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This paper deals with a new formulation of the optimal operation of electrical distribution networks problem in regular working state. In the new deregulated energy market providing reliable and economical service to customers is a primary task. The multiobjective formulation of the reconfiguration and compensation problem used in this paper considers as a primary object also the minimisation of the load nodes unavailability (UA) expressed in probabilistic terms. Therefore, the objectives to be attained through the optimisation strategy are: minimal power losses operation, minimum UA of the load nodes, load balancing among the HV/MV transformers, and voltage profile regularisation. The application carried out uses an evolutionary algorithm and a particular normalisation technique for the multiple objectives formulation. In the considered automated network, the remote control of capacitor banks and tie‐switches is possible and their layout is the optimisation variable. After a brief description of the optimal reconfiguration and compensation problem for automated distribution networks, the most recent papers on the topic are reported and commented. Then the problem formulation and the solution algorithm are described in detail. Finally, the test results on a large MV distribution network are reported and discussed.

This paper proposes to identify a new model of the fixed voltage nodes (PV nodes) for medium voltage distribution systems analysis. The model is used within backward/forward (b/f) analysis method applied to solve radial and weakly meshed systems.

The model is based on the compensation currents method for multi‐port systems which has been extensively used, within b/f analysis methods, to take into account the presence of meshes and PV nodes.

Test results prove the approach to be more efficient and precise than previous methodologies and put into evidence the good performance of the proposed model in terms of speed and convergence properties.

The model is developed to be used within b/f methodology.

Utilities are quite interested in such items, since the deregulation electricity market in last years has determined an evolution of the medium voltage distribution networks, due to the presence of distributed generation. So it is necessary to take into account and to model in an opportune way the nodes with distributed generators (PV nodes), also in weakly meshed networks, for the load flow analysis.

The paper presents a new model to treat the fixed voltage nodes (PV nodes) for medium voltage distribution networks analysis.

Identify a new methodology for fault characterization, identification and location in electrical distribution systems, based on the use of matrix algebra.

The developed diagnostic methodology is based on a high precision analytical model of the network using a distributed parameters representation.

Test results have proved the approach to be efficient and precise, while providing a generalized quadripolar model of a line affected by the most common kinds of fault.

Generalization to a greater number of fault cases, experimental tests.

Utilities are quite interested in such items, since the new required quality standards put severe constraints on faults management and clearance. On the other hand, the system requires a rather complete measurement equipment of secondary substations.

The paper presents a new diagnostic technique for faults identification, location and characterization in distribution systems.

Introduces the fourth and final chapter of the ISEF 1999 Proceedings by stating electric and magnetic fields are influenced, in a reciprocal way, by thermal and mechanical fields. Looks at the coupling of fields in a device or a system as a prescribed effect. Points out that there are 12 contributions included ‐ covering magnetic levitation or induction heating, superconducting devices and possible effects to the human body due to electric impressed fields.

This paper aims to extend the small body of literature on energy industry transitions on firm level. A growing number of articles shed light on paradigm shifts in the energy industry and the influence of renewable energies on industry structures. In the majority of cases, the authors analyze changes on a global or national level.

Energy companies’ forecasting capabilities are particularly important to enable them to react in time to upcoming changes in industry structures. In this context, we analyze annual reports of German energy companies to evaluate their economic and technological forecasting competencies.

Big energy providers offer high economic forecasting quality, but seem to be less able to derive valid forecasts in terms of renewable energies from the currently unstable political frameworks. On the contrary, renewable energy companies do not seem to suffer from these difficulties and provide good foresting accuracy in terms of renewable energy development, but show less accurate economic forecasting quality.

Big energy providers need to find the means of responding to the challenges and integrate changing political guidelines and support into their forecasting system. Renewable energy companies, in contrast, should focus on company-level profitability and the respective economic forecasting competencies.

This paper makes a significant contribution to the literature on the subject of energy industry transitions by providing insights from publicly available data on firm level. The findings are highly relevant for managers of the energy industry and policy makers in this field.

