Optimal Energy Management Strategy Research Articles

Hierarchical Energy Management of a Hybrid Propulsion System Considering Speed Profile Optimization

This paper proposes a hierarchical optimization energy management strategy (EMS) considering speed profile to explore energy-saving potential and achieve reasonable power distribution for the hybrid propulsion system of a tramway composed of the catenary, a battery pack and an ultracapacitor pack. For the higher layer, a speed profile is optimized in the space domain through a particle swarm optimization algorithm, which balances energy consumption, running time and passenger comfort. For the lower layer, based on the optimized speed profile, an EMS is designed, using dynamic programming. The boundary range calculation of state variables for the dynamic programming is carried out in advance, based on the working mode analysis to reduce the computation load. In addition, a supervisory controller is developed to realise real-time control. Finally, to verify the proposed method, real rail line data in China is used as a case study, and the results show that the proposed method offers better performance.

Energy Management Control Strategy for Renewable Energy System Based on Spotted Hyena Optimizer

Hydrocarbons, carbon monoxide and other pollutants from the transportation sector harm human health in many ways. Fuel cell (FC) has been evolving rapidly over the past two decades due to its efficient mechanism to transform the chemical energy in hydrogen-rich compounds into electrical energy. The main drawback of the standalone FC is its slow dynamic response and its inability to supply rapid variations in the load demand. Therefore, adding energy storage systems is necessary. However, to manage and distribute the power-sharing among the hybrid proton exchange membrane (PEM) fuel cell (FC), battery storage (BS), and supercapacitor (SC), an energy management strategy (EMS) is essential. In this research work, an optimal EMS based on a spotted hyena optimizer (SHO) for hybrid PEM fuel cell/BS/SC is proposed. The main goal of an EMS is to improve the performance of hybrid FC/BS/SC and to reduce the amount of hydrogen consumption. To prove the superiority of the SHO method, the obtained results are compared with the chimp optimizer (CO), the artificial ecosystem-based optimizer (AEO), the seagull optimization algorithm (SOA), the sooty tern optimization algorithm (STOA), and the coyote optimization algorithm (COA). Two main metrics are used as a benchmark for the comparison: the minimum consumed hydrogen and the efficiency of the system. The main findings confirm that the minimum amount of hydrogen consumption and maximum efficiency are achieved by the proposed SHO based EMS.

Cyber Physical Energy Optimization Control Design for PHEVs Based on Enhanced Firework Algorithm

Energy management strategy (EMS) plays a vital role in improving the fuel economy of plug-in hybrid electric vehicle (PHEV). By virtue of excellent real-time performance, deterministic rule-based (DRB) method is widely introduced into EMS for the control of actual PHEV. However, fixed parameters are usually used as thresholds in traditional DBR control, which makes it difficult for PHEV to achieve excellent fuel economy. To solve this problem, relevant parameters need to be optimized, but the resulting time-consuming and complex process is an obstacle for practical application of this scheme. Nowadays, the emergence of technologies, such as wireless communication, remote monitoring and so on, has gave birth to the concept of cyber-physical system (CPS). It provides an opportunity to optimize parameters of DRB EMS. Motivated by this, this paper proposes a cyber physical energy optimization control design for PHEVs. Among them, DRB control is designed to allocate power tasks for the engine and electric motor (EM), according to state of the charge (SOC) of battery and demand power of vehicle. Moreover, to further improve performance of EMS, an enhanced firework algorithm (EFWA) is firstly proposed to optimize parameters of controller. Compared with original algorithm, a novel selection mechanism for non-CF is introduced in EFWA. It makes the excellent parameters could be obtained in a shorter time, which is more suitable for the complex optimization of EMS. Finally, the effectiveness of proposed EMS is verified and evaluated. The results show that it improves the fuel economy of PHEV by 10% and 12% over that using the unoptimized rule-based EMS, under the China typical urban driving cycle and real-world driving cycle, respectively.

