Energy Management In Networks Research Articles

Real-Time Power Management of Plug-in Electric Vehicles and Renewable Energy Sources in Virtual Prosumer Networks with Integrated Physical and Network Security Using Blockchain

This paper presents a blockchain-enabled Multi-Agent System (MAS) for real-time power management in Virtual Prosumer (VP) Networks, integrating Plug-in Electric Vehicles (PEVs) and Renewable Energy Sources (RESs). The proposed framework addresses critical challenges related to scalability, security, and operational efficiency by developing a hierarchical MAS architecture that optimizes the scheduling and coordination of geographically distributed PEVs and RESs. Unlike conventional business management systems, this study integrates a blockchain-based security mechanism within the MAS framework, leveraging Proof of Authority (PoA) consensus to enhance transaction security while addressing scalability and energy consumption concerns. The system further employs advanced Particle Swarm Optimization (PSO) to dynamically compute optimal power set-points, enabling adaptive and efficient energy distribution. Additionally, hierarchical aggregation of transactions at lower MAS layers enhances computational efficiency and system resilience under high-traffic and partial network failure conditions. The proposed framework is validated through large-scale simulations spanning four major cities in Greece, demonstrating its scalability, reliability, and efficiency under diverse operational scenarios. Results confirm that the system effectively balances energy supply and demand while maintaining secure and transparent transactions. Despite these advancements, practical deployment challenges remain, including synchronization delays in geographically distributed agents, legacy system integration, and blockchain energy consumption. Future research directions include investigating more advanced consensus mechanisms (e.g., Proof of Task), machine learning-driven predictive optimization, real-world large-scale testing, and federated learning models for decentralized decision-making. The proposed framework offers a scalable, secure, and efficient solution for decentralized real-time energy management in Virtual Prosumer Networks.

Energy management in smart distribution networks: Synergizing network reconfiguration, energy storage, and electric vehicles with disjunctive convex hull relaxation

Energy management in smart distribution networks: Synergizing network reconfiguration, energy storage, and electric vehicles with disjunctive convex hull relaxation

Safe deep reinforcement learning-assisted two-stage energy management for active power distribution networks with hydrogen fueling stations

Safe deep reinforcement learning-assisted two-stage energy management for active power distribution networks with hydrogen fueling stations

Optimizing residential flexibility for sustainable energy management in distribution networks

Optimizing residential flexibility for sustainable energy management in distribution networks

Eco-power management system with operation and voltage security objectives of distribution system operator considering networked virtual power plants with electric vehicles parking lot and price-based demand response

Eco-power management system with operation and voltage security objectives of distribution system operator considering networked virtual power plants with electric vehicles parking lot and price-based demand response

Energy Management in the Nodes of Telecommunications Network Systems

Currently there is a significant increase in energy consumption, due to the use of electronic devices, at the same time the use of renewable energy has grown to reduce the impact of greenhouse gases. Therefore, the importance of implementing energy management in telecommunications networks to reduce costs and negative environmental impacts. So in this article we propose the implementation of an intelligent EMS architecture for telecommunications networks with the use of ZigBee and communication and data transfer elements. In addition, it has a server that collects and calculates energy generation and consumption data to establish usage and purchase patterns and creates useful information for statistical analysis. Finally, it is expected that this scheme will optimize the energy of the telecommunications network and result in energy savings.

UAVs Revolutionizing Efficiency: Integrating DRL and Random Walrus for Enhanced Energy and Spectral Resource Management

The improvement of energy efficiency and spectral efficiency in networks is achieved by seamlessly integrating energy harvesting, cognitive radio technologies, and Non-Orthogonal Multiple Access (NOMA) techniques. These complementary strategies optimize resource usage and address challenges related to energy consumption. Additionally, the adaptability and versatility of Unmanned Aerial Vehicles (UAVs) offer innovative solutions for enhancing coverage performance, thereby improving connectivity, efficiency, and reliability. We introduce a novel approach called the Deep Reinforcement Learning-Random Walrus (DRL-RW) algorithm, which combines Deep Reinforcement Learning (DRL) with the Random Walrus optimization (RWO) technique for efficient spectrum resource allocation and energy harvesting management in dynamic environments. The DRL-RW algorithm enables UAVs to learn optimal spectrum-sharing strategies and energy harvesting policies, while the RWO enhances the algorithm's adaptability and speed in exploring diverse solutions. Simulation results demonstrate the effectiveness of the DRL-RW algorithm, showing significant improvements in several performance metrics, including reduced energy consumption, enhanced computation time, improved convergence, increased signal-to-noise ratio, higher throughput, extended network lifetime, and increased harvested energy. These findings underscore the efficacy of the DRL-RW approach in addressing challenges associated with energy management in cognitive radio networks. The integration of UAVs, NOMA networks, and this novel algorithm represents a promising direction for developing energy-efficient communication systems.

