concept

energy management

Also known as: optimal energy management, energy management optimization, energy management

synthesized from dimensions

Energy management is a multifaceted discipline focused on the strategic optimization of power scheduling, consumption, and distribution within complex systems such as smart grids, microgrids, and individual households. At its core, the field seeks to balance the competing requirements of cost minimization, grid stability, and the efficient integration of renewable energy sources. By leveraging advanced computational frameworks, energy management systems transform raw energy data into actionable strategies that facilitate demand flattening, peak shifting, and the reliable operation of distributed energy resources.

The technical foundation of energy management relies on a diverse array of algorithmic techniques categorized into deterministic and stochastic approaches. Deterministic methods, including linear programming (LP) (LP definition), nonlinear programming (NLP), and mixed integer nonlinear programming (MINLP), are frequently employed to seek global optima in structured environments (deterministic methods, deterministic methods seeking global optima). Conversely, stochastic and metaheuristic methods—such as Reinforcement Learning (RL) (RL as Reinforcement Learning), Particle Swarm Optimization (PSO) (PSO as Particle Swarm Optimization), Dynamic Programming (DP) (DP as Dynamic Programming), and Model Predictive Control (MPC) (MPC as Model Predictive Control)—are utilized to navigate uncertainty and improve cost efficiency (SSA and BWOA improving cost efficiency). While stochastic methods are iterative and do not always guarantee a global optimum (stochastic methods), hybrid algorithms often demonstrate superior convergence speeds compared to their non-hybrid counterparts (hybrid superiority).

Operational strategies within this field often involve the categorization of loads into flexible and non-flexible groups, which enables effective off-peak scheduling and demand response (flexible loads enabling off-peak scheduling, appliance categorization). Frameworks such as the Residential Energy Management System (REMS) utilize time-slot-based scheduling to ensure scalability across various residential settings (REMS framework). Furthermore, the integration of forecasting models—covering both generation and demand—is critical for smoothing grid profiles and managing the inherent variability of renewable energy (strategy with forecasting, grid smoothing strategy).

The significance of energy management extends beyond technical efficiency to include broader societal and ethical dimensions. Modern research increasingly emphasizes the role of energy management in supporting energy justice principles (energy justice principles), ensuring that the benefits of smart infrastructure are equitably distributed. As systems grow to include multi-home electric vehicle (EV) networks and complex microgrid architectures, energy management serves as the essential mechanism for maintaining grid resilience and enabling the digital transformation of the energy sector (resilient strategies for networked microgrids, multi-home EVs).

Model Perspectives (2)

openrouter/x-ai/grok-4.1-fast definitive 92% confidence

Energy management encompasses strategies and optimization techniques for efficient power scheduling and usage in settings like smart grids, residential microgrids, and smart homes, often integrating renewable energy sources, electric vehicles (EVs), and demand forecasting. Pascual et al. (2015) proposed a strategy for renewable-based residential microgrids using generation and demand forecasting (strategy with forecasting). Rehman et al. (2021), published in IEEE Access by Springer-referenced authors, focused on optimal power usage scheduling in smart grids with renewables (optimal scheduling). Arcos-Aviles et al. (2017) developed a low-complexity strategy for grid profile smoothing in residential microgrids using forecasting (grid smoothing strategy). Awais et al. (2018) applied heuristic algorithms for single homes and communities under pricing tariffs (heuristic for homes). Optimization approaches include deterministic methods like linear programming (LP) (LP definition), nonlinear programming (NLP), and mixed integer nonlinear programming (MINLP) to reduce consumption, as listed in Springer sources (deterministic methods). Stochastic methods are iterative but non-guaranteeing of global optima (stochastic methods). Numerous metaheuristic and hybrid algorithms from Springer contexts enhance performance, such as hybrid genetic algorithm (HGA), grey wolf optimization (GWO) (GWO acronym), whale optimization (WOA), and hybrids showing faster convergence than non-hybrids (hybrid superiority). Techniques like categorizing appliances into flexible and non-flexible groups enable peak shifting (appliance categorization), while Residential Energy Management System (REMS) uses time slots for scalability (REMS framework). Studies by Bayati et al. (2024, Nature) and Prum et al. optimize multi-home EV-equipped systems for grid stability (multi-home EVs). Broader links include energy justice per Lin et al. (2020, Penn State) and digital transformation benefits (Springer).

