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Smart Grid Technologies
Published in Stuart Borlase, Smart Grids, 2017
Organized competitive markets provide most of these services in separate markets for day-ahead, hour-ahead, and “real-time” trading and scheduling. Power generation plants and demand management services that comparably satisfy necessary conditions can operate in many different markets on a given day. A major increase in the need for ramping capacity for grid balancing is in response to the large amount of variable renewable resources on the grid, particularly wind generation and PV generation. Demand management can be also be used for load management at specific locations on the distribution system. The greatest value from demand management is when it can serve multiple purposes—such as for the high-voltage grid, customer needs, and the distribution grid. The flexibility of demand management to serve these purposes depends on the hours of availability and the trigger(s) used to activate and thus harness the demand management.
Clean energy and SDGs
Published in Ulisses Manuel de Miranda Azeiteiro, J. Paulo Davim, Higher Education and Sustainability, 2019
Grid balancing – Since solar and wind power are intermittent sources, keeping the frequency of the current stable is a problem. Deep learning method can be used to balance the demand and supply side. With the help of smart meters, constant monitoring of demand and supply happen leading to creation of large data sets (Kempener, Komor, & Hoke, 2013). Deep learning algorithms fix any irregularities in large data sets and learn on their own. Along with this, AI can include data about weather, time, region, zones, seasons and provide better clarity on arranging power for the loads in short response time.
Smart Energy Resources: Supply and Demand
Published in Stuart Borlase, Smart Grids, 2018
Stuart Borlase, Sahand Behboodi, Thomas H. Bradley, Miguel Brandao, David Chassin, Johan Enslin, Christopher McCarthy, Stuart Borlase, Thomas Bradley, David P. Chassin, Johan Enslin, Gale Horst, Régis Hourdouillie, Salman Mohagheghi, Casey Quinn, Julio Romero Aguero, Aleksandar Vukojevic, Bartosz Wojszczyk, Eric Woychik, Alex Zheng, Daniel Zimmerle
Organized competitive markets provide most of these services in separate markets for day-ahead, hour-ahead, and “real-time” trading and scheduling. Power generation plants and demand management services that comparably satisfy necessary conditions can operate in many different markets on a given day. A major increase in the need for ramping capacity for grid balancing is in response to the large amount of variable renewable resources on the grid, particularly wind generation and PV generation.
Multi-criteria PSO-based optimal design of grid-connected hybrid renewable energy systems
Published in International Journal of Green Energy, 2020
Fariborz Mansouri Kouhestani, James Byrne, Daniel Johnson, Locke Spencer, Bryson Brown, Paul Hazendonk, Jeremy Scott
Grid-connected or stand-alone hybrid generation systems are composed of various parallel-connected energy resources such as photovoltaic cells, wind turbines, and storage batteries. When demand exceeds the renewable generation plus battery capacity, energy must be drawn from the conventional utility grid, which can usually be assumed to be fossil fuel based (Akram, Khalid, and Shafiq 2017; Kellogg et al. 1996). When the renewable generated electricity exceeds the demand and the storage batteries are fully charged, extra power is diverted to the grid, balancing the demand load and generated power (Owayjan et al. 2013). In effect, the utility grid plays a backup role here. Each of these electricity sources possesses specific economic, technical, and environmental characteristics which can be improved by incorporating individual sources into a hybrid generation system. The aim of this incorporation and the attempt to determine the optimum design for it is to balance multiple conflicting objectives in order to meet the energy demand, while simultaneously optimizing a set of desirable economic, environmental, and operational-reliability measures (Wang and Singh 2008, 2009). In addition, the integration of PV and wind decreases the required battery storage capacity compared with the single utilization of renewables (Bhandari et al. 2015). The reliability of the energy generated by the hybrid system considering the weather condition variability together with the overall system cost are two crucial design concerns. The main objective here is to ascertain the optimal size of the hybrid system components while satisfying the preceding criteria. To determine the optimum wind turbine, PV, and battery size, the hybrid system may be considered as an autonomous (off-grid) system (Akram, Khalid, and Shafiq 2017; Xu et al. 2013). In this context, producing extra energy and selling it to the grid is not considered to be desirable (Alsayed et al. 2013). The environmental advantages of renewable hybrid systems are compromised by purchasing power from the utility grid; hence, minimizing the amount of power purchased is considered as an optimization objective (Xu et al. 2013).