Supervisory Power Management Research Articles

Supervisory Power Management Scheme of a Laboratory Scale Wind-PV Based LVDC Microgrid Integrated With Hybrid Energy Storage System

Supervisory Power Management Scheme of a Laboratory Scale Wind-PV Based LVDC Microgrid Integrated With Hybrid Energy Storage System

Frequency Based Supervisory Power Management Approach for Battery-Supercapacitor Electric Vehicle Considering Road Slope

This article proposes a real time strategy based on frequency separation for energy management of battery/Supercapacitor (SC) based Hybrid Energy Storage System (HESS) in Electric Vehicle (EV). The performance and life cycle of battery are important parameters in determining the efficiency of EVs. With the aim of improving the effectiveness of battery in battery/SC based EVs, a supervisory Symlets wavelet based Energy Management System (EMS) using Fuzzy Controller (FC) is proposed which considers real time road slope and vehicle condition. Actual road grade values are computed from Contour Positioning System (CPS) using Google Earth software. Symlets wavelet control technique is utilised to separate the high frequency transients from the required load power. After separation of high frequency components, a FC is developed to provide an additional reference power for battery and SC. Then, the high frequency transients along with additional power obtained from FC are allocated to SC and the rest is handled by the battery. Three road slope conditions viz. city tour, downhill and uphill and two drive cycles viz. NYCC and UDDS are applied to examine the capability of proposed EMS. The proposed Fuzzy Symlets Wavelet based controller is observed to improve the battery Ampere hour (Ah) throughput and Root Mean Square (RMS) value of battery current in comparison to only Symlets wavelet based controller for city tour, uphill and downhill journeys. The results indicate the robustness and superiority of the designed Fuzzy Symlets Wavelet based control technique as compared to Symlets Wavelet technique in handling practical conditions and improving vehicle performance.

Adaptive Fuzzy Logic-Based Control and Management of Photovoltaic Systems with Battery Storage

Renewable energy sources (RESs) such as solar photovoltaic (PV) systems are increasingly used as distributed generation for replacing the conventional energy. At the same time, energy storage systems like battery (BAT) must be applied for maintaining the balance between fluctuating energy production and load consumption. BAT’s state of charge (SOC) should be maintained within their design limits unaffected by RES intermittency and/or load power variations. This necessitates advanced power control and management methodologies for overcoming challenging conditions. This paper discusses and evaluates an optimal DC bus voltage regulation approach: an intelligent controller using an adaptive fuzzy logic controller (FLC) and a novel supervisory power management strategy for PV systems with BAT. The objectives are to keep a stable power flow in the system and guarantee the continuity of service by ensuring that the system components do not exceed their limits. In this manner, the DC bus voltage regulation of the PV/BAT system can be improved in comparison with conventional regulation. Therefore, the most important contributions of this work are as follows. (1) Development of comprehensive and modular novel energy management system (EMS): its originality is related to the inclusion of the control system limits with faster SOC balancing and smaller DC bus voltage fluctuation. (2) Providing a simple power flow management implementation that considers the optimal energy flow between PV system, BAT system, and load: a balance between minimal energy flow in the connecting line and the least requirements of BAT capacity is kept, reducing component constraints with a very straightforward structure. (3) Furthermore, FLC offers high robustness and smooth performances. FLC is added to the control strategy design requirements to reduce DC bus voltage deviation. (4) Real-time simulation/experimentation-based complete cases utilizing Matlab/Simulink and DSpace are illustrated to testify the effectiveness of the proposed FLC and EMS.

