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In this paper, a robust optimization strategy is developed to handle the uncertainties for domestic electric water heater load scheduling. At first, the uncertain parameters, including hot water demand and ambient temperature, are described as the intervals, and are further divided into different robust levels in order to control the degree of the conservatism. Based on this, traditional load scheduling problem is rebuilt by bringing the intervals and robust levels into the constraints, and are thus transformed into the equivalent deterministic optimization problem, which can be solved by existing tools. Simulation results demonstrate that the schedules obtained under different robust levels are of complete robustness. Furthermore, in order to offer users the most optimal robust level, the trade-off between the electricity bill and conservatism degree are also discussed.
Jidong Wang; Yingchen Shi; Kaijie Fang; Yue Zhou; Yinqi Li. A Robust Optimization Strategy for Domestic Electric Water Heater Load Scheduling under Uncertainties. Applied Sciences 2017, 7, 1136 .
AMA StyleJidong Wang, Yingchen Shi, Kaijie Fang, Yue Zhou, Yinqi Li. A Robust Optimization Strategy for Domestic Electric Water Heater Load Scheduling under Uncertainties. Applied Sciences. 2017; 7 (11):1136.
Chicago/Turabian StyleJidong Wang; Yingchen Shi; Kaijie Fang; Yue Zhou; Yinqi Li. 2017. "A Robust Optimization Strategy for Domestic Electric Water Heater Load Scheduling under Uncertainties." Applied Sciences 7, no. 11: 1136.
In the context of climate change and energy crisis around the world, an increasing amount of attention has been paid to developing clean energy and improving energy efficiency. The penetration of distributed generation (DG) is increasing rapidly on the user’s side of an increasingly intelligent power system. This paper proposes an optimization method for industrial task-continuous load management in which distributed generation (including photovoltaic systems and wind generation) and energy storage devices are both considered. To begin with, a model of distributed generation and an energy storage device are built. Then, subject to various constraints, an operation optimization problem is formulated to maximize user profit, renewable energy efficiency, and the local consumption of distributed generation. Finally, the effectiveness of the method is verified by comparing user profit under different power modes.
Jidong Wang; Kaijie Fang; Jiaqiang Dai; Yuhao Yang; Yue Zhou. Optimal Scheduling of Industrial Task-Continuous Load Management for Smart Power Utilization. Applied Sciences 2017, 7, 281 .
AMA StyleJidong Wang, Kaijie Fang, Jiaqiang Dai, Yuhao Yang, Yue Zhou. Optimal Scheduling of Industrial Task-Continuous Load Management for Smart Power Utilization. Applied Sciences. 2017; 7 (3):281.
Chicago/Turabian StyleJidong Wang; Kaijie Fang; Jiaqiang Dai; Yuhao Yang; Yue Zhou. 2017. "Optimal Scheduling of Industrial Task-Continuous Load Management for Smart Power Utilization." Applied Sciences 7, no. 3: 281.