AF
A. Fu
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
12 records found
1
Conference paper
(2025)
-
J.J. Alpizar Castillo, Aihui Fu, L.M. Ramirez Elizondo, Milos Cvetkovic, Pavol Bauer
The energy transition encourages using heat pumps at the residential level, which results in a multi-carrier energy system when combined with PV and battery storage. Optimally controlling such systems has proven challenging. The numerous constraints required, different response times per energy carrier, and the need for forecasting methods also increase the complexity and computational cost. We propose an adaptable energy management system strategy for any system architecture with a reduced number of constraints using genetic algorithms with a discrete-continuous approach for the power setpoints. Using random forest regression, we also created short-term estimation models for the PV generation and electric and thermal demand, with error distributions centred near 0 %. Our results demonstrate that the strategy can solve the power allocation problem in the order of 1 s, including forecasting 60 minutes, minimizing electric costs, and ensuring thermal comfort.
...
The energy transition encourages using heat pumps at the residential level, which results in a multi-carrier energy system when combined with PV and battery storage. Optimally controlling such systems has proven challenging. The numerous constraints required, different response times per energy carrier, and the need for forecasting methods also increase the complexity and computational cost. We propose an adaptable energy management system strategy for any system architecture with a reduced number of constraints using genetic algorithms with a discrete-continuous approach for the power setpoints. Using random forest regression, we also created short-term estimation models for the PV generation and electric and thermal demand, with error distributions centred near 0 %. Our results demonstrate that the strategy can solve the power allocation problem in the order of 1 s, including forecasting 60 minutes, minimizing electric costs, and ensuring thermal comfort.
Journal article
(2025)
-
Shengren Hou, Aihui Fu, Edgar Mauricio Salazar Duque, Peter Palensky, Qixin Chen, Pedro P. Vergara
The integration of distributed energy resources (DERs) has escalated the challenge of voltage magnitude regulation in distribution networks. Model-based approaches, which rely on complex sequential mathematical formulations, cannot meet the real-time demand. Deep reinforcement learning (DRL) offers an alternative by utilizing offline training with distribution network simulators and then executing online without computation. However, DRL algorithms fail to enforce voltage magnitude constraints during training and testing, potentially leading to serious operational violations. To tackle these challenges, we introduce a novel safe-guaranteed reinforcement learning algorithm, the DistFlow safe reinforcement learning (DF-SRL), designed specifically for real-time voltage magnitude regulation in distribution networks. The DF-SRL algorithm incorporates a DistFlow linearization to construct an expert-knowledge-based safety layer. Subsequently, the DF-SRL algorithm overlays this safety layer on top of the agent policy, recalibrating unsafe actions to safe domains through a quadratic programming formulation. Simulation results show the DF-SRL algorithm consistently ensures voltage magnitude constraints during training and real-time operation (test) phases, achieving faster convergence and higher performance, which differentiates it apart from (safe) DRL benchmark algorithms.
...
The integration of distributed energy resources (DERs) has escalated the challenge of voltage magnitude regulation in distribution networks. Model-based approaches, which rely on complex sequential mathematical formulations, cannot meet the real-time demand. Deep reinforcement learning (DRL) offers an alternative by utilizing offline training with distribution network simulators and then executing online without computation. However, DRL algorithms fail to enforce voltage magnitude constraints during training and testing, potentially leading to serious operational violations. To tackle these challenges, we introduce a novel safe-guaranteed reinforcement learning algorithm, the DistFlow safe reinforcement learning (DF-SRL), designed specifically for real-time voltage magnitude regulation in distribution networks. The DF-SRL algorithm incorporates a DistFlow linearization to construct an expert-knowledge-based safety layer. Subsequently, the DF-SRL algorithm overlays this safety layer on top of the agent policy, recalibrating unsafe actions to safe domains through a quadratic programming formulation. Simulation results show the DF-SRL algorithm consistently ensures voltage magnitude constraints during training and real-time operation (test) phases, achieving faster convergence and higher performance, which differentiates it apart from (safe) DRL benchmark algorithms.
Journal article
(2024)
-
Aihui Fu, Aleksandra Lekić, Kyriaki Nefeli D. Malamaki, Georgios C. Kryonidis, Juan M. Mauricio, Charis S. Demoulias, Peter Palensky, Miloš Cvetković
The extensive integration of distributed renewable energy resources (DRES) can lead to several issues in power grids, particularly in distribution grids, due to their inherent intermittency. This paper presents a stochastic simulation-based approach to estimate the maximum permissible penetration level of DRES and to determine the optimal capacity of centralized battery energy storage systems (BESS) in distribution networks while adhering to technical constraints. The stochastic method creates a wide range of scenarios under various conditions. For each scenario, our proposed approach calculates the maximum allowable penetration level of DRES and the required BESS capacity with different DRES control logics. The maximum allowable penetration level of DRES and the requirements of the BESS capacity are determined by an analysis of various simulation results. This paper's unique contribution lies in equipping distribution system operators (DSOs) with the ability to compare results and select the most appropriate voltage control and power smoothing methods. This aids in mitigating challenges associated with overvoltage and intermittency issues arising from DRES-generated power, thereby enhancing the overall resilience and reliability of the power grid. Case studies that include four voltage control algorithms and three power smoothing methods demonstrate the universality and effectiveness of the proposed approach.
...
