• Energy Research

This paper presents method to discriminate between transient voltage stability and voltage sag. The discrete wavelet transform (WT) is a powerful tool in the analysis of the transient phenomena in power systems because of its ability to extract information in both the time and frequency domain. This paper introduces a technique for accurate discrimination by combining WTs with neural networks (NNs). The WT is first applied to decompose the signals into a series of detailed wavelet components. The wavelet components are calculated and then employed to train a NN. The simulated results presented clearly show that the proposed technique can accurately discriminate between transient voltage stability and voltage sag in power system protection.

The emergence of novel demand-side management strategies is provoking that residential consumers cannot be longer categorized as inflexible loads. In that sense, new paradigms such as flexible demand and vehicle-to-home capabilities have appeared on pursuing a more efficient management of the different domestic assets such as smart appliances, small renewable generators and storage facilities. Consideration of such kind of management strategies plays a vital role on designing stages of electrifications systems for isolated homes. In this regard, a proper evaluation of possible demand-side capabilities enables a more accurate evaluation of the electrification project. This paper tackles this issue by developing a Mixed Integer Linear Programming formulation for optimal planning of electrification systems for off-grid dwellings. The developed framework allows to incorporate and analyse advanced demand-side strategies such as deferrable loads and vehicle-to-home processes by evaluating different project costs over various time horizons. The proposed approach is applied to a benchmark off-grid residential case study and various results are provided and analysed. As the most relevant result, it is worth remarking that total project cost can be reduced by ~21.2% and ~25.73% by considering flexible demand and vehicle-to-home capabilities, respectively. Nevertheless, the highest monetary savings were achieved when both strategies are jointly adopted, reducing the total project cost up to ~41.5%. The reported results demonstrate the importance of considering the different demand-side strategies on planning stages of electrification systems for off-grid dwellings.

With the emergence of smart appliances and communication infrastructures, Home Energy Management Systems have gained importance to help home users to reduce their electricity bills. A Home Energy Management System is a tool able to optimally coordinate the different home assets such as controllable appliances, onsite generators and storage facilities, among others. Such kind of tools has become more complex with the appearance of dynamic pricing tariffs and novel appliances such as electric vehicles. In this context, scheduling tools must attain a high level of robustness against uncertainties as well as being able to consider the particular behaviour of unpredictable energy pricing and vehicle routines, while some complementary objectives like thermal comfort are not ignored. This paper addresses this issue by developing a novel hybrid robust-multi objective home energy management approach. The novel proposal is based on information gap decision theory and lexicographic optimization, which are combined in an original way to attain a scheduling plan immune against the negative effect of uncertainties, while the economy and comfort of the building are jointly considered. The developed mathematical formulation is Mixed Integer Linear Programming, employing advanced linearization techniques to overcome the problems arisen from nonlinear models. Its particular integer-linear structure makes the developed optimization problem easily tractable by conventional solvers and average machines. Also, further capabilities are explored such as the possibility of selling energy to the grid and the vehicle-to-home ability of electric vehicles. Extensive simulations are performed on a benchmark prosumer environment considering real time pricing and time-of-use tariffs. The result serves to prove that the developed methodology is able to deal with uncertainties whereas different objective functions are jointly accounted.

Abstract Many methods have been applied to achieve optimal site and size of distributed generation systems. This paper introduces a new hybrid method, which employs discrete particle swarm optimization and optimal power flow to overcome this shortcoming. The main technical constraints are imposed for utilities, which could apply this approach to search the best sites to connect distributed generation systems in a distribution network choosing among a large number of potential combinations. A fair comparison between the proposed algorithm and other methods is performed. For such goal, convergence curves of objective function versus number of iterations are computed. The proposed algorithm reaches a better solution than Genetic Algorithms considering similar number of evaluations.

Despite that most of power systems can be categorized as well-conditioned, ill-conditioned cases are becoming more frequent. Consequently, developing new robust LF techniques is necessary to efficiently solve these cases. In this paper, an effective load-flow (LF) approach based on Gauss-Newton’s formulation is proposed. Moreover, efficient strategies for exploring the unsolvable region and calculating the solution space boundary are developed. The proposed LF approach is comprehensively validated using a wide variety of ill-conditioned systems in loadbase conditions, near of the maximum loadability points and considering generator’ reactive limits. The studied systems range from 1888-bus to 70000-bus. The results prove the efficiency and superiority of proposed approach over other well-known LF methods.

In the original publication [...]