With the rapid development of renewable energy, the deployment of photovoltaic (PV) inverters has grown exponentially. As the proportion of renewable energy capacity in the power grid increases, higher demands are placed on grid stability. Many aging solar inverters, particularly certain types of solar inverter, fail to meet the latest grid requirements due to inadequate grid compliance performance, including issues with maximum power point tracking (MPPT), automatic generation control (AGC) response rates, primary frequency response, and control accuracy. In this study, I explore strategies to enhance the internal response speed of inverters, optimize power distribution execution, and refine MPPT step sizes through algorithmic improvements. These measures aim to boost the response rate and power execution accuracy of older inverters, address grid adaptability issues, and increase the energy yield of aging PV power stations. Various types of solar inverter, such as those with outdated communication modules or primitive MPPT algorithms, are the focus of this research.
The grid compliance technical requirements for renewable energy plants, including wind farms and PV power stations, mandate that they utilize active power control systems or standalone control devices to implement active power-frequency droop control. This enables them to participate in rapid grid frequency regulation at the point of interconnection. The fast frequency response is characterized by an active power-frequency droop特性, achieved through a predefined piecewise function of frequency and active power, expressed as:
$$ P = P_0 – \frac{f – f_N}{f_N} \cdot \frac{P_N}{\delta} $$
where \( f_d \) represents the frequency response dead band, typically ranging from 49.94 Hz to 50.06 Hz for PV power stations. \( f_N \) is the system rated frequency, \( P_N \) is the rated power, \( P_0 \) is the initial active power value, and \( \delta \) is the frequency response droop rate, set at 3% for PV stations. The droop characteristic ensures that inverters adjust their output based on frequency deviations, enhancing grid stability. Key performance criteria include: a step disturbance in regulation target of at least 10% of rated output, frequency control deviation within ±1% of rated output, frequency measurement resolution no greater than 0.003 Hz, frequency sampling period not exceeding 100 ms, and an active power control cycle for fast frequency response of no more than 1 second. These requirements are critical for all types of solar inverter to ensure seamless integration into modern power systems.
To address grid compliance issues, I propose several enhancement schemes focused on improving the response times and control algorithms of aging inverters. One major challenge is the prolonged internal response delay in common types of solar inverter. Typically, these inverters use communication modules to receive external commands, which are then relayed via serial ports to power execution modules, resulting in reception and transmission delays of approximately 2 seconds. Upon receiving commands, the power execution unit often employs MPPT power ramp algorithms to track the target power, requiring an additional 1 to 3 seconds to reach the desired level. This cumulative delay hinders compliance with fast frequency response requirements, which demand response times under 2 seconds. For instance, in many older types of solar inverter, the communication pathway introduces significant latency that must be reduced to around 1 second through hardware or firmware upgrades. However, the power execution module’s response time, which is within 5 seconds, generally meets technical standards and does not require modification. By optimizing the internal communication protocols and processing speeds, we can significantly cut down delays, making these inverters more responsive to grid signals.
Another critical aspect is the active power optimization scheme for PV generation units. These units are commonly modeled as controlled current sources in electromechanical transient simulations, allowing decoupled control of active and reactive power. The reactive power control section can receive dynamic reactive power commands from the PV plant’s reactive power control system, calculating the reactive reference current \( I_{qcmd} \) based on control strategies. The implementation involves:
$$ I_{qcmd} = f(Q_{gen}, V_{\max}, V_{\min}, Q_{cmd}) $$
where \( Q_{gen} \) is the measured reactive power, \( V_{\max} \) and \( V_{\min} \) are the maximum and minimum terminal voltages of the PV unit, and \( Q_{cmd} \) is the reactive power reference value dynamically assigned by the plant’s control system. The thresholds \( I_q^{\max} \) and \( I_q^{\min} \) for the reactive reference current are determined by the converter current limiting module, with control parameters \( K_{Qi} \) and \( K_{Vi} \) fine-tuned for optimal performance. This decoupled approach is essential for improving the grid support capabilities of various types of solar inverter, especially those in aging installations that lack advanced reactive power management.
