Smart Grid Services Research Articles

AI Services-Oriented Dynamic Computing Resource Scheduling Algorithm Based on Distributed Data Parallelism in Edge Computing Network of Smart Grid

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization.

Anomaly‐detection‐based learning for real‐time data processing in non‐intrusive load monitoring

AbstractA power system can be regarded as a cyber‐physical system with physical power networks and a cyber system based on increasing engagement with information communication technologies for smart grid functionalities for more efficient operations and control. Non‐intrusive load monitoring (NILM), an emerging smart‐grid technology, can be used to better understand the electricity usage profile and composition of smart meters using advanced data analysis algorithms. Although NILM enables various smart grid services, wider applications of NILM require addressing the challenges regarding cyber security and data privacy risks. Anomaly detection in appliance data is one of the most effective measures against potential cyber intrusions from a data perspective. This study proposes a framework of anomaly detection‐based learning algorithms to identify the anomalous periods of electricity loading data, which may be a subject for potential cyber‐attacks. Comparison studies with the hidden Markov model are performed to validate the proposed approaches. The simulation results show that these anomaly detection‐based learning algorithms work well and can precisely determine anomalous loading periods. Moreover, these trained models perform well on the testing dataset without prior knowledge of the data, providing the possibility of the real‐time assessment of power‐ loading states. The proposed framework can also be used to develop protective measures to ensure secure system operation and user data privacy.

Whale Optimization for Cloud-Edge-Offloading Decision-Making for Smart Grid Services.

As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services' computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time.

Service scheduling strategy for microservice and heterogeneous multi-cores-based edge computing apparatus in smart girds with high renewable energy penetration

The microservice-based smart grid service (SGS) organization and the heterogeneous multi-cores-based computing resource supply are the development direction of edge computing in smart grid with high penetration of renewable energy sources and high market-oriented. However, their application also challenges the service schedule for edge computing apparatus (ECA), the physical carrier of edge computing. In the traditional scheduling strategy of SGS, an SGS usually corresponds to an independent application or component, and the heterogeneous multi-core computing environment is also not considered, making it difficult to cope with the above challenges. In this paper, we propose an SGS scheduling strategy for the ECA. Specifically, we first present an SGS scheduling framework of ECA and give the essential element of meeting SGS scheduling. Then, considering the deadline and importance attributes of the SGS, a microservice scheduling prioritizing module is proposed. On this basis, the inset-based method is used to allocate the microservice task to the heterogeneous multi-cores to utilize computing resources and reduce the service response time efficiently. Furthermore, we design the scheduling unit dividing module to balance the delay requirement between the service with early arrival time and the service with high importance in high concurrency scenarios. An emergency mechanism (EM) is also presented for the timely completion of urgent SGSs. Finally, the effectiveness of the proposed service scheduling strategy is verified in a typical SGS scenario in the smart distribution transformer area.

A cognitive spectrum allocation scheme for data transmission in smart distribution grids

As the communication needs in the smart distribution grid continue to rise, using existing resources to meet this growing demand poses a significant challenge. This paper researches on spectrum allocation strategies utilizing cognitive radio (CR) technology. We consider a model containing strong time-sensitive and regular communication service requirements such as distribution terminal communication services, which can be seen as a user with primary data (PD) and weak time-sensitive services such as power quality monitoring, which can be seen as a user with secondary data (SD). To fit the diversity of services in smart distribution grids (SDGs), we formulate an optimization problem with two indicators, including the sum of SD transmission rates and the maximum latency of them. Then, we analyze the two convex sub-problems and utilize convex optimization methods to obtain the optimal power and frequency bandwidth allocation for the users with SD. The simulation results indicate that, when the available transmission power of SD is low, Maximization of Transmission Sum Rate (MTSR) achieves lower maximum transmit time. Conversely, when the available transmission power is high, the performance of Minimization of the Maximum Latency (MML) is better, compared with MTSR.

Boosting 5G on Smart Grid Communication: A Smart RAN Slicing Approach

Fifth-generation (5G) and beyond systems are expected to accelerate the ongoing transformation of power systems towards the smart grid. However, the inherent heterogeneity in smart grid services and requirements pose significant challenges towards the definition of a unified network architecture. In this context, radio access network (RAN) slicing emerges as a key 5G enabler to ensure interoperable connectivity and service management in the smart grid. This article introduces a novel RAN slicing framework which leverages the potential of artificial intelligence (AI) to support IEC 61850 smart grid services. With the aid of deep reinforcement learning, efficient radio resource management for RAN slices is attained, while conforming to the stringent performance requirements of a smart grid self-healing use case. Our research outcomes advocate the adoption of emerging AI-native approaches for RAN slicing in beyond-5G systems, and lay the foundations for differentiated service provisioning in the smart grid.

