Energy Management Strategy Of System Research Articles

Rule-Based Operation Mode Control Strategy for the Energy Management of a Fuel Cell Electric Vehicle

Hydrogen, due to its high energy density, stands out as an energy storage method for the car industry in order to reduce the impact of the automotive sector on air pollution and global warming. The fuel cell electric vehicle (FCEV) emerges as a modification of the electric car by adding a proton exchange membrane fuel cell (PEMFC) to the battery pack and electric motor, that is capable of converting hydrogen into electric energy. In order to control the energy flow of so many elements, an optimal energy management system (EMS) is needed, where rule-based strategies represent the smallest computational burden and are the most widely used in the industry. In this work, a rule-based operation mode control strategy for the EMS of an FCEV validated by different driving cycles and several tests at the strategic points of the battery state of charge (SOC) is proposed. The results obtained in the new European driving cycle (NEDC) show the 12 kW battery variation of 2% and a hydrogen consumption of 1.2 kg/100 km compared to the variation of 1.42% and a consumption of 1.08 kg/100 km obtained in the worldwide harmonized light-duty test cycle (WLTC). Moreover, battery tests have demonstrated the optimal performance of the proposed EMS strategy.

Charging and Discharging Process Analysis of Energy Management System Strategy Towards Battery Aging in Series Configuration Hybrid Vehicle

Charging and Discharging Process Analysis of Energy Management System Strategy Towards Battery Aging in Series Configuration Hybrid Vehicle

A hierarchical two-level MILP optimization model for the management of grid-connected BESS considering accurate physical model

This work proposes a new Energy Management System (EMS) for Battery Energy Storage Systems (BESS). The goal is to make a BESS profitable in the new environment considering massive use of batteries that can be foreseen in the next future, due to the predictive increase of clean energy resources. The developed EMS considers two levels of optimization. The first level models the participation of the BESS in an Ancillary Service Market and schedules the BESS. The second level, the most innovative, is responsible for optimally distributing the power set-points obtained previously among the various battery banks considering, in addition to the battery aging, also the different efficiencies of battery banks, converters, and transformers. Moreover, this second-level manages both active and reactive power flows, and losses. Both optimization algorithms have been modeled as Mixed Integer Linear Programming (MILP) and implemented in GAMS using CPLEX as a solver. The results are encouraging: compared with the common industrial practice in which the load profile is equally shared among the individual batteries within a BESS, the two new proposed EMS strategies guarantee for a long period of operation (10-years) a consistent reduction in the number of batteries replacement (around 47%), thus ensuring significant cost savings. Moreover, the proposed BESS model accurately approximates the real physical behavior of the system, leading to an average error in State-of Energy (SoE) evaluation below 0.6%, which is almost one order of magnitude lower than the ones obtained by simpler models from literature with degradation only SoE-dependent.

High-power electric vehicle charging: Low-carbon grid integration pathways with stationary lithium-ion battery systems and renewable generation

The energy transition in the mobility sector is well underway. The electrification of road transport is resulting in a shift of the energy demand from the oil and gas sector to the electricity grid. Increasingly aggressive targets for low charging times for Electric Vehicles (EVs) are slated to raise the demand for High-Power Charging (HPC). This is likely to lead to bottlenecks and overloading in vulnerable sections of the electricity grid. Battery Assistance (BA) is a promising grid integration measure for High-Power Charging (HPC) to mitigate these problems. As decarbonization is the primary objective of the energy transition, the determination and comparison of the Global Warming Potential (GWP) footprints for HPC stations with BA is crucial. A comprehensive mathematical framework for the modelling and quantification of GWP footprints for HPC has been developed. The Levelized Emissions of Energy Supply (LEES) methodology has been extended and generalized to handle energy from the grid. A new state variable for the Battery Energy Storage System (BESS) — the State of Carbon Intensity (SOCI) has been introduced to calculate the operation phase GWP footprint of the BESS. The energy consumption GWP footprint for the load is also described by a new quantity — the Load Energy Consumption (LEC) emissions. The effect of incorporation of local Photovoltaic Solar (PV) generation in the energy flows is also investigated. An optimized Energy Management System (EMS) strategy with rolling horizon optimization to minimize emissions has been implemented to regulate energy flows in scenarios with BA and local PV generation. The Levelized Emissions of Energy Supply (LEES) values are obtained for all simulated scenarios and compared against a baseline rule-based EMS strategy. In combination with on-site PV generation, BA could achieve a reduction of 24% in the LEES vis-á-vis the baseline strategy. For reference, two scenarios with Grid Reinforcement (GR) for the grid section with and without local PV generation have also been simulated. With Grid Reinforcement (GR), a reduction of over 2% can be achieved with respect to the baseline EMS strategy for BA. Grid Reinforcement (GR) in conjunction with local PV generation can bring about a further reduction of about 6% with respect to the baseline EMS strategy for BA.

