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In recent years, the energy landscape has undergone significant transformations, characterized by the integration of renewable energy sources, smart grids, and the proliferation of IoT-enabled devices. As a result, the efficient management of energy resources has become paramount, requiring advanced methodologies in load forecasting and clustering. This article presents an enhanced methodology for short-term load forecasting that focuses on load consumption profile recognition within a smart building environment. The methodology is designed to analyze and identify recurring load consumption profiles and measures of sensors, thereby enhancing load consumption profile recognition capabilities within the smart building context. The interaction between single and grouped datasets is explored to enhance the accuracy and interpretability of predictions, contributing to optimized energy consumption and providing valuable information for demand response programs. The default forecasting methods used in the methodology are artificial neural networks and k-nearest neighbors. For comparing results and evaluating the proposed approach, XGBoost is also employed. The dataset is selected from a specific database, and the clustering method, partitioning type, is applied with k-means. The results, validated with error evaluation models and statistics, reveal the advantages of the proposed approach, especially with three clusters, where the results achieved by the Artificial Neural Network are the best. The clustering process, particularly the partitioning type, demonstrates a strong capability in improving load forecasting in smart buildings and helps understand load consumption patterns and achieve energy savings.

Background Oculofaciocardiodental (OFCD) syndrome is a rare genetic disorder affecting ocular, facial, dental, and cardiac systems, being an X-linked condition caused by pathogenic variants in the BCL-6 corepressor gene (BCOR). We report a case series of three female patients with OFCD syndrome with severe glaucoma.Results Three female patients with OFCD syndrome with different variants involving BCORgene, in heterozygosity: a seven-years-old girl with an insertion (c.2037_2038dupCT), a nine years-old girl with a microdeletion in the X (p21.2-p11.4)) spanning the BCORgene; and a 25 years-old female with a deletion (c.3858_3859del). Systemic involvement is variable among patients ranging from one patient mainly with ocular and dental involvement to one with associated intra-auricular and intra-ventricular defects. All the patients presented with congenital cataracts diagnosed in the first days of life. Cataract surgery was performed without incidents between 6 and 16 weeks of age in all the patients. Postoperatively, the three patients developed ocular hypertension and glaucoma with the need for surgical interventions, including trabeculectomy, Ahmed valve implantation, and cyclophotocoagulation.Conclusion OFCD syndrome characterizes by a severe ocular involvement with glaucoma as a characteristic feature. Ocular hypertension after cataract surgery in these patients is challenging, almost always needing surgery during childhood. Therefore, we consider BCORdisruption may predispose to a higher incidence of glaucoma due to its aggressiveness and early onset on our case series. The awareness of these complications is crucial to an adequate follow-up of the patients.

Energy communities are of the utmost importance for the promotion of active participation in local energy markets under the current agenda of the European Union. Thus, it is crucial that all operations requiring energy transactions keep the stability of electrical grids. To solve this problem, this paper proposes a methodology capable of preventing and solving congestion line-by-line in an electrical network. The methodology consists of a power flow analysis model and two demand response programs. By using the two demand response methods independently or in combination, the methodology gains robustness and can handle complex scenarios that need a significant amount of flexibility. The robustness of the method is demonstrated in the IEEE Low Voltage European test feeder, where multiple periods needed to apply both demand response programs to solve the congestion, maintaining the operational state of the network.

Many studies have examined the potential of the flexible loads within the power systems, taking advantage of Demand Response (DR) programs and Data Mining (DM) to optimize the system response and achieve sustainability through load management. The authors propose aggregation models to study their impact on the power system, using unsupervised DM technique which is Clustering (k-means). The scientific contribution of this paper is related to providing peak-reduction using clustering for load aggregation. A case study has been provided, consisting of three scenarios based on the load aggregation model. The results indicate the system responses to the different scenarios and illustrate the features of each load aggregation model. Furthermore, the results demonstrate how using DR programs combined with DM can effectively provide benefits to the system stability.

Electrical buildings are composed by smart grids technologies intended on improving the energy efficiency. Nowadays, forecasting algorithms are crucial to formulate advance decisions resulting in lower energy costs. This paper uses two forecasting algorithms known as artificial neural networks and k-nearest neighbors to obtain accurate energy predictions in a target week with the support of an annual historic with energy and auxiliary sensors devices data. Green computing is also addressed in this paper to value the environmental sustainability of computing devices. This is possible by reducing the computational effort of the GPU device dedicated on forecasting activities. Therefore, the historic and predictions of this paper are contextualized in five minutes periods and hour schedules with energy activity behaviors.

