Application Of Energy Management System Research Articles

Experimental investigation of power management and control of a PV/wind/fuel cell/battery hybrid energy system microgrid

This paper presents an experimental study of a standalone hybrid microgrid system. The latter is dedicated to remote area applications. The system is a compound that utilizes renewable sources that are Wind Generator (WG), Solar Array (SA), Fuel Cell (FC) and Energy Storage System (ESS) using a battery. The power electronic converters play a very important role in the system; they optimize the control and energy management techniques of the various sources. For wind and solar subsystem, the speed and Single Input Fuzzy Logic (SIFL) controllers are used respectively to harvest the maximum power point tracking (MPPT). To maintain a balance of energy in the hybrid system, an energy management strategy based on the battery state of charge (SOC) has been developed and implemented experimentally. The AC output voltage regulation was achieved using a Proportional Integral (PI) controller to supply a resistive load with constant amplitude and frequency. According to the obtained performances, it was concluded that the proposed system is very promising for potential applications in hybrid renewable energy management systems.

Embedded Digital Twins in future energy management systems: paving the way for automated grid control

Abstract Emerging real-time applications in information technology, and operational technology enable new innovative concepts to design and operate cyber-physical systems. A promising approach, which has been discovered recently as key technology by several industries is the Digital Twin (DT) concept. A DT connects the virtual representation of a physical object, system or process by available information and sensor data streams, which allows to gather new information about the system it mirrors by applying analytic functions. Thereby the DT technology can help to fill sensor data gaps, e. g., to support anomaly detection, and to predict future operating conditions and system states. This paper discusses a dynamic power system DT as a cornerstone instance of a new generation of EMS, and a prospective new EMS architecture, to support the increasingly complex operation of electric power systems. Unlike in traditional offline power system models, the parameters are updated dynamically using measurement information from the supervisory control and data acquisition (SCADA) and a wide area monitoring system (WAMS) to tune the model. This allows to derive a highly accurate virtual representation of the mirrored physical objects. A simulation engine, the Digital Dynamic Mirror (DDM) is introduced, in order to be able to reproduce the state of a reference network in real-time. The validation of the approach is carried out by a case study. In a closed loop within EMS applications, the DDM can help to assess contingency mitigation strategies, thus it can support the decision-making process under variable system conditions. The next generation of control centre Energy Management System (EMS) can benefit from this development by augmentation of the dynamic observability, and the rise of operator situation awareness.

An Autonomous Home Energy Management System Using Dynamic Priority Strategy in Conventional Homes

With the growth in smart technology, customers have a chance to contribute to demand response programs and reduce their bills of electricity actively. This paper presents an intelligent wireless smart plug demonstration, which is designed to control the electrical appliances in the home energy management system (HEMS) application with a response to the utility company’s signal. Besides, a linear model of an energy management system utilizing a dynamic priority for electrical appliances is used as an energy management strategy. This can be useful for decreasing energy consumption in peak hours. Proposed hardware is tested with two different price strategies such as real-time pricing and a combination of this and incremental block rate (IBR) pricing. A small one-story house with ordinary electrical appliances is used as a test-bed for the proposed hardware and strategy. Initial results show that intelligent plugs can decrease the energy cost by 9% per day with an effective peak-to-average ratio deduction compared to the domicile without deploying intelligent plugs and controllers.

Multi-objective MILP model for PMU allocation considering enhanced gross error detection: A weighted goal programming framework

This paper proposes a multi-objective mixed-integer linear programming (MILP) model for the optimal phasor measurement unit (PMU) placement (OPP) problem. A majority of the solutions presented for the OPP problem focus on minimizing cost while guaranteeing system observability. While this is a good approach to the OPP problem, the effect of PMU allocation on various energy management system applications should also be considered. This paper addresses the OPP problem considering PMU installation costs, system observability, and gross error detection. In order to allocate PMUs considering gross error detection, the Vulnerability Index (VI) is used to quantify the vulnerability of each element in the system. A weighted goal programming (WGP) framework, which allows for the optimization of contradictory goals, which is a characteristic of the goals considered in this paper, is presented. The goals and weights of the framework are user inputs to the program besides a hard budget restriction. The results of this paper show how the WGP framework allows the priorities of the decision maker to influence the final PMU allocation presented by the MILP, making this model a valuable framework for utilities planning to install PMUs on their network.

