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

Introduction

Methods

Results

Discussion

Conclusion

References

Abstract

     The industry 4.0 has created a paradigm shift in how industrial equipment could be monitored and diagnosed with the help of emerging technologies such as artificial intelligence (AI). AI-driven troubleshooting tools play an important role in high-efficacy diagnosis and monitoring processes, especially for systems consisting of several components including wind turbines (WTs). The utilization of such approaches not only reduces the troubleshooting and diagnosis time but also enables fault prevention by predicting the behavior of different components and calculating the probability of near future failure. This not only decreases the costs of repair by providing constant component’s monitoring and identifying faults’ causes but also increases the efficacy of the apparatus by lowering the downtimes due to the AI-driven early warning system. This article evaluated, compared, and contrasted eight different artificial neural network (ANN) models for diagnosis and monitoring of WTs that predict the machinery’s system failure based on internal components’ sensor signals and generation temperature. This article employed a machine learning model approach with two hidden layers using multilayer linear regression to achieve its objective. The developed system predicted the output of the WT’s generator temperature with an accuracy of 99.8% with 2 months in advance measurement prediction.

Introduction

     Industry 4.0 introduced a new paradigm to the machineries’ monitoring and diagnosis procedure. Enabled by advances in artificial intelligence (AI) and notions such as Internet of Things (IoT) in recent years, Industry 4.0 has proved to be an effective and reliable trend toward digitization and automation (Haag and Anderl 2018). Industry 4.0 is the fourth paradigm shift and major breakthrough in industrial revolution made possible by the advancements in electronics and information technology; a continuation of the evolution of automation commenced from the invention of steam engines and mass production as a result of assembly lines and standardization (Xu, Xu, and Li 2018).

     The wind power industry and the whole renewable energy sector could benefit significantly from the employment of industry 4.0. Most of the machineries including wind turbines (WTs) produce a huge amount of data related to power consumption, current, voltage, vibration, and environmental factors that are not necessarily utilized. The information processed from these gathered data could be used to improve the troubleshooting, monitoring, and maintenance procedures. Sensors attached to different parts of WTs will provide important data of the health state of the apparatus, which require interpretation and processing.

Conclusion

     Eight different ML models were developed with different sensors’ data based on SCADA data collected from nine WTs over 10 years received from the Westmill Windfarm located in Swindon, United Kingdom, to predict the generator failure in WTs in advance using pattern recognition based on historical data. The results of each model’s accuracy in terms of minimum, maximum, and standard deviation offsets between the predicted and actual generator temperature values were compared and contrasted, and the effect of the input sensor data was explored. Overall, this research showed the possibility of utilizing ML-driven regression algorithms to predict WTs’ generator failure caused by heat, lowering the maintenance costs related to downtime and staff,and, at the same time, improving the operational availability of the apparatuses. For the future works, the authors of this article aim to explore the possibility of implementing transfer learning for fast adaptation and deployment of the trained models to new WTs, allowing quick training of new assets and lowering the readiness time required for the model.

Highlights

Abstract

Keywords

1. Introduction

2. Cyber–physical DC microgrids

3. Attack detection design

4. Performance validation

5. Conclusion

CRediT authorship contribution statement

Declaration of Competing Interest

Acknowledgments

Appendix.

References

Abstract

DC microgrids are considered as the next generation of power systems because of the possibility of connecting various renewable energy sources to different types of loads based on distributed networks. However, due to the strong reliance on communication networks, DC microgrids are vulnerable to intentional cyber-attacks. Therefore, in this paper, a robust cyber-attack detection scheme is proposed for DC microgrid systems. Utilizing the parity-based method, a multi-objective optimization problem is formulated to achieve robust detection against electrical parameter perturbations and unknown disturbances. An analytical solution is then provided using the singular value decomposition approach. With the disturbance decoupling scheme, the presented detection strategy can monitor the system with only local knowledge of the DC microgrid. The proposed method is easy to design and with less computation complexity. The performances of the provided scheme are validated by simulation tests and experimental results.

