Adaptive Energy Management System Research Articles

An Adaptive Energy Management Approach for Battery-Supercapacitor Hybrid Energy Storage System

Energy storage systems (ESS) are indispensable in daily life and have two types that can offer high energy and high power density. Hybrid energy storage systems (HESS) are obtained by combining two or more energy storage units to benefit both types. Energy management systems (EMS) are essential in ensuring HESS's reliability, high performance, and efficiency. One of the most critical parameters for EMS is the battery state of health (SoH). Continuous monitoring of the SoH provides essential information regarding the system's status, detects unusual performance degradations and enables planned maintenance, prevents system failures, helps keep efficiency at a consistently high level, and helps ensure energy security by reducing downtime. The SoH parameter depends on parameters such as Depth of Discharge (DoD), charge and discharge rate (C-Rate), and temperature. Optimal values of these parameters directly affect the lifetime and operating performance of the battery. The proposed Adaptive Energy Management System (AEMS) uses the SoH parameter of the battery as the control input. It provides optimal control by dynamically updating the C-Rate and DoD parameters. In addition, the supercapacitor integrated into the system with filter-based power separation prevents deep discharge of the batteries. Under the proposed AEMS control, HESS has been observed to generate 6.31% more energy than a system relying solely on batteries. This beneficial relationship between supercapacitors and batteries efficiently managed by AEMS opens new possibilities for advanced energy management in applications ranging from electric vehicles to renewable energy storage systems.

An Adaptive ECMS Based on Traffic Information for Plug-in Hybrid Electric Buses

Nowadays, a growing interest is in improving the efficiency of a parallel hybrid electric bus (PHEB). This paper proposes an adaptive energy management strategy based on an equivalent consumption minimization strategy (ECMS) by using real-time traffic information described by the average speed, average acceleration, and standard deviation of speed for different road sections. The whole adaptive energy management system consists of offline and online components. Three velocity characteristic parameters are derived by utilizing the data from the vehicle. Firstly, the road segments are classified through the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means. Then three Markov transition matrices on different types of roads are constructed with the velocity feature parameters which can be used to predict the speed in the following time. On the other hand, the online component implements the energy management strategy (EMS) involved. The EMS incorporates predicted vehicle speed and road information into the ECMS. Based on this, the predicted vehicle speed and traffic information are applied to make adaptive changes to the equivalent factors in the ECMS. Finally, the performance of the proposed strategy is evaluated through a comparative simulation study and the results show the superiority of the approach in improving fuel economy. Comparative outcomes from HIL tests additionally illustrate that the proposed strategy's fuel efficiency is compatible with the original design intent.

Cascaded dual fuzzy logic controller for stable microgrid operation mitigating effects of natural uncertainty in solar and wind energy sources

In a microgrid system, the perturbations due to variable solar irradiance, fluctuating wind speed, and even the sudden fluctuation in load lead to the deviation in system voltage, frequency, DC-Link voltage, etc., causing instability. Thus, this paper proposes a robust and adaptive energy management system using the cascaded dual-fuzzy logic-based control approach to mitigate such issues. Besides, the system considers the auto-tuned PI-based voltage regulator with the reference voltage 415 V for inverter control and nine controllable/non-controllable system parameters for the proposed cascaded control approach. The proposed algorithm can control the battery operation, mode of microgrid, and load management to improve stability. It is analyzed for experimental conditions like reduced renewable output, microgrid transition, and unhealthy grid/microgrid conditions. In addition, the proposed method has been compared with the existing control topologies by assessing load voltage, frequency, and DC-Link voltage. The proposed approach has been validated for stable microgrid operation using MATLAB-Software. As per observations, it maintains the limits of 0.25% frequency deviation, 0.57% voltage deviation, and 0.877% DC link voltage deviation, which are acceptable as per IEEE 1547 standard, IEEE 2030 standard, and Indian standard CERC 2011 for the concerned system parameters.

Adaptive Mixed-Integer Linear Programming-Based Energy Management System of Fast Charging Station with Nuclear–Renewable Hybrid Energy System

The concept of transportation electrification is proliferating due to its high impact on emission reduction. However, the increased usage of electric vehicles strains the power grid’s charging infrastructure. As a result, to reduce demand on the power grid, lower the emissions, and solve the intermittency problem of Renewable Energy Sources (RESs), a Nuclear–renewable Hybrid Energy System (N-R HES) is proposed in this research to support the load demand of a Fast Charging Station (FCS). Fulfilling the power demand of the FCS while reducing the generation cost and waste of energy is a vital issue, and hence, energy management with optimization is a must for the hybrid energy system. To address this issue, a model reference adaptive control with a mixed-integer linear programming-based energy management method was modelled to accomplish the charging station’s extensive performance. MATLAB/Simulink software has been used to model and simulate the proposed system, and the results are analyzed. The assessment shows that the proposed energy management system offers an optimized performance of the fast charging station integrating with nuclear and renewable energy.

