Distributed Energy Management Research Articles

Hardware Implementation of a Resilient Energy Management System for Networked Microgrids

A networked microgrid is composed of multiple nearby microgrids linked together to gain additional flexibility for resilient operations. Networked microgrids collaborate to prevent power shortages in microgrid clusters by sharing critical renewable and energy storage resources. However, controlling the local resources of each microgrid, including the energy storage systems’ charging and discharging, maintaining the DC bus voltage, and even overseeing the power shared by multiple microgrids, is challenging. Therefore, a microgrid control technique and distributed energy management are used cooperatively in this study to handle the shared power between a system of networked microgrids incorporating photovoltaics and battery energy storage systems. Numerical simulation results from a networked microgrid system verify the accuracy and soundness of the suggested distributed energy management under several operating conditions, including renewable uncertainties and sequential load variations in different zones. The applicability of the suggested technique is confirmed by hardware implementation, and several operational scenarios further evaluate the proposed system on a practical two-microgrid system located in the Florida International University (FIU) testbed.

Event-Trigger-Based Resilient Distributed Energy Management Against FDI and DoS Attack of Cyber–Physical System of Smart Grid

Event-Trigger-Based Resilient Distributed Energy Management Against FDI and DoS Attack of Cyber–Physical System of Smart Grid

Hierarchically Distributed Energy Management in Distribution Systems: An Error-Tolerant and Asynchronous Approach

Hierarchically Distributed Energy Management in Distribution Systems: An Error-Tolerant and Asynchronous Approach

Optimal and distributed energy management in interconnected energy hubs

Recently, multi-carrier energy systems (MCESs) have been rapidly developed as flexible multi-generation systems aiming to satisfy load demands by purchasing, converting, and storing different energy carriers. This study specifically focuses on the optimal and robust large-scale coordination of interconnected energy hubs (IEHs) in an iterative consensus-based procedure considering distribution network losses. Furthermore, a new robust-based hybrid IGDT/consensus algorithm is introduced to achieve risk-averse optimal energy management in IEHs under uncertainty. The fast convergence, needless to collect the total information from all hubs, minimal computational burden, and more robust communication system are the most important features of the proposed distributed consensus algorithm in this study. The effectiveness of the proposed consensus algorithm is verified by simulation results considering various energy trading structures in IEHs at different scales. The obtained results highlight the scalability capability of the proposed method. Regarding an IEHS of 30 energy hubs, the computation burden is lightened by 0.53 (s) and 0.1917 (s), respectively with and without uncertainty. Considering distribution network losses, the total purchasing costs can be increased by 8%. The simulation results also reveal an increase of 11% in the total power trading under the uncertainty.

Distributed Energy Management of Multi-Entity Integrated Electricity and Heat Systems: A Review of Architectures, Optimization Algorithms, and Prospects

Distributed Energy Management of Multi-Entity Integrated Electricity and Heat Systems: A Review of Architectures, Optimization Algorithms, and Prospects

Distributed energy management of multi-area integrated energy system based on multi-agent deep reinforcement learning

This paper addresses the problem of distributed energy management in multi-area integrated energy systems (MA-IES) using a multi-agent deep reinforcement learning approach. The MA-IES consists of interconnected electric and thermal networks, incorporating renewable energy sources and heat conversion systems. The objective is to optimize the operation of the system while minimizing operational costs and maximizing renewable energy utilization. We propose a distributed energy management strategy that makes hierarchical decisions on intra-area heat energy and inter-area electric energy. The strategy is based on a multi-agent deep reinforcement learning framework, where each agent represents a component or unit in the MA-IES. We formulate the problem as a Markov Decision Process and employ Q-learning with experience replay and double networks to train the agents. The proposed strategy is evaluated using a simulation of a four-area MA-IES. The results demonstrate significant improvements in energy management compared to traditional methods, with higher renewable energy utilization and lower operational costs. Specifically, the strategy achieves 100% utilization of wind power, and decreases operational costs by 5.53%. Furthermore, it leverages the generalization capabilities of reinforcement learning to respond in real-time to uncertainties in demand and wind power output. The results highlight the advantages of the proposed strategy, making it a promising solution for optimizing the operation of multi-area integrated energy systems.