The active consumers' role in the power and energy market is changing. To deal with the volatile behavior from the Distributed Generation (DG) resources, load flexibility must be provided from the demand side. So, Demand Response (DR) events are triggered. To reduce the response uncertainty, the authors proposed a Trustworthy Rate (TR) to classify the performance of the active consumers in a community. For comparison with previous works' approaches, the innovation from the present study introduces classification methods for deciding the best approach for selecting the DR program participants (load shifting) for specific contexts and reduction goals. From the results, the Decision Tree model created had a lower accuracy performance than the Artificial Neural Network one. To evaluate the effectiveness of the proposed model, the mean absolute error (MAE) was used by the authors.

Ever-increasing electricity prices due to inflation has become one of the main problems for manufacturing industries to deal with, mainly in a competitive market. The usage of Renewable Energy Resources (RER)s can be a great way to mitigate the energy costs of the electricity market, by not only covering the energy consumption but also turning a profit with the surplus. This paper aims to address these issues by proposing an intelligent production line scheduling system, in a flexible job shop configuration, that focuses on reducing energy costs. It considers dynamic pricing, RERs usage and surplus selling, and complex constraints applied in the production plan. To achieve these results, a Genetic Algorithm (GA) is employed. A case study from the literature, that uses real production data, is presented which does not take into account energy selling. Results show that with the proposed GA energy costs can be further reduced by 24.6% when considering RERs surplus selling.

Effective and active consumers providing flexibility through Demand Response (DR) programs have three important aspects: rating each consumer according to previous participation, remuneration of that participation, and determining the rebound effect of consumption after the event. In this article, the authors design a rate to classify and select the proper participants for a DR event considering the context in which the event is triggered. The aggregator estimated the shifting of consumption to periods after the event is modeled, and the respective remuneration is estimated under different scenarios. This shifting can be done in several time frames in the future. The scenarios are developed to test the acceptable time range in which the load should be allocated according to the rebound effect. The results show that a higher time range can avoid huge peak consumption, optimizing the system operation with benefits for consumers, DSO, and the aggregator.

The flexibility provided by the demand side will be crucial to take a step forward to increase the penetration of renewable energy resources in the system. The proposed methodology provides the aggregator with information about the most reliable consumers, attributing a trustworthy rate that characterizes their performance on Demand Response (DR) events. The innovation relies on applying rates and evaluating the context in which the event is triggered and the factors that influence such rates. The authors find that context is essential to understand which participants are available for the event and achieve the reduction target successfully. Also, the proposed methodology focuses on the performance and the proper motivation for continuous participation, reducing the uncertainty of the response in DR events by giving higher economic compensation to the active consumers with better results. Distributed generation is also optimally managed by the aggregator. Findings prove the feasibility of the proposed methodology supporting the Aggregator in communities and smart cities management.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.respol.2010.05.003 Byline: Pedro de Faria (a), Francisco Lima (b), Rui Santos (c) Keywords: Innovation; Cooperation; Community Innovation Survey Abstract: This paper analyses the importance of cooperation partners for the development of innovation activities. We contribute to the literature on cooperation in innovation activities that seeks to identify the characteristics differentiating cooperative from non-cooperative firms by proposing a different approach to this problem. We distinguish firms according to their evaluation of cooperation partners in the development of innovation activities. We apply a probit selection model to account for the firm's decision to cooperate (or not). The data we use come from the Portuguese Third Community Innovation Survey. Our estimation results show that firms from high-technological industries, with higher levels of absorptive capacity and of innovation investment, who give importance to incoming spillovers management, and who cooperate with firms from the same group or with suppliers, place greater value on cooperation partners in the innovation process. Additionally, we also find that the factors influencing the importance attributed to cooperation activities are different from the ones behind the decision to cooperate. Author Affiliation: (a) Department of Innovation Management and Strategy, Faculty of Economics and Business, University of Groningen, Nettelbosje 2, 9747 AE Groningen, The Netherlands (b) Instituto Superior Tecnico and CEG-IST, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisboa, Portugal (c) CESNOVA, Faculdade de CiA*ncias Sociais e Humanas, Universidade Nova de Lisboa, Avenida de Berna, 26-C, 1069-061 Lisboa, Portugal Article History: Received 7 January 2008; Revised 21 April 2010; Accepted 6 May 2010