1. Introduction

DC microgrids (MGs), known as next-generation power systems, have received significant attention in recent years because of their ability to transmit power from renewable energy sources and energy storage devices to various loads with greater efficiency and reliability than the AC grid. As a distributed power supply, the DC MG can be operated independently or connected to the utility grid. Such applications can be found in power generations [1], smart houses [2], transportation systems [3], etc.

Due to the rapid developments of the Industry 4.0 paradigm, information technology-based solutions have been widely used in industrial processes. The revolutionary changes have seen the emergence of cyber–physical systems where large amounts of data are exchanged between multiple devices in real time [4]. Accordingly, the framework of DC MG tends to be more distributed, intelligent and tightly integrated with the network. However, due to the strong reliance on the communication technologies, DC MGs are more vulnerable to security threats [5] and have a higher risk of being compromised by malicious attackers.

In general, the functionality of a potential microgrid controller relies heavily on the reliability of the data received by the measurement devices or sensors. For example, if the sensors or communication links are compromised by an attacker, the controllers may receive faulty data and therefore make inappropriate control decisions [6], leading to the undesirable power-sharing [7], frequency oscillating [8] and stability issues [9]. As a result, renewable energy generating units may not be able to produce the maximum amount of available power from nature or meet the appropriate power sharing between microgrids, and energy storage devices may not be able to provide the required amount of power or operate with optimal economic dispatch [10]. More seriously, attackers may be able to disrupt the system without adequate security protection in terms of hardware or software policies, leading to significant social losses. Examples include the nuclear facility struck by Stuxnet malware [11], power outage event [12], [13] and nuclear plant blackout accident [14]. Considering the huge impact of attacks on microgrid systems, it is vital to provide an effective detection scheme to counter cyber attacks.

Abstract

     Concrete is a primary material in infrastructure projects and is a significant contributor to global climate emissions. However, there is a lack of readily available cement and ready-mix concrete inventory data for evaluating the environmental performance of the industries. This study describes the development of cradle-to-gate inventories for U.S. ready-mix concrete and gate-to-gate inventories for portland cement production technologies. These life-cycle inventories provide baselines for the environmental releases associated with concrete that is used for major infrastructure projects. The inventories are incorporated into the publicly available Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET) model. The life-cycle inventories are created using facility-level environmental release data from U.S. datasets normalized to activity levels which are based on production capacity and utilization data provided by Portland Cement Association (PCA) and the U.S. Geological Survey Minerals Yearbook. Unit processes for limestone quarrying, sand and gravel quarrying, and wet-mix concrete batch plants are developed on the basis of national total point-source environmental releases and production statistics, coupled with corresponding flows associated with off-road fuel consumption and other non-point-source emissions. Midpoint impact assessment results are normalized to provide insight into their relative significance in the context of U.S.

Introduction

     Concrete is a primary structural material central to the construction projects throughout the world. It is the second most-consumed material in the world, after water (Miller et al., 2018b). Cement is the binder that holds concrete aggregates together, enabling structural performance. Global cement production of about 4.1 billion tonnes year after year since 2013 (IEA, 2021). Of this, 102 million tonnes, or 2.5%, were consumed in the U.S. Concrete can be transported, pumped, and poured to fill frames in a wide range of forms, and once cured, it is rigid and durable, making it ideal for structural elements, especially distributed transportation infrastructure. As a result, concrete is used in a wide range of construction projects. The Portland Cement Association (PCA) reports that in 2015, 30% of the cement use in the U.S. was for transportation infrastructure, 28% for residential buildings, 18% for nonresidential buildings, 13% for public utilities and water/wastewater systems, and 10% for other applications (PCA, 2016).

Conclusions

     This study provides transparent emissions data for infrastructure modeling within the GREET framework, as well as robust inventories for openLCA modeling, available for free download on the Federal LCA Commons. The four primary kiln technologies employed to produce cement are all available, in addition to the U.S. national average blend, enabling specification of blends that may be more regionally appropriate for specific LCA studies. The ready-mix concrete inventory is appropriate for commonly available truck-delivered concrete and is also adaptable for specified cement blends, including adjustments for modeling batch plants that may be set up for larger infrastructure projects. These data enable transparent assessments of concrete applications with complete inventories across a full suite of life-cycle impact categories. Researchers and decision-makers can use GREET or the data on the Federal Commons to assess the impacts associated with major concrete infrastructure projects.

