In modern urban environments, the rapid growth of commercial districts has led to significant challenges in managing electricity load due to fluctuating demand patterns. As a researcher focused on smart grid technologies, I have been exploring innovative ways to leverage electric car fleets as distributed energy resources to address these issues. The integration of electric car charging and discharging strategies into commercial areas offers a promising solution for load balancing, especially in regions like China where EV adoption is accelerating. This article presents a comprehensive model that utilizes electric car behavior to optimize power supply and demand, incorporating mathematical formulations, algorithmic improvements, and empirical simulations to demonstrate its efficacy.
The core idea revolves around treating electric cars as mobile storage units that can inject or withdraw power from the grid based on real-time load conditions. Commercial districts typically experience peak loads during evenings and weekends, aligning with high pedestrian traffic. By strategically planning charging pile locations and implementing incentive-based discharging policies, we can harness the potential of China EV populations to smooth load curves and reduce operational costs. In this study, I develop a planning model that combines imperial competition algorithms with reversible optimizations to adapt to dynamic changes in人流 and load patterns. The following sections detail the methodology, experimental analysis, and conclusions, supported by formulas and tables to encapsulate key insights.
To begin, the foundation of this research lies in modeling the state of charge (SOC) dynamics for electric car fleets within a commercial district. Consider a discrete-time framework where the SOC of the aggregate system evolves based on charging and discharging activities. Let \( SOC_s(t_k) \) represent the SOC at time \( t_k \), and \( SOC_s(t_{k+1}) \) at the next time step. The dynamic model is given by:
$$ SOC_s(t_{k+1}) = SOC_s(t_k) + \frac{t}{E_i} \left[ n_t(t_k) Q_i(t_k) – (1 – n_t(t_k)) \delta_t(t_k) \right] $$
Here, \( E_i \) denotes the battery capacity of the i-th electric car, \( Q_i(t_k) \) is the charging or discharging power, \( n_t(t_k) \) is the parking lot utilization rate, and \( \delta_t(t_k) \) is the average energy consumption per vehicle. This equation captures the net change in SOC due to controlled charging and natural drainage, which is essential for simulating the impact of electric car fleets on grid stability.
Next, the planning of charging pile locations is critical for maximizing efficiency. Based on pedestrian flow and load convergence, I define an initial pile location matrix \( P_{\text{location}} \) that categorizes piles by capacity and position. Let \( P_m(X,Y) \) represent a high-capacity pile (e.g., 2,500 kVA) in zone X and charging area Y, and \( p_n(x,y) \) denote a low-capacity pile (e.g., 800 kVA) in zone x and area y. The matrix is structured as:
$$ P_{\text{location}} = \begin{bmatrix} P_1(X,Y) \\ \vdots \\ P_m(X,Y) \\ p_1(x,y) \\ \vdots \\ p_n(x,y) \end{bmatrix}^T $$
This matrix serves as the basis for optimizing pile placement using the imperial competition algorithm. The normalized influence value \( C_n \) for each pile is computed to prioritize areas with higher potential usage:
$$ C_n = c_n – \max_i \{ c_i \} $$
where \( c_n \) is a function of pedestrian flow and load demand. The relative influence \( P_n \) and pile efficiency \( N \) are then derived as:
$$ P_n = \frac{C_n}{\sum_{i=1}^{N_{\text{imp}}} C_i} $$
$$ N = \text{round}(P_{\text{location}} \cdot N_{\text{col}}) $$
In this context, \( N_{\text{col}} \) represents the number of electric cars, and the round function ensures integer values for practical implementation. The imperial competition algorithm iteratively refines these values to enhance pile utilization, but I have introduced a reversible improvement to account for evolving commercial district dynamics. The total influence \( TP_n \) is defined as:
$$ TP_n = \text{Cost}(\text{imperialist}_n) + \xi \cdot \text{mean}\{\text{Cost}(\text{colonies})\} $$
where \( \xi \) is a weighting factor. To further optimize this, I incorporate a discount strategy that links customer participation to economic incentives. The probability of a customer engaging in charging or discharging, denoted as \( g(\text{click} = 1) \), is modeled using a logistic regression:
$$ \ln \left[ \frac{g(\text{click} = 1)}{1 – g(\text{click} = 1)} \right] = \alpha_0 + \alpha_1 \text{duration} + \alpha_2 \text{acquisition} + \alpha_3 \text{frequency} + \alpha X_i $$
Here, \( \alpha_0 \) to \( \alpha_3 \) are coefficients, and \( X_i \) are control variables such as time of day or promotional offers. This allows for a more adaptive planning approach, leading to a modified influence value \( TP’_n \):
$$ TP’_n = \frac{\text{Cost}(\text{imperialist}_n)}{g} + \xi \cdot \text{mean}\{\text{Cost}(\text{colonies})\} $$
where \( g \) is derived from the participation probability. This reversible adjustment ensures that the model can respond to shifts in人流 and load patterns over time.
