Hanane Dagdougui

Smart greenhouses can be defined as cutting-edge technological systems that efficiently control indoor climate conditions to protect crops f… (voir plus)rom harsh outdoor conditions to increase their productivity. In this article, we developed and implemented a robust model predictive control approach that relies on a recursive state estimation method to cope with the impact of measurement and process signal errors. The aim of this approach is to optimally control the internal environment of intelligent greenhouses. A feedback policy problem is decomposing signals for the accessibility of uncertainties. Then, a robust feasibility set can be defined by determining the ellipsoid set on uncertainty to obtain solvable constrained optimization in the CPLEX solver. In the overall formulation, each greenhouse is considered as an independent element. This method can improve the quality of set-point tracking while reducing the computation time required to arrive at a solution. Extensive numerical simulations involving the application of an innovative and robust algorithm to a cluster of greenhouses were conducted to demonstrate the algorithm’s performance and effectiveness.

2024-12-31

IEEE Transactions on AgriFood Electronics (publié)

The Bifurcation Method: White-Box Observation Perturbation Attacks on Reinforcement Learning Agents on a Cyber Physical System

KIERNAN BRODA-MILIAN

Ranwa Al Mallah

2024-09-19

International Conference on Machine Learning and Computing (publié)

Neural differential equations for temperature control in buildings under demand response programs

Mathieu Le Cam

2024-07-31

Applied Energy (publié)

Learn-To-Design: Reinforcement Learning-Assisted Chemical Process Optimization

Eslam G. Al-Sakkari

Ahmed Ragab

Mohamed Ali

Daria C. Boffito

Mouloud Amazouz

This paper proposes an AI-assisted approach aimed at accelerating chemical process design through causal incremental reinforcement learning … (voir plus)(CIRL) where an intelligent agent is interacting iteratively with a process simulation environment (e.g., Aspen HYSYS, DWSIM, etc.). The proposed approach is based on an incremental learnable optimizer capable of guiding multi-objective optimization towards optimal design variable configurations, depending on several factors including the problem complexity, selected RL algorithm and hyperparameters tuning. One advantage of this approach is that the agent-simulator interaction significantly reduces the vast search space of design variables, leading to an accelerated and optimized design process. This is a generic causal approach that enables the exploration of new process configurations and provides actionable insights to designers to improve not only the process design but also the design process across various applications. The approach was validated on industrial processes including an absorption-based carbon capture, considering the economic and technological uncertainties of different capture processes, such as energy price, production cost, and storage capacity. It achieved a cost reduction of up to 5.5% for the designed capture process, after a few iterations, while also providing the designer with actionable insights. From a broader perspective, the proposed approach paves the way for accelerating the adoption of decarbonization technologies (CCUS value chains, clean fuel production, etc.) at a larger scale, thus catalyzing climate change mitigation.

2024-07-08

Systems and Control Transactions (publié)

A Novel Bifurcation Method for Observation Perturbation Attacks on Reinforcement Learning Agents: Load Altering Attacks on a Cyber Physical Power System

KIERNAN BRODA-MILIAN

Ranwa Al Mallah

Components of cyber physical systems, which affect real-world processes, are often exposed to the internet. Replacing conventional control m… (voir plus)ethods with Deep Reinforcement Learning (DRL) in energy systems is an active area of research, as these systems become increasingly complex with the advent of renewable energy sources and the desire to improve their efficiency. Artificial Neural Networks (ANN) are vulnerable to specific perturbations of their inputs or features, called adversarial examples. These perturbations are difficult to detect when properly regularized, but have significant effects on the ANN's output. Because DRL uses ANN to map optimal actions to observations, they are similarly vulnerable to adversarial examples. This work proposes a novel attack technique for continuous control using Group Difference Logits loss with a bifurcation layer. By combining aspects of targeted and untargeted attacks, the attack significantly increases the impact compared to an untargeted attack, with drastically smaller distortions than an optimally targeted attack. We demonstrate the impacts of powerful gradient-based attacks in a realistic smart energy environment, show how the impacts change with different DRL agents and training procedures, and use statistical and time-series analysis to evaluate attacks' stealth. The results show that adversarial attacks can have significant impacts on DRL controllers, and constraining an attack's perturbations makes it difficult to detect. However, certain DRL architectures are far more robust, and robust training methods can further reduce the impact.

