Optimal Energy Scheduling in Smart Buildings with Electric Vehicle and Demand Response

In a number of countries, smart microgrid (SMG) technology is being exploited in the energy system infrastructure along with other generators such as electric and storage systems. In the framework of SMGs, communication links between generation and demand sides have been established to optimise energy consumption by means of economic signals. The framework of energy management for Smart Home appliances is presented in the present paper, taking account of appliance and electric vehicle participation. The approach to the energy schduling at day-ahead is defined as three strategies, such as optimum use of existing appliances, optimal production of electricity resources and optimal discharge and charging for electrical vehicles. Reducing energy generation costs and minimizing emissions of air pollutants are key objectives. Two case studies with the aim of reducing costs and emissions while maximizing system flexibility are taken into account to demonstrate preference and viability of home energy scheduling

Optimal Energy Scheduling in Smart Buildings with Electric Vehicle and Demand Response

In a number of countries, smart microgrid (SMG) technology is being exploited in the energy system infrastructure along with other generators such as electric and storage systems. In the framework of SMGs, communication links between generation and demand sides have been established to optimise energy consumption by means of economic signals. The framework of energy management for Smart Home appliances is presented in the present paper, taking account of appliance and electric vehicle participation. The approach to the energy schduling at day-ahead is defined as three strategies, such as optimum use of existing appliances, optimal production of electricity resources and optimal discharge and charging for electrical vehicles. Reducing energy generation costs and minimizing emissions of air pollutants are key objectives. Two case studies with the aim of reducing costs and emissions while maximizing system flexibility are taken into account to demonstrate preference and viability of home energy scheduling

Sustainable resource allocation for power generation: The role of big data in enabling interindustry architectural innovation

Economic, social and environmental requirements make planning for a sustainable electricity generation mix a demanding endeavour. Technological innovation offers a range of renewable generation and energy management options which require fine tuning and accurate control to be successful, which calls for the use of large-scale, detailed datasets. In this paper, we focus on the UK and use Multi-Criteria Decision Making (MCDM) to evaluate electricity generation options against technical, environmental and social criteria. Data incompleteness and redundancy, usual in large-scale datasets, as well as expert opinion ambiguity are dealt with using a comprehensive grey TOPSIS model. We used evaluation scores to develop a multi-objective optimization model to maximize the technical, environmental and social utility of the electricity generation mix and to enable a larger role for innovative technologies. Demand uncertainty was handled with an interval range and we developed our problem with multi-objective grey linear programming (MOGLP). Solving the mathematical model provided us with the electricity generation mix for every 5 min of the period under study. Our results indicate that nuclear and renewable energy options, specifically wind, solar, and hydro, but not biomass energy, perform better against all criteria indicating that interindustry architectural innovation in the power generation mix is key to sustainable UK electricity production and supply

Multicriteria methodologies for the appraisal of smart grid projects when flexibility competes with grid expansion

The severe consequences expected due to the increased frequency and intensity of extreme weather events call for improving the environmental sustainability of our society. The electricity sector is pivotal in the path toward a climate-neutral society. Nowadays, the massive use of renewable energy sources requires that electricity demand follows energy production. Demand has to be flexible, as well as the renewable generation and the grid infrastructures. The power system has to assume a decentralised structure and integrate the transportation and cooling and heating sectors. All customers connected to the electrical grid have to contribute to the power system management and participate in the related markets. The power system has to become smart; all technical and market processes have to be digitalised to enable new functionalities and services. The power system transformation requires rethinking planning and operation practices to accommodate the changes and take advantage of the related opportunities. The novel features and services available in the active and flexible power system will influence the customers' daily habits; therefore, the impacts generated by planning initiatives will cross the power system borders by impacting society as a whole. Since the power system will be operated closer to its technical limits, it is crucial to enhance the management of uncertainties by the increased accuracy of load and generation forecast. This thesis addresses the ongoing power system transformation by focusing on the distribution system, which will face unprecedented changes. This thesis concerns novel approaches for appraising the project initiatives based on the use of the users' flexibility connected to the grid. Traditional appraisal tools are no longer effective; therefore, decision-makers have to be supported with tools capable of capturing the complexity of the future power system in which flexibility measures compete with grid expansion. In this thesis, an assessment framework for smart grid initiatives which combines the cost-benefit analysis and the multi-criteria analysis proposed. Based on international guidelines, this framework allows for a systematic and simultaneous assessment of tangible and the intangible impacts considering conflicting criteria. To complete the assessment framework, a novel methodology which combines Regret Theory and multi-criteria analysis is proposed. The proposed methodology represents one of the main contributions of this dissertation. It supports the decision-maker to identify the most valuable option by decomposing the complex decision-making problem of smart grid planning and rejecting personal biases by avoiding the need for defining the evaluation criteria relevance. However, the stakeholders’ perspective can be included in terms of constraints for the minimax optimisation problem. In conclusion, the contribution of the thesis is to provide decision-making support tools for strategical power system planning. The research activities described in this document have been aimed at supporting system operators and regulatory bodies by providing tools for smart grid project appraisal and improving the accuracy of power system studies considering the novel context features

