Konflikte um Erneuerbare-Energien-Anlagen im kommunalen Bereich

Given the key role of municipalities in the transformation of the energy system and observing the increased occurrence of conflicts about the construction of renewable energy plants, the author analyses conflicts relating to renewable energy plants from a sociological perspective. For this purpose, she undertakes three case studies on the construction of biogas and wind power plants, focusing in particular on the parties involved in the conflicts and their positions, perceptions, actions and potency. She shows that the conflicting parties were either in favour of the construction of the plants or advocated the preservation of the sites on which the plants were proposed to be built.

Characterizing power transformer frequency responses using bipolar pseudo-random current impulses

The behaviour of a power transformer is complex and difficult to predict during transient conditions or during operation at frequencies below or above its nominal frequency, a phenomenon common in renewable energy plants due to harmonic distortion. Furthermore, the accuracy of a power system simulation depends on the models of critical subsystems such as the power transformers. This paper presents the use of a unique excitation waveform comprising of pseudo-random current impulses to accurately identify the wideband characteristics of a power transformer. By injecting the excitation waveform to the relevant transformer terminals, frequency responses are determined by cross-correlation of the perturbation signal, and the measured response. Compared to the traditional transformer identification methods, the pseudo-random current impulses offer a wideband excitation with a higher degree of controllability such that its spectral energy can be focused in the frequency band of interest. The proposed method was investigated on a 16 kVA, 22 kV/240 V single-phase transformer. The obtained wideband frequency responses provide useful information in harmonic penetration and over-voltage studies and are also used to estimate, with a high degree of accuracy, the lumped parameters of the equivalent transformer broadband circuit model.

The Impact of Electricity Price on Power-Generation Structure: Evidence From China

Being affected by a variety of factors, power-generation structure plays an essential role in a high-quality and sustainable development. The focus of this paper is to evaluate the influence of electricity price on it. First, we provide a microeconomic framework to understand the impact mechanism. We discuss two effects through which price level can affect power generation, and then the power-generation structure. After that, an empirical test is conducted using provincial panel data, and the results of it are robust. We also test the above-mentioned mechanism empirically. There are two main conclusions. First, the electricity price has a positive effect on the share of thermal power in electricity generation. Second, the mechanism test shows that an increase of electricity price can not only improve efficiency of power plants but also propel firms to invest in more renewable energy plants.

Renewable Energy Plants and Business Models: A New Rural Development Perspective

The paper evaluates the rural development (RD) contribution of local economic activities (LEAs), whether generated or affected by the proximity of renewable energy plants (REPs). The study also informs about LEAs’ role as co-players in the fight against climate change. Semi-structured research interviews have been applied to identify LEAs’ BM (business model) in Andalusia, Murcia, and Catalonia, autonomous communities of Spain. Most LEAs present a BM based on the RE plant, and others do not, but they still contribute to RD, rural communities’ well-being and global sustainability. Results show, first, that certain LEAs, due to their inter-connection with large REPs, can innovate and create a significant number of stable jobs. Second, land leasing to REPs allows for temporary farms’ diversification, which is conditioned to its bargaining power. Third, advice on integration RE projects in RD strategies should be provided. Conclusions suggest the need for new governance to favor energy transition coherent with the Sustainable Development Goals (SDGs).

Hydrogen research: technology first, society second?

Hydrogen futures are in the making right in front of our eyes and will determine socio-ecological path dependencies for decades to come. However, expertise on the societal effects of the hydrogen transition is in its infancy. Future energy research needs to include the social sciences, humanities and interdisciplinary studies: energy cultures have to be examined as well as power relations and anticipation processes since the need for (green) hydrogen is likely to require a massive expansion of renewable energy plants.

Simulation of a microgrid for a non-interconnected zone that integrates renewable energies

This paper develops a simulation of a small electrical network (Microgid) that integrates renewable energies, the model of the micro network is made up of a solar energy source, a wind energy source, an energy storage element, a non-renewable source such as a diesel generator. The model of the microgrid represent a non-interconnected area from the electrical network in Colombia. The non-interconnected areas sometimes depend on unreliable connections to the grid integration of renewable energies could be the best option to guarantee energy in these sectors and allow generating projects with social impact. A possible solution to this deficit of energy is to supplement the production of energy with renewable energy plants from resources as sun or wind. The simulated model allowed to study the effects of the network in island mode and in interconnected mode, showing the imbalances that can be obtained by integrating renewable energies and storage systems. It is verified that with an inclusion of more than 30% of power in renewable energies there is the possibility of having load imbalances, which affect the frequency and cause instability in the network. It also verifies how a control system can regulate the load balance but must interact with the other energy sources.

Solar power generation forecasting using ensemble approach based on deep learning and statistical methods

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.