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About 10,000 papers for “Microgrid Control and Optimization”

Distributed Control and Dynamic Optimization of a Microgrid

Jameel Ahmad, Muhammad Aqil Aslam, Muhammad Tahir et al. · 2020

In this paper dynamics of a power system consisting of distributed generators in a microgrid is modeled as a nonlinear system. The specific objectives are to optimize non-liner economic-emission dispatch and control the dynamics of power sharing of distributed generators in a microgrid. For efficient coordination of distributed generators, a multi-agent system with shared communication framework employing self-triggered data sampler is proposed. The system is tested for load as well as source side transients. The performance evaluation results show that distributed optimized control with proposed self-triggered strategy significantly reduces the bandwidth requirement. Numerical results show that approximately thirty-six times less data transmission is required during steady state while data transmission requirement during transients reduces twenty times, leading to significant reduction in the demanded communication bandwidth.

Cited by 0

Microgrid management system: power flow control and optimization

Gabriel Guimarães Vasconcelos de Sousa · Institutional Repository of the Federal Technological University of Paraná (RIUT) (Federal University of Technology – Paraná) · 2019

This work proposes a management system for microgrids to optimize the operation cost. Theoptimization minimizes the financial losses caused by power factor irregularities, however, thepossible limitation of the active power is also addressed to weight the optimization, avoidingwasting energy at the primary source. Another contribution of this work is a methodology forreactive power sales allowing power distribution companies to use the microgrid as an activeresource for distribution network control. Mathematical analysis of the proposed algorithmsand Hardware in the Loop validation are presented.

Cited by 0Open access

Optimization and Control for Microgrid and Power Electronic Converters

Rasouli Disfani, Vahid · Digital Commons - University of South Florida (University of South Florida) · 2015

The proposed dissertation research investigates Optimization and Control for Microgrid and Power Electronic Converters. The research has two major parts:\ni- Microgrid Operation and Control,\nii- Power Electronic Converter Control and Optimization.\nIn the first part, three focuses are investigated. First, a completely distributed algorithm is developed for dc optimal power flow problem for power distribution systems as one of the necessary functions considered in unit-commitment problem in day-ahead markets. This method is derived based upon the partial primal-dual representation of the economic dispatch problem, which is finally translated to DC-OPF problem. Second, the optimal interaction between the utility and communities will be studied, due to its improtance in real-time markets. The objective of this section will be to develop an iterative agent-based algorithm for optimal utility-community control. The algorithm will consider the AC power system constraints to maintain power system stability. In this algorithm, a simplified model of microgrid is considered. In the third focus, a comprehensive model of microgrid is taken into account. The optimal operation of the microgrid considering energy storage systems and renewable energy resources is investigated. The interaction of such microgrids with the main grid to define the optimal operation of the entire embedded system is studied through two iterative methods. In the microgrid's internal problem, a moving-horizon algorithm is considered to define the optimal dispatch of all distributed energy resources while considering the time-correlated constraints of energy storage systems. A thorough analysis of the effects of the size of storage systems on energy and reserve market parameters are also performed.\nIn the second part, the focus of research is to develop optimal control strategies for Power Electronic Converters. A Model Predictive Control (MPC) switching method is proposed for Modular Multilevel Converters (MMC). The optimal solution of MPC problem is then represented as an optimization problem. Due to lack of efficient algorithms to seek the optimal solution, a fast algorithm will be proposed in this research. The method proposed reduces the number of possible solutions and computation efforts dramatically.

Cited by 0Open access

REinforcement learning for Microgrid Optimization and TEmperature Control (REMOTEC)

Ferrarini, Luca, Valentini, Alberto, Politecnico di Milano · Zenodo (CERN European Organization for Nuclear Research) · 2025

<p> This report documents the experimental validation of a Meta-Reinforcement Learning (Meta<br>RL) framework applied to building temperature control under uncertain thermal dynamics. The<br> work was conducted at the SYSLAB Risø Campus, Danish Technical University, within the<br> scope of the ERIGrid 2.0 Lab Access programme. The goal was to investigate how a controller<br> trained in simulation, using an approximate building model, could adapt to real-world conditions<br> through data-driven adaptation.<br> The experimental activities encompassed initial model identification, parameter uncertainty<br> analysis, simulation-based controller training, real-world data collection, and deployment of<br> a learning-based adaptation mechanism. The targeted building, Power Flexhouse 03, was<br> equipped with temperature sensors and actuators connected to a central control platform. Key<br> findings demonstrate that the proposed Meta-RL framework substantially reduces energy con<br>sumption without compromising thermal comfort, with the adaptation mechanism effectively<br> correcting initial modeling inaccuracies after only three days of data.<br> This project shows that Meta-RL is a viable, scalable solution for adaptive control in buildings<br> and can serve as a foundation for next-generation intelligent HVAC systems</p>

Cited by 0Open access

Advanced Modeling and Research in Hybrid Microgrid Control and Optimization

Nicu Bizon · 2021

This book presents the latest solutions in fuel cell (FC) and renewable energy implementation in mobile and stationary applications. The implementation of advanced energy management and optimization strategies are detailed for fuel cell and renewable microgrids, and for the multi-FC stack architecture of FC/electric vehicles to enhance the reliability of these systems and to reduce the costs related to energy production and maintenance. Cyber-security methods based on blockchain technology to increase the resilience of FC renewable hybrid microgrids are also presented. Therefore, this book is for all readers interested in these challenging directions of research.

