IEE-TUGraz · BenjaminStoeckl · Apr 21, 2026 · Apr 21, 2026 · Apr 21, 2026 · Apr 21, 2026
diff --git a/README.md b/README.md
@@ -41,6 +41,7 @@ For comprehensive guides, API reference, and tutorials, visit the official docum
 - **Multiple Partitioning Algorithms** - K-means, K-medoids, DBSCAN, HDBSCAN, and hierarchical clustering
 - **Distance Metrics** - Euclidean for local coordinates, Haversine for geographic data
 - **Electrical Distance** - Partition based on PTDF-derived electrical proximity
+- **Price-Based Clustering** - Partition based on Locational Marginal Price (LMP) profiles
 - **Voltage-Aware Clustering** - Respects voltage levels and transformer boundaries
 - **Flexible Aggregation** - Sum, average, or custom strategies for node/edge properties
 - **Extensible Design** - Easy to add your own partitioning or aggregation strategies

diff --git a/docs/api/partitioning.rst b/docs/api/partitioning.rst
@@ -33,3 +33,10 @@ Voltage-Aware Partitioning
    :members:
    :show-inheritance:
    :no-index:
+
+Locational Marginal Price Partitioning
+--------------------------------------
+.. automodule:: npap.partitioning.locational_marginal_price
+   :members:
+   :show-inheritance:
+   :no-index:
diff --git a/docs/user-guide/available-strategies.md b/docs/user-guide/available-strategies.md
@@ -164,29 +164,43 @@ Combines electrical distance (PTDF approach) with voltage level and AC island co
 
 **Required attributes**: Nodes: `voltage`, `ac_island` | Edges: `x` (reactance)
 
+### Locational Marginal Price Partitioning
+
+Clusters nodes based on nodal prices (LMP) or price profiles.
+
+| Strategy | Algorithm | Description |
+|----------|-----------|-------------|
+| `lmp_hierarchical_connected` | Hierarchical | Spatially contiguous price-based clustering |
+
+**Required attributes**: Nodes: `lmp`
+
 ### Choosing a Partitioning Strategy
 
 ```{mermaid}
 flowchart TD
     A[Start] --> B{Multi-voltage?}
     B -->|Yes| C{Need electrical distance?}
-    B -->|No| D{Need electrical distance?}
-    C -->|Yes| E[va_electrical_*]
-    C -->|No| F[va_geographical_*]
-    D -->|Yes| G[electrical_*]
-    D -->|No| H{AC islands?}
-    H -->|Yes| I[geographical_kmedoids_*]
-    H -->|No| L[geographical_*]
+    B -->|No| D{Price-based?}
+    D -->|Yes| E[lmp_*]
+    D -->|No| F{Need electrical distance?}
+    C -->|Yes| G[va_electrical_*]
+    C -->|No| H[va_geographical_*]
+    F -->|Yes| I[electrical_*]
+    F -->|No| J{AC islands?}
+    J -->|Yes| K[geographical_kmedoids_*]
+    J -->|No| L[geographical_*]
 
     style A fill:#2993B5,stroke:#1d6f8a,color:#fff
     style B fill:#FFBF00,stroke:#cc9900,color:#1e293b
     style C fill:#FFBF00,stroke:#cc9900,color:#1e293b
     style D fill:#FFBF00,stroke:#cc9900,color:#1e293b
     style E fill:#0fad6b,stroke:#076b3f,color:#fff
-    style F fill:#0fad6b,stroke:#076b3f,color:#fff
+    style F fill:#FFBF00,stroke:#cc9900,color:#1e293b
     style G fill:#0fad6b,stroke:#076b3f,color:#fff
-    style H fill:#FFBF00,stroke:#cc9900,color:#1e293b
+    style H fill:#0fad6b,stroke:#076b3f,color:#fff
     style I fill:#0fad6b,stroke:#076b3f,color:#fff
+    style J fill:#FFBF00,stroke:#cc9900,color:#1e293b
+    style K fill:#0fad6b,stroke:#076b3f,color:#fff
     style L fill:#0fad6b,stroke:#076b3f,color:#fff
 ```
 

diff --git a/docs/user-guide/index.md b/docs/user-guide/index.md
@@ -142,11 +142,12 @@ G = nx.MultiDiGraph()
 
 Different strategies require specific node and edge attributes:
 