An Efficient Supervisory Power Management Scheme for a Wind–Battery-Assisted Hybrid Autonomous System

This article presents an efficient power management and protection scheme for a wind–battery-assisted hybrid autonomous system. To operate the wind power generation system at the maximum power point (MPP), a drift-free perturb and observe algorithm is adapted. A unified controller for a bidirectional converter (BDC) is incorporated for the dc-bus voltage regulation along with the charge/discharge control of the battery. The small-signal analysis of the BDC and the buck converter is presented in detail. The performance of the proposed power management scheme (PMS) is validated through a time-domain simulation in MATLAB/Simulink along with the hardware implementation using the DS1103 digital platform. Initially, the PMS with only maximum power point tracking (MPPT) and pitch controller is unable to maintain the dc-bus voltage during the battery overcharge or deep-discharge condition. To address this issue, an <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> -MPPT controller is proposed to regulate the dc-bus voltage. The effectiveness of all the operating modes of the proposed PMS is verified in the developed laboratory prototype, and the performance is also compared with that of the classical droop control method. The experimental results confirm the ability of the PMS to handle power sharing and dc-bus voltage fluctuation during wind speed and load change and satisfy the load demand. This ensures that the improved performance of the developed hybrid autonomous system and changeover from the MPP to the generator or battery protection mode is seamless and fast.

An MPC-based power management of standalone DC microgrid with energy storage

Standalone dc microgrid faces a significant reliability risk due to continuous variation in power from renewable energy generation and the load demand. Usually, the output power from the renewable generators fluctuates with the weather conditions. The load is also varying all the time. This leads to a continuous risk of power mismatch in the system. Thus, a complementary generation or a storage system is required for maintaining the power balance in the system. This paper introduces a supervisory power management strategy (PMS) for a standalone dc microgrid with multiple distributed generations, load, and a battery energy storage system. The PMS is designed based on the model predictive control (MPC) approach. The complete mathematical model of the system has been developed, and it has been utilized in the MPC controller to solve an optimization problem with operating constraints. A forecasting technique is also incorporated in the proposed MPC to predict the environmental and load demand parameters. Simulation results illustrate the effectiveness of the presented MPC-based power management approach. Accordingly, the proposed approach effectively manages the power among the generators, load, and the battery and stabilizes the dc voltage. The paper also evaluates the effects of various prediction methods on the controller performance.

A Supervisory Power Management System for a Hybrid Microgrid With HESS

This paper proposes a supervisory power management system (PMS) for a grid interactive microgrid with a hybrid energy storage system. The key feature of the proposed PMS is reduced number of sensors required to implement the PMS. The PMS considers renewable power variation, grid availability, electricity pricing, and changes in local loads. It can detect the operating mode of system without measuring load currents and powers. A single-phase voltage source converter (VSC) transfers real power between dc grid and utility grid besides offering ancillary services such as harmonic mitigation, reactive power support, and unity power factor at the point of common coupling (PCC). In the proposed system, a better dc-link voltage regulation is achieved and the usage of supercapacitors reduces the current stress on the battery. The PMS also addresses extreme operating conditions such as load shedding, off-maximum power point tracking operation of photovoltaic, elimination of critical oscillation of hybrid energy storage systems power, islanded operation, and resynchronization with grid. The performance of the proposed PMS is verified by digital simulation and experimental studies.

A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. In this paper, we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared with the solution derived with dynamic programming using the average cost criterion. Both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.

Supervisory Power Management Control Algorithms for Hybrid Electric Vehicles: A Survey

The growing necessity for environmentally benign hybrid propulsion systems has led to the development of advanced power management control algorithms to maximize fuel economy and minimize pollutant emissions. This paper surveys the control algorithms for hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) that have been reported in the literature to date. The exposition ranges from parallel, series, and power split HEVs and PHEVs and includes a classification of the algorithms in terms of their implementation and the chronological order of their appearance. Remaining challenges and potential future research directions are also discussed.

Thermostatic Control for Series Hydraulic Hybrid Vehicle (SHHV) Energy Management

The main contributions of this paper are the development of forward-facing model of a series hydraulic hybrid vehicle (SHHV) power-train for medium size trucks, of which the fundamental architecture is described, together with dynamic equations and basic features of subsystem modules. A thermostatic SoC supervisory power management control algorithm is assessed, with the expectation that series configuration would maximize the fuel economy as engine is decoupled from the wheels. Simulation results over the urban driving cycle represent a significant departure from the conventional wisdom of operating the engine near its sweet spot and indicate what is preferred from the system stand-point, and also demonstrate the potential of the selected hybrid system to substantially improve vehicle fuel economy.