Amidst the worldwide efforts to decarbonize power networks, Local Electricity Markets (LEMs) in distribution networks are gaining importance due to the increased adoption of renewable energy sources and growing active participation of consumers and prosumers. Considering that LEMs involve data exchange among independent entities, privacy and cybersecurity are some of the main practical challenges in LEM design. This presentation will introduce a novel secure market protocol using innovations from distributed optimization and Secure MultiParty Computation (SMPC). The considered LEM is formulated as an uncertainty-aware joint market for energy and reserves with affine balancing policies. To achieve scalability and enable the use of SMPC, market clearing is solved using the Consensus ADMM algorithm. Subsequently, the data exchange among participants via ADMM iterations is protected using the Shamir secret-sharing scheme to ensure privacy. The market protocol is further reinforced by a secure and verifiable settlement process that uses SMPC and ElGamal commitments to verify market quantities and by a secure recovery scheme for missing network measurements. Finally, the feasibility and performance of the proposed LEM are evaluated on a 15-bus test network.

In this talk, we deal with data-driven stochastic optimization, which has become a powerful tool to address decision-making problems across various domains by leveraging data in an effective manner. Despite its consolidated position, a significant gap remains in terms of theory and methodologies to assess the sensitivity of decision performance to the fundamental input of stochastic optimization: the dataset.

Against this background, we propose a methodology based on Distributionally Robust Optimization (DRO) to perform this sensitivity analysis by formally defining the marginal value of the quality of a dataset. Central to our approach is the utilization of the Wasserstein metric, which serves as a natural measure to encode the quality of data and allows us to posit a Wasserstein DRO formulation for computing the marginal value of datasets sourced from multiple providers. These datasets may pertain to the same uncertain input parameter or to different ones.

Finally, we use paradigmatic problems from the field of power system operations to illustrate how our methodology can be exploited to guide decision-making and data valuation and acquisition.

We describe a novel hierarchical optimization-based control architecture for coordination of Distributed Energy Resources (DERs) in Distribution Networks (DNs). At a high level, the proposed control system enables the DN in aggregate to quickly respond to power set-point requests from the transmission level while maintaining local DN constraints. More granularly, the proposal allows for multiple independently-managed areas within the DN to optimize their local resources while coordinating to support the TN, and while maintaining data privacy; the only required inter-area communication is between physically adjacent areas within the DN control hierarchy. Using tools from variational analysis and control theory, we provide rigorous closed-loop stability guarantees and tuning recommendations. The proposal is further validated via case simulation studies on multiple three-phase unbalanced grids, ranging from 123 to 8500 buses, demonstrating scalability to tens of thousands of DERs under control of dozens of independent stakeholders.

An important challenge in the online convex optimization (OCO) setting is to incorporate generalized inequalities and time-varying constraints. The inclusion of constraints in OCO widens the applicability of such algorithms to dynamic but safety-critical settings such as the online optimal power flow (OPF) problem. In this work, we propose the first projection-free OCO algorithm admitting time-varying linear constraints and convex generalized inequalities: the online interior-point method for time-varying equality constraints (OIPM-TEC). We derive simultaneous sublinear dynamic regret and constraint violation bounds for OIPM-TEC under standard assumptions. For applications where a given tolerance around optima is accepted, we propose a new OCO performance metric – the epsilon-regret – and a more computationally efficient algorithm, the epsilon OIPM-TEC, that possesses sublinear bounds under this metric. Finally, we showcase the performance of these two algorithms on an online OPF problem and compare them to another OCO algorithm from the literature.