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Cesar Diaz-Londono

Publications and source records attributed to Cesar Diaz-Londono.

5 recordsLinked to original sources

Quantifying Realizable Flexibility Limits in Fast and Ultra-Fast EV Charging Using Real-World Data

The rapid growth of electric vehicles (EVs) is increasing the need to accurately quantify their flexibility as a resource for power system operation. However, most existing approaches rely on simplified or power-controllable models that overlook the intrinsic constraints of fast and ultra-fast DC charging. In practice, flexibility is fundamentally shaped by battery management system (BMS) behavior, connection time availability, and battery-protection limits. This paper introduces a trajectory-aware data-driven framework to quantify EV charging flexibility as an energy-bounded and time-constrained process. Based on 252 real charging sessions, 141 representative Power-SoC profiles are reconstructed to capture real-world charging dynamics. Unidirectional flexibility is defined through bounds on the maximum shiftable charging energy, while bidirectional flexibility is quantified as the bounds of the maximum extractable discharge energy under feasibility constraints. Results show that flexibility depends on charging state and connection time. Charging beyond 80% SoC increases duration with limited gains, while higher charger power saturates due to BMS limits. Charging time in the 20%-80% range drops by over 60%, and mean power increases by up to 40%. The maximum extractable bidirectional energy can exceed twice its value depending on the point at which flexibility is activated. These results highlight that EV flexibility is not a controllable resource, but a bounded and time-dependent capability. As such, the proposed framework provides actionable limits that can be directly used by system operators and aggregators for scheduling, peak shaving, and short-duration flexibility services.

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A Simulation Tool for V2G Enabled Demand Response Based on Model Predictive Control

Integrating electric vehicles (EVs) into the power grid can revolutionize energy management strategies, offering both challenges and opportunities for creating a more sustainable and resilient grid. In this context, model predictive control (MPC) emerges as a powerful tool for addressing the complexities of Grid-to-vehicle (G2V) and vehicle-to-grid (V2G) enabled demand response management. By leveraging advanced optimization techniques, MPC algorithms can anticipate future grid conditions and dynamically adjust EV charging and discharging schedules to balance supply and demand while minimizing operational costs and maximizing flexibility. However, no standard tools exist to evaluate novel energy management strategies based on MPC approaches. Our research focuses on harnessing the potential of MPC in G2V and V2G applications, by providing a simulation tool that allows to maximize EV flexibility and support demand response initiatives while mitigating the impact on EV battery health. In this paper, we propose an open-source MPC controller for G2V and V2G-enabled demand response management. The proposed approach is capable of tackling the uncertainties inherent in demand response operations. Through extensive simulation and analysis, we demonstrate the efficacy of our approach in maximizing the benefits of G2V and V2G while assessing the impact on the longevity and reliability of EV batteries. Specifically, our controller enables Charge Point Operators (CPOs) to optimize EV charging and discharging schedules in real-time, taking into account fluctuating energy prices, grid constraints, and EV user preferences.

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EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking

As electric vehicle (EV) numbers rise, concerns about the capacity of current charging and power grid infrastructure grow, necessitating the development of smart charging solutions. While many smart charging simulators have been developed in recent years, only a few support the development of Reinforcement Learning (RL) algorithms in the form of a Gym environment, and those that do usually lack depth in modeling Vehicle-to-Grid (V2G) scenarios. To address the aforementioned issues, this paper introduces the EV2Gym, a realistic simulator platform for the development and assessment of small and large-scale smart charging algorithms within a standardized platform. The proposed simulator is populated with comprehensive EV, charging station, power transformer, and EV behavior models validated using real data. EV2Gym has a highly customizable interface empowering users to choose from pre-designed case studies or craft their own customized scenarios to suit their specific requirements. Moreover, it incorporates a diverse array of RL, mathematical programming, and heuristic algorithms to speed up the development and benchmarking of new solutions. By offering a unified and standardized platform, EV2Gym aims to provide researchers and practitioners with a robust environment for advancing and assessing smart charging algorithms.

cs.SE

Power-to-Gas in a gas and electricity distribution network: a sensitivity analysis of modeling approaches

Power-to-Gas (P2G) has been one of the most frequently discussed technologies in the last few years. This technology allows producing CO2 free fuels. Thanks to its high flexibility, it may offer services to the power system, fostering Variable Renewable Energy Sources (VRES) and the electricity demand match, mitigating the issues related to VRES overproduction. The role of P2G plants connected to the transmission system as flexibility asset has been extensively analyzed in the literature. Conversely, the analysis of these systems used at distribution level has only been dealt with in a few studies: however, in this case critical operation conditions can easily arise, both on electrical and gas infrastructure. This article presents a methodological analysis on the impact of different simulation approaches when P2G is installed at distribution system level. The choice of the most appropriate modeling approaches for electricity and distribution grids is required in order to avoid overestimating or underestimating the potential flexibility that P2G plants connected to distribution networks can offer. The aim of this paper is to understand the impact of different modeling approaches in order to determine whether, and under which conditions, this is acceptable. An illustrative case study has been developed to perform this kind of analysis. The results demonstrated that it is important to take into account the electric distribution network topology, as the performance of P2G plants could be affected by their placement in the network. Neglecting the dynamics of a gas network or the interactions between P2G plant components under a low gas demand condition can lead to an underestimation of the flexibility of the entire system. If the demand for gas is high enough, the use of simplified assumptions that do not consider the dynamics of the gas network and P2G systems may be acceptable.

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Techno-economic analysis of Power-to-Gas plants in a gas and electricity distribution network system with high renewable energy penetration

Distributed generation, based on the exploitation of Renewable Energy Sources (RES), has increased in the last few decades to limit anthropogenic carbon dioxide emissions, and this trend will increase in the future. However, RES generation is not dispatchable, and an increasing share of RES may lead to inefficiencies and even problems for the electricity network. Flexible resources are needed to handle RES generation in order to support the delicate electricity generation and demand balance. Energy conversion technologies (P2X, Power to X) allow the flexibility of energy systems to be increased. These technologies make a connection between different energy sectors (e.g., electricity and gas) possible, and thus create new synergies within an overall multi energy system. This paper analyzes how the P2G technology can be used at the distribution network level (both gas and electricity) to optimize the use of RES. In fact, in order to coordinate P2X resources, it is necessary to take into account the whole multi energy scenario, and not just the electrical side: it therefore becomes fundamental to recognize the pros and cons that Balancing Service Providers (BSPs), composed of a number of P2G plants (representing the Balancing Responsible Providers, BRPs), may have when offering services to an electricity network. Moreover, the convenience of the decarbonization of the gas grid has been evaluated through the calculation of the levelized cost of Synthetic Natural Gas (LCSNG) for cost scenarios for the years 2030 and 2050, considering different assumptions about the cost of the surplus utilization of RES. The results show that LCSNG may vary from 47 to 319 EURO/MWh, according to the different configurations, i.e., only in the best case scenario is the SNG cost comparable with the cost of natural gas, and hence does the P2G technology result to be profitable

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