arXiv subjects

Giovanni Sansavini

Publications and source records attributed to Giovanni Sansavini.

At least 19 recordsLinked to original sources

arXiv2026

Prosumer Synchronisation Risk: Impacts of Time-Varying Tariffs on Distribution Network Expansion

The rapid deployment of distributed energy resources, including heat pumps, electric vehicles, photovoltaics, and battery storage, is reshaping the operation of low-voltage networks. While distribution system operators often aim to develop time-varying tariffs to incentivise network-friendly behaviour and defer reinforcements, they risk triggering prosumer synchronisation. As prosumers follow price signals, their behaviour may synchronise, avoiding existing load peaks while creating undesirable peaks at other times. This paper quantifies the impact on low-voltage network reinforcement needs when shifting from flat tariffs to two time-varying tariffs: Time-of-Use and Real-Time Pricing. Applying a mixed-integer linear model across 471 low-voltage reference networks in Switzerland, we evaluate expansion requirements under projections of distributed energy resource deployment for 2050. In the model, prosumers operate their distributed energy resources in accordance with their subscribed electricity tariff to minimise costs, and the distribution system operator subsequently optimises reinforcements. Our results demonstrate that while adopting time-varying tariffs can reduce reinforcement investment costs by more than 50%, high adoption rates can reverse this declining trend. Specifically, once prosumer adoption exceeds the optimal thresholds of 50% for Time-of-Use and 40% for Real-Time Pricing, reinforcement needs increase by 39% and 134%, respectively, for an additional 20% of tariff adoption.

eess.SY↗

arXiv2026

Estimating Density Functions for Probabilistic Power Flow Using Invertible Neural Networks

Probabilistic power flow (PPF) is essential for quantifying operational uncertainty in modern power systems with high penetrations of renewable generation and flexible loads. Conventional PPF methods primarily rely on Monte Carlo (MC)- based power flow (PF) simulations or simplified approximations of voltage probability density functions. Although MC methods provide high accuracy, they incur substantial computational and data-storage costs, whereas simplified approximations often sacrifice accuracy. In this paper, we propose a novel PPF density approximation framework that avoids repeated PF simulations during inference and can, in principle, approximate complex voltage distributions without restrictive distributional assumptions. The core idea is to learn an explicit invertible mapping between stochastic power injections and system voltages using invertible neural networks (INNs). By combining this mapping with the change-of-variables theorem, the proposed framework directly evaluates voltage probability densities without repeatedly solving the PF equations. Extensive numerical studies demonstrate that the proposed framework achieves state-of-the-art performance both as an accurate PF surrogate and as an efficient PPF density estimator.

eess.SY↗

arXiv2026

Physics-Informed Graph Neural Networks for Robust AC-Optimal Power Flow

We present PINCO, an unsupervised learning framework that integrates Graph Neural Networks with physics-informed neural networks for AC optimal power flow (AC-OPF) solutions. Unlike state-of-the-art unsupervised methods that require prescreened datasets containing only feasible instances, our approach operates on unfiltered data, including ill-conditioned cases. The framework addresses two critical gaps in the literature: (1) robustness to topology changes up to N-2 contingencies, and (2) detecting optimal power flow instances that are infeasible without relying on traditional solvers for data filtering. PINCO embeds physical laws into the learning process via augmented Lagrangian multipliers. In addition, it introduces a clustering branch with learnable centroids that automatically separate feasible from infeasible solutions based on constraint-violation patterns. We evaluate the framework across systems of varying complexity, including the IEEE 30-bus, IEEE 57-bus, and Swiss transmission networks, demonstrating scalability and robustness under diverse loading conditions and topology variations. Benchmarking against DeepOPF-FT and the IPOPT solver shows that PINCO achieves comparable constraint satisfaction while delivering two to three orders of magnitude computational speedup compared to IPOPT and lower operational costs across all configurations.

eess.SY↗

arXiv2026

Does Central Planning Fail Locally? Evaluating EV Charging Flexibility Optimization Across Grid Levels

