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Prof. Birgitte Bak-Jensen
Aalborg University

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Research Keywords & Expertise

0 Demand Response
0 Power Distribution Systems
0 integration of renewable energy
0 storage and delivery system
0 smart grid and distributed systems

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Demand Response
smart grid and distributed systems
integration of renewable energy

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Journal article
Published: 04 August 2021 in Energies
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With the growing application of green energy, the importance of effectively handling the volatile nature of these energy sources is also growing in order to ensure economic and operational viability. Accordingly, the main contribution of this work is to evaluate the revenue potential for wind parks with integrated storage systems in the day-ahead electricity markets using genetic algorithm. It is achieved by the concept of flexible charging–discharging of the Energy Storage System (ESS), taking advantage of the widespread electricity prices that are predicted using a feedforward-neural-network-based forecasting algorithm. In addition, the reactive power restrictions posed by grid code that are to be followed by the wind park are also considered as one of the constraints. Moreover, the profit obtained with a Battery Energy Storage System (BESS) is compared with that of a Thermal Energy Storage System (TESS). The proposed method gave more profitable results when utilizing BESS for energy arbitrage in day-ahead electricity markets than with TESS. Moreover, the availability of ESS at wind park has reduced the wind power curtailment.

ACS Style

Pavani Ponnaganti; Birgitte Bak-Jensen; Brian Wæhrens; Jesper Asmussen. Assessment of Energy Arbitrage Using Energy Storage Systems: A Wind Park’s Perspective. Energies 2021, 14, 4718 .

AMA Style

Pavani Ponnaganti, Birgitte Bak-Jensen, Brian Wæhrens, Jesper Asmussen. Assessment of Energy Arbitrage Using Energy Storage Systems: A Wind Park’s Perspective. Energies. 2021; 14 (16):4718.

Chicago/Turabian Style

Pavani Ponnaganti; Birgitte Bak-Jensen; Brian Wæhrens; Jesper Asmussen. 2021. "Assessment of Energy Arbitrage Using Energy Storage Systems: A Wind Park’s Perspective." Energies 14, no. 16: 4718.

Journal article
Published: 09 June 2021 in Energies
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Implementation of alternative energy supply solutions requires the broad involvement of local communities. Hence, smart energy solutions are primarily investigated on a local scale, resulting in integrated community energy systems (ICESs). Within this framework, the distributed generation can be optimally utilised, matching it with the local load via storage and demand response techniques. In this study, the boat demand flexibility in the Ballen marina on Samsø—a medium-sized Danish island—is analysed for improving the local grid operation. For this purpose, suitable electricity tariffs for the marina and sailors are developed based on the conducted demand analysis. The optimal scheduling of boats and battery energy storage system (BESS) is proposed, utilising mixed-integer linear programming. The marina’s grid-flexible operation is studied for three representative weeks—peak tourist season, late summer, and late autumn period—with the combinations of high/low load and photovoltaic (PV) generation. Several benefits of boat demand response have been identified, including cost savings for both the marina and sailors, along with a substantial increase in load factor. Furthermore, the proposed algorithm increases battery utilisation during summer, improving the marina’s cost efficiency. The cooperation of boat flexibility and BESS leads to improved grid operation of the marina, with profits for both involved parties. In the future, the marina’s demand flexibility could become an essential element of the local energy system, considering the possible increase in renewable generation capacity—in the form of PV units, wind turbines or wave energy.

ACS Style

Dawid Jozwiak; Jayakrishnan Pillai; Pavani Ponnaganti; Birgitte Bak-Jensen; Jan Jantzen. Optimising Energy Flexibility of Boats in PV-BESS Based Marina Energy Systems. Energies 2021, 14, 3397 .

AMA Style

Dawid Jozwiak, Jayakrishnan Pillai, Pavani Ponnaganti, Birgitte Bak-Jensen, Jan Jantzen. Optimising Energy Flexibility of Boats in PV-BESS Based Marina Energy Systems. Energies. 2021; 14 (12):3397.

Chicago/Turabian Style

Dawid Jozwiak; Jayakrishnan Pillai; Pavani Ponnaganti; Birgitte Bak-Jensen; Jan Jantzen. 2021. "Optimising Energy Flexibility of Boats in PV-BESS Based Marina Energy Systems." Energies 14, no. 12: 3397.

Journal article
Published: 27 December 2020 in Applied Sciences
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Integration of PV power generation systems at distribution grids, especially at low-voltage (LV) grids, brings in operational challenges for distribution system operators (DSOs). These challenges include grid over-voltages and overloading of cables during peak PV power production. Battery energy storage systems (BESS) are being installed alongside PV systems by customers for smart home energy management. This paper investigates the utilization of those BESS by DSOs for maintaining the grid voltages within limits. In this context, an incentive price based demand response (IDR) method is proposed for indirect control of charging/discharging power of the BESS according to the grid voltage conditions. It is shown that the proposed IDR method, which relies on a distributed computing application, is able to maintain the grid voltages within limits. The advantage of the proposed distributed implementation is that the DSOs can compute and communicate the incentive prices thereby encouraging customers to actively participate in the demand response program. An iterative distributed algorithm is used to compute the incentive prices of individual BESS to minimize the costs of net power consumption of the customer. The proposed IDR method is tested by conducting simulation studies on the model of a Danish LV grid for few study cases. The simulation results show that by using the proposed method for the control of BESS, node voltages are maintained within limits as well as the costs of net power consumption of BESS owners are minimized.

