Updated thesis
This commit is contained in:
Victor Mylle
@@ -78,6 +78,15 @@
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long = Pinball Loss Function
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}
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% Electricity Market Terms
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\DeclareAcronym{NRV}{
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short = NRV,
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@@ -141,3 +150,18 @@
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short = MW,
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long = Megawatt
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}
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\DeclareAcronym{ACE}{
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short = ACE,
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long = Area Control Error
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}
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\DeclareAcronym{MIP}{
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short = MIP,
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long = Marginal price of upward activation
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}
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\DeclareAcronym{MDP}{
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short = MDP,
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long = Marginal price of downward activation
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}
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@@ -1,12 +1,12 @@
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\relax
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\providecommand\hyper@newdestlabel[2]{}
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\@writefile{toc}{\contentsline {section}{\numberline {A}Appendix}{56}{appendix.A}\protected@file@percent }
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\@writefile{lof}{\contentsline {figure}{\numberline {21}{\ignorespaces Comparison of the autoregressive models with the diffusion model\relax }}{56}{figure.caption.35}\protected@file@percent }
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\newlabel{fig:ar_linear_gru_comparison}{{21}{56}{Comparison of the autoregressive models with the diffusion model\relax }{figure.caption.35}{}}
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\@writefile{lof}{\contentsline {figure}{\numberline {22}{\ignorespaces Comparison of the non-autoregressive models with the diffusion model\relax }}{57}{figure.caption.36}\protected@file@percent }
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\newlabel{fig:ar_linear_gru_comparison}{{22}{57}{Comparison of the non-autoregressive models with the diffusion model\relax }{figure.caption.36}{}}
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\@writefile{toc}{\contentsline {section}{\numberline {A}Appendix}{57}{appendix.A}\protected@file@percent }
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\@writefile{lof}{\contentsline {figure}{\numberline {21}{\ignorespaces Comparison of the autoregressive models with the diffusion model\relax }}{57}{figure.caption.35}\protected@file@percent }
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\newlabel{fig:ar_linear_gru_comparison}{{21}{57}{Comparison of the autoregressive models with the diffusion model\relax }{figure.caption.35}{}}
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\@writefile{lof}{\contentsline {figure}{\numberline {22}{\ignorespaces Comparison of the non-autoregressive models with the diffusion model\relax }}{58}{figure.caption.36}\protected@file@percent }
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\newlabel{fig:ar_linear_gru_comparison}{{22}{58}{Comparison of the non-autoregressive models with the diffusion model\relax }{figure.caption.36}{}}
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\@setckpt{sections/appendix}{
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\setcounter{page}{58}
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\setcounter{page}{59}
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\setcounter{equation}{8}
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\setcounter{enumi}{0}
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\setcounter{enumii}{0}
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@@ -28,7 +28,7 @@
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\setcounter{continuedfloat}{0}
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\setcounter{subfigure}{4}
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\setcounter{subtable}{0}
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\setcounter{NAT@ctr}{31}
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\setcounter{NAT@ctr}{32}
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\setcounter{section@level}{0}
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\setcounter{Item}{0}
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\setcounter{Hfootnote}{0}
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@@ -60,4 +60,7 @@
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\setcounter{g@acro@aFRR@int}{1}
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\setcounter{g@acro@mFRR@int}{1}
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\setcounter{g@acro@MW@int}{0}
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\setcounter{g@acro@ACE@int}{0}
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\setcounter{g@acro@MIP@int}{0}
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\setcounter{g@acro@MDP@int}{0}
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}
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@@ -7,10 +7,11 @@
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\providecommand*\caption@xref[2]{\@setref\relax\@undefined{#1}}
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\newlabel{tab:parties}{{1}{4}{Overview of the most important parties in the electricity market\relax }{table.caption.1}{}}
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\ACRO{recordpage}{BRP}{5}{1}{4}
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\ACRO{recordpage}{TSO}{6}{1}{5}
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\citation{elia_tariffs_2022 }
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\citation{noauthor_fcr_nodate}
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\citation{noauthor_afrr_nodate}
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\citation{noauthor_mfrr_nodate}
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\ACRO{recordpage}{TSO}{7}{1}{6}
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\ACRO{recordpage}{FCR}{7}{1}{6}
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\ACRO{recordpage}{BSP}{7}{1}{6}
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\ACRO{recordpage}{aFRR}{7}{1}{6}
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@@ -110,4 +111,7 @@
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\setcounter{g@acro@aFRR@int}{1}
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\setcounter{g@acro@mFRR@int}{1}
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\setcounter{g@acro@MW@int}{0}
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\setcounter{g@acro@ACE@int}{0}
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\setcounter{g@acro@MIP@int}{0}
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\setcounter{g@acro@MDP@int}{0}
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}
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@@ -26,15 +26,15 @@ The electricity market consists of many different parties who all work together
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\label{tab:parties}
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\end{table}
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The most important aspect of the electricity market is that the grid needs to be balanced at all times. This means that the amount of electricity consumed and generated must be equal at all times. If this is not the case, the grid can become unstable which can lead to blackouts and disrupt equipment. One company is responsible for keeping the grid balanced. This company is called the Transmission System Operator (TSO). In Belgium, this party is Elia. The TSO keeps the grid balanced by activating reserves when needed. These reserves, however, are expensive and need to be paid by the market participants. The prices paid for the activations of these reserves are called the imbalance price.
