Design Of Energy Management System Research Articles

Design of a hybrid energy management system using designedrule‐basedcontrol strategy and genetic algorithm for the series‐parallel plug‐in hybrid electric vehicle

Design of a hybrid energy management system using designed<scp>rule‐based</scp>control strategy and genetic algorithm for the series‐parallel plug‐in hybrid electric vehicle

Microgrid Energy Management Systems Design by Computational Intelligence Techniques

With the capillary spread of multi-energy systems such as microgrids, nanogrids, smart homes and hybrid electric vehicles, the design of a suitable Energy Management System (EMS) able to schedule the local energy flows in real time has a key role for the development of Renewable Energy Sources (RESs) and for reducing pollutant emissions. In the literature, most EMSs proposed are based on the implementation of energy systems prediction which enable to run a specific optimization algorithm. Such strategy, known as Rolling Time Horizon (RTH), demonstrated very effective when the supporting prediction system performs well. However, it is featured by high operational times. In this work, different lightweight EMS models synthesized through machine learning algorithms have been compared considering six different simulation scenarios. Results shows that an RTH-based EMS owns the best overall performances. However, in some case studies, also other EMSs show competitive results, especially those based on Adaptive Neuro Fuzzy Inference Systems (ANFIS) trained by clustering, which in one case outperform RTH EMSs, and in other 3 cases (out of 6) yields performances close to RTH EMSs within 5%. A second contribution concerns the RTH EMS implementation on a small micro-controller, highlighting the high computational effort which can range in the order of minutes. Conversely, the ANFIS EMS shows always almost negligible computational costs (less than one second) and therefore can be used in realistic scenarios on cheap devices at run time. The paper also proposed a novel graphic tool to better represent, observe and analyze microgrid energy flows in each time slot or along the overall considered dataset.

A suitable state-space model for renewable source-based microgrids with hydrogen as backup for the design of energy management systems

This paper presents a methodological foundation to obtain a general state-space model for renewable source-based microgrids. The main novelty lies in developing a general control-oriented model that, taking into account all the parameters (technical and economic), enables the design of efficient energy management system (EMS) to make the best decisions both in the short-term (such as to adapt demand and production) and long-term (such as extending the lifespan of the most expensive and sensitive microgrid elements). The proposed approach guarantees a better approximation when compared to traditional models because most of them are linearized systems around a single operating point. The proposed model is more comprehensive since, apart from describing electrical performance for each device that makes up the microgrid, it also includes technical and economical parameters such as operating efficiency, degradation, losses and costs needed for microgrid optimization. The experimental results show the quality and excellent performance of the developed model. Additionally, a comparison between the reference models from the literature and the one proposed in this paper is undertaken. The results reflect a good performance in the evolution of the main parameters of the microgrid. Moreover, the use of the proposed methodology, based on the definition and calculation of the linear model parameters in each discretization period, guarantees a good fit of the model with respect to the non-linear behaviour of the microgrid.

Design of Hybrid Energy Management System for Wireless Sensor Networks in Remote Areas

Energy is an essential parameter for any work to be done. An electric energy is a primary requirement for any wireless transmission system. Transmitting the information from a source to destination requires a continuous and strengthen wireless network connection to avoid interruption from electromagnetic disturbance. Powering such wireless networks can be done by giving direct current through a battery or from direct supply. The challenge comes when the wireless sensor network is fixed in a remote place or designed as a moving system for sensing analysis. Battery sourcing power system will be a better choice for such movable wireless system. The paper proposes a hybrid energy management system for wireless sensor networks (WSNs) connected in remote areas and it analyses the performance of the energy management system in terms of amount of energy can be generated for a battery source.

Optimal Energy Management System Design Based on Dynamic Programming for Battery Electric Vehicles

Abstract An optimal control strategy for the energy management system on battery electric vehicles with only one power source is proposed. Firstly, this paper builds the control-oriented models for the drivetrain, battery thermal management and battery aging. Then, it is stated that, taking drivetrain power loss, battery thermal management requirements, and battery health performance into consideration simultaneously would lead to a multi-objective optimal control problem. Dynamic programming is adopted to solve the optimization problem. The simulation results could testify the feasibility and efficiency of the proposed energy management strategy in this paper.

