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Utility Grid with Hybrid Energy System
Published in Yatish T. Shah, Hybrid Power, 2021
The improved flexibility of the smart grid permits greater penetration of highly variable renewable energy sources such as solar power and wind power, even without the addition of energy storage. For very large level of RES penetration, energy storage appears necessary. Current network infrastructure is not built to allow for many distributed feed-in points, and typically even if some feed-in is allowed at the local (distribution) level, the transmission-level infrastructure cannot accommodate it. Rapid fluctuations in distributed generation, such as due to cloudy or gusty weather, present significant challenges to power engineers who need to ensure stable power levels through varying the output of the more controllable generators such as gas turbines and hydroelectric generators. Smart grid technology is a necessary condition for very large amounts of renewable electricity on the grid for this reason. Market enabling
Solar Power Fields
Published in Anco S. Blazev, Solar Technologies for the 21st Century, 2021
The smart grid will allow greater use of variable renewable energy sources such as solar and wind power, even without the addition of energy storage—which is impossible at present, since the grid cannot handle many distributed feed-in points and power fluctuations, due to cloudy or gusty weather. Smart grid technology, therefore, is a necessary condition for introducing very large amounts of renewable electricity on the grid.
Clean renewable energy growth management
Published in Henry K. H. Wang, Renewable Energy Management in Emerging Economies, 2020
Various Latin American countries have been promoting clean renewable power. Some countries have achieved high shares of electricity generation with variable renewable energy. Two good examples include Honduras which managed to supply near to 10% of its electricity with solar PV and Uruguay which has used wind power to supply over 20% of its electricity consumption recently. In addition, a number of Caribbean islands, including Aruba, Curacao, Bonaire and St Eustatius, have managed to achieve clean renewable energy shares of over 10% in their total power mix.
A GA-PSO Hybrid Algorithm Based Neural Network Modeling Technique for Short-term Wind Power Forecasting
Published in Distributed Generation & Alternative Energy Journal, 2018
Yordanos Kassa Semero, Jianhua Zhang, Dehua Zheng, Dan Wei
Wind power is becoming increasingly important in electric power industry. It has relatively low cost of electricity production and large resources are available. Wind power significantly reduces emissions and air pollution caused by conventional power generation techniques. Wind power generation technologies now contribute a significant coverage in the growing clean energy market worldwide [1, 2]. Nevertheless, the intermittency and uncertainty associated with wind flow poses challenges in regulation of power systems. The fluctuating nature of wind is caused by various environmental and weather factors such as season, terrain, temperature, air pressure, and so on [3, 4]. As maintaining the balance between supply and demand in a key requirement in electric power systems, the integration of highly variable renewable energy sources like wind adds more difficulties to power regulation. Wind power forecasting plays an important role in mitigating the challenges. Accurate forecasts of wind power production are important inputs for optimal generation scheduling, maintenance scheduling, load shedding and other operational decisions.
Evaluation of the energy autonomy of urban areas as an instrument to promote the energy transition
Published in Energy Sources, Part B: Economics, Planning, and Policy, 2022
Manuel Ayala-Chauvin, Genis Riba Sanmartí, Carles Riba, Patricio Lara
The large-scale deployment of DERs at all levels of the electricity system (high, medium and low voltage) requires a back-up of fast-response generation systems to ensure grid stability and quality of supply (Joos et al. 2017). When different variable renewable energy systems are combined or deployed over large geographical areas, variability of renewable energy sources is easier to manage as they often compensate for each other (Hrnčić et al. 2021). Characterizing DERs over large areas is a task necessary to optimize the energy mix to reduce variability (Syranidou et al. 2020). DERs generation simulations are a first step toward this optimization (Icaza et al. 2018; Reyes et al. 2017), but they lack comparison with electricity demand.
The Carbon Value of Nuclear Power Plant Lifetime Extensions in the United States
Published in Nuclear Technology, 2022
Son H. Kim, Temitope A. Taiwo, Brent W. Dixon
All technologies in GCAM compete based on their costs. GCAM implements the discrete choice method for determining the technology portfolio, where a probabilistic approach, using a nested logit model and technology costs, determines the technology choice.30 The selected technology portfolio contains larger shares of lower-cost technologies that simulate a least-cost paradigm but avoid a “winner take all” result. Technology costs are separated into fuel or energy costs and nonfuel costs that include capital, maintenance, and operations and management (O&M) costs. Future technology assumptions play a vital role in determining the growth of the energy system and the choice of technologies over time. The median capital costs assumed for 2015, 2050, and 2100; capacity factor; and lifetime assumptions for all electric power plants represented in GCAM are shown in Table II (Ref. 31). Capital costs for all technologies are assumed to improve over time at varied rates of improvement dependent on their technical maturity.31 Electric power technologies in GCAM compete based on the levelized cost of electricity (LCOE). Capacity factors, as well as capital, O&M, and fuel costs, have a significant impact on the LCOE. Fuel costs are determined endogenously from energy resource markets and power plant efficiencies. For variable renewable energy technologies, such as wind and solar without dedicated energy storage, additional cost for backup capacity and grid integration is applied as a function of total variable renewable share of power generation. Higher systems costs are incurred with increasing variable renewable penetration.32