Energy Informatics最新文献

Battery-based electric vehicles (BEVs) in the United States (U.S.) set a new sales record in 2022, driven by technology, policy, environmental, and economic objectives. However, the rapid deployment of BEVs and charging infrastructure without a careful review of their integration with the electric grid can have negative economic impacts on reliable and resilient electricity supply. Bi-directional power transfer (Bi-Di) vehicle-grid integration technologies and services such as vehicle-to-home or building (V2H/B) and vehicle-to-grid (V2G) can potentially lower local and system peak demand, improve economics for grid operators, and benefit BEV customers. Original equipment manufacturers (OEMs) in the automotive industry are exploring technologies and economics (techno-economics) for Bi-Di services. The study conducted a literature review of eleven case studies in the U.S. and Europe that featured Bi-Di demonstrations from 2005 to 2022 to highlight insights and techno-economic opportunities and challenges for OEMs. The findings should motivate the OEMs to prioritize technology innovation and business models to increase BEV sales and gain continuous revenue from Bi-Di services, which can potentially transition "car makers" to "technology solution" companies.

Unified power quality conditioner is chiefly employed to offer power quality improvement, especially in grid connected mode of operation in microgrid applications. This article proposes an artificial ecosystem optimized neural network for control of photovoltaic system and battery powered UPQC for microgrid applications. The intelligent routine implemented by the proposed controller helps tune parameters such as the error between load voltage references and measured load voltage signals so that the optimal performance of the system can be reached as its exploratory and exploitation capabilities are leveraged in controller design. A prototype of a three-phase system with a dually powered conditioner is tested and validated in MATLAB-Simulink environment in a variety of dynamic scenarios that are commonly present in a contemporary distribution network, such as grid voltage changes, grid inaccessibility, variation in photovoltaic power output, and nonlinear load. It is shown that the proposed controller, being aware of the instantaneous values of grid voltages, was able to adequately compensate in magnitude and phase under all dynamic scenarios to maintain the load voltage constant at the nominal value and sinusoidal. When the system switches automatically from grid-connected mode to islanded mode due to a grid fault, it was observed that the controller prioritizes delivering uninterrupted power to critical loads and enables fast discharge from the battery. The total harmonic distortion percentages of grid currents and load voltages are found to be within the limits as per IEEE-519 standards.

Renewable energy systems depend on the weather, and weather information, thus, plays a crucial role in forecasting time series within such renewable energy systems. However, while weather data are commonly used to improve forecast accuracy, it still has to be determined in which input shape this weather data benefits the forecasting models the most. In the present paper, we investigate how transformations for weather data inputs, i. e., station-based and grid-based weather data, influence the accuracy of energy time series forecasts. The selected weather data transformations are based on statistical features, dimensionality reduction, clustering, autoencoders, and interpolation. We evaluate the performance of these weather data transformations when forecasting three energy time series: electrical demand, solar power, and wind power. Additionally, we compare the best-performing weather data transformations for station-based and grid-based weather data. We show that transforming station-based or grid-based weather data improves the forecast accuracy compared to using the raw weather data between 3.7 and 5.2%, depending on the target energy time series, where statistical and dimensionality reduction data transformations are among the best.

The production of green hydrogen and its scale-up require the enginering and installation of new electrolysis plants. Modular electrolysis plants ease the scale-up as they allow to add further modules with growing demand. While many engineering methods focus on the detailed planning of the plants and their automation systems, the early engineering phases are scarcely considered, supported or formalized. However, especially these phases are crucial in the current scale-up of modular electrolysis plants. In this paper, an intention-based engineering approach for the early engineering phases Requirements Engineering and Conceptual Engineering for modular electrolysis plants is presented and evaluated based on three different use cases. The approach is based on Goal-oriented Requirements Engineering from Software Engineering and relies on an early, systematic as well as formalized description and analysis of intentions of different engineering disciplines.

The transition to green energy systems is vital for addressing climate change, with a focus on renewable sources like wind and solar. This change requires substantial investment, societal adaptations, and managing a complex energy ecosystem. However, no existing evaluation methods support this purpose. The "energy metaverse" is proposed as a digital platform that mirrors the energy ecosystem, enabling the design, trial, and assessment of new technologies, business models, and value chains before real-world deployment. Drawing from State-of-the-Art technologies and methodologies, this paper introduces a conceptual framework for the energy metaverse, comprising five essential components: a versatile energy ecosystem data space, an interoperable virtual ecosystem living lab, an energy system models and artificial intelligent algorithms sandbox, a circular value chain co-design toolbox, and an ecosystem lifecycle evaluation software tool. This paper also suggests specific methods and technologies to develop each of these five components of the energy metaverse.

