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Shantanu Chakraborty
Energy Transition Hub, The University of Melbourne, Australia

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Journal article
Published: 28 November 2019 in Applied Energy
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This paper presents an automated peer-to-peer negotiation strategy for settling energy contracts among prosumers in a Residential Energy Cooperative considering heterogeneity prosumer preferences. The heterogeneity arises from prosumers’ evaluation of energy contracts through multiple societal and environmental criteria and the prosumers’ private preferences over those criteria. The prosumers engage in bilateral negotiations with peers to mutually agree on periodical energy contracts/loans consisting of the energy volume to be exchanged at that period and the return time of the exchanged energy. The negotiating prosumers navigate through a common negotiation domain consisting of potential energy contracts and evaluate those contracts from their valuations on the entailed criteria against a utility function that is robust against generation and demand uncertainty. From the repeated interactions, a prosumer gradually learns about the compatibility of its peers in reaching energy contracts that are closer to Nash solutions. Empirical evaluation on real demand, generation and storage profiles – in multiple system scales – illustrates that the proposed negotiation based strategy can increase the system efficiency (measured by utilitarian social welfare) and fairness (measured by Nash social welfare) over a baseline strategy and an individual flexibility control strategy representing the status quo strategy. We thus elicit system benefits from peer-to-peer flexibility exchange already without any central coordination and market operator, providing a simple yet flexible and effective paradigm that complements existing markets.

ACS Style

Shantanu Chakraborty; Tim Baarslag; Michael Kaisers. Automated peer-to-peer negotiation for energy contract settlements in residential cooperatives. Applied Energy 2019, 259, 114173 .

AMA Style

Shantanu Chakraborty, Tim Baarslag, Michael Kaisers. Automated peer-to-peer negotiation for energy contract settlements in residential cooperatives. Applied Energy. 2019; 259 ():114173.

Chicago/Turabian Style

Shantanu Chakraborty; Tim Baarslag; Michael Kaisers. 2019. "Automated peer-to-peer negotiation for energy contract settlements in residential cooperatives." Applied Energy 259, no. : 114173.

Preprint
Published: 26 November 2019
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This paper presents an automated peer-to-peer negotiation strategy for settling energy contracts among prosumers in a Residential Energy Cooperative considering heterogeneity prosumer preferences. The heterogeneity arises from prosumers' evaluation of energy contracts through multiple societal and environmental criteria and the prosumers' private preferences over those criteria. The prosumers engage in bilateral negotiations with peers to mutually agree on periodical energy contracts/loans consisting of the energy volume to be exchanged at that period and the return time of the exchanged energy. The negotiating prosumers navigate through a common negotiation domain consisting of potential energy contracts and evaluate those contracts from their valuations on the entailed criteria against a utility function that is robust against generation and demand uncertainty. From the repeated interactions, a prosumer gradually learns about the compatibility of its peers in reaching energy contracts that are closer to Nash solutions. Empirical evaluation on real demand, generation and storage profiles -- in multiple system scales -- illustrates that the proposed negotiation based strategy can increase the system efficiency (measured by utilitarian social welfare) and fairness (measured by Nash social welfare) over a baseline strategy and an individual flexibility control strategy representing the status quo strategy. We thus elicit system benefits from peer-to-peer flexibility exchange already without any central coordination and market operator, providing a simple yet flexible and effective paradigm that complements existing markets.

ACS Style

Shantanu Chakraborty; Tim Baarslag; Michael Kaisers. Automated Peer-to-peer Negotiation for Energy Contract Settlements in Residential Cooperatives. 2019, 1 .

AMA Style

Shantanu Chakraborty, Tim Baarslag, Michael Kaisers. Automated Peer-to-peer Negotiation for Energy Contract Settlements in Residential Cooperatives. . 2019; ():1.

Chicago/Turabian Style

Shantanu Chakraborty; Tim Baarslag; Michael Kaisers. 2019. "Automated Peer-to-peer Negotiation for Energy Contract Settlements in Residential Cooperatives." , no. : 1.

