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| Yiwen Wang |
| University of Massachusetts, Amherst |
The energy industry is essential in powering our daily lives and fueling economic growth. This sector revolves around corporations that produce and supply primary energy products (crude oil, natural gas, and renewables) and secondary energy (electricity, gasoline, and heat). As the cornerstone of various social aspects, the energy industry imposes significant economic and environmental impacts. However, it faces challenges that restrict the sustainable impact on societal development. This article examines these challenges, offers insights into the energy sector’s potential prospects, and proposes operations research frameworks that enhance and sustain the industry’s contributions to society.
The energy industry is highly dynamic and evolving rapidly. Global energy demand has surged 91% from 2000 to 2022.1 Adding to this problem, cities have growing heating and cooling needs to combat extreme weather. Furthermore, the emergence of new technologies has also made people more dependent on energy to power households’ needs. For instance, electric vehicle (EV) charging can significantly increase families’ nighttime electricity consumption. The overall digitalization trend in recent decades has also imposed substantial requirements for facilities such as data centers, which now account for 2% of electricity use in the US (DOE). On the supply side, air pollutants and greenhouse gas emissions heated critiques on the extensive use of fossil fuels in the energy system. From 2000 to 2022, more than 1 million MW of renewable energy was generated under the influence of environmental and climate pledges. Renewable energy is typically sustainably created using sources such as hydropower, solar, wind, biomass, and geothermal. However, the uncertainty and inconsistency of these resources pose challenges to the electricity system operations, especially during severe weather. To make matters worse, the unstable energy prices and security further enhance the complexity of the energy industry.
Considering all these challenges, let us envision a transformed energy industry shaped by operations research (OR) tools. In this blueprint, the energy industry is guided by three core objectives:
- to minimize energy consumption and costs
- to reduce emissions and pollution
- to enhance energy security and affordability
In an ideal future, the energy industry will become efficient, sustainable, and reliable.
Efficient
Transportation accounts for 27% of total energy consumption in the US (EIA, 2023). Promoting EVs and replacing Internal Combustion Engine Vehicles (ICEV) is a promising yet challenging task to improve energy use in the transportation industry. EVs, powered by batteries, have an efficiency close to 90%. This number is substantially higher than ICEVs, which is around 40%. Therefore, EVs only consume half the primary energy of a gasoline-powered vehicle. Besides, EVs also have clear environmental advantages. For instance, EVs have near-zero emissions, while ICEVs consume gasoline and diesel that produce greenhouse gases and toxic pollutants like CO, SO2, and NOx. Despite these advantages, EVs may be less convenient than ICEVs in practice. The range of EV per charge is 250 miles on average, compared to a typical gasoline car’s range of 500 miles. Mileage anxiety has held back many individuals from purchasing an EV. Furthermore, gas stations are far more accessible than charging stations. This causes some EV owners to experience "range anxiety" as they fear the vehicle may run out of power before reaching a charging point.
Range anxiety can be resolved in the future by optimally designing the EV chargers network through optimization algorithms. A promising project published on Google Blog (Kollias and Gollapudi, 2021) introduces route optimization for EVs in Google Maps using a modified shortest path algorithm. These researchers built a network with charging stations as intermediate nodes between the origin and destination. Next, each node was replicated to represent different levels of "entering" and "exiting" battery status. Through this method, EV drivers can choose routes based on their traveling time preferences and battery status without worrying about power shortages on the highway.
Sustainable
Fossil fuels has dominated energy production for centuries. As shown by Figure 2, 84.3% of global total energy is still generated from fossil fuels including oil, gas, and coal. More than 63.3% of global electricity is from these unsustainable resources. Such a production system face issues such as fluctuating prices which impose uncertainty on energy supply (IEA, 2022). Furthermore, fossil fuels release severe air pollutants and greenhouse gases in the power and transportation sectors, which cause severe threats to public health in many regions. Fortunately, acknowledging the scarcity, climate, and environmental consequences of fossil fuels, many countries have navigated a more sustainable and climate-friendly energy path.
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| Figure 2: Global Energy and Electricity Supply Mix by Energy Source |
Renewable energy, such as solar and wind, offers a clean alternative to fossil fuels. However, renewable energy also faces challenges, especially with implementation and adoption. One of the primary motivations for replacing fossil fuels with renew- able energy is the lower electricity prices from zero-cost generation inputs. Although sunlight and wind are free resources, they have inconsistent supply and may not meet the general demand patterns. A good example is the duck curve observed in California’s electricity market, as shown in Figure 3. The figure shows the real-time electricity demand minus solar production in California throughout the day. A duck curve pattern appears because solar production peaks at midday while demand is at its lowest; as solar production wanes, the demand quickly ramps up to its evening peak. Despite being a worldwide leader in solar adoption with abundant sunlight, California faces this unmatched pattern between power supply and demand. The challenge for the grid operators is quickly boosting other energy resources needed to compensate solar as the sun falls down(Jones-Albertus, 2017). The ideal strategy is to harness more electricity from solar when it is available and only use thermal generators (mostly fossil fuels) as standbys to continue the production. Nonetheless, the availability of sunlight varies by weather and season and can only be predicted with short foresight. It becomes a challenging task to secure the supply while maintaining grid reliability simultaneously.