Cited by 0Open access

CONTROL STRATEGIES AND HYBRID OPTIMIZATION SCHEME FOR AN AUTONOMOUS MICROGRID

Sarbajit Behera, Rakesh Sahu, Tapas Chhual Singh et al. · International Research Journal of Modernization in Engineering Technology and Science · 2022

The rapid deployment and optimal operation of Micro-grids is an essential step in increasing the adoption of solar power, improving grid resilience, and expanding electrical power availability in developing countries. Micro-grids integrate local energy generation and storage, local loads, and run in isolation or with a grid connection. Given time-of-day grid energy pricing, predictions of solar energy production, and knowledge of upcoming loads, it is possible to determine the optimal operating policy. This paper establishes an optimal control strategy that can efficiently manage an electric grid comprised of various renewable and non-renewable energy sources for a medium sized community. A hybrid model predictive control scheme is implemented for choosing an optimal mode and set of inputs for the system for tracking both a constant and load-varying power demand profile.

Cited by 0Open access

Stability-oriented Optimization and Consensus Control for Inverter-based Microgrid

Weihang Yan, Wei Gao, Wenzhong Gao et al. · 2018

In this paper, the small-signal model and stability-oriented consensus control scheme for inverter-based microgrid systems is proposed. By perturbating four identified dominant control parameters of inverter-based microgrid intentionally, the corresponding incremental matrix of microgrid system can be formed. Then, Matrix Perturbation Theory is employed in this paper to reduce the computational burden caused by repetitive calculations of system eigenvalues during stability analysis. Furthermore, the objective function is formulated based on the small-signal stability index and consensus index of microgrid system. Finally, the optimal control algorithm based on the joint application of Matrix Perturbation Theory and Artificial Fish Swarm Algorithm is introduced in this paper so that the stability and consensus rate of microgrid can be ensured. Besides, the effectiveness of proposed method can be verified in Matlab/Simulation results.

Cited by 0

Distributed microgrid energy optimization using transactive control and heuristic strategy

Minjie Ji, Peichao Zhang, Yizhi Cheng · 2017

This paper proposes a distributed energy optimization method for microgrids using transactive control and heuristic strategy. The optimality of the method is theoretically proved to be equivalent to that of the conventional centralized method. Then, the paper puts forward heuristic bidding strategies for distributed energy resources (DER) such as distributed generators (DG), loads, energy storage and tie lines. At last, a case study validates that the method can obtain the quasioptimal solution, support standardized communication and have better performance in privacy protection and scalability than centralized energy optimization methods.

Cited by 8

Research on stochastic game and optimization in complex microgrid control

Hong ZHOU, Ruotian YAO, Chang YU et al. · SHILAP Revista de lepidopterología · 2020

A microgrid which integrated by a large scale of distributed generators,storage equipment and dynamic loads has stochastic characteristics in both the power generation and the demand-side.The purpose was to discuss various decision-making and optimal control approaches in the integrated energy microgrid based on a stochastic game.Firstly,the characteristic and architecture of the future complex microgrid were investigated.Secondly,the form and interact of the group game were explained in details,respectively.Then,the stochastic characteristics of different players and the topology that affecting the game on complex microgrids were presented.Finally,a stochastic game optimization approach and its control architecture was proposed.By discussing the key issues of microgrid synchronization,stability and game optimization in different perspectives,the new ideas for promoting clean energy consumption,supply-demand side interaction and maintaining the stability of power grids were provided.

Cited by 0Open access

Dynamic Optimization and Control of a Renewable Microgrid Incorporating Ammonia

Baiwen Kong, Qi Zhang, Pródromos Daoutidis · 2023

A renewable microgrid using green ammonia and hydrogen for energy storage is proposed in the work. Wind and solar energy are captured as power inputs for water electrolysis to produce hydrogen which can be further transformed to ammonia through the Haber-Bosch process. Gensets are dispatched to generate power from hydrogen or ammonia for meeting residential demands. Local control structures are designed for each module in the framework, which takes hourly commands from an upper level dynamic real-time optimization (D-RTO) layer. Case studies in Duluth, MN are conducted for summer and winter in a 24-hour time horizon, demonstrating the stability of system under disturbances in renewable sources. Results confirm that ammonia is more preferred for long-term energy storage.

