Energy Management Mechanism Research Articles

A Scalable Energy Management Mechanism for Peer-to-Peer Electricity Market

A Scalable Energy Management Mechanism for Peer-to-Peer Electricity Market

Feasibility assessment of behind-the-meter batteries under typical tariff structures for commercial and industrial customers

This paper presents a comprehensive analysis of cost savings and payback periods for the deployment of batteries by commercial and industrial customers, considering several typical tariff structures in the context of Australia. To achieve this, a battery energy management mechanism is proposed that enables battery charging, both from the solar photovoltaic system and the grid, and discharging precisely to maximize cost savings and minimize payback periods for commercial and industrial customers while minimizing battery degradation. The proposed mechanism directs the battery to charge from the grid during the most economical periods, and grid charging amounts are optimized based on the battery size and demand profiles, and to discharge during demand charge-, peak rate-, and shoulder rate-applicable periods in succession to reap maximum benefits. Furthermore, sensitivity analyses are conducted to showcase the economic viability of battery trial tariffs (tariffs introduced in Australia to expedite the uptake of batteries for commercial and industrial customers) compared to standard tariffs (usual tariffs imposed on commercial and industrial customers in Australia), considering various scenarios such as changes in battery sizes, customer profiles, and coulomb-ratings. The simulation results suggest that the battery trial tariff is more profitable for a sample commercial and industrial customer compared to the standard tariff, enabling the reduction of payback periods by between 1.53% and 38.05%, for example, under the Tariff 1 structure (one example of a tariff structure introduced by a typical service provider in Australia).

Green building energy: Patents analysis and analytical hierarchy process evaluation

In the dynamic sphere of building energy systems, this study explores advancements in energy integration, storage technologies, management practices, and occupant behavior, assessing sustainable energy practices, including emerging technologies like fuel cells and energy storage systems. It underscores the significance of efficient energy management, considering both renewable and conventional energy mechanisms. The study comprises four key strata: (i) a thorough literature review of recent energy trends, (ii) a comparative study of global energy patents using the World Intellectual Property Organization (WIPO) database, (iii) a comprehensive analysis of building-energy patents, and (iv) expert-guided Analytic Hierarchy Process (AHP) evaluation. These realms encompass five primary sources: (i) energy-efficient building design, (ii) intelligent building automation, (iii) optimizing energy systems integration, (iv) energy storage, and (v) energy management and optimization. Findings reveal energy storage's dominance, with water energy storage and emerging hydrogen technology leading the trajectory. Global energy patent scrutiny underscores China, the United States, and Japan as influential players in optimizing energy markets. The research shapes energy futures, identifies gaps, and drives sustainable energy practices within the built environment, serving as a compass for policymakers and researchers.

Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle

In response to the challenges posed by the low energy utilization of single-power pure electric vehicles and the limited lifespan of power batteries, this study focuses on the development of a compound power system. This study constructs a composite power system, analyzes the coupling characteristics of multiple systems, and investigates the energy management and optimal control mechanisms. Firstly, a power transmission scheme is designed for a hybrid electric vehicle. Then, a multi-state model is established to assess the electric vehicle’s performance under complex working conditions and explore how these conditions impact system coupling. Next, load power is redistributed using the Haar wavelet theory. The super capacitor is employed to stabilize chaotic and transient components in the required power, with low-frequency components serving as input variables for the controller. Further, power distribution is determined through the application of fuzzy logic theory. Input parameters include the system’s power requirements, power battery status, and super capacitor state of charge. The result is the output of a composite power supply distribution factor. To fully exploit the composite power supply’s potential and optimize the overall system performance, a global optimization control strategy using the dynamic programming algorithm is explored. The optimization objective is to minimize power loss within the composite power system, and the optimal control is calculated through interpolation using the interp function. Finally, a comparative simulation experiment is conducted under UDDS cycle conditions. The results show that the composite power system improved the battery discharge efficiency and reduced the number of discharge cycles and discharge current of the power battery. Under the cyclic working condition of 1369 s, the state of charge of the power battery in the hybrid power system decreases from 0.9 to 0.69, representing a 12.5% increase compared to the single power system. The peak current of the power battery in the hybrid power system decreases by approximately 20 A compared with that in the single power system. Based on dynamic programming optimization, the state of charge of the power battery decreases from 0.9 to 0.724. Compared with that of the single power system, the power consumption of the proposed system increases by 25%, that of the hybrid power fuzzy control system increases by 14.2%, and that of the vehicle decreases by 14.7% after dynamic programming optimization. The multimode energy shunt relationship is solved through efficient and reasonable energy management and optimization strategies. The performance and advantages of the composite energy storage system are fully utilized. This approach provides a new idea for the energy storage scheme of new energy vehicles.

