BKW Energie AG (BKW, formerly known as Bernische Kraftwerke AG) is a power production and distribution utility with its headquarters in Bern, Switzerland. It also provides gas and heat through a number of subsidiaries or partner companies. It mainly operates in the Canton of Bern, but is also present in Italy, Germany. and Austria.
The transition to sustainable energy systems necessitates innovative solutions for managing the increasing complexity of electricity distribution networks. The BKW ProgRés Explorer is a cutting-edge application designed to facilitate stochastic network planning, addressing the challenges posed by the integration of renewable energy sources and the electrification of heat and transport. Accessible to all stakeholders, BKW ProgRés Explorer aims to highlight the pivotal role of distributed system operators (DSOs) in enabling the energy transition. This paper demonstrates how the widespread integration of photovoltaic systems (PVs), electric vehicles (EVs), and heat pumps (HPs) in low-voltage (LV) networks necessitates proactive planning approaches. It explores the complexity of these approaches, evaluating hundreds of scenarios and the measures required to maintain grid stability. The BKW ProgRés Explorer aligns with national energy perspectives, incorporating projections for renewable technology deployment up to 2050 [1]. This foresight is crucial as distribution network expansion costs are estimated to reach CHF 30 billion under base scenarios, with potential costs soaring to CHF 66 billion in adverse conditions [2], ultimately impacting consumer network tariffs and with the risk of slowing down the energy transition.
Condition-based assessment of high-voltage components like hydro generator insulation is crucial for optimizing the lifespan and avoid unexpected outages, but the effectiveness depends on reliable results and cost-efficient implementation. Early detection of insulation degradation in stator bars provides significant benefits, yet no sole diagnostic method will provide full coverage of all types of insulation defects. This study evaluates four commonly used techniques: partial discharge (PD) measurement, loss angle measurement (tan δ), polarization index (PI) measurement, and DC ramp test (DCR), with the main focus on the DCR. Thirteen stator bars with artificial defects—such as moisture, punctures, cracks, and thermal degradation—were analysed. A reference baseline was established using old, defect-free non-service spare bars. The performance of each method was assessed based on sensitivity to specific defects, ease of use, and interpretability of results. Results reveal that combining DCR and PD methods enables the detection of all tested defects. DCR stands out for its simplicity and comprehensive defect information, whereas PD excels in defect origin insights but requires more complex execution and analysis. The study is limited to single stator bars.
In a changing power system with increasing penetrations of distributed energy resources, traditional network tariffs might not be able to meet the underlying requirements. Therefore, it is necessary to assess suitable alternatives. We propose a new two-stage process and evaluation framework to support an informed decision process and test them in a Swiss environment. In the first stage, stakeholder interviews determine the relevant design criteria. In the second step, these are translated into a quantitative evaluation framework. The single indicators are weighted by expert weighting, following the analytic hierarchy process, to arrive at the final ranking. The application in a case study shows that the final ranking of the examined tariff structures depends on expert weighting. It is therefore vital to work on a shared understanding of the importance of the different criteria. Moreover, in a scenario with high shares of distributed energy resources, the volumetric tariff shows the lowest performance independent of expert weighting. This result stresses the importance of adapting network tariffs for a future power system with high penetrations of distributed energy sources. Our open-source evaluation tool can help with an informed and transparent decision process.
Frequency response analysis (FRA), and short- circuit impedance measurements, are considered the state-of-the-art techniques, to discover mechanical deformations to transformer windings [1] . Such deformations may be the result of mechanical shock during transport to site or reduction over time of winding clamping pressure. Mechanical deformations, like "free buckling" [2] of the low voltage transformer winding, may be the result of radial short-circuit forces, acting upon the inner low voltage and the outer high voltage windings during an over-current, short-circuit event as windings tend to be more resilient to tensile, than compressive stress, hence leading to "buckling". The frequency response of a transformer winding is a signature of its distributed network parameters of frequency dependent resistances (R), capacitances (C) and inductances (L), which are unique to a specific transformer’s mechanical structure. By injecting a low voltage signal as a function of frequency into one terminal, whilst measuring the response at another, the transformer FRA signature is obtained as the signal’s voltage ratio in gain (dB) and phase (°). Should an over-current event result in winding displacements, the transformer signature will change. In order to recognize a change in signature, a comparison with previously recorded fingerprint data is required. The signatures can be compared phase-wise or with sister units of similar design, if fingerprint data is not available. Since historical fingerprint FRA data of older units is often not available, the latter case is common engineering field practice. The assessment of the FRA signature is mostly based upon visual comparison of the graphical FRA representation with the FRA reference signature by experienced engineers. Experience shows that in practice, there will always, to some degree, be differences between the actual FRA measurements and the reference FRA data. In the vast majority of cases, these deviations do not indicate winding damage, but rather reflect differences of measurement technique, state or structural features of the transformer. First of all, the frequency range must be considered. The frequency response of a transformer is distinguished by four frequency bands up to 10 MHz, which are broadly speaking dominated by the core (<1 kHz), the inter-winding space (1-10 kHz), the winding structure (10- 1000 kHz), and the leads and earthing method (>1 MHz). Differences in the low frequency range may result from different degrees of residual core magnetization. Close attention to the connection technique is required in order to make reproducible FRA measurement. Differences in the high frequency range may arise from poor or not reproducible connections (i.e. the factory acceptance signatures may be different) or poor grounding, etc. Differences in winding signatures between the phases are normal due to small geometrical differences (i.e. connection leads from windings to terminals). Small structural differences between sister units may result in slightly different FRA signatures. Such structural variations are a natural consequence of updated or improved transformer designs as well as differences in the workmanship during manufacture.
We aim to demonstrate the importance of an optimal network tariff model for future electric energy systems. Due to the lack of available household consumption data and the uncertainty of new actors impact (e.g. electric cars), there are no reliable environments to perform such simulations. We propose a new simulation environment in order to evaluate the impact of different network tariff models in a representative network region. The core of this new simulation environments are advanced machine learning models for household consumption, electric cars charger stations, solar panels productions and the local optimization of batteries behavior at the prosumer level. We propose a new method to evaluate the fairness of the network distribution costs. The presented results demonstrate that existing network tariffs will not be able to fairly distribute the costs and that the penetration of energy storage could put the system at risk if the network tariffs are not correctly adapted for this new environment.
