Minimum Hydrogen Consumption Research Articles

Research on Power Optimization for Energy System of Hydrogen Fuel Cell Wheel-Driven Electric Tractor

Hydrogen fuel cell tractors are emerging as a new power source for tractors. Currently, there is no mature energy management control method available. Existing methods mostly rely on engineers’ experience to determine the output power of the fuel cell and the power battery, resulting in relatively low energy utilization efficiency of the energy system. To address the aforementioned problems, a power optimization method for the energy system of hydrogen fuel cell wheel-driven electric tractor was proposed. A dynamic model of tractor ploughing conditions was established based on the system dynamics theory. From this, based on the equivalent hydrogen consumption theory, the charging and discharging of the power battery were equivalent to the fuel consumption of the hydrogen fuel cell, forming an equivalent hydrogen consumption model for the tractor. Using the state of charge (SOC) of the power battery as a constraint, and with the minimum equivalent hydrogen consumption as the objective function, an instantaneously optimized power allocation method based on load demand in the energy system is proposed by using a traversal algorithm. The optimization method was simulated and tested based on the MATLAB simulation platform, and the results showed under ploughing conditions, compared with the rule-based control strategy, the proposed energy system power optimization method optimized the power output of hydrogen fuel cells and power batteries, allowing the energy system to work in a high-efficiency range, reducing the equivalent hydrogen consumption of the tractor by 7.79%, and solving the energy system power distribution problem.

Developing an Approach for Calculating Theoretical Minimum Hydrogen Consumption during Catalytic Hydrotreating of Diesel.

Identifying the unnecessary H2 consumption existing in diesel hydrotreating process and calculating theoretical minimum H2 consumption are extremely critical for reducing H2 consumption in consideration of carbon reduction and resource utilization improvement. In this work, chemical reactions happened during diesel hydrotreating were categorized into hydrodesulfurization (HDS), hydrodenitrogenation (HDN), saturation of monocyclic aromatic hydrocarbons (MAHs), saturation of polycyclic aromatic hydrocarbons (PAHs), hydrogenation of olefins (HGO) and hydrocracking reactions (HCR). Then, in order to gain insights into where and how much H2 can be reduced, the ideal molecular compositions of the products were analyzed when theoretical minimum H2 was achieved for each type of reactions, which can give a genuine value of average relative molecular weight and average number of moles of H2 consumed per mole of reactants, leading to the establishment of method for calculating theoretical minimum H2 consumption. Additionally, the above method was used to calculate theoretical minimum H2 consumption of five diesel feedstocks with different properties to study the influence of content of S, N and PAHs in the feed on theoretical minimum H2 consumption. This method can provide guidance for experiments of H2 consumption reduction, and also help the refineries to save potential costs of H2.

Equivalent Minimum Hydrogen Consumption of Fuzzy Control-Based Fuel Cells: Exploration of Energy Management Strategies for Ships

Aiming to solve the problems of insufficient dynamic responses, the large loss of energy storage life of a single power cell, and the large fluctuation in DC (direct current) bus voltage in fuel cell vessels, this study takes a certain type of fuel cell ferry as the research object and proposes an improved equivalent minimum hydrogen consumption energy management strategy, based on fuzzy logic control. First, a hybrid power system including a fuel cell, a lithium–iron–phosphate battery, and a supercapacitor is proposed, with the simulation of the power system of the modified mother ship. Second, a power system simulation model and a double-closed-loop PI (proportion integration) control model are established in MATLAB/Simulink to design the equivalent hydrogen consumption model and fuzzy logic control strategy. The simulation results show that, under the premise of meeting the load requirements, the control strategy designed in this paper improves the Li-ion battery’s power, the Li-ion battery’s SOC (state of charge), the bus voltage stability, and the equivalent hydrogen consumption significantly, compared with those before optimization, which improves the stability and economy of the power system and has certain practical engineering value.

