Application Of Energy Management System Research Articles

A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema

This paper introduces a portable framework for developing, scaling and maintaining energy management and information systems (EMIS) applications using an ontology-based approach. Key contributions include an interoperable layer based on Brick schema, the formalization of application constraints pertaining metadata and data requirements, and a field demonstration. The framework allows for querying metadata models, fetching data, preprocessing, and analyzing data, thereby offering a modular and flexible workflow for application development. Its effectiveness is demonstrated through a case study involving the development and implementation of a data-driven anomaly detection tool for the photovoltaic systems installed at the Politecnico di Torino, Italy. During eight months of testing, the framework was used to tackle practical challenges including: (i) developing a machine learning-based anomaly detection pipeline, (ii) replacing data-driven models during operation, (iii) optimizing model deployment and retraining, (iv) handling critical changes in variable naming conventions and sensor availability (v) extending the pipeline from one system to additional ones.

Three‐phase state estimation in transmission networks with unbalanced loading utilising SCADA measurements

AbstractState estimation stands as one of the most crucial applications in energy management systems of control centres, ensuring the secure operation of the power system. The real‐time availability of accurate system variables, including voltages, currents, and power flows, presents an excellent opportunity to consider the actual network characteristics, including imbalances. This paper presents a linear and non‐iterative method to solve the three‐phase state estimation problem in transmission systems with unbalanced loading. The method utilises supervisory control and data acquisition measurements obtained from remote terminal units. The formulation is based on complex variables derived from traditional supervisory control and data acquisition measurements, with assumptions made regarding the availability of certain three‐phase bus voltages, as well as a number of three‐phase currents and active and reactive power flows. The proposed algorithm employs the classical weighted least squares technique for solving the three‐phase state estimation problem. Due to its straightforward and linear nature, the method ensures no convergence issues. To validate its effectiveness, the proposed method is validated on a three‐bus network and the IEEE 39‐bus network simulated using the three‐phase model within the DIgSILENT Power Factory environment.

Machine Learning Applications in Energy Management Systems for Smart Buildings

Machine Learning Applications in Energy Management Systems for Smart Buildings

Applying a model-driven approach to the development of power plant SCADA/EMS software

In this paper, we propose and apply a model-driven approach to develop applications that compose a suite known as Energy Management System (EMS). EMS is an essential tool for the operation of electrical systems. Although models of cyber–physical phenomena are extensively used in planning, designing, and operating power systems, the development of EMS applications often uses such models only as support and documentation, while the main artifact throughout the software development process is source code. In other words, the models are used merely to document and communicate some aspects of a system. The evolution of such a system is expensive and error-prone, requiring specialized personnel, skilled in both software engineering and power systems. Some of the problems arising from this manual process include: (i) substantial implementation effort in interpreting models and manually translating them into programs; (ii) greater risk of introducing software defects; (iii) risk of leaving documentation outdated, or implementation inconsistent with the intended design. Model-Driven Engineering (MDE) is an approach to constructing computing systems in which models are used as primary engineering artifacts, while programs (and other products) are automatically generated from the models, thus addressing the above limitations. MDE approaches have been adopted in several industrial areas, including smart grid automation, in which specialized modeling languages and tools facilitate the production of executable software from high-level models. For SCADA/EMS software development, however, a dedicated approach is still absent. We address these issues by proposing D-SPADES: a Model-Driven Engineering approach tailored to the EMS domain. D-SPADES defines a domain-specific modeling language based on block diagram notation, called EMSML, specifically targeted to describe the behavior of EMS applications. It also defines the associated mapping strategies and harnesses a set of tools for automatically transforming models into source code of applications that can be integrated into existing SCADA platforms. We have applied D-SPADES to model and automatically generate source code for two different applications from the Itaipu power plant: a voltage and reactive power controller and a component for a system-wide special protection scheme. Both applications were validated using a power system simulator and performed satisfactorily. The special protection scheme was deployed to the production system. Hence we demonstrate that D-SPADES is a viable approach to developing mission-critical applications.

Preparation of Microencapsulated Phase Change Materials from Sulfonated Graphene Stabilized Pickering Emulsion.

