Power system is undergoing a transition toward renewable energy sources (RES). While the high penetration of RES at the distribution level offers significant benefits, it also introduces challenges-particularly the reduction of rotational inertia, which poses serious risks to frequency stability and grid reliability. To ensure stable operation, it is essential to analyze and accurately estimate system inertia to maintain frequency stability. This paper reviews the emerging concept of an enhanced predictive estimation model that integrates Future Load Profile Nominations (FLPN). FLPN provides utilities with improved foresight into upcoming demand changes by enabling early identification of periods and locations that may experience high inertia stress. This review consolidates current research on system inertia and explores how FLPN can improve existing demand estimation practices to support better coordination in distributed energy networks. Aligned with the United Nations's Sustainable Development Goals (SDG) 7 (Affordable and Clean Energy) and 13 (Climate Action), the findings suggest that FLPN holds potential as a foundation for future developments in control strategies, virtual inertia planning, and the broader goal of establishing resilient, low-inertia power systems.

Future Trip Profile Nomination (FTPN) is proposed as a proactive framework for electric vehicle (EV) routing and traffic coordination. Built upon the broader concept of Future Behavior Nomination (FBN), FTPN allows EV users to voluntarily submit anticipated trip information—including origin, destination, departure time, and optional waypoints. These submissions are aggregated to support optimized routing decisions that minimize traffic congestion and ensure sufficient battery charge levels, taking into account each vehicle's range and the availability of charging infrastructure. Foundational models are introduced to integrate these behavioral nominations with conventional forecast data, incorporating timedependent accuracy and varying user participation rates. A simulation-based validation demonstrates that the proposed models can significantly improve trip prediction accuracy by fusing user nominations with conventional forecasts. By shifting from reactive, demand-driven routing to a behavior-informed paradigm, FTPN supports the development of Proactive Vehicle Traffic Management (PVTM) systems. This framework offers a scalable and intelligent approach to mobility planning that aligns with United Nations (UN) Sustainable Development Goals (SDG), particularly SDG 11 on sustainable cities and communities, and SDG 13 on climate action through improved energy efficiency and reduced transportation emissions.

Traditional pseudo-measurements often rely on operator forecasts, which lack consumer intent and adaptability. This paper proposes an enhanced Weighted Least Squares (WLS) State Estimation framework that integrates Future Load Profile Nominations (FLPNs), forward-declared load expectations with consumer-declared confidence levels, to improve estimation accuracy in low-SCADA or distribution-level networks. Unlike static forecast-based methods, FLPNs are modeled as inverse-variance-weighted pseudo-measurements, enabling direct consumer participation in grid monitoring. The framework is evaluated on the IEEE 14-bus test system in two configurations: (1) FLPN-only, where SCADA data at participating buses is fully replaced, and (2) FLPN-augmented, where both SCADA and FLPN data are fused. A 4 by 4 sensitivity analysis across FLPN participation$(25-100 \%)$and accuracy levels$(\mathbf{2 5}-\mathbf{1 0 0 \%})$shows that the FLPN-augmented mode consistently achieves the lowest voltage RMSE across most conditions, achieving up to$\mathbf{1 9. 3 \%}$lower voltage RMSE than FLPN-only. These findings demonstrate that integrating participatory declarations significantly enhances estimation accuracy and supports scalable, consumer-integrated grid operations. The study establishes a foundation for future extensions to dynamic and distributed estimation frameworks, advancing intelligent grid monitoring aligned with SDGs 7, 9, and 11.