Active communities are emerging thanks to the necessity of creating a cleaner and safer energy system. The growing concern regarding climate change urges a solution to remove fossil fuels from the production equation. The Distributed Generation (DG) technologies are presented as a substitute, but the main resources’ behavior is highly uncertain. Flexibility from the demand side is needed. In this way, the authors resort to mixed-integer linear programming optimization to schedule the active resources introduced by the Smart Grid concept: DG, Demand Response programs, and Energy Storage Systems. In this study, the last one is the focus where the impact of these technologies in an active community is analyzed and discussed. The authors performed a clustering method to identify patterns on Energy Storage System (ESS) profiles, finding the optimal number of clusters first. The results show the importance of ESS from both Aggregator and active consumer perspectives.

The environmental concerns around energy, namely electricity, have driven attention to innovative approaches to fostering consumers participation in the whole energy system management. Accordingly, the concept of demand response provides incentives and signals no consumers to change the normal consumption patterns to increase the use of renewables, for example. The problem is that such response of consumers has a large amount of uncertainty. This paper proposes a methodology in which different demand response programs are activated and deactivated during an event to cover the demand response deviations from the target. Even after achieving the response target, if the actual response of consumers is reduced to a critical level, additional programs are activated. The proposed approach considers consumers participating in an aggregate way, supported by an aggregator. The case study in this paper accommodates three demand response programs, showing how different consumers are activated and remunerated for the provision of consumption reduction. It has been seen that the proposed methodology is flexible as desired to accommodate the uncertainty of consumers’ responses.

With the growing concern regarding climate change, solutions such as distributed generation, namely renewable-based, increased in the energy system. However, their volatile behavior needed more flexibility from the demand side to balance – resorting to demand response programs. Active consumers play a critical role in this new paradigm. In this way, the uncertainty of their response to triggered events should be modeled. The authors developed a contextual consumer rate to properly select the participants in a demand response event according to their previous events in similar contexts. The innovation in the present paper lies in the classification of new active consumers with no prior experience. A decision tree method was then used to attribute a trustworthy rate. A sensitivity study on the number of leaf nodes used is explored. The results prove that the use of private information related to active consumer increase the performance of the algorithm.

New energy approaches and models require an ample amount of data to be collected in order to be tested and validated, however ordinary on-site load data measuring from buildings or households can be expensive and time-consuming. This paper creates a dataset of a residential community by attributing sample consumption and photovoltaic production profiles and disaggregating the total power consumption of these households into the consumption of several individual appliances commonly used. Initially, a few consumption and photovoltaic production profiles and an extensive library of appliance profiles were collected, allowing the tool created to randomly assign the appliances’ load profile, if possible, into the available consumption areas of each end-user and in certain intervals previously defined by the user. In the case study, we generated a community with 49 households and a public building (municipal library) in 50 buildings and attributed 15 of these with renewable solar energy. The results show the creation of a flexible energy community where the appliances allocated can occupy approximately 40% of the total consumption profiles.

The energy sector, as many, is being adapted to meet environmental concerns and avoid fossil fuels. So, Smart Grids concept is promoted, penetrating Distributed Generation into the grid, namely renewable-based energy, providing an environmentally friendly alternative. Also, the consumers’ role is empowered through Demand Response (DR). The consumers are incentivized to actively modify their consumption behavior receiving the proper remuneration. With this, the power system will decrease operation costs and DR can be used as an alternative to generation. However, manage these new and active resources as well as their transactions in the energy market is a complex task due to the uncertainty associated. Many factors can cause a non-response and the Aggregator must be able to manage these situations mainly when a certain target of reduction is required from the wholesale market. The authors proposed an approach including a Trustworthy Rank to select consumers for on DR events: consumers participate considering their reliability. In the present paper, the effects of the approach will be compared between two seasons, proving the viability on giving the correct information to the community manager and understanding how variable is the behavior of this rank at different times of the year.