Journal article
Published: 30 September 2019 in Sustainability
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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.

ACS Style

Eric Galvan; Paras Mandal; Shantanu Chakraborty; Tomonobu Senjyu. Efficient Energy-Management System Using A Hybrid Transactive-Model Predictive Control Mechanism for Prosumer-Centric Networked Microgrids. Sustainability 2019, 11, 5436 .

AMA Style

Eric Galvan, Paras Mandal, Shantanu Chakraborty, Tomonobu Senjyu. Efficient Energy-Management System Using A Hybrid Transactive-Model Predictive Control Mechanism for Prosumer-Centric Networked Microgrids. Sustainability. 2019; 11 (19):5436.

Chicago/Turabian Style

Eric Galvan; Paras Mandal; Shantanu Chakraborty; Tomonobu Senjyu. 2019. "Efficient Energy-Management System Using A Hybrid Transactive-Model Predictive Control Mechanism for Prosumer-Centric Networked Microgrids." Sustainability 11, no. 19: 5436.

Journal article
Published: 29 September 2019 in Applied Sciences
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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.

ACS Style

Akihiro Yoza; Kosuke Uchida; Shantanu Chakraborty; Narayanan Krishna; Mitsunaga Kinjo; Tomonobu Senjyu; Zengfeng Yan. Optimal Scheduling Method of Controllable Loads in Smart Home Considering Re-Forecast and Re-Plan for Uncertainties. Applied Sciences 2019, 9, 4064 .

AMA Style

Akihiro Yoza, Kosuke Uchida, Shantanu Chakraborty, Narayanan Krishna, Mitsunaga Kinjo, Tomonobu Senjyu, Zengfeng Yan. Optimal Scheduling Method of Controllable Loads in Smart Home Considering Re-Forecast and Re-Plan for Uncertainties. Applied Sciences. 2019; 9 (19):4064.

Chicago/Turabian Style

Akihiro Yoza; Kosuke Uchida; Shantanu Chakraborty; Narayanan Krishna; Mitsunaga Kinjo; Tomonobu Senjyu; Zengfeng Yan. 2019. "Optimal Scheduling Method of Controllable Loads in Smart Home Considering Re-Forecast and Re-Plan for Uncertainties." Applied Sciences 9, no. 19: 4064.

Journal article
Published: 17 May 2019 in Sustainability
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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.

ACS Style

Abdul Conteh; Mohammed Elsayed Lotfy; Kiptoo Mark Kipngetich; Tomonobu Senjyu; Paras Mandal; Shantanu Chakraborty. An Economic Analysis of Demand Side Management Considering Interruptible Load and Renewable Energy Integration: A Case Study of Freetown Sierra Leone. Sustainability 2019, 11, 2828 .

AMA Style

Abdul Conteh, Mohammed Elsayed Lotfy, Kiptoo Mark Kipngetich, Tomonobu Senjyu, Paras Mandal, Shantanu Chakraborty. An Economic Analysis of Demand Side Management Considering Interruptible Load and Renewable Energy Integration: A Case Study of Freetown Sierra Leone. Sustainability. 2019; 11 (10):2828.

Chicago/Turabian Style

Abdul Conteh; Mohammed Elsayed Lotfy; Kiptoo Mark Kipngetich; Tomonobu Senjyu; Paras Mandal; Shantanu Chakraborty. 2019. "An Economic Analysis of Demand Side Management Considering Interruptible Load and Renewable Energy Integration: A Case Study of Freetown Sierra Leone." Sustainability 11, no. 10: 2828.

Journal article
Published: 23 February 2019 in Sustainability
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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.

ACS Style

David Konneh; Harun Howlader; Ryuto Shigenobu; Tomonobu Senjyu; Shantanu Chakraborty; Narayanan Krishna. A Multi-Criteria Decision Maker for Grid-Connected Hybrid Renewable Energy Systems Selection Using Multi-Objective Particle Swarm Optimization. Sustainability 2019, 11, 1188 .

