Load flexibility management is a promising approach to face the problem of balancing generation and demand in electrical grids. This problem is becoming increasingly difficult due to the variability of renewable energies. Thermostatically-controlled loads can be aggregated and managed by a virtual battery, and they provide a cost-effective and efficient alternative to physical storage systems to mitigate the inherent variability of renewable energy sources. However virtual batteries require that an accurate control system is capable of tracking frequency regulation signals with minimal error. A real-time control system allowing virtual batteries to accurately track frequency or power signals is developed. The performance of this controller is validated for a virtual battery composed of 1000 thermostatically-controlled loads. Using virtual batteries equipped with the developed controller, a study focused on residential thermostatically-controlled loads in Spain is performed. The results of the study quantify the potential of this technology in a country with different climate areas and provides insight about the feasibility of virtual batteries as enablers of electrical systems with high levels of penetration of renewable energy sources.

In recent years, the interest in electric direct current (DC) technologies (such as converters, batteries, and electric vehicles) has increased due to their potential in energy efficiency and sustainability. However, the vast majority of electric systems and networks are based on alternating current (AC) as they also have certain advantages regarding cost-effective transport and robustness. In this paper, an AC/DC optimal power flow method for hybrid microgrids and several key performance indicators (KPIs) for its techno-economic assessment are presented. The combination of both calculations allows users to determine the viability of their hybrid microgrids. AC/DC networks have been modeled considering their most common elements. For the power flow method, polynomial optimization is formulated considering four different objective functions: the minimization of energy losses, voltage deviation, and operational costs and the maximization of the microgrid generation. The power flow method and the techno–economic analysis are implemented in Python and validated in the Centro de Desarrollo de Energías Renovables (CEDER) demonstrator for TIGON. The results show that the calculated power flow variables and those measured at CEDER are practically the same. In addition, the KPIs are obtained and compared for four operating scenarios: baseline, no battery, battery flexibility, and virtual battery (VB) flexibility. The last scenario results in the most profitable option.

The aggregate demand flexibility of a set of residential thermostatically controlled loads (TCLs) can be represented by a virtual battery (VB) in order to manage their participation in the electricity markets. For this purpose, it is necessary to know in advance and with a high level of reliability the maximum power that can be supplied by the aggregation of TCLs. A probability function of the power that can be supplied by a VB is introduced in this paper. This probability function is used to predict the demand flexibility using a rigorous experimental probabilistic method based on a combination of Monte Carlo simulation and extremum search by bisection (MC&ESB) algorithm. As a result, the maximum flexibility power that a VB can provide is obtained. MC&ESB performs the demand flexibility prediction with a given confidence level and taking into account TCLs and users’ real‐time constraints, which is a novel contribution. The performance and validity of the proposed method are demonstrated and discussed in three different case studies where a VB bids its aggregate power in the Spanish electricity balancing markets (SEBMs).

The growing share of variable renewable energy sources in power systems is increasing the need for short-term operational flexibility—particularly from large industrial electricity consumers. This study proposes a practical, two-stage optimization framework to unlock this flexibility in cement manufacturing and support participation in electricity balancing markets. In Stage 1, a mixed-integer linear programming model minimizes electricity procurement costs by optimally scheduling the raw milling subsystem, subject to technical and operational constraints. In Stage 2, a flexibility assessment model identifies and evaluates profitable deviations from this baseline, targeting participation in Spain’s manual Frequency Restoration Reserve market. The methodology is validated through a real-world case study at a Spanish cement plant, incorporating photovoltaic (PV) generation and battery energy storage systems (BESS). The results show that flexibility services can yield monthly revenues of up to €800, with limited disruption to production processes. Additionally, combined PV + BESS configurations achieve electricity cost reductions and investment paybacks as short as six years. The proposed framework offers a replicable pathway for integrating demand-side flexibility into energy-intensive industries—enhancing grid resilience, economic performance, and decarbonization efforts.

The aggregate demand flexibility of a set of thermostatically controlled residential loads (TCLs) can be represented by a virtual battery (VB) in order to manage their participation in the electricity markets. For this purpose, it is necessary to know in advance and with a high level of reliability the maximum power that can be supplied by the aggregation of TCLs. A probability function of the power that can be supplied by a VB is introduced. This probability function is used to predict the demand flexibility using a new experimental probabilistic method based on a combination of Monte Carlo simulation and extremum search by bisection algorithm (MC&ESB). As a result, the maximum flexibility power that a VB can provide with a certain guaranteed probability is obtained. The performance and validity of the proposed method are demonstrated in three different case studies where a VB bids its aggregate power in the Spanish electricity balancing markets (SEBM).