Smart Grid Strategies for Tackling the Duck Curve: A Qualitative Assessment of Digitalization, Battery Energy Storage, and Managed Rebound Effects Benefits

Abstract

Modern utilities face unprecedented pressures as trends in digital transformation and democratized energy choice empower consumers to engage in peak shaving, flexible load management, and adopt grid automation and intelligence solutions. A powerful confluence of architectural, technological, and socio-economic forces is transforming the U.S. electricity market, triggering significant changes in electricity production, transmission, and consumption. Utilities are embracing digital twins and repurposed Utility 2.0 concepts—distributed energy resources, microgrids, innovative electricity market designs, real-time automated monitoring, smart meters, machine learning, artificial intelligence, and advanced data and predictive analytics—to foster operational flexibility and market efficiency. This analysis qualitatively evaluates how digitalization, Battery Energy Storage Systems (BESSs), and adaptive strategies to mitigate rebound effects collectively advance smart duck curve management. By leveraging digital platforms for real-time monitoring and predictive analytics, utilities can optimize energy flows and make data-driven decisions. BESS technologies capture surplus renewable energy during off-peak periods and discharge it when demand spikes, thereby smoothing grid fluctuations. This review explores the benefits of targeted digital transformation, BESSs, and managed rebound effects in mitigating the duck curve problem, ensuring that energy efficiency gains translate into actual savings. Furthermore, this integrated approach not only reduces energy wastage and lowers operational costs but also enhances grid resilience, establishing a robust framework for sustainable energy management in an evolving market landscape.

1. Introduction

The transformative convergence in the power and utilities industry—where advanced grid infrastructure, distributed renewable energy technologies, and evolving market and regulatory forces merge—is fundamentally reshaping the U.S. electricity markets. This confluence reflects a shift from centralized, fossil fuel-based generation to decentralized systems that prioritize clean power grids and energy efficiency [1,2]. The integration of smart grid technologies, enhanced energy storage, and sophisticated communication systems is catalyzing a new power grid operational paradigm that accommodates variable renewable energy resources. A direct consequence of this evolution is the emergence of the duck curve, defined by a pronounced midday dip in net load followed by a steep evening ramp as solar output declines [3,4,5]. Historically, utilities maintained grid stability with predictable baseload generation; however, the rapid expansion of solar photovoltaics (PV) has disrupted this equilibrium, leading to significant load fluctuations that challenge grid stability and operational planning. In response, utilities and regulators are rethinking energy management strategies by integrating demand response mechanisms, advanced storage solutions, and modernized grid controls to enhance flexibility and resilience of power grids [6]. Concurrently, market and policy drivers—such as environmental mandates and renewable incentives—are accelerating the development of integrated solutions designed to mitigate the impacts of the duck curve, ensuring a more robust and adaptive power system [7].

Digitalization in the energy sector has emerged as a critical enabler in addressing the challenges posed by the duck curve. Smart grids now integrate advanced sensors, smart meters, and IoT devices to continuously collect and analyze real-time data, enabling precise load forecasting, dynamic demand response, and proactive grid management [8,9]. Through cloud and fog computing infrastructures, massive datasets are being processed to optimize the dispatch of energy resources, thus ensuring that surplus renewable energy is effectively stored or redirected. Battery Energy Storage Systems (BESSs) play a pivotal role in this context; they absorb excess generation during midday periods of high photovoltaic output and discharge during steep evening ramp-ups, thereby smoothing out the net load curve and enhancing grid stability [10,11]. However, while digitalization and BESSs provide technical solutions for energy imbalances, rebound effects pose a counterintuitive challenge. Under rebound effects, an improvement in energy efficiency inadvertently leads to increased overall energy consumption. These effects occur as consumers, benefiting from cost savings and improved appliance performance, may expand their usage, ultimately offsetting the anticipated reductions in energy demand [12,13]. Therefore, the integration of robust digital tools with BESS management becomes essential not only to capture and redistribute surplus energy but also to monitor consumption patterns and mitigate behavioral shifts that drive rebound phenomena.

In smart grids, the interplay among digitalization, BESSs, and rebound effects creates a dynamic framework for taming the duck curve [4,14]. Digital platforms facilitate the aggregation of granular consumption data, which in turn informs adaptive control algorithms that govern BESS operations. This integrated approach enables grid operators to deploy storage resources optimally—charging during periods of low demand and discharging during peak load—to counteract the rapid ramps that define the duck curve [10]. Simultaneously, real-time analytics provide insight into consumer behavior, allowing for the detection of rebound effects and the implementation of targeted demand response strategies. By coupling predictive analytics with strategic BESS deployment, smart grids can pre-emptively adjust to fluctuating conditions, thereby ensuring that energy efficiency improvements translate into genuine load reductions rather than unintended consumption increases [7,15,16]. This comprehensive, data-driven management paradigm not only stabilizes grid operations but also supports long-term sustainability goals, underscoring the critical need for continued research into the synergistic effects of digitalization, BESSs, and rebound dynamics in modern energy systems.

This paper contributes to the literature by assessing the synergistic role of digital platforms, BESSs, and rebound-effect-aware policies in managing the duck curve—a critical challenge in modern power grids with a high PV penetration like the California Independent System Operator (CAISO), Australian Energy Market Operator’s (AEMO) South Australia, Germany’s various regional grid operators, Hawaiian Electric and Japan’s regional utilities like Kyushu Electric Power Company [3,17]—by enhancing grid responsiveness and flexibility. Section 1 introduces the paper’s context, defining the duck curve, digital transformation, BESS technologies, and rebound effects, while outlining the paper’s scope. Section 2 delves into digitalization and data analytics, examining how smart grid technologies, IoT devices, and advanced analytics enhance grid monitoring and control. Section 3 focuses on BESSs, detailing their principles, operational strategies, integration with renewables, and economic, environmental, and safety considerations. Section 4 investigates rebound effects, exploring their definition, impact on energy efficiency, methods to monitor them, and strategies for mitigation through policy and behavioral interventions. Section 5 presents integrated approaches that combine digitalization, BESSs, and rebound dynamics, supported by case studies, demand response mechanisms, and comparative analyses. Section 6 presents discussions and policy implications, while Section 7 concludes by outlining future directions, emphasizing emerging smart grid trends, research gaps, regulatory challenges, and actionable recommendations tailored for the power and utilities industry.

2. Digital Technologies in Smart Energy Management

2.1. Role of Digital Transformation in Modern Energy Systems

Digitalization has fundamentally transformed grid management by ushering in the era of smart grids, IoT, Artificial Intelligence and Machine Learning (AI/ML), cloud and edge computing, and blockchain technology [18,19]. A smart grid is defined as an electrical network that integrates cutting-edge communication, automation, and sensing technologies to enable real-time monitoring, control, and optimization of power generation, transmission, and distribution [20,21]. This evolution from traditional, unidirectional grids to smart, adaptive systems has been driven by the need to incorporate renewable energy sources and to address the dynamic demands of modern energy consumption. Digital solutions—comprising distributed sensors, smart meters, and advanced control algorithms—facilitate enhanced visibility into network operations by providing granular, continuous data on voltage levels, load profiles, and power flows. In turn, this increased transparency empowers grid operators to implement adaptive control strategies that improve operational flexibility and system resilience. Moreover, the integration of digital technologies has allowed for the decentralization of control functions, thereby enabling localized management of distributed energy resources (DERs) and fostering an environment where both prosumers and traditional generators interact seamlessly within the grid [14]. By automating routine monitoring and control tasks, smart grids reduce the reliance on manual interventions and enable a more robust, self-healing network architecture that is capable of dynamically reconfiguring itself in response to disturbances.

Building on this digital foundation, modern grid operations rely on seven key smart grid control elements that together ensure efficient, resilient, and adaptive grid performance. At the forefront is the supervisory control and data acquisition (SCADA) system, which provides continuous real-time monitoring and remote control of grid assets. Complementing SCADA, the energy management system (EMS) is critical for optimizing generation scheduling, load forecasting, and balancing supply and demand. The distribution management system (DMS) further supports grid performance by overseeing power delivery and maintaining voltage regulation across distribution networks. Enhancing these functions, the advanced distribution management system (ADMS) integrates real-time analytics and automated decision support to improve overall grid resilience, enabling the network to adjust dynamically to operational challenges [22]. Additionally, the distributed energy resources management system (DERMS) plays a vital role in coordinating diverse DERs—such as rooftop solar panels and wind turbines—ensuring that these renewable assets are integrated seamlessly into the grid. During periods of high demand, the demand response management system (DRMS) actively manages consumer loads, adjusting consumption patterns to preserve stability, while the microgrid control system (MCS) facilitates the autonomous operation of localized grids, including islanding and reconnection functions. These seven control elements not only address immediate operational challenges through real-time responsiveness but also supports long-term strategic planning and renewable energy integration, thereby reducing reserve requirements and mitigating the propagation of errors across the system.

In addition to the control elements, the electric grid elements form an essential backbone of smart grid systems. This layer encompasses the tangible infrastructure required for electricity generation, transmission, and distribution. It integrates diverse power sources, including conventional thermal, hydro, and nuclear plants, alongside renewable installations such as solar farms and wind turbines, and DERs that are increasingly critical to today’s energy mix. High-voltage transmission lines, substations, transformers, and switching equipment work in concert to transport bulk power over extensive distances, while the distribution network—using medium and low-voltage lines, feeders, and smart meters—ensures that electricity is delivered reliably to end-users. Energy storage systems and protection/control devices further enhance grid stability, safety, and rapid fault response, collectively forming a robust foundation that supports advanced grid management and communication functions. As illustrated in Figure 1, which shows the key components of a smart grid, the integration of the smart grid control architecture with a resilient physical infrastructure creates an ecosystem capable of dynamic adaptation and sustainable performance [23]. This synergy between the control elements and the electric physical assets not only enhances the operational efficiency of current grids but also lays the groundwork for future developments in grid optimization, renewable energy integration, and overall system sustainability [20].

2.2. Digital Technologies in Network Management: AI, Edge Computing, IoT and Smart Meters

Digital technologies in network management are redefining utility sector operations through the integration of AI, edge computing, and IoT-based solutions. AI-driven systems enhance demand forecasting, predictive maintenance, and grid control by analyzing real-time and historical data [18]. Edge computing empowers utilities with low-latency, localized processing, reducing reliance on centralized systems and enabling faster decision-making at the grid edge [19]. IoT devices and sensors, when embedded throughout utility infrastructure, generate high-frequency data that informs fault detection, smart meter analytics, and energy optimization [14]. Digital twins—virtual replicas of grid assets—combine AI, big data, and IoT to support asset management and resilient operations [18]. These tools enable utilities to simulate, predict, and adapt to fluctuating grid conditions. Moreover, the convergence of cloud computing, ML, and advanced communications protocols is enhancing the flexibility and scalability of smart grid architectures. As utilities shift toward more distributed energy systems, edge computing facilitates localized grid coordination, supports smart home integration, and improves cybersecurity response times.