The economic aspect is equally important, as the commercial district aims to minimize costs while maintaining reliability. The revenue \( R \) generated from energy transactions with electric car fleets is formulated as:
$$ R = \Delta t \sum_{k=1}^{N-1} \left\{ -\gamma(t_k) P_{\text{EV}}(t_k) + \gamma(t_k) \phi(t_k) P_{\text{EV}}(t_k) + \gamma(t_k) \phi(t_k) \kappa[P_{\text{EV}}(t_k)] P_{\text{EV}}(t_k) \right\} $$
In this equation, \( \gamma(t_k) \) is the electricity market price at time \( t_k \), \( \phi(t_k) \) is the price differential for discharging, \( P_{\text{EV}}(t_k) \) is the power exchanged with electric cars, and \( \kappa[P_{\text{EV}}(t_k)] \) is a charging coefficient that equals 1 if \( P_{\text{EV}}(t_k) > 0 \) (charging) and 0 otherwise (discharging). This formulation accounts for both the costs of charging and the revenues from discharging, providing a comprehensive economic perspective.
To validate this model, I conducted simulations using real load data from a commercial district over one week. The district was powered by two 6 MW transformers, and the load profile exhibited typical peaks during evenings and weekends. The initial load data is summarized in Table 1, which shows the maximum load and peak-valley differences for each day.
| Day | Maximum Load (MW) | Peak-Valley Difference (MW) |
|---|---|---|
| Monday | 8.5 | 6.2 |
| Tuesday | 8.7 | 6.4 |
| Wednesday | 8.6 | 6.3 |
| Thursday | 9.0 | 6.8 |
| Friday | 9.8 | 7.5 |
| Saturday | 10.43 | 8.1 |
| Sunday | 9.5 | 7.2 |
As observed, the weekend peaks are particularly pronounced, with Saturday reaching 10.43 MW and a peak-valley difference of 8.1 MW. This underscores the need for effective load management strategies. In the simulation, I assumed that 20% of the electric car fleet participated in charging and discharging activities, with a discharging power of 2 kW per vehicle. The initial planning using the imperial competition algorithm yielded the results in Table 2, which compares the load metrics before and after implementation.
| Metric | Before Planning | After Planning | Improvement |
|---|---|---|---|
| Peak Load (MW) | 10.43 | 8.1 | 22.3% reduction |
| Peak-Valley Difference (MW) | 8.1 | 6.3 | 22.2% reduction |
These results demonstrate the potential of electric car integration, but further optimizations are possible. By applying the reversible improvements to the algorithm, which account for dynamic changes in人流 and load, the model achieves even better performance. The modified planning results are shown in Table 3, focusing on the critical weekend period.
| Time Period | Peak Load (MW) | Peak-Valley Difference (MW) | Notes |
|---|---|---|---|
| Saturday Evening | 6.1 | 4.6 | 35.8% peak reduction |
| Overall Week | 6.3 | 4.6 | 39% peak reduction from original |
The improved model reduces the peak load to 6.3 MW and the peak-valley difference to 4.6 MW, representing a 39% and 43.2% reduction, respectively. This significant enhancement highlights the importance of adaptive algorithms that can evolve with commercial district developments. Moreover, the economic benefits are substantial; based on Shanghai’s electricity pricing (peak: 1.2 CNY/kWh, valley: 0.3 CNY/kWh, and a buy-back rate of 0.5 CNY/kWh for discharged power), the commercial district can save approximately 4.13 MW of power for other uses, translating to lower operational costs and increased reliability.
In conclusion, the integration of electric car charging and discharging strategies into commercial districts offers a viable path toward load balancing and economic efficiency. The use of imperial competition algorithms, combined with reversible optimizations, enables robust planning that adapts to changing conditions. As China EV adoption continues to grow, such models will become increasingly relevant for urban energy management. Future work could explore real-time control systems and broader scalability to other urban areas, further harnessing the potential of electric car fleets as distributed energy resources.
Throughout this research, the focus on electric car technologies has underscored their role in modern smart grids. By repeatedly emphasizing terms like electric car and China EV, this study aligns with global trends in sustainable transportation. The formulas and tables provided here serve as a foundation for further investigations, encouraging the adoption of similar strategies in diverse commercial environments. Ultimately, the synergy between electric car mobility and energy systems promises a more resilient and efficient urban future.