A Smart Management Approach Investigation for Hybrid Autonomous Power System

A novel design of system management based on multi-agents approach applied to an autonomous hybrid power system is proposed and investigated. The system under study integrates few elements, some serve to provide power requirements, while the others used to store energy. Among these items, we can mention a Solar Power Source namely (SPS) which works as primary source to feed a DC electric load. The system integrates also a secondary power source namely Power Recovery Source (PRS) based on a fuel cell technology used to compensate the power deficit if required. More than two kinds of energy storage, the first called Hydrogen Generation Element (HGE) including a water electrolyzer to store the energy in hydrogen form, while the second uses an Ultracapacitor Element (UE) to store the energy in its electrical form. To reach the well functioning of the system in order to satisfy the load requirements whatever the facts, an intelligent energy management approach based on multi-agent modeling is implemented and verified. Hence, the reliability and the effectiveness of the applied management strategy, which allows the coordination between the different energy sources and protects the system against any fluctuation, are proved by the obtained results from Matlab/Simulink

Model Advancement And Hil Setup For Testing A P2 Phev Supervisory Controller

Teams participating in Advanced Vehicle Technology Competitions such as EcoCAR3 are often bound by limited time and resources. Moreover, vehicle and component downtime due to mechanical and electrical issues reduce the time available for testing activities demanded by the Controls/Systems Modeling and Simulation teams. Therefore, the teams would benefit from identifying new approaches and being more pragmatic and productive in order to achieve satisfactory progress in the competition. This thesis summarizes the approach taken to improve the simulation accuracy of the Wayne State University EcoCAR3 team’s Pre-transmission Parallel Hybrid Electric Vehicle plant model and HIL setup. Focus is on testing the Hybrid Supervisory Controller energy management and diagnostic functionality to be successful in the emissions and energy consumption event. After thorough literature research it is determined that a varying fidelity forward dynamic HEV plant model can produce accurate energy consumption simulation results. Initially, data obtained from manufacturers is used to model the components such as IC Engine, Electric Machine, Energy Storage System (ESS), transmission, differential, chassis and the ECUs. Later, test benches are setup to optimize and refine the individual model parameters by comparing the simulated results with the actual results obtained from component testing and on-road vehicle testing. Finally, the total vehicle plant model is validated by comparing the simulated results with the P2 PHEV on-road test data. The accuracy of the plant model determines the ability to optimize the Hybrid Supervisory Controller code to achieve maximum energy efficiency. Apart from model accuracy improvement, the Hardware In Loop (HIL) test setup is also discussed. HIL system is essential for validating the Hybrid Supervisory Controller’s functionalities in real time. The challenges during modeling and HIL setup are discussed and more improvements that can be done during the final year are recommended based on the research

Modeling for simulation of hybrid drivetrain components

Designing a hybrid drivetrain is a complex task, due to the unknown sensitivity of vehicle performance to system components specifications, the interaction between systems components, and the ability to operate the system components at different set points at any time. Therefore, many researchers have made efforts formulating, and developing holistic hybrid drivetrain analysis, design, and optimization models including the top-level vehicle system control. However, an integral design approach is usually characterized by large computation times, complex design problem formulations, multiple subsystem simulations, analyses, and non-smooth, or non-continuous models. In this paper, the influence of the component efficiencies, whereby the engine operation strategy (engine-, or system optimal operation) on the fuel economy, and the energy management strategy (EMS) is investigated. Thereby, a relative simple rule-based (RB) EMS is used, and is compared with the strategy based on dynamic programming (DP). The series-parallel transmission of the Toyota Prius has been used as a case study. The component modeling, and simulation results from the RB EMS, and DP are compared with results from the simulation platform ADVISOR. Finally, it is shown, that modeling the component efficiencies by only a few characteristic parameters, and using the RB EMS, the fuel consumption can be calculated very quickly, and with sufficient accuracy. In future work, the influence of topology choice on the fuel economy, and the EMS will also be investigate

Modelling driving behaviour and its impact on the energy management problem in hybrid electric vehicles