The extensive integration of distributed renewable energy resources (DRES) can lead to several issues in power grids, particularly in distribution grids, due to their inherent intermittency. This paper presents a stochastic simulation-based approach to estimate the maximum permissible penetration level of DRES and to determine the optimal capacity of centralized battery energy storage systems (BESS) in distribution networks while adhering to technical constraints. The stochastic method creates a wide range of scenarios under various conditions. For each scenario, our proposed approach calculates the maximum allowable penetration level of DRES and the required BESS capacity with different DRES control logics. The maximum allowable penetration level of DRES and the requirements of the BESS capacity are determined by an analysis of various simulation results. This paper's unique contribution lies in equipping distribution system operators (DSOs) with the ability to compare results and select the most appropriate voltage control and power smoothing methods. This aids in mitigating challenges associated with overvoltage and intermittency issues arising from DRES-generated power, thereby enhancing the overall resilience and reliability of the power grid. Case studies that include four voltage control algorithms and three power smoothing methods demonstrate the universality and effectiveness of the proposed approach.
Self-organizing voltage regulation in the distribution networks
Insights into planning, operation and validation
Global energy trends are experiencing a significant shift characterized by a growing movement toward the integration of Distributed Renewable Energy Sources (DRES), such as wind and solar energy, into the power grid. Accelerated by technological advancements and supportive policy initiatives, this transition aims to reduce our reliance on fossil fuels, promote local energy generation, and improve energy security. However, the extensive penetration of DRES into the power grid presents its unique set of challenges.
A big challenge associated with the integration of DRES is their innate intermittency and unpredictability, which induce fluctuations in power availability and demand. Such fluctuations could lead to voltage instability, frequency deviations, and general power quality problems within the power grid. Moreover, the traditional power grid, which is largely unidirectional in design, cannot manage the bidirectional power flow resulting from DRES integration. As a result, ensuring the stability and reliability of the power grid becomes essential with the widespread integration of DRES. Furthermore, incorporating DRES requires innovative grid planning and operation methodologies to optimize resources and prevent potential congestion.
Motivated by these challenges, this thesis develops and implements the method on identifying the main barriers to increasing the integration of DRES in distribution networks (DN) and developing the solution that can enable high DRES penetration levels in power grids, thereby supporting the transition to a future 100\% renewable energy system. This thesis provides a solution for three critical phases for future smart power grids: planning, operation, and validation.
Planning phase: The thesis introduces a stochastic simulation-based approach to assess DRES penetration levels and the capacity requirements for the central Battery Energy Storage System (BESS) in DNs while ensuring technical constraints. The stochastic method creates a wide range of scenarios under various conditions. For each scenario, my proposed approach calculates the maximum allowable DRES penetration level and the required BESS capacity with different DRES control logic. The maximum allowable DRES penetration level and the BESS capacity requirements are then determined by analyzing various simulation results. The unique contribution lies in equipping distribution system operators (DSO) with the ability to compare results and select the most appropriate voltage control and power smoothing methods. This helps address the challenges associated with voltage violation and intermittency issues arising from DRES-generated power, thus improving the overall resilience and reliability of the power grid. Moreover, data analysis techniques are utilized to compare the efficacy of various local voltage and BESS control methodologies, offering valuable insights for network planners.
Operation phase (DSO): Building on the foundational knowledge acquired in the planning phase about DRES high penetration level network, a novel algorithm is proposed to achieve optimal voltage regulation through the self-organizing actions of agents. This algorithm empowers distributed agents to coordinate and collaborate in real time to regulate voltage in DNs with high DRES penetration. The proposed method can minimize the number of agents involved in the voltage regulation and the change of required power for voltage regulation, which together minimizes the need for re-dispatching, i.e., the impact of voltage regulation on the exchange of energy. Moreover, the proposed method performs online optimization, i.e., the value of the decision variable is physically implemented as a controller set-point at each iteration, which reduces the response time. The presented algorithm is benchmarked against the alternating direction method of multipliers (ADMM) algorithm and centralized optimization to validate its efficiency.
Operation phase (Energy community): While coordinating DRES strategies from the grid's viewpoint is vital, it's equally important to consider the energy management of energy communities. I propose a comprehensive four-stage energy management approach that employs receding-horizon optimization to stabilize power fluctuations in a residential energy community system. This system comprises a photovoltaic (PV) installation, a BESS, and a hydrogen system with an electrolyzer, a fuel cell, and a hydrogen storage. This innovative approach uniquely integrates four optimization stages, i.e., yearly, monthly, day-ahead, and intra-day. It blends long-term and short-term optimization techniques in EMS development to utilize hydrogen generated via electrolysis as seasonal storage. The introduced algorithm incorporates three modes with distinct objective functions for enhanced user adaptability. The approach is tested through simulations and operational analysis of an on-site PV–BESS–electrolyzer–fuel cell energy system field lab, including an in-depth analysis of system failure rates, system efficiency evaluation, and performance comparison across different modes of operation.
Validation Phase: To make our research more hands-on and highlight the challenges of DRES, I developed a practical simulation tool named The Illuminator. The Illuminator helps illustrate the challenges of DRES integration, acts as a sandbox for testing new research concepts in real and nonreal time, and allows real-world equipment simulations to check an algorithm before it is fully used. The Illuminator technology is primarily a modular software platform developed on a Raspberry Pi cluster. It is open-source, available on GitHub and developed in Python. The Illuminator comprises models of common energy technologies, such as PV panels, wind turbines, BESS, and hydrogen systems. The uniqueness of The Illuminator is in its modularity and flexibility to reconfigure scenarios and cases on the fly, even by non-experts in a plug-and-play fashion. I introduce The Illuminator and show its performance in two simple case studies.
This research improves the collective understanding of DRES integration by developing practical tools and methodologies that can significantly influence the design and operation of future power grids. Consequently, it paves the way for a cleaner, more efficient, and reliable energy system. ...
