Journal articles on the topic 'Energy Management Strategy (EMS)'
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1
Yao, Dongwei, Xinwei Lu, Xiangyun Chao, Yongguang Zhang, Junhao Shen, Fanlong Zeng, Ziyan Zhang, and Feng Wu. "Adaptive Equivalent Fuel Consumption Minimization Based Energy Management Strategy for Extended-Range Electric Vehicle." Sustainability 15, no. 5 (March 4, 2023): 4607. http://dx.doi.org/10.3390/su15054607.
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Unlike battery electric vehicles, extended-range electric vehicles have one more energy source, so a reasonable energy management strategy (EMS) is crucial to the fuel economy of the vehicles. In this paper, an adaptive equivalent fuel consumption minimization strategy (A-ECMS)-based energy management strategy is proposed for the extended-range electric vehicle. The equivalent fuel consumption minimization strategy (ECMS), which utilizes Pontryagin’s minimum principle (PMP), is introduced to design the EMS. Compared with other ECMS strategies, an adaptive equivalent factor algorithm, based on state of charge (SOC) feedback and a proportional–integral (PI) controller is designed to update the equivalent factor under different working conditions. Additionally, a start–stop penalty is added to the objective function to take the dynamic start–stop process of the range extender into account. As a result, under the WLTC driving cycle, the proposed strategy can achieve 6.78 L/100 km comprehensive fuel consumption, saving 6.2% and 3.4% fuel consumption compared with the conventional rule-based thermostat strategy and the power following strategy. Moreover, the proposed EMS achieves the lowest ampere-hour flux among the three EMSs, indicating its ability to improve battery life.
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Chen, Xu, Guangdi Hu, Feng Guo, Mengqi Ye, and Jingyuan Huang. "Switched Energy Management Strategy for Fuel Cell Hybrid Vehicle Based on Switch Network." Energies 13, no. 1 (January 3, 2020): 247. http://dx.doi.org/10.3390/en13010247.
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Environmentally friendly and pollution-free fuel cell/lithium battery hybrid vehicles have received the attention of the community in recent years. It is imperative for fuel cell/lithium battery hybrid vehicles to use the energy management strategy (EMS) to distribute the output power of each power source to improve fuel economy and system life. In practical application, inconsistency of battery pack will lead to security hazard and capacity degradation. However, few EMS take the inconsistency of battery pack into account. Also, the current battery equalization strategy rarely discusses how to perform the equilibrium process while meeting the power demand of vehicle. To solve these issues, a novel equalization energy management strategy (EEMS) based on the switch network is proposed at first. Then, a switched energy management strategy (SEMS) that switches between the EEMS and the equivalent consumption minimization strategy (ECMS) is proposed and implemented in the fuel cell/lithium battery hybrid system to validate its effectiveness. The results show that the proposed SEMS can ameliorate the inconsistency of series lithium battery pack while meeting the power demand of vehicle’s normal operation. It can improve the safety and durability of the system and reduce the equalization time. Besides, it has good expansibility and no energy waste.
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Michel, Pierre, Alain Charlet, Guillaume Colin, Yann Chamaillard, Cédric Nouillant, and Gérard Bloch. "3WCC Temperature Integration in a Gasoline-HEV Optimal Energy Management Strategy." Advances in Mechanical Engineering 6 (January 1, 2014): 802597. http://dx.doi.org/10.1155/2014/802597.
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For a gasoline-hybrid electric vehicle (HEV), the energy management strategy (EMS) is the computation of the distribution between electric and gasoline propulsion. Until recently, the EMS objective was to minimize fuel consumption. However, decreasing fuel consumption does not directly minimize the pollutant emissions, and the 3-way catalytic converter (3WCC) must be taken into account. This paper proposes to consider the pollutant emissions in the EMS, by minimizing, with the Pontryagin minimum principle, a tradeoff between pollution and fuel consumption. The integration of the 3WCC temperature in the EMS is discussed and finally a simplification is proposed.
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Bakht, Muhammad Paend, Zainal Salam, Abdul Rauf Bhatti, Waqas Anjum, Saifulnizam A. Khalid, and Nuzhat Khan. "Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding." Applied Sciences 11, no. 10 (May 18, 2021): 4601. http://dx.doi.org/10.3390/app11104601.
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This study investigates the potential application of Stateflow (SF) to design an energy management strategy (EMS) for a renewable-based hybrid energy system (HES). The SF is an extended finite state machine; it provides a platform to design, model, and execute complex event-driven systems using an interactive graphical environment. The HES comprises photovoltaics (PV), energy storage units (ESU) and a diesel generator (Gen), integrated with the power grid that experiences a regular load shedding condition (scheduled power outages). The EMS optimizes the energy production and utilization during both modes of HES operation, i.e., grid-connected mode and the islanded mode. For islanded operation mode, a resilient power delivery is ensured when the system is subjected to intermittent renewable supply and grid vulnerability. The contributions of this paper are twofold: first is to propose an integrated framework of HES to address the problem of load shedding, and second is to design and implement a resilient EMS in the SF environment. The validation of the proposed EMS demonstrates its feasibility to serve the load for various operating scenarios. The latter include operations under seasonal variation, abnormal weather conditions, and different load shedding patterns. The simulation results reveal that the proposed EMS not only ensures uninterrupted power supply during load shedding but also reduces grid burden by maximizing the use of PV energy. In addition, the SF-based adopted methodology is envisaged to be a useful alternative to the popular design method using the conventional software tools, particularly for event-driven systems.
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Ferahtia, Seydali, Hegazy Rezk, Rania M. Ghoniem, Ahmed Fathy, Reem Alkanhel, and Mohamed M. Ghonem. "Optimal Energy Management for Hydrogen Economy in a Hybrid Electric Vehicle." Sustainability 15, no. 4 (February 10, 2023): 3267. http://dx.doi.org/10.3390/su15043267.
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Fuel cell hybrid electric vehicles (FCEVs) are mainly electrified by the fuel cell (FC) system. As a supplementary power source, a battery or supercapacitor (SC) is employed (besides the FC) to enhance the power response due to the slow dynamics of the FC. Indeed, the performance of the hybrid power system mainly depends on the required power distribution manner among the sources, which is managed by the energy management strategy (EMS). This paper considers an FCEV based on the proton exchange membrane FC (PEMFC)/battery/SC. The energy management strategy is designed to ensure optimum power distribution between the sources considering hydrogen consumption. Its main objective is to meet the electric motor’s required power with economic hydrogen consumption and better electrical efficiency. The proposed EMS combines the external energy maximization strategy (EEMS) and the bald eagle search algorithm (BES). Simulation tests for the Extra-Urban Driving Cycle (EUDC) and New European Driving Cycle (NEDC) profiles were performed. The test is supposed to be performed in typical conditions t = 25 °C on a flat road without no wind effect. In addition, this strategy was compared with the state machine control strategy, classic PI, and equivalent consumption minimization strategy. In terms of optimization, the proposed approach was compared with the original EEMS, particle swarm optimization (PSO)-based EEMS, and equilibrium optimizer (EO)-based EEMS. The results confirm the ability of the proposed strategy to reduce fuel consumption and enhance system efficiency. This strategy provides 26.36% for NEDC and 11.35% for EUDC fuel-saving and efficiency enhancement by 6.74% for NEDC and 36.19% for EUDC.
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Li, Pei, Jun Yan, Qunzhang Tu, Ming Pan, and Jinhong Xue. "A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle." Mathematical Problems in Engineering 2018 (October 29, 2018): 1–15. http://dx.doi.org/10.1155/2018/8450213.
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The performance and fuel consumption of hybrid electric vehicle heavily depend on the EMS (energy management strategy). This paper presents a novel EMS for a series hybrid electric rescue vehicle. Firstly, considering the working characteristics of engine and battery, the EMS combining logic threshold and fuzzy control is proposed. Secondly, a fuzzy control optimization method based on IQGA (improved quantum genetic algorithm) is designed to achieve better fuel efficiency. Then, the modeling and simulation are completed by using MATLAB/Simulink; the results demonstrate that the fuel consumption can be decreased by 5.17% after IQGA optimization and that the optimization effect of IQGA is better than that of GA (genetic algorithm) and QGA (quantum genetic algorithm). Finally, the HILS (hardware in loop simulation) platform is constructed with dSPACE; the HILS experiment shows that the proposed EMS can effectively improve the vehicle working efficiency, which can be applied to practical application.
