Imthias Ahamed

APJ Abdul kalam technological university, Centre for Artificial Intelligence, Faculty Member

King Saud University, College of Engineering, Faculty Member

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David Pierre Leibovitz

Carleton University

Don Ross

University College Cork

Armando Marques-Guedes

UNL - New University of Lisbon

Dr. Rohit Bhakar

Malaviya National Institute of Technology, Jaipur

Gary Feinman

Field Museum

Reza Arghandeh

Western Norway University of Applied Science

Sam Koohi-Kamali

University of Malaya, Malaysia

Iain Staffell

Imperial College London

Faisal Rahiman Pazheri

King Saud University

Canan Acar

University of Twente

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Papers by Imthias Ahamed

Centralized power management and control of a Low Voltage DC Nanogrid

Energy Reports, Oct 1, 2023

Residential Load Scheduling With Renewable Generation in the Smart Grid: A Reinforcement Learning Approach

IEEE Systems Journal, Sep 1, 2019

The significance and need of demand response (DR) programs is realized by the utility as a means ... more The significance and need of demand response (DR) programs is realized by the utility as a means to reduce the additional production cost imposed by the accelerating energy demand. With the development in smart information and communication systems, the price-based DR programs can be effectively utilized for controlling the loads of smart residential buildings. Nowadays, the use of stochastic renewable energy sources like photovoltaic (PV) by a small domestic consumer is increasing. In this paper, a generalized model for the residential load scheduling or load commitment problem (LCP) in the presence of renewable sources for any type of tariff is presented. Reinforcement learning (RL) is an efficient tool that has been used to solve the decision making problem under uncertainty. An RL-based approach to solve the LCP is also proposed. The novelty of this paper lies in the introduction of a comprehensive model with implementable solution considering consumer comfort, stochastic renewable power, and tariff. Simulation experiments are conducted to test the efficacy and scalability of the proposed algorithm. The performance of the algorithm is investigated by considering a domestic consumer with schedulable and nonschedulable appliances along with a PV source. Guidelines are given for choosing the parameters of the load.

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A function approximation approach to Reinforcement Learning for solving unit commitment problem with Photo voltaic sources

Day ahead scheduling of power generation resources has turned out to be complex and challenging o... more Day ahead scheduling of power generation resources has turned out to be complex and challenging optimization problem with the integration of Renewable energy sources to the power grid. Photovoltaic sources are the most significant and abundant renewable energy source being integrated to the power system. Due to the stochastic nature of the solar irradiance and the associated power, scheduling of the various generating sources including solar power turns to be a stochastic optimization problem. Reinforcement Learning is an optimization method which can accommodate the uncertain nature of the problem environment very effectively. When the solution space becomes very large, simple reinforcement Learning solution through look up table approach becomes more complex and time consuming. In this paper we propose a Neural Network based Reinforcement learning method for the optimal commitment in the presence of Photo voltaic sources. The algorithm is found to be effective for day ahead solution in a smart grid with stochastic renewable energy sources.

Conceptual Framework for Load Controlling: With Demand Reduction Bidding & Consumer Retention

Advancement in demand side management strategies enables smart grid to cope with the increasing e... more Advancement in demand side management strategies enables smart grid to cope with the increasing energy demand and provide economic benefit to all of its stakeholders. Moreover, emerging concept of smart pricing and advances in load control and communication generate new business opportunities as a third party entities between the electricity supply system and the consumers. In this research, a mathematical framework for the future third party energy service providers with an aim to benefit all the stake holders and also facilitate consumers to actively participate in Demand Side Management (DSM) by demand reduction bidding against power reduction with some constraints. This work also develops a novel scheduling algorithm with consumer retention for aggregated demand response in smart distribution network.

Short Term Hydrothermal Scheduling Using Reinforcement Learning

An optimal solution to the problem of hydrothermal scheduling, using Reinforcement Learning, is p... more An optimal solution to the problem of hydrothermal scheduling, using Reinforcement Learning, is presented in this paper. The objective is to minimize the cost of the thermal unit, by making use of the available hydropower, subjected to various equality and inequality constraints. The cost function with and without valve loading effect have been considered. MATLAB software was used to schedule a base system comprising a single hydrothermal unit. The Reinforcement Learning algorithm employed uses the Q learning technique for obtaining the optimal policy. The Epsilon greedy algorithm has been used to select the actions during the learning phase. The results obtained for the simple hydrothermal system for both cost functions have been presented. The optimal schedule obtained incurs a cost that is much below that obtained in literature.