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The most important aspect of the electricity market is that the grid needs to be balanced. This means that the amount of electricity consumed and generated must be equal at all times. If this is not the case, the grid can become unstable which can lead to blackouts and disrupt equipment. One company is responsible for keeping the grid balanced. This company is called the Transmission System Operator (TSO). In Belgium, this party is Elia. The TSO keeps the grid balanced by activating reserves when needed. These reserves, however, are expensive and need to be paid by the market participants. The prices paid for the activations of these reserves are called the imbalance prices.
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At every access point of the grid, there is a designated \acf{BRP}. This party may be a producer, major consumer, energy supplier or trader. The BRP must take all reasonable measures to maintain the balance between injections, offtakes and commercial power trades within its portfolio. Each day, the BRP submits a daily balance schedule for the next day to the TSO. This schedule contains the expected physical injections and offtakes from the grid as well as the commercial power trades with other BRPs or other countries. These schedules are forecasts and are not always 100\% accurate. A lot of factors can influence the production and consumption of electricity like the weather, the economy, the time of day etc. The BRP must take all reasonable measures to be balanced on a quarter-hourly basis. This can be done by day-ahead or intra-day trading with other BRPs. If the BRP is not balanced for a certain quarter, it will need to pay the imbalance price for the deviation. The imbalance of a BRP is the quarter-hourly difference between total injections and offtakes from the grid. \cite{noauthor_role_nodate}
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The imbalance price, which is a crucial factor in the management of electricity grids, is set by the Transmission System Operator (TSO). This price is calculated based on the total imbalance within the grid. The net regulation volume (NRV) plays a key role in this process. The NRV represents the amount of energy that Elia, the TSO for Belgium, utilizes to ensure the stability and balance of the electricity grid within the Elia control area.
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The imbalance price is a crucial factor in the management of electricity grids, set by the Transmission System Operator (TSO) to ensure grid stability and reliability. This price is mainly calculated based on the Net Regulation Volume (NRV), which represents the amount of reserves activated by the TSO to maintain balance in the grid. Essentially, the NRV reflects the net control volume of energy used for upward or downward regulation within the TSO's control area.
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The Area Control Error (ACE) is another important concept in this context. It refers to the discrepancy between the planned (scheduled) and the actual power exchanges in the Belgian control area. Essentially, it measures how much the actual conditions deviate from what was anticipated.
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Another key concept is the Area Control Error (ACE), which measures the discrepancy between planned (scheduled) and actual power exchanges in the control area. Specifically, ACE quantifies the difference between the scheduled values and actual values of power exchanges, accounting for frequency deviations. In the Belgian control area, this measurement indicates how much the actual conditions deviate from what was anticipated, providing a real-time assessment of grid balance.
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The System Imbalance (SI) is derived by subtracting the NRV from the ACE. This value, the SI, directly influences the calculation of the imbalance price. The TSO uses the magnitude of the System Imbalance to determine the appropriate imbalance price, ensuring that costs are allocated to market participants based on their contribution to the overall grid imbalance. By calculating the imbalance price in this way, the TSO incentivizes market participants to adhere closely to their scheduled injections and offtakes, thereby promoting grid stability and reliability.
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The System Imbalance (SI) is derived by subtracting the NRV from the ACE. This calculation provides a measure of the overall grid imbalance, where SI = ACE - NRV. The value of the SI directly influences the calculation of the imbalance price. The TSO uses the magnitude of the System Imbalance to determine the appropriate imbalance price, ensuring that costs are allocated to market participants based on their contribution to the overall grid imbalance. \cite{elia_tariffs_2022 }
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The Transmission System Operator (TSO) can activate reserves to maintain grid stability, and these reserves are supplied by entities known as Balancing Service Providers (BSPs). BSPs are crucial participants in the electricity market as they provide the necessary reserve capacity that the TSO can call upon in times of need. Each BSP submits bids to the TSO for the potential activation of these reserves. These bids are detailed and include several key components: the specific type of reserve being offered, the total volume of energy available for activation (measured in megawatt-hours, MWh), the price per MWh at which the BSP is willing to provide this reserve, and a start price which initiates the reserve's deployment. Through this bidding process, the TSO selects the most cost-effective and appropriate offers to ensure the grid's stability and balance.
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@@ -44,25 +44,27 @@ Elia, the \acf{TSO} in Belgium, maintains grid stability by activating three typ
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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 \acsp{BSP}. \cite{noauthor_fcr_nodate}
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2) \textbf{ \acf{aFRR}} \\
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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. This reserve can also be offered by the BSPs. \cite{noauthor_afrr_nodate}
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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. \cite{noauthor_afrr_nodate}
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3) \textbf{ \acf{mFRR}} \\
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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. This reserve is the slowest and is used when the other reserves are not sufficient. This reserve can also be offered by the BSPs. \cite{noauthor_mfrr_nodate}
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The order in which the reserves are activated is FCR, aFRR, and mFRR. The reserves are activated in this order because of the response time of the reserves. The FCR is the fastest reserve and can respond automatically in seconds. The aFRR is the second reserve and can respond in 7.5 minutes. The mFRR is the slowest reserve and can respond in 15 minutes. The reserves are activated in this order to ensure that the grid remains stable and that the frequency remains within the required operational limits.