Analysis and application of building heating and thermal energy management system

Objective: Through the analysis and application of building heating and thermal energy management system, this paper proposes a new thermal energy control strategy to improve the automation level of building heating optimization. Method: This study analyzes the principle of indoor heat balance in buildings. Aiming at the different heating needs of different buildings, a new control strategy is proposed by combining neural network models and fuzzy control theory. Finally, this strategy is applied to the actual building heating, and the practical application value of the strategy proposed by this study is verified through experiments. Result: In the heating stage, after applying the control strategy, the maximum relative error of the temperature is 0.047, and the average error is 0.013. In the antifreeze stage, the maximum error is 0.143 and the average error is 0.09. After the implementation of the control strategy, the temperature fluctuations in the room change little and remain almost between 19 ?C and 21 ?C. Buildings consume less heat with the highest energy saving rate of 14.37% and the average energy saving rate of 9.23%. Conclusion: The control strategy proposed in this study can adjust the indoor temperature according to the actual situation and achieve the purpose of reasonable heat use. Moreover, it has certain energy-saving effects and can be applied to building heating.

Analysis and use of building heating and thermal energy management system

To explore the role and influence of thermal energy management system on building heating, by building a thermal energy management system based on the Internet of Things, the situation of heating system and building heating is analyzed, the heat utilization rate of building heating, the stability of heating temperature, the change of heating energy consumption are mainly studied, and the energy consumption of building and the comprehensive effect of thermal energy management system and residents? satisfaction are analyzed. The research results show that through the role of the Internet of Things thermal energy management system, the heat utilization rate of heating buildings has increased from about 65% to about 80%, about 15%. The fluctuation of heating water temperature is reduced from 12?C before the system is adopted to 4 ?C, which improves significantly. The coal consumption per hour of heating system is reduced from 63 kg/h to 50 kg/h, and the coal saving is about 15%. This not only saves resources but also reduces environmental pollution. The heat management system based on the Internet of Things has significantly improved the heating system and building heating. Through the application of thermal energy management system, not only the heat utilization rate is increased, but also the consumption of resources is reduced and the environment is protected. Meanwhile, it solves the problem of building heating and the maximization of efficiency in the operation of heating companies. The research on building heating and thermal energy management system has a positive effect on the follow-up research.

Energy Management System Application for Sustainable Development in Wood Industry Enterprises

This study analyzes the energy management approach as one of the driving factors for sustainable development, not only in enterprises, but also countries. Sustainability of an enterprise is defined as its tendency to meet the need of customers and stakeholders through creative and innovative business strategies as well as activities that have a positive influence on society and environment. In this study, sustainability is measured through the application of an energy management system, but in the wood industry, which has an increasingly significant impact on the economic development in one country. The aim of this research is to determine the current situation regarding the application of energy management practices in production and service systems. The ISO 50001 standard for energy management systems considers the energy performance achieved by an enterprise. The connections between critical factors and the level of application requirements for the energy management system were statistically analyzed in this research. It was found that in enterprises that have a certified system of quality and environmental management, a higher level of energy management system implementation is evident. The research results provide data on the scientific level for the national policy improvement, as well as a developed model based on the Plan Do Check Act (PDCA) cycle for energy management system application.

