Abstract
When batteries supply behind-the-meter services such as arbitrage or peak load management, an optimal controller can be designed to minimize the total electric bill. The limitations of the batteries, such as on voltage or state-of-charge, are represented in the model used to forecast the system’s state dynamics. Control model inaccuracy can lead to an optimistic shortfall, where the achievable schedule will be costlier than the schedule derived using the model. To improve control performance and avoid optimistic shortfall, we develop a novel methodology for high performance, risk-averse battery energy storage controller design. Our method is based on two contributions. First, the application of a more accurate, but non-convex, battery system model is enabled by calculating upper and lower bounds on the globally optimal control solution. Second, the battery model is then modified to consistently underestimate capacity by a statistically selected margin, thereby hedging its control decisions against normal variations in battery system performance. The proposed model predictive controller, developed using this methodology, performs better and is more robust than the state-of-the-art approach, achieving lower bills for energy customers and being less susceptible to optimistic shortfall.
Highlights
B ATTERY Energy Storage Systems (BESS) are becoming an integral part of a resilient and efficient electrical system
The Energy Reservoir Model (ERM) is widely used in battery energy storage control problems [10], [11], [15]–[17] and has the advantage of being linear in charge and discharge power
The resulting schedules are distinguished by the tags ‘calculated’, which stands for the optimal schedules calculated using the ERM or Charge Reservoir Model (CRM), and ‘achieved’, which stands for the results of simulating the calculated schedule using the extended CRM
Summary
B ATTERY Energy Storage Systems (BESS) are becoming an integral part of a resilient and efficient electrical system. The simplest BESS model assumes that changes in SoC are proportional to the energy charged or discharged from ac point of interconnection This approach to optimal control represents the state-of-the-art and has been used for improving wind farm dispatch in Australian electricity markets [10], and achieving distribution feeder dispatchability [11]. Another common approach, based on the need for improved accuracy, is to use a BESS model that assumes that changes in SoC are proportional to the charge, in amp-hours, supplied or absorbed by the battery itself.
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