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The present work describes a charging management with integrated charging optimization for electric vehicles that can be provided to a fleet by a fleet operator or to a group of private customers by a loading infrastructure operator. Aim of this charging optimization is the calculation of an optimized charging plan by considering the electricity price as well as hard capacity boundaries and user requirements. This charging optimization is designed and implemented within the scope of this work.
Demand Response --- Optimierung --- Elektromobilität --- Evolutionäre AlgorithmenElectromobility --- Evolutionary Algorithms --- Optimization --- Smart Grid
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Increasing the shares of Renewable Energy Sources (RES) and Distributed Energy Resources (DER) is one of the most important levers in many countries to cope with the environmental, political, and economic challenges of future energy supply. The underlying research question of this thesis is whether Distributed Storage Systems (DSS) at the end consumer level can economically foster the integration of intermittent and non-dispatchable resources by providing demand-side flexibility.
load and price forecasting --- economic analysis --- batteries --- distributed storage systems --- demand response
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In this work a newly developed techno-economic optimization model of a household system with electric vehicle (EV) is used to endogenously dimension both the PV system and the stationary battery storage system (SBS). It maximizes the net present values (NPV) of these two systems. The NPV are highly affected by the load shifting potentials of EV and SBS, electricity tariff design and further general conditions.
stationäre Batteriespeicher-Systeme --- Optimierungsmodell --- Elektromobilität --- electric mobility --- optimization model --- Photovoltaik --- Laststeuerung --- photovoltaics --- stationary battery systems --- demand response
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At present, the impact of distributed energy resources in the operation of power and energy systems is unquestionable at the distribution level, but also at the whole power system management level. Increased flexibility is required to accommodate intermittent distributed generation and electric vehicle charging. Demand response has already been proven to have a great potential to contribute to an increased system efficiency while bringing additional benefits, especially to the consumers. Distributed storage is also promising, e.g., when jointly used with the currently increasing use of photovoltaic panels. This book addresses the management of distributed energy resources. The focus includes methods and techniques to achieve an optimized operation, to aggregate the resources, namely, by virtual power players, and to remunerate them. The integration of distributed resources in electricity markets is also addressed as a main drive for their efficient use.
autonomous operation --- energy management system --- stochastic programming --- co-generation --- Unit Commitment (UC) --- distributed system --- demand-side energy management --- virtual power plant --- Powell direction acceleration method --- average consensus algorithm (ACA) --- transmission line --- interval optimization --- renewable energy --- microgrids --- scheduling --- business model --- non-cooperative game (NCG) --- domestic energy management system --- time series --- energy trading --- decision-making under uncertainty --- Demand Response Unit Commitment (DRUC) --- real-time simulation --- distributed generation --- discrete wavelet transformer --- microgrid (MG) --- probabilistic programming --- optimal bidding --- ac/dc hybrid microgrid --- building energy flexibility --- storage --- uncertainty --- Cat Swarm Optimization (CSO) --- advance and retreat method --- multiplier method --- microgrid --- Demand Response (DR) --- electricity markets --- aggregators --- fault localization --- aggregator --- consensus algorithm --- black start --- microgrid operation --- local controller --- thermal comfort --- diffusion strategy --- optimal operation --- power system restoration (PSR) --- energy flexibility --- ARIMA --- pricing strategy --- clustering --- adaptive droop control --- multi-agent system (MAS) --- hierarchical game --- energy flexibility potential --- demand response
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Driven by new regulations, new market structures, and new energy resources, the smart grid has been the trigger for profound changes in the way that electricity is generated, distributed, managed, and consumed. The smart grid has raised the traditional power grid by using a two-way electricity and information flow to create an advanced, automated power supply network. However, these pioneering smart grid technologies must grow to adapt to the demands of the current digital society. In today’s digital landscape, we can access feasible data and knowledge that were merely inconceivable. This Special Issue aims to address the landscape in which smart grids are progressing, due to the advent of pervasive technologies like the Internet of Things (IoT). It will be the advanced exploitation of the massive amounts of data generated from (low-cost) IoT sensors that will become the main driver to evolve the concept of the smart grid, currently focused on infrastructure, towards the digital energy network paradigm, focused on service. Furthermore, collective intelligence will improve the processes of decision making and empower citizens. Original manuscripts focusing on state-of-the-art IoT networking and communications, M2M communications, cyberphysical system architectures, big data analytics or cloud computing applied to digital energy platforms, including design methodologies and practical implementation aspects, are welcome.