A big challenge associated with the integration of DRES is their innate intermittency and unpredictability, which induce fluctuations in power availability and demand. Such fluctuations could lead to voltage instability, frequency deviations, and general power quality problems within the power grid. Moreover, the traditional power grid, which is largely unidirectional in design, cannot manage the bidirectional power flow resulting from DRES integration. As a result, ensuring the stability and reliability of the power grid becomes essential with the widespread integration of DRES. Furthermore, incorporating DRES requires innovative grid planning and operation methodologies to optimize resources and prevent potential congestion.
Motivated by these challenges, this thesis develops and implements the method on identifying the main barriers to increasing the integration of DRES in distribution networks (DN) and developing the solution that can enable high DRES penetration levels in power grids, thereby supporting the transition to a future 100\% renewable energy system. This thesis provides a solution for three critical phases for future smart power grids: planning, operation, and validation.
Planning phase: The thesis introduces a stochastic simulation-based approach to assess DRES penetration levels and the capacity requirements for the central Battery Energy Storage System (BESS) in DNs while ensuring technical constraints. The stochastic method creates a wide range of scenarios under various conditions. For each scenario, my proposed approach calculates the maximum allowable DRES penetration level and the required BESS capacity with different DRES control logic. The maximum allowable DRES penetration level and the BESS capacity requirements are then determined by analyzing various simulation results. The unique contribution lies in equipping distribution system operators (DSO) with the ability to compare results and select the most appropriate voltage control and power smoothing methods. This helps address the challenges associated with voltage violation and intermittency issues arising from DRES-generated power, thus improving the overall resilience and reliability of the power grid. Moreover, data analysis techniques are utilized to compare the efficacy of various local voltage and BESS control methodologies, offering valuable insights for network planners.
Operation phase (DSO): Building on the foundational knowledge acquired in the planning phase about DRES high penetration level network, a novel algorithm is proposed to achieve optimal voltage regulation through the self-organizing actions of agents. This algorithm empowers distributed agents to coordinate and collaborate in real time to regulate voltage in DNs with high DRES penetration. The proposed method can minimize the number of agents involved in the voltage regulation and the change of required power for voltage regulation, which together minimizes the need for re-dispatching, i.e., the impact of voltage regulation on the exchange of energy. Moreover, the proposed method performs online optimization, i.e., the value of the decision variable is physically implemented as a controller set-point at each iteration, which reduces the response time. The presented algorithm is benchmarked against the alternating direction method of multipliers (ADMM) algorithm and centralized optimization to validate its efficiency.
Operation phase (Energy community): While coordinating DRES strategies from the grid's viewpoint is vital, it's equally important to consider the energy management of energy communities. I propose a comprehensive four-stage energy management approach that employs receding-horizon optimization to stabilize power fluctuations in a residential energy community system. This system comprises a photovoltaic (PV) installation, a BESS, and a hydrogen system with an electrolyzer, a fuel cell, and a hydrogen storage. This innovative approach uniquely integrates four optimization stages, i.e., yearly, monthly, day-ahead, and intra-day. It blends long-term and short-term optimization techniques in EMS development to utilize hydrogen generated via electrolysis as seasonal storage. The introduced algorithm incorporates three modes with distinct objective functions for enhanced user adaptability. The approach is tested through simulations and operational analysis of an on-site PV–BESS–electrolyzer–fuel cell energy system field lab, including an in-depth analysis of system failure rates, system efficiency evaluation, and performance comparison across different modes of operation.
Validation Phase: To make our research more hands-on and highlight the challenges of DRES, I developed a practical simulation tool named The Illuminator. The Illuminator helps illustrate the challenges of DRES integration, acts as a sandbox for testing new research concepts in real and nonreal time, and allows real-world equipment simulations to check an algorithm before it is fully used. The Illuminator technology is primarily a modular software platform developed on a Raspberry Pi cluster. It is open-source, available on GitHub and developed in Python. The Illuminator comprises models of common energy technologies, such as PV panels, wind turbines, BESS, and hydrogen systems. The uniqueness of The Illuminator is in its modularity and flexibility to reconfigure scenarios and cases on the fly, even by non-experts in a plug-and-play fashion. I introduce The Illuminator and show its performance in two simple case studies.
This research improves the collective understanding of DRES integration by developing practical tools and methodologies that can significantly influence the design and operation of future power grids. Consequently, it paves the way for a cleaner, more efficient, and reliable energy system. ...
Global energy trends are experiencing a significant shift characterized by a growing movement toward the integration of Distributed Renewable Energy Sources (DRES), such as wind and solar energy, into the power grid. Accelerated by technological advancements and supportive policy initiatives, this transition aims to reduce our reliance on fossil fuels, promote local energy generation, and improve energy security. However, the extensive penetration of DRES into the power grid presents its unique set of challenges.
A big challenge associated with the integration of DRES is their innate intermittency and unpredictability, which induce fluctuations in power availability and demand. Such fluctuations could lead to voltage instability, frequency deviations, and general power quality problems within the power grid. Moreover, the traditional power grid, which is largely unidirectional in design, cannot manage the bidirectional power flow resulting from DRES integration. As a result, ensuring the stability and reliability of the power grid becomes essential with the widespread integration of DRES. Furthermore, incorporating DRES requires innovative grid planning and operation methodologies to optimize resources and prevent potential congestion.
Motivated by these challenges, this thesis develops and implements the method on identifying the main barriers to increasing the integration of DRES in distribution networks (DN) and developing the solution that can enable high DRES penetration levels in power grids, thereby supporting the transition to a future 100\% renewable energy system. This thesis provides a solution for three critical phases for future smart power grids: planning, operation, and validation.