An integrated energy storage framework with significant energy management and absorption mechanism for machine learning assisted electric vehicle application

Regarding environmental friendliness, low maintenance needs, and statuses as a renewable technology, Hybrid Electric Vehicles (HEV) have become more and more popular around the world. In this, the energy management system is crucial for the effective storage of power and regulation of the energy flow system. As a result, Hybrid Energy Storage Systems (HESS) has increased interest due to their superior capabilities in system performance and battery capacity when compared to solo energy sources. Additionally, the primary problem interaction applications, including such battery electric vehicles, are the energy storage system. Multiple energy storage technologies, including battery packs, flywheels, super-capacitors and fuel cells, are combined into a HESS due to their complementing properties. The goal of this setup is to make renewable energy sources more reliable by storing power generated from intermittent sources or by providing backup energy generation from traditional energy sources. A HESS could be utilized as an alternate energy storage system to help them make up for their lack of power density. HESS needs a smart Energy Management System (EMS) to function properly since it combines the dynamic characteristics of a battery and a SuperCapacitor (SC). The motive of the study is to suggest an actual power management control system to accomplish these objectives. The plan is built using a wavelet transform, deep learning mechanism, and fuzzy logic together. A useful tool for separating the various frequency elements of a load's power requirements to reflect the properties of a battery or supercapacitor is the wavelet transform. It is challenging to immediately apply it in a system, though. Because of this, the traditional optimization-model-based facility energy management system encounters substantial difficulties with online forecast and calculation. To solve this problem, the paper proposes a ML technique dependent on a Long Short-Term Memory (LSTM). The suggested control system structure allows for the separation of the offline and online stages of the LSTM technique. The LSTM is being used to map states (inputs) to decisions (outputs) based on system training during the offline stage. As a result, the supercapacitor receives an online calculation and distribution of the high-frequency power requirement. The SOC of the supercapacitor is kept within the appropriate range via fuzzy logic control. To evaluate the efficacy of the suggested energy management control technique, a 70 V battery with 92 V supercapacitor hybrid energy storage devices for hardware platforms have been created.

Energy Management and Networking for Sensor-based Transmission Line State Monitoring

The transmission line condition monitoring sensor network is large in scale and difficult to maintain. Its long-chain network topology differs from general wireless sensor networks, so it is necessary to develop a self-organizing sensor network with self-energy harvesting. In this regard, a three-layer network model is designed. Three types of node models are constructed. The sensor nodes implement round-robin sleep/wake and energy management mechanisms, powered by self-harvested energy. Additionally, reliable routing between nodes is established. Simulation results show that the developed simulation model is suitable for the application of transmission line condition monitoring. The expected functions of each module have been realized, and the stable and reliable transmission of perceived data of key components of transmission lines has been achieved, thereby providing technical and data basis for the deployment of practical networks.

