With the rapid growth in renewable energy and battery storage technologies, there exists significant opportunity to improve energy efficiency and reduce costs through optimization. However, optimization algorithms must take into account the underlying dynamics and uncertainties of the various interconnected subsystems in order to fully realize this potential. To this end, we formulate and solve an energy management optimization problem as a Markov Decision Process (MDP) consisting of battery storage dynamics, a stochastic demand model, a stochastic solar generation model, and an electricity pricing scheme. The stochastic model for predicting solar generation is constructed based on weather forecast data from the National Oceanic and Atmospheric Administration (NOAA). A near-optimal policy design is proposed based on stochastic dynamic programming. Simulation results are presented in the context of a storage and solar-integrated residential building environment. Results indicate that the near-optimal policy significantly reduces the operating costs compared to several heuristic alternatives. The framework proposed can be used to aid the design and evaluation of energy management policies with configurable demand-supply-storage parameters while accounting for weather-induced uncertainties.
Also centralised but considering renewable energy technologies to improve energy efficiency and reduce costs through optimisation algorithms, approaches in the work by the authors of REF focus on the context of microgrids and storage at residential and commercial building environments.
Designing Near-Optimal Policies for Energy Management in a Stochastic Environment
ArXiv
ArXiv
Computer Science Engineering Mathematics