Paula Hernamperez

The growing integration of distributed energy resources and electrification of end users is driving the need for greater system flexibility in modern power grids. Various electrical assets can contribute to this flexibility, either inherently or through external control mechanisms, although their suitability varies even within the same category of assets. This paper presents a novel methodological approach to assess the flexibility potential of electrical assets based on their inherent technical characteristics and their intended installation context. Inspired by the principles of the Smart Readiness Indicator (SRI) for buildings, the proposed method employs a scoring system to evaluate a set of key functionalities that determine an asset’s readiness to contribute to system flexibility, then through a weighted sum a final index is obtained. These scores are combined through a weighted aggregation to produce a single, easy-to-interpret index that synthesizes multiple characteristics, enabling comparisons across different technologies. Unlike the SRI, this approach is not focused on certification but rather on providing a decision-support tool for end-users. The applicability of the method is demonstrated through a case study evaluating a photovoltaic inverter, followed by a sensitivity analysis to assess the robustness of the weighting scheme. Results indicate that the proposed index provides a transparent and replicable means of quantifying flexibility potential, supporting more informed planning and investment decisions.

In portfolio theory, the investment portfolio optimization problem is one of those problems whose complexity grows exponentially with the number of assets. By backtesting classical and quantum computing algorithms, we can get a sense of how these algorithms might perform in the real world. This work establishes a methodology for backtesting classical and quantum algorithms in equivalent conditions, and uses it to explore four quantum and three classical computing algorithms for portfolio optimization and compares the results. Running 10000 experiments on equivalent conditions we find that quantum can match or slightly outperform classical results, showing a better escalability trend. To the best of our knowledge, this is the first work that performs a systematic backtesting comparison of classical and quantum portfolio optimization algorithms. In this work we also analyze in more detail the Variational Quantum Eigensolver algorithm, applied to solve the portfolio optimization problem, running on simulators and real quantum computers from IBM. The benefits and drawbacks of backtesting are discussed, as well as some of the challenges involved in using real quantum computers of more than 100 qubits. Results show quantum algorithms can be competitive with classical ones, with the advantage of being able to handle a large number of assets in a reasonable time on a future larger quantum computer.