Updated thesis
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@@ -51,15 +51,26 @@ 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}.
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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.
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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.
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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.
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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.
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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 (\acs{MW}), bid price (per MWh) and start price (per MWh).
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The start price is used to cover the costs of starting a unit.
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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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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}.
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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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% 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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\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 (MIP) 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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\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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\end{itemize}
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\begin{table}[h]
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\centering
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@@ -86,7 +97,7 @@ The imbalance price calculation includes the following variables: \\
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TODO: Add more information about the imbalance price calculation, alpha?
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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.
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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.
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\section{Generative modeling}
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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 for a whole day. 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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\babel@toc {english}{}\relax
|
||||
\contentsline {section}{\numberline {1}Introduction}{2}{section.1}%
|
||||
\contentsline {section}{\numberline {2}Electricity market}{3}{section.2}%
|
||||
\contentsline {section}{\numberline {3}Generative modeling}{6}{section.3}%
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||||
\contentsline {subsection}{\numberline {3.1}Quantile Regression}{6}{subsection.3.1}%
|
||||
\contentsline {subsection}{\numberline {3.2}Autoregressive vs Non-Autoregressive models}{9}{subsection.3.2}%
|
||||
\contentsline {subsection}{\numberline {3.3}Model Types}{10}{subsection.3.3}%
|
||||
\contentsline {subsubsection}{\numberline {3.3.1}Linear Model}{10}{subsubsection.3.3.1}%
|
||||
\contentsline {subsubsection}{\numberline {3.3.2}Non-Linear Model}{11}{subsubsection.3.3.2}%
|
||||
\contentsline {subsubsection}{\numberline {3.3.3}Recurrent Neural Network (RNN)}{11}{subsubsection.3.3.3}%
|
||||
\contentsline {subsection}{\numberline {3.4}Diffusion models}{12}{subsection.3.4}%
|
||||
\contentsline {subsubsection}{\numberline {3.4.1}Overview}{12}{subsubsection.3.4.1}%
|
||||
\contentsline {subsubsection}{\numberline {3.4.2}Applications}{13}{subsubsection.3.4.2}%
|
||||
\contentsline {subsubsection}{\numberline {3.4.3}Generation process}{13}{subsubsection.3.4.3}%
|
||||
\contentsline {subsection}{\numberline {3.5}Evaluation}{15}{subsection.3.5}%
|
||||
\contentsline {section}{\numberline {4}Policies}{17}{section.4}%
|
||||
\contentsline {subsection}{\numberline {4.1}Baselines}{17}{subsection.4.1}%
|
||||
\contentsline {subsection}{\numberline {4.2}Policies based on NRV generations}{18}{subsection.4.2}%
|
||||
\contentsline {section}{\numberline {5}Literature Study}{19}{section.5}%
|
||||
\contentsline {subsection}{\numberline {5.1}Electricity Price Forecasting}{19}{subsection.5.1}%
|
||||
\contentsline {subsection}{\numberline {5.2}Policies for Battery Optimization}{20}{subsection.5.2}%
|
||||
\contentsline {section}{\numberline {6}Results \& Discussion}{21}{section.6}%
|
||||
\contentsline {subsection}{\numberline {6.1}Data}{21}{subsection.6.1}%
|
||||
\contentsline {subsection}{\numberline {6.2}Quantile Regression}{22}{subsection.6.2}%
|
||||
\contentsline {subsubsection}{\numberline {6.2.1}Linear Model}{22}{subsubsection.6.2.1}%
|
||||
\contentsline {subsubsection}{\numberline {6.2.2}Non-Linear Model}{29}{subsubsection.6.2.2}%