The increasing penetration of renewable energy sources led to the development of several energy management approaches. One of the main topics in this field is related to the load forecast in buildings, which can contribute to more intelligent and sustainable energy consumption. However, it is necessary to build a proper forecast model, capable of detecting an accurate consumption profile. The minimum effort to achieve this is to extract a historic with energy consumptions to use as input. Additional information should be considered in order to achieve improvements in forecasting results. This way, information regarding the day of the week is discussed as a reliable source of information that may enhance the load forecast. In this paper, two forecasting techniques, namely neural networks and support vector machine, are used to predict the energy consumption of a building for all 5 min from a period. The proposed model finds the best forecasting technique and determines if the additional information regarding the day of the week enhances the load forecast. In this case study, a period of two years and a half data with a 5-minute time interval is used. Moreover, several tests are performed for varied inputs to understand if the insights are consistent for these tests. This data has been adapted from an office building to illustrate the advantages of the proposed methodology.

Considering the technological advances of the industrial sector today, it appears that the management of energy resources has become increasingly prominent. Thus, to make this management more efficient, it is necessary to take into account the production planning and scheduling concept, since it allows influencing the scheduling of production at the level of cost and efficiency. Thus, the objective of this paper is to present a methodology that allows making the best possible scheduling of a textile production line to optimize it. This optimization is elaborated with the help of genetic algorithms, and, as it can be verified in this paper, it is possible to make an optimization of the production line at the level of energy cost or the level of energy consumption or optimization of both. Thus, the case study of this paper is based on a textile production line that produces a variety of products through three machines capable of performing numerous tasks, which can be done on more than one machine. Likewise, this production line enjoys photovoltaic production. This paper presents several case studies that allow for highlighting the impact of the methodology covered in the respective production line, where it is illustrated through different graphics.

This paper aims to promote the importance and advantages that the clustering method brings to the world of industry, making it possible to increase production efficiency and to manage the energy resources available better. The purpose of this paper is to group the consumption profiles of a task, in order to be able to determine which is the typical load profile of the task through the Fuzzy C-Means clustering method. The case study of this paper focuses on a task performed by three machines that make up a textile production line that makes several products. Each product, when going through a task performed by a specific machine, has a specific consumption and duration. Thus, by machine, it is determined which is the typical profile of ideal consumption to perform the designated task. In the same way, the general consumption profile of the task is highlighted, that is, the possible consumption profile to be expected when executing this task on one of the three machines.

This paper proposes an optimization approach to minimize the power consumption of an office building considering user comfort and key performance indicators. The present approach implements the power reduction by reducing power consumption of lights and air conditioners as reducible loads, and implements load shifting by considering dishwasher as shift able load. Two key performance indicators and several comfort parameters are defined to limit the power reduction of lights and air conditioners to consider the user comfort. In the context of load shifting, the complete operation cycle of dishwasher should be shifted without interruption based on the priority weight of the periods and available power. In the present optimization approach, sufficient number of periods have been considered for running the optimization, considering operational constraints and multiple levels for key performance indicators to evaluate their effectiveness. The case study of the work contains six different scenarios to validate different characteristics of the approach. Real consumption data of 20 lights, 10 air conditioners and 1 dishwasher in 20 working days have been applied to implement six different scenarios to propose a real comparable view of present approach.

Demand Response programs have been assuming lot of importance in the simulations of electric users’ loads’ profiles. The evolution of these simulations helps defining new models able to predict power consumption trends for different user types. In order to better match consumption and production energy curves, highly precise forecasts of loads’ profiles are needed. This goal can be achieved also thanks to the study of the elasticity factor, that identifies the will of a user to have his consumptions reduced after a remuneration. In this paper, a way to obtain it has been presented, together with an interpolation able to predict it. Its definition is also supposed to help building scenarios that consider the impact of the long-term use of RTP remuneration (Real Time Price). Importance of having a real-time elasticity value able to adapt to specific situations is discussed, as for example user’s habits during the weekends or weekdays and weather forecasts.

Center pivot systems are widely used to overcome the irrigation needs of agricultural fields. In this paper, an autonomous approach is proposed in order to improve the low efficiency of irrigation by developing a system based on the water requirement of the plantations, through field data. The data are local temperature, local wind, soil moisture, precipitation forecast, and soil evapotranspiration calculation. This information enables the system to calculate the real evapotranspiration for not being necessary to restrict to lysimetric measures. By this way, the system schedules the irrigation for the lower cost periods, considering the produced energy by the local resources, and the price of energy purchased from the utility grid. Also, it is considered that the irrigation must be carried out within the time interval in which the plantations do not reach the wilding point, so it will be carried out at the periods with the lowest cost. This will optimize the overall operational costs of the irrigation.

