Smart grid and energy district mutual interactions with demand response programs. Issue 1 (8th October 2019)

• Record Type: Journal Article
• Authors: Ali, Sahibzada Muhammad; Ullah, Zahid; Mokryani, Geev; Khan, Bilal; Hussain, Iqrar; Mehmood, Chaudhry Arshad; Farid, Umar; Jawad, Muhammad
• Is Part Of: IET energy systems integration. Volume 2:Issue 1(2020)
• Page Start: 1
• Page End: 8
• Publication Date: 2019-10-08
• Subjects: demand side management -- power markets -- distributed power generation -- smart power grids -- tariffs -- stochastic processes -- cloud computing -- pricing -- load management -- power generation economics -- power engineering computing -- wind power plants; smart grid -- energy district -- demand response programs -- bi‐directional energy flow -- SG -- load‐sharing -- peak‐load shaving -- load reduction -- energy market programs -- ancillary services‐based energy transactions -- mutual beneficial frameworks -- load variations -- intermittent wind speed -- stochastic energy price -- wind energy prosumers interaction -- stochastic bi‐directional energy management model -- BEMM -- cloud‐based service level agreement -- WEPs interactions -- stochastic wind estimation model -- stochastic energy pricing model -- energy generation -- energy price model -- real‐time pricing -- day‐a‐head pricing mechanisms -- efficient energy trade; Power resources -- Periodicals; Energy conservation -- Periodicals; Power resources; Energy conservation; Periodicals; 333.79
• DOI: 10.1049/iet-esi.2019.0032
• Languages: English
• ISSNs: 2516-8401

Abstract

Abstract : The bi‐directional energy flow between prosumers (wind energy) and smart grid (SG) provides pertinent benefits, such as (i) load‐sharing, (ii) peak‐load shaving, (iii) load reduction with energy market programs, (iv) ancillary services‐based energy transactions, and (v) mutual beneficial frameworks based on rewards and penalties. However, the load variations of SG, intermittent wind speed in energy district (ED) of prosumers, and stochastic energy price are the major constraints that must be considered in wind energy prosumers (WEPs) interaction with utility. Further, the interfacing and interactions of WEPs with SG incur an enormous volume of data to be processed, stored, accessed, and managed. Therefore, the authors proposed a stochastic bi‐directional energy management model (BEMM) to manage the aforementioned constraints. Moreover, the BEMM is empowered with cloud‐based service level agreement (C‐SLA) that provides massive storage capabilities to the enormous data incurred due to WEPs interactions with SG. Two sub‐models of BEMM are incorporated, namely stochastic wind estimation model and stochastic energy pricing model. The wind estimation model deals the stochasticity of wind speed for energy generation, while energy price model manages and controls the uncertainty of pricing tariffs based on real‐time pricing and day‐a‐head pricing mechanisms for efficient energy trade between SG and WEPs under the principle of C‐SLA.