Information and Communication Technology (ICT) is vital for the operation of modern power systems, giving rise to Cyber-Physical Energy Systems (CPESs). ICT enables the grid services (GSs) needed for monitoring and controlling the physical parameters of the power system, especially for remedying the impact of disturbances. But the ICT integration makes the overall system more complex, leading to new and unforeseen disturbances. This motivates the need for a resilient system design capable of absorbing and recovering from such disturbances. The current state of the art lacks a comprehensive resilience assessment of ICT-enabled GSs in CPESs. To address this, a novel method and metrics to assess the resilience of GSs in CPESs are presented in this paper. An operational state model of a GS, with three states, i.e., normal, limited and failed, is used to capture its performance, which is essential for quantifying its resilience. Sequential Monte Carlo simulations are performed with the model to capture the behaviour of ICT components to compute the operational state trajectory of the GSs. Metrics are then derived to quantify the resilience and its constituting phases. The method is demonstrated using two ICT system designs for the CIGRE MV benchmark grid, considering the state estimation as an exemplary GS. The simulation results show that the proposed method can capture the differences between ICT system designs with regard to resilience metrics. The contribution can, therefore, be used to analyse, compare and potentially improve the resilience of ICT system designs for CPES.

In the smart grid of the future, accurate load forecasts on the level of individual clients can help to balance supply and demand locally and to prevent grid outages. While the number of monitored clients will increase with the ongoing smart meter rollout, the amount of data per client will always be limited. We evaluate whether a Transformer load forecasting model benefits from a transfer learning strategy, where a global univariate model is trained on the load time series from multiple clients. In experiments with two datasets containing load time series from several hundred clients, we find that the global training strategy is superior to the multivariate and local training strategies used in related work. On average, the global training strategy results in 21.8% and 12.8% lower forecasting errors than the two other strategies, measured across forecasting horizons from one day to one month into the future. A comparison to linear models, multi-layer perceptrons and LSTMs shows that Transformers are effective for load forecasting when they are trained with the global training strategy.

Flexibility potentials are mostly provided by centrally coordinated flexibility resources such as natural-gas-fired power plants. However, the decentralization of power generation combined with the decarbonization of the sector due to the energy transition requires the exploration of new types of flexibility resources. In particular, to reduce dependence on natural-gas-fired power plants, it would be desirable to replace them with alternative flexibility resources. Therefore, the objective of this paper is to analyze, using Germany as an example, to what extent the flexibility potential of already existing battery storage systems can replace the flexibility potential of natural-gas-fired power plants. The methodology used is based on a multi-time-scale flexibility model together with an approach for temporal aggregation of flexibility potentials and two approaches for spatial aggregation of flexibility potentials. Based on the methodology and an analysis of publicly available data, a comprehensive potential analysis is carried out. This analysis shows, among others, that existing battery storage systems have a promising potential to replace a considerable number of natural-gas-fired power plants in Germany in terms of their flexibility potentials.

Bidirectional charging allows energy from the electric vehicles (EV) to be fed back into the grid, offering the possibility of price-optimized charging. However, such strategies cause higher charging cycles, which affect the cyclic aging of the battery and reduce its service life, resulting in additional costs for the user. Various approaches are used to account for battery degradation in optimizations models of bidirectional charging use-cases. In this paper, a systematic literature review is carried out to identify existing battery degradation models and to determine the most suitable one. In the models under review, degradation is integrated into the optimization’s objective function. The review shows that there are mainly two strategies suitable for vehicle-to-grid (V2G) optimization problems: A weighted Ah-throughput model (wAh-model) with a constant degradation cost factor and a performance based model (pb-model) linking the degradation to measurable parameters such as capacity loss. Both models were implemented and analyzed. The results show that the wAh-model is the better optimization option, as in the pb-model the current state of health of the battery has an excessively large impact on the calculated degradation cost. It leads to excess costs due to a higher aging rate at the beginning of life which proves to be not ideal in the optimization. The sensitivity analysis reveals that altering the initial State of Health (SoH) from 95 % in the base scenario to 100 % leads to an increase in average degradation costs by factor 9.71 in the pb-model. From the evaluated base scenario the average degradation costs for the pb-model are 0.45 cent/kWh and for the wAh-model 0.23 cent/kWh.