arXiv subjects

Aldo Bischi

Publications and source records attributed to Aldo Bischi.

11 recordsLinked to original sources

arXiv2026

From Crop and Energy Data to Optimized Lighting Scheduling: A Surrogate-Based MILP Framework for Vertical Farming

Vertical farming enables high productivity and stable crop production under controlled conditions, but its economic feasibility remains limited by electricity demand for artificial lighting and climate control. This study develops a surrogate-based optimization framework for cost-minimizing lighting management in hydroponic vertical farms. A key contribution is a procedure for converting crop-energy response data into relationships suitable for mathematical programming. The method can be applied to experimental datasets or outputs from validated dynamic models, provided that relevant ranges of control variables, crop stages, and energy responses are represented. The framework is applied to lettuce cultivation in an industrial-scale vertical farm in northern Italy. Synthetic data generated by a verified agri-energy model are used to derive surrogate relationships for fresh biomass growth, leaf area index, and electricity demand for lighting, heating, and cooling. These relationships depend on photosynthetic photon flux density, crop stage, and outdoor temperature. Different temporal aggregation levels are examined to evaluate the trade-off among surrogate accuracy, scheduling flexibility, and computational compactness. The surrogate model reproduced crop and energy trends of the reference model with sufficient accuracy for optimization. Compared with fixed-lighting benchmarks, optimized schedules reduced electric energy consumption by up to 15.9% and electricity cost by up to 17.8%, while achieving the target harvest weight. Results indicate that moderate light intensities combined with flexible photoperiods are more cost-effective than high-intensity lighting under the investigated conditions. The proposed framework offers a tractable and generalizable approach for integrating crop growth, energy demand, and electricity price variability into vertical farming lighting strategies.

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arXiv2026

Can sunlight replace LEDs in vertical farms? A critical assessment of optical-fiber daylighting strategies

Vertical farming is a promising approach for increasing food production in controlled environments while reducing land use, water consumption, and dependence on external climatic conditions. However, its large electricity demand, mainly driven by artificial lighting, still limits its energetic and economic sustainability. This study evaluates the techno-economic potential of an optical-fiber daylighting system designed to transport concentrated sunlight directly to the crop growing zones of a vertical farm. A validated transient vertical farm model was coupled with Tonatiuh ray-tracing simulations to assess a single-axis solar-tracked Fresnel concentration system. Five optical fiber typologies and three daylighting strategies were compared in terms of crop production, specific electrical energy consumption, and light cost, and benchmarked against light-pipe and rooftop photovoltaic solutions. The optical-fiber system achieved an annual useful-PAR delivery efficiency of 19-27%, depending on fiber typology. By distributing daylight across all rack shelves and limiting heat gains through a UV-IR filter, it reduced electricity consumption by 35-89%, compared with 12% for the light-pipe configuration. The daylight-only strategy achieved the lowest SEEC, equal to 1.81 kWh kg^-1, but caused a 52% crop productivity reduction. Hybrid LED-optical-fiber strategies avoided this penalty and achieved electricity savings of about 50%, with a minimum SEEC of 3.16 kWh kg^-1. However, current optical fiber costs make the system economically unattractive, requiring an estimated capital cost reduction of about 90% to become competitive. Rooftop photovoltaics covered 22% of annual electricity demand and achieved an 11-12-year payback, making PV the most economically viable rooftop solar option under current market conditions.

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arXiv2026

Solar Daylighting to Offset LED Lighting in Vertical Farming: A Techno-Economic Study of Light Pipes

Vertical farming is a controlled-environment agriculture (CEA) approach in which crops are grown in stacked layers under regulated climate and lighting, enabling predictable production but requiring high electricity input. This study quantifies the techno-economic impact of roof-mounted daylighting in a three-tier container vertical farm using a light-pipe (LP) system that delivers sunlight to the upper tier. The optical chain, comprising a straight duct and a tilting aluminum-coated mirror within a rotating dome, was modelled in Tonatiuh to estimate crop-level photon delivery and solar gains. These outputs were coupled with a transient AGRI-Energy model to perform year-round simulations for Dubai. Tier-3 strategies were compared against a fully LED benchmark, including daylight-only operation, on/off supplementation, PWM dimming, UV-IR filtering, variable-transmittance control, and simple glazing. Ray-tracing predicted an overall LP optical efficiency of 45%-75%, depending on solar position, quantifying the fraction of incident daylight at the collector aperture delivered to the target growing zone. Daylight-only operation reduced the total three-tier yield by 17% and was not economically viable despite 27-29% electricity savings. Hybrid daylight-LED strategies preserved benchmark yield while reducing electricity use. PWM dimming combined with UV-IR filtering achieved the lowest specific electricity energy consumption (6.32 kWh/kg), 14% below the benchmark. Overall, viability remains CAPEX-limited because achievable electricity savings are insufficient to offset the added investment and thus improves mainly under high electricity and carbon-price contexts, although the LP system delivers a 15-38% lower light cost than an optical-fiber reference under identical incident daylight.