Modeling, Simulation and Optimization of Wind Farms and Hybrid Systems

The reduction of greenhouse gas emissions is a major governmental goal worldwide. The main target, hopefully by 2050, is to move away from fossil fuels in the electricity sector and then switch to clean power to fuel transportation, buildings and industry. This book discusses important issues in the expanding field of wind farm modeling and simulation as well as the optimization of hybrid and micro-grid systems. Section I deals with modeling and simulation of wind farms for efficient, reliable and cost-effective optimal solutions. Section II tackles the optimization of hybrid wind/PV and renewable energy-based smart micro-grid systems

Modeling, Simulation and Optimization of Wind Farms and Hybrid Systems

The reduction of greenhouse gas emissions is a major governmental goal worldwide. The main target, hopefully by 2050, is to move away from fossil fuels in the electricity sector and then switch to clean power to fuel transportation, buildings and industry. This book discusses important issues in the expanding field of wind farm modeling and simulation as well as the optimization of hybrid and micro-grid systems. Section I deals with modeling and simulation of wind farms for efficient, reliable and cost-effective optimal solutions. Section II tackles the optimization of hybrid wind/PV and renewable energy-based smart micro-grid systems

Modeling, Simulation and Optimization of Wind Farms and Hybrid Systems

The reduction of greenhouse gas emissions is a major governmental goal worldwide. The main target, hopefully by 2050, is to move away from fossil fuels in the electricity sector and then switch to clean power to fuel transportation, buildings and industry. This book discusses important issues in the expanding field of wind farm modeling and simulation as well as the optimization of hybrid and micro-grid systems. Section I deals with modeling and simulation of wind farms for efficient, reliable and cost-effective optimal solutions. Section II tackles the optimization of hybrid wind/PV and renewable energy-based smart micro-grid systems

Sensitivity analysis for multi-objective optimization weights in energy systems

Abstract. This master’s thesis dealt with the production of district heating, the popularity of which is growing due to its low-cost production and environmental friendliness. In the experimental part, multi-objective optimization of district heating production and its consumption was considered. The aim was to maximize profits and minimize emissions on the production side by identifying the optimal weights for the presented objective function. In addition to this, a study was made of how the integration of heat pumps into the district heating network affected the emissions and profits of the production plant. The multi-objective optimization of the experimental part was simulated using MATLAB® software. The prediction horizon was two days (48 hours). The study focused on tuning parameters in the determined objective function, namely weights for profits and emissions. Simulation scenarios included high and low electricity prices and different numbers of heat pumps. The theory part of the master’s thesis introduced the energy systems of the future and how they can be turned into more sustainable solutions. Based on the results of multi-objective optimization, it can be concluded that there is no single optimal solution that would suit every situation, regardless of the electricity price and the number of heat pumps. However, when comparing all the results, it can be noted that when more heat pumps are integrated into the district heating network, the profits tend to increase and emissions decrease during periods of low and high electricity prices.Herkkyysanalyysi energiajärjestelmien monitavoiteoptimoinnin painokertoimille. Tiivistelmä. Tämä diplomityö käsitteli kaukolämmön tuotantoa, jonka suosio on kasvamassa sen edullisen tuotannon ja ympäristöystävällisyyden vuoksi. Kokeellisessa osassa simuloitiin monitavoiteoptimointia Oulun kaupungin lämmöntuotantolaitokselle sekä kaupungin rakennuksille. Tavoitteena oli maksimoida tuotantolaitoksen tulos sekä minimoida päästöt määrittämällä esitetylle kustannusfunktiolle optimaaliset parametrit. Tämän lisäksi simuloinneilla tutkittiin, miten lämpöpumppujen integrointi osaksi kaukolämpöverkkoa vaikuttaa tuotantolaitoksen päästöihin sekä tulokseen. Kokeellisen osan monitavoiteoptimointi simuloitiin MATLAB® ohjelmistotyökalun avulla. Ennustehorisonttina oli kaksi vuorokautta eli 48 tuntia. Työssä keskityttiin määrittämään optimaaliset painokertoimet taloudellinen tulos- ja tuotannon päästöt -muuttujille kustannusfunktiossa. Simulointiskenaarioissa muuttuvina tekijöinä olivat sähkön hinta sekä lämpöpumppujen määrä. Tämän lisäksi verrattiin kalliin ja edullisen sähkönhinnan vaikutusta tuotannon tulokseen sekä päästöihin. Diplomityön teoriaosuudessa tutustuttiin tulevaisuuden energiajärjestelmiin, ja siihen miten niistä voidaan tehdä kestävämpiä. Monitavoiteoptimoinnista saatujen tulosten perusteella voidaan todeta, että yhtä optimaalista ratkaisua ei saada, joka sopisi jokaiseen tilanteeseen sähkönhinnasta ja lämpöpumppujen määrästä huolimatta. Kuitenkin kaikkia tuloksia verrattaessa voidaan todeta, että mitä enemmän lämpöpumppuja on integroituna kaukolämpöverkkoon, sitä suurempi on tulos. Tämän lisäksi myös päästöjen kokonaismäärä näyttää laskevan kaupunkitasolla