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Hu, Tengda, Yunwu Li, Zhi Zhang, Ying Zhao, and Dexiong Liu. "Energy Management Strategy of Hybrid Energy Storage System Based on Road Slope Information." Energies 14, no. 9 (April 21, 2021): 2358. http://dx.doi.org/10.3390/en14092358.
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To maximize the performance of power batteries and supercapacitors in a hybrid energy storage system (HESS) and to resolve the conflict between the high power demands of electric vehicles and the limitations of high-current charging and discharging of the power battery, a vehicle power demand model incorporating road slope information has been constructed. This paper takes a HESS composed of power battery and supercapacitor as the object, and a rule-based energy management strategy (EMS) based on road slope information is proposed to realize the reasonable distribution and management of energy under the slope condition. According to the slope information of the road ahead, the energy consumption in the next period was predicted, and the supercapacitor is charged and discharged in advance to meet the energy demand of uphill and the energy recovery capacity of downhill to avoid the high current charge and discharge of the battery. Subsequently, the improved EMS performance was simulated under the New York City Cycle (NYCC) driving conditions with additional slope driving conditions. The simulated results indicate that compared to the existing EMS, the proposed EMS based on slope information can effectively distribute the power demand between the power battery and the supercapacitor, can reduce the discharge current and the duration of high-power discharge, and has a 20.4% higher energy recovery efficiency, effectively increasing the cruising range.
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Wang, Yaqian, and Xiaohong Jiao. "Dual Heuristic Dynamic Programming Based Energy Management Control for Hybrid Electric Vehicles." Energies 15, no. 9 (April 28, 2022): 3235. http://dx.doi.org/10.3390/en15093235.
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This paper investigates an adaptive dynamic programming (ADP)-based energy management control strategy for a series-parallel hybrid electric vehicle (HEV). This strategy can further minimize the equivalent fuel consumption while satisfying the battery level constraints and vehicle power demand. Dual heuristic dynamic programming (DHP) is one of the basic structures of ADP, combining reinforcement learning, dynamic programming (DP) optimization principle, and neural network approximation function, which has higher accuracy with a slightly more complex structure. In this regard, the DHP energy management strategy (EMS) is designed by the backpropagation neural network (BPNN) as an Action network and two Critic networks approximating the control policy and the gradient of value function concerning the state variable. By comparing with the existing results such as HDP-based and rule-based control strategies, the equivalent consumption minimum strategy (ECMS), and reinforcement learning (RL)-based strategy, simulation results verify the robustness of fuel economy and the adaptability of the power-split optimization of the proposed EMS to different driving conditions.
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Abdulhadi Abdulsalam Abulifa, Azura Che Soh, Mohd Khair Hassan, Raja Mohd Kamil Raja Ahmad, and Mohd Amran Mohd Radzi. "Control strategies for energy management system in electric vehicle using high-level supervisory control." International Journal of Science and Technology Research Archive 3, no. 2 (October 30, 2022): 037–44. http://dx.doi.org/10.53771/ijstra.2022.3.2.0116.
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Energy Management System (EMS) is a computer-supported device utilized by drivers of electrical frameworks to maintain management and to optimize the efficiency of transmission systems. In this paper, a control strategy for EMS using on the High-level Supervisory Control (HLSC) has been reviewed. This HLSC strategy with an intelligent management algorithm technique has been evolving rapidly particularly in EMS for Electrical Vehicles (EVs). Their revolutionary applications provide efficient control strategies for EMS that increase capabilities, efficiency and accuracy, as well as reducing energy consumption in EVs. Applying EMS with HLSC control strategy with an intelligent management algorithm that is able reallocate the electrical power flow inside the EVs system to boost power efficiency and obtain optimum effectiveness. Such innovative solutions can enhance the efficiency of smart EMS in EVs as the future sustainable transportation.
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Wang, Hao, Hongwen He, Jianwei Li, Yunfei Bai, Yuhua Chang, and Beizhan Yan. "Adaptive MPC Based Real-Time Energy Management Strategy of the Electric Sanitation Vehicles." Applied Sciences 11, no. 2 (January 6, 2021): 498. http://dx.doi.org/10.3390/app11020498.
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Electric sanitation vehicles have increasingly been applied to cleaning work due to the requirement of air pollution control. The power distribution and energy management strategy (EMS) influence the vehicle’s performance a lot both in the aspects of cleaning effect and electricity consumption. Aiming to improve energy economy and ensure clean tasks, first, the electricity consumption percentages of the vehicle onboard devices are analyzed and the main contributors are clarified, and the power requirement model of the working motor is built based on experimental data. Second, a universal modeling method of garbage distribution on the road surface is proposed, which implements a nonlinear autoregressive neural network as the predictor. Third, an adaptive model predictive control (AMPC)-based EMS is proposed and verified. The results show the AMPC method can accurately predict the garbage density and the proposed EMS can approximate the energy consumption of the DP-based EMS with little deviation. Compared to conventional rule-based EMS, the AMPC-based EMS achieved a 15.5% decrease in energy consumption as well as a 14.6% decrease in working time.
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Sun, Mou, Chuan Shan, Kang-wen Sun, and Yu-hong Jia. "Energy Management Strategy for High-Altitude Solar Aircraft Based on Multiple Flight Phases." Mathematical Problems in Engineering 2020 (December 9, 2020): 1–13. http://dx.doi.org/10.1155/2020/6655031.
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Making use of solar energy to fly is an up-and-coming technology in the human aviation field since solar energy is renewable and inexhaustible, and more and more attention and efforts have been directed to the development of high-altitude solar aircraft (HSA). Due to the technical constraints of the rechargeable battery, the HSA must carry sufficient batteries to meet the flight power consumption at night, which seriously limits the flight endurance of HSA. To solve this contradiction, the paper has proposed a new energy management strategy (EMS) of multiple flight phases for HSA based on the gravitational energy storage and mission altitude, which aims to achieve the goal of long-endurance flight for HSA. The integrated model of this new EMS includes the aerodynamic model, the kinematic model, the solar irradiation model, the battery model, and the energy management model. Compared with the current EMS of level flight, the flight path of HSA in the new EMS has been divided into five phases: the lower altitude level flight at night, the maximum power ascending for mission altitude, the level flight at mission altitude, the maximum power ascending for higher altitude, and the longest gliding endurance. At last, the calculation of the new EMS for Zephyr 7 is studied by MATLAB/Simulink, and the calculation results indicate that about 22.9% of energy surplus can be stored in battery with the new EMS for Zephyr 7 compared with the current EMS, which is equal to reducing the rechargeable battery weight from 16.0 kg to 12.3 kg. Besides, the results of simulation in the four seasons also show that the new EMS is a very promising way to achieve the long-endurance goal for high-altitude HSA when the flight conditions satisfy some constraints like the deficiency of solar flux and the limit of battery mass.
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Ghosh, Subarto Kumar, Tushar Kanti Roy, Md Abu Hanif Pramanik, Ajay Krishno Sarkar, and Md Apel Mahmud. "An Energy Management System-Based Control Strategy for DC Microgrids with Dual Energy Storage Systems." Energies 13, no. 11 (June 10, 2020): 2992. http://dx.doi.org/10.3390/en13112992.
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In this work, a control strategy is developed for different components in DC microgrids where set points for all controllers are determined from an energy management system (EMS). The proposed EMS-based control scheme is developed for DC microgrids with solar photovoltaic (PV) systems as the primary generation units along with energy storage systems. In this work, the concept of dual energy storage systems (DESSs) is used, which includes a battery energy storage system (BESS) and supercapacitor (SC). The main feature of this DESS is to improve the dynamic performance of DC microgrids during severe transients appearing from changes in load demands as well as in the output power from solar PV units. Furthermore, the proposed EMS-based control scheme aims to enhance the lifetime of the BESS in DC microgrids with DESSs and voltage stability as compared to the same without SCs. The proposed EMS-based control strategy uses proportional-integral (PI) controllers to regulate the switching control actions for different converters within the DC microgrid based on the decision obtained from the EMS in order to achieve the desired control objectives. The performance of the proposed scheme was analyzed through simulation results in terms of improving the voltage stability, maintaining the power balance, and enhancing the lifetime of BESSs within a DC microgrid framework incorporated with the DESS. The simulations are carried out in the MATLAB/SIMULINK simulation platform and compared with a similar approach having only a single energy storage system, i.e., the BESS.
