As the global energy landscape shifts toward low-carbon transformation, the integration of renewable energy sources like wind and solar into power systems has gained significant attention. In China, the rapid adoption of electric vehicles (EVs) presents a unique opportunity to enhance the flexibility and efficiency of integrated energy systems (IES). Electric vehicles, particularly in the context of China’s EV market, can serve as mobile energy storage units, contributing to grid stability and renewable energy utilization. This paper explores the coordination of multi-park IES, incorporating industrial parks, residential areas, and charging stations, with a focus on leveraging electric vehicle clusters for optimized energy management. We propose a cooperative framework based on game theory to address energy trading and benefit distribution among these entities, ensuring both overall system efficiency and individual cost reductions.
The development of IES has been driven by the need to improve energy flexibility and reduce carbon emissions. In China, electric vehicles are becoming increasingly prevalent, with policies supporting their integration into the energy grid. China EV initiatives emphasize the role of EVs in demand response and energy storage, making them a critical component in modern IES. By considering electric vehicle batteries as distributed storage resources, we can enhance the system’s ability to handle peak loads and integrate intermittent renewables. This approach aligns with China’s goals for sustainable energy development and carbon neutrality.
In this study, we model a multi-park IES consisting of three main operators: Industrial Park Operator (IPO), Residential Park Operator (RPO), and Charging Station Operator (PSO). The IPO is equipped with combined heat and power (CHP) units, wind turbines (WT), gas boilers (GB), and batteries, while the RPO relies on electric boilers (EB) and batteries. The PSO manages electric vehicle charging and discharging, treating EVs as a collective energy storage system. The cooperation among these operators is formulated using Nash bargaining theory to ensure fair benefit distribution, and the alternating direction method of multipliers (ADMM) is employed for distributed solving to protect privacy. Our results demonstrate that this cooperative strategy significantly reduces overall costs and enhances system independence compared to independent operation.
System Framework and Modeling
The proposed multi-park IES framework integrates energy flows among industrial parks, residential areas, and charging stations. Each operator has distinct energy generation and consumption profiles. The IPO utilizes CHP units coupled with carbon capture systems (CCS) and power-to-gas (P2G) devices to reduce emissions, while the RPO depends on electricity purchases and local generation. The PSO aggregates electric vehicle charging loads, enabling V2G (vehicle-to-grid) services to participate in peak shaving and valley filling. This structure allows for spatial and temporal complementarity, improving overall energy efficiency. The electric vehicle cluster, representing China EV trends, acts as a flexible resource, adjusting charging patterns based on grid conditions and renewable availability.
Key equipment models include the CHP system with CCS and P2G, which can be represented mathematically. For instance, the CHP output is given by:
$$ P_{\text{CHP},t} = V_{\text{CHP},t} \eta_{\text{CHP}} Q_{\text{CH4}} $$
where \( P_{\text{CHP},t} \) is the power generation, \( V_{\text{CHP},t} \) is natural gas consumption, \( \eta_{\text{CHP}} \) is efficiency, and \( Q_{\text{CH4}} \) is the lower heating value of natural gas. The integration of P2G and CCS modifies the output, as shown in:
$$ V_{\text{P2G},t} = \alpha P_{\text{P2G},t} $$
$$ W_{\text{CO2},t} = \beta P_{\text{P2G},t} $$
$$ P_{\text{CCS},t} = \frac{W_{\text{CO2},t}}{\chi} $$
Here, \( \alpha \) and \( \beta \) are conversion coefficients, and \( \chi \) is the efficiency of CCS. Constraints such as power limits ensure feasible operation:
$$ P_{\text{min}}^{\text{P2G}} \leq P_{\text{P2G},t} \leq P_{\text{max}}^{\text{P2G}} $$
$$ P_{\text{min}}^{\text{CCS}} \leq P_{\text{CCS},t} \leq P_{\text{max}}^{\text{CCS}} $$
For electric vehicles, the daily driving distance and charging behavior are modeled using probability distributions. The energy consumption of an electric vehicle is:
$$ P_{\text{EVs}}^{\text{cons}} = \kappa D $$
where \( \kappa \) is energy consumption per km, and \( D \) is the distance traveled. The charging power of the EV cluster at time \( t \) is aggregated as:
$$ P_{\text{EVs},t} = \sum_{n=1}^{N} P_{\text{EV,ch},n,t}^{\text{rated}} $$
with constraints on state of charge (SOC):
$$ E_{\text{EV},n,t} = E_{\text{EV},n,t-1} + \eta_{\text{EV,ch}} P_{\text{EV,ch},n,t} – \eta_{\text{EV,dis}} P_{\text{EV,dis},n,t} $$
$$ E_{\text{min}} \leq E_{n,t} \leq E_{\text{max}} $$
These models highlight the role of electric vehicles in providing storage and flexibility, which is crucial for China’s EV integration strategies.
