Optimum residential load management strategy for real time pricing (RTP) demand response programs Juan M. Lujano-Rojas a , Cla ´ udio Monteiro b,c , Rodolfo Dufo-Lo ´ pez a , Jose ´ L. Bernal-Agustı ´n a,n a Department of Electrical Engineering, University of Zaragoza, Spain b FEUP, Faculty of Engenharia University of Porto, Portugal c INESC-Instituto de Engenharia de Sistemas e Computadores do Porto, Porto, Portugal article info Article history: Received 3 November 2011 Accepted 8 March 2012 Available online 28 March 2012 Keywords: Smart grid Demand response Electric vehicle abstract This paper presents an optimal load management strategy for residential consumers that utilizes the communication infrastructure of the future smart grid. The strategy considers predictions of electricity prices, energy demand, renewable power production, and power-purchase of energy of the consumer in determining the optimal relationship between hourly electricity prices and the use of different household appliances and electric vehicles in a typical smart house. The proposed strategy is illustrated using two study cases corresponding to a house located in Zaragoza (Spain) for a typical day in summer. Results show that the proposed model allows users to control their diary energy consumption and adapt their electricity bills to their actual economical situation. & 2012 Elsevier Ltd. All rights reserved. 1. Introduction Energy demand is increasing in many countries as a result of economic and industrial developments; consequently, many gov- ernments are working to provide reliable electrical energy. How- ever, problems related with restrictions in electricity prices by means of a price ceiling and flat rates have produced a difference between marginal electricity generation costs and energy con- sumption cost of electricity. This increases the growth of demand faster than the growth of generation capacity. In addition, the volatility of wholesale electricity prices affects the retailer’s ability to generate profit and increase the investment uncertainty (Kim and Shcherbakova, 2011). The demand response (DR) is defined as changes in the electricity consumption patterns of end consumers to reduce the instantaneous demand in times of high electricity prices. A change in consumption patterns could be made by means of a change in the price of electricity (Price-Based Programs) or incentive payments (Incentive-Based Programs Albadi and El-Saadany, 2008). Time-of-Use (TOU) is a particular type of Price-Based Program whereby peak periods have higher prices than prices during off-peak periods; consequently, the users change their use of electricity. This type of DR program is particularly convenient for residential users (Eissa, 2011). Recently, many studies have been carried out to determine how end-users can adjust their load level according to a determined DR program. Molderink et al. (2010) developed an algorithm for the control of energy streams on a single house and a large group of houses. It is assumed that every house has microgenerators, heat and electricity buffers, appliances, and a local controller. In this approach, global and local controllers are used in three steps. First, a prediction is made for production and consumption for one day ahead, and then the local controller determines the aggregated profile and sends it to the global controller. Second, the plan for each house is made for the next day. Third, the algorithm decides how the demand is matched. Two examples were analyzed, and the results showed that it is possible to plan for a fleet of houses based on a one-day prediction; however, any forecasting error affects the outcomes of this approach. Houwing et al. (2011) analyzed the importance of the micro- Combined Heat and Power systems (micro-CHP) as a special type of distributed generation (DG) technology. The model-predictive control (MPC) proposed to make a demand response, minimizing the cost of the domestic energy use, subject to operational con- straints and assuming the perfect prediction of energy demand and electricity prices. The results showed that the costs are between 1% and 14% lower than the standard control strategies. Mohsenian-Rad and Leon-Garcia (2010) developed a residen- tial load control for real-time pricing (RTP) environments where the electricity payment and the waiting time for the operation of each appliance are minimized in response to the variable real- time prices. First, a price prediction is made for a determined scheduling horizon. Next, an objective function that considers the total electricity payment and the total cost of waiting (cost of using appliances at later hours) into the scheduling horizon is minimized. Mohsenian-Rad et al. (2010) presented a demand side Contents lists available at SciVerse ScienceDirect journal homepage: www.elsevier.com/locate/enpol Energy Policy 0301-4215/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enpol.2012.03.019 n Corresponding author. Tel.: þ34 976761921; fax: þ34 976762226. E-mail address: jlbernal@unizar.es (J.L. Bernal-Agustı ´n). Energy Policy 45 (2012) 671–679