A general methodology for optimal load management with distributed renewable energy generation and storage in residential housing. Issue 2 (4th March 2017)

• Record Type: Journal Article
• Authors: Georges, E.; Braun, J. E.; Lemort, V.
• Is Part Of: Journal of building performance simulation. Volume 10:Issue 2(2017)
• Page Start: 224
• Page End: 241
• Publication Date: 2017-03-04
• Subjects: net metering -- load matching -- heat pump -- optimal control -- linear programming; 690.0113
• DOI: 10.1080/19401493.2016.1211738
• Languages: English
• ISSNs: 1940-1493

Abstract

Abstract : 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 net metering programmes. The fast spread of such distributed power generation represents additional challenges for the management of the electricity grid and has led to increased interest in smart control of building loads and demand response programmes. This paper presents a general methodology for assessing opportunities associated with optimal load management in response to evolving utility incentives for residential buildings that employ renewable energy sources and energy storage. An optimal control problem is formulated for manipulating thermostatically controlled domestic loads and energy storage in response to the availability of renewable energy generation and utility net metering incentives. The methodology is demonstrated for a typical American house built in the 1990s and equipped with a single-speed air-to-air heat pump, an electric water heater and photovoltaic (PV) collectors. The additional potential associated with utilizing electrical batteries is also considered. Load matching performance for on-site renewable energy generation is characterized in terms of percentage of the electricity production consumed on-site and the proportion of the demand covered. For the purpose of assessing potential, simulations were performed assuming perfect predictions of the electrical load profiles. The method also allows determination of the optimal size of PV systems for a given net metering programme. Results of the case study showed significant benefits associated with control optimization including an increase of load matching between 3% and 28%, with the improvement dependent on the net metering tariff and available storage capacity. The estimated cost savings for the consumer ranged from 6.4% to 27.5% compared to no optimization with a unitary buy-back ratio, depending on the available storage capacity. Related reduction in CO2 emissions were between 11% and 46%. Optimal load management of the home thermal systems allowed an increase in the optimal size of the PV system in the range of 13–21%.