Modern power systems face challenges amidst the increased integration of distributed energy resources such as photovoltaic (PV) systems, battery energy storage systems (BESS), and electric vehicles (EVs), which introduce complexity and uncertainty of demand profiles. Hence, this study proposes an optimization framework incorporating Future Load Profile Nominations (FLPN) as proactive, consumer-declared forecasts for energy dispatch planning, enabling a Proactive Energy Management (PEM) approach to grid operations. The IEEE 14-bus system is used as a testbed, with PV, BESS, and EV assets scaled proportionally to static loads at each bus. Dispatch optimization is performed under Philippine time-of-use pricing using a Monte Carlo approach to model FLPN uncertainty. Three scenarios are evaluated: (i) baseline dispatch without FLPN, (ii) FLPN-assisted dispatch with flexible EV charging, and (iii) FLPN-assisted dispatch with bidirectional Vehicle-to-Grid participation. Results show that while FLPN visibility alone yields limited operational benefits, its integration with flexible and controllable EVs achieves substantial improvements, reducing dispatch costs by up to 6.8% and lowering grid dependency up to 3%. This approach aims to contribute to achieving United Nation Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy) and 13 (Climate Action) by promoting proactive, more resilient, and consumer-integrated energy management strategies.

This study introduces a novel privacy-preserving data transformation framework for load forecasting that uses hybrid signal component analysis (SCA) techniques. Addressing the growing need for accurate energy predictions while protecting consumer privacy, the framework incorporates Future Load Profile Nomination (FLPN) with Load Profile Data (LPD) prior to transformation. A variety of basic and SCA-based methods including Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Wavelet Transform (WT) were tested, both individually and in hybrid combinations, to address the challenge of balancing privacy and forecasting accuracy. Evaluation showed that WT-based combinations significantly boost privacy, achieving a privacy Mean Squared Error (MSE) as high as 4.680, but this severely decreased forecasting accuracy to an MSE of 4.786. These findings show that hybrid transformation methods can be systematically evaluated to optimize the tradeoff between utility and privacy. This transformation-based approach offers an alternative to methods like Federated Learning and Differential Privacy, contributing to reliable, accurate, and privacy-respecting power systems aligned with SDG 7 and 11.

Power system state estimation is highly sensitive to the quality of input data, particularly in distribution grids where consumer-driven load variability can introduce significant discrepancies between expected and actual power usage. Traditional residual-based detection methods, including classical cumulative sum (CUSUM), often misclassify these natural fluctuations as anomalies due to their reliance on static baselines and lack of normalization. This study proposes an enhanced anomaly detection framework that integrates Future Load Profile Nominations (FLPNs) into a modified two-sided CUSUM structure. The method evaluates normalized residuals relative to time-aware, user-declared load expectations, and incorporates drift compensation to suppress the accumulation of small benign deviations. Simulation results on the IEEE 14-bus system across 100 trials revealed that the Modified CUSUM with FLPN achieved up to 82% reduction in false alarm rate and up to 69% reduction in missed detection rate compared to traditional CUSUM (without FLPN) approach. This underscores the efficacy of integrating FLPNs and normalization in enhancing anomaly detection performance under dynamic consumer load behavior.

The Future Load Profile Nomination (FLPN) framework is introduced as a novel approach to enhance power system load forecasting by enabling proactive participation from the demand side. As a specific application of the broader concept of Future Behavior Nomination (FBN) within power systems, FLPN addresses a key limitation of traditional forecasting methods, which depend primarily on historical and real-time data and often fail to anticipate deviations driven by future-oriented consumer behavior. FLPN empowers consumers to voluntarily nominate their anticipated electricity consumption in advance, effectively shifting the demand side from reactive to proactive. Mathematical models are developed at both the individual and system-aggregated levels, incorporating dynamic parameters such as participation rates, forecast blending, and time-varying accuracy. A simulation of a non-historical load anomaly validates the framework, demonstrating a significant reduction in forecast error even with imperfect user input. FLPN contributes to the broader objective of Proactive Energy Management (PEM) and directly supports the United Nations (UN) Sustainable Development Goals (SDG) by promoting efficient grid operations that reduce reliance on carbon-intensive spinning reserves (SDG 13), increase the hosting capacity for renewables (SDG 7), and empower consumers for responsible consumption (SDG 12).