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Low temperature (LT) heat sources (HS) below 100 °Ccontain high amounts of renewable energy available in the form of air, water, and ground. If leveraged properly, these heat sourcescan be utilized through large-scale heat pumps and other conversion techniques to supply district heating networks and assist in the energy transition. Therefore, all local municipal heat sources need to be assessed for their potential and availability. Individually assessing each heat source, including air (ambient and exhaust), water (rivers, lakes, oceans, wastewater), and soil (surface and deep geothermal) will contribute to more informed decision-making for municipal energy planners. In this regard, this paper suggests an indicator-based heat source evaluation method using open-source data to analyze potential and availability of local renewable heat sources to support large-scale heat pump implementation into district heating in Germany. The authors present a five-step method that introduces a comprehensive indicator-based procedure: (i) identify heat sources, (ii) evaluate the heat source availability and potential based on technical, economical, ecological and regulatory indicators including an assessment of the quantitative data condition and relevance, (iii) approximate the cost of heat source extraction and supply, (iv) compare the potentials and costs and (v) give recommendations to municipal heat planners. The method is demonstrated in a case study, covering the German city of Fellbach. Following this, insights into promising heat sources are discussed to increase the application and understanding for decision-makers, as well as the risks involved with the temporal uncertainty of heat supply. This article serves as guidance to understand the evaluation of low-temperature renewable heat sources based on a step-by-step indicator calculation.

In optimizing performance and extending the lifespan of lithium batteries, accurate state prediction is pivotal. Traditional regression and classification methods have achieved some success in battery state prediction. However, the efficacy of these data-driven approaches heavily relies on the availability and quality of public datasets. Additionally, generating electrochemical data predominantly through battery experiments is a lengthy and costly process, making it challenging to acquire high-quality electrochemical data. This difficulty, coupled with data incompleteness, significantly impacts prediction accuracy. Addressing these challenges, this study introduces the End of Life (EOL) and Equivalent Cycle Life (ECL) as conditions for generative AI models. By integrating an embedding layer into the CVAE model, we developed the Refined Conditional Variational Autoencoder (RCVAE). Through preprocessing data into a quasi-video format, our study achieves an integrated synthesis of electrochemical data, including voltage, current, temperature, and charging capacity, which is then processed by the RCVAE model. Coupled with customized training and inference algorithms, this model can generate specific electrochemical data for EOL and ECL under supervised conditions. This method provides users with a comprehensive electrochemical dataset, pioneering a new research domain for the artificial synthesis of lithium battery data. Furthermore, based on the detailed synthetic data, various battery state indicators can be calculated, offering new perspectives and possibilities for lithium battery performance prediction.

With California's ambitious goal to achieve decarbonization of the electrical grid by the year 2045, significant challenges arise in power system investment planning. Existing modeling methods and software focus on computational efficiency, which is currently achieved by simplifying the associated unit commitment formulation. This may lead to unjustifiable inaccuracies in the cost and constraints of gas-fired generation operations, and may affect both the timing and the extent of investment in new resources, such as renewable energy and energy storage. To address this issue, this paper develops a more detailed and rigorous mixed-integer model, and more importantly, a solution methodology utilizing surrogate level-based Lagrangian relaxation to overcome the combinatorial complexity that results from the enhanced level of model detail. This allows us to optimize a model with approximately 12 million binary and 100 million total variables in under 48 hours. The investment plan is compared with those produced by E3's RESOLVE software, which is currently employed by the California Energy Commission and California Public Utilities Commission. Our model produces an investment plan that differs substantially from that of the existing method and saves California over 12 billion dollars over the investment horizon.