arXiv subjects

Luis Ramirez Camargo

Publications and source records attributed to Luis Ramirez Camargo.

7 recordsLinked to original sources

arXiv2026

The electricity system value of the local acceptance of onshore wind in Europe

The large-scale deployment of wind power is central to Europe`s energy transition but faces challenges due to its social and environmental impacts on communities. Here we assess how the tolerance of local stakeholders to such impacts translates across spatial scales to shape the cost and design of the continent`s net-zero electricity system using a soft-linked modelling framework. We find that lower impact tolerance can reduce the role of onshore wind in Europe reaching net-zero by up to 84% relative to a future where wind enjoys higher acceptance, with other low carbon sources needing to be scaled up to compensate. This translates into total European electricity system costs increasing by between 2-14% while some countries see costs escalating by 20% or more. Our results show that the local acceptance of onshore wind is a key structural driver of the system and highlight the system value of policies to promote it.

physics.soc-ph↗

arXiv2024

TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithm

Energy management systems (EMS) have traditionally been implemented using rule-based control (RBC) and model predictive control (MPC) methods. However, recent research has explored the use of reinforcement learning (RL) as a promising alternative. This paper introduces TreeC, a machine learning method that utilizes the covariance matrix adaptation evolution strategy metaheuristic algorithm to generate an interpretable EMS modeled as a decision tree. Unlike RBC and MPC approaches, TreeC learns the decision strategy of the EMS based on historical data, adapting the control model to the controlled energy grid. The decision strategy is represented as a decision tree, providing interpretability compared to RL methods that often rely on black-box models like neural networks. TreeC is evaluated against MPC with perfect forecast and RL EMSs in two case studies taken from literature: an electric grid case and a household heating case. In the electric grid case, TreeC achieves an average energy loss and constraint violation score of 19.2, which is close to MPC and RL EMSs that achieve scores of 14.4 and 16.2 respectively. All three methods control the electric grid well especially when compared to the random EMS, which obtains an average score of 12 875. In the household heating case, TreeC performs similarly to MPC on the adjusted and averaged electricity cost and total discomfort (0.033 EUR/m$^2$ and 0.42 Kh for TreeC compared to 0.037 EUR/m$^2$ and 2.91 kH for MPC), while outperforming RL (0.266 EUR/m$^2$ and 24.41 Kh).

cs.LG↗

arXiv2022

Safe reinforcement learning for multi-energy management systems with known constraint functions

Reinforcement learning (RL) is a promising optimal control technique for multi-energy management systems. It does not require a model a priori - reducing the upfront and ongoing project-specific engineering effort and is capable of learning better representations of the underlying system dynamics. However, vanilla RL does not provide constraint satisfaction guarantees - resulting in various potentially unsafe interactions within its safety-critical environment. In this paper, we present two novel safe RL methods, namely SafeFallback and GiveSafe, where the safety constraint formulation is decoupled from the RL formulation. These provide hard-constraint, rather than soft- and chance-constraint, satisfaction guarantees both during training a (near) optimal policy (which involves exploratory and exploitative, i.e. greedy, steps) as well as during deployment of any policy (e.g. random agents or offline trained RL agents). This without the need of solving a mathematical program, resulting in less computational power requirements and a more flexible constraint function formulation (no derivative information is required). In a simulated multi-energy systems case study we have shown that both methods start with a significantly higher utility (i.e. useful policy) compared to a vanilla RL benchmark and Optlayer benchmark (94,6% and 82,8% compared to 35,5% and 77,8%) and that the proposed SafeFallback method even can outperform the vanilla RL benchmark (102,9% to 100%). We conclude that both methods are viably safety constraint handling techniques applicable beyond RL, as demonstrated with random policies while still providing hard-constraint guarantees.

eess.SY↗

arXiv2022

Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costs

This paper presents a solution to a predict then optimise problem which goal is to reduce the electricity cost of a university campus. The proposed methodology combines a multi-dimensional time series forecast and a novel approach to large-scale optimization. Gradient-boosting method is applied to forecast both generation and consumption time-series of the Monash university campus for the month of November 2020. For the consumption forecasts we employ log transformation to model trend and stabilize variance. Additional seasonality and trend features are added to the model inputs when applicable. The forecasts obtained are used as the base load for the schedule optimisation of university activities and battery usage. The goal of the optimisation is to minimize the electricity cost consisting of the price of electricity and the peak electricity tariff both altered by the load from class activities and battery use as well as the penalty of not scheduling some optional activities. The schedule of the class activities is obtained through evolutionary optimisation using the covariance matrix adaptation evolution strategy and the genetic algorithm. This schedule is then improved through local search by testing possible times for each activity one-by-one. The battery schedule is formulated as a mixed-integer programming problem and solved by the Gurobi solver. This method obtains the second lowest cost when evaluated against 6 other methods presented at an IEEE competition that all used mixed-integer programming and the Gurobi solver to schedule both the activities and the battery use. The code and data used for the paper are publicly available.

cs.LG↗

arXiv2022

Renewable energy communities: do they have a business case in Flanders?