ACS Style

Karthikeyan Nainar; Jayakrishnan Radhakrishna Pillai; Birgitte Bak-Jensen. Incentive Price-Based Demand Response in Active Distribution Grids. Applied Sciences 2020, 11, 180 .

AMA Style

Karthikeyan Nainar, Jayakrishnan Radhakrishna Pillai, Birgitte Bak-Jensen. Incentive Price-Based Demand Response in Active Distribution Grids. Applied Sciences. 2020; 11 (1):180.

Chicago/Turabian Style

Karthikeyan Nainar; Jayakrishnan Radhakrishna Pillai; Birgitte Bak-Jensen. 2020. "Incentive Price-Based Demand Response in Active Distribution Grids." Applied Sciences 11, no. 1: 180.

Research article
Published: 22 July 2020 in IET Generation, Transmission & Distribution
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The economic dispatch of the inter-regional power grid with multiple uncertain sources and loads is focused in this study. As the tie-line can transmit the power between regional grids, a coordination mechanism between the tie-line power schedule and the economic dispatch of regional power grids is built to solve the focused dispatch problem under the uncertain environment. Furthermore, the quality of service (QoS) method is introduced in this study to consider and study the service attribute in the power dispatch process, which is quantified by the responding behaviour of the price-sensitive consumers. Then, a model-free hierarchical optimisation method based on the learning technique is designed. The hierarchical structure consists of two levels and multiple agents, where the agents learn knowledge from the interaction between themselves and the environment. An improved reinforcement learning algorithm is adopted to find the optimal dispatch policy for each agent, which realises an online optimisation with the operation samples rather than the support of an accurate system model. Finally, the simulation results are shown to validate the effectiveness of the designed method. Specifically, the optimisation process and the obtained dispatch policies are analysed, and the impact of the QoS index on the optimisation is introduced.

ACS Style

Kai Lv; Hao Tang; Birgitte Bak‐Jensen; Jayakrishnan Radhakrishna Pillai; Qi Tan; Qianli Zhang. Hierarchical learning optimisation method for the coordination dispatch of the inter‐regional power grid considering the quality of service index. IET Generation, Transmission & Distribution 2020, 14, 3673 -3684.

AMA Style

Kai Lv, Hao Tang, Birgitte Bak‐Jensen, Jayakrishnan Radhakrishna Pillai, Qi Tan, Qianli Zhang. Hierarchical learning optimisation method for the coordination dispatch of the inter‐regional power grid considering the quality of service index. IET Generation, Transmission & Distribution. 2020; 14 (18):3673-3684.

Chicago/Turabian Style

Kai Lv; Hao Tang; Birgitte Bak‐Jensen; Jayakrishnan Radhakrishna Pillai; Qi Tan; Qianli Zhang. 2020. "Hierarchical learning optimisation method for the coordination dispatch of the inter‐regional power grid considering the quality of service index." IET Generation, Transmission & Distribution 14, no. 18: 3673-3684.

Journal article
Published: 30 April 2020 in Energy
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This paper aims to visualize how the planned electrification of heat demand together with the utilization of energy flexibility in buildings will influence the performance of local electricity network. Thereby, the work contributes to the research on smart energy system in the residential sector. The flexibility service is provided by two demand-side-management strategies a) activation of the thermal mass to modulate load profile of a heat pump, b) control of household appliances’ starting times. Three configurations of load mix in the local electricity network are investigated: a) domination of non-renovated houses, b) with equal share of high and low heat demand houses, c) domination of energy efficient houses. The model is soft-coupled and anchored in existing low voltage (LV) network and existing residential buildings. The energy flexible buildings enhance the LV network performance, by decreasing the afternoon peaks, without compromising the occupants’ thermal comfort. The highest impact is for the LV network dominated by energy efficient houses. There are also new challenges, namely the newly created peak loads and transformer overloading during night time. It is a consequence of uniform price signal sent to all flexible customers and electrification of heating demand without parallel improvement of energy performance.

ACS Style

Anna Marszal-Pomianowska; Joakim Widén; Jérôme Le Dréau; Per Heiselberg; Birgitte Bak-Jensen; Iker Diaz De Cerio Mendaza. Operation of power distribution networks with new and flexible loads: A case of existing residential low voltage network. Energy 2020, 202, 117715 .

AMA Style

Anna Marszal-Pomianowska, Joakim Widén, Jérôme Le Dréau, Per Heiselberg, Birgitte Bak-Jensen, Iker Diaz De Cerio Mendaza. Operation of power distribution networks with new and flexible loads: A case of existing residential low voltage network. Energy. 2020; 202 ():117715.

Chicago/Turabian Style

Anna Marszal-Pomianowska; Joakim Widén; Jérôme Le Dréau; Per Heiselberg; Birgitte Bak-Jensen; Iker Diaz De Cerio Mendaza. 2020. "Operation of power distribution networks with new and flexible loads: A case of existing residential low voltage network." Energy 202, no. : 117715.