EPPSQ: Achieving efficient and privacy-preserving statistics queries over encrypted data in smart grids

Outsourcing encrypted data and query services to clouds have been widely adopted by data owners, such as in the energy, healthcare, and transportation sectors, usually for economic considerations. However, this is usually prone to data privacy breaches. In smart grids, although many privacy-preserving data query solutions have been presented in the literature, they either suffer from low query efficiencies or limited capabilities of statistics queries (say the aggregation, variance, or extrema only). To mitigate this gap, in this paper we propose an efficient and privacy-preserving statistics query scheme over encrypted data in smart grids, coined EPPSQ, which can achieve diverse statistics queries in an efficient and privacy-preserving way. Specifically, we first develop a public key encryption based secure data collection protocol for data owners. Then, we design a two-server model based privacy-preserving statistics query protocol for data requesters. In addition, four algorithms respectively for securely computing the statistics (i.e., max/min, variance, mean, and count) over the encrypted data in smart grids are crafted. Security analysis shows that the proposed EPPSQ scheme can effectively guarantee data privacy while outsourcing the encrypted data and query services in smart grids. Further, extensive experiments demonstrate that the proposed EPPSQ scheme outperforms existing CKKS-based or BFV-based studies in terms of computational efficiency.

A Super-Efficient GSM Triplexer for 5G-Enabled IoT in Sustainable Smart Grid Edge Computing and the Metaverse.

Global concerns regarding environmental preservation and energy sustainability have emerged due to the various impacts of constantly increasing energy demands and climate changes. With advancements in smart grid, edge computing, and Metaverse-based technologies, it has become apparent that conventional private power networks are insufficient to meet the demanding requirements of industrial applications. The unique capabilities of 5G, such as numerous connections, high reliability, low latency, and large bandwidth, make it an excellent choice for smart grid services. The 5G network industry will heavily rely on the Internet of Things (IoT) to progress, which will act as a catalyst for the development of the future smart grid. This comprehensive platform will not only include communication infrastructure for smart grid edge computing, but also Metaverse platforms. Therefore, optimizing the IoT is crucial to achieve a sustainable edge computing network. This paper presents the design, fabrication, and evaluation of a super-efficient GSM triplexer for 5G-enabled IoT in sustainable smart grid edge computing and the Metaverse. This component is intended to operate at 0.815/1.58/2.65 GHz for 5G applications. The physical layout of our triplexer is new, and it is presented for the first time in this work. The overall size of our triplexer is only 0.007 λg2, which is the smallest compared to the previous works. The proposed triplexer has very low insertion losses of 0.12 dB, 0.09 dB, and 0.42 dB at the first, second, and third channels, respectively. We achieved the minimum insertion losses compared to previous triplexers. Additionally, the common port return losses (RLs) were better than 26 dB at all channels.

Engineering IoT-Based Open MAS for Large-Scale V2G/G2V

In this paper, we aimed to demonstrate how to engineer Internet of Things (IoT)-based open multiagent systems (MASs). Specifically, we put forward an IoT/MAS architectural framework, along with a case study within the important and challenging-to-engineer vehicle-to-grid (V2G) and grid-to-vehicle (G2V) energy transfer problem domain. The proposed solution addresses the important non-functional requirement of scalability. To this end, we employed an open multiagent systems architecture, arranging agents as modular microservices that were interconnected via a multi-protocol Internet of Things platform. Our approach allows agents to view, offer, interconnect, and re-use their various strategies, mechanisms, or other algorithms as modular smart grid services, thus enabling their seamless integration into our MAS architecture, and enabling the solution of the challenging V2G/G2V problem. At the same time, our IoT-based implementation offers both direct applicability in real-world settings and advanced analytics capabilities via enabling digital twin models for smart grid ecosystems. We have described our MAS/IoT-based architecture in detail; validated its applicability via simulation experiments involving large numbers of heterogeneous agents, operating and interacting towards effective V2G/G2V; and studied the performance of various electric vehicle charging scheduling and V2G/G2V-incentivising electricity pricing algorithms. To engineer our solution, we used ASEME, a state-of-the-art methodology for multiagent systems using the Internet of Things. Our solution can be employed for the implementation of real-world prototypes to deliver large-scale V2G/G2V services, as well as for the testing of various schemes in simulation mode.