Adaptive Hierarchical Energy Management Design for a Plug-In Hybrid Electric Vehicle

To promote the real-time application of the advanced energy management system in hybrid electric vehicles (HEVs), this paper proposes an adaptive hierarchical energy management strategy for a plug-in HEV. In this paper, deep learning (DL) and genetic algorithm (GA) are synthesized to derive the power split controls between the battery and internal combustion engine. First, the architecture of the multimode powertrain is founded, wherein the particular control actions, state variables, and optimization objective are explained. Then, the hierarchical framework for control actions generation is introduced. GA is utilized to search the global optimal controls based on the powertrain model provided in MATLAB/Simulink. DL is applied to train the neural network model that is connecting the inputs and control actions. Finally, the effectiveness of the presented integrated energy management strategy is validated via comparing with the original charge depleting/charge sustaining policy. Simulation results indicate that the proposed technique can highly improve the fuel economy. Furthermore, a hardware-in-the-loop is conducted to evaluate the adaptive and real-time characteristics of the designed energy management system.

Enhancing Power System Cyber-Security With Systematic Two-Stage Detection Strategy

State estimation estimates the system condition in real-time and provides a base case for other energy management system (EMS) applications including real-time contingency analysis and security-constrained economic dispatch. Recent work in the literature shows malicious cyber-attack can inject false measurements that bypass traditional bad data detection in state estimation and cause actual overloads. Thus, it is very important to detect such cyber-attack. In this paper, multiple metrics are proposed to monitor abnormal load deviations and suspicious branch flow changes. A systematic two-stage approach is proposed to detect false data injection (FDI) cyber-attack. The first stage determines whether the system is under attack while the second stage identifies the target branch. Numerical simulations verify that FDI can cause severe system violations and demonstrate the effectiveness of the proposed two-stage FDI detection (FDID) method. It is concluded that the proposed FDID approach can efficiently detect FDI cyber-attack and identify the target branch, which will substantially improve operators' situation awareness in real-time.

Melamine Foam-Supported Form-Stable Phase Change Materials with Simultaneous Thermal Energy Storage and Shape Memory Properties for Thermal Management of Electronic Devices.

Paraffin wax (PW) is widely used as a phase change material (PCM) in the thermal energy storage field, whereas the leakage and strong rigidity of PW have hindered its practical applications. In this work, binary melamine foam (MF)/PW blends with simultaneous thermal energy storage and shape memory properties were prepared through vacuum impregnation. Herein, PW performs as a latent heat storage material and as a switching phase for shape fixation and MF serves as a supporting material to prevent the leakage and as a permanent phase for shape recovery. Due to the light weight and super-elasticity of MF, the MF/PW PCMs possess not only good encapsulation ability and a high latent heat, but also excellent shape-fixing and recovery properties (shape-fixing and recovery ratios are about 100%). Besides, the MF/PW PCMs can be fabricated into arbitrary shapes using MF as a template, and they exhibit excellent shape memory cyclic performance and thermal reliability. A temperature-sensitive and temperature-controlled deployable panel is further established, which can be installed in the electronic device and used for temperature protection. With high thermal energy storage capability, excellent shape memory properties, shape designability, and stable cycling reliability, this multifunctional MF/PW PCM shows a promising application in thermal energy management systems.

“Stiff–Soft” Binary Synergistic Aerogels with Superflexibility and High Thermal Insulation Performance

AbstractDesigning aerogel materials featuring both high thermal insulation property and excellent mechanical robustness is of great interest for applications in superior integrated energy management systems. To meet the above requirements, composite aerogels based on hierarchical “stiff–soft” binary networks are reported, in which secondary mesoporous polymethylsilsesquioxane domains intertwined by bacterial cellulose nanofibrillar networks are connected in tandem. The resulting composite aerogels are characterized by highly porous (93.6%) and nanosized structure with a surface area of 660 m2 g−1, leading to the excellent thermal insulation performance with a low thermal conductivity of 15.3 mW m−1 K−1. The integrated “stiff–soft” binary nature also endows the composite aerogels with high flexibility that can conform to various substrates as well as large tensile strength that can withstand more than 2.70 × 104 times its own weight. These composite aerogels show multifunctionality in terms of efficient wearable protection, controllable thermal management, and ultrafast oil/water separation. These favorable multifeatures present composite aerogels ideal for aerospace, industrial, and commercial applications.