In the last decade Demand Response (DR) programs have been influencing loads’ profiles of electric users who participate in these programs. The evolution of the simulations able to study them brought to the possibility of defining new models that can consider power consumption profiles for different types of user (MAT, AT, MT, BTE, BTN-2, BTN-1) but, in order to better match consumption and production energy curves, highly precise predictions of loads’ profiles are still needed. This goal can be achieved also thanks to the study of the price elasticity factor. A way to obtain it will be examined in this paper: price and power absorption variations will be considered because it is defined as the ratio of their relative variations before and after DR. This work focuses on the profiles of price variations ΔP with respect to the absorbed power variation ΔQ: users indeed are expected to vary their consumptions according to different values of remunerations. Moreover, different ranges of elasticities have been evaluated in order to study the behavior of ΔP profiles for the more representing users. Finally, effects of a wrong interpolation have been discussed in order to see their consequences on the actual available power.

The current Energy Market is not yet ready for the integration of the Smart Grid context. Concepts such as Demand Response and Distributed Generation, namely renewable energy resources, are not yet included in current business models in order to the system flow properly. Therefore, the authors propose a methodology that gathers all these concepts through the optimization, aggregation and remuneration of resources. The purpose of this paper will be to study the influence of the tariff used for the remuneration and incentive of the participants in the formation of the groups in the aggregation phase. Three studies were performed: aggregation with only the result of the optimization (schedule power for each resource); this result and the fixed tariff associated with each resource; result and a new tariff that considers real-time values.

Rapid growth of energy consumption in last decades, made the world persuaded to energy optimization and management. Therefore, producers and prosumers should be equipped with the automation infrastructures to perform the management programs, like demand response. Office buildings are considering as a proper case for implementing energy consumption minimization since they are responsible for a huge portion of total energy consumption. This paper proposes a multi-period optimization algorithm implemented in Supervisory Control and Data Acquisition system of an office building. The developed optimization algorithm is an efficient solution considered for minimizing the power consumption of air conditioners by considering the user comfort constraints. A case study with several scenarios is implemented to verify the performance of proposed algorithm in real life using real data of the building. The obtained results show the impacts of different comfort constraints of algorithm while the main target of the optimization has been reached.

The demand response program explained in this article is designed to be implemented in communities seeking to achieve a self-sustaining system, namely through renewable energy such as photovoltaic energy. This article, through concepts such as prosumer and clustering, aims to make the most efficient management of the resources provided by the energy community. The developed demand response clusters the different consumers who have the same type of consumption throughout the day. That is, it brings together those whose behavior of the respective loads resemble each other and can be viewed from the perspective of an individual load or even clustered with one or more loads. The study comprises three villages with different numbers of consumers and charges, where, through their participation, it is estimated that there are reductions in electricity bills and, for those who collaborated for the study, it is attributed a remuneration according to their performance.

With the new challenges and complexity in power electricity management, demand response programs can be a way to reduce stress and strengthen power grids. However, as demand response implies end users to intentionally change their consuming patterns to adapt to grids needs, some decision-making support tools are necessary. The present paper proposes an energy management and controlling tool to assist electricity end users to make their decision to change consumption pattern in a DR scenario while using key performance indicators. The tool was tested using a group of 20 end users and showed a consistent result throughout all the elements in the sample.

Local energy communities with information from the real-time market may improve the market operation but also increase the complexity of the management problem thanks to the uncertainty associated with the actual response of these resources. For instance, consumers with price knowledge may change their power consumption to lower-cost periods. The authors present a model to deal with uncertainty from the Aggregator perspective: apply reliability rates to each consumer according to their actual response in events of Demand Response (DR). The consumers with higher rates are chosen to participate in the local flexibility markets. To compute the final rate, three different independent rates are used: Historical rate with past information, Cut-rate from the response in the actual period and the Last Day Rate which is the final reliability rate from the previous day. In the present paper, the influence of each independent rate, through the weight used, is studied.

