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| Fig. 1: Castaic Power Plant, CA, (1.25 GW). (Source: Wikimedia Commons) |
The U.S. state of California has repeatedly increased the mandated deployment of variable renewable resources (VRE) for the purpose of power generation, including wind and solar. [1] Due to the variable nature of renewable resources such as solar and wind power, the time mismatch between the demand and supply renders higher levels of renewable penetration more difficult. Without means to time-shift the excess generation from renewable resources to meet demand, the grid cannot be solely dependent on variable generation. [1] Below the certain threshold of VRE penetration, the renewable energy generated can be partially or fully consumed by the current energy demand without the crucial need for energy storage. [1-3] Above the minimum threshold, the need to time-shift excess generation is a limiting factor for reaching higher penetration levels. [3] However, the current cost of available grid storage is much higher than the value of stored energy. [1] This means that it is cheaper to cap the VRE generation at the threshold (or curtail it to a certain level) and use natural gas for electricity generation.
This problem can be demonstrated through a high-level comparison of the energy cost ($ kWh-1) before and after adding a grid-storage system. In fact, the energy cost would vary significantly depending on the storage technology used and the details of the electricity market. [4] The three dominant and most mature grid-scale energy storage technologies are Pumped-hydro energy storage (PES), Compressed air energy storage (CAES), and Battery energy storage systems (BESS). [4] Other technologies, however, are still studied and deployed on smaller levels such as hydrogen energy storage system (HESS). One way to calculate the energy cost of these different storage systems in a manner that allows a logical comparison between them is through the levelized cost of energy (LCOE). This is the average net present cost of generating electricity for a specific energy project. In other words, it is the minimum price at which electricity can be sold for the energy project to break even. It can be calculated through the equation
| LCOE | = | Capital Cost × Capital Recovery Factor (CRF)
+ O&M Cost 8760 × Capacity Factor |
| + Fuel Cost × Heat Rate |
where the capital cost is in $ kW-1, O&M cost is in $ kW-1 y-1, and the capacity factor is the fraction of the year during which the project is generating power. The capital recovery factor (CRF) is calculated according to
| CRF | = | i (1+i)n (1+i)n - 1 |
where i is the interest rate in y-1 and n is the cost recovery period in years of the project. In the context of energy storage systems, LCOE is strongly sensitive to storage efficiency, electricity buy price and electricity sell price. [4] Table 1 shows the LCOE for the several storage technologies all evaluated at 100 MW power capacity for the two durations of 4 and 10 hours. The technologies are lithium-ion phosphate (LFP) batteries, lithium-ion nickel manganese cobalt (NMC) batteries, lead- acid batteries, compressed-air energy storage (CAES), and pumped storage hydro (PSH), and hydrogen energy storage (HESS). The LCOE for combined-cycle gas turbine (CCGT) is also included for comparison.
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| Table 1: Levelized cost of energy for energy storage systems and combined cycle gas turbine. [2,5] |
Note that the currently dominant technology, PSH, along with CAES, are the cheapest storage option for the two durations examined. Fig.1 shows the pumped storage hydroelectric Castaic power plant located on Castaic lake in California. Clearly, the cost of generating electricity with gas-fired turbines is much lower than that with any energy storage technology presented here. The cost of certain storage systems is expected to decrease in the next decade. [3] Until the cost of such technologies are made competitive with the cost of conventional peaking capacities, the increase of renewable penetration in the grid would be limited. [3]
© Ghufran Alkhamis. 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] P. Denholm and R. Margolis, "The Potential for Energy Storage to Provide Peaking Capacity in California under Increased Penetration of Solar Photovoltaics," U.S. National Renewable Energy Laboratory, NREL/TP-6A20-70905, March 2018.
[2] K. Mongrid et al., 2020 Grid Energy Storage Technology Cost and Performance Assessment," U.S. Department of Energy, DOE/PA-0204, December 2020.
[3] P. Denholm and R. Margolis, "Energy Storage Requirements for Achieving 50% Solar Photovoltaic Energy Penetration in California," U.S. National Renewable Energy Laboratory, NREL/TP-6A20-66595, August 2016.
[4] M. Obi et al., "Calculation of Levelized Costs of Electricity For Various Electrical Energy Storage Systems," Renew. Sustain. Energy Rev. 67, 908 (2016).
[5] "Projected Costs of Generating Electricity, 2020 Edition," International Energy Agency, 2020.