Analytical Formulation for Power, Energy, and Efficiency Measurement of Ultracapacitor Using Fractional Calculus

Reliability of any electrical system heavily depends on the accuracy of the model used for representing the integrated device components. The design of renewable power sources and energy management systems using ultracapacitor demands adoption of precise mathematical representation of ultracapacitor dynamics. An appropriate model of ultracapacitor not only allows an accurate assessment of its charge/discharge characteristics but also provides a precise estimation of energy storage and power delivery capabilities. However, due to the lack in the usability of data/information provided by the manufacturer, conventional calculus-based ultracapacitor modeling and energy estimation approach often leads to its inaccurate and inefficient utility. In this regard, this paper has focused on fractional calculus-based analytical formulation for power, energy, and efficiency measurement of ultracapacitor and has deployed an upgraded model of the ultracapacitor, which in turn provides a better estimation of its energy efficiency. The work presented in this paper has been supported by extensive experimentation with the ultracapacitor under different input excitations, considering the dependence of energy storage (state of energy) and power delivery capacity of ultracapacitor on the charge/discharge profile. Furthermore, the hybrid optimization-based analysis recommends the requirement and challenges for the design and development of optimal charging/discharging of ultracapacitors for achieving higher energy efficiency.

Modelling and Design of Real-Time Energy Management Systems for Fuel Cell/Battery Electric Vehicles

Modelling and design of real-time energy management systems for optimising the operating costs of a fuel cell/battery electric vehicle are presented in this paper. The proposed energy management system consists of optimally sharing the propulsion power demand between the fuel cell and battery by enabling them to support each other for operating cost minimisation. The optimisation is achieved through real-time minimisation of a cost function, which accounts for fuel cell and battery degradation, hydrogen consumption and charge sustaining costs. A detailed analysis of each term of the overall cost function is performed and presented, which enables the development of a real-time, advanced energy management system for improving a previously presented simplified version using more accurate modelling and by considering cost function minimisation over a given time horizon. The performance of the proposed advanced energy management system are verified through numerical simulations over different driving cycles; particularly, simulations were performed in MATLAB-Simulink by considering a hysteresis-based energy management system and both simplified and advanced versions of the proposed energy management system for comparison.

Energy storage and management system design optimization for a photovoltaic integrated low-energy building

This study aims to analyze and optimize the photovoltaic-battery energy storage (PV-BES) system installed in a low-energy building in China. A novel energy management strategy considering the battery cycling aging, grid relief and local time-of-use pricing is proposed based on TRNSYS. Both single-criterion and multi-criterion optimizations are conducted by comprehensively considering technical, economic and environmental performances of the system based on decision-making strategies including the weighted sum and minimum distance to the utopia point methods. The single-criterion optimizations achieve superior performances in the energy supply, battery storage, utility grid and whole system aspect respectively over the existing scenario of the target building. The multi-criterion optimization considering all performance indicators shows that the PV self-consumption and PV efficiency can be increased by 15.0% and 48.6% while the standard deviation of net grid power, battery cycling aging and CO2 emission can be reduced by 3.4%, 78.5% and 34.7% respectively. The significance and impact of design parameters are further quantified by both local and global sensitivity analyses. This study can provide references for the optimum energy management of PV-BES systems in low-energy buildings and guide the renewable energy and energy storage system design to achieve higher penetration of renewable applications into urban areas.

Energy management system design and economic feasibility evaluation for a hybrid wind power/pumped hydroelectric power plant

Renewable energies are reaching the maturity of their technological development, but before they are ready to become the main or even the only power sources in the energy mix, they must become profitably manageable. Currently there are many research topics regarding the integration of renewable energies in power systems, such as the combination of two or more sources into hybrid systems, the addition of energy storage systems, etc. The present paper studies the economic feasibility of converting an existing pumped-storage hydro power plant into a hybrid hydro-wind power plant through the integration of a wind farm in its surroundings. For this purpose, the estimated economic benefits of the existing pumped-storage hydropower plant are compared with the potential benefits of the proposed hybrid hydro-wind configuration. Furthermore, two energy management systems are conceived in order to estimate the energy generated and consumed by the hybrid hydro-wind plant, as well as the income and expenses resulting from the energy purchase-sale. The results point out the economic feasibility of the project, as well as an increased participation of the hybrid plant in the power system.

Optimized use of Energy Charged from Shore in Plug-in Hybrid Marine Vessels

The paper presents a methodology for the design and tuning of real-time power and energy management systems for plug-in marine vessels. Main contributions are: (1) Method to design and tune the energy management strategy that will optimally share the load between on-board, fixed speed, diesel generator units and on-board energy storage, in such way that fuel consumption is minimized for a given expected load probability distribution (2) Method adaption for cases where the crew, for operational or safety reasons, decides to run with non-optimal number of diesel engines. (3) Outline of possible inclusion of adaptive tuning to cope with uncertain or unknown load probability distribution. The presented methodology applies to plug-in vessels where the battery storage cannot cover all energy needed for the planned trip, such that one need to combine the use of stored electrical energy from shore with the direct use of energy produced by on-board diesel generators. The proposed method uses the expected time of operation at each load level as key input for the optimization. Although getting such data upfront can be difficult, it is expected to be easier to get a good estimate of the load probability distribution rather than the exact load profile as function of time that is used by the existing methods.