Building upon this digital transformation, robust data acquisition constitutes the backbone of modern smart energy management by enabling the continuous, high-resolution monitoring of grid parameters and fostering real-time decision-making. The systematic deployment of IoT devices, smart meters, and advanced sensors throughout the grid infrastructure facilitates the capture of granular data on energy generation, consumption, voltage, current, and system performance. These devices—integrated using standardized communication protocols such as IEEE 802.15.4 [24] and 6LoWPAN [24]—create an interconnected sensor network that supports dynamic monitoring and adaptive control. Smart meters, in particular, serve as critical nodes that not only record precise consumption metrics but also communicate bidirectionally with central management systems, thereby enabling advanced DRMS and fault detection capabilities [19]. By continuously collecting real-time data, this robust acquisition framework underpins the transformation of traditional power grids into intelligent, resilient systems capable of anticipating and responding to operational fluctuations. Moreover, the high-fidelity datasets generated are essential for sophisticated data analytics, which drive optimization strategies such as load balancing, predictive maintenance, and renewable integration—ultimately enhancing grid reliability and energy efficiency [25].

Despite these advancements, the effective utilization of such comprehensive data is impeded by challenges related to interoperability, data security, and privacy, which must be systematically addressed to fully leverage data analytics for grid optimization. Interoperability issues emerge from the diverse array of sensors and communication standards deployed across the grid; hence, the adoption of unified protocols and middleware solutions is imperative to ensure seamless data exchange between heterogeneous devices. Concurrently, the extensive interconnection of IoT devices exposes the grid to cybersecurity risks, as vulnerabilities may lead to unauthorized data access, manipulation, or even large-scale cyber-physical attacks that compromise system integrity and consumer privacy [26]. To mitigate these risks, advanced security measures—such as end-to-end encryption, blockchain-based distributed ledgers, and ML–driven anomaly detection—are being integrated within the data acquisition framework [19,27]. Overcoming these challenges is critical; ensuring the integrity and confidentiality of the data not only bolsters trust among stakeholders but also maximizes the potential of big data analytics to optimize resource allocation, predict load variations, and enhance overall grid resilience. In this integrated approach, robust, secure, and interoperable data collection is the keystone that enables smart energy management systems (EMS) to evolve into adaptive, efficient, and sustainable infrastructures [25] capable of meeting the demands of the future energy landscape.

2.3. Advanced Analytics for Load Forecasting and Grid Control

To address the interoperability, data security, and privacy-related challenges, advanced ML, predictive analytics, and cloud-based edge analytics are revolutionizing load forecasting accuracy, anomaly detection, and proactive grid management in smart grids by leveraging a spectrum of forecasting methods that range from classical statistical models to state-of-the-art deep learning techniques. Traditional approaches such as autoregressive integrated moving average (ARIMA), exponential smoothing, and linear regression models offer baseline forecasts but often fail to capture the complex, nonlinear relationships inherent in modern energy consumption patterns. To overcome these limitations, ML methods—including support vector regression (SVR), random forest regression, and ensemble tree models—have been increasingly adopted to improve prediction accuracy by learning hidden patterns in large historical datasets [28,29]. In parallel, deep learning models, particularly long short-term memory (LSTM) networks and their variants such as Bi-LSTM and hybrid LSTM-ELM models, have demonstrated superior performance in short-term load forecasting (STLF) by effectively modeling time dependencies and capturing seasonal, weather, and behavioral influences [30]. Enhanced with attention mechanisms, these models not only provide high-precision predictions but also offer explainable insights into critical events like the steep ramp-up periods that are characteristic of the duck curve [30]. These AI-powered methods offer substantial improvements in anomaly detection, allowing for the early identification of irregular load patterns, especially during extreme weather events [18] and potential cyber-physical threats, which, if unaddressed, could compromise grid reliability and efficiency. Additionally, reinforcement learning algorithms and ensemble forecasting strategies further refine predictions by integrating multiple model outputs to address uncertainties across very short-term, short-term, medium-term, and long-term horizons. This integration of cutting-edge ML algorithms with predictive models fosters a resilient, adaptive grid that not only forecasts future energy requirements but also optimizes control strategies for balancing supply and demand.

Cloud-to-edge architectures [31] further amplify these forecasting capabilities by synergizing the high computational power of cloud platforms with the low-latency responsiveness of edge devices. In this hybrid paradigm, edge computing is optimally employed for very short-term load forecasting (VSTLF) where rapid, localized data processing is crucial to address minute-to-minute variations and imminent anomalies. Conversely, the hybrid cloud-to-edge paradigm leverages advanced AI and deep learning algorithms to process large-scale datasets, thereby supporting longer forecasting horizons and enabling robust predictive maintenance, capacity planning and localized processing power available at the network edge [32,33]. This architectural synergy facilitates the continuous updating of forecasting models with real-time data from IoT devices, smart meters, and advanced sensors distributed throughout the grid. The integration of cloud and edge analytics ensures that critical factors such as weather conditions, calendar effects, economic indicators, and consumer behavior are dynamically incorporated into predictive models, thus enhancing their accuracy and reliability. Moreover, by employing techniques like sequence-to-sequence learning, convolutional neural networks (CNNs), and hybrid deep learning frameworks, utilities can effectively detect anomalies and predict ramp-up events in renewable-dominated grids, thereby smoothing the steep ramp characteristic of the duck curve [9,34]. In essence, the strategic deployment of a diverse array of forecasting methods within a robust cloud-to-edge framework is pivotal for enabling proactive, resilient, and sustainable grid management that meets the evolving challenges of modern energy systems [1,29,30].

2.4. Challenges and Opportunities in Digital Energy Management

Digital transformation of power systems is confronted by significant technical and operational challenges that must be surmounted to realize its full potential. Cybersecurity vulnerabilities are paramount, as smart grids with high DER penetration become increasingly exposed to threats from open network environments and insecure IoT devices [19,27,35]. The integration of legacy systems poses another critical hurdle; many existing grids were built on outdated architectures that lack compatibility with modern ICT solutions, necessitating costly and complex retrofits [36]. High implementation costs of modernizing the existing architectures further compound these issues, as substantial capital investments are required to upgrade the generation, distribution and transmission infrastructure, establish secure communication channels, and deploy advanced data analytics platforms. Moreover, the absence of standardized communication protocols—such as those defined by IEEE 802.15.4 and 6LoWPAN—creates interoperability problems among diverse devices and systems, impeding seamless data exchange and system integration [9,37]. Recent studies have illustrated these challenges; for instance, pilot projects in smart buildings [31] and resilient community microgrids [20,26,36,38] have encountered technical complexities and elevated upfront costs that hinder digital transformation, underscoring the need for cost-effective, interoperable, and secure solutions. Additionally, the fragmented nature of ancillary service markets [39] further complicates digitalization efforts, with inconsistent technical and procurement practices across regions leading to varied outcomes in grid stability and frequency control.

Despite these challenges, the digitalization of power grids presents substantial opportunities to enhance grid resilience, optimize renewable integration, and enable innovative services such as automated DRMS. For example, cloud-to-edge architectures are emerging as a transformative solution, enabling real-time processing of vast datasets collected from smart meters, IoT devices, and advanced sensors; such systems have demonstrated improved load forecasting accuracy and more effective anomaly detection [32,33,40]. Advanced ML techniques, particularly LSTM networks enhanced with attention mechanisms, have been successfully deployed in recent pilot studies to predict and smooth the steep ramp-up events of the duck curve, thereby facilitating more reliable grid operation under high renewable penetration [30]. Moreover, data-driven energy management system (EMS) in smart buildings have proven capable of integrating renewable resources with automated DRMS strategies, yielding measurable improvements in energy efficiency and occupant comfort [36]. Case studies from decentralized community microgrids further illustrate that when robust cybersecurity measures and standardized communication protocols are implemented, digital transformation can significantly reduce operational inefficiencies while enhancing overall grid stability and sustainability [41].

3. Battery Energy Storage Systems (BESSs) for Duck Curve Mitigation

3.1. Fundamentals and Principles of Battery Energy Storage Systems

In addition to grid digitization, BESSs provides a fast charge/discharge and serve as a flexible resource assisting DER-powered systems to track generation plan. BESSs comprise core components such as electrochemical cells, inverters, and advanced control systems that collectively store and release electrical energy through reversible chemical reactions. At their heart, cells perform the energy conversion by undergoing oxidation–reduction reactions, while inverters convert the direct current (DC) generated during discharge to alternating current (AC) for grid compatibility. Complementary control systems—including battery management systems (BMS) that integrate sensors, digital controllers, and communication modules—ensure safety, regulate operating conditions, and optimize charge/discharge cycles [35,42]. This integration enables precise monitoring of key parameters like state-of-charge (SOC) and state-of-health (SOH), critical for maintaining performance and extending the lifespan of the system. Moreover, the physical design of BESSs frequently incorporates modular architectures that allow for scalability and redundancy, thereby mitigating risks associated with individual cell failure and enhancing overall system reliability.

The technological evolution of battery storage has progressed significantly from early lead–acid systems to today’s advanced lithium–ion chemistries and emerging alternatives. Early systems, characterized by low energy density and modest cycle life, have gradually been replaced by lithium–ion batteries that offer superior energy density, faster charge/discharge rates, and extended cycle life [43]. These improvements have made BESSs increasingly effective in grid applications by facilitating rapid response times for frequency regulation and peak shaving. Key performance metrics—including energy density, power density, efficiency, and cycle life—directly influence a BESS’s ability to deliver reliable energy under varying grid conditions. Higher energy density allows for more compact installations, while improved charge/discharge rates and cycle durability contribute to both operational efficiency and economic viability. Additionally, deployment considerations now encompass technical aspects such as optimal system sizing, thermal management, and integration with renewable energy sources such as solar PV and wind energy, as well as economic and environmental factors including capital costs, lifecycle cost reduction, and minimized ecological impact [44]. Digital monitoring systems play a pivotal role in this context by leveraging real-time data, cloud computing, and digital twin technologies to continuously assess and predict battery performance. Such innovations in advanced monitoring technologies enable proactive maintenance, facilitate accurate SOC and SOH estimations, and support decision-making processes that optimize energy management and extend the battery’s useful life [35,44]. Together, these innovations ensure that BESSs not only meet the rigorous demands of modern power grids but also adapt dynamically to evolving market, customer expectations and regulatory conditions, ultimately contributing to a more resilient and sustainable energy infrastructure.

