Reprint

Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids

Edited by
February 2024
612 pages
  • ISBN978-3-7258-0087-2 (Hardback)
  • ISBN978-3-7258-0088-9 (PDF)

This book is a reprint of the Special Issue Emerging Technologies towards Energy Cooperation between Smart Grid and Microgrids that was published in

Business & Economics
Environmental & Earth Sciences
Social Sciences, Arts & Humanities
Summary

The growing propagation of microgrids and their remarkable effects on the operation of the smart grid is leading to the development of a sustained environment which is moving away from traditional frameworks. Therefore, tending to microgrid systems to increase their range of benefits can play a significant role in outlining an effective negotiation framework for the microgrids that are connected to the smart grid.

In recent years, numerous research and development projects have been carried out to design energy transactions and economic models and implement local control platforms for manufacturers, consumers, and microgrids. Furthermore, the attention paid to peer-to-peer constructions for energy exchanges and management has grown significantly.

This Reprint highlights and discusses the appropriate negotiation structures to maximize the benefits of microgrids that are connected to the smart grid and contributes to the derivation of sustainable future energy systems. In addition to modern techniques for managing uncertainty parameters regarding the microgrid and smart grid, this Reprint also examines multilateral economic distribution frameworks that need to be implemented easily and efficiently without the need for a central agent, with a limited exchange of information comprising the amount and price of the energy exchange.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
hybrid renewable energy system; bonobo optimizer; annualized system cost; optimal solution; convergence rate; renewable energy fraction; artificial intelligent algorithms; energy conservation; pareto analysis; energy consumption; eQUEST; Revit; value methodology; distributed generator; D-STATCOM; fuzzy logic controller; genetic algorithm; power quality; wind power integration; low-frequency oscillations (LFOs); doubly-fed induction generators (DFIGs); power system stability; damping characteristics; dynamic economic environmental dispatch; cow search algorithm; seagull optimization algorithm; tunicate swarm algorithm; firefly algorithm; optimal scheduling; optimization methods; dynamic thermal rating; probabilistic energy demand; two points approximation scheme; wind power; congestion management; electricity optimization; MPSO multi-objective optimization; suitability; power system stability; power system stabilizer; static synchronous series compensator; type-2 fuzzy lead–lag controller; differential evolution; pattern search; smart grid; modern transportation system; hybrid blockchain technology; EV mobility; machine learning; synergetic control (SC) law; fuzzy neural network (FNN) approximator; fast terminal synergetic controller (FTSC); finite-time convergence; DC/DC buck converter; market price prediction; modeling and management; optimization; renewable energy sources; energy storage system (ESS); synchronverter; wind energy; frequency modulation (FM); capacity configuration; microgrid; binary orientation search algorithm; demand side management; real-time pricing; energy management; multi-objective management; generation power uncertainty; operating cost; demand response; energy; smart-grid; grid operator; industrial customer; Stackelberg–particle swarm optimization; MPPT; incremental conductance (IncCond); differential inverter; differential flyback inverter (DFI); high-frequency transformer (HFT); continuous modulation scheme (CMS); harmonic compensation; renewable Resources; microgrids; generation efficiency; energy management; battery energy storage system; service scheduling; hybrid genetic algorithms; simulated annealing algorithms; tourism services; sustainability tourism; gas-insulated switchgear; failure prediction; parameter optimization; improved particle swarm optimization algorithm; active distribution network; relay protection; vector inspection; back-to-back converter; stability; smart-grid; microgrid control; distribution networks; power electronics; Gaussian distribution; M-estimator; power system state estimation; precision; robustness; weighted least square method; electric vehicles; energy storage systems; transmission lines planning; renewable energy sources; multi-objective Gazelle optimization algorithm; smart grid; energy internet; energy router; common bus voltage; cooperative control; model transformation; demand response; optimum power flow; improved teaching–learning optimization; heat and electrical demands; combined heat and electricity systems; photovoltaic cells; modeling; parameters estimation; MSGO algorithm; optimization; artificial neural network (ANN); solar photovoltaic (PV); maximum power point tracking; Levenberg–Marquardt (LM); Bayesian regularization (BR); scaled conjugate gradient (SCG); resilient backpropagation (RP); blockchain; local energy trading; microgrids; oracle networks; peer-to-peer; virtual power plant; distributed energy resource; distribution network planning; distributed energy storage system; flexibility quantization; improved k-means clustering algorithm; economic analysis; hosting capacity; market price; microgrid; bi-level optimization; renewable energy sources; sodium–sulfur batteries; uncertainty