This paper aims to focus on how different types of knowledge are exchanged within innovation networks in the German energy industry. External factors such as market pressure through liberalization, de-carbonization and decentralization challenge established actors in the industry. Answers to these challenges cannot be found by single actors but require networks to gather and concentrate innovation activities. This implies a need for knowledge transfer among energy providers. The authors aim at exploring knowledge exchange relations in-depth by treating them as multidimensional flows which can comprise technological, market, managerial or regulatory knowledge. In detail, the authors examine patterns of knowledge exchange on network-, dyad- and firm-level. Furthermore, first, empiric results are provided on how two of these patterns, namely, a firm’s propensity to form multiplex instead of uniplex ties as well as the composition of externally acquired knowledge concerning the four types, influence organizational innovativeness.

The authors address their research questions by conducting an in-depth investigation of the largest network of municipal utilities in Germany. The analysis is based on quantitative data collected via standardized online questionnaires drawing on socio-metric methods to reconstruct knowledge exchange networks as well as traditional approaches from socio-empiric research to evaluate firm innovativeness.

The findings indicate that while technological, market, managerial and regulatory knowledge represent different types of knowledge with different exchange patterns, these transfers are interdependent. In particular, the analysis reveals non-hierarchical relations of complementarity. The authors furthermore provide evidence for the existence of ideal profiles for attaining different types of innovation. One central tendency across all of these profiles is that outperformers acquire regulatory knowledge to a significantly lesser degree than other firms and focus more on the other types instead.

This paper solely focusses on the largest network of municipal utilities whereby it is questionable how representative it is for the whole industry. Additionally, due to the cross-sectional design, the paper cannot fully rule out issues of endogeneity in the quantitative analysis.

This paper delivers valuable insights for managers in the energy sector who seek to either enter and manage inter-organizational networks or apply external knowledge to foster innovation. In particular, the authors reveal benchmark profiles for external knowledge acquisition which may serve as templates for strategic collaboration and innovation management.

To the authors’ knowledge, this paper presents the first innovation-related network analysis in the energy industry. Rather than operationalizing knowledge transfer as a simplex flow relation, the authors examine different types of knowledge, their patterns of exchange and their distinct effects on process, product and administrative innovations.

The aim is to develop a nonlinear transformer model to achieve an accurate model to obtain the frequency components of the magnetizing current based on the harmonic voltages at the primary and secondary side. So, it can easily be implemented in a harmonic load‐flow program.

The transformer model is based on the harmonic balance method. The electric and magnetic equations of the transformer are derived from the electric and magnetic equivalent circuits.

The transformer model can be easily implemented in a harmonic load‐flow program. The accuracy of the model has been shown by comparing it with a finite element simulation. The transformer model can be used with asymmetrical supply voltages, because different saturation levels of the phases can occur. There is a coupling between the phases which can be concluded out of the asymmetrical currents in the transformer under symmetrical supply voltages.

The transformer model does not consider the iron losses and the interharmonics. In future work the transformer model will be used to study the harmonic losses in distribution networks, so the transformer losses due to these harmonics have to be considered. This can be achieved with a postcalculation process where the magnetic flux density is used to calculate the eddy current losses and the magnetic field intensity will be applied in a static Preisach model to quantify the hysteresis losses.

The model can be used in a harmonic load‐flow program in order to obtain more accurate simulations for the power system analysis and design.

The model presented in this paper is more detailed than similar papers found in literature (saturation of the yokes, coupling between the phases, interaction between different harmonics) and still it takes a brief simulation time.

An electrical company is responsible for the maintenance of a transmission network of high voltage electricity. The maintenance schedule must be planned so as to minimize outage costs, taking into consideration various factors such as system security/reliability, system availability, and manpower utilization. With the rapid growth of organization, planning engineers are required to fulfill additional roles in order to increase productivity. To this end, a fast response and accurate mechanism is required to assist the planning engineers in dealing with the daily operation. This paper describes how a proposed maintenance schedule can be obtained automatically by the adoption of genetic algorithm. The main aim is to determine the maintenance schedule of circuit outage with minimizing the maintenance cost and maximizing the circuit availability under certain unavoidable system constraints. Further, an additional search mechanism called “final tuning search” is developed to enhance the system performance.

This paper proposes an integrated approach of genetic algorithms, Tabu search and simulated annealing for multi‐stage (dynamic) transmission network expansion planning. The proposed algorithm integrates the most interesting and best features of the above individual algorithms. The efficiency and reliability of the proposed algorithm is proved with the modified Garver's six‐bus network. Finally, a real‐world application (Sri Lankan transmission network) of the integrated algorithm is presented for multi‐stage transmission expansion planning.