Feasibility Approaches to Reduce the Unreliability of Gas, Nuclear, Coal, Solar and Wind Electricity Production

Feasibility Approaches to Reduce the Unreliability of Gas, Nuclear, Coal, Solar and Wind Electricity Production

Roy L. Nersesian, Kenneth David Strang
Copyright: © 2017 |Pages: 16
DOI: 10.4018/IJRCM.2017010104
(Individual Articles)
No Current Special Offers


This paper examines the feasibility of three scenarios for reducing the unreliability inherent in traditional and clean energy creation. One scenario uses pumped hydro to store non-peak energy from solar or wind plants. Another scenario investigates the use of pumped hydro for storing non-peak energy from gas and coal fired plants. Another scenario explores the possibility of selling stored energy as well as meeting peak demand for all types of energy creation methods. The authors used the action research technique with a case study in USA. They applied parametric and simulation techniques to develop several models. The models include variables representing base loads at plants serving a utility, the buying and selling of electricity contracts at appropriate times, and the possibility of diverting a stream to feed the upper reservoir of a pumped storage plant.
Article Preview

Methods And Case Study

We are undertaking an action research method to assist several power plants to examine clean energy models and methods to improve the reliability of electricity generation. We will apply parametric statistical techniques including descriptive analysis, distribution analysis, and simulation.

We will use several plants in the USA as source of power generation statistics (capacity and demand). Since this is a simulation, and the data is competitively sensitive, the actual power plant names will not be disclosed. Our case study operations with the daily load curve summarized in Figure 1. This was derived by the use of two beta distribution, whose general formula is: y = k(x-a)α(b-x)β (1). The parametric values for both beta distributions were determined by Evolver optimization with the result shown in Figure 1, which shows that this approach can effectively model a bimodal distribution of day time electricity demand (Nersesian, 2012).

Figure 1.

Daily load curve for a utility (megawatts)


Complete Article List

Search this Journal:
Volume 13: 1 Issue (2024): Forthcoming, Available for Pre-Order
Volume 12: 1 Issue (2023): Forthcoming, Available for Pre-Order
Volume 11: 4 Issues (2022): 1 Released, 3 Forthcoming
Volume 10: 4 Issues (2021)
Volume 9: 4 Issues (2020)
Volume 8: 4 Issues (2019)
Volume 7: 4 Issues (2018)
Volume 6: 4 Issues (2017)
Volume 5: 4 Issues (2016)
Volume 4: 4 Issues (2015)
Volume 3: 4 Issues (2014)
Volume 2: 4 Issues (2013)
Volume 1: 4 Issues (2012)
View Complete Journal Contents Listing