Optimal Energy Management Strategy Research Articles

A Multi-Objective Demand/Generation Scheduling Model-Based Microgrid Energy Management System

In recent years, microgrids (MGs) have been developed to improve the overall management of the power network. This paper examines how a smart MG’s generation and demand sides are managed to improve the MG’s performance in order to minimize operating costs and emissions. A binary orientation search algorithm (BOSA)-based optimal demand side management (DSM) program using the load-shifting technique has been proposed, resulting in significant electricity cost savings. The proposed optimal DSM-based energy management strategy considers the MG’s economic and environmental indices to be the key objective functions. Single-objective particle swarm optimization (SOPSO) and multi-objective particle swarm optimization (MOPSO) were adopted in order to optimize MG performance in the presence of renewable energy resources (RERs) with a randomized natural behavior. A PSO algorithm was adopted due to the nonlinearity and complexity of the proposed problem. In addition, fuzzy-based mechanisms and a nonlinear sorting system were used to discover the optimal compromise given the collection of Pareto-front space solutions. To test the proposed method in a more realistic setting, the stochastic behavior of renewable units was also factored in. The simulation findings indicate that the proposed BOSA algorithm-based DSM had the lowest peak demand (88.4 kWh) compared to unscheduled demand (105 kWh); additionally, the operating costs were reduced by 23%, from 660 USD to 508 USD, and the emissions decreased from 840 kg to 725 kg, saving 13.7%.

Multi-agent energy management optimization for integrated energy systems under the energy and carbon co-trading market

With the development trends of carbon neutrality, carbon trading is graduating embedded into the energy management of integrated energy systems (IESs). The dual benefices of carbon emission reduction and economics can be achieved by coordinatively optimizing the complementarity and flexibility between multiple IESs. However, this leads to the increased complexity of the market transactions, which poses significant challenges in terms of the benefits distribution among multiple entities, the convergence of trading processes, and the privacy-preserving issues. The multi-agent reinforcement learning (MARL) is capable of solving complex sequential-decision problems and acquiring the optimal strategies for each entity through the interactions between the multiple agents and the market. The MARL deployed on the local agent can provide online trading decisions for individual market entities considering their own interests, which offers new potential to solve the abovementioned difficulties. In this paper, we proposed an IESs co-trading market including electricity, natural gas and carbon trading. The multi-agent energy management coordinative optimization problem is solved by an improved Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm to achieve fair trade and entity privacy protection. The case study results verify that the proposed optimal energy management strategy based on the improved MADDPG algorithm can efficiently guide the IESs in the energy and carbon co-trading market.

Convex Performance Envelope for Minimum Lap Time Energy Management of Race Cars

The optimization of the energy management of modern hybrid-electric or fully electric race cars for minimum lap time requires a description of the vehicle dynamics performance envelope, that is, of the tires’ grip limit in corners, braking zones and during acceleration. In this paper, we present a computationally efficient performance envelope model in the form of convex constraints on the achievable longitudinal and lateral acceleration, on the assumption that the path on the track is given. The proposed acceleration limits are modeled velocity-dependent to take into account the effect of aerodynamic downforce present in many circuit race cars. The formulation as linear equality, inequality and second-order cone constraints allows to embed the model in a convex energy management optimization framework. To showcase the approach, we identify the model with data obtained from a state-of-the-art hybrid-electric Formula 1 car and present results for the Silverstone and Spa-Francorchamps circuits. The optimal energy management strategies can be evaluated with a computational time of less than 1 s. The optimal velocity profile subject to the performance envelope constraints is close to the measured one. The good agreement between the optimal solution and the measurement data shows that the proposed model captures the vehicle dynamics accurately enough for the purposes of energy management optimization.

