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Updated thesis

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Victor Mylle
2024-05-13 11:33:00 +02:00
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Reports/Thesis/sections/background.tex
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@@ -33,13 +33,17 @@ The electricity market consists of many different parties who all work together
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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.
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.
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.
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.
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 to the transmission system operator. 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 traded 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 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, 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.
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 \ac{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 \acf{SI} is the Area Control Error minus the \ac{NRV}. Using the System Imbalance, the imbalance price is calculated.
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.
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.
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.
Elia, the \acf{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:

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20
Reports/Thesis/verslag.toc
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@@ -2,18 +2,18 @@
\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}{5}{section.3}%
\contentsline {section}{\numberline {3}Generative modeling}{6}{section.3}%
\contentsline {subsection}{\numberline {3.1}Quantile Regression}{6}{subsection.3.1}%
\contentsline {subsection}{\numberline {3.2}Autoregressive vs Non-Autoregressive models}{8}{subsection.3.2}%
\contentsline {subsection}{\numberline {3.3}Model Types}{9}{subsection.3.3}%
\contentsline {subsubsection}{\numberline {3.3.1}Linear Model}{9}{subsubsection.3.3.1}%
\contentsline {subsubsection}{\numberline {3.3.2}Non-Linear Model}{10}{subsubsection.3.3.2}%
\contentsline {subsubsection}{\numberline {3.3.3}Recurrent Neural Network (RNN)}{10}{subsubsection.3.3.3}%
\contentsline {subsection}{\numberline {3.4}Diffusion models}{11}{subsection.3.4}%
\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}{12}{subsubsection.3.4.2}%
\contentsline {subsubsection}{\numberline {3.4.3}Generation process}{12}{subsubsection.3.4.3}%
\contentsline {subsection}{\numberline {3.5}Evaluation}{14}{subsection.3.5}%
\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}%

3
src/data/dataset.py
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@@ -169,8 +169,7 @@ class NrvDataset(Dataset):
all_features = torch.cat(all_features_list, dim=0)
else:
all_features_list = [nrv_features + self.]
all_features_list = [nrv_features.unsqueeze(1)]
if self.forecast_features.numel() > 0:
history_forecast_features = self.forecast_features[

3
src/trainers/diffusion_trainer.py
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@@ -80,11 +80,12 @@ class DiffusionTrainer:
data_processor: DataProcessor,
device: torch.device,
policy_evaluator: PolicyEvaluator = None,
noise_steps: int = 300,
):
self.model = model
self.device = device
self.noise_steps = 300
self.noise_steps = noise_steps
self.beta_start = 0.0001
self.beta_end = 0.02
self.ts_length = 96

20
src/training_scripts/diffusion_training.py
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@@ -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=[1024, 1024] (300 steps), lr=0.0001, time_dim=8 + NRV + L + W + PV + NP",
task_name="Diffusion Training: hidden_sizes=[256, 256] (30 steps), lr=0.0001, time_dim=8",
)
task.execute_remotely(queue_name="default", exit_process=True)
@@ -19,16 +19,16 @@ from src.policies.PolicyEvaluator import PolicyEvaluator
data_config = DataConfig()
data_config.NRV_HISTORY = True
data_config.LOAD_HISTORY = True
data_config.LOAD_FORECAST = True
data_config.LOAD_HISTORY = False
data_config.LOAD_FORECAST = False
data_config.PV_FORECAST = True
data_config.PV_HISTORY = True
data_config.PV_FORECAST = False
data_config.PV_HISTORY = False
data_config.WIND_FORECAST = True
data_config.WIND_HISTORY = True
data_config.WIND_FORECAST = False
data_config.WIND_HISTORY = False
data_config.NOMINAL_NET_POSITION = True
data_config.NOMINAL_NET_POSITION = False
data_config = task.connect(data_config, name="data_features")
@@ -42,7 +42,7 @@ print("Input dim: ", inputDim)
model_parameters = {
"epochs": 15000,
"learning_rate": 0.0001,
"hidden_sizes": [1024, 1024],
"hidden_sizes": [256, 256],
"time_dim": 8,
}
@@ -71,6 +71,6 @@ policy_evaluator = PolicyEvaluator(baseline_policy, task)
#### Trainer ####
trainer = DiffusionTrainer(
model, data_processor, "cuda", policy_evaluator=policy_evaluator
model, data_processor, "cuda", policy_evaluator=policy_evaluator, noise_steps=30
)
trainer.train(model_parameters["epochs"], model_parameters["learning_rate"], task)