-| Strategy Type | Node Attributes | Edge Attributes |
-|---------------|-----------------|-----------------|
-| Geographical | `lat`, `lon` | — |
-| Electrical | `ac_island` | `x` (reactance) |
-| Voltage-Aware | `lat`, `lon`, `voltage`, `ac_island` | `x`, `type` |
+| Strategy Type             | Node Attributes | Edge Attributes |
+|---------------------------|-----------------|-----------------|
+| Geographical              | `lat`, `lon` | — |
+| Electrical                | `ac_island` | `x` (reactance) |
+| Voltage-Aware             | `lat`, `lon`, `voltage`, `ac_island` | `x`, `type` |
+| Locational Marginal Price | `lmp` | — |
 
 ## Error Handling
 

diff --git a/docs/user-guide/partitioning/index.md b/docs/user-guide/partitioning/index.md
@@ -4,12 +4,13 @@ Partitioning divides a network into clusters of nodes based on distance metrics.
 
 ## Overview
 
-| Strategy Family | Distance Metric            | Use Case                                  |
-|-----------------|----------------------------|-------------------------------------------|
-| Geographical | Euclidean / Haversine      | Spatial proximity clustering              |
-| Electrical | PTDF-based                 | Electrical behavior clustering            |
-| VA Geographical | Geographical + Voltage aware | Multi-voltage-level networks              |
-| VA Electrical | PTDF + Voltage aware       | Multi-voltage-level electrical clustering |
+| Strategy Family                  | Distance Metric                   | Use Case                                  |
+|----------------------------------|-----------------------------------|-------------------------------------------|
+| Geographical                     | Euclidean / Haversine             | Spatial proximity clustering              |
+| Electrical                       | PTDF-based                        | Electrical behavior clustering            |
+| VA Geographical                  | Geographical + Voltage aware      | Multi-voltage-level networks              |
+| VA Electrical                    | PTDF + Voltage aware              | Multi-voltage-level electrical clustering |
+| Locational Marginal Prices (LMP) | Duals of nodal balance constraint | Congestion sensitive clustering           |
 
 ## Basic Usage
 
@@ -47,6 +48,7 @@ print(partition_result.strategy_name)
 
