This paper deals with distributed control of variable efficiency heterogeneous battery storage systems connected in a microgrid. The nonlinear efficiency of battery charging is taken from converters’ datasheet and identified battery internal resistance, and a corresponding nonlinear model is derived dependent on state of energy. Individual battery control, as a local nonlinear problem, is solved by using a sequential linear program. Distributed control with the central supervisor is then used to coordinate individual batteries within the storage system and ensure improved overall system operation while respecting the joint constraint of the grid connection power capacity. Asymmetric projection algorithm is used for distributed control, based on iterative convergence of individual battery control actions towards the optimal operation of the whole system. The algorithm is implemented in a realistic simulation scenario of six different batteries with their corresponding characteristics, real electricity prices and measured load consumption. Presented results show that distributed control satisfies global constraint, while obtaining better cost than the decentralized approach with previously reported 7% of increased savings of nonlinear over constant efficiency.

Charging electric vehicles (EVs), whose number is increasing, is a great challenge for the power grid due to the charging load variability. Coordinated charging and schedule optimization with seized demand response opportunities are well-known conceptual solutions to that. Still, the main challenge is to adequately predict availability and parameters of electric vehicles which is crucial for determining the charging schedule and the demand response potential. We propose a method to represent a population of electric vehicles that on the one hand enables prediction via machine learning and on the other it enables an accurate optimization of the charging schedule and demand response ability. The method essence is to use five discrete-time signals spanned over a prediction horizon period which are related to envelopes of feasible charging power and charging states for the EV population on that horizon. We also introduce a robust conversion of any sequence of these signals into individual EVs data. It enables to pose and solve the optimization problem of charging scheduling with included demand response for a predicted population in the introduced representation. The proposed method is validated by schedule optimization using first the original data and then using reconstructed population data. The validation results show that the proposed EV population representation method preserves the valuable information needed for the charging schedule optimization and demand response.

The article is focused on buildings with legacy one-pipe heating systems that have inherent problems with high losses and maintaining comfort when classical zone hysteresis and constant forward medium temperature control are applied to them. We show a great potential of predictive control to improve both comfort and energy efficiency in these systems by exploiting the possibilities existing in synchronized operation between rooms and the central heating station of the building. Optimal day-ahead scheduling based on predictive control is applied to assess the efficient operation mode of such a system which is then compared with classical reactive controls. The case study of primary school in Strem, Austria is considered and significant comfort improvement and savings possibilities are revealed.

This paper presents a microgrid energy flow optimization algorithm with variable battery storage efficiency in order to achieve energy savings and expand the lifespan of the components. The converter efficiency curve is deduced from converter’s datasheets and approximated with mathematical functions. The power loss on the battery internal resistance is also included in order to achieve a more accurate model of the complete storage system. The obtained nonlinear model is used in model predictive control formulation and solved by using a sequential linear program (SLP) algorithm. The SLP algorithm iteratively linearizes the model around the current solution and uses corresponding efficiencies over the prediction horizon. Simulations in MATLAB are performed for a 7-day period and compared with a conventional, constant-efficiency battery system model. The results show an improved performance regarding the charging and discharging battery power and the overall savings of 7% in comparison with the conventional model used in model predictive control.

The control system for energy-efficient train operation with the inclusion of a detailed train motion model and train traction system energy efficiency is presented in the paper. A piecewise affine train model is constructed with the parameters obtained for the electromotive train of an industrial manufacturer. The model encompasses intrinsic features of the train system such as linearized resistance force, a set of traction and braking force physical limitations and passengers comfort constraints. The resulting quadratic optimization problem is solved parametrically through dynamic programming giving the off-line precomputed optimal control law that is a function of train speed and traversed path. The on-line computed traction force profile is then tuned with respect to the traction system energy efficiency. The developed control system is evaluated on a detailed real case study scenario put together with a railway operator and the train manufacturer. The presented results show the possibility of significant energy consumption reductions achieved by energy-efficient train control.

Non-conservative set-based characterization of Kalman filter state estimation error under the influence of bounded disturbances acting on the system under consideration is needed to analyze the corresponding control system performance comprehensively. One important newly rising application of such error characterization is also in cyber-attack detection. This work outlines how the estimation error set characterization is performed by relying on set-theoretic methods in control. In particular, minimal robust positively invariant sets correspond to the smallest such sets and thus represent their least conservative characterization. The procedure is applied to the automatic generation control problem in an exemplary electrical transmission grid configuration with two control areas. A simulation study is performed under a random sequence of bounded disturbances of production-consumption mismatch and frequency/power measurement noises to demonstrate the correct and non-conservative estimation error localization.

The paper presents a railway energy management system based on hierarchical coordination of electric traction substation energy flows and on-route trains energy consumption. The railway system is divided into energy-efficient individual trains energy consumption management as lower level, and the price-efficient electric traction substation energy flows management as higher level. The levels are coordinated through parametric hierarchical model predictive control with the main goal of additionally increasing the energy efficiency and decreasing the operational costs of the overall system. Through interactions with the power grid on the higher level, the system is able to provide ancillary services and respond to various grid requests. At the same time, lower level trains driving profiles are adjusted to attain the minimal cost of system operation with timetables and on-route constraints respected. The developed algorithm is verified on a detailed real case study scenario put together with a railway operator and a trains manufacturer. The presented results show significant cost and energy consumption reductions achieved by simultaneous coordination of several trains supplied from the same traction substation.

This paper presents a predictive control method for power variation reduction and maximum power limitation in building heating systems. A case study with heating supply pipeline of a skyscraper floor with 13 zones is chosen. Simulation environment is developed to simulate floor thermal dynamics with fan coils as heating elements. Fan coils operation in different zones is orchestrated via model predictive control in order to achieve as smooth as possible cumulative heating power consumption profile while respecting individual comfort requirements in zones. The approach inherently limits the maximum power measured on a calorimeter. Compared to conventional decentralized hysteresis-based temperature control in zones, simulation results show that the heating power variance is successfully reduced and fan coils smooth operation is ensured while the overall energy efficiency increased. By replicating the presented control system through all the segments of a building, this approach yields important benefits of smoothing the building power consumption curve and significant savings in contracted heating power.

The winding system of high voltage machines is usually composed of pre-formed coils. To facilitate the winding fitting process stator slots are usually wide opened. These wide opened slots are known to cause disturbances of the magnetic field distribution. Thus losses are increased and machine's efficiency is reduced. A common way to counteract this drawback is given by placing magnetic slot wedges in the slots. During operation the wedges are exposed to high magnetic and mechanical forces. As a consequence wedges can get loose and finally fall out into the air-gap. State-of-the-art missing slot wedge detection techniques deal with the drawback that the machine must be disassembled, what is usually very time consuming. In this paper a method is investigated which provides the possibility of detecting missing magnetic slot wedges based only on measurement of electrical quantities and without machine disassembling. The method is based on exploitation of machine reaction on transient voltage excitation. The resulting current response contains information on machine's magnetic state. This information is composed of several machine asymmetries including the fault (missing wedge) induced asymmetry. A specific signal processing chain provides a distinct separation of all asymmetry components and delivers a high sensitive fault indicator. Measurements for several fault cases are presented and discussed. A sensitivity analysis shows the high accuracy of the method and the ability to detect even partially missing slot wedges.