A general methodology for optimal load management with distributed renewable energy generation and storage in residential housing

摘要

In the US, buildings represent around 40% of the primary energy consumption and 74% of the electrical energy consumption[U.S. Department of Energy (DOE). 2012. 2011 Buildings Energy Data Book. Energy Efficiency & Renewable Energy].Incentives to promote the installation of on-site renewable energy sources have emerged in different states, including netmetering programmes. The fast spread of such distributed power generation represents additional challenges for the managementof the electricity grid and has led to increased interest in smart control of building loads and demand responseprogrammes. This paper presents a general methodology for assessing opportunities associated with optimal load managementin response to evolving utility incentives for residential buildings that employ renewable energy sources and energystorage. An optimal control problem is formulated for manipulating thermostatically controlled domestic loads and energystorage in response to the availability of renewable energy generation and utility net metering incentives. The methodologyis demonstrated for a typical American house built in the 1990s and equipped with a single-speed air-to-air heat pump, anelectric water heater and photovoltaic (PV) collectors. The additional potential associated with utilizing electrical batteriesis also considered. Load matching performance for on-site renewable energy generation is characterized in terms of percentageof the electricity production consumed on-site and the proportion of the demand covered. For the purpose of assessingpotential, simulations were performed assuming perfect predictions of the electrical load profiles. The method also allowsdetermination of the optimal size of PV systems for a given net metering programme. Results of the case study showedsignificant benefits associated with control optimization including an increase of load matching between 3% and 28%, withthe improvement dependent on the net metering tariff and available storage capacity. The estimated cost savings for the consumerranged from 6.4% to 27.5% compared to no optimization with a unitary buy-back ratio, depending on the availablestorage capacity. Related reduction in CO2 emissions were between 11% and 46%. Optimal load management of the homethermal systems allowed an increase in the optimal size of the PV system in the range of 13–21%.