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In recent times, the energy consumed by buildings facilities became considerable. Efficient local energy management is vital to deal with building power demand penalties. This operation becomes complex when a hybrid energy system is included in the power system. This study proposes new energy management between photovoltaic (PV) system, Battery Energy Storage System (BESS) and the power network in a building by controlling the PV/BESS inverter. The strategy is based on explicit model predictive control (MPC) to find an optimal power flow in the building for one-day ahead. The control algorithm is based on a simple power flow equation and weather forecast. Then, a cost function is formulated and optimised using genetic algorithms-based solver. The objective is reducing the imported energy from the grid preventing the saturation and emptiness of BESS. Including other targets to the control policy as energy price dynamic and BESS degradation, MPC can optimise dramatically the efficacy of the global building power system. The strategy is implemented and tested successfully using MATLAB/SimPowerSystems software, compared to classical hysteresis management, MPC has given 10% in energy cost economy and 25% improvement in BESS lifetime.

The building sector is one of the main consumers of energy. Therefore, heating and cooling concepts for renewable energy sources become increasingly important. For this purpose, low-temperature systems such as thermo-active building systems (TABS) are particularly suitable. This paper presents results of the use of a novel adaptive and predictive computation method, based on multiple linear regression (AMLR) for the control of TABS in a passive seminar building. Detailed comparisons are shown between the standard TABS and AMLR strategies over a period of nine months each. In addition to the reduction of thermal energy use by approx. 26% and a significant reduction of the TABS pump operation time, this paper focuses on investment savings in a passive seminar building through the use of the AMLR strategy. This includes the reduction of peak power of the chilled beams (auxiliary system) as well as a simplification of the TABS hydronic circuit and the saving of an external temperature sensor. The AMLR proves its practicality by learning from the historical building operation, by dealing with forecasting errors and it is easy to integrate into a building automation system.

In this study, a high-performance controller is proposed for single-phase grid-tied energy storage systems (ESSs). To control power factor and current harmonics and manage time-shifting of energy, the ESS is required to have low steady-state error and fast transient response. It is well known that fast controllers often lack the required steady-state accuracy and trade-off is inevitable. A hybrid control system is therefore presented that combines a simple yet fast proportional derivative controller with a repetitive controller which is a type of learning controller with small steady-state error, suitable for applications with periodic grid current harmonic waveforms. This results in an improved system with distortion-free, high power factor grid current. The proposed controller model is developed and design parameters are presented. The stability analysis for the proposed system is provided and the theoretical analysis is verified through stability, transient and steady-state simulations.

There is a growing trend for the use of thermo-active building systems (TABS) for the heating and cooling of buildings, because these systems are known to be very economical and efficient. However, their control is complicated due to the large thermal inertia, and their parameterization is time-consuming. With conventional TABS-control strategies, the required thermal comfort in buildings can often not be maintained, particularly if the internal heat sources are suddenly changed. This paper shows measurement results and evaluations of the operation of a novel adaptive and predictive calculation method, based on a multiple linear regression (AMLR) for the control of TABS. The measurement results are compared with the standard TABS strategy. The results show that the electrical pump energy could be reduced by more than 86%. Including the weather adjustment, it could be demonstrated that thermal energy savings of over 41% could be reached. In addition, the thermal comfort could be improved due to the possibility to specify mean room set-point temperatures. With the AMLR, comfort category I of the comfort norms ISO 7730 and DIN EN 15251 are observed in about 95% of occasions. With the standard TABS strategy, only about 24% are within category I.

—Research in Microgrid has taken recently more attentions in the context of solving the conventional centralized power network constraints. Combining information and power technologies, the efficiency and reliability of the power network have been improved dramatically. As an additional advantage, including renewable energy sources and storage in to form distributed energy systems has proven its aptitude to solved some economic and technical penalties appearing in classical centralized systems. However, appropriate energy management algorithms in hybrid microgrids are primordial to reach the objectives mentioned previously, for which, this paper has given a significant contribution. This work proposes a practical and efficient solution to implement a real-time energy management policy on a microgrid prototype based on commercial products. This method is implemented and tested successfully to manage the power flow between 6.3 KWp photovoltaic system; 4.5 KW lead-acid batteries and 3.6 KW programmable loads. The objective is to maximize the use of renewable energy while driving efficiently the storage system. Another target summarized in operating the microgrid in island mode as much as possible to improve its reliability.

This work proposes and simulates a finite set model predictive control technique for the photovoltaic inverter. The power topology is a 30 KW PV array with two power conversion stages: DC/DC boost converter controlled with maximum power point tracking to extract the maximum available photovoltaic power and two-level voltage source inverter with passive filter to convert direct current to alternative current and inject it into a medium voltage grid via a transformer, a 30 KW lead-acid batteries system connected to the inverter DC bus through buck-boost converter. Two control levels appear for the inverter: a simple hysteresis-based power management which sends the power reference to the second level according to the battery state of charge value. The second level is a finite set model predictive controller which makes the inverter inject desired high-quality currents according to the reference. This technique doesn't need a modular for switching and the digital simulation with MATLAB/Simulink software proves the performance of MPC controller in term of injected currents quality with THD value of 0.91% compared to conventional hysteresis controller with THD of 1.57%.