Planning phase: The thesis introduces a stochastic simulation-based approach to assess DRES penetration levels and the capacity requirements for the central Battery Energy Storage System (BESS) in DNs while ensuring technical constraints. The stochastic method creates a wide range of scenarios under various conditions. For each scenario, my proposed approach calculates the maximum allowable DRES penetration level and the required BESS capacity with different DRES control logic. The maximum allowable DRES penetration level and the BESS capacity requirements are then determined by analyzing various simulation results. The unique contribution lies in equipping distribution system operators (DSO) with the ability to compare results and select the most appropriate voltage control and power smoothing methods. This helps address the challenges associated with voltage violation and intermittency issues arising from DRES-generated power, thus improving the overall resilience and reliability of the power grid. Moreover, data analysis techniques are utilized to compare the efficacy of various local voltage and BESS control methodologies, offering valuable insights for network planners.
Operation phase (DSO): Building on the foundational knowledge acquired in the planning phase about DRES high penetration level network, a novel algorithm is proposed to achieve optimal voltage regulation through the self-organizing actions of agents. This algorithm empowers distributed agents to coordinate and collaborate in real time to regulate voltage in DNs with high DRES penetration. The proposed method can minimize the number of agents involved in the voltage regulation and the change of required power for voltage regulation, which together minimizes the need for re-dispatching, i.e., the impact of voltage regulation on the exchange of energy. Moreover, the proposed method performs online optimization, i.e., the value of the decision variable is physically implemented as a controller set-point at each iteration, which reduces the response time. The presented algorithm is benchmarked against the alternating direction method of multipliers (ADMM) algorithm and centralized optimization to validate its efficiency.
Operation phase (Energy community): While coordinating DRES strategies from the grid's viewpoint is vital, it's equally important to consider the energy management of energy communities. I propose a comprehensive four-stage energy management approach that employs receding-horizon optimization to stabilize power fluctuations in a residential energy community system. This system comprises a photovoltaic (PV) installation, a BESS, and a hydrogen system with an electrolyzer, a fuel cell, and a hydrogen storage. This innovative approach uniquely integrates four optimization stages, i.e., yearly, monthly, day-ahead, and intra-day. It blends long-term and short-term optimization techniques in EMS development to utilize hydrogen generated via electrolysis as seasonal storage. The introduced algorithm incorporates three modes with distinct objective functions for enhanced user adaptability. The approach is tested through simulations and operational analysis of an on-site PV–BESS–electrolyzer–fuel cell energy system field lab, including an in-depth analysis of system failure rates, system efficiency evaluation, and performance comparison across different modes of operation.
Validation Phase: To make our research more hands-on and highlight the challenges of DRES, I developed a practical simulation tool named The Illuminator. The Illuminator helps illustrate the challenges of DRES integration, acts as a sandbox for testing new research concepts in real and nonreal time, and allows real-world equipment simulations to check an algorithm before it is fully used. The Illuminator technology is primarily a modular software platform developed on a Raspberry Pi cluster. It is open-source, available on GitHub and developed in Python. The Illuminator comprises models of common energy technologies, such as PV panels, wind turbines, BESS, and hydrogen systems. The uniqueness of The Illuminator is in its modularity and flexibility to reconfigure scenarios and cases on the fly, even by non-experts in a plug-and-play fashion. I introduce The Illuminator and show its performance in two simple case studies.
This research improves the collective understanding of DRES integration by developing practical tools and methodologies that can significantly influence the design and operation of future power grids. Consequently, it paves the way for a cleaner, more efficient, and reliable energy system.
A big challenge associated with the integration of DRES is their innate intermittency and unpredictability, which induce fluctuations in power availability and demand. Such fluctuations could lead to voltage instability, frequency deviations, and general power quality problems within the power grid. Moreover, the traditional power grid, which is largely unidirectional in design, cannot manage the bidirectional power flow resulting from DRES integration. As a result, ensuring the stability and reliability of the power grid becomes essential with the widespread integration of DRES. Furthermore, incorporating DRES requires innovative grid planning and operation methodologies to optimize resources and prevent potential congestion.
Motivated by these challenges, this thesis develops and implements the method on identifying the main barriers to increasing the integration of DRES in distribution networks (DN) and developing the solution that can enable high DRES penetration levels in power grids, thereby supporting the transition to a future 100\% renewable energy system. This thesis provides a solution for three critical phases for future smart power grids: planning, operation, and validation.
Planning phase: The thesis introduces a stochastic simulation-based approach to assess DRES penetration levels and the capacity requirements for the central Battery Energy Storage System (BESS) in DNs while ensuring technical constraints. The stochastic method creates a wide range of scenarios under various conditions. For each scenario, my proposed approach calculates the maximum allowable DRES penetration level and the required BESS capacity with different DRES control logic. The maximum allowable DRES penetration level and the BESS capacity requirements are then determined by analyzing various simulation results. The unique contribution lies in equipping distribution system operators (DSO) with the ability to compare results and select the most appropriate voltage control and power smoothing methods. This helps address the challenges associated with voltage violation and intermittency issues arising from DRES-generated power, thus improving the overall resilience and reliability of the power grid. Moreover, data analysis techniques are utilized to compare the efficacy of various local voltage and BESS control methodologies, offering valuable insights for network planners.
Operation phase (DSO): Building on the foundational knowledge acquired in the planning phase about DRES high penetration level network, a novel algorithm is proposed to achieve optimal voltage regulation through the self-organizing actions of agents. This algorithm empowers distributed agents to coordinate and collaborate in real time to regulate voltage in DNs with high DRES penetration. The proposed method can minimize the number of agents involved in the voltage regulation and the change of required power for voltage regulation, which together minimizes the need for re-dispatching, i.e., the impact of voltage regulation on the exchange of energy. Moreover, the proposed method performs online optimization, i.e., the value of the decision variable is physically implemented as a controller set-point at each iteration, which reduces the response time. The presented algorithm is benchmarked against the alternating direction method of multipliers (ADMM) algorithm and centralized optimization to validate its efficiency.
