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- Daily Operation Optimization of a Hybrid Energy System Considering a Short-Term Electricity Price Forecast SchemePublication . Bento, P.M.R.; Nunes, H.G.G.; Pombo, José Álvaro Nunes; Calado, M. do Rosário; Mariano, S.The scenario where the renewable generation penetration is steadily on the rise in an increasingly atomized system, with much of the installed capacity “sitting” on a distribution level, is in clear contrast with the “old paradigm” of a natural oligopoly formed by vertical structures. Thereby, the fading of the classical producer–consumer division to a broader prosumer “concept” is fostered. This crucial transition will tackle environmental harms associated with conventional energy sources, especially in this age where a greater concern regarding sustainability and environmental protection exists. The “smoothness” of this transition from a reliable conventional generation mix to a more volatile and “parti-colored" one will be particularly challenging, given escalating electricity demands arising from transportation electrification and proliferation of demand-response mechanisms. In this foreseeable framework, proper Hybrid Energy Systems sizing, and operation strategies will be crucial to dictate the electric power system’s contribution to the “green” agenda. This paper presents an optimal power dispatch strategy for grid-connected/off-grid hybrid energy systems with storage capabilities. The Short-Term Price Forecast information as an important decision-making tool for market players will guide the cost side dispatch strategy, alongside with the storage availability. Different scenarios were examined to highlight the effectiveness of the proposed approach.
- A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimizationPublication . Nunes, H.G.G.; Pombo, José Álvaro Nunes; Mariano, S.; Calado, M. do Rosário; Felippe de Souza, J.A.M.Determining the mathematical model parameters of photovoltaic (PV) cells and modules represents a great challenge. In the last few years, several analytical, numerical and hybrid methods have been proposed for extracting the PV model parameters from datasheets provided by the manufacturers or from experimental data, although it is difficult to determine highly reliable solutions quickly and accurately. In this paper, we propose a new method for determining the PV parameters of both the single-diode and the double-diode models, based on the guaranteed convergence particle swarm optimization (GCPSO), using experimental data under different operating conditions. The main advantage of this method is its ability to avoid premature convergence in the optimization of complex and multimodal objective functions, such as the function that determines PV parameters. To validate performance, the GCPSO method was compared with several analytical, numerical and hybrid methods found in the literature. This validation considered three different case studies. The first two are important reference case studies in the literature and have been widely used by researchers. The third was performed in an experimental environment, in order to test the proposed method under a real implementation. The proposed methodology can find highly accurate solutions while demanding a reduced computational cost. Comparisons with other published methods demonstrate that the proposed method produces very good results in the extraction of the PV model parameters.
- Glowworm Swarm Optimization for photovoltaic model identificationPublication . Nunes, H.G.G.; Pombo, José; Fermeiro, J.B.L.; Mariano, S.; Calado, M. do RosárioThis paper presents a new algorithm for finding the parameters that characterize a photovoltaic panel by using the Glowworm Swarm Optimization algorithm. This new algorithm shows great simplicity, flexibility and precision, being able to precisely locate the global optimum point or multiple global optimum points, independently of the initial conditions. The approach here adopted allows the utilization of the algorithm in several existing models to characterize a photovoltaic panel in the current literature.
- Collaborative swarm intelligence to estimate PV parametersPublication . Nunes, H.G.G.; Pombo, José Álvaro Nunes; Bento, P.M.R.; Mariano, S.; Calado, M. Do RosárioTo properly evaluate, control and optimize photovoltaic (PV) systems, it is crucial to accurately estimate the equivalent electric circuit parameters from the respective mathematical models that characterize the PV cells or modules behavior. This is currently a hot research topic that has attracted the attention of numerous researchers. In this paper, we propose a new hybrid methodology that combines diversification and intensification mechanisms from different metaheuristics (MHs) to estimate PV parameters precisely. The proposed methodology has the capacity to adapt to the specific optimization problem and maintain diversity when building solutions, thus mitigating premature convergence and population stagnation. This methodology can incorporate several MHs (two or more swarms) with different potentialities, enabling a good balance between diversification and intensification mechanisms. Furthermore, it is able to explore a multidimensional search space in different regions simultaneously. To validate its performance, the proposed methodology was compared with other wellestablished MHs in several benchmark functions, and used to estimate PV parameters in single and double-diode models in two case studies, the first using standard literature data, and the second using measured data from a real application with and without the occurrence of partial shading. The proposed methodology was able to find highly accurate solutions with reduced computational cost and high reliability. Comparisons with the other MHs demonstrate that the proposed methodology presents a very competitive performance when solving the PV parameter estimation problem.
- A Modified Multidimension Diode Model for PV Parameters Identification Using Guaranteed Convergence Particle Swarm Optimization AlgorithmPublication . Nunes, H.G.G.; Bento, P.M.R.; Pombo, José Álvaro Nunes; Mariano, S.; Calado, M. do RosárioThis paper proposes a modified multidimension diode model to identify the photovoltaic (PV) parameters using the guaranteed convergence particle swarm optimization algorithm. The main advantage of this model is that it allows adjusting the number of diodes of the PV model by finding the configuration that most accurately characterizes a PV device under a certain operating condition and of different PV technologies. The proposed model was validated from experimental data measured at different irradiance and temperature levels, as well as for six different PV technologies. The results show that the model is able to accurately characterize the behaviour of PV devices.
- Daily Operation Optimization for Grid-Connected Hybrid System Considering Short-Term Electricity Price Forecast SchemePublication . Bento, P.M.R.; Nunes, H.G.G.; Álvaro Nunes Pombo, José; Mariano, S.; Calado, M. do RosárioWith 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 PVwind with hydro and battery storage capabilities for a gridconnected 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.