Perfect knowledge of future driving conditions can be rarely assumed on real applications when optimally splitting power demands among different energy sources in a hybrid electric vehicle. Since performance of a control strategy in terms of fuel economy and pollutant emissions is strongly affected by vehicle power requirements, accurate predictions of future driving conditions are needed. This paper proposes different methods to model driving patterns with a stochastic approach. All the addressed methods are based on the statistical analysis of previous driving patterns to predict future driving conditions, some of them employing standard vehicle sensors, while others require non-conventional sensors (for instance, global positioning system or inertial reference system). The different modelling techniques to estimate future driving conditions are evaluated with real driving data and optimal control methods, trading off model complexity with performance.Guardiola García, C.; Plá Moreno, B.; Blanco Rodriguez, D.; Reig Bernad, A. (2014). Modelling driving behaviour and its impact on the energy management problem in hybrid electric vehicles. International Journal of Computer Mathematics. 91(1):147-156. doi:10.1080/00207160.2013.829567S147156911Ericsson, E. (2001). Independent driving pattern factors and their influence on fuel-use and exhaust emission factors. Transportation Research Part D: Transport and Environment, 6(5), 325-345. doi:10.1016/s1361-9209(01)00003-7Q. Gong, P. Tulpule, V. Marano, S. Midlam-Mohler, and G. Rizzoni,The role of ITS in PHEV performance improvement, 2011 American Control Conference, June–July, San Francisco, CA, 2011, pp. 2119–2124.C. Guardiola, B. Pla, S. Onori, and G. Rizzoni,A new approach to optimally tune the control strategy for hybrid vehicles applications, IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling E-COSM’12, October, Rueil-Malmaison, France, 2012.Johannesson, L., Asbogard, M., & Egardt, B. (2007). Assessing the Potential of Predictive Control for Hybrid Vehicle Powertrains Using Stochastic Dynamic Programming. IEEE Transactions on Intelligent Transportation Systems, 8(1), 71-83. doi:10.1109/tits.2006.884887Liu, S., & Yao, B. (2008). Coordinate Control of Energy Saving Programmable Valves. IEEE Transactions on Control Systems Technology, 16(1), 34-45. doi:10.1109/tcst.2007.903073Paganelli, G. (2001). General supervisory control policy for the energy optimization of charge-sustaining hybrid electric vehicles. JSAE Review, 22(4), 511-518. doi:10.1016/s0389-4304(01)00138-2Rizzoni, G., Guzzella, L., & Baumann, B. M. (1999). Unified modeling of hybrid electric vehicle drivetrains. IEEE/ASME Transactions on Mechatronics, 4(3), 246-257. doi:10.1109/3516.789683Control of hybrid electric vehicles. (2007). IEEE Control Systems, 27(2), 60-70. doi:10.1109/mcs.2007.338280L. Serrao, S. Onori, and G. Rizzoni,ECMS as realization of Pontryagin's minimum principle for HEV control, 2009 American Control Conference, June, Saint Louis, MO, 2009, pp. 3964–3969.Serrao, L., Onori, S., & Rizzoni, G. (2011). A Comparative Analysis of Energy Management Strategies for Hybrid Electric Vehicles. Journal of Dynamic Systems, Measurement, and Control, 133(3). doi:10.1115/1.4003267Stockar, S., Marano, V., Canova, M., Rizzoni, G., & Guzzella, L. (2011). Energy-Optimal Control of Plug-in Hybrid Electric Vehicles for Real-World Driving Cycles. IEEE Transactions on Vehicular Technology, 60(7), 2949-2962. doi:10.1109/tvt.2011.2158565Sundström, O., Ambühl, D., & Guzzella, L. (2009). On Implementation of Dynamic Programming for Optimal Control Problems with Final State Constraints. Oil & Gas Science and Technology – Revue de l’Institut Français du Pétrole, 65(1), 91-102. doi:10.2516/ogst/2009020O. Sundström and L. Guzzella,A generic dynamic programming Matlab function, 18th IEEE International Conference on Control Applications Part of 2009 IEEE Multi-conference on Systems and Control, July, Saint Petersburg, 2009, pp. 1625–1630.R. Wang and S.M. Lukic,Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles, Vehicle Power and Propulsion Conference (VPPC), 2011 IEEE, September 6–9, Raleigh, NC, 2011, pp. 1–7