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The order in which the reserves are activated is FCR, aFRR, and mFRR. The reserves are activated in this order because of the response time of the reserves. The FCR is the fastest reserve and can respond automatically in seconds. The aFRR is the second reserve and can respond in 7.5 minutes. The mFRR is the slowest reserve and can respond in 15 minutes. The reserves are activated in this order to ensure that the grid remains stable and that the frequency remains within the required operational limits. The BSPs submit bids to Elia for the activation of these reserves. When reserves need to be activated, Elia selects the bids based on the order of activation and then the price.
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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. The imbalance price calculation is shown in Table \ref{tab:imbalance_price}. Four possible scenarios can happen. The System Imbalance (SI) can be positive or negative and the imbalance of the balance responsible party can be positive or negative. These factors determine in which direction the payments are made. It is possible the BRP needs to pay Elia for the imbalance or that Elia needs to pay the BRP. A positive imbalance corresponds with a surplus of injections to the grid. On the other hand, a negative imbalance indicates a deficit in the injections or an excess of offtakes from the grid.
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The highest marginal price paid for upward or downward activation, for a given quarter-hour, determines the imbalance price. This means that the last bid that is activated determines the imbalance price. The imbalance price calculation is shown in Table \ref{tab:imbalance_price}. Four possible scenarios can happen. The System Imbalance (SI) can be positive or negative and the imbalance of the balance responsible party can be positive or negative. These factors determine in which direction the payments are made. It is possible the BRP needs to pay Elia for the imbalance or that Elia needs to pay the BRP. A positive imbalance corresponds with a surplus of injections to the grid. On the other hand, a negative imbalance indicates a deficit in the injections or an excess of offtakes from the grid.
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% TODO: Check MDP and MIP again and the directions of the payments
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% list the scenarios
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\begin{itemize}
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\item \textbf{Positive SI + Positive BRP Imbalance }\\
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This means that the BRP injects more energy into the grid than it takes out. The BRP has a positive imbalance. The System Imbalance is also positive which means that the grid has a surplus of injections. The BRP will need to pay Elia for the surplus injections. The price paid by the BRP is the Marginal price of downward activation (MDP) minus an extra parameter \(\alpha\).
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This means that the BRP injects more energy into the grid than it takes out. The BRP has a positive imbalance. The System Imbalance is also positive which means that the grid has a surplus of injections. The BRP will need to pay Elia for the surplus injections. The price paid by the BRP is the MDP minus an extra parameter \(\alpha\).
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\item \textbf{Positive SI + Negative BRP Imbalance }\\
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The BRP takes more energy out of the grid than it injects. The BRP has a negative imbalance. The System Imbalance is positive which means that the grid has a surplus of injections. Elia will need to downward activate reserves to balance the grid. Elia needs to pay the BRP for the surplus of offtakes. The price paid by Elia is the Marginal price of downward activation (MDP) minus an extra parameter \(\alpha\).
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The BRP takes more energy out of the grid than it injects. The BRP has a negative imbalance. The System Imbalance is positive which means that the grid has a surplus of injections. Elia will need to downward activate reserves to balance the grid. Elia needs to pay the BRP for the surplus of offtakes. The price paid by Elia is the MDP minus an extra parameter \(\alpha\).
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\item \textbf{Negative SI + Positive BRP Imbalance }\\
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The BRP injects more energy into the grid than it takes out. The BRP has a positive imbalance. The System Imbalance is negative which means that the grid has a deficit of injections. Elia will need to upward activate reserves to balance the grid. Elia needs to pay the BRP for the surplus of injections. The price paid by Elia is the Marginal price of upward activation (MIP) plus an extra parameter \(\alpha\).
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The BRP injects more energy into the grid than it takes out. The BRP has a positive imbalance. The System Imbalance is negative which means that the grid has a deficit of injections. Elia will need to upward activate reserves to balance the grid. Elia needs to pay the BRP for the surplus of injections. The price paid by Elia is the MIP plus an extra parameter \(\alpha\).
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\item \textbf{Negative SI + Negative BRP Imbalance }\\
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The BRP takes more energy out of the grid than it injects. The BRP has a negative imbalance. The System Imbalance is negative which means that the grid has a deficit of injections. The BRP will need to pay Elia for the deficit of injections or surplus of offtakes. The price paid by the BRP is the Marginal price of upward activation (MIP) plus an extra parameter \(\alpha\).
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The BRP takes more energy out of the grid than it injects. The BRP has a negative imbalance. The System Imbalance is negative which means that the grid has a deficit of injections. The BRP will need to pay Elia for the deficit of injections or surplus of offtakes. The price paid by the BRP is the MIP plus an extra parameter \(\alpha\).
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\end{itemize}
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\begin{table}[h]
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@@ -89,7 +91,7 @@ The imbalance price calculation includes the following variables: \\
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The formulas used to calculate the imbalance price can change. Elia publishes the tariffs and formulas used to calculate the imbalance price \cite{elia_tariffs_2022}.
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Given the bids of the BSPs for a certain quarter or day and knowing System Imbalance, the imbalance price can be reconstructed using the calculation provided by Elia. 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. To reconstruct the imbalance price from the NRV value for a certain quarter, the bids of the BSPs are needed. These bids can be transformed into a bid ladder. This bid ladder aggregates the bids of the BSPs and shows the total price for the activation of a certain volume of energy. This way, the highest marginal prices can easily be determined for the activation of a certain volume of energy. A bid ladder is shown in Figure \ref{fig:bid_ladder}.