energy management system --- n/a --- home energy management system --- nanogrids --- electric energy storage --- genetic algorithm --- voltage unbalance --- harmonics --- self-consumption --- prosumer --- power system reliability --- microgrid --- communication --- low-cost solutions --- distributed energy resources --- LoRa technology --- smart inverter --- direct load control --- distributed generation --- power quality --- mixed-integer linear programming --- PV monitoring --- islanded operation --- wireless --- frequency variations --- demand response
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This book represents the Special Issue of Energies, entitled “Demand-Response in Smart Buildings”, that was published in the section “Energy and Buildings”. This Special Issue is a collection of original scientific contributions and review papers that deal with smart buildings and communities. Demand response (DR) offers the capability to apply changes in the energy usage of consumers—from their normal consumption patterns—in response to changes in energy pricing over time. This leads to a lower energy demand during peak hours or during periods when an electricity grid’s reliability is put at risk. Therefore, demand response is a reduction in demand designed to reduce peak load or avoid system emergencies. Hence, demand response can be more cost-effective than adding generation capabilities to meet the peak and/or occasional demand spikes. The underlying objective of DR is to actively engage customers in modifying their consumption in response to pricing signals. Demand response is expected to increase energy market efficiency and the security of supply, which will ultimately benefit customers by way of options for managing their electricity costs leading to reduced environmental impact.
demand response --- artificial neural network --- power predictions --- energy management --- genetic algorithm --- optimisation --- microgrid --- smart grid --- requests time --- cloud computing --- response time --- processing time --- resource allocation --- fog computing --- energy resource --- energy security --- energy sources --- Slovakia --- energy flexibility --- retrofitting interventions --- residential consumption --- electrification in the built environment --- smart cities --- smart energy management --- India --- energy efficiency --- low-carbon mobility --- water-energy nexus
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The Special Issue Distributed Energy Resources Management 2018 includes 13 papers, and is a continuation of the Special Issue Distributed Energy Resources Management. The success of the previous edition shows the unquestionable relevance of distributed energy resources in the operation of power and energy systems at both the distribution level and at the wider power system level. Improving the management of distributed energy resources makes it possible to accommodate the higher penetration of intermittent distributed generation and electric vehicle charging. Demand response programs, namely the ones with a distributed nature, allow the consumers to contribute to the increased system efficiency while receiving benefits. This book addresses the management of distributed energy resources, with a focus on methods and techniques to achieve an optimized operation, in order to aggregate the resources namely in the scope of virtual power players and other types of aggregators, and to remunerate them. The integration of distributed resources in electricity markets is also addressed as an enabler for their increased and efficient use.
n/a --- virtual power plant --- bidding strategy --- local flexibility market --- multi-period optimal power flow --- flexibility service --- occupant comfort --- unbalanced networks --- decentralized energy management system --- autonomous control --- optimization --- energy storage --- microgrids --- energy efficiency --- distributed energy --- control system --- DSM --- optimal scheduling --- adaptability --- synergistic optimization strategy --- teaching-learning --- distributed generation --- energy storage system --- stackelberg dynamic game --- IoT (Internet of Things) --- supply and demand --- comprehensive benefits --- distributed generator --- frequency bus-signaling --- active distribution networks --- swarm intelligence --- wind --- multi-agent technology --- solar --- power system management --- fault-tolerant control --- indoor environment quality --- multi-temporal optimal power flow --- multi-agent synergetic estimation --- smart grids --- local energy trading --- active power control --- prosumer --- microgrid --- trade agreements --- healthy building --- smart grid --- nonlinear control --- algorithm design and analysis --- batteries --- droop control --- distributed energy resources --- aggregator --- multi-agent system --- frequency control --- particle swarm optimization --- distribution system operator --- building climate control --- low voltage networks --- demand Response --- clustering --- distributed coordination --- demand-side management --- demand response
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Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area.