Fuel efficiency optimization Techniques in Hybrid Vehicle

The hybrid electric vehicle (HEV) has the potential of sufficing drive needs with better fuel efficiency. The complex nature of hybrid vehicle provides multiple opportunities for obtaining better fuel efficiency. The mathematical modeling is a necessity for understanding the behavior of HEV. The fuel efficiency is obtained through various means including the design of energy management system, component sizing, emission control, and other methods. In the present paper, a mathematical model of HEV is presented for a parallel HEV with a review of various methods used for fuel efficiency optimization.

Design and development of advanced smart energy management system integrated with IoT framework in smart grid environment

The day-to-day increased usage of power appliance by consumers is a growing concern in the energy sector, which creates an imbalance in the ratio of demand and supply. Demand-side energy management is an imperative tool to avoid significant deficiency from the supply end and improve energy efficiency. The trend in energy management lays focus on reducing the overall cost of electricity without limiting the consumption counterpart by instead choosing to reduce the power consumption during peak hours. The above issue seeks for design and development of a flexible and portable system to cover a wide variety of consumers for balancing the overall system. The design of smart energy management system is intended to replace the scenario of a complete power outage in a region with partial load shedding in a controlled manner as per the consumer’s preference. Demonstration of experimental work is carried out assuming demand response event and also, considering the maximum demand limit constraint with different cases and changing the order of priority assigned to an appliance. Cost optimization algorithms based on time of usage and user comfort level with sensory information features are embedded within SEMS. Reliable ZigBee communication for home area network is established and also, an IoT environment is developed for data storage and analytics.

Demonstrating the Scope of a Switched Supercapacitor Circuit for Energy Harvesting Powered Wireless Sensor Loads

This paper describes the design of a high-efficiency energy management system for low-power, low-voltage energy harvesting powered wireless sensor systems. With typical voltages of less than 1 V and power levels lower than 1 mW, there are significant challenges when applying energy harvesting sources to supply pulsed power levels of up to 120 mW at voltage levels of 1.8–4 V as required by wireless sensor loads. The proposed approach integrates energy storage elements within a voltage step-up circuit to produce a new high-efficiency energy management circuit that converts the energy produced by a low-power, low-voltage source into a series of high power pulses. An optimized switched supercapacitor energy buffer circuit including a self-powered control circuit is proposed. Efficiencies of up to 91% are shown for an indoor solar cell source with a power level of 0.5 mW, supplying an equivalent wireless sensor with pulsed power levels of 10–120 mW. This is significantly higher than 83% achieved for the dc–dc stage of the existing best solution under the same source conditions, but requires an additional conversion step to provide high power pulses.

Design of Energy Management System using Autonomous Hybrid Micro-Grid under IOT Environment

In this paper, the automated power consumption unit is implemented by smart system technology. The interfacing could be done between the user and power sensing unit in which the collection of data is segregated and processed by the common interface bridge unit. Then, the data is transmitted to the power grid unit where the the processing and sensing power consumption is executed for the consumer in aspects of bill generation. All this kind of environment is controlled by the Internet of Things (IoT) substances and regulated by the configured power supplies. The entire setup wire diagram is simulated using Lab VIEW. The another advantage of the proposed system is equipped with highly cyber security point

Optimal Load Management in a Shipyard Drydock

With the proliferation of enabling smart grid technologies, industries are increasingly looking to reduce their electricity costs through the adoption of renewable energy sources and efficient load management strategies. In this context, the realization of lower electricity costs requires the design of efficient energy management systems (EMSs). An EMS needs to factor in the unique operational requirements of the industry for which it is designed. Consequently, there has been a lot of research interest in designing EMSs for various industrial applications. However, the existing EMS formulations and models are not suitable for a shipyard drydock. Shipyard drydocks may be treated as grid-connected microgrids containing pump loads, interruptible loads, critical loads and heterogeneous generation sources. This paper proposes three modules, which can constitute a shipyard drydock energy management system (SEMS). A load forecasting module generates short term and medium term load forecasts using historical load demand data and ship arrival schedules as inputs. A multilayer feed-forward neural network with backpropagation is used to perform the load forecasting. A contracted capacity optimization module uses the medium term load forecast to find the optimal contracted capacity for the drydock. The short term load forecast and the optimal contracted capacity are used by an optimal scheduling module to reduce the total electricity cost incurred by the shipyard drydock. Case studies performed using data from a local shipyard demonstrate the efficacies of the proposed load forecasting and optimal scheduling modules in improving the accuracies of the load forecasts and reducing the overall electricity cost, respectively.

