MES (multi-energy systems): An overview of concepts and evaluation models

This paper presents a new method based on the cost-benefit analysis for optimal sizing of an energy storage system in a microgrid (MG). The unit commitment problem with spinning reserve for MG is considered in this method. Time series and feed-forward neural network techniques are used for forecasting the wind speed and solar radiations respectively and the forecasting errors are also considered in this paper. Two mathematical models have been built for both the islanded and grid-connected modes of MGs. The main problem is formulated as a mixed linear integer problem (MLIP), which is solved in AMPL (A Modeling Language for Mathematical Programming). The effectiveness of the approach is validated by case studies where the optimal system energy storage ratings for the islanded and grid-connected MGs are determined. Quantitative results show that the optimal size of BESS exists and differs for both the grid-connected and islanded MGs in this paper.

Sizing of Energy Storage for Microgrids

Spinning reserve (SR) allows system operators to compensate for unpredictable imbalances between load and generation caused by sudden outages of generating units, errors in load forecasting or unexpected deviations by generating units from their production schedules. As the proportion of power produced by wind farms increases, it becomes more difficult to predict accurately the total amount of power injected by all generators into the power system. This added uncertainty must be taken into account when setting the requirement for SR. This paper proposes a technique to calculate the optimal amount of SR that the system operator should provide to be able to respond not only to generation outages but also to errors in the forecasts for load and wind power production. Using a Monte Carlo simulation, the proposed technique for setting the SR requirements is then compared with the traditional deterministic criterion (i.e., the capacity of the largest online infeed), an approach to cope with wind imbalances and an approach that combines the traditional criterion with the approach to cope with wind imbalances. The results show that, contrary to what is commonly believed, an increased wind power penetration does not necessarily require larger amounts of SR.

Estimating the Spinning Reserve Requirements in Systems With Significant Wind Power Generation Penetration

The objective of this paper is to provide a review of distributed control and management strategies for the next generation power system in the context of microgrids. This paper also identifies future research directions. The next generation power system, also referred to as the smart grid, is distinct from the existing power system due to its extensive use of integrated communication, advanced components such as power electronics, sensing, and measurement, and advanced control technologies. At the same time, the need for increased number of small distributed and renewable energy resources can exceed the capabilities of an available computational resource. Therefore, the recent literature has seen a significant research effort on dividing the control task among different units, which gives rise to the development of several distributed techniques. This paper discusses features and characteristics of these techniques, and identifies challenges and opportunities ahead. The paper also discusses the relationship between distributed control and hierarchical control.

Distributed Control Techniques in Microgrids

This paper presents an overview of existing research on wind speed and power forecasting. It first discusses state-of-the-art wind speed and power forecasting approaches. Then, forecasting accuracy is presented based on variable factors. Finally, potential techniques to improve the accuracy of forecasting models are reviewed. A full survey on all existing models is not presented, but attempts to highlight the most promising body of knowledge concerning wind speed and power forecasting.

Current status and future advances for wind speed and power forecasting

Due to increasing wind power penetration, the need for and usage of wind power prediction systems have increased. At the same time, much research has been done in this field, which has led to a significant increase in the prediction accuracy recently. With many ongoing research programs in the field of numerical weather prediction (NWP), as well as in the power output prediction models (transforming wind speed into electrical power output), one can expect further improvements in the future. For the time being, three measures are taken as best practices to reduce prediction errors: Combinations of different models can be done with power output forecast models as well as with NWP models (multimodel and multischeme approaches). Reductions in RMSE of up to 20% were shown with intelligent combinations. As expected, a shorter forecast horizon leads to lower prediction errors. However, the organization of the electricity market as well as the conventional generation pool has a large influence on the needed forecast horizon. The forecast error depends on the number of wind turbines and wind farms and their geographical spread. In Germany, typical forecast errors for representative wind farm forecasts are 10-15% RMSE of installed power, while the error for the control areas calculated from these representative wind farms is typically 6-7% and that for the whole of Germany only 5-6%. Whenever possible, aggregating wind power over a large area should be performed as it leads to significant reduction of forecast errors as well as short-term fluctuations. a large area should be performed as it leads to significant reduction of forecast errors as well as short-term fluctuations.

Predicting the Wind

/pdf/port-of-new-york-and-new-jersey-drayage-electrification-1if4fg91.pdf

Port of New York and New Jersey Drayage Electrification Analysis

This report outlines a holistic view of pathways to a sustainable aviation ecosystem, focusing on low-net-carbon aircraft energy carriers (fuels), airport ecosystems (airports and bases), and developments in sustainable aircraft components (aircraft). Taking this holistic ecosystem perspective, we identify critical components that contribute to sustainable energy solutions necessary to achieve deep decarbonization of the aviation industry, as well as the integrated energy system interfaces that must be comprehensively understood, planned for, and realized. Further, we outline necessary R&D needs to achieve sustainability across the aviation ecosystem, including advancing breakthrough innovations to rapidly achieve scalable solutions, with attention to their cross-sectoral dependencies and implications.

/pdf/a-roadmap-toward-a-sustainable-aviation-ecosystem-pn7wq6i6.pdf

A Roadmap Toward a Sustainable Aviation Ecosystem

Integration of wind-power plants into the electric power system presents challenges to power-system planners and operators. These challenges stem primarily from the natural characteristics of wind plants, which differ in some respects from conventional plants. Wind plants operate when the wind blows, and their power levels vary with the strength of the wind. Hence, they are not dispatchable in the traditional sense, which lessens the ability of system operators to control them while maintaining the system's balance between load and generation.

Accommodating wind's natural behavior

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Predicting the Wind

Port of New York and New Jersey Drayage Electrification Analysis

A Roadmap Toward a Sustainable Aviation Ecosystem

Accommodating wind's natural behavior