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Given the bids of the BSPs for a certain quarter or day and knowing System Imbalance, the imbalance price can be reconstructed using the calculation provided by Elia. 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. To reconstruct the imbalance price from the NRV value for a certain quarter, the bids of the BSPs are needed. These bids can be transformed into a bid ladder. This bid ladder aggregates the bids of the BSPs and shows the total price for the activation of a certain volume of energy. This way, the highest marginal prices can easily be determined for the activation of a certain volume of energy. A bid ladder example is shown in Figure \ref{fig:bid_ladder}.
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\begin{figure}[H]
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\centering
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@@ -101,7 +103,7 @@ Given the bids of the BSPs for a certain quarter or day and knowing System Imbal
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\section{Generative modeling}
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Forecasting the imbalance price is a difficult task. The price is influenced by many different factors like the weather, time of day, ... but also by the formulas used by the TSO to calculate the imbalance price. The formulas can change which results in a different imbalance price distribution. This makes it hard to train a model to forecast the imbalance price using historical data. Another method to forecast the imbalance price is to forecast the Net Regulation Volume (NRV) and then use the formulas provided by the TSO to calculate the imbalance price. This way, the model does not need to learn the imbalance price distribution but only the NRV distribution.
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Another problem occurs when just forecasting the NRV. Forecasting a time series is a difficult task because of the uncertainty in the data and the many different factors that can influence the data. 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 full-day generations of the NRV. This can give a better understanding of the uncertainty of the NRV. Better decisions can then be made based on multiple generations of the NRV.
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Forecasting a time series is a difficult task because of the uncertainty in the data and the many different factors that can influence the data. 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 full-day generations of the NRV. This can give a better understanding of the uncertainty of the NRV. More informed decisions can then be made based on multiple generations of the NRV.
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Generative modeling is a type of machine learning that is used to generate new data samples that look like the training data. The goal of generative modeling is to learn the true data distribution and use this distribution to generate new samples. Generative modeling is used in many different fields including image generation, text generation, audio generation etc.
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\setcounter{g@acro@aFRR@int}{0}
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\setcounter{g@acro@mFRR@int}{0}
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\setcounter{g@acro@MW@int}{0}
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\setcounter{g@acro@ACE@int}{0}
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\setcounter{g@acro@MIP@int}{0}
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\setcounter{g@acro@MDP@int}{0}
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}
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@@ -3,12 +3,12 @@ The electricity market is a complex system influenced by numerous factors. The r
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Market participants with big enough flexible assets (e.g., industrial batteries) can help keep the grid stable. This helps Elia to use fewer of its reserves, which in turn makes the system cheaper for everyone. The market participants are then paid for their services by Elia. The main goal of the market participants is not to help stabilize the grid, but to make a profit. They can do this by buying electricity when it is cheap and selling it when Elia pays a high price for it.
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The integration of renewable energy sources has significantly increased the complexity and volatility of the electricity market. Unlike traditional energy sources, renewables such as wind and solar power are inherently variable and less predictable. This variability leads to frequent imbalances between electricity supply and demand, necessitating a greater reliance on reserves to stabilize the grid. Consequently, the needed reserves are increasing as the share of renewable energy in the energy mix grows, making it more challenging for the \ac{TSO} to maintain system stability and manage costs. For instance, the proportion of quarter-hours with negative system imbalances has grown, reflecting the increased volatility introduced by renewables. This increase in volatility directly impacts imbalance prices, often causing them to spike during periods of high renewable generation and drop when renewable output is low. \cite{commission_for_electricity_and_gas_regulation_creg_study_2023}
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The integration of renewable energy sources has significantly increased the complexity and volatility of the electricity market. Unlike traditional energy sources, renewables such as wind and solar power are inherently variable and less predictable. This variability leads to frequent imbalances between electricity supply and demand, necessitating a greater reliance on reserves to stabilize the grid. Consequently, the needed reserves are increasing as the share of renewable energy in the energy mix grows, making it more challenging for the \ac{TSO} to maintain system stability and manage costs. For instance, the proportion of quarter-hours with negative system imbalances has grown, reflecting the increased volatility introduced by renewables. \cite{commission_for_electricity_and_gas_regulation_creg_study_2023}
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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.
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The goal of this thesis is to generatively model the Belgian electricity market. This allows the reconstruction of 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.
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The goal of this thesis is to generatively model the imbalance prices in the Belgian electricity market. Using these models, imbalance price forecasts can be generated for a certain day. These forecasts can then be used to optimize a simple policy to utilize a battery to maximize profit. The policy will charge and discharge the battery based on the imbalance price forecasts which results in better buying and selling decisions. This policy can then be compared to other baseline policies to evaluate its performance.
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Forecasting the system imbalance will become increasingly important as the share of renewable energy sources continues to grow.
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This thesis can be divided into two main parts. The first part focuses on modeling the \ac{NRV} of the Belgian electricity market for the next day. This modeling is conditioned on multiple inputs that can be obtained from data provided by Elia. The second part of the thesis focuses on optimizing a simple policy using the \ac{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 \ac{NRV} and compared to each other based on their profit optimization.
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This thesis can be divided into two main parts. The first part focuses on modeling the \ac{NRV} of the Belgian electricity market for the next day. The NRV can later be used to reconstruct the imbalance price. This modeling is conditioned on multiple inputs that can be obtained from data provided by Elia. The second part of the thesis focuses on optimizing a simple policy using the \ac{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 \ac{NRV} and are then compared to each other based on their profit optimization.
