Abstract
This paper presents a battery-aware stochastic control framework for residential energy management systems (EMS) equipped with energy harvesting, that is, photovoltaic panels, and storage capabilities. The model and control rationale takes into account the dynamics of load, the weather, the weather forecast, the utility, and consumer preferences into a unified Markov decision process. The embedded optimization problem is formulated to determine the proportion of energy drawn from the battery and the grid to minimize a cost function capturing a user-defined tradeoff between battery degradation and financial expense by user preferences. Numerical results are based on real-world weather data for Golden, Colorado, and load traces. The results illustrate the ability of the system to limit battery degradation assessed using the Rain flow counting method for lithium ion batteries.
Highlights
Due to their intermittent nature, the integration of renewable sources in the energy grid poses several challenges
In Reference [12], an Internet of Things (IoT) based home energy management system (HEMS) comprised of appliances, renewable energy source (RES), energy storage source (ESS), and a plug-in electric vehicle (PEV) is proposed to satisfy a demand model which takes into consideration the total power consumption of appliance operation, the operation period for the appliance, the length of run-time and appliance priority
Since we model the contributing elements of the home energy system as Markov chains to be used in a Markov decision process for control optimization, the system is subject to the curse of dimensionality
Summary
Due to their intermittent nature, the integration of renewable sources in the energy grid poses several challenges. The key to accomplish this objective is the ability of the controller to predict future load and output of the photovoltaic panels To this aim, based on real-world data, we build an underlying stochastic model capturing the temporal dynamics of the load and solar irradiance. Using Dynamic Programming [3], the controller evaluates the long-term cost of actions determining the fraction of load satisfied, taking energy from the grid and battery. The former trivially contributes to the financial cost of operations.
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