Operation phase (Energy community): While coordinating DRES strategies from the grid's viewpoint is vital, it's equally important to consider the energy management of energy communities. I propose a comprehensive four-stage energy management approach that employs receding-horizon optimization to stabilize power fluctuations in a residential energy community system. This system comprises a photovoltaic (PV) installation, a BESS, and a hydrogen system with an electrolyzer, a fuel cell, and a hydrogen storage. This innovative approach uniquely integrates four optimization stages, i.e., yearly, monthly, day-ahead, and intra-day. It blends long-term and short-term optimization techniques in EMS development to utilize hydrogen generated via electrolysis as seasonal storage. The introduced algorithm incorporates three modes with distinct objective functions for enhanced user adaptability. The approach is tested through simulations and operational analysis of an on-site PV–BESS–electrolyzer–fuel cell energy system field lab, including an in-depth analysis of system failure rates, system efficiency evaluation, and performance comparison across different modes of operation.
Validation Phase: To make our research more hands-on and highlight the challenges of DRES, I developed a practical simulation tool named The Illuminator. The Illuminator helps illustrate the challenges of DRES integration, acts as a sandbox for testing new research concepts in real and nonreal time, and allows real-world equipment simulations to check an algorithm before it is fully used. The Illuminator technology is primarily a modular software platform developed on a Raspberry Pi cluster. It is open-source, available on GitHub and developed in Python. The Illuminator comprises models of common energy technologies, such as PV panels, wind turbines, BESS, and hydrogen systems. The uniqueness of The Illuminator is in its modularity and flexibility to reconfigure scenarios and cases on the fly, even by non-experts in a plug-and-play fashion. I introduce The Illuminator and show its performance in two simple case studies.
This research improves the collective understanding of DRES integration by developing practical tools and methodologies that can significantly influence the design and operation of future power grids. Consequently, it paves the way for a cleaner, more efficient, and reliable energy system.
Journal article
(2024)
-
Jingxuan Wu, Shuting Li, Aihui Fu, Miloš Cvetković, Peter Palensky, Juan C. Vasquez, Josep M. Guerrero
The increasing proportion of renewable energy introduces both long-term and short-term uncertainty to power systems, which restricts the implementation of energy management systems (EMSs) with high dependency on accurate prediction techniques. A hierarchical online EMS (HEMS) is proposed in this paper to economically operate the Hybrid hydrogen–electricity Storage System (HSS) in a residential microgrid (RMG). The HEMS dispatches an electrolyzer-fuel cell-based hydrogen energy storage (ES) unit for seasonal energy shifting and an on-site battery stack for daily energy allocation against the uncertainty from the renewable energy source (RES) and demand side. The online decision-making of the proposed HEMS is realized through two parallel fuzzy logic (FL)-based controllers which are decoupled by different operating frequencies. An original local energy estimation model (LEEM) is specifically designed for the decision process of FL controllers to comprehensively evaluate the system status and quantify the electricity price expectation for the HEMS. The proposed HEMS is independent of RES prediction or load forecasting, and gives the optimal operation for HSS in separated resolutions: the hydrogen ES unit is dispatched hourly and the battery is operated every minute. The performance of the proposed method is verified by numerical experiments fed by real-world datasets. The superiority of the HEMS in expense-saving manner is validated through comparison with PSO-based day-ahead optimization methods, fuzzy logic EMS, and rule-based online EMS.
...
The increasing proportion of renewable energy introduces both long-term and short-term uncertainty to power systems, which restricts the implementation of energy management systems (EMSs) with high dependency on accurate prediction techniques. A hierarchical online EMS (HEMS) is proposed in this paper to economically operate the Hybrid hydrogen–electricity Storage System (HSS) in a residential microgrid (RMG). The HEMS dispatches an electrolyzer-fuel cell-based hydrogen energy storage (ES) unit for seasonal energy shifting and an on-site battery stack for daily energy allocation against the uncertainty from the renewable energy source (RES) and demand side. The online decision-making of the proposed HEMS is realized through two parallel fuzzy logic (FL)-based controllers which are decoupled by different operating frequencies. An original local energy estimation model (LEEM) is specifically designed for the decision process of FL controllers to comprehensively evaluate the system status and quantify the electricity price expectation for the HEMS. The proposed HEMS is independent of RES prediction or load forecasting, and gives the optimal operation for HSS in separated resolutions: the hydrogen ES unit is dispatched hourly and the battery is operated every minute. The performance of the proposed method is verified by numerical experiments fed by real-world datasets. The superiority of the HEMS in expense-saving manner is validated through comparison with PSO-based day-ahead optimization methods, fuzzy logic EMS, and rule-based online EMS.
With the maritime industry poised on the cusp of a hybrid revolution, the design and analysis of advanced vessel systems have become paramount for engineers. This paper presents AC and DC electrical hybrid power system models in ETAP, the simulation software that can be adapted to engineer future hybrid vessels. These models are also a step towards a digital twin model that can help in troubleshooting and preventing issues, reducing risk and engineering time. The testing of the models is focused on time domain analysis, short-circuit currents, and protection & coordination. The models are based on actual vessels and manufacturer parameters are used where available.
...