openrouter/x-ai/grok-4.1-fast definitive 85% confidence

Energy management, especially in optimization for smart grids, microgrids, and households, relies on diverse algorithmic techniques to minimize costs, enhance stability, and integrate renewables. Springer sources define key methods like RL as Reinforcement Learning, PSO as Particle Swarm Optimization, DP as Dynamic Programming, MPC as Model Predictive Control, and others such as IPSO, TLGO, and GA. Researchers like Nguyen HT, Nguyen DT, and Le LB developed strategies for households with solar-assisted thermal loads accounting for renewables and price uncertainty, while Hussain A, Bui V-H, and Kim H-M proposed resilient strategies for networked microgrids. PLOS ONE highlights flexible loads enabling off-peak scheduling, CSUA for energy strategies, and grey wolf algorithm prioritizing comfort. Springer notes deterministic methods seeking global optima, while Nature reports SSA and BWOA improving cost efficiency. Applications span EVs, RES integration, and demand flattening, with Penn State linking it to energy justice principles.

Facts (113)

Sources

A comprehensive overview on demand side energy management ... link.springer.com Springer Mar 13, 2023 94 facts

referencePascual et al. (2015) proposed an energy management strategy for renewable-based residential microgrids that integrates generation and demand forecasting.

claimIn the context of energy management optimization, HGA stands for Hybrid genetic algorithm.

referenceRehman et al. (2021) published 'An optimal power usage scheduling in smart grid integrated with renewable energy sources for energy management' in IEEE Access, volume 9, pages 84619–84638.

claimIn the context of energy management optimization, GTA stands for Game theory algorithms.

claimIn the context of energy management optimization, LWMCSO stands for Levy Whale Modified Crow Search Optimizer.

referenceArcos-Aviles et al. published a study titled 'Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting' in Applied Energy in 2017.

claimIn the context of energy management optimization, ICA stands for Imperialist competitive algorithm.

referenceAwais M, Javaid N, Aurangzeb K, Haider SI, Khan ZA, and Mahmood D published 'Towards effective and efficient energy management of single home and a smart community exploiting heuristic optimization algorithms with critical peak and real-time pricing tariffs in smart grids' in Energies in 2018.

claimHybrid optimization techniques used to address energy management problems have shown better performance compared to non-hybrid techniques due to their faster convergence speed.

referenceDeterministic methods used in energy management to reduce electricity consumption include linear programming (LP), nonlinear programming (NLP), gradient base (GB), Lagrangian algorithms, Lagrange–Newton, interior point method, Lyapunov techniques, and mixed integer nonlinear programming (MINP).

claimIn the context of energy management optimization, MILP stands for Mixed integer linear programming.

claimIn the context of energy management optimization, BSA stands for Backtracking Search Optimization.

claimIn the context of energy management optimization, BCSA stands for Bat-crow search algorithm.

claimIn the context of energy management optimization, WDGWO stands for Wind driven grey wolf optimization.

referenceShuja SM, Javaid N, Khan S, Akmal H, Hanif M, Fazalullah Q, and Khan ZA published 'Efficient scheduling of smart home appliances for energy management by cost and PAR optimization algorithm in smart grid' in 2019.

claimIn the context of energy management optimization, CBO stands for Colliding body optimization.

claimIn the context of energy management optimization, EDE stands for Effective Differential Evolution.

claimIn the context of energy management optimization, CA stands for Cultural algorithm.

claimIn the context of energy management optimization, RUOA stands for Runner Updation Optimization Algorithm.

claimIn the context of energy management optimization, HBFPSO stands for Hybrid beamforming particle swarm optimization.

claimIn the context of energy management optimization, NLP stands for Nonlinear programming.

claimStochastic optimization methods in energy management are iterative algorithms that utilize the unpredictable nature of energy usage to identify optimal solutions, though they do not guarantee a global optimal solution.

