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\title{Integrating Renewables in Jiangsu Province, China}
%\subtitle{---}
\author{
  Tom Brown, Stefan Langanke, Thomas Ackermann (Energynautics); \\
 Sven Teske (Greenpeace)
}

\institute{
  Frankfurt Institute of Advanced Studies, Goethe-Universit\"at Frankfurt

  (Presented work carried out at Energynautics GmbH for Greenpeace East Asia)

  \vspace{0.1cm}

  \href{mailto:brown@fias.uni-frankfurt.de}{brown@fias.uni-frankfurt.de}, \href{mailto:t.ackermann@energynautics.com}{t.ackermann@energynautics.com}

}

\date{Wind Integration Workshop, Brussels, 22nd October 2015}


\titlegraphic{%
  \vspace{6.5cm}
    \includegraphics[width=4cm]{FIAS-logo}\hspace{3.6cm}
    \includegraphics[width=3cm]{ena_logo}~%
}

\begin{document}

\maketitle


\begin{frame}

  \frametitle{Table of Contents}
  \setbeamertemplate{section in toc}[sections numbered]
  \tableofcontents[hideallsubsections]
\end{frame}

\section{Introduction}


\begin{frame}[fragile]
  \frametitle{Jiangsu Province}



\begin{columns}[t]
\begin{column}{5cm}
\vspace{-0.5cm}
\begin{itemize}
  \item Jiangsu is a highly industrialised Province on the east coast of China (north-west of Shanghai)
  \item Population around 80 million
  \item Peak load of 80~GW in 2014
  \item Population and load comparable to Germany; area three times smaller
\end{itemize}
\end{column}
\begin{column}[T]{7.5cm}
\centering
\includegraphics[width=7cm]{Jiangsu_in_China}
\source{Wikipedia}
\end{column}
\end{columns}

\end{frame}






\begin{frame}[fragile]
  \frametitle{Greenpeace Energy [R]evolution for Jiangsu in 2020}

\begin{itemize}
\item China 15\% non-fossil primary energy target by 2020\\ $\implies$ $\sim 30\%$ renewable electricity by 2020 (currently $\sim 23\%$ RE)
  \item Today 80\% of electricity in Jiangsu comes from locally-burned coal, with 11\% imports and some nuclear, gas and wind
  \item In 2013 around 2.5~GW of wind and 1~GW of PV in Jiangsu (1\% RE)
  \item By 2020 government plans 10~GW each of wind and solar (5\% RE)
  \item Greenpeace Energy [R]evolution 3 times as ambitious: \\ 30~GW of wind, 30~GW of PV and 3~GW of biomass by 2020
  \item Jiangsu would then cover 14\% of demand with local renewables; 17\% if renewable imports included
  \item Greenpeace East Asia commissioned Energynautics to investigate consequences for Jiangsu's power system
\end{itemize}

\end{frame}

\begin{frame}[fragile]
  \frametitle{Greenpeace Energy [R]evolution for Jiangsu in 2020}

\centering
\includegraphics[width=11cm]{power_energy-both_years.pdf}

\end{frame}



\section{Data Inputs \& Methodology}


\begin{frame}
  \frametitle{Network Model of Jiangsu}

\begin{columns}[T]
\begin{column}{6.5cm}
\includegraphics[width=7.5cm]{load_cities_lines}
\end{column}
\begin{column}{3cm}
\begin{itemize}
\item Load mostly in south (grey circles)
\item Backbone of transmission network is 500~kV network (red)
\item Inter-province 1000~kV lines under construction (blue)
\end{itemize}

\end{column}
\end{columns}

\end{frame}


\begin{frame}
  \frametitle{Generation capacities}


\ra{1.05}
\begin{table}[!t]
	\centering
	\begin{tabular}{@{}lrrrr@{}}
\toprule
Source &  Capacity  in      &    Capacity in & Full Load & Capacity\\
& 2013 (GW)& 2020 (GW) & Hours & factor (\%)\\
\midrule
Wind onshore & 2.3 & 12.0 & 1510 & 17.1\\
Wind nearshore &0.2 & 18.0&2550 & 29.1 \\
Solar PV &1.0& 30.0&1000 &  11.4\\
Biomass &0.9& 3.0&6000 & 68.5\\
Coal &65.7&73.0&4903 & 56.0\\
Gas &6.9&45.0&4960 & 56.6\\
Nuclear &2.0 &4.0 & 7885& 90.0\\
Pumped-Hydro &1.1&1.1& & \\
\midrule
Peak load & 80.0 & 130.0 &&\\
\bottomrule
	\end{tabular}
\end{table}

