A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

A secure cloud computing based framework for big data information management of smart grid

Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address these challenges since it has several good properties such as energy saving, cost saving, agility, scalability, and flexibility. In this paper, we propose a secure cloud computing based framework for big data information management in smart grids, which we call 'Smart-Frame.' The main idea of our framework is to build a hierarchical structure of cloud computing centers to provide different types of computing services for information management and big data analysis. In addition to this structural framework, we present a security solution based on identity-based encryption, signature and proxy re-encryption to address critical security issues of the proposed framework.

Smart Grid e Carros Elétricos - Influência da Carga de Carros Elétricos Sobre o Sistema Elétrico

The technologies are advancing at a pace so expressive that allow the increase of the power quality from generation until the distribution to end customers. This improvement has been made possible through the automation of the energy that follows to a better quality of the energy provided, a lower energy supply disruptions and a very short recovery time. The trend of today and the near future is the distributed energy generation. To keep the automated control of the chain, the presence of Smart Grids is needed and that will be the most efficient and economical way to manage the entire system. Within this theme, is going to be necessary analyze the electric cars that promise to promote a more sustainable transport because it doesn’t uses fossil fuels, and more healthy because it does not emit pollutants into the atmosphere. The popularization of this type of vehicle is estimated to happen in a few decades and the case study analyzing its influence on the demand of the electrical system is something that will be very important in the near future. This paper presents a study of the influence of the inclusion of charges refering to electric cars

Desenvolvimento de um sistema de gerenciamento de energia (EMS - Energy Management System) para a rede elétrica inteligente (Smart Grid)

Pós-graduação em Engenharia Elétrica - FEIS

Modelo de simulação NS-2 para o protocolo DNP3 sobre o protocolo de rede sem fio IEEE 802.15.4 para simulação de baixo custo de aplicação smart grid

Pós-graduação em Engenharia Elétrica - FEIS

Power Converters and Electric Drives for Smart Grid Applications

The present dissertation aims to explore, theoretically and experimentally, the problems and the potential advantages of different types of power converters for “Smart Grid” applications, with particular emphasis on multi-level architectures, which are attracting a rising interest even for industrial requests. The models of the main multilevel architectures (Diode-Clamped and Cascaded) are shown. The best suited modulation strategies to function as a network interface are identified. In particular, the close correlation between PWM (Pulse Width Modulation) approach and SVM (Space Vector Modulation) approach is highlighted. An innovative multilevel topology called MMC (Modular Multilevel Converter) is investigated, and the single-phase, three-phase and "back to back" configurations are analyzed. Specific control techniques that can manage, in an appropriate way, the charge level of the numerous capacitors and handle the power flow in a flexible way are defined and experimentally validated. Another converter that is attracting interest in “Power Conditioning Systems” field is the “Matrix Converter”. Even in this architecture, the output voltage is multilevel. It offers an high quality input current, a bidirectional power flow and has the possibility to control the input power factor (i.e. possibility to participate to active and reactive power regulations). The implemented control system, that allows fast data acquisition for diagnostic purposes, is described and experimentally verified.

Increasing Utilization of US Electric Grids via Smart Technologies: Integration of Load Management, Real Time Pricing, Renewables, EVs and Smart Grid Sensors

