over time as energy demand has outstripped the feasibility to bring additional hydropower capacity online, the contribution of hydropower has declined in the nation’s energy mix while, at the same time, annual total and per capita electricity usage in the U.S. has been increasing [2]. The feasibility of satisfying expanding energy needs from alternative renewable energy sources (e.g., wind, solar and biomass fuel) has been increasing with the rapid decline of their costs and improvements in their efficiency. Indeed, the leading U.S. federal labs have reported a consistent annual decline of the levelized cost of energy (LCOE) for land-based- and offshore-windfarms, and utility-scale solar installations in recent years. For instance, the LCOE value of a typical land-based wind project in the U.S. declined from $71/Megawatt hour (MWh) in 2010 to $49/MWh in 2016, and it declined from $225/MWh in 2010 to $173–207/MWh in 2016 for a typical offshore windfarm [3,4]. Hydroelectricity generation from impounded water is still viewed as providing a high level of energy supply services, probably because even in the recent context of other non-traditional sources becoming cheaper, it contributed a substantial 16.4% of the global energy mix in 2016 with an estimated total installed capacity of 1096 GW [5]. From the energy production perspective, three main types of hydro-projects, namely reservoir-based, run-of-river and pumped-storage have been identified; each with its own inherent merits and demerits. Perhaps the most important appeal of a hydroelectric plant is its capacity to load balance instantly as the electricity demand varies on a diurnal basis [6]. Initially, hydropower was promoted as a “carbon-emission free” energy resource and a panacea to mitigate atmospheric pollution; however, recent studies, emerging from tropical to temperate areas—including the U.S., have debunked those assertions by reporting significant emission of greenhouse gases (GHGs) from reservoirs into the atmosphere [7,8].