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Yi, Fengyan, Dagang Lu, Xingmao Wang, Chaofeng Pan, Yuanxue Tao, Jiaming Zhou, and Changli Zhao. "Energy Management Strategy for Hybrid Energy Storage Electric Vehicles Based on Pontryagin’s Minimum Principle Considering Battery Degradation." Sustainability 14, no. 3 (January 21, 2022): 1214. http://dx.doi.org/10.3390/su14031214.
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The development of energy management strategy (EMS), which considers how power is distributed between the battery and ultracapacitor, can reduce the electric vehicle’s power consumption and slow down battery degradation. Therefore, the purpose of this paper is to develop an EMS for hybrid energy storage electric vehicles based on Pontryagin’s minimums principle (PMP) considering battery degradation. To verify the EMS, the hybrid energy storage electric vehicle model is first established. In the meantime, the battery cycle life trials are finished in order to develop a battery degradation model. Following that, a rule-based control approach and the PMP optimization algorithm are used to allocate power in a hybrid energy storage system (HESS) in a reasonable manner. Finally, a simulation experiment under urban dynamometer driving schedule (UDDS) settings verifies the established EMS, and the findings reveal that the suggested EMS has a lower energy consumption rate and battery deterioration rate than the rule-based method.
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MOHD SABRI, MOHAMAD FAIZRIZWAN, MAIMUN HUJA HUSIN, SHAMSIAH SUHAILI, and SHARIFAH MASNIAH WAN MASRA. "ADAPTIVE ENERGY MANAGEMENT STRATEGY FOR SUSTAINABLE OPERATION OF HYBRID ELECTRIC VEHICLE." JOURNAL OF SUSTAINABILITY SCIENCE AND MANAGEMENT 17, no. 6 (June 30, 2022): 159–71. http://dx.doi.org/10.46754/jssm.2022.06.012.
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Hybrid electric vehicles (HEVs) in the transportation sector is spearheading the industry towards embracing green technology to ensure the sustainability of the environment. HEVs offer significant reduction of exhaust emissions while retaining vehicle performance achieved via a revolutionary energy management strategy (EMS) which reforms the management of power flow from the dual energy sources of a HEV. However, researchers are faced with challenges to extract the maximum performance out of HEVs due to the contradicting nature between its main objectives, namely vehicle performance, fuel consumption and cost. In this study, an investigation focusing on the fuel-saving potential of a power-split type HEV using a fuzzy logic-based EMS is conducted. The purpose of this research is to explore methods to improve fuel efficiency of a HEV through a smart and adaptive EMS. The power flow in the proposed model is decided based on its current vehicle speed and the global discharge rate value derived from the real-time battery state-of-charge and remaining trip distance. From simulations over standard drive cycles, the proposed controller is able to outperform a rule-based EMS by an improvement of up to 65.4% in terms of fuel consumption which subsequently reduces the volume of pollutants released to the atmosphere.
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Yahyaoui, Imene, and Natalia Vidal de la Peña. "Energy Management Strategy for an Autonomous Hybrid Power Plant Destined to Supply Controllable Loads." Sensors 22, no. 1 (January 4, 2022): 357. http://dx.doi.org/10.3390/s22010357.
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This paper proposes an energy management strategy (EMS) for a hybrid stand-alone plant destined to supply controllable loads. The plant is composed of photovoltaic panels (PV), a wind turbine, a diesel generator, and a battery bank. The set of the power sources supplies controllable electrical loads. The proposed EMS aims to ensure the power supply of the loads by providing the required electrical power. Moreover, the EMS ensures the maximum use of the power generated by the renewable sources and therefore minimizes the use of the genset, and it ensures that the batteries bank operates into the prefixed values of state of charge to ensure their safe operation. The EMS provides the switching control of the switches that link the plant components and decides on the loads’ operation. The simulation of the system using measured climatic data of Mostoles (Madrid, Spain) shows that the proposed EMS fulfills the designed objectives.
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Teekaraman, Yuvaraja, K. A. Ramesh Kumar, Ramya Kuppusamy, and Amruth Ramesh Thelkar. "SSNN-Based Energy Management Strategy in Grid Connected System for Load Scheduling and Load Sharing." Mathematical Problems in Engineering 2022 (January 10, 2022): 1–9. http://dx.doi.org/10.1155/2022/2447299.
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The proposed research work focused on energy management strategy (EMS) in a grid connected system working in islanding mode with the connected renewable energy resources and battery storage system. The energy management strategy developed provides a balancing operation at its output by utilizing perfect load sharing strategy. The EMS technique using smart superficial neural network (SSNN) is simulated, and numerical analyses are presented to validate the effectiveness of the centralized energy management strategy in a grid connected islanded system. A SSNN prediction model is unified to forecast the associated household load demand, PV generation system under various time horizons (including the disaster condition), EV availability, and status on EV section and distance. SSNN is one the most reliable forecasting methods in many of the applications. The developed system is also accounted for degradation battery model and its associated cost. The incorporation of energy management strategy (EMS) reduces the amount of energy drawn from the grid connected system when compared with the other optimized systems.
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Price, Thomas, Gordon Parker, Gail Vaucher, Robert Jane, and Morris Berman. "Microgrid Energy Management during High-Stress Operation." Energies 15, no. 18 (September 8, 2022): 6589. http://dx.doi.org/10.3390/en15186589.
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We consider the energy management of an isolated microgrid powered by photovoltaics (PV) and fuel-based generation with limited energy storage. The grid may need to shed load or energy when operating in stressed conditions, such as when nighttime electrical loads occur or if there is little energy storage capacity. An energy management system (EMS) can prevent load and energy shedding during stress conditions while minimizing fuel consumption. This is important when the loads are high priority and fuel is in short supply, such as in disaster relief and military applications. One example is a low-power, provisional microgrid deployed temporarily to service communication loads immediately after an earthquake. Due to changing circumstances, the power grid may be required to service additional loads for which its storage and generation were not originally designed. An EMS that uses forecasted load and generation has the potential to extend the operation, enhancing the relief objectives. Our focus was to explore how using forecasted loads and PV generation impacts energy management strategy performance. A microgrid EMS was developed exploiting PV and load forecasts to meet electrical loads, harvest all available PV, manage storage and minimize fuel consumption. It used a Model Predictive Control (MPC) approach with the instantaneous grid storage state as feedback to compensate for forecasting errors. Four scenarios were simulated, spanning a stressed and unstressed grid operation. The MPC approach was compared to a rule-based EMS that did not use load and PV forecasting. Both algorithms updated the generator’s power setpoint every 15 min, where the grid’s storage was used as a slack asset. While both methods had similar performance under unstressed conditions, the MPC EMS showed gains in storage management and load shedding when the microgrid was stressed. When the initial storage was low, the rule-based EMS could not meet the load requirements and shed 16% of the day’s electrical load. In contrast, the forecast-based EMS managed the load requirements for this scenario without shedding load or energy. The EMS sensitivity to forecast error was also examined by introducing load and PV generation uncertainty. The MPC strategy successfully corrected the errors through storage management. Since weather affects both PV energy generation and many types of electrical loads, this work suggests that weather forecasting advances can improve remote microgrid performance in terms of fuel consumption, load satisfaction, and energy storage requirements.
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Tayab, Usman Bashir, Junwei Lu, Seyedfoad Taghizadeh, Ahmed Sayed M. Metwally, and Muhammad Kashif. "Microgrid Energy Management System for Residential Microgrid Using an Ensemble Forecasting Strategy and Grey Wolf Optimization." Energies 14, no. 24 (December 16, 2021): 8489. http://dx.doi.org/10.3390/en14248489.
