Development of a photovoltaic MPPT control based on neural network

The Maximum Power Point Tracking (MPPT) is an important factor to increase the efficiency of the solar photovoltaic (PV) system. This paper presents a solar PV system containing a solar PV array, a DC/DC boost converter and a load. Di↵erentMPPT algorithms have been established with their features. The conventional algorithms (Perturb and Observe, Incremental Conductance and Open Circuit Voltage) show a lot of drawbacks. The major issue is the tracking of the Maximum Power Point (MPP) when environmental conditions change faster. So, a MPPT technique based on Neural Network (NN) was developed and which can enhance the efficiency and gathers the advantages of a lot of techniques. A multi layer neural network with back-propagation algorithm is used in order to have a small Mean Squared Error (MSE). The inputs of NN are irradiance, temperature and the output is the duty cycle that controls the boost converter. Finally, it is discussed the results and made comparison in terms of performance of the di↵erent algorithms, covering the overshoot, time response, oscillation and stability.info:eu-repo/semantics/publishedVersio

Development of a photovoltaic MPPT control based on neural network

The Maximum Power Point Tracking (MPPT) is an important factor to increase the efficiency of the solar photovoltaic (PV) system. This paper presents a solar PV system containing a solar PV array, a DC/DC boost converter and a load. Different MPPT algorithms have been established with their features. The conventional algorithms (Perturb and Observe, Incremental Conductance and Open Circuit Voltage) show a lot of drawbacks. The major issue is the tracking of the Maximum Power Point (MPP) when environmental conditions change faster. So, a MPPT technique based on Neural Network (NN) was developed and which can enhance the efficiency and gathers the advantages of a lot of techniques. A multi layer neural network with back-propagation algorithm is used in order to have a small Mean Squared Error (MSE). The inputs of NN are irradiance, temperature and the output is the duty cycle that controls the boost converter. Finally, it is discussed the results and made comparison in terms of performance of the different algorithms, covering the overshoot, time response, oscillation and stability.info:eu-repo/semantics/publishedVersio

Development of technical economic analysis for optimal sizing of a hybrid power system: a case study of an industrial site in Tlemcen Algeria

The current study aimed to develop an optimal sizing simulation model for an off-grid photovoltaic-wind hybrid power system of an industrial site in Algeria. The loss of power supply probability algorithm was used for sizing our hybrid system. The technical and economic evaluation for the case study showed that the storage system occupied the most critical part of the total investment cost of the hybrid system. The investment cost analysis indicated a unique optimal configuration for each size of the batteries bank. For one day's autonomy, the best size of the hybrid system corresponded to 61 PV panels and 9 wind turbines. Based on a levelized cost of energy analysis, the cost of the batteries represented for this combination is 52% of the total investment cost. The wind turbines accounted for 42% and the PV panels for only 3%. This combination of the hybrid system resulted in an energy cost that was very competitive with most European countries. However, the public energy grid cost in the case study region was still six times lower due to government subsidies. The findings are very encouraging and can help decision-makers adopt alternative and more sustainable solutions in energy policy. These results will aid in determining future research directions in Algeria's hybrid renewable energy systems.National funds funded Luís Frölén Ribeiro through FCT - Fundação para a Ciência e Tecnologia, through project UIDB/50022/2020 – LAETAinfo:eu-repo/semantics/publishedVersio

Particle swarm optimization for micro-grid power management and load scheduling

A smart power management strategy is needed to economically manage local production and consumption while maintaining the balance between supply and demand. Finding the best-distributed generators’ set-points and the best city demand scheduling can lead to moderate and judicious use out of critical moments without compromising smart city residents’ comfort. This paper aimed at applying the Particle Swarm Optimization (PSO) to minimize the operating cost of the consumed energy in a smart city supplied by a micro-grid. Two PSO algorithms were developed in two steps to find the optimal operating set-points. The first PSO algorithm led to the optimal set-points powers of all micro-grid generators that can satisfy the non-shiftable needs of the smart city demand with a low operating cost. While the second PSO algorithm aimed at scheduling the shiftable city demand in order to avoid peak hours when the operating cost is high. The results showed that the operating costs during the day were remarkably reduced by using optimal distributed generators’ set-points and scheduling shiftable loads out of peaks hours. To conclude, the main advantages of the proposed methodology are the improvement in the local energy efficiency of the micro-grid and the reduction in the energy consumption costs

Smart microgrid management: a hybrid optimisation approach

The association of distributed generators, energy storage systems and controllable loads close to the energy consumers gave place to a small-scale electrical network called microgrid. The stochastic behavior of renewable energy sources, as well as the demand variation, can lead in some cases to problems related to the reliability of the microgrid system. On the other hand, the market price of electricity from mainly non-renewable sources becomes a concern for a simple consumer due to its high costs. An innovative optimization method, combining linear programming, based on the simplex method, with the particle swarm optimisation algorithm is used to develop an energy management system. The management is performed considering a smart city’s consumption profile, two management scenarios have been proposed to characterize the relation price versus gas emissions for optimal energy management. The simulation results have demonstrated the reliability of the optimisation approach on the energy management system in the optimal scheduling of the microgrid generators power flows, having achieved a better energy price compared to a previous study with the same data. The computational results identified the optimal set-points of generators in a smart city supplied by a microgrid while ensuring consumer comfort, minimising greenhouse gas emissions and guarantee an appropriate operating price for all consumers in the smart city. The energy management system based on the proposed optimisation approach gave an inverse correlation between economic and environmental aspects, in fact, a multi-objective optimisation approach is performed as a continuation of the work proposed in this paper.This work has been supported by Fundação La Caixa and FCT — Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020info:eu-repo/semantics/publishedVersio