Search Results (169)

Demand-side management (DSM) is a security-critical function in residential smart grids. The same communication and sensing infrastructure that enables fine-grained load flexibility also exposes schedulers to corrupted measurements, price manipulation, and delayed control signals. Conventional DSM formulations generally treat cyber and communication impairments as external disturbances, which are addressed only after the schedule has already been calculated. This study proposes and evaluates Cyber-Resilient and QoS-Aware Demand-Side Management (CQ-DSM) as a hierarchical optimization framework that embeds cyber-risk likelihood and communication quality-of-service (QoS) directly into the scheduling objective. Local home energy management systems (HEMSs) solve mixed-integer linear programs at the appliance level, and central aggregators broadcast compact coordination signals based on real-time prices, measured QoS, and a sliding-window GRU-feature MLP risk estimator. The key intuition is to convert uncertainty about trust and actuation reliability into scheduling prices: high cyber risk discourages exposed loads during vulnerable periods, whereas poor QoS increases the value of locally preserving thermal flexibility. Under the simulation conditions (NYISO August pricing, P = 50 prosumers, Seed 42), CQ-DSM reduces overall system costs by 5.75% and imbalance procurement costs relative to an attack-unaware baseline under normal operation, limits the FDI-induced cost increase to 0.46% versus 0.83% (44% reduction in cost overrun), and reduces thermal-violation penalties by 81% under degraded QoS. The ablation results are consistent with cyber-risk pricing and QoS-aware fallback being complementary rather than redundant under the scenarios tested. Full article

This paper develops a bi-level optimization framework for community energy systems to improve grid stability and strengthen resilience against supply–demand mismatches, with potential applicability to weather-driven operational stress. By incorporating demand-side response resources, with particular emphasis on the thermal storage potential of buildings, the proposed framework enhances the operational security and regulation capability of the system. At the upper level, energy operators determine dynamic electricity pricing strategies aimed at not only maximizing economic returns but also shaping load profiles toward smoother and more stable operation. At the lower level, a building thermal dynamic model is established, and the schedulable characteristics of flexible appliances, including electric water heaters, dishwashers, and washing machines, are exploited to reduce user-side energy costs while supporting peak load mitigation. Through iterative coordination between the two levels, the proposed method enables effective joint optimization of supply and demand. Simulation results indicate that the framework increases operator revenues through differentiated pricing and, at the same time, substantially lowers users’ electricity expenditures. In addition, by aggregating distributed flexible resources as a virtual buffering capacity, the proposed strategy helps reconcile the interests of both operators and users and further improves the resilience of the local power community energy system. Full article

This paper presents an optimisation model for scheduling in-house energy consumption to improve efficiency and sustainability. Focus is on the integration of advanced scheduling techniques to improve the overall performance of the house appliances and energy storage system. The proposed model applies constraint programming and satisfiability (CP-SAT) techniques to analyse complex schedules. A sensitivity analysis was conducted by perturbing key input parameters, including electricity price variations and demand profiles, while tracking output metrics such as total cost, load distribution, and computational performance. The model incorporates real-world constraints, including fluctuating electricity prices and renewable energy availability, to improve efficiency and reduce operational costs. The optimisation of the scheduling task was set for a 36 h time period with time resolutions of 15 min, equal to the electricity price time step. The proposed approach is evaluated through simulation using representative household consumption profiles and real day-ahead electricity prices data. The performance of the proposed CP-SAT model was evaluated, and the model’s response to the input parameter change has been analysed. The computational performance and cost outcomes of the proposed CP-SAT approach are comparable to those reported for established HEMS optimisation methods. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