Demand Response with Receiving Power Variable Control of a Battery Power Conditioner and Energy Management System Application

Demand Response with Receiving Power Variable Control of a Battery Power Conditioner and Energy Management System Application

Day-ahead probabilistic PV generation forecast for buildings energy management systems

The photovoltaic (PV) generation forecast is a key element to an efficient building energy management system (EMS) operation. The forecast’s uncertainties and generation variabilities expose the loads to misplanning, and hence decrease building autonomy, self-sufficiency, and potential costs savings. In this paper, a novel approach for a day-ahead PV power generation probabilistic forecast is proposed that is especially optimized for building EMS applications. The model consists of several modules to develop the probabilistic forecast. Initially, a clear sky model is tuned to incorporate the system and temperature losses and partial shading. The deviation of the PV power from the clear sky power is used to train a bagging regression tree, which produces a deterministic point forecast. The probabilistic forecast is developed based on the probabilistic analysis of the point forecast and regenerating it based on the given weather conditions. The model is developed based on the available data in buildings such as the historic PV measurements acquired from the inverter and the weather forecasts. The probabilistic forecast was validated over a complete-year data set of a rooftop PV system in Munich, Germany, where the results showed its capability to provide an accurate and reliable forecast for EMS applications.

Research on Three Times Control of Microgrid Based on Artificial Bee Colony Algorithm

Due to the wide application of the micro grid energy management system and its important has been studied in this paper, artificial bee colony algorithm, based on a control and two control, three times of the control layer energy management system are analyzed, and the algorithm into the three controller, through the algorithm to achieve the maximum power penetration of the micro grid management, the micro grid system is more economical and efficient. Finally, the simulation platform is built, and the effectiveness of the proposed control strategy is proved by the simulation results.

Energy and comfort performance benefits of early detection of building sensor and actuator faults

This paper presents a building performance simulation-based investigation to better understand the energy and comfort performance benefits of early detection of common sensor and actuator faults. Five types of air-handling unit faults and four types of zone-level faults were implemented to the energy management system application of the building performance simulation tool EnergyPlus. During 50-year simulation periods, the faults were randomly permitted to affect 75 different components of an archetype medium-sized office building model. The sensitivity of the simulation results with respect to three variables was studied: fault recurrence period, fault repair period, and discomfort threshold for simulated complaints. The results indicate that the energy use intensity and the predicted percentage of dissatisfied exhibit a power–law relationship with time, in which most of the performance reductions occur in the first 10 years. If the work-orders are issued only upon occupant complaints, the faults were estimated to cause a 16–62% increase in the energy use intensity for heating, ventilation, and air-conditioning and a 11–38% increase in the predicted percentage of dissatisfied at the end of the 50-year simulation periods. The results indicate that if the faults can be detected within a month after their first appearance, almost all their detrimental effects on a building’s energy and comfort performance can be mitigated. Practical application: The methodology and results presented in this article are of practical use for those who study on-going commissioning, fault detection and diagnostics, and energy management systems in buildings. The simulation-based parametric analysis approach can be used to estimate the range of energy and comfort savings expected through early detection of common sensor and actuator faults in commercial buildings. Insights gathered from such an analysis can be used in planning the frequency of retro-commissioning and investments for automated fault detection and diagnostics systems.

