Cyber-physical power systems integrate the various devices, which provide ancillary system services. In this paper, the design and implementation of a fully controllable cyber-physical system are presented. This system simulates the behavior of the real power systems and additionally assures controllable repeatable testing conditions, enabling investigations of energy storage systems. Ancillary system services provided by energy storages are especially crucial in the context of renewable energy sources and electromobility sector development. Credible tests of control strategies, realizing system services, require controllability of test parameters. Such investigations are impossible in a real power system, due to its inherent variability. The novel approach presented in this paper enables the management of power system resources supplied by various producers and ensures flexibility in the realization of assumed scenarios of power system operation. Such an controllable cyber-physical power system constitutes a suitable environment for tests of the effectiveness of ancillary services provided by energy storage systems because the system is independent of inherent variability of the real power system and enables flexible realization of the control algorithms developed for all of the system components (power source, loads, and energy storages). The voltage profile improvement in low-voltage grids has been shown in a case study confirming the applicability of the proposed approach.INDEX TERMS Electrical engineering, power engineering and energy, electronic equipment testing, energy storage, power electronics, power system control.
Abstract:The growth in renewable power generation and more strict local regulations regarding power quality indices will make it necessary to use energy storage systems with renewable power plants in the near future. The capacity of storage systems can be determined using different methods most of which can be divided into either deterministic or stochastic. Deterministic methods are often complicated with numerous parameters and complex models for long term prediction often incorporating meteorological data. Stochastic methods use statistics for ESS (Energy Storage System) sizing, which is somewhat intuitive for dealing with the random element of wind speed variation. The proposed method in this paper performs stabilization of output power at one minute intervals to reduce the negative influence of the wind farm on the power grid in order to meet local regulations. This paper shows the process of sizing the ESS for two selected wind farms, based on their levels of variation in generated power and also, for each, how the negative influences on the power grid in the form of voltage variation and a shortterm flicker factor are decreased.
The number of electric vehicles (EV) on the roads, as well as the share of EVs in use, will inevitably increase in coming decades. This creates a number of problems. A large EV fleet is a significant additional load in the power system that is impossible to accurately predict. Another related problem is the limited distribution network capacity, which is not ready for the additional load from the widespread EV infrastructure. There is a need for an EV charging coordination algorithm capable of fulfilling the charging EV needs, while using as low demanded power as possible and using the lowest power values in each EV charging profile. We propose an EV coordinating algorithm that is capable of ensuring that all connected EVs in the considered parking lot will be charged at the user-defined departure time. The algorithm also controls the charging/discharging power of every connected EV in such a way that the parking lot as a whole will use minimal possible peak power while minimizing the charging power of every EV. The proposed algorithm is also capable of responding to demand response (DR) signals. The paper also includes the results of simulation with a statistical summary of the proposed algorithm effectiveness.
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