For MPPT maximum power point tracking, early types of solar inverter often employed the hill-climbing method due to its simplicity and ease of implementation. However, with advancements in PV technology and digital signal processing (DSP) capabilities, more sophisticated algorithms have become feasible for engineering applications. The variable-step perturbation and observation (P&O) MPPT algorithm introduces a variable step size coefficient \( N \), where the step size is proportional to the derivative of power with respect to voltage, \( \frac{dP}{dU} \). As the system approaches the maximum power point, \( \frac{dP}{dU} \) decreases, necessitating smaller step sizes to minimize oscillations and enhance stability and energy efficiency. The voltage reference value in this algorithm is given by:
$$ U_{\text{ref}_{i+1}} = U_{\text{ref}_i} + N \frac{dP}{dU} $$
Here, \( U_{\text{ref}_{i+1}} \) and \( U_{\text{ref}_i} \) represent the voltage references after the \( i+1 \)-th and \( i \)-th perturbations, respectively. The step size coefficient \( N \) is crucial for balancing the algorithm’s speed and precision; an optimal \( N \) ensures rapid convergence without sacrificing accuracy. This improvement is particularly beneficial for older types of solar inverter that use fixed-step methods, as it reduces power losses and increases overall energy harvest. By integrating such advanced MPPT strategies, we can elevate the performance of aging inverters to match that of modern types of solar inverter.
To validate these enhancement schemes, I conducted active power control tests on a retrofitted inverter. The tests involved both load reduction and load increase scenarios, with detailed measurements of response time, settling time, steady-state control deviation, and overshoot. For example, in load reduction tests, the inverter’s active power was initially at 1400.2 kW, and the system commanded a step-down to 1200 kW. The inverter achieved a maximum power of 1198.7 kW, with a response time of 0.74 seconds and a settling time of 0.89 seconds. Similar tests were performed at various power levels, and the results are summarized in the table below. All parameters met the specified requirements, demonstrating the effectiveness of the upgrades. Additionally, by optimizing the inverter’s software程序和 algorithms, including the MPPT strategy, the energy yield of a single inverter unit increased by approximately 2% to 5%. This gain is significant for aging PV stations employing outdated types of solar inverter, as it directly translates to improved economic viability and grid support.
| Test | Initial Load (kW) | Target Load (kW) | Final Load (kW) | Response Time (s) | Settling Time (s) | Max Steady-State Deviation (%) | Max Overshoot (kW) |
|---|---|---|---|---|---|---|---|
| 1 | 1400.2 | 1200 | 1198.7 | 0.74 | 0.89 | 0.85 | 17.2 |
| 2 | 1193.1 | 1000 | 986.9 | 0.88 | 0.89 | 0.82 | 21.3 |
| 3 | 995.8 | 800 | 785.6 | 0.85 | 0.95 | 0.89 | 39.6 |
| 4 | 783.5 | 600 | 586.7 | 0.86 | 1.67 | 0.93 | 47.4 |
| 5 | 585.7 | 400 | 387.6 | 0.80 | 1.53 | 0.95 | 55.3 |
| 6 | 384.5 | 600 | 584.8 | 0.75 | 1.54 | 0.92 | 26.7 |
| 7 | 586.4 | 800 | 787.4 | 0.74 | 1.53 | 0.88 | 20.4 |
| 8 | 782.2 | 1000 | 990.3 | 0.79 | 1.17 | 0.92 | 17.5 |
| 9 | 984.2 | 1200 | 1193.5 | 0.69 | 0.91 | 0.77 | 14.5 |
| 10 | 1187.5 | 1400 | 1397.6 | 0.98 | 1.21 | 0.43 | 8.5 |
In conclusion, this study focuses on the performance deficiencies of aging PV inverters, particularly in the context of grid compliance requirements. By analyzing the impact of regulatory standards on older types of solar inverter and identifying key shortcomings, I have developed targeted optimization strategies for critical elements such as response speed and MPPT algorithms. The implementation of these strategies, centered on control algorithm refinements and response time reductions, has proven effective in enhancing inverter performance and boosting the energy output of aging PV stations. This approach not only addresses immediate grid adaptability issues but also extends the operational life of existing infrastructure, making it a cost-effective solution for the renewable energy sector. Future work could explore the integration of these enhancements across diverse types of solar inverter to further validate their scalability and impact.