Simulation and retrofitting of mass exchange networks in fertilizer plants

This paper presents a simulation technique for optimizing a hydrogen integration network. By applying this technique, the minimum fresh hydrogen consumption can be determined. Quantitative relationship between sources and sinks streams were studied to get the flow rates of coupled source and sink, hydrogen consumption and hydrogen concentration in each stream. The introduced technique was applied on twelve sources and twelve sinks with any purity of hydrogen concentration. The hydrogen integration network was designed through two steps, the first step considers applying the data given in the LINGO program, while the second step considers using the LINGO results in the introduced excel program to obtain the retrofitted hydrogen integration network. The proposed technique was applied on several case studies to achieve the minimum consumption of fresh hydrogen for the obtained hydrogen integrated networks. The introduced model for simulation and retrofitting of mass exchange networks is easy to understand and the results showed that this model is more efficient for fertilizer, petrochemical and refinery plants.

Optimal Hybridization with Minimum Fuel Consumption of the Hybrid Fuel Cell Train

This paper describes a numerical study of the optimal distribution of energy between fuel cells and auxiliary energy storages in the hybrid train. Internal combustion engines (ICEs) are currently under pressure from environmental agencies due to their harmful gas emissions, and pure battery vehicles have a short range; a hybrid train powered by fuel cells, batteries, and supercapacitors can provide a viable propulsion solution. In this study, special energy management on the mountain railway with optimal power distribution and minimum hydrogen consumption is proposed. Considering the characteristics of the mountain railway, the vehicle uses recuperation of regenerative braking energy and thus charges additional power devices, and hybridization optimization gives favorable power to each power source device with a minimum consumption of hydrogen in the fuel cell. In this study, a simulation model was created in a Matlab/Simulink environment for the optimization of hybridized power systems on trains, and it can be easily modified for the hybridization of any type of train. Optimization was performed by using Sequential quadratic programming (SQP). The results show that this hybrid train topology has the ability to recover battery and supercapacitor state of charge (SOC) while meeting vehicle speed and propulsion power requirements. The effect of battery and supercapacitor parameters on power distribution and fuel consumption was also simulated.

Minimum hydrogen consumption‐based energy management strategy for hybrid fuel cell unmanned aerial vehicles using direction prediction optimal foraging algorithm

AbstractIn hybrid energy storage systems of fuel cell unmanned aerial vehicles (UAVs), achieving energy management while minimizing hydrogen consumption is the main goal for economic aspects and endurance enhancement. The external energy maximization strategy (EEMS) and the equivalent consumption minimization strategy (ECMS) are commonly used energy management strategies. However, they use a gradient descent approach, which converges slowly and does not guarantee the optimal solution. Thus, this paper proposes an optimization method based on a direction prediction optimal foraging algorithm (OFA/DP), which has the advantages of high optimization capability and simple parameter definition. In this study, the hybrid energy storage system comprises fuel cells and lithium‐ion batteries for powering UAVs. To verify the validity of the proposed strategy, it is compared with rule‐based and optimized methods of state machine control, fuzzy logic control based on frequency separation, ECMS, EEMS, and genetic algorithm. The obtained results confirm the superiority of the proposed OFA/DP‐based EEMS method with an efficiency of 88.65% and a minimum hydrogen consumption of 19.06 g. Furthermore, it achieves optimal power distribution and leads to 38.62% minimization in hydrogen consumption.

Genetic algorithm-based fuzzy optimization of energy management strategy for fuel cell vehicles considering driving cycles recognition

The energy management in fuel cell vehicles (FCVs) is crucial to maintain the economical operation of FCVs and the fuzzy logic control (FLC) is mainly used to manage the energy split between the fuel cell and other energy sources. To overcome the limitation of traditional FLC, the dependence on expert knowledge leading to the insufficient energy split, this paper proposes strategy optimization based on FLC with driving cycles recognition achieve near-optimal fuel economy and stable battery charge sustenance. Initially, the whole FCV model is established, which includes electrical system, vehicle dynamic system, energy management system. Additionally, with the objective function which is the minimum equivalent hydrogen consumption of four typical driving cycles, the centers and widths of FLC membership function are optimized by genetic algorithm (GA), respectively. Finally, the driving cycles recognition is achieved based on K-means clustering method, and characteristic parameters are extracted and classified. Compared with GA-optimized fuzzy EMS and the traditional fuzzy EMS, the simulation results demonstrate that the equivalent hydrogen consumption based on proposed strategy is reduced by 16.55% and 40.50%, respectively. Therefore, the proposed strategy can effectively smooth the output of proton exchange membrane fuel cell (PEMFC) and enhance the total fuel economy.