3.2. BESS Operational Strategies in Smoothing the Duck Curve

BESS operational strategies leverage advanced scheduling algorithms, real-time monitoring, and digital control systems to optimize charging cycles while ensuring system flexibility, grid stability, and cost-effective operation. Sophisticated scheduling algorithms—often based on genetic algorithms, deep learning, or rolling horizon optimization—enable the dynamic adjustment of charging and discharging profiles by predicting optimal time windows in response to dynamic pricing signals and grid constraints [45]. These algorithms integrate comprehensive data, including electricity tariff fluctuations, forecasted grid limitations, and real-time battery state parameters, to orchestrate charging cycles that both maximize battery life and minimize operating costs. In parallel, digital controls and cloud-based monitoring platforms harness IoT connectivity and digital twin models to continuously track key metrics such as SOC and SOH. By assimilating real-time information from DERs and grid operators, these digital control systems adjust charging rates and schedule interruptions or delays as needed, ensuring that surplus energy is efficiently stored and delivered precisely when required. This integrated approach not only enhances the reliability of peak shaving and load leveling strategies [46] but also facilitates ancillary services, such as frequency regulation, that are critical for balancing intermittent renewable outputs.

Furthermore, BESS operational strategies extend beyond the optimization of individual charging cycles to encompass a holistic suite of grid services, including peak shaving, load leveling, and frequency regulation (Figure 2). By employing multi-objective, scenario-based security-constrained unit commitment models and bi-level programming approaches, BESSs can be scheduled to charge during low-price periods and discharge during peak demand, thus reducing stress on the grid and curbing energy costs [38]. These strategies facilitate real-time adjustments to charging profiles, which are essential for maintaining grid balance amid fluctuating renewable energy inputs and variable demand. Moreover, decentralized control frameworks allow multiple BESS installations to collectively contribute to load leveling, as aggregated responses from distributed systems can effectively mitigate sudden load spikes and prevent overloading. Digital monitoring systems, integrated with advanced forecasting techniques such as LSTM networks, further enhance these capabilities by providing precise, near-real-time updates on grid conditions and pricing signals. This enables automated decision-making that dynamically modulates charging currents to optimize both battery performance and grid reliability. In this way, BESSs not only support immediate operational needs but also contribute to long-term energy sustainability by reducing greenhouse gas emissions and facilitating a more resilient, adaptable energy infrastructure [45,47].

3.3. Integration of BESSs with Renewable Energy Sources and Grid Stability

Integrating BESSs with renewable energy sources fundamentally mitigates the intermittency challenges inherent in solar, wind, and other variable resources by providing a robust buffer that absorbs excess generation and releases stored energy during periods of deficit [1]. By dynamically charging when renewable output exceeds demand and discharging during low production, BESSs significantly reduce ramp rates and stabilize voltage and frequency variations, thereby enhancing overall grid resilience. Technical and operational coupling of BESSs with DER generators involves sophisticated synchronization mechanisms, adherence to stringent grid connection standards, and the use of digital control systems that continuously monitor parameters such as SOC and system inertia. For instance, integrated hybrid PV–BESS installations in regions like New York State and Europe employ advanced power conversion topologies and digital twin frameworks to optimize control strategies in real time, ensuring seamless synchronization between DERs and storage assets [49,50]. Moreover, deployments in weak power grids have demonstrated that grid-forming BESSs can actively counteract voltage fluctuations and provide primary frequency control [51], thereby offsetting the reduced inertia typically associated with high solar PV and wind energy penetration [39,52]. In this context, careful consideration of inverter control strategies, converter dynamics, and compliance with grid codes is essential to achieve reliable and safe integration [9], as technical guidelines and benchmark requirements ensure that BESSs operate efficiently alongside intermittent renewable sources.

Operationally, the integration of BESSs not only enhances grid stability through ancillary services—such as frequency regulation, load leveling, and peak shaving—but also opens up valuable energy arbitrage opportunities that improve the economic performance of power systems. Advanced scheduling algorithms and digital controls, which incorporate predictive modeling and real-time data analytics, optimize the charging and discharging cycles to align with fluctuating renewable generation and dynamic pricing signals. Field deployments across diverse regions, as evidenced by recent case studies, illustrate that strategic integration of BESSs leads to reduced ramp rates, improved grid reliability, and more efficient load management, ultimately lowering operating costs and mitigating energy supply uncertainties [53]. Future trends point toward increasingly hybrid renewable–storage systems that combine multiple storage technologies with next-generation digital control architectures, thereby enhancing scalability and responsiveness. Emerging technologies—such as high-energy-density battery chemistries, ML–driven optimization algorithms [49], and improved communication protocols [24]—are poised to further revolutionize system integration, offering enhanced control over power flows and greater adaptability in the face of evolving grid challenges. These advancements will not only drive technical innovations but also support a transformative shift toward resilient, low-carbon, and economically efficient power systems that fully leverage the synergies between renewable energy and BESSs.

3.4. Enabling Battery Digital Twins for Intelligent Decision-Making

Digital twins for battery energy storage systems offer transformative capabilities for intelligent decision-making by integrating computationally efficient physical modeling with real-time data collection and robust data assimilation techniques. At their core, battery digital twins simulate the physical behavior of battery cells through a hybrid modeling approach that leverages both physics-based equations and data-driven machine learning methods [54]. This dual strategy enables digital twins to capture complex battery phenomena such as SOC, SOH, and degradation patterns with high precision. For instance, studies have demonstrated that hybrid models, which combine detailed physical insights with adaptive data assimilation frameworks, achieve superior accuracy and consistency in performance prediction [44]. In practical terms, a battery digital twin connects components, devices, and systems for manufacturing and operational insights and can rapidly update its predictions by incorporating live sensor data, thereby providing actionable insights for preventive maintenance, cycle life optimization, and dynamic control strategies (Figure 3). The integration of a stochastic optimization framework further supports intelligent decision-making by enabling rapid response to variations in operating conditions, thereby enhancing overall system resilience. Moreover, real-time data collection across critical quantities ensures that the digital twin remains a true reflection of its physical counterpart, facilitating informed, data-backed decisions in complex operational environments.

A holistic design approach is fundamental to ensuring that digital twins encompass the entire lifecycle of battery energy systems—from manufacturing and operation to recycling. As depicted in Figure 4, which outlines the main life cycle phases, a comprehensive digital twin must integrate data streams from mining and fabrication through to end-of-life analysis. During manufacturing, accurate physical modeling captures the intrinsic material properties and design specifications; during operation, continuous real-time monitoring collects data on performance metrics such as capacity fade, thermal behavior, and cycle stress; and during recycling, data assimilation techniques help identify degradation root causes and inform decisions on material recovery or reconditioning [44]. Such a lifecycle-wide integration not only enables rapid and reliable root-cause analysis of battery failures but also provides a secure framework for managing proprietary information. By restricting details of sensitive processes while still capturing essential battery performance data, digital twins can protect intellectual property and maintain competitive advantages for manufacturers. This balance is achieved through the implementation of secure data governance protocols and role-based data access controls, ensuring that only authorized stakeholders—such as original equipment manufacturers (OEMs) and utility operators—have access to critical system information [55,56]. Consequently, the digital twin serves not only as a tool for operational excellence but also as a strategic asset for continuous innovation and risk mitigation throughout the battery’s lifecycle.

Ensuring interoperability among digital twin data standards across multiple stakeholders is critical for effective electric power operations. To address this challenge, digital twin platforms must adopt standardized communication protocols and open-source frameworks that facilitate seamless data exchange among manufacturers, OEMs, and utility operators. By adhering to common data formats and standardized interfaces, digital twins can harmonize diverse datasets from various components and systems, thereby enabling comprehensive analysis and collaborative decision-making. Moreover, the hybrid approach—integrating physics-based modeling with data-driven techniques—ensures that the digital twin remains flexible and scalable, capable of assimilating large volumes of heterogeneous data while maintaining accuracy and consistency [44,54]. Such strategies are further reinforced by the development of centralized data and communication hubs that act as the backbone for multi-level integration, as seen in emerging frameworks like the “system of digital twin systems (SDTSs)” proposed by Song et al. [57]. These hubs not only standardize data formats but also provide robust security features to protect proprietary information. Consequently, the successful implementation of interoperable battery digital twin systems relies on combining advanced data assimilation methods, secure data-sharing protocols, and industry-wide standards that together enhance intelligent decision-making, ensure operational reliability, and drive the evolution of sustainable BESS solutions.

3.5. Economic, Environmental, and Regulatory Considerations for BESS Deployment

The deployment of BESS technologies is influenced by a confluence of economic, environmental, and regulatory factors that collectively determine its feasibility and market penetration. Economically, the cost dynamics of BESSs hinge on significant capital expenditures, ongoing operational and maintenance costs, and the financial benefits derived from ancillary services such as frequency regulation and energy arbitrage. Economic models demonstrate that favorable payback periods, sometimes as low as 3–4 years, can justify investments, particularly when grid benefits and revenue stacking are taken into account [58,59]. However, the high cost of critical raw materials and the challenges associated with end-of-life management—such as recycling and second-life applications—impose environmental burdens that can affect the overall lifecycle impact of battery technologies [60]. In addition, the extraction and processing of materials like lithium, cobalt, and nickel entail significant ecological and human health risks, thereby underscoring the need for robust circular economy strategies. From a regulatory perspective, the evolving policy landscape—characterized by emerging standards (such as IEEE 1547 for interconnection, UL 9540 for safety, and IEC 62,933 for battery energy storage) [61], incentive mechanisms, and market rules—plays a pivotal role in either facilitating or impeding BESS integration. Regulatory frameworks that precisely define energy storage assets and establish clear guidelines for their interconnection and operation have been shown to reduce market uncertainties and encourage early adoption [60,61].

Policy frameworks and market mechanisms are critical levers for overcoming current barriers and spurring future investments in BESSs. Proactive policy measures—such as tax incentives, subsidies, and streamlined permitting processes—can reduce the upfront capital burden and lower the levelized cost of storage, thereby accelerating market adoption [62]. Additionally, the integration of BESSs into deregulated electricity markets, where innovative market designs allow for dynamic pricing and revenue stacking, creates an environment conducive to private investments by aligning financial returns with grid support services. Industry trends indicate that modular and scalable BESS deployments, along with the adoption of second-life batteries, offer further cost savings and environmental benefits by extending the lifecycle of battery assets and reducing raw material consumption. Collaborative regulatory initiatives that foster stakeholder engagement and establish common standards across regions can enhance interoperability and ensure a level playing field. In this context, policy recommendations should address the development of clear definitions for energy storage systems, incentives for recycling and repurposing batteries, and the implementation of market mechanisms that reward flexibility and ancillary service provision [58]. Such measures not only address the existing barriers but also pave the way for a more resilient, sustainable, and economically attractive energy storage market, ultimately influencing future investments and driving the transition toward a low-carbon economy.