Electric vehicles (EVs) are a key enabler of global decarbonization, and their charging flexibility is crucial for integrating high shares of renewable energy into future power systems. Research on EV charging flexibility in future power systems has largely focused on either the transmission or the distribution level, with limited integration between the two. However, these two scales cannot be separated: EV charging optimized at the national transmission level for system-wide planning is ultimately activated in local distribution grids, where it may cause voltage and loading issues. This study bridges that gap by evaluating EV charging flexibility across both grid levels, using two different control approaches. First, a top-down approach disaggregates the optimal charging demand obtained from a centralized planner at the transmission grid down to the distribution grid, explicitly accounting for grid constraints and individual driving requirements. Second, a bottom-up approach optimizes EV charging locally under distribution-grid constraints using electricity price signals from the centralized planning model. Controlled charging from the top-down and bottom-up approaches is then compared with uncontrolled charging. Results show that the top-down approach produces EV charging schedules that are compatible with individual driving behavior and reduce grid violations compared to uncontrolled charging. The bottom-up approach identifies an optimal profile similar to the centrally optimized profile, showing that electricity price signals are a suitable coordination mechanism. These findings suggest that centrally optimized EV charging remains effective when implemented at the distribution level, in systems with high DER penetration.

eess.SY↗

arXiv2026

Double interior-point regularization for large-scale capacity expansion

Capacity expansion is a key tool for planning future energy systems. However, weather-dependent generation and long-duration storage result in problem sizes that exceed the computational limits of conventional interior-point solvers, making it impossible to plan renewable systems that are cost-efficient and reliable across a wide range of weather conditions. To tackle such large problems, this paper introduces the double interior-point regularization (DIP-set) for Benders Decomposition, combining the advantages of traversing the interior of the solution space while remaining close to a reference solution. We benchmark the method on a power-sector problem and an energy-system problem, varying problem size and the level of foresight during operations. Results demonstrate that DIP-set outperforms competing regularizations in all test cases. The speed-up increases with size, reaching 30-50% for the largest problems, which are the most critical for planning renewable systems and are too large for state-of-the-art methods. The key benefit of DIP-set is its ability to mitigate the sharp decrease in convergence as BD approaches the optimal solution.

eess.SY↗

arXiv2026

Efficient Multi-Market Scheduling of Virtual Power Plants via Spectral Representation of Uncertainty

As the penetration of distributed energy resources increases, harnessing their flexibility becomes critical for power system operations. Virtual power plants (VPPs) offer a promising solution. However, existing VPP market scheduling tools exhibit a tradeoff between economic performance and tractability. Stochastic formulations provide probabilistically optimal decisions but are computationally intractable for large systems due to scenario explosion. Robust approaches are more tractable but often yield conservative decisions. This paper addresses this gap by proposing a stochastic multi-market VPP scheduling framework that represents uncertainty in the spectral domain via intrusive Polynomial Chaos Expansion (PCE). The resulting reformulation yields a low-dimensional deterministic spectral counterpart that preserves the stochastic structure and can be solved efficiently with standard optimization tools. The proposed spectral approach is demonstrated on a DER-based VPP operating on a realistic Swiss low-voltage grid and benchmarked against a state-of-the-art scenario-based solution. Results show that intrusive PCE achieves solution quality comparable to the scenario-based benchmark, with up to a 137 times reduction in computational effort, while yielding highly accurate bidding decisions. Finally, to facilitate adoption and reproducibility, we release an open-source, application-agnostic projection tool that automates the spectral reformulation for generic single- and two-stage stochastic programs.

eess.SY↗

arXiv2026

Risk-Aware Multi-Market Scheduling of Virtual Power Plants with Dynamic Network Tariffs

As the penetration of distributed energy resources (DERs) increases, harnessing their flexibility becomes critical for power system operations. Virtual power plants (VPPs) offer a promising solution. However, most existing scheduling tools rely on simplified DER or grid models and largely overlook local flexibility procurement mechanisms such as dynamic network tariffs. This paper proposes a two-stage stochastic optimization framework for VPP multi-market scheduling that integrates detailed device-level constraints, network limitations, and operational and market uncertainties. Conditional value-at-risk is incorporated to represent risk preferences, and Benders decomposition ensures tractability with extensive scenario sets. The model jointly optimizes bidding across energy and reserve markets while explicitly accounting for local flexibility procurement through dynamic network tariffs. The results from a realistic case study show that both risk-neutral and risk-averse strategies exploit arbitrage opportunities. However, risk aversion reduces profit volatility through closer alignment with physical dispatch. Dynamic tariffs unlock local flexibility by shifting demand across the day, though strong tariff signals reduce expected profitability by up to 65% with limited additional flexibility gains.