Downgrade power allocation for multi-fuel cell system (MFCS) based on minimum hydrogen consumption

At present, multi-fuel cell system (MFCS) is used to meet the high-power requirement, when a single stack fuel cell system is not sufficient. For the issue of non-optimal solutions in the low power range of multi-stack systems, the scheme of downgrade strategy power allocation for multi-stack systems is implemented by combining with Hydrogen consumption fitting curve and Lagrange function to solve condition constraint. The analytical solution of the scheme allows the power allocation of the n-dimensional system to be performed, which is expressed as an expression for the power allocation, hydrogen consumption, and the intersection of hydrogen consumption of the multi-stack system and the downgraded system. The solution meets the load power demand and better optimizes the low power output. The experimental results also verified the effectiveness of the optimized scheme by four 1kW proton exchange membrane fuel cell (PEMFC) systems.

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.

Minimum hydrogen consumption power allocation strategy for the multi-stack fuel cell (MFC) system based on a discrete approach

In high-powered application scenarios, a multi-stack fuel cell system (MFCS) could have advantages such as higher robustness, lifetime, and reliability than a single-stack fuel cell system. In particular, MFCS could maintain a high efficiency and increase system redundancy by power configuration between subsystems. In order to reduce the operational expenses for systems, a reasonable power management strategy is necessary to minimize the hydrogen consumption of MFCS. First, the power-hydrogen consumption curve of the single-stack fuel cell system is discretized from experimental measurements. Next, the discrete data are reassembled by the inverse derivation of the dynamic programming method to produce a minimum solution for the hydrogen consumption in the output power range. It is found that the strategy varies depending on activated state On or Off. Finally, two power allocation strategies are developed and modeled in a lookup-table block considering the activated state. The optimal stack output power strategy is analyzed with four stacks. The results indicate that the hydrogen consumption is smaller and more efficient than the other allocation strategies. It can respond to the load demand with a high efficiency sooner than the average strategy.

Improve hydrogen economy for vehicular fuel cell system via investigation and control of optimal operating oxygen excess ratio

This study investigates and controls the optimal operating oxygen excess ratio (OER) for PEMFC, which effectively prevents oxygen starvation and improves the hydrogen economy of proton exchange membrane fuel cells (PEMFC). Firstly, the PEMFC output characteristic model and the five-order nonlinear air supply system model are established. Moreover, an adaptive algebraic observer was developed to observe the partial pressure of gas in PEMFC and further reconstruct OER. Secondly, to achieve the minimum hydrogen consumption under the required power, the reference OER is determined by analyzing the PEMFC system output power with its minimum current. Finally, the super-twisting algorithm is adopted to track reference OER. Simulation results show that the average absolute observation errors of oxygen, nitrogen, and cathode pressures under the Highway Fuel Economy Test are 1351.1 Pa (5.1%), 1724.2 Pa (0.9%), and 409.9 Pa (1.6%), respectively. The OER adjust average absolute error is 0.03. Compared with the commonly used fixed OER (e.g., OER of 1.5 and 2.3), the optimal OER strategy can reduce the hydrogen consumption of the PEMFC system by 5.2% and 1.8%, respectively. Besides, a DSP hardware in loop test is conducted to show the real-time performance of the proposed optimal method.