AMA Style

David Konneh, Harun Howlader, Ryuto Shigenobu, Tomonobu Senjyu, Shantanu Chakraborty, Narayanan Krishna. A Multi-Criteria Decision Maker for Grid-Connected Hybrid Renewable Energy Systems Selection Using Multi-Objective Particle Swarm Optimization. Sustainability. 2019; 11 (4):1188.

Chicago/Turabian Style

David Konneh; Harun Howlader; Ryuto Shigenobu; Tomonobu Senjyu; Shantanu Chakraborty; Narayanan Krishna. 2019. "A Multi-Criteria Decision Maker for Grid-Connected Hybrid Renewable Energy Systems Selection Using Multi-Objective Particle Swarm Optimization." Sustainability 11, no. 4: 1188.

Journal article
Published: 12 December 2018 in IFAC-PapersOnLine
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A landlocked country, Afghanistan, located between energy surplus regions and energy deficit areas is blessed with abundant renewable energy sources (RESs) which can exploit not only to cover its power demand but also to earn remarkable export revenue. This paper focuses on generation scheduling problem along with the optimal sizing of the hybrid renewable energy system (HRES) integrated with the northeast power system (NEPS) of Afghanistan to electrify northeast region of Afghanistan as well as to meet power shortages of Afghanistan’s longest shared border neighbor, Pakistan. The NEPS of Afghanistan is an isolated power system supplied by Afghanistan’s own generations and imported powers from Uzbekistan and Tajikistan. Genetic Algorithm (GA) is used to schedule all units’ output power as well as to find the on/off status of thermal units and the optimal values of the total area occupied by the set of photovoltaic (PV) panels, total swept area by the rotating turbines’ blades and the volume of the upper reservoir of pumped hydro energy storage (PHES) system. The objective of this research is to minimize the total operation cost of thermal units, aggregate imports power tariffs, and the total net present cost of HRES and to maximize the income from selling electricity to Pakistan.

ACS Style

Mohammad Masih Sediqi; Abdul Matin Ibrahimi; Mir Sayed Shah Danish; Tomonobu Senjyu; Shantanu Chakraborty; Paras Mandal. An Optimization Analysis of Cross-border Electricity Trading between Afghanistan and its Neighbor Countries. IFAC-PapersOnLine 2018, 51, 25 -30.

AMA Style

Mohammad Masih Sediqi, Abdul Matin Ibrahimi, Mir Sayed Shah Danish, Tomonobu Senjyu, Shantanu Chakraborty, Paras Mandal. An Optimization Analysis of Cross-border Electricity Trading between Afghanistan and its Neighbor Countries. IFAC-PapersOnLine. 2018; 51 (28):25-30.

Chicago/Turabian Style

Mohammad Masih Sediqi; Abdul Matin Ibrahimi; Mir Sayed Shah Danish; Tomonobu Senjyu; Shantanu Chakraborty; Paras Mandal. 2018. "An Optimization Analysis of Cross-border Electricity Trading between Afghanistan and its Neighbor Countries." IFAC-PapersOnLine 51, no. 28: 25-30.

Conference paper
Published: 01 October 2018 in 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)
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In recent years, much attention has been paid to Demand Response (DR) from the viewpoint of protecting the environment. The fluctuations in supply and demand are increased by the introduction of DR, which is not considered in most previous studies. The unit commitment that corresponds to the uncertainty of load demand and the operational maintaining the spinning reserves can be achieved by the proposed method in this paper. In this research, DR prediction for electricity prices was performed using a neural network.

ACS Style

Hiroki Aoyagi; Shantanu Chakraborty; Paras Mandal; Ryuto Shigenobu; Abdul Conteh; Tomonobu Senjyu. Unit Commitment Considering Uncertainty of Price-Based Demand Response. 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) 2018, 406 -410.

AMA Style

Hiroki Aoyagi, Shantanu Chakraborty, Paras Mandal, Ryuto Shigenobu, Abdul Conteh, Tomonobu Senjyu. Unit Commitment Considering Uncertainty of Price-Based Demand Response. 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). 2018; ():406-410.