3.6. Advances in Battery Technologies and Their Impact on Grid Flexibility

Recent advances in lithium-ion, sodium-sulfur (NaS), lead-acid, and vanadium redox flow batteries have significantly enhanced the operational envelope of BESSs in flexible grid applications. Among these, lithium-ion batteries dominate the current energy storage market due to their high round-trip efficiency, fast response time, and favorable energy-to-power density ratios, making them suitable for fast-acting grid services such as frequency regulation, voltage support, and spinning reserve [53,63]. NaS batteries, with their high energy capacity and thermal resilience, have demonstrated efficacy in load leveling and peaking capacity support, particularly in arid or industrialized regions with high base-load demands [11]. Flow batteries, including vanadium redox technologies, offer the added advantage of decoupling power and energy capacity, enabling long-duration storage and renewable firming over multi-hour timeframes, which is critical for mitigating solar PV and wind generation volatility [64]. These battery types, when integrated with advanced control strategies such as droop-based frequency response or voltage-guided dispatch, have been shown to stabilize fluctuations and improve real-time grid balancing capabilities [63,65]. Collectively, these electrochemical innovations enable a 40–50% penetration of variable renewable energy (VRE) sources into electric grids with minimal retrofitting of legacy infrastructure [11].

Equally transformative is the emergence of novel energy storage systems—hydrogen-based batteries with fuel cells, molten metal batteries, and gravity storage technologies—that extend the temporal scope of grid flexibility beyond traditional BESS timeframes [65]. Hydrogen energy storage (HES), utilizing electrolyzers and fuel cells, enables multi-day and seasonal energy arbitrage with minimal degradation, offering strategic value in high-renewable scenarios marked by prolonged generation–demand imbalances [66]. Molten metal batteries provide high-temperature resilience and long discharge durations, positioning them as viable candidates for industrial-scale grid services and contingency reserve applications [53]. Gravity-based storage, while mechanically constrained, offers low operational overhead and geographic scalability for load shifting and peak shaving in off-grid and mountainous regions. Importantly, all of these storage architectures are increasingly coupled with edge-deployed optimization and predictive control systems that regulate the SOC, minimize degradation, and extend battery lifetime in grid-interactive modes [63]. These advancements redefine the techno-economic boundaries of dispatchable storage, enabling enhanced resilience, ride-through capability during blackouts, and greater alignment with DER variability and smart grid imperatives (Figure 5) [53,64]. By integrating such emerging technologies, modern grids are transitioning toward a hybridized, adaptive storage ecosystem that underpins a reliable and decarbonized electricity future.

4. Rebound Effects: Understanding and Mitigation Strategies

4.1. Rebound Effects in the Energy Context

The rebound effect, defined as the increase in energy consumption that occurs when efficiency improvements lower the effective cost of energy services, manifests in both direct and indirect forms [13,15]. Direct rebound effects occur when consumers and businesses, faced with lower operating costs, increase their energy-intensive activities due to reduced operational costs—such as driving more when vehicles become more fuel efficient [67]—a phenomenon originally described by Jevons as the Jevons paradox or “backfire” [68]. Indirect rebound effects, on the other hand, emerge when the monetary savings from efficiency gains are reallocated to other energy-consuming activities, thereby offsetting the expected energy savings [69]. This dual phenomenon is further compounded by economy-wide effects, wherein the aggregate response of multiple economic sectors amplifies energy demand beyond the immediate context of the efficiency improvement [70]. For instance, studies by Gillingham et al. [71] and Borenstein [72] provide empirical evidence that efficiency improvements in the energy sector may yield savings that are partially negated by subsequent increases in consumption across households and industries. Moreover, digitalization has been identified as a key trigger of rebound effects; as digital technologies lower production and operational costs, they can inadvertently stimulate additional energy use in areas such as data processing and transmission [68]. The interplay between these dynamics suggests that even well-intended energy efficiency policies may fall short if rebound effects are not thoroughly accounted for, thereby challenging policymakers to adopt a more holistic approach in their energy strategies.

The implications for effective energy policy and renewable energy integration are profound. A comprehensive theoretical framework that incorporates both the microeconomic behavior of individual consumers and the macroeconomic dynamics captured by models such as the Cobb–Douglas production function and the Solow residual are essential for accurately predicting net energy savings. When combined with insights from Leontief and Constant Elasticity of Substitution (CES) production functions, such a framework allows policymakers to quantify the extent to which rebound effects may erode anticipated energy conservation gains [12,69]. Empirical studies have shown that while direct rebound effects tend to be moderate, indirect and economy-wide effects can be significantly larger, suggesting that improvements in energy efficiency might not linearly translate to lower overall energy use [15]. This reality places a premium on the design of energy policies that are robust against such counteractive behaviors. For instance, unanticipated increases in energy use due to rebound effects can exacerbate the intermittency challenges associated with renewable sources, complicating grid management strategies and potentially necessitating further reliance on fossil fuel backups, thereby undermining sustainability goals [1,73]. Policymakers should therefore consider measures such as dynamic pricing, enhanced energy monitoring, and targeted incentives for low-carbon technologies to mitigate rebound-induced demand surges. Furthermore, addressing digital transformation’s impact requires integrated approaches that coordinate technology deployment with energy regulation to prevent unintended escalations in consumption. Understanding the nuanced interplay between direct, indirect, and economy-wide rebound effects thus is crucial for the formulation of energy policies that not only promote efficiency improvements but also ensure that these gains lead to genuine reductions in energy use and greenhouse gas emissions [71,73], thereby supporting the broader transition to renewable energy systems.

4.2. Impact of Rebound Effects on Electricity Grid Operations

Rebound effects can significantly complicate electricity grid operations and management, including load forecasting, demand management, grid stability, and renewable energy integration, by introducing unpredictable fluctuations in energy demand patterns. For instance, behavioral and economic responses by the consumers and businesses to both the direct and indirect rebound effects present challenges for electricity grid operators, who rely heavily on accurate load forecasts to balance energy supply and demand, especially when integrating intermittent renewable sources like wind or solar (Figure 6). Consequently, rebound effects increase electricity demand volatility, undermine the reliability of demand-response strategies, necessitate the need for additional capacity reserves or backup generation, and ultimately compromise the stability and resilience of electricity grids and operations. Empirical evidence from multiple contexts highlights considerable variability in rebound effects, which significantly affect energy policy effectiveness. In residential electricity use, direct rebound effects have been quantified through time-series modeling, with studies like Belaid and Mikayilov [74] reporting rebounds ranging from 41% to 71% across different regions in Saudi Arabia. These substantial rebounds illustrate how households significantly increase their energy usage following efficiency improvements, particularly in developing regions where the sensitivity to reduced energy costs is pronounced [15]. Similarly, research by Davis et al. [75] on appliance replacement programs in Mexico showed dramatic rebounds—up to 72% for refrigerators and even backfire for air conditioners—demonstrating how added product features can magnify rebound effects and erase anticipated energy savings. Such variability underscores the importance of context-specific factors, such as consumer behavior, appliance characteristics, and economic conditions, all of which must be accounted for in forecasting energy demand and assessing efficiency policy outcomes.

At the economy-wide level, modeling studies further emphasize the complexity and magnitude of rebound effects, which vary substantially depending on methodological frameworks and regional contexts. Computable General Equilibrium (CGE) models have reported rebound effects ranging from approximately 50% to nearly 100%, indicating a significant reduction or even full offsetting of potential energy savings due to systemic economic responses [76]. For example, Freire-González and Ho [77], using a CGE model for Catalonia, demonstrated that comprehensive energy efficiency policies must be combined with complementary measures—such as carbon taxes and behavioral shifts—to effectively counteract rebound effects. Similarly, Likewise, Gillingham et al. [71] highlighted the importance of accurately accounting for rebound effects at the macroeconomic level, noting substantial variability due to factors such as induced innovation and productivity growth, and cautioning that ignoring these rebounds systematically underestimates true energy consumption.

Further highlighting variability, recent empirical analyses have also shown how rebound magnitudes differ significantly based on sectoral, technological, and spatial factors. Nyangon and Byrne [13], employing a spatial Durbin error model (SDEM) to examine residential energy efficiency measures (EEMs) adoption in New York, found strong correlations between residential energy-efficiency uptake and spatial spillover effects among neighboring ZIP codes. Their results demonstrated that adoption rates varied significantly based on building characteristics—such as multifamily structures and gas-heated homes—and were influenced by localized socioeconomic factors, creating uneven spatial patterns in rebound effects. Moreover, Peng and Qin [73] revealed that technological changes like digitalization further complicate rebound dynamics; their study in Chinese cities indicated digitalization-driven rebounds of up to 19%, primarily due to rising electricity demands from data centers and digital infrastructures. These diverse empirical findings underscore the critical need for context-specific policy designs and periodic reassessment of incentives, ensuring that energy-efficiency initiatives genuinely deliver intended energy demand reductions and support reliable grid operations.

4.3. Digital Tools for Monitoring and Quantifying Rebound Effects

Digitalization is increasingly redefining electric power operations by not only enhancing efficiency but also by revealing the complex dynamics of rebound effects through advanced, high-resolution data collection. In modern grids, smart meters, IoT sensors, and advanced analytics provide near real-time measurements of electricity consumption, enabling utilities to capture granular shifts in usage patterns immediately following efficiency improvements. Quantitatively, studies have reported rebound effects that range between 10% and 20% on a macroeconomic level—with some models, such as the German energy-economy model PANTA RHEI, estimating mesoeconomic rebounds between 7% and 12% and macroeconomic rebounds up to 18% in the medium term [78]. Such quantitative assessments are often derived from methodologies that integrate econometric models, simulation studies, and indices like the ODeX, which help quantify the extent to which initial energy savings are offset by subsequent increases in consumption. For instance, when energy efficiency improvements reduce the effective price of energy, the elasticity of demand can lead to a partial or even complete erosion of the anticipated savings—a phenomenon rigorously modeled using panel data techniques and panel smooth transition regression [73]. These advanced quantitative models not only provide estimates of rebound magnitudes but also enable scenario analyses where the interplay of efficiency gains and energy demand is dynamically simulated under varying technical and behavioral parameters.

Complementing these quantitative insights, qualitative analyses reveal that digitalization fundamentally alters consumer behavior and system operations, thereby influencing the rebound effect in more nuanced ways. On the one hand, real-time monitoring systems empower utilities to implement dynamic demand response programs, whereby consumption can be curtailed or shifted during peak periods, effectively mitigating potential rebound spikes [79]. On the other hand, the lower operational costs resulting from efficiency gains often lead to increased usage, i.e., a behavioral response that is difficult to predict without detailed data analytics. Advanced simulation studies that combine life cycle assessment (LCA), input–output modeling, and re-spending modeling have demonstrated that the rebound phenomenon is not uniform; rather, it varies significantly across different sectors, regions, and consumer groups. For example, certain households may exhibit rebound effects as high as 15% to 25%, particularly when efficiency gains prompt additional investments in energy-dependent technologies, whereas industrial consumers might experience lower relative rebounds due to economies of scale and more controlled consumption patterns [80]. Furthermore, agent-based modeling approaches have been employed to capture the heterogeneity in consumer responses, highlighting how social and economic factors—such as income levels and regional energy policies—contribute to the observed variability in rebound effects [81]. Such qualitative findings underscore the need for a systems-thinking approach that integrates technical metrics with behavioral data, ensuring that digitalization’s efficiency benefits are not overestimated by neglecting the compensatory increase in energy use [79].

