IoT Integration for Renewable Energy Storage: A Systematic Literature Approach ^†

Abstract

The global transition from fossil fuels to renewable energy has become a critical priority in addressing environmental and sustainability challenges. However, the intermittent nature of renewable energy sources, such as solar and wind, poses significant challenges in ensuring a stable and reliable energy supply. This article explores the role of Internet of Things (IoT) technology in optimizing renewable energy storage systems to address these challenges. Using a systematic literature review (SLR) approach, the study identifies recent innovations in IoT technologies, including the integration of artificial intelligence (AI), big data analytics, and smart energy management systems, which enhance efficiency, sustainability, and reduce operational costs of energy storage systems. IoT enables real-time data monitoring, energy demand prediction, and decentralized energy management, reducing energy losses, extending the lifespan of storage devices, and supporting grid stability. The findings highlight that IoT integration offers innovative solutions to mitigate the intermittency of renewable energy, supporting the global energy transition toward more reliable, cost-effective, and environmentally friendly energy systems. This study provides valuable insights into the potential of IoT in advancing renewable energy storage technologies and contributes to the development of sustainable energy solutions for the future.

1. Introduction

The global transition from fossil fuels to renewable energy has become a top priority in addressing environmental and sustainability challenges [1]. Conventional energy sources such as coal, oil, and natural gas, while serving as the backbone of global energy needs for decades [2], have significant negative impacts on the environment. Greenhouse gas emissions from fossil fuel combustion are the primary drivers of global warming and climate change [3,4,5]. Additionally, the finite availability of fossil fuel reserves raises concerns about future energy crises [6]. Consequently, transitioning to renewable energy has become a strategic step to reduce dependence on fossil fuels while significantly lowering carbon emissions [7,8].

Renewable energy sources, such as solar, wind, hydro, and biomass, offer cleaner, environmentally friendly, and sustainable solutions [9]. These resources are not only naturally available but also produce minimal or zero carbon emissions [10]. As a result, renewable energy contributes to reducing environmental harm, supporting ecosystem sustainability, and enhancing a nation’s energy resilience [11,12]. However, despite their immense potential, the intermittent nature of renewable energy—relying on natural conditions such as sunlight intensity or wind speed—poses a major challenge in ensuring a stable and reliable energy supply [13].

This intermittency creates an urgent need for reliable and efficient energy storage systems [14]. Energy storage systems enable surplus energy generated during peak production periods, such as midday for solar energy or during strong winds for wind energy, to be stored and utilized when energy production declines. However, current storage technologies face several limitations, including limited capacity, high costs, low efficiency, and environmental impacts associated with their lifecycle [15]. For instance, lithium-ion batteries, one of the most commonly used storage technologies, have limited capacity, high costs, and pose environmental risks due to electronic waste [16,17]. Therefore, innovative approaches are needed to optimize renewable energy storage systems.

The Internet of Things (IoT) offers an innovative solution to address the challenges of renewable energy storage. IoT technology enables real-time monitoring, control, and optimization of storage systems [18]. By utilizing sensors connected through the internet, IoT can continuously collect data on storage capacity, energy charging and discharging levels, and the condition of storage components [19,20]. This data is then analyzed to quickly identify issues or efficiency losses, ensuring the storage system operates optimally. Furthermore, IoT supports predictive management by leveraging historical and real-time data to forecast future energy needs [21]. For example, IoT can predict when energy demand will increase or when renewable energy supply will decrease, such as during cloudy weather or nighttime, allowing storage systems to be better prepared for energy fluctuations in a more stable and planned manner [18].

Another advantage of IoT is its ability to reduce operational and maintenance costs. Through automated monitoring, IoT enables early detection of potential issues and more scheduled maintenance. Maintenance is performed as needed, reducing the inefficiency of routine maintenance. Additionally, IoT helps extend the lifespan of storage systems by monitoring the condition of batteries or other components in real-time, avoiding situations that could accelerate degradation, such as overcharging or extreme temperature fluctuations [19].

