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- A New Approach for Dynamic Energy Storage SystemPublication . Barbara, João; Fermeiro, J.B.L.; Pombo, José Álvaro Nunes; Mariano, S.; Calado, M. do RosárioBattery packs with a large number of cells are becoming more and more prominent technology. Monitoring and controlling these packs is now a subject with increased importance. The proposed implemented system consists of only three switches per cell, allowing several types of connection between the cells. The battery management system relies on a distributed hierarchical architecture with n-1 local controllers and a global controller. In the proposed algorithm the cells act as a single swarm functioning autonomously and depending on the parameters (voltage, current and temperature) of each cell of the pack. The hardware for each cell is explained and demonstrated. The results obtained both in simulation and in experimental tests show the excellent performance of the proposed methodology. Also, the charging approach based on the traditional multistage method exhibited an excellent performance on charging supercapacitors, concerning its efficiency, charging time and overvoltage protection.
- Design and Implementation of MPPT System Based on PSO AlgorithmPublication . Calvinho, Gonçalo; Pombo, José Álvaro Nunes; Mariano, S.; Calado, M. Do RosárioThis paper presents a method for maximum power point tracking (MPPT) based on the particle swarm optimization (PSO) algorithm with variable step size in order to prevent steady state oscillations. This will avoid the impact of partial shading conditions in the efficiency of photovoltaic (PV) systems. To optimize power output of the solar panels a DC-DC boost converter is used. Special emphasis is put on software development and implementation in the TMS320F28027 microcontroller in Texas Instruments Code Composer Studio.
- A procedure to specify the weighting matrices for an optimal load-frequency controllerPublication . Mariano, S.; Pombo, José Álvaro Nunes; Calado, M. Do Rosário; Ferreira, Luís António Fialho MarcelinoThe linear quadratic optimal regulator is one of the most powerful techniques for designing multivariable control systems. The performance of the system is specified in terms of a cost, which is the integral of a weighted quadratic function of the system state and control inputs, that is to be minimized by the optimal controller. The components of the state cost weighting matrix, Q, and the control cost weighting matrix, R, are ours to choose in mathematically specifying the way we wish the system to perform. Changing these matrices, we can modify the transient behavior of the closed-loop system. This paper addresses the stabilization and performance of the load-frequency controller by using the theory of the optimal control. A new technique, based on pole placement using optimal regulators, to overcome the difficulties of specifying weighting matrices Q and R is proposed. The design method employs successive shifting of either a real pole or a pair of complex conjugate poles at a time. The proposed technique builds Q and R in such a way that the system response also obeys conventional criteria for the system pole location. The effectiveness of the proposed method is illustrated by numerical examples.
- A bat optimized neural network and wavelet transform approach for short-term price forecastingPublication . Bento, P.M.R.; Pombo, José Álvaro Nunes; Calado, M. do Rosário; Mariano, S.In the competitive power industry environment, electricity price forecasting is a fundamental task when market participants decide upon bidding strategies. This has led researchers in the last years to intensely search for accurate forecasting methods, contributing to better risk assessment, with significant financial repercussions. This paper presents a hybrid method that combines similar and recent day-based selection, correlation and wavelet analysis in a pre-processing stage. Afterwards a feedforward neural network is used alongside Bat and Scaled Conjugate Gradient Algorithms to improve the traditional 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 was applied in a day-ahead framework to historical data pertaining to Spanish and Pennsylvania-New Jersey-Maryland (PJM) electricity markets, revealing positive forecasting results in comparison with other state-of-the-art methods.
- 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.
- Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage SystemPublication . Faria, João; Pombo, José; Calado, M. do Rosário; Mariano, S.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.
- Damping of Power System Oscillations with Optimal RegulatorPublication . Mariano, S.; Pombo, José Álvaro Nunes; Calado, M. Do Rosário; Souza, J. A. M. Felippe DeThis chapter presents a study of the small signal stability applied to an electric power system, with the consideration of the Power System Stabilizer and using the optimal control theory. A new technique is proposed, which is based on pole placement using optimal state feedback for damping electromechanical oscillation under small signal. The proposed technique builds the weighting matrices of the quadratic terms for the state vector Q and control vector R in such a way that the system response also obeys conventional criteria for the system pole location. Besides, when the number of output variables is less than the order of the system, it is proposed an optimal output feedback approach, where a set of closed-loop system poles is allocated to an arbitrary position by means of a suitable output feedback. The Power Sensitivity Model is used to represent the electric power system. Information about the stability of the electric power system, when subjected to small disturbances, is illustrated by using numerical examples.
- 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.
- Modelos optimizados para sistemas de miniprodução híbridos instalados em edifícios e áreas envolventesPublication . Pombo, José Álvaro Nunes; Mariano, Sílvio José Pinto SimõesAtualmente, um dos grandes desafios deste século consiste na transição para um futuro de energia elétrica sustentável proveniente de fontes endógenas renováveis. Essa sustentabilidade, exigirá mudanças não apenas no modo como a energia elétrica é produzida e distribuída, mas também no modo como é usada. Como resultado o setor de energia elétrica tem vindo a sofrer profundas transformações. Em particular, o regime Português de auto consumidor permitiu que os clientes da rede elétrica de média e de baixa tensão podem ser produtores/consumidores de energia elétrica, contribuindo de forma ativa para uma maior eficiência energética. É neste contexto, onde se aposta na diversificação e na produção de energia através de fontes endógenas renováveis e numa maior participação do produtor/consumidor, que a complementaridade dos recursos renováveis (sistemas híbridos de energia elétrica) vai desempenhar um papel fundamental. Assim, o trabalho apresentado nesta tese refere-se a um estudo de investigação sobre vários temas relacionados com os sistemas híbridos de energia elétrica com capacidade de armazenamento. Em concreto, na tecnologia de produção de energia fotovoltaica (mais utilizada pelos produtores/ consumidores) foram desenvolvidas técnicas de modelação de confiança que permitam prever com rigor a produção de energia elétrica. Além disso, por forma maximizar a produção de energia, em todo o instante de tempo, foram desenvolvidas e testadas diversas técnicas de procura do ponto de máxima de potência com base em algoritmos de otimização. Outra questão de grande interesse num futuro próximo é a inclusão de sistemas de armazenamento de energia que possibilitem ao produtor/consumidor o controlo das suas instalações, de forma a gerirem os seus recursos e consumos consoante as suas próprias estratégias de atuação. Nesse sentido, foi desenvolvido um sistema capaz de monitorizar um sistema de armazenamento de potência infinita (teoricamente) e proposto um método de carregamento que possui a capacidade de se adaptar às condições das células. Por outro lado, para permitir que os produtores/consumidores retirem benefícios do seu sistema de produção com armazenamento, foi desenvolvida uma ferramenta computacional permitindo avaliar diferentes arquiteturas e tecnologias de produção/armazenamento de um sistema híbrido de energia elétrica. Além disso, é proposta uma estratégia de operação diária para um sistema híbrido de energia interligado com a rede elétrica com base na previsão dos preços de energia elétrica.
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