Device-independent metrics for explicit, a priori measurement of energy flexibility

Given the unpredictable nature of renewable energy generation, flexibility is an aspect of key importance. Specifically, being able to numerically quantify flexibility is an important topic; however, explicit, a priori measurement of flexibility objects is mostly an unexplored field, despite it being valuable for assessing the value of flexibility, e.g., for trading. A proposal of such metrics was done for FlexOffers (FO), a specific device-independent, scalable and accurate flexibility model; however, those metrics have several serious shortcomings. This paper i) examines those metrics and their shortcomings, ii) extends those metrics for more advanced FO types, iii) proposes new metrics that improve the existing ones, iv) determines mathematical properties that flexibility metrics should have, and v) compares the proposed metrics with respect to those properties, and to their correlation to an existing, a posteriori metric: economic assessment. We show that different metrics support different properties, and only one supports all of them. We also measure how well the metrics can predict two economic indicators, without any prior knowledge on prices: energy consumption price (i.e., economic cost), and the difference between maximum and minimum consumption price (i.e., risk). This helps aggregators to quickly estimate value and risk of FOs on the energy market. Regarding heat pumps, some metrics have a very high correlation (above 99.6%) with the maximum-minimum difference, and have moderate correlation (almost 48%) with respect to consumption price; however, for EVs, the correlation between the metrics and cost/risk is much lower. Therefore, our proposed metrics are better predictors for cost and risk for devices without time flexibility, such as heat pumps, compared to EVs.