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It is imperative to increase the connectable capacity (i.e., hosting capacity) of distributed generation in order to decarbonise electricity distribution networks. Hybrid generation that exploits complementarity in resource characteristics among different renewable types potentially provides value for minimising technical constraints and increasing the effective use of the network. Tidal, wave and wind energy are prominent offshore renewable energy sources. It is of importance to explore their potential complementarity for increasing network integration. In this work, the novel introduction of these distinct offshore renewable resources into hosting capacity evaluation enables the quantification of the benefits of various resource combinations. A scenario reduction technique is adapted to effectively consider variation of these renewables in an AC optimal power flow-based nonlinear optimisation model. Moreover, the beneficial impact of active network management (ANM) on enhancing the renewable complementarity is also investigated. The combination of complementary hybrid generation and ANM, specifically where the maxima of the generation profiles rarely co-occur with each other and with the demand minimum, is found to make the best use of the network components.

The bioeconomy occupies the centre of the Green Deal, the EU’s plan to support transformative growth following the COVID-19 episode. However, parts of the EU, such as countries in Central and Eastern Europe (CEE) continue to lag behind in harnessing the potential held by the bioeconomy. This article argues that in CEE countries, where the primary and conventional bioeconomy sectors play a more important role, ‘early’ transition pathways such as improvements in productivity and practice- as well as commercialisation-oriented innovation (the do–use–interact model: DUI) are just as important as approaches based on (generally publicly supported) R&D, innovation adoption, and technology transfer (science–technology–innovation model: STI), typically associated with high-value bioindustrial applications. The argument is tested by conducting a survey of 352 experts in the region that gives an insight into the CEE macro-region’s assets with respect to deploying the bioeconomy’s potential and assessing the transition pathways relevant to the better performance of bioeconomy (primary, manufacturing, and other related) sectors. The results show the particular relevance of consolidating the primary and traditional sectors to support improvements in productivity based on the vertical and horizontal interaction typically associated with DUI, while the relevance of STI is mostly linked to advanced sectors, which are narrowly distributed across the region. The findings are relevant to policy given that the EU’s bioeconomy policy has thus far chiefly focused on STI support that better corresponds to the needs of countries at more advanced stages of developing the bioeconomy, but is less appropriate for the specific context and needs of CEE.

Daily operations and planning in a smart grid require a day-ahead load forecasting of its customers. The accuracy of day-ahead load-forecasting models has a significant impact on many decisions such as scheduling of fuel purchases, system security assessment, economic scheduling of generating capacity, and planning for energy transactions. However, day-ahead load forecasting is a challenging task due to its dependence on external factors such as meteorological and exogenous variables. Furthermore, the existing day-ahead load-forecasting models enhance forecast accuracy by paying the cost of increased execution time. Aiming at improving the forecast accuracy while not paying the increased executions time cost, a hybrid artificial neural network-based day-ahead load-forecasting model for smart grids is proposed in this paper. The proposed forecasting model comprises three modules: (i) a pre-processing module; (ii) a forecast module; and (iii) an optimization module. In the first module, correlated lagged load data along with influential meteorological and exogenous variables are fed as inputs to a feature selection technique which removes irrelevant and/or redundant samples from the inputs. In the second module, a sigmoid function (activation) and a multivariate auto regressive algorithm (training) in the artificial neural network are used. The third module uses a heuristics-based optimization technique to minimize the forecast error. In the third module, our modified version of an enhanced differential evolution algorithm is used. The proposed method is validated via simulations where it is tested on the datasets of DAYTOWN (Ohio, USA) and EKPC (Kentucky, USA). In comparison to two existing day-ahead load-forecasting models, results show improved performance of the proposed model in terms of accuracy, execution time, and scalability.

Islands face unique challenges on their journey towards achieving carbon neutrality by the mid-century, due to the lack of energy interconnections, limited domestic energy resources, extensive fossil fuel dependence, and high load variance requiring new technologies to balance demand and supply. At the same time, these challenges can be turned into a great opportunity for economic growth and the creation of jobs with non-interconnected islands having the potential to become transition frontrunners by adopting sustainable technologies and implementing innovative solutions. This paper uses an advanced energy–economy system modeling tool (IntE3-ISL) accompanied by plausible decarbonization scenarios to assess the medium- and long-term impacts of energy transition on the energy system, emissions, economy, and society of the island of Mayotte. The model-based analysis adequately captures the specificities of Mayotte and examines the complexity, challenges, and opportunities to decarbonize the island’s non-interconnected energy system. The energy transition necessitates the adoption of ambitious climate policy measures and the extensive deployment of low- and zero-carbon technologies both in the demand and supply sides of the energy system, accounting for the unique characteristics of each individual sector, while sectoral integration is also important. To reduce emissions from hard-to-abate sectors, such as transportation and industry, the measures and technologies can include the installation and use of highly efficient equipment, the electrification of end uses (such as the widespread adoption of electric vehicles), the large roll-out of renewable energy sources, as well as the production and use of green hydrogen and synthetic fuels.

