The recent deployment of distributed battery units in prosumer premises offer new opportunities for providing aggregated flexibility services to both distribution system operators and balance responsible parties. The optimization problem presented in this paper is formulated with an objective of cost minimization which includes energy and battery degradation cost to provide flexibility services. A decomposed solution approach with the alternating direction method of multipliers (ADMM) is used instead of commonly adopted centralised optimization to reduce the computational burden and time, and then reduce scalability limitations. In this work we apply a modified version of ADMM that includes two new features with respect to the original algorithm: first, the primal variables are updated concurrently, which reduces significantly the computational cost when we have a large number of involved prosumers; second, it includes a regularization term named Proximal Jacobian (PJ) that ensures the stability of the solution. A case study is presented for optimal battery operation of 100 prosumer sites with real-life data. The proposed method finds a solution which is equivalent to the centralised optimization problem and is computed between 5 and 12 times faster. Thus, aggregators or large-scale energy communities can use this scalable algorithm to provide flexibility services.

This article proposes a multiobjective hybrid energy management system that minimizes both the electricity expenses and the household greenhouse gas emissions released due to consumption, considering the entire life cycle of the generation assets used to provide energy. The global warming potential indicator is used to decide if it is more sustainable to purchase electricity from the grid or use the household's flexible generation sources like photovoltaic panels and the energy storage system. Results prove that it is possible to reduce greenhouse gas emissions without incurring expensive electricity bill costs thanks to the hybrid-based home energy management system approach. This method gives the end-users a more influential role in the climate change solution, allowing them to give more or less importance to the economic or environmental component, according to their preferences.

Accurate day-ahead demand forecasting is crucial for optimizing the performance of home energy management systems. Traditional forecasting methods often decouple the forecasting task and the subsequent decision marking, resulting in imbalanced economic penalties from load deviations. Furthermore, the rise of digitization has led to a massive increase in fine-grained smart meter data stored daily, posing significant challenges to customers' data privacy and security. To address these technical challenges, this study proposes a personalized federated learning methodology that incorporates a cost-oriented loss function. This methodology is designed to learn end-user-specific patterns, reduce penalization costs, and preserve customer privacy. Comparative analyses reveal that the proposed method, which utilizes a cost-oriented loss function and $L^{2}$ regularization, outperforms traditional symmetric loss functions in terms of efficiency and economic benefits. The results confirm that this personalized federated learning approach consistently achieves the lowest error rates and penalization costs compared to other methods. Additionally, sensitivity analyses indicate that even households with limited historical consumption data can achieve accurate load predictions using the personalized federated learning approach.

Electricity-intensive industries are highly impacted by electricity prices and their fluctuations. While these industries can change their consumption profiles to avoid peak price periods, they must also adhere to production schedules dictated by their unique manufacturing processes and related constraints. Balance Responsible Parties (BRPs), seek to optimize the electricity costs of their portfolio of customers and have limited access to real-time industrial flexibility. So, the concept of Daily Industrial Flexibility (DIF) is introduced as a trade-off between real-time flexibility and predetermined industrial schedules. Each factory can propose different fixed day-ahead consumption profiles to the BRP, each with a specific activation cost. In this context, this paper proposes a day-ahead flexibility service to minimize the BRP’s costs of electricity purchase on the day-ahead market, activating DIFs within the portfolio of industrial customers. A forecasting model for the electricity price is implemented and an optimization model is formulated to minimize day-ahead portfolio costs. Finally, the case study of a Catalan BRP is presented, considering DIF offers from its largest customers. Results demonstrate that the proposed approach enables accurate predictions of the BRP’s customers operation before DAM closure, successfully identifying alternative schedules to reduce portfolio costs.