A Solution to Prevent a Blackout Crisis: Determining the Behavioral Potential and Capacity of Solar Power
Author(s) -
Saeed Vedadi Kalantar,
Amir Ali Saifoddin,
Ahmad Hajinezhad,
Mohammad Hossein Ahmadi
Publication year - 2021
Publication title -
international journal of photoenergy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.426
H-Index - 51
eISSN - 1687-529X
pISSN - 1110-662X
DOI - 10.1155/2021/2092842
Subject(s) - blackout , computer science , flexibility (engineering) , base load power plant , peak demand , grid , power (physics) , markov chain , reliability engineering , environmental economics , peaking power plant , environmental science , demand response , automotive engineering , electric power system , simulation , electricity , statistics , economics , electrical engineering , mathematics , engineering , physics , geometry , quantum mechanics , machine learning
Increasing the power grid peak in the summer time causes power outages in industries and residential areas in Iran. The most obvious example of this issue is the power outages in the summer of 2018. Management of the demand-side is the most important strategy to reduce the grid peak due to the high cost of the development of the power plant capacity (500$ per kilowatt). In the present study, the effect of behavioral parameters in decreasing the power grid peak was identified. The behavioral simulation was done as an agent-based model using the raw data of the time-use survey (TUS) of the Statistics Center of Iran. 4228 urban households were surveyed, and the quality of people’s behavior was determined in each time step of 15 minutes during the day and night with 2 deterministic and stochastic approaches. In the stochastic approach, the Markov chain method was used. It showed that the power grid peak can only be reduced by 10% with behavioral flexibility and up to 25% by upgrading technology. In addition, based on the power deficit in 2018, 2000 megawatts of solar power capacity must be added to the network at peak times to meet grid demand.
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