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Baseload power production from wind turbine arrays coupled to compressed air energy storage
Abstract
An analysis is presented of compressed air energy storage (CAES) and its
potential for mitigating the intermittency of wind power, facilitating
access to remote wind resources and transforming wind into baseload
power. Although CAES has traditionally served other grid support
applications, it is also well suited for wind balancing applications due
its ability to provide long duration storage, its fast ramp rates and
its high part load efficiencies. In addition, geologies potentially
suitable for CAES appear to be abundant in regions with high-quality
wind resources. This is especially true of porous rock formations, which
have the potential to be the least costly air storage option for CAES.
The characteristics of formations suitable for CAES storage and the
challenges associated with using air as a storage fluid are discussed.
An optimization framework is developed for analyzing the cost of
baseload plants comprised of wind turbine arrays backed by natural
gas-fired generating capacity and/or CAES. The optimization model
analyzes changes to key aspects of the system configuration such as the
wind turbine rating, the relative capacities of the system components,
the size of the CAES storage reservoir and the wind turbine spacing. The
response of the optimal system configuration to changes in natural gas
price, greenhouse gas (GHG) emissions price, capital cost, and wind
resource is also considered. Wind turbine rating is given focused
attention because of its substantial impact on system configuration and
output behavior. The generation cost of baseload wind is compared to
that of other baseload options. To highlight the carbon-mitigation
potential of baseload wind, the competition with coal power (with and
without CO2 capture and storage, CCS) is given prominent attention. The
ability of alternative options to compete under dispatch competition is
explored thereby clarifying the extent to which baseload wind can defend
high capacity factors in the market. This analysis indicates that CAES
might be well suited for balancing wind power output and enabling wind
to achieve deep reductions in GHG emissions from power generation
globally.
This article studies the impact on CO2 emissions of electrical storage systems in power systems with high penetrations of wind generation. Using the Irish All-Island power system as a case-study, data on the observed dispatch of each large generator for the years 2008 to 2012 was used to estimate a marginal emissions factor of 0.547 kgCO2/kWh. Selected storage operation scenarios were used to estimate storage emissions factors – the carbon emissions impact associated with each unit of storage energy used. The results show that carbon emissions increase in the short-run for all storage technologies when consistently operated in ‘peak shaving and trough filling’ modes, and indicate that this should also be true for the GB and US power systems. Carbon emissions increase when storage is operated in ‘wind balancing’ mode, but reduce when storage is operated to reduce wind power curtailment, as in this case wind power operates on the margin. For power systems where wind is curtailed to maintain system stability, the results show that energy storage technologies that provide synthetic inertia achieve considerably greater carbon reductions. The results highlight a tension for policy makers and investors in storage, as scenarios based on the operation of storage for economic gains increase emissions, while those that decrease emissions are unlikely to be economically favourable. While some scenarios indicate storage increases emissions in the short-run, these should be considered alongside long-run assessments, which indicate that energy storage is essential to the secure operation of a fossil fuel-free grid.
The integration of increasingly available renewable energy sources, such as wind energy, into the power grid will have the potential to reduce dependence on fossil fuels and minimize greenhouse gas emission. However, due to the stochastic nature of renewable generation, balancing of generation and load becomes difficult. Energy storage is expected to play a major role in promoting the development of renewable energy by intermittent power source balancing, storing surplus generation, and providing electricity during high demands. One of the various emerging energy storage technologies is Compressed Air Energy Storage (CAES). In this paper, we model a wind generation-CAES system which can generate, store, and sell electricity to the grid. In addition, two optimization methodologies based on particle swarm optimization (PSO) are used to optimize the short-term operation and long-term planning of the wind generation-CAES system. The goal is to determine the optimum capacities of these resources as well as the optimum day-to-day operation strategy in order to maximize profit. The variables considered in this study include electricity market price, wind speed, gas price, etc., from a local electric utility. A number of sensitivity analyses are performed to evaluate the profitability of the wind generation-CAES system and the impact of different factors on the results.
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