Preparation, characterisation and property modification of a calcium chloride hexahydrate phase change material slurry with additives for thermal energy transportation

Efficient thermal energy distribution and rational energy management are effective solutions to address the significant and fast-ascending energy consumption in building heating, ventilation, and air conditioning (HVAC). Phase change material (PCM) slurries with excellent thermal energy transportation and storage characteristics have been recognised as promising secondary refrigerants to simultaneously facilitate efficient heat/cold transport and effective thermal energy storage in air conditioning systems. Among various PCM slurries, salt hydrate PCM slurries have attracted increasing attention in recent years due to their many inherent advantages. This paper presented the preparation, characterisation, and property modification of a CaCl2·6H2O slurry based on a methodology proposed for optimal salt hydrate PCM slurry development. Theoretical modelling was first carried out to assess the availability of salt hydrate PCM slurries based on the phase diagram and solid fraction, followed by a series of property measurements to reveal the mechanisms and address the problems behind the phase transition, crystal particle morphology, stability and rheologic characteristics, using nucleating agents, surfactants and stabilisers. It was found that the salt hydrate PCM slurry developed provides a unit volume enthalpy twice that of water over the phase change temperature range of 5°C, together with acceptable fluidity. The results also showed that the PCM particle size can be well controlled, and the slurry can be stabilised, with the assistance of surfactants and stabilisers. Compared to other types of PCM slurries, the proposed salt hydrate PCM slurry has the advantages of being cost-effectiveness, easy preparation, adjustable phase change temperature, etc., which is promising to facilitate energy saving and rationalisation in building HVAC.

Enhancing Electrical Power Demand Prediction Using LSTM-Based Deep Learning Models for Local Energy Communities

The pursuit of accurate electrical power demand forecasting has led to the application of deep learning algorithms, notably demonstrating promising outcomes despite the prerequisite of substantial training data. This study pioneers a new learning paradigm, employing sophisticated deep learning models specifically, Long Short-Term Memory networks and recurrent neural networks (RNNs). Leveraging historical load data, temperature, wind speed, and day-ahead predicted spot prices, this approach follows a structured flow involving data preprocessing, sequence generation, model training, and subsequent prediction of future load demand using LSTM-based RNN variants. The study’s paramount findings underscore the substantial advancement achieved by this proposed methodology over prevailing techniques. The method significantly improves prediction accuracy by over 11%, demonstrating the efficacy of deep learning models and a significant leap forward in forecasting precision. Beyond its superior predictive capabilities, this novel strategy serves as a catalyst for enhancing energy distribution management in local energy communities. Its effectiveness lies not only in its precision but also in enabling the optimization and cost-effective control of energy distribution, vital for sustainable energy management in these communities. Ultimately, this pioneering approach presents a robust solution poised to revolutionize the landscape of electrical power demand forecasting and its practical application in local energy systems.

IoT Based Design and Implementation of Intelligent Energy Distribution Management with Photovoltaic System

Abstract: The grid operators have difficulties as a result of the increasing penetration of renewable sources due to reduced environmental contamination. Unrestrained energy use will undoubtedly deepen environmental harm and accelerate global warming. A best practice for effectively managing energy use is energy management. IoT technology can be leveraged by technological advancements to facilitate energy management. This paper offers a novel way to energy management using a solar system and the Internet of Things (IoT). The suggested system tracks, evaluates, and optimizes solar power use in real time with the goal of increasing energy distribution's intelligence and efficiency. The project's goal is to build a net energy meter (NEM), a unique billing system that gives solar PV system owners credit for the entire retail value of the electricity their system produces. Under NEM, a customer's electric meter tracks how much energy they use as well as how much extra energy the system produces and feeds back into the electric utility grid.

