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.

New technologies, such as HVDC-links, will play a major role in future transmission system operation. The application of HVDC systems or flexible AC transmission systems (FACTS), introduce new fast dynamics, which add controllability to the power system. Additionally, state of the art technology such as phasor measurement unit (PMU) based wide area monitoring systems (WAMS) enables new methods for the next generation of power system monitoring and control systems. To develop new functionalities for control centers a new development platform for dynamic simulation studies of control and operation functionalities for AC-HVDC systems is introduced. Therefore, a detailed understanding of every component of the distributed system is required. Hence, this paper discusses the components of the development platform comprising a real-time hardware in the loop (HIL) simulation system, the communication system and the control center itself. Furthermore, first new control center functionalities are also described in this paper.

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.

Wind power plants have become a mainstay of German renewable energy production and accounted for 12.3% of gross electricity production in 2015 [1]. Nonetheless, wind turbines do not produce power continuously and require active and reactive power depending on their operating point. Annual energy consumption incurs significant operating costs, which can only be reduced by making technical changes to the existing infrastructure or by modernizing the installed wind turbines. Unlike conventional static power factor compensation systems, battery storage systems (BSS) additionally provide active power and thus double benefits. This paper first describes selected technical constraints of wind farms and defines battery storage system use cases to reduce their operating costs. Furthermore a design algorithm for storage parameters is proposed and the technical configuration is optimized to maximize cost reduction. Experiences and operation data of an 1 MW BSS under test has been factored into use case simulations and results are valued in a cost-benefit-analysis.

The intensive electrification of the automotive sector means that the energy system must be able to adapt to the current market situation. The increase in energy demand is a major factor associated with electric vehicles. The study analyzed the operation of a grid-connected facility operating a vehicle fleet providing transport services in the region Halle/Saale, Germany. Measurement data were used in the analysis, including global positioning system data of the vehicles and technical data, including average fuel consumption on a given route section, daily load demand of the industrial facility, and energy generation from photovoltaics. This paper shows the impact of using a battery electric vehicles (BEVs) fleet in the load distribution for the industrial facility considered. The NEDC energy consumption profile for the Nissan e-NV200 were used in this study. Furthermore, the paper presented simulation results allowing one to determine the usage potential, energy demand, and consumption of EVs using real data, reliably representing the processes related to EV daily use. The measurement data were captured using available specialized equipment: Dako-Key (GPS data), PV power generation (Siemens 7KM PAC4200), and load (Janitza UMG 604-Pro) in September, 2018. On this basis, it is possible to identify the effects and variations in load on the power grid during the replacement of combustion vehicle fleets used currently by EVs for the provision of transport services. Three models were presented, making it possible to calculate changes in energy demand for each scenario. In the first model, EVs were charged exclusively from the distribution network. In the second, the energy generation from a renewable source was considered and the possibility of compensating the energy demand of the vehicles from this source was demonstrated. In the third model, the daily load profile and the period of maximum load in the electricity grid were considered. The results are presented in graphical and tabular form. Finally, the potential of using an EV fleet to increase the functionality of a modern industry object was determined and discussed. Based on data for the adopted scenarios, electrification of transport can increase demand for energy by 40.9% for individual enterprises. The electrification of the automotive sector will increase the instantaneous energy demand of businesses, forcing the integration of renewable energy sources during designing new invests.