Factories account for more than 42% of global energy consumption. In order to contribute to reduce carbon footprint and increase energy efficiency, it is important to optimize the tasks and time of product manufacturing according to the renewable generation and lower prices of the grid but without compromising production quality and output. This paper aims to develop flexible optimization platform for industrial production processes. The proposed production scheduling model is formulated as a 15-minute interval of one week time-span adopting mixed-integer linear optimization model and solved in TOMLAB. The model considers general production constraints for different products and takes into account with the photovoltaic generation of the factory as well as the dynamic price of the grid. The results are compared with a reference case without photovoltaic and where the dynamic price is not considered. The energy cost savings can amount up to 29% or 100 € in the considered example.

The adoption of distributed resources by households, e.g., storage units and renewables, open the possibility of self-consumption (on-site generation), sell energy to the grid as a small producer, or do both according to the context of operation. In this paper, a framework capturing the interactions between an aggregator and a large number of households is envisaged. We consider households equipped with distributed resources and simple smart technologies that look for the reduction of energy bills and can perform demand response actions. A mixed-integer linear programming formulation that provides optimal scheduling of household devices and minimal operation costs is developed. Results show that the model can be applied considering up to 10000 households. Moreover, households can reduce up to 20% of their energy bill on average using storage units and demand response. Besides, the aggregator can attain profits by offering the resulting flexibility to upper-level players of the energy chain, such as the distribution system operator.

Non-state and subnational climate actors have become central to global climate change governance. Quantitatively assessing climate mitigation undertaken by these entities is critical to understand the credibility of this trend. In this Perspective, we make recommendations regarding five main areas of research and methodological development related to evaluating non-state and subnational climate actions: defining clear boundaries and terminology; use of common methodologies to aggregate and assess non-state and subnational contributions; systematically dealing with issues of overlap; estimating the likelihood of implementation; and addressing data gaps.

Individuals vaccinated against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), when infected, can still develop disease that requires hospitalization. It remains unclear whether these patients differ from hospitalized unvaccinated patients with regard to presentation, coexisting comorbidities, and outcomes.

Climate change mitigation scenarios are finding a wider set of users, including companies and financial institutions. Increased collaboration between scenario producers and these new communities will be mutually beneficial, educating companies and investors on climate risks while grounding climate science in real-world needs.

Corporate climate action is increasingly considered important in driving the transition towards a low-carbon economy. For this, it is critical to ensure translation of global goals to greenhouse-gas (GHG) emissions reduction targets at company level. At the moment, however, there is a lack of clear methods to derive consistent corporate target setting that keeps cumulative corporate GHG emissions within a specific carbon budget (for example, 550–1,300 GtCO2between 2011 and 2050 for the 2 °C target). Here we propose a method for corporate emissions target setting that derives carbon intensity pathways for companies based on sectoral pathways from existing mitigation scenarios: the Sectoral Decarbonization Approach (SDA). These company targets take activity growth and initial performance into account. Next to target setting on company level, the SDA can be used by companies, policymakers, investors or other stakeholders as a benchmark for tracking corporate climate performance and actions, providing a mechanism for corporate accountability.

The Smart Grid environment allows the integration of resources of small and medium players through the use of Demand Response programs. Despite the clear advantages for the grid, the integration of consumers must be carefully done. This paper proposes a system which simulates small and medium players. The system is essential to produce tests and studies about the active participation of small and medium players in the Smart Grid environment. When comparing to similar systems, the advantages comprise the capability to deal with three types of loads – virtual, contextual and real. It can have several loads optimization modules and it can run in real time. The use of modules and the dynamic configuration of the player results in a system which can represent different players in an easy and independent way. This paper describes the system and all its capabilities.

The increasing and intensive integration of distributed energy resources into distribution systems requires adequate methodologies to ensure a secure operation according to the smart grid paradigm. In this context, SCADA (Supervisory Control and Data Acquisition) systems are an essential infrastructure. This paper presents a conceptual design of a communication and resources management scheme based on an intelligent SCADA with a decentralized, flexible, and intelligent approach, adaptive to the context (context awareness). The methodology is used to support the energy resource management considering all the involved costs, power flows, and electricity prices leading to the network reconfiguration. The methodology also addresses the definition of the information access permissions of each player to each resource. The paper includes a 33-bus network used in a case study that considers an intensive use of distributed energy resources in five distinct implemented operation contexts.

•A security-based model is proposed for the scheduling of BES in the joint energy and reserve markets.•The coupling between the security and energy management problems is analyzed.•The pre and post contingency intervals are modeled.•The frequency excursion and rate of change of frequency are considered as constraints of optimization problem.