COST-EFFECTIVE ENERGY MANAGEMENT SYSTEMS STRATEGY IN OPTIMIZATION OF PHOTOVOLTAIC FOR GRID-CONNECTED SYSTEM

Renewable Energy Source (RES) based Distributed Generation (DG) like Photovoltaic (PV) is widely integrated into the distribution network, particularly for residential. With proper planning, the installation of optimal PV in the network is capable of minimizing the dependency on the power grid generation. However, the optimum use of solar energy has been limited by the weather and the load variation. At the particular time, the generated PV output is not fully utilized during the minimum load. The excessive generation of PV power occurs and causes an increase in the cost of electricity consumption. Therefore, the purpose of this paper is to optimize the PV size for the grid-connected system considering the Battery Energy Storage System (BESS) and the proper Energy Management System (EMS) Strategy in order to reduce the grid power consumption. BESS is introduced to store the excess PV power generated during peak hours, while the cost-effective EMS strategy is proposed to ensure the RES is fully employed. The number of PV panels is optimized using Particle Swarm Optimization (PSO) technique. The implementation of PSO in optimising PV size can reduce the number of PV panels by 21% compared to the conventional method.

Overview of Smart Grid Technology as a Renewable Energy Source

A smart grid is an electricity system that uses digital communications technology to detect, respond to, and take appropriate action in response to changes in demand and a variety of other problems. The secret to effective utilization of distributed energy resources is smart grid technology. Opportunities for renewable energy systems to address electricity generation appear to be growing, especially in light of the importance of climate change, the rising cost of petroleum products, and the falling cost of renewable energy power systems. However, an effective energy management strategy of system must be addressed in order to achieve commercialization and widespread use. Electric power networks have recently benefited from the effective application of the smart grid idea. This research work examines the overview of smart grid technology as a renewable energy source. The concept of a smart grid as well as the advantages and disadvantages of a smart grid renewable energy. The success of promoting renewable energy is significantly influenced by pricing. As a result, it is crucial to evaluate the distinctive qualities connected with alternative sources of renewable energy in order to get insight into the pricing of renewable energy. A survey of recent work in renewable smart grid systems shows the exciting future potential of such research traits. Both policymakers and those who create and use renewable energy systems would benefit from this.

Optimal energy management of multiple electricity-hydrogen integrated charging stations

Hydrogen is considered promising for the replacement of fossil fuels in integrated energy systems through hydrogen energy storage (HES). This paper considers multiple electricity-hydrogen integrated charging stations (EHI-CSs) as a unit consisting of photovoltaic systems and HES systems for charging plug-in electric vehicles and refilling hydrogen fuel vehicles. In the multiple EHI-CSs unit, a set of interconnected EHI-CSs can be supplied by other EHI-CSs or power utilities and an EHI-CSs aggregator can manage the individual EHI-CSs through controllable facilities (i.e., HES system) and adjustment methods (i.e., energy transportation between subsystems). Meanwhile, a two-stage energy management system (EMS) strategy is proposed to coordinate the day-ahead scheduling and real-time dispatch. In the day-ahead scheduling stage, the aggregator minimizes the cost of the overall multiple EHI-CSs unit through optimization, and in the real-time dispatching stage, the intraday energy dispatch based model predictive control (MPC) is carried out to minimize the penalty cost. The proposed two-stage EMS strategy is evaluated through simulations and the numerical results confirm that the proposed solution outperforms the baseline solution with additional economic benefit.

