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In this paper five different flexibility options are analysed from a techno-economic perspective as alternatives to traditional grid expansion for a specific distribution grid in Germany. The options are: two reactive power control strategies with photovoltaic inverters (as a function of the power feed-in, or of the voltage at the connection point), one residential and two large scale battery storage applications (primary control reserve with autonomous reactive power control or self consumption maximisation strategy with autonomous reactive power control). For the pilot grid located in Southern Germany a photovoltaic expansion pathway is determined. The main goal of this work is to quantify the grid expansion actions that can be avoided by applying these five flexibility options for the assumed expansion pathway, focusing on large scale battery storages. It is shown that the five flexibility options increase the hosting capacity for PV systems, compared to a scenario without, by up to 45%. Furthermore, the results of the economic assessment indicate that the analysed flexibility options might be a viable alternative to traditional grid expansion as all of them show a cost reduction potential for the pilot region. These results could encourage DSOs to consider the integration of additional PV and battery storage systems not as a problem which triggers grid expansion, but as part of the solution reducing future grid expansion costs. [ABSTRACT FROM AUTHOR]

The decentralized feed-ins from distributed energy resources (DER) represent a significant change in the manner in which the power grid is used. If this leads to high loads on electrical equipment, its aging can be accelerated. This applies in particular with regard to the thermal aging of older generations of power cables, namely paper insulated lead covered (PILC) cables. This type of power cable can still be found frequently in medium voltage (MV) networks. If aging of these cables is significantly accelerated in the presence of DER, distribution system operators (DSO) could face unplanned premature cable failures and a high replacement demand and costs. Therefore, this paper investigates the thermal aging of PILC cables in a MV distribution network benchmark for different load scenarios, using standardized load profiles and representative expansion scenarios for wind power and photovoltaics plants in particularly affected network areas in Germany. A main objective of this paper is to present a methodology for estimating the thermal degradation of PILC cables. An approach is used to draw simplified conclusions from the loading of cables to their conductor or insulation temperature. For this purpose, mainly Joule losses are considered. In addition, thermal time constants are used for the heating and cooling processes. Based on the insulation temperature, thermal aging is determined using the Arrhenius law or the Montsinger rule. However, it is important to note that there is an urgent need for research on reference data in this area. For this reason, the results of the lifetime estimation presented in this paper should only be considered as an approximation if the selected reference data from the literature for the aging model are actually applicable. The lifetime assessment is performed for a highly utilized line segment of the network benchmark. Accordingly, extreme values are examined. Different operational control strategies of DSO to limit cable utilization are investigated. The results show that the expansion of DER can lead to a short but high cable utilization, although the average utilization does not increase or increases only slightly. This can lead to significantly lower cable lifetimes. The possible influence of these temporarily high loads is shown by comparing the resulting cable lifetime with previous situations without DER. It is also shown that DSO could already reduce excessive aging of PILC cables by preventing overloads in a few hours of a year. In addition to these specific results, general findings on the network load due to the influence of DER are obtained, which are of interest for congestion management. [ABSTRACT FROM AUTHOR]

In the context of climate change mitigation and sustainable energy infrastructure development, this paper focuses on the successful implementation of microgrids supplied by renewable energies in very diverse environments. Taking into account the challenges of reaching economies of scale, this paper is targeted at identifying success factors for microgrid implementation. The success factors are derived from case study analyses of microgrids implemented in the Global South and in communities of Germany. The goal of the research is to develop a mutual understanding of common values and so support future developments. The analysis covers the categories of ownership and participation; technology and system design; and policy and financing. The results show that microgrids in the Global South and renewable-energy-based communities in Germany share a number of success factors. The results demonstrate that a high share of ownership by users and the flexibility to expand the microgrid with user needs are particularly promising features. To verify the application of the identified success factors, the latter are applied to analyze the microgrid concept of “swarm electrification”. The analysis concludes that the concept of swarm electrification is consistent with the success factors, making it a high-potential approach for renewable-energy-based electrification. [ABSTRACT FROM AUTHOR]

The intermittent and stochastic nature of solar energy generation systems, climate change, and the inefficiency of modern power systems due to zero inertia have created many challenges for on-grid operators. Solar forecasting systems based on machine learning algorithms are an emerging and effective solution that uses Big Data related to weather phenomena. However, the predictive ability of these algorithms is hampered by the sporadic nature of solar energy generation. In this article, a robust hybrid machine learning system that utilizes multiple linear regression (MLR) and a Pearson correlation coefficient (PCC) was tested on solar power plant sites of varying capacities in Germany (100–8500 kW). The volume of Big Data features can be reduced by focusing on the features that significantly improve the reliability of the mid-term forecasting system. In this way, drastic fluctuations in the prediction of photovoltaic (PV) power generation can be avoided. The results of our approach are evaluated regarding real-world data using the extreme gradient boosting (XGBoost) with feature engineering, and principal component analysis (PCA), in order to forecast PV energy, rank, and track the importance of feature engineering for different PV capacities. Furthermore, we found that the need for selectivity and reduction of performance error was supported by ridge regression. In addition, the proposed novel XGBoost forecast system decreased the root-mean-square error (RMSE) and mean absolute error (MAE) by 30% and 18%, respectively, compared to the Autoencoder and long short-term memory (LSTM) for same datasets. Furthermore, the CoD determination coefficient ($R^{2}$) increased by 85% compared to the statistical model's autoregressive integrated moving average (ARIMA). [ABSTRACT FROM AUTHOR]