Optimal Energy Management Scheme of Battery Supercapacitor-Based Bidirectional Converter for DC Microgrid Applications

Because of the splendid front of sustainable energy reassets in a DC Microgrid, it is profoundly willing to variances in energy age. A hybrid energy storage system (HESS) which includes a battery and a supercapacitor (SC) is used to decrease in-built fluctuations. The two different characteristics of the battery and supercapacitor make it a great match for HESS applications. The HESS is connected to the DC Microgrid through a bidirectional converter, which allows energy to be exchanged between the battery and supercapacitor. This paper discusses a converter presenting an approach for a double-input bidirectional converter. Related to this, a regulator was designed for use as a voltage regulation in a DC Microgrid. The designed controllers accelerated PV generation and load disturbance DC link voltage restoration, in addition to effective power balancing among the battery and the SC. The conventional PI, proposed PI, and predictive PI control techniques are effectively validated using MATLAB Simulink. Experimental findings with low power have been used to validate the operation of the predictive PI control technique. The DC grid voltage profile showed substantial improvement while using the predictive PI control in comparison with the proposed and conventional PI control techniques in terms of setting time and maximum peak overshoot.

Impact of the number of planetary gears on the energy efficiency of electrified powertrains

Planetary gears (PGs) play a critical role in hybrid electric vehicles (HEVs) by combining the output torques of different powertrain components and delivering the resulting torque to the wheels. Whilst previous studies show that the number of planetary gears affects performance of HEVs, there is no prior study to systematically investigate such effects on energy consumption. This paper quantifies the energy efficiency improvement of HEVs due to increasing the number of PGs from one to two, and from two to three. This is done by comparing the minimum energy consumption for different topologies when the rest of the powertrain components – namely electric motors, batteries and engine – are the same. To calculate the minimum energy consumption, the paper proposes an optimal energy management strategy (EMS) for each topology to find the optimum sequence of clutch engagement and torque distribution. The minimum energy consumption of a vehicle with different number of PGs is then evaluated using the automotive simulation models (ASM) from dSPACE. Results show that, for the same electric motors and engine, increasing the number of PGs from one to two and from two to three reduces energy consumption by 5% and 1.5%, respectively.

Optimal Fuzzy-Based Energy Management Strategy to Maximize Self-Consumption of PV Systems in the Residential Sector in Ecuador

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.

Power System Design Optimization for a Ferry Using Hybrid-Shaft Generators

Ferry contributing a significant amount of greenhouse gas is one of the critical vessels to be electrified. Designing a power system for a ferry with hybrid-shaft generators is different from designing a power system for other vessels because of its fixed route. More clearly, ferries repeatedly travel between their port of origin and port of destination, and before the next voyage, the battery must be recharged to the initial state so that the optimal energy management scheme can be repeatedly applied. Furthermore, the flexibility of hybrid-shaft generators, which allow more fuel saving, increases design complexity. In this paper, a mixed-integer non-linear programming problem is first formulated, and a power management algorithm with an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">initialization</i> step for fulfilling the battery recharging requirement and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">refinement</i> step for minimizing the fuel consumption is proposed. The simulation results obtained from data of an actual ferry show that the proposed power management algorithm can fully recharge the battery and consumes less fuel than a rule-based power management scheme. Simulations also reveal that fuel consumption depends on available shore power, highlighting the necessity to develop charging infrastructure for practical electrification. Because of its speed, the algorithm can support hardware sizing, e.g., battery sizing.

Optimization of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on Dynamic Programming

Fuel cell hybrid electric vehicles have attracted a large amount of attention in recent years owing to their advantages of zero emissions, high efficiency and low noise. To improve the fuel economy and system durability of vehicles, this paper proposes an energy management strategy optimization method for fuel cell hybrid electric vehicles based on dynamic programming. Rule-based and dynamic-programming-based strategies are developed based on building a fuel cell/battery hybrid system model. The rule-based strategy is improved with a power distribution scheme of dynamic programming strategy to improve the fuel economy of the vehicle. Furthermore, a limit on the rate of change of the output power of the fuel cell system is added to the rule-based strategy to avoid large load changes to improve the durability of the fuel cell. The simulation results show that the equivalent 100 km hydrogen consumption of the strategy based on the dynamic programming optimization rules is reduced by 6.46% compared with that before the improvement, and by limiting the rate of change of the output power of the fuel cell system, the times of large load changes are reduced. Therefore, the strategy based on the dynamic programming optimization rules effectively improves the fuel economy and system durability of vehicles.