Journal article
Published: 21 April 2020 in IEEE Access
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The daily load profiles modeling is of great significance for the economic operation and stability analysis of the distribution network. In this paper, a flow-based generative network is proposed to model daily load profiles of the distribution network. Firstly, the real samples are used to train a series of reversible functions that map the probability distribution of real samples to the prior distribution. Then, the new daily load profiles are generated by taking the random number obeying the Gaussian distribution as the input data of these reversible functions. Compared with existing methods such as explicit density models, the proposed approach does not need to assume the probability distribution of real samples, and can be used to model different loads only by adjusting the structure and parameters. The simulation results show that the proposed approach not only fits the probability distribution of real samples well, but also accurately captures the spatial-temporal correlation of daily load profiles. The daily load profiles with specific characteristics can be obtained by simply classification.

ACS Style

Leijiao Ge; Wenlong Liao; Shouxiang Wang; Birgitte Bak-Jensen; Jayakrishnan Radhakrishna Pillai. Modeling Daily Load Profiles of Distribution Network for Scenario Generation Using Flow-Based Generative Network. IEEE Access 2020, 8, 77587 -77597.

AMA Style

Leijiao Ge, Wenlong Liao, Shouxiang Wang, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai. Modeling Daily Load Profiles of Distribution Network for Scenario Generation Using Flow-Based Generative Network. IEEE Access. 2020; 8 (99):77587-77597.

Chicago/Turabian Style

Leijiao Ge; Wenlong Liao; Shouxiang Wang; Birgitte Bak-Jensen; Jayakrishnan Radhakrishna Pillai. 2020. "Modeling Daily Load Profiles of Distribution Network for Scenario Generation Using Flow-Based Generative Network." IEEE Access 8, no. 99: 77587-77597.

Journal article
Published: 11 April 2020 in International Journal of Electrical Power & Energy Systems
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The decentralised infrastructure of the Danish thermal and electricity infrastructure has improved security, efficiency and reliability in energy transmission and consumption in each of the systems. To further improve this trend, this paper investigates the concept of decentralised coupling of electrified thermal and transportation system in low voltage residential network to identify their operational flexibility. In this paper, diagnosis based on actual data from the energy distributors and surveys is used to understand and improve the flexibility of an integrated energy system. The main contribution is a set up model with an autonomous control system that can assess the potential flexibility from thermal units (eg heat pumps and storages) and electric vehicles (EV) charging systems, in the low voltage distribution network as a multi-energy system. Each thermal system and EV charging has its respective individual controller. The proposed control technique manages to successfully operate and control the thermal units and EVs charging system within the recommended operating limits of grid voltage, and by sharing flexibility within the specific network integrated with multi-carrier energy systems. It has the capability of sensing local key control parameters like node voltage, state of charge of EV, temperature and level of hot water in the storage tank. These control parameters allow scheduling, re-scheduling, and decision making on the operation of individual thermal and EV charging-unit with operational priorities. This enhances the sharing of flexibility for proper coordination, control, and management of thermal and EV charging systems in low voltage (LV) distribution networks with mutual technical benefits. From the results of the steady-state analysis of power system, the application of the proposed control architecture is found to be effective to manage grid congestions and local voltage control, satisfying the thermal energy requirements of the customer as well as charging needs of EV.

ACS Style

Rakesh Sinha; Birgitte Bak-Jensen; Jayakrishnan Radhakrishna Pillai. Operational flexibility of electrified transport and thermal units in distribution grid. International Journal of Electrical Power & Energy Systems 2020, 121, 106029 .

AMA Style

Rakesh Sinha, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai. Operational flexibility of electrified transport and thermal units in distribution grid. International Journal of Electrical Power & Energy Systems. 2020; 121 ():106029.

Chicago/Turabian Style

Rakesh Sinha; Birgitte Bak-Jensen; Jayakrishnan Radhakrishna Pillai. 2020. "Operational flexibility of electrified transport and thermal units in distribution grid." International Journal of Electrical Power & Energy Systems 121, no. : 106029.

Journal article
Published: 24 December 2019 in Energies
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Active use of heat accumulators in the thermal system has the potential for achieving flexibility in district heating with the power to heat (P2H) units, such as electric boilers (EB) and heat pumps. Thermal storage tanks can decouple demand and generation, enhancing accommodation of sustainable energy sources such as solar and wind. The overview of flexibility, using EB and storage, supported by investigating the nature of thermal demand in a Danish residential area, is presented in this paper. Based on the analysis, curve-fitting tools, such as neural net and similar day method, are trained to estimate the residential thermal demand. Utilizing the estimated demand and hourly market spot price of electricity, the operation of the EB is scheduled for storing and fulfilling demand and minimizing energy cost simultaneously. This demonstrates flexibility and controlling the EB integrated into a multi-energy system framework. Results show that the curve fitting tool is effectively suitable to acknowledge thermal demands of residential area based on the environmental factor as well as user behaviour. The thermal storage has the capability of operating as a flexible load to support P2H system as well as minimize the effect of estimation error in fulfilling actual thermal demand simultaneously.

