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Advanced microgrid optimization using price-elastic demand response and greedy rat swarm optimization for economic and environmental efficiency

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SCIENTIFIC REPORTS 2025年第1期15卷 1-29页

作者： Singh, Arvind R. Dey, Bishwajit Bajaj, Mohit Kadiwala, Sahil Kumar, Rangu Seshu Dutta, Soham Zaitsev, Ievgen Hanjiang Normal Univ Sch Phys & Elect Engn Dept Elect Engn Shiyan Peoples R China Manipal Univ Dept Elect Engn Jaipur Rajasthan India Graphic Era Deemed Univ Dept Elect Engn Dehra Dun 248002 India AL Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman Jordan Univ Business & Technol Coll Engn Jeddah 21448 Saudi Arabia Adani Inst Infrastruct Engn Dept Elect Engn Ahmadabad 382421 Gujarat India Deemed Univ Vignans Fdn Sci Technol & Res Dept Elect & Elect Engn Guntur India Manipal Acad Higher Educ Manipal Inst Technol Dept Elect & Elect Engn Manipal India Natl Acad Sci Ukraine Inst Electrodynam Dept Theoret Elect Engn & Diagnost Elect Equipment Beresteyskiy 56Kyiv 57 UA-03680 Kyiv Ukraine Natl Acad Sci Ukraine Ctr Informat Analyt & Tech Support Nucl Power Faci Akademika Palladina Ave34-A Kyiv Ukraine

In this paper, a comprehensive energy management framework for microgrids that incorporates price-based demand response programs (DRPs) and leverages an advanced optimization method-Greedy Rat Swarm Optimizer (GRSO) is proposed. The primary objective is to minimize the generation cost and environmental impact of microgrid systems by effectively scheduling distributed energy resources (DERs), including renewable energy sources (RES) such as solar and wind, alongside fossil-fuel-based generators. Four distinct demand response models-exponential, hyperbolic, logarithmic, and critical peak pricing (CPP)-are developed, each reflecting a different price elasticity of demand. These models are integrated with a flexible elasticity matrix to assess the dynamic consumer response to fluctuating electricity prices. The study evaluates four operational scenarios, focusing on grid participation, DER utilization, and the impact of real-time pricing (RTP), time of use (TOU), and critical peak pricing strategies. Quantitative results demonstrate the significant cost-saving potential of integrating DRPs with microgrid operations. In the optimal scenario, the GRSO achieved a minimum generation cost of 882 & YEN;for the base load profile. Further, when critical peak pricing (CPP) was applied, the generation cost was reduced to 746 & YEN;, representing a 15.4% reduction. For a scenario where the grid's participation was limited, the logarithmic-based demand response model decreased the generation cost to 817 & YEN;, while full grid interaction led to higher cost reductions. Additionally, our results show a significant reduction in peak load, with load factor improvements of up to 87.7% across the studied demand profiles. Furthermore, limiting the grid's upstream power capacity to 30 kW resulted in a 7% increase in generation cost across all cases, confirming the importance of grid participation in reducing operational costs. The GRSO algorithm outperformed traditional metaheuristics in

关键词： microgrid optimization Demand response programs Greedy rat Swarm Optimizer Price elasticity Renewable energy integration Cost minimization Environmental efficiency Distributed energy resources

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Residential microgrid optimization using grey-box and black-box modeling methods

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ENERGY AND BUILDINGS 2021年 235卷 110705-110705页

作者： Green, Christy Garimella, Srinivas Georgia Inst Technol GWW Sch Mech Engn Sustainable Thermal Syst Lab Atlanta GA 30332 USA

The use of data-driven black-box HVAC and water heater models in the optimization of a single-home residential microgrid is investigated. Data-driven models do not rely on the specification of system geometry or thermal parameters, instead using time-variant, system-specific data to develop models of each controllable appliance. A comparison of modeling accuracy of grey-box models based on those found in the literature and black-box models developed using extreme gradient boosting is made. A multi objective optimization problem is then developed with the objectives of minimizing energy cost and consumption while maximizing thermal comfort and the consumption of locally generated PV energy. Results demonstrate that data-driven black-box models have the potential to be used in place of grey box models, which are more prone to the introduction of error by the user. optimization using the grey-box and black-box modeling methods was performed using a genetic algorithm to verify the similarity of results using the two modeling methods. optimization resulted in a maximum total energy savings during the optimized period of 32.3%, a maximum total peak energy reduction of 39.8%, and a maximum cost reduction of 38.1%. CO 2020 Published by Elsevier B.V.