A trip distance adaptive real-time optimal energy management strategy for a plug-in hybrid vehicle integrated driving condition prediction

To achieve better fuel economy for plug-in hybrid electric vehicles (PHEVs), this paper proposes a novel improved adaptive equivalent consumption minimization strategy (A-ECMS) integrated driving condition prediction using artificial neural network (ANN) combined the least-squares with forgetting factors. Firstly, the ANN method and the least-square with forgetting factors are used to predict the velocity of the vehicle and the slope of the road. Then a trip adaptive ECMS is proposed which the equivalent factor (EF) is adjusted in real-time according to the remaining distance. Furthermore, the driving condition prediction technology is integrated into A-ECMS to decrease fuel consumption further. Besides, the impact of different preview horizon lengths on fuel consumption is analyzed. Finally, a simulation study is conducted for applying the proposed strategy to a practical trip path in the Fuzhou road network. Simulation results show that, compared with CD-CS, the A-ECMS combined with driving condition prediction can achieve better fuel economy with a fuel consumption reduction by 12.1%, which effectively improves the fuel economy of the PHEV.

Optimal energy management strategy of a novel hybrid dual-motor transmission system for electric vehicles

To improve the performance of powertrain configuration and heuristic strategy used in electric commercial vehicles currently for complex traffic scenarios, we propose a blended optimal rule extraction method for a novel hybrid dual-motor transmission (hDMT) system. Dynamic programming (DP) is applied to determine the optimal working points on the real-world cycles. However, it is challenging to extract online rules in real-time due to overlapping working points induced by compound running conditions. To create a trade-off between optimum and practicability, a novel blended optimal rule extraction method of energy management strategy is proposed for the integrated powertrain system. To be specific, DP is applied offline to find optimal working points from compound running scenarios. Then, a self-organizing feature map (SOM) is utilized to classify overlapped working points with reference to the characteristics of sample points. To solve the over-classification issue, the recursive k-means method is used to cluster the best matching unit generated by SOM. Based on the above, the optimal strategy is developed systematically and implemented online. Furthermore, the hardware-in-the-loop experiments and simulation results demonstrate that the proposed powertrain configuration and method can improve the economic performance of EVs. The online performance of the blended optimal strategy is highly consistent with the simulation, which can reduce energy consumption by 29.02% compared to the preliminary strategy. Compared with traditional powertrain configuration, the hDMT system also has better energy-saving potential.

Nonlinear Model Predictive Control-Based Optimal Energy Management for Hybrid Electric Aircraft Considering Aerodynamics-Propulsion Coupling Effects

Hybrid electric propulsion systems have been identified as feasible solutions for regional jets and narrow-body aircraft to reduce block fuel burn, emissions, and operating costs. In this article, a nonlinear model predictive control-based optimal energy management scheme (MPC-EMS) has been proposed to minimize the block fuel burn during flight. First, the artificial neural network (ANN) model is adopted to predict turbofan engine performance; meanwhile, gas turbine–electrical powertrain integration is investigated and analyzed for typical operating conditions. Then, by combining a point-mass aircraft dynamic model, nonlinear MPC with the cross-entropy method (CEM) is proposed to obtain optimal energy management based on a fully coupled aerodynamics-propulsion hybrid electric aircraft model. Besides, this state-constrained optimal control problem is reformulated as a state-unconstrained problem with a penalty function to reduce the computational load. Finally, the proposed MPC-EMS algorithm is applied to Boeing 737-800 aircraft with mechanically parallel hybrid electric propulsion configuration to minimize the block fuel burn and compared with the optimization results using global genetic algorithm (GA)-based EMS and equivalent consumption minimization strategy (ECMS). The simulation results indicate that the proposed MPC-EMS can effectively reduce the computational time compared with global GA-based EMS while achieving global optimization performance with only a minor difference of 1.71% of block fuel burn and emissions reductions.

