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This paper presents grid-code considerations that must be addressed when connecting large electrolyzer plants to the electrical grid. A model for an electrolyzer module based on a grid-connected three-phase H-bridge converter is presented, together with a scaling method, for investigating relevant aspects. A low-voltage-ride-through strategy is implemented into the converter and the performance of the strategy is tested against running the system with no low-voltage-ride-through strategy. Finally, the paper will conclude on the ability of the electrolyzer plant to provide reactive power support and the considerations that must be made if this support is desired.

Precise forecasting of thermal loads is a critical factor for economic and efficient operation of district heating and cooling networks. If thermal loads are known with high accuracy in advance, use of renewable energies can be maximized, and – in combination with thermal storage units – fossil generation, in particular in peaking units, can be avoided. Machine learning has proven to be a powerful tool for time series forecasting, and has demonstrated significant advancements in recent years. This paper presents the scientific methodology and first results of the publicly funded research project “deepDHC”, which aims at a broad benchmarking of traditional and advanced machine learning methods for thermal load forecasting in district heating and cooling applications. The analysis covers autoregressive forecasting approaches, decision trees such as “adaptive boosting”, but also latest “deep learning” techniques such as the “long short-term memory” (LSTM) neural network. This work is based on data from the district heating network of the city of Ulm in Germany. First, different performance metrics for evaluating forecasting qualities are introduced. Second, approaches for data screening and results of a linear and non-linear correlation analysis are presented. Third, the machine learning tuning process is described. For thermal load forecasting, weather data are key input parameters. This work uses hourly weather forecasts from weather models provided by the German meteorological service. These weather data are updated automatically, and have been statistically corrected in order to represent very accurate point forecasts for up to ten days ahead. In addition, a user-friendly web interface has been developed for use by the district heating network operator. The performance of different machine-learning algorithms is compared based on 72 h heating load forecasts.

Solution of environmental and climate issues remains relevant today. Since the signing of the Paris Agreements in 2015 many countries have begun to change legislative provisions and introduce new regulations aimed at reducing greenhouse gas emissions and reducing the environmental load on the environment. The authors pay special attention to the problem of disposal of municipal solid waste (MSW) and consideration of this process as a climate project. The aim of the study is to develop an economic model of MSW utilization and assess the economic effect of applying the mechanism of public–private partnership (PPP) on the example of the Novosibirsk region. The research method is a comprehensive simulation modeling with the use of assessment of investment projects’ efficiency. The model takes into account the possibility of providing containers for separate waste collection (SWC-containers) to all residents of Novosibirsk and the expansion of facilities for the collection and sorting of MSW. It is shown that the state support in the form of public–private partnership is crucial. Participation of the state in the project implementation contributes to provision of the city with a sufficient number of infrastructure facilities related to accumulation and utilization of MSW to reduce landfills and greenhouse gas emissions from them. The authors proposed to enhance the positive effect of the climate project by conducting an environmental marathon for the population. The expected results of the marathon were confirmed in practice.

AbstractConventional generation units encounter a changing role in modern societies’ energy supply. With increased need for flexible operation, engineers and project managers have to evaluate the benefits of technical improvements. For this purpose, a valuation tool has been developed, comparing economical cornerstones and technical constraints of generation units to European Energy Exchange prices for PHELIX 2014. It enables the user to relate a change in technical parameters to an economic effect and possible revenues. Four different types of conventional power plants are investigated in scenarios with increasing CO2 and fuel prices to determine the impact of different flexibility options. Results show that an increased ramp rate has not the same magnitude of positive economic impact as reduced minimum operation load, based on an observation on a price signal with resolution of fifteen minutes.

The idea of exporting dispatchable solar electricity from the North African region to Europe is still the subject of several bilateral meetings, workshops, and conferences, despite doubts and previous failures with the Desertec concept. Given that each North African country has its own privacy policy, energy strategy, and distinct approach to the concept of renewable energy exports, while the potential of renewable energy sources varies from country to country, it would be very helpful to assess the potential and export opportunities of each country. In this respect, this paper focuses on the potential of Algeria. Several key factors that can make Algeria an attractive place to supply Europe with dispatchable solar electricity have been identified and discussed. The factors have been identified from the European perspective and can be used to analyze the potentials of neighboring countries. The barriers that could impede the export of dispatchable solar electricity from Algeria to Europe have also been identified. Based on certain given criteria, the paper provides some scenarios of possible solar electricity import corridors from Algeria to Europe. This work is timely as the Algerian government has expressed interest in exporting its surplus electricity to Europe and is studying an electricity interconnection project with Italy.

In the European renewable energy portfolio, wind has a sizeable share in the total energy production. The Nordic and Baltic energy systems in particular are benefiting from wind energy to reach the greenhouse gas emissions reduction objectives set by the EU. The wind energy production varies with time, and this intermittent characteristic imposes a challenge for full utilization of renewable energy potential. The power system operator needs to ensure timely power supply of demand. An accurate estimation of power output from a non-dispatchable generation resource such as a wind farm is essential for the operator to ensure the supply–demand balance and adequate sizing of reserve power capacity. Existing methods of feature extraction and prediction such as linear regression often overlook the significant variations or do not utilize in the model building. However, this method misinterprets the trend in data. Understanding the properties of the variations in more details would reduce the uncertainty and significantly improve the feature extraction to aid in decision making. Furthermore, as the volume, shape and type of dataset start to increase and new methods are required to extract meaningful information from the patterns in the big data. The objective of the paper is to present a novel Ramping BehaviourAnalysis (RBAθ) model that identifies and quantifies the variations in a time-varying dataset. The variations are classified into significant and stationary events. The former refers to the significant swings beyond a set threshold range and the latter refers to the swings that are relatively within the threshold limits. The features associated to each event include start time, end time, change in magnitude, persistence of an event, angle at which the event took place and frequency of occurrences of the features. In addition, the rain-flow cycles count is extracted from the original data for each event as a sum of half cycles and full cycles. The model is validated using simulated wind power production data from a virtual wind park spread across Estonia and the results are elaborated. The spatial dynamics of the virtual windfarm are captured through localized spatial autocorrelation of the events with the geospatial locations of the turbines. The results demonstrate that RBAθprecisely and accurately identify and quantify the time varying power generation into events with subsequent features. The volume of the data is significantly reduced in the process of summarizing time series data into a series of events. Thereby RBAθcan be also used for data compression and reconstruction with minor losses. The system operators can use the proposed algorithm in operational scheduling, maintenance and investment-capacity building decisions.