Parallel Concatenated Gallager Code with Single Encoder for Cognitive Radio Applications

2019 9th International Conference on Advances in Computing and Communication (ICACC)

Recently, wireless communication operators identify the spectrum scarcity as a major problem to b... more Recently, wireless communication operators identify the spectrum scarcity as a major problem to be solved. The revolutionary concept of Cognitive radio addresses this issue by providing opportunistic access of licensed bands to unlicensed users. The technology offers many challenges among which the reliable transmission of secondary user data is the significant one. The constant interference from the licensed users affects the secondary user communication thereby affecting the reliable communication in cognitive radio networks. Error control coding can be used to reduce the impact of interference from incumbent users. Low density parity check codes are used in cognitive radio networks for ensuring reliable data transmission. However, as the block length increases, these codes have high encoding and decoding complexity. Parallel concatenated Gallager codes overcome these drawbacks. We analyse the performance of proposed parallel concatenated Gallager codes with single encoder for secondary users in a dynamic network. Results confirm that these codes attained the performance of a dedicated code with reduced complexity and less decoding delay.

Investigation of Choice of State Space for Reinforcement Learning Based Battery Scheduling in Microgrid

2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

Optimal Microgrid Battery Scheduling Using Simulated Annealing

2020 International Conference on Power Electronics and Renewable Energy Applications (PEREA), 2020

Microgrid represents a propitious concept capable of solving numerous problems in conventional po... more Microgrid represents a propitious concept capable of solving numerous problems in conventional power systems such as accuracy and economical energy supplied by integrating various renewable/distributed energy resources. The intermit- tency and variability of such resources are a great concern for the microgrid operation. This issue can be solved by using battery energy storage system. In this paper, an optimization of battery scheduling for residential microgrid application is done using Simulated Annealing optimization tool. Optimal battery scheduling is to be done, in order to reduce the energy consumption from main grid and thus, reduce the energy cost for the end users.

Design and Simulation of Stand-alone DC Microgrid with Energy Storage System

2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), 2019

Use of renewable energy sources significantly reduces the fuel consumption for electricity genera... more Use of renewable energy sources significantly reduces the fuel consumption for electricity generation which in turn trims down the greenhouse gas emissions. The concept of Microgrid and Smart grid has evolved from the deployment of Renewable Energy based power generation plants, their integration into the existing utility grid. Simulation and analysis of a standalone DC Microgrid is presented in this paper. Solar PV (SPV) system is the main source of the grid, battery is used as Energy Storage System (ESS). A simulation model of DC Microgrid is built in MATLAB/Simulink. The designed system is simulated under various input conditions, load variations tostudy and analyze the performance of whole system. The simulation results obtained under different conditions from various stages of the DC microgrid components are presented and discussed.

Learning Automata and Soft Computing Techniques Based Maximum Power Point Tracking for Solar PV Systems

Intelligent Paradigms for Smart Grid and Renewable Energy Systems, 2020

GA-Optimized Fuzzy-Based MPPT Technique for Abruptly Varying Environmental Conditions

Journal of The Institution of Engineers (India): Series B, 2021

This paper presents and discusses the Fuzzybased MPPT technique optimized using the Genetic Algor... more This paper presents and discusses the Fuzzybased MPPT technique optimized using the Genetic Algorithm (GA). The proposed GA simultaneously produces optimized ranges of both membership functions and the rule base of fuzzy. MATLAB coded GA to optimize the Fuzzy Logic Controller (FLC) is integrated with the Simulink model of the Photovoltaic (PV) system and training is performed online by operating the PV system for different conditions. GA provides the optimized membership functions and rule base of FLC upon completion of training. FLC is developed using the optimized values obtained from the training. The SPV system model with the GA-optimized fuzzy MPPT is built and simulation is performed. For a more realistic study, analysis of PV system under abruptly varying weather conditions is carried out using real-time data of a particular day on which the changes are very frequent. Besides, simulation of solar PV system is carried out with fuzzy MPPT and Artificial Neuro Fuzzy Inference System (ANFIS) MPPT for similar cases, the results are presented and discussed. The results show that the GA-optimized FLC-based MPP tracking method has better performance with improved tracking accuracy and faster response under all weather conditions. Keywords Maximum power point tracking Á Genetic algorithm Á Fuzzy logic control Á ANFIS Á MATLAB