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.

Abstract

     Existing theories on the diffusion of innovations fail to sufficiently account for contextual factors such as institutions, infrastructure, and supply-side dynamics. This paper presents a novel framework to analyse technology diffusion from a sociotechnical systems perspective, intended as an analytical tool to identify and assess drivers and barriers to diffusion that could be addressed through policy or business strategy. This framework, referred to as the diffusion innovation system (DIS) approach, is positioned within the innovation systems literature. The framework is applied to two empirical cases of renewable energy technology diffusion in Sweden: solar photovoltaics (PV) and wind power. The cases illustrate how key factors related to institutions, infrastructure, adopters, and supply co-develop over time as the technologies diffuse, hence demonstrating the merits of the framework. As these changes are both a reaction to and a cause of diffusion, the sociotechnical diffusion system develops through positive feedback loops. Although the systems’ development is largely conducive of diffusion, some remaining and potential barriers are identified.

Introduction

     The diffusion of innovations – i.e., the process through which a new technology is adopted by an increasing number of actors throughout society – is important for economic, social, and ecological sustainable development. For example, to mitigate climate change, renewable energy technology diffusion is needed. Understanding how different factors shape diffusion patterns is important as this helps policy makers and business leaders facilitate diffusion.

     There is a broad and diverse literature on how innovations emerge and diffuse. First, sociotechnical systems approaches to technological transitions (Markard and Truffer, 2008; Weber and Rohracher, 2012) such as the multi-level perspective (e.g. Geels, 2010, Geels, 2002; Schot and Kanger, 2018; Smith et al., 2010) and different innovation system frameworks (e.g. Binz and Truffer, 2017; Carlsson and Stankiewicz, 1991; Edquist, 1997) emphasise factors beyond the diffusing technology itself and its adopters, including dynamic and complex relationships between institutions and actor networks. These approaches traditionally treat technology development, production, and diffusion as occurring together in one and the same system, typically not emphasising the diffusion part. While these approaches suit immature technologies struggling to diffuse beyond their earliest applications, they are not appropriate to analyse the diffusion of more mature technologies (Mignon and Bergek, 2016). For example, the technological innovation systems approach has been widely used as a hands-on analytical tool for informing policy on how to support e.g. renewable energy technologies and electric vehicles in early stages of these technologies’ development. However, several sustainable technologies are now mature and (ready to) diffuse beyond their initial markets. Hence – as stated in recent calls for research in the sociotechnical transitions literature (Köhler et al., 2019; Mignon and Bergek, 2016; Palm, 2017a) – there is a need for a diffusion-focused system-oriented analytical tool.

Synthesis and conclusions

     The two case studies illustrate that the DIS framework is appropriate for analysing the diffusion of innovations from a sociotechnical systems perspective. The cases reveal that, as the technologies have diffused, much development has occurred through the DIS framework’s four key processes. This development has been crucial to facilitate diffusion, although some barriers to diffusion remain. The processes both induced and responded to diffusion, and they developed symbiotically fuelling one another. Hence, positive feedback mechanisms were important for the system’s development. Overall, the national system boundary made sense for the two cases as the processes’ development was – besides the import of standardised artifacts – largely determined by factors within the system. Although the system boundaries were by no means perfect, they proved good enough to serve as analytical constructs.