Chicago/Turabian Style

Hiroki Aoyagi; Shantanu Chakraborty; Paras Mandal; Ryuto Shigenobu; Abdul Conteh; Tomonobu Senjyu. 2018. "Unit Commitment Considering Uncertainty of Price-Based Demand Response." 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) , no. : 406-410.

Conference paper
Published: 01 October 2018 in 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)
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The increasing number of residential energy cooperatives raises the importance of forming a local prosumer marketplace that is capable of managing energy and flexibility exchange efficiently. However, the coordination and control of independently operated flexible resources (e.g. storage, demand response) imposes critical challenges arising from the heterogeneity of the flexible resources, conflict of interests, and impact on the grid. Therefore, designing a simple yet efficient coordination mechanism that works on these distributed resources is of the utmost importance. We introduce a simulation model to study energy exchange with flexibility coordination while working towards an efficient allocation mechanism. A case study analysing different allocation mechanisms and consequent losses (compared to a base-case of No flexibility) in numerical experiments over real demand/generation profiles of the Pecan Street dataset elucidates the efficacy in energy and flexibility allocation while promoting cooperation between co-located flexibilities in residential cooperatives through local exchange.

ACS Style

Shantanu Chakraborty; Pablo Hernandez-Leal; Michael Kaisers. Coordinating Distributed and Flexible Resources: A Case-study of Residential Cooperatives. 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) 2018, 1 -6.

AMA Style

Shantanu Chakraborty, Pablo Hernandez-Leal, Michael Kaisers. Coordinating Distributed and Flexible Resources: A Case-study of Residential Cooperatives. 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). 2018; ():1-6.

Chicago/Turabian Style

Shantanu Chakraborty; Pablo Hernandez-Leal; Michael Kaisers. 2018. "Coordinating Distributed and Flexible Resources: A Case-study of Residential Cooperatives." 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) , no. : 1-6.

Conference paper
Published: 01 October 2018 in 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
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This paper presents an automated peer-to-peer (P2P) negotiation strategy for settling energy contracts among prosumers in a Residential Energy Cooperative (REC) considering heterogeneous prosumer preferences. The heterogeneity arises from prosumers' evaluation of energy contracts through multiple societal and environmental criteria and the prosumers' private preferences over those criteria. The prosumers engage in bilateral negotiations with peers to mutually agree on periodical energy contracts/loans that consist of an energy volume to be exchanged at that period and the return time of the exchanged energy. The prosumers keep an ordered preference profile of possible energy contracts by evaluating the contracts from their own valuations on the entailed criteria, and iteratively offer the peers contracts until an agreement is formed. A prosumer embeds the valuations into a utility function that further considers uncertainties imposed by demand and generation profiles. Empirical evaluation on real demand, generation and storage profiles illustrates that the proposed negotiation based strategy is able to increase the system efficiency (measured by utilitarian social welfare) and fairness (measured by Nash social welfare) over a baseline strategy and an individual flexibility control strategy. We thus elicit system benefits from P2P flexibility exchange already with few agents and without central coordination, providing a simple yet flexible and effective paradigm that may complement existing markets.

ACS Style

Shantanu Chakraborty; Tim Baarslag; Michael Kaisers. Energy Contract Settlements through Automated Negotiation in Residential Cooperatives. 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) 2018, 1 -6.

AMA Style

Shantanu Chakraborty, Tim Baarslag, Michael Kaisers. Energy Contract Settlements through Automated Negotiation in Residential Cooperatives. 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). 2018; ():1-6.

Chicago/Turabian Style

Shantanu Chakraborty; Tim Baarslag; Michael Kaisers. 2018. "Energy Contract Settlements through Automated Negotiation in Residential Cooperatives." 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) , no. : 1-6.