|
||||
\contentsline {subsubsection}{\numberline {6.2.3}GRU Model}{32}{subsubsection.6.2.3}%
|
||||
\contentsline {subsection}{\numberline {6.3}Diffusion}{36}{subsection.6.3}%
|
||||
\contentsline {subsection}{\numberline {6.4}Comparison}{40}{subsection.6.4}%
|
||||
\contentsline {section}{\numberline {7}Policies for battery optimization}{43}{section.7}%
|
||||
\contentsline {subsection}{\numberline {7.1}Baselines}{43}{subsection.7.1}%
|
||||
\contentsline {subsection}{\numberline {7.2}Policy using generated NRV samples}{44}{subsection.7.2}%
|
||||
\contentsline {section}{\numberline {A}Appendix}{48}{appendix.A}%
|
||||
\contentsline {section}{\numberline {3}Generative modeling}{7}{section.3}%
|
||||
\contentsline {subsection}{\numberline {3.1}Quantile Regression}{7}{subsection.3.1}%
|
||||
\contentsline {subsection}{\numberline {3.2}Autoregressive vs Non-Autoregressive models}{10}{subsection.3.2}%
|
||||
\contentsline {subsection}{\numberline {3.3}Model Types}{11}{subsection.3.3}%
|
||||
\contentsline {subsubsection}{\numberline {3.3.1}Linear Model}{11}{subsubsection.3.3.1}%
|
||||
\contentsline {subsubsection}{\numberline {3.3.2}Non-Linear Model}{12}{subsubsection.3.3.2}%
|
||||
\contentsline {subsubsection}{\numberline {3.3.3}Recurrent Neural Network (RNN)}{12}{subsubsection.3.3.3}%
|
||||
\contentsline {subsection}{\numberline {3.4}Diffusion models}{13}{subsection.3.4}%
|
||||
\contentsline {subsubsection}{\numberline {3.4.1}Overview}{13}{subsubsection.3.4.1}%
|
||||
\contentsline {subsubsection}{\numberline {3.4.2}Applications}{14}{subsubsection.3.4.2}%
|
||||
\contentsline {subsubsection}{\numberline {3.4.3}Generation process}{14}{subsubsection.3.4.3}%
|
||||
\contentsline {subsection}{\numberline {3.5}Evaluation}{16}{subsection.3.5}%
|
||||
\contentsline {section}{\numberline {4}Policies}{18}{section.4}%
|
||||
\contentsline {subsection}{\numberline {4.1}Baselines}{18}{subsection.4.1}%
|
||||
\contentsline {subsection}{\numberline {4.2}Policies based on NRV generations}{19}{subsection.4.2}%
|
||||
\contentsline {section}{\numberline {5}Literature Study}{20}{section.5}%
|
||||
\contentsline {subsection}{\numberline {5.1}Electricity Price Forecasting}{20}{subsection.5.1}%
|
||||
\contentsline {subsection}{\numberline {5.2}Policies for Battery Optimization}{21}{subsection.5.2}%
|
||||
\contentsline {section}{\numberline {6}Results \& Discussion}{22}{section.6}%
|
||||
\contentsline {subsection}{\numberline {6.1}Data}{22}{subsection.6.1}%
|
||||
\contentsline {subsection}{\numberline {6.2}Quantile Regression}{23}{subsection.6.2}%
|
||||
\contentsline {subsubsection}{\numberline {6.2.1}Linear Model}{23}{subsubsection.6.2.1}%
|
||||
\contentsline {subsubsection}{\numberline {6.2.2}Non-Linear Model}{30}{subsubsection.6.2.2}%
|
||||
\contentsline {subsubsection}{\numberline {6.2.3}GRU Model}{33}{subsubsection.6.2.3}%
|
||||
\contentsline {subsection}{\numberline {6.3}Diffusion}{37}{subsection.6.3}%
|
||||
\contentsline {subsection}{\numberline {6.4}Comparison}{41}{subsection.6.4}%
|
||||
\contentsline {section}{\numberline {7}Policies for battery optimization}{44}{section.7}%
|
||||
\contentsline {subsection}{\numberline {7.1}Baselines}{44}{subsection.7.1}%
|
||||
\contentsline {subsection}{\numberline {7.2}Policy using generated NRV samples}{45}{subsection.7.2}%
|
||||
\contentsline {section}{\numberline {A}Appendix}{49}{appendix.A}%
|
||||
|
||||
@@ -59,11 +59,8 @@ def sample_diffusion(
|
||||
|
||||
# evenly spaces 4 intermediate samples to append between 1 and noise_steps
|
||||
if intermediate_samples:
|
||||
first_quarter_end = (noise_steps - 1) // 4
|
||||
spacing = (first_quarter_end - 1) // 4
|
||||
|
||||
# save 1, 1 + spacing, 1 + 2*spacing, 1 + 3*spacing
|
||||
if i % spacing == 1 and i <= first_quarter_end:
|
||||
spacing = (noise_steps - 1) // 4
|
||||
if i % spacing == 0:
|
||||
intermediate_samples_list.append(x)
|
||||
|
||||
x = torch.clamp(x, -1.0, 1.0)
|
||||
|
||||
@@ -2,7 +2,7 @@ from src.utils.clearml import ClearMLHelper
|
||||
|
||||
clearml_helper = ClearMLHelper(project_name="Thesis/NrvForecast")
|
||||
task = clearml_helper.get_task(
|
||||
task_name="Diffusion Training: hidden_sizes=[256, 256] (30 steps), lr=0.0001, time_dim=8",
|
||||
task_name="Diffusion Training: hidden_sizes=[256, 256] (100 steps), lr=0.0001, time_dim=8",
|
||||
)
|
||||
task.execute_remotely(queue_name="default", exit_process=True)
|
||||
|
||||
@@ -71,6 +71,6 @@ policy_evaluator = PolicyEvaluator(baseline_policy, task)
|
||||
|
||||
#### Trainer ####
|
||||
trainer = DiffusionTrainer(
|
||||
model, data_processor, "cuda", policy_evaluator=policy_evaluator, noise_steps=30
|
||||
model, data_processor, "cuda", policy_evaluator=policy_evaluator, noise_steps=100
|
||||
)
|
||||
trainer.train(model_parameters["epochs"], model_parameters["learning_rate"], task)
|
||||
|
||||
Reference in New Issue
Block a user