The success of the energy transition in Europe depends on the engagement of citizens and the sustainable replacement of conventional generation with renewable production. Highlights of the PAN European Technology Energy Research Approach (PANTERA) project, a H2020 coordination and support action, are presented in this paper. In broad terms, PANTERA offers a forum for actors in the smart grid to support the expansion of activities in smart grid research, demonstration, and innovation, especially in the below-average spending member states in the European Union (EU). The focus of this paper is on those activities of the project consortium related to the identification of gaps and barriers in regulations, standards, and network codes that hinder the sustainable engagement of energy citizens in the energy transition. The paper summarises the challenges to citizen engagement in the energy transit and considers the enablers that make the engagement of citizens viable, e.g., demand response (DR), renewable energy resources (RESs), and modern designs for local energy markets (LEMs). We focus on the barriers to the enablers that are explicitly and implicitly related to regulations, standards, and network codes and explore aspects of the relevant regulations and standards of the sample below-average spending member states. A specific case study of a research and demonstration project in Ireland for updating the network codes is also presented to demonstrate the ways in which member states are attempting to remove the barriers and enable citizen participation in the smart energy transition. Finally, the opportunities to foster smart grid research and innovation through shared knowledge and insights offered by the PANTERA European Interconnection for Research Innovation and Entrepreneurship (EIRIE) platform are highlighted.

The development of new climate change policies has increased the motivation to reduce energy use in buildings, as reflected by a stringent regulatory landscape. The construction industry is expected to adopt new methods and strategies to address such requirements, focusing primarily on reducing energy demand, improving process efficiency and reducing carbon emissions. However, the realisation of these emerging requirements has been constrained by the highly fragmented nature of the industry, which is often portrayed as involving a culture of adversarial relationships and risk avoidance, which is exacerbated by a linear workflow. Recurring problems include low process efficiency, delays and construction waste. Building information modelling (BIM) provides a unique opportunity to enhance building energy efficiency (EE) and to open new pathways towards a more digitalised industry and society. BIM has the potential to reduce (a) waste and carbon emissions, (b) the endemic performance gap, (c) in-use energy and (d) the total lifecycle impact. BIM also targets to improve the whole supply chain related to the design, construction as well as the management and use of the facility. However, the construction workforce is required to upgrade their skills and competencies to satisfy new requirements for delivering BIM for EE. Currently, there is a real gap between the industry expectations for employees and current training and educational programmes. There is also a set of new requirements and expectations that the construction industry needs to identify and address in order to deliver more informed BIM for EE practices. This paper provides an in-depth analysis and gap identification pertaining to the skills and competencies involved in BIM training for EE. Consultations and interviews have been used as a method to collect requirements, and a portfolio of use cases have been created and analysed to better understand existing BIM practices and to determine current limitations and gaps in BIM training. The results show that BIM can contribute to the digitalisation of the construction industry in Europe with adapted BIM training and educational programmes to deliver more informed and adapted energy strategies.

Research and Innovation (R&I) are a key part of the EU strategy towards stronger growth and the creation of more and better jobs while respecting social and climate objectives. In the last decades, improvements in costs and performance of low-carbon technologies triggered by R&I expenditures and learning-by-doing effects have increased their competitiveness compared to fossil fuel options. So, in the context of ambitious climate policies as described in the EU Green Deal, increased R&I expenditures can increase productivity and boost EU economic growth and competitiveness, especially in countries with large innovation and low-carbon manufacturing base. The analysis captures the different nature of public and private R&I, with the latter having more positive economic implications and higher efficiency as it is closer to industrial activities. Public R&D commonly focuses on immature highly uncertain technologies, which are also needed to achieve the climate neutrality target of the EU. The model-based assessment shows that a policy portfolio using part of carbon revenues for public and private R&D and development of the required skills can effectively alleviate decarbonisation costs, while promoting high value-added products and exports (e.g., low-carbon technologies), creating more high-quality jobs and contributing to climate change mitigation.