eess.SY↗

arXiv2026

Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility

Buildings account for approximately 40% of global energy consumption, and with the growing share of intermittent renewable energy sources, enabling demand-side flexibility, particularly in heating, ventilation and air conditioning systems, is essential for grid stability and energy efficiency. This paper presents a safe deep reinforcement learning-based control framework to optimize building space heating while enabling demand-side flexibility provision for power system operators. A deep deterministic policy gradient algorithm is used as the core deep reinforcement learning method, enabling the controller to learn an optimal heating strategy through interaction with the building thermal model while maintaining occupant comfort, minimizing energy cost, and providing flexibility. To address safety concerns with reinforcement learning, particularly regarding compliance with flexibility requests, we propose a real-time adaptive safety-filter to ensure that the system operates within predefined constraints during demand-side flexibility provision. The proposed real-time adaptive safety filter guarantees full compliance with flexibility requests from system operators and improves energy and cost efficiency -- achieving up to 50% savings compared to a rule-based controller -- while outperforming a standalone deep reinforcement learning-based controller in energy and cost metrics, with only a slight increase in comfort temperature violations.

eess.SY↗

arXiv2026

A day-ahead market model for power systems: benchmarking and security implications

Power system security assessments, e.g. via cascading outage models, often use operational set-points based on optimal power flow (OPF) dispatch. However, driven by cost minimization, OPF provides an ideal, albeit unrealistic, clearing of the generating units that disregards the complex interactions among market participants. In addition, existing market modeling tools often utilize economic dispatch and unit commitment to minimize total system costs, often disregarding the profit-driven behavior of market participants. The security of the system, therefore, may be overestimated. To address this gap, we introduce a social-welfare-based day-ahead market-clearing model. The security implications are analyzed using Cascades, a model for cascading failure analysis. We apply this model to the IEEE-118 bus system with three independent control zones. The results show that market dispatch leads to an increase in demand not served (DNS) of up to 80% higher than OPF, highlighting a significant security overestimation. This is especially pronounced in large-scale cascading events with DNS above 100MW. A key driver is the increased dispatch of storage and gas units, which can place the system in critical operating conditions. Operators can use this information to properly estimate the impact of the market on system security and plan efficient expansion strategies.

eess.SY↗

arXiv2025

Accurately modeling long-term storage with minimum representative hours in large-scale renewable energy systems

Energy system optimization often relies on time series aggregation to ensure computational tractability. Aggregation generally loses the chronology of time steps, which renders the storage level representation challenging. Typically, this challenge is addressed by using representative days (RD) to utilize intra-day chronology, even though representative hours (RH) can describe the input time series more accurately at fewer representative time steps than RD. However, until now, the use of RH storage representation methods has been limited by either high computational complexity, poor accuracy in clustering and storage representation, or restricted applicability. Here, we present a novel storage representation method based on RH that combines the high accuracy of RH time series aggregation with the high computational efficiency of methods based on RD. Through benchmarking the four most established storage representation methods on a model of a net-zero European energy system, we find that the proposed method can reduce the solving time by over 95% for the same objective value compared to the most established RD and RH methods. The proposed method exhibits particular strengths at strong aggregations of around 100 to 500 representative hours per year, making the method especially applicable to large-scale and sector-coupled transition pathway models. The developed method for accurately modeling both short-term and long-term storage, along with the presented findings, is of practical relevance to energy system modelers who seek computational tractability in large-scale applications while avoiding the misallocation of storage and conversion capacities.

math.OC↗

arXiv2025

Wasserstein-based Kernel Principal Component Analysis for Clustering Applications