A Real-Time Rule-Based Energy Management Strategy With Multi-Objective Optimization for a Fuel Cell Hybrid Electric Vehicle

Energy management strategy (EMS) has a great impact on securing fuel cell durability, battery charge sustenance, and fuel saving in fuel cell hybrid electric vehicles (FCHEVs). This study aims to develop EMS that can be applied in real-time to satisfy above conditions. Real time power separation was performed using rule-based EMS. A genetic algorithm (GA) was implemented to calculate the optimal battery charge/discharge criterion that simultaneously satisfies the minimum hydrogen consumption rate, battery charge rate preservation, and high fuel cell efficiency. The battery charge/discharge parameter values vary according to driving patterns, and in this paper, typical suburban, urban, and highway driving conditions are considered. For the real-time application of this research method, the effectiveness was demonstrated by applying the driving conditions of unknown patterns. The effect on the initial battery SOC on EMS was analyzed, and in order to verify the superiority of this method, it was compared and analyzed with EMS results using dynamic programming and fuzzy logic under the same driving cycles. The effectiveness of this research method was verified through simulation, and it was confirmed through experiments for real-time application. Since there is a limit to the experiment using an actual fuel cell vehicle, the experiment was performed using a fuel cell and battery. This method can be applied to real fuel cell vehicles in the same way.

Increment-oriented online power distribution strategy for multi-stack proton exchange membrane fuel cell systems aimed at collaborative performance enhancement

The multi-stack fuel cell system based on proton exchange membrane fuel cell has gained increasing attraction in high-power transportation applications. However, the real-time coordinated optimization among multiple fuel cell systems still remains a promising problem. To achieve the online collaborative performance enhancement between fuel economy and durability for the parallel multi-stack architecture, an increment-oriented power distribution strategy is proposed in this paper. It is inspired by the quadratic polynomial formulation derived from the hydrogen consumption analysis of integrated fuel cell system. The quantitative correlation between the fuel economy and durability is obtained with analytical power increments. To improve the strategy applicability with fault tolerance, iterative high-order sliding-mode differentiation procedure is utilized in the initial condition determination. Besides, performance-dominated power limits are considered in the global switching sequence calculation. The effectiveness and practicability of the proposed strategy are verified by two designed scenario cases with one-cycle short-term and life-cycle long-term evaluations. Detailed simulation results demonstrate that the proposed strategy can guarantee fault tolerance operation and collaborative performance enhancement for multi-stack fuel cell systems with minimum hydrogen consumption and maximum service life compared with other advanced strategies. • The hydrogen consumption characteristics for PEM fuel cell system is analyzed. • An increment-oriented power distribution strategy for MFCS is proposed. • Coupling between fuel economy and durability for MFCS is characterized. • Fault tolerance performance of the proposed strategy are investigated. • Collaborative performance between fuel economy and durability is improved.

Bat-optimized fuzzy controller with fractional order adaptive super-twisting sliding mode control for fuel cell/battery hybrid power system considering fuel cell degradation

This paper proposes a bat-optimized fuzzy controller with fractional-order (FO) adaptive supertwisting sliding mode control (ASTSMC) applied to fuel cell/battery hybrid power system for the vehicular application, which can improve the economy and durability of the fuel cell system. The proposed energy management strategy is developed based on the bat-optimized fuzzy controller with fractional-order adaptive supertwisting sliding mode control (bat-fuzzy with FO-ASTSMC). The main objective of the proposed bat-fuzzy with FO-ASTSMC scheme is to provide an optimal output power of the fuel cell system and battery as well as to stabilize the DC bus voltage. The bat optimization algorithm is employed to obtain optimal parameters of fuzzy membership functions with minimization of the equivalent cost function, considering the fuel cell degradation model. Then, FO-ASTSMC control loops are proposed to control the fuel cell and battery currents to follow their given reference values. The stability of the proposed FO-ASTSMC via a closed-loop system is theoretically verified using the Lyapunov theory. The forward simulation model of the fuel cell/battery hybrid power system is established on MATLAB/Simulink, and the highway fuel economy driving cycle (HWFET) and federal test procedure driving cycle (FTP) are used for investigation. The findings show that the proposed bat-fuzzy-FO-ASTSMC provides minimum total hydrogen consumption (0.0613 kg under HWFET, 0.0739 kg under FTP) compared to the particle swarm optimization-Fuzzy-FO-ASTSMC (0.070 97 kg under HWFET, 0.092 37 kg under FTP) and the state machine strategy (0.081 25 kg under HWFET, 0.1094 kg under FTP). The proposed bat-fuzzy-FO-ASTSMC scheme not only minimizes the hydrogen consumption but can also improve the efficiency of fuel cell system. In addition, the proposed FO-ASTSMC scheme was obtained better-tracking performance with less fluctuation compared to the standard supertwisting sliding mode control (STSMC).