A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model

The photovoltaic (PV) systems generate green energy from the sunlight without any pollution or noise. The PV systems are simple, convenient to install, and seldom malfunction. Unfortunately, the energy generated by PV systems depends on climatic conditions, location, and system design. The solar radiation forecasting is important to the smooth operation of PV systems. However, solar radiation detected by a pyranometer sensor is strongly nonlinear and highly unstable. The PV energy generation makes a considerable contribution to the smart grids via a large number of relatively small PV systems. In this paper, a high-precision deep convolutional neural network model (SolarNet) is proposed to facilitate the solar radiation forecasting. The proposed model is verified by experiments. The experimental results demonstrate that SolarNet outperforms other benchmark models in forecasting accuracy as well as in predicting complex time series with a high degree of volatility and irregularity.

Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting

Real-time energy management systems that are designed to support consumer supply and demand spectrums of electrical energy continue to face challenges with respect to designing accurate and reliable real-time forecasts due to the stochasticity of model construction data and the model’s inability to disseminate both the short- and the long-term electrical energy demand (G) predictions. Using real G data from Queensland, Australia’s second largest state, and employing the support vector regression (SVR) model integrated with an improved version of empirical mode decomposition with adaptive noise (ICEEMDAN) tool, this study aims to propose a novel hybrid model: ICEEMDAN-PSO-SVR. Optimization of the model’s weights and biases was performed using the particle swarm optimization (PSO) algorithm. ICEEMDAN was applied to improve the hybrid model’s forecasting accuracy, addressing non-linear and non-stationary issues in time series inputs by decomposing statistically significant historical G data into intrinsic mode functions (IMF) and a residual component. The ICEEMDAN-PSO-SVR model was then individually constructed to forecast IMFs and the residual datasets and the final G forecasts were obtained by aggregating the IMF and residual forecasted series. The performance of the ICEEMDAN-PSO-SVR technique was compared with alternative approaches: ICEEMDAN-multivariate adaptive regression spline (MARS) and ICEEMDAN-M5 model tree, as well as traditional modelling approaches: PSO-SVR, MARS and M5 model tree algorithms. To develop the models, data were partitioned into different subsets: training (70%), validation (15%), and testing (15%), and the tuned forecasting models with near global optimum solutions were applied and evaluated at multiple horizons: short-term (i.e., weekends, working days, whole weeks, and public holidays), and long-term (monthly). Statistical metrics including the root-mean square error (RMSE), mean absolute error (MAE) and their relative to observed means (RRMSE and MAPE), Willmott’s Index (WI), the Legates and McCabe Index (ELM) and Nash–Sutcliffe coefficients (ENS), were used to assess model accuracy in the independent (testing) period. Empirical results showed that the ICEEMDAN-PSO-SVR model performed well for all forecasting horizons, outperforming the alternative comparison approaches: ICEEMDAN-MARS and ICEEMDAN-M5 model tree and the PSO-SVR, PSO-MARS and PSO-M5 model tree algorithm. Due to its high predictive utility, the two-phase ICEEMDAN-PSO-SVR hybrid model was particularly appropriate for whole week forecasts (ENS=0.95, MAPE=0.89%, RRMSE=1.22%, and ELM=0.79), and monthly forecasts (ENS=0.70, MAPE=2.18%, RRMSE=3.18%, and ELM=0.56). The excellent performance of the ICEEMDAN-PSO-SVR hybrid model indicates that the two-phase hybrid model should be explored for potential applications in real-time energy management systems.

Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming

Energy Management System (EMS) applications of modern power networks like microgrids have to respond to a number of stringent challenges due to current energy revolution. Optimal resource dispatch tasks must be handled with specific regard to the addition of new resource types and the adoption of novel modeling considerations. In addition, due to the comprehensive changes concerning the multi cell grid structure, new policies should be fulfilled via microgrids’ EMS. At the same time achieving a variety of (conflicting) goals in different microgrids requires a universal and a multi criteria optimization tool. Few of recent works in this area have considered the different perspectives of network operation with high amount of constraints and decision criteria. In this paper two dispatch-optimizers for a centralized EMS (CEMS) as a universal tool are introduced. An improved real-coded genetic algorithm and an enhanced mixed integer linear programming (MILP) based method have been developed to schedule the unit commitment and economic dispatch of microgrid units. In the proposed methods, network restrictions like voltages and equipment loadings and unit constraints have been considered. The adopted genetic algorithm features a highly flexible set of sub-functions, intelligent convergence behavior, as well as diversified searching approaches and penalty methods for constraint violations. Moreover, a novel method has been introduced to deal with the limitations of the MILP algorithm for handling the non-linear network topology constraints. A new aging model of a Lithium-Ion battery based on an event-driven aging behavior has been introduced. Ultimately, the developed GA-based and MILP-based optimizers have been applied to a test microgrid model under different operation policies, and the functionality of each method has been evaluated and compared together.

Cyber-Physical Attack-Resilient Wide-Area Monitoring, Protection, and Control for the Power Grid

Cybersecurity and resiliency of wide-area monitoring, protection, and control (WAMPAC) applications is critically important to ensure secure, reliable, and economical operation of the bulk power system. WAMPAC relies heavily on the security of measurements and control commands transmitted over wide-area communication networks for real-time operational, protection, and control functions. The current “N–1” security criterion for grid operation is inadequate to address malicious cyber events; therefore, it is important to fundamentally redesign WAMPAC and to enhance energy management system applications to make them attack resilient. In this paper, we present three key contributions to enhance the cybersecurity and resiliency of WAMPAC. First, we describe an end-to-end attack-resilient cyber–physical security framework for WAMPAC applications encompassing the entire security life cycle including risk assessment, attack prevention, attack detection, attack mitigation, and attack resilience. Second, we describe a defense-in-depth architecture that incorporates attack resilience at both the infrastructure layer and the application layer by leveraging domain-specific security approaches at the WAMPAC application layer in addition to traditional cybersecurity measures at the information technology infrastructure layer. Third, we discuss several attack-resilient algorithms for WAMPAC that leverage measurement design and cyber–physical system model-based anomaly detection and mitigation along with illustrative case studies. We believe that the research issues and solutions identified in this paper will open up several avenues for research in this area. In particular, the proposed framework, architectural concepts, and attack-resilient algorithms would serve as essential building blocks to transform the “fault-resilient” grid of today into an “attack-resilient” grid of the future.

Experimental Study of 6LoPLC for Home Energy Management Systems

Ubiquitous connectivity is already transforming residential dwellings into smart homes. As citizens continue to embrace the smart home paradigm, a new generation of low-rate and low-power communication systems is required to leverage the mass market presented by energy management in homes. Although Power Line Communication (PLC) technology has evolved in the last decade, the adaptation of PLC for constrained networks is not fully charted. By adapting some features of IEEE 802.15.4 and IPv6 over Low-power Wireless Personal Area Network (6LoWPAN) into power lines, this paper demonstrates a low-rate, low-power PLC system over the IPv6 network (referred to as 6LoPLC), for Home Energy Management System (HEMS) applications. The overall idea is to provide a framework for assessing various scenarios that cannot be easily investigated with the limited number of evaluation hardware available. In this respect, a network model is developed in NS-3 (Version 21) to measure several important characteristics of the designed system and then validated with experimental results obtained using the Hanadu evaluation kits. Following the good agreement between the two, the NS-3 model is utilised to investigate more complex scenarios and various use-cases, such as the effects of impulsive noise, the number of nodes and packet size on the latency and Bit Error Rate (BER) performances. We further demonstrate that for different network and application configurations, optimal data sizes exist. For instance, the results reveal that in order to guarantee 99% system reliability, the HEMS application data must not exceed 64 bytes. Finally, it is shown that with impulsive noise in a HEMS network comprising 50 appliances, provided the size of the payload does not exceed 64 bytes, monitoring and control applications incur a maximum latency of 238.117 ms and 248.959 ms, respectively; both of which are within acceptable limits.