Browsing by Subject "Microgrids"

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Open Access
Adaptive contextual learning for unit commitment in microgrids with renewable energy sources
(Institute of Electrical and Electronics Engineers, 2018) Lee, H. -S.; Tekin, Cem; van der, Schaar, M.; Lee, J. -W.
In this paper, we study a unit commitment (UC) problem where the goal is to minimize the operating costs of a microgrid that involves renewable energy sources. Since traditional UC algorithms use a priori information about uncertainties such as the load demand and the renewable power outputs, their performances highly depend on the accuracy of the a priori information, especially in microgrids due to their limited scale and size. This makes the algorithms impractical in settings where the past data are not sufficient to construct an accurate prior of the uncertainties. To resolve this issue, we develop an adaptively partitioned contextual learning algorithm for UC (AP-CLUC) that learns the best UC schedule and minimizes the total cost over time in an online manner without requiring any a priori information. AP-CLUC effectively learns the effects of the uncertainties on the cost by adaptively considering context information strongly correlated with the uncertainties, such as the past load demand and weather conditions. For AP-CLUC, we first prove an analytical bound on the performance, which shows that its average total cost converges to that of the optimal policy with perfect a priori information. Then, we show via simulations that AP-CLUC achieves competitive performance with respect to the traditional UC algorithms with perfect a priori information, and it achieves better performance than them even with small errors on the information. These results demonstrate the effectiveness of utilizing the context information and the adaptive management of the past data for the UC problem.
Open Access
Energy management in microgrids with plug-in electric vehicles, distributed energy resources and smart home appliances
(Springer, Singapore, 2015) Arslan, Okan; Karaşan, Oya Ekin; Rajakaruna, S.; Shahnia, F.; Ghosh, A.
Smart Grid is transforming the way energy is being generated and distributed today, leading to the development of environment-friendly, economic and efficient technologies such as plug-in electric vehicles (PEVs), distributed energy resources and smart appliances at homes. Among these technologies, PEVs pose both a risk by increasing the peak load as well as an opportunity for the existing energy management systems by discharging electricity with the help of Vehicle-to-grid (V2G) technology. These complications, together with the PEV battery degradation, compound the challenge in the management of existing energy systems. In this context, microgrids are proposed as an aggregation unit to smartly manage the energy exchange of these different state-of-the-art technologies. In this chapter, we consider a microgrid with a high level of PEV penetration into the transportation system, widespread utilization of smart appliances at homes, distributed energy generation and community-level electricity storage units. We propose a mixed integer linear programming energy management optimization model to schedule the charging and discharging times of PEVs, electricity storage units, and running times of smart appliances. Our findings show that simultaneous charging and discharging of PEV batteries and electricity storage units do not occur in model solutions due to system energy losses.