claimIn the context of energy management optimization, FPA stands for Flower pollination algorithm.

claimIn the context of energy management optimization, WDGA stands for Wind driven genetic algorithms.

referenceKumar A, Rizwan M, and Nangia U (2022) developed a hybrid optimization technique for energy management within a smart grid environment, published in the International Journal of Hydrogen Energy.

claimIn the context of energy management optimization, MKL stands for Math Kernel Library.

claimHybrid optimization techniques used to address energy management problems have shown better performance compared to other techniques due to their faster convergence speed.

claimIn the context of energy management optimization, BFOA stands for Bacterial foraging optimization algorithm.

referenceDeterministic methods used in energy management to reduce electricity consumption include Linear Programming (LP) (Erol-Kantarci and Mouftah 2011; Zhu et al. 2012), Nonlinear Programming (NLP) (Althaher et al. 2015), Gradient Base (GB) (Huang et al. 2015), Lagrangian algorithms (Boyd; Gatsis and Giannakis 2011), Lagrange–Newton (Dong et al. 2012), Interior Point Method (Samadi et al. 2012), Lyapunov techniques (Guo et al. 2012), and Mixed Integer Nonlinear Programming (MINP) (Behrangrad et al. 2010).

claimIn the context of energy management optimization, LP stands for Linear programming.

claimIn the context of energy management optimization, PBO stands for Polar bear optimization.

claimIn the context of energy management optimization, GSA stands for Gravitational Search Algorithm.

referenceBruni G, Cordiner S, Mulone V, Rocco V, and Spagnolo F studied energy management in domestic micro-grids using model predictive control strategies, published in Energy Conversion and Management in 2015.

claimIn the context of energy management optimization, HEDE stands for Hybrid Effective Differential Evolution.

claimIn the context of energy management optimization, EHOANFIS stands for Elephant herding optimization neuro fuzzy.

claimIn the context of energy management optimization, MINLP stands for Mixed integer nonlinear programming.

claimIn the context of energy management optimization, HGPO stands for Hybrid genetic particle swarm optimization.

referenceHussain, Bui, and Kim (2016) proposed a resilient and privacy-preserving energy management strategy for networked microgrids in IEEE Transactions on Smart Grid.

claimIn the context of energy management optimization, ELPSO stands for Enhanced leader particle swarm optimization.

referenceElmouatamid A, Ouladsine R, Bakhouya M, El Kamoun N, Khaidar M, and Zine-Dine K (2020) published 'Review of control and energy management approaches in micro-grid systems' in Energies, volume 14, issue 1, page 168.

claimIn the context of energy management optimization, WOA stands for Whale optimization algorithm.

referencePanwar et al. (2017) developed a strategic energy management method for microgrids that incorporates electric vehicles and distributed resources under operation window constraints.

claimIn the context of energy management optimization, MGWO stands for Mixed grey wolf optimization.

claimIn the context of energy management optimization, BAT stands for Bat algorithm.

claimIn the context of energy management optimization, DE stands for Differential evolution.

claimIn the context of energy management optimization, BBO stands for Biogeography based optimization.

claimIn the context of energy management optimization, EHO stands for Elephant herding optimization.

claimIn the context of energy management optimization, ABC stands for Artificial bee colony.

claimIn the context of energy management optimization, GWO stands for Grey wolf optimization.

referenceRehman AU, Wadud Z, Elavarasan RM, Hafeez G, Khan I, Shafiq Z, and Alhelou HH published a study in 2021 titled 'An optimal power usage scheduling in smart grid integrated with renewable energy sources for energy management' in IEEE Access.

claimIn the context of energy management optimization, HGPDO stands for Hybrid genetic particle wind driven optimization.

claimIn the context of energy management optimization, RL stands for Reinforcement learning.

claimIn the context of energy management optimization, IPSO stands for Improved particle swarm optimization.

referenceNguyen HT, Nguyen DT, and Le LB published the paper 'Energy management for households with solar assisted thermal load considering renewable energy and price uncertainty' in IEEE Transactions on Smart Grid, volume 6, issue 1, pages 301–314, in 2014.

