Energy efficient festival electrification can be viewed as a middle-consumption problem, standing between smart household applications and larger commercial consumers. The optimal deployment of different resources, such as local renewable energy production (RES), diesel generators (DG) and energy storage systems (ESS) may bring about significant financial gain for the organizer, and is usually framed as an off-grid or limited grid-connection problem. This makes online planning particularly challenging due to the uncertainty related to the RES production, constraints regarding the operation of the diesel generators and limitations of the grid connection. In this paper a new online planning algorithm based on two-stage stochastic programming is proposed in order to address the aforementioned challenges and provide minimal-cost, uninterrupted, and sustainable electrification of festivals under dynamically priced grid energy. Data based on real festival events are used in order to illustrate the effectiveness of the proposed methodology.

Social media have become dominant in everyday life during the last few years where users share their thoughts and experiences about their enjoyable events in posts. Most of these posts are related to different categories related to: activities, such as dancing, landscapes, such as beach, people, such as a selfie, and animals such as pets. While some of these posts become popular and get more attention, others are completely ignored. In order to address the desire of users to create popular posts, several researches have studied post popularity prediction. Existing works focus on predicting the popularity without considering the category type of the post. In this paper we propose category specific post popularity prediction using visual and textual content for action, scene, people and animal categories. In this way we aim to answer the question What makes a post belonging to a specific action, scene, people or animal category popular? To answer to this question we perform several experiments on a collection of 65K posts crawled from Instagram.

Smart grid solutions with enabling technologies such as energy management systems (EMSs) and smart meters promote the vision of smart households, which also allows for active demand side in the residential sector. These technologies enable the control of residential consumption, local small-scale generation, and energy storage systems to respond to time-varying prices. However, shifting loads simultaneously to lower price periods is likely to put extra stress on distribution system assets such as distribution transformers. Especially, additional new types of loads/appliances such as electric vehicles (EVs) can introduce even more burden on the operation of these assets, which is an issue that needs special attention. Such extra stress can cause accelerated aging of distribution system assets and significantly affect the reliability of the system. In this paper, the impact of a smart neighborhood load on distribution transformer aging is investigated. The EMS of each household is designed to respond to prices and other signals emitted by the responsive load serving entity within the relevant demand response strategy. An optimization framework based on mixed-integer linear programming is presented in order to define the EMS structure. Then, the equivalent aging of the distribution transformer is examined with a thermal model under different scenarios. The case studies that are presented indicate that the integration of EVs in residential premises may indeed cause accelerated aging of the distribution transformers, while the need to investigate the efficiency of dynamic pricing mechanisms is rendered evident.

In this paper, a novel real-time rolling horizon optimization framework for the optimal operation of a smart household is presented. A home energy management system (HEMS) model based on mixed-integer linear programming (MILP) is developed in order to minimize the energy procurement cost considering that the household is enrolled in a dynamic pricing tariff scheme. Several assets such as a photovoltaic (PV) installation, an electric vehicle (EV) and controllable appliances are considered. Additionally, the energy from the PV and the EV can be used either to satisfy the household demand or can be sold back to the grid. The uncertainty of the PV production is estimated using time-series models and performing forecasts on a rolling basis. Also, appropriate distribution is used in order to model the uncertainty related to the EV. Besides, several parameters can be updated in real-time in order to reflect changes in demand and consider the end-user's preferences. The optimization algorithm is executed on a regular basis in order to improve the results against uncertainty.

In this paper, the optimal operation of a neighborhood of smart households in terms of minimizing the total energy procurement cost is analyzed. Each household may comprise several assets such as electric vehicles, controllable appliances, energy storage and distributed generation. Bi-directional power flow is considered both at household and neighborhood level. Apart from the distributed generation unit, technological options such as vehicle-to-home and vehicle-to-grid are available to provide energy to cover self-consumption needs and to inject excessive energy back to the grid, respectively. The energy transactions are priced based on the net-metering principles considering a dynamic pricing tariff scheme. Furthermore, in order to prevent power peaks that could be harmful for the transformer, a limit is imposed to the total power that may be drawn by the households. Finally, in order to resolve potential competitive behavior, especially during relatively low price periods, a simple strategy in order to promote the fair usage of distribution transformer capacity is proposed.

The introduction of smart end-users is a vital point towards the smart grid vision. In this respect, smart households have drawn significant attention recently. Home energy management systems (HEMS) in smart households and transformer energy management units have different objectives that can sometimes be conflicting. Thus, a novel interactive optimum operating strategy that considers all possible aspects in each smart household together with internal bi-directional power flows within the household structure from economic (minimization of household daily electricity usage costs) and technical (avoidance of transformer overloading) perspective is proposed in this study. To the best knowledge of the authors, this is the first study in the literature combining all of the possible operational possibilities in HEMS of smart households together with internal household bi-directional power flows and the technical limitations of a transformer unit serving a neighborhood of smart households under a mixed-integer linear programming (MILP) framework.

As a recently increasing trend among different applications of smart grid vision, smart households as a new implementation area of demand response (DR) strategies have drawn more attention both in research and in engineering practice. On the other hand, optimum sizing of renewable energy based small scale hybrid systems is also a topic that is widely covered by the existing literature. In this study, the sizing of additional distributed generation and energy storage systems to be applied in smart households, which due to DR activities have a different daily demand profile compared with normal household profiles, is investigated. To the best knowledge of the authors, this is the first attempt in the literature to consider the impact of DR on sizing. The study is conducted using a mixed-integer linear programming framework for home energy management system modeling and techno-economical sizing. Also, different sensitivity analyses considering the impacts of variation of economic inputs on the provided model are realized.

In this study the case of the energy consumption profile of the Aristotle University of Thessaloniki, in Greece, is presented and statistically analyzed by clustering methods on the basis of seasonal daily load curves and load shape factors, using data from real-time measurements. The results indicate that the categorization of active power demand in university buildings is an extremely useful tool for understanding and predicting the seasonal, hourly and daily energy consumption changes, which is the first step towards adopting energy efficiency policies in such scale premises as well as performing demand-side actions aiming to achieve a more economical and environmentally sustainable energy usage.

As a recently increasing trend among different applications of smart grid vision, smart households as a new implementation area of demand response (DR) strategies have drawn more attention both in research and in engineering practice. On the other hand, optimum sizing of renewable energy based small scale hybrid systems is also a topic that is widely covered by the existing literature. In this study, the sizing of additional distributed generation (DG) and energy storage systems (ESSs) to be applied in smart households, that due to DR activities have a different daily demand profile compared with normal household profiles, is investigated. To the best knowledge of the authors this is the first attempt in the literature to investigate this issue, also including step-wise decreasing cost functions for DC and ESS, varying load and DG production profiles seasonally, and weekday-weekend horizons for a long-term analysis period. The study is conducted using a mixed-integer linear programming (MILP) framework for home energy management system (HEM) modeling and techno-economical sizing. Also, different sensitivity analyses considering the impacts of variation of economic inputs on the provided model are realized. (C) 2015 Elsevier Ltd. All rights reserved.