Hierarchical reinforcement learning based energy management strategy of plug-in hybrid electric vehicle for ecological car-following process

The economy-oriented automated hybrid eclectic vehicles (HEV) provide great potential to save energy by optimizing both driving behaviors and power distribution. Recent advances in the ecological car following issue of HEV focus on fusing adaptive cruise control (ACC) and energy management system (EMS) by collaborative optimization. However, series control frameworks ACC+EMS breaks the internal coupling relation between motion control and energy distribution, leading to the natural limitation of its optimization. On the opposite, integrated ACC-EMS promises energy-saving improvement but brings complex optimization problems with multi-scale objectives and large exploration space. The huge computation load restricts the online application of ACC-EMS. To address these problems, a hierarchical reinforcement learning based ACC-EMS strategy is proposed with a hierarchical policy and non-hierarchical execution. The upper layer learns to plan state-of-charge and time-headway trajectories, while the low layer policy learns to achieve the expected goals by outputting control variables executed by the host vehicle. The proposed ACC-EMS strategy were self-learning by interaction in car-following scenario constructed with GPS data on I-880 highway. Comprehensive simulations show the proposed strategy has significantly improved the training speed and stability, compared to the offline global optimum, achieving the energy consumption difference of less than 3% and computational load of less than 600 times.

An SOC Based Adaptive Energy Management System for Hybrid Energy Storage System Integration to DC Grid

In this paper, an efficient adaptive energy management strategy (EMS) is presented for a hybrid energy storage system (HESS) application to compensate power fluctuation. The HESS consists of a battery and super-capacitor, which are integrated into the DC grid using a modified triple active bridge converter (m-TAB). The conventional EMS uses a low pass filter (LPF) to distribute high and low-frequency components of power. However, the time constant of the LPF is fixed throughout the process, which discharges the battery at the same rate even near the low state of charge (SOC) region. This increases the degradation rate of the battery. The proposed EMS employs an adaptive time constant LPF-based power allocation method to distribute the total power between the energy storage devices. The adaptive feature of the EMS changes the power distribution ratio dynamically to increase the utilization of the super-capacitor and battery operation time, thereby enhancing the life cycle. The proposed EMS is simulated in MATLAB/SIMULINK platform with the m-TAB converter, and the results are compared with the conventional EMS to prove its superiority. The system is also tested on an experimental prototype of 500 W for different test cases, and the outcomes are presented to support the proposed EMS.

Neural network-based adaptive control and energy management system of a direct methanol fuel cell in a hybrid renewable power system

Due to fluctuations in electricity demand and weather patterns, the solar energy-based system should be integrated with energy storage for efficient energy management. However, using only a battery cannot cope with the long-term power demand. Direct methanol fuel cells (DMFCs) have been considered one of the most promising backup power supplies for household applications. Therefore, the photovoltaic and solar thermal collector system incorporated with battery and DMFC is proposed along with the energy management system. In this study, a neural network-based adaptive control is developed to control the DMFC to meet persistent residential power demand and follow the technical requirements. Simulation results show that the neural network-based control outperforms the proportional-integral-derivative control in set-point change, disturbance, and model mismatch cases. Implementing the controlled DMFC in the hybrid system results in lower power requirements from the grid, which is less than twice that of a hybrid system without the DMFC. Furthermore, when solar power was insufficient to supply the load demand, the battery lifecycle in the system integrated with the DMFC seems more extended than that of a system without the DMFC.

