• Energy Research

Since the emergence of the virus that causes COVID-19 (the SARS-CoV-2) in Wuhan in December 2019, societies all around the world have had to change their normal life patterns due to the restrictions and lockdowns imposed by governments. These changes in life patterns have a direct reflection on energy consumption. Thanks to Smart Grid technologies, specifically to the Advance Metering Infrastructure at secondary distribution network, this impact can be evaluated even at the customer level. Thus, this paper analyzes the consumption behavior and the impact that this crisis has had using Smart Meter data. The proposed approach includes the selection and normalization of features, automatic clustering, the obtaining of the estimated consumption without considering the crisis (at short and mid-terms) and the impact evaluation. The proposed approach has been tested on a case with a real Smart Meter infrastructure from Manzanilla (Huelva, Spain). The results of this use case showed that residential customers have increased their consumption around 15% during full lockdown and 7.5% during the reopening period. In contrast, globally, non-residential customers have decreased their consumption 38% during full lockdown and 14.5% during the reopening period. However, referring to non-residential customers, five different consumption profiles were found with different short-term and mid-term behaviors during the COVID crisis. The different behavior found shows customers who have maintained their normal consumption during the lockdown, others who have reduced it (to a greater or lesser extent) and have not recovered it after the removal of the restrictions, and others who have reduced the consumption but then they recovered it when the restrictions were removed. The metadata of the customers in each behavior cluster found are highly correlated to the restrictions imposed to control the spread of the virus. This study shows evidence about the proposed approach usefulness to analyze the behavior and the impact at customer level during the COVID-19 crisis.

Nowadays, Distribution System Operators are increasing the digitalization of their smart grids, making it possible to measure and manage their state at any time. However, with the massive eruption of change-distributed generation (e.g., renewable resources, electric vehicles), the grid operation have become more complex, requiring specific technologies to balance it. In this sense, the demand-side management is one of its techniques; the demand response is a promising approach for providing Flexibility Services (FSs) and complying with the regulatory directives of the energy market. As a solution, this paper proposes the use of the OpenADR (Open Automated Demand Response) standard protocol in combination with a Decentralized Permissioned Market Place (DPMP) based on Blockchain. On one hand, OpenADR hierarchical architecture based on distributed nodes provides communication between stakeholders, adding monitoring and management services. Further, this architecture is compatible with an aggregator schema that guarantees the compliance with the strictest regulatory framework (i.e., European market). On the other hand, DPMP is included at different levels of this architecture, providing a global solution to Flexibility Service Providers (FSP) that can be adapted depending on the regulation of a specific country. As a proof of concept, this paper shows the result of a real experimental case, which implements a Capacity Bidding Program where the OpenADR protocol is used as a communication method to control and monitor energy consumption. In parallel, the proposed DPMP based on Blockchain makes it possible to manage the incentives of FSs, enabling the integration of local and global markets.

Microgrids (MGs) have emerged as a potential solution for the integration of Distributed Energy Resources (DERs) into the distribution network. In this sense, to effectively manage MGs, it is essential to implement Energy Management Systems (EMSs). This entails not only performing the unit commitment but also considering the voltage and reactive power technical constraints and managing ancillary services. This paper contributes with a comprehensive EMS for the optimal management of active and reactive power of a generic grid-tied MG composed of Renewable Energy Sources (RESs), Battery Energy Storage Systems (BESSs), Diesel Generator (DGs) units and loads, with the goal of reducing the operating costs of the facility. The EMS includes models for the power electronics units to apply reactive power management and a generic formulation for the management of the startup and shutdown cycles of dispatchable units. Furthermore, a detailed modeling of BESS and DG units is presented, reflecting the actual behavior of the devices. The MG is modeled as a multi-busbar network, with the application of the power flow equations to establish the link between power flows and nodal voltages. All the constraints are linearized to formulate the EMS as a Mixed-Integer Linear Programming (MILP) optimization problem. The EMS is validated in a real facility: the CATEPS Microgrid Living-Lab. The results demonstrate the operational effectiveness of the EMS in different seasons, exhibiting a reduction in costs ranging from 21.84 % in summer to a 5.69 % in winter compared to a scenario with RES production but without energy management. In addition, a comprehensive examination of reactive power and voltage management is presented. Furthermore, an empirical assessment of the power flow equations linearization demonstrated minimal discrepancy in the results when compared with those obtained with the non-linear equations, exhibiting a mean absolute error of 8.8e-5 p.u. and 3.2e-5 rad in voltage magnitude and phase angle, respectively, in the most unfavorable scenario. A sensitivity analysis of the startup and shutdown cycles management of the BESS reveals a negligible effect on operational costs, yet it provides a mechanism for managing the battery stress by reducing the number of startups in a complete week from 28 to 16 in summer and from 37 to 24 in winter. The dependence between the maximum charging and discharging power on the state of charge of the BESS is also assessed in the use case.