A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home

In recent years, energy demand has grown significantly relative to its production. The power companies have also offered a variety of schemes such as energy consumption management to meet this growing consumer demand. Energy consumption management is a set of strategies used to optimize energy consumption which includes a set of interconnected activities between the utility and customers to transfer the load from peak hours to off-peak hours. This reduces the electricity bill. This paper presents an optimal schedule for the consumption of residential appliances based on improved multi-objective antlion optimization algorithm to minimize the electrical cost and the user comfort. To prevent peaks, the peak-to-average ratio is considered as a constraint for the energy cost function. Also, two different tariff signals have been used to measure energy costs. The real-time pricing and critical peak pricing are considered as energy tariffs. The simulations results are compared with other meta-heuristic algorithms, including multi-objective particle swarm optimization, the second version of the non-dominated sorting genetic algorithm, and the basic antlion optimizer algorithm to show the superiority of the proposed algorithm. Final results show that using the proposed scheme reaches electricity bills less than 80%.

Adaptive real-time optimal energy management strategy based on equivalent factors optimization for hybrid fuel cell system

Fuel cell, a new kind of energy supply equipment, has several advantages such as high efficiency, low noise, and no emission. Proton exchange membrane fuel cell (PEMFC) is considered to have the potential to take the place of the conventional engine on unmanned underwater vehicle (UUV). Besides the power sources in the hybrid power system, the energy management system (EMS) is crucial to operating performance. In this paper, an on-line adaptive equivalent hydrogen consumption minimization strategy (ECMS) is proposed to solve the problem of prior knowledge demand and poor adaptability of current energy management algorithms. In this presented method, a battery state of charge (SOC) constituted penalty term is designed to calculate the equivalent factor (EF), and then the equivalent factor obtained by optimization is substituted into the original objective equation to realize the real-time energy regulation. In this paper, a typical UUV load curve is used to verify the control effect under different working conditions, and the performance is compared with three conventional algorithms’. Simulation results show that the hydrogen consumption of proposed algorithm is close to the optimal solution obtained in offline environment, and it is reduced by more than 3.79% compared with the traditional online methods.

Offline optimal energy management strategies considering high dynamics in batteries and constraints on fuel cell system power rate: From analytical derivation to validation on test bench

For a fuel cell hybrid train, offline optimal energy management strategies using the Pontryagin’s minimum principle and dynamic programming are developed and presented in this contribution. The dynamics in the voltages over various parallel resistance-capacitor branches in the batteries are considered. In addition, dynamic limitation of the fuel cell power is taken into account by choosing the fuel cell power rate as the control variable instead of the fuel cell power, as found so far in all literature with related topics. The correctness of the Pontryagin’s minimum principle and the dynamic programming-based strategies are mutually validated. The corresponding results provide more precise references than the offline strategies without the resistance-capacitor branches in batteries taken into account. A damping factor is then introduced into the cost function to reduce unnecessary high dynamic oscillations of the operating points of the fuel cell system without compromising fuel economy. Finally, the results of the offline strategies are validated with measurements on the test bench at the Center for Mobile Propulsion of the RWTH Aachen University. Only a difference of 0.15% was determined between the measured and the offline calculated hydrogen consumption. .

Optimal energy management strategy for a renewable‐based microgrid considering sizing of battery energy storage with control policies

SummaryMicrogrids (MGs) are known as suitable options to accommodatethe high penetration of renewable energies, like solar and wind. MGs have provided the requirements of controlling and adjusting these sources. In addition, batteries are becoming indispensable components of MGs because of their capabilities in addressing the renewable energies' power output intermittency. In MGs, the problem of smart energymanagement along with battery sizing has been introduced as the necessity to ensure the efficient use of renewable sources and decrease traditional fossil‐fuel‐based generation technology penetration level in power systems. Accordingly, a novel method is presented in this paper to effectively address the above‐mentioned requirements, utilizing the modified shuffled frog leaping algorithm (MSFLA), applied to different case studies. The results, obtained from the numerical simulation, are compared to several well‐established optimization approaches to verify the performance of MSFLA. In terms of computational efficiency and quality of the obtained solution, the MSFLA has demonstrated promising outcomes, together with superior performance in comparison with other algorithms. The results show that the presented framework, including the battery sizing, would be very beneficial to minimize the operating cost of MGs.