electricity load forecasting --- smart grid --- feature selection --- Extreme Learning Machine --- Genetic Algorithm --- Support Vector Machine --- Grid Search --- AMI --- TL --- SG --- NB-PLC --- fog computing --- green community --- resource allocation --- processing time --- response time --- green data center --- microgrid --- renewable energy --- energy trade contract --- real time power management --- load forecasting --- optimization techniques --- deep learning --- big data analytics --- electricity theft detection --- smart grids --- electricity consumption --- electricity thefts --- smart meter --- imbalanced data --- data-intensive smart application --- cloud computing --- real-time systems --- multi-objective energy optimization --- renewable energy sources --- wind --- photovoltaic --- demand response programs --- energy management --- battery energy storage systems --- demand response --- scheduling --- automatic generation control --- single/multi-area power system --- intelligent control methods --- virtual inertial control --- soft computing control methods --- n/a
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This book focuses on the analysis, design and implementation of future smart grid systems. This book contains eleven chapters, which were originally published after rigorous peer-review as a Special Issue in the International Journal of Energies (Basel). The chapters cover a range of work from authors across the globe and present both the state-of-the-art and emerging paradigms across a range of topics including sustainability planning, regulations and policy, estimation and situational awareness, energy forecasting, control and optimization and decentralisation. This book will be of interest to researchers, practitioners and scholars working in areas related to future smart grid systems.
industry 4.0 --- digitalization --- demand response --- HVAC control --- dynamic programming --- nonlinear optimization --- energy storage --- regulatory barriers --- storage policy --- market regulations --- SWOT analysis --- deep neural networks --- short-term load forecasting --- renewable energy --- sustainability --- island communities --- demand flexibility --- energy management --- optimization --- hydrogen economy --- cost analysis --- life cycle costing --- methane reforming --- water electrolysis --- centralised hydrogen production --- smart grids (intelligent networks) --- phasor machine learning --- binary logistic regression --- wireless network --- Sensors --- decentralized --- community energy management --- lithium-ion battery --- capacity prediction --- state of health estimation --- time–frequency image analysis --- continuous wavelet transform (CWT) --- two-stage optimization --- risk-based hybrid demand response --- uncertainties --- conditional value at risk --- improved multi-layer artificial bee colony algorithm --- interconnected power system --- cybersecurity --- FPID controller --- automatic intrusion mitigation unit --- Virtual Oscillator Control --- parameter tuning --- voltage-mode inverter --- microgrid
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The synergy between artificial intelligence and power and energy systems is providing promising solutions to deal with the increasing complexity of the energy sector. Multi-agent systems, in particular, are widely used to simulate complex problems in the power and energy domain as they enable modeling dynamic environments and studying the interactions between the involved players. Multi-agent systems are suitable for dealing not only with problems related to the upper levels of the system, such as the transmission grid and wholesale electricity markets, but also to address challenges associated with the management of distributed generation, renewables, large-scale integration of electric vehicles, and consumption flexibility. Agent-based approaches are also being increasingly used for control and to combine simulation and emulation by enabling modeling of the details of buildings’ electrical devices, microgrids, and smart grid components. This book discusses and highlights the latest advances and trends in multi-agent energy systems simulation. The addressed application topics include the design, modeling, and simulation of electricity markets operation, the management and scheduling of energy resources, the definition of dynamic energy tariffs for consumption and electrical vehicles charging, the large-scale integration of variable renewable energy sources, and mitigation of the associated power network issues.
EV charging --- multi-agent system --- digital twin --- customer satisfaction indicator --- smart microgrid --- energy management system --- real-time optimization --- immune system algorithm --- economic dispatch --- energy consumption --- wireless sensor network --- cooperation --- collaboration --- ontology --- energy sector --- scoping review --- decision-aid --- distributed energy resources --- distribution system operator --- reactive power management --- uncertainty --- day-ahead market --- balancing market --- bilateral trading --- market design --- variable renewable energy --- agent-based simulation --- MATREM system --- congestion management --- dynamic tariff --- agent-based distribution networks --- demand response --- routing protocols --- performance parameters --- Wireless Sensor Network (WSN)
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