Highlights

Abstract

Keywords

1. Introduction

2. Policy context

3. Electricity system modelling assumptions

4. Scenarios

5. Modelling results

6. Cost-benefit estimations

7. Conclusion and policy implications

Credit author statement

Declaration of competing interest

References

Abstract

Long distance power transmission is commonly considered as an option for reducing carbon footprint in future electricity systems. Accordingly, this article presents economic insights in a transcontinental power interconnection linking four Asian countries with Europe. Enhanced electricity trade through the interconnected countries is assessed via techno-economic modelling. For this purpose two electricity system scenarios are developed for the year 2040: (i) a Reference Scenario, where electricity system development follows the plans of the involved system operators and (ii) a so-called Trans-Asia Scenario, where additional power transmission capacities are added to strengthen the electricity trading route crossing the interconnected countries: Turkey, Georgia, Azerbaijan and Kazakhstan. Economic benefits arising from the proposed Trans-Asia Scenario are estimated as a change in social welfare in the electricity system. Modelling results show a 140 M€ increase in annual social welfare for the Trans-Asia Scenario. The subsequent cost-benefit analysis results in a net present value in the range of −221 M€ to 534 M€, at a discount rate of 4%. This implies that over a life-cycle period of 40 years, the evaluated economic benefit may compensate investments between 1598 M€ and 3251 M€ needed for the additional power transmission capacities.

1. Introduction

Long distance power transmission is regularly presented as an enabler for high deployment of centralized renewable energy sources (RES) [1]. Several projects have been implemented providing flexibility in transcontinental power balance between electricity demand and generation [2]. Wu and Zhang [3] presented a feasibility study of an intercontinental electricity trade between Europe and China with 100% RES generation in 2050. This interconnection significantly decreases annual electricity system running costs. However, electricity systems of countries along the Europe-China connection route are not considered. In this regard, Assembayeva et al. [4] developed an electricity market model of Kazakhstan, with possible further expanding modelling capacity in the Central Asian region. Assembayeva et al. [4] indicates power flow congestions in the electricity grid of Kazakhstan during high electricity demand in winter. According to Gea-Bermúdez et al. [5]; grid congestions can be reduced along with electricity production costs through a sound long-term electricity system planning. In the light of the above considerations, this study presents an elaborated techno-economic modelling approach giving insights in investments in additional electricity trade capacity along a route connecting Europe with a selection of Asian countries in 2040.

Transmission grid reinforcements are proposed on top of the planned transmission infrastructure development plans to allow a cross-border electricity trade capacity of 2000 MW in a selected route from Turkey to Kazakhstan, passing through Georgia, Azerbaijan and the Caspian Sea. This route derives from a study by Ardelean and Minnebo [6] and interlinks with the European transmission grid as shown in Fig. 1. The proposed increase in cross-border trade capacity between the Asian countries to 2000 MW is close to the envisaged electricity trade capacity between Bulgaria-Turkey and Greece-Turkey in the ENTSO-E 2040 scenarios [7].

Building energy use is expected to grow by more than 40% in the next 20 years. Electricity remains the largest energy source consumed by buildings, and that demand is growing. To mitigate the impact of the growing demand, strategies are needed to improve buildings’ energy efficiency. In residential buildings home appliances, water, and space heating are answerable for the increase of energy use, while space heating and other miscellaneous equipment are behind the increase of energy utilization in non-residential buildings. Building energy management systems support building managers and proprietors to increase energy efficiency in modern and existing buildings, non-residential and residential buildings can benefit from building energy management system to decrease energy use. Base on the type of building, different management strategies can be used to achieve energy savings. This paper presents a review of management strategies for building energy management systems for improving energy efficiency. Different management strategies are investigated in non-residential and residential buildings. Following this, the reviewed researches are discussed in terms of the type of buildings, building systems, and management strategies. Lastly, the paper discusses future challenges for the increase of energy efficiency in building energy management system.

1. Introduction

Buildings such as residential, education, office, healthcare, and industrial are emerging as critical consumers in energy consumption. Energy consumption for buildings represents 30–45% of global energy use [1–3], with a larger part of the energy used by the building subsystems, which consist of cooling and heating systems; safety, water, lighting, and similarly combined subsystems. In this context, efforts at this time are focused on the fulfillment of the requirements for energy-efficient in buildings, by guaranteeing the operative needs with the base conceivable energy cost and environmentally friendly [4]. In many developing and developed countries, energy efficiency is viewed as the best mechanism to address and defeat ever-rising energy needs [5]. In any case, advancing the energy efficiency of these subsystems is very testing since they typically have to comply with complex working requirements, dynamic energy necessity, and comfort needs [6].

