This paper aims to present a novel control strategy for Modular Multilevel Converters (MMC) based on differential flatness theory (DFT), in which instantaneous active and reactive power values are considered as the flat outputs. To this purpose, a mathematical model of the MMC taking into account dynamics of the ac-side current and the dc-side voltage of the converter is derived in a d-q reference frame. Using this model, the flat outputs-based dynamic model of MMC is obtained to reach the initial value of the proposed controller inputs. In order to mitigate the negative effects of the input disturbance, model errors, and system uncertainties on the operating performance of the MMC, the integral-proportional terms of the flat output errors are added to the initial inputs. This can be achieved through defining a control Lyapunov function which can ensure the stability of the MMC under various operating points. Moreover, the small-signal linearization method is applied to the proposed flat output-based model to separately evaluate the variation effects of controller inputs on flat outputs. The proficiency of the proposed method is researched via MATLAB simulation. Simulation results highlight the capability of the proposed controller in both steady-state and transient conditions in maintaining MMC currents and voltages, through managing active and reactive power.

This paper addresses an optimized management of a storage energy battery which is part of a microgrid with a connection to the main grid and is supplied by a photovoltaic (PV) power plant. The main contribution of this paper is to consider uncertainty in electricity price while managing the battery storage. The forecasted value for demand and PV unit are predicted by a seasonal autoregressive integrated moving average model (SARIMA)—capable of accurately characterizing both seasonality effects and tail fatness. The optimal operation of the battery is determined by resolving a linear optimization program in which the objective function comprises the conditional value at risk (CVaR). Using CVaR ensures that the demand is fully supplied while minimizing the risk and operational cost. The cost function is the difference between power sold and bought subject to the charging and discharging rates for the battery and defining upper and lower bounds for the level of battery charge. The simulation results confirm that the risk consideration has a significant effect on the optimized management of a storage energy battery in a photovoltaic grid-connected microgrid.

Aiming to take full advantage of Electric Vehicles' (EVs) batteries, this paper proposes a two-level hierarchical model predictive controller coupled with an innovative charging-discharging scheduler for EVs in Building Microgrids (BMGs). This paper provides a complete framework for the design of this control structure and analyses its performance regarding the state of charge of the EVs at departure time, the self-consumption rate, and the coverage rate, considering a residential BMG equipped with photovoltaic panels and static Li-ion batteries. The results and performance of the proposed control architecture are compared to two other solutions: a hierarchical predictive controller with no scheduler and a rule-based algorithm. A technological and economical study is also performed considering variables such as the dimension of the EV's park, the price of energy, the cost of maintenance, the possibility to discharge or not into the grid, and the execution time of the control architecture. The simulation results conducted in MATLAB Simulink demonstrated that the proposed control structure ensures the full charging of all vehicles at departure time while also improving the self-consumption rate of the BMG with a relatively low stress on the needed computation capacities, even when considering a large fleet of vehicles.

This paper presents a multi-objective energy management system (EMS) to manage the power dispatch of a hybrid power plant (HPP), consisting of a grid-connected wind farm and a Li-ION battery storage system on the island of Guadeloupe’s electrical grid. Via a controller based on Model Predictive Control (MPC), the EMS solves the problem of optimization by considering the production forecast data and managing several operation rules, which ensures meet energy targets considered for a sustainable power dispatching plan. The proposed strategy is tested in a PowerFactory/MATLAB co-simulation environment.

Microgrids can potentially improve security, quality, reliability and availability of electricity supply for many applications. In order to achieve these benefits when a microgrid with a substantial amount of stochastic generation operates in grid connected mode but also during islanding mode, the use of storage systems is essential. The main goal of the storage is to perform power balance during islanding mode and to avoid the effect of the renewable energy sources intermittency and load fluctuations to the grid. However, none of the currently available Energy Storage Systems (ESS) has the necessary high power and energy densities to face the effect of different disturbances coming from the load or renewable energy sources. Thus, the association of more than one storage technology in a Hybrid Energy Storage System (HESS) can be used to satisfy the above mentioned requirements. Generally, separate DC/DC converters are required to interconnect the different ESS with an inverter in order to supply the distributed system in a microgrid. The present work proposes the association of a Vanadium-Redox flow battery (VRB) with supercapacitors and a Three-level Neutral Point Clamped Inverter Interface for energy management in a microgrid. The power converter control and the energy management are also detailed. Simulation results are presented to prove the feasibility of the proposed system.

This paper presents a three-phase Active Power Conditioner to improve power quality in microgrids based on renewable energy. A microgrid is a weak electrical grid which can be easily subject to disturbances. The Active Power Conditioner (APC) presented in this paper acts as an interface between renewable energy sources and the AC bus of a microgrid and uses an improved control strategy, which makes possible to inject energy in the microgrid, compensate the current harmonics and correct the power factor. Moreover, the proposed control strategy allows the line current at the point of common coupling (PCC) to be balanced and sinusoidal even when the load is unbalanced. Consequently, the voltage at the PPC becomes balanced. Simulation results show the validity of the innovative control strategy.