MODEL FOR SOLAR ENERGY HARVESTING AND OPTIMIZATION IN WIRELESS SENSOR NETWORKS

Presented in this thesis is the energy harvesting and management model concerning wireless sensor network. Wireless sensor network, bears resemblance as routers. However, wireless sensor network cannot afford large batteries because of their small size. Continues operation of these devices result in rapid consumption of available energy. It is difficult to replace and recharge the battery due to limited accessibility. Sometime these devices are installed in remote areas or areas with uneven terrain such as hilly areas. One approach to counter this problem is to harness the ambient energy which is unevenly distributed in our environment. Availability of this energy is random and uncertain. Ambient energy has been successfully harnessed by solar energy harvesting technique. We present a feasible approach of management model with appropriate energy management for wireless sensor network. The proposed solar energy harvesting and management model not only saves energy, it also assures the connection and communication within the network by optimum consumption of energy along with the management of the harvested and battery energy.

Contextual Deep Reinforcement Learning for Flow and Energy Management in Wireless Sensor and IoT Networks

Contextual Deep Reinforcement Learning for Flow and Energy Management in Wireless Sensor and IoT Networks

A Two-Stage Hybrid Stochastic–Robust Coordination of Combined Energy Management and Self-Healing in Smart Distribution Networks Incorporating Multiple Microgrids

This paper presents a two-stage hybrid stochastic–robust coordination of energy management and self-healing in smart distribution networks with multiple microgrids. A multi-agent systems approach is first used for coupling energy management and self-healing strategies of microgrids, based on expert system rules. The second stage problem, a framework similar to that of the first stage, is then established for the smart distribution networks. Then, hybrid stochastic–robust optimization is used to model the uncertainties of demand, energy price, power generation of renewable energy sources, demand of electric vehicles, and accessibility of zone agents. Further, the grey wolf algorithm is used to solve the formulated optimization problem and achieve an optimal and reliable solution. The proposal is validated on a 69-bus distribution network consisting of three microgrids. The results validate that the proposal minimizes microgrids’ utilization indices, such as energy costs, energy losses, and network voltage drops, while simultaneously managing a flexible distribution network. It is also verified that the proposed multi-agent system design provides a high-speed and optimized self-healing solution for the network.

Neural network energy management strategy for plug-in hybrid electric combine harvesters based on quasi-periodic samples

Neural network energy management strategy for plug-in hybrid electric combine harvesters based on quasi-periodic samples

Optimal coordinated energy management in active distribution networks considering battery energy storage and price‐responsive demand

AbstractContemporary distribution networks can be seen with diverse dispatchable and non‐dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non‐dispatchable resources can provide several techno‐economic and social benefits. Since battery energy storage systems (BESSs) and microturbine units are capital intensive. A thorough investigation of their coordinated scheduling on a purely economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty of renewable power generation and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and microturbine units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility. In this study, a recently explored modified African buffalo optimization algorithm is employed. The key attributes of the proposed methodology are comprised of mean price‐based adaptive scheduling embedded within a decision mechanism system to maximize arbitrage benefits. Decision mechanism system keeps a track of system states as a‐priori thus guides the artificial intelligence‐based solution technique for sequential optimization. This may also reduce the computational burden of complex real‐life engineering optimization problems. Further, a novel concept of fictitious charges in coordination with BESS management algorithm is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33‐bus test distribution system highlights the importance of the proposed methodology.

Integration of RES and AI for Grid Distribution Network Energy Management

In an era where sustainability and efficient resource utilization are paramount, optimizing energy management in grid distribution networks is a top priority. This research introduces a cutting-edge approach that harnesses the power of Renewable Energy Sources (RES), Artificial Intelligence (AI), Long Short-Term Memory (LSTM) networks, Quadratic Regression, and Demand Response mechanisms for Grid Distribution Network Energy Management. By fuzing these state-of-the-art technologies, we unlock the potential to revolutionize energy load forecasting with unprecedented precision and foresight. LSTM models, enriched with historical load data, weather conditions, and demand-response patterns, empower us to anticipate grid requirements with exceptional accuracy. Our approach consistently achieves an average Mean Absolute Percentage Error (MAPE) below 5% and a Root Mean Square Error (RMSE) under 2% for load predictions with an overall grid distribution efficiency is 98%, surpassing conventional forecasting methodologies. Furthermore, the integration of demand response strategies results in a remarkable 20% peak shaving ratio, contributing to a 15% reduction in energy demand during high-demand periods. This research not only enhances smart grid technologies but also ushers in a more resilient, adaptive, and eco-friendly energy infrastructure for the future.

