Optimizing operational costs and PV production at utility scale: An optical fiber network analogy for solar park clustering

Abstract

Modeling analogy with optical network design for optimal PV site clustering • Enhanced risk management of operational costs and energy stability • Comparative study with an existing GIS-optimization model on real data • Novel implementation strategy for PV site selection at utility scale

Quantifying the capacity credit of IDC‐based demand response in smart distribution systems

“…Compare the calculated value of $$EEN{S}^{{\rm{base}}}$$ and $$EEN{S}^{{\rm{DR}}}$$. If $$EEN{S}^{base} < EEN{S}^{DR}$$, then set the CC of IDC demand response is 0; Otherwise, perform the following steps: (4‐1) Define two auxiliary variables $${G}_{\max }$$ and $${G}_{\min }$$, and let $${G}_{\max } = {G}^{rat}$$, $${G}_{\min } = 0$$, where $${G}^{rat}$$ is a positive real number given artificially. (4‐2) In the scenario where IDC does not participate in the demand response, it is assumed that a virtual benchmark generator unit is added at the IDC grid connected location, its configured capacity is $${G}^{bm} = ( {{G}_{\max } + {G}_{\min }} )/2$$, and the reliability parameter FOR is equal to 0.08%, which is the same as the current conventional generator set [39]. (4‐3) The sequential Monte‐Carlo simulation method is used to evaluate the EENS index of the power system and record it as $$EEN{S}^\nu $$. (4‐4) Compare the obtained $$EEN{S}^\nu $$ and $$EEN...$…”