Cloud computing-based real-time global optimization of battery aging and energy consumption for plug-in hybrid electric vehicles

This paper addresses two conflicts in the energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs). One is the conflict between fuel economy optimization and battery state of health preservation, and the other is the conflict between global optimality and real-time capability. Inspired by the hierarchy structure of a computer, a two-layer internet-distributed EMS (ID-EMS) is developed using cloud computing and the internet of vehicles. The top layer in the cloud, which possesses powerful calculating capability, focuses on global optimality by utilizing machine learning technology and stochastic dynamic programming. The bottom layer on board, with limited computing power, employs a fuzzy controller to respond to real-time conditions while trying not to deviate too far from the global solution. Thus, a real-time global optimal EMS can be achieved. The ID-EMS is implemented on an internet-distributed vehicle-in-the-loop simulation platform whose in-loop vehicle makes it possible to test the ID-EMS in an on-road driving experiment. The ID-EMS outperforms a rule-based EMS in terms of overall cost by 6.8%, but it is surpassed by an acausal EMS with dynamic programming by 7%. These results suggest directions for the future development of EMS for PHEVs using cloud computing.

Optimization-Based Fuzzy Energy Management Strategy for PEM Fuel Cell/Battery/Supercapacitor Hybrid Construction Excavator

Fuel cell hybrid electric construction equipment (FCHECE) is known as a promising solution to achieve the goal of energy saving and environment protection. Energy management strategy is a key technology of FCHECE, which splits the energy flow between power sources. This paper presents a novel optimal energy management strategy for a hybrid electric-powered hydraulic excavator system to enhance power performance, power sources lifespan, and fuel economy. As for the proposed powertrain configuration, fuel cell serves as a primary energy source, and supercapacitor and battery are considered as energy storages. The integration of supercapacitor and battery in fuel cell vehicle has advantages of improving power performance and storing the regenerative energy for future usage. An energy management strategy based on fuzzy logic control and a rule-based algorithm is proposed to effectively distribute the power between the three sources and reuse the regenerative energy. Furthermore, the parameters of the fuzzy logic system are optimized using the combination of a backtracking search algorithm which provides a good direction to the global optimal region and sequential dynamic programming as a local search method to fine-tune the optimal solution in order to reduce the hydrogen consumption and prolong the lifetime of the power sources. Simulation results show that the proposed energy management strategy enhances the vehicle performance, improves fuel economy of the FCHECE by 10.919%, increase battery and supercapacitor charge-sustaining capability as well as efficiency of the fuel cell system.

Energy planning of renewable applications in high-rise residential buildings integrating battery and hydrogen vehicle storage

This study presents a robust energy planning approach for hybrid photovoltaic and wind energy systems with battery and hydrogen vehicle storage technologies in a typical high-rise residential building considering different vehicle-to-building schedules. Multiple design criteria including the supply performance, grid integration and lifetime net present value are adopted to size the hybrid system and select the optimal energy management strategy. Four decision-making strategies are further applied to search the final optimum solution for major stakeholders with different preferences. The study result indicates that the energy management strategy with battery storage prior to hydrogen storage is suitable for hybrid systems with large photovoltaic, wind and battery installation capacities to achieve the optimum supply-grid integration-economy performance. The energy management strategy with hydrogen storage prior to battery storage has a wider applicability, and this strategy should be selected when focusing on the supply-grid integration or supply-economy performance. The annual average self-consumption ratio, load cover ratio and hydrogen system efficiency are about 84.79%, 76.11% and 77.06% respectively in the end-user priority case. The annual absolute net grid exchange is about 4.55 MWh in the transmission system operator priority case. The lifetime net present value of the investor priority case is about 3.64 million US$, 29.88% less than the equivalent priority case. Final optimum solutions show positive environmental impacts with negative annual carbon emissions. Such a techno-economic-environmental feasibility analysis of the hybrid system provides major stakeholders with valuable energy planning references to promote renewable applications in urban areas.