Improvement on the Power Compensation for the ITER Pulsed Power Electrical Network

The reliable and controllable operation of the ITER machine relies on a compatible and stable power grid. The ITER Pulsed Power Electrical Network (PPEN) already has employed the Static Var Compensator (SVC), but issues, such as poor dynamic characteristics, insufficient capacity, and lack of means to cope with active power shock are still existed. To meet the updated needs of ITER's future operation, this paper proposes the Modular Multilevel Converter (MMC) combined with a supercapacitor (SC) energy storage scheme (E-STATCOM) to realize active and reactive power compensation. The Energy Management Mechanism (EMM) is detailed for the operation of the entire system. Furthermore, a cooperative control method based on an improved second-order filter is presented to achieve coordination with the existing SVC system, and the control decoupling are explored in the frequency domain. The detailed models are built in MATLAB/Simulink and demonstrate that the recommended scheme can effectively and stably achieve the power compensation objective. The paper provides reference design for the upgrade of ITER machine, as well as the power compensation solution for the load with large capacity, high uncertainty and significant impulse characteristics.

A three-stage mechanism for flexibility-oriented energy management of renewable-based community microgrids with high penetration of smart homes and electric vehicles

A multi-stage mechanism for flexibility-oriented energy management (FOEM) of the distribution system is developed in this article, which main novelty is providing the flexibility requirements of the main grid by resources within renewable-based microgrids (RBMGs), including distributed generations (DGs), storage systems (SDs), internet-of-things enabled smart homes (IESHs), and plug-in electric vehicles (PEVs). In the first stage, IESHs are programmed in a decentralized space subject to satisfying the residents' satisfaction index (SI). In the second stage, RBMGs are programmed according to the announced plan of IESHs. Ultimately, in the third stage, the main grid operator calculates the required flexibility capacities of the system according to the announced plans of RBMGs, where a risk-averse strategy is adopted to manage the risk of programming. Next, the main grid operator announces the required flexibility capacities to RBMGs and IESHs so that they can re-plan with the possibility of participating in flexibility markets. The numerical results clarify that the participation of RBMGs, IESHs, and plug-in electric vehicles (PEVs) in providing flexibility capacities not only reduces the dependence of the distribution system on the upstream grid (UG), but also reduces the total daily costs by 30.7%.

Integration of EVs through RES with Controlled Interfacing

Abstract Electric cars have a lot of promise in future energy markets as a manageable load. A popular vehicle-to-grid control interface, which enables the aggregation of the charging mechanism for energy management in the distribution grid, is one of the most significant road blocks to realize this opportunity. Understanding the ecology of electric transportation and integrating it in local communities to alleviate the energy shortage at peak hours is very complicated. In this research paper, recent standardization initiatives aimed at overcoming obstacles such as the integration of electric cars into smart grids are discussed. A charge control scheme focused on vehicle-to-grid connectivity is implemented. It is observed that the rise of environmentally sustainable energy sources, such as photovoltaic (PV) and wind energy, is straining the power network and their infrequent power generation is causing problems in power system operation, regulation and planning. The introduction of electric vehicles (EVs) into the electricity grid has been proposed to overcome grid load variations. Finally, the article discusses the incorporation of renewable energy sources and latest potential solutions involving electric vehicles.

Non-Intrusive Load Monitoring (NILM), Interests and Applications

In developing effective energy management mechanisms, new concepts have been developed to provide new approaches. Non-intrusive load monitoring (NILM) is an approach that was originally developed to allow the occupants of a room to identify the contribution of each appliance to the total electricity consumption of the room through a single point measurement device. The aim is to provide customers with information that will enable them to act as `` ` consum'actors", i.e., people who undertake to change their electricity consumption habits for an objective cause. The progress of artificial intelligence in its various forms (machine learning, big data, internet of things) have greatly contributed to increase the interest of NILM among researchers in different fields. Indeed, some of them are adapting this concept to research areas such as water, transport, health, the environment and agriculture. In this context, applications in these fields have been developed to show the potential and benefits of using this approach. In addition to presenting non-intrusive load monitoring (NILM) in its general framework, this article presents the interests and applications of this approach in various fields.