Predictive Control of Adaptive Micro-Grid Energy Management System Considering Electric Vehicles Integration

This paper addresses the problems of control and energy management in micro-grid with the incorporation of renewable energy generation, hybrid storage technologies, and the integration of the electric vehicles (EVs) with vehicle to grid (V2G) technology. The adaptive model predictive control (AMPC) technique is used to optimize the charge/discharge of the EVs in a receding horizon manner in order to reduce operational cost in a renewable energy-based micro-grid. V2G systems integration can be a crucial element in the assurance of network reliability against variability in loads. In this context, the paper presents an AMPC algorithm for the optimization of a micro-grid coupled with a V2G system consisting of six electric vehicle charging stations. The proposed algorithm effectively manages the use of renewable energy sources, vehicles charge, energy storage units, and the purchase and sale of electric power to the external network. Two scenarios are investigated in this paper to examine the performance of the proposed controller to manage the renewable energy sources in the micro-grid system. The first case uses a load shifting mechanism to solve the charge management problem during a known interval of parking time. The second case introduces the EVs with V2G capabilities when connected with the micro-grid. In this case, the vehicle battery collaborates with the ESS of the micro-grid to maximize costs benefits and mitigate the intermittency of renewable generation. Furthermore, other benefits of V2G concepts, such as voltage and frequency control for the micro-grid stability, are investigated. Therefore, it is evident from the obtained results that the proposed control algorithm was able to effectively manage the renewable energy sources, energy storage units, vehicles charge, and the purchase and sale of electric power with the grid. Keywords: Adaptive model predictive control, Energy management system, Electric vehicles, Vehicle to grid technology, Grid reliability, Load shifting, Optimization problem and MATLAB/Simulink.

OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration

As microgrids have gained increasing attention over the last decade, more and more applications have emerged, ranging from islanded remote infrastructures to active building blocks of smart grids. To optimally manage the various microgrid assets towards maximum profit, while taking into account reliability and stability, it is essential to properly schedule the overall operation. To that end, this paper presents an optimal scheduling framework for microgrids both for day-ahead and real-time operation. In terms of real-time, this framework evaluates the real-time operation and, based on deviations, it re-optimises the schedule dynamically in order to continuously provide the best possible solution in terms of economic benefit and energy management. To assess the solution, the designed framework has been deployed to a real-life microgrid establishment consisting of residential loads, a PV array and a storage unit. Results demonstrate not only the benefits of the day-ahead optimal scheduling, but also the importance of dynamic re-optimisation when deviations occur between forecasted and real-time values. Given the intermittency of PV generation as well as the stochastic nature of consumption, real-time adaptation leads to significantly improved results.

Adaptive Energy Management System of Hybrid Solar/Wind/Battery Power Sources Integrated in Dc Microgrid for Smart University Based Fuzzy Modified Super Twisting Algorithm

Adaptive Energy Management System of Hybrid Solar/Wind/Battery Power Sources Integrated in Dc Microgrid for Smart University Based Fuzzy Modified Super Twisting Algorithm

Adaptive Energy Management System for Smart Hybrid Microgrids

The energy management will play an important role in the future smart grid by managing loads in an intelligent way. Energy management programs, realized via House Energy Management systems (HEMS) for smart cities, provide many benefits; consumers enjoy electricity price savings, and utility operates at reduced peak demand. This paper proposed an adaptive energy management system for islanded mode and grid-connected mode. In this paper, a hybrid system that includes distribution electric grid, photovoltaics, and batteries are employed as energy sources in the residential of the consumer in order to meet the demand. The proposed system permits coordinated operation of distributed energy resources to concede necessary active power and additional service whenever required. This paper uses home energy management system which switches between the distributed energy and the grid power sources. The home energy management system incorporates controllers for maximum power point tracking, battery charge and discharge and inverter for effective control between different sources depending upon load requirement and availability of sources at maximum powerpoint. Also, in this paper, the Maximum Power Point Tracking (MPPT) technique is applied to the photovoltaic station to extract the maximum power from hybrid power system during variation of the environmental conditions. The operation strategy of energy storage systems is proposed to solve the power changes from photovoltaics and houses loads fluctuations locally, instead of reflecting those disturbances to the utility grid. Furthermore, the energy storage systems energy management scheme will help to achieve the peak reduction of the houses daily electrical load demand. The simulation results have verified the effectiveness and feasibility of the introduced strategy and the capability of the proposed controller for a hybrid microgrid operating in different modes.

Self-Learning Data-Based Models as Basis of a Universally Applicable Energy Management System

In the transfer from fossil fuels to renewable energies, grid operators, companies and farms develop an increasing interest in smart energy management systems which can reduce their energy expenses. This requires sufficiently detailed models of the underlying components and forecasts of generation and consumption over future time horizons. In this work, it is investigated via a real-world case study how data-based methods based on regression and clustering can be applied to this task, such that potentially extensive effort for physical modeling can be decreased. Models and automated update mechanisms are derived from measurement data for a photovoltaic plant, a heat pump, a battery storage, and a washing machine. A smart energy system is realized in a real household to exploit the resulting models for minimizing energy expenses via optimization of self-consumption. Experimental data are presented that illustrate the models’ performance in the real-world system. The study concludes that it is possible to build a smart adaptive forecast-based energy management system without expert knowledge of detailed physics of system components, but special care must be taken in several aspects of system design to avoid undesired effects which decrease the overall system performance.