Optimization of Ship Power Supply Network and Intelligent Energy Management Strategy Under Multiple Energy Modes

<p>Purpose: This article aims to address the issues of incomplete management models and slow convergence speed of optimization models in energy management in ship multi energy systems, and to construct a comprehensive dynamic optimization objective model. Method: Firstly, establish an optimization design model with the objective functions of energy storage system cost, grid power fluctuation smoothing, and energy supply and demand balance; Then, in the optimization process of the objective function, a bus voltage coordination control strategy is adopted, and for the parameter optimization problem in the strategy, a cuckoo search algorithm based on population feature feedback is used for optimization. Result: Through simulation experiments, the method proposed in this article provides effective guidance for capacity configuration and energy management of multi energy ship microgrids, improving quality and efficiency.</p> <p> </p>

A new iterative mixed integer linear programming based real time energy efficient management of AC-DC distribution networks

A new iterative mixed integer linear programming based real time energy efficient management of AC-DC distribution networks

Decentralized robust operation of the unbalanced microgrids in distribution networks: A convex relaxation approach

Decentralized robust operation of the unbalanced microgrids in distribution networks: A convex relaxation approach

A novel reinforcement learning based routing algorithm for energy management in networks

A novel reinforcement learning based routing algorithm for energy management in networks

Ensemble-based classification algorithm to enhance stability of energy management in IoT-based smart grid networks

Ensemble-based classification algorithm to enhance stability of energy management in IoT-based smart grid networks

Optimized energy management and small cell activation in ultra-dense networks through a data-driven approach.

With the exponential expansion of the wireless industry, the demand for improved data network throughput, capacity, and coverage has become critical. Heterogeneous ultra-dense networks (UDNs) have emerged as a promising solution to meet these escalating requirements for high data rates and capacity. However, effectively deploying and managing small cells within UDNs presents significant challenges, particularly amidst varying traffic loads and the necessity for efficient resource utilization to minimize energy consumption, especially in environments with high interference levels. Inadequate deployment of small cells can lead to excessive interference, resulting in suboptimal profitability and inefficient energy resource utilization. Addressing these challenges demands innovative approaches such as data-driven deployment strategies and efficient energy efficient resource (EER) management for small cells. Leveraging data-driven methodologies, operators can optimize small cell deployment locations and configurations based on real-time traffic patterns and environmental conditions, thereby maximizing network performance while minimizing energy consumption. This research investigates the effectiveness of a data-driven mechanism in enhancing the average achievable data rate of small cells within Heterogeneous UDNs. Our proposed approach Data Driven Opportunistic Sleep Strategy (D-DOSS) employs stochastic geometry based mathematical model for the heterogeneous networks (HetNets) wireless network will assess the impact of strategic small cell deployment on network performance in respect of energy savings. The results from Monte Carlo simulations reveal that D-DOSS outperforms traditional strategies by improving energy efficiency (EE) by 20% and achieving a 15% higher average data rate. Additionally, D-DOSS achieves a coverage probability of 50% at a signal-to-interference-plus-noise ratio (SINR) threshold of 5 dB, significantly better than random sleep mode (RSM) and load aware sleep (LAS) strategies. Overall, our findings underscore the significance of data-driven deployment and management strategies in optimizing the performance of HetNets UDNs. By embracing such approaches, wireless operators can meet the escalating demands for high-speed data transmission while achieving greater EE and sustainability in wireless network operations.

Artificial Intelligence and Energy Efficiency of 5G Radio Access Network

Purpose: This paper is a pioneering study that investigates the integration of Artificial Intelligence (AI) to enhance energy efficiency in 5G Radio Access Networks (RANs). This paper aims to identify AI-driven strategies that can significantly optimize energy consumption in the rapidly evolving 5G network infrastructure, which is essential for meeting the increasing demand for high-speed connectivity.
 Methodology: The methodology used for this research is a detailed review and analysis of the 5G RAN architecture and its energy dynamics, alongside the exploration of AI applications in optimizing network operations. The study focuses on AI techniques such as resource allocation, traffic prediction, adaptive sleep modes, and fault detection, proposing a holistic approach to energy management in 5G networks. A key contribution of this research is its in-depth examination of AI's role in 5G energy efficiency, highlighting its practical implications and potential for future applications. The paper offers novel insights into the implementation of AI in real-world 5G scenarios and addresses the challenges in transitioning from theoretical models to practical solutions.
 Findings: The findings reveal that AI integration is a vital step towards reducing the environmental footprint of 5G networks, with AI-based solutions showing promise in enhancing efficiency beyond the inherent capabilities of current 5G technologies. Despite many AI applications being in nascent stages, their potential impact on energy efficiency is significant.
 Unique contributor to theory, policy and practice: This paper is a valuable guide for researchers, industry professionals, and policymakers in telecommunications and environmental sustainability. It provides a clear roadmap for leveraging AI in 5G networks, emphasizing the synergy between technological innovation and ecological responsibility.