Scheduling Optimization Model
The optimization aims to minimize total costs, including energy purchase, operation, maintenance, carbon emissions, and demand response costs. The objective function for each operator \( i \) is:
$$ \min C_i = C_i^{\text{P2P}} + C_i^{\text{TRAD}} + C_i^{\text{OPE}} + C_i^{\text{CO2}} + C_i^{\text{DR}} $$
where:
$$ C_i^{\text{P2P}} = \sum_{t}^{T} \sum_{j}^{I} c_{i-j}^{\text{P2P},t} P_{i-j}^{\text{P2P},t} \Delta t $$
$$ C_i^{\text{TRAD}} = \sum_{t}^{T} (c_{\text{grid},t} P_{\text{grid},t} + c_{\text{fuel}} P_{\text{fuel},t}) \Delta t $$
$$ C_i^{\text{OPE}} = \sum_{n}^{N} \sum_{t}^{T} c_n P_{t,n} \Delta t $$
$$ C_i^{\text{CO2}} = \varepsilon_t (W_t^0 – W_t) $$
$$ C_i^{\text{DR}} = \sum_{t}^{T} (\lambda_{e,\text{cut}} P_{e,t,\text{cut}} + \lambda_{e,\text{tran}} P_{e,t,\text{tran}} + \lambda_{h,\text{cut}} P_{h,t,\text{cut}} + \lambda_{h,\text{tran}} H_{h,t,\text{tran}}) $$
Carbon emission mechanisms are incorporated to align with China’s carbon reduction goals. The initial carbon quota and actual emissions are calculated as:
$$ W_t^0 = \kappa_e P_{\text{buy},t} + \kappa_h (P_{\text{CHP},t} + h_m H_{\text{CHP},t} + H_{\text{GB},t}) $$
$$ W_t = \sum_{t}^{T} [a_1 (P_{E,t} + h_m H_{\text{CHP},t}) + b_1 H_{\text{CB},t} + c_1 + (a_2 + b_2 P_{\text{buy},t} + c_2 P_{\text{buy},t}^2) – C_{\text{cc},t}] $$
Constraints include power balance and transaction limits:
$$ P_{\text{P2P},t}^{i-j} \leq P_{\text{max}}^{\text{P2P}} $$
$$ \sum_{j} P_{\text{P2P},t}^{i-j} = 0 $$
This model ensures that energy transactions among operators are feasible and beneficial, with electric vehicles playing a key role in balancing supply and demand.
