Intelligent Energy Management System for Residential and Community Applications

Part 11: Integration of Power Electronics Systems with ICT - IInternational audienceThis paper presents a Smart Storage System able to manage the energy and the smart home devices of a house for optimizing the local consumption of energy, even if there is not renewable generation. The proposed system is composed by two main systems. On the one hand, the Local Energy Management units that will be located in the houses, which are able to maintain the power consumption under a maximum reference value and to switch on/off the devices in the house by using domotic protocols. On the other hand, the Central Energy Management and Intelligent System, that receives operation data from each local unit, analyzes them using behavioral and optimization algorithms and determines the best way in which each local unit has to operate, communicating the operation references back to them

Systematic categorization of optimization strategies for virtual power plants

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development

Optimal scheduling of industrial task-continuous load management for smart power utilization

In the context of climate change and energy crisis around the world, an increasing amount of attention has been paid to developing clean energy and improving energy efficiency. The penetration of distributed generation (DG) is increasing rapidly on the user’s side of an increasingly intelligent power system. This paper proposes an optimization method for industrial task-continuous load management in which distributed generation (including photovoltaic systems and wind generation) and energy storage devices are both considered. To begin with, a model of distributed generation and an energy storage device are built. Then, subject to various constraints, an operation optimization problem is formulated to maximize user profit, renewable energy efficiency, and the local consumption of distributed generation. Finally, the effectiveness of the method is verified by comparing user profit under different power modes

Investigation of computer-oriented technologies for the optimization of electric supply and energy saving of railway transport

The analysis of the current state of power electric equipment of traction substations of railways, power supply systems for traction and control systems based on what was shown that current trends to ensure a high level of efficiency of the power industry are directly related to its informatization and the development of distributed computer systems and networks intellectual technology for monitoring, identifying and intellectualizing the management of energy saving modes of electric supply systems. The temporary decomposition of the tasks of management of electric networks of railways and the methodology of the organization of intelligent electric power traction networks are proposed. The methods for registering primary information monitoring parameters of normal and emergency modes of electric networks based on the use of mathematical tools of differential transformations and the presentation of data in the form of T-spectra are developed. On the basis of the system-wide principle of a single information space and the results of experimental studies, the architecture of the computer environment is studied, which reflects the range of possibilities for intellectualizing the traction network based on the characteristics of electricity supply to railways, taking into account restrictions and specificity of consumption. And at its base, modern computer-intelligent technologies have been created for managing energy saving in the process of power supply to railways at the level of traction substations, power supply distances and the upper level of the railway as a whole. The possibilities of the proposed structure of the intellectual traction electric network of Ukrzaliznytsia developed as a result of the mutual integration of the topology of the traction network of power supply and architecture of the computer environment, which is the infrastructure for managing power supply for railway transport, as well as the possibility of integrating real-time monitoring, state, optimization of consumption and management of energy saving in the process during the maintenance of electricity for traction at all levels of management of the railway in market condition

Using an intelligent method for microgrid generation and operation planning while considering load uncertainty

The integration of distributed generation (DG), energy storage systems (ESS), and controllable loads near the place of consumption has led to the creation of microgrids. However, the uncertain nature of renewable energy sources (wind and photovoltaic), market prices, and loads have caused issues with guaranteeing power quality and balancing generation and consumption. To solve these issues, microgrids should be managed with an energy management system (EMS), which facilitates the minimization of operating (performance) costs, the emission of pollutants, and peak loads while meeting technical constraints. To this effect, this research attempts to adjust parameters by defining indicators related to the best possible conditions of the microgrid. Generation planning, the storage of generated power, and exchange with the main grid are carried out by defining a dual-purpose objective function, which includes reducing the operating cost of power generation, as well as the pollution caused by it in the microgrid, by means of the SALP optimization algorithm. Moreover, in order to make the process more realistic and practical for microgrid planning, some parameters are considered as indefinite values, as they do not have exact values in their natural state. The results show the effect of using the introduced intelligent optimization method on reducing the objective function value (cost and pollution)

Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

[EN] In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-to-infrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. To show the HEV powertrain characteristics, a case study is given with the speed planning and energy management strategy.Xu, F.; Tsunogawa, H.; Kako, J.; Hu, X.; Eben Li, S.; Shen, T.; Eriksson, L.... (2022). Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution. Control Theory and Technology. 20:145-160. https://doi.org/10.1007/s11768-022-00086-y14516020Zhou, Q., Zhao, D., Shuai, B., Li, Y., Williams, H., & Xu, H. (2021). Knowledge implementation and transfer with an adaptive learning network for real-time power management of the plug-in hybrid vehicle. IEEE Transactions on Neural Networks and Learning Systems, 32(12), 5298–5308. https://doi.org/10.1109/TNNLS.2021.3093429Xu, F., & Shen, T. (2021). Decentralized optimal merging control with optimization of energy consumption for connected hybrid electric vehicles. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2021.3054903Zhuang, W., Li, S., Zhang, X., et al. (2020). A survey of powertrain configuration studies on hybrid electric vehicles. Applied Energy, 262, 114553.Wang, S., Chen, K., Zhao, F., & Hao, H. (2019). Technology pathways for complying with corporate average fuel consumption regulations up to 2030: A case study of China. Applied Energy, 241, 257–277.Zhang, J., Shen, T., & Kako, J. (2020). Short-term optimal energy management of power-split hybrid electric vehicles under velocity tracking control. IEEE Transactions on Vehicular Technology, 69(1), 182–193.Asaei, B. (2010). A fuzzy-genetic algorithm approach for finding a new HEV control strategy idea. 1st Power Electronic and Drive Systems and Technologies Conference, pp. 224 – 229. Tehran, Iran.Wu, J., Zhang, C. H., & Cui, N. X. (2008). PSO algorithm-based parameter optimization for HEV powertrain and its control strategy. International Journal of Automotive Technology, 9(1), 53–59.Lin, C. C., Peng, H., Grizzle, J. W., & Kang, J.-M. (2003). Power management strategy for a parallel hybrid electric truck. IEEE Transactions on Control Systems Technology, 11(6), 839–849.Luján, J. M., Guardiola, C., Pla, B., & Reig, A. (2018). Analytical optimal solution to the energy management problem in series hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 67(8): 6803 – 6813.Larsson, V., Johannesson, L., & Egardt, B. (2014). Analytic solutions to the dynamic programming subproblem in hybrid vehicle energy management. IEEE Transactions on Vehicular Technology, 64(4), 1458–1467.Serrao, L., Onori, S., & Rizzoni, G. (2009). ECMS as a realization of Pontryagin’s minimum principle for HEV control. American Control Conference, pp. 3964-3969. St. Louis, MO, USA.Kim, N., Cha, S., & Peng, H. (2011). Optimal equivalent fuel consumption for hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 20(3), 817–825.Rezaei, A., Burl, J. B., Solouk, A., Zhou, B., et al. (2017). Catch energy saving opportunity (CESO), an instantaneous optimal energy management strategy for series hybrid electric vehicles. Applied Energy, 208, 655–665.Xie, S., Hu, X., Qi, S., & Lang, K. (2018). An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles. Energy, 163, 837–848.Zhang, J., & Shen, T. (2016). Real-time fuel economy optimization with nonlinear MPC for PHEVs. IEEE Transactions on Control Systems Technology, 24(6), 2167–2175.Sciarretta, A., Serrao, L., Dewangan, P. C., et al. (2014). A control benchmark on the energy management of a plug-in hybrid electric vehicle. Control Engineering Practice, 29, 287–298.Lars, E. (2019). An overview of various control benchmarks with a focus on automotive control. Control Theory and Technology, 17(2), 121–130.Moura, S. J., Fathy, H. K., Callaway, D. S., & Stein, J. L. (2010). A stochastic optimal control approach for power management in plug-in hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 19(3), 545–555.Sun, C., Hu, X., Moura, S. J., & Sun, F. (2014). Velocity predictors for predictive energy management in hybrid electric vehicles. IEEE Transactions on Control Systems Technology, 23(3), 1197–1204.Xiang, C., Ding, F., Wang, W., & He, W. (2017). Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control. Applied Energy, 189, 640–653.Sun, C., Sun, F., & He, H. (2017). Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles. Applied Energy, 185, 1644–1653.Zhang, F., Hu, X., Langari, R., & Cao, D. (2019). Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook. Progress in Energy and Combustion Science, 73, 235–256.Yang, C., Zha, M., Wang, W., Liu, K., & Xiang, C. (2020). Efficient energy management strategy for hybrid electric vehicles/plug-in hybrid electric vehicles: Review and recent advances under intelligent transportation system. IET Intelligent Transport Systems, 14(7), 702–711. https://doi.org/10.1049/iet-its.2019.0606Zhang, J., Xu, F., Zhang, Y., & Shen, T. (2019). ELM-based driver torque demand prediction and real-time optimal energy management strategy for HEVs. Neural Computing and Applications, 32: 14411C14429.Zhang, B., Zhang, J., Xu, F., & Shen, T. (2020). Optimal control of power-split hybrid electric powertrains with minimization of energy consumption. Applied Energy, 266, 114873.Zhang, F., Xi, J., & Langari, R. (2016). Real-time energy management strategy based on velocity forecasts using V2V and V2I communications. IEEE Transactions on Intelligent Transportation Systems, 18(2), 416–430.Li, J., Zhou, Q., He, Y., et al. (2019). Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles. Applied Energy, 253, 113617.Qi, X., Wu, G., Hao, P., Boriboonsomsin, K., & Barth, M. J. (2017). Integrated-connected eco-driving system for PHEVs with co-optimization of vehicle dynamics and powertrain operations. IEEE Transactions on Vehicular Technology, 2(1), 2–13.Uebel, S., Murgovski, N., B$$\ddot{\rm a}$$ker, B., & Sj$$\ddot{\rm o}$$berg, J. (2019). A two-level mpc for energy management including velocity control of hybrid electric vehicles. IEEE Transactions on Vehicular Technology, 68(6): 5494–5505.Chen, B., Evangelou, S. A., & Lot, R. (2019). Hybrid electric vehicle two-step fuel efficiency optimization with decoupled energy management and speed control. IEEE Transactions on Vehicular Technology, 68(12), 11492–11504.Wang, S., & Lin, X. (2020). Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios. Applied Energy, 271, 115233.Zhang, J., & Xu, F. (2020). Real-time optimization of energy consumption under adaptive cruise control for connected HEVs. Control Theory and Technology, 18(2), 182–192.Fu, Q., Xu, F., Shen, T., & Takai, K. (2020). Distributed optimal energy consumption control of HEVs under MFG-based speed consensus. Control Theory and Technology, 18(2), 193–203.Chen, B., Evangelou, S. A., & Lot, R. (2019). Series hybrid electric vehicle simultaneous energy management and driving speed optimization. IEEE/ASME Transactions on Mechatronics, 24(6), 2756–2767.Hu, Q., Amini, M. R., Feng, Y., Yang, Z., Wang, H., Kolmanovsky, I., & Seeds, J. B. (2020). Engine and aftertreatment co-optimization of connected HEVs via multi-range vehicle speed planning and prediction. SAE Technical Paper, -01-0590.Xu, F., & Shen, T. (2020). Look-ahead prediction-based real-time optimal energy management for connected HEVs. IEEE Transactions on Vehicular Technology, 69(3), 2537–2551.Xu, F., & Shen, T. (2019). MPC-based optimal control for diesel engine coupled with lean NOx trap system. SICE Journal of Control, Measurement, and System Integration, 12(3), 94–101