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Microgrid (MG) is a small-scale grid that consists of multiple distributed energy resources and load demand. The microgrid energy management system (M-EMS) is the decision-making centre of the MG. An M-EMS is composed of four modules which are known as forecasting, scheduling, data acquisition, and human-machine interface. However, the forecasting and scheduling modules are considered the major modules from among the four of them. Therefore, this paper proposed an advanced microgrid energy management system (M-EMS) for grid-connected residential microgrid (MG) based on an ensemble forecasting strategy and grey wolf optimization (GWO) based scheduling strategy. In the forecasting module of M-EMS, the ensemble forecasting strategy is proposed to perform the short-term forecasting of PV power and load demand. The GWO based scheduling strategy has been proposed in scheduling module of M-EMS to minimize the operating cost of grid-connected residential MG. A small-scale experiment is conducted using Raspberry Pi 3 B+ via the python programming language to validate the effectiveness of the proposed M-EMS and real-time historical data of PV power, load demand, and weather is adopted as inputs. The performance of the proposed forecasting strategy is compared with ensemble forecasting strategy-1, particle swarm optimization based artificial neural network, and back-propagation neural network. The experimental results highlight that the proposed forecasting strategy outperforms the other strategies and achieved the lowest average value of normalized root mean square error of day-ahead prediction of PV power and load demand for the chosen day. Similarly, the performance of GWO based scheduling strategy of M-EMS is analyzed and compared for three different scenarios. Finally, the experimental results prove the outstanding performance of the proposed scheduling strategy.
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Dou, Haishi, Youtong Zhang, and Likang Fan. "Design of Optimized Energy Management Strategy for All-Wheel-Drive Electric Vehicles." Applied Sciences 11, no. 17 (September 4, 2021): 8218. http://dx.doi.org/10.3390/app11178218.
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The performance of the all-wheel-drive electric vehicle is inseparable from the energy management strategy (EMS). An outstanding EMS could extend the cycling mileage, coordinating the power output of the battery and exerts the advantage of the motor comprehensively. However, the current EMS has poor performance in real-time, and this paper proposes the dynamic programming coordination strategy (DPCS) to solve the problem. Firstly, the EMS based on a rule-based control strategy (RBCS) is applied in a different driving cycle. Secondly, the dynamic programming algorithm (DP) is proposed in the process. The DPCS cooperated the advantage of RBCS and DP, extracting the boundary parameters along with the demand power and vehicle speed. Finally, the number of motors joined in the driving condition is elucidated and the method obtains the optimal torque split ratio through a partly-known driving cycle. By incorporating the thought of a basis of rules, the DPCS determines the torque of each motor that confirm the motor working in an efficient range that incorporates the mind of dynamic programming. The method is validated through the simulation. The results show that the strategy can significantly improve the mileage of the driving cycle, with comprehensive performance in energy distribution and utilization.
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Madhavaram, Poornachandra Reddy, and Manimozhi M. "Smart Energy Management Strategy for Microgrids Powered by Heterogeneous Energy Sources and Electric Vehicles’ Storage." Energies 15, no. 20 (October 19, 2022): 7739. http://dx.doi.org/10.3390/en15207739.
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The rapid growths of power demand and renewable resources have led to numerous challenges. Constructing more resilient microgrids (MGs) provides an opportunity to avoid dependency on the main grid. This article proposes an innovative Energy Management Strategy (EMS) for microgrids that uses non-conventional energy sources such as solar power, wind power, and the storage of electric vehicles (EVs). Numerous studies have been published on MG EMSs using storage; however, in real-time scenarios, predominant factors limit their straightforward implementation. In this article, an attempt is made to address key aspects of EV storage exploitation to support MGEMSs. Minimizing the total MG energy cost is the key objective, considering EV battery longevity and technical limitations. The proposed EMS was implemented in three layers: Optimal Storage Distribution (OSD), Optimal Power Usage (OPU), and EV Selection (EVS). A novel probabilistic approach was implemented in the EVS process (using a Fuzzy Logic Controller (FLC)) to minimize battery degradation. Various case studies were analyzed in a grid-connected MG by implementing the proposed EMS.
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Alhumade, Hesham, Hegazy Rezk, Mohamed Louzazni, Iqbal Ahmed Moujdin, and Saad Al-Shahrani. "Advanced Energy Management Strategy of Photovoltaic/PEMFC/Lithium-Ion Batteries/Supercapacitors Hybrid Renewable Power System Using White Shark Optimizer." Sensors 23, no. 3 (January 30, 2023): 1534. http://dx.doi.org/10.3390/s23031534.
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The slow dynamic response of a proton exchange membrane fuel cell (PEMFC) to high load change during deficit periods must be considered. Therefore, integrating the hybrid system with energy storage devices like battery storage and/or a supercapacitor is necessary. To reduce the consumed hydrogen, an energy management strategy (EMS) based on the white shark optimizer (WSO) for photovoltaic/PEMFC/lithium-ion batteries/supercapacitors microgrid has been developed. The EMSs distribute the load demand among the photovoltaic, PEMFC, lithium-ion batteries, and supercapacitors. The design of EMSs must be such that it minimizes the use of hydrogen while simultaneously ensuring that each energy source performs inside its own parameters. The recommended EMS-based-WSO was evaluated in regard to other EMSs regarding hydrogen fuel consumption and effectiveness. The considered EMSs are state machine control strategy (SMCS), classical external energy maximization strategy (EEMS), and optimized EEMS-based particle swarm optimization (PSO). Thanks to the proposed EEMS-based WSO, hydrogen utilization has been reduced by 34.17%, 29.47%, and 2.1%, respectively, compared with SMCS, EEMS, and PSO. In addition, the efficiency increased by 6.05%, 9.5%, and 0.33%, respectively, compared with SMCS, EEMS, and PSO.
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Ranjan, Alok, and Sanjay B. Bodkhe. "Fuzzy Logic Controller Based Modified Energy Management Strategy for Battery and UC with Improved Battery Performance." ECS Transactions 107, no. 1 (April 24, 2022): 2457–69. http://dx.doi.org/10.1149/10701.2457ecst.
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The ultracapacitor (UC) acts as a stress reducer for the battery. It is also considered as a fruitful companion for a battery in battery-UC hybrid energy storage system (HESS). Management of power between battery, UC, and drive motor, along with a suitable topology, decides the performance of the battery and drive system. Rule based energy management strategy (EMS) is simple, real-time implementable, ease in control, and based on engineer’s experience. But it lacks in controlling the state-of-charge (SOC) of battery and UC, battery current rate, and its magnitude, dc link voltage control, and UC voltage regulation. In this paper, low pass filter based conventional rule-based EMS is studied and implemented in MATLAB. Then fuzzy logic based modified rule-based EMS is proposed and simulated for the same load profile. Comparative study between conventional and modified EMS shows the proposed EMS is capable to provide constant dc link voltage, stable UC SOC operation, fast response, and reduces the large variation in speed. Proposed EMS saves the 10% of the battery energy for 20 sec load profiles as compared with battery only system. So, by use of UC, shared bus topology, and effective EMS, driving range of the vehicle and battery life can be improved and range anxiety can be reduced.
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Zhang, Qi, and Xiaoling Fu. "A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition." Applied Sciences 10, no. 2 (January 19, 2020): 696. http://dx.doi.org/10.3390/app10020696.
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Aiming at the problems inherent in the traditional fuzzy energy management strategy (F-EMS), such as poor adaptive ability and lack of self-learning, a neural network fuzzy energy management strategy (NNF-EMS) for hybrid electric vehicles (HEVs) based on driving cycle recognition (DCR) is designed. The DCR was realized by the method of neural network sample learning and characteristic parameter analysis, and the recognition results were considered as the reference input of the fuzzy controller with further optimization of the membership function, resulting in improvement in the poor pertinence of F-EMS driving cycles. The research results show that the proposed NNF-EMS can realize the adaptive optimization of fuzzy membership function and fuzzy rules under different driving cycles. Therefore, the proposed NNF-EMS has strong robustness and practicability under different driving cycles.
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Yao, Gang, Changbo Du, Quanbo Ge, Haoyu Jiang, Yide Wang, Mourad Ait-Ahmed, and Luc Moreau. "Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles." Energies 12, no. 23 (November 21, 2019): 4426. http://dx.doi.org/10.3390/en12234426.