The rapid growth of distributed solar energy, such as rooftop photovoltaics (PVs), has revolutionized conventional power systems into more distributed networks, enabling end-users to engage in and trade within the energy market. Maximizing the benefits of rooftop PV panels for residential end-users, including increased renewable energy use and reduced reliance on the utility grid, remains an essential challenge in conventional centralized markets. Moreover, reducing energy consumption may lead to increased peak demand, decreased self-consumption, reduced system flexibility, and reduced grid stability. Therefore, this study presents a transactive energy market framework that integrates home energy management systems (HEMSs) with multi-objective optimization and an aggregator-based, distributed peer-to-peer (P2P) trading strategy to increase rooftop PV utilization and reduce grid dependency within an intra-residential community. The HEMS is structured to integrate rooftop PV production, battery energy storage systems, and smart appliances to offer flexibility through demand response programs in balancing supply and demand by scheduling appliances during periods of rooftop PV production and lower grid prices. Multi-objective (i.e., minimizing energy consumption cost and peak load) optimization problems are solved using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) by achieving a Pareto-optimal solution. To validate the reliability and optimality of the NSGA-II results, the same problem formulation is solved using a mixed-integer linear programming approach. Moreover, a Strategic Double Auction with Dynamic Pricing (SDA-DP) strategy is proposed to support P2P trading among consumers and prosumers and thereafter compared with a rule-based zero-intelligence strategy with market-matching rules to analyze the trading performance of the proposed SDA-DP. The results of this comparative analysis (for 10 households, year-long simulation with 15 min time resolution) demonstrate that compared to the baseline case, integrating NSGA-II optimization with SDA-DP trading significantly enhances rooftop PV utilization by 35.11%, reduces grid dependency by 34.04%, and reduces electricity consumption costs by 30.53%, with savings of €1.93 to €6.67 for a single day after participating in the proposed P2P market. Full article

As modern power systems increasingly depend on demand-side flexibility, accurately modeling building multi-energy demand under uncertainty has become essential for achieving reliable and flexible grid operation. This study proposes an agent-based framework to conduct uncertainty-aware modeling of building multi-energy demand and to assess demand-side flexibility under different demand response mechanisms. Firstly, an agent-based modeling framework is established to connect occupant activities, electrical appliance usage, and building thermal dynamics, characterizing the explicit relationship between Markovian behavioral uncertainties and multi-energy demands. Secondly, an integrated thermal load model is constructed based on a resistance–capacitance network, coupled with behavior-driven internal heat gains and building morphology-driven shading and radiative microclimate conditions. Then, the flexibility potential of electrical and thermal loads is quantified at both individual and aggregated scales. Finally, the demand response flexibilities of the multi-energy loads were assessed under price-based self-scheduling and incentive-based centralized optimization scenarios. The results demonstrate that the proposed approach effectively captures behavior-driven uncertainties and their impacts on the temporal pattern and magnitude of building energy demand, as well as on the resulting demand-side flexibility. In addition, the proposed demand response strategies effectively reduce electricity costs and achieve peak shaving and valley filling, while maintaining schedulable flexibility within acceptable operational limits. Full article

In the face of escalating energy demand, this research proposes a demand-side management (DSM) strategy that focuses on appliance-level load shifting in residential environments. The proposed approach utilizes detailed energy consumption forecasts that are generated by ensemble machine learning models, which predict usage at both whole-household and individual appliance levels. This granular forecasting enables the development of customized load-shifting schedules for controllable devices. These schedules are optimized using a metaheuristic genetic algorithm (GA) with the objectives of minimizing consumer energy costs and reducing peak demand. The iterative nature of GA allows for continuous fine-tuning, thereby adapting to dynamic energy market conditions. The implemented DSM technique yields significant results, successfully reducing the daily energy consumption cost for shiftable appliances. Overall, the proposed system decreases the per-day consumer electricity cost from 237 cents (without DSM) to 208 cents (with DSM), achieving a 12.23% cost saving. Furthermore, it effectively mitigates peak demand, reducing it from 3.4 kW to 1.2 kW, which represents a substantial 64.7% reduction. These promising outcomes demonstrate the potential for substantial consumer savings while concurrently enhancing the overall efficiency and reliability of the power grid. Full article