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@@ -68,4 +68,7 @@
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\setcounter{g@acro@aFRR@int}{1}
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\setcounter{g@acro@mFRR@int}{1}
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\setcounter{g@acro@MW@int}{0}
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\setcounter{g@acro@ACE@int}{0}
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\setcounter{g@acro@MIP@int}{0}
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\setcounter{g@acro@MDP@int}{0}
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}
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\setcounter{g@acro@aFRR@int}{1}
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\setcounter{g@acro@mFRR@int}{1}
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\setcounter{g@acro@MW@int}{0}
|
||||
\setcounter{g@acro@ACE@int}{0}
|
||||
\setcounter{g@acro@MIP@int}{0}
|
||||
\setcounter{g@acro@MDP@int}{0}
|
||||
}
|
||||
|
||||
@@ -122,6 +122,7 @@ A comparison of the baselines and the best-performing models is shown in Table \
|
||||
\multicolumn{4}{l}{\textbf{Models}} \\
|
||||
\midrule
|
||||
|
||||
AQR: Linear & 190,501.34 & 282.94 & -4.17\% \\
|
||||
AQR: Non-Linear (4 - 512, All) & 196,999.03 & 284.88 & -0,91\% \\
|
||||
AQR: GRU (2 - 256, Only NRV) & 196,655.36 & 283.81 & -1.08\% \\
|
||||
|
||||
|
||||
@@ -121,34 +121,35 @@
|
||||
\bibcite{commission_for_electricity_and_gas_regulation_creg_study_2023}{{1}{}{{Commission for Electricity and Gas Regulation (CREG)}}{{}}}
|
||||
\bibcite{noauthor_geliberaliseerde_nodate}{{2}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_role_nodate}{{3}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_fcr_nodate}{{4}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_afrr_nodate}{{5}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_mfrr_nodate}{{6}{}{{noa}}{{}}}
|
||||
\bibcite{elia_tariffs_2022}{{7}{}{{Elia}}{{}}}
|
||||
\bibcite{goodfellow_generative_2014}{{8}{}{{Goodfellow et~al.}}{{Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, and Bengio}}}
|
||||
\bibcite{kingma_auto-encoding_2022}{{9}{}{{Kingma and Welling}}{{}}}
|
||||
\bibcite{rezende_variational_2015}{{10}{}{{Rezende and Mohamed}}{{}}}
|
||||
\bibcite{sohl-dickstein_deep_2015}{{11}{}{{Sohl-Dickstein et~al.}}{{Sohl-Dickstein, Weiss, Maheswaranathan, and Ganguli}}}
|
||||
\bibcite{koenker_regression_1978}{{12}{}{{Koenker and Bassett}}{{}}}
|
||||
\bibcite{ho_denoising_2020}{{13}{}{{Ho et~al.}}{{Ho, Jain, and Abbeel}}}
|
||||
\bibcite{gneiting_strictly_2007}{{14}{}{{Gneiting and Raftery}}{{}}}
|
||||
\bibcite{weron_electricity_2014}{{15}{}{{Weron}}{{}}}
|
||||
\bibcite{poggi_electricity_2023}{{16}{}{{Poggi et~al.}}{{Poggi, Di~Persio, and Ehrhardt}}}
|
||||
\bibcite{lago_forecasting_2018}{{17}{}{{Lago et~al.}}{{Lago, De~Ridder, and De~Schutter}}}
|
||||
\bibcite{hagfors_modeling_2016}{{18}{}{{Hagfors et~al.}}{{Hagfors, Bunn, Kristoffersen, Staver, and Westgaard}}}
|
||||
\bibcite{lu_scenarios_2022}{{19}{}{{Lu et~al.}}{{Lu, Qiu, Lei, and Zhu}}}
|
||||
\bibcite{dumas_deep_2022}{{20}{}{{Dumas et~al.}}{{Dumas, Wehenkel, Lanaspeze, Cornélusse, and Sutera}}}
|
||||
\bibcite{rasul_autoregressive_2021}{{21}{}{{Rasul et~al.}}{{Rasul, Seward, Schuster, and Vollgraf}}}
|
||||
\bibcite{dumas_probabilistic_2019}{{22}{}{{Dumas et~al.}}{{Dumas, Boukas, de~Villena, Mathieu, and Cornélusse}}}
|
||||
\bibcite{narajewski_probabilistic_2022}{{23}{}{{Narajewski}}{{}}}
|
||||
\bibcite{noauthor_welcome_nodate}{{24}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_imbalance_nodate}{{25}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_measured_nodate}{{26}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_photovoltaic_nodate}{{27}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_wind_nodate}{{28}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_intraday_nodate}{{29}{}{{noa}}{{}}}
|
||||
\bibcite{dhariwal_diffusion_2021}{{30}{}{{Dhariwal and Nichol}}{{}}}
|
||||
