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Optimizing Fairness in Photovoltaic Curtailments for Voltage Regulation in Power Distribution Networks

Core Concepts
This work compares two approaches to improve fairness in photovoltaic curtailments for voltage regulation in power distribution networks: (1) adding a fairness objective to the optimization problem, and (2) using a feedback-based approach that assigns different weights to individual photovoltaic plants based on their previous curtailment actions.
The paper analyzes and compares two fairness-promoting schemes for photovoltaic (PV) curtailment in power distribution networks to address over-voltage issues. The first scheme incorporates an additional fairness objective in the optimization problem, which aims to minimize the variance in curtailments across different PV plants. The second scheme uses a feedback-based approach, where the weights assigned to individual PV plants in the curtailment minimization objective are determined based on their previous curtailment actions. The authors combine these two schemes into a single formulation and evaluate their performance in terms of fairness and net curtailments on several benchmark distribution networks. The results show that the feedback-based approach can improve fairness, as quantified by the Jain Fairness Index and Gini coefficient, compared to the case without any fairness considerations. The comparison of the two fairness schemes reveals that incorporating past curtailment information can enhance fairness, especially for the objective of minimizing electricity bills. However, for the objective of minimizing PV curtailments, the impact of including past curtailment information is less significant. The authors also observe that the Jain Fairness Index and Gini coefficient may not always align in their assessment of fairness, suggesting the need to carefully select appropriate fairness metrics.
The net PV curtailments for the CIGRE LV network range from 24.2% to 32.0% across the different fairness schemes and simulation durations. The Jain Fairness Index values range from 0.81 to 0.96 for the different schemes and networks. The Gini coefficient values range from 0.12 to 0.25 for the different schemes and networks.
"The work in [13] proposes fair power curtailment by exploiting sensitivity matrix information in a P-V droop control scheme." "In [14], an additional cost term is added to the curtailment minimization problem; this term reduces the variance of the curtailments across different PV plants." "In [16], a fairness cost function is introduced, aiming to curtail proportionally to the energy exported."

Deeper Inquiries

How can the fairness metrics be further improved or combined to provide a more comprehensive assessment of fairness in PV curtailment schemes?

Fairness metrics play a crucial role in evaluating the equity and just distribution of benefits in PV curtailment schemes. To enhance the assessment of fairness, a more comprehensive approach can be adopted by combining multiple metrics and refining existing ones. One way to improve fairness metrics is by incorporating dynamic weighting factors that adjust based on the changing conditions of the distribution network. This dynamic weighting can account for factors such as network congestion, time of day, or seasonal variations in PV generation. Additionally, a multi-dimensional approach to fairness assessment can be beneficial. By combining metrics like Jain Fairness Index (JFI) and Gini coefficient with other indicators such as social welfare impact, environmental benefits, or economic considerations, a more holistic view of fairness can be achieved. This multi-dimensional approach can provide a comprehensive assessment of fairness that considers various aspects of the PV curtailment scheme's impact on different stakeholders. Furthermore, incorporating qualitative feedback from stakeholders, such as customer satisfaction surveys or community engagement initiatives, can offer valuable insights into the perceived fairness of the curtailment schemes. By integrating qualitative data with quantitative metrics, a more nuanced understanding of fairness can be obtained.

How can the proposed fairness-aware schemes be extended to consider uncertainty in PV generation and load forecasts, and their impact on the fairness outcomes?

To address uncertainty in PV generation and load forecasts within fairness-aware schemes for PV curtailment, several strategies can be implemented: Probabilistic Modeling: Integrate probabilistic forecasting techniques to account for uncertainties in PV generation and load forecasts. By incorporating probabilistic models, the fairness-aware schemes can adapt to varying forecast accuracies and adjust curtailment decisions accordingly. Robust Optimization: Implement robust optimization techniques that optimize curtailment decisions while considering the worst-case scenarios of forecast uncertainties. This approach ensures that the fairness outcomes are resilient to forecast errors and variations. Scenario Analysis: Conduct scenario analysis by considering multiple forecast scenarios to evaluate the robustness of the fairness-aware schemes. By analyzing a range of possible outcomes based on different forecast scenarios, the schemes can be designed to be more adaptive and flexible. Real-Time Adjustments: Develop real-time adjustment mechanisms that dynamically update curtailment decisions based on the latest forecast information. By continuously monitoring forecast updates and adjusting curtailments in real-time, the schemes can respond effectively to forecast uncertainties. By incorporating these strategies, the proposed fairness-aware schemes can be extended to consider uncertainty in PV generation and load forecasts, ensuring more robust and adaptive decision-making processes that enhance fairness outcomes in PV curtailment schemes.

What other factors, beyond past curtailment actions, could be considered to enhance fairness in PV curtailment decisions?

In addition to past curtailment actions, several other factors can be considered to enhance fairness in PV curtailment decisions: Geographical Location: Take into account the geographical location of PV plants within the distribution network. Plants located in areas with higher voltage fluctuations or congestion may require different curtailment strategies to ensure fairness across all locations. Grid Constraints: Consider the existing grid constraints and limitations when determining curtailment decisions. By factoring in grid constraints such as line capacities, transformer limitations, and voltage profiles, the curtailment schemes can be optimized to maintain grