Abstract
This paper proposes a novel hybrid particle swarm optimization methodology to solve the problem of optimal participation in multiple electricity markets. The decision time is usually very important when planning the participation in electricity markets. This environment is characterized by the time available to take action, since different electricity markets have specific rules, which requires participants to be able to adapt and plan their decisions in a short time. Using metaheuristic optimization, participants' time problems can be resolved, because these methods enable problems to be solved in a short time and with good results. This paper proposes a hybrid resolution method, which is based on the particle swarm optimization metaheuristic. An exact mathematical method, which solves a simplified, linearized, version of the problem, is used to generate the initial solution for the metaheuristic approach, with the objective of improving the quality of results without representing a significant increase of the execution time.

Abstract
We address sampled–data nonlinear Model Predictive Control (MPC) schemes, in particular we address methods to efficiently and accurately solve the underlying continuous-time optimal control problems (OCP). In nonlinear OCPs, the number of discretization points is a major factor affecting the computational time. Also, the location of these points is a major factor affecting the accuracy of the solutions. We propose the use of an algorithm that iteratively finds the adequate time–mesh to satisfy some pre–defined error estimate on the obtained trajectories. The proposed adaptive time–mesh refinement algorithm provides local mesh resolution considering a time–dependent stopping criterion, enabling an higher accuracy in the initial parts of the receding horizon, which are more relevant to MPC. The results show the advantage of the proposed adaptive mesh strategy, which leads to results obtained approximately as fast as the ones given by a coarse equidistant–spaced mesh and as accurate as the ones given by a fine equidistant–spaced mesh. © Springer International Publishing Switzerland 2017.

Abstract

Abstract
A vital element in making a sustainable world is correctly managing the energy in the domestic sector. Thus, this sector evidently stands as a key one for to be addressed in terms of climate change goals. Increasingly, people are aware of electricity savings by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Much of the reduction was due to technological improvements, however with the advancing of the years new types of control arise. Domestic appliances with the purpose of heating and cooling rely on thermostatic regulation technique. The study in this paper is focused on the subject of an alternative power management control for home appliances that require thermal regulation. In this paper a Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat with the aim of minimizing the cooling energy consumption through the minimization of the energy cost while satisfying the adequate temperature range for the human comfort. In addition, the Model Predictive Control problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. For this purpose, the typical consumption of a 24 h period of a summer day was simulated a three-level tariff scheme was used. The new contribution of the proposal is a modulation scheme of a two-level Model Predictive Control's control signal as an interface block between the Model Predictive Control output and the domestic appliance that functions as a two-state power switch, thus reducing the Model Predictive Control implementation costs in home appliances with thermal regulation requirements.

Abstract
The hybrid microgrids (MGs) are introduced to form the future distribution systems that utilize the advantages of both DC and AC grids. In the hybrid MG, renewables, DC and AC loads, storage devices and distributed energy resources (DERs) are integrated and connected through the separated AC and DC buses. The control and management of the hybrid MG are more complicated than an individual AC or DC microgrid. In this paper, first, the two control strategies are implemented in a typical hybrid MG. Then, the performance of these control strategies in the view of the primary control are discussed and compared in response to the occurrence of a defined contingency. Simulation results show the different performance of each control strategy in frequency and voltage control of the hybrid MG in response to the same contingency.

Abstract