PEMODELAN STRATEGI MANAJEMEN ENERGI BERBASIS SOLUSI ANALITIS UNTUK KENDARAAN HIBRIDA LISTRIK-HIDROGEN

Abstract - Energy Management Strategy (EMS) implemented in the fuel cell hybrid propulsion system aims to minimize the fuel consumption needed to provide the required power demand. Various EMS methods have been built to solve the optimization problem in minimizing the fuel consumption. This paper provides a modeling of new alternative approach of EMS method calLED analytical solution which is based on the minimization of the losses in the vehicle propulsion system. This method ensures that the minimization in the propulsion losses matches the minimization in the fuel consumption. Since the minimization is applied to the propulsion losses, this method has shifted the EMS objective to minimize the losses in the fuel cell hybrid propulsion system, still enabling to supply the demanded power for traction. The important advantage of this method is that it enables the real time implementation.Keywords: Energy Management Strategy, analytical solution, Fuel Cell Hybrid Electric Vehicle

Optimization of Experimental Model Parameter Identification for Energy Storage Systems

The smart grid approach is envisioned to take advantage of all available modern technologies in transforming the current power system to provide benefits to all stakeholders in the fields of efficient energy utilisation and of wide integration of renewable sources. Energy storage systems could help to solve some issues that stem from renewable energy usage in terms of stabilizing the intermittent energy production, power quality and power peak mitigation. With the integration of energy storage systems into the smart grids, their accurate modeling becomes a necessity, in order to gain robust real-time control on the network, in terms of stability and energy supply forecasting. In this framework, this paper proposes a procedure to identify the values of the battery model parameters in order to best fit experimental data and integrate it, along with models of energy sources and electrical loads, in a complete framework which represents a real time smart grid management system. The proposed method is based on a hybrid optimisation technique, which makes combined use of a stochastic and a deterministic algorithm, with low computational burden and can therefore be repeated over time in order to account for parameter variations due to the battery's age and usage

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Comprehensive Methodology for Sustainable Power Supply in Emerging Countries