Impacts of partial-load service on energy, exergy, environmental and economic performances of low-temperature compressed air energy storage system

An energy analysis of the off-design operation of a low-temperature adiabatic compressed air energy storage system has recently been presented. However, it is still unknown how the partial-load operation of this system could affect its exergy parameters, economic feasibility, and environmental impacts. Therefore, this study investigates the effects of the partial-load service of a low-temperature adiabatic compressed air energy storage system on its technical (energy and exergy), economic and environmental performance. The findings of the study will be very useful for optimal energy management and bidding strategy determination of power plants using this technology as the storage unit. This will be primarily of more importance for an energy storage unit that is connected to a renewable power plant, and thus, is under higher risk of operating in off-design conditions. The results indicate that the energy and exergy efficiencies of the system at the nominal load can be as high as 65%, while at 30% load, neither of these can be higher than 45%. The economic analysis shows that in the Danish electricity system as the case study, the payback period can be as short as 12 years if the system continuously works at the nominal load while for 80% operation load, the payback period will be about 22 years. Similarly, the emission reduction level achievable by using the system significantly decreases if it works in off-design conditions.

Jump Linear Quadratic Control for Microgrids with Commercial Loads

Due to the aging power-grid infrastructure and increased usage of renewable energies, microgrids (μGrids) have emerged as a promising paradigm. It is reasonable to expect that they will become one of the fundamental building blocks of a smart grid, since effective energy transfer and coordination of μGrids could help maintain the stability and reliability of the regional large-scale power-grid. From the control perspective, one of the key objectives of μGrids is load management using local generation and storage for optimized performance. Accomplishing this task can be challenging, however, particularly in situations where local generation is unpredictable both in quality and in availability. This paper proposes to address that problem by developing a new optimal energy management scheme, which meets the requirements of supply and demand. The method that will be described in the following models μGrids as a stochastic hybrid dynamic system. Jump linear theory is used to maximize storage and renewable energy usage, and Markov chain theory is applied to model the intermittent generation of renewable energy based on real data. Although the model itself is quite general, we will focus exclusively on solar energy, and will define the performance measure accordingly. We will demonstrate that the optimal solution in this case is a state feedback law with a piecewise constant gain. Simulation results are provided to illustrate the effectiveness of such an approach.

Optimal energy management for formula-E cars with regulatory limits and thermal constraints

In this paper, novel solutions are proposed for energy and thermal management in Formula-E cars using optimal control theory. Optimal control techniques are used to optimize net energy consumption (accounting for loss-reductions from energy recovery from regenerative braking) to achieve minimal lap time which is a crucial element in developing a competitive race strategy in Formula E races. A thermal battery model is used to impose thermal constraints on the optimal energy management strategy in order to realistically capture working constraints during a race. The effects of energy and thermal constraints on the proposed strategy are then demonstrated and two different pedal lifting techniques were introduced. Both the current second generation and a concept third generation type of formula-E cars are studied and compared. While third generation is significantly more efficient with 10% to 30% less energy consumption, it potentially faces more critical thermal issues with more than 60% more heat generation.