El turismo es un elemento de ocio en auge que cada vez aglutina a un sector más grande de la población, incluyo a determinados sectores que hasta hace poco no podían permitirse este tipo de actividades. Dentro de este ámbito, cuando se realiza un gasto extraordinario en estos viajes se quiere aprovechar los mismos en la mayor medida posible, siendo necesario un proceso previo de planificación y búsqueda de información acerca del lugar que se vaya a visitar. Este proceso previo suele ser más tedioso que el viaje en sí, lo cual provoca innumerables problemas al respecto. Es por ello que surge este proyecto, destinado a desarrollar un sistema experto de ayuda a la planificación de rutas turísticas, permitiendo además almacenar la experiencia del usuario, que será clave para el entrenamiento continuo del sistema. Gracias a esta aplicación el usuario podrá crear un itinerario perfecto y totalmente adaptado a sus necesidades y gustos en cuestión de apenas unos segundos, reduciendo ese 18% del tiempo invertido a un 1%, o en su defecto, a un máximo de un 5% en el caso de que desee investigar más acerca de los lugares recomendados en la ruta planificada, o quiera probar otras configuraciones. Tourism is a growing element of leisure that increasingly brings together a larger sector of the population, including certain sectors that until recently could not afford this type of activity. Within this scope, when an extraordinary expense is made in these trips, it is wanted to take advantage of them as much as possible, being necessary a previous planning process and search of information about the place that is going to visit. This previous process is usually more tedious than the trip itself, which causes innumerable problems in this regard. That is why this project arises, aimed at developing an expert system to help with the planning of tourist routes, allowing also to store the user experience, which will be key to the continuous training of the system. Thanks to this application the user can create a perfect itinerary and fully adapted to their needs and tastes in just a few seconds, reducing that 18% of the time spent to 1%, or failing that, to a maximum of 5% In case you want to investigate more about the recommended places in the planned route, or want to try other configurations.

The complexity of power systems is rising mainly due to the expansion of renewable energy generation. Due to the enormous variability and uncertainty associated with these types of resources, they require sophisticated planning tools so that they can be used appropriately. In this sense, several tools for the simulation of renewable energy assets have been proposed. However, they are traditionally focused on the simulation of the generation process, leaving the operation of these systems in the background. Conversely, more expert SCADA operators for the management of renewable power plants are required, but their training is not an easy task. SCADA operation is usually complex, due to the wide set of information available. In this sense, simulation or co-simulation tools can clearly help to reduce the learning curve and improve their skills. Therefore, this paper proposes a useful simulator based on a JavaScript engine that can be easily connected to any renewable SCADAs, making it possible to perform different simulated scenarios for novel operator training, as if it were a real facility. Using this tool, the administrators can easily program those scenarios allowing them to sort out the lack of support found in setting up facilities and training of novel operator tasks. Additionally, different renewable energy generation models that can be implemented in the proposed simulator are described. Later, as a use example of this tool, a study case is also performed. It proposes three different wind farm generation facility models, based on different turbine models: one with the essential generation turbine function obtained from the manufacturer curve, another with an empirical model using monotonic splines, and the last one adding the most important operational states, making it possible to demonstrate the usefulness of the proposed simulation tool.