\end{frame}



\begin{frame}
  \frametitle{Load curve for 2020}

\centering
\includegraphics[width=11cm]{load_curve}

Load minimum during Spring Festival in February;

\centering
air-conditioning peak in late July.

\end{frame}



\begin{frame}
  \frametitle{Wind Full Load Hours from REatlas}




\begin{columns}[T]
\begin{column}{7cm}
\includegraphics[width=8cm]{wind_flh}
\end{column}
\begin{column}{3cm}
\begin{itemize}
\item Wind and solar data from RE Atlas from Aarhus University
\item Provided hourly for 2014 for 480 points
\item Wind FLH best in sea and along coastline
\end{itemize}
\end{column}
\end{columns}



\end{frame}



\begin{frame}
  \frametitle{Wind Park Placement}


\begin{columns}[T]
\begin{column}{6.5cm}
\includegraphics[width=7.5cm]{Wind_Offshore-Wind_Onshore-Wind_Onshore_ENA-distribution}
\end{column}
\begin{column}{3cm}
\begin{itemize}
\item Plans already for 18~GW of nearshore and first 3~GW of onshore
\item Remaining 9~GW of onshore placed by us
\item Poor inland sites reduce onshore FLHs from $\sim 1950$ to $\sim 1500$
\end{itemize}

\end{column}
\end{columns}


\end{frame}

\begin{frame}
  \frametitle{Comparison of REatlas to measured data}



\begin{columns}[T]
\begin{column}{7.8cm}

\includegraphics[width=8.5cm]{Dongtai_Wind_Park-2014-02-01-2014-02-24}

\includegraphics[width=8.5cm]{Dafeng_Wind_Park-2014-04-25-2014-05-30}
\end{column}
\begin{column}{2cm}


\vspace{0.7cm}


Dongtai Wind Park in February 2014

\vspace{1.5cm}


Dafeng Wind Park in April/May 2014


\end{column}
\end{columns}



\end{frame}

\begin{frame}
  \frametitle{Methodology for Network Extension Calculation}

Optimal Power Flow performed with Energynautics' ENAPLAN tool to minimise necessary grid extensions. Objective function for each hour is:
\begin{equation*}
  f(\mathbf{d}_{g,i,t},\mathbf{P}_{\ell}) = \sum_{g,i} c_{g} \mathbf{d}_{g,i,t} + \sum_{\ell} c_{\ell} \mathbf{P}_{\ell}
  \label{eq:objective}
\end{equation*}
$c_{\star}$ are costs (line extension costs dominate); $\mathbf{d}_{g,i,t}$ is dispatch of technology $g$ at node $i$ and time $t$;
$\mathbf{P}_{\ell}$ is additional capacity of line $\ell$. Constraints:
\begin{eqnarray*}
  D_{g,i} & \geq & D_{g,i,t}\,\geq \, \mathbf{d}_{g,i,t} \,\geq \, \hat{D}_{g,i,t}   \hspace{0.5cm} \textrm{(Generator)}\\
  s\mathbf{P}_{\ell}  + s\hat{P}_\ell & \geq & \left| p_{\ell,t} \right| = \Big| \sum_i PTDF_{\ell i} \,p_{i,t} \Big|  \hspace{0.5cm} \textrm{(Linear power flow)} \\
  p_{i,t} & =& \sum_{g} \mathbf{d}_{g,i,t} - \ell_{i,t}  \hspace{0.5cm} \textrm{(Nodal power imbalance)} \\
  0 & =& \sum_{i} p_{i,t} \hspace{0.5cm} \textrm{(Global power balance)}
\end{eqnarray*}