Supervisory Energy-Management Systems for Microgrids: Modeling and Formal Verification

This article presents the modeling and verification of supervisory energy-management systems (EMSs) for microgrids using timed automata (TA) and a formal verification approach. The EMS plays an essential role in managing the power flow among different components in the microgrid system for its safe and reliable operation. The modeling of the EMS is based on predefined invariants with allowable and nonallowable operating modes, which are the conditions that do not change over time. The failure of invariants could have severe effects on microgrid system functionality, which highlights the importance of verification during the initial stage of EMS design. Conventional approaches, such as simulation and/or experimental verification, require manual checking and skilled professional knowledge to check EMS design correctness. Also, there may be a corner case that could lead to system failure that goes unidentified by manual analysis.

Online modeling and identification of plug-in electric vehicles sharing a residential station

In this paper, a novel plug-in electric vehicle (PEV) modeling approach is proposed for residential charging stations. This methodology can be used in the design of autonomous energy management systems (EMS) with the purpose of providing the charging schedules using minimum input measured data. The proposed method is composed of two main steps. In the first step, unlike other similar works, online PEV recognition is performed by means of an artificial neural network as a supervised classification method. The required feature space for classification is provided using the power spectral density estimation and the statistical analysis of individual PEVs charging current. The second step deals with the statistical modeling of the charging habits to facilitate the scheduling by predicting the charging demand using the current measurements on the grid side. In this respect, each PEV charging habit is modeled based on the correlation among the plug-in time, departure time, required energy, and weekdays using kernel density estimation. The performance of the suggested method is validated using real data collected from a charging station. The final results confirm the applicability of the proposed methodology with a satisfactory precision. The effectiveness of the method is demonstrated using a comparative analysis in terms of the recognition performance.

Cost Minimization Energy Control Including Battery Aging for Multi-Source EV Charging Station

A Multi-Source Electric Vehicle Charging Station (MS-EVCS) is a local entity that combines the grid energy with Distributed Energy Resources (DERs) with the aim of reducing the grid impact due to electric vehicles (EVs) charging events. The integration of stationary and in-vehicle Energy Storage Systems (ESSs) in MS-EVCSs has gained increasing interest thanks to the possibility of storing energy at off-peak hours to be made available at peak-hours. However, the ESS technology and the vehicle-to-grid (V2G) concept show several issues due to cost, battery life cycle, reliability, and management. The design of the MS-EVCS energy management system is of primary importance to guarantee the optimal usage of the available resources and to enhance the system benefits. This study presents a novel energy management strategy for Real-Time (RT) control of MS-EVCS considering DERs, stationary ESS, and V2G. The proposed energy management control allows defining the MS-EVCS control policy solving several cascaded-problems with the aim of achieving the minimum operating cost when the battery degradation and the stochastic nature of the sources are considered. The key feature of the proposed methodology is the lower computational effort with respect to traditional optimal control methodologies while achieving the same optimal solution.

A review of the relevance of driving condition mapping and vehicle simulation for energy management system design

With the emerging trend of transportation electrification, energy management strategies have become of significant importance in automotive engineering. Control systems are developed with the objective to increase vehicle efficiency, thus lowering fuel consumption and harmful gas emissions. Both the methodology and tools for powertrain systems design as well as vehicle performance assessment are challenging with a constant necessity for improvement. For both aspects, the importance of developing realistic driving scenarios accounting for real and sometimes random driving conditions is irrefutable. This paper provides an overview of the current state-of-the-art technologies in energy management strategies (EMSs), highlighting how driving conditions and journey mapping can influence the performance of these systems. In addition, a review of vehicle simulators is performed emphasising how such tools could support the system level vehicle development.

A daytime optimal stochastic energy management for EV commercial parking lots by using approximate dynamic programming and hybrid big bang big crunch algorithm

The design of optimal energy management systems has been an important problem, in particular for daytime plug-in electric vehicles’ (PEVs’) parking lots at workplaces and commercial buildings, where the number of vehicles, their arrival and departure times are typically unknown and time-varying. The paper addresses this problem by introducing a two-stage optimization based on Approximate Dynamic Programing (ADP) and Hybrid Big Bang Big Crunch (HBB-BC) algorithm considering a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) which predicts the electricity price. The proposed optimal energy management minimizes the cost of parking lot owner with respect to the Time of Use (TOU) Demand Response (DR) program without reducing the welfare of EV owners. The two-stage optimization manages the charge scheduling subject to short and long term data coming from the MLP-ANN and intelligent transportation system. The stochastic features of the commercial parking are fully addressed into the problem. The effectiveness of the proposed charging technology is assessed and discussed by using real electricity data from ERCOT, under various stochastic conditions. The results demonstrate optimal energy management during peak and off time periods at the minimum cost.