This paper presents a Genetic Algorithm-based Home Energy Management System designed to exploit the energy flexibility of user-dependent loads by identifying and recommending optimal operating schedules that minimize electricity costs. To determine the most advantageous 15 min activation slot for the following day for each load, the algorithm uses as input the forecasted consumption profile of non-optimizable loads and photovoltaic generation, both obtained through an LSTM-based model, along with the contracted power, applicable tariffs, and the load profiles of the selected appliances. Unlike previous approaches, the proposed framework allows users to select which loads to optimize and define specific operational constraints. Additionally, a user-friendly interface was developed to facilitate seamless interaction between the user and the system. To validate the proposed framework, a case study was conducted on a residential household with four occupants located in Portugal, considering user-dependent flexible loads such as a washing machine, tumble dryer, and dishwasher. The results demonstrated that the developed system operated effectively, reducing electricity costs by approximately 9% compared to a scenario without the proposed solution. Full article

Home energy management systems (HEMSs) have attracted considerable research interest in residential appliance management. Although optimal scheduling of home appliances has been extensively studied, these problems are fundamentally dynamic multi-objective optimization problems. This paper proposes a dynamic appliance scheduling model under time-of-use electricity pricing based on user’s preferences, to minimize energy costs and user dissatisfaction. A knee point-based manifold transfer algorithm (KPMT-DMOEA) is proposed to solve the scheduling problem. This approach leverages high-quality knee points from previous environments to generate optimized initial populations in response to environmental changes, thereby improving solution quality and convergence speed. The experimental results validate the effectiveness and feasibility of the proposed scheduling framework. By making a comparison with state-of-the-art algorithms, the experimental results demonstrate that the proposed method outperforms others and is able to efficiently generate optimal schedules for each appliance under different environments. Full article

This article presents the development and implementation of an IoT-enabled, off-grid solar power supply prototype designed to power a range of electrical devices. The developed system comprises a Photovoltaic panel, a Maximum Power Point Tracking (MPPT) charger, a 2.5 kWh/24 V high-performance LiFePO4 battery bank with a Battery Management System, an embedded controller with IoT connectivity, and DC/DC and DC/AC converters. The PV panel serves as the primary energy source, with the MPPT controller optimizing battery charging, while the DC/DC and DC/AC converters supply power to the connected electrical devices. The article includes a case study of a developed platform for powering an information and advertising system. The system features a predictive energy management algorithm, which optimizes the appliance operation based on daily solar irradiance forecasts and real-time battery State-of-Charge monitoring. The IoT-enabled controller obtains solar irradiance forecasts from an online meteorological service via API calls and uses these data to estimate energy availability for the next day. Using this prediction, the system schedules and prioritizes the operations of connected electrical devices dynamically to optimize the performance and prevent critical battery discharge. The IoT-based controller is equipped with both Wi-Fi and an LTE modem, enabling communication with online services via wireless or cellular networks. Full article

This paper presents an intelligent hybrid framework for short-term energy consumption forecasting and real-time energy management in photovoltaic (PV)-integrated smart homes with Vehicle-to-Home (V2H) systems, tailored to the Tunisian context. The forecasting module employs an Attention-based Long Short-Term Memory (LSTM) neural network, whose hyperparameters (learning rate, hidden units, temporal window size) are optimized using the Harris Hawks Optimization (HHO) algorithm. Simulation results show that the proposed LSTM-HHO model achieves a Root Mean Square Error (RMSE) of 269 Wh, a Mean Absolute Error (MAE) of 187 Wh, and a Mean Absolute Percentage Error (MAPE) of 9.43%, with R² = 0.97, substantially outperforming conventional LSTM (RMSE: 945 Wh, MAPE: 51.05%) and LSTM-PSO (RMSE: 586 Wh, MAPE: 28.72%). These accurate forecasts are exploited by the Energy Management System (EMS) to optimize energy flows through dynamic appliance scheduling, HVAC load shifting, and coordinated operation of home and EV batteries. Compared with baseline operation, PV self-consumption increased by 18.6%, grid reliance decreased by 25%, and household energy costs were reduced by 17.3%. Cost savings are achieved via predictive and adaptive control that prioritizes PV utilization, shifts flexible loads to surplus periods, and hierarchically manages distributed storage (home battery for short-term balancing, EV battery for extended deficits). Overall, the proposed LSTM-HHO-based EMS provides a practical and effective pathway toward smart, sustainable, and cost-efficient residential energy systems, contributing directly to Tunisia’s energy transition goals. Full article

