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Energy Analytics: From Data Acquisition to Data-Driven Business Models
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Smart meters roll-out enables access to residential end-users’ energy consumption data. Distribution Network Operators (DNOs), power utilities and energy retailers are now offered information that allows for more efficient asset and client management. Europe is paving the way on this topic, especially within the Energy Union strategy that was launched in 2015. Smart meters are expected to replace 80% of the legacy ones as an attempt to reduce emissions and energy consumption, while smart grid and data protection regulation is prepared or even already in place. This new landscape sparks energy retailers’ interest in energy services and data-driven business models. Energy disaggregation is of critical importance and usually, the cornerstone on which such services are built. However, depending on the services, different stakeholders want to implement and offer to their end-users, distinct categories and volume of data might be needed.
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... potential of data associated with microgrid operations. Microgrid data, often harvested via IoT devices operating within a microgrid, offer powerful insights that could fundamentally alter operational strategies, pinpoint bad practices, guide capital investments, and uncover untapped potential [12]. This valuable data resource is rarely accessible in an open format, particularly for small-to medium-scale microgrids. ...
... It is therefore imperative to investigate and propose real-world infrastructures, besides communication systems [18] and network management schemes [19,20], that support the monitoring and operational analytics of microgrids, utilizing state-of-the-art computing resources and digital architectures that cater to technologies found within typical microgrids (e.g., power meters, edge intelligence). The significance of the latter is thoroughly investigated in [12], where relevant data categorization, data acquisition, and energy disaggregation tools are investigated in the context of building data-driven business models for net-zero power grids. ...
Microgrids are considered a viable solution for achieving net-zero targets and increasing renewable energy integration. However, there is a lack of conceptual work focusing on practical data analytics deployment schemes and case-specific insights. This paper presents a scalable and flexible physical and digital architecture for extracting data-driven insights from microgrids, with a real-world microgrid utilized as a test-bed. The proposed architecture includes edge monitoring and intelligence, data-processing mechanisms, and edge–cloud communication. Cloud-hosted data analytics have been developed in AWS, considering market arrangements between the microgrid and the utility. The analysis involves time-series data processing, followed by the exploration of statistical relationships utilizing cloud-hosted tools. Insights from one year of operation highlight the potential for significant operational cost reduction through the real-time optimization and control of microgrid assets. By addressing the real-world applicability, end-to-end architectures, and extraction of case-specific insights, this work contributes to advancing microgrid design, operation, and adoption.
... However, nowadays energy companies have access to more data from thousands of internet-of-things (IoT) endpoints within the smart grid, even at low-voltage level. This new landscape creates new opportunities for energy services and datadriven business models, e.g., grid operation and maintenance of distribution networks, fault detection, detection of non-technical losses, load forecasting and demand response [1]. ...
... Turn-on events are detected by applying adaptive thresholding. Specifically, a time instant n is considered as event onset if the following condition is met (1) where is the adaptive threshold for are determined by means of trial-and-error testing. In case of detecting several close in time onset candidates, they are merged into a single onset. ...
Non-intrusive load monitoring (NILM) is a topic that lately attracts both the academic and the industrial interest. NILM is used to reveal useful information regarding the consumption breakdown on appliance or activity level, thus can be a key solution to unlock various smart-home services and opportunities. To that end, deep learning has arisen as a prominent solution. Although most of the known solutions so far focus on a predefined number of home appliances, this paper proposes a multi-class NILM system which can detect in real-time any number of appliances and can be efficiently embedded into simple microprocessors. The key feature for the identification of the appliances is the processing of measured turn-on active power transient responses sampled at 100 Hz. The NILM system includes three stages; adaptive thresholding event detection method, convolutional neural network and k-nearest neighbors classifier. For future extensions, it is capable to automatically identify new appliances; thus, no retraining and additional modeling is required.
This paper reviews digitalisation in energy sector by looking at the business models of 40 interesting new start-up energy companies from around the world. These start-ups have been facilitated by the rise of distributed generation, much of it intermittent in nature. We review Artificial Intelligence (AI), Machine Learning, Deep Learning and Blockchain applications in energy sector. We discuss the rise of prosumers and small-scale renewable generation, highlighting the role of Feed-in-Tariffs (FITs), the Distribution System Platform concept and the potential for Peer-to-Peer (P2P) trading. Our aim is to help energy regulators calibrate their support new business models. Abstract This paper reviews digitalisation in energy sector by looking at the business models of 40 interesting new start-up energy companies from around the world. These start-ups have been facilitated by the rise of distributed generation, much of it intermittent in nature. We review Artificial Intelligence (AI), Machine Learning, Deep Learning and Blockchain applications in energy sector. We discuss the rise of prosumers and small-scale renewable generation, highlighting the role of Feed-in-Tariffs (FITs), the Distribution System Platform concept and the potential for Peer-to-Peer (P2P) trading. Our aim is to help energy regulators calibrate their support new business models.