\end{frame}



\section{Results}



\begin{frame}
  \frametitle{Scenario Assumption and Results}

\ra{1.1}
\begin{table}[!t]
	\begin{tabular}{@{}ll@{}}
\toprule
\textbf{Scenario} &  \textbf{Description}  \\
\midrule
Load Expansion Only & No RE, just load increase \\
Basis & With RE \\
Dynamic RE & With RE and DLR\\
Unlimited Wind Management & With RE and  curtailment to 0\% \\
2\% Wind Management & With RE and curtailment to 70\% \\
1\% Wind Management & With RE and curtailment to 80\%  \\
\midrule
Lower Load, No RE & No RE, peak load just 113.5~GW \\
Lower Load, With RE& With RE, peak load just 113.5~GW \\
\bottomrule
	\end{tabular}
\end{table}

\end{frame}




\begin{frame}
  \frametitle{Load Extension Only Scenario (No RE)}

\begin{columns}[T]
\begin{column}{6.5cm}
\includegraphics[width=7.5cm]{no_load_extension}
\end{column}
\begin{column}{3cm}
\begin{itemize}
\item Rising load alone requires 2200 GVAkm of network extensions by 2020
\item Corresponds
to a 3.3\% increase in the capacity of the network model
\item Costs of 4.4 billion Yuan
\end{itemize}

\end{column}
\end{columns}

\end{frame}


\begin{frame}
  \frametitle{Basis Scenario}



\begin{columns}[T]
\begin{column}{6.5cm}
\includegraphics[width=7.5cm]{141207-204247-basis-second}
\end{column}
\begin{column}{3cm}
\begin{itemize}
\item Build out determined by wind feed-in along coastline
\item Network extensions cost only 52\% more than Load Expansion Only scenario
\item Increase of 5.1\% over already-planned network (in MVAkm)
\end{itemize}

\end{column}
\end{columns}

\end{frame}




\begin{frame}
  \frametitle{Dynamic Line Rating}

Dynamic Line Rating (DLR) can safely raise the thermal limits when there is an additional cooling of the overhead transmission lines due to the wind and cold outside temperature.

\vspace{0.5cm}

\includegraphics[width=11cm]{dlr_in_feb}

\end{frame}



\begin{frame}
  \frametitle{Dynamic RE Scenario}



\begin{columns}[T]
\begin{column}{6.5cm}
\includegraphics[width=7.5cm]{141211-145346-dlr-second}
\end{column}
\begin{column}{3cm}
\begin{itemize}
\item Same RE capacity as Basis scenario
\item DLR allows us to avoid over half of network extension costs
\item DLR raises capacity at same time that wind feed-in overloads network
\end{itemize}

\end{column}
\end{columns}


\end{frame}


\begin{frame}
  \frametitle{Scenario Results: Costs for Network Extensions}
\includegraphics[width=10cm]{scenario_comparison-Yuan}


Cost assumption: 2000 Yuan/MVA/km for network extension.

\end{frame}


\begin{frame}
  \frametitle{Scenario Results: Costs for Network Extensions and Curtailment over 40 years}
\includegraphics[width=10cm]{scenario_comparison-Yuan-withCurtailment}



\end{frame}



\begin{frame}
  \frametitle{Flexibility Requirements}

Histogram of changes to the load and residual load within one hour for the Basis scenario in 2020:

\includegraphics[width=10cm]{histogram_load-no_title}



\end{frame}


\begin{frame}
  \frametitle{Two-Week Dispatch}

Coal run as baseload, unless forced down by residual load:

\includegraphics[width=10cm]{all-stacked-two_weeks}


\end{frame}






\section{Conclusions}



\begin{frame}
  \frametitle{Conclusions}

  \begin{itemize}
  \item Jiangsu can triple its RE targets for 2020 and thus meet 14\% of its electrical demand from local renewables.
\item Some grid upgrades are necessary to accommodate the predicted rise in load and  transport wind from the coast to load centres...
\item ...but over half of the extensions can be avoided by using technologies such as Dynamic Line Rating, which raises grid capacities where there is a cooling effect due to the wind.
\item The additional grid extensions are small (2-3\%) compared to the planned network size and the annualised grid extension costs are minimal ($\sim 0.05\%$) compared to annual electricity costs.


  \end{itemize}

\end{frame}


\end{document}