To cope with the increasing global demand of energy and significant energy wastage caused by the use of different home appliances, smart load monitoring is considered a promising solution to promote proper activation and scheduling of devices and reduce electricity bills. Instead of installing a sensing device on each electric appliance, non-intrusive load monitoring (NILM) enables the monitoring of each individual device using the total power reading of the home smart meter. However, for a high-accuracy load monitoring, efficient artificial intelligence (AI) and deep learning (DL) approaches are needed. To that end, this paper thoroughly reviews traditional AI and DL approaches, as well as emerging AI models proposed for NILM. Unlike existing surveys that are usually limited to a specific approach or a subset of approaches, this review paper presents a comprehensive survey of an ensemble of topics and models, including deep learning, generative AI (GAI), emerging attention-enhanced GAI, and hybrid AI approaches. Another distinctive feature of this work compared to existing surveys is that it also reviews actual cases of NILM system design and implementation, covering a wide range of technical enablers including hardware, software, and AI models. Furthermore, a range of new future research and challenges are discussed, such as the heterogeneity of energy sources, data uncertainty, privacy and safety, cost and complexity reduction, and the need for a standardized comparison. Full article

Microgrids play a crucial role in the development of future smart grids, with multiple interconnected microgrids forming large-scale multi-microgrid systems that operate as smart grids. Multi-agent system (MAS)-based control solutions are the most suitable for addressing such control challenges. This paper presents a demand-side management (DSM) strategy using a meta-heuristic optimization technique for minimizing the household energy consumption cost using MAS. The binary grey wolf optimization algorithm (BGWOA) optimizes load scheduling, reducing electricity costs, without compromising consumer preferences using time-of-day (ToD) tariffs. The communication agents and load agents comprise the MAS used to streamline load control operations. The results demonstrate that MAS-based load control using metaheuristic optimization techniques enhances demand-side management, thus minimizing the electricity costs while adhering to contradictory parameters like user preferences, appliance duration, and load atomicity. This makes renewable energy integration more cost-effective in smart grids, thereby ensuring affordable, reliable, and sustainable energy for all. Full article

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

With the increasing integration of renewable energy and smart technologies in residential energy systems, proactive household energy management (HEM) have become critical for reducing costs, enhancing grid stability, and achieving sustainability goals. This study proposes a ultra-short-term forecasting-driven proactive energy consumption optimization strategy that integrates advanced forecasting models with multi-objective scheduling algorithms. By leveraging deep learning techniques like Graph Attention Network (GAT) architectures, the system predicts ultra-short-term household load profiles with high accuracy, addressing the volatility of residential energy use. Then, based on the predicted data, a comprehensive consideration of electricity costs, user comfort, carbon emission pricing, and grid load balance indicators is undertaken. This study proposes an enhanced mixed-integer optimization algorithm to collaboratively optimize multiple objective functions, thereby refining appliance scheduling, energy storage utilization, and grid interaction. Case studies demonstrate that integrating photovoltaic (PV) power generation forecasting and load forecasting models into a home energy management system, and adjusting the original power usage schedule based on predicted PV output and water heater demand, can effectively reduce electricity costs and carbon emissions without compromising user engagement in optimization. This approach helps promote energy-saving and low-carbon electricity consumption habits among users. Full article