By synthesizing both quantitative data and qualitative insights, researchers and policymakers are now better positioned to design interventions that account for the rebound effect’s dual nature. This integrated framework promotes a more nuanced understanding of energy dynamics, where advanced digital monitoring and sophisticated econometric models converge to inform real-time adjustments in electric grid operations and DRMS strategies. Ultimately, the interplay between digitalization and rebound effects presents a complex yet navigable challenge—one that requires coordinated policy responses, dynamic system modeling, and an appreciation of the behavioral dimensions of energy consumption. Such comprehensive analysis is critical for ensuring that the promise of digitalization in electric power operations translates into genuine, sustainable energy savings without being undermined by unintended increases in electricity demand [69].

4.4. Policy and Behavioral Strategies to Mitigate Rebound Effects

An array of policy interventions has been developed to mitigate the rebound effects in various settings, and these measures are crucial for preserving the intended benefits of energy efficiency policies [2,9,13,82]. Dynamic pricing mechanisms, for instance, adjust electricity tariffs in real time based on demand fluctuations, thereby discouraging excessive use during peak periods and aligning consumer behavior with grid capacity constraints [14,82]. Energy taxes, including carbon and energy production taxes, serve as fiscal instruments that elevate the cost of energy use post-efficiency gains, effectively reducing the incentive to overconsume by internalizing the external costs of energy production and associated emissions [77,83]. Additionally, incentive schemes—such as rebates for reduced consumption or penalties for excessive use—further ensure that the savings achieved from technological improvements are not offset by a surge in electricity demand. Studies employing CGE models have demonstrated that these market-based instruments, when carefully calibrated, can limit rebound effects at relatively low economic costs while even contributing positively to GDP in the long run [9,77].

Complementing these fiscal and regulatory measures are behavioral strategies aimed at modifying consumer attitudes and practices regarding energy usage. Public awareness campaigns and educational programs play a pivotal role in informing consumers about the full cost of energy consumption, including the environmental and societal impacts that are often hidden behind low marginal costs following efficiency gains [82,84]. By leveraging clear, data-driven messaging and interactive platforms, these initiatives encourage consumers to adopt energy-conserving behaviors even when energy appears cheaper, thus bridging the gap between technological potential and actual energy savings. For example, targeted information sessions, online tools, and community-based workshops have been used to illustrate how small changes in behavior—such as moderating thermostat settings or reducing appliance standby times—can collectively contribute to significant reductions in energy demand [77]. This approach not only raises awareness about rebound effects but also fosters a culture of sustainability where behavioral adjustments reinforce the efficacy of policy interventions.

One exemplary pilot project in a European city, which combined a dynamic pricing algorithm with a consumer education campaign to effectively manage load peaks while promoting energy conservation achieved a measurable 15% reduction in electricity peak load without compromising service reliability; this project also saw a 7% decrease in overall electricity consumption during critical peak periods [77]. In parallel, the project conducted a series of interactive workshops, digital tutorials, and community outreach programs that reached over 80% of participating households, significantly enhancing public awareness of the true costs associated with energy usage and the rebound effect [82]. Financial incentives were provided in the form of bill credits of up to EUR 50 for households that sustained energy reductions during peak times, thereby bolstering consumer participation. Comprehensive monitoring and evaluation, employing both automated analytics and manual surveys, enabled continuous refinement of the pricing model and validated the quantitative outcomes. Additionally, complementary studies integrating advanced battery scheduling with deep reinforcement learning algorithms demonstrated a further 23% improvement in reducing the duck curve’s peak-to-average ratio [14]. These robust, data-driven results underscore the capacity of coordinated policy interventions to produce significant energy savings. These results confirm that integrating technology, regulation, and behavior is essential for lasting energy savings. Ultimately, this case study illustrates that only a comprehensive approach—one that unites technological advances, effective regulatory measures, and proactive consumer behavior modifications—can effectively mitigate rebound effects and secure sustainable energy efficiency in electric power operations.

4.5. Advantages and Disadvantages of Predictive Analytics and Regulatory Strategies Under Comprehensive Digitalization and Intelligence

Table 1. Advantages and Disadvantages of Predictive Regulatory Digitalization.

Advantage Predictive Analytics and Regulatory Strategies	Disadvantages and Risk Mitigation
Enhanced forecasting accuracy: Predictive analytics enables more precise load, generation, and price forecasting, reducing uncertainty in grid operations.	Model overfitting or data bias: Over-reliance on historical data may embed systemic bias. Mitigation: Employ robust model validation techniques and include diverse, real-time datasets for training.
Proactive grid management: Allows grid operators to anticipate and respond to potential instabilities before they escalate.	False positives and alert fatigue: Excessive automated alerts may lead to desensitization. Mitigation: Design tiered alert systems and human-in-the-loop controls to manage response prioritization.
Optimization of DERs and storage assets: AI can dynamically schedule distributed energy resources (DERs) for grid efficiency and economic gains.	Cybersecurity vulnerabilities: Digital control systems increase exposure to cyberattacks. Mitigation: Apply zero-trust architecture, real-time threat detection, and regulatory enforcement of cybersecurity standards.
Improved regulatory monitoring: Digitalization enables real-time compliance tracking and market transparency for regulators.	Data privacy concerns: Granular smart meter and user data may raise privacy issues. Mitigation: Implement secure data anonymization protocols and user consent frameworks.
Adaptive regulatory frameworks: Regulations can evolve dynamically to accommodate new technologies and market structures.	Regulatory lag: Technology often outpaces policy response. Mitigation: Use regulatory sandboxes and horizon scanning tools to test and co-develop adaptive regulations.
System resilience against extreme events: AI-based simulations and digital twins allow scenario-based planning for shocks like weather or supply disruptions.	Limited transparency in AI decisions: Black-box models hinder stakeholder trust. Mitigation: Mandate explainable AI methods and audit trails in high-stakes applications.
Stakeholder engagement and market innovation: Open data and digital platforms promote new market entrants and consumer participation (e.g., demand response).	Digital divide and equity gaps: Vulnerable populations may lack access or understanding. Mitigation: Develop inclusive regulatory provisions and targeted digital literacy programs.

outlines advantages and disadvantages of predictive analytics and regulatory strategies in the context of comprehensive digitalization and intelligent energy systems, including risk mitigation considerations.

5. Integrated Approaches for Smart Duck Curve Management

5.1. Synergistic Integration of Digitalization, BESSs, and Managed Rebound Dynamics

An integrated platform that combines digitalization, BESSs, and managed rebound effects is crucial for addressing the challenges posed by the duck curve in renewable energy grid operations. Digitalization, which encompasses real-time data acquisition, advanced analytics, and automated control, enables seamless communication between DERs and grid operators. By leveraging interoperable platforms that support standardized communication protocols, digital tools facilitate dynamic monitoring and control, ensuring that information from solar panels, wind turbines, and other renewable sources is efficiently processed and acted upon. For instance, state-of-the-art algorithms—such as deep reinforcement learning techniques outlined by Watari et al. [14]—are employed to optimize battery scheduling and dynamically adjust pricing signals, which in turn can mitigate the rapid fluctuations typical of the duck curve.

BESSs play a central role in this integrated strategy by acting as both a buffer and a provider of flexibility. These systems store excess energy during periods of low demand or high renewable generation and release it during peak demand times, thus helping to balance the net load. The integration of BESSs with digital control systems allows for precise scheduling of charging and discharging cycles, which is particularly important when renewable generation is highly variable. Additionally, digital platforms can coordinate the operation of multiple BESS units distributed across the grid, effectively transforming them into a virtual power plant capable of providing ancillary services such as frequency regulation and load balancing [85]. On the other hand, managed rebound effects add another layer of sophistication to this strategy. In many conventional EMS, simultaneous responses to external signals can result in overcompensation, leading to undesirable load spikes or oscillatory behavior. By incorporating mechanisms that regulate rebound effects—such as improved locking and randomization techniques proposed by Mahmud et al. [86]—the integrated platform can smooth out these fluctuations. This regulation ensures that when a large number of EMS respond to a drop or surge in load, the collective action does not inadvertently destabilize the grid. Such control is essential not only for maintaining grid stability but also for ensuring that energy storage systems operate efficiently without propagating errors throughout the system.

The operational synergies created by this integrated approach are significant. Real-time coordination between digital platforms and BESSs allows for adaptive responses to both forecasted changes in renewable output and unexpected deviations in energy demand [35,87]. This adaptive capacity is key to managing the steep ramps and deep valleys characteristic of the duck curve, especially in regions with high solar PV penetration. Furthermore, by facilitating a continuous feedback loop between energy generation, storage, and consumption, integrated digitalization helps mitigate the risks associated with intermittent renewable energy sources and controlled rebound phenomena [4,6]. Ultimately, the combination of these technologies not only enhances grid reliability and operational efficiency but also contributes to cost reduction and improved environmental performance, thus supporting a more resilient and sustainable power system.

5.2. Current State-of-the-Art Implementations and Case Studies

Recent advancements in digitalization, BESSs, and managed rebound effects have culminated in integrated platforms that not only enhance grid resilience but also validate sophisticated theoretical models through real-world operational performance. In several pioneering implementations, digital control architectures have been coupled with advanced BESS management strategies, employing real-time monitoring, dynamic pricing algorithms, and predictive analytics to mitigate the pronounced effects of the duck curve [9,88]. For instance, case studies from Japan have demonstrated that dynamic pricing coupled with deep reinforcement learning for battery scheduling can significantly reduce net load variability, achieving improvements in the standard deviation and peak-to-average ratio by up to 57.1% and 23%, respectively [14]. Similarly, utility-scale projects in Malaysia have utilized metaheuristic optimization to determine optimal BESS placement and sizing, thereby curtailing system losses while addressing steep load ramp constraints [85]. These integrated strategies rely on interoperable communication protocols and standardized digital platforms, ensuring that control signals, energy storage operations, and rebound management measures are synchronized effectively. The convergence of these technologies has enabled pilot projects to replicate and scale best practices across diverse geographical regions, providing robust quantitative and qualitative validation of theoretical performance metrics.