2D2PS: A demand-driven privacy-preserving scheme for anonymous data sharing in smart grids

In smart grid services, electricity consumption data sharing may lead to privacy breaches and the potential for misuse of personal data. Existing data aggregation approaches mainly rely on homomorphic encryption techniques to achieve privacy protection in data sharing, however, lack consideration of controlled anonymity and fine-grained access control. This paper proposes a demand-driven privacy-preserving scheme for anonymous data sharing in smart grids to solve these problems. Specifically, an authenticable pseudonym assignment method is first proposed to achieve controlled anonymity. In addition, a proxy re-encryption technique and a data separation algorithm are implemented to achieve fine-grained access control. Finally, we strictly prove that our scheme achieves privacy protection and authentication, and demonstrate its feasibility and effectiveness through simulations.

Coupling OMNeT++ and Mosaik for Integrated Co-Simulation of ICT-Reliant Smart Grids

The increasing integration of renewable energy resources requires so-called smart grid services for monitoring, control and automation tasks. Simulation environments are vital for evaluating and developing innovative solutions and algorithms. Especially in smart energy systems, we face a variety of heterogeneous simulators representing, e.g., power grids, analysis or control components and markets. The co-simulation framework mosaik can be used to orchestrate the data exchange and time synchronization between individual simulators. So far, the underlying communication infrastructure has often been assumed to be optimal and therefore, the influence of e.g., communication delays has been neglected. This paper presents the first results of the project cosima, which aims at connecting the communication simulator OMNeT++ to the co-simulation framework mosaik to analyze the resilience and robustness of smart grid services, e.g., multi-agent-based services with respect to adaptivity, scalability, extensibility and usability. This facilitates simulations with realistic communication technologies (such as 5G) and the analysis of dynamic communication characteristics by simulating multiple messages. We show the functionality and benefits of cosima in experiments with 50 agents.

A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring.

Non-intrusive load monitoring (NILM) considers different approaches for disaggregating energy consumption in residential, tertiary, and industrial buildings to enable smart grid services. The main feature of NILM is that it can break down the bulk electricity demand, as recorded by conventional smart meters, into the consumption of individual appliances without the need for additional meters or sensors. Furthermore, NILM can identify when an appliance is in use and estimate its real-time consumption based on its unique consumption patterns. However, NILM is based on machine learning methods and its performance is dependent on the quality of the training data for each appliance. Therefore, a common problem with NILM systems is that they may not generalize well to new environments where the appliances are unknown, which hinders their widespread adoption and more significant contributions to emerging smart grid services. The main goal of the presented research is to apply a domain adversarial neural network (DANN) approach to improve the generalization of NILM systems. The proposed semi-supervised algorithm utilizes both labeled and unlabeled data and was tested on data from publicly available REDD and UK-DALE datasets. The results show a 3% improvement in generalization performance on highly uncorrelated data, indicating the potential for real-world applications.

Enhanced Crow Search with Deep Learning-Based Cyberattack Detection in SDN-IoT Environment

The paradigm shift towards the Internet of Things (IoT) phenomenon and the rise of edge-computing models provide massive potential for several upcoming IoT applications like smart grid, smart energy, smart home, smart health and smart transportation services. However, it also provides a sequence of novel cyber-security issues. Although IoT networks provide several advantages, the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for cyber-attackers. Cyberattacks result in financial loss and data breaches for organizations and individuals. So, it becomes crucial to secure the IoT environment from such cyberattacks. With this motivation, the current study introduces an effectual Enhanced Crow Search Algorithm with Deep Learning-Driven Cyberattack Detection (ECSADL-CAD) model for the Software-Defined Networking (SDN)-enabled IoT environment. The presented ECSADL-CAD approach aims to identify and classify the cyberattacks in the SDN-enabled IoT environment. To attain this, the ECSADL-CAD model initially pre-processes the data. In the presented ECSADL-CAD model, the Reinforced Deep Belief Network (RDBN) model is employed for attack detection. At last, the ECSA-based hyperparameter tuning process gets executed to boost the overall classification outcomes. A series of simulations were conducted to validate the improved outcomes of the proposed ECSADL-CAD model. The experimental outcomes confirmed the superiority of the proposed ECSADL-CAD model over other existing methodologies.