Cited by 5

Multi-objective coordinated control and optimization for photovoltaic microgrid scheduling

D.C. Yu, Kai Hou, Lin Xu et al. · Frontiers in Energy Research · 2025

The stability and economic dispatch efficiency of photovoltaic (PV) microgrids is influenced by various internal and external factors, and they require a well-designed optimization plan to enhance their operation and management. This paper proposes a multi-objective coordinated control and optimization system for PV microgrids. To address the challenges of slow convergence and local optima in traditional PV microgrid scheduling methods, this study introduced an improved multiple objective particle swarm optimization (IMOPSO) algorithm that integrates an adaptive inertia weight adjustment strategy based on optimal similarity and a multi-directional iterative Pareto solution archive update mechanism. A tri-objective optimization model is formulated to minimize operational costs, environmental pollution, and grid output fluctuation variance, with decision-making supported by the Entropy Weight TOPSIS method. The proposed algorithm is validated through a practical case study of a PV microgrid located in Suzhou, China, and the results demonstrate that IMOPSO achieves a 4.4% reduction in total operational costs under time-of-use pricing (from 50.73 USD to 48.49 USD) and a 4.6% reduction under fixed pricing (from 54.93 USD to 52.38 USD), alongside a maximum safety variance reduction of 45% (from 22.16 to 12.15). The Pareto front distribution exhibits enhanced diversity and uniformity compared to the original MOPSO. While single-objective optimization yields lower costs in isolated scenarios (e.g., 28.50 USD for economic cost minimization), it significantly compromises environmental performance (20.44 USD) and grid stability (14.05 variance). In contrast, IMOPSO ensures coordinated control and effectively balances economic efficiency, environmental sustainability, and operational safety. This study provides a robust framework for multi-objective coordinated control and microgrid scheduling, advancing sustainable energy transition.

Cited by 2Open access

Distributed Optimization and Control of Microgrid Considering Environmental Protection and Stability

Hao Yu, Xiao Bo Dou, Zai Jun Wu et al. · Advanced materials research · 2013

In order to reduce pollutant emissions to improve environmental protection, and maintain microgrid stability during real-time operation, a distributed energy optimization scheduling and stability control strategy was proposed. According to the distributed nature of the microgrid, as well as operational objectives of different microsources, an optimal scheduling model for microgrid environmental protection was designed. Based on the proposed model, the tasks of each unit in optimal scheduling and stability control were described. Genetic algorithm (GA) and user datagram protocol (UDP) were used to implement distributed optimization and control of the microgrid. The simulation indicates that, compared with the traditional centralized optimization and control, the proposed distributed optimization and control strategy can clearly show the characteristics of each unit, and have a faster computation speed. Meanwhile, it can timely response once the voltage fluctuates due to power imbalance, so as to keep microgrid stability in real-time operation.

Cited by 0

Dynamic Control and Optimization of Distributed Energy Resources in a Microgrid

Trudie Wang, Daniel O’Neill, Haresh Kamath · IEEE Transactions on Smart Grid · 2015

As we transition toward a power grid that is increasingly based on renewable resources like solar and wind, the intelligent control of distributed energy resources (DERs) including photovoltaic (PV) arrays, controllable loads, energy storage, and plug-in electric vehicles (EVs) will be critical to realizing a power grid that can handle both the variability and unpredictability of renewable energy sources as well as increasing system complexity. Realizing such a decentralized and dynamic infrastructure will require the ability to solve large scale problems in real-time with hundreds of thousands of DERs simultaneously online. Because of the scale of the optimization problem, we use an iterative distributed algorithm previously developed in our group to operate each DER independently and autonomously within this environment. The algorithm is deployed within a framework that allows the microgrid to dynamically adapt to changes in the operating environment. Specifically, we consider a commercial site equipped with on-site PV generation, partially curtailable load, EV charge stations and a battery electric storage unit. The site operates as a small microgrid that can participate in the wholesale market on the power grid. We report results for simulations using real-data that demonstrate the ability of the optimization framework to respond dynamically in real-time to external conditions while maintaining the functional requirements of all DERs.

Cited by 237

Distributed Predefined-Time Optimization and Control for Multi-Bus DC Microgrid

Yan‐Wu Wang, Yu Zhang, Xiao‐Kang Liu et al. · IEEE Transactions on Power Systems · 2024

As an extension of single-bus DC microgrid, multi-bus DC microgrid has become a popular research topic due to its better availability and reliability and more reconfiguration options. The fundamental and challenging control objectives for multi-bus DC microgrids are bus voltage regulation and current sharing with transmission loss minimization. In this paper, a multi-objective optimization problem with penalty factors is formulated, and the global optimal solution is presented explicitly. A distributed predefined-time optimization and control approach is designed by employing a time-dependent function and a distributed predefined-time observer. Under the proposed approach, bus voltage, output current, and power flow converge within a predefined settling time. The performance of the proposed approach is evaluated by simulations and hardware-in-the-loop experiments in terms of trade-off ability, impact of parameter changes, robustness to load variation and grid reconfiguration, plug-and-play functionality, and comparison case.

Cited by 55