Optimal home energy management including batteries and heterogenous uncertainties

Energy storage will play a vital role in the decarbonization of the electricity sector, especially in domestic installations. In such kind of systems, home energy management applications are becoming essential. These kinds of tools enable active control of domestic appliances and storage systems to pursue a more efficient use of energy in household installations. However, the emergence of renewable generators and electric vehicles makes the operation of residential assets more difficult, as multiple uncertainties should be managed on a whole. These unknowns have a different character, in fact, some of them can be easily predicted while others are subject to a high level of randomness. This paper addresses this issue by developing a novel home energy management tool that accounts for the different levels of the randomness of the uncertainties involved in home operation. To this end, a novel Lexicographic-Interval formulation of the home energy management problem is presented, by which the uncertainties can be easily modelled using interval notation. Unlike conventional tools, the new proposal sorts the uncertainties according to their level of randomness. Then, the energy management mechanism performs the scheduling plan according to the predefined classification so that the more random parameters rule the impact of others, thus giving more importance to those uncertainties that are hardly predictable. A benchmark case study is performed to validate the new proposal and illustrate its capabilities. Different tariffs are compared, showing that the Time-of-Use mechanism is normally more expensive than Real-Time-Pricing tariffs, increasing the electricity bill by 12 % in some cases. However, the level of robustness achieved with Time-of-Use tariffs seems higher, allowing to assume an abruptly unexpected reduction of photovoltaic generation.

A Near-Optimal Energy Management Mechanism Considering QoS and Fairness Requirements in Tree Structure Wireless Sensor Networks.

The rapid development of AIOT-related technologies has revolutionized various industries. The advantage of such real-time sensing, low costs, small sizes, and easy deployment makes extensive use of wireless sensor networks in various fields. However, due to the wireless transmission of data, and limited built-in power supply, controlling energy consumption and making the application of the sensor network more efficient is still an urgent problem to be solved in practice. In this study, we construct this problem as a tree structure wireless sensor network mathematical model, which mainly considers the QoS and fairness requirements. This study determines the probability of sensor activity, transmission distance, and transmission of the packet size, and thereby minimizes energy consumption. The Lagrangian Relaxation method is used to find the optimal solution with the lowest energy consumption while maintaining the network's transmission efficiency. The experimental results confirm that the decision-making speed and energy consumption can be effectively improved.

Peer-to-peer energy trading in energy local area network considering decentralized energy routing

Peer-to-peer (P2P) energy trading is perceived as a promising energy management mechanism for decentralized renewable energy sources. The concept of energy local area network (e-LAN) is proposed as a new system framework for future microgrids to facilitate P2P trading where the end-to-end power delivery is achieved by energy routing. Decentralized P2P trading in e-LAN requires a perfectly competitive market and flexible end-to-end power delivery. In this paper, a new P2P trading model incorporating decentralized energy routing is proposed for e-LAN. The proposed model is composed of a P2P energy market and a loss compensation market. In the P2P energy market, a tripartite graph model is proposed to represent transactions where trading decisions and energy routing are jointly considered for each participant to pursue its optimal benefit. Considering the size problem and nonlinear weights of the tripartite graph, an extended depth-first branch-and-bound algorithm is proposed to prune low-efficiency paths. Based on the pruned tripartite graph, the competitive P2P market is captured by a multi-leader multi-follower Stackelberg game model. A loss compensation market is established to coordinate energy routing paths planned in a decentralized manner. By means of setting the price for utilizing each transmission line, the loss compensation market avoids network congestion and ensure a normal operation of e-LAN. Simulation results show that the proposed model can bring higher utilities for P2P participants compared with the existing P2P trading models in e-LAN. Additionally, the effectiveness of the proposed model in resolving path conflicts in a decentralized fashion and improving computation efficiency, are verified.