Recent developments in the eastern Mediterranean, such as significant gas finds; disagreements over the demarcation of maritime boundaries; large-scale violence and political instability following the Arab Spring; mass migration via sea routes; Great Power dynamics in the region; and environmental hazards, make the political entities along the shores of the eastern Mediterranean part of a regional security complex and create strong incentives for regional coordination on maritime security. Material international relations theories predict that growing security challenges (realism) coupled with expected gains (liberalism) will facilitate regional cooperation. Yet, the political entities in the region rely mainly on unilateral actions, or limited quasi-alliances in response to these challenges. The article shows the puzzling gap between the theoretical expectation and practical outcome in the region and explains why regional cooperation in the maritime domain fails to occur. It argues that cooperation on a regional scale fails to take place due to three complementing reasons: 1) lack of shared ideational features like cultural traits, set of values and regime type; 2) enduring rivalries between political entities in the region (Israel–Palestine; Turkey–Greece–Cyprus) coupled with internal strife within other regional political entities (Libya; Syria); and unequal political standing and lack of sovereignty of some of the political entities in the region (Northern Cyprus; the Palestinian Authority and the Gaza Strip).
This paper reviews digitalisation in energy sector by looking at the business models of 40 interesting new start-up energy companies from around the world. These start-ups have been facilitated by the rise of distributed generation, much of it intermittent in nature. We review Artificial Intelligence (AI), Machine Learning, Deep Learning and Blockchain applications in energy sector. We discuss the rise of prosumers and small-scale renewable generation, highlighting the role of Feed-in-Tariffs (FITs), the Distribution System Platform concept and the potential for Peer-to-Peer (P2P) trading. Our aim is to help energy regulators calibrate their support new business models.
The present paper examines the role that green bonds can play in financing the
transition to low carbon economy. We first establish the need for central banks to
respond to climate change challenges and we present the main ways in which they can
get involved. We explain why green bonds should be used to instrument of choice for
financing the low carbon transition, based, on the one hand, on the theoretical
argument of intergenerational burden sharing and, on the other hand, on the practical
need of large long-term infrastructure investments. After defining green bonds, we
present their main characteristics. We then summarize the development of the green
bond market in the last decade. We conclude by presenting ways in which to respond
to existing challenges and barriers, so that the green bonds market develops further.
European Union (EU)’s energy and policy targets necessitate the penetration of new propulsion systems in the automotive industry. In this direction, the deployment of electric vehicles (EVs) and plug-in hybrid electric vehicles that rely on low greenhouse gas emission electricity generation has great potential to significantly alleviate the dependence on fossil fuel consumption in the transport sector. In this study, the policy incentives that different countries have implemented for stimulating the market of EVs are presented. Different policy instruments are examined, in order to support the deployment of EVs in Greece. Based on data from previous study, where total costs of ownership have been calculated, three alternative policy support incentives have been developed and analysed to promote the use of EVs until 2030 in Greece. Results indicate the direct subsidy scheme is more favourable over a carbon taxation system. It is suggested that EV will be competitive with ICE in the year 2022/2023, if no additional policies are implemented in Greece. This study fills the gap in the literature by offering a policy view of the main measures as well of actions that need to be taken in Greece, in order to promote electromobility.
The high level of reliance on external energy supplies, mainly from Russia, is considered a major risk to the energy security in Southeast Europe. This dependency is being addressed by the European Union’s new contractual and institutional framework. The construction of new gas pipelines and related infrastructure is being undertaken in a timely manner to secure supplies from alternative energy sources and enhance their sustainability. Still, the EU’s policies have run up against Russia’s systematic and stubborn resistance as it struggles to preserve its dominance in Europe’s energy market and thwart the implementation of competing energy projects. Despite this, Southeast Europe’s energy markets will soon be able to ensure a higher level of diversification and secure its supplies.
Solar power as a renewable energy resource is discussed. Concentrated solar power generation is explained. Use of linear Fresnel reflector and its compact version is presented. Photovoltaic generation of power is discussed. Solar cells, modules, and array are explained. Tracking system and batteries for solar power installations are considered. Wind power generation as another energy resource is considered next. Wind turbines—horizontal axis type and vertical axis type—are described with sketches. Rotor blades and blade material are mentioned. Geothermal energy as another resource for renewable energy is discussed. Modes of geothermal energy extraction from earth’s crust and processes used are discussed. Dry steam type, flash type, and their combinational processes are treated. Biomass and tidal power generation are briefly mentioned. Distributed power generation, microgrid management demand response, and online voltage stability monitoring are other aspects covered in this chapter. Integration of these systems with grid is discussed. Battery storage systems are considered.
It has been seen that the approaches related to green-manufacturing have significant role in textile industries upon improvement of awareness on environmentally friendly point of view.
Environmental impacts of the material should be taken into consideration for improving new textile structures. Materials should be composed of environmentally friendly raw material and the harmful emissions of whole production processes should be limited, while the material should be easily disposable with no detrimental effects on the environment after use.
The aim of this study is to develop textile structures which have sustainable, environmentally friendly and functional characteristics. Antibacterial knitted fabrics have been improved in this study. Raw materials were selected among environmentally friendly new generation fibers. PLA (Polylactic acid), lyocell and chitosan fibers were mixed, the single jersey knitted fabric composed of 80% PLA 15% Lyocell 5% Chitosan having thickness of 30/1 were produced. Production processes which minimize harmful emissions to the environment were used. Antibacterial efficiency of the designed fabric was tested according to AATCC100. In addition, biodegradation of the improved knitted fabric was tested in soil burial test under standardized laboratory conditions for a defined test period of 4, 12 and 24 weeks. After soil burial test, the ecotoxicological assessment of soil was performed with plants growth test.