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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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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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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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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.

History of engineering & technology --- 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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• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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