Further, empirical evidence from distributed EMS in microgrid applications has underscored the importance of coordinated rebound management, particularly when uncoordinated responses can lead to significant load spikes. In one notable study, researchers modeled both load-based and price-based EMS to explore the rebound effect inherent in battery and electric vehicle charging operations during peak periods [86]. These investigations revealed that strategic locking and randomization techniques can substantially attenuate the inadvertent propagation of errors, thereby stabilizing the overall grid performance. In addition to improved control algorithms, enhanced digital platforms facilitate the aggregation of distributed BESS units into virtual power plants that dynamically adjust to fluctuations in solar PV generation. This integrated approach not only confirms the efficacy of predictive models but also elucidates best practices in digital-physical system coordination, highlighting the need for comprehensive interoperability frameworks that support both supply-side flexibility and demand-side management. The case studies demonstrate that by mitigating rebound effects through precise digital controls, operational results consistently align with the predicted outcomes of advanced simulation models, thereby reinforcing the validity of contemporary theoretical constructs.

Moreover, a comparative analysis of performance metrics across various implementations provides critical insights into the scalability and replicability of these integrated solutions. Quantitative performance metrics—such as load variance reduction, peak shaving efficacy, and energy cost savings—have been systematically evaluated in pilot projects spanning residential, commercial, and maritime applications [89]. These studies indicate that the integration of digitalization with BESSs and managed rebound strategies can reduce energy consumption by 10–20% by 2050, as digital practices inherently facilitate a reduction in overall energy demand [90]. However, the empirical data also reveals certain limitations, such as residual rebound-induced oscillations during simultaneous EMS switching events, which underscore the necessity for continuous refinement of control algorithms. In practice, best practices continue to emerge that emphasize the importance of robust communication networks, real-time data analytics, and adaptive control strategies that are capable of accommodating both forecasted and unforeseen disturbances [19,26,79]. These findings serve as a benchmark for current capabilities and provide a clear directive for future improvements, ensuring that integrated digitalization, BESSs, and rebound management strategies remain at the forefront of sustainable, resilient energy grid operations.

5.3. Demand Response and Adaptive Control Systems

Digitalization enables DRMS programs that automatically adjust consumer loads in real time based on prevailing grid conditions. In the context of smart duck curve management, DR programs are deployed through advanced digital platforms that integrate data from smart meters, IoT sensors, and automated control systems. These systems continuously monitor critical parameters such as voltage, frequency, and load profiles, thereby providing a comprehensive view of grid status. When an imminent imbalance is detected—whether due to a sudden spike in consumer demand or variability in renewable output—the automated DR system initiates corrective actions. For instance, predefined load-shedding schemes or load-shifting strategies are triggered to reduce demand during peak intervals, thereby attenuating ramp rates and preventing overloads. Digital analytics, enhanced by ML algorithms, further optimize these DR actions by predicting transient events and fine-tuning load adjustments to minimize disruptions. This proactive approach ensures that consumer loads are modulated seamlessly, effectively balancing the grid while simultaneously mitigating the adverse impacts associated with the duck curve phenomenon [79]. Moreover, these automated DR solutions are integrated within an overarching energy management system that orchestrates a coordinated response across multiple grid assets, including distributed BESSs. This integration allows DR programs to work synergistically with BESS operations, whereby stored energy is judiciously deployed to smooth out abrupt changes in net load and stabilize the grid during periods of renewable intermittency. Figure 7 and Figure 8 illustrate types of DR programs in the U.S. and their development trends. DR programs are broadly categorized as dispatchable or reactive. Load control, wholesale, and retail programs contribute about 62%, 27%, and 8%, respectively, to potential peak load reduction [91]. While dispatchable programs dominate enrollment, smart meters have boosted reactive programs by enhancing customer-utility interaction. U.S. trends show DR evolving beyond reliability, aiming to improve system efficiency and flexibility.

Adaptive control systems, operating in tandem with DR programs and BESSs, further enhance grid reliability by providing a dynamic, coordinated response to fluctuations in energy supply and demand. These systems employ advanced control algorithms—such as iterative learning controllers or reinforcement learning-based approaches—to continuously recalibrate BESS charging and discharging cycles based on real-time grid data. By adjusting the operational parameters of BESSs in response to instantaneous fluctuations in renewable generation or sudden shifts in consumer demand, adaptive control systems effectively buffer the grid against rapid changes and potential overloads. This dynamic regulation not only diminishes the steepness of load ramps but also contributes to maintaining a steady grid frequency and voltage profile during critical periods. For example, when renewable output drops unexpectedly due to intermittent cloud cover over photovoltaic installations, the adaptive control mechanism swiftly commands BESSs to release stored energy, thereby compensating for the deficit and preventing severe frequency deviations [36,87]. Additionally, digital analytics integrated within the control framework allow for continuous performance monitoring and optimization, ensuring that both DR and BESS operations align with forecasted grid conditions. This integrated approach can yield significant benefits: it reduces ramp rates, mitigates overload risks, and sustains grid balance even under volatile operating conditions. Furthermore, coordinated responses from adaptive control systems help in reducing the overall strain on generation assets by pre-emptively managing load variations, thus enhancing the resilience of the power grid against extreme events and supporting the seamless integration of renewable energy sources [92,93].

5.4. Comparative Analysis of Integrated Strategies

Integrated strategies for managing the duck curve have evolved to address the technical, economic and policy and regulatory challenges posed by high renewable energy penetration. Energy storage systems, such as BESSs and pumped hydro, offer rapid response and peak shaving capabilities by absorbing excess generation and releasing power during steep ramp periods, although their deployment is often limited by high capital costs and site-specific constraints [3]. Demand-side management (DSM) techniques, including pre-cooling and dynamic load shifting, provide scalable, cost-effective solutions that optimize load profiles but rely heavily on consumer participation and advanced communication infrastructure [4]. Grid flexibility is further enhanced by interconnections that enable distributed balancing and resource sharing, yet these benefits can be undermined by regulatory barriers and inconsistent market rules. Flexible generation assets like natural gas-fired peaker and hybrid plants supply immediate backup during peak demand; however, they face long-term limitations due to emissions concerns and evolving environmental regulations (Ahmad et al. [94]). Digitization through smart grids, IoT, and AI forecasting improves system responsiveness by enabling monitoring and adaptive control, although significant investment in technology upgrades and cybersecurity measures is required. Comparative evaluation of these integrated approaches reveals that while technical strengths are evident in rapid response and scalability, limitations persist in economic feasibility and regulatory support (

Table 2. Comparative Analysis of technical strategies to smart duck curve management.

Specific Integrated Technical Strategy	Key Strengths and Benefits	Major Challenges and Drawbacks	Scalability and Deployment Potential	References/ Supporting Sources
Energy storage (BESS + pumped hydro)	Fast response to ramping and peak shifting needs (seconds to minutes) Supports frequency regulation and peak shaving Supports renewable integration.	High capital and operational costs. Limited duration for BESSs; site constraints for pumped hydro Environmental and land-use concerns (esp. hydro)	BESSs: Modular, urban-friendly, rapidly scalable Pumped hydro: High capacity but location-dependent Long-term scalability requires policy and cost reductions	[3,10,51,62]
DSM	Cost-effective way to reduce peak loads Enhances grid flexibility without infrastructure overhaul Can leverage consumer behavior and automation	Needs widespread smart meter adoption and consumer education Limited control in critical ramp hours Highly variable effectiveness depending on participation	Scalable through digital platforms and policy mandates Success depends on utility-customer coordination Requires integration into market structures	[4,13,27,28,79,96]
Grid flexibility and interconnections	Enables regional energy balancing and load sharing Reduces curtailment of renewables across regions Enhances reliability during generation shortfalls	High capital costs and long permitting timelines Grid congestion and inter-jurisdictional challenges Technical harmonization required between regions	Scalable with regional cooperation and strong governance Suited for large, diverse power systems Long lead times can delay benefits	[1,36,94]
Flexible generation (e.g., gas peakers, hybrid plants)	Fast ramping and dispatchable for evening peaks Reliable, dispatchable, mature technology Bridging fuel source; complements intermittent renewables	Fossil fuel-based; increases emissions if not coupled with CCUS Risk of stranded assets in decarbonization pathways Lower efficiency compared to baseload units	Scalable in the short term for transitional stability Long-term use limited by climate policies Hybrid plants (solar/storage/gas) offer greater scalability and transitional flexibility	[1,36,40]
Digitization (smart grids, IoT, AI forecasting)	Real-time monitoring and predictive analytics Facilitates automated DSM and DER integration Enhances grid responsiveness to variable conditions	Cybersecurity and data privacy risks High upfront investment in IT and comms infrastructure Requires workforce reskilling and digital literacy	Highly scalable through cloud-based platforms Integration-friendly with legacy and future systems Depends on regulatory support for digital innovation	[17,19,26,95]

). Ultimately, the effectiveness of these strategies depends on the interplay between technological maturity, market dynamics, and regulatory environments, ensuring integrated solutions deliver reliability and cost-effectiveness in mitigating the duck curve phenomenon [95].

From an economic standpoint, market-based mechanisms such as time-of-use (TOU) pricing offer a robust foundation for aligning consumer behavior with grid requirements by providing direct financial incentives for shifting demand. Comparative analyses in

Table 3. Comparative analysis of economic strategies for smart duck curve management.

Specific Integrated Economic Strategy	Key Strengths and Benefits	Major Challenges and Drawbacks	Scalability and Deployment Potential	References/ Supporting Sources
Time-of-use (TOU) pricing	Shifts load from peak to off-peak, reducing ramp pressures Provides clear price signals to consumers Low-cost and easy to implement with smart meters	Limited impact without consumer automation May disproportionately affect vulnerable customers Requires consumer education and awareness campaigns	Highly scalable with smart infrastructure Low barrier to entry for utilities Works best in regions with dynamic pricing culture	[97,98]
Capacity and ancillary services markets	Monetizes grid flexibility and reliability services Encourages investment in fast-responding resources Helps defer infrastructure upgrades	Complex to design and operate fairly Barriers for small-scale and distributed resources Risk of market manipulation without oversight	Scalable in liberalized electricity markets Requires mature regulatory and metering systems Performance depends on market liquidity and access	[5,9,39]
Curtailment strategies	Simple, immediate solution to overgeneration Prevents grid instability during surplus events No new infrastructure needed	Wastes renewable energy; economic and environmental loss Undermines investor confidence in renewables Can become habitual if not paired with better solutions	Not a sustainable long-term strategy Suitable as a last resort or during transition Scalable only with frequent review and compensation mechanisms	[3,9,94]
Managed rebound effects (post-DSM or load shifting)	Smooths out demand post-suppression or load deferment Improves DSM effectiveness with rebound prediction tools Prevents secondary peaks from load resumption	Requires advanced modeling and forecasting tools Complexity in consumer behavior modeling Difficult to integrate into basic DSM strategies	Scalable with AI-based demand forecasting Depends on digitization and automated control systems Needs regulatory frameworks for rebound management	[68,88,94]

indicate that TOU pricing is economically attractive due to its low implementation cost and high scalability in regions equipped with smart metering infrastructure. However, efficiency of TOU is often tempered by variable consumer responsiveness and divergent regulatory policies [97,98]. In contrast, capacity and ancillary services markets create a more dynamic framework by monetizing flexibility, yet they require sophisticated market structures and real-time data, which can limit their broad applicability in less mature markets [5]. Curtailment strategies—despite their straightforward operational simplicity—tend to incur significant economic inefficiencies by wasting renewable energy potential, thereby challenging both economic and environmental objectives [3,9,94]. Alternatively, managed rebound effects emerging from post-DSM or load shifting integrate technical and economic measures to optimize load recovery. Although promising in mitigating abrupt consumption spikes, these approaches necessitate advanced predictive modeling and regulatory oversight to avoid counterintuitive increases in energy use [77,88,94]. Overall, variations in market structures, regulatory mandates, and technological maturity critically modulate the effectiveness of these integrated strategies. The scalability of each approach ultimately depends on harmonized policy frameworks and the availability of cutting-edge control systems that can dynamically balance supply and demand, ensuring a more efficient management of the duck curve.