2024-07-05

ArXiv (prépublication)

arxiv.org

Towards a framework selection for assessing the performance of photovoltaic solar power plants: criteria determination

Meryam Chafiq

Ismail Belhaj

Abdelali Djdiaa

Hicham Bouzekri

Abdelaziz Berrado

Mastery of Key Performance Indicators (KPIs) in the realm of photovoltaic solar power plants is pivotal for evaluating their effectiveness a… (voir plus)nd fine-tuning their operational efficiency. The assessment of these plants' performance has con-sistently stood as a focal point in scientific research. Nevertheless, the investigation into the process of selecting a framework for classifying KPIs, particularly through their categorization based on criteria, sub-criteria, or aspects, has been relatively limited in research. This article addresses this gap by conducting a comprehensive literature review on various KPIs and, drawing upon both literature and practical experience, formulating a set of criteria to serve as the foundation for a Multi-Criteria Decision Analysis (MCDA) method. This intricate taxonomic framework enhances the understanding of infrastructure performance for stakeholders in the solar industry. By streamlining decision-making, it simplifies the selection of KPIs tailored to specific requirements, thus mitigating the complexity arising from the abundance of KPIs in the literature. As a result, decision-makers can make well-informed choices regarding the monitoring and evaluation framework that best suits the performance goals of their solar plant.

2024-05-15

2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) (publié)

ADMM-Based Hierarchical Single-Loop Framework for EV Charging Scheduling Considering Power Flow Constraints

Sina Kiani

Keyhan Sheshyekani

This article presents a three-layer hierarchical distributed framework for optimal electric vehicle charging scheduling (EVCS). The proposed… (voir plus) hierarchical EVCS structure includes a distribution system operator (DSO) at the top layer, electric vehicle aggregators (EVAs) at the middle layer, and electric vehicles (EVs) charging stations at the bottom layer. A single-loop iterative algorithm is developed to solve the EVCS problem by combining the alternating direction method of multipliers (ADMM) and the distribution line power flow model (DistFlow). Using the single-loop structure, the primal variables of all agents are updated simultaneously at every iteration resulting in a reduced number of iterations and faster convergence. The developed framework is employed to provide charging cost minimization at the EV charging stations level, peak load shaving at the EVAs level, and voltage regulation at the DSO level. In order to further improve the performance of the optimization framework, a neural network-based load forecasting model is implemented to include the uncertainties related to non-EV residential load demand. The efficiency and the optimality of the proposed EVCS framework are evaluated through numerical simulations, conducted for a modified IEEE 13 bus test feeder with different EV penetration levels.

2024-02-29

IEEE Transactions on Transportation Electrification (publié)

Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities

Eslam G. Al-Sakkari

Ahmed Ragab

Daria C. Boffito

Mouloud Amazouz

Carbon capture, utilization, and sequestration (CCUS) is a promising solution to decarbonize the energy and industrial sector to mitigate cl… (voir plus)imate change. An integrated assessment of technological options is required for the effective deployment of CCUS large-scale infrastructure between CO2 production and utilization/sequestration nodes. However, developing cost-effective strategies from engineering and operation perspectives to implement CCUS is challenging. This is due to the diversity of upstream emitting processes located in different geographical areas, available downstream utilization technologies, storage sites capacity/location, and current/future energy/emissions/economic conditions. This paper identifies the need to achieve a robust hybrid assessment tool for CCUS modeling, simulation, and optimization based mainly on artificial intelligence (AI) combined with mechanistic methods. Thus, a critical literature review is conducted to assess CCUS technologies and their related process modeling/simulation/optimization techniques, while evaluating the needs for improvements or new developments to reduce overall CCUS systems design and operation costs. These techniques include first principles- based and data-driven ones, i.e. AI and related machine learning (ML) methods. Besides, the paper gives an overview on the role of life cycle assessment (LCA) to evaluate CCUS systems where the combined LCA-AI approach is assessed. Other advanced methods based on the AI/ML capabilities/algorithms can be developed to optimize the whole CCUS value chain. Interpretable ML combined with explainable AI can accelerate optimum materials selection by giving strong rules which accelerates the design of capture/utilization plants afterwards. Besides, deep reinforcement learning (DRL) coupled with process simulations will accelerate process design/operation optimization through considering simultaneous optimization of equipment sizing and operating conditions. Moreover, generative deep learning (GDL) is a key solution to optimum capture/utilization materials design/discovery. All of these developed methods will be generalizable where the extracted knowledge can be transferred to future works to help cutting the costs of CCUS value chain.