Distributed energy management of electric vehicle charging stations based on hierarchical pricing mechanism and aggregate feasible regions

With the rapid development of electric vehicle charging stations, effective management of them has become challenging due to the high uncertainty of electric vehicles, the pricing mechanisms of charging stations, and their coupling with distribution networks. To address these challenges, this paper proposes a two-stage framework for energy management at charging stations. In the first stage, a resource allocation model considering the profits of distribution systems, charging stations, and electric vehicle users is established based on the aggregate feasible power regions of charging stations. The aggregate feasible region is obtained based on the combination of Minkowski summation and the data-driven method, which can preserve the privacy of electric vehicle data and reduce the computational burden. In the second stage, a novel hierarchical pricing mechanism is developed, which encompasses both the clearing price between charging stations and distribution networks and the retail electricity price between charging stations and electric vehicle users. Notably, charging stations participate in the power clearing of distributed networks based on the aggregate feasible power region, while a two-stage robust pricing strategy is established between electric vehicle users and charging stations. The model is finally optimized through a distributed coordination mechanism with a clear physical interpretation. The simulation results show that the proposed aggregation method enables charging stations to achieve a total economic profit at least 1.76 % higher than three competitive methods. The hierarchical pricing mechanism allows charging stations to achieve total economic profits 18.60 % and 2.94 % higher than those in the centralized dispatch and price-taker modes, respectively, while simultaneously reducing operating costs for the distributed network by 25.96 % and 27.99 %.

An attack-resilient distributed energy management strategy for integrated energy systems

The increasing integration of various energy sources and the adoption of smart grid technologies have revolutionized the way we generate, distribute, and consume energy. While these advancements offer numerous benefits, they also introduce vulnerabilities to cyber–physical attacks. To this aim, this paper investigates a resilient energy management strategy for integrated energy systems. By adopting a switched control approach and incorporating a local estimation mechanism, we have developed a resilient distributed energy management strategy to tackle the energy management problem (EMP) in integrated energy systems (IESs). This methodology demonstrates strong robustness and resilience, successfully detecting data integrity attacks and denial-of-service (DoS) attacks. Finally, we provide a case study to demonstrate the effectiveness of our proposed strategy in a real-world scenario.

Security Event-Trigger-Based Distributed Energy Management Of Cyber-Physical Isolated Power System With Considering Nonsmooth Effects.

Due to cyber-physical fusion and nonsmooth characteristics of energy management, this article proposes a security event-trigger-based distributed approach to address these issues with developed smoothing technique. To tackle with nonconvex and nondifferentiable issue, a randomized gradient-free-based successive convex approximation is developed to smooth economic objective function. Due to resilience ability against security issue, a security event-triggered mechanism-based distributed energy management is proposed to optimize social welfare, which coordinately controls both power generators and load demand. The security event-triggered mechanism is designed to reduce power system security risks, and relieve communication burden caused by smoothing calculation, the convergence of proposed distributed algorithm is also properly proved. According to those obtained results on both IEEE 9-bus and IEEE 39-bus systems, it reveals that the proposed approach can achieve good convergence performance and have less security risks than other alternatives, which also proves that the proposed approach can be a viable and promising way for tackling with energy management issue of cyber-physical isolated power system.

Efficient economic energy scheduling in smart cities using distributed energy resources

Machine learning provides a powerful mechanism to enhance the capabilities of the next generation of smart cities. Whether healthcare monitoring, building automation, energy management, or traffic management, use cases of capability enhancement using machine learning have been significant in recent years. This paper proposes a modeling approach for scheduling energy consumption within smart homes based on a non-dominated sorting genetic algorithm (NSGA). Distributed energy management plays a significant role in reducing energy consumption and carbon emissions as compared to centralized energy generation. Multiple energy consumers can schedule energy-consuming household tasks using home energy management systems in coordination to reduce economic costs and greenhouse gas emissions. In this work, such a home energy management system is used to collect energy price data from the electricity company via an embedded device-enabled smart meter and schedule energy consumption tasks based on this data. We schedule daily power consumption tasks using a multiobjective optimization method that considers environmental and economic sustainability. Two conflicting objectives are minimizing daily energy costs and reducing carbon dioxide emissions. Based on electricity tariffs, CO2 intensity, and the window of time during which electricity is consumed, energy consumption tasks involving distributed energy resources (DERs) and electricity consumption are scheduled. The proposed model is implemented in a model smart building consisting of 30 homes under 3 pricing schemes. The energy demand is spread out across a 24-hour period for points A2–A4 under CPP-PDC, which produces a more flattened curve than point A1. There are competing goals between electricity costs and carbon footprints at points B2–B4 under the CPP-PDC, where electricity demand is set between 20:00 and 0:00. Power grids’ peak energy demand is comparatively low when scheduling under CPP-PDC for points A5 and B5. Reducing carbon emissions, CPP-PDC reduces the maximum demand for electricity from the grid and the overall demand above the predetermined level. The maximum power demand from the grid is minimized for points A5 and B5, reducing up to 22% compared to A2. The proposed method minimizes both energy costs as well as CO2 emissions. A Pareto curve illustrates the trade-off between cost and CO2 emissions.

