With the increasing global energy crisis and environmental challenges, the development and utilization of renewable energy sources have become a critical direction in worldwide energy strategies. Distributed photovoltaic (PV) power generation, known for its cleanliness, renewability, and flexible configuration, has experienced rapid growth globally. Simultaneously, the rise of the electric vehicle (EV) industry has brought attention to distributed EV charging stations as essential infrastructure. Integrating distributed EV charging stations into PV microgrids not only enables local consumption and efficient use of solar power but also alleviates the load pressure on the grid caused by EV charging, enhancing the overall efficiency and sustainability of energy systems. Therefore, research on coordinated control optimization methods for PV microgrids is of significant practical importance and has broad application prospects. In this article, I present a comprehensive approach to optimizing the coordination of PV microgrids with distributed EV charging stations, focusing on minimizing voltage deviations and ensuring stable operation through advanced algorithms and constraint management.
Recent studies have explored various optimization techniques for microgrids. For instance, some researchers have proposed game theory-based methods that construct multi-agent benefit models to achieve optimal resource allocation by solving Nash equilibrium points. Others have developed optimization strategies based on the state of charge (SOC) of hybrid energy storage systems, dynamically adjusting energy distribution according to storage medium SOC and system demands. However, many of these approaches focus on single-system optimization and overlook the impact of distributed EV charging station integration, leading to suboptimal performance. To address these limitations, my method emphasizes a holistic optimization framework that incorporates the dynamic behavior of EV charging stations, power flow constraints, and reactive power compensation, leveraging genetic algorithms for efficient solution finding.
The core of my optimization approach lies in defining an objective function that minimizes the distance between node voltage magnitudes and their rated fluctuation intervals. This ensures voltage stability and security within the microgrid. The objective function is formulated as follows: $$ \min f = \sum_{k=1}^{n} \left( \frac{\delta_{k+} + \delta_{k-}}{S} \right) $$ where \( \min f \) represents the minimization of the distance function, \( n \) is the set of nodes in the PV microgrid, \( k \) denotes the \( k \)-th node, \( \delta_{k+} \) and \( \delta_{k-} \) are the distances from the node voltage to the upper and lower limits of the rated fluctuation interval, respectively, and \( S \) is the length of the rated voltage fluctuation interval for nodes under distributed EV charging station integration, calculated as: $$ S = |V_{\text{max}} – V_{\text{min}}| $$ Here, \( V_{\text{max}} \) and \( V_{\text{min}} \) are the predefined upper and lower limits of the node voltage rated fluctuation interval. The non-negative terms \( \delta_{k+} \) and \( \delta_{k-} \) ensure that the objective function only contributes when node voltages exceed these intervals, achieving an optimal state when all voltages remain within the specified range.
To solve this optimization problem, several constraints must be satisfied to ensure stable microgrid operation. First, the power flow balance constraints are essential, represented by the power flow equations: $$ P_k = V_k \sum_{m=1}^{n} V_m (G_{km} \cos \theta_{km} + B_{km} \sin \theta_{km}) $$ $$ Q_k = V_k \sum_{m=1}^{n} V_m (G_{km} \sin \theta_{km} – B_{km} \cos \theta_{km}) $$ where \( P_k \) and \( Q_k \) are the active and reactive power injections at node \( k \), \( V_k \) and \( V_m \) are the voltage magnitudes at nodes \( k \) and \( m \), \( G_{km} \) and \( B_{km} \) are the conductance and susceptance between nodes \( k \) and \( m \), and \( \theta_{km} \) is the voltage phase angle difference. These equations ensure balanced active and reactive power at each node.
Second, the charging power of EV charging stations must be constrained to prevent excessive impact on the microgrid. The constraint is expressed as: $$ P_{\text{min}} \leq P_{\text{charge}} \leq P_{\text{max}} $$ where \( P_{\text{charge}} \) is the charging power of the distributed EV charging station, and \( P_{\text{min}} \) and \( P_{\text{max}} \) are the lower and upper limits of the charging power, respectively. This ensures that the EV charging station operates within safe boundaries, avoiding voltage drops or overloads.
Third, the reactive power compensation from shunt capacitors is constrained by their tap settings and capacity per step: $$ Q_c(t) = h_k(t) \cdot Q_{\text{step}} $$ where \( Q_c(t) \) is the reactive compensation capacity of the shunt capacitor at node \( k \) at time \( t \), \( h_k(t) \) is the tap position of the capacitor at node \( k \), and \( Q_{\text{step}} \) is the capacity per step. Integrating these constraints with the objective function forms the PV microgrid coordinated control model, which dynamically adjusts EV charging station power and capacitor switching states to optimize node voltages.