Application of alternating direction method of multipliers algorithm in energy management of fuel cell vehicles

In this paper, a method based on alternating direction method of multipliers (ADMM) algorithm is proposed to solve the energy management problem for fuel cell vehicles (FCVs). Taking the minimum hydrogen consumption as the objective function, a new fuel cell dynamical system and a whole vehicle model that conform to the property of convex function are constructed, and an augmented Lagrangian equation with its scaled dual form is established, which satisfies the standard normal form of ADMM algorithm. For the first time, the ADMM algorithm is applied to energy management system of FCVs. In the meantime, a cyclic constraint inspection strategy is introduced to have a further precise control of the power of the fuel cell system so that it can meet the dynamic change constraints of the power. Compared with dynamic programming (DP) algorithm off-line calculation results, the simulation results of the proposed method indicate that the calculation of this algorithm is approximately 20–200 times faster than the former in 9 standard driving cycles. And under the two energy management modes by power maintenance and consumption, the deviation results in terms of the hydrogen consumption derived from the proposed method and DP were 2.37% and 1.06% respectively.

Integrated Renewable Energy Management System for Reduced Hydrogen Consumption using Fuel Cell

The hybrid energy sources and their behavior may be controlled by monitoring and sensing with the help of a single or multiple control strategies incorporated in the energy management system. Utilization of the battery state of charge (SOC) and reduction in the consumption of hydrogen are the main objectives of battery and fuel cell (FC) based renewable hybrid power systems. The lifespan of the hydrogen storage as well as battery may be improved while improving the cost reduction benefits using these parameters. These objectives are achieved by designing an integrated energy management system (IEMS). A battery, supercapacitor (SC), proton-exchange membrane fuel cell (PEMFC) and Photovoltaic (PV) cell are combined to provide the required power to a predetermined load to form a renewable hybrid power system (RHPS). During daylight, PV is the master power source in RHPS. During the shading or night time, FC is the secondary power source. When high load power is required, the FC is supported by the battery. Load fast change or load transient operation is performed by the SC. Maximum SOC value and minimum hydrogen consumption value is obtained simultaneously based on predetermined functions that aids in switching between the state machine control, frequency decoupling and fuzzy logic based integrated strategies in the proposed energy management model. When compared to the stand-alone strategies, the integrated model achieves increased SOC and reduced hydrogen consumption. When maximum value of PV power is attained, the surplus power is displayed at the load. The battery is charged using this surplus power. The stand-alone strategies and integrated strategy results are compared. The attainment of the goal of IEMS is confirmed from this comparison.

Real-time energy management of fuel cell hybrid electric buses: Fuel cell engines friendly intersection speed planning

In this paper, a novel fuel cell engines friendly real-time energy management is proposed, which considers the intersection speed planning to reduce frequent load change conditions in the process of driving. The main advantage of this energy management is that it can improve road transportation efficiency, promote the goal of minimum hydrogen consumption and extend the service life of the fuel cell engines. For the intersection speed planning method, the information of the vehicle in front of the driving route and the traffic signal light states are considered based on dynamic programming (DP). With the intersection speed planning, the corresponding control variable is applied for the model predictive control (MPC) based energy management when the bus is 100m away from the traffic light. The simulation results show that the equivalent hydrogen saving rate can improve by approximately 3.04% and reduce 3.4% idle working conditions compared with the MPC based without intersection speed planning energy management. The hardware in the loop test show that the vehicle speed can follow the target speed, and the equivalent hydrogen consumption error is within 2.5%, which meets the allowable error range.

