Large-scale battery storage will become an essential part of the future smart grid. This paper investigates the optimal bidding strategy for battery storage in power markets. Battery storage could increase its profitability by providing fast regulation service under a performance-based regulation mechanism, which better exploits a battery's fast ramping capability. However, battery life might be decreased by frequent charge-discharge cycling, especially when providing fast regulation service. It is profitable for battery storage to extend its service life by limiting its operational strategy to some degree. Thus, we incorporate a battery cycle life model into a profit maximization model to determine the optimal bids in day-ahead energy, spinning reserve, and regulation markets. Then a decomposed online calculation method to compute cycle life under different operational strategies is proposed to reduce the complexity of the model. This novel bidding model would help investor-owned battery storages better decide their bidding and operational schedules and investors to estimate the battery storage's economic viability. The validity of the proposed model is proven by case study results.

In the future power system with high penetration of renewables, renewable energy is expected to undertake part of the responsibility for frequency regulation, just as the conventional generators. Wind power and battery storage are complementary in accuracy and durability when providing frequency regulation. Therefore, it would be profitable to combine wind power and battery storage as a physically connected entity or a virtual power plant to provide both energy and frequency regulation in the markets. This paper proposes a real-time cooperation scheme to exploit their complementary characteristics and an optimal bidding strategy for them in joint energy and regulation markets, considering battery cycle life. The proposed cooperation scheme is adopted in a real-time battery operating simulation and then incorporated into the optimal bidding model. The scheme could improve the wind regulation performance score and allow for more regulation bids without affecting the battery life, thus significantly increasing the overall revenue. The validity of the proposed scheme and strategy are proved by the case study.

Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies. Despite the recent progress in artificial intelligence, anomaly detection methods are not customized for or validated in realistic battery settings due to the complex failure mechanisms and the lack of real-world testing frameworks with large-scale datasets. Here, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems and configured by social and financial factors. We test our detection algorithm on released datasets comprising over 690,000 LiB charging snippets from 347 EVs. Our model overcomes the limitations of state-of-the-art fault detection models, including deep learning ones. Moreover, it reduces the expected direct EV battery fault and inspection costs. Our work highlights the potential of deep learning in improving LiB safety and the significance of social and financial information in designing deep learning models.

Sector coupling via H ₂ could significantly reduce the cost of energy system decarbonization by providing extra flexibility for variable renewable energy integration.

Solar and wind resources are vital for the sustainable energy transition. Although renewable potentials have been widely assessed in existing literature, few studies have examined the statistical characteristics of the inherent renewable uncertainties arising from natural randomness, which is inevitable in stochastic-aware research and applications. Here we develop a rule-of-thumb statistical learning model for wind and solar power prediction and generate a year-long dataset of hourly prediction errors of 30 provinces in China. We reveal diversified spatiotemporal distribution patterns of prediction errors, indicating that over 60% of wind prediction errors and 50% of solar prediction errors arise from scenarios with high utilization rates. The first-order difference and peak ratio of generation series are two primary indicators explaining the uncertainty distribution. Additionally, we analyze the seasonal distributions of the provincial prediction errors that reveal a consistent law in China. Finally, policies including incentive improvements and interprovincial scheduling are suggested.

In addition to energy, a concentrating solar power (CSP) plant with thermal energy storage (TES) could also provide ancillary service (AS) in the reserve and regulation markets. On one hand, providing AS contributes to the flexibility of the power systems and increases the revenue of CSP plants. On the other hand, the flexibility of CSP plants to accommodate solar energy, which is of great uncertainty, might be significantly weakened by an inappropriate offering strategy, e.g., offering excessive AS. Insufficient flexibility might cause massive solar energy curtailment and reduce the potential revenue. This paper develops a general model framework on the optimal offering strategy for CSP plants in joint day-ahead energy, reserve and regulation markets, which is robust for solar energy uncertainty and stochastic for market price uncertainty. On this basis, given the optimal day-ahead offering strategy, the offering curves to provide incremental AS capacities in the supplemental AS markets are further derived considering the opportunity cost. A new index, the maximum acceptable curtailment rate, is introduced to formulate the tradeoff of CSP plants between supplying AS to the system and reserving the flexibility for solar energy accommodation. The case study results demonstrate the validity of the proposed model.