Proceedings article
Published: 01 September 2018 in 2018 North American Power Symposium (NAPS)
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This paper presents a transactive control (TC) mechanism for the management of battery energy storage systems (BESS) in residential networked microgrids (MGs) that contain loads, electric vehicles (EVs), and rooftop solar photovoltaic systems (PV). The goals of the TC are to maximize the savings of consumers and prosumers and to reduce peak load on local transformers. This is accomplished by utilizing local hybrid PV-BESS resources from prosumer community groups (PCGs), which are scheduled to offset peak loads. A model predictive control (MPC) based method is utilized to optimize the BESS scheduling. In the proposed TC, the PCGs are incentivized by the distribution system operator (DSO) through a dynamic price signal that is being updated hourly based on the MG local conditions. To evaluate the proposed TC, case studies are conducted on residential MGs located in an IEEE 33-bus test system. The evaluation indicates that the proposed TC can improve the savings of prosumers/consumers, reduce peak demand caused by EV charging in the distribution networks, and is able to alleviate undesired grid effects, e.g., transformer overloads.

ACS Style

Eric Galvan; Paras Mandal; Shantanu Chakraborty; Ahmed Y. Saber. Efficient Transactive Control for Energy Storage Management System in Prosumer-Centric Networked Microgrids. 2018 North American Power Symposium (NAPS) 2018, 1 -6.

AMA Style

Eric Galvan, Paras Mandal, Shantanu Chakraborty, Ahmed Y. Saber. Efficient Transactive Control for Energy Storage Management System in Prosumer-Centric Networked Microgrids. 2018 North American Power Symposium (NAPS). 2018; ():1-6.

Chicago/Turabian Style

Eric Galvan; Paras Mandal; Shantanu Chakraborty; Ahmed Y. Saber. 2018. "Efficient Transactive Control for Energy Storage Management System in Prosumer-Centric Networked Microgrids." 2018 North American Power Symposium (NAPS) , no. : 1-6.

Dataset
Published: 01 March 2018 in ENERGYO
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This paper presents a determination methodology for finding optimal operation schedules of thermal units (namely unit commitment) integrated with an energy storage system (ESS) to minimize total operating costs. A generic ESS formulation along with a method for solving unit commitment (UC) of thermal units with ESS is proposed to serve this purpose. The problem of unit commitment with an ESS is solved using the Priority List method. Intelligent Genetic algorithm (GA) is included in the algorithm for generating new and potential solutions. The proposed method consists of two steps. The first step is to determine the schedule of ESS and the schedule of thermal units. The second step is to dispatch the hourly output of thermal units and the ESS which comply a minimized total production cost. The proposed method is applied to a power system with ten thermal units and a large ESS. The presented simulation results show that the schedule of thermal units with an ESS of a particular life cycle, achieved by the proposed method, minimizes the operating cost. The discussion regarding the determination of schedule thermal units (TU) along with the integrated ESS may interest many types of ESS due to their generalized formulations.

ACS Style

Tomonobu Senjyu; Shantanu Chakraborty; Ahmed Yousuf Saber; Atsushi Yona; Toshihisa Funabashi. Determination of an Optimal Operating Schedule for Thermal Units with an Energy Storage System. ENERGYO 2018, 1 .

AMA Style

Tomonobu Senjyu, Shantanu Chakraborty, Ahmed Yousuf Saber, Atsushi Yona, Toshihisa Funabashi. Determination of an Optimal Operating Schedule for Thermal Units with an Energy Storage System. ENERGYO. 2018; ():1.

Chicago/Turabian Style

Tomonobu Senjyu; Shantanu Chakraborty; Ahmed Yousuf Saber; Atsushi Yona; Toshihisa Funabashi. 2018. "Determination of an Optimal Operating Schedule for Thermal Units with an Energy Storage System." ENERGYO , no. : 1.

Conference paper
Published: 01 October 2017 in 2017 17th International Conference on Control, Automation and Systems (ICCAS)
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ACS Style

Masahiro Furukakoi; Mohammad Masih Sediqi; Shantanu Chakraborty; Mohamed Ahmed Moustafa Hassan; Tomonobu Senjyu. Multi-objective optimal operation with demand management and voltage stability. 2017 17th International Conference on Control, Automation and Systems (ICCAS) 2017, 383 -388.