Research on Energy Management Strategy of Electric Vehicle Hybrid System Based on Reinforcement Learning

From the perspective of energy management, the demand power of a hybrid electric vehicle driving under random conditions can be considered as a random process, and the Markov chain can be used for modeling. In this article, an energy management strategy based on reinforcement learning with real-time updates is proposed to reasonably allocate the energy flow of the hybrid power system under unknown working conditions. The hybrid system is powered by a supercapacitor and a lithium battery, which uses the characteristics of each component to reduce the energy loss of the system, reduce the rate of change of the lithium battery current, and prolong the service life of the components. The strategy takes the change of the transition probability matrix under real-time working conditions as the basis. The system judges whether it is necessary to use the new transition probability to calculate and update the energy management strategy of the system by calculating the Pearson similarity between the transition probability matrix at the current time and previous time. The simulation results validate the proposed method.

A Review of Mathematical Models of Building Physics and Energy Technologies for Environmentally Friendly Integrated Energy Management Systems

The Energy Management System (EMS) is an efficient technique to monitor, control and enhance the building performance. In the state-of-the-art, building performance analysis is separated into building simulation and control management: this may cause inaccuracies and extra operating time. Thus, a coherent framework to integrate building physics with various energy technologies and energy control management methods is highly required. This framework should be formed by simplified but accurate models of building physics and building energy technologies, and should allow for the selection of proper control strategies according to the control objectives and scenarios. Therefore, this paper reviews the fundamental mathematical modeling and control strategies to create such a framework. The mathematical models of (i) building physics and (ii) popular building energy technologies (renewable energy systems, common heating and cooling energy systems and energy distribution systems) are first presented. Then, it is shown how the collected mathematical models can be linked. Merging with two frequently used EMS strategies, namely rule-based and model predictive controls, is discussed. This work provides an extendable map to model and control buildings and intends to be a foundation for building researchers, designers and engineers.

Comparison of Two Energy Management System Strategies for Real-Time Operation of Isolated Hybrid Microgrids

The propagation of hybrid power systems (solar–diesel–battery) has led to the development of new energy management system (EMS) strategies for the effective management of all power generation technologies related to hybrid microgrids. This paper proposes two novel EMS strategies for isolated hybrid microgrids, highlighting their strengths and weaknesses using simulations. The proposed strategies are different from the EMS strategies reported thus far in the literature because the former enable the real-time operation of the hybrid microgrid, which always guarantees the correct operation of a microgrid. The priority EMS strategy works by assigning a priority order, while the optimal EMS strategy is based on an optimization criterion, which is set as the minimum marginal cost in this case. The results have been obtained using MATLAB/Simulink to verify and compare the effectiveness of the proposed strategies, through a dynamic microgrid model to simulate the conditions of a real-time operation. The differences in the EMS strategies as well as their individual strengths and weaknesses, are presented and discussed. The results show that the proposed EMS strategies can manage the system operation under different scenarios and help power system operator obtain the optimal operation schemes of the microgrid.

Turning Base Transceiver Stations into Scalable and Controllable DC Microgrids Based on a Smart Sensing Strategy.

This paper describes a practical approach to the transformation of Base Transceiver Stations (BTSs) into scalable and controllable DC Microgrids in which an energy management system (EMS) is developed to maximize the economic benefit. The EMS strategy focuses on efficiently managing a Battery Energy Storage System (BESS) along with photovoltaic (PV) energy generation, and non-critical load-shedding. The EMS collects data such as real-time energy consumption and generation, and environmental parameters such as temperature, wind speed and irradiance, using a smart sensing strategy whereby measurements can be recorded and computing can be performed both locally and in the cloud. Within the Spanish electricity market and applying a two-tariff pricing, annual savings per installed battery power of 16.8 euros/kW are achieved. The system has the advantage that it can be applied to both new and existing installations, providing a two-way connection to the electricity grid, PV generation, smart measurement systems and the necessary management software. All these functions are integrated in a flexible and low cost HW/SW architecture. Finally, the whole system is validated through real tests carried out on a pilot plant and under different weather conditions.

An Overview of the Building Energy Management System Considering the Demand Response Programs, Smart Strategies and Smart Grid

Electricity demand is increasing, as a result of increasing consumers in the electricity market. By growing smart technologies such as smart grid and smart energy management systems, customers were given a chance to actively participate in demand response programs (DRPs), and reduce their electricity bills as a result. This study overviews the DRPs and their practices, along with home energy management systems (HEMS) and load management techniques. The paper provides brief literature on HEMS technologies and challenges. The paper is organized in a way to provide some technical information about DRPs and HEMS to help the reader understand different concepts about the smart grid, and be able to compare the essential concerns about the smart grid. The article includes a brief discussion about DRPs and their importance for the future of energy management systems. It is followed by brief literature about smart grids and HEMS, and a home energy management system strategy is also discussed in detail. The literature shows that storage devices have a huge impact on the efficiency and performance of energy management system strategies.

Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids

Energy Storage Systems (ESS) and Distributed Generation (DG) are topics in a large number of recent research works. Moreover, given the increasing adoption of EVs, high capacity EV batteries can be used as ESS, as most vehicles remain idle for long periods during work or home parking. However, the high EV penetration introduces some issues related to the charging power requirements, thereby increasing the peak demand for microgrids where EV chargers are installed. In addition, photovoltaic distributed generation is becoming another issue to deal with in EV charging microgrids. Therefore, this new scenario requires an Energy Management System (EMS) able to deal with charging demand, as well as with generation intermittency. This paper presents an EMS strategy for Microgrids that contain an EV parking lot (EVM), Photovoltaic (PV) arrays, and dynamic loads connected to the grid considering a Point of Common Coupling (PCC). The EVM-EMS utilizes the projections of future PV generation and future demand to accomplish a dynamic programming technique that optimizes the EVs’ charging (G2V) or discharging (V2G) profiles. This algorithm attends to user preferences while reducing the demand grid dependences and improves the microgrid efficiency.

Deep reinforcement learning enabled self-learning control for energy efficient driving

To address the air pollution problems and reduce greenhouse gas emissions (GHG), plug-in hybrid electric vehicles (PHEV) have been developed to achieve higher fuel efficiency. The Energy Management System (EMS) is a very important component of a PHEV in achieving better fuel economy and it is a very active research area. So far, most of the existing EMS strategies are either just simply following predefined rules that are not adaptive to changing driving conditions; or heavily relying on accurate prediction of future traffic conditions. Deep learning algorithms have been successfully applied to many complex problems and proved to even outperform human beings in some tasks (e.g., play chess) in recent years, which shows the great potential of such methods in practical engineering problems. In this study, a deep reinforcement learning (Deep Q-network or DQN) based PHEV energy management system is designed to autonomously learn the optimal fuel/electricity splits from interactions between the vehicle and the traffic environment. It is a fully data-driven and self-learning model that does not rely on any prediction, predefined rules or even prior human knowledge. The experiment results show that the proposed model is capable of achieving 16.3% energy savings (with the designed PHEV simulation model) on a typical commute trip, compared to conventional binary control strategies. In addition, a dueling Deep Q-network with dueling structure (DDQN) is also implemented and compared with single DQN in particular with respect to the convergence rate in the training process.

Energy Consumption Control and Optimization of Large Power Grid Operation Based on Artificial Neural Network Algorithm

Green, energy-saving, efficient and reliable smart grids have become one of the most important development areas in the world. An artificial neural network algorithm based on cranial nerve principle proposed in this paper provides reference for power flow analysis, reasonable dispatching and control decision-making of smart grids. At the same time, new EMS (Energy Management System) structure and strategy are introduced to optimize resource allocation and energy management. Based on the single-layer network control theory of large power grid, BP neural network including genetic algorithm is constructed and combined with regression analysis to realize the optimization of power analysis. In addition, a neuron controller is set up in the distributed unit to collect, monitor and control the parameters and send them to the high-level central controller, forming a multi-level three-dimensional network to analyze and make decisions, accurately predict the energy consumption of power grids, and improve the control level of the energy consumption of smart grids.

Impact of Different Driving Cycles and Operating Conditions on CO2 Emissions and Energy Management Strategies of a Euro-6 Hybrid Electric Vehicle