Advancements in IoT technology, including the integration of artificial intelligence (AI), big data analytics, and smart energy management systems, open significant opportunities for optimizing renewable energy storage. AI algorithms connected to IoT networks can predict energy demand and supply patterns based on historical data and current conditions [22]. Moreover, big data analytics enables the collection and analysis of large volumes of data, including energy consumption patterns, environmental conditions, and storage system performance [23]. Smart energy management systems, which utilize data from AI and big data, allow for the automated regulation of energy charging and discharging processes based on real-time conditions detected by sensors [24]. These systems not only improve operational efficiency but also support long-term sustainability.

This study aims to explore the role of IoT in addressing the challenges of renewable energy storage, which remains one of the main barriers to the global energy transition. By systematically reviewing the existing literature, this research identifies the latest innovations in IoT technology and analyzes their impact on the efficiency, stability, and sustainability of energy storage systems. The novelty of this study lies in its focus on integrating IoT with renewable energy storage technologies, a topic that has not been extensively explored in the previous literature. Additionally, this research provides new insights into how IoT can be utilized to optimize energy storage on a large scale, supporting the global energy transition toward greener sources.

The urgency of this research is heightened by the pressing need to reduce carbon emissions and combat climate change. By leveraging IoT technology, renewable energy storage systems can become more efficient, reliable, and sustainable, ultimately supporting global sustainability goals. This study is expected to make a significant contribution to developing innovative solutions for future energy challenges while accelerating the adoption of renewable energy across various sectors.

2. Materials and Methods

This study adopts a Systematic Literature Review (SLR) method to comprehensively explore the integration of Internet of Things (IoT) in optimizing renewable energy storage systems. The rationale behind this approach is grounded in the need to provide a structured and evidence-based analysis of existing research, enabling the identification, evaluation, and synthesis of relevant literature. The methodology was designed to ensure both transparency and reliability and aligns with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

2.1. Research Questions

To focus the review and analysis, the following three research questions were formulated:

1.

How can IoT technology improve the efficiency of renewable energy storage in addressing the intermittent nature of energy sources such as solar and wind?

The intermittency of renewable energy—such as solar energy’s dependency on sunlight and wind energy’s reliance on wind speed—presents significant challenges to achieving stable and reliable energy supply systems [25,26]. While the potential of IoT to mitigate these challenges through real-time monitoring [27], predictive management [28], and automated system adjustments [27] has been discussed in the prior literature, this study seeks to deepen the understanding of how IoT practically enhances storage efficiency within this context.

2.

What are the latest innovations in IoT technology that can be integrated with renewable energy storage systems to enhance sustainability and reduce operational costs?

With the rise in emerging technologies such as artificial intelligence (AI), big data analytics, and smart energy management systems, IoT has shown increasing potential in improving both sustainability and cost efficiency [24]. However, due to high operational costs still posing barriers to renewable energy adoption [29], identifying innovations that directly contribute to cost reduction is essential [30,31]. This question addresses the necessity of evaluating current advancements to determine which technologies are most effective and scalable.

3.

How does IoT integration impact energy loss reduction and the extension of the lifespan of renewable energy storage systems?

Energy losses during storage and degradation of storage components are major issues [32], leading to reduced system efficiency [33] and shorter component lifespans [34]. IoT offers the potential to address these problems through real-time monitoring [35], early detection [36], and operational optimization [37], which collectively can contribute to both reduced energy waste and extended battery life [38]. This research question investigates these impacts comprehensively.

2.2. Literature Search Strategy

The literature search was carried out using the Scopus database, focusing on peer-reviewed journal articles, high-quality conference proceedings, and industry reports. To ensure currency and relevance, the scope was limited to publications from 2020 to 2024.

The search utilized the following keywords:

• “Internet of Things” (IoT),
• “renewable energy storage”,
• “energy optimization”.

The inclusion criteria for selected articles were:

• The study explicitly discusses IoT integration in renewable energy storage;
• The research evaluates IoT’s impact on storage efficiency, sustainability, or operational cost;
• The article contains empirical results or conceptual frameworks with practical relevance;
• The article is peer-reviewed and published in English.

Exclusion criteria included:

• Articles without empirical or analytical content;
• Studies not focused on energy storage;
• Reports not accessible or not published in final form;
• Duplicated or outdated publications.

