Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicles driving schedules
- Technical Univ. of Lisbon (Portugal)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Center for Energy and Innovation Technologies (Austria)
- NEC Laboratories American Inc., Irving, TX (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
The large scale penetration of electric vehicles (EVs) will introduce technical challenges to the distribution grid, but also carries the potential for vehicle-to-grid services. Namely, if available in large enough numbers, EVs can be used as a distributed energy resource (DER) and their presence can influence optimal DER investment and scheduling decisions in microgrids. In this work, a novel EV fleet aggregator model is introduced in a stochastic formulation of DER-CAM [1], an optimization tool used to address DER investment and scheduling problems. This is used to assess the impact of EV interconnections on optimal DER solutions considering uncertainty in EV driving schedules. Optimization results indicate that EVs can have a significant impact on DER investments, particularly if considering short payback periods. Furthermore, results suggest that uncertainty in driving schedules carries little significance to total energy costs, which is corroborated by results obtained using the stochastic formulation of the problem.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- AC02-05CH11231
- OSTI ID:
- 1165008
- Report Number(s):
- LBNL-6471E
- Journal Information:
- Energy (Oxford), Vol. 64; ISSN 0360-5442
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
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