🤝 Making P2P Energy Trading Fair and Grid-Friendly

Peer-to-peer (P2P) energy trading lets small consumers and generators exchange electricity directly.
It promotes community energy use and renewable adoption — but it also interacts with the physical grid.

The problem?

Some trades stress the grid far more than others — but current pricing treats everyone the same.

This can cause:

• voltage violations
• line congestion
• increased system losses
• unfair cost allocation

This paper introduces a causality-based cost allocation method that charges peers based on the actual impact their trades impose on the distribution network.
:contentReference[oaicite:1]{index="1"}

---

⚡ Why Existing Approaches Fall Short

1) Universal cost allocation

Everybody pays the same network fee per MWh.

Issue:
Harmless peers subsidize harmful ones → inefficient & unfair.

---

2) Distance or zone-based fees

Uses electrical distance or geographic grouping.

Issue:
Distance ≠ actual physical impact.

---

3) Hard constraints

Block trades that violate limits.

Issue:
Overly restrictive → reduces social welfare.

---

🧠 Key Idea: Charge Peers According to Causality

Instead of spreading costs evenly, the proposed method:

1. Observes each trade.
2. Measures how much that trade changes:
   • system losses
   • line flows
   • voltage profiles
3. Allocates cost only to the peers responsible for those impacts.
4. Lets peers optimally respond to network-aware price signals.

Result:

Grid-friendly trades are encouraged; harmful trades become naturally discouraged.

No bans.
No one-size-fits-all fees.
Just physics-based, causal pricing.

---

🧮 Core Mathematical Formulation (Expert View)

The core of the model is the network-constrained welfare maximization problem, with causality-based marginal-impact pricing derived from it.

Let each seller ( s ) and buyer ( b ) engage in bilateral trading ( t_{sb} ).

1. Social-welfare maximization

where:

• (U_b) = buyer utility
• (C_s) = seller cost
• (f_k(\mathbf{t})) = network constraint (loss, voltage, congestion)
• (\lambda_k) = dual variable (shadow price)

2. DistFlow or linearized power-flow constraints

3. Causality-based marginal impact

For each trade ( t_{sb} ), the method computes its incremental physical impact:

Examples:

• Loss impact:
• Voltage impact:

4. Causal network charge

In words:
The price charged to a trade equals the value of the network constraints it pushes.

This ensures that:

• trades that cause congestion pay congestion
• trades that cause voltage drop pay voltage penalties
• harmless trades pay very little

This is the mathematical heart of the paper and what differentiates the approach from conventional averaging-based fees.
:contentReference[oaicite:2]{index="2"}

---

🗺 Test System: IEEE 33-Bus Feeder

---

📊 Scenario 1 — Only Loss Cost Considered

Table omitted for brevity – included in original content.

Key takeaways:

• Universal charges cause everyone to trade less.
• Causality-based charges reduce only loss-causing trades.
• System loss ↓ 22.8%
• Welfare ↑ most of all policies

---

📈 Scenario 2 — Voltage, Congestion, and Loss

Table omitted for brevity – included in original content.

Interpretation:

• Universal policy overreacts → grid underutilized.
• Causality-based pricing identifies who causes violations.
• Harmful trades shrink; beneficial trades grow.
• Welfare preserved while grid stays secure.

---

🔧 Who Actually Pays More?

The method identifies:

• Peers near bottleneck lines → congestion charges
• Peers causing undervoltage → voltage charges
• Peers with minimal impact → low or zero charge

This is true cost causation, not heuristic spreading.

---

💡 Why This Matters

✔ Fair

Peers pay based on their actual physical impact.

✔ Efficient

Trading adjusts intelligently instead of shrinking uniformly.

✔ Higher welfare

Good trades increase; harmful ones reduce.

✔ Ensures grid security

Voltage & congestion remain within limits — without blocking trades.

✔ Better than universal allocation

Universal = simple but inefficient.
Causality-based = principled + practical.

---

📘 Reference

Kim, H. J., Song, Y. H., & Kim, J. “Causality-based cost allocation for peer-to-peer energy trading in distribution system.” Electric Power Systems Research, 2024. [link]