Efficiency of integration between sensor networks and clouds

Numerous wireless sensor networks (WSN) applications include monitoring and controlling various conditions in the environment, industry, healthcare, medicine, military affairs, agriculture, etc. The life of sensor nodes largely depends on the power supply type, communication ability, energy storage capacity and energy management mechanisms. The collection and transmission of sensor data streams from sensor nodes lead to the depletion of their energy. At the same time, the storage and processing of this data require significant hardware resources. Integration between clouds and sensor networks is an ideal solution to the limited computing power of sensor networks, data storage and processing. One of the main challenges facing systems engineers is to choose the appropriate protocol for integrating sensor data into the cloud structure, taking into account specific system requirements. This paper presents an experimental study on the effectiveness of integration between sensor networks and the cloud, implemented through three protocols HTTP, MQTT and MQTT-SN. A model for studying the integration of sensor network - Cloud with the communication models for integration - request-response and publish- subscribe, implemented with HTTP, MQTT and MQTT-SN. The influence of the number of transmitted data packets from physical sensors to the cloud on the transmitted data delay to the cloud, the CPU and memory load was studied. After evaluating the results of sensor network and cloud integration experiments, the MQTT protocol is the most efficient in terms of data rate and power consumption.

Event-triggered model predictive control for dynamic energy management of electric vehicles in microgrids

The rapidly increasing charging demand of electric vehicles (EVs) represents a significant new load with myriad uncertainties. This paper proposes an event-triggered model predictive control (ET-MPC) method for dynamic energy management of EVs in microgrids (MGs). The study novelties lie in the forecasting models of EV status in an environment of MGs and the event-triggered mechanism for dynamic energy management of EVs. Firstly, the characteristics of EVs from different types of MGs are analysed by fitting the EV states with different probability distributions to improve the accuracy of forecasting of their status. Secondly, an ET-MPC method focusing on the energy management of EVs is first proposed to achieve coordination between the computational efficiency and optimisation impact of EVs. The event-triggered mechanism is achieved by monitoring the errors between the real EV states and their forecast values, which is only required to carry out optimisation when the forecast error of the EV states meet a set trigger level. Numerical simulations show that unlike time-triggered MPC methods—with long calculation times and fixed mechanisms—the ET-MPC method proposed achieves nearly the same energy management impact with a significantly shorter calculation overhead.

Peer-to-Peer Energy Trading With Energy Path Conflict Management in Energy Local Area Network

The increasing penetration of distributed renewable generations has given rise to a novel energy management mechanism, peer-to-peer (P2P) energy trading. The concept of Energy Internet (EI) is proposed as a new energy system framework to facilitate P2P energy trading where all distributed electrical devices are interconnected via energy routers (ERs). A proper market clearing approach is required to coordinate decentralized decision making in P2P market for demand-supply balance and feasible end-to-end energy delivery. In this paper, a decentralized market clearing mechanism considering energy path conflict management is proposed for P2P energy trading in energy local area network (e-LAN). An adjusted conflict-based search (ACBS) algorithm is proposed to deal with the non-convexity of the social welfare maximization problem where the nonconvex problem is transformed into multiple independent convex problems. Considering the privacy protection of participants, a dual decomposition based approach is proposed to solve these convex problems in a decentralized manner where self rationality of each participant can be satisfied by maximizing its personal welfare. In addition, energy path conflicts caused by decentralized optimization is resolved by imposing penalty fee and observing the rule of energy sharing maximization. Numerical simulations are presented to verify the effectiveness of the proposed market clearing approach.

Energy management system for a small-scale microgrid

In recent years, the power system has been evolved into microgrids, which are little pockets of self-contained entities. Different distributed, interconnected generation units, loads, and energy storage units make up a typical microgrid system. The increased energy efficiency of these units on microgrids is gaining popularity day by day. Because of their stochastic behavior, renewable generation causes an imbalance in the power system, which needs microgrid energy management. To solve these issues, a variety of novel approaches have been explored in the literature. For the stand-alone microgrid in this research, efficient energy management and control mechanism is adopted. A photovoltaic system, a wind turbine, and a battery energy storage device make up this stand-alone microgrid. The power stability of the hybrid system is ensured by a sophisticated controller. The main purpose of this study is to regulate the DC/DC bidirectional converter (DBC), which connects the Li-ion battery to the DC bus of the stand-alone microgrid. This paper describes the development of a wind/photovoltaic power generation system to the load, as well as MPPT techniques like perturb and observe (P&O). The system is simulated and the results are presented using MPPT techniques. There is no requirement for a specific power model in the suggested method. Only power and voltage system data are used by DBC. A stand-alone microgrid system was simulated using MATLAB.