Microencapsulated phase change materials (MCPCM) as a green energy storage material not only prevent leakage of phase change materials but also increase the heat transfer area of phase change materials. Extensive previous work has shown that the performance of MCPCM depends on the shell material and MCPCM with polymers, as the shell material suffers from low mechanical strength and low thermal conductivity. In this study, a novel MCPCM with hybrid shells of melamine-urea-formaldehyde (MUF) and sulfonated graphene (SG) was prepared by in situ polymerization using SG-stabilized Pickering emulsion as a template. The effects of SG content and core/shell ratio on the morphology, thermal properties, leak-proof properties, and mechanical strength of the MCPCM were investigated. The results showed that the incorporation of SG into the shell of MUF effectively improved the contact angles, leak-proof performance, and mechanical strength of the MCPCM. Specifically, the contact angles of MCPCM-3SG were reduced by 26°, the leakage rate was reduced by 80.7%, and the breakage rate after high-speed centrifugation was reduced by 63.6% compared to MCPCM without SG. These findings suggest that the MCPCM with MUF/SG hybrid shells prepared in this study has great potential for application in thermal energy storage and management systems.

Fuzzy logic-based energy management for isolated microgrid using meta-heuristic optimization algorithms

Rural areas have increased directly with the growth of the world population. Although, isolated rural areas do not have the energy infrastructure to supply essential services such as electricity. In Ecuador, as in the rest of the world, these areas of the rural population usually present access problems due to the topography of the place. This makes the construction of power lines to connect them to the Interconnected National Energy System unfeasible. In this regard, isolated microgrids have emerged as a great solution to cover the energy demands in these locations. However, an optimal implementation of isolated microgrids depends on several factors, such as geographical location, weather conditions, sizing, load demand, operating costs, and social impacts. Therefore, this study proposes the design of a new energy management system (EMS) for isolated microgrids comprising a photovoltaic system, diesel generator, and battery energy storage system (ESS). Since fuzzy logic control (FLC) has proven to be a powerful tool for dealing with the nonlinearities of a microgrid and the application of fuzzy-based EMS for isolated microgrids is rarely reported in the literature, this study proposes the application of an FLC for the EMS's design of an isolated microgrid. The proposed fuzzy-based EMS uses generation and demand forecasts, enhances the operating time of diesel generators (DLG), and takes advantage of the available solar resource to supply the energy required by a community while preserving the ESS lifetime. An adjustment of the FLC parameters by Particle Swarm Optimization (PSO) and Cuckoo Search (CS) algorithms is performed to improve the behavior of the proposed EMS. Furthermore, a battery degradation model is applied to estimate the ESS State of Health (SOH). To highlight the advantages of the proposed approach, a case study in a specific community in Ecuador is presented. In this location, the proposed EMS is compared with an EMS without parameter adjustment developed in previous work, demonstrating improved performance in DLG limits, preserving the battery lifespan by controlling the battery SOC limits more efficiently, and minimizing the microgrid operating costs by maximizing the use of photovoltaic power (i.e., reducing the wasted photovoltaic energy). The results provide evidence that the EMS adjusted with the PSO algorithm presents an enhanced behavior than the one adjusted by the CS algorithm. Finally, the fuzzy-EMS is validated using Matlab® and Hardware-in-the-Loop Typhoon HIL-402 device. Results demonstrate that the proposed approach limits DLG usage, make the most of the available solar resources, and extends the battery life by controlling overcharges and deep discharges.

Development of an Energy Management System for a Renewable Energy Community and Performance Analysis via Global Sensitivity Analysis

This paper presents the development of an energy management system (EMS) for a renewable energy community (REC) with the load-generation balancing objective. In this regard, rule-based and optimization mechanisms are proposed for the REC management in line with the scope of a field trial and considering the scarcity of the measurement and historical data. This typical data scarcity along with the intermittent behaviour of renewable energy resources introduce an unavoidable level of uncertainty— not being adequately addressed in the EMS literature— that might ultimately affect the proper REC management. Hence, a comprehensive performance analysis of the proposed EMS has been conducted via global sensitivity analysis (GSA). Particularly, variance-based sensitivity analysis has been employed to investigate how the variability of a set of selected indicators of the REC performance is apportioned to the different sources of uncertainty specifically related to the forecast and flexibility availability. Results show that the EMS performance is consistent with the EMS objective. The application of GSA reveals though interesting findings that contradict antecedent misconceptions about how different uncertainty sources affect the EMS performance. Although being related to the specific REC under study, the present work specializes GSA method in novel ways that pave the path for its reusability in the context of other EMS applications with different boundary conditions. By highlighting the necessity of GSA and showcasing its suitability to study the EMS performance under an uncertainty framework, the present work offers a precious tool to support system operators in their decision making process.

