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Study on Electricity Settlement Mechanism Considering Competitiveness of Electricity Markets

Fangping Gao

Fangping Gao

Settlement Statistics Department, Guizhou Power Exchange CenterGuiyang, China
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Gao, Fangping
 und 
Mingli Li

Mingli Li

Settlement Statistics Department, Guizhou Power Exchange CenterGuiyang, China
Suche nach diesem Autor auf
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Li, Mingli
  
29. Sept. 2025
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Online veröffentlicht: 29. Sept. 2025
Eingereicht: 28. Dez. 2024
Akzeptiert: 23. Apr. 2025
DOI: https://doi.org/10.2478/amns-2025-1098
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© 2025 Fangping Gao et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Solution flow diagram of multi-agent reinforcement learning
Solution flow diagram of multi-agent reinforcement learning

Figure 2.

MADDPG framework
MADDPG framework

Figure 3.

Flowchart of MADDPG solving two-layer model
Flowchart of MADDPG solving two-layer model

Figure 4.

Distribution system of the IEEE33 nodes
Distribution system of the IEEE33 nodes

Figure 5.

Tripartite power supply game results of node 24 under different scenarios
Tripartite power supply game results of node 24 under different scenarios

Figure 6.

The three forces in each round at t=14
The three forces in each round at t=14

Figure 7.

Tripartite benefits in each round at t=14
Tripartite benefits in each round at t=14

Figure 8.

Convergence curve of quotation strategy variable αg,tstrategy$$\alpha_{g,t}^{strategy}$$ under two algorithms
Convergence curve of quotation strategy variable αg,tstrategy$$\alpha_{g,t}^{strategy}$$ under two algorithms

Figure 9.

Comparison of the best quotation strategy variable under the two algorithms
Comparison of the best quotation strategy variable under the two algorithms

Benefits and partial costs in different scenarios

DNO LA DGO
Profit/yuan Call gas turbine cost / yuan Purchase IL cost /yuan Safeguard the cost of the power supply / yuan Profit/ yuan IL sells electrical benefits/ yuan Profit/ yuan
Scene 1 2157.16 492.51 0 0 2074.31 0 2924.35
Scene 2 2341.52 468.84 0 0 2342.58 0 3116.24
Scene 3 2738.67 174.38 254.17 0 2617.63 254.17 3435.19

Tripartite game relationship

Players
Market transaction load number DNO DGO LA
8 √ × √
24 √ √ √
30 √ √ ×

Air storage and optical storage system parameters

ID Capacity/ (kW-kW·h) Maximum charge and discharge power /kW Minimum storage power / kW·h Charge and discharge efficiency Final period charge requirement / kW·h
8 PV-BESS 550-800 50 120 0.9 400
24 WG-BESS 700-800 70 60 0.9 300
PV-BESS 700-800 70 60 0.9 400
30 WG-BESS 550-800 60 60 0.9 300

Other simulation example parameters

Algorithm μ(αg,tstrategy)$$\mu \left( {\alpha_{g,t}^{strategy}} \right)$$($/MWh) σ(αg,tstrategy)$$\sigma \left( {\alpha_{g,t}^{strategy}} \right)$$($/MWh) Average round total return($) Standard deviation of total return($)
DQN 0.2617 0.04021 812.38 542.13
MADDPG 0.2504 0.03257 2206.52 478.24
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