AMA Style

Masahiro Furukakoi, Mohammad Masih Sediqi, Shantanu Chakraborty, Mohamed Ahmed Moustafa Hassan, Tomonobu Senjyu. Multi-objective optimal operation with demand management and voltage stability. 2017 17th International Conference on Control, Automation and Systems (ICCAS). 2017; ():383-388.

Chicago/Turabian Style

Masahiro Furukakoi; Mohammad Masih Sediqi; Shantanu Chakraborty; Mohamed Ahmed Moustafa Hassan; Tomonobu Senjyu. 2017. "Multi-objective optimal operation with demand management and voltage stability." 2017 17th International Conference on Control, Automation and Systems (ICCAS) , no. : 383-388.

Proceedings article
Published: 26 December 2016 in 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)
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This paper presents a robust and optimal operation tracking Energy Management System (EMS) for Mobile Base Transceiver Station (BTS) Microgrid equipped with Battery, PV panels and Diesel Engine Generator (DEG) for unreliable grid. The contribution is particularly focused on minimizing the DEG fuel (with DEG ON/OFF frequency) at the time of power-grid outage (i.e. Blackout). The EMS is designed to work with less sophisticated local BTS controller. The prediction (load predictor, PV power generator predictor and blackout duration predictor) modules and the optimization modules are envisioned to be hosted in a server. The optimization module provides optimal battery control policies that are robust against the temporal uncertainty imposed by unknown blackout duration while minimizing the expected DEG fuel cost. The blackout duration predictor is facilitated by combining Linear Logistic Regression classifier and blackout duration frequency distribution. The load predictor utilizes Support Vector Regression for creating predictive models. The optimizer performs a local search over candidate solution space to seek the optimal control policy. Numerical simulation corroborates that the designed EMS, applied on 14 BTS sites in India, is capable of reducing 26% (on average) of DEG fuel compared to that of current operation over 7-Days.

ACS Style

Shantanu Chakraborty; Alexander Viehweider. Intelligent operation of a base transceiver station-microgrid EMS with unreliable grid supply. 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia) 2016, 675 -680.

AMA Style

Shantanu Chakraborty, Alexander Viehweider. Intelligent operation of a base transceiver station-microgrid EMS with unreliable grid supply. 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia). 2016; ():675-680.

Chicago/Turabian Style

Shantanu Chakraborty; Alexander Viehweider. 2016. "Intelligent operation of a base transceiver station-microgrid EMS with unreliable grid supply." 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia) , no. : 675-680.

Conference paper
Published: 12 December 2016 in 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm)
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A Power Producer and Supplier (PPS) in the deregulated energy market requires forward market interactions to procure energy as precisely as possible in order to reduce imbalance energy (on-line demand vs. supply gap). This paper presents, 1) (off-line) an effective demand aggregation based strategy for creating a number of balancing groups that leads to higher predictability of group-wise demand, 2) (on-line) a robust energy storage scheduling that minimizes the imbalance for a particular balancing group considering the demand prediction uncertainty. The group formation is performed by a probabilistic programming approach using Bayesian Markov Chain Monte Carlo (MCMC) method, applied on the historical demand statistics. An upper-limit of the storage rating/capacity is derived as a by-product of Bayesian MCMC, which is utilized in the on-line operation. A robust energy storage scheduling method is proposed that minimizes the on-line imbalance energy and cost (a non-linear function of imbalance energy) while incorporating the demand uncertainty of a particular group. The proposed methods are applied on the real apartment buildings' demand data in Tokyo (Japan). Simulation results are presented to verify the effectiveness of the proposed methods.

ACS Style

Shantanu Chakraborty; Toshiya Okabe. Energy storage scheduling for imbalance reduction of strategically formed balancing groups. 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm) 2016, 278 -283.

AMA Style

Shantanu Chakraborty, Toshiya Okabe. Energy storage scheduling for imbalance reduction of strategically formed balancing groups. 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm). 2016; ():278-283.