Abstract

For reducing the energy use of buildings, the application of insulation materials can be a possible solution to improve energy efficiency. However, with the continuously tightening regulations, thicker and thicker insulations should be used (e.g., up to 20–25 cm in Hungary and about 40–50 cm in the Nordic countries) to fulfil the directives. In the past decades, developmental research started to focus on both decreasing the thickness and decreasing the thermal conductivity of materials parallels. With this research direction, the investigations of the new type of insulation materials took place. The use of materials having thermal conductivity with less than 0.020 W/mK spread over. These materials are the so-called super insulation materials. Besides Vacuum Insulation Panels (VIPs), aerogel-based materials belong to this group, too. This paper will present laboratory tests executed on two altered types of protected Vacuum Insulation Panels: a one side and a 2 side protected VIP, also called Vacuum Insulation Sandwich (VIS). The first one, type “A” contains 20 mm thick laminated fumed silica, covered with 10 mm thick expanded polystyrene on one side, while type “B” was the one where the 10 mm thick laminated fumed silica core was packed between two 10 mm thick EPS slabs. Firstly, thermal insulation performance was investigated by the so-called guarded hot-box method for U-value, where the samples were placed on a solid brick wall. Furthermore, the thermal insulation performance of protected VIPs was also tested with Hukseflux heat flux plates. To reveal the applicability limits caused by the thermal bridges infrared thermography images (IR) were taken, too. From the images and the measurement results, a theory was formed regarding the heat flow directions inside the encapsulated panels. The current article emphasizes the equivalent thermal conductivity for the assessment of the thermal protection performance of vacuum insulation panels. The paper states that by using VIPs at least, 70% reduction can be reached in the U-value compared to the value of the solid brick wall. Moreover, the paper points out that the use of EPS protection layer on both sides of the VIP has a promising effect by reducing the thermal bridges. Furthermore, the VIP protected with EPS slabs on both sides results in less equivalent thermal conductivity.

1. Introduction

Nowadays, reducing significantly the energy consumption is an important issue for the building sector, but the reduction of energy use cannot neglect the thermal comfort requirements. Heating and cooling account for at least 40% of the final energy use of the European Union. Moreover, it was predicted by the International Energy Agency (IEA) by the energy use of the countries all over the world could reach up the unwanted 50% until the year 2030 [1–3]. That is why the application of thermal insulation materials in the buildings is very significant. Several types of insulation materials are available [4], but nowadays the use of super insulation materials (SIM) is also widespread, resulted in the development of the industry sector [5]. In cases where greater thickness (15 ~ 20 cm) cannot be used, materials with less thermal conductivity must be applied. It is mentionable that there is no exact definition of SIMs but the IEA EBC Annex 65 ‘‘SIM” Subtask 1 [6] defines the elements of these groups. According to this Annex, these materials are said to be the part of this group: (a) Vacuum Insulation Panels (VIP); (b) Advanced Porous Materials (APM), for example, different types of aerogels (monoliths or fibrous ones). Their most important property is the thermal conductivity, possibly less than 0.022 W/ mK [7]. These insulation materials are also called nanostructured materials, due to their nano-sized pore structure [6]. The application limits of these mentioned materials are written in [8,9] but now the vacuum insulation panels will be in focus.

1- Introduction

2- Circuit configuration

3- Theoretical analysis

4- SPICE simulation

5- Experiments

6- Conclusion

References

Abstract

For vibration energy harvesting, we propose an inductor-less step-up ac/dc converter in this paper. To realize the inductor-less design, the proposed ac/dc converter consists of two converter blocks: Cockcroft–Walton circuit and charge pump. Unlike existing ac/dc converters for vibration energy harvesting, the proposed ac/dc converter can achieve less electro-magnetic interference (EMI), because no magnetic component is necessary. Furthermore, owing to the Cockcroft–Walton circuit, a fullbridge circuit is not necessary to convert vibration energy. Therefore, small vibration energy, namely, as 0.3 V@1 MHz, can be converted directly to dc voltage. Through theoretical analysis and simulation program with integrated circuit emphasis (SPICE) simulation, the performance of the proposed converter with 6× voltage gain is investigated, where the proposed converter is designed by assuming 0.18 µm CMOS process. The proposed converter demonstrates that about 62% power efficiency can be provided, where the output power is 30 µW, the output voltage is about 1.6 V, and the ripple factor is 0.8%. Furthermore, the feasibility of the proposed ac/dc converter is confirmed by breadboard experiments. The inductor-less design provides us to integrate the proposed ac/dc converter into a hybrid IC chip.

