The number of large energy storage units installed in the power system has increased over the last few years. This fact remains closely linked to the increase in the share of renewable energy in electricity generation. This is necessary to maintain the stability of the grid, which is becoming increasingly difficult to maintain due to the growing number of renewable energy sources (RES). Energy production from these sources is difficult to estimate, and possible unplanned shortages and surpluses in production are the cause of voltage and frequency fluctuations, which is an undesirable state. Consequently, the use of energy storage not only contributes to the regulation of grid operation but can also, under appropriate conditions, constitute an additional load if too much energy is generated by RES, or the source when the generation from RES is insufficient. The main contributions of this paper are as follows: A presentation of practical results achieved by implementing two optimal control strategies for a 1 MW (0.5 MWh) battery energy storage (BES) cooperating with a large 144 MW photovoltaic farm. In the first case, the BES was used to generate curtailment at photovoltaic farm to avoid power grid overload. The second case focuses on maximizing profits from selling the energy produced in periods when the unit price for energy was the highest according to energy market forecasts. In both cases, the storage was used simultaneously to cover the producer’s own demand, which eliminated the costs associated with the purchase of energy from the operator, especially during the night supply. A technical and economic evaluation was prepared for both cases, considering the real profits from the investment. The potential of using the BES to increase the functionality of photovoltaic energy sources was determined and discussed in the paper.

In fact electric mobility becomes in different fields of research an important matter, realistic models of electric vehicles (EV) become more significant. There are just small amount of EVs existing and produced compared to conventional vehicles. The existing types of EVs are differing significant in concept and technology so that their physical models for mobility simulations and their electrical model for grid simulations have to be created accurately. EV models which reflect the energy consumption while driving and the characteristics for charging and discharging of EVs are required for a mobile storage management. For this reason an EV fleet consisting of different types of EVs was equipped with measurement devices and information and communication technique (ICT) to collect and evaluate data of the potential mobile storages. Based on this research, procedures for an automatism to construct mathematical models of the EVs out of the measured data are developed and presented in this paper.

In this contribution power quality measurements of electric vehicles (EVs) will be presented. In the coming years EVs will become a more relevant asset in the power system. Due to infrastructural reasons and typical usage the EVs will be mainly connected to the low voltage power grid. That includes charging and - in case of vehicle-to-grid concepts - discharging processes with different power levels. The investigation of different kinds of EVs considers voltage drops, harmonics and power factors that allow for scaling up the impact on the power system. To interpret the results common standards and technical requirements are taken into account that describes the connection of loads as well as generators to the grid.

The use of lithium-ion battery energy storage (BES) has grown rapidly during the past year for both mobile and stationary applications. For mobile applications, BES units are used in the range of 10–120 kWh. Power grid applications of BES are characterized by much higher capacities (range of MWh) and this area particularly has great potential regarding the expected energy system transition in the next years. The optimal operation of BES by an energy storage management system is usually predictive and based strongly on the knowledge about the state of charge (SOC) of the battery. The SOC depends on many factors (e.g. material, electrical and thermal state of the battery), so that an accurate assessment of the battery SOC is complex. The SOC intermediate prediction methods are based on the battery models. The modeling of BES is divided into three types: fundamental (based on material issues), electrical equivalent circuit (based on electrical modeling) and balancing (based on a reservoir model). Each of these models requires parameterization based on measurements of input/output parameters. These models are used for SOC modelbased calculation and in battery system simulation for optimal battery sizing and planning. Empirical SOC assessment methods currently remain the most popular because they allow practical application, but the accuracy of the assessment, which is the key factor for optimal operation, must also be strongly considered. This scientific contribution is divided into two papers. Paper part I will present a holistic overview of the main methods of SOC assessment. Physical measurement methods, battery modeling and the methodology of using the model as a digital twin of a battery are addressed and discussed. Furthermore, adaptive methods and methods of artificial intelligence, which are important for the SOC calculation, are presented. In paper part II, examples of the application areas are presented and their accuracy is discussed.

Transforming the transport sector to zero emission is an integral part of changes to the energy sector worldwide. This effects not only the electrification of the private sector but also the commercial sector. The aim of this study is to develop methodologies, algorithms and associated requirements for the integration of electric vehicles into a logistics application with a possible reduction in operating costs. The most favorable solution for a company was evaluated using the analytic hierarchy process algorithm considering three main aspects: economic, environmental and technical. An analysis of the environmental impact of the vehicle fleet in terms of atmospheric emissions was also conducted, based on the data available for combustion and electric vehicles, considering the well-to-tank approach. The costs associated with operating an electric vehicle were identified and compared to the current costs associated with operating a standard diesel-based fleet. Incorporating the identified costs of electrifying the vehicle fleet, an algorithm was implemented to reduce the number of vehicles in the company and, thereby, significantly reducing the costs associated with fleet maintenance.

Electric vehicles (EVs) can be utilized as mobile storages in a power system. The use of battery chargers can cause current harmonics in the supplied AC system. In order to analyze the impact of different EVs with regardto their number and their emission of current harmonics, a generic harmonic current model of EV types was built and implemented in the power system simulation tool PSS®NETOMAC. Based on the measurement data for different types of EVs three standardized harmonic EV models were developed and parametrized. Further, the identified harmonic models are used by the computation of load flow in a modeled, German power distribution system. As a benchmark, a case scenario was studied regarding a high market penetration of EVs in the year 2030 for Germany. The impact of the EV charging on the power distribution system was analyzed and evaluated with valid power quality standards.