Chicago/Turabian Style

Shantanu Chakraborty; Toshiya Okabe. 2016. "Energy storage scheduling for imbalance reduction of strategically formed balancing groups." 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm) , no. : 278-283.

Research article
Published: 01 November 2016 in IET Generation, Transmission & Distribution
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This study introduces a demand-side distributed and secured energy commitment framework and operations for a power producer and supplier (PPS) in deregulated environment. Due to the diversity of geographical location as well as customers’ energy profile coupled with high number of customers, managing energy transactions and resulting energy exchanges are challenging for a PPS. The envisioned PPS maintains several aggregators (e.g. microgrids), named as sub service provider (SSP) that manage customers/subscribers under their domains. The SSPs act as agents that perform local energy matching (inside their domains) and distributed energy matching within SSPs to determine the energy commitment. The goal of the distributed energy matching is to reduce the involvement of external energy supplier (e.g. Utility) while providing a platform to demand side players to be a part of energy transaction. A distributed assignment problem is designed that requires minimum and aggregated information exchange (hence, secured) and solved by linear programming that provides the distributed matching decision. The communicative burden among SSPs due to the exchange of energy information is reduced by applying an adaptive coalition formation method. The simulations are conducted by implementing a synchronous distributed matching algorithm while showing the effectiveness of the proposed framework.

ACS Style

Shantanu Chakraborty; Toshiya Okabe. Optimal demand side management by distributed and secured energy commitment framework. IET Generation, Transmission & Distribution 2016, 10, 3610 -3621.

AMA Style

Shantanu Chakraborty, Toshiya Okabe. Optimal demand side management by distributed and secured energy commitment framework. IET Generation, Transmission & Distribution. 2016; 10 (14):3610-3621.

Chicago/Turabian Style

Shantanu Chakraborty; Toshiya Okabe. 2016. "Optimal demand side management by distributed and secured energy commitment framework." IET Generation, Transmission & Distribution 10, no. 14: 3610-3621.

Journal article
Published: 01 November 2016 in Energy
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Imbalance (on-line energy gap between contracted supply and actual demand, and associated cost) reduction is going to be a crucial service for a Power Producer and Supplier (PPS) in the deregulated energy market. PPS requires forward market interactions to procure energy as precisely as possible in order to reduce imbalance energy. This paper presents, 1) (off-line) an effective demand aggregation based strategy for creating a number of balancing groups that leads to higher predictability of group-wise aggregated demand, 2) (on-line) a robust energy storage scheduling that minimizes the imbalance energy and cost of a particular balancing group considering the demand prediction uncertainty. The group formation is performed by a Probabilistic Programming approach using Bayesian Markov Chain Monte Carlo (MCMC) method after applied on the historical demand statistics. Apart from the group formation, the aggregation strategy (with the help of Bayesian Inference) also clears out the upper-limit of the required storage capacity for a formed group, fraction of which is to be utilized in on-line operation. For on-line operation, a robust energy storage scheduling method is proposed that minimizes expected imbalance energy and cost (a non-linear function of imbalance energy) while incorporating the demand uncertainty of a particular group. The proposed methods are applied on the real apartment buildings' demand data in Tokyo, Japan. Simulation results are presented to verify the effectiveness of the proposed methods.

ACS Style

Shantanu Chakraborty; Toshiya Okabe. Robust energy storage scheduling for imbalance reduction of strategically formed energy balancing groups. Energy 2016, 114, 405 -417.

AMA Style

Shantanu Chakraborty, Toshiya Okabe. Robust energy storage scheduling for imbalance reduction of strategically formed energy balancing groups. Energy. 2016; 114 ():405-417.

Chicago/Turabian Style

Shantanu Chakraborty; Toshiya Okabe. 2016. "Robust energy storage scheduling for imbalance reduction of strategically formed energy balancing groups." Energy 114, no. : 405-417.