A new stochastic gain adaptive energy management system for smart microgrids considering frequency responsive loads

Islanded microgrids as flexible, adaptive and sustainable smart cells of distribution power systems should be operated in accordance to both techno-economic purposes. Motivated by this need, the microgrid operators are in charge to elevate the active accommodation of both demand-side and supply-side distributed energy resources. To that end, in this paper, a new flexible frequency dependent energy management system is proposed through which distributed generators have time varying droop controllers with a gain-adaptive strategy. Besides to cope economically with uncertainty arise frequency excursions, a new, comfort-aware and versatile frequency dependent demand response program is mathematically formulated and conducted to the energy management system. It is aimed to co-optimize the microgrid energy resources such a way the day-ahead operational costs are managed subject to a secure frequency control portfolio. The presented model is solved using a two-stage stochastic programming and by a tractable efficient mixed integer linear programming approach. The simulation results are derived in 24-h scheduling time horizon and implemented on a typical test microgrid. The effectiveness of the proposed hourly gain assignment and frequency responsive load management program has been verified thoroughly by analyzing the results.

An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses

In this paper, an adaptive energy management system consisting of off-line and online parts is presented to improve the energy efficiency of a parallel hybrid electric bus. The off-line part discusses how to precisely recognize the driver's driving style based on the hybrid algorithm. Specially, K nearest neighbor method is applied to reduce the number of samples as the preprocessor, and the expectation maximization method is leveraged to speculate the class using similar training samples. Furthermore, the driving style recognition module is built and employed to classify the driver's driving style from aggressive to conservative at each time step. On the other hand, the online part performs the involved energy management strategy, which incorporates driver's driving style into the equivalent consumption minimization strategy (ECMS). Simulation results demonstrate that the performance of charging sustainability and equivalent fuel consumption in the proposed strategy exceed those in the ECMS, as for both aggressive and conservative drivers. Comparative results obtained from HIL tests also indicate that the fuel economy of the proposed strategy is improved by 9.54% and 7.03% for aggressive and conservative drivers compared with ECMS, respectively.

태양광전원 임베디드장치를 위한 적응적 에너지관리 시스템

임베디드 장치는 환경이나 크기 등의 제약으로 인해 전력 공급이 제한적이며, 일반적으로 배터리에 의존하는 경우가 많다. 임베디드 장치들이 야외 환경에 설치되는 경우 이러한 전력 공급의 제한을 해결하기 위해 태양광 전원의 사용을 고려할 수 있다. 그러나 태양광 에너지는 날씨 및 시간에 따라 전력공급이 일정하지 않은 문제점이 있으며, 이는 임베디드 장치에서 제공하는 서비스의 안정성과 지속성에 영향을 줄 수 있다. 본 논문에서는 태양광 전원을 이용하는 임베디드 장치를 위한 에너지 관리 시스템과 환경 변화에 따른 수행률 조정 기법을 제안한다. 제안된 에너지 관리 시스템은 임베디드 시스템의 서비스가 중단되지 않도록 하며 에너지 공급 환경의 변화를 고려하여 최대의 수행률을 달성함을 목표로 한다. 제안된 기법과 시스템은 실제 하드웨어에 탑재되어 구현되었으며, 실험결과는 제안된 시스템이 태양광 발전량을 고려하여 임베디드 장치의 동작이 멈추지 않도록 배터리 사용량을 제어할 수 있음을 보인다.

Optimized power management based on adaptive-PMP algorithm for a stationary PEM fuel cell/battery hybrid system

This research develops an efficient and robust polymer electrolyte membrane (PEM) fuel cell/battery hybrid operating system. The entire system possesses its own rapid dynamic response benefited from hybrid connection and power split characteristics due to DC/DC buck-boost converter. An indispensable energy management system (EMS) plays a significant role in achieving optimal fuel economy and in a promising running stability. EMS as an indispensable part plays a significant role in achieving optimal fuel economy and promising operation stability. This study aims to develop an adaptive supervisory EMS that comprises computer-aided engineering tools to monitor, control, and optimize the performance of the hybrid power system. A stationary fuel cell/battery hybrid operating system is optimized using adaptive-Pontryagin's minimum principle (A-PMP). The proposed algorithm depends on the adaptation of the control parameter (i.e., fuel cell output power) from the state of charge (SOC) and load power feedback. The integrated model simulated in a Matlab/Simulink environment includes the fuel cell, battery, DC/DC converter, and power requirements models by analyzing the three different load profiles. Real-time experiments are performed to verify the effectiveness of EMS after analyzing the simulated operating principle and control scheme.