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In the field of Fuel Cell Electric Vehicles (FCEVs), a fuel-cell stack usually works together with a battery to improve powertrain performance. In this hybrid-power system, an Energy Management Strategy (EMS) is essential to configure the hybrid-power sources to provide sufficient energy for driving the FCEV in different traffic conditions. The EMS determines the overall performance of the power supply system; accordingly, EMS research has important theoretical significance and application values on the improvement of energy-utilization efficiency and the serviceability of vehicles’ hybrid-power sources. To overcome the deficiency of apparent filtering lag and improve the adaptability of an EMS to different traffic conditions, this paper proposes a novel EMS based on traffic-condition predictions, frequency decoupling and a Fuzzy Inference System (FIS). An Artificial Neural Network (ANN) was designed to predict traffic conditions according to the vehicle’s running parameters; then, a Hull Moving Average (HMA) algorithm, with filter-window width decided by the prediction result, is introduced to split the demanded power and keep low-frequency components in order to meet the load characteristics of the fuel cell; afterward, an FIS was applied to manage power flows of the FCEV’s hybrid-power sources and maintain the State of Change (SoC) of the battery in a predefined range. Finally, an FCEV simulation platform was built with MATLAB/Simulink and comparison simulations were carried out with the standard test cycle of the Worldwide harmonized Light vehicle Test Procedures (WLTPs). Simulation results showed that the proposed EMS could efficiently coordinate the hybrid-power sources and support the FCEV in following the reference speed with negligible control errors and sufficient power supply; the SoC of the battery was also maintained with good adaptability in different driving conditions.
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Kim, Tae-Gyu, Hoon Lee, Chang-Gyun An, Junsin Yi, and Chung-Yuen Won. "Hybrid AC/DC Microgrid Energy Management Strategy Based on Two-Step ANN." Energies 16, no. 4 (February 10, 2023): 1787. http://dx.doi.org/10.3390/en16041787.
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In grid-connected operations, a microgrid can solve the problem of surplus power through regeneration; however, in the case of standalone operations, the only method to solve the surplus power problem is charging the energy storage system (ESS). However, because there is a limit to the capacity that can be charged in an ESS, a separate energy management strategy (EMS) is required for stable microgrid operation. This paper proposes an EMS for a hybrid AC/DC microgrid based on an artificial neural network (ANN). The ANN is composed of a two-step process that operates the microgrid by outputting the operation mode and charging and discharging the ESS. The microgrid consists of an interlinking converter to link with the AC distributed system, a photovoltaic converter, a wind turbine converter, and an ESS. The control method of each converter was determined according to the mode selection of the ANN. The proposed ANN-based EMS was verified using a laboratory-scale hybrid AC/DC microgrid. The experimental results reveal that the microgrid operation performed stably through control of individual converters via mode selection and reference to ESS power, which is the result of ANN integration.
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Guo, Zhiqi, Jianhua Guo, Liang Chu, Chong Guo, Jincheng Hu, and Zhuoran Hou. "A Hierarchical Energy Management Strategy for 4WD Plug-In Hybrid Electric Vehicles." Machines 10, no. 10 (October 18, 2022): 947. http://dx.doi.org/10.3390/machines10100947.
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In the field of new energy vehicles, 4WD PHEVs show strong energy-saving potential. A single energy management strategy, nevertheless, has difficulty achieving the energy-saving potential due to the complex, nonlinear energy system of the 4WD PHEV. To cope with it, a hierarchical energy management strategy (H-EMS) for 4WD PHEVs is proposed in this paper to achieve energy management optimization. Firstly, the future speed information is predicted by the speed prediction method, and the upper energy management strategy adopts the model predictive control (MPC) based on the future speed information to carry out the power source distribution between the engine and the battery. Secondly, the lower energy management strategy performs the power component distribution of the front motor and the rear motor based on an equivalent consumption minimization strategy (ECMS). Finally, the simulation based on MATLAB/Simulink is performed, validating that the proposed method has more energy-saving capabilities, and the economy is improved by 11.87% compared with the rule-based (RB) energy management strategies.
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Wei, Changyin, Yong Chen, Xiuxiu Sun, and Yue Zhang. "Optimal Equivalent Consumption Minimization Strategy for Plug-In Hybrid Electric Vehicle with Improved Genetic Algorithm." SAE International Journal of Electrified Vehicles 9, no. 2 (December 31, 2020): 143–54. http://dx.doi.org/10.4271/14-09-02-0009.
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The equivalent consumption minimization strategy (ECMS) is a promising energy management approach to low-fuel economy with the outstanding features of high efficiency. In this article, an optimal ECMS by Improved Genetic Algorithm (IGA) is proposed. To this end, we improved the genetic algorithm (GA) from the coding method, initialization mode, and cross and mutation process. And based on the comprehensive energy consumption and Pontryagin’s minimum principle, the equivalent factor was derived. The IGA was used to optimize the equivalent factor. To evaluate the performance of the proposed energy management strategy (EMS), the average efficiency of the engine and the motor was analyzed in an urban area, high-speed area, and the whole area. The comprehensive fuel consumption was used as the energy consumption index, and the battery capacity loss under the transient conditions was amplified to 10 years as the evaluation battery life index. The simulation results show that under the New European Driving Cycle (NEDC), the proposed strategy improves the fuel economy and battery life index by 14.64% and 36.76%, respectively, compared with the rule-based EMS.
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Gadge, Gaurav, and Yogesh Pahariya. "Grey Wolf Optimization Based Energy Management Strategy for Hybrid Electrical Vehicles." International Journal of Electrical and Electronics Research 10, no. 3 (September 30, 2022): 772–78. http://dx.doi.org/10.37391/ijeer.100359.
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Electric vehicles (EVs) are seen as a necessary component of transportation's future growth. However, the performance of batteries related to power density and energy density restricts the adoption of electric vehicles. To make the transition from a conventional car to a pure electric vehicle (PEV), a Hybrid Electric Vehicle's (HEV) Energy Management System (EMS) is crucial. The HEVs are often powered with hybrid electrical sources, therefore it is important to select the optimal power source to improve the HEV performance, minimize the fuel cost and minimize hydrocarbon and nitrogen oxides emission. This paper presents the Grey Wolf Optimization (GWO) algorithm for the control of the power sources in the HEVs based on power requirement and economy. The proposed GWO-based EMS provides optimized switching of the power sources and economical and pollution free control of HEV.
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Azuara-Grande, Luis Santiago, Santiago Arnaltes, Jaime Alonso-Martinez, and Jose Luis Rodriguez-Amenedo. "Comparison of Two Energy Management System Strategies for Real-Time Operation of Isolated Hybrid Microgrids." Energies 14, no. 20 (October 17, 2021): 6770. http://dx.doi.org/10.3390/en14206770.
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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.
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Rodríguez-Licea, Martín-Antonio, Francisco-J. Perez-Pinal, Allan-Giovanni Soriano-Sánchez, and José-Antonio Vázquez-López. "Noninvasive Vehicle-to-Load Energy Management Strategy to Prevent Li-Ion Batteries Premature Degradation." Mathematical Problems in Engineering 2019 (May 23, 2019): 1–9. http://dx.doi.org/10.1155/2019/8430685.
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Today, electric vehicles available in the market aspire to offer different connections to the end user, for instance, Vehicle to Grid (V2G), Vehicle to Building (V2B), Vehicle to Home (V2H), Vehicle to Vehicle (V2V), and Vehicle to Load (V2L), among others. Notwithstanding these versatility options toward the development of a sustainable society, the additional degradation of the energy storage systems once those operate in extra discharge modes is inevitable. Therefore, in this paper, an energy management strategy (EMS) which operates autonomously and noninvasively as an additional layer to the battery management system (BMS) is proposed. The EMS limits the current flow avoiding high and low temperatures, low state of charge (SoC), high deep of discharge (DoD), noncentered DoD around an optimal SoC point, and high charge and discharge rates. The proposed EMS is evaluated by long-term simulations with a Li-Ion battery degradation model and realistic weather conditions, during standard driving cycles including the V2L operation. The effectiveness and simplicity of tuning of the proposed EMS allow estimating and increasing the life expectancy of the Li-Ion battery bank, by limiting the energy used for V2L operation.
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Li, Tao, Wei Cui, and Naxin Cui. "Soft Actor-Critic Algorithm-Based Energy Management Strategy for Plug-In Hybrid Electric Vehicle." World Electric Vehicle Journal 13, no. 10 (October 18, 2022): 193. http://dx.doi.org/10.3390/wevj13100193.
