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Open Access Publications from the University of California

Real-time Battery Control Method for Microgrid Energy Management

  • Author(s): XUE, YUN
  • Advisor(s): Martinez-Morales, Alfredo
  • Barth, Matthew
  • et al.
Abstract

Renewable energy has been playing an increasingly important role worldwide recently. For 2012 and 2013, renewables contributed 19% to energy consumption and 22% to electricity generation. In 2014, solar energy represented 36% of new generating capacity in the U.S., second only to natural gas. Due to the intermittent nature of renewable sources, energy storage systems have been integrated into the architecture of Microgrid systems to make them more efficient and robust. Within a Microgrid system, the battery energy storage system (BESS) can be used to reduce the electricity cost by delivering energy during the On-Peak rate period and storing energy (i.e. charging) during the Off-Peak rate period. This thesis proposes a real-time battery control method to reduce the electrical bill in an energy intensive environment, and more effectively utilize a BESS within Microgrid testbed system at the College of Engineering Center for Environmental Research and Technology (CE-CERT). The monthly electrical bill is based on the rate schedule time-of-use (TOU) for Large General and Industrial Service in the city of Riverside. Each month, this rate schedule has both demand charge (kW) and energy consumption (kWh) charge for three different rate periods: On-Peak, Mid-Peak, and Off-Peak. The real-time control method considers both rates for different rate periods separately. The main parts of the control method are the control algorithms, which comprise two model predictive control (MPC) algorithms, for the On-Peak rate period. The first algorithm is called the constant threshold MPC (CT-MPC) algorithm, and it is implemented in the system with the relatively stable solar generation and building load profiles in the winter season. This control algorithm can maintain the On-Peak demand below the constant threshold during the entire On-Peak rate period. The second one is called the adjusting demand threshold MPC (ADT-MPC) algorithm. The ADT-MPC algorithm fits a system with unpredictable solar generation and building load profiles. During the On-Peak rate period, by applying the ADT-MPC algorithm, the On-Peak threshold can be adjusted to optimized values. Both algorithms can maintain a low level of energy import from the external grid (i.e. Riverside Public Utilities grid) during the On-Peak rate period. For the other two rate periods, the Off-Peak and Mid-Peak control algorithms are also developed. With the real-time battery control method, the BESS continuously maintains the lowest demand and energy consumption for the entire day.

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