Design and Implementation of a Cloud-IoT-Based Home Energy Management System

The advances in the Internet of Things (IoT) and cloud computing opened new opportunities for developing various smart grid applications and services. The rapidly increasing adoption of IoT devices has enabled the development of applications and solutions to manage energy consumption efficiently. This work presents the design and implementation of a home energy management system (HEMS), which allows collecting and storing energy consumption data from appliances and the main load of the home. Two scenarios are designed and implemented: a local HEMS isolated from the Internet and relies on its processing and storage duties using an edge device and a Cloud HEMS using AWS IoT Core to manage incoming data messages and provide data-driven services and applications. A testbed was carried out in a real house in the city of Valparaiso, Chile, over a one-year period, where four appliances were used to collect energy consumption using smart plugs, as well as collecting the main energy load of the house through a data logger acting as a smart meter. To the best of our knowledge, this is the first electrical energy dataset with a 10-second sampling rate from a real household in Valparaiso, Chile. Results show that both implementations perform the baseline tasks (collecting, storing, and controlling) for a HEMS. This work contributes by providing a detailed technical implementation of HEMS that enables researchers and engineers to develop and implement HEMS solutions to support different smart home applications.

High-Precision Timing Method of BeiDou-3 System Based on Reinforcement Learning

All aspects of smart grid operation require precise timing technology to improve the timeliness and precision of the network. However, due to the complex and time-varying network environment and the low precision of device timing, the existing timing technology cannot meet the timing precision required by smart grid services. Therefore, according to the multi-armed bandit (MAB) theory and the upper confidence bound (UCB) algorithm, this paper proposes a high-precision timing method for power transmission and distribution park named adaptive timing-aware upper confidence bound (AUCB). The long-term average reward is maximized based on local information and historical decision-making experience. The simulation results show that the proposed high-precision timing method of AUCB in the power transmission and distribution park can effectively improve the device timing precision and realize self-adaptive timing for power system devices.

Smart Meter Data Obfuscation With a Hybrid Privacy-Preserving Data Publishing Scheme Without a Trusted Third Party

Smart electricity meters as a prominent instance of the Internet of Things (IoT) have driven more efficient energy services in smart grids but also created growing concerns of consumer privacy. Homomorphic encryption of consumption data is a conventional solution for privacy protection, but it incurs a high computational burden to the resource-restrained smart meters (SMs) due to the encryption of high-frequency consumption readings. Perturbation is another major approach in providing the privacy protection of SM readings which is highly efficient. However, most existing perturbation-based works inadequately balance the tradeoff between the need of hiding individual consumption profiles and the need of retaining utility’s quality. This article proposes a hybrid privacy-preserving electricity consumption data publishing scheme without a trusted third party which utilizes the desirable properties of both perturbation and cryptography for better privacy-utility tradeoff and efficiency. The proposed scheme for fine-grained SM consumption data consists of two phases, which are noise generation and noise distribution. In the first phase, a distributed perturbation method is designed to provide differential privacy protection for high-frequency consumption while retaining the accuracy of energy services like regional load forecasting. In the second phase, a private noise distribution protocol, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$nn$ </tex-math></inline-formula> -PND, securely distributes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> noise elements generated by an energy distribution operator to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> SMs in a semihonest adversarial model. Formal proofs of correctness and privacy of the scheme are provided. Experiments of regional short-term electricity consumption forecast using real-world data sets demonstrate the preservation of the utility over the masked data of this scheme.

Resource placement strategy optimization for smart grid application using 5G wireless networks

&lt;p&gt;With the evolution of 5G-network, wireless mobile networks are growing to take a strong stand in attempts to achieve ubiquitous large-scale acquisition, connectivity and processing. Smart-grids are among the critical areas that can benefit from the capabilities of the 5G-network, especially internet of things (IoT) applications such as massive machine-type-communications or ultra-reliable low-latency communications. A distributed cloud-services use the cloud, fog and edge computing infrastructures and applications to take advantage of every available resource including network equipment and connected objects to optimize cost, energy, and latency depending on the planned optimization criteria. In this article, we present smart-grid solution based on cloud-services and 5G-network, then we study the integration of smart-grid services in the cloud based on: placement in the cloud and in the end-device, and finally we introduce our proposed solution based on Intelligent placement strategy. The scenarios are evaluated by the iFogSim simulator, and the analyzed results compare the standard cloud placement, edge placement and our intelligent placement with regard to the optimization of the energy consumption, latency, and network usage. The findings show that cloud energy consumption can be substantially reduced using Intelligent Placement while respecting the potential central processing unit (CPU) processing power-limit for each IoT-device used and network constraints in smart-grid.&lt;/p&gt;

AuGrid: Edge-Enabled Distributed Load Management for Smart Grid Service Providers

