Risk‐constrained energy management of PV integrated smart energy hub in the presence of demand response program and compressed air energy storage

Multi‐carrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among different energy hubs’ facilities and various operational parameters on the scheduling of the energy hub systems. This paper deals with the problem o...

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Vydané v:IET renewable power generation Ročník 13; číslo 6; s. 998 - 1008
Hlavní autori: Jadidbonab, Mohammad, Dolatabadi, Amirhossein, Mohammadi‐Ivatloo, Behnam, Abapour, Mehdi, Asadi, Somayeh
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: The Institution of Engineering and Technology 01.04.2019
Predmet:
boiler
boilers
cogeneration
combined heat and power unit
compressed air energy storage
compressed air energy storage system
conditional value‐at‐risk methodology
cooling demands
demand response program
demand side management
electrical demands
electrical power
electricity market prices
energy hub system scheduling
energy management systems
future energy systems
Monte Carlo methods
Monte Carlo simulation method
multicarrier energy systems
operational parameters
optimal scheduling
photovoltaic integrated system
photovoltaic power systems
power generation economics
power generation scheduling
power markets
power unit
pricing
PV integrated smart energy hub
PV integrated SREH scheduling
PV system
risk analysis
risk aversion parameter
risk‐constrained energy management
risk‐constrained two‐stage stochastic programming model
scenario‐reduction algorithm
smart power grids
smart residential energy hub
solar radiation
solar radiation uncertainties
stochastic programming
thermal demands
thermal energy storage
time 24.0 hour
ISSN:1752-1424, 1752-1416, 1752-1424
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Popis
Shrnutí:Multi‐carrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among different energy hubs’ facilities and various operational parameters on the scheduling of the energy hub systems. This paper deals with the problem of optimal scheduling of smart residential energy hub (SREH) considering the different uncertain parameters. The effect of the market prices, demands and solar radiation uncertainties on the SREH scheduling problem is characterised through a risk‐constrained two‐stage stochastic programming model. The objective of the proposed scheduling problem is to determine the least‐cost 24 h operation of the facilities that would cover the cooling, thermal and electrical demands. The Monte Carlo simulation method is applied to model the inaccuracies of solar radiation, energy demands, and electricity market prices. Additionally, a proper scenario‐reduction algorithm is employed to reduce the number of scenarios and simulation burden. The proposed approach evaluates the impacts of different values of risk aversion parameter and the utility of the demand response program on the optimal solution of the proposed PV integrated SREH scheduling. Finally, an illustrative example is provided to confirm the efficiency and the applicability of the proposed approach.
ISSN:1752-1424
1752-1416
1752-1424
DOI:10.1049/iet-rpg.2018.6018