Although Hybrid Electric Vehicles (HEVs) represent one of the key technologies to reduce CO2 emissions, their effective potential in real world driving conditions strongly depends on the performance of their Energy Management System (EMS) and on its capability to maximize the efficiency of the powertrain in real life as well as during Type Approval (TA) tests. Attempting to close the gap between TA and real world CO2 emissions, the European Commission has decided to introduce from September 2017 the Worldwide Harmonized Light duty Test Procedure (WLTP), replacing the previous procedure based on the New European Driving Cycle (NEDC). The aim of this work is the analysis of the impact of different driving cycles and operating conditions on CO2 emissions and on energy management strategies of a Euro-6 HEV through the limited number of information available from the chassis dyno tests. The vehicle was tested considering different initial battery State of Charge (SOC), ranging from 40% to 65%, and engine coolant temperatures, from −7 °C to 70 °C. The change of test conditions from NEDC to WLTP was shown to lead to a significant reduction of the electric drive and to about a 30% increase of CO2 emissions. However, since the specific energy demand of WLTP is about 50% higher than that of NEDC, these results demonstrate that the EMS strategies of the tested vehicle can achieve, in test conditions closer to real life, even higher efficiency levels than those that are currently evaluated on the NEDC, and prove the effectiveness of HEV technology to reduce CO2 emissions.

Blended Rule-Based Energy Management for PHEV: System Structure and Strategy

This paper proposes a blended rule-based energy management system (EMS) for a plug-in hybrid electric vehicle (PHEV). The proposed EMS is formulated over driving information and vehicle trip energy and not over vehicle speed profiles, as usually seen. The proposed EMS design structure and strategy is described followed by its evaluation. This is the first time a platform for a rule-based acausal EMS has been designed. Performance metrics such as the fuel economy and the number of engine stop–starts are compared with conventional rule-based EMS over real-world destinations with uncertain trip demand. Its performance metrics compared with a conventional EMS for a full parallel PHEV was found to be superior in this paper.

Opportunities and Challenges of Integrating Renewable Energy in Smart Grid System

Smart grid technology is the key for an efficient use of distributed energy resources. Noting the climate change becomes an important issue the whole world is currently facing, the ever increasing price of petroleum products and the reduction in cost of renewable energy power systems, opportunities for renewable energy systems to address electricity generation seems to be increasing. However, to achieve commercialization and widespread use, an efficient energy management strategy of system needs to be addressed. Recently, the concept of smart grid has been successfully applied to the electric power systems. This paper presents the study of integrating renewable energy in smart grid system. The introductory sections provide the role of renewable energy and distributed generation in smart grid system. Subsequent sections cover the concept of smart grid as well as benefits and barrier of smart grid renewable energy system. Pricing is a significant variable in success of renewable energy promotion. Thus, it is important to gain insight to renewable energy pricing by considering unique characteristics associated with renewable energy alternatives. A review of work done in renewable smart grid systems in recent years indicates the promising potential of such research characteristics in the future. This would be useful to developers and practitioners of renewable energy systems and to policy makers.

WEEC 2008 Energy Management Systems Pushing Your Energy Management System to the Limit

ABSTRACT Our planet is in the midst of an energy crisis. Our traditional energy resources (oil, gas, coal) are limited. Due to ever-increasing competition for the finite supply of traditional energy sources, costs continue to escalate. The first step to combat rising energy costs is to make the most efficient use of what you have. This article summarizes several strategies to reduce energy costs by fully utilizing energy management system (EMS) features available with current building automation systems. Experience has shown that many facilities do not fully exploit the potential energy conservation tools at their disposal. The typical site energy management strategy does little more than time-of-day scheduling. There are numerous energy conservation measures that can be employed to reduce energy consumption—EMS strategies (utilizing all energy conservation features), continuous commissioning, and measurement and verification, to name a few. Designers must look for opportunities to fully specify all the energy conservation capabilities in an EMS. Building energy managers must be educated on building systems, look for energy conservation opportunities and utilize all the features of the EMS to help drive down energy costs. Additionally as more EMS features are utilized, the energy benefits increase. The incremental cost of additional energy features is typically low which helps the financial performance of the project. To ensure EMS features get utilized in new buildings, the designer must specify in detail which features are required and the expectations. Simply stating “the system must be capable of” does not mean that the EMS feature will be used. The training should be specific to the EMS and detailed enough to properly educate the users. Building energy managers must dedicate time to understand how to use the EMS and all of its functionality. Without the proper training and education, the building energy manager may not recognize energy conservation opportunities (ECOs). This knowledge is essential to developing a site energy management strategy. When a building manager pushes the EMS to the limit, the end result is lower operating costs, increased occupant comfort, and lower maintenance costs due to planned maintenance rather than emergency maintenance.