In this paper, we propose and design AuGrid, an LSTM-based model for geographically aware smart grid service providers, which predicts the hourly load requests from users. We also develop a pricing model which depends on the predictions obtained from AuGrid for deciding per unit cost of energy in contrast to the existing schemes that focused solely on the load requests. The crux of this work is that the suppliers may plan better with forecasts than being in uncertainty. Since smart grids are well connected, logically neighboring smart grids may exchange information and energy on the requirement. We train AuGrid with a lookback set to 2 using real-world datasets and demonstrate its robustness by predicting the load requests for different suppliers. We propose deploying the AuGrid system on geographically aware suppliers for facilitating intelligence on the edge while reducing the user sample space and increasing data security. On extensive implementation and deployment, we observe that AuGrid offers minuscule loss (below 0.1) and the pricing model offers a reduction in per-unit cost by almost 75% in comparison to existing solutions. Additionally, AuGrid requires 30% CPU and 40% RAM of single processor boards on deployment, which illustrates its suitability for resource-constrained devices.

Secure and Efficient Federated Learning for Smart Grid With Edge-Cloud Collaboration

With the prevalence of smart appliances, smart meters, and Internet of Things (IoT) devices in smart grids, artificial intelligence (AI) built on the rich IoT big data enables various energy data analysis applications and brings intelligent and personalized energy services for users. In conventional AI of Things (AIoT) paradigms, a wealth of individual energy data distributed across users’ IoT devices needs to be migrated to a central storage (e.g., cloud or edge device) for knowledge extraction, which may impose severe privacy violation and data misuse risks. Federated learning, as an appealing privacy-preserving AI paradigm, enables energy data owners (EDOs) to cooperatively train a shared AI model without revealing the local energy data. Nevertheless, potential security and efficiency concerns still impede the deployment of federated-learning-based AIoT services in smart grids due to the low-quality shared local models, non-independently and identically distributed (non-IID) data distributions, and unpredictable communication delays. In this article, we propose a secure and efficient federated-learning-enabled AIoT scheme for private energy data sharing in smart grids with edge-cloud collaboration. Specifically, we first introduce an edge-cloud-assisted federated learning framework for communication-efficient and privacy-preserving energy data sharing of users in smart grids. Then, by considering non-IID effects, we design a local data evaluation mechanism in federated learning and formulate two optimization problems for EDOs and energy service providers. Furthermore, due to the lack of knowledge of multidimensional user private information in practical scenarios, a two-layer deep reinforcement-learning-based incentive algorithm is developed to promote EDOs’ participation and high-quality model contribution. Extensive simulation results show that the proposed scheme can effectively stimulate EDOs to share high-quality local model updates and improve the communication efficiency.

Optimal Energy Trading With Demand Responses in Cloud Computing Enabled Virtual Power Plant in Smart Grids

The increasing penetration of renewable energy sources and electric vehicles (EVs) poses a significant challenge for the power grid operator in terms of increasing peak load and power quality reduction. Moreover, there is a growing demand for fast charging services in smart grids. Addressing the growing demand from fast charging services is challenging. To overcome this challenge, in this article, we propose a new computational architecture combining energy trading and demand responses based on cloud computing for managing virtual power plants (VPPs) in smart grids. In the proposed system, EVs can be charged at high charging rates without affecting the operation of the power grid by purchasing energy through the energy trading platform in the cloud. In addition, users with storage devices can sell energy surplus to the market. On the one hand, the energy trading platform can be regarded as an internal market of the VPP that aims to maximize its revenue. The interest of the EV owners, on the other hand, is to minimize the cost for charging. Therefore, we model the interactions between the EV owners and the VPP as a non-cooperative game. To search for the Nash equilibrium (NE) of the game, we design an algorithm and then analyze its computational complexity and communication overhead. We utilize real data from the California Independent System Operator (CAISO) to evaluate the performance of the proposed algorithm. Our results illustrate that the users with only storage devices can obtain nearly <inline-formula><tex-math notation="LaTeX">$200\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>200</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="chung-ieq1-3118563.gif"/></alternatives></inline-formula> higher revenue on average by participating in the proposed internal market. Moreover, users with only EVs can reduce their charging costs by nearly <inline-formula><tex-math notation="LaTeX">$50\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>50</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="chung-ieq2-3118563.gif"/></alternatives></inline-formula> in average. Users with both EVs and storage devices can reduce the charging costs even further by approximately <inline-formula><tex-math notation="LaTeX">$120\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>120</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="chung-ieq3-3118563.gif"/></alternatives></inline-formula> where the users get profit by utilizing the internal market.