ACS Style

Rakesh Sinha; Birgitte Bak-Jensen; Jayakrishnan Radhakrishna Pillai; Hamidreza Zareipour. Flexibility from Electric Boiler and Thermal Storage for Multi Energy System Interaction. Energies 2019, 13, 98 .

AMA Style

Rakesh Sinha, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Hamidreza Zareipour. Flexibility from Electric Boiler and Thermal Storage for Multi Energy System Interaction. Energies. 2019; 13 (1):98.

Chicago/Turabian Style

Rakesh Sinha; Birgitte Bak-Jensen; Jayakrishnan Radhakrishna Pillai; Hamidreza Zareipour. 2019. "Flexibility from Electric Boiler and Thermal Storage for Multi Energy System Interaction." Energies 13, no. 1: 98.

Journal article
Published: 04 November 2019 in Smart Cities
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The smart active residential buildings play a vital role to realize intelligent energy systems by harnessing energy flexibility from loads and storage units. This is imperative to integrate higher proportions of variable renewable energy generation and implement economically attractive demand-side participation schemes. The purpose of this paper is to develop an energy management scheme for smart sustainable buildings and analyze its efficacy when subjected to variable generation, energy storage management, and flexible demand control. This work estimate the flexibility range that can be reached utilizing deferrable/controllable energy system units such as heat pump (HP) in combination with on-site renewable energy sources (RESs), namely photovoltaic (PV) panels and wind turbine (WT), and in-house thermal and electric energy storages, namely hot water storage tank (HWST) and electric battery as back up units. A detailed HP model in combination with the storage tank is developed that accounts for thermal comforts and requirements, and defrost mode. Data analytics is applied to generate demand and generation profiles, and a hybrid energy management and a HP control algorithm is developed in this work. This is to integrate all active components of a building within a single complex-set of energy management solution to be able to apply demand response (DR) signals, as well as to execute all necessary computation and evaluation. Different capacity scenarios of the HWST and battery are used to prioritize the maximum use of renewable energy and consumer comfort preferences. A flexibility range of 22.3% is achieved for the scenario with the largest HWST considered without a battery, while 10.1% in the worst-case scenario with the smallest HWST considered and the largest battery. The results show that the active management and scheduling scheme developed to combine and prioritize thermal, electrical and storage units in buildings is essential to be studied to demonstrate the adequacy of sustainable energy buildings.

ACS Style

Viktor Stepaniuk; Jayakrishnan Radhakrishna Pillai; Birgitte Bak-Jensen; Sanjeevikumar Padmanaban. Estimation of Energy Activity and Flexibility Range in Smart Active Residential Building. Smart Cities 2019, 2, 471 -495.

AMA Style

Viktor Stepaniuk, Jayakrishnan Radhakrishna Pillai, Birgitte Bak-Jensen, Sanjeevikumar Padmanaban. Estimation of Energy Activity and Flexibility Range in Smart Active Residential Building. Smart Cities. 2019; 2 (4):471-495.

Chicago/Turabian Style

Viktor Stepaniuk; Jayakrishnan Radhakrishna Pillai; Birgitte Bak-Jensen; Sanjeevikumar Padmanaban. 2019. "Estimation of Energy Activity and Flexibility Range in Smart Active Residential Building." Smart Cities 2, no. 4: 471-495.

Journal article
Published: 12 June 2019 in Energies
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This paper presents a unique integrated approach to meter placement and state estimation to ensure the network observability of active distribution systems. It includes observability checking, minimum measurement utilization, network state estimation, and trade-off evaluation between the number of real measurements used and the accuracy of the estimated state. In network parameter estimation, observability assessment is a preliminary task. It is handled by data analysis and filtering followed by calculation of the triangular factors of the singular, symmetric gain matrix using an algebraic method. Usually, to cover the deficiency of essential real measurements in distribution systems, huge numbers of virtual measurements are used. These pseudo measurements are calculated values, which are based on the network parameters, real measurements, and forecasted load/generation. Due to the application of a huge number of pseudo-measurements, large margins of error exists in the calculation phase. Therefore, there is still a high possibility of having large errors in estimated states, even though the network is classified as being observable. Hence, an integrated approach supported by forecasting is introduced in this work to overcome this critical issue. Finally, estimation of the trade-off in accuracy with respect to the number of real measurements used has been evaluated in order to justify the method’s practical application. The proposed method is applied to a Danish network, and the results are discussed.

ACS Style

Basanta Raj Pokhrel; Birgitte Bak-Jensen; Jayakrishnan R. Pillai. Integrated Approach for Network Observability and State Estimation in Active Distribution Grid. Energies 2019, 12, 2230 .

AMA Style

Basanta Raj Pokhrel, Birgitte Bak-Jensen, Jayakrishnan R. Pillai. Integrated Approach for Network Observability and State Estimation in Active Distribution Grid. Energies. 2019; 12 (12):2230.

Chicago/Turabian Style

Basanta Raj Pokhrel; Birgitte Bak-Jensen; Jayakrishnan R. Pillai. 2019. "Integrated Approach for Network Observability and State Estimation in Active Distribution Grid." Energies 12, no. 12: 2230.

