Research on the impact mechanism of power structure based on green certificate for renewable energy investment decision in the electricity supply chain

Jingwen Liao

doi:10.3934/jimo.2024091

Article Contents

2025, Volume 21, Issue 1: 474-503. Doi: 10.3934/jimo.2024091

This issue Previous Article Parameter optimization of a novel grey model EGM(1, 1, $ \sum{} $t^c) and its application in China's GDP per capita prediction Next Article Integration of pricing, sustainability and 3PL delivery time according to freshness date in a dual-channel fruit supply chain: A game theoretic approach

Research on the impact mechanism of power structure based on green certificate for renewable energy investment decision in the electricity supply chain

Jingwen Liao^,

School of Business, Hong Kong Baptist University, China

Corresponding author: Jingwen Liao
Corresponding author: Jingwen Liao

Received: June 2022

Revised: September 2023

Early access: July 2024

Published online: July 2024

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Abstract

This paper constructs a two-level electricity supply chain consisting of the generator and retailer based on the green certificate mechanism (GCM) and considers three different power structure models, including the Vertical Nash game model (model A), the Generator Stackelberg game model (model B), and the Retailer Stackelberg game model (model C), to investigate the renewable energy investment decision problem in the electricity generator under GCM. By comparing the optimal equilibrium solutions under three different power structures, the renewable energy investment is lowest in model B. When the renewable energy preference coefficient is small, the renewable energy investment is maximum in model A. When the renewable energy preference coefficient is large, the renewable energy investment is maximum in model C. When the renewable energy preference coefficient is small, the leader’s profit is the largest, and the follower’s profit is the smallest. Furthermore, the increase in the renewable energy preference coefficient will lead to an increase in the renewable energy investments, electricity prices, and retailer profits, while the increase in the renewable energy cost coefficient will be the opposite. Therefore, the government should encourage electricity enterprises to research and develop technologies related to renewable energy.

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Mathematics Subject Classification: Primary: 91A35, 91B06; Secondary: 91B32.

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Figure 1. Structure of the electricity supply chain.

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Figure 2. Impact of t on $ w^{Z*} $.

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Figure 3. Impact of t on $ p^{Z*} $.

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Figure 4. Impact of t on $ q^{Z*} $.

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Figure 5. Impact of t on $ k^{Z*} $.

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Figure 6. Impact of t on $ \pi_{m}^{Z*} $.

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Figure 7. Impact of t on $ \pi_{r}^{C*} $.

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Figure 8. Impact of t on $ \pi_{m}^{Z*} + \pi_{r}^{Z*} $.

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Table 1. Notations.

Notations	Descriptions
Model parameters
a	Potential market demand.
b	Renewable energy preference coefficient, where $ 0< b< 1 $.
c	Renewable energy investment cost coefficient.
d	Traditional energy production cost coefficient.
t	Green certificate unit price.
r	Renewable energy electricity proportion, where $ 0< r< 1 $.
$ \alpha $	Electricity price elasticity coefficient, where $ 0< \mathit{\alpha}< 1 $.
Decision variables
w	Wholesale electricity price, $ w> c $.
k	Renewable energy investment.
p	Retail electricity price, $ p> w $.
Other notations
q	Electricity demand.
$ \pi_{m} $, $ \pi_{r} $	Generator and retailer’s profits.

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