A blackout is usually the result of load increasing beyond the transmission capacity of the power system. A collapsing system enters a contingency state before the blackout. This contingency state is characterized by a decline in the bus voltage magnitudes. To avoid blackouts, power systems may start shedding load when a contingency state occurs called under voltage load shedding (UVLS). The success of a UVLS scheme in arresting the contingency state depends on shedding the optimum amount of load at the optimum time and location. This paper proposes a hybrid algorithm based on genetic algorithms (GA) and particle swarm optimization (PSO). The proposed algorithm can be used to find the optimal amount of load shed for systems under stress (overloaded) in smart grids. The proposed algorithm uses the fast voltage stability index (FVSI) to determine the weak buses in the system and then calculates the optimal amount of load shed to recover a collapsing system. The performance analysis shows that the proposed algorithm can improve the voltage profile by 0.022 per units with up to 75% less load shedding and a convergence time that is 53% faster than GA.

Automated demand response in residential sectors is critical for grid stability, but centralized control strategies fail to address the unique energy profiles of individual households. This paper introduces a decentralized control framework using multi-agent deep reinforcement learning. We assign an independent Soft Actor–Critic (SAC) agent to each building’s battery energy storage system (BESS), enabling it to learn a control policy tailored to local conditions while responding to shared grid signals. Evaluated in a high-fidelity simulation environment of CityLearn using real-world data, our multi-agent system demonstrated a reduction of approximately 50% in both electricity costs and carbon emissions. Crucially, this decentralized approach considerably outperformed all benchmarks, including a rule-based controller, tabular Q-learning, and even a centralized single-agent SAC controller. At the district level, learned policies flatten the net load profile, lowering daily peaks by 16% and ramping by 26%, and improve the load factor. The resulting dispatch patterns are interpretable and consistent with operator objectives such as peak shaving and valley filling. These findings indicate that decentralized reinforcement learning can translate local optimization into system-level benefits and offers a scalable pathway for aggregators and utilities to operationalize the flexibility of residential storage at scale.

Demand response is shifting towards continuous coordination of flexible demand, storage, and distributed generation across buildings and prosumer communities. Multi-agent reinforcement learning has gained attention because it can support decentralized execution under partial observability while still learning coordinated behavior through centralized training. This systematic review follows PRISMA 2020 guidance and synthesizes $n=70$ peer-reviewed studies published in the 2021 to 2025 window, covering building clusters, grid-aware district coordination, program-level aggregation, industrial demand response, and transactive energy mechanisms. The results show that the dominant evaluation context is grid-responsive building clusters, with growing reliance on benchmark environments that standardize interfaces and encourage reproducible multi-KPI reporting. Across the methods, centralized training with decentralized execution is the prevailing pattern, often combined with attention-based critics or value factorization to handle heterogeneity and global rewards. Reward design and constraint handling emerge as primary determinants of stability, since objectives mix cost, peak, ramp, comfort, and emissions, while rebound and synchronized behavior are recurring risks. A descriptive and cross-variable quantitative synthesis is also provided, showing that publication activity increased from three studies (4.3%) in 2021 to 28 studies (40.0%) in 2025, with the strongest concentration in 2024–2025. Quantitatively, grid-responsive building clusters accounted for 26 of 70 studies (37.1%), actor–critic methods for 24 studies (34.3%), CityLearn for 16 studies (22.9%), and cost-based evaluation was reported in 64 studies (91.4%), whereas robustness testing appeared in only 16 studies (22.9%). Across the reviewed studies, peak reduction was reported in 55 (78.6%) studies, whereas robustness testing appeared in only 16 studies (22.9%) and transferability or deployment realism in 11 (15.7%), indicating that evaluation remains much stronger for operational performance than for real-world generalization.

Automated demand response in residential sectors is critical for grid stability, but centralized control strategies fail to address the unique energy profiles of individual households. This limitation becomes more pronounced in districts where buildings differ in load demand, photovoltaic (PV) production and battery energy storage system (BESS) behavior, while electricity prices and grid carbon intensity vary hourly. Conventional rule-based controllers can exploit patterns, but they require tuning and do not generalize across heterogeneous buildings. Existing centralized reinforcement learning methods improve adaptivity, yet they often learn compromise policies and scale poorly as the number of buildings increases. To address these issues, this paper proposes an AttentionKAN-based multi-agent reinforcement learning controller for district-level BESS scheduling. The method uses centralized training with decentralized execution, where each building is controlled by its own actor and a centralized critic models cross-building interactions through a multi-head query-key-value attention mechanism. To improve approximation accuracy under nonlinear and constrained battery dynamics, multilayer perceptron (MLP) blocks in the actor and critic are replaced with Kolmogorov-Arnold Networks (KANs), whose spline-parameterized univariate functions capture saturation effects, tariff discontinuities and couplings among state of charge, PV availability and carbon intensity. Implemented in CityLearn and evaluated on a residential net-zero community dataset, the proposed controller is assessed using building-level and district-level indicators for cost, ${\mathrm{CO}}_2$ emissions, peak demand, ramping and load shape. The learned policy charges during solar-rich hours and discharges during evening peaks, achieving the strongest performance among benchmark controllers, including an approximately 50% cost reduction versus the reference case and emissions reduction. From a sustainability perspective, the results indicate that coordinated multi-building BESS control can support low-carbon residential electrification through emission reduction, lowering electricity expenditure and improving renewable-energy utilization and providing grid-supportive flexibility through reduced peaks and ramping.

Incentive-based demand response (DR) programs rely on accurate and trustworthy quantification of customer performance to ensure fair compensation and market efficiency. Estimating the customer baseline load is an important part of this process. It shows how much electricity would be used if there were no DR occurrence. Unlike conventional load forecasting, baseline modeling is inherently unobservable, economically sensitive, and vulnerable to strategic manipulation. With the growing penetration of distributed energy resources, electric vehicles, and intelligent control technologies, traditional baseline estimation approaches face increasing limitations. This paper offers a thorough and future-oriented synthesis of baseline load estimation for incentive-based DR strategies. Current approaches are carefully classified into rule-based, statistical, probabilistic, machine learning (ML), and hybrid intelligence techniques, and their appropriateness for various DR services and client categories is rigorously evaluated. Beyond modeling accuracy, this paper emphasizes market-oriented requirements, including incentive compatibility, simplicity, transparency, privacy preservation, and deployment feasibility. Furthermore, emerging digital trust enablers such as blockchain and FL are reviewed, along with baseline-free and baseline-light alternatives for performance evaluation. Finally, open research challenges and future directions toward interpretable, robust, and market-ready baseline intelligence are discussed.