With the maritime industry poised on the cusp of a hybrid revolution, the design and analysis of advanced vessel systems have become paramount for engineers. This paper presents AC and DC electrical hybrid power system models in ETAP, the simulation software that can be adapted to engineer future hybrid vessels. These models are also a step towards a digital twin model that can help in troubleshooting and preventing issues, reducing risk and engineering time. The testing of the models is focused on time domain analysis, short-circuit currents, and protection & coordination. The models are based on actual vessels and manufacturer parameters are used where available.
Conference paper
(2023)
-
Aihui Fu, Aleksandra Lekić, Eleftherios O. Kontis, Kyriaki-Nefeli D. Malamaki, Georgios C. Kryonidis, Juan Manuel Mauricio, Charis S. Demoulias, Milos Cvetkovic
This paper deals with a systematic assessment of the power system frequency dynamics under high penetration of converter-interfaced renewable energy sources (CI-RESs). Specifically, the concept of the virtual synchronous generator (VSG) is implemented in the CI-RESs located at the transmission system (TS) side and/or the distribution network (DN) side. Dynamic RMS simulations are performed on a testbed consisting of the IEEE 9-bus TS grid and the CIGRE medium-voltage DN grid under different CI-RES penetration levels and VSG control parameters to assess the VSG impact on the power system frequency dynamics. It is shown that the decommissioning of conventional power plants coupled via synchronous generators can be safely performed in case the VSG concept is adopted correctly.
...
This paper deals with a systematic assessment of the power system frequency dynamics under high penetration of converter-interfaced renewable energy sources (CI-RESs). Specifically, the concept of the virtual synchronous generator (VSG) is implemented in the CI-RESs located at the transmission system (TS) side and/or the distribution network (DN) side. Dynamic RMS simulations are performed on a testbed consisting of the IEEE 9-bus TS grid and the CIGRE medium-voltage DN grid under different CI-RES penetration levels and VSG control parameters to assess the VSG impact on the power system frequency dynamics. It is shown that the decommissioning of conventional power plants coupled via synchronous generators can be safely performed in case the VSG concept is adopted correctly.
The Illuminator
An Open Source Energy System Integration Development Kit
Conference paper
(2023)
-
Aihui Fu, Raghav Saini, Remko Koornneef, Arjen van der Meer, Peter Palensky, Milos Cvetkovic
This paper introduces a flexible and extendable easy-to-use energy system integration development kit: the Illuminator. The Illuminator illustrates challenges arising from the energy transition. Hence, it is suitable in education and for demonstration. It also acts as a sandbox for testing new research concepts, and particularly, distributed energy coordination algorithms in real and non-real time. The Illuminator technology is primarely a modular software platform developed to run on a Raspberry Pi (RasPi) cluster. It is open-source, available at GitHub and developed in Python. The Illuminator comprises models of common energy technologies, such as photovoltaic (PV) panels, wind turbines, batteries, and hydrogen systems. The uniqueness of the Illuminator is in its modularity and flexibility to reconfigure scenarios and cases on the fly, even by non-experts in a plug-and-play fashion. This paper introduces the Illuminator and shows its performance in a simple case study.
...
This paper introduces a flexible and extendable easy-to-use energy system integration development kit: the Illuminator. The Illuminator illustrates challenges arising from the energy transition. Hence, it is suitable in education and for demonstration. It also acts as a sandbox for testing new research concepts, and particularly, distributed energy coordination algorithms in real and non-real time. The Illuminator technology is primarely a modular software platform developed to run on a Raspberry Pi (RasPi) cluster. It is open-source, available at GitHub and developed in Python. The Illuminator comprises models of common energy technologies, such as photovoltaic (PV) panels, wind turbines, batteries, and hydrogen systems. The uniqueness of the Illuminator is in its modularity and flexibility to reconfigure scenarios and cases on the fly, even by non-experts in a plug-and-play fashion. This paper introduces the Illuminator and shows its performance in a simple case study.
Conference paper
(2023)
-
Kyriaki-Nefeli D. Malamaki, Aihui Fu, Juan Manuel Mauricio, Milos Cvetkovic, Charis S. Demoulias
As the penetration of Converter-Interfaced Dis-tributed Renewable Energy Sources (CI-DRES) increases, several problems are revealed in electric power systems, e.g., power quality issues, reverse power flows and frequency instability. A solution to tackle these issues is the mitigation of high CI-DRES active power ramp-rates (RRs) by utilizing energy storage systems (ESS). In many grid-codes at transmission system (TS) level, it is specified that the CI-DRES limit their RRs, while also the utilization of a central ESS has been proposed to limit the RRs. Nevertheless, this approach involves only large energy market players. Although various RRL methods have been proposed for CI-DRES, a remaining gap is the evaluation of the RR of a Distribution Network (DN) containing CI-DRES and loads together with the influence of distributed ESS in the DN. Towards this direction, in this paper, this evaluation is performed in order to study the RRL capability of a low-voltage (LV) DN considering both central and distributed ESS. The analysis is conducted in the LV CIGRE DN via quasi-steady-state and RMS simulations in PowerFactory considering several techno-economic parameters, e.g., ESS size, type, per unit cost. This evaluation will help towards the integration of the RRL control in the grid codes in DNs so that it can be considered as a new ancillary service to be remunerated in respective markets where also small CI-DRES owners will be able to participate.
...