A hybrid approach for optimal energy management system of internet of things enabled residential buildings in smart grid

Nowadays, a large number of smart devices in residential buildings is integrated with the evolution of internet of things (IoT), where it is necessary to effectively manage energy to meet the increase in demand. Therefore, in this paper, an optimal energy management strategy using the hybrid Gradient Boosting Decision Tree -Artificial Transgender Longicorn Algorithm (GBDT-ALTA) is proposed for IoT-enabled residential buildings. The main objective of the proposed approach is to minimize the electricity bill of customer, thereby Peak-to-Average Ratio (PAR) is reduced. Due to the scheduling process of residential electric devices, the proposed approach utilizes the waiting time threshold. Here, three types of appliances, such as shiftable electrical devices, thermostatically controlled electrical devices, and generally operated electrical devices are considered. Related to real-time price signals (RTPS) in utilities, scheduling the shiftable electrical devices is processed. The cost minimization with less acceptable time of waiting is achieved by GBDT-ATLA method, which considers the trade-off between the electricity cost and waiting time. Moreover, the proposed approach maintains the stability of the grid, because the stability of grid depends on the PAR. The proposed method is carried out in MATLAB/Simulink site and the simulated results prove that the better performance of GBDT-ATLA approach compared to the existing approaches, like Slime Mould Optimization (SMO), Chaos Game Optimization Algorithm (CGO), and Side-Blotched Lizard Optimization Algorithm (SBLO). The efficiency of the proposed approach under 100, 200, 500, and 1000 trails are 99.7300%, 99.6513%, 99.8363%, and 99.7916% respectively.

Configuration optimization and energy management of hybrid energy system for marine using quantum computing

Improving the technology of hybrid energy systems is an important development direction for the greening of ships, the configuration optimization and energy management of ship hybrid energy system is vital to enhance the marine electric power system reliability, economic efficiency, and sustainability. Focusing on this problem, this paper has carried out a study on the multi-objective configuration optimization method (COM) and energy management strategy (EMS) for ship hybrid energy system based on quantum computing. First, the mathematical model of distributed power modules of the hybrid energy system is established, and a configuration optimization objective function that aims at low-cost, long equipment life, and high reliability of power supply is constructed. Then, the combination with fuzzy rules and quantum multi-objective artificial bee colony algorithm to solve the objective function, the configuration scheme satisfying multiple constraints is obtained. On this basis, an energy management optimization objective function that meets both low-cost operation and maximum clean energy utilization for ship electric power system is established, the objective function is optimized in multi-objective quantum particle swarm optimization (QPSO) algorithm, and the real-time optimal scheduling for hybrid energy systems of the ship is realized. Experiments on simulative navigation data verify the feasibility of the multi-objective configuration optimization method using the quantum artificial bee colony (QABC) algorithm. Furthermore, energy management experiments with different strategies, experimental results show that the energy management strategy proposed in this paper outperforms other methods- and effectively reduces the operating costs, fuel costs, and pollutant emissions of marine power system, meeting the environmental requirements of the Energy Efficiency Operating Index (EEOI) for ships.

Energy Management of Hybrid Electric Urban Bus by Off-Line Dynamic Programming Optimization and One-Step Look-Ahead Rollout

