Even though modern control has emerged in numerous control applications, a building automation is still a field where the position of the classical control is almost exclusive. The main reason is that for the synthesis of a predictive controller a decent model for control is needed. In the field of building climate control, it is still problem to obtain a model of large building in an explicit form suitable for control. Most of the approaches either use building modeling software to get detailed model, which is unfortunately in implicit form; or the model is built-up as a first principle model, which usually ends-up as an extreme simplification of the reality. In this paper, a building model identification procedure is presented, wherein the building model is built-up as a first-principle model using a simulation software (detailed, precise, however in implicit form), and then a state-space model is identified by means of subspace identification methods. The main focus of the paper lays on a case study of a large office building, and the entire process of its identification.

 At many places, the use of wind energy is confined by the connecting conditions dictated by the electrical grid. Particularly in low-power grid sections, the maximal installable power of the wind energy converter is considerably reduced by the anticipated mains pollutions. In order to avoid an expansion of the electrical grid and achieve an economical utilization of wind energy earlier it is advisable to use low-pollution and efficient wind energy converters. Within the scope of the research project AMOEVES (Autonomous, modular Energy supply systems) a speed variable wind energy converter has been designed and tested on a 22 kW-model test facility. The system has been particularly designed to make use of wind energy at remote places with excellent wind conditions.One of the focusses was laid upon the control concept. For low-power grids, a safe and interruption-free operation must be guaranteed at maximal energy outputs. This requires a control concept which provides sufficient control reserves on the one hand and features a constant behaviour at considerably fluctuating system parameters on the other.

 In this paper we present a system architecture and suitable control methodologies for the management and control of Distributed Generation (DG) units, Renewable Energy Resources (RES), Active Demand (AD) and storage units, being these Electric Vehicles (EV) or Uninterruptible Power Supply (UPS). Within the proposed platform, control methodologies allow to adapt unit generation profiles and active loads to ensure economic benefits to each actor. The key aspect is the organization in two levels of control: at residential level a Smart Home Controller (SHC) monitors and controls smart appliances while at higher level a Community Energy Management System (CEMS) coordinates generation units, negotiates consumption with SHCs and sets power grid energy withdrawals. Proposed control methodologies involve the solution of a Walrasian market equilibrium and the design of a distributed solution of a dynamic programming problem

 In this paper we outline a novel approach for the design of an electric vehicle (EV) aggregator, a controller whose objective is to optimally manage the charging operations of an EV fleet. The control strategy we derive is based on model predictive control and allows to achieve costs minimization, also enabling the aggregator (hence, the EV fleet) to participate to the provisioning of active demand services to upper level market players. Explicative simulations are presented and discussed in order to show the effectiveness of the approach and also to investigate the role of vehicle to grid power.

 In this work, new analysis and prediction strategies for the power control in wind generators that use a doubly fed induction generator are addressed. The most widely used wind generator model and control strategies are first presented, including strategies used to increase punctually the power generation in order to respond to power demands derived from electrical grid frequency deviations. A new on-line prediction strategy based on a wind observer plus the use of the different norms that the dynamic model presents from the inputs (the wind or the power demand) to the outputs (rotor speed, accelerations, and power generation) is addressed. For that purpose, the wind observer and the computational derivation of the different input-output performances are developed via optimization over polynomials techniques. The developed analysis and predicting strategies are applied to a wind generator model and controller proposed in the literature, showing the effectiveness of the presented approach.

 Building automation systems (BAS) provide automatic control of indoor environments conditions. Their primary goal is to realize significant energy savings, to reduce costs and to increase users comfort. In this paper improvements of the control system of a Building and Home Automation system previously presented by the author are presented. The system integrates energy-consuming sources for heat and light power supply, such as heat pumps and artificial lights with green energy-supplying sources like natural radiation and natural illuminance. In particular, in the present work, control solutions as a new thermal control policy, an anti-glare logic, and a logic for accounting of the solar radiation are introduced. This controller works satisfactory reducing the need for energy-consuming sources and it reaches good control performances and energy efficiency by making the best of the advantages of intelligence building.