referenceKumar A, Rizwan M, and Nangia U (2022) developed a hybrid optimization technique to improve energy management within smart grid environments.

claimIn the context of energy management optimization, TLGO stands for Teacher learning genetic optimization.

claimIn the context of energy management optimization, DP stands for Dynamic programming.

referenceAwais et al. published a study titled 'Towards effective and efficient energy management of single home and a smart community exploiting heuristic optimization algorithms with critical peak and real-time pricing tariffs in smart grids' in the journal Energies in 2018.

claimIn the context of energy management optimization, MPC stands for Model predictive control.

claimIn the context of energy management optimization, JOA stands for Jaya Optimization Algorithm.

claimIn the context of energy management optimization, GAPSO stands for Genetic algorithm particle swarm optimization.

claimIn the context of energy management optimization, CSUA stands for Candidate solution updation algorithm.

claimIn the context of energy management optimization, SFL stands for Shuffling frog leap.

claimIn the context of energy management optimization, EA stands for Expert advisors.

referenceHussain A, Bui V-H, and Kim H-M published 'A resilient and privacy-preserving energy management strategy for networked microgrids' in 2016 in IEEE Transactions on Smart Grid, volume 9, issue 3, pages 2127–2139.

claimIn the context of energy management optimization, AIS stands for Artificial immune system.

claimIn the context of energy management optimization, BFO stands for Bacterial foraging optimization.

referenceNguyen et al. (2014) developed an energy management strategy for households with solar-assisted thermal loads that accounts for renewable energy availability and price uncertainty.

claimIn the context of energy management optimization, ANFIZ stands for Adaptive neuro fuzzy logic.

claimIn the context of energy management optimization, MCSA stands for Modified clonal selection algorithm.

claimIn the context of energy management optimization, EWA stands for Earth Worm Algorithm.

claimIn the context of energy management optimization, GA stands for Genetic algorithms.

claimIn the context of energy management optimization, FL stands for Fuzzy logic.

claimIn the context of energy management optimization, SSO stands for Social spider optimization.

claimIn the context of energy management optimization, LWOA stands for Levy Whale Optimization Algorithm.

claimDeterministic optimization methods in energy management aim to find a universally optimal solution using the analytic properties of the problem, with the likelihood of discovering global solutions increasing as problem constraints shrink.

claimIn the context of energy management optimization, QP stands for Quadratic programming.

claimIn the context of energy management optimization, UACNFC stands for Unmanned aerial vehicle neural-fuzzy classification.

claimIn the context of energy management optimization, MSO stands for Mosquito Host Seeking.

referencePanwar LK, Konda SR, Verma A, Panigrahi BK, and Kumar R published the paper 'Operation window constrained strategic energy management of microgrid with electric vehicle and distributed resources' in IET Generation, Transmission & Distribution, volume 11, issue 3, pages 615–626, in 2017.

claimIn the context of energy management optimization, PAR stands for Peak to average ratio.

claimIn the context of energy management optimization, ANN stands for Artificial neural network.

referenceDurairasan M, Ramprakash S, and Balasubramanian D (2021) published 'System modeling of micro-grid with hybrid energy sources for optimal energy management—a hybrid elephant herding optimization algorithm-adaptive neuro fuzzy inference system approach' in the International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, volume 34, issue 6, page e2915.

claimIn the context of energy management optimization, KKT stands for Karush–Kuhn–Tucker.

claimIn the context of energy management optimization, ACO stands for Ant colony optimization.

claimIn the context of energy management optimization, FIS stands for Fuzzy logic interfere.

claimIn the context of energy management optimization, WBPSO stands for Wind driven binary particle swarm optimization.

claimIn the context of energy management optimization, TLBO stands for Teacher and learning-based optimization.

claimIn the context of energy management optimization, HGWD stands for Hybrid genetic wind-driven.

claimIn the context of energy management optimization, DRL stands for Deep Reinforcement Learning.

claimIn the context of energy management optimization, GHSA stands for Genetic harmony search algorithms.

referenceArcos-Aviles D, Pascual J, Guinjoan F, Marroyo L, Sanchis P, and Marietta MP published 'Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting' in Applied Energy in 2017.

claimIn the context of energy management optimization, PSO stands for Particle swarm optimization.

referencePascual J, Barricarte J, Sanchis P, and Marroyo L published the paper 'Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting' in Applied Energy, volume 158, pages 12–25, in 2015.