 geographical
 electrical
+locational_marginal_price
 va-geographical
 va-electrical
 ```
@@ -114,13 +116,14 @@ flowchart TD
 
 ### Recommendations by Use Case
 
-| Use Case                     | Recommended Strategy |
-|------------------------------|---------------------|
-| Geographical clustering      | `geographical_kmeans` |
-| Geographical with AC islands | `geographical_kmedoids_haversine` |
-| Electrical behavior grouping | `electrical_kmedoids` |
-| Multi-voltage network        | `va_geographical_kmedoids_haversine` |
-| Unknown cluster count        | `geographical_dbscan_*` or `geographical_hdbscan_*` |
+| Use Case                        | Recommended Strategy                                |
+|---------------------------------|-----------------------------------------------------|
+| Geographical clustering         | `geographical_kmeans`                               |
+| Geographical with AC islands    | `geographical_kmedoids_haversine`                   |
+| Electrical behavior grouping    | `electrical_kmedoids`                               |
+| Multi-voltage network           | `va_geographical_kmedoids_haversine`                |
+| Unknown cluster count           | `geographical_dbscan_*` or `geographical_hdbscan_*` |
+| Identification of Bidding Zones | `lmp_hierarchical_connected`                     |
 
 ### Performance Comparison
 

diff --git a/docs/user-guide/partitioning/locational_marginal_price.md b/docs/user-guide/partitioning/locational_marginal_price.md
@@ -0,0 +1,91 @@
+# Locational Marginal Price Partitioning
+
+Clusters nodes based on Locational Marginal Prices (LMP) or price profiles. The implementation of this strategy is based on the findings in the
+paper "Congestion-Sensitive Grid Aggregation for DC Optimal Power Flow" by B. Stöckl et al. ([2025](https://doi.org/10.1109/PowerTech59965.2025.11180585)).
+
+## Required Attributes
+
+- **Nodes**: `lmp` (can be a scalar value or a time-series vector/array)
+
+## Available Strategies
+
+| Strategy                     | Algorithm    | Description                                            | AC-Island Aware |
+|------------------------------|--------------|--------------------------------------------------------|-----------------|
+| `lmp_hierarchical_connected` | Hierarchical | Agglomerative clustering with connectivity constraints | Yes             |
+
+## Overview
+
+LMP-based partitioning is specifically designed for market-based network analysis. It identifies regions with similar price behaviors, which is often
+used to define bidding zones or identify structural congestion in a power system.
+
+The strategy calculates the Euclidean distance between LMP profiles (vectors of prices over time) to determine nodal similarity.
+
+### Connectivity Constraint
+
+By default, the `lmp_hierarchical_connected` strategy uses the graph's topology as a constraint. This ensures that resulting clusters are **spatially
+contiguous**—nodes in the same cluster must be physically connected in the network. This prevents "scattered" clusters that would be unrealistic for
+geographical price zones.
+
+### Mathematical Background
+
+Locational Marginal Prices (LMPs) are often used in power systems to reflect the cost of supplying the next increment of load at a specific location,
+considering generation costs and network constraints.
+
+In an optimal power flow (OPF) with a bus angle formulation, they can be mathematically defined as the dual variables $\lambda$ associated with the
+nodal balance constraints.
+
+In an optimal power flow implementation based on PTDF power flows, the LMPs can be expressed as:
+$$\mathrm{LMP} = \lambda_{slack} + \mathrm{PTDF}^\top (\bar\varphi - \underline\varphi)$$
+where:
+
+- $ \lambda_{slack} $ is the LMP at the slack bus (reference node).
+- $ \mathrm{PTDF} $ is the Power Transfer Distribution Factor matrix, which describes how power injections at nodes affect line flows.
+- $ \bar\varphi $ and $ \underline\varphi $ are the dual variables associated with the upper and lower line flow constraints, respectively.
+
+### AC-Island Awareness
+
+If the graph contains `ac_island` data (automatically set by `va_loader`), the strategy respects AC island boundaries by assigning infinite distance
+between nodes in different islands. This ensures that price zones do not span across asynchronous regions.
+
+## Usage
+
+```python
+import npap
+
+manager = npap.PartitionAggregatorManager()
+manager.load_data("va_loader", node_file="nodes.csv", line_file="lines.csv")
+
+# Partition based on LMP profiles
+partition = manager.partition(
+    strategy="lmp_hierarchical_connected",
+    n_clusters=15,
+    lmp_attr="lmp",  # Node attribute containing prices
+    use_connectivity=True  # Ensure contiguous clusters
+)
+```
+
+## Configuration
+
+The LMP strategy uses {py:class}`~npap.partitioning.locational_marginal_price.LMPConfig`:
+
+```python
+from npap.partitioning.locational_marginal_price import LMPConfig
+
+config = LMPConfig(
+    hierarchical_linkage="complete",
+    infinite_distance=1e4,
+    use_connectivity=True
+)
+
+partition = manager.partition(
+    "lmp_hierarchical_connected",
+    n_clusters=15,
+    config=config
+)
+```
+
+| Parameter              | Default      | Description                                                   |
+|------------------------|--------------|---------------------------------------------------------------|
+| `hierarchical_linkage` | `"complete"` | Linkage criterion for hierarchical clustering.                |
+| `infinite_distance`    | `1e4`        | Value used to separate nodes in different AC islands.         |
+| `use_connectivity`     | `True`       | Whether to use graph connectivity as a clustering constraint. |
diff --git a/npap/managers.py b/npap/managers.py
@@ -14,7 +14,7 @@
     TopologyStrategy,
 )
 from npap.logging import LogCategory, log_debug, log_info, log_warning
-from npap.partitioning import VAElectricalDistancePartitioning
+from npap.partitioning import LMPPartitioning, VAElectricalDistancePartitioning
 
 
 class InputDataManager:
@@ -156,6 +156,9 @@ def _register_default_strategies(self) -> None:
             algorithm="hierarchical"
         )
 
+        # Locational Marginal Price partitioning strategies
+        self._strategies["lmp_hierarchical_connected"] = LMPPartitioning(algorithm="hierarchical")
+
         log_debug("Registered default partitioning strategies", LogCategory.MANAGER)
 
 

diff --git a/npap/partitioning/__init__.py b/npap/partitioning/__init__.py
@@ -18,12 +18,14 @@
 
 from .electrical import ElectricalDistancePartitioning
 from .geographical import GeographicalPartitioning
+from .locational_marginal_price import LMPPartitioning
 from .va_electrical import VAElectricalDistancePartitioning
 from .va_geographical import VAGeographicalPartitioning
 
 __all__ = [
     "ElectricalDistancePartitioning",
     "GeographicalPartitioning",
+    "LMPPartitioning",
     "VAElectricalDistancePartitioning",
     "VAGeographicalPartitioning",
 ]