Minimum hydrogen consumption based control strategy of fuel cell/PV/battery/supercapacitor hybrid system using recent approach based parasitism-predation algorithm

In hybrid renewable energy sources containing different storage devices like fuel cells, batteries, and supercapacitors, minimizing the hydrogen consumption is the main target for economic aspects and operation enhancement. External energy maximization strategy (EEMS) is the most popular energy management strategy used with hybrid renewable energy sources. However, gradient-based method is employed in EEMS which has low convergence, moreover it doesn’t guarantee the optimum solution. Therefore, this paper proposes for first time an energy management strategy based on recent metaheuristic optimizer of parasitism-predation algorithm employed in hybrid source comprises photovoltaic/fuel cell/battery/supercapacitor for supplying aircraft in emergency state during landing. The main target is hydrogen consumption minimization, this helps in enhancing the power durability to the aircraft in case of curtailment of the main power source. The selection of parasitism-predation algorithm (PPA) is due to requirement of less parameters defined by the user and its high convergence ability. The proposed strategy is compared to other conventional and programmed approaches of state machine control, water cycle algorithm, dynamic differential annealed optimization, spotted hyena optimizer, EEMS, marine predator algorithm, and mayfly optimization algorithm. The obtained results confirmed the superiority of the proposed method achieving efficiency of 95.34% and minimum hydrogen consumption of 15.7559 gm.

Coupling pinch analysis and rigorous process simulation for hydrogen networks with light hydrocarbon recovery

In refineries, some hydrogen-rich streams contain considerable light hydrocarbons that are important raw materials for the chemical industry. Integrating hydrogen networks with light hydrocarbon recovery can enhance the reuse of both hydrogen and light hydrocarbons. This work proposes an automated method for targeting hydrogen networks with light hydrocarbon recovery. A pinch-based algebraic method is improved to determine the minimum fresh hydrogen consumption and hydrogen sources fed into the light hydrocarbon recovery unit automatically. Rigorous process simulation is conducted to determine the mass and energy balances of the light hydrocarbon recovery process. The targeting procedures are developed through combination of the improved pinch method and rigorous process simulation. This hybrid method is realized by coupling the Matlab and Aspen HYSYS platforms. A refinery hydrogen network is analyzed to illustrate application of the proposed method. The integration of hydrogen network with light hydrocarbon recovery further reduces fresh hydrogen requirement by 463.0 m3·h−1 and recovers liquefied petroleum gas and gasoline of 1711.5 kg·h−1 and 643 kg·h−1, respectively. A payback period of 9.2 months indicates that investment in light hydrocarbon recovery is economically attractive.

A comparison of real-time energy management strategies of FC/SC hybrid power source: Statistical analysis using random cycles

This paper presents a comparative study of two promising real-time energy management strategies for fuel cell electric vehicle applications: adaptive equivalent consumption minimization strategy (A-ECMS), and stochastic dynamic programming (SDP). An off-line algorithm –classic dynamic programming - provides reference results. On-line and off-line strategies are tested both in simulation and using a dedicated test bench completely consistent with an electric scooter powertrain.The hybrid power source combines a fuel cell, a supercapacitor pack and two related power converters. The system model is carefully calibrated using experimental data. This allows meaningful identification of parameters of the various strategies. The model data is determined using a motorcycle certification driving cycle.The robustness of each strategy is then analyzed using a large number of random driving cycles. Experimental and simulation results show that a specific SDP approach, based on Markov chain modeling, has the best overall performance in real-world driving conditions. It achieves the minimum average hydrogen consumption while respecting the state-sustaining constraint. Conversely, the A-ECMS results lack robustness and show poor performance indexes when facing unknown real world power demand profile. In conclusion, the present results indicate SDP is an interesting approach for future hybrid source energy allocation.