A proposal for climate change resilience management through fuzzy controllers

We aim towards the implementation of a set of fuzzy controllers capable to perform automated estimation of the period of time necessary to recover a resilience level through the non-linear influence of a set of interrelated climate change resilience indicators constrained by social-based variables. This fuzzy controller set, working together with a fuzzy inference system type Mamdani, will be capable to estimate the proper adjustments to be done onto system’s elements in order to achieve a certain resilience level, while a general estimation of required costs is appraised. The final tool can then be used to provide guidelines for strategic vulnerability planning and monitoring through a clear understanding between investments and results, while an open evaluation and scrutiny of applied policies is made. In this paper the main strategy to achieve the mentioned objectives is presented and discussed.Peer ReviewedPostprint (author's final draft

Model predictive control for microgrid functionalities: review and future challenges

ABSTRACT: Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.info:eu-repo/semantics/publishedVersio

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin

IoT and semantic web technologies for event detection in natural disasters

This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Natural disasters cannot be predicted well in advance, but it is still possible to decrease the loss of life and mitigate the damages, exploiting some peculiarities that distinguish them. Smart collection, integration, and analysis of data produced by distributed sensors and services are key elements for understanding the context and supporting decision making process for disaster prevention and management. In this paper, we demonstrate how Internet of Things and Semantic Web technologies can be effectively used for abnormal event detection in the contest of an earthquake. In our proposal, a prototype system, which retrieves the data streams from IoT sensors and web services, is presented. In order to contextualize and give a meaning to the data, semantic web technologies are applied for data annotation. We evaluate our system performances by measuring the response time and other parameters that are important in a disaster detection scenario.Peer ReviewedPostprint (author's final draft

A feasibility study for the development of sustainable theoretical framework for smart air-conditioning

Air-conditioning as a technical solution to protect inhabitants from excessive heat exposure creates the challenge of expanding global warming and climate change. While air-conditioning has mostly been applied as an improvement to living conditions, health and environmental problems associated with its use frequently occur. Therefore, this study challenges and extends existing knowledge on sustainability-related to smart air-conditioning systems, where social, environmental and economic dynamics were considered. For instance, when exploring renewable-based options, advanced smart control techniques and profitability measures of air-conditioning reinforce the three pillars of sustainability. In addition to eradicating indoor health effects, this also helps to combat climate change through the system’s sustainability. As an exercise in conceptual modelling, the principal component analysis accounts for sustainable planning and its integration into the theoretical framework. The newly proposed photovoltaic solar air-conditioning was optimised using Polysun to demonstrate the significant application of solar energy in air-conditioning systems, thereby reducing the level of energy consumption and carbon emissions. The newly proposed fuzzy proportional-integral-derivative controller and backpropagation neural network were optimised using Matlab to control the indoor temperature and CO2 level appropriately. The controller of the indoor environment was designed, and the proportional-integral-derivative control was utilised as a result of its suitability. The smart controllers were designed to regulate the parameters automatically to ensure an optimised control output. The performance of photovoltaic solar air-conditioning in different temperate climates of Rome, Toulouse and London districts achieved a higher coefficient of performance of 3.37, 3.69 and 3.97, respectively. The system saved significant amount of energy and carbon emissions. The indoor temperature and indoor CO2 possess an appropriate time constant and settling time, respectively. The profitability assessment of the system revealed its adequate efficiency with an overall payback period of 5.5 years