ORCID Profile
0000-0001-8881-4903
Current Organisation
Schneider Electric (Australia)
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Publisher: Institution of Engineering and Technology (IET)
Date: 11-2016
Publisher: MDPI AG
Date: 23-02-2019
DOI: 10.3390/SU11041188
Abstract: Combating climate change issues resulting from excessive use of fossil fuels comes with huge initial costs, thereby posing difficult challenges for the least developed countries in Sub-Saharan Africa (SSA) to invest in renewable energy alternatives, especially with rapid industrialization. However, designing renewable energy systems usually hinges on different economic and environmental criteria. This paper used the Multi-Objective Particle Swarm Optimization (MOPSO) technique to optimally size ten grid-connected hybrid blocks selected amongst Photo-Voltaic (PV) panels, onshore wind turbines, biomass combustion plant using sugarcane bagasse, Battery Energy Storage System (BESS), and Diesel Generation (DG) system as backup power, to reduce the supply deficit in Sierra Leone. Resource assessment using well-known methods was done for PV, wind, and biomass for proposed plant sites in Kabala District in Northern and Kenema District in Southern Sierra Leone. Long term analysis was done for the ten hybrid blocks projected over 20 years whilst ensuring the following objectives: minimizing the Deficiency of Power Supply Probability (DPSP), Diesel Energy Fraction (DEF), Life Cycle Costs (LCC), and carbon dioxide (CO 2 ) emissions. Capacity factors of 27.41 % and 31.6 % obtained for PV and wind, respectively, indicate that Kabala district is the most feasible location for PV and wind farm installations. The optimum results obtained are compared across selected blocks for DPSP values of 0–50% to determine the most economical and environmentally friendly alternative that policy makers in Sierra Leone and the region could apply to similar cases.
Publisher: Institution of Engineering and Technology (IET)
Date: 03-2014
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 06-2011
Publisher: MDPI AG
Date: 17-05-2019
DOI: 10.3390/SU11102828
Abstract: Like in most developing countries, meeting the load demand and reduction in transmission grid bottlenecks remains a significant challenge for the power sector in Sierra Leone. In recent years, research attention has shifted to demand response (DR) programs geared towards improving the supply availability and quality of energy markets in developed countries. However, very few studies have discussed the implementation of suitable DR programs for developing countries, especially when utilizing renewable energy (RE) resources. In this paper, using the Freetown’s peak load demand data and the price elasticity concept, the interruptible demand response (DR) program has been considered for maximum demand index (MDI) customers. Economic analysis of the energy consumption, customer incentives, benefits, penalties and the impact on the load demand are analyzed, with optimally designed energy management for grid-integrated battery energy storage system (BESS) and photovoltaic (PV)-hybrid system using the genetic algorithm (GA). Five scenarios are considered to confirm the effectiveness and robustness of the proposed scheme. The results show the economic superiority of the proposed DR program’s approach for both customers and supplier benefits. Moreover, RE inclusion proved to be a practical approach over the project lifespan, compared to the diesel generation alternative.