Journal article
Published: 18 April 2019 in Energies
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This paper aims to unleash the potential of a heat pump (HP) and its storage system, as a flexible consumer load, in the low-voltage (LV) distribution network by introducing an autonomous controller. Steady-state analysis using DigSILENT Power Factory, a power system analysis tool, is performed to verify the proposed hypothesis. The proposed controller manages to operate the individual HP and storage within the recommended operating limits of grid voltage, by sharing flexibility within the specific network. It has the capability of sensing local key control parameters for scheduling, re-scheduling, and decision-making on the operation of individual HPs. It also takes the thermal energy comfort of individual consumers into consideration. Measurement of local parameters such as grid voltage, supply temperature and level of cold water in the storage tank defines the priority for operation of HPs based on operating delays for turning it on and off. This enhances the sharing of flexibility for proper coordination, control, and management of HP systems in LV distribution networks with mutual technical benefits. From the results, the application of the proposed controller is found to be effective to manage grid congestions and local voltage regulation, satisfying the thermal energy requirements of the customer.

ACS Style

Rakesh Sinha; Birgitte Bak-Jensen; Jayakrishnan Radhakrishna Radhakrishna Pillai. Autonomous Controller for Flexible Operation of Heat Pumps in Low-Voltage Distribution Network. Energies 2019, 12, 1482 .

AMA Style

Rakesh Sinha, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Radhakrishna Pillai. Autonomous Controller for Flexible Operation of Heat Pumps in Low-Voltage Distribution Network. Energies. 2019; 12 (8):1482.

Chicago/Turabian Style

Rakesh Sinha; Birgitte Bak-Jensen; Jayakrishnan Radhakrishna Radhakrishna Pillai. 2019. "Autonomous Controller for Flexible Operation of Heat Pumps in Low-Voltage Distribution Network." Energies 12, no. 8: 1482.

Journal article
Published: 21 February 2019 in IEEE Transactions on Industrial Electronics
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This paper proposes a novel structure for Industrial Demand Response Aggregators (IDRA) to provide operational flexibility for the power system. A robust self-scheduling approach is formulated for the first time to optimize different sub-processes of the whole production line of heavy industries. The new approach satisfies the customer order with the lowest energy cost. Numerical studies are implemented on 8 integrated cement factories, from Khorasan Regional Electric Company (KREC), in the east of Iran. The results show that the integrated model of heavy industries provides guaranteed flexibility to the system when a power shortage occurs or system reliability is jeopardized.

ACS Style

Hessam Golmohamadi; Reza Keypour; Birgitte Bak-Jensen; Jayakrishnan R. Pillai; Mohammad Hassan Khooban. Robust Self-Scheduling of Operational Processes for Industrial Demand Response Aggregators. IEEE Transactions on Industrial Electronics 2019, 67, 1387 -1395.

AMA Style

Hessam Golmohamadi, Reza Keypour, Birgitte Bak-Jensen, Jayakrishnan R. Pillai, Mohammad Hassan Khooban. Robust Self-Scheduling of Operational Processes for Industrial Demand Response Aggregators. IEEE Transactions on Industrial Electronics. 2019; 67 (2):1387-1395.

Chicago/Turabian Style

Hessam Golmohamadi; Reza Keypour; Birgitte Bak-Jensen; Jayakrishnan R. Pillai; Mohammad Hassan Khooban. 2019. "Robust Self-Scheduling of Operational Processes for Industrial Demand Response Aggregators." IEEE Transactions on Industrial Electronics 67, no. 2: 1387-1395.

Journal article
Published: 11 February 2019 in IEEE Transactions on Power Systems
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In this paper, a model-based predictive control method is proposed for utilization of flexible resources such as battery energy storage systems and heating systems effectively to provide demand response in low-voltage distribution networks with solar PV. The contributions of this paper are twofold. Firstly, a linear power flow method based on relaxation of branch power losses applicable to radial distribution networks is proposed and formulated. Secondly, a flexible resources controller which solves a multi-objective linear optimization problem in recedinghorizon fashion is formulated taking into account system states, forecasts of generation and loads. Using the proposed control algorithm, flexibility from network resources can be utilized for low-voltage network management with assurance of quality of service to the customers. Simulations are conducted for summer and winter cases on a simplified Danish low-voltage network using Matlab/Simulink to study the performance of the proposed control method. Compared to the methods in state of the art, the proposed linear power flow method is proven to be accurate for the calculation of network power flows. Simulation results also show that proposed flexible resources controller can meet the network control objectives while satisfying the network constraints and operation limits of the flexible resources.

ACS Style

Nainar Karthikeyan; Jayakrishnan Radhakrishna Pillai; Birgitte Bak-Jensen; John William Simpson-Porco. Predictive Control of Flexible Resources for Demand Response in Active Distribution Networks. IEEE Transactions on Power Systems 2019, 34, 2957 -2969.

AMA Style

Nainar Karthikeyan, Jayakrishnan Radhakrishna Pillai, Birgitte Bak-Jensen, John William Simpson-Porco. Predictive Control of Flexible Resources for Demand Response in Active Distribution Networks. IEEE Transactions on Power Systems. 2019; 34 (4):2957-2969.

Chicago/Turabian Style

Nainar Karthikeyan; Jayakrishnan Radhakrishna Pillai; Birgitte Bak-Jensen; John William Simpson-Porco. 2019. "Predictive Control of Flexible Resources for Demand Response in Active Distribution Networks." IEEE Transactions on Power Systems 34, no. 4: 2957-2969.

