Greenhouse cultivation stands out for the ongoing food production challenge due to its ability to maintain a specific microclimate and allow crops to grow under highly variable weather conditions. Since the energy resources in greenhouses are limited, an energy management system based on evolving fuzzy prediction intervals is proposed to correctly define a proper irrigation and water extraction schedule, assuming a limited amount of energy stored. The energy management system schedules the crop irrigation to fulfill a defined daily irrigation volume while managing the water extraction according to the future photovoltaic power. In addition, evolving fuzzy prediction intervals are used to forecast the photovoltaic power and estimate its worst-case scenario from the interval's lower bound. With this information, the energy management system can be implemented using a robust model predictive controller. The proposed controller is tested assuming a reduction in the real solar power the system receives, which resembles a change in the system dynamics due to shadows and dust. Then, the controller's performance is compared against conventional fuzzy prediction intervals. Simulation results show that the proposed energy management system fulfills the reference irrigation and achieves a 33% higher state of energy and 14% higher water availability, on average, during the simulation. Thus, the proposal is better prepared for energy and water shortages due to the controller's robust approach and the model's evolving nature.

Due to global warming and population growth, preserving and guaranteeing clean water and electricity access has become harder. For this purpose, Energy-water microgrids (EWMGs) have been proposed to manage both resources efficiently. In these systems, resource management is traditionally performed at the tertiary control level, on large time windows, whereas integration of renewable energy sources requires faster controllers. Several works proposed moving energy cost management to the secondary control level as a solution, achieving quick responses to perturbations. Inspired by this idea, We propose to solve the water-energy co-optimization at a secondary control level timescale, using the Karush-Khun-Tacker (KKT) conditions of the centralized economic dispatch (ED) of an EWMG. The proposal is validated through simulation, achieving an 11% operational cost reduction. While our simulations were executed on only one type of EWMG topology, the approach presented can be generalized to any topology.

This paper proposes a distributed predictive secondary controller that restores frequency deviations and handles the active power sharing of multiple generation units in a sea harbor microgrid. The proposed control strategy is based on a multi-objective optimization framework, where each distributed generation unit is handled by its own predictive optimization problem. The main contribution of this approach is the avoidance of weighting factors on each optimization problem, which in most works in the literature have to be tuned for the specific application case. Instead, in this proposal the control actions applied to the microgrid are determined by the obtention of a Pareto front defined in terms of the different control objectives in a microgrid. This controller is validated via simulation by connecting and disconnecting loads in a sea harbor microgrid model. The reported results confirm that the proposed distributed controller based on multi-objective optimization can handle the operation of multiple generation units while complying with the frequency restoration and power consensus condition.

This paper proposes a two-level hierarchical energy management system (EMS) with demand side management (DSM) capabilities for grid-connected microgrids (MGs). The proposed strategy is based on model predictive control (MPC) with prediction intervals obtained through the fuzzy numbers method. While the Main Grid level EMS aims for auto-consumption within the MG, i.e., minimise the energy drawn for the main grid, the Microgrid level tracks power and consumption references, sent from the higher level, to manage the MG resources and the load consumption. Furthermore, fuzzy prediction intervals are used to determine the best-case and worst-case scenarios of operation and modify the load profile while the overall load during the MG operation is maintained. Operation data for generation and consumption from a real urban community is used to validate the performance of the proposed EMS. The results show that the proposed hierarchical EMS with DSM and an adequate prediction case can reduce weekly costs while maintaining overall consumption and a healthy battery usage compared to an EMS that has no way to modify the load. This concludes that a microgrid can improve its performance with the correct predictions and the commitment of the consumers.

This paper proposes a distributed predictive secondary controller to tackle together frequency and voltage regulation, realize the economic dispatch and reactive power sharing of generation units in isolated AC microgrids. Contrary to most approaches, the proposed predictive controller achieves consensus objectives (economic dispatch of generation and reactive power sharing) with soft constraints (keep both frequency and average voltage within predefined bands instead of restoring them to their nominal values). Extensive simulation work validates the effectiveness of the predictive controller for communication problems and in the presence of plug-and-play scenarios.

Hybrid AC/DC microgrids are of special interest due to their flexibility, low infrastructure investments, and reliability against failures on the utility grid. The economic dispatch in the AC and DC subgrids requires communication to achieve near optimal solutions, which makes cooperative control a promising and feasible approach to be used by the microgrid's interlinking converter. This paper proposes a fully distributed finite-time control strategy over the interlinking converter, which ensures an economic operation and additionally takes care about the microgrid utilisation. The interlinking converter uses incremental costs and average powers from distributed regulators in AC and DC sides. The performance is verified through simulations in software PLECS. The results show that the proposed strategy is able to perform a trade-off between the two control objectives while achieving a fast convergence.