[Tex/LaTex] IEEEtrans does not show the keywords

ieeeconfieeetrankeywords

I downloaded this IEEEtrans template:
https://www.ctan.org/tex-archive/macros/latex/contrib/IEEEtran/?lang=en

I want to add keywords to my document. I followed the package's documentation and inserted the required commands to add keywords. However, when I compile the script below, the keywords do not show up in the PDF.

\documentclass[compsoc, conference, letterpaper, 10pt, times]{IEEEtran}
\ifCLASSOPTIONcompsoc
  \usepackage[nocompress]{cite}
\else
  \usepackage{cite}
\fi
\ifCLASSINFOpdf
\else
\fi

\hyphenation{op-tical net-works semi-conduc-tor}

\begin{document}
\title{Test Document}

% make the title area
\maketitle

\IEEEtitleabstractindextext{
    \begin{abstract}
    We propose ...
    \end{abstract}
    \begin{IEEEkeywords}
    keyword1, keyword2, keyword3.
    \end{IEEEkeywords}
} %end \IEEEtitleabstractindextext

\section{Introduction}

\section{Conclusion}
The conclusion goes here.

\end{document}

Best Answer

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Here is an intact template for you to work with:

\documentclass[compsoc, conference, letterpaper, 10pt, times]{IEEEtran}
\ifCLASSOPTIONcompsoc
  \usepackage[nocompress]{cite}
\else
  \usepackage{cite}
\fi
\ifCLASSINFOpdf
\else
\fi

\hyphenation{op-tical net-works semi-conduc-tor}

\begin{document}


\title{Characterization of Forward Electricity Market Price Variations and Price-Responsive Demands}


% Authors names and emails
\author{Al-Motasem I. Aldaoudeyeh, \IEEEmembership{Student Member, IEEE}, and Second Author, \IEEEmembership{Life Fellow, IEEE}
    % Information about the authors (in the margins)
    \thanks{Al-Motasem I. Aldaoudeyeh and Second Author are with the Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND, 58102 USA e-mail: almotasem.aldaoudeye@ndsu.edu}
}



% Make titles available (this command goes here for IEEE style)
\maketitle
% This command is important for both title and author names and information. Without it, none of the previously mentioned appears on the text.



\begin{abstract}

This paper addresses two problems of interest in the present time, namely, the characterization of price variations and the corresponding load response to them.
The paper begins by defining Price Elasticity Matrices (PEMs) and shows how they can be used to model demands deviation from their scheduled levels due to price differentials. It then explains the reason as to why extending PEMs characterization is necessary. Afterward, we show how Normal distribution lacks accuracy in modeling the variation of market clearing prices from one day to the next. Based on empirical data from the Midcontinent Independent System Operator (MISO) area, we propose the use of Stable distribution and demonstrate how such statistical model is very accurate to characterize electricity market price variations.
The models mentioned above are used in a Monte Carlo (MC) simulation to find the probability that different Direct Load Control (DLC) levels would not be enough to maintain the peak demand below its reference value. Since MC simulations are so computationally intensive, we also implement Importance Sampling Monte Carlo (ISMC) to substantially reduce the computational burden without sacrificing the accuracy.

\end{abstract}


\begin{IEEEkeywords}
%   
peak load, real time pricing, electricity markets,  consumption scheduling, smart metering
%   
\end{IEEEkeywords}


\section{Introduction}
\IEEEPARstart{D}{emand} Response (DR) in Smart Grids has (in principle) the potential to achieve excellent levels of reliability. More than two-thirds of residential demands can be either stored in forms of thermal energy (e.g., water heating), advanced or deferred (e.g., washing machines) with marginal impact on customers convenience. The instant, stable, and predictable response that can be achieved using customers equipment facilitates a potential firm capacity solution. The reliability of these equipment stems from the fact that hundreds of thousands (or millions) of customers and their devices would have less failure chance than a single generator has. Additionally, with Real-Time Pricing (RTP), customers can manage to consume more yet pay less by shifting their consumption to off-peak periods. Despite the advantages just mentioned, there are also some drawbacks for DR which include:


\section{Conclusion}
This paper explains why PEM models need to be extended -- if we neglect the way that off-diagonal elements in PEMs are distributed, we will have  PEMs with the same characterization, but different results when applied to find the actual power consumption. Consequently, we devise extensions of existing PEMs. We then present a Stable distribution model for clearing price variations and show its accuracy against empirical data of the day-ahead market of MISO control area. Unlike Normal distribution, Stable distribution has a tail parameter $(\alpha)$ that can be used to adjust the heaviness of its tail, resulting in a more accurate match with the price variation data that exhibit heavy-tailed distribution.

We employ both of the PEMs and Stable distribution model in MC simulations to find the failure probability of DLC DR programs in keeping the peak demand within its reference value. Since MC simulations can take unreasonably long time, we use ISMC and demonstrate how results of both methods are relatively similar, but with the advantage of a more reasonable simulation time with ISMC.



\end{document}

You should not worry about \thanks, \IEEEmembership, and \IEEEPARstart if they did not have an effect on the text. The IEEEtran class automatically ignore or use them depending on the class of document your prepare.

As an aside, try not to add too many customizations to the class options, unless the alternation is recommended by the kind of transactions you are writing for. Good luck.

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