Electric power grids throughout the world suffer from serious inefficiencies associated with under-utilization due to demand patterns, engineering design and load following approaches in use today. These grids consume much of the world’s energy and represent a large carbon footprint. From material utilization perspectives significant hardware is manufactured and installed for this infrastructure often to be used at less than 20-40% of its operational capacity for most of its lifetime. These inefficiencies lead engineers to require additional grid support and conventional generation capacity additions when renewable technologies (such as solar and wind) and electric vehicles are to be added to the utility demand/supply mix. Using actual data from the PJM [PJM 2009] the work shows that consumer load management, real time price signals, sensors and intelligent demand/supply control offer a compelling path forward to increase the efficient utilization and carbon footprint reduction of the world’s grids. Underutilization factors from many distribution companies indicate that distribution feeders are often operated at only 70-80% of their peak capacity for a few hours per year, and on average are loaded to less than 30-40% of their capability. By creating strong societal connections between consumers and energy providers technology can radically change this situation. Intelligent deployment of smart sensors, smart electric vehicles, consumer-based load management technology very high saturations of intermittent renewable energy supplies can be effectively controlled and dispatched to increase the levels of utilization of existing utility distribution, substation, transmission, and generation equipment. The strengthening of these technology, society and consumer relationships requires rapid dissemination of knowledge (real time prices, costs & benefit sharing, demand response requirements) in order to incentivize behaviors that can increase the effective use of technological equipment that represents one of the largest capital assets modern society has created.

Middleware architectures for the Smart Grid : survey and challenges in the foreseeable future

The traditional power grid is just a one-way supplier that gets no feedback data about the energy delivered, what tariffs could be the most suitable ones for customers, the shifting daily needs of electricity in a facility, etc. Therefore, it is only natural that efforts are being invested in improving power grid behavior and turning it into a Smart Grid. However, to this end, several components have to be either upgraded or created from scratch. Among the new components required, middleware appears as a critical one, for it will abstract all the diversity of the used devices for power transmission (smart meters, embedded systems, etc.) and will provide the application layer with a homogeneous interface involving power production and consumption management data that were not able to be provided before. Additionally, middleware is expected to guarantee that updates to the current metering infrastructure (changes in service or hardware availability) or any added legacy measuring appliance will get acknowledged for any future request. Finally, semantic features are of major importance to tackle scalability and interoperability issues. A survey on the most prominent middleware architectures for Smart Grids is presented in this paper, along with an evaluation of their features and their strong points and weaknesses.

A semantic middleware architecture focused on data and heterogeneity management within the Smart Grid

There is an increasing tendency of turning the current power grid, essentially unaware of variations in electricity demand and scattered energy sources, into something capable of bringing a degree of intelligence by using tools strongly related to information and communication technologies, thus turning into the so-called Smart Grid. In fact, it could be considered that the Smart Grid is an extensive smart system that spreads throughout any area where power is required, providing a significant optimization in energy generation, storage and consumption. However, the information that must be treated to accomplish these tasks is challenging both in terms of complexity (semantic features, distributed systems, suitable hardware) and quantity (consumption data, generation data, forecasting functionalities, service reporting), since the different energy beneficiaries are prone to be heterogeneous, as the nature of their own activities is. This paper presents a proposal on how to deal with these issues by using a semantic middleware architecture that integrates different components focused on specific tasks, and how it is used to handle information at every level and satisfy end user requests.

Business models in the Smart Grid: challenges, opportunities and proposals for prosumer profitability.

Considering that non-renewable energy resources are dwindling, the smart grid turns out to be one of the most promising and compelling systems for the future of energy. Not only does it combine efficient energy consumption with avant-garde technologies related to renewable energies, but it is also capable of providing several beneficial utilities, such as power monitoring and data provision. When smart grid end users turn into prosumers, they become arguably the most important value creators within the smart grid and a decisive agent of change in terms of electricity usage. There is a plethora of research and development areas related to the smart grid that can be exploited for new business opportunities, thus spawning another branch of the so-called ?green economy? focused on turning smart energy usage into a profitable business. This paper deals with emerging business models for smart grid prosumers, their strengths and weaknesses and puts forward new prosumer-oriented business models, along with their value propositions.

Vehicle to vehicle energy exchange in smart grid applications

This paper presents a novel vehicle to vehicle energy exchange market (V2VEE) between electric vehicles (EVs) for decreasing the energy cost to be paid by some users whose EVs must be recharged during the day to fulfil their daily scheduled trips and also reducing the impact of charging on the electric grid. EVs with excess of energy in their batteries can transfer this energy among other EVs which need charge during their daily trips. These second type of owners can buy the energy directly to the electric grid or they can buy the energy from other EV at lower price. An aggregator is responsible for collecting all information among vehicles located in the same area at the same time and make possible this energy transfer.