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Wideband spectrum sensing using multicoset sampling and extended orthogonal matching pursuit

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MICROELECTRONICS, SIGNALS AND SYSTEMS 2019, 2020

Spectrum sensing is one among the major functions of Cognitive radio systems. Meanwhile, wideband... more Spectrum sensing is one among the major functions of Cognitive radio systems. Meanwhile, wideband spectrum sensing is a challenging issue with these systems. Conventional wideband techniques require analog-to-digital converters operating at Nyquist sampling rates. Sub-Nyquist sampling or compressed sensing techniques, however, require low rate analog-to-digital converters. In order to avoid interference to primary user transmissions, the cognitive users should perform continuous spectrum sensing and identify the active primary bands even at low-signal-to-noise ratios. In this paper, we propose a sub-Nyquist wideband spectrum sensing technique using multicoset sampling and revised orthogonal matching pursuit (OMP). Conventional orthogonal matching pursuit algorithm uses n iterations to recover n-sparse signal. Extending the iterations beyond n further improves the performance, for instance, extended OMP (OMP α) and sparsity unaware OMP (OMP ∞) achieves a better detection capability by increasing the number of iterations beyond n. We analyse the performance of multicoset sampling based wideband spectrum sensing with various OMP algorithms.

Residential load scheduling considering maximum demand using binary particle swarm optimisation

International Journal of Advanced Intelligence Paradigms, 2020

Demand response (DR) programs are gaining importance in smart grid, owing to the continuously inc... more Demand response (DR) programs are gaining importance in smart grid, owing to the continuously increasing energy demand. The primary objective of DR programs is to motivate consumers to change the power consumption pattern to limit the maximum demand. The success of residential DR programs largely depends on schedulable loads and the nature of utility tariff. Binary particle swarm optimisation (BPSO) is an effective tool for solving scheduling problems. In this paper, a BPSO based solution is presented through a case study for the residential load scheduling problem including the consumer demand constraints and maximum demand (MD) limit specified by the utility. The objective of the algorithm is to automatically schedule the consumer's load so as to minimise the energy cost subject to various constraints. Simulation experiments are conducted under different tariff and MD limit conditions. Test results show that the proposed method saves energy cost of the domestic consumer and reduces the maximum demand on the system.

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Learning Automata based fuzzy MPPT controller for solar photovoltaic system under fast changing environmental conditions

Journal of Intelligent & Fuzzy Systems, 2017

Optimization of fuzzy Maximum Power Point Tracking (MPPT) controller using Learning Automata (LA)... more Optimization of fuzzy Maximum Power Point Tracking (MPPT) controller using Learning Automata (LA) algorithm is proposed in this paper. The optimal duty cycle of the DC-DC converter circuit is obtained using LA for various environmental conditions through learning process. The fuzzy MPPT controller is developed using the information collected by LA through the learning process. The proposed model is developed and tested using MATLAB for standard test conditions of PV, constant temperature and varying irradiation level, constant irradiation and varying temperature level, and varying temperature and varying irradiation level. The results obtained using the proposed fuzzy MPPT are compared with the conventional Perturb and Observe (P&O) MPPT and variable step size Fuzzy MPPT based PV system. The experimental set up is developed and the test is conducted under different conditions for the solar PV system with P&O MPPT and the proposed LA Fuzzy MPPT. The results show that the proposed LA based Fuzzy MPPT method is more accurate and its tracking response is faster.

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The Consumer Rationality Assumption in Incentive Based Demand Response Program via Reduction Bidding

Journal of Electrical Engineering and Technology, 2015

Maximum power point tracking system for stand alone solar PV power system using Adaptive Neuro-Fuzzy Inference System

2016 Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy (PESTSE), 2016

A Maximum Power Point Tracking controller for stand alone solar PV power system is presented in t... more A Maximum Power Point Tracking controller for stand alone solar PV power system is presented in this paper. A solar PV power system consists of PV Module, DC-DC boost converter and an Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller is developed in MATLAB/Simulink. The operation of the DC-DC Boost converter in the PV system is controlled by ANFIS MPPT controller. The ANFIS model is trained using randomly selected data obatined for various temperature and irradiation levels. The cell temperature and irradiation level are the input to the controller and the output of the controller is duty cycle. The model is simulated for various weather conditions and the simulation results are obtained. Performance of proposed ANFIS MPPT controller is analysed by comparing its results with the PV systems without MPPT and PV systems with Incremental Conductance MPPT.

