arXiv subjects

Lorenzo Zapparoli

Publications and source records attributed to Lorenzo Zapparoli.

6 recordsLinked to original sources

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.

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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.

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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.

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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.

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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.

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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.

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