Distribution of microservices for hardware interoperability in the Smart Grid

Due to the significant increase of population and their natural desire of improving their standard of living, usage of energy extracted from world commodities, especially shaped as electricity, has increased in an intense manner during the last decades. This fact brings up a challenge with a complicated solution, which is how to guarantee that there will be enough energy so as to satisfy the energy demand of the world population. Among all the possible solutions that can be adopted to mitigate this problem one of them is almost of mandatory adoption, which consists of rationalizing energy utilization, in a way that its wasteful usage is minimized and it can be leveraged during a longer period of time. One of the ways to achieve it is by means of the improvement of the power distribution grid, so that it will be able to react in a more efficient manner against common issues, such as energy demand peaks or inaccurate electricity consumption forecasts. However, in order to be able to implement this improvement it is necessary to use technologies from the ICT (Information and Communication Technologies) sphere that often present challenges in some key areas: advanced metering infrastructure integration, interoperability and interconnectivity of the devices, interfaces to offer the applications, security measures design, etc. All these challenges may imply slowing down the adoption of the smart grid as a system to prolong the lifespan and utilization of the available energy. A proposal for an intermediation architecture that will make possible solving these challenges is put forward in this Master Thesis. Besides, one implementation and the tests that have been carried out to know the performance of the presented concepts have been included as well, in a way that it can be proved that the challenges set out by the smart grid can be resolved. RESUMEN. Debido al incremento significativo de la población y su deseo natural de mejorar su nivel de vida, la utilización de la energía extraída de las materias primas mundiales, especialmente en forma de electricidad, ha aumentado de manera intensa durante las últimas décadas. Este hecho plantea un reto de solución complicada, el cual es cómo garantizar que se dispondrá de la energía suficiente como para satisfacer la demanda energética de la población mundial. De entre todas las soluciones posibles que se pueden adoptar para mitigar este problema una de ellas es de casi obligatoria adopción, la cual consiste en racionalizar la utilización de la energía, de tal forma que se minimice su malgasto y pueda aprovecharse durante más tiempo. Una de las maneras de conseguirlo es mediante la mejora de la red de distribución de electricidad para que ésta pueda reaccionar de manera más eficaz contra problemas comunes, tales como los picos de demanda de energía o previsiones imprecisas acerca del consumo de electricidad. Sin embargo, para poder implementar esta mejora es necesario utilizar tecnologías del ámbito de las TIC (Tecnologías de la Información y la Comunicación) que a menudo presentan problemas en algunas áreas clave: integración de infraestructura de medición avanzada, interoperabilidad e interconectividad de los dispositivos, interfaces que ofrecer a las aplicaciones, diseño de medidas de seguridad, etc. Todos estos retos pueden implicar una ralentización en la adopción de la red eléctrica inteligente como un sistema para alargar la vida y la utilización de la energía disponible. En este Trabajo Fin de Máster se sugiere una propuesta para una arquitectura de intermediación que posibilite la resolución de estos retos. Además, una implementación y las pruebas que se han llevado a cabo para conocer el rendimiento de los conceptos presentados también han sido incluidas, de tal forma que se demuestre que los retos que plantea la red eléctrica inteligente pueden ser solventados.

Short-term Load Forecasting Using Neural Network For Future Smart Grid Application

Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised. This thesis outlines some aspects and functions of smart meter. It also presents different policies and current statuses as well as future projects and objectives of SG development in several countries. Then the thesis compares main aspects about latest products of smart meter from different companies. Lastly, three types of prediction models are established in MATLAB to emulate the functions of smart grid in the short-term load forecasting, and then their results are compared and analyzed in terms of accuracy. For this thesis, more variables such as dew point temperature are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.