Smart grids are envisaged as infrastructures able to accommodate all centralized and distributed energy resources (DER), including intensive use of renewable and distributed generation (DG), storage, demand response (DR), and also electric vehicles (EV), from which plug-in vehicles, i.e. gridable vehicles, are especially relevant. Moreover, smart grids must accommodate a large number of diverse types or players in the context of a competitive business environment.

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant.

Linking innovation with firms’ growth and performance is still a challenge for research. It is difficult to observe the effect of new products and processes on firms’ performance, because it may take a long time between innovation and the economic gain of firms. This paper intends to address this challenge and look at the relationship between innovation and firm performance, distinguishing product and process innovation. We join two different databases: the third Community Innovation Survey (CIS III) survey and the Portuguese Quadros de Pessoal. We observe an impact of innovation activities in performance gains three years later. This effect is observed only for small firms (less than 50 employees), and it is stronger for newer firms, thus leading to conclude that innovation activities may be a source of competitive advantage, especially for new firms.

Purpose To report the safety and efficacy of intravitreal recombinant tissue plasminogen activator (rtPA) with gas for managing submacular hemorrhage.Methods Patients with submacular hemorrhage centered in or close to the fovea underwent hemorrhage displacement with intravitreal injection of rtPA (50 μg/0.05 mL) followed by gas injection (0.3 mL SF6). Anatomic and visual outcomes are described.Results Exudative age-related macular degeneration (AMD) (n = 4) and blunt trauma (n = 2) were the etiologies of submacular hemorrhage in this sample. Intravitreal injection of rtPA decreased the extent of submacular hemorrhage in all eyes, with complete hemorrhage displacement in 2 and partial displacement in 4. Visual acuity remained stable (n = 4) or improved (n = 2) after the procedure. Improvements in anatomic and visual outcomes were less evident in exudative AMD cases, which also had longer hemorrhage duration. Recurrence occurred only in 1 eye. No evident rtPA-associated retinal toxicity was observed.Conclusions Untreated submacular hemorrhage has poor visual prognosis. Our results suggest that rtPA injection is a minimally invasive, simple, inexpensive procedure with few related complications. Cost-benefit of this injection seems acceptable.

Smart energy community, energy community or many other variants of this name has been researched since the 70s but has gained more prominence in the last decade. In order to better understand the concept of energy community and analyze the main research trends in this subject, it was performed a systematic literature review using six databases and a set of carefully chosen keywords obtained after preliminary analysis of energy community concept and its many variations. As a result, the literature review shows that there are many concepts and overlapping of definitions, which creates confusion between researches and readers and the need to standardize the terminology used. Results also highlight the need to analyze the synergistic improvement in multi-purpose energy communities and to research energy islands as smart communities in a way to simulate, create and implement solutions that can be adapt in land communities.

Physical and transitional risks resulting from climate change are already inducing significant direct and indirect impacts on organizations—such as damages to assets, disruption to supply chains, or shifts in supply and demand for certain commodities, products or services. The current short-termism of most companies suggests the importance of raising awareness among the private sector about the potential risks of climate change. However, companies increasingly are reporting and disclosing climate risks and associated costs as asked for benchmarking by financial institutions and to comply with regulations with respect to sustainable finance. A guidance on how to do a climate risk assessment and to estimate the costs of physical climate risk as well as transitional and systemic risk concerning their operations and value chain management is lacking. This paper presents a stepwise blueprint on climate risk assessment and financial disclosures that support companies on reorienting capital flows towards more sustainable investments and with their disclosure process to foster transparency and long-termism in financial and economic activity in line with the action plan on sustainable finance adopted by the European Commission in March 2018 to achieve sustainable and inclusive growth.

This paper presents a comprehensive analysis on the latest advances in transactive energy systems. The main contribution of this work is centered on the definition of transactive energy concepts and how such systems can be implemented in the smart grid paradigm. The analyzed works have been categorized into three lines of research: (i) transactive network management; (ii) transactive control; and (iii) peer-to-peer markets. It has been found that most of the current approaches for transactive energy are available as a model, lacking the real implementation to have a complete validation. For that purpose, both scientific and practical aspects of transactive energy should be studied in parallel, implementing adequate simulation platforms and tools to scrutiny the results.

Several letters to the editor are presented in response to articles in previous issues including buying the digital issue of "Ultrarunning" magazine and enjoying it, and another commending the December 2013 cover.