Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study

Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG(Lab)(2)) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications

Genetic Algorithm Optimization of an Energy Storage System Design and Fuzzy Logic Supervision for Battery Electric Vehicles

This chapter presents a methodology to optimize the capacity and power of the ultracapacitor (UC) energy storage device and also the fuzzy logic supervision strategy for a battery electric vehicle (BEV) equipped with electrochemical battery (EB). The aim of the optimization was to prolong the EB life and consequently to permit financial economies for the end-user of the BEV. Eight variables were used in the optimization process: two variables that control the energy storage capacity and power of the UC device and six variables that change the membership functions of the fuzzy logic supervisor. The results of the optimization, using a genetic algorithm from MATLAB®, are showing an increase of the financial economy of 16%

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Fuzzy logic control of an artificial neural network-based floating offshore wind turbine model integrated with four oscillating water columns

Renewable energy induced by wind and wave sources is playing an indispensable role in electricity production. The innovative hybrid renewable offshore platform concept, which combines Floating Offshore Wind Turbines (FOWTs) with Oscillating Water Columns (OWCs), has proven to be a promising solution to harvest clean energy. The hybrid platform can increase the total energy absorption, reduce the unwanted dynamic response of the platform, mitigate the load in critical situations, and improve the system's cost efficiency. However, the nonlinear dynamical behavior of the hybrid offshore wind system presents an opportunity for stabilization via challenging control applications. Wind and wave loads lead to stress on the FOWT tower structure, increasing the risk of damage and failure, and raising maintenance costs while lowering its performance and lifespan. Moreover, the dynamics of the tower and the platform are extremely sensitive to wind speed and wave elevation, which causes substantial destabilization in extreme conditions, particularly to the tower top displacement and the platform pitch angle. Therefore, this article focuses on two main novel targets: (i) regressive modeling of the hybrid aero-hydro-servo-elastic-mooring coupled numerical system and (ii) an ad-hoc fuzzy-based control implementation for the stabilization of the platform. In order to analyze the performance of the hybrid FOWT-OWCs, this article first employs computational Machine Learning (ML) techniques, i.e., Artificial Neural Networks (ANNs), to match the behavior of the detailed FOWT-OWCs numerical model. Then, a Fuzzy Logic Control (FLC) is developed and applied to establish a structural controller mitigating the undesired structural vibrations. Both modeling and control schemes are successfully implemented, showing a superior performance compared to the FOWT system without OWCs. Experimental results demonstrate that the proposed ANN-based modeling is a promising alternative to other intricate nonlinear NREL 5 MW FOWT dynamical models. Meanwhile, the proposed FLC improves the platform's dynamic behavior, increasing its stability under a wide range of wind and wave conditions.This work was supported in part by the Basque Government through project IT1555-22 and through the projects RTI2018-094902-B-C22 (MCIU/AEI/FEDER, UE), PID2021-123543OB-C21 and C22 funded by MCIN/AEI/10.13039/501100011033. The authors would also like to thank the UPV/EHU for the financial support through the Maria Zambrano grant MAZAM22/15 funded by the European Union-Next Generation EU and through grant PIF20/299

A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

Quantitative and qualitative risk-informed energy investment for industrial companies

© 2023 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In the ongoing energy transition, small and medium-sized industrial companies are making energy equipment investments due to the obsolescence of their current equipment as well as social, political and market pressures. These firms typically choose investments with low risk exposure based on a combination of criteria that are not always quantifiable. However, published studies on energy investment to date have not been suitable for industrial SMEs because they do not assess the value of the investment over time, ignore the qualitative aspects of decision-making, and do not consider uncertainties. To fill this gap in the literature, this paper proposes a methodology that considers both quantitative and qualitative parameters and risks over time through an extended two-stage risk-informed approach. The proposed methodology includes fuzzy and statistical techniques for evaluating both qualitative and quantitative parameters, as well as their uncertainties, at the time of decision-making and over the investment lifetime. Fuzzy logic is used in the first stage of the optimisation process to measure qualitative parameters and their uncertainty, while quantitative parameters are expressed using probability density functions to account for their uncertainty and measure the quantitative risk assumed by the investor. This methodology is applied to a case study involving a real industrial SME, and the results show that considering both quantitative and qualitative parameters and uncertainties in the optimisation process leads to a more balanced consideration of economic, environmental and social criteria and reduces the variability of the outcome compared to economic-only approaches that do not account for risks. Specifically, the case study shows that considering these parameters and uncertainties resulted in a 15.7% reduction in the size of the cogeneration system due to its environmental and social impacts, and 4.2% reduction in the variability of the economic result.Peer ReviewedPostprint (published version