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Plug-in hybrid electric vehicles (PHEVs) are equipped with more than one power source, providing additional degrees of freedom to meet the driver’s power demand. Therefore, the reasonable allocation of the power demand of each power source by the energy management strategy (EMS) to keep each power source operating in the efficiency zone is essential for improving fuel economy. This paper proposes a novel model-free EMS based on the soft actor-critic (SAC) algorithm with automatic entropy tuning to balance the optimization of energy efficiency with the adaptability of driving cycles. The maximum entropy framework is introduced into deep reinforcement learning-based energy management to improve the performance of exploring the internal combustion engine (ICE) as well as the electric motor (EM) efficiency interval. Specifically, the automatic entropy adjustment framework improves the adaptability to driving cycles. In addition, the simulation is verified by the data collected from the real vehicle. The results show that the introduction of automatic entropy adjustment can effectively improve vehicle equivalent fuel economy. Compared with traditional EMS, the proposed EMS can save energy by 4.37%. Moreover, it is able to adapt to different driving cycles and can keep the state of charge to the reference value.
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Boukhnifer, Moussa, Nadir Ouddah, Toufik Azib, and Ahmed Chaibet. "Intelligent energy management for hybrid fuel cell/battery system." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 35, no. 5 (September 5, 2016): 1850–64. http://dx.doi.org/10.1108/compel-08-2015-0309.
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Purpose The purpose of this paper is to propose two energy management strategies (EMS) for hybrid electric vehicle, the power system is an hybrid architecture (fuel cell (FC)/battery) with two-converters parallel configuration. Design/methodology/approach First, the authors present the EMS uses a power frequency splitting to allow a natural frequency decomposition of the power loads and second the EMS uses the optimal control theory, based on the Pontryagin’s minimum principle. Findings Thanks to the optimal approach, the control objectives will be easily achieved: hydrogen consumption is minimized and FC health is protected. Originality/value The simulation results show the effectiveness of the control strategy using optimal control theory in term of improvement of the fuel consumption based on a comparison analysis between the two strategies.
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Dou, Haishi, Hongqian Wei, Youtong Zhang, and Qiang Ai. "Configuration Design and Optimal Energy Management for Coupled-Split Powertrain Tractor." Machines 10, no. 12 (December 7, 2022): 1175. http://dx.doi.org/10.3390/machines10121175.
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High-power tractors are regarded as effective operation tools in agriculture, and plugin hybrid tractors have shown potential as agricultural machinery, due to their wide application in energy conservation. However, the allocation of the output power of the motors and engine is a challenging task, given that the energy management strategy (EMS) is nonlinearly constrained. On the other hand, the structure of the continuous variable transmission (CVT) system is complicated, and affects the price of tractors. In this paper, a variable configuration of a tractor that could have the same performance as a complex CVT system is proposed. To address the EMS issues that have shown poor performance in real time, where the programming runs online, firstly a demand power prediction algorithm is proposed in a rotary tillage operation mode. Secondly, an equivalent fuel consumption minimization strategy (ECMS) is used to optimize the power distribution between the engine and the motors. In addition, the equivalent factor is optimized with an offline genetic algorithm. Thirdly, the equivalent factor is converted into a lookup table, and is used for an online power distribution with different driving mileages and state-of-charge (SOC). The simulation results indicate that the equivalent fuel consumption is reduced by 8.4% and extends the operating mileage of pure electric power. Furthermore, the error between the actual and forecasted demand power is less than 1%. The online EMS could improve the mileage of the tractor working cycle with a more feasible fuel economy based on demand power predictions.
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Li, Wang, Chao Wang, Haoying Pei, Chunmei Xu, Gengyi Lin, Jiangming Deng, Dafa Jiang, and Yuanju Huang. "An Improved Energy Management Strategy of Diesel-Electric Hybrid Propulsion System Based on FNN-DP Strategy." Electronics 12, no. 3 (January 17, 2023): 486. http://dx.doi.org/10.3390/electronics12030486.
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Diesel-electric hybrid propulsion system (HPS) is widely applied for shunting locomotive due to the characteristics of flexible configuration, economic and environmental protection in the world. Energy management strategy (EMS) is an important design factor of HPS that can optimize the energy distribution of each power sources, improve system efficiency, and reduce fuel consumption. In this paper, the model of HPS for shunting locomotive and system operating profile are firstly carried out. Then the EMS consist of the conventional rule-based (RB) strategy rule, and a fuzzy neural network base on dynamic programming (FNN-DP) strategy are studied. Finally, the simulations were carried out with these EMSs in the system model at full operating conditions to derive the fuel consumption. The conclusion is that the theoretical optimal solution of DP provides reference and guidance for the fuzzy neural network strategy to improve the rules, and the fuel consumption of the FNN-DP strategy is 10.2% lower than the conventional RB strategy.
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Jbari, Hatim, Rachid Askour, and Badr Bououlid Idrissi. "Fuzzy logic-based energy management strategy on dual-source hybridization for a pure electric vehicle." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (October 1, 2022): 4903. http://dx.doi.org/10.11591/ijece.v12i5.pp4903-4914.
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This paper presents a fuzzy logic controller (FLC) based energy management strategy (EMS), combined with power filtering for a pure electric vehicle. The electrical power supply is provided by a hybrid energy storage system (HESS), including Li-Ion battery and supercapacitors (SCs), adopting a fully active parallel topology. The vehicle model was organized and constructed using the energetic macroscopic representation (EMR). The main objective of this work is to ensure an efficient power distribution in the proposed dual source, in order to reduce the battery degradation. To evaluate the impact of the developed design and the efficiency of the developed EMS, the proposed FLC strategy is compared to a classical EMS using SCs-filtering strategy and architecture based on battery storage model. To validate the proposed topology, simulation results are provided for the new European driving cycle (NEDC) using MATLAB/Simulink environment.
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Tapia, Cristian, Diana Ulloa, Mayra Pacheco-Cunduri, Jorge Hernández-Ambato, Jesús Rodríguez-Flores, and Victor Herrera-Perez. "Optimal Fuzzy-Based Energy Management Strategy to Maximize Self-Consumption of PV Systems in the Residential Sector in Ecuador." Energies 15, no. 14 (July 16, 2022): 5165. http://dx.doi.org/10.3390/en15145165.
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This paper proposes a fuzzy-based energy management strategy (EMS) to maximize the self-consumption from a PV installation with an energy storage system (ESS) for the residential sector adapted to the Ecuadorian electricity market. The EMS includes two control levels: Energy management at the end-user level (Fuzzy-based EMS and optimized by genetic Algorithm) and Energy management at the distribution grid level (Fuzzy-based EMS). Both strategies aim to maximize the use of the energy generated at home (taking into account the local solar generation profile), fulfilling the loads’ demand and injecting the energy surplus into the main grid to be economically compensated. Additionally, this paper presents economical modeling according to the electricity market in Ecuador. The main results showed a cost reduction in the electricity bill up to 83.64% from the base case (residential consumption without a PV system). In the scenario of a community electricity market (still not contemplated under the Ecuadorian electricity law), the potential economic savings may be more than double compared to the exact case but only with a self-consumption system.
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Jbari, Hatim, Mohamed Haidoury, Rachid Askour, and Badr Bououlid Idrissi. "Fuzzy Logic Controller for an EV’s Dual-Source Hybridization." E3S Web of Conferences 297 (2021): 01039. http://dx.doi.org/10.1051/e3sconf/202129701039.
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This paper presents an energy management system (EMS) based on fuzzy logic control (FLC) strategy combined with power filtering. This strategy is developed for an Electric Vehicle (EV) hybrid energy storage systems (HESS). The proposed control and energy management strategy (EMS) aims to ensure an efficient power split guaranteeing that battery and supercapacitors (SC) provide the continuous and transient-power, respectively, adopting a pure electric vehicle fully-active parallel topology. In order to develop the studied system model, the Energetic Macroscopic Representation (EMR) approach is adopted. Considering SC’s control criterion, and battery root mean square RMS current reducing, an evaluation of the proposed EMS and developed model was conducted using MATLAB/SIMULINK simulation under New European Driving Cycle (NEDC) and compared to the classical only battery storage configuration.