The Effect of Probabilistic Window Opening Behavior of Occupants on Adaptive Thermal Comfort (The Case of Courtyard House in Yazd, Bandar Abbas, and Tabriz)

Considering the global energy crisis and the need to reduce energy consumption while providing thermal comfort to occupants, building performance prediction using building simulation programs requires higher accuracy of output data. Therefore, it seems necessary to study the impact of occupant behavior, which is the main source of uncertainty in residential buildings. The traditional courtyard houses, which are recognized as a successful passive house model, respond to different climatic conditions. Therefore, this research focuses on this building type to analyze occupant window opening control scenarios and determine which control works better. For this purpose, several probabilistic controls and their effects on the adaptive thermal comfort of occupants in zones around a central courtyard were compared in the three cities of Yazd, Bandar Abbas, and Tabriz. Energy Plus was used as a simulation program for the application of Grasshopper's energy management system (EMS) along with the Ladybug and Honeybee environmental plugins. The results show that the window control algorithms can increase the adaptive thermal comfort of occupants by 25.7%, 32.2%, and 20.3% in each of the climates of Yazd, Bandar Abbas, and Tabriz cities, respectively. Indoor and outdoor temperature were the most significant variables for opening windows in the warm and cold seasons, respectively.

Energy Management System with Power Offering Strategy for a Microgrid Integrated VPP

In the context of both the Virtual Power Plant (VPP) and microgrid (MG), the Energy Management System (EMS) is a key decision-maker for integrating Distributed renewable Energy Resources (DERs) efficiently. The EMS is regarded as a strong enabler of providing the optimized scheduling control in operation and management of usage of disperse DERs and Renewable Energy reSources (RES) such as a small-size wind-turbine (WT) and photovoltaic (PV) energies. The main objective to be pursued by the EMS is the minimization of the overall operating cost of the MG integrated VPP network. However, the minimization of the power peaks is a new objective and open issue to a well-functional EMS, along with the maximization of profit in the energy market. Thus, both objectives have to be taken into account at the same time. Thus, this paper proposes the EMS application incorporating power offering strategy applying a nature-inspired algorithm such as Particle Swarm Optimization (PSO) algorithm, in order to find the optimal solution of the objective function in the context of the overall operating cost, the coordination of DERs, and the energy losses in a MG integrated VPP network. For a fair DERs coordination with minimized power fluctuations in the power flow, the power offering strategies with an active power control and re-distribution are proposed. Simulation results show that the proposed MG integrated VPP model with PSO-based EMS employing Egalitarian reDistribution (ED) power offering strategy is most feasible option for the overall operating cost of VPP revenue. The total operating cost of the proposed EMS with ED strategy is 40.98$ compared to 432.8$ of MGs only without EMS. It is concluded that each MGs in the proposed VPP model intelligently participates in energy trading market compliant with the objective function, to minimize the overall cost and the power fluctuation.

Data-Driven Detection of Stealthy False Data Injection Attack Against Power System State Estimation

Power system state estimation (PSSE) is the foundation of energy management system applications. Hence, operators impose stringent requirements on PSSE data integrity. False data injection attacks (FDIAs) can cause risks to PSSE data-driven operations and demand mitigation. In this article, we present a two-step FDIA detector design. In step one, we study a novel stealthy attack policy by simultaneously considering the attacker’s cost reduction and damage production. In step two, with the aid of a deep autoencoding Gaussian mixture model (DAGMM), we design an unsupervised detection scheme to detect the stealthy attack. The DAGMM-based detector can meet the requirement of rapidity, unsupervisedness, and data imbalance tolerance. Eventually, we simulate and validate the stealthy attack policy and the corresponding detector using the benchmark IEEE 39-bus and 118-bus systems.

Fuzzy Based Energy Management Strategy for Hybrid Micro Grid with Battery Storage System