As the penetration of Converter-Interfaced Dis-tributed Renewable Energy Sources (CI-DRES) increases, several problems are revealed in electric power systems, e.g., power quality issues, reverse power flows and frequency instability. A solution to tackle these issues is the mitigation of high CI-DRES active power ramp-rates (RRs) by utilizing energy storage systems (ESS). In many grid-codes at transmission system (TS) level, it is specified that the CI-DRES limit their RRs, while also the utilization of a central ESS has been proposed to limit the RRs. Nevertheless, this approach involves only large energy market players. Although various RRL methods have been proposed for CI-DRES, a remaining gap is the evaluation of the RR of a Distribution Network (DN) containing CI-DRES and loads together with the influence of distributed ESS in the DN. Towards this direction, in this paper, this evaluation is performed in order to study the RRL capability of a low-voltage (LV) DN considering both central and distributed ESS. The analysis is conducted in the LV CIGRE DN via quasi-steady-state and RMS simulations in PowerFactory considering several techno-economic parameters, e.g., ESS size, type, per unit cost. This evaluation will help towards the integration of the RRL control in the grid codes in DNs so that it can be considered as a new ancillary service to be remunerated in respective markets where also small CI-DRES owners will be able to participate.
Door middel van een zo representatief mogelijk rekenmodel voor het middenspanningsnet van de Amsterdamse gebieden Buiksloterham-Zuid/Overhoeks (BZOH), heeft het onderzoeksteam een detailanalyse kunnen uitvoeren naar de verwachte leveringscongestie in dit gebied zoals aangekondigd door Liander op 24 juni 2021. De detailanalyse laat een grootschalige toename in elektriciteitsverbruik in 3 jaar tijd zien, voornamelijk vanwege de stedelijke ontwikkeling in Overhoeks. Uit de beperkte beschikbare data en de resultaten uit het rekenmodel blijkt dat deze toename in vermogensvraag in het middenspanningsnet boven de normale beleidsgrenzen komt totdat de realisatie van de geplande netuitbreiding halverwege 2023 gereed is. De capaciteit is vooral ontoereikend in storings- en/of onderhoudssituaties, ook wel verschakelde toestand genoemd, in bepaalde delen van het netwerk.
De studie laat zien dat het effectief verschakelen van het netwerk door Liander een groot deel van het capaciteitsprobleem vermindert. Er bestaan meerdere mogelijkheden om de configuratie van het net aan te passen om zowel in normaal bedrijf als in storings- en/of onderhoudssituaties belastingen beter in het netwerk te kunnen integreren.
Een optimale netwerktopologie is daarom noodzakelijk om capaciteit vrij te spelen. In combinatie met een alternatieve reservestelling voor storing en onderhoud (t.o.v. de huidige reservecapaciteit in het netwerk) blijkt dat kritieke netsituaties voorkomen kunnen worden. Om tot een kosteneffectieve en uitvoerbare inschatting te komen voor de dimensionering en locatie van de alternatieve reservestelling, is het detailniveau van netanalyse cruciaal en is voor het toepassen van een alternatieve reservestelling in BZOH een kalibratie van deze studie door Liander noodzakelijk. Daarbij kan de detailanalyse inzichten bieden om in tijden van congestie het overschrijden van de normale beleidsgrenzen omtrent kabelbelasting tijdelijk toe te staan onder de veilige omstandigheden. Op basis van de resultaten uit deze studie zijn deze opties voor reservestelling vanuit energetisch perspectief kansrijk voor BZOH, zonder een verdere uitwerking te bieden voor implementatie. ...
De studie laat zien dat het effectief verschakelen van het netwerk door Liander een groot deel van het capaciteitsprobleem vermindert. Er bestaan meerdere mogelijkheden om de configuratie van het net aan te passen om zowel in normaal bedrijf als in storings- en/of onderhoudssituaties belastingen beter in het netwerk te kunnen integreren.
Een optimale netwerktopologie is daarom noodzakelijk om capaciteit vrij te spelen. In combinatie met een alternatieve reservestelling voor storing en onderhoud (t.o.v. de huidige reservecapaciteit in het netwerk) blijkt dat kritieke netsituaties voorkomen kunnen worden. Om tot een kosteneffectieve en uitvoerbare inschatting te komen voor de dimensionering en locatie van de alternatieve reservestelling, is het detailniveau van netanalyse cruciaal en is voor het toepassen van een alternatieve reservestelling in BZOH een kalibratie van deze studie door Liander noodzakelijk. Daarbij kan de detailanalyse inzichten bieden om in tijden van congestie het overschrijden van de normale beleidsgrenzen omtrent kabelbelasting tijdelijk toe te staan onder de veilige omstandigheden. Op basis van de resultaten uit deze studie zijn deze opties voor reservestelling vanuit energetisch perspectief kansrijk voor BZOH, zonder een verdere uitwerking te bieden voor implementatie. ...
Door middel van een zo representatief mogelijk rekenmodel voor het middenspanningsnet van de Amsterdamse gebieden Buiksloterham-Zuid/Overhoeks (BZOH), heeft het onderzoeksteam een detailanalyse kunnen uitvoeren naar de verwachte leveringscongestie in dit gebied zoals aangekondigd door Liander op 24 juni 2021. De detailanalyse laat een grootschalige toename in elektriciteitsverbruik in 3 jaar tijd zien, voornamelijk vanwege de stedelijke ontwikkeling in Overhoeks. Uit de beperkte beschikbare data en de resultaten uit het rekenmodel blijkt dat deze toename in vermogensvraag in het middenspanningsnet boven de normale beleidsgrenzen komt totdat de realisatie van de geplande netuitbreiding halverwege 2023 gereed is. De capaciteit is vooral ontoereikend in storings- en/of onderhoudssituaties, ook wel verschakelde toestand genoemd, in bepaalde delen van het netwerk.
De studie laat zien dat het effectief verschakelen van het netwerk door Liander een groot deel van het capaciteitsprobleem vermindert. Er bestaan meerdere mogelijkheden om de configuratie van het net aan te passen om zowel in normaal bedrijf als in storings- en/of onderhoudssituaties belastingen beter in het netwerk te kunnen integreren.