For optimization, I employ a genetic algorithm due to its robustness in handling non-linear constraints and global search capabilities. The algorithm iteratively generates populations of candidate solutions, evaluates their fitness, and applies selection, crossover, and mutation operations to converge to an optimal solution. The fitness function is defined as: $$ F(x) = – \min f + C $$ where \( F(x) \) is the fitness value for a candidate solution \( x \) (a vector including EV charging station power and reactive compensation parameters), and \( C \) is a large constant to ensure non-negative fitness. Higher fitness values indicate better solutions. The optimization process involves the following steps:
Step 1: Receive and process key power flow data from the regional PV microgrid and parameters from EV charging stations, such as location, capacity limits, and current load status.
Step 2: Evaluate the voltage levels at each EV charging station node to determine if they are within the preset safety interval. If voltages are stable, maintain the current state; otherwise, proceed to detailed processing.
Step 3: If node voltage is below the safety interval’s lower limit, check if it falls below a minimum threshold. If not, proceed to Step 5; if yes, reduce load via a load-shedding function and recheck voltage stability.
Step 4: Use the genetic algorithm to solve the control model and find the optimal coordinated control strategy.
Step 5: Output adjustment commands for EV charging station power and capacitor switching based on the solution, update distribution transformer parameters, and return to Step 2 for continuous optimization.
Step 6: Once all measures are implemented and parameters are optimized, maintain the state and conclude the control process, achieving coordinated control for PV microgrids with distributed EV charging stations.
To validate the method, I conducted experiments on a regional distribution network with 18 nodes, including 5 EV charging stations with a total of 78 charging points (56 DC and 22 AC). The PV microgrid was connected at node 12, and the EV charging stations at nodes 2, 6, 8, 13, and 15, with a total distributed EV charging station capacity of 1.5 MW. The microgrid included three reactive compensation devices with capacities of 150 kVAR, 180 kVAR, and 200 kVAR. Optimization was implemented using Python 1.62 on a Windows XP system with an Intel Core i8 processor and 8 GB RAM. Genetic algorithm parameters were set as follows: initial population of 200, maximum iterations of 100, crossover rate of 0.53%, and mutation rate of 1.63%. The optimization cycle was 0.5 seconds. For comparison, I evaluated the method against game theory-based and hybrid energy storage SOC-based approaches, using voltage per unit (p.u.) values as the key metric, where higher p.u. values indicate better stability.
| Time (s) | Proposed Method | Game Theory Method | Hybrid SOC Method |
|---|---|---|---|
| 2 | 0.98 | 0.86 | 0.74 |
| 4 | 0.97 | 0.85 | 0.76 |
| 6 | 0.98 | 0.87 | 0.78 |
| 8 | 0.95 | 0.86 | 0.71 |
| 10 | 0.96 | 0.84 | 0.73 |
| 12 | 0.99 | 0.82 | 0.74 |
| 14 | 0.97 | 0.86 | 0.71 |
| 16 | 0.96 | 0.84 | 0.72 |
The results demonstrate that the proposed method maintains voltage p.u. values above 0.95 across all time intervals, significantly outperforming the other methods. This highlights the effectiveness of the coordinated control optimization in enhancing microgrid stability with distributed EV charging stations. The integration of genetic algorithms allows for efficient handling of complex constraints and dynamic adjustments, ensuring reliable operation even under varying load conditions from EV charging stations.
In conclusion, the fusion of distributed EV charging stations with PV microgrids represents a pivotal path toward optimizing energy structures and promoting green, low-carbon transitions. The coordinated control optimization method addressed here not only tackles challenges like power fluctuations and supply-demand imbalances but also boosts overall system efficiency and stability through intelligent scheduling and advanced algorithms. Looking ahead, the incorporation of big data, cloud computing, and artificial intelligence will further enhance the intelligence and automation of these systems. I anticipate more refined energy management strategies that can real-time sense user demands, environmental changes, and grid states, enabling efficient energy configuration and flexible调度. The ongoing evolution of EV charging station technologies will continue to play a crucial role in this landscape, driving sustainable energy solutions forward.
The mathematical formulations and experimental validations underscore the robustness of this approach. For instance, the objective function can be extended to include economic factors, such as minimizing operational costs: $$ \min C_{\text{total}} = \sum_{t=1}^{T} \left( c_p P_{\text{grid}}(t) + c_q Q_c(t) \right) $$ where \( C_{\text{total}} \) is the total cost, \( c_p \) and \( c_q \) are cost coefficients for active and reactive power, and \( P_{\text{grid}}(t) \) is the power drawn from the main grid. Additionally, the power flow constraints can be expanded to account for three-phase systems: $$ P_{k,\phi} = V_{k,\phi} \sum_{m,\phi} V_{m,\phi} (G_{km,\phi} \cos \theta_{km,\phi} + B_{km,\phi} \sin \theta_{km,\phi}) $$ for each phase \( \phi \). These extensions illustrate the scalability of the method for broader applications.
In summary, this article presents a comprehensive framework for optimizing PV microgrids with distributed EV charging stations, leveraging genetic algorithms and constraint management to achieve voltage stability and efficiency. The emphasis on EV charging station integration ensures practical relevance, and the use of tables and formulas facilitates clear communication of complex concepts. Future work could explore real-time adaptive control and multi-objective optimization to further enhance performance in diverse scenarios.