\bibcite{ho_classifier-free_2022}{{31}{}{{Ho and Salimans}}{{}}}
|
||||
\bibcite{noauthor_elia_nodate}{{4}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_fcr_nodate}{{5}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_afrr_nodate}{{6}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_mfrr_nodate}{{7}{}{{noa}}{{}}}
|
||||
\bibcite{elia_tariffs_2022}{{8}{}{{Elia}}{{}}}
|
||||
\bibcite{goodfellow_generative_2014}{{9}{}{{Goodfellow et~al.}}{{Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, and Bengio}}}
|
||||
\bibcite{kingma_auto-encoding_2022}{{10}{}{{Kingma and Welling}}{{}}}
|
||||
\bibcite{rezende_variational_2015}{{11}{}{{Rezende and Mohamed}}{{}}}
|
||||
\bibcite{sohl-dickstein_deep_2015}{{12}{}{{Sohl-Dickstein et~al.}}{{Sohl-Dickstein, Weiss, Maheswaranathan, and Ganguli}}}
|
||||
\bibcite{koenker_regression_1978}{{13}{}{{Koenker and Bassett}}{{}}}
|
||||
\bibcite{ho_denoising_2020}{{14}{}{{Ho et~al.}}{{Ho, Jain, and Abbeel}}}
|
||||
\bibcite{gneiting_strictly_2007}{{15}{}{{Gneiting and Raftery}}{{}}}
|
||||
\bibcite{weron_electricity_2014}{{16}{}{{Weron}}{{}}}
|
||||
\bibcite{poggi_electricity_2023}{{17}{}{{Poggi et~al.}}{{Poggi, Di~Persio, and Ehrhardt}}}
|
||||
\bibcite{lago_forecasting_2018}{{18}{}{{Lago et~al.}}{{Lago, De~Ridder, and De~Schutter}}}
|
||||
\bibcite{hagfors_modeling_2016}{{19}{}{{Hagfors et~al.}}{{Hagfors, Bunn, Kristoffersen, Staver, and Westgaard}}}
|
||||
\bibcite{lu_scenarios_2022}{{20}{}{{Lu et~al.}}{{Lu, Qiu, Lei, and Zhu}}}
|
||||
\bibcite{dumas_deep_2022}{{21}{}{{Dumas et~al.}}{{Dumas, Wehenkel, Lanaspeze, Cornélusse, and Sutera}}}
|
||||
\bibcite{rasul_autoregressive_2021}{{22}{}{{Rasul et~al.}}{{Rasul, Seward, Schuster, and Vollgraf}}}
|
||||
\bibcite{dumas_probabilistic_2019}{{23}{}{{Dumas et~al.}}{{Dumas, Boukas, de~Villena, Mathieu, and Cornélusse}}}
|
||||
\bibcite{narajewski_probabilistic_2022}{{24}{}{{Narajewski}}{{}}}
|
||||
\bibcite{noauthor_welcome_nodate}{{25}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_imbalance_nodate}{{26}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_measured_nodate}{{27}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_photovoltaic_nodate}{{28}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_wind_nodate}{{29}{}{{noa}}{{}}}
|
||||
\bibcite{noauthor_intraday_nodate}{{30}{}{{noa}}{{}}}
|
||||
\bibcite{dhariwal_diffusion_2021}{{31}{}{{Dhariwal and Nichol}}{{}}}
|
||||
\bibcite{ho_classifier-free_2022}{{32}{}{{Ho and Salimans}}{{}}}
|
||||
\@input{sections/appendix.aux}
|
||||
\ACRO{total-barriers}{1}
|
||||
\ACRO{usage}{QR=={0}}
|
||||
@@ -178,8 +179,11 @@
|
||||
\ACRO{usage}{aFRR=={1}}
|
||||
\ACRO{usage}{mFRR=={1}}
|
||||
\ACRO{usage}{MW=={0}}
|
||||
\ACRO{usage}{ACE=={0}}
|
||||
\ACRO{usage}{MIP=={0}}
|
||||
\ACRO{usage}{MDP=={0}}
|
||||
\ACRO{pages}{BRP=={5@1@4}}
|
||||
\ACRO{pages}{TSO=={3@1@2|6@1@5}}
|
||||
\ACRO{pages}{TSO=={3@1@2|7@1@6}}
|
||||
\ACRO{pages}{FCR=={7@1@6}}
|
||||
\ACRO{pages}{BSP=={7@1@6}}
|
||||
\ACRO{pages}{aFRR=={7@1@6}}
|
||||
@@ -189,4 +193,4 @@
|
||||
\ACRO{pages}{MSE=={44@1@43|45@1@44}}
|
||||
\ACRO{pages}{MAE=={44@1@43|45@1@44}}
|
||||
\ACRO{pages}{NRV=={3@1@2|4@1@3|47@1@46|48@1@47}}
|
||||
\gdef \@abspage@last{58}
|
||||
\gdef \@abspage@last{59}
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
\begin{thebibliography}{31}
|
||||
\begin{thebibliography}{32}
|
||||
\providecommand{\natexlab}[1]{#1}
|
||||
\providecommand{\url}[1]{\texttt{#1}}
|
||||
\expandafter\ifx\csname urlstyle\endcsname\relax
|
||||
@@ -18,16 +18,20 @@ De geliberaliseerde elektriciteitsmarkt omvat vele partijen die allen samen moet
|
||||
Role of {BRP}, {\natexlab{b}}.
|
||||
\newblock URL \url{https://www.elia.be/en/electricity-market-and-system/role-of-brp}.
|
||||
|
||||
\bibitem[noa({\natexlab{c}})]{noauthor_fcr_nodate}
|
||||
{FCR}, {\natexlab{c}}.
|
||||
\bibitem[noa({\natexlab{c}})]{noauthor_elia_nodate}
|
||||
Elia: de electriciteitsmarkt en -systeem, {\natexlab{c}}.
|
||||
\newblock URL \url{https://www.elia.be/nl/elektriciteitsmarkt-en-systeem}.