Journal article
Published: 12 December 2018 in International Journal of Electrical Power & Energy Systems
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Today’s power systems are subject to the high penetration of renewable power. Volatility and intermittency of the renewable power need to be compensated through alternative forms of flexibility. This paper proposes a novel agent-based structure to integrate the flexibility potential of industrial and residential demands. In this approach, a central demand response provider (DRP) is suggested to coordinate the responsive plans of industrial and residential demand response aggregators (IDRA, RDRA). The suggested IDRA integrates the flexibility potential of whole production lines for two energy-intensive heavy industries, i.e. cement manufacture and metal smelting. Besides, the RDRA uses the thermal and electrical storage capabilities of thermostatically-controlled appliances (TCAs) and electrical storage systems linked with roof-top photovoltaic (ESS-RPV) sites through home energy management systems (HEMS). The integrated flexibility is traded in the electricity market to maximize the profit of the market participants in a competitive environment, instead of subsidizing the responsive consumers by supportive regulations. Finally, the suggested structure is tested on the Danish sector of the Nordic Electricity Market to show applicability and proficiency of the proposed approach. The results show that the integrated flexibility can safeguard the future of power systems against the intermittent power.

ACS Style

Hessam Golmohamadi; Reza Keypour; Birgitte Bak-Jensen; Jayakrishnan R. Pillai. A multi-agent based optimization of residential and industrial demand response aggregators. International Journal of Electrical Power & Energy Systems 2018, 107, 472 -485.

AMA Style

Hessam Golmohamadi, Reza Keypour, Birgitte Bak-Jensen, Jayakrishnan R. Pillai. A multi-agent based optimization of residential and industrial demand response aggregators. International Journal of Electrical Power & Energy Systems. 2018; 107 ():472-485.

Chicago/Turabian Style

Hessam Golmohamadi; Reza Keypour; Birgitte Bak-Jensen; Jayakrishnan R. Pillai. 2018. "A multi-agent based optimization of residential and industrial demand response aggregators." International Journal of Electrical Power & Energy Systems 107, no. : 472-485.

Journal article
Published: 05 November 2018 in Sustainable Cities and Society
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Prosumers have a different interaction with the distribution network in comparison with traditional consumers. They have a bi-directional power exchange with the grid, meaning they receive from as well as deliver electricity to the network. The emergence of prosumers is expected to challenge the way network operators control the residential low-voltage (LV) distribution networks. Commonly, the metering of received and delivered electricity at the residential level is conducted on 1-hour basis, thus only hourly load/generation profiles are available for the system operators to conduct the power system impact analysis. Yet, it is relatively difficult to have an accurate prediction of the real system performance if the intra-hour phenomena are not considered. A better estimation requires employment of higher temporal resolution profiles during the power system studies. To address these challenges, which future smart cities and communities might face, this paper presents a methodology for generating 1-minute load profiles based on the hourly readings from smart meters. Secondly, it demonstrates gain in information about LV network by employing the high resolution profiles for power system impact analysis. Finally, it highlights the problems of smart residential networks with high share of prosumers for two LV network scenarios during winter and summer weeks.

ACS Style

Anna Marszal-Pomianowska; Iker Diaz De Cerio Mendaza; Birgitte Bak-Jensen; Per Heiselberg. A performance evaluation of future low voltage grids in presence of prosumers modelled in high temporal resolution. Sustainable Cities and Society 2018, 44, 702 -714.

AMA Style

Anna Marszal-Pomianowska, Iker Diaz De Cerio Mendaza, Birgitte Bak-Jensen, Per Heiselberg. A performance evaluation of future low voltage grids in presence of prosumers modelled in high temporal resolution. Sustainable Cities and Society. 2018; 44 ():702-714.

Chicago/Turabian Style

Anna Marszal-Pomianowska; Iker Diaz De Cerio Mendaza; Birgitte Bak-Jensen; Per Heiselberg. 2018. "A performance evaluation of future low voltage grids in presence of prosumers modelled in high temporal resolution." Sustainable Cities and Society 44, no. : 702-714.

Contributors
Published: 19 October 2018 in Smart Power Distribution Systems
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ACS Style

Sandhya Armoogum; Birgitte Bak-Jensen; Vandana Bassoo; Bishnu P. Bhattarai; Rajeev Kumar Chauhan; Kalpana Chauhan; Weirong Chen; Bo Chen; B. Chitti Babu; S.S. Choi; Sanchari Deb; Sanjoy Debbarma; Ali Ehsan; Xinli Fang; Deqiang Gan; Yajing Gao; Ying Han; Xiaoqing Han; Yujin Huang; Le Jiang; Karuna Kalita; Mitja Kolenc; Qi Li; Jinghua Li; Bo Lu; Jin Ma; Pinakeswar Mahanta; Yuhong Mo; Qitian Mu; Kurt S. Myers; Bonu Ramesh Naidu; Gayadhar Panda; Feng Qiao; M.M. Rana; Mohammad Seydali Seyf Abad; Rituraj Shrivastwa; Nermin Suljanović; Robert J. Turk; Zhen Wang; Eric Wang; Zhibang Wang; ShanYang Wei; Kit Po Wong; Wei Xiang; Zhengqing Yang; Qiang Yang; Hanqing Yang; Ting Yang; Matej Zajc; Jiasheng Zhou; Xiaojie Zhou; Jing Zhu; Yi Zong. List of contributors. Smart Power Distribution Systems 2018, 1 .