关键词： microgrid optimization Data-driven modeling Genetic algorithm Solar photovoltaics

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Smart grid management: Integrating hybrid intelligent algorithms for microgrid energy optimization

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ENERGY REPORTS 2024年 12卷 2997-3019页

作者： Pramila, V. Kannadasan, R. Bharathsingh, J. Rameshkumar, T. Alsharif, Mohammed H. Kim, Mun-Kyeom BS Abdur Rahman Crescent Inst Sci & Technol Dept Elect & Elect Engn Chennai 600048 India Sri Venkateswara Coll Engn Dept Elect & Elect Engn Sriperumbudur 602117 India Kalasalingam Acad Res & Educ Krishnankoil Dept Comp Sci & Engn Srivilliputhur 626126 India KPR Inst Engn & Technol Elect & Elect Engn Coimbatore 641407 India Sejong Univ Coll Elect & Informat Engn Dept Elect Engn 209 Neungdong Ro Seoul 05006 South Korea Chung Ang Univ Sch Energy Syst Engn 84 Heukseok Ro Seoul 06974 South Korea

The increasing global demand for power drives the need for advancements in generation, control, and supply technologies. While bulk power generation remains cost-effective, it depends on transmission and distribution systems for delivery. Integrating distributed generators into the grid enhances reliability and direct power supply to consumers. Micro Grids (MGs) are a promising solution, offering smoother and more reliable operations. This study explores MGs, incorporating the latest loads and distributed generators to minimize operating costs for electrical power providers. This work utilizes intelligent algorithms, including the Firefly algorithm, Spider Monkey optimization (SMO), and a hybrid approach combining both. The Firefly algorithm, inspired by swarm intelligence, mimics fireflies' light emission to optimize solutions. SMO, based on spider monkeys' social behavior, balances exploration and exploitation to achieve optimal results. A novel hybrid algorithm is developed to minimize MG operating costs by combining SMO's exploration with the Firefly algorithm's neighborhood optimization;enhancing navigation through the complex constraint solution space of MGs. Case studies on a modified IEEE 33-bus test feeder are conducted. The study compares three metaheuristic methods with nonconvex solution spaces to MG operational algorithms. The results demonstrate the hybrid method's efficacy in determining the optimal location and size of real and reactive power support devices for MG operation. Numerical results show the hybrid algorithm's superiority over existing methods, reducing operating costs by 7.5 %, real power loss by 36.5 %, and reactive power loss by 1.7 %, and voltage deviation by 16.895. By considering MG constraints, the hybrid algorithm consistently delivers better outcomes, optimizing operating costs and ensuring efficient power supply. This study presents a novel MG optimization approach, enhancing future energy systems with improved solutions.

关键词： microgrid optimization Intelligent Algorithms Distributed Generation Firefly Algorithm Spider Monkey optimization Hybrid optimization Algorithms

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Multi-Objective optimization of Combined Heat and Power Industrial microgrid

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JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME 2020年第5期142卷 051004页

作者： Mehrabadi, Elham Sheikhi Sathiakumar, Swamidoss Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

microgrids play a critical role in the transition from conventional centralized power systems to the smart distributed networks of the future. To achieve the greatest outputs from microgrids, a comprehensive multi-objective optimization plan is necessary. Among various conflicting planning objectives, emissions and cost are primary concerns in microgrid optimization. In this work, two novel procedures, i.e., non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective particle swarm optimization (MOPSO), were developed to minimize emissions and cost in combined heat- and power-based (CHP) industrial microgrids (IMGs) simultaneously, by applying the most practical constraints and considering the variable loads. Two different scenarios, the presence and absence of photovoltaics (PV) and PV storage systems, were analyzed. The results concluded that when considering PVs and PV storage systems, the NSGA-II algorithm provides the most optimized solution in minimizing economic and environmental objectives.