|
||||
|
||||
\bibitem[noa({\natexlab{d}})]{noauthor_fcr_nodate}
|
||||
{FCR}, {\natexlab{d}}.
|
||||
\newblock URL \url{https://www.elia.be/en/electricity-market-and-system/system-services/keeping-the-balance/fcr}.
|
||||
|
||||
\bibitem[noa({\natexlab{d}})]{noauthor_afrr_nodate}
|
||||
{aFRR}, {\natexlab{d}}.
|
||||
\bibitem[noa({\natexlab{e}})]{noauthor_afrr_nodate}
|
||||
{aFRR}, {\natexlab{e}}.
|
||||
\newblock URL \url{https://www.elia.be/en/electricity-market-and-system/system-services/keeping-the-balance/afrr}.
|
||||
|
||||
\bibitem[noa({\natexlab{e}})]{noauthor_mfrr_nodate}
|
||||
{mFRR}, {\natexlab{e}}.
|
||||
\bibitem[noa({\natexlab{f}})]{noauthor_mfrr_nodate}
|
||||
{mFRR}, {\natexlab{f}}.
|
||||
\newblock URL \url{https://www.elia.be/en/electricity-market-and-system/system-services/keeping-the-balance/mfrr}.
|
||||
|
||||
\bibitem[{Elia}()]{elia_tariffs_2022}
|
||||
@@ -144,28 +148,28 @@ Michał Narajewski.
|
||||
\newblock Probabilistic forecasting of german electricity imbalance prices.
|
||||
\newblock URL \url{http://arxiv.org/abs/2205.11439}.
|
||||
|
||||
\bibitem[noa({\natexlab{f}})]{noauthor_welcome_nodate}
|
||||
Welcome — elia open data portal, {\natexlab{f}}.
|
||||
\bibitem[noa({\natexlab{g}})]{noauthor_welcome_nodate}
|
||||
Welcome — elia open data portal, {\natexlab{g}}.
|
||||
\newblock URL \url{https://opendata.elia.be/pages/home/}.
|
||||
|
||||
\bibitem[noa({\natexlab{g}})]{noauthor_imbalance_nodate}
|
||||
Imbalance prices per quarter-hour (historical data), {\natexlab{g}}.
|
||||
\bibitem[noa({\natexlab{h}})]{noauthor_imbalance_nodate}
|
||||
Imbalance prices per quarter-hour (historical data), {\natexlab{h}}.
|
||||
\newblock URL \url{https://opendata.elia.be/explore/dataset/ods047/information/?sort=datetime}.
|
||||
|
||||
\bibitem[noa({\natexlab{h}})]{noauthor_measured_nodate}
|
||||
Measured and forecasted total load on the belgian grid (historical data), {\natexlab{h}}.
|
||||
\bibitem[noa({\natexlab{i}})]{noauthor_measured_nodate}
|
||||
Measured and forecasted total load on the belgian grid (historical data), {\natexlab{i}}.
|
||||
\newblock URL \url{https://opendata.elia.be/explore/dataset/ods001/table/?sort=datetime}.
|
||||
|
||||
\bibitem[noa({\natexlab{i}})]{noauthor_photovoltaic_nodate}
|
||||
Photovoltaic power production estimation and forecast on belgian grid (historical), {\natexlab{i}}.
|
||||
\bibitem[noa({\natexlab{j}})]{noauthor_photovoltaic_nodate}
|
||||
Photovoltaic power production estimation and forecast on belgian grid (historical), {\natexlab{j}}.
|
||||
\newblock URL \url{https://opendata.elia.be/explore/dataset/ods032/table/?sort=datetime}.
|
||||
|
||||
\bibitem[noa({\natexlab{j}})]{noauthor_wind_nodate}
|
||||
Wind power production estimation and forecast on belgian grid (historical), {\natexlab{j}}.
|
||||
\bibitem[noa({\natexlab{k}})]{noauthor_wind_nodate}
|
||||
Wind power production estimation and forecast on belgian grid (historical), {\natexlab{k}}.
|
||||
\newblock URL \url{https://opendata.elia.be/explore/dataset/ods031/information/}.
|
||||
|
||||
\bibitem[noa({\natexlab{k}})]{noauthor_intraday_nodate}
|
||||
Intraday implicit net position (belgium's balance), {\natexlab{k}}.
|
||||
\bibitem[noa({\natexlab{l}})]{noauthor_intraday_nodate}
|
||||
Intraday implicit net position (belgium's balance), {\natexlab{l}}.
|
||||
\newblock URL \url{https://opendata.elia.be/explore/dataset/ods022/information/?sort=datetime}.
