# Intelligent Energy Management of Electrical Power Systems
## 2 Advances on Intelligent Energy Management Systems  
There are six papers focused on multi-source systems optimization regarding the techno-economic dispatching, system design, and sizing. A multi-source/multi-energy system including wind power, photovoltaic power, hydropower stations, and thermal power plants combined with energy storage devices is considered in [1], for which a multi-objective scheduling optimization model considering different operation strategies is presented. Taking into account the longest life cycle and the optimum economic efficiency, the proposed model provides effective decision-making support for designing the optimal plan for system operation. Aimed at achieving the most economic energy scheduling and based on stochastic model predictive control, [2] develops an interesting approach to coordinate scheduling of multi-source/multi-energy system. Scenarios for aggregated electric vehicles (EVs) and their battery degradation are taken into account and a finite-horizon optimal control solution is presented while the economic objective and operational constraints are included. Considering an integrated energy system, a standardized matrix modeling method is presented in [3]. Based on the proposed model, the system optimization can be achieved regarding the energy management, system design and sizing, as well as the system operation. Using a genetic algorithm with the Pareto front, an optimal design for hybrid photovoltaic (PV)-storage-grid-connected systems is developed in [4]. The optimization minimizes the total cost of ownership and the voltage common bus deviation while considering the direct and indirect costs for the prosumer and highlights the profitability of the optimal system configuration on storage prices. The energy-coupling relationship between the energy-supply side and the demand side is introduced in [5]. Taking into consideration economic performances, this paper proposes an optimized energy-conversion interface model that simplifies the complex multi-energy system into a multi-input-multi-output dual-port one and provides a decision-making method for system planning. A high-dimensional mixed-integer optimization problem is proposed in [6], in which, for a high-dimensional multi-source system, the power system unit commitment, i.e., the techno-economic sources scheduling, is solved by applying a novel binary competitive swarm optimizer. The power management methods of multi-source systems lead often on application field such as the building sector or EVs charging stations. Four papers are included on this topic. A direct current (DC) microgrid based on PV-storage is presented in [7; 8]. For the islanding operation mode that requires a backup power source, the classical diesel generator, which start-up is assisted by supercapacitors, is used in [7]; the proposed power management strategy controls a dynamic load as well as economic operating mode of the diesel generator. Based on mixed-integer linear procedure, [8] suggests the optimal operation planning of a DC microgrid supplying EVs optimizing daily operational costs considering the PV production and EVs exploitation forecast. On the other hand, EVs changing station may help the power grid to improve the automatic generation control performance by cost consensus algorithm applications [9] or to minimize the load peak by adopting an intelligent scheduling system [10]. An intelligent energy management system implies also many other components. Some of them, like electric springs, smart meter, and data, are presented in [11; 12; 13]. Reference [11] introduces the state of the art of electric springs as a new solution for stabilizing power grid fed by intermittent renewable energy sources. Regarding data, two papers are included in this Special Issue. The result of the analysis presented in [12] shows that revealing characteristics from smart meter data is feasible and the proposed machine learning methods have a good accuracy.