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In this paper, the reliability of cyber-physical energy hubs (CPEHs) is evaluated based on the Monte Carlo simulation (MCS) method. Previous works in the related literature are generally focused on the physical layer of energy hubs (EHs). However, the security of EHs heavily relies on the reliable and safe performance of the cyber system. Therefore, this study considers the failure of cyber-physical components, data packet errors, and information transmission delay to improve the reliability assessment accuracy. The physical layer of the proposed CPEH is divided into two sections, namely heat and power sections, and reliability indices are calculated for both sections. Various scenarios are suggested to precisely analyze the effects of the cyber layer consideration on the CPEH reliability results. Sensitivity analysis on the failure rate of the components is also conducted to understand better the performance of the CPEH in situations with a higher risk of heat components’ failure. The notable impacts of the cyber layer disturbances on the CPEH reliability evaluation are demonstrated through the simulation results.

The increasing share of renewables in the electricity generation mix comes along with an increasing uncertainty in power supply. In the recent years, distributionally robust optimization has gained significant interest due to its ability to make informed decisions under uncertainty, which are robust to misrepresentations of the distributional information (e.g., from probabilistic forecasts). This is achieved by introducing an ambiguity set that describes the potential deviations from an empirical distribution of all uncertain parameters. However, this set typically overlooks the inherent dependencies of uncertainty, e.g., spatial dependencies of weather-dependent energy sources. This paper goes beyond the state-of-the-art models by embedding such dependencies within the definition of ambiguity set. In particular, we propose a new copula-based ambiguity set which is tailored to capture any type of dependencies. The resulting problem is reformulated as a conic program which is kept generic such that it can be applied to any decision-making problem under uncertainty in power systems. Given the Optimal Power Flow (OPF) problem as one of the main potential applications, we illustrate the performance of our proposed distributionally robust model applied to i) a DC-OPF problem for a meshed transmission system and ii) an AC-OPF problem using LinDistFlow approximation for a radial distribution system.

Real-time electricity prices are economic signals incentivizing market players to support real-time system balancing. These price signals typically switch between low- and high-price regimes depending on whether the power system is in surplus or shortage of generation, which is hard to capture. In this context, we propose a new Transformer-based model to assist the short-term trading strategies of market players. The proposed model offers high-performance probabilistic forecasts of real-time prices while providing insights into its inner decision-making process. Transformers rely on attention mechanisms solely computed via feed-forward networks to explicitly learn temporal patterns, which allows them to capture complex dependencies such as regime switching. Here, we augment Transformers with subnetworks dedicated to endogenously quantify the relative importance of each input feature. Hence, the resulting forecaster intrinsically provides the temporal attribution of each input feature, which foster trust and transparency for subsequent decision makers. Our case study on real-world market data of the Belgian power system demonstrates the ability of the proposed model to outperform state-of-the-art forecasting techniques, while shedding light on its most important drivers.

By sharing common assets such as the power grid, prosumers are closely interrelated by their actions and interests. Game theory provides powerful tools for increased coordination among the prosumers to optimize the energy resources. However, depending on the prosumer profiles and the market rules, the individual bills may notably differ and prove to be unfair. In this work, we analyze the outcomes of three relevant game-theoretical billing methods, which are innovatively transposed to the day-ahead scheduling of energy exchange within a liberalized residential community dominated by distributed energy resources. The first two approaches rely on a (static) daily billing scheme, while the third considers a multi-temporal (continuous) billing. The Nash equilibria are computed using distributed algorithms, hence ensuring individual decision-making and avoiding third-party dependencies. The cost distributions are assessed using both a qualitative and a quantitative comparison based on various prosumer profiles in a modern smart grid. It is shown that, depending on the billing option, either the contribution towards the entity (i.e., the ability to improve the global solution) or the individual empowerment (i.e., the ability to bargain) can be preferentially incentivized.