Publisher: IEEE
Date: 12-2012
Publisher: Wiley
Date: 31-08-2009
DOI: 10.1002/TEE.20454
Publisher: IEEE
Date: 09-2018
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 2018
Publisher: Wiley
Date: 31-08-2009
DOI: 10.1002/TEE.20453
Publisher: IEEE
Date: 10-2009
Publisher: IEEE
Date: 12-2008
Publisher: Springer Japan
Date: 06-11-2014
Publisher: IEEE
Date: 11-2015
Publisher: Elsevier BV
Date: 06-2015
Publisher: Elsevier BV
Date: 11-2016
Publisher: Elsevier BV
Date: 2015
Publisher: Institution of Engineering and Technology (IET)
Date: 2011
Publisher: IEEE
Date: 2013
Publisher: Institution of Engineering and Technology (IET)
Date: 11-2009
Publisher: IEEE
Date: 10-2010
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 11-2009
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 12-2012
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 04-2022
Publisher: IEEE
Date: 10-2014
Publisher: IEEE
Date: 10-2018
Publisher: IEEE
Date: 10-2018
Publisher: Wiley
Date: 12-06-2012
DOI: 10.1002/TEE.21759
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2013
Publisher: Elsevier BV
Date: 2009
Publisher: MDPI AG
Date: 30-09-2019
DOI: 10.3390/SU11195436
Abstract: With the development of distributed energy resources (DERs) and advancements in technology, microgrids (MGs) appear primed to become an even more integral part of the future distribution grid. In order to transition to the smart grid of the future, MGs must be properly managed and controlled. This paper proposes a microgrid energy management system (MGEMS) based on a hybrid control algorithm that combines Transactive Control (TC) and Model Predictive Control (MPC) for an efficient management of DERs in prosumer-centric networked MGs. A locally installed home energy management system (HEMS) determines a charge schedule for the battery electric vehicle (BEV) and a charge–discharge schedule for the solar photovoltaic (PV) and battery energy storage system (BESS) to reduce residential customers’ operation cost and to improve their overall savings. The proposed networked MGEMS strategy was implemented in IEEE 33-bus test system and evaluated under different BEV and PV-BESS penetration scenarios to study the potential impact that large amounts of BEV and PV-BESS systems can have on the distribution system and how different pricing mechanisms can mitigate these impacts. Test results indicate that our proposed MGEMS strategy shows potential to reduce peak load and power losses as well as to enhance customers’ savings.
Publisher: IEEE
Date: 06-2011
Publisher: Wiley
Date: 12-06-2012
DOI: 10.1002/TEE.21761
Publisher: Public Library of Science (PLoS)
Date: 16-03-2017
Publisher: IEEE
Date: 11-2016
Publisher: MDPI AG
Date: 29-09-2019
DOI: 10.3390/APP9194064
Abstract: Renewable energies (REs) such as photovoltaic generation (PV) have been gaining attention in distribution systems. Recently, houses with PV and battery systems, as well as electric vehicles (EV) are expected to contribute to not only the suppression of global warming but also reducing electricity bill on the consumer side. However, there are numerous challenges with the introduction of REs at the demand side such as the actual output of REs often deviating from the forecasted output, which causes fluctuation of the power flow and this is challenging for the distribution or transmission system operator. For this challenge, it is expected that smart grid technology using controllable loads such as a fixed battery or EV battery, can suppress fluctuation of power flow. This paper presents a decision method of optimal scheduling of controllable loads to suppress the fluctuation of power flow by PV output in the smart home. An optimization method to cope with uncertainties such as variability of PV power and effective forecasting methods are considered in the proposed scheme. In order to decrease the expected operational cost and to validate the robustness for the uncertainty’s optimization approach, statistical analysis is executed for the optimal scheduling scheme. From the optimization results, the proposed methodology suppressed the fluctuation of power flow in the smart home and also minimized the consumer operational cost.
Publisher: Information Processing Society of Japan
Date: 2014
Publisher: IEEE
Date: 10-2018
Publisher: Elsevier BV
Date: 06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: IEEE
Date: 10-2010
Publisher: IEEE
Date: 10-2013
Publisher: Walter de Gruyter GmbH
Date: 03-04-0029
Abstract: In the framework of the competitive electricity markets, electricity price forecasting is important for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested interval of the peak electricity price forecasting. Forecasting the peak price is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. The choice of the forecasting model becomes the important influence factor how to improve price forecasting accuracy. This paper proposes new approach to reduce the prediction error at occurrence time of the peak electricity price, and aims to enhance the accuracy of the next day electricity price forecasting. In the proposed method, the weekly variation data is used for input factors of the ANN at occurrence time of the peak electricity price in order to catch the price variation. Moreover, learning data for the ANN is selected by rough sets theory at occurrence time of the peak electricity price. This method is examined by using the data of the PJM electricity market. From the simulation results, it is observed that the proposed method provides a more accurate and effective forecasting, which helpful for suitable bidding strategy and risk management tool for market participants in a deregulated electricity market.
Publisher: IEEE
Date: 11-2016
Publisher: IEEE
Date: 10-2009
Publisher: Elsevier BV
Date: 12-2012
No related grants have been discovered for Shantanu Chakraborty.