scholarly journals Trade-Off between Precision and Resolution of a Solar Power Forecasting Algorithm for Micro-Grid Optimal Control

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3565
Author(s):  
Jean-Laurent Duchaud ◽  
Cyril Voyant ◽  
Alexis Fouilloy ◽  
Gilles Notton ◽  
Marie-Laure Nivet
Keyword(s):  
Optimal Control ◽  
Energy Management ◽  
Management Strategies ◽  
Time Discretization ◽  
Time Step ◽  
Trade Off ◽  
Forecast Models ◽  
Error Metric ◽  
Forecast Time ◽  
Solar Power Forecasting

With the development of micro-grids including PV production and storage, the need for efficient energy management strategies arises. One of their key components is the forecast of the energy production from very short to long term. The forecast time-step is an important parameter affecting not only its accuracy but also the optimal control time discretization, hence its efficiency and computational burden. To quantify this trade-off, four machine learning forecast models are tested on two geographical locations for time-steps varying from 2 to 60 min and horizons from 10 min to 6 h, on global irradiance horizontal and tilted when data was available. The results are similar for all the models and indicate that the error metric can be reduced up to 0.8% per minute on the time-step for forecasts below one hour and up to 1.7% per ten minutes for forecasts between one and six hours. In addition, it is shown that for short term horizons, it may be advantageous to forecast with a high resolution then average the results at the time-step needed by the energy management system.

Intelligent energy management for hybrid fuel cell/battery system

2016 ◽  
Vol 35 (5) ◽  
pp. 1850-1864 ◽  
Author(s):  
Moussa Boukhnifer ◽  
Nadir Ouddah ◽  
Toufik Azib ◽  
Ahmed Chaibet
Keyword(s):  
Optimal Control ◽  
Fuel Cell ◽  
Control Theory ◽  
Energy Management ◽  
Optimal Control Theory ◽  
Hybrid Electric Vehicle ◽  
Minimum Principle ◽  
Management Strategies ◽  
Hybrid Architecture ◽  
Content Type

Purpose The purpose of this paper is to propose two energy management strategies (EMS) for hybrid electric vehicle, the power system is an hybrid architecture (fuel cell (FC)/battery) with two-converters parallel configuration. Design/methodology/approach First, the authors present the EMS uses a power frequency splitting to allow a natural frequency decomposition of the power loads and second the EMS uses the optimal control theory, based on the Pontryagin’s minimum principle. Findings Thanks to the optimal approach, the control objectives will be easily achieved: hydrogen consumption is minimized and FC health is protected. Originality/value The simulation results show the effectiveness of the control strategy using optimal control theory in term of improvement of the fuel consumption based on a comparison analysis between the two strategies.

scholarly journals Characterization and Enhancement of Flight Planning Predictability under Wind Uncertainty

2019 ◽  
Vol 2019 ◽  
pp. 1-29 ◽  
Author(s):  
Javier García-Heras ◽  
Manuel Soler ◽  
Daniel González-Arribas
Keyword(s):  
Optimal Control ◽  
Performance Indicators ◽  
Flight Time ◽  
Time Step ◽  
Flight Planning ◽  
Robust Optimal Control ◽  
Single Origin ◽  
Probabilistic Forecasts ◽  
Forecast Time ◽  
Control Methodology

A novel study is presented aiming at characterizing and illustrating potential enhancements in flight planning predictability due to the effects of wind uncertainty. A robust optimal control methodology is employed to calculate robust flight plans. Wind uncertainty is retrieved out of Ensemble Probabilistic Forecasts. Different wind approximation functions are compared, typifying errors, and illustrating its importance for accurate solving of the robust optimal control problem. A set of key performance indicators is defined for the quantification of uncertainty in terms of flight time and fuel consumption. Two different case studies are presented and discussed. The first one is based on a representative sample of the whole 2016 year for a single origin-destination and a forecast time step of 6 hours. As for the second, we select the most uncertain day together with a multiorigin-destination set of flights with forecast time steps up to 2 days.

Microgrid energy management strategies assessment through coupled thermal-electric considerations

2021 ◽  
Vol 228 ◽  
pp. 113711
Author(s):  
Spyridon Chapaloglou ◽  
Athanasios Nesiadis ◽  
Konstantinos Atsonios ◽  
Nikos Nikolopoulos ◽  
Panagiotis Grammelis ◽  
...  
Keyword(s):  
Energy Management ◽  
Management Strategies ◽  
Microgrid Energy Management

scholarly journals Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 172
Author(s):  
Sunny Katyara ◽  
Muhammad Fawad Shaikh ◽  
Shoaib Shaikh ◽  
Zahid Hussain Khand ◽  
Lukasz Staszewski ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Fuzzy Inference System ◽  
Distribution System ◽  
Fuzzy Inference ◽  
Management Strategies ◽  
Test System ◽  
Optimal Placement ◽  
Inference System ◽  
Simulated Environment

With the rising load demand and power losses, the equipment in the utility network often operates close to its marginal limits, creating a dire need for the installation of new Distributed Generators (DGs). Their proper placement is one of the prerequisites for fully achieving the benefits; otherwise, this may result in the worsening of their performance. This could even lead to further deterioration if an effective Energy Management System (EMS) is not installed. Firstly, addressing these issues, this research exploits a Genetic Algorithm (GA) for the proper placement of new DGs in a distribution system. This approach is based on the system losses, voltage profiles, and phase angle jump variations. Secondly, the energy management models are designed using a fuzzy inference system. The models are then analyzed under heavy loading and fault conditions. This research is conducted on a six bus radial test system in a simulated environment together with a real-time Power Hardware-In-the-Loop (PHIL) setup. It is concluded that the optimal placement of a 3.33 MVA synchronous DG is near the load center, and the robustness of the proposed EMS is proven by mitigating the distinct contingencies within the approximately 2.5 cycles of the operating period.

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