By connecting it to the hybrid micro grid system, this explains the combination of wind, solar, and fuel cell power. Renewable energy is frequently used today. Distributed energy sources like wind, solar, fuel cells, etc., can operate concurrently with a larger utility. The majority of people today want to use sustainable energy sources like solar, wind, tidal, geothermal, wave, etc. A tiny grid produces the necessary electricity. In this project, energy storage and utilisation with a micro grid are demonstrated. This project serves as an example of how a micro grid can be used to store and use power. We usually allow the Micro Grid to use us by producing this energy. The system is a composite that utilises renewable resources such fuel cell, a solar array, a wind generator, and an energy storage system (ESS) that uses a battery as one of its renewable energy sources. Power Electronics converters are crucial to the system since they enhance power management and control strategies for various sources. The single-input Fuzzy logic and speed controllers are utilised to track the maximum power point for the wind and PV subsystems, respectively (MPPT). A power management method based on battery state of charge (SOC) was created and put into use to maintain the hybrid system's energy balance. A proportional integrative (PI) controller was used to supply a resistive load with a constant amplitude and frequency in order to regulate the AC output voltage was obtained. The proposed solution is deemed to be very promising for possible applications in hybrid renewable energy management systems based on the received bids. MATLAB/SIMULINK is to be used to develop the simulation.

Reinforcement Learning: Theory and Applications in HEMS

The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article. It surveys the use of RL in various home energy management system (HEMS) applications. There is a focus on deep neural network (DNN) models in RL. The article provides an overview of reinforcement learning. This is followed with discussions on state-of-the-art methods for value, policy, and actor–critic methods in deep reinforcement learning (DRL). In order to make the published literature in reinforcement learning more accessible to the HEMS community, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using standard machine learning terminology. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. It suggests that research in this direction is still in its infancy. Lastly, the article proposes four performance metrics to evaluate RL methods.

Development and Application of Smart Home Energy Management System Based on Wireless Network Technology

In order to solve the problem of integration of home energy management system and wireless network technology, the author proposes the development and application of a smart home energy management system based on wireless network technology. Divide the NB-IoT into different simulation levels, simulate the physical layer of the NB-IoT, and according to the household energy conservation rules, calculate the household energy distribution parameters, convert the intelligent control signal into a signal constellation point, calculate the deviation value of the control command corresponding to the energy equipment, build an intelligent control simulation algorithm, and finally complete the simulation of the intelligent control system. Experimental results show the following: The simulation method in the intelligent home language input terminal control system is based on the Internet of Things, the average time of simulating the test point of energy equipment is about 4.9 s, the required simulation time is long, and in the intelligent control system of the mobile road tunnel lighting vehicle, the simulation time required by the designed simulation method is about 6.8 s, and the simulation time is the longest. The average time of the method proposed by the author to simulate the energy distribution instruction is about 3.3 s. Conclusion. The application prospect of a smart home energy management system based on wireless network technology is very broad.

Application of intelligent energy management and control system in industrial enterprises

Under the background of carbon peaking and carbon neutrality, the efficient use of energy has become the focus of research in the energy field. The article first analyzes the challenges in the energy management and control system based on the development status and trends of the intelligent energy management and control system of industrial enterprises; then proposes the system architecture and design of the intelligent energy management and control system of industrial enterprises, from energy monitoring, energy management, energy forecasting, energy analysis, energy diagnosis, energy optimization and other aspects provide comprehensive and strong support for the energy management of industrial enterprises, and realize the integration and automation of energy and production data.

Topological Approach for Identifying Critical Measurements and Sets in State Estimation

Power system state estimation (SE) is an energy management system application responsible for providing a consistent real-time database, instrumental in monitoring the system. SE input data are redundant observations (measurements) of the system state (complex bus voltages) taken in a given network configuration. Measurement redundancy is an essential requirement for the SE results' reliability, determined by the quantity, location, and type of measurements received for processing. There are two ways to approach the observability/ criticality analysis, namely topological (a graph theory-based) and numerical (performed by arithmetic operations on matrices). From the conceptual viewpoint, topological methods are adequate since the problem in question is considered structural-natured, dependent on the network topology and the type/placement of measurements. Criticality analysis has been considered vital to reveal the different network observability degrees established by the measuring system. The most severe conditions are related to the occurrence of single critical measurements and critical sets of measurements. These conditions refer to imminent network unobservability and SE limitation to detect/identify the presence of spurious measurements. This paper proposes a graph theory-based method devoted to identifying essential elements to SE. Conventional measurements (branch power flows and bus power injections) coming from SCADA systems, as well as synchrophasors (phase angles and branch current measurements), are considered. Simulation results carried out on the IEEE 14-bus test system are provided.

Deep Reinforcement Learning for Autonomous Water Heater Control

Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand Response (DR) and building energy management system (BEMS) applications. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use (TOU) electricity pricing policy by only using standard DR commands. In this approach, a set of RL agents, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on an unseen pair of price and hot water usage profiles. The testing results showed that the RL agents can help save electricity cost in the range of 19% to 35% compared to the baseline operation without causing any discomfort to end users. Additionally, the RL agents outperformed rule-based and model predictive control (MPC)-based controllers and achieved comparable performance to optimization-based control.