Internet of Things and Intelligent Technologies for Efficient Energy Management in a Smart Building Environment

Internet of Things (IoT) is attempting to transform modern buildings into energy efficient, smart, and connected buildings, by imparting capabilities such as real-time monitoring, situational awareness and intelligence, and intelligent control. Digitizing the modern day building environment using IoT improves asset visibility and generates energy savings. This dissertation provides a survey of the role, impact, and challenges and recommended solutions of IoT for smart buildings. It also presents an IoT-based solution to overcome the challenge of inefficient energy management in a smart building environment. The proposed solution consists of developing an Intelligent Computational Engine (ICE), composed of various IoT devices and technologies for efficient energy management in an IoT driven building environment. ICE’s capabilities viz. energy consumption prediction and optimized control of electric loads have been developed, deployed, and dispatched in the Real-Time Power and Intelligent Systems (RTPIS) laboratory, which serves as the IoT-driven building case study environment. Two energy consumption prediction models viz. exponential model and Elman recurrent neural network (RNN) model were developed and compared to determine the most accurate model for use in the development of ICE’s energy consumption prediction capability. ICE’s prediction model was developed in MATLAB using cellular computational network (CCN) technique, whereas the optimized control model was developed jointly in MATLAB and Metasys Building Automation System (BAS) using particle swarm optimization (PSO) algorithm and logic connector tool (LCT), respectively. It was demonstrated that the developed CCN-based energy consumption prediction model was highly accurate with low error % by comparing the predicted and the measured energy consumption data over a period of one week. The predicted energy consumption values generated from the CCN model served as a reference for the PSO algorithm to generate control parameters for the optimized control of the electric loads. The LCT model used these control parameters to regulate the electric loads to save energy (increase energy efficiency) without violating any operational constraints. Having ICE’s energy consumption prediction and optimized control of electric loads capabilities is extremely useful for efficient energy management as they ensure that sufficient energy is generated to meet the demands of the electric loads optimally at any time thereby reducing wasted energy due to excess generation. This, in turn, reduces carbon emissions and generates energy and cost savings. While the ICE was tested in a small case-study environment, it could be scaled to any smart building environment

