In this paper, an optimal procedure for the economic schedule of a network of interconnected microgrids with hybrid energy storage system is carried out through a control algorithm based on distributed model predictive control (DMPC). The algorithm is specifically designed according to the criterion of improving the cost function of each microgrid acting as a single system through the network mode operation. The algorithm allows maximum economical benefit of the microgrids, minimizing the degradation causes of each storage system, and fulfilling the different system constraints. In order to capture both continuous/discrete dynamics and switching between different operating conditions, the plant is modeled with the framework of mixed logic dynamic. The DMPC problem is solved with the use of mixed integer linear programming using a piecewise formulation, in order to linearize a mixed integer quadratic programming problem.

This paper studies the economic dispatch problem for a microgrid that interacts with external agents. First, the microgrid acts in the day-ahead market in order to find the optimal schedule for its internal resources, considering the consumption and generation forecast and the energy price prediction. Once the day-ahead market has closed, external agents such as other microgrids in the same network, aggregators or electric vehicles can interact with the microgrid through a local energy market. If an external agent requests a specific energy profile that offers an economic benefit, the microgrid controller will propose supplying the closest profile to that requested, depending on the benefit offered. The proposed solution allows the microgrid to respond to external requests, thus optimizing its economic benefit. The problem is solved using Model Predictive Control (MPC), and the operation and degradation costs are included in the objective function. In order to capture both continuous/discrete dynamics and switching between different operating conditions, the plant is modeled using the Mixed Logic Dynamic (MLD) framework. The MPC problem is solved by means of mixed-integer quadratic programming (MIQP).

This paper presents the application of a distributed model predictive controller (DMPC) to the control of an accurate model of an actual irrigation canal in Spain. The canal is modeled using the Saint-Venant equations and implemented using the well-known Simulation of Irrigation Canals (SIC) modeling software for irrigation canals. The DMPC algorithm has been implemented in Matlab and interfaced to SIC. In the distributed-control algorithms, the local controllers exchange information so that their control policies are optimal in the sense of getting the best value of a performance index. The results show that the proposed distributed-control algorithm obtains better control performance than a more-conventional decentralized control scheme without information exchange. This better performance translates directly into money and resource savings.

This paper focuses on designing a robust control law to manage the demand response of islanded microgrids composed of shifting and adjusting loads. On one side, Hybrid Dynamical System theory is suited here, because the microgrid model is composed of continuous-time dynamics (the energy stored in the battery and the adjustable loads), and discrete-time dynamics (the shifting loads). On the other side, Multi Agent System theory is used to control the adjusting loads in order to guarantee a consensus between them. Hence, non-uniform convergence of the State of Charge of the battery to a given reference is ensured. Robustness with respect to plug and play of any load and parameter variations is also ensured. Experimental results from a laboratory-scale microgrid validate the approach.

La microrred como elemento agregador de fuentes de generación, cargas y sistemas de almacenamiento de energía aparece como tecnología clave para dotar a los sistemas eléctricos de suficiente flexibilidad para una transición energética basada en fuentes renovables. Sin embargo, el problema de control para la gestión de energía se vuelve complejo cuando se incrementa el número de sistemas conectados a una misma microrred. De igual forma, se requiere flexibilidad para integrar a los vehículos eléctricos. La interacción entre las distintas microrredes y los vehículos hacen necesarias herramientas avanzadas de control para resolver el problema de optimización. El objeto del presente trabajo es presentar distintas herramientas de control predictivo basado en el modelo (Model Predictive Control, MPC) para resolver el problema de control asociado a este tipo de sistemas. En concreto, se abordan dos problemas: la conexión de vehículos eléctricos a la microrred y la interconexión de varias microrredes. Para el primer caso se analizan dos escenarios, según que el intercambio de energía sea uni o bidireccional y se presenta la forma de optimizar la operación usando MPC. En el segundo caso se aborda el problema usando técnicas de control distribuido.