Due to the growing air quality concern in urban areas and rising fuel prices, urban bus fleets are progressively turning to hybrid electric vehicles (HEVs) which show higher efficiency and lower emissions in comparison with conventional vehicles. HEVs can reduce fuel consumption and emissions by combining different energy sources (i.e., fuel and batteries). In this sense, the performance of HEVs is strongly dependent on the energy management strategy (EMS) which coordinates the energy sources available to exploit their potential. While most EMSs are calibrated for general driving conditions, this paper proposes to adapt the EMS to the specific driving conditions on a particular bus route. The proposed algorithm relies on the fact that partial information on the driving cycle can be assumed since, in the case of a urban bus, the considered route is periodically covered. According to this hypothesis, the strategy presented in this paper is based on estimating the driving cycle from a previous trip of the bus in the considered route. This initial driving cycle is used to compute the theoretical optimal solution by dynamic programming. The obtained control policy (particularly the cost-to-go matrix) is stored and used in the subsequent driving cycles by applying one-step look-ahead roll out, then, adapting the EMS to the actual driving conditions but exploiting the similarities with previous cycles in the same route. To justify the proposed strategy, the paper discusses the common patterns in different driving cycles of the same bus route, pointing out several metrics that show how a single cycle captures most of the key parameters for EMS optimization. Then, the proposed algorithm (off-line dynamic programming optimization and one-step look-ahead rollout) is described. Results obtained by simulation show that the proposed method is able to keep the battery charge within the required range and achieve near-optimal performance, with only a 1.9% increase in fuel consumption with regards to the theoretical optimum. As a reference for comparison, the equivalent consumption minimization strategy (ECMS), which is the most widespread algorithm for HEV energy management, produces an increase in fuel consumption with respect to the optimal solution of 11%.

Mode Switching Frequency of Electrohydraulic-Power-Coupled Electric Vehicles with Different Delay Control Times

The variability of vehicle operating conditions and the multiplicity of coupler dynamics inevitably increase the frequency and complexity of cooperative power control. In this study, a novel electromechanical–hydraulic-power-coupled electric vehicle is developed and investigated. This vehicle integrates a conventional electric motor with a hydraulic pump/motor to interconvert electrical, mechanical, and hydraulic energies, while a rule-based dynamic optimal energy management strategy is designed to achieve dynamic switching of operating modes according to the operating conditions. Thus, the power-switching sensitivity is reduced by adding a delay determination link to the Stateflow. Results show that the addition of the delay link has a small effect on classical road conditions and significant suppression of road conditions with high-power-switching frequency. Therefore, the method proposed in this paper improves the energy efficiency, stability, and economic performance of electrohydraulic-power-coupled electric vehicles, which will hopefully provide a good reference for the development of electrohydraulic vehicles.

Optimal energy management strategy for a renewable‐based microgrid considering uncertainty

ABSTRACT Today, the use of renewable energy is increasing rapidly due to the reduction of pollution and the optimal use of available energy. Microgrids (MGs) are used as systems to control and exploit various resources and loads. Due to the connection of different resources in MGs, control and operation of this system are complex and important. In this paper, the optimal utilization of MGs is presented by considering various parameters including production sources, different loads and energy storage. In this paper for energy management in systems with various MGs, we aim to apply the multi-objective optimization algorithm to obtain the optimal energy management in MG, while simultaneously satisfying pollution and economic objectives. The multi-objective particle swarm optimization (MOPSO) method and non-dominated sorting genetic algorithm (NSGA-II) are used to solve the problem and their results are compared. Various factors are considered in the cost function including operation cost, losses, pollution and other operation characteristics. Using multi-objective optimization functions considering cost functions and various constraints ensures optimal operation of the MG. Simulation results verify various models and effectiveness of the proposed methods. Simulation results show that using NSGA-II for scheduling DG resources gives more optimal results in terms of reducing operation cost and losses such that considering operation cost and pollution cost using NSGA-II reduces operation cost and losses by 10% and 4%, respectively.

Energy Saving Performance of Agricultural Tractor Equipped with Mechanic-Electronic-Hydraulic Powertrain System

