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Updated Thesis and linear baseline

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Reports/Thesis/verslag.tex
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\usepackage{tabularx}
\usepackage{array}
\usepackage{amsmath}
\usepackage{mathtools}
\usepackage{multirow}
\usepackage{float}
\usepackage{bbm}
\newcolumntype{C}{>{\centering\arraybackslash}X}
@@ -58,9 +61,6 @@
% Input for title page
%----------------------
% The title
\thesubtitle{February Intermediate Report}
%% Note: a stricter UGent style could be achieved with, e.g.:
\usepackage{ulem} % for colored underline
\renewcommand{\ULthickness}{2pt} % adjust thickness of underline
@@ -133,126 +133,23 @@
% \vspace{1cm}
% {\large Mentor: dr. ir. Femke De Backere\par}
% {\large TechWolf supervisor: ir. Jens-Joris Decorte}
% \end{titlepage}
\newpage
\section{Introduction}
The electricity market is a complex system influenced by numerous factors. The rise of renewable energy sources adds to this complexity, introducing greater volatility compared to traditional energy sources. Renewables, with their unpredictable nature, exacerbate the challenge of maintaining a stable balance between supply and demand. This critical balance is managed by the transmission system operator, Elia, which utilizes reserves to mitigate any potential shortages or surpluses, directly influencing electricity prices.
(TODO: Market participants met flexible assets (Groot genoeg), zij willen grote winst maken. Elia moet minder eigen reserves gebruiken -> goedkoper voor iedereen)
Forecasting the imbalance price is vital for market participants engaged in buying or selling electricity. It enables them to make informed decisions on the optimal times to buy or sell, aiming to maximize their profits. However, current industry practices often rely on simplistic policies, such as adhering to a fixed price for transactions. This approach is not optimal and overlooks the potential benefits of adaptive policies that consider the forecasted imbalance prices.
The goal of this thesis is to generatively model the Belgian electricity market. This allows to reconstruct the imbalance price for a given day which can then be used by other simple policies to make decisions on when to buy or sell electricity. These policies can then be compared to the current industry practices to assess their performance.
Forecasting the system imbalance will become increasingly important as the share of renewable energy sources continues to grow.
\section{Background}
% Achtergrond informatie
% Generatief modelleren
% -> enkel forecast is vaak brak -> reinforcement learning is lastig -> generatief modelleren, veel generaties om mee te trainen
% - Achtergrond electrititetismarkt
% - Achtergrond Generatief modelleren (van NRV)
\subsection{Electricity market}
The electricity market consists of many different parties who all work together and want to make a profit in the end. An overview of the most important parties can be found in Table \ref{tab:parties}.
% table
\begin{table}[h]
\centering
\begin{tabularx}{\textwidth}{|C|C|}
\hline
\textbf{Party} & \textbf{Description} \\
\hline
Producers & Generates electricty. The electricity can be generated using coal, nuclear energy, wind parks etc. \\
\hline
Consumers & Uses electricity. This can be normal households, companies but also industry. \\
\hline
Transmission system operator (TSO) & Party responsible for reliable transmission of electricity from generation plants to local distribution networks. This is done over the high-voltage grid. In Belgium, this party is Elia.\\
\hline
Distribution system operator (DSO) & Party responsible for the distribution of electricity to the end users. Here, the electricity is transported over the low-voltage grid. \\
\hline
Balancing responsible party (BRP) & These parties forecast the electricity consumption and generation of their clients. They make balanced nominations to Elia.
\\
\hline
Balancing Service Provider (BSP) & Parties that provide the TSO (Elia) with balancing services. They submit Balancing Energy Bids to Elia. If needed, they will provide balancing energy at a set price. \\
\hline
\end{tabularx}
\caption{Overview of the most important parties in the electricity market}
\label{tab:parties}
\end{table}
Elia, the Transmission system operator (TSO) in Belgium is responsible for keeping the grid stable. They do this by balancing the electricity consumption and generation. If there is an imbalance, Elia will use reserves to balance the grid. These reserves are expensive and are paid by the market participants. The price of these reserves is called the imbalance price. Keeping the grid balanced is a very important but also a very difficult task. If the grid is not balanced, it can lead to blackouts but also other problems like damage to equipment.
\\\\
Balance responsible parties (BRPs) forecast the electricity consumption and generation of their portfolio to effectively manage the balance between supply and demand within the grid they operate in. They submit a daily balance schedule for their portfolio the day before. This consists of the expected physical injections and offtakes from the grid and the commercial power trades. The power trades can be purchases and sales between BRPs or they can even be trades with other countries. BRPs must provide and deploy all reasonable resources to be balanced on a quarter-hourly basis. They can exchange electricity with other BRPs for the following day or the same day. There is one exception where a BRP can deviate from the balance schedule. This is when the grid is not balanced and they can help Elia to stabilize the grid. In this case, they will receive a compensation for their help. When a BRP deviates from the balance schedule in a way that destabilizes the grid, it will need to pay the imbalance price for the deviation.