Load Frequency Control (LFC) Strategies in Renewable Energy‐Based Hybrid Power Systems:A Review

The hybrid power system is a combination of renewable energy power plants and conventional energy power plants. This integration causes power quality issues including poor settling times and higher transient contents. The main issue of such interconnection is the frequency variations caused in the hybrid power system. Load Frequency Controller (LFC) design ensures the reliable and efficient operation of the power system. The main function of LFC is to maintain the system frequency within safe limits, hence keeping power at a specific range. An LFC should be supported with modern and intelligent control structures for providing the adequate power to the system. This paper presents a comprehensive review of several LFC structures in a diverse configuration of a power system. First of all, an overview of a renewable energy-based power system is provided with a need for the development of LFC. The basic operation was studied in single-area, multi-area and multi-stage power system configurations. Types of controllers developed on different techniques studied with an overview of different control techniques were utilized. The comparative analysis of various controllers and strategies was performed graphically. The future scope of work provided lists the potential areas for conducting further research. Finally, the paper concludes by emphasizing the need for better LFC design in complex power system environments

Hybrid Optimized Fuzzy Pitch Controller of a Floating Wind Turbine with Fatigue Analysis

Floating offshore wind turbines (FOWTs) are systems with complex and highly nonlinear dynamics; they are subjected to heavy loads, making control with classical strategies a challenge. In addition, they experience vibrations due to wind and waves. Furthermore, the control of the blade angle itself may generate vibrations. To address this issue, in this work we propose the design of an intelligent control system based on fuzzy logic to maintain the rated power of an FOWT while reducing the vibrations. A gain scheduling incremental proportional–derivative fuzzy controller is tuned by genetic algorithms (GAs) and combined with a fuzzy-lookup table to generate the pitch reference. The control gains optimized by the GA are stored in a database to ensure a proper operation for different wind and wave conditions. The software Matlab/Simulink and the simulation tool FAST are used. The latter simulates the nonlinear dynamics of a real 5 MW barge-type FOWT with irregular waves. The hybrid control strategy has been evaluated against the reference baseline controller embedded in FAST in different environmental scenarios. The comparison is assessed in terms of output power and structure stability, with up to 23% and 33% vibration suppression rate for tower top displacement and platform pitch, respectively, with the new control scheme. Fatigue damage equivalent load (DEL) of the blades has been also estimated with satisfactory results.This work has been partially supported by the Spanish Ministry of Science and Innovation under the project MCI/AEI/FEDER number RTI2018-094902-B-C21 and PDI2021-123543OB-C21

The Application of Artificial Intelligence in Project Management Research: A Review

The field of artificial intelligence is currently experiencing relentless growth, with innumerable models emerging in the research and development phases across various fields, including science, finance, and engineering. In this work, the authors review a large number of learning techniques aimed at project management. The analysis is largely focused on hybrid systems, which present computational models of blended learning techniques. At present, these models are at a very early stage and major efforts in terms of development is required within the scientific community. In addition, we provide a classification of all the areas within project management and the learning techniques that are used in each, presenting a brief study of the different artificial intelligence techniques used today and the areas of project management in which agents are being applied. This work should serve as a starting point for researchers who wish to work in the exciting world of artificial intelligence in relation to project leadership and management