Intelligent Approaches For Modeling And Optimizing Hvac Systems

Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This research will evaluate model-based optimization processes (OP) for HVAC systems utilizing MATLAB, genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC models and optimizing the process (minimizing energy use) will be tested using data collected from the HVAC system servicing the Academic building on the campus of NC A&T State University. Intelligent approaches for modeling and optimizing HVAC systems are developed and validated in this research. The optimization process (OP) including the STMs with genetic algorithms (GA) enables the ideal operation of the building’s HVAC systems when running in parallel with a building automation system (BAS). Using this proposed optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature (Ts), supply duct static pressure (Ps), chilled water supply temperature (Tw), minimum outdoor ventilation, reheat (or zone supply air temperature, Tz), and chilled water differential pressure set-point (Dpw) are optimized with respect to energy use of the HVAC’s cooling side including the chiller, pump, and fan. HVAC system component models were developed and validated against both simulated and monitored real data of an existing VAV system. The optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE’s new ventilation standard 62.1-2013. The proposed optimization process is validated on an existing VAV system for three summer months (May, June, August). This proposed research deals primarily with: on-line, self-tuning, optimization process (OLSTOP); HVAC design principles; and control strategies within a building automation system (BAS) controller. The HVAC controller will achieve the lowest energy consumption of the cooling side while maintaining occupant comfort by performing and prioritizing the appropriate actions. Recent technological advances in computing power, sensors, and databases will influence the cost savings and scalability of the system. Improved energy efficiencies of existing Variable Air Volume (VAV) HVAC systems can be achieved by optimizing the control sequence leading to advanced BAS programming. The program’s algorithms analyze multiple variables (humidity, pressure, temperature, CO2, etc.) simultaneously at key locations throughout the HVAC system (pumps, cooling coil, chiller, fan, etc.) to reach the function’s objective, which is the lowest energy consumption while maintaining occupancy comfort

Airport connectivity optimization for 5G ultra-dense networks

The rapid increase of air traffic demand and complexity of radio access network motivate developing scalable wireless communications by adopting system intelligence. The lack of adaptive reconfiguration in radio transmission systems may cause dramatic impacts on the traffic management concerning congestion and demand-capacity imbalances driving the industry to jointly access licensed and unlicensed bands for improved airport connectivity. Therefore, intelligent system is embedded into fifth generation (5G) ultra-dense networks (UDNs) to provision dense and irregular deployments that maintain extended coverage and also to improve the energy-efficiency for the entire airport network providing high speed services. To define the technical aspects of this solution, this paper addresses new intelligent technique that configures the coverage and capacity factors of radio access network considering the changes in air traffic demands. This technique is analysed through mathematical models that employ power consumption constraints to support dynamic traffic control requirements to improve the overall network capacity. The presented problem is formulated and exactly solved for medium or large airport air transportation network. The power optimization problem is solved using linear programming with careful consideration to latency and energy efficiency factors. Specifically, an intelligent pilot power method is adopted to maintain the connectivity throughout multi-interface technologies by assuming minimum power requirements. Numerical and system-level analysis are conducted to validate the performance of the proposed schemes for both licenced macrocell Long-Term Evolution (LTE) and unlicensed wireless fidelity (WiFi) topologies. Finally, the insights of problem modelling with intelligent techniques provide significant advantages at reasonable complexity and brings the great opportunity to improve the airport network capacit

An integrated metro operation optimization to minimize energy consumption

Energy efficient techniques are receiving increasing attention because of rising energy prices and environmental concerns. Railways, along with other transport modes, are facing increasing pressure to provide more intelligent and efficient power management strategies. This paper presents an integrated optimization method for metro operation to minimize whole day substation energy consumption by calculating the most appropriate train trajectory (driving speed profile) and timetable configuration. A train trajectory optimization algorithm and timetable optimization algorithm are developed specifically for the study. The train operation performance is affected by a number of different systems that are closely interlinked. Therefore, an integrated optimization process is introduced to obtain the optimal results accurately and efficiently. The results show that, by using the optimal train trajectory and timetable, the substation energy consumption and load can be significantly reduced, thereby improving the system performance and stability. This also has the effect of reducing substation investment costs for new metros