Comprehensive framework for smart residential demand side ... nature.com Nature Mar 22, 2025 8 facts

referenceA., Bayati, N., and Charoenlarpnopparut, C. proposed an energy management scheme for optimizing multiple smart homes equipped with electric vehicles in 2024.

claimThe Residential Energy Management System (REMS) divides a 24-hour observation period into equal time slots for energy management, with scalability for longer durations and complex systems.

referenceA., Bayati, N. & Charoenlarpnopparut, C. proposed an energy management scheme for optimizing multiple smart homes equipped with electric vehicles in the journal Energies, volume 17, issue 1, published in 2024.

referencePrum et al. introduced an energy management scheme for optimizing multiple smart homes equipped with electric vehicles, focusing on cooperative control strategies to enhance local grid stability.

claimPrum et al. introduced an energy management scheme for optimizing multiple smart homes equipped with electric vehicles, focusing on cooperative control strategies to enhance local grid stability.

claimComparative analyses of the Sparrow Search Algorithm (SSA) and the Black Widow Optimization Algorithm (BWOA) demonstrate that these techniques provide improvements in cost efficiency, grid stability, and energy management compared to existing literature.

claimThe smart scheduler application described in the REM framework flattens demand peaks and distributes load by integrating Renewable Energy Sources (RES), electric vehicles, and energy management strategies, leading to a more sustainable and cost-effective energy ecosystem for residential prosumers.

claimA. Bayati and C. Charoenlarpnopparut proposed an energy management scheme designed to optimize multiple smart homes equipped with electric vehicles in 2024.

Demand side management using optimization strategies for efficient ... journals.plos.org PLOS ONE Mar 21, 2024 6 facts

claimCategorizing appliances into flexible and non-flexible groups allows for targeted energy management, such as shifting flexible appliance usage away from peak demand hours to reduce grid strain and household costs, while maintaining a baseline load from non-flexible appliances.

claimFlexible loads allow for energy management strategies, such as scheduling intensive computational tasks during off-peak hours or adjusting air conditioning when areas of an office are unoccupied.

claimThe Candidate Solution Updating Algorithm (CSUA) has been introduced as a strategy for energy management.

claimThe grey wolf accretive satisfaction algorithm is a strategy that prioritizes user comfort while striving for the lowest possible energy expenditure, taking inspiration from the hunting behavior of grey wolves to provide an adaptive energy management solution.

procedureLoad clipping and load shifting strategies for energy management were developed and simulated using MATLAB/Simulink, with further optimization performed by an Artificial Neural Network (ANN) algorithm.

referenceTools available for monitoring faults and improving system performance in energy management include extra-tree, bagging k-nearest neighbors (KNN), voting regressor, random forest, and boosting algorithms.

Energy Transition Literature - PSU Center for Energy Law and Policy celp.psu.edu Penn State Center for Energy Law and Policy May 20, 2024 2 facts

claimLin, Liou, and Chou (2020) assert that the theory of energy justice is connected to the principles of energy management.

claimThe theory of energy justice is connected with the principles of energy management.

Global perspectives on energy technology assessment and ... link.springer.com Springer Oct 30, 2025 2 facts

claimThe ETA framework provides policymakers with a common approach to examine the trade-offs and potential negative consequences of utilizing artificial intelligence solutions in energy management.

claimDigital transformation offers opportunities to increase energy management, improve functional efficiencies, and integrate renewable energy sources more effectively.

A critical review on techno-economic analysis of hybrid renewable ... link.springer.com Springer Dec 6, 2023 1 fact

claimDeveloping efficient energy management strategies and integrating flow power systems with existing grids or microgrids is a complex task.