Meanwhile, policy and regulatory environments play a pivotal role in shaping the effectiveness and scalability of integrated strategies for managing the duck curve phenomenon in power grids. Renewable Portfolio Standards (RPS) and mandates [2,9], for example, offer a structured framework that drives investments in renewable energy and ensures predictable market signals, although their rigid design can limit adaptive responses to rapid technological shifts [35,82,94]. In contrast, net metering and feed-in tariffs (FiT) provide economic incentives for distributed generation, yet these measures may inadvertently lead to grid imbalances and cost-shifting challenges if not periodically reformed [1,5]. Moreover, energy storage incentives and subsidies have demonstrated significant strengths by promoting the deployment of BESSs, which help mitigate ramping issues inherent in the duck curve; however, such fiscal measures can strain public budgets and create uneven competitive landscapes, particularly in regions with variable renewable adoption [3]. Digitally enabled policies—such as smart tariffs and dynamic grid codes—further enhance real-time responsiveness and grid flexibility by leveraging data analytics and communication technologies, though their success is contingent on robust digital infrastructure and a regulatory willingness to embrace innovation [35,94]. Comparative analyses, as summarized in

Table 4. Comparative analysis of policy and regulatory strategies for smart duck curve management.

Specific Integrated Policy Strategy	Key Strengths and Benefits	Major Challenges and Drawbacks	Scalability and Deployment Potential	References/ Supporting Sources
Renewable Portfolio Standards (RPS) and mandates	Drives large-scale renewable adoption Provides long-term market certainty Encourages innovation and cost reductions	May accelerate duck curve without storage/flexibility mandates Risk of compliance gaps and uneven enforcement Can oversaturate grids without supporting infrastructure	Highly scalable and adopted globally Most effective when paired with flexible resource incentives Requires regular updates to targets and technologies	[2,35,36,94]
Net metering and feed-in tariffs (fit)	Promotes distributed generation (especially rooftop PV) Reduces consumer bills and boosts prosumer engagement Simple entry point for renewable energy users	Grid cost recovery challenges for utilities Exacerbates mid-day oversupply (worsens duck curve) May lead to equity issues among ratepayers	Scalable in early-stage markets Needs restructured tariffs over time (net billing, time-variant FiTs) Dependent on regulatory support and utility adaptation	[1,5]
Storage incentives and subsidies	Directly accelerates storage adoption (BESSs, V2G, etc.) Reduces peak demand and stabilizes renewables Attracts private sector investment in flexibility	Requires sustained government funding or tax credits Risk of market distortion or dependency Difficult to target optimally without robust analytics	Highly scalable if designed with phase-out mechanisms Drives early-stage markets toward maturity Effective when combined with market-based compensation	[3,9,82]
Digitally enabled policy (e.g., smart tariffs, grid codes)	Supports interoperability and standards for digital assets Aligns policy with tech evolution (blockchain, AI, DERs) Facilitates fast and granular grid decisions	Regulatory lag in digital innovation adoption High coordination needed among stakeholders Challenges in policy enforcement and verification	Scalable through modular and open standards Requires harmonization across regions and vendors Needs adaptive regulation frameworks (sandboxing, pilots)	[2,18,48,79]

, reveal that jurisdictions with flexible, adaptive regulatory frameworks tend to integrate these approaches more effectively [5]. Ultimately, while RPS and net metering lay the foundational policy groundwork, storage incentives and digital solutions provide the necessary operational agility to address the duck curve’s challenges. Variations in regulatory environments critically influence both the scalability and overall effectiveness of these strategies, underscoring the need for integrated, adaptive policy design [79].

To this end, what integrated electricity grid operation solutions show the most promise for managing the duck, and under what conditions do they achieve optimal performance? Integrated solutions that combine technical flexibility, advanced storage technologies, digital innovation, and managed rebound effects are increasingly recognized as the most promising for future deployment to manage the duck curve. In particular, BESSs have demonstrated considerable potential in flattening duck curves and stabilizing power grids under high renewable penetration, as evidenced by recent optimization models and empirical analyses [36,94]. These systems achieve optimal performance when deployed in regions with robust policy incentives, dynamic pricing mechanisms, and supportive market conditions that encourage both centralized and decentralized energy management [14,94,98]. Moreover, the integration of smart grid technologies, real-time data analytics, and digital twins enhances operational resilience by enabling precise state-of-charge monitoring and adaptive control strategies [44]. Furthermore, managed rebound effects—particularly post-DSM or load shifting strategies—play a critical role in smoothing out demand following load suppression, improving DSM effectiveness through sophisticated rebound prediction tools, and preventing secondary peaks when deferred loads resume [16,99,100]. Coupling BESSs with advanced DSM and flexible generation sources, such as gas turbines with fast ramping capability, hydropower plants with reservoir storage, and hybrid solar + battery storage systems, creates a responsive ecosystem that not only minimizes rapid ramping challenges but also optimizes both economic efficiency and emissions reductions. Digitalization further contributes to flattening the duck curve by streamlining interdependencies between energy sectors, thereby facilitating coordinated energy dispatch and improved asset performance [88]. These integrated solutions deliver the best performance when supported by adaptive regulatory frameworks, transparent market signals, and comprehensive AI/ML-driven approaches that align technological capabilities with long-term sustainability objectives. This convergence not only effectively mitigates operational risks but also significantly strengthens energy security and promotes electric power systems resiliency.

6. Discussions and Policy Implications

6.1. Emerging Trends in Utility-Focused Smart Grid Technologies

The rapid evolution of smart grid systems is fundamentally altering how utilities address challenges such as the duck curve, integrate DERs, and enhance overall grid flexibility and resilience. Emerging technologies—including AI, edge computing, and real-time analytics platforms—are now central to this transformation, enabling utilities to optimize energy flows and balance supply and demand more effectively. Advanced metering infrastructure (AMI) and ADMS have transitioned the traditional, centralized grid into an interactive, data-driven network, thereby permitting a more dynamic response to load fluctuations [9,101]. In this context, digitalization plays a pivotal role by integrating a multitude of renewable energy sources into the grid, while simultaneously facilitating the efficient management of DERs. Consequently, utilities are not only mitigating the pronounced challenges of the duck curve but are also establishing a smart grid resilient framework that can adapt to both anticipated and unforeseen energy demands, as evidenced by the strategic implementations observed in pioneering utility projects across North America and Europe [102,103].

Moreover, the integration of AI and ML into grid management systems has introduced significant innovations that enhance operational efficiency and reliability of electricity systems. AI-driven predictive maintenance systems analyze vast datasets from next-generation sensors and real-time monitoring devices to anticipate equipment failures and schedule proactive repairs, thereby reducing downtime and maintenance costs [104]. Simultaneously, ML algorithms are employed to refine electricity demand forecasting models, enabling utilities to predict peak loads with greater precision and adjust resource allocation accordingly [105]. In addition, blockchain technology is emerging as a secure solution for managing energy transactions, ensuring transparency and trust across decentralized networks of prosumers and utility operators. This amalgamation of AI, ML, and blockchain, coupled with the deployment of edge computing—which processes data near the source to minimize latency—has fostered a new paradigm in electricity grid monitoring and control. These innovations not only streamline electricity grid operations but also provide utilities with the agility needed to respond rapidly to real-time fluctuations in energy supply and demand, ultimately smoothing load profiles and reducing the stress associated with peak demand periods [106].

Finally, several utilities are already demonstrating the transformative potential of these technologies through practical implementations that set industry benchmarks. For instance, leading utilities in California, such as Southern California Edison (SCE) and Pacific Gas and Electric Company (PG&E), have harnessed the capabilities of advanced digital platforms to coordinate the operation of DERs and renewable energy installations, thereby effectively mitigating the steep demand ramps of the duck curve. Similarly, Duke Energy’s integration of AI for demand forecasting and predictive maintenance has not only optimized energy distribution but also enhanced grid stability and reduced operational costs [99]. These case studies underscore how the convergence of emerging digital technologies enables utilities to achieve a more resilient, flexible, and efficient grid. Furthermore, the adoption of sophisticated EMS that incorporate next-generation sensors and real-time analytics is proving critical for the timely identification of faults and rapid response to disturbances. As regulatory frameworks evolve to support these technological advances, the ongoing digital transformation in the utilities sector is poised to deliver substantial improvements in energy efficiency and sustainability. Thus, by embracing innovations such as AI, edge computing, and blockchain, utilities are not only addressing current grid operational challenges but are also laying the groundwork for a future where power systems are more adaptive, secure, and sustainable [102,105].

6.2. Research Gaps in Flattening the Duck Curve and Emerging Opportunities

Despite considerable advancements in digital technologies and energy storage integration, significant research gaps remain that impede utilities from effectively managing the duck curve, i.e., the steep ramping requirements during peak demand hours. One critical gap is the insufficient understanding of the long-term performance and scalability of BESSs and integrated digital solutions. While pilot studies have demonstrated short-term benefits in smoothing demand fluctuations, there is a pressing need for comprehensive longitudinal research that examines system degradation, maintenance cycles, and overall reliability under diverse operational conditions [105]. This research should extend beyond controlled experiments to capture real-world complexities that affect BESS performance over time. Another vital area is the interoperability between legacy infrastructure and modern digital platforms. Many utilities continue to operate with outdated control systems that lack the flexibility required to support advanced analytics, real-time data exchange, and ML applications. The absence of standardized communication protocols severely hampers the integration of renewable energy forecasting tools with grid operations, leading to inefficiencies and limited responsiveness in load management [9]. Consequently, there is a clear imperative to develop and implement standardized middleware solutions that facilitate seamless interaction between old and new systems.