Adaptive Energy Management System Based on a Real-Time Model Predictive Control With Nonuniform Sampling Time for Multiple Energy Storage Electric Vehicle

The performance of a dual energy storage electric vehicle system mainly depends on the quality of its power and energy managements. A real-time management strategy supported by a model predictive control (MPC) using the nonuniform sampling time concept is developed and fully addressed in this paper. First, the overall multiple energy storage powertrain model including its inner control layer is represented with the energetic macroscopic representation and used to introduce the energy strategy level. The model of the system with its inner control layer is translated into the state-space domain in order to develop an MPC approach. The management algorithm based on mixed short- and long-term predictions is compared to rule-based and constant sampling time MPC strategies in order to assess its performance and its ability to be used in a real vehicle. The real-time simulation results indicate that, compared to other strategies, the proposed MPC strategy can balance the power and the energy of the dual energy storage system more effectively, and reduce the stress on batteries. Moreover, battery and supercapacitor key variables are kept within safety limits, increasing the lifetime of the overall system.

An Adaptive Energy Management System for Electric Vehicles Based on Driving Cycle Identification and Wavelet Transform

Since driving cycle greatly affects load power demand, driving cycle identification (DCI) is proposed to predict power demand that can be expected to prepare for the power distribution between battery and supercapacitor. The DCI is developed based on a learning vector quantization (LVQ) neural network method, which is assessed in both training and validation based on the statistical data obtained from six standard driving cycles. In order to ensure network accuracy, characteristic parameter and slide time window, which are two important factors ensuring the network accuracy for onboard hybrid energy storage system (HESS) applications in electric vehicles, are discussed and designed. Based on the identification results, Multi-level Haar wavelet transform (Haar-WT) is proposed for allocating the high frequency components of power demand into the supercapacitor which could damage battery lifetime and the corresponding low frequency components into the battery system. The proposed energy management system can better increase system efficiency and battery lifetime compared with the conventional sole frequency control. The advantages are demonstrated based on a randomly generated driving cycle from the standard driving cycle library via simulation.

Adaptive intelligent energy management system of plug-in hybrid electric vehicle

Efficient energy management in hybrid vehicles is the key for reducing fuel consumption and emissions. To capitalize on the benefits of using PHEVs (Plug-in Hybrid Electric Vehicles), an intelligent energy management system is developed and evaluated in this paper. Models of vehicle engine, air conditioning, powertrain, and hybrid electric drive system are first developed. The effect of road parameters such as bend direction and road slope angle as well as environmental factors such as wind (direction and speed) and thermal conditions are also modeled. Due to the nonlinear and complex nature of the interactions between PHEV–Environment–Driver components, a soft computing based intelligent management system is developed using three fuzzy logic controllers. The crucial fuzzy engine controller within the intelligent energy management system is made adaptive by using a hybrid multi-layer adaptive neuro-fuzzy inference system with genetic algorithm optimization. For adaptive learning, a number of datasets were created for different road conditions and a hybrid learning algorithm based on the least squared error estimate using the gradient descent method was proposed. The proposed adaptive intelligent energy management system can learn while it is running and makes proper adjustments during its operation. It is shown that the proposed intelligent energy management system is improving the performance of other existing systems.

Adaptive Energy Management for Dynamically Reconfigurable Processors

We present an adaptive energy management system for dynamically reconfigurable processors that chooses an energy-minimizing set of custom instructions (CIs) and then power-gates the temporarily unused subset of CIs. It requires a comprehensive power model to estimate the power consumption of different CIs at run time. We deploy our new energy management in two state-of-the-art reconfigurable processors (RISPP and Molen) and perform an elaborative evaluation of energy savings under various area and performance constraints for different technology nodes. We demonstrate the energy benefits by comparing it to state-of-the-art power-gating techniques for FPGAs. The work is implemented as a prototype on a Xilinx FPGA platform.