|
||||
|
||||
\bibitem[Dhariwal and Nichol()]{dhariwal_diffusion_2021}
|
||||
|
||||
@@ -8,6 +8,8 @@ A level-1 auxiliary file: sections/literature_study.aux
|
||||
The style file: unsrtnat.bst
|
||||
A level-1 auxiliary file: sections/appendix.aux
|
||||
Database file #1: references.bib
|
||||
Warning--entry type for "noauthor_elia_nodate" isn't style-file defined
|
||||
--line 2 of file references.bib
|
||||
Warning--entry type for "noauthor_welcome_nodate" isn't style-file defined
|
||||
--line 365 of file references.bib
|
||||
Warning--entry type for "noauthor_imbalance_nodate" isn't style-file defined
|
||||
@@ -35,6 +37,7 @@ Warning--entry type for "noauthor_mfrr_nodate" isn't style-file defined
|
||||
Warning--empty year in commission_for_electricity_and_gas_regulation_creg_study_2023
|
||||
Warning--empty year in noauthor_geliberaliseerde_nodate
|
||||
Warning--empty year in noauthor_role_nodate
|
||||
Warning--empty year in noauthor_elia_nodate
|
||||
Warning--empty year in noauthor_fcr_nodate
|
||||
Warning--empty year in noauthor_afrr_nodate
|
||||
Warning--empty year in noauthor_mfrr_nodate
|
||||
@@ -79,45 +82,45 @@ Warning--empty year in noauthor_wind_nodate
|
||||
Warning--empty year in noauthor_intraday_nodate
|
||||
Warning--empty year in dhariwal_diffusion_2021
|
||||
Warning--empty year in ho_classifier-free_2022
|
||||
You've used 31 entries,
|
||||
You've used 32 entries,
|
||||
2481 wiz_defined-function locations,
|
||||
728 strings with 11247 characters,
|
||||
and the built_in function-call counts, 10379 in all, are:
|
||||
= -- 895
|
||||
731 strings with 11360 characters,
|
||||
and the built_in function-call counts, 10599 in all, are:
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
+ -- 198
|
||||
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|
||||
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|
||||
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|
||||
add.period$ -- 113
|
||||
call.type$ -- 31
|
||||
change.case$ -- 62
|
||||
* -- 821
|
||||
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|
||||
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|
||||
call.type$ -- 32
|
||||
change.case$ -- 64
|
||||
chr.to.int$ -- 20
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
format.name$ -- 154
|
||||
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|
||||
int.to.chr$ -- 12
|
||||
int.to.str$ -- 32
|
||||
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|
||||
int.to.chr$ -- 13
|
||||
int.to.str$ -- 33
|
||||
missing$ -- 10
|
||||
newline$ -- 182
|
||||
newline$ -- 186
|
||||
num.names$ -- 62
|
||||
pop$ -- 348
|
||||
pop$ -- 360
|
||||
preamble$ -- 1
|
||||
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|
||||
purify$ -- 32
|
||||
quote$ -- 0
|
||||
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|
||||
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|
||||
stack$ -- 0
|
||||
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|
||||
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|
||||
substring$ -- 318
|
||||
swap$ -- 90
|
||||
text.length$ -- 3
|
||||
text.prefix$ -- 0
|
||||
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|
||||
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|
||||
warning$ -- 47
|
||||
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|
||||
warning$ -- 48
|
||||
while$ -- 61
|
||||
width$ -- 0
|
||||
write$ -- 403
|
||||
(There were 59 warnings)
|
||||
write$ -- 413
|
||||
(There were 61 warnings)
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) (preloaded format=pdflatex 2023.9.17) 20 MAY 2024 00:35
|
||||
This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023) (preloaded format=pdflatex 2023.9.17) 20 MAY 2024 15:30
|
||||
entering extended mode
|
||||
restricted \write18 enabled.
|
||||
file:line:error style messages enabled.
|
||||
@@ -840,6 +840,9 @@ Package: ulem 2019/11/18
|
||||
\c@g@acro@aFRR@int=\count366
|
||||
\c@g@acro@mFRR@int=\count367
|
||||
\c@g@acro@MW@int=\count368
|
||||
\c@g@acro@ACE@int=\count369
|
||||
\c@g@acro@MIP@int=\count370
|
||||
\c@g@acro@MDP@int=\count371
|
||||
)
|
||||
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||||
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||||
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||||
|
||||
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||||
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||||
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||||
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||||
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|
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|
||||
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|
||||
Package: epstopdf-base 2020-01-24 v2.11 Base part for package epstopdf
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@@ -941,8 +944,8 @@ Package epstopdf-base Info: Redefining graphics rule for `.eps' on input line 48
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||||
(/usr/local/texlive/2023/texmf-dist/tex/latex/latexconfig/epstopdf-sys.cfg
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File: epstopdf-sys.cfg 2010/07/13 v1.3 Configuration of (r)epstopdf for TeX Live
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[]\T1/LinuxLibertineT-TLF/m/n/12 FCR, . URL [][]$\T1/LinuxLibertineMonoT-TLF/regular/n/12 https : / / www . elia . be / en / electricity-[]market-[]and-[]system /
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@@ -30,7 +30,7 @@ from src.models.time_embedding_layer import TimeEmbedding
|
||||
#### Data Processor ####
|
||||
data_config = DataConfig()
|
||||
|
||||
data_config.NRV_HISTORY = False
|
||||
data_config.NRV_HISTORY = True
|
||||
data_config.LOAD_HISTORY = False
|
||||
data_config.LOAD_FORECAST = False
|
||||
|
||||
@@ -53,7 +53,7 @@ data_processor.set_full_day_skip(True)
|
||||
#### Hyperparameters ####
|
||||
data_processor.set_output_size(96)
|
||||
inputDim = data_processor.get_input_size()
|
||||
epochs = 5
|
||||
epochs = 300
|
||||
|
||||
# add parameters to clearml
|
||||
quantiles = task.get_parameter("general/quantiles", cast=True)
|
||||
@@ -68,7 +68,7 @@ else:
|
||||
|
||||
model_parameters = {
|
||||
"learning_rate": 0.0001,
|
||||
"hidden_size": 512,
|
||||
"hidden_size": 256,
|
||||
"num_layers": 2,
|
||||
"dropout": 0.2,
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user