Computational intelligence techniques for short term generation scheduling in a hybrid energy system

1998 ◽  
pp. 272-281 ◽  
Author(s):  
C.C. Fung ◽  
V. Iyer ◽  
C. Maynard
Keyword(s):  
Computational Intelligence ◽  
Energy System ◽  
Short Term ◽  
Hybrid Energy System ◽  
Hybrid Energy ◽  
Generation Scheduling

scholarly journals Performance Analysis of APSO and Firefly Algorithm for Short Term Optimal Scheduling of Multi-Generation Hybrid Energy System

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 177549-177569
Author(s):  
Sheroze Liaquat ◽  
Muhammad Salman Fakhar ◽  
Syed Abdul Rahman Kashif ◽  
Akhtar Rasool ◽  
Omer Saleem ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Firefly Algorithm ◽  
Energy System ◽  
Optimal Scheduling ◽  
Short Term ◽  
Hybrid Energy System ◽  
Hybrid Energy ◽  
Generation Hybrid

scholarly journals Energy Economic Management of Hybrid Energy System Based on Short-term Generation and Demand Response

2020 ◽  
Vol 24 (1) ◽  
pp. 653-668
Author(s):  
Heydar Chamandoust ◽  
Neda Peyvand
Keyword(s):  
Demand Response ◽  
Energy System ◽  
Linear Programs ◽  
Short Term ◽  
Economic Management ◽  
Hybrid Energy System ◽  
Hybrid Energy ◽  
Time Price ◽  
Buying In ◽  
Operation Cost

Abstract Energy economic management based on economic principles and demand response (DR) is one of the significant issues for consumers and Distribution Companies (DisCos). In this paper, presents energy management for hybrid energy system is in connecting mode to the network and based on short-term optimal scheduling in a house. The presented modelling, minimizing the operation cost of resources and electricity bought from network in the Real-Time Price (RTP) plan. Modelling of the objective function based on mixed integral linear programs (MILP) has been implemented and the result shows that management of sources and demand in short-term scheduling causes minimizing operation cost in aspects as financial, technical and electricity buying in RTP plan.

scholarly journals A Short-Term Hybrid Energy System Robust Optimization Model for Regional Electric-Power Capacity Development Planning under Different Pollutant Control Pressures

2021 ◽  
Vol 13 (20) ◽  
pp. 11341
Author(s):  
Jixian Cui ◽  
Chenghao Liao ◽  
Ling Ji ◽  
Yulei Xie ◽  
Yangping Yu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Robust Optimization ◽  
Electric Power ◽  
Optimization Model ◽  
Energy System ◽  
Short Term ◽  
System Planning ◽  
Hybrid Energy System ◽  
Hybrid Energy ◽  
Energy System Planning

This paper is aimed at proposing a short-term hybrid energy system robust optimization model for regional energy system planning and air pollution mitigation based on the inexact multi-stage stochastic integer programming and conditional value-at-risk method through a case study in Shandong Province, China. Six power conversion technologies (i.e., coal-fired power, hydropower, photovoltaic power, wind power, biomass power, and nuclear power) and power demand sectors (agriculture, industry, building industry, transportation, business, and residential department) were considered in the proposed model. The optimized electricity generation, capacity expansion schemes, and economic risks were selected to analyze nine defined scenarios. Results revealed that electricity generations of clean and new power had obvious increasing risks and were key considerations of establishing additional capacities to meet the rising social demands. Moreover, the levels of pollutants mitigation and risk-aversion had a significant influence on different power generation schemes and on the total system cost. In addition, the optimization method developed in this paper could effectively address uncertainties expressed as probability distributions and interval values, and could avoid the system risk in energy system planning problems. The proposed optimization model could be valuable for supporting the adjustment or justification of air pollution mitigation management and electric power planning schemes in Shandong, as well as in other regions of China.

An intelligent strategy for hybrid energy system management

2019 ◽  
Vol 17 ◽  
pp. 550-554 ◽  
Author(s):  
I. Riverón ◽  
◽  
J.F. Gómez ◽  
B. González ◽  
J. Albino Méndez
Keyword(s):  
Energy System ◽  
System Management ◽  
Hybrid Energy System ◽  
Hybrid Energy

Nuclear Hybrid Energy System Reliability Model Progress Report

2017 ◽  
Author(s):  
Askin Guler Yigitoglu ◽  
Thomas Harrison ◽  
Michael Scott Greenwood
Keyword(s):  
System Reliability ◽  
Energy System ◽  
Progress Report ◽  
Reliability Model ◽  
Hybrid Energy System ◽  
Hybrid Energy
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