Many data clustering applications must handle objects that cannot be represented as vectors. In this context, the bag-of-vectors representation describes complex objects through discrete distributions, for which the Wasserstein distance provides a well-conditioned dissimilarity measure. Kernel methods extend this by embedding distance information into feature spaces that facilitate analysis. However, an unsupervised framework that combines kernels with Wasserstein distances for clustering distributional data is still lacking. We address this gap by introducing a computationally tractable framework that integrates Wasserstein metrics with kernel methods for clustering. The framework can accommodate both vectorial and distributional data, enabling applications in various domains. It comprises three components: (i) an efficient approximation of pairwise Wasserstein distances using multiple reference distributions; (ii) shifted positive definite kernel functions based on Wasserstein distances, combined with kernel principal component analysis for feature mapping; and (iii) scalable, distance-agnostic validity indices for clustering evaluation and kernel parameter optimization. Experiments on power distribution graphs and real-world time series demonstrate the effectiveness and efficiency of the proposed framework.

cs.LG↗

arXiv2025

Multi-level informed optimization via decomposed Kriging for large design problems under uncertainty

Engineering design involves demanding models encompassing many decision variables and uncontrollable parameters. In addition, unavoidable aleatoric and epistemic uncertainties can be very impactful and add further complexity. The state-of-the-art adopts two steps, uncertainty quantification and design optimization, to optimize systems under uncertainty by means of robust or stochastic metrics. However, conventional scenario-based, surrogate-assisted, and mathematical programming methods are not sufficiently scalable to be affordable and precise in large and complex cases. Here, a multi-level approach is proposed to accurately optimize resource-intensive, high-dimensional, and complex engineering problems under uncertainty with minimal resources. A non-intrusive, fast-scaling, Kriging-based surrogate is developed to map the combined design/parameter domain efficiently. Multiple surrogates are adaptively updated by hierarchical and orthogonal decomposition to leverage the fewer and most uncertainty-informed data. The proposed method is statistically compared to the state-of-the-art via an analytical testbed and is shown to be concurrently faster and more accurate by orders of magnitude.

eess.SY↗

arXiv2025

Optimal Duration of Reserve Capacity Ancillary Services for Distributed Energy Resources

The increasing integration of distributed energy resources (DERs) into power systems presents opportunities and challenges for ancillary services (AS) provision. Technical requirements of existing AS (i.e., duration, reliability, ramp rate, and lead time) have been designed for traditional generating units, making their provision by DER aggregates particularly challenging. This paper proposes a method to design the duration of reserve capacity AS products considering the operational constraints of DERs and the temporal dynamics of system imbalances. The optimal product duration is determined by maximizing product availability and aligning the supply profile with the system's balancing needs. We apply the methodology to a realistic Swiss low-voltage network with a diverse DER portfolio. The results reveal that (i) shorter product durations maximize average availability and (ii) long product durations improve the alignment with system balancing needs. This paper offers valuable insights for system operators to design AS products tailored for DER participation.

eess.SY↗

arXiv2025

Power Reserve Capacity from Virtual Power Plants with Reliability and Cost Guarantees

The growing penetration of renewable energy sources is expected to drive higher demand for power reserve ancillary services (AS). One solution is to increase the supply by integrating distributed energy resources (DERs) into the AS market through virtual power plants (VPPs). Several methods have been developed to assess the potential of VPPs to provide services. However, the existing approaches fail to account for AS products' requirements (reliability and technical specifications) and to provide accurate cost estimations. Here, we propose a new method to assess VPPs' potential to deliver power reserve capacity products under forecasting uncertainty. First, the maximum feasible reserve quantity is determined using a novel formulation of subset simulation for efficient uncertainty quantification. Second, the supply curve is characterized by considering explicit and opportunity costs. The method is applied to a VPP based on a representative Swiss low-voltage network with a diversified DER portfolio. We find that VPPs can reliably offer reserve products and that opportunity costs drive product pricing. Additionally, we show that the product's requirements strongly impact the reserve capacity provision capability. This approach aims to support VPP managers in developing market strategies and policymakers in designing DER-focused AS products.

eess.SY↗

arXiv2025

Future Deployment and Flexibility of Distributed Energy Resources in the Distribution Grids of Switzerland