AMA Style

Sandhya Armoogum, Birgitte Bak-Jensen, Vandana Bassoo, Bishnu P. Bhattarai, Rajeev Kumar Chauhan, Kalpana Chauhan, Weirong Chen, Bo Chen, B. Chitti Babu, S.S. Choi, Sanchari Deb, Sanjoy Debbarma, Ali Ehsan, Xinli Fang, Deqiang Gan, Yajing Gao, Ying Han, Xiaoqing Han, Yujin Huang, Le Jiang, Karuna Kalita, Mitja Kolenc, Qi Li, Jinghua Li, Bo Lu, Jin Ma, Pinakeswar Mahanta, Yuhong Mo, Qitian Mu, Kurt S. Myers, Bonu Ramesh Naidu, Gayadhar Panda, Feng Qiao, M.M. Rana, Mohammad Seydali Seyf Abad, Rituraj Shrivastwa, Nermin Suljanović, Robert J. Turk, Zhen Wang, Eric Wang, Zhibang Wang, ShanYang Wei, Kit Po Wong, Wei Xiang, Zhengqing Yang, Qiang Yang, Hanqing Yang, Ting Yang, Matej Zajc, Jiasheng Zhou, Xiaojie Zhou, Jing Zhu, Yi Zong. List of contributors. Smart Power Distribution Systems. 2018; ():1.

Chicago/Turabian Style

Sandhya Armoogum; Birgitte Bak-Jensen; Vandana Bassoo; Bishnu P. Bhattarai; Rajeev Kumar Chauhan; Kalpana Chauhan; Weirong Chen; Bo Chen; B. Chitti Babu; S.S. Choi; Sanchari Deb; Sanjoy Debbarma; Ali Ehsan; Xinli Fang; Deqiang Gan; Yajing Gao; Ying Han; Xiaoqing Han; Yujin Huang; Le Jiang; Karuna Kalita; Mitja Kolenc; Qi Li; Jinghua Li; Bo Lu; Jin Ma; Pinakeswar Mahanta; Yuhong Mo; Qitian Mu; Kurt S. Myers; Bonu Ramesh Naidu; Gayadhar Panda; Feng Qiao; M.M. Rana; Mohammad Seydali Seyf Abad; Rituraj Shrivastwa; Nermin Suljanović; Robert J. Turk; Zhen Wang; Eric Wang; Zhibang Wang; ShanYang Wei; Kit Po Wong; Wei Xiang; Zhengqing Yang; Qiang Yang; Hanqing Yang; Ting Yang; Matej Zajc; Jiasheng Zhou; Xiaojie Zhou; Jing Zhu; Yi Zong. 2018. "List of contributors." Smart Power Distribution Systems , no. : 1.

Proceedings article
Published: 01 August 2018 in 2018 IEEE Power & Energy Society General Meeting (PESGM)
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This study presents an algorithm to optimally aggregate spatially distributed flexible resources at strategic microgrid/smart-grid locations. The aggregation reduces a distribution network having thousands of nodes to an equivalent network with a few aggregated nodes, thereby enabling distribution system operators (DSOs) to make faster operational decisions. Moreover, the aggregation enables flexibility from small distributed flexible resources to be traded to different power and energy markets. A hierarchical control architecture comprising a combination of centralized and decentralized control approaches is proposed to practically deploy the aggregated flexibility. The proposed method serves as a great operational tool for DSOs to decide the exact amount of required flexibilities from different network section(s) for solving grid constraint violations. The effectiveness of the proposed method is demonstrated through simulation of three operational scenarios in a real low voltage distribution system having high penetrations of electric vehicles and heat pumps. The simulation results demonstrated that the aggregation helps DSOs not only in taking faster operational decisions, but also in effectively utilizing the available flexibility.

ACS Style

Bishnu Bhattarai; Iker Diaz De Cerio Mendaza; Kurt Myers; Birgitte Bak-Jensen; Sumit Paudyal. Optimum Aggregation and Control of Spatially Distributed Flexible Resources in Smart Grid. 2018 IEEE Power & Energy Society General Meeting (PESGM) 2018, 1 -1.

AMA Style

Bishnu Bhattarai, Iker Diaz De Cerio Mendaza, Kurt Myers, Birgitte Bak-Jensen, Sumit Paudyal. Optimum Aggregation and Control of Spatially Distributed Flexible Resources in Smart Grid. 2018 IEEE Power & Energy Society General Meeting (PESGM). 2018; ():1-1.

Chicago/Turabian Style

Bishnu Bhattarai; Iker Diaz De Cerio Mendaza; Kurt Myers; Birgitte Bak-Jensen; Sumit Paudyal. 2018. "Optimum Aggregation and Control of Spatially Distributed Flexible Resources in Smart Grid." 2018 IEEE Power & Energy Society General Meeting (PESGM) , no. : 1-1.

