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    Comparative Study between On- and Off-Grid Photovoltaic to Reduce Peak Demand in Residential Houses
    (2026-01-01)
    Jettanasen, Chaiyan
    ;
    Sottiyaphai, Chayanut
    ;
    Bunjongjit, Sulee
    ;
    Songsukthawan, Panapong
    ;
    Phannil, Natthanon
    This study proposes a programmable logic controller (PLC)-based energy management system integrated with an off-grid photovoltaic (PV) system and battery storage to reduce residential peak demand. The proposed system dynamically manages power supply between the distribution grid, PV generation, and battery storage based on real-time power demand measurements. When the measured power exceeds a predefined threshold, stored renewable energy is utilized to support high-load conditions and mitigate peak demand. Experimental results obtained from a residential-scale test system demonstrate that the proposed off-grid PV system with PLC control can reduce peak demand by up to 29.68% and 15.57% under office-working and work-from-home scenarios, respectively, compared with conventional grid supply and on-grid PV systems without storage. In addition, the system achieves electricity cost reductions of up to 13.63% under Time-of-Use tariffs and up to 11.42% under normal electricity rates, depending on load behavior. These results indicate that integrating PLC-based control with off-grid PV and battery storage can effectively mitigate residential peak demand and reduce electricity expenses under realistic operating conditions.
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    Medium-term operation for an industrial customer considering demand side management and risk management
    (2015-09-14)
    Ding, Zhaohao
    ;
    Sarikprueck, Piampoom
    ;
    Lee, Wei Jen
    Under a deregulated market environment, industrial customers can participate in multiple markets with different time range to purchase electricity. Transactions in different markets make the industrial customer involve in different level of cost uncertainties and risks. To solve this energy procurement portfolio problem, a medium-term operation model is proposed. The risk-term is measured and managed by mean-variance approach. The uncertainties in the proposed model are characterized by stochastic day-ahead and real-time prices generated based on ERCOT historical data. A sample case study is provided to illustrate and verify the proposed model.