Tractors are usually applied in field operations, road transport, and other operations. Modern agriculture has higher design requirements for tractor powertrains due to the complicated working environments and various operations. To meet the driving requirements of the tractor under multiple operations, a mechanic-electronic-hydraulic powertrain system (MEH-PS) for tractors has been designed according to the characteristics of the hydro-mechanical composite transmission and electromechanical hybrid system. The principle of multiple driven and transmission modes of MEH-PS are introduced, the speed regulation characteristic curve of hydro-mechanical transmission (HMT) is given, and the related power element model, tractor model, and efficiency model are established. The HMT optimal economy transmission ratio control strategy and hybrid rule-based optimization energy management strategy were developed. Take three typical tractor operations for analysis: ploughing, harvesting, and transport. The results show that the engine operating points are mainly distributed in the higher load area, the tractor maintains high system efficiency, and the relative error between simulated and tested fuel consumption is within 5%, which further proves the reliability of the model. The solution also showed lower fuel consumption in all three operations compared to DLG’s announced PowerShift tractors and CVT tractors. Thus, the powertrain system can meet the tractor’s drive requirements under complex operating conditions and maintain high efficiency and is therefore suitable for tractors that need to operate frequently in the field and on the road.

Energy management strategy of four-wheel drive SUV electric-hydraulic hybrid (EHH) power system based on optimal instantaneous efficiency

To address the problem of a large inrush current in pure electric vehicles under high-power driving and braking conditions, which damages the cycle life of the battery, a hydraulic auxiliary drive system is used to design a four-wheel-drive sport utility vehicle (SUV) electric–hydraulic hybrid(EHH) power system. Energy management strategy (EMS) research on this EHH power system is conducted. An EMS based on optimal instantaneous efficiency is proposed. The simulation results under the China light-duty vehicle test cycle for passenger car (CLTC-P) show that control effect of the EMS can improve by 3.27% than the EMS based on rule and achieve 97.43% of control effect of the dynamic programming(DP) global optimal EMS in terms of economic performance. Then the model predictive control (MPC) method is used, and the instantaneous efficiency optimal EMS is optimized. The simulation results under the CLTC-P show that the instantaneous efficiency optimal EMS optimized by MPC has an economic performance improvement of 1.13% compared with the performance before optimization. It can improve by 4.36% than the EMS RB and achieve 98.54% of control effect of the dynamic programming(DP) global optimal EMS in terms of economic performance.

Energy management strategy integrating self‐adaptive adjustment and Pontryagin's minimum principle‐based optimization for fuel‐cell hybrid electric vehicle

AbstractA hierarchical energy management strategy (EMS) integrating self‐adaptive adjustment and Pontryagin's minimum principle‐based optimization is proposed for a fuel cell hybrid electrical vehicle. First, the proposed EMS estimate the future power requirement by using Markov chain Metropolis–Hastings sampling, second the parameters of the low‐pass filter is adjusted according to the state of charge estimation of the super capacitor, finally the best fuel economy is realized through Pontryagins minimum principle‐based optimization. The evaluation of the EMS is verified via simulation and real driving cycle, the results are compared with another global optimization EMS. The results show that the proposed EMS can fully exert the function of the super capacitor and improve the fuel economy 0.3% and 8.8% for simulation and real driving cycle, respectively.

Energy Management of a Parallel Hybrid Electric Vehicle using Model Predictive Static Programming

Energy management of Parallel Hybrid Electric Vehicles (PHEVs) involves computation of energy efficient torque-speed set-points for the engine and the motor. Application of Nonlinear Model Predictive Control (NMPC) for this problem has been reported in the recent literature. In this work, a fast iterative implementation of the NMPC, known as Model Predictive Static Programming (MPSP) is deployed for the first time for energy management of PHEVs, with an appropriate multi-objective cost function and the applicable equality and inequality constraints. Additionally, a systematic approach based on the Theory of Dominance is used to tune parameters of the controller efficiently. Performance is evaluated with a validated PHEV model on the ADVISOR simulation environment, in terms of various performance metrics on standard drive cycles. The results show that the performance of the MPSP-based technique is close to the ideal performance achieved with Dynamic Programming (DP) and improved over a standard implementation using the Linear Time-Varying MPC (LTV-MPC) and Sequential Quadratic Programming MPC (SQP-MPC) algorithm. Moreover, its execution time on an industry standard micro-controller board is seen to be significantly less than that of LTV-MPC and SQP-MPC. This algorithm therefore appears to be one of the best candidates for on-board vehicle implementation of an optimal energy management strategy for a PHEV.