Introduction

Over the past few decades, wireless sensor network (WSN) systems [1] are receiving much attention for monitoring various environment parameters. The bottleneck of the WSN systems is the limited lifetime of WSN nodes, because the WSN nodes are usually driven by a battery source. For this reason, in order to extend lifetime of WSN nodes, some of wasted energy are captured by energy harvesting collectors and are used as a power supply source in recent years. There are several types of ambient energy sources, such as solar, wind, vibration, thermoelectric. Among others, we focused on vibration energy, which is a kind of kinetic energy, because it widely exists in the ambient, including the vibration of industrial machinery, transportation vibration, body movement, and so on. In the energy harvesting collectors, the ambient energy is converted into electrical energy by power converters. To utilize vibration energy, an ac/dc converter is necessary as a vital component of energy harvesting collectors. For this reason, several types of ac/dc converters have been proposed in recent years. For example, Shi et al. [2] suggested the ac/dc converter by combining a full-bridge circuit with two buck-boost converters. Wang et al. [3] designed the ac/dc converter by combining a full-bridge circuit with a synchronized switching harvesting on inductor. Shareef et al. [4] realized the ac/dc converter without full-bridge circuits. However, due to the magnetic component, such as inductors, these existing converters becomes bulky. Furthermore, these converters are applied only for low frequency ac input. To solve these problems, we undertake the development of an inductor-less ac/dc converter for vibration energy harvesting.

1- Introduction

2- System architecture

3- Modelling of the WECS

4- Performance analysis of proposed DC nanogrid topology

5- Conclusion

References

Abstract

Wind energy conversion system (WECS) based DC nanogrid topology is proposed in this paper. The WECS topology is based on self-excited induction generator (SEIG) which is cost effective, and has rugged construction and combination of diode bridge rectifier (DBR) with DC-DC converter to implement the maximum power point tracking technique. The combination of diode DBR with DC-DC converter allows simple control structure due to one controllable switch as compared to complex control in the conventional controlled/semi-controlled rectifier with six controllable switches. Further, proposed DC nanogrid topology enhances the SEIG efficiency due to the reduction of harmonic injection on machine side from DBR implementation. The detailed harmonic spectrum analysis is carried out by developing the laboratory scale prototype of DBR based SEIG topology for WECS to justify the selection of DBR instead of the controlled rectifier. Further, to analyze the effect of different types DC-DC converter to make combination with the DBR. Afterwards, the dynamic performance of the proposed DC nanogrid topology is analyzed to understand the selection of the DC-DC converter with DBR for different DC grid voltage conditions.

Introduction

Electricity plays a vital role in energy supply, however, the electricity generation is the single largest source of CO2 emission, which is more than 40% of CO2 emission related to global energy. By the year 2030, global electricity demand is likely to double [1,2]. Hence, climate change mitigation and increasing energy demand motivates for the development of renewable energy resources. The renewable energy resources such as solar and wind energy are distributed in nature which favors the electricity generation and consumption on site. With technological advancement and regulatory changes, the concept of supplying the load locally has led to a concept of nanogrid. Thus, the development of renewable based nanogrid are gaining attention in recent past for providing environment-friendly, sustainable and economical electricity [3]. Further, the development of power electronics has transformed the load profile of end user as the modern appliances operate on DC supply [4]. Having this understanding, the concept of DC nanogrid has been proposed in the literature to elude multiple AC-DC conversion and vice-versa [5]. The DC distribution enables easy control, no issue of frequency, power angle, phase sequence, and power factor. Mashood et al.