2024-02-29

Science of The Total Environment (publié)

An Analytic Hierarchy Process based approach for assessing the performance of photovoltaic solar power plants

Meryam Chafiq

Ismail Belhaj

Abdelali Djdiaa

Hicham Bouzekri

Abdelaziz Berrado

2023-12-31

IFAC-PapersOnLine (publié)

Game Theoretical Formulation for Residential Community Microgrid via Mean Field Theory: Proof of Concept

Mohamad Aziz

Issmail ElHallaoui

Incentive-based demand response aggregators are widely recognized as a powerful strategy to increase the flexibility of residential communit… (voir plus)y MG (RCM) while allowing consumers’ assets to participate in the operation of the power system in critical peak times. RCM implementing demand response approaches are of high interest as collectively, they have a high impact on shaping the demand curve during peak time while providing a wide range of economic and technical benefits to consumers and utilities. The penetration of distributed energy resources such as battery energy storage and photovoltaic systems introduces additional flexibility to manage the community loads and increase revenue. This letter proposes a game theoretical formulation for an incentive-based residential community microgrid, where an incentive-based pricing mechanism is developed to encourage peak demand reduction and share the incentive demand curve with the residential community through the aggregator. The aggregator’s objective is to maximize the welfare of the residential community by finding the optimal community equilibrium electricity price. Each household communicates with each other and with the distributed system operator (DSO) through the aggregator and aims to minimize the local electricity cost.

2023-12-31

IEEE Control Systems Letters (publié)

Deep Learning Model for Multi-Step Ahead Prediction of Solar Irradiance: Case of Study of Morocco

Saad Benbrahim

Ismail Belhaj

Abdelali Djdiaa

Hicham Bouzekri

Abdelaziz Berrado

Accurate solar irradiance forecasting is crucial for managing energy generation and consumption in the rapidly evolving landscape of renewab… (voir plus)le energy. It enables renewable energy operators to make informed decisions and maximize their output. This study employs deep learning-based forecasting models to predict the Global Horizontal Irradiance (GHI) of the R&D platform situated in Ouarzazate, Morocco. A sensitivity analysis was conducted on multiple scenarios for a one day-ahead horizon. Moreover, a forecasting technique that encompasses numerous horizons, ranging from one day to three days in advance, was evaluated. The study's findings suggest that the encoder-decoder model we proposed exhibited superior performance compared to the other models tested and produced dependable predictions.

2023-11-21

2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA) (publié)

Towards an Effective Electrical Market Design: Identifying and Defining Key Criteria for Decision-Making

Souhaila Chiguer

Ismail Belhaj

Abdelali Djdiaa

Hicham Bouzekri

Abdelaziz Berrado

In our changing energy landscape, electricity is taking a major role in achieving decarbonization goals. Electricity can be a clean and effi… (voir plus)cient source of energy, and it is well-suited to help countries meet their climate goals. However, the electrical market is complex and constantly evolving, and it is important to carefully choose the design elements of the market to ensure that it is meeting its objectives. In this context, evaluating an electrical market's effectiveness requires a multifaceted approach that takes into account a range of elements, from environmental impact to economic viability. This paper provides an overview of several evaluation methods for different objectives to finally select the key criteria to consider in assisting decision-makers, regulators, and stakeholders in developing an electricity market that is not only effective but also reliable and sustainable.

2023-11-21

2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA) (publié)