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arXiv2025

Beyond yield: integrating energy, water, cost, and carbon to benchmark indoor vertical farming viability

The interest in vertical farming arises from its ability to ensure consistent, high-quality, and pest-free vegetable production while supporting synergies with energy systems and urban development. While previous studies have assessed energy use and cost-effectiveness in vertical farming using simplified models, this study fills a gap by providing a comprehensive analysis of how individual input parameters affect efficiency, sustainability, and economic viability through a detailed modeling framework with sensitivity and correlation analyses. 162 scenarios were evaluated by combining three levels of temperature, photosynthetic photon flux density, and CO2 concentration across three distinct localities, namely Trondheim, Shanghai, and Dubai regions, which differ from a socio-environmental viewpoint. Two insulation thicknesses were also tested in each scenario. Results indicate that, due to the heating, ventilation, and air conditioning and dehumidification system, crop productivity could be kept optimal regardless of insulation or the external climate. Photosynthetic photon flux density was the dominant growth factor (correlation: 0.85), followed by CO2 (0.36) and temperature (0.22), and also the driver of energy consumption (0.73). The lowest specific energy consumption coincided with the lowest productivity (55 kg/m2). Levelized cost of lettuce identified the most cost-effective setup as 24{\deg}C, 250 umol/m2s photosynthetic photon flux density, 1400ppm CO2, with insulation (102kg/m2). Only decarbonized energy systems can support vertical farming without increasing CO2 emissions compared to imported lettuce. These findings guide practitioners and policymakers in selecting cost-effective, sustainable vertical farming strategies and provide validated data for research and implementation across diverse climatic and energy contexts.

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arXiv2025

Indoor thermal comfort management: A Bayesian machine-learning approach to data denoising and dynamics prediction of HVAC systems

The optimal management of a building's microclimate to satisfy the occupants' needs and objectives in terms of comfort, energy efficiency, and costs is particularly challenging. This complexity arises from the non-linear, time-dependent interactions among all the variables of the control problem and the changing internal and external constraints. Focusing on the accurate modeling of the indoor temperature, we propose a data-driven approach to address this challenge. We account for thermal inertia, non-linear effects, small perturbations of the indoor climate dynamics caused by ventilation and weather variations, as well as for the stochastic nature of the control system due to the observed noise in the input signal. Since the prohibitive cost of quality data acquisition and processing limits the implementation of data-driven approaches for real-life problems, we applied a method that merges several Bayesian machine learning and deep learning architectures that are suitable for predicting complex system dynamics, while relaxing the dataset quality requirements. Our framework includes a built-in deep Kalman filter, which makes it deployable even with low-accuracy temperature sensors. It achieves state-of-the-art performance, best performing with a 150-minute prediction horizon with an RMSE of 0.2455, an MAE of 0.162, and an $R^2$ of 0.926. The model's performance remains consistent even when exposed to highly noisy data. Finally, we show how our approach can be extended to other applications including demand response event duration prediction and equipment failure detection.

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arXiv2023

Mixed Integer Linear Program model for optimized scheduling of a vanadium redox flow battery with variable efficiencies, capacity fade, and electrolyte maintenance

Redox Flow Batteries are a promising option for large-scale stationary energy storage. The vanadium redox flow battery is the most widely commercialized system thanks to its chemical stability and performance. This work aims to optimize the scheduling of a vanadium flow battery that stores energy produced by a renewable power plant, keeping into account a thorough characterization of the battery performance, with variable efficiencies and capacity fade effects. A detailed characterization of the battery performance improves the calculation of the optimal number of cycles and revenue associated with the battery use if compared to the results obtained using simpler models, which take into account constant efficiencies and no capacity fade effects. The presented problem is nonlinear due to the functions of the battery efficiency, which depend upon charging and discharging powers and state of charge with nonlinear, non-convex correlations. The problem is linearized using convex hulls. The optimization program also calculates the progressive battery capacity fade due to undesired secondary electrochemical reactions and the economic impact of capacity restoration through periodic maintenance. The final problem is solved as a Mixed-Integer Linear Program (MILP) to guarantee the global optimality of the linearized problem. The proposed optimization model has been applied to two different case studies: a case of energy arbitrage and a case of load-shifting. The optimization results have been compared to those obtained with constant battery efficiency models, which do not consider the capacity fade effects. Results show that simpler models overestimate the optimal number of cycles of the battery and the revenue by up to 15% if they do not take into account the degradation model of the battery, and respectively up to 32% and 42% if they also assume constant efficiency for the battery.

math.OC↗

arXiv2021

Congestion management via increasing integration of electric and thermal energy infrastructures