关键词： microgrid optimization combined heat and power (CHP) non-dominated sorting genetic algorithm-II (NSGA-II) multi-objective particle swarm optimization (MOPSO) environment photovoltaics solar storage system

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One-parameter battery degradation model for optimization of islanded microgrid system

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2023年第1期45卷 1257-1273页

作者： Tomic, Andrej Zvonimir Marinic-Kragic, Ivo Barbir, Frano Univ Split Fac Elect Engn Mech Engn & Naval Architecture Split Croatia Univ Split Ctr Excellence STIM Split Croatia Sveuciliste Splitu Fak elektrotehnike strojarstva Grp Novel Thermal Energy Technol Split Croatia Sveuciliste Splitu Fak elektroteh nike strojarstva brodogradnje Grp Numer Modeling & Comp Applicat Split Croatia Univ Split FESB R Boskov 32 Split 21000 Croatia

Methods for optimization of islanded microgrid systems are usually based on hourly models where each subcomponent is described by a simple algebraic model. There are many studies on this topic, which are usually based on the minimization of total lifetime cost by determining the number of required batteries, wind turbines, PV panels, the positioning of PV panels, etc. In this paper, we further improve the modeling of the microgrid system optimization process by developing a simplified algebraic model that uses one parameter to simulate accelerated battery degradation with respect to depth of discharge. The model consists of simply linearly increasing the degradation of the battery when the state of charge (SOC) becomes lower than a fixed value, and the only model constant is the factor of degradation f. The objective of the paper is to examine the effect of the degree of degradation on the obtained optimal microgrid system parameters. The analysis was performed for several different systems, and the results show that optimal parameters of the system and the overall system cost strongly depend on battery degradation characteristics. The overall system cost can be reduced by 1-6% for lower battery degradation rates and up to 20% for high degradation factor cases. Increasing the degradation factor also has an influence on the ratio of wind turbines to PV panels, and the optimal size of the battery system.

关键词： Islanded microgrid microgrid optimization battery degradation PV panel positioning sizing

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Constrained multiobjective optimization for microgrid based on nondominated immune algorithm

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2015年第4期10卷 376-382页

作者： Huang, Daizheng Gong, Renxi Gong, Shu Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Guangxi Med Univ Sch Preclin Med Nanning Peoples R China

The optimization of a microgrid is a typical multivariable nonlinear programming problem with multiple hybrid constraints. In this paper, a kind of constrained multiobjective optimization based on nondominated immune algorithm is used, in which the constraint is transformed into a target function, a relatively isolated nondominated antibody is selected as the active antibody, and ratio cloning, recombination, and mutation are performed in order, according to the crowding distance of the active antibody, thus obtaining the convergence of an ideal Pareto front and a uniform distribution of the Pareto-optimal solution. The optimization model of the power output of the microgrid and the simulation of the operation in two microgrids for 24 h are carried out by utilizing the algorithm. The results show that good environmental protection is obtained at the lowest cost of running and maintenance, thus satisfying the stability of power supply requirements for users. (c) 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： microgrid optimization constrained multiobjective problem nondominated immune algorithm cost

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Comparison of different demand response optimization goals on an isolated microgrid

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2018年第Dec.期30卷 209-215页

作者： Neves, Diana Pina, Andre Silva, Carlos A. Univ Lisbon Inst Super Tecn Ctr Innovat Technol & Policy Res IN Lisbon Portugal

Demand response (DR) is often studied from the consumer point of view to achieve economic benefits, especially when dealing with prosumers. However, DR may also be promoted by the grid manager to optimize the supply system. This study aims at addressing the impacts of these possible conflicting perspectives, in isolated microgrids in presence of centralized versus local renewable energy (RE) generation. Terceira Island in Azores is used as case study. First, DR is optimized through genetic algorithms to decrease the grid operation costs. Second, DR is optimized through linear programming to decrease direct prosumer costs, assessing also the corresponding impact on the grid. As output for both, the economic dispatch costs, renewable share and emissions are compared. Results show that DR optimization from the microgrid point of view lead to 1.9% savings on dispatch costs and emissions, while representing an increase of 5.4% regarding the renewable share. On the other hand, the optimization considering the prosumer interest resulted in 1.3% savings for the islands dispatch costs and a 3.2% increase on the renewable share. As such, although both optimizations lead to better grid management, they still show different impacts, urging a common approach for local RE deployment on isolated systems.

关键词： Demand response microgrid optimization Renewable integration Prosumers

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Optimizing microgrid design and operation: A decision-making framework for residential distributed energy systems in Brazil

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2025年 214卷 251-268页

作者： Amorim, Ana Paula Alves Pontes, Karen Valverde Dorneanu, Bogdan Arellano-Garcia, Harvey Univ Fed Bahia Ind Engn Program Salvador Da Bahia Brazil Brandenburg Tech Univ Cottbus Senftenberg FG Prozess & Anlagentechn Cottbus Germany