Preprint
Published: 30 August 2016
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Imbalance (on-line energy gap between contracted supply and actual demand, and associated cost) reduction is going to be a crucial service for a Power Producer and Supplier (PPS) in the deregulated energy market. PPS requires forward market interactions to procure energy as precisely as possible in order to reduce imbalance energy. This paper presents, 1) (off-line) an effective demand aggregation based strategy for creating a number of balancing groups that leads to higher predictability of group-wise aggregated demand, 2) (on-line) a robust energy storage scheduling that minimizes the imbalance for a particular balancing group considering the demand prediction uncertainty. The group formation is performed by a Probabilistic Programming approach using Bayesian Markov Chain Monte Carlo (MCMC) method after applied on the historical demand statistics. Apart from the group formation, the aggregation strategy (with the help of Bayesian Inference) also clears out the upper-limit of the required storage capacity for a formed group, fraction of which is to be utilized in on-line operation. For on-line operation, a robust energy storage scheduling method is proposed that minimizes expected imbalance energy and cost (a non-linear function of imbalance energy) while incorporating the demand uncertainty of a particular group. The proposed methods are applied on the real apartment buildings' demand data in Tokyo, Japan. Simulation results are presented to verify the effectiveness of the proposed methods.

ACS Style

Shantanu Chakraborty; Toshiya Okabe. Robust Energy Storage Scheduling for Imbalance Reduction of Strategically Formed Energy Balancing Groups. 2016, 1 .

AMA Style

Shantanu Chakraborty, Toshiya Okabe. Robust Energy Storage Scheduling for Imbalance Reduction of Strategically Formed Energy Balancing Groups. . 2016; ():1.

Chicago/Turabian Style

Shantanu Chakraborty; Toshiya Okabe. 2016. "Robust Energy Storage Scheduling for Imbalance Reduction of Strategically Formed Energy Balancing Groups." , no. : 1.

Conference paper
Published: 01 November 2015 in 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm)
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This paper introduces a demand-side distributed energy matching framework and operations for an Energy Service Provider (ESP). Due to the diversity, geographical location and high number of customers, managing energy transactions and resulting energy exchanges are challenging for an ESP. The envisioned ESP maintains several aggregators (e.g. Microgrids), named as Sub Service Provider (SSP) that manage customers/subscribers under their domains. The service requires customers to provide pre-committed energy profiles along with preferences over certain attributes on a day-ahead basis. The SSPs (that work under the ESP) act as agents that perform local energy matching within their own domain of customers and distributed energy matching with the colleague SSPs. The goal of the distributed energy matching is to reduce the involvement of utility (in both the day-ahead market and spot-market). In order to perform the distributed matching operation, an assignment problem is designed and solved by Linear Programming. The communicative burden among SSPs due to the exchange of energy information (i.e. to converge in the distributed matching process) is reduced by applying an adaptive microgrid (SSP) coalition formation method.

ACS Style

Shantanu Chakraborty; Toshiya Okabe. Distributed energy matching and exchange scheme for demand-side optimal operation. 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm) 2015, 163 -168.

AMA Style

Shantanu Chakraborty, Toshiya Okabe. Distributed energy matching and exchange scheme for demand-side optimal operation. 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm). 2015; ():163-168.

Chicago/Turabian Style

Shantanu Chakraborty; Toshiya Okabe. 2015. "Distributed energy matching and exchange scheme for demand-side optimal operation." 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm) , no. : 163-168.

Journal article
Published: 01 June 2015 in Expert Systems with Applications
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ACS Style

Shantanu Chakraborty; Shin Nakamura; Toshiya Okabe. Real-time energy exchange strategy of optimally cooperative microgrids for scale-flexible distribution system. Expert Systems with Applications 2015, 42, 4643 -4652.

AMA Style

Shantanu Chakraborty, Shin Nakamura, Toshiya Okabe. Real-time energy exchange strategy of optimally cooperative microgrids for scale-flexible distribution system. Expert Systems with Applications. 2015; 42 (10):4643-4652.

Chicago/Turabian Style

Shantanu Chakraborty; Shin Nakamura; Toshiya Okabe. 2015. "Real-time energy exchange strategy of optimally cooperative microgrids for scale-flexible distribution system." Expert Systems with Applications 42, no. 10: 4643-4652.