Congestion caused in the electrical network due to renewable generation can be effectively managed by integrating electric and thermal infrastructures, the latter being represented by large scale District Heating (DH) networks, often fed by large combined heat and power (CHP) plants. The CHP plants could further improve the profit margin of district heating multi-utilities by selling electricity in the power market by adjusting the ratio between generated heat and power. The latter is possible only for certain CHP plants, which allow decoupling the two commodities generation, namely the ones provided by two independent variables (degrees-of-freedom) or by integrating them with thermal energy storage and Power-to-Heat (P2H) units. CHP units can, therefore, help in the congestion management of the electricity network. A detailed mixed-integer linear programming (MILP) optimization model is introduced for solving the network-constrained unit commitment of integrated electric and thermal infrastructures. The developed model contains a detailed characterization of the useful effects of CHP units, i.e., heat and power, as a function of one and two independent variables. A lossless DC flow approximation models the electricity transmission network. The district heating model includes the use of gas boilers, electric boilers, and thermal energy storage. The conducted studies on IEEE 24 bus system highlight the importance of a comprehensive analysis of multi-energy systems to harness the flexibility derived from the joint operation of electric and heat sectors and managing congestion in the electrical network.

math.OC↗

arXiv2020

Pricing in Integrated Heat and Power Markets

There is a growing interest in the integration of energy infrastructures to increase systems' flexibility and reduce operational costs. The most studied case is the synergy between electric and heating networks. Even though integrated heat and power markets can be described by a convex optimization problem, prices derived from dual values do not guarantee cost recovery. In this work, a two-step approach is presented for the calculation of the optimal energy dispatch and prices. The proposed methodology guarantees cost-recovery for each of the energy vectors and revenue-adequacy for the integrated market.

econ.GN↗

arXiv2020

Data-driven control of micro-climate in buildings: an event-triggered reinforcement learning approach

Smart buildings have great potential for shaping an energy-efficient, sustainable, and more economic future for our planet as buildings account for approximately 40% of the global energy consumption. Future of the smart buildings lies in using sensory data for adaptive decision making and control that is currently gloomed by the key challenge of learning a good control policy in a short period of time in an online and continuing fashion. To tackle this challenge, an event-triggered -- as opposed to classic time-triggered -- paradigm, is proposed in which learning and control decisions are made when events occur and enough information is collected. Events are characterized by certain design conditions and they occur when the conditions are met, for instance, when a certain state threshold is reached. By systematically adjusting the time of learning and control decisions, the proposed framework can potentially reduce the variance in learning, and consequently, improve the control process. We formulate the micro-climate control problem based on semi-Markov decision processes that allow for variable-time state transitions and decision making. Using extended policy gradient theorems and temporal difference methods in a reinforcement learning set-up, we propose two learning algorithms for event-triggered control of micro-climate in buildings. We show the efficacy of our proposed approach via designing a smart learning thermostat that simultaneously optimizes energy consumption and occupants' comfort in a test building.

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arXiv2019

Non-ideal Linear Operation Model for a Li-ion Battery

Currently, the characterization of electric energy storage units used for power system operation and planning models relies on two major assumptions: charge and discharge efficiencies, and power limits are constant and independent of the electric energy storage state of charge. This approach can misestimate the available storage flexibility. This work proposes a detailed model for the characterization of steady-state operation of Li-ion batteries in optimization problems. The model characterizes the battery performance, including non-linear charge and discharge power limits and efficiencies, as a function of the state of charge and requested power. We then derive a linear reformulation of the model without introducing binary variables, which achieves high computational efficiency, while providing high approximation accuracy. The proposed model characterizes more accurately the performance and technical operational limits associated with Li-ion batteries than those present in classical ideal models. The developed battery model has been compared with three modelling approaches: the complete non-convex formulation; an ideal model typically used in the power system community; and a mixed integer linear reformulation approach. The models have been tested on a network-constrained economic dispatch for a 24-bus system. Based on the simulations, we observed approximately 12% of energy mismatches between schedules that use an ideal model and those that use the model proposed in this study.

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arXiv2018

Flexible unit commitment of a network-constrained combined heat and power system

Large Combined Heat and Power (CHP) plants are often employed in order to feed district heating networks, in Europe, in post soviet countries and China. Traditionally they have been operated following the thermal load with the electric energy considered as a by-product, while the modern trend includes them in the electric market to take advantage of the flexibility they could provide. This implies the necessity to consider the impact on the electric grid while filling the thermal load requests. A detailed Mixed Integer Linear Programming (MILP) optimization model for the solution of the network-constrained CHP unit commitment of the day-ahead operation is introduced. The developed model accounts for lossless DC network approximation of the electric power flow constraints, as well as a detailed characterization of the CHP units with useful effect, heat and power, function of one and two independent variables ("degrees-of-freedom"), and thermal energy storage. A computational validation of the outlined model on a CHP test system with multiple heating zones is presented in the form of computational test cases. The test cases illustrate the impact on the flexibility of the implementation of the energy storage, network constraints and joint multi-system operation. The conducted studies have highlighted the importance of a comprehensive and integrated analysis of multi-energy systems to exploit the operational flexibility provided by the cogeneration units. The joint operation of the thermal and electric system allows to reap economic, operational efficiency, and environmental benefits. The developed model can be easily extended to include diverse multi-energy systems and technologies, as well as more complex representations of the energy transmission networks, and the modeling of renewable energy resources dependent of one or more independent, weather-related, variables.

math.OC↗