A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV

Load-forecasting problems have already been widely addressed with different approaches, granularities and objectives. Recent studies focused not only on deep learning methods but also on forecasting loads on single building level. This study aims to research problems and possibilities arising by using different load-forecasting techniques to manage loads. For that behavior of two neural networks, Long Short-Term Memory and Feed-Forward Neural Network as well as two statistical methods, standardized load profiles and personalized standardized load profiles are analyzed and assessed by using a sliding-window forecast approach. The results show that personalized standardized load profiles (MAE: 3.99) can perform similar to deep learning methods (for example, LSTM MAE: 4.47). However, because of the simplistic approach, load profiles are not able to adapt to new patterns. As a case study for evaluating the support of load-forecasting for applications in energy management systems, the integration of charging stations into an existing building is simulated by using load-forecasts to schedule the charging procedures. It is shown that forecast- based controlled charging can have a significant impact by lowering overload peaks exceeding the house connection point power limit (controlled charging 20.24 kW; uncontrolled charging: 65.15 kW) while slightly increasing average charging duration. It is concluded that integration of high flexible loads can be supported by using forecast-based energy management systems with regards to their limitations.

The application of building energy management system based on IoT technology in smart city

To introduce new energy management (EM) systems that apply solar energy, geothermal energy, and wind energy to intelligent building (IB), so as to reduce the energy consumption of traditional buildings, and integrate it into the building equipment management system (EMS) to make the application of new energy more transparent and rationalized. The research results show that according to the three characteristics of Internet of Things (IoT)—“Sensor, Internet, Intelligent”, the problem of data connection between IB EMS and IoT is solved. Then, through the user’s demand analysis of application services, it is determined the service content that the system can provide for the user. It can be concluded that the application development environment of the IoT data application platform is used to design and develop a new energy application management system based on the IoT, which lays a theoretical foundation for realizing the large-scale EM of “smart city”.

Application of energy management system in auxiliary power consumption at combined cycle power plant

Reliability and efficiency are a power-plant’s commitment to the government in anticipating and overcoming the threat of energy crisis and the environmental impact of energy consumption. The demand for a highly-efficient operation has sparked competitions among power generation industries. The Energy Management System was adopted to evaluate the energy consumption for powering-up electrical equipment in a power plant operation. It is also defined as in-house usage. This study was conducted to analyse Energy Management System compatibility in combined-cycle power plants, utilising Pareto Diagram to discover the most significant consumption of electrical equipment, resulting in the likelihoods of energy and cost savings. This study proposed a mathematical opportunity of saving 342 MWh energy and boosting revenue by 410 million Rupiahs or 28 thousand US Dollars in one year.

Ti3C2Tx@Polyvinyl alcohol foam-Supported Phase Change Materials with Simultaneous Enhanced Thermal Conductivity and Solar-Thermal Conversion Performance

The phase change materials (PCMs) are attractive and environmental-friendly way to improve the utilization rate in the field of photothermal conversion and solar radiation storage. However, the low photothermal conversion efficiency and low thermal conductivity characteristics of PCMs limit their practical applications. In order to improve the performance of these materials, we developed a novel polyethylene glycol (PEG 2000) PCMs composites supported by Ti3C2Tx@polyvinyl alcohol (PVA) foam skeleton with several advantages like low-cost, scalable and simple preparation method by freeze-drying after PEG being introduced into the skeleton via vacuum impregnation. The thermal conductivity of Ti3C2Tx/PEG composite containing 7.68 wt% skeleton was notably high as 0.428 W/(m·K)，which was 4.2 times higher than PEG 2000 (0.101 W/(m·K)). The improvement of thermal conductivity was mainly derived from the three-dimensional interconnected structures of the foamy skeleton. The actual phase change enthalpy of composite searched up to 131.1 J/g, and its photothermal conversion efficiency achieved 96.5%, demonstrating excellent photothermal conversion and storage ability. Furthermore, the Ti3C2Tx/PEG composite exhibited structural stable property that avoids leakage of liquefied PCM during melting process. This multifunctional Ti3C2Tx@PVA/PEG PCM provided a prospective application in thermal energy management systems regarding its high thermal conductivity, excellent photothermal conversion efficiency, thermal energy storage (TES) capability and structural stability.