2.3. Screening and Selection Process

The selection process followed the PRISMA flow as shown in Figure 1. A total of 33 records were initially identified. After applying the inclusion/exclusion criteria:

• 28 articles were excluded during screening;
• 5 articles were assessed for full eligibility and were ultimately included in the review.

Reasons for exclusion included mismatch in publication year (1 articles), irrelevant research topics (6 articles), unsuitable document types (5 articles), non-final publication status (articles), and lack of access (articles).

2.4. Data Extraction and Analysis

From the final pool of selected articles, relevant data were extracted regarding:

• Research objectives and methodologies;
• Variables and metrics studied;
• Key findings and contributions to IoT-based energy storage optimization.

A qualitative thematic analysis was conducted to identify recurring themes, technological trends, and application patterns. Key areas of focus included:

• IoT’s role in real-time energy monitoring and predictive control;
• Integration of AI and machine learning algorithms to support decision-making;
• Use of IoT for cost minimization and efficiency improvement;
• Impact on battery lifecycle and energy loss reduction;
• Implementation of multi-agent systems, optimization algorithms (e.g., Binary Particle Swarm Optimization (BPSO), Improved Satin Bowerbird Optimizer (ISBO)), and edge-cloud architectures for intelligent energy flow control.

The analysis aimed to consolidate insights on the effectiveness, challenges, and future directions of IoT in the context of renewable energy storage systems.

Figure 1. PRISMA diagram. * Records identified from the Scopus database. ** Records excluded during screening for not meeting the inclusion criteria (see eligibility assessment).

Figure 1. PRISMA diagram. * Records identified from the Scopus database. ** Records excluded during screening for not meeting the inclusion criteria (see eligibility assessment).

3. Results

3.1. IoT Technology and Renewable Energy Storage Efficiency

3.1.1. Key Mechanisms to Improve Efficiency

The integration of Internet of Things (IoT) technology in renewable energy systems enhances the efficiency of energy storage, particularly in addressing the intermittent nature of sources such as solar and wind. Key findings include the following:

• Real-Time Data Monitoring: IoT sensors enable continuous monitoring of energy production and consumption. For example, real-time monitoring from renewable sources and storage systems was demonstrated in Ali M. Eltamaly et al. (2021) [39].
• Energy Prediction and Analysis: Machine learning (e.g., Artificial Neural Network (ANN) [40]) helps forecast demand, as shown by Preetha R. et al. (2023) [41], allowing energy adjustments aligned with consumption needs.
• Decentralized Energy Management: IoT with edge computing reduces latency and enhances decision-making, as highlighted by Amal Nammouchi et al. (2021) [42].
• Energy Storage Optimization: Aggregated data helps determine whether to store or sell energy. Amal Nammouchi et al. (2021) [42] demonstrated cloud-based optimization.
• Peak Load Reduction: Algorithms like BPSO reduce peak demand. Bilal Naji Alhasnawi et al. (2022) [43] showed improved renewable energy utilization.
• Communication Standards: Integration of IEC 61850 supports device interoperability, as per Ali M. Eltamaly et al. (2021) [39].
• Energy Waste Reduction: Accurate demand predictions improve system efficiency, reducing energy waste, as noted by Preetha R. et al. (2023) [41].

3.1.2. IoT Process Flow

The process flow of IoT in energy storage systems follows these steps (see Figure 2):

• Input Data: Collection from sources, batteries, weather, and usage;
• Data Processing: Analysis via ML and optimization algorithms;
• Decision-Making: Whether to store, use, or distribute energy;
• Output: Action implementation to improve efficiency;
• Feedback Loop: Continuous updates refine the system.

Figure 2. IoT Process flow diagram for energy storage.

Figure 2. IoT Process flow diagram for energy storage.

3.2. Latest Innovations in IoT for Sustainability and Cost Reduction

3.2.1. Key Innovations

Recent innovations in IoT enhance sustainability and cut operational costs:

• Edge-Cloud Architecture and AI: Real-time data and AI prediction optimize energy usage, demonstrated by Amal Nammouchi et al. (2021) [42].
• Hybrid IoT Architecture: Integration with HRES supports real-time monitoring and reduces operational costs [39].
• Multi-Agent Systems and DSM: MAS facilitates efficient communication and DSM shifts load to reduce costs [43].
• Machine Learning for Prediction: ANN-based forecasting directs surplus energy for optimized use [41].
• FOPID Controllers and ISBO Algorithm: High-precision control strategies minimize fluctuations and costs [12].

