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This paper develops a two-step procedure for commercial buildings to optimize the frequency regulation service provision by leveraging the heating, ventilation, and air conditioning (HVAC) systems. Both day-ahead and real-time operations of the HVAC system are simulated by using a typical commercial building's model, the PJM market prices, and dynamic regulation signals. The simulation results show that it is beneficial for buildings to provide dynamic regulation services where the capacity reserved for regulation up and down are the same. The mean reverting characteristic of the dynamic regulation signal enables commercial buildings to increase regulation capacity with minimal impact on the comfort level of occupants. The proposed frequency regulation provisioning scheme yields a high performance score (>0.9). The simulation results also reveal that there exists a trade-off between frequency regulation performance and climate control performance of the building. Finally, the economic benefits of frequency regulation provisions of commercial buildings are analyzed.
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Data-Driven Capacity Bidding for Frequency Regulation
Frequency regulation is crucial for balancing the supply and demand of modern electricity grids. To provide regulation services, it is important to understand the capability of flexible resources to track regulation signals. This paper studies the problem of submitting capacity bids to a forward regulation market based on historical regulation signals. We consider an aggregator who manages a group of flexible resources with linear dynamic constraints. He seeks to find the optimal capacity bid, so that real-time regulation signals can be followed with an arbitrary guaranteed probability. We formulate this problem as a chance-constrained program with unknown regulation signal distributions. A sampling and discarding algorithm is proposed. It provably provides near-optimal solutions at a guaranteed probability of success without knowing the distribution of the regulation signals. This result holds for resources with arbitrary linear dynamics and allows arbitrary intra-hour data correlations. We validate the proposed algorithm with real data via numerical simulations. Two cases are studied: (1) CAISO market, where providers separately submit capacity estimates for regulation up and regulation down signals, (2) PJM market, where regulation up and down capacities are the same. Simulation results show that the proposed algorithm provides near-optimal capacity estimates for both cases.
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- Award ID(s):
- 1646612
- PAR ID:
- 10213780
- Date Published:
- Journal Name:
- 2019 American Control Conference (ACC)
- Page Range / eLocation ID:
- 3901 to 3908
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/ACC.2019.8815383