Moreover, the optimization of storage dispatch strategies in real-world scenarios remains an open research question. Existing models (including stochastic programming, robust optimization, chance-constrained programming, model predictive control (MPC), and fuzzy logic-based models) often fail to adequately incorporate the unpredictable variables associated with renewable energy outputs and dynamic demand profiles [94,107]. Consequently, adaptive control algorithms that leverage big data and ML are urgently needed to predict and respond to fluctuations more effectively. Equally important is the integration of customer behavior into grid management [108]. For example, interdisciplinary research combining engineering, economics, and behavioral science is essential to understand how rebound effects influence energy consumption patterns and grid stability [82]. Finally, there is a significant opportunity for innovation through public–private partnerships and collaborative pilot programs that bring together utilities, major technology vendors and supplies (especially Home Depot, Lowes, Samsung, General Electric, LG, Whirlpool, and others), and academic institutions. Such collaborations can expedite the development of utility-specific control algorithms, adaptive demand response programs, and innovative tariff structures designed to incentivize load shifting, thereby improving grid efficiency, reliability, and resiliency.

6.3. Policy Frameworks and Regulatory Considerations

As mentioned in Section 3.4, current policies and regulatory frameworks present a complex landscape that simultaneously supports and hinders the adoption of smart grid technologies and BESSs in the utilities sector. In the United States, progressive initiatives such as New York’s Reforming the Energy Vision (REV), Illinois’s NextGrid program, and California’s Energy Savings and Performance Incentive (ESPI) have been at the forefront of grid modernization. These initiatives foster the integration of DERs, AMI, and real-time data analytics, thereby promoting digitalization and customer-centric service models. Such frameworks have shifted the focus from traditional, capital-intensive investments toward more dynamic, service-based models that recognize the dual role of BESSs in stabilizing grid operations and providing essential ancillary services. However, significant challenges persist due to fragmented regulatory approaches that lead to inconsistent interconnection standards and outdated dynamic pricing models. These shortcomings result in misaligned incentives, particularly in regions still reliant on legacy systems. Moreover, despite proactive measures in leading jurisdictions, regulatory disparities across states and international markets generate uncertainty that can deter investment. The absence of harmonized standards and coordinated policy efforts undermines the development of a resilient grid infrastructure. Utilities and policymakers now face a dual challenge: refining existing frameworks while accommodating rapid technological progress in the smart grid and energy storage sectors. This scenario necessitates targeted regulatory reforms that balance operational flexibility with the imperative for secure, reliable energy delivery.

Addressing these challenges requires regulatory reforms that are adaptive, forward-looking, and foster collaborative engagement among utilities, federal and state agencies, and industry consortia [109]. A critical aspect of such reform is the urgent need to refine dynamic pricing models that leverage real-time analytics and granular tariff structures to provide clear economic signals to consumers. These signals should not only promote effective DSM but also help mitigate operational issues like the duck curve [19]. Harmonizing grid interconnection standards across different Regional Transmission Organizations/Independent System Operators (RTOs/ISOs) is equally essential, as it minimizes technical barriers and facilitates the seamless integration of legacy systems with emerging digital platforms. International experiences offer valuable lessons in this regard; the United Kingdom’s RIIO (Revenue = Incentives + Innovation + Outputs) framework, Germany’s Energiewende, and Australia’s Electricity Network Transformation Roadmap (ENTR) have each successfully aligned regulatory incentives with performance-based outcomes to drive comprehensive grid modernization. Moreover, evolving incentive structures from one-off rewards to sustained, performance-based mechanisms can stimulate continuous investments by consistently rewarding improvements in grid reliability, emissions reduction, and energy efficiency. In addition, robust cybersecurity and data privacy measures must be embedded within these regulatory frameworks to safeguard digitalized systems from emerging cyber threats. Equally important is the need for close collaboration among all stakeholders, which allows for anticipatory policy adjustments as new technologies and market challenges emerge. Through these strategic, coordinated policy efforts, utilities will be better equipped to navigate the growing complexities of modern energy systems, ultimately building a more resilient, efficient, and sustainable grid for the future [49].

6.4. Stakeholder Engagement and Public Acceptance in Energy Transition

Stakeholder perceptions play a central role in shaping the public acceptance of digital platforms, BESSs, and strategies for managing rebound effects. Although digital platforms are increasingly deployed for real-time energy monitoring and demand-side optimization, acceptance remains uneven due to data transparency concerns, cybersecurity risks, and skepticism about algorithmic decision-making. Nonetheless, empirical studies suggest that users perceive net benefits—such as enhanced control, convenience, and cost savings—as outweighing these drawbacks [13,84]. In the case of BESSs, while economic viability is improving, public perceptions are still influenced by uncertainty around battery lifespan, environmental risks, and regulatory clarity [44,61]. Advances like digital twins offer a path to mitigate these concerns through predictive diagnostics and asset-specific optimization, but the lack of standardized data sharing frameworks between stakeholders impedes trust and industrial uptake. Critically, many stakeholders view policy targets for clean energy adoption as misaligned with local realities, undermining support for infrastructure projects and further delaying deployment [2]. In rebound effect mitigation, perception gaps between technical experts and end users persist. Efficiency-led innovations can paradoxically stimulate higher consumption unless aligned with behavioral insights and lifecycle emissions metrics [13,79]. This dissonance underscores the need for systemic approaches that incorporate social acceptance, not just technical feasibility. As communities increasingly expect localized benefits and transparent governance, stakeholder engagement becomes both a social license and a determinant of adoption success. Addressing stakeholder perception across these domains is therefore critical to ensuring an equitable and resilient energy transition.

Table 5. Stakeholder roles and public acceptance across digital platforms, BESSs, and managed rebound effects of the energy transition.

Stakeholder Category	List of Stakeholders	Determinants of Public Acceptance in Energy Transition
Digital platforms	Smart grid technology providers Data analytics and AI firms Digital twin developers Utility IT/OT teams EMS vendors Cybersecurity firms	Trust in data privacy and system transparency Perceived value in improved reliability and efficiency Concerns over surveillance, data misuse and digital exclusion Acceptance improves with participatory design and accessibility
BESSs	BESS manufacturers Transmission and distribution utilities Project developers Local governments Environmental NGOs Community organizations	Acceptance shaped by safety, land-use, and environmental impact Benefits include grid reliability and renewable energy integration Concerns around fire risk, NIMBYism and limited community engagement Perception can be improved by transparent sitting and community engagement
Managed rebound effects	Policymakers Behavioral economists Energy regulators Academic institutions Energy efficiency advocates Consumer groups	Public acceptance influenced by understanding energy savings vs. behavioral impacts Public support increases with clear communication, behavioral insights, equitable pricing, and education campaigns Resistance can stem from perceived behavioral restrictions or unclear cost–benefit trade-offs.

summarizes stakeholder engagement and public acceptance in energy transition, organized by key thematic areas—digital platforms, BESSs, and managed rebound effects.

7. Conclusions and Recommendations

The rapid evolution of smart grid technologies offers a transformative pathway for utilities and system operators to address the persistent challenges posed by the duck curve. Innovations such as AI, edge computing, BESSs, and real-time analytics are central to this transition. When combined with forward-looking operational and regulatory strategies, these tools not only improve grid flexibility and predictive precision but also support more efficient energy management. The key contributions of this work can be grouped into two main categories: (a) technological advancements, and (b) policy and regulatory implications, as outlined below.

(1)

Technological contributions:

• Deployment of AI-driven predictive models to improve peak load forecasting accuracy.
• Use of edge computing to reduce latency by processing data locally from sensors.
• Integration of AMI, DMS, and ADMS to enable near-real-time data capture for responsive grid operations.
• Adoption of advanced digital platforms that merge real-time analytics with AI to support adaptive control strategies.
• Strategic enhancement of BESS deployment to perform peak shaving and energy arbitrage.
• Implementation of dynamic DRMS to incentivize load shifting during peak demand periods.
• Overall facilitation of a more resilient and adaptive grid, capable of addressing operational challenges such as high renewable integration and the duck curve.

(2)

Policy contributions

• Recommendation for a strategic overhaul of current outdated utility practices, emphasizing investment in digital platforms and operational innovation.
• Encourage utilities to adopt a dual approach that combines technological deployment with dynamic demand-side measures.
• Advocate for continuous innovation and adaptive policy measures to match the pace of technological advancement in grid management.
• Support pilot digital transformation programs and collaborative research initiatives to gather insights and refine both technical standards and regulatory frameworks [18,22,107].
• Promote regulatory engagement and knowledge sharing to accelerate adoption of next-generation grid solutions and enhance policy alignment with emerging technologies.

Looking ahead, actionable steps for utilities must be outlined in both the short and long term to ensure grid stability and operational excellence. In the immediate term, utilities should focus on integrating digital monitoring systems with existing infrastructure, ensuring that real–time data flows from AMI and ADMS are fully exploited for enhanced load forecasting and DRMS. Short–term measures include upgrading legacy systems to be interoperable with modern digital platforms, thereby enabling smoother data integration and quicker response times to the fluctuating demand. In parallel, targeted investments in BESSs can provide a buffer against sudden load changes, with advanced control algorithms optimizing charging and discharging cycles to extend battery life and reduce operational costs [103]. In the longer term, strategic initiatives should aim to embed adaptive policy measures that support continuous technological evolution. This involves developing standardized communication protocols for grid components, establishing comprehensive data security frameworks, and promoting flexible regulatory models that accommodate rapid innovation. The industry must also invest in collaborative research that bridges the gap between theoretical advancements and practical applications, ensuring that innovative solutions are rigorously tested and validated in real-world conditions. Such initiatives will not only enhance grid reliability but will also mitigate operational risks associated with renewable integration and volatile electricity demand patterns [109]. By aligning technical investments with proactive policy reforms, utilities can balance stringent environmental imperatives with economic viability more prudently.

Finally, by harnessing AI, edge computing, and real-time analytics in tandem with robust BESS solutions, utilities can transform traditional grid operations into dynamic, responsive, and efficient systems. Utility executives and grid planners are encouraged to pursue a holistic planning strategy that combines immediate technological upgrades with long-term policy and regulatory reforms. Such a dual approach will not only stabilize the grid during peak electricity demand periods but also foster a resilient, climate-proof energy system capable of adapting to rapid market and technological changes, as well as extreme weather events [18]. Through strategic investments in digital platforms, enhanced BESS deployment, and advanced DRMS mechanisms, the industry can achieve significant improvements in operational efficiency and grid stability. Ultimately, a collaborative effort involving utilities, regulators, and technology innovators is essential to drive continuous improvement and secure a sustainable energy future in an increasingly complex and dynamic power landscape.