Stealthy Data Integrity Attack Against Consensus-Based Distributed Energy Management Algorithm

Stealthy Data Integrity Attack Against Consensus-Based Distributed Energy Management Algorithm

Distributed Energy Management Method With EEOI Limitation for the Ship-Integrated Energy System

Distributed Energy Management Method With EEOI Limitation for the Ship-Integrated Energy System

Homomorphic Encryption Based Resilient Distributed Energy Management Under Cyber-Attack of Micro-Grid With Event-Triggered Mechanism

Homomorphic Encryption Based Resilient Distributed Energy Management Under Cyber-Attack of Micro-Grid With Event-Triggered Mechanism

An IoT-fog-cloud consensus-based energy management algorithm of multi-agent smart energy hubs considering packet losses and uncertainty

—As a result of the increasing demand for different energies with technological development, and with the rapid development of energy hubs (EHs), issues such as computation speed, big data and information privacy, robustness, etc. are becoming increasingly vital. Also, the problem of communication packet loss leads to an incompatibility between the generated and consumed energy. In response to the above problems, this paper proposes an Internet of things-Fog-Cloud distributed consensus-based energy management algorithm to optimally solve the energy mismatch problem in an EH environment considering communication information loss and uncertainties of some EH elements. A MATLAB/Simulink-based model of a hypothetical EH system is modified to support real-time communication. Two case studies are applied to explain the performance and effectiveness of the proposed algorithm under different packet loss scenarios. The system's sensitivity was studied depending on the number of iterations and the uncertainties of the renewable energy sources, energy prices, and load demands. Also, a comparison was made between the results obtained by the proposed method and the optimization results using the genetic algorithm and particle swarm optimization techniques. The results prove the applicability and feasibility of the proposed method for the demand management system in EHs. The results show that the proposed algorithm decreases the daily system operating cost from 372.421 to 369.941 $ and the total CO2 emissions cost from 4848.5 to 4103.1 $. In addition, the daily energy mismatch between the generated and consumed values decreases from 579.85 to 0 kWh in the electrical system and from 435.14 to 0.073 kWhth in the heating system. Based on the previous results, the proposed algorithm of EHs contributes significantly to the development of sustainable cities.

Hierarchical and distributed energy management framework for AC/DC hybrid distribution systems with massive dispatchable resources

Hierarchical and distributed energy management framework for AC/DC hybrid distribution systems with massive dispatchable resources

Retraction Notice: Distributed Energy Management in Smart Grids Based on Cloud-Fog Layer Architecture Considering PHEVs

Retraction Notice: Distributed Energy Management in Smart Grids Based on Cloud-Fog Layer Architecture Considering PHEVs

CASL: A Novel Collusion Attack Against Distributed Energy Management Systems

Recently, the security issues of smart grids have received wide attention. In particular, false data injection attacks against distributed energy management systems (DEMSs) are considered of high importance as they are able to cause economic losses or even damages to system stability in subtle ways. Existing studies have identified many such attacks and proposed corresponding countermeasures. However, in this paper, we show that DEMS is still insecure and in risk of economic loss by proposing and analyzing a novel attack called Collusion Attack between a Storage and a Load (CASL). In CASL, a distributed energy storage device colludes with a load by injecting an abnormal extra power supply to the load in a secret way through using two secret functions. In this way, the colluding pair behaves as normal ones in both computational and communicational viewpoints. We theoretically prove the convergence of DEMS in presence of the attack, and derive an upper bound of the social welfare losses of DEMS caused by CASL. We demonstrate the effectiveness of CASL through simulations.