Een optimale netwerktopologie is daarom noodzakelijk om capaciteit vrij te spelen. In combinatie met een alternatieve reservestelling voor storing en onderhoud (t.o.v. de huidige reservecapaciteit in het netwerk) blijkt dat kritieke netsituaties voorkomen kunnen worden. Om tot een kosteneffectieve en uitvoerbare inschatting te komen voor de dimensionering en locatie van de alternatieve reservestelling, is het detailniveau van netanalyse cruciaal en is voor het toepassen van een alternatieve reservestelling in BZOH een kalibratie van deze studie door Liander noodzakelijk. Daarbij kan de detailanalyse inzichten bieden om in tijden van congestie het overschrijden van de normale beleidsgrenzen omtrent kabelbelasting tijdelijk toe te staan onder de veilige omstandigheden. Op basis van de resultaten uit deze studie zijn deze opties voor reservestelling vanuit energetisch perspectief kansrijk voor BZOH, zonder een verdere uitwerking te bieden voor implementatie.
De studie laat zien dat het effectief verschakelen van het netwerk door Liander een groot deel van het capaciteitsprobleem vermindert. Er bestaan meerdere mogelijkheden om de configuratie van het net aan te passen om zowel in normaal bedrijf als in storings- en/of onderhoudssituaties belastingen beter in het netwerk te kunnen integreren.
Een optimale netwerktopologie is daarom noodzakelijk om capaciteit vrij te spelen. In combinatie met een alternatieve reservestelling voor storing en onderhoud (t.o.v. de huidige reservecapaciteit in het netwerk) blijkt dat kritieke netsituaties voorkomen kunnen worden. Om tot een kosteneffectieve en uitvoerbare inschatting te komen voor de dimensionering en locatie van de alternatieve reservestelling, is het detailniveau van netanalyse cruciaal en is voor het toepassen van een alternatieve reservestelling in BZOH een kalibratie van deze studie door Liander noodzakelijk. Daarbij kan de detailanalyse inzichten bieden om in tijden van congestie het overschrijden van de normale beleidsgrenzen omtrent kabelbelasting tijdelijk toe te staan onder de veilige omstandigheden. Op basis van de resultaten uit deze studie zijn deze opties voor reservestelling vanuit energetisch perspectief kansrijk voor BZOH, zonder een verdere uitwerking te bieden voor implementatie.
As the penetration of distributed energy resources (DERs) increases significantly in the distribution networks, their integration reshapes distribution system power flows and leads to serious voltage quality problems in distribution networks. In this paper, a novel distributed cooperation voltage regulation method is proposed for future distribution networks. The proposed method can minimize the number of agents involved in the voltage regulation and it minimizes the change of required power for voltage regulation, which together minimizes the need for re-dispatching, i.e. the impact of voltage regulation on exchange of energy. Moreover, the proposed method performs online optimization, i.e the value of the decision variable is physically implemented as a controller set-point at each iteration which reduces the response time. The presented algorithm is benchmarked against ADMM and centralized optimization. The results show the voltage regulation effectiveness. Compared to the centralized method, the proposed method performs better in the case of a single agent failure, and compared to the ADMM method, the proposed method greatly reduces the action time and does not require retuning if network topology or agent participation changes.
...
As the penetration of distributed energy resources (DERs) increases significantly in the distribution networks, their integration reshapes distribution system power flows and leads to serious voltage quality problems in distribution networks. In this paper, a novel distributed cooperation voltage regulation method is proposed for future distribution networks. The proposed method can minimize the number of agents involved in the voltage regulation and it minimizes the change of required power for voltage regulation, which together minimizes the need for re-dispatching, i.e. the impact of voltage regulation on exchange of energy. Moreover, the proposed method performs online optimization, i.e the value of the decision variable is physically implemented as a controller set-point at each iteration which reduces the response time. The presented algorithm is benchmarked against ADMM and centralized optimization. The results show the voltage regulation effectiveness. Compared to the centralized method, the proposed method performs better in the case of a single agent failure, and compared to the ADMM method, the proposed method greatly reduces the action time and does not require retuning if network topology or agent participation changes.
The rapid growth of rooftop solar, and on the rise technologies such as electric vehicles and heat pumps, is leading to congestion problems in low voltage distribution networks. If not dealt with, the congestion will prevent the installation of further units limiting the pace of the energy transition. Energy Storage Systems (ESS) are often seen as the technology with a high potential for congestion reduction. In this paper, we propose to use Relocatable Energy Storage System (RESS) fleet, to physically move the ESS throughout the distribution grid in an effort to alleviate congestion. The proposed method consists of two parts. First, a Mixed Integer Linear Program (MILP) is created to minimise the amount of RESS used and penalise displacement. Second, an algorithm with a Minimum Cost Maximum Matching objective is used to determine the dispatch of each individual RESS. The results on a CIGRE test system show the benefits of this method compared to stationary placed ESS in the grid.
...
The rapid growth of rooftop solar, and on the rise technologies such as electric vehicles and heat pumps, is leading to congestion problems in low voltage distribution networks. If not dealt with, the congestion will prevent the installation of further units limiting the pace of the energy transition. Energy Storage Systems (ESS) are often seen as the technology with a high potential for congestion reduction. In this paper, we propose to use Relocatable Energy Storage System (RESS) fleet, to physically move the ESS throughout the distribution grid in an effort to alleviate congestion. The proposed method consists of two parts. First, a Mixed Integer Linear Program (MILP) is created to minimise the amount of RESS used and penalise displacement. Second, an algorithm with a Minimum Cost Maximum Matching objective is used to determine the dispatch of each individual RESS. The results on a CIGRE test system show the benefits of this method compared to stationary placed ESS in the grid.