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Kandidayeni, Mohsen, Alvaro Macias, Loïc Boulon, and João Pedro F. Trovão. "Online Modeling of a Fuel Cell System for an Energy Management Strategy Design." Energies 13, no. 14 (July 19, 2020): 3713. http://dx.doi.org/10.3390/en13143713.
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An energy management strategy (EMS) efficiently splits the power among different sources in a hybrid fuel cell vehicle (HFCV). Most of the existing EMSs are based on static maps while a proton exchange membrane fuel cell (PEMFC) has time-varying characteristics, which can cause mismanagement in the operation of a HFCV. This paper proposes a framework for the online parameters identification of a PMEFC model while the vehicle is under operation. This identification process can be conveniently integrated into an EMS loop, regardless of the EMS type. To do so, Kalman filter (KF) is utilized to extract the parameters of a PEMFC model online. Unlike the other similar papers, special attention is given to the initialization of KF in this work. In this regard, an optimization algorithm, shuffled frog-leaping algorithm (SFLA), is employed for the initialization of the KF. The SFLA is first used offline to find the right initial values for the PEMFC model parameters using the available polarization curve. Subsequently, it tunes the covariance matrices of the KF by utilizing the initial values obtained from the first step. Finally, the tuned KF is employed online to update the parameters. The ultimate results show good accuracy and convergence improvement in the PEMFC characteristics estimation.
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Ramalingeswar, J. T., and K. Subramanian. "A novel energy management strategy to reduce gird dependency using electric vehicles storage in coordination with solar power." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 2207–23. http://dx.doi.org/10.3233/jifs-210930.
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The effective coordination of solar photovoltaic (solar PV) with Electrical Vehicles (EV) can substantially improve the micro grid(MG) stability and economic benefits. This paper presents a novel Energy Management System (EMS) that synchronizes EV storage with Solar PV and load variability. Reducing grid dependency and energy cost of the MGs are the key objectives of the proposed EMS. A smart EV prioritization based control strategy is developed using fuzzy controller. Probabilistic approach is designed to estimate the EV usage expectancy in the near time zone that helps smart decision on choosing EVs. Minimizing battery degradation and maximizing EV storage exploitation are the key objectives of EV prioritization. On the other hand, Water Filling Algorithm (WFA) is used for Optimal Storage Distribution (OSD) in each zone of energy need for load flattening. The proposed EMS is implemented in a real time on-grid MG scenario and different case studies have been investigated to realize the impact of proposed EMS. A comprehensive cost analysis has been conducted and the efficacy of the proposed EMS is analysed.
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Marín, Luis Gabriel, Mark Sumner, Diego Muñoz-Carpintero, Daniel Köbrich, Seksak Pholboon, Doris Sáez, and Alfredo Núñez. "Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control." Energies 12, no. 23 (November 22, 2019): 4453. http://dx.doi.org/10.3390/en12234453.
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This paper presents a two-level hierarchical energy management system (EMS) for microgrid operation that is based on a robust model predictive control (MPC) strategy. This EMS focuses on minimizing the cost of the energy drawn from the main grid and increasing self-consumption of local renewable energy resources, and brings benefits to the users of the microgrid as well as the distribution network operator (DNO). The higher level of the EMS comprises a robust MPC controller which optimizes energy usage and defines a power reference that is tracked by the lower-level real-time controller. The proposed EMS addresses the uncertainty of the predictions of the generation and end-user consumption profiles with the use of the robust MPC controller, which considers the optimization over a control policy where the uncertainty of the power predictions can be compensated either by the battery or main grid power consumption. Simulation results using data from a real urban community showed that when compared with an equivalent (non-robust) deterministic EMS (i.e., an EMS based on the same MPC formulation, but without the uncertainty handling), the proposed EMS based on robust MPC achieved reduced energy costs and obtained a more uniform grid power consumption, safer battery operation, and reduced peak loads.
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Sellali, Mehdi, Alexandre Ravey, Achour Betka, Abdellah Kouzou, Mohamed Benbouzid, Abdesslem Djerdir, Ralph Kennel, and Mohamed Abdelrahem. "Multi-Objective Optimization-Based Health-Conscious Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles." Energies 15, no. 4 (February 11, 2022): 1318. http://dx.doi.org/10.3390/en15041318.
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The Energy Management Strategy (EMS) in Fuel Cell Hybrid Electric Vehicles (FCHEVs) is the key part to enhance optimal power distribution. Indeed, the most recent works are focusing on optimizing hydrogen consumption, without taking into consideration the degradation of embedded energy sources. In order to overcome this lack of knowledge, this paper describes a new health-conscious EMS algorithm based on Model Predictive Control (MPC), which aims to minimize the battery degradation to extend its lifetime. In this proposed algorithm, the health-conscious EMS is normalized in order to address its multi-objective optimization. Then, weighting factors are assigned in the objective function to minimize the selected criteria. Compared to most EMSs based on optimization techniques, this proposed approach does not require any information about the speed profile, which allows it to be used for real-time control of FCHEV. The achieved simulation results show that the proposed approach reduces the economic cost up to 50% for some speed profile, keeping the battery pack in a safe range and significantly reducing energy sources degradation. The proposed health-conscious EMS has been validated experimentally and its online operation ability clearly highlighted on a PEMFC delivery postal vehicle.
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Deufel, Felix, Martin Gießler, and Frank Gauterin. "Optimal Control of Electrified Powertrains in Offline and Online Application Concerning Dimensioning of Li-Ion Batteries." Vehicles 4, no. 2 (May 19, 2022): 464–81. http://dx.doi.org/10.3390/vehicles4020028.
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Various energy management systems (driving strategies) have been developed to improve the efficiency of electrified vehicle drives. These include strategies from the field of offline optimization to determine the theoretical optimum for a given system, as well as online strategies designed for an on-board application in the vehicle. In this paper, investigations are performed on an SUV electrified by a 48 V hybrid system in P14 topology regarding both offline and online strategies. To calculate the global optimum, the performance of Dynamic Programming (DP) compared to an Equivalent Consumption Minimization Strategy (ECMS) with an iteratively determined equivalence factor is shown. Furthermore, with regard to online energy management strategies (EMS), it is presented how a predictive Online ECMS achieves additional fuel savings compared to a robust, non-predictive implementation. The simulation-based vehicle development allows detailed investigations regarding interactions between battery requirements and EMS. In this context, it is shown how various battery capacities are exploited by the discussed EMS.
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Bui, Truong M. N., Truong Q. Dinh, James Marco, and Chris Watts. "Development and Real-Time Performance Evaluation of Energy Management Strategy for a Dynamic Positioning Hybrid Electric Marine Vessel." Electronics 10, no. 11 (May 27, 2021): 1280. http://dx.doi.org/10.3390/electronics10111280.
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Hybridisation of energy sources in marine vessels has been recognized as one of the feasible solutions to improve fuel economy and achieve global emission reduction targets in the maritime sector. However, the overall performance of a hybrid vessel system is strongly dependent on the efficiency of the energy management system (EMS) that regulates the power-flow amongst the propulsion sources and the energy storage system (ESS). This study develops a simple but production-feasible and efficient EMS for a dynamic positioning (DP) hybrid electric marine vessel (HEMV) and real-time experimental evaluation within a hardware-in-the-loop (HIL) simulation environment. To support the development and evaluation, map-based performance models of HEMVs’ key components are developed. Control logics that underpin the EMS are then designed and verified. Real-time performance evaluation to assess the performance and applicability of the proposed EMS is conducted, showing the improvement over those of the conventional control strategies. The comparison using key performance indicators (KPIs) demonstrates that the proposed EMS could achieve up to 4.8% fuel saving per voyage, while the overall system performance remains unchanged as compared to that of the conventional vessel.
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Antonopoulos, Spyros, Klaas Visser, Miltiadis Kalikatzarakis, and Vasso Reppa. "MPC Framework for the Energy Management of Hybrid Ships with an Energy Storage System." Journal of Marine Science and Engineering 9, no. 9 (September 11, 2021): 993. http://dx.doi.org/10.3390/jmse9090993.