This paper explores the optimization of microgrid design and operation for residential distributed energy systems in Brazil, addressing the growing demand for sustainable energy in the context of climate change. A decisionmaking framework based on Mixed-Integer Nonlinear Programming (MINLP) is proposed to integrate distributed energy resources (DERs) such as solar, wind, and biogas. Key challenges include managing the variability of renewable resources and complying with local regulations, while also addressing gaps in literature, particularly the impact of time-dependent efficiency profiles on energy sharing within microgrids. By employing innovative analyses and clustering techniques, the research optimizes microgrid configurations, accounting for seasonal demand fluctuations and the influence of incentive policies on system feasibility. The findings reveal that incorporating a time-dependent efficiency model can reduce total costs by 45 %. This reduction underscores the importance of accurate efficiency predictions, as the model captures variations in energy generation and utilization efficiency over time, improving system optimization. Additionally, the findings reveal that a wellstructured optimization model can meet 100% of electricity and hot water demands across all scenarios, with customized incentives playing a crucial role in reducing costs and promoting sustainability.

关键词： Distributed energy systems (DES) microgrid optimization Mixed-integer nonlinear programming (MINLP) Renewable energy incentives Time-dependent efficiency

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Novel abstractions and experimental validation for digital twin microgrid design: Lab scale studies and large scale proposals

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APPLIED ENERGY 2025年 377卷

作者： Sifat, Md. Mhamud Hussen Choudhury, Safwat Mukarrama Das, Sajal K. Pota, Hemanshu Yang, Fuwen Rajshahi Univ Engn & Technol Rajshahi 6204 Bangladesh Univ New South Wales Canberra ACT Australia Griffith Univ Gold Coast Qld Australia

Managing the current high penetration of renewable energy sources globally poses a significant challenge due to the distributed and diverse nature of generation components. To maximize real-time power generation, these resources need to perform optimally. Digital twin technology offers a comprehensive framework for managerial support by replicating grid features in a digital environment. This research creates a digital twin of the microgrid to optimize power generation, focusing on computational efficiency and self-healing control. The framework is tested in a laboratory microgrid, with modeling performed using a polynomial regression algorithm. optimization is achieved through a gradient descent algorithm, and the self-healing model is implemented using a logistic regression algorithm. Real-time data extracted from the microgrid drives this process. The results can be utilized for predictive analysis before deploying a microgrid or to optimize generation in existing systems using the digital twin model. Even though the research focuses on a single microgrid unit, it introduces a framework proposal for extensively distributed microgrids integrating multiple renewable energy sources.

关键词： Digital twin microgrid control Energy management Self-healing microgrid optimization

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optimization of a standalone photovoltaic-based microgrid with electrical and hydrogen loads

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ENERGY 2021年 235卷 121218-121218页

作者： Mah, Angel Xin Yee Ho, Wai Shin Hassim, Mimi H. Hashim, Haslenda Ling, Gabriel Hoh Teck Ho, Chin Siong Ab Muis, Zarina Univ Teknol Malaysia UTM Fac Engn Sch Chem & Energy Engn Utm Johor Bahru 81310 Johor Malaysia Univ Teknol Malaysia UTM Res Inst Sustainable Environm RISE Proc Syst Engn Ctr PROSPECT Utm Johor Bahru 81310 Johor Malaysia Univ Teknol Malaysia Inst Future Energy Ctr Hydrogen Energy Utm Johor Bahru 81310 Johor Malaysia Univ Teknol Malaysia UTM Fac Built Environm & Surveying Program Urban & Reg Planning UTM Low Carbon Asia Res Ctr Johor Baharu Malaysia

Despite the ability of renewables to decarbonize energy use, their intermittent nature causes inconsistent energy generation, thus energy storage is required to tackle the supply-demand mismatch. While the use of hybrid battery-hydrogen energy storage for microgrids has been extensively studied, there is a lack of study on the integration of electricity and hydrogen supply systems. In other words, the concurrent targeting of hydrogen and electrical loads in a microgrid with hybrid battery-hydrogen storage is lacking. This study presents an optimization framework for the design and operation of a standalone microgrid with electrical and hydrogen loads. Two energy management strategies have been proposed and the optimization model is solved using particle swarm optimization algorithm. The proposed methodology was demonstrated through a case study and the levelized cost of energy ranges from 0.4551 USD/kWh to 0.4572 USD/kWh for the base case scenario. The optimal microgrid design in base case scenario is found to have a high value of potential energy waste possibility, indicating that the solar panel is oversized to reduce energy storage requirement. Sensitivity analysis results showed that a significant cost reduction can be achieved when only 95% of loads are targeted. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： microgrid optimization Solar tracking system Particle swarm optimization Hybrid energy storage Distributed energy system

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