The temporal mismatches in the varying demand and supply pose a major challenge for today’s U.S. electricity grid. Demand response (DR), aiming at reducing demand on the grid during times of electricity generation capacity shortage and very high wholesale prices, is one of many approaches to address this challenge. Unlike the sophisticated automatic controls to operate appliances (such as lights and air-conditioning) on shifted or reduced schedules, which are more common in the commercial sector, the proposed DR scheme discharges storage when demand on the grid is high so as to enable DR without affecting actual appliance usage. As small-scale storage technologies and residential demand response tariffs (e.g., time-of-use tariffs, which charge in differing rates for peak times and off-peak times) become more available, distributed energy storage for the residential sector DR is now technically ready and has the opportunity to generate financial incentives for residential consumers. However, such storage-based DR is still largely underutilized in the residential sector, partly due to consumers’ concerns about cost-effectiveness of storage.
Thus, these concerns call for a comprehensive economic analysis to answer the following two questions: 1) Could storage yield actual profits (i.e., electricity cost reduction via arbitrage minus levelized storage cost) for residential consumers? And 2) Which particular combination of storage technology and tariff yields the highest profit? In addition, from the perspective of the grid, a third question is yet to be answered: If a large portion of households were to apply economically optimized storage-based DR systems, what would be the implications and emission impacts (i.e., CO₂, NOₓ, and SO₂ emissions) for the grid?
To address the above questions, I 1) develop a levelized storage cost model, based on the simulated storage lifetime — a hybrid of the total-energy-throughput lifetime and the calendar lifetime. Storage technologies included in this dissertation are conventional and flow batteries, flywheel, magnetic storage, pumped hydro, compressed air, and capacitor; 2) devise an agent-based, appliance-level demand model to simulate demand profiles for an average household in the U.S.; 3) dispatch storage via loadshifting (to time-shift energy requirements from peak times to off-peak times) and peak shaving (to reduce peak power, i.e., kW, demands and smooth demand profiles) strategies, under realistic tariffs (Con Edison, New York); and 4) optimize the storage capacity (in kWh) and the demand limit on the grid (in kW; above which the strategy will attempt to use stored electricity in addition to grid electricity to satisfy appliance demand; used for the peak shaving strategy only) and determine the potential profits (or losses). I find that: 1) For economically viable technologies, annual profits range from <1% to 28% of the household’s non-DR electricity bill by utilizing the loadshifting strategy and from <1% to 37% by implementing the peak shaving strategy, depending on the storage technologies; 2) Of the two DR strategies, the peak shaving strategy can render more storage technologies economically viable.
To evaluate the potential implications for the New York state grid, the electricity consumption features of households in New York state are then fed into the demand model. A dispatch curve is then developed, based on the marginal generation cost of each power plant, to simulate the dispatch order of the available power plants in New York state. The potential implications and emission impacts are investigated by comparing the statewide demand profiles as well as generation emissions with and without residential sector storage and DR. I find that: 1) Although yielding substantial financial incentives for households, the peak shaving strategy only leads to minor impact on the grid (assuming 15% household participation rate); 2) The loadshifting strategy would cause extra grid stress, and likely lead to brownouts, when all participating households start to re-charge their storage by purchasing inexpensive electricity uncoordinatedly; and 3) The overall emission impacts for both strategies are less than 5% of the total non-DR emissions in the state of New York.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D89P315H |
| Date | January 2015 |
| Creators | Zheng, Menglian |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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