Emeline Georges

Smart grid energy flexible buildings through the use of heat pumps and building thermal mass as energy storage in the Belgian context

The management of electricity grids requires the supply and demand of electricity to be in balance at any point in time. To this end, electricity suppliers have to nominate their electricity bids on the day-ahead electricity market so that the forecast supply and demand are in balance. One way to reduce the cost of electricity supply is to minimize the procurement costs of electricity by shifting flexible loads from peak to off-peak hours. This can be done by offering consumers time-of-use variable electricity tariffs as an incentive to shift their demand. This study provides typologies of smart grid energy ready buildings within the context of the Belgian residential building stock and the Belgian day-ahead electricity market. Typical new residential buildings are considered, equipped with air-to-water heat pumps that supply either radiators or a floor heating system. Five heating control strategies are compared in terms of thermal comfort, energy use, cost, and flexibility. Flexibility is quantified in terms of load volumes shifted and in terms of procurement costs avoided. The first three are rule-based control strategies, whereas the last two are a smart grid-oriented optimal predictive control strategy responding to a time-varying electricity price profile. The results show that the smart grid control strategies allow reduction of procurement costs by up to 15% and the consumers cost by 13%. The flexibility, defined in terms of loads volume shifted, is increased by 3% to 14% with the same thermal comfort. The impact of building insulation level and thermal mass is also evaluated. The flexibility for load shifting is higher when shifting from a low-energy (average U-value of 0.458 W/m2K) to a very-low-energy house (average U-value of 0.152 W/m2K).

Direct control service from residential heat pump aggregation with specified payback

This paper addresses the problem of an aggregator controlling residential heat pumps to offer a direct control flexibility service. The service is defined by a 15 minute power modulation, upward or downward, followed by a payback of one hour and 15 minutes. The service modulation is relative to an optimized baseline that minimizes the energy costs. The potential amount of modulable power and the payback effect are computed by solving mixed integer linear problems. Within these problems, the building thermal behavior is modeled by an equivalent thermal network made of resistances and lumped capacitances whose parameters are identified from validated models. Simulations are performed on 100 freestanding houses. For an average 4.3 kW heat pump, results show a potential of 1.2 kW upward modulation with a payback of 600 Wh and 150 Wh of overconsumption. A downward modulation of 500 W per house can be achieved with a payback of 420 Wh and 120 Wh of overconsumption.

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

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%.