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| Figure 3: Duck Curve in California |
One approach to sustaining the future energy industry operation is through more accurate forecasts of renewable energy availability. Stochastic optimization, for example, can be a helpful OR method for integrating the uncertainty about renewable energy into modeling. Zheng et al. (2015) reviewed works that used stochastic optimization to improve the NP-hard deterministic unit commitment model for power plant scheduling. Zakaria et al. (2020) summarized the state-of-the-art stochastic optimization techniques for renewable energy applications, including Monte Carlo Simulation, Markov Chain, stochastic programming, and dynamic programming.
Reliable
Beyond technical challenges, many other factors need to be addressed in the transformation process. This includes risk tolerance, technological innovation beliefs, conflicting objective resolution, and equity justice. However, these elements are usually difficult to measure or build models. Equity justice is particularly the case, which is a challenging objective to quantify in decision-making. Therefore, some victim neighborhoods are overlooked in energy policy decision-making despite experiencing energy crises. For example, many regions have launched rooftop solar and EV programs with incentives like electricity bill reduction and tax credits. Low-income communities are often more vulnerable to energy price variations, yet the high investment cost restricts their participation in these programs.
Energy crises such as shortages or grid failures during extreme weather significantly impact society. The future energy industry must remain reliable under emergent conditions. This requires careful and attentive energy planning. Decision-making methods can help policymakers find a set of robust and practical solutions. Policymakers can apply the utility theory to quantify preferences in outcomes and multi-criteria decision-making to find the optimal point between the trade-offs with conflicting objectives (Pohekar and Ramachandran, 2004). For example, Heleno et al. (2022) use a multi-objective model for energy justice and examine the trade-offs between intervention costs and energy burden reduction.
Conclusion
The future energy industry has many promising opportunities with the advancement of technology and the increasing concerns for the environment. This article proposes to apply OR to assist in optimizing networks, predicting resources, and engaging in long-term planning under uncertainty. Even though how OR can play a bigger role in the transition of the energy industry is still an open question, it holds great potential for shaping a more efficient, sustainable, and reliable energy future.
References
DOE. Data centers and servers. URL: https://www.energy.gov/eere/buildings/data-centers-and-servers.
EIA, 2023. U.S. Energy Information Administration Monthly Energy Review, Table 2.1. URL: https://www.eia.gov/energyexplained/ use-of-energy/transportation.php.
Heleno, M., Sigrin, B., Popovich, N., Heeter, J., Figueroa, A.J., Reiner, M., Reames, T.G., 2022. Optimizing equity in energy policy interventions: A quantitative decision-support framework for energy justice. Applied Energy 325, 119771.
IEA, 2022. Global Energy Crisis – Topics - IEA — iea.org. https://www.iea.org/topics/global-energy-crisis.
Jones-Albertus, B., 2017. Confronting the Duck Curve: How to Address Over-Generation of Solar Energy — energy.gov. https://www.energy.gov/eere/articles/confronting-duck-curve-how-address-over-generation-solar-energy.
Kollias, K., Gollapudi, S., 2021. Addressing range anxiety with smart electric vehicle routing. URL: https://blog.research.google/ 2021/01/addressing-range-anxiety-with-smart.html?m=1.
Pohekar, S., Ramachandran, M., 2004. Application of multi-criteria decision making to sustainable energy planning—a review. Renewable and Sustainable Energy Reviews 8, 365–381. doi:10.1016/j.rser.2003.12.007.
Zakaria, A., Ismail, F.B., Lipu, M.H., Hannan, M., 2020. Uncertainty models for stochastic optimization in renewable energy applications. Renewable Energy 145, 1543–1571. doi:10.1016/j.renene.2019.07.081.
Zheng, Q.P., Wang, J., Liu, A.L., 2015. Stochastic optimization for unit commitment—a review. IEEE Transactions on Power Systems 30, 1913–1924. doi:10.1109/tpwrs.2014.2355204.
Acknowledgments: I would like to thank Sen Li for taking time to review this article.
1. Numbers in this article are from ourworldindata unless otherwise cited.