This thesis aims to assess some of the most relevant potential implications of a recently approved new methodology for establishing the cost of energy of the Spanish regulated electricity tariffs for low-voltage consumers, which substitutes a much different previous methodology: * The previous methodology was based on quarterly auctions (so-called CESUR auctions). The resulting price of these auctions was used to set the cost of energy to be included in the regulated retail tariffs for the next quarter, and so, it was a methodology that allowed the consumers to pay a price for electricity which was known ex-ante by them, and which was stable for the following quarter. * The new methodology is based on a real-time pricing (RTP) approach. It implies that the last-resort retailers will charge their consumers in their (usually bimonthly) bills, a cost of energy based on an electricity-market average-hourly price of the billed period. This average-hourly price is calculated using real hourly prices of day-ahead and intraday markets, as well as an hourly load curve for each consumer. This hourly load curve could be either the actual one if the consumer has a smart meter allowing for hourly metering, or an average one calculated by the system operator if the consumer does not have such a smart meter. Thus, this new methodology opens the door, to those consumers with smart meters, to save costs by implementing new demand-response actions by shifting their demand from hours with high prices to hours with low prices. More concretely, the specific questions that this thesis attempts to answer are: * Will the consumers pay more or less than what they used to pay with the previous methodology? How much? * What will be the maximum savings that the consumers with a smart meter may obtain if they optimize their consumption profile? * What will be the consequences if, instead, they follow the worst possible profile? To answer the above, this thesis proposes and implements an innovative approach, which goes beyond current state of the art, providing the following original contributions: * A sophisticated bottom-up model for generating realistic consumers’ load-profiles, based on: - A detailed representation of the technical characteristics of electric domestic devices or services (DoS); - A realistic characterization of the use of these DoS in Spain based on statistics on socio-demographic data. * A detailed optimization model to simulate optimal demand-response strategies of individual consumers that minimizes their electricity costs by shifting their demand to hours with lower prices subject to restrictions based on both technical and behavioral considerations. In this thesis, this approach has been applied to a realistic case study based on data of 2010 for Spain. The main conclusions of the analysis carried out are: * If the new methodology for establishing the cost of energy would had been approved in 2010, the average annual electricity costs’ savings obtained by those consumers without smart meters which are billed according to the average standard load profile published by the system operator would had been of 12.55% with respect to the actual costs they paid for electricity that year, with an standard deviation of 1.02%.3 * Those consumers with smart meters, that are billed according to their actual consumption profile, would had obtained slightly higher annual savings of 12.78% in average, with a standard deviation of 1.59%, just by following the same consumption profile they had without responding to real time price signals. * At a monthly level, the higher savings are obtained those months with lower average and higher standard deviation on hourly prices. Main reason for this is that for those months with lower hourly prices, the cost of energy established ex-ante through the CESUR auctions was significantly higher than actual hourly prices. So just by changing the methodology to an ex-post establishment of the cost of energy based on actual electricity hourly prices, significant savings would have been obtained. * If those consumers with smart meters, instead of following the same demand profile they actually had, would had responded to real time prices, shifting their demand to those hours with lower prices, they could have achieved additional annual costs’ savings of 6.33% in average. On the other hand, non-rational consumers following the worst load profile possible would have suffered average costs’ increases of 3.21%. *Above figures are based on perfect-flexibility assumptions. That is, they could only be obtained by consumers equipped with a kind of smart-box that would allow them to optimize and manage each DoS without restrictions regarding when to switch on and off them. On a more realistic situation with consumers not equipped with this device, considering that, usually, they will not be awake at certain hours to switch on or off some DoS (i.e.: from 1 am to 7am), the average annual maximum costs’ savings estimated by the case study of this thesis are of 4.35%. In this case, an average consumer following the worst profile possible would have seen its annual costs increasing by 2.92%. * On a monthly basis, those months presenting a higher volatility with regard to day-ahead hourly prices are those in which rational consumers obtain higher savings. This is due to the fact that higher volatility implies higher differences of prices between different hours, and so, more opportunities to obtain savings by shifting demand from expensive to cheap hours. In the analyzed cases, these months are usually those with the lowest average day-ahead price. The reason for that is that these months were usually those with a higher penetration of variable wind energy, which introduced higher volatility. * Although maximum annual average savings obtained through demand-response strategies in both cases, with (6.33%) or without a smart-box (4.35%), are not negligible, when expressed on absolute terms, these would have meant annual average savings of 8.78 euros and 5.96 euros respectively in 2010. Thus, consumers with a smart-box that could shift their demand to hours between 1 am and 7 am, could obtain 2.82 euros more of additional average annual savings than consumers without such a smart- box. On the other hand, non-rational consumers following the worst profile possible do not obtain significantly different losses when they can shift their demand to the period 1 am – 7am and when they cannot. * In any case, the observed savings are probably a low incentive for consumers to change their consuming behavior (although higher market electricity prices could increase this incentive). Thus, this thesis suggests that in a context of electricity market prices similar to those in 2010, it seems that, if regulatory authorities still aim to modify consumers’ demand profile, provided that they consider this important enough to reduce other system costs (i.e.: system operation costs; required additional investments on new generation and network capacities to cope with peak demand; etc.), they will have to think on additional measures beyond a mere real time pricing of electricity to provide incentives attractive enough for consumers to modify their behavior.