Application of Optimal Energy Management Strategies for a Building Powered by PV/Battery System in Corsica Island

The use of renewable energy sources, and in particular photovoltaics, can effectively reduce the supply of household energy from the main grid, contributing to a more sustainable community. In this paper, several energy management strategies were applied to an existing microgrid with photovoltaic (PV) production and battery storage in view to supply in electricity a building and an electric vehicle located in Ajaccio, France. The purpose was to determine how the choice of a management strategy can impact the cost and the energy share in the microgrid, using the actual electricity tariff in France as well as an over-cost due to the island situation. For some strategies, a forecasting tool was introduced and its influence on the performances of the microgrid was discussed. It appears that the performance of the strategy increased with its complexity and the use of PV forecasting.

Design and energy utilization of electro-hydrostatic hydraulic hybrid system for battery bus

In order to solve the problems of large peak torque and short battery life when the city battery bus starts or accelerates, a new electro-hydrostatic hydraulic hybrid powertrain is designed in this paper that can operate efficiently in all speed conditions with a certain type of electric bus running in Chengdu. The improvement of comprehensive energy efficiency of power system by switching between different power modes under typical operating conditions is studied. Through the co-simulation of AMESim and MATLAB/Simulink-stateflow software, the vehicle and control models are established and the rule-based dynamic optimal energy management strategy is set. The dynamic performance and energy utilization characteristics of New European Driving Cycle and the cycle condition of investigation under actual driving environments are analyzed. The core problems of energy coupling of electro-hydrostatic hydraulic hybrid powertrain are studied experimentally to verify the correctness of the design thought and simulation model. The results show that under the premise of satisfying the dynamic performance, the designed electro-hydrostatic hydraulic hybrid system can effectively improve the overall average efficiency of operating point of the electric motor and greatly reduce the peak torque. The peak torque is reduced by 36.4% and the battery power consumed by the selected cycle is reduced by 33.98% under New European Driving Cycle conditions. Under the actual cycle conditions, the peak torque is reduced by 28.9%, and the battery power consumed by the selected cycle is reduced by 32.3%.

A novel real‐time energy management strategy for plug‐in hybrid electric vehicles based on equivalence factor dynamic optimization method

The universal adaptive equivalent consumption minimization strategy (A-ECMS) has the potential of being implemented in real-time for plug-in hybrid electric vehicles (PHEVs). However, the imprecise prediction of a long-term future driving cycle and biggish computation burdens remain the barriers for further real vehicle application. Thus, it is of great significance to develop a real-time optimal energy management strategy for PHEVs by weakening the influence of future driving cycle to the control accuracy and improving its computation efficiency. In this paper, a novel real-time energy management strategy for PHEVs based on equivalence factor (EF) dynamic optimization method is proposed. Firstly, a novel proportional plus integral adaption law for calculating the dynamic optimal EF is established for A-ECMS using only instantaneous information of current vehicle speed and battery state of charge. Second, three key coefficients are obtained and converted into a three-dimensional look up tables, so as to determine the dynamic optimal EF. Finally, the method of fast searching the optimal engine torque is proposed, which significantly enhances the computational efficiency. Compared with A-ECMS, the computational time of A-ECMS2 is decreased near 94.8% and the deviation of fuel consumption is controlled within 4.4%. Both the numerical results and hardware-in-loop results prove that the proposed novel energy management strategy A-ECMS2 has better real-time performance and less computing burden than the general A-ECMS.

Optimal energy management of a renewable microgrid integrating water supply systems

The interdependence between the power grid and water supply system (WSS) provides opportunities for energy conservation. However, the existing water supply flexibility has not been fully developed, resulting in an unsatisfactory effect. To address the problem, this paper proposes an optimal energy management strategy for a renewable microgrid (MG) integrating WSSs. First, a novel supply model that evaluates the interdependence between the large WSS and several islanded sub-systems, hydraulic stability, and desalination characteristic is established, in order to reduce the hydraulic coupling constraints and ensure water supply safety. To achieve an optimal performance of the MG integrating WSSs, a novel two-stage dispatching method is designed, where the day-head decisions fully consider the real-time power fluctuation, and the real-time stage corrects renewable generation deviation and supports external grid. Additionally, a two-stage algorithm is proposed to efficiently solve the scheduling problem. Simulation studies on a MG integrating WSSs indicate the proposed energy management strategy can reduce the water supply loss by 5.2%, and improve the MG economy by 19.5%, while supporting the external grid (EG).