A Performance Comparison of Machine Learning Algorithms for Load Forecasting in Smart Grid

With the rapid increase in the world’s population, the global electricity demand has increased drastically. Therefore, it is required to adopt efficient energy management mechanisms. Since the energy consumption trends are rather dynamic. Therefore, precise energy demand estimation and short and/or long-term forecasting results with higher accuracy are required to develop the optimization and control mechanism. Consequently, the machine learning (ML) techniques along with distributed demand response programs are being adopted to predict the future energy demand requirement with satisfactory results. In this paper, different state-of-the-art ML algorithms such as logistic regression (LR), support vector machines (SVM), naive Bayes (NB), decision tree classifier (DTC), K-nearest neighbor (KNN), and neural networks (NNs), have been implemented to analyze their performance. The main objective of this paper is to present a comparative analysis of ML algorithms for short-term load forecasting (STLF) regarding accuracy and forecast error. Based on the implementation and analysis, we have identified that, among other algorithms, the DTC provides comparatively better results. Therefore, we devised the enhanced DTC (EDTC) by integrating fitting function, loss function, and gradient boosting in DTC mathematical model for fine-tuning the control variables. The implementation results show that the proposed EDTC algorithm provides better forecast results (i.e., 99.9 % recall, 100% F1, 100% precision, 99.21 % training accuracy, and 99.70% testing accuracy.)

Application of Real-Time Distribution Grid Monitoring for Grid Forecasting and Control Considering Incomplete Information of Resources Behind-the-Meter

This paper presents a rolling horizon optimal energy management mechanism using real-time grid monitoring data. The proposed mechanism includes novel approaches in terms of real-time data processing, net-load forecasting, and optimal scheduling of battery energy storage systems. The proposed real-time data processing and net-load forecasting techniques use fast training and computationally efficient methods based on grid monitoring data. The data processing and parameter forecasting methods are based on auto-regressive with exogenous variables (ARX). Two sets of features including similar values in previous hours, days, and weeks as well as calendar effects are used for training the forecast model. Furthermore, the impact of additional non-synchronized weather features adopted from meteorology databases on the forecasts’ accuracy is discussed. Finally, a real-time optimal scheduling is proposed to optimize battery energy storage systems (BESS), maximizing the self-consumption at grid and community levels. The application of real-time grid measurement in the proposed algorithm allows handling the impacts of loads and generators behind the meter without having their detailed information. The developed method is being effectively used in a real low voltage distribution grid in Switzerland.

Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) are prone to highly constrained resources, as a result ensuring the proper functioning of the network is a requirement. Therefore, an effective WSN management system has to be integrated for the network efficiency. Our objective is to model, design, and propose a homogeneous WSN hybrid architecture. This work features a dedicated power utilization optimization strategy specifically for WSNs application. It is entitled Hybrid Energy-Efficient Power manager Scheduling (HEEPS). The pillars of this strategy are based on the one hand on time-out Dynamic Power Management (DPM) Intertask and on the other hand on Dynamic Voltage and Frequency Scaling (DVFS). All tasks are scheduled under Global Earliest Deadline First (GEDF) with new scheduling tests to overcome the Dhall effect. To minimize the energy consumption, the HEEPS predicts, defines and models the behavior adapted to each sensor node, as well as the associated energy management mechanism. HEEPS’s performance evaluation and analysis are performed using the STORM simulator. A comparison to the results obtained with the various state of the art approaches is presented. Results show that the power manager proposed effectively schedules tasks to use dynamically the available energy estimated gain up to 50%.