Edmund Handschin

Influences of wind energy on the operation of transmission systems

In this work the impact of wind energy on the power flow is analyzed. After the development of a reduced sample network, possible network congestions are identified and the costs for the required redispatch of the generation are evaluated. To avoid or reduce the probability of congestions, different network upgrades can be installed. The efficiency of additional lines and power flow controlling devices is discussed on the basis of power flow calculations and dynamic simulations.

Flow-based evaluation of congestions in the electric power transmission system

In this paper the practical implementation of a flow-based method for the allocation of limited cross-border transmission capacity is modeled. In order to evaluate the efficiency of the approach compared to traditional methods based on bilateral conventions for the trading transactions, economic and technical performance indicators are presented. The developed approach is applied to a model of the Central Western European region. The results show that the flow-based method can improve the network efficiency leading to lower generation cost and higher economic welfare in the regarded region. The necessary simplifications of the load flow equations cause inevitable deviations of the estimated flows but the general behavior is uncritical. Apart from that, single situations require a sensitive monitoring by the involved parties.

Analysis of market coupling based on a combined network and market model

In this paper the possible implementation and the efficiency of multilateral market coupling are evaluated. Market coupling is known as the cooperation of power exchanges and other market players in order to utilize the limited capacity of a meshed electricity transmission system in an efficient way. Based on a three-part combined network and market model of the Central Western European region, the congestion management is simulated and possible alternatives in the different steps of the practical execution are compared to each other. The results are rated from an economic and technical point of view in terms of system-wide welfare as well as utilization and security of the congested lines.

Distributed Learning Strategies for Collaborative Agents in Adaptive Decentralized Power Systems

For regenerative electric power the traditional top- down and long-term power management is obsolete, due to the wide dispersion and high unpredictability of wind and solar based power facilities. In the R&D DEZENT1 project we developed a multi-level bottom- up solution where autonomous software agents negotiate available energy quantities and needs on behalf of consumers and producer groups. We operate within very short time intervals of assumedly constant demand and supply, in our case 0.5 sec (switching delay for a light bulb). We prove security against a relevant variety of malicious attacks. In this paper the main contribution is to make the negotiation strategies themselves adaptive across periods. We adapted a reinforcement Learning approach for defining and discussing learning strategies for collaborative autonomous agents that are clearly superior to previous (static) procedures. We report briefly on extensive comparative simulation.

Establishing Large-Scale Renewable Reserve Capacity through Distributed Multi-Agent Support

A world-wide trend towards renewable and ecologically clean forms of energy has been steadily growing. Private investments are encouraged and heavily subsidized in most of the European countries, through tax deductions, and even more through a very favorable refund program for feeding electric power from renewable sources into the public network. Due to the limited predictability of the output of renewable power capacities it has long become the policy of grid operators and large power distributors to cover the differences between demand and supply with immense reserve and balancing power capacities based on fossil, and thus predictable, energy sources. With growing renewable power feed-in the demand for reserve and balancing power grows over-proportionally. In 2005 the European Union for the Coordination of Transmission of Electricity (UCTE) demanded to impose an obligation on grid operators to reduce integration costs for renewable energy capacities. This could obviously be possible once the renewable capacities sources could serve as reserve capacity. Since these are widely distributed and dispersed, their combined effect may well be used to guarantee a stable supply. The remaining problem behind is that the largely unpredictable character of wind and solar power supply is to be administered financially and in terms of timely transmission. We introduce a novel solution for the distributed negotiation process, which is compatible with electric distribution procedures. This is part of our DEZENT (decentralized management of electric power distribution) project.

