|
|
| Fig. 1: Hybrid System Featuring Wind Turbines and Solar Panels. (Source: Wikimedia Commons) |
The past decade of progress in solar, wind, and tidal energy infrastructure has achieved many aspirations of renewable hopes. As of 2017, compared to the global average cost for gas generated electricity of $0.05/kWh, the average costs of wind and solar electricity dropped to $0.06/kWh and $0.10/kWh, respectively. [1,2] Although both these renewables still cost at least 20 percent more thatn energy from conventional sources, projections for 2020 predict much more competitive prices at $0.05/kWh for wind and $0.06/kWh for solar. [2] However, beyond the sticker price of these electricity costs, the intermittent nature of these renewables has introduced a bottleneck on the extent of their utilization. [3,4] In both cases of solar and wind, the available resource used for energy harvesting exhibits volatile behavior. While weather forecasting technology has improved, the backup power generator is typically less adaptable to weather fluctuations in the short term. [3] The steady baseload energy generated thus creates compatibility issues with the volatile energy outputs of renewable infrastructure. As such, the increased dependence on solar and wind energy would carry significant risk by putting intermittency-associated strain on the system. Moving forward, raising the ceiling on renewables penetration would require one or more investments in current risk reduction techniques.
Perhaps the most intuitive method for reducing the variance of energy output involves integrating complementary renewable sources. Take solar and wind power for example (See Fig. 1). Whereas wind energy might dominate in the winter, solar power would peak during the summer. [4] Even at the intraday level, peak energy generation across these two sources is usually complementary. [4] While the particular resource availability and trends might vary on a regional basis, any investment in renewable infrastructure should consider the allocation that most decreases supply variance while matching day-to-day energy demands.
To complement strategies on the supply side, intelligent energy management systems represent an enticing opportunity to further increase utilization rates through the demand side. As the name suggests, demand side responses represent a body of methods leveraging both incentives and automated systems to better align energy usage with anticipated energy supply. [5] For instance, time-of-use tariffs set different prices on energy rates based on typical energy availability during that time of day. [5] Ideally, these tariffs would encourage more environmentally-friendly consumption patterns while minimizing the burden on each individual. The induced behavioral changes could be as simple as changing the time of day dedicated to laundry. Building off of intraday energy pricing, these intelligent systems could warn of an impending energy shortage in order to shift the demand distribution during the day. [4,5] If energy levels are anticipated with high probability to plummet at night the next day as a result of some emergency, the household can preemptively heat the house prior to the warning time. On the other end of this spectrum, institutions like the Lawrence Berkeley National Laboratory are developing a demand response that is fully automated by a central controller. [4,5] In this way, the controller can continuously make real time decisions using load models to maximize power savings.
While there might not be a silver bullet solution to renewable intermittency, the plethora of existing solutions can and should be leveraged as suited to a particular region's natural energy sources, consumption patterns, and technological resources. All told, advancing the potential of renewables begins not with a panacea, but rather with a combination of steps aiming towards a more energy-efficient future.
© David Lin. The author warrants that the work is the author's own and that Stanford University provided no input other than typesetting and referencing guidelines. The author grants permission to copy, distribute and display this work in unaltered form, with attribution to the author, for noncommercial purposes only. All other rights, including commercial rights, are reserved to the author.
[1] A. Evans, V. Strezov, and T. J. Evans, "Assessment of Sustainability Indicators For Renewable Energy Eechnologies," Renew. Sustain. Energy Rev. 13, 1082 (2009).
[2] "Renewable Power Generation Costs in 2017," International Renewable Energy Agency, 2018.
[3] W. Kempton and J. Tomić, "Vehicle-to-Grid Power Implementation: From Stabilizing the Grid to Supporting Large-Scale Renewable Energy," J. Power Sources 144, 268 (2005).
[4] P. Nema, R. K. Nema, and S. Rangnekar, "A Current and Future State of Art Development of Hybrid Energy System Using Wind and PV-Solar: A Review," Renew. Sustain. Energy Rev. 13, 2096 (2009).
[5] P. Palensky, "Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads," IEEE Trans. Ind. Inform. 7, 381 (2011).