[EN] Electricity has become one of the main driving forces for development, especially in remote areas where the lack of energy is linked to poverty. Traditionally, in these areas power is supplied by grid extension projects, which are expensive, or stand-alone systems based on fossil fuels. An actual alternative to these solutions is community micro-grid projects based on distributed renewable energy sources. However, these solutions need to introduce a holistic approach in order to be successfully implemented in real cases. The main purpose of this research work is the definition and development of a comprehensive methodology to encourage the use of decentralized renewable power systems to provide power supply to non-electrified areas. The methodology follows a top-down approach. Its main novelty is that it interlinks a macro and micro analysis dimension, considering not only the energy context of the country where the area under study is located and its development towards a sustainable scenario; but also the potential of renewable power generation, the demand side management opportunities and the socio-economic aspects involved in the final decision on what renewable energy solution would be the most appropriate for the considered location. The implementation of this methodology provides isolated areas a tool for sustainable energy development based on an environmentally friendly and socially participatory approach. Results of implementing the methodology in a case study showed the importance of introducing a holistic approach in supplying power energy to isolated areas, stating the need for involving all the different stakeholders in the decision-making process. Despite final raking on sustainable power supply solutions may vary from one area to another, the implementation of the methodology follows the same procedure, which makes it an inestimable tool for governments, private investors and local communities.This research was funded by Universitat Politecnica de Valencia and Generalitat Valenciana, grant references SP20180248 and GV/2017/023, respectively.Peñalvo-López, E.; Pérez-Navarro, Á.; Hurtado-Perez, E.; Cárcel Carrasco, FJ. (2019). Comprehensive Methodology for Sustainable Power Supply in Emerging Countries. Sustainability. 11(19):1-22. https://doi.org/10.3390/su11195398S1221119LOKEN, E. (2007). Use of multicriteria decision analysis methods for energy planning problems. Renewable and Sustainable Energy Reviews, 11(7), 1584-1595. doi:10.1016/j.rser.2005.11.005Cherni, J. A., Dyner, I., Henao, F., Jaramillo, P., Smith, R., & Font, R. O. (2007). Energy supply for sustainable rural livelihoods. A multi-criteria decision-support system. Energy Policy, 35(3), 1493-1504. doi:10.1016/j.enpol.2006.03.026Gabaldón-Estevan, D., Peñalvo-López, E., & Alfonso Solar, D. (2018). The Spanish Turn against Renewable Energy Development. Sustainability, 10(4), 1208. doi:10.3390/su10041208Ouyang, W., Cheng, H., Zhang, X., & Yao, L. (2010). Distribution network planning method considering distributed generation for peak cutting. Energy Conversion and Management, 51(12), 2394-2401. doi:10.1016/j.enconman.2010.05.003Chaurey, A., Ranganathan, M., & Mohanty, P. (2004). Electricity access for geographically disadvantaged rural communities—technology and policy insights. Energy Policy, 32(15), 1693-1705. doi:10.1016/s0301-4215(03)00160-5CARCEL CARRASCO, F. J., PEÑALVO LOPEZ, E., & DE MURGA, G. (2018). OFICINAS AUTO-SOSTENIBLES PARA LAS AGENCIAS DE AYUDA INTERNACIONAL EN ZONAS GEOGRÁFICAS REMOTAS. DYNA INGENIERIA E INDUSTRIA, 94(1), 272-277. doi:10.6036/8507Erdinc, O., & Uzunoglu, M. (2012). Optimum design of hybrid renewable energy systems: Overview of different approaches. Renewable and Sustainable Energy Reviews, 16(3), 1412-1425. doi:10.1016/j.rser.2011.11.011Al-falahi Monaaf D.A., Jayasinghe, S. D. G., & Enshaei, H. (2017). A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system. Energy Conversion and Management, 143, 252-274. doi:10.1016/j.enconman.2017.04.019Bajpai, P., & Dash, V. (2012). Hybrid renewable energy systems for power generation in stand-alone applications: A review. Renewable and Sustainable Energy Reviews, 16(5), 2926-2939. doi:10.1016/j.rser.2012.02.009Pérez-Navarro, A., Alfonso, D., Ariza, H. E., Cárcel, J., Correcher, A., Escrivá-Escrivá, G., … Vargas, C. (2016). Experimental verification of hybrid renewable systems as feasible energy sources. Renewable Energy, 86, 384-391. doi:10.1016/j.renene.2015.08.030Al-Alawi, A., & Islam, S. . (2004). Demand side management for remote area power supply systems incorporating solar irradiance model. Renewable Energy, 29(13), 2027-2036. doi:10.1016/j.renene.2004.03.006Ardakani, F. J., & Ardehali, M. M. (2014). Novel effects of demand side management data on accuracy of electrical energy consumption modeling and long-term forecasting. Energy Conversion and Management, 78, 745-752. doi:10.1016/j.enconman.2013.11.019Kavrakoǧlu, I., & Kiziltan, G. (1983). Multiobjective strategies in power systems planning. European Journal of Operational Research, 12(2), 159-170. doi:10.1016/0377-2217(83)90219-9Pohekar, S. D., & Ramachandran, M. (2004). Application of multi-criteria decision making to sustainable energy planning—A review. Renewable and Sustainable Energy Reviews, 8(4), 365-381. doi:10.1016/j.rser.2003.12.007Kabak, M., & Dağdeviren, M. (2014). Prioritization of renewable energy sources for Turkey by using a hybrid MCDM methodology. Energy Conversion and Management, 79, 25-33. doi:10.1016/j.enconman.2013.11.036Peñalvo-López, E., Cárcel-Carrasco, F., Devece, C., & Morcillo, A. (2017). A Methodology for Analysing Sustainability in Energy Scenarios. Sustainability, 9(9), 1590. doi:10.3390/su9091590HOMER Pro® Microgrid Software, the Micro-Power Optimization Model; HOMER Pro 3.13, HOMER Energyhttps://www.homerenergy.com/products/pro/index.htmlSuper Decisions Softwarehttps://www.superdecisions.com/ENRGYPLAN Advanced Energy System Analysishttp://www.energyplan.eu/LEAP Code Energy Analysishttps://www.energycommunity.org/default.asp?action=introductionRodríguez-García, Ribó-Pérez, Álvarez-Bel, & Peñalvo-López. (2019). Novel Conceptual Architecture for the Next-Generation Electricity Markets to Enhance a Large Penetration of Renewable Energy. Energies, 12(13), 2605. doi:10.3390/en12132605Huld, T., Müller, R., & Gambardella, A. (2012). A new solar radiation database for estimating PV performance in Europe and Africa. Solar Energy, 86(6), 1803-1815. doi:10.1016/j.solener.2012.03.006Fischer, G., & Schrattenholzer, L. (2001). Global bioenergy potentials through 2050. Biomass and Bioenergy, 20(3), 151-159. doi:10.1016/s0961-9534(00)00074-xHurtado, E., Peñalvo-López, E., Pérez-Navarro, Á., Vargas, C., & Alfonso, D. (2015). Optimization of a hybrid renewable system for high feasibility application in non-connected zones. Applied Energy, 155, 308-314. doi:10.1016/j.apenergy.2015.05.09