Energy management and demand response with intelligent learning for multi-thermal-zone buildings

This paper presents an optimal building energy management strategy for the demand response of multi-thermal-zone buildings in the smart electricity grid environment. The proposed method includes a machine learning model, based on a neural network, for a building heating ventilation and air conditioning system. The learned model is then applied to an optimization problem to determine the optimal management scheduling of building loads. The goal of the optimization problem is to minimize building electricity costs and reduce the overall building energy consumption during peak load hours while satisfying human comfort demand. To overcome the coupling issue between the building internal-heat-gain loads and the building heating ventilation and air conditioning system, an iterative algorithm is proposed to solve the optimization problem. In each iteration, a mixed-integer linear programming technique is used to solve a sub-optimization problem for the building internal-heat-gain loads and its results are then applied to another sub-optimization problem, solved by using a particle swarm technique, for the building heating ventilation and air conditioning system. The iterative optimization algorithm stops when convergence between the optimization for the building heating ventilation and air conditioning system and the optimization for the building internal-heat-gain loads is properly reached. EnergyPlus is used to build and simulate complex buildings with multiple-thermal zones according to real-life conditions. The simulation model is also used to test and evaluate the effectiveness of the proposed machine-learning model and the iterative optimization algorithm and the improvement of building energy management in terms of energy consumption efficiency, cost saving, and satisfaction of human comfort.

Optimal energy management strategy of fuel‐cell battery hybrid electric mining truck to achieve minimum lifecycle operation costs

Proton exchange membrane fuel cell (PEMFC) electric vehicle is an effective solution for improving fuel efficiency and onboard emissions, taking advantage of the high energy density and short refuelling time. However, the higher cost and short life of the PEMFC system and battery in an electric vehicle prohibit the fuel cell electric vehicle (FCEV) from becoming the mainstream transportation solution. The fuel efficiency-oriented energy management strategy (EMS) cannot guarantee the improvement of total operating costs. This paper proposes an EMS to minimize the overall operation costs of FCEVs, including the cost of hydrogen fuel, as well as the cost associated with the degradations of the PEMFC system and battery energy storage system (ESS). Based on the PEMFC and battery performance degradation models, their remaining useful life (RUL) models are introduced. The control parameters of the EMS are then optimized using a meta-model based global optimization algorithm. This study presents a new optimal control method for a large mining truck operating on a real closed-road operation cycle, using the combined energy efficiency and performance degradation cost measures of the PEMFC system and lithium-ion battery ESS. Simulation results showed that the proposed EMS could improve the total operating costs and the life of the FCEV.

Optimization of distributed energy resources for electric vehicle charging and fuel cell vehicle refueling

A grid-connected set of distributed energy resources that supply power for electric vehicle charging and hydrogen production is investigated through detailed simulation studies. This work uses a genetic algorithm to find the minimum cost for a set of distributed energy resources, the component sizes and energy management strategy are optimized simultaneously. It was found that the optimal component sizes and optimal energy management strategy have a significant influence on each other, and therefore, simultaneous optimization of the two aspects is suggested for such distributed energy resources. The presented approach yields higher time resolution in the simulation compared to previous work. Hence, the model can capture short term changes in dynamic loads and generation, making the simulated energy-management performance more representative of real conditions. The stochastically varying load from electric vehicle charging is modeled based on probabilistic data from existing charging infrastructure. With the present model, the performance of several energy management strategies can be examined. The simulation results show that using the battery storage for peak shaving minimizes the distributed energy resources overall cost while simultaneously decreasing its dependence on the utility grid. Moreover, the results of this study suggest that local energy generation with photovoltaic arrays, in combination with local energy storage and connection to the utility grid, is a viable option.