Nash Bargaining Model and Solution
To handle benefit distribution in the cooperative framework, we use Nash bargaining theory. The standard Nash bargaining model is:
$$ \max \prod_{i=1}^{I} (C_i^0 – C_i) \quad \text{subject to} \quad C_i^0 \geq C_i $$
where \( C_i^0 \) is the cost without cooperation. This non-convex problem is decomposed into two subproblems: coalition cost minimization (P1) and benefit allocation (P2). For P1:
$$ \text{(P1)} \quad \min -\sum_{i=1}^{I} C_i $$
and for P2:
$$ \text{(P2)} \quad \min \sum_{i}^{I} -\ln(C_i^0 – C_i – C_{\text{TRAD}}) $$
We solve these using ADMM to preserve privacy. The Lagrangian for P1 is:
$$ L_{\text{P1}}^{i-j,t} = C_N^i + \sum_{j}^{I} \sum_{t=1}^{T} \lambda_{1}^{i-j} (P_{\text{P2P},i-j,t} + P_{\text{P2P},j-i,t}) + \sum_{j}^{I} \frac{\rho_1}{2} \sum_{t=1}^{T} \| P_{\text{P2P},i-j,t} + P_{\text{P2P},j-i,t} \|_2^2 $$
Iterative updates are performed until convergence. Similarly, for P2, the Lagrangian is:
$$ L_{\text{P2}}^{i-j,t} = -\ln(C_i^0 – C_i + U_i^0) + \sum_{j \neq i}^{I} \sum_{t=1}^{T} \left[ \lambda_{2}^{i-j} (c_{\text{P2P},i-j,t} + c_{\text{P2P},j-i,t}) + \frac{\rho_2}{2} \sum_{t=1}^{T} \| c_{\text{P2P},i-j,t} + c_{\text{P2P},j-i,t} \|_2^2 \right] $$
This approach ensures that all operators, including those managing electric vehicle resources, achieve fair cost reductions while maintaining data privacy.
Case Study and Results
We simulate the proposed framework using real-world data from China, including load profiles and renewable generation. The convergence of the ADMM algorithm for the coalition cost minimization problem is shown to be efficient, reaching stability after 116 iterations. In terms of scheduling, the cooperative strategy allows for better utilization of wind power and CHP units. For instance, the IPO can supply excess wind energy to other operators during off-peak hours, reducing overall costs. The electric vehicle cluster, managed by the PSO, provides flexibility by adjusting charging schedules based on renewable availability.
Key results include a significant reduction in total costs. The following table summarizes cost comparisons between independent and cooperative operation:
| Cost Type | Independent Operation (USD) | Cooperative Operation (USD) | Cost Reduction (USD) |
|---|---|---|---|
| IPO | 149,165.8 | 25,320.7 | 123,845.1 |
| RPO | 54,095.6 | 51,703.0 | 2,392.6 |
| PSO | 7,810.2 | 7,347.9 | 462.3 |
| Total | 211,071.6 | 84,371.7 | 126,699.9 |
Additionally, the independence performance index (IPI) improves from 0.4734 to 0.621, indicating reduced reliance on the external grid. The Nash bargaining results show that transaction prices between operators fall within grid buying and selling prices, ensuring mutual benefits. For example, the energy trading price between RPO and IPO varies with time, but always remains advantageous compared to grid tariffs.
The integration of electric vehicles in China EV contexts proves crucial in achieving these outcomes. By acting as mobile storage, electric vehicles help shave peak loads and store excess renewable energy, contributing to system stability. This aligns with China’s policies promoting electric vehicle adoption and smart grid development.
Conclusion
In this paper, we present a cooperative framework for multi-park integrated energy systems that incorporates electric vehicle clusters as flexible resources. The use of Nash bargaining and ADMM ensures fair benefit distribution and privacy preservation. Our case study demonstrates that cooperation leads to significant cost reductions and improved system independence. The role of electric vehicles, especially in the context of China EV growth, is highlighted as a key enabler for renewable integration and energy efficiency. Future work could explore dynamic pricing mechanisms and larger-scale implementations to further enhance the benefits of electric vehicle participation in IES.
The rapid expansion of China’s electric vehicle market offers immense potential for energy system optimization. By leveraging electric vehicle batteries for storage and grid services, we can address challenges related to renewable intermittency and peak demand. This approach not only supports China’s carbon neutrality goals but also paves the way for smarter, more resilient energy networks. As electric vehicle technology evolves, their integration into IES will become increasingly important, making studies like this essential for sustainable energy planning.