The decarbonization goals worldwide drive the energy transition of power distribution grids, which operate under increasingly volatile conditions and closer to their technical limits. In this context, localized operational data with high temporal and spatial resolution is essential for their effective planning and regulation. Nevertheless, information on grid-connected distributed energy resources, such as electric vehicles, photovoltaic systems, and heat pumps, is often fragmented, inconsistent, and unavailable. This work introduces a comprehensive database of distributed energy resources and non-controllable loads allocated in Switzerland's medium- and low-voltage distribution grid models, covering over 2 million points of connection. Remarkably, this data specifies the flexibility capabilities of the controllable devices, with a set of projections aligned with national forecasts for 2030, 2040, and 2050. The database supports studies on flexibility provision of distributed energy resources, distribution grid resilience, and national energy policy, among other topics. Importantly, its modular structure allows users to extract national- and local-scale information across medium- and low-voltage systems, enabling broad applicability across locations.

eess.SY↗

arXiv2025

Integrating the Expected Future in Load Forecasts with Contextually Enhanced Transformer Models

Accurate and reliable energy forecasting is essential for power grid operators who strive to minimize extreme forecasting errors that pose significant operational challenges and incur high intra-day trading costs. Incorporating planning information -- such as anticipated user behavior, scheduled events or timetables -- provides substantial contextual information to enhance forecast accuracy and reduce the occurrence of large forecasting errors. Existing approaches, however, lack the flexibility to effectively integrate both dynamic, forward-looking contextual inputs and historical data. In this work, we conceptualize forecasting as a combined forecasting-regression task, formulated as a sequence-to-sequence prediction problem, and introduce contextually-enhanced transformer models designed to leverage all contextual information effectively. We demonstrate the effectiveness of our approach through a primary case study on nationwide railway energy consumption forecasting, where integrating contextual information into transformer models, particularly timetable data, resulted in a significant average mean absolute error reduction of 26.6%. An auxiliary case study on building energy forecasting, leveraging planned office occupancy data, further illustrates the generalizability of our method, showing an average reduction of 56.3% in mean absolute error. Compared to other state-of-the-art methods, our approach consistently outperforms existing models, underscoring the value of context-aware deep learning techniques in energy forecasting applications.

cs.CY↗

arXiv2024

Policy-relevance of a Model Inter-comparison: Switzerland in the European Energy Transition

The energy transition is reshaping electricity systems, bringing new challenges, and emphasizing the need for strategic planning. Energy policies play a crucial role in guiding this transition. However, assessing their impacts often requires robust modeling involving multiple models and going beyond a single country's scope, analyzing international interactions. In this study, we examine three Swiss energy policies, analyzing their impacts on both the national energy system and the cross-border electricity flows. We use a model inter-comparison approach with four electricity system models to explore scenarios involving Swiss renewable generation targets, the Swiss market integration, and the Swiss winter import limitations, in the context of various European electricity developments. The results indicate that a renewable generation target leads to a reduction in net imports and electricity prices. Additionally, reduced market integration impacts both Swiss and European energy transitions by limiting trade benefits, underutilizing Variable Renewable Energy Sources (VRES), and increasing electricity supply costs. Lastly, we observe that limiting Swiss winter imports adversely affects electricity trading, driving up both supply costs and electricity prices.

physics.soc-ph↗

arXiv2024

PowerGraph: A power grid benchmark dataset for graph neural networks

Power grids are critical infrastructures of paramount importance to modern society and, therefore, engineered to operate under diverse conditions and failures. The ongoing energy transition poses new challenges for the decision-makers and system operators. Therefore, developing grid analysis algorithms is important for supporting reliable operations. These key tools include power flow analysis and system security analysis, both needed for effective operational and strategic planning. The literature review shows a growing trend of machine learning (ML) models that perform these analyses effectively. In particular, Graph Neural Networks (GNNs) stand out in such applications because of the graph-based structure of power grids. However, there is a lack of publicly available graph datasets for training and benchmarking ML models in electrical power grid applications. First, we present PowerGraph, which comprises GNN-tailored datasets for i) power flows, ii) optimal power flows, and iii) cascading failure analyses of power grids. Second, we provide ground-truth explanations for the cascading failure analysis. Finally, we perform a complete benchmarking of GNN methods for node-level and graph-level tasks and explainability. Overall, PowerGraph is a multifaceted GNN dataset for diverse tasks that includes power flow and fault scenarios with real-world explanations, providing a valuable resource for developing improved GNN models for node-level, graph-level tasks and explainability methods in power system modeling. The dataset is available at https://figshare.com/articles/dataset/PowerGraph/22820534 and the code at https://github.com/PowerGraph-Datasets.

cs.LG↗