This paper presents the concept of an 'empty network' and shows how the power balance can be controlled in such a system. In this study, an 'empty network' is defined as a transmission system in which no rotating mass is present. All generators are connected to distributed systems and 'hidden' behind power electronic interfaces. One generator creates a neat 50 Hz voltage that serves as a frequency reference for the other generators. Consequently, a power imbalance cannot be detected in the classical way, as an altered system frequency. Therefore, a novel control system to maintain the power balance is needed. In this paper, voltage deviations are used to detect power imbalances, and remedies to eliminate the negative consequences of using the voltage deviations to detect the power imbalances are proposed and discussed. Keywords: empty network; power balance control; distributed generation; distribution systems; power electronic interfaces; voltage deviations; power imbalances.@en

In a liberalized electricity market, the use of phase shift ing transformers or other power ¿ow controlling devices allows the transmission system operator to utilize the avail able grid infrastructure in a more optimal way. However, each phase shifter adds a degrees of freedom to the control problem, making optimization more dif¿cult. In this paper, the coordination problem is solved by using Particle Swarm Optimization (PSO). The goal is to give an overview of how PSO is used to solve this particular problem, and to demon strate a new area of application for the method.@en

This paper presents the concept of an "empty network" and shows how the power balance can be maintained in such a system. In this study, an "empty network" is defined as a power system in which no rotating mass is present; all generators are grid-connected via power electronic interfaces, so that the possible inertia of the generators is 'hidden' from the network. One generator creates a neat 50 Hz voltage that serves as a frequency reference for the other generators. Conventional power systems are mainly supplied by large centralized synchronous generators. These generators are connected directly to the grid so that there is a coupling between the generator rotor speed, and thus the system frequency, and the power balance in the system. Therefore, an unbalance manifests itself by an altered system frequency. In the empty network, the frequency is fixed, as it is created by a power electronic device, and an unbalance cannot be detected in the classical way. In this paper, an empty network is simulated on an RTDS (real time digital simulator). Voltage deviations are used to detect the power unbalances. Simple test systems that consist of 1,2 and 3 buses are applied, in which all generators are modeled as current sources, except for the frequency reference, which is a voltage source. A load jump is simulated to cause a power unbalance in the system. The study shows that by using voltage deviations as control signals, the power balance can be maintained in an empty network.@en

Stochastic generation, i.e., electrical power production by an uncontrolled primary energy source, is expected to play an important role in future power systems. A new power system structure is created due to the large-scale implementation of this small-scale, distributed, non-dispatchable generation; the `horizontally-operated¿ system. Modeling methodologies that can deal with the operational uncertainty introduced by these power units should be used for analyzing the impact of this generation to the system. In this contribution, the principles for this modeling are presented, based on the decoupling of the single stochastic generator behavior (marginal distribution-stochastic unit capacity) from the concurrent behavior of the stochastic generators (stochastic dependence structure-stochastic system dispatch). Subsequently, the stochastic bounds methodology is applied to model the extreme power contribution of the stochastic generation to the system, based on two new sampling concepts (comonotonicity¿countermonotonicity). The application of this methodology to the power system leads to the definition of clusters of positively correlated stochastic generators and the combination of different clusters based on the sampling concepts. The stochastic decomposition and clustering concepts presented in this contribution provide the basis for the application of new uncertainty analysis techniques for the modeling of stochastic generation in power systems. Keywords: Stochastic power generation; Distributed generation; Steady-state analysis; Uncertainty analysis; Monte-Carlo simulation; Risk management@en