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This paper proposes an advanced shipboard energy management strategy (EMS) based on model predictive control (MPC). This EMS aims to reduce mission-scale fuel consumption of ship hybrid power plants, taking into account constraints introduced by the shipboard battery system. Such constraints are present due to the boundaries on the battery capacity and state of charge (SoC) values, aiming to ensure safe seagoing operation and long-lasting battery life. The proposed EMS can be used earlier in the propulsion design process and requires no tuning of parameters for a specific operating profile. The novelties of the study reside in (i) studying the impact of mission-scale effects and integral constraints on optimal fuel consumption and controller robustness, (ii) benchmarking the performance of the proposed MPC framework. A case study carried out on a naval vessel demonstrates near-optimal and robust behaviour of the controller for several loading sequences. The application of the proposed MPC framework can lead to up to 3.5% consumption reduction due to utilisation of long term information, considering specific loading sequences and charge depleting (CD) battery operation.
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Abbas, Furqan A., Adel A. Obed, Mohammed A. Qasim, Salam J. Yaqoob, and Seydali Ferahtia. "An efficient energy-management strategy for a DC microgrid powered by a photovoltaic/fuel cell/battery/supercapacitor." Clean Energy 6, no. 6 (December 1, 2022): 827–39. http://dx.doi.org/10.1093/ce/zkac063.
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Abstract The outcome of this paper is to suggest an efficient energy-management strategy (EMS) for a direct-current (DC) microgrid (MG). The typical MG is composed of two renewable energy sources [photovoltaic (PV) systems and fuel cells (FCs)] and two energy-storage elements (lithium-ion battery and supercapacitor). An EMS was proposed to ensure optimal bus voltage with a power-sharing arrangement between the load and the sources. As a result, in the suggested DC MG, non-linear flatness control theory was used instead of the traditional proportional-integral control approach. The suggested EMS is intended to supply high power quality to the load under varying load conditions with fluctuating solar irradiation while considering the FC status. To validate and prove the effectiveness of the proposed EMS, a MATLAB® environment was used. In addition, the output power of the PV system was maximized using the particle swarm optimization algorithm as a maximum power point tracking (MPPT) technique to track the MPP of the 3000-W PV system under different irradiance conditions. The results show that the suggested EMS delivers a stable and smooth DC bus voltage with minimum overshoot value (0.1%) and improved ripple content (0.1%). As a result, the performance of the DC MG was enhanced by employing the flatness control theory, which provides higher power quality by stabilizing the bus voltage.
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Fu, Xiaoling, Huixuan Wang, Naxin Cui, and Chenghui Zhang. "Energy Management Strategy Based on the Driving Cycle Model for Plugin Hybrid Electric Vehicles." Abstract and Applied Analysis 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/341096.
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The energy management strategy (EMS) for a plugin hybrid electric vehicle (PHEV) is proposed based on the driving cycle model and dynamic programming (DP) algorithm. A driving cycle model is constructed by collecting and processing the driving data of a certain school bus. The state of charge (SOC) profile can be obtained by the DP algorithm for the whole driving cycle. In order to optimize the energy management strategy in the hybrid power system, the optimal motor torque control sequence can be calculated using the DP algorithm for the segments between the traffic intersections. Compared with the traditional charge depleting-charge sustaining (CDCS) strategy, the test results on the ADVISOR platform show a significant improvement in fuel consumption using the EMS proposed in this paper.
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Ben Ali, Ines, Mehdi Turki, Jamel Belhadj, and Xavier Roboam. "Fuzzy Logic for Solving the Water-Energy Management Problem in Standalone Water Desalination Systems." International Journal of Fuzzy System Applications 12, no. 1 (February 3, 2023): 1–28. http://dx.doi.org/10.4018/ijfsa.317104.
Full textAbstract:
This work investigates an important topic of energy and water security (water-energy nexus). For this purpose, Water-Energy Management Strategy (W-EMS) for a standalone water desalination system powered by PV-Wind source is designed. The proposed W-EMS is based on fuzzy logic. In this context, authors focus on the design phase of the Fuzzy Inference System (FIS) through which three design methods are described and analyzed. The influence of FIS design on W-EMS performance is highlighted. First, it is shown that based on the designer's knowledge, the handmade-FIS can offer good performance for the W-EMS. Then, the water-energy management is formulated as an optimization problem. Therefore, genetic algorithm is used to optimize the FIS design to reduce iterative hand-tuning trials. Furthermore, the design of the fuzzy W-EMS can be addressed by a data-driven approach as a third step. This method shows its good performance in terms of water production and energy efficiency compared to the designed FISs by the two previous methods (handmade tuning and genetic algorithm).
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Trinh, Hoai-An, Van-Du Phan, Hoai-Vu-Anh Truong, and Kyoung Kwan Ahn. "Energy Management Strategy for PEM Fuel Cell Hybrid Power System Considering DC Bus Voltage Regulation." Electronics 11, no. 17 (August 30, 2022): 2722. http://dx.doi.org/10.3390/electronics11172722.
Full textAbstract:
Developing an energy management strategy (EMS) is an important requirement to satisfy the load power demand for a proton-exchange membrane fuel cell (PEMFC) hybrid system under different working conditions. For this objective, this paper proposes an EMS to control the power distribution between the PEMFC, battery (BAT), and supercapacitor (SC) and regulate the DC bus voltage for matching the load power demand. In this strategy, fuzzy logic rules (FLRs) and low-pass filters (LPFs) are utilized to determine the reference currents for energy sources based on their dynamic response. In addition, current and voltage control loops are designed to provide the appropriate gains for compensators that can maintain a stable voltage on the DC bus. Finally, simulations are conducted in the MATLAB/Simulink environment to validate and compare the effectiveness of the proposed strategy with others. The simulation results present that the proposed EMS achieves the highest distributed power accuracy with an error of (−2.1→2.6) W, while reducing the DC bus voltage ripple by 1% under various load working conditions in comparison to the other approaches.
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Ferahtia, Seydali, Ali Djeroui, Tedjani Mesbahi, Azeddine Houari, Samir Zeghlache, Hegazy Rezk, and Théophile Paul. "Optimal Adaptive Gain LQR-Based Energy Management Strategy for Battery–Supercapacitor Hybrid Power System." Energies 14, no. 6 (March 17, 2021): 1660. http://dx.doi.org/10.3390/en14061660.
Full textAbstract:
This paper aims at presenting an energy management strategy (EMS) based upon optimal control theory for a battery–supercapacitor hybrid power system. The hybrid power system consists of a lithium-ion battery and a supercapacitor with associated bidirectional DC/DC converters. The proposed EMS aims at computing adaptive gains using the salp swarm algorithm and load following control technique to assign the power reference for both the supercapacitor and the battery while achieving optimal performance and stable voltage. The DC/DC converter model is derived utilizing the first-principles method and computes the required gains to achieve the desired power. The fact that the developed algorithm takes disturbances into account increases the power elements’ life expectancies and supplies the power system with the required power.
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Battula, Amrutha Raju, Sandeep Vuddanti, and Surender Reddy Salkuti. "A Day Ahead Demand Schedule Strategy for Optimal Operation of Microgrid with Uncertainty." Smart Cities 6, no. 1 (February 3, 2023): 491–509. http://dx.doi.org/10.3390/smartcities6010023.
Full textAbstract:
A microgrid energy management system (EMS) with several generation and storage units is crucial in attaining stable and reliable operation. Optimal scheduling of energy resources in EMS becomes arduous due to uncertainty in the forecasting of intermittent renewable sources, electricity pricing, and load demand. However, with the demand response (DR) approaches the operational benefits in the EMS framework can be maximized. In order to improve the cost-effectiveness of the microgrid, a novel day-ahead energy management strategy is proposed for optimal energy allocation of the distributed generators with environmental consideration. An incentive load control-based demand response program is developed to improve the operational results. The forecasting uncertainties are handled using probability-based Hong’s 2 m approximation method. The suggested approach uses a metaheuristic genetic algorithm (GA) to solve the constrained convex problem in determining optimal load shifting. Incentive pricing is developed to adapt to the demand shifting for the benefit of the customers and utility operators. Two case studies with grid-connected and islanded modes are studied to assess the strategy. Results indicate that the proposed technique reduces the overall cost fitness by 12.28% and 18.91% in the two cases, respectively. The consistency in operational parameters with popular methods confirms the effectiveness and robustness of the method for day-ahead energy management.