Renewable energy communities (RECs) are prominent initiatives to provide end consumers an active role in the energy sector, raise awareness on the importance of renewable energy (RE) technologies and increase their share in the energy system thus reducing greenhouse gas emissions. The economic viability of RECs though, depends on multiple interdependent factors that require careful examination for each individual context. This study aims at investigating the impact of electricity tariffs, ratio of electrification of heating and transportation sectors, prices of RE technologies and storage systems, and internal electricity exchange prices on the annual cost for electricity provision of a REC. A mixed-integer linear model is developed to minimize energy provision costs for a representative REC in Flanders, Belgium. The results indicate that RECs have the potential to reduce these costs by 10 to 26% compared to business-as-usual. This cost reduction depends on the type of electricity tariffs and the level of uptake of flexible assets such as heat pumps and electric vehicles. The shift towards a higher power component in the electricity tariff makes electricity storage systems more attractive, which leads to higher electricity self-consumption. The introduction of flexible assets adds the possibility to shift demand when tariffs are lower and makes larger sizes of photovoltaic systems economically viable due to the increase in the total electricity demand. However, RECs cost reduction compared to individual smart-homes amounts to only 4% - 6% in the best cases. Uncertainties stemming from the regulation and the costs of setting up a REC may reduce the estimated benefits.

physics.soc-ph↗

arXiv2021

Model-predictive control and reinforcement learning in multi-energy system case studies

Model-predictive-control (MPC) offers an optimal control technique to establish and ensure that the total operation cost of multi-energy systems remains at a minimum while fulfilling all system constraints. However, this method presumes an adequate model of the underlying system dynamics, which is prone to modelling errors and is not necessarily adaptive. This has an associated initial and ongoing project-specific engineering cost. In this paper, we present an on- and off-policy multi-objective reinforcement learning (RL) approach, that does not assume a model a priori, benchmarking this against a linear MPC (LMPC - to reflect current practice, though non-linear MPC performs better) - both derived from the general optimal control problem, highlighting their differences and similarities. In a simple multi-energy system (MES) configuration case study, we show that a twin delayed deep deterministic policy gradient (TD3) RL agent offers potential to match and outperform the perfect foresight LMPC benchmark (101.5%). This while the realistic LMPC, i.e. imperfect predictions, only achieves 98%. While in a more complex MES system configuration, the RL agent's performance is generally lower (94.6%), yet still better than the realistic LMPC (88.9%). In both case studies, the RL agents outperformed the realistic LMPC after a training period of 2 years using quarterly interactions with the environment. We conclude that reinforcement learning is a viable optimal control technique for multi-energy systems given adequate constraint handling and pre-training, to avoid unsafe interactions and long training periods, as is proposed in fundamental future work.

eess.SY↗

arXiv2020

Simulation of long-term time series of solar photovoltaic power: is the ERA5-land reanalysis the next big step?

Modelling long time series of photovoltaic electricity generation in high temporal resolution using reanalysis data has become a commonly used alternative to assess the viability of systems with high shares of renewables, their risks of failure and probability of extreme events. While there is a considerable amount of literature evaluating the accuracy of the original solar radiation and temperature variables in these data sets, the validation of the calculated output of photovoltaic installations is scarce and usually limited to locations in Europe. This work combines the new ERA5-land reanalysis data set and PV_LIB to generate hourly time series of photovoltaic electricity generation for several years and validates the results using individual data of 57 large photovoltaic plants located in Chile. Results are also compared with PV output for these locations calculated using renewables.ninja, a platform relying on MERRA-2, a global reanalysis with five times lower spatial resolution. Accuracy and bias indicators are satisfactory for plants that do not present severe anomalies in their generation profiles and where basic plant characteristics such as size and orientation match our model assumptions. However, the improvements in indicators over results obtained with renewables.ninja from MERRA-2 are minor. The validation process serves not only to confirm the suitability of the proposed workflow to model the output of individual photovoltaic plants, but also to list and discuss data quality and availability issues. Efforts towards availability and standardization of data of individual installations are necessary to improve the basis for further developments.

physics.soc-ph↗