Modeling, Simulation and Analysis of Photovoltaic Modules under Partially Shaded Conditions

Indian Journal of Science and Technology, 2016

The power output of photovoltaic (PV) cells is influenced by the environmental conditions, partic... more The power output of photovoltaic (PV) cells is influenced by the environmental conditions, particularly by solar irradiation level. The irradiation level of light falling on all the cells in the module or the PV modules in an array is uneven under the cloudy weather conditions; this produces multiple peaks in the output of PV modules. It is essential to have a model of PV module in which the partial shading effect can be analyzed so that an effective Maximum Power Point Tracking (MPPT) system can be implemented for a PV system. This paper presents the modeling and performance analysis of PV modules under partial shaded conditions. A generalized PV module model is developed in MATLAB based on the mathematical equation of PV cell. The model is simulated for analysis of partially shaded cells in the PV module and also for partial shaded modules in the PV string for various weather conditions. The obtained results are satisfactory and confirm the accuracy of the developed PV module model. The developed model can be used for simulation and analysis of any PV modules under partially shaded conditions and also for development of PV power system with MPPT.

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Load scheduling problems under demand response schemes: A survey

2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), 2015

Demand Response is a major component of Smart Grid. For Demand Response(DR) schemes to succeed, c... more Demand Response is a major component of Smart Grid. For Demand Response(DR) schemes to succeed, consumers should schedule their load. This problem is an active area of research now. Various optimization models have been proposed by researchers. There are works related to specific appliance models which are more energy consuming and models which pertains to general schedulable loads. The various DR methods provide objective function for minimization of energy cost along with satisfying the Maximum Demand limit and device operating constraints. This paper surveys the different models being developed by various researchers for Demand Response in residential power sector and the solution methods suggested for the same. It gives an overview of the current status of research in this field.

Application of Reinforcement Learning Algorithm for Scheduling of Microgrid

2019 Global Conference for Advancement in Technology (GCAT), Oct 1, 2019

Automation is a main option for improving the flexibility of irrigation canals. Control systems a... more Automation is a main option for improving the flexibility of irrigation canals. Control systems are required for this task. The main element of the control systems is a control algorithm. There are different control algorithms which have been developed. Investigation of the developed algorithms showed they have some limitations in terms of accuracy and time. These limitations make the researchers develop and introduce new and better control algorithms. Reinforcement learning (RL), which is a branch of artificial intelligence systems, is a powerful control algorithm successfully applied in robot control and in industry. In this research, a mathematical model of the RL algorithm was developed to control water depth upstream of check structures. RL learns the map of water depth-check structures adjustment by maximizing a reward function. To test the RL algorithm, a mathematical model of a flume was used. Different scenarios of inflow increase and decrease were simulated. System response time (SRT), maximum absolute error (MAE), and integral of absolute magnitude of error (IAE) indicators were used to evaluate RL. Results were obtained and analysed. Low depth variations and performance indicators were shown. The maximum values of SRT, MAE and IAE were obtained as 252 s, 3.07% and 0.152%, respectively. Results showed the RL algorithm is a beneficial control system and can be applied in irrigation canals.

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Comparison of pursuit and &#x03B5;-Greedy algorithm for load scheduling under real time pricing

ABSTRACT Demand Response (DR) is a useful tool to develop a balance between the available generat... more ABSTRACT Demand Response (DR) is a useful tool to develop a balance between the available generation and loads under smart grid environment. There are various price based schemes to implement DR and flatten the load profile. Hence, for the benefit of customers, proper load scheduling is required to lower the usage of electricity during peak load periods in order to decrease the electricity cost. This work formulates load scheduling as multi stage decision making problem or Markov Decision Problem (MDP). Reinforcement learning (RL) has been used to solve many decision making problems under stochastic environment. ε-Greedy algorithm is the most popular exploration method used in RL. In this paper, pursuit algorithm is developed to achieve a balance between exploration and exploitation process of the RL. The performance of both the algorithms is compared which shows the supremacy of Pursuit Algorithm over ε-greedy algorithm.

Centralized power management and control of a Low Voltage DC Nanogrid

Energy Reports, Oct 1, 2023

Residential Load Scheduling With Renewable Generation in the Smart Grid: A Reinforcement Learning Approach

IEEE Systems Journal, Sep 1, 2019

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A function approximation approach to Reinforcement Learning for solving unit commitment problem with Photo voltaic sources