A novel spontaneous self-adjusting controller of energy storage system for maximum demand reductions under penetration of photovoltaic system

Lee Cheun Hau, Yun Seng Lim and Serena Miao San Liew

Applied Energy, 2020, vol. 260, issue C, No S0306261919319816

Abstract: Customers are subject to varying charges for their electricity consumption (kWh) as well as monthly maximum demands (kW) depending on the charging schemes for commercial and industrial customers. Generally, maximum demand charges may account for as high as 30% of the total electricity bills. Although on-site photovoltaic (PV) systems can help customers reduce their maximum demand charges, PV may not be as effective in reducing some of the peak demands due to the intermittent power output of PV. The inclusion of a battery-based energy storage system (BESS), on the other hand, can reduce those unexpected peaks by supplying power at the appropriate time and magnitude. Research efforts have therefore been carried out to develop control strategies for BESS to reduce the peak demands of PV customers. However, some of the existing controllers that rely on forecasted next-interval net demands to supply power for the next interval may fail to reduce peak demands effectively when the actual next-interval net demands are different from the forecasted ones. Hence, a spontaneous self-adjusting controller has been developed and presented in this paper to overcome this issue. It employs model predictive control and dynamic programming with anticipatory, preparatory and recovery actions to achieve a maximum demand reduction of at least 11.00% over the monthly maximum demand. Throughout an experimental peak reduction period of 4 months, the controller has also proven to achieve a reduction of at least 74.11% of the ideal reductions as compared to 68.00% and 65.00% reductions demonstrated by the preceding active and fuzzy controllers.

Keywords: Peak demand reductions; Maximum demand reductions; Battery energy storage; Photovoltaic; Spontaneous self-adjusting; Model predictive control (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2019.114294

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