Article
Published: 05 October 2017 in WIREs Energy and Environment
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In power systems, the installed generation capacity must exceed the annual peak demand, even though some capacity is kept idle most of the time. However, if it is uneconomical or not feasible to augment a sufficient capacity, the demand might exceed the available capacity. This mandates the system operator to shed the load in order to maintain security of the system. With the advent of advanced smart metering infrastructure, communication between system operator and end-use customers makes it possible to adjust/curtail/shift the demand with respect to the state of the system. The response of the demand commonly termed as demand response (DR) can be attained either by incentive-based or price-based. With the help of DR, the renewable energy generation capacity can be increased by tuning the demand to match the variable and unpredictable power from renewable generation. It can also bring other benefits such as peak shaving, hosting capacity enhancement, and generation cost reduction. Furthermore, electric vehicles, heat pumps, and electric water heater can also be used as distributed storage resources to contribute to ancillary services, such as frequency/voltage regulation, peak-shaving power or help to integrate fluctuating renewable resources. All these DR modes of operation need conventional regulatory frameworks and market design for capitalizing the available resources. Therefore, the objective of the study is to discuss the DR classification and their control strategies, DR role in microgrids and integration of renewable energy resources. Also, highlighted the opportunities and challenges along with the insights for the research scope associated with DR. WIREs Energy Environ 2018, 7:e271. doi: 10.1002/wene.271 This article is categorized under:

ACS Style

Pavani Ponnaganti; Jayakrishnan R Pillai; Birgitte Bak-Jensen. Opportunities and challenges of demand response in active distribution networks. WIREs Energy and Environment 2017, 7, e271 .

AMA Style

Pavani Ponnaganti, Jayakrishnan R Pillai, Birgitte Bak-Jensen. Opportunities and challenges of demand response in active distribution networks. WIREs Energy and Environment. 2017; 7 (1):e271.

Chicago/Turabian Style

Pavani Ponnaganti; Jayakrishnan R Pillai; Birgitte Bak-Jensen. 2017. "Opportunities and challenges of demand response in active distribution networks." WIREs Energy and Environment 7, no. 1: e271.

Journal article
Published: 24 March 2017 in IEEE Transactions on Smart Grid
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This study presents an algorithm to optimally aggregate spatially distributed flexible resources at strategic microgrid/smart-grid locations. The aggregation reduces a distribution network having thousands of nodes to an equivalent network with a few aggregated nodes, thereby enabling distribution system operators (DSOs) to make faster operational decisions. Moreover, the aggregation enables flexibility from small distributed flexible resources to be traded to different power and energy markets. A hierarchical control architecture comprising a combination of centralized and decentralized control approaches is proposed to practically deploy the aggregated flexibility. The proposed method serves as a great operational tool for DSOs to decide the exact amount of required flexibilities from different network section(s) for solving grid constraint violations. The effectiveness of the proposed method is demonstrated through simulation of three operational scenarios in a real low voltage distribution system having high penetrations of electric vehicles and heat pumps. The simulation results demonstrated that the aggregation helps DSOs not only in taking faster operational decisions, but also in effectively utilizing the available flexibility.

ACS Style

Bishnu Prasad Bhattarai; Iker Diaz De Cerio Mendaza; Kurt S. Myers; Birgitte Bak-Jensen; Sumit Paudyal. Optimum Aggregation and Control of Spatially Distributed Flexible Resources in Smart Grid. IEEE Transactions on Smart Grid 2017, 9, 5311 -5322.

AMA Style

Bishnu Prasad Bhattarai, Iker Diaz De Cerio Mendaza, Kurt S. Myers, Birgitte Bak-Jensen, Sumit Paudyal. Optimum Aggregation and Control of Spatially Distributed Flexible Resources in Smart Grid. IEEE Transactions on Smart Grid. 2017; 9 (5):5311-5322.

Chicago/Turabian Style

Bishnu Prasad Bhattarai; Iker Diaz De Cerio Mendaza; Kurt S. Myers; Birgitte Bak-Jensen; Sumit Paudyal. 2017. "Optimum Aggregation and Control of Spatially Distributed Flexible Resources in Smart Grid." IEEE Transactions on Smart Grid 9, no. 5: 5311-5322.

Journal article
Published: 01 January 2017 in Energies
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This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator’s perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The simulation results demonstrate that HCA efficiently utilizes demand flexibility stemming from EVs to solve grid unbalancing and congestions with simultaneous maximization of economic benefits to the participating actors. This is ensured by enabling EV participation in day-ahead, balancing, and regulation markets. For the given network configuration and pricing structure, HCA ensures the EV owners to get paid up to five times the cost they were paying without control.

ACS Style

Bishnu P. Bhattarai; Kurt S. Myers; Birgitte Bak-Jensen; Sumit Paudyal. Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks. Energies 2017, 10, 37 .

AMA Style

Bishnu P. Bhattarai, Kurt S. Myers, Birgitte Bak-Jensen, Sumit Paudyal. Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks. Energies. 2017; 10 (1):37.

Chicago/Turabian Style

Bishnu P. Bhattarai; Kurt S. Myers; Birgitte Bak-Jensen; Sumit Paudyal. 2017. "Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks." Energies 10, no. 1: 37.