3.2.2. IoT Integration Process

The structured IoT integration process (see Figure 3) includes the following:

• Input Data: Sensor data from sources, storage, and loads;
• Data Processing: Analysis using AI and ML;
• Decision-Making: Optimal use, storage, or sale of energy;
• Output: Cost-effective and sustainable management.

Figure 3. Process diagram of IoT integration in energy system.

Figure 3. Process diagram of IoT integration in energy system.

3.3. Impact on Energy Loss Reduction and Storage Lifespan

3.3.1. Key Impacts

IoT significantly contributes to energy loss minimization and storage system longevity:

• Real-Time Monitoring: Reduces inefficiencies by monitoring production, consumption, and storage [42].
• Battery Cycle Management: Optimized charging/discharging cycles extend battery life [39].
• Energy and Storage Optimization: Algorithms like BPSO optimize energy use, reducing stress on batteries [43].
• ML for Efficiency: Accurate energy demand forecasting enhances charge/discharge management [41].
• Precision Algorithms: FOPID and ISBO dynamically select sources, cut losses, and preserve battery health [12].

3.3.2. IoT Lifecycle Optimization Process

Steps in lifecycle optimization (see Figure 4) include:

• Input Data: Sensor readings on system status;
• Data Analysis: AI processing of energy and storage trends;
• System Decision-Making: Optimal energy flow actions;
• Output: Reduced loss, prolonged storage lifespan.

Figure 4. Process diagram of IoT Lifecycle Optimization Process.

Figure 4. Process diagram of IoT Lifecycle Optimization Process.

4. Discussion

The integration of IoT technology with renewable energy storage systems marks a transformative shift in energy management. IoT enables real-time monitoring, decentralized control, and predictive analytics that directly address the intermittency and unpredictability of solar and wind energy. By implementing real-time data acquisition, demand forecasting, and adaptive energy flow decisions, energy systems become more resilient and efficient.

The reviewed literature illustrates that IoT not only enhances energy storage and distribution but also plays a vital role in reducing energy waste and operational costs. From the implementation of edge computing and cloud platforms to sophisticated algorithms like BPSO and FOPID, the advancements facilitate dynamic energy balancing, reduce grid dependency, and support local autonomy in microgrids.

Furthermore, the precision in battery cycle management contributes to a longer lifespan for energy storage units, thereby reducing replacement frequency and lifecycle costs. This extends the return on investment and enhances sustainability in the long term.

From a broader perspective, the integration of IoT can serve as a foundational element in smart city initiatives, where energy efficiency, sustainability, and cost-effectiveness are essential. As energy demands grow and the shift toward renewable sources accelerates, the role of IoT becomes increasingly indispensable. Future research should focus on scalable, secure, and interoperable IoT frameworks that can adapt to various renewable infrastructures globally.

5. Conclusions

The integration of Internet of Things (IoT) technology has a significant impact on the management of renewable energy systems, particularly in improving efficiency, sustainability, and reducing operational costs. With its ability to perform real-time data monitoring, energy demand prediction using machine learning algorithms, and decentralized management, IoT provides effective solutions to address the intermittent nature of energy sources such as solar and wind. Furthermore, recent innovations such as AI-based edge-cloud architecture, multi-agent systems (MAS), and optimization algorithms like FOPID and ISBO enable more precise energy management, enhance grid stability, and reduce energy waste. IoT also supports energy loss reduction through optimized storage cycles that prevent overcharging and over-discharging, ultimately extending the lifespan of energy storage devices such as batteries. By incorporating processes such as data collection, predictive analysis, and optimal system decision-making, IoT contributes to the creation of more efficient and sustainable renewable energy systems. This conclusion underscores the critical role of IoT in supporting the transition toward more reliable, cost-effective, and environmentally friendly energy systems in the future.