\\\\
The imbalance price is determined based on which reserves Elia needs to activate to stabilize the grid. The imbalance of a BRP is the quarter-hourly difference between total injections and offtakes from the grid. The Net Regulation Volume (NRV) is the net control volume of energy that Elia applies to maintain balance in the Elia control area. The Area Control Error is the current difference between the scheduled values and the actual values of power exchanged in the Belgian control area. The imbalance of the system (SI) is the Area Control Error minus the NRV. Using the System Imbalance, the imbalance price is calculated.
\\\\
Elia, the Transmission System Operator (TSO) in Belgium, maintains grid stability by activating three types of reserves, each designed to address specific conditions of imbalance. These reserves are crucial for ensuring that the electricity supply continuously meets the demand, thereby maintaining the frequency within the required operational limits. The reserves include:
1) \textbf{Frequency Containment Reserve (FCR)} \\
FCR is a reserve that responds automatically to frequency deviations in the grid. The reserve responds automatically in seconds and provides a proportional response to the frequency deviation. Elia must provide a minimal share of this volume within the Belgian control area. This type of volume can also be offered by the BSPs.
\\\\
2) \textbf{Automatic Frequency Restoration Process (aFRR)} \\
aFRR is the second reserve that Elia can activate to restore the frequency to 50Hz. The aFRR is activated when the FCR is not sufficient to restore the frequency. Every 4 seconds, Elia sends a set-point to the BSPs. The BSPs use this set-point to adjust their production or consumption. The BSPs have a 7.5-minute window to activate the full requested energy volume.
\\\\
3) \textbf{Manual Frequency Restoration (mFRR)} \\
Sometimes the FCR and aFRR are not enough to restore the imbalance between generation and consumption. Elia activates the mFRR manually and the requested energy volume is to be activated in 15 minutes.
The order in which the reserves are activated is as follows: FCR, aFRR and mFRR. BSPs provide bids for the aFRR and mFRR volumes. The provided bids consist of the type (aFRR or mFRR), bid volume (MW), bid price (per MWh) and start price (per MWh).
The start price is used to cover the costs of starting a unit.
\\\\
Elia selects the bids based on the order of activation and then the price. The highest marginal price paid for upward or downward activation determines the imbalance price. This means that the last bid that is activated determines the imbalance price. This price is paid by the BRPs that are not balanced. The imbalance price calculation is shown in Table \ref{tab:imbalance_price}.
\begin{table}[h]
\centering
\begin{tabular}{|c|c|c|}
\hline
& \multicolumn{2}{c|}{\textbf{System Imbalance}} \\
\cline{2-3}
\textbf{Imbalance of the balance responsible party} & \textbf{Positive} & \textbf{Negative or zero} \\
\hline
\textbf{Positive} & MDP - \(\alpha\) & MIP + \(\alpha\) \\
\hline
\textbf{Negative} & MDP - \(\alpha\) & MIP + \(\alpha\) \\
\hline
\end{tabular}
\caption{Prices paid by the BRPs}
\label{tab:imbalance_price}
\end{table}
The imbalance price calculation includes the following variables: \\
- MDP: Marginal price of downward activation \\
- MIP: Marginal price of upward activation \\
- \(\alpha\): Extra parameter dependent on System Imbalance \\
\\
% TODO: Add more information about the imbalance price calculation, alpha?
TODO: Add more information about the imbalance price calculation, alpha?
The imbalance price can be reconstructed given the bids of a certain quarter/day and the System Imbalance. During this thesis, the system imbalance is assumed to be almost the same as the Net Regulation Volume. This is a simplification but it is a good approximation. The goal of this thesis is to model the Net Regulation Volume which can then be used to reconstruct the imbalance price and to make decisions on when to buy or sell electricity.
\subsection{Generative modeling}
Simple forecasting of the NRV is often not accurate and defining a policy using this forecast will lead to wrong decisions. A better method would be to try to model the NRV and sample multiple generations of the NRV. This gives a better prediction and confidence intervals can be calculated from this.
\\\\
Generative modeling is a type of machine learning that is used to generate new data samples. The goal of generative modeling is to learn the true data distribution of the training data. From this learned distribution, new samples can be generated. Generative modeling is used in many different fields including image generation, text generation etc.
\\\\
TODO: Formulas of generative modeling
\\\\
In this thesis, generative modeling can be used to model the NRV of the Belgian electricity market using different input features like the weather, the electricity price etc. The model can then be used to generate new samples of the NRV.
\\\\
Multiple methods can be used to generatively model the NRV.
\include{sections/introduction}
\include{sections/background}
\section{Literature Study}
- Literatuur forecasting imbalance price
- Literatuur policies adhv forecasts
\section{Modellen + resultaten}
\section{TODO: Better title for this section}
This thesis can be divided into two main parts. The first part focuses on modeling the Net Regulation Volume (NRV) of the Belgian electricity market for the next day. This modeling is conditioned on multiple inputs that can be obtained from Elia (TODO: add citation to the open data of Elia). The second part of the thesis focuses on optimizing a simple policy using the NRV generations for the next day. The policy tries to maximize profit by charging and discharging a battery and thereby buying and selling electricity on the market. Multiple models are trained and tested to model the NRV and compared to each other based on their profit optimization.
\input{sections/nrv_prediction}
\end{document}