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.

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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.

Background: 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.Method: In this work, an energy management system was developed based on an innovative optimization method, combining linear programming, based on the simplex method, with particle swarm optimisation algorithm. Two scenarios have been proposed to characterise the relation price versus gas emissions for optimal energy management. The objective of this study is to nd the optimal setpoints of generators in a smart city supplied by a microgrid in order to ensure consumer comfort, minimising the emission of greenhouse gases and ensure an appropriate operating price for all smart city consumers. Results: 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 ows, having achieved a better energy price compared to a previous study with the same data. Conclusion: The energy management system based on the proposedoptimisation 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.