Dezentrale vernetzte Energiebewirtschaftung (DEZENT) im Netz der Zukunft

KernpunkteZiel des Projekts DEZENTist die Entwicklung eines verteilten Energiemanagementsystems, mit dem sich eine Vielzahl dezentraler Energieumwandlungsanlagen (erneuerbarer Energien) zu einem großen regionalen Netz zusammenschließen lassen.Technische, wirtschaftliche und ökologische Randbedingungen werden berücksichtigt.Die Netzleistung steht sowohl der allgemeinen Versorgung wie auch der Reserveregelung zur Verfügung.Das Management des Netzes wird durch ein verteiltes adaptives sicherheitskritisches Realzeit-Multiagentensystem realisiert.Die dezentrale Führung einer Vielzahl kooperierender, heterogener Systeme kann hinsichtlich Preis bzw. bereitgestellter Energie günstiger sein als bei zentral gesteuerter Versorgung.AbstractGiven the sharply rising costs for traditional energy (based on coal, fossil oil or gas etc.) over the past few years renewable energy sources such as wind, sun, water, or seed oil have favorably come into the picture as economically desirable and ecologically clean alternatives. The corresponding power facilities (e.g. wind power stations, solar panels, block heat & power plants) are widely distributed and are quite heterogeneous regarding their productivity (capacity and reliability) yet at the same time transport paths and costs are at a minimal level. (In the near future it will be possible to cover all needs from such sources.) For guaranteeing both the adaptive integration of the diverse facilities and a balanced level of supply under (locally) highly unpredictable energy production we present a bottom-up power grid management architecture. A key novelty of our approach is a completely decentralized management for negotiating the available power supply and needs. This is realized through a safety-critical, real-time multi-agent system where bids and offers are negotiated on the (electrically) shortest time basis of 0.5 sec. While the system is secure against malicious attacks it exhibits a high amount of fault tolerance where the latter corresponds well to the exceptional production safety of the widely distributed facilities. Beyond the high supply reliability under the decentralized management of distributed facilities we demonstrate that both operation costs and consumer prices could be assumed lower than under a centralized management and architecture.

Estimation of the costs due to renewable energies for a transmission system operator

By nature the generation from renewable energies sources is not performed on demand, but on offer. This yields the need of converting the energy to the actual demand. In Germany the execution of this process is obliged to the four transmission system operators. In this paper the occurring costs of the process of purchasing and transmitting renewable energies in Germany are analyzed for sample control areas. These costs include the expenses for additive required control power as well as the expenses caused by the special balancing group for renewable energies. During the operation of this balancing group accrue costs for balancing energy and as a result of trading transactions at electricity markets. Through this paper, the main question of the sensitivity of these costs from parameters in the observed control area is addressed.

Visualization and Analysis of Voltage Stability Using Self-Organizing Neural Networks

On the basis of a compelling mathematical description of voltage stability in electrical power systems and its indication using the minimum singular value of the load flow Jacobian the application of a self-organizing Kohonen-Neural-Network (KNN) is presented for a fast and secure indication and visualization of voltage stability. The advantage of the structural representation of the system condition by the KNN is worked out bypassing the disabilities of standard voltage stability indicators. In addition the application of KNN aims at the analysis of measures for the improvement of voltage stability. All examples are calculated using a model of a real power transmission system.

State estimation by orthogonalization and splitting

Abstract State estimation is a real-time problem and must be solved online. It is shown how state-estimation methods may be adapted for implementation on the new, mostly parallel and distributed computing systems currently being developed. The method of splitting is introduced and shown to be very efficient if the subnetworks are not too small. Another method of solving state estimation, namely orthogonalization, is also reviewed. It is then shown that computing errors may go undetected if the least-squares method is used. Finally, it is shown that use of orthogonalization would increase both accuracy and speed, even without splitting, but with splitting, the proposed method is more accurate, more stable, more reliable and requires less computing time than the methods used at present.

Management decision simulation for electrical utilities

Abstract Management decisions in electrical utilities have to be taken with a view to both economical and technical constraints. In particular, decisions concerning the operation of existing nuclear power stations and the construction of new ones require the study of very long-range planning problems. A management game is described which takes this aspect into consideration. The model simulates a utility over a period of 16 years. At the beginning of the simulation, the electrical energy requires the construction of new generation and transmission facilities. The discussion of the management game covers the following aspects: the definition of various objectives for the model; the choice of decision areas to be covered; the selection of decisions to be trained; the setup of the tutorial role of a game-monitoring person (teacher); the definition of boundary values for every game parameter; and the model development. The second part of the paper describes the implementation of the management game. The initial conditions and the boundary parameters have to be defined by the teacher at the beginning of the simulation. For every year, a number of financial and technical decisions have to be taken, keeping the a priori specified objective of the simulation in mind. Since the different submodels are heavily interconnected, each decision requires a detailed analysis of the results obtained in the previous year. These results are given annually in a balance sheet and a summary of the electrical-economic results. The third part of the paper contains a discussion of typical results which have been obtained from practical applications of the management game. The concluding remarks address the benefits and limits of the simulation approach towards energy management decisions.