• Energy Research

Abstract This paper addresses the importance of the probabilistic analysis in determining the wave energy potential to support the design of electrical generators for wave energy conversion. The irregular activity of ocean waves induces variable frequencies and amplitudes of operation for point absorbers electrical generators, which may lead to the construction of inefficient electrical machines. Applying probabilistic models to describe the ocean wave's behavior allows the determination of the values most likely to be sampled for ocean-state parameters. Knowing these values, the most expected ocean wave potential can be determined and a proper device design can be performed. This work also refers to the importance of using the joint probability models instead of the marginal ones. Seven locations at the Portuguese coast are evaluated in order to determine their wave energy resource potential.

Short-Term Load Forecasting is critical for reliable power system operation, and the search for enhanced methodologies has been a constant field of investigation, particularly in an increasingly competitive environment where the market operator and its participants need to better inform their decisions. Hence, it is important to continue advancing in terms of forecasting accuracy and consistency. This paper presents a new deep learning-based ensemble methodology for 24 h ahead load forecasting, where an automatic framework is proposed to select the best Box-Jenkins models (ARIMA Forecasters), from a wide-range of combinations. The method is distinct in its parameters but more importantly in considering different batches of historical (training) data, thus benefiting from prediction models focused on recent and longer load trends. Afterwards, these accurate predictions, mainly the linear components of the load time-series, are fed to the ensemble Deep Forward Neural Network. This flexible type of network architecture not only functions as a combiner but also receives additional historical and auxiliary data to further its generalization capabilities. Numerical testing using New England market data validated the proposed ensemble approach with diverse base forecasters, achieving promising results in comparison with other state-of-the-art methods.

Abstract Short-term load forecasting is very important for reliable power system operation, even more so under electricity market deregulation and integration of renewable resources framework. This paper presents a new enhanced method for one day ahead load forecast, combing improved data selection and features extraction techniques (similar/recent day-based selection, correlation and wavelet analysis), which brings more “regularity” to the load time-series, an important precondition for the successful application of neural networks. A combination of Bat and Scaled Conjugate Gradient Algorithms is proposed to improve neural network learning capability. Another feature is the method's capacity to fine-tune neural network architecture and wavelet decomposition, for which there is no optimal paradigm. Numerical testing using the Portuguese national system load, and the regional (state) loads of New England and New York, revealed promising forecasting results in comparison with other state-of-the-art methods, therefore proving the effectiveness of the assembled methodology.

Photovoltaic (PV) generation systems have been increasingly used to generate electricity from renewable sources, attracting a growing interest. Recently, grid connected PV micro-generation facilities in individual homes have increased due to governmental policies as well as greater attention by industry. As low voltage (LV) distribution systems were built to make energy flow in one direction, the power feed-in of PV generation in rural low-voltage grids can influence power quality (PQ) as well as facility operation and reliability. This paper presents results on PQ analysis of a real PV generation facility connected to a rural low-voltage grid. Voltage fluctuations and voltage harmonic contents were observed. Statistical analysis shows a negative impact on PQ produced by this PV facility and also that only a small fraction of the energy available during a sunny day is converted, provoking losses of revenue and forcing the converter to work in an undesirable operating mode. We discuss the disturbances imposed upon the grid and their outcome regarding technical and economic viability of the PV system, as well as possible solutions. A low-voltage grid strengthening has been suggested and implemented. After that a new PQ analysis shows an improvement in the impact upon PQ, making this facility economically viable.

Standalone microgrids with photovoltaic (PV) solutions could be a promising solution for powering up off-grid communities. However, this type of application requires the use of energy storage systems (ESS) to manage the intermittency of PV production. The most commonly used ESSs are lithium-ion batteries (Li-ion), but this technology has a low lifespan, mostly caused by the imposed stress. To reduce the stress on Li-ion batteries and extend their lifespan, hybrid energy storage systems (HESS) began to emerge. Although the utilization of HESSs has demonstrated great potential to make up for the limitations of Li-ion batteries, a proper power management strategy is key to achieving the HESS objectives and ensuring a harmonized system operation. This paper proposes a novel power management strategy based on an artificial neural network for a standalone PV system with Li-ion batteries and super-capacitors (SC) HESS. A typical standalone PV system is used to demonstrate and validate the performance of the proposed power management strategy. To demonstrate its effectiveness, computational simulations with short and long duration were performed. The results show a minimization in Li-ion battery dynamic stress and peak current, leading to an increased lifespan of Li-ion batteries. Moreover, the proposed power management strategy increases the level of SC utilization in comparison with other well-established strategies in the literature.

With an increasing public and governmental awareness regarding environment protection and sustainable resources, hand-in-hand with an escalating electricity demand, the exponential growth of renewable energy generation capacity has been the “answer”. Mitigating the environmental harms associated with the more conventional energy sources such as: coal, oil, gas and nuclear. Nonetheless, some challenges remain, particularly concerning the integration of these technologies into the conventional generation mix. Hybrid energy systems allow a paradigm shift from a concentrated conventional generation to a more distributed one. This paper discusses the optimized PV-wind with hydro and battery storage capabilities for a grid-connected application considering the Short-Term Price Forecast information. The proposed technique has been tested in different scenarios, and results demonstrate the effectiveness of the proposed approach.

Microgeneration is the small-scale generation of heat or electric power or both, by individuals or buildings to meet their own needs. Recently, microgeneration is being regarded as a means to decentralize the power production of renewable energies, reducing the impacts on the grid caused by unexpected energy demands. Given the increase in microgeneration facilities, determining the quantity of energy produced and the power quality assumes growing importance in low, medium, or high voltage facilities. This paper presents a power quality analysis of two different facilities with photovoltaic generation localized in a rural area of Portugal, describing the voltage and frequency behaviour, the harmonic contents, and the total harmonic distortion. Statistical data are presented regarding the number of voltage events and occurrence of dips and swells in both facilities as a percentage of rated voltage. We conclude that some PV systems can severely affect voltage quality, forcing the grid to work at and even above the maximum voltage standard limit.

Abstract This paper addresses the optimization of the linear switched reluctance generator with tubular topology to be applied in a sea wave energy conversion system. Two new algorithms to optimize the geometry of the generators are proposed. The algorithms are based on both particle swarm and Box´s complex optimization methods. The optimization procedures consist of a multidimensional optimal value search. First the initial variable vectors are specified throughout the feasible search space. Then, an iterative procedure is applied with the goal of finding the variable values that minimize the objective function. The proposed algorithms are suitable for the optimization problem considered since the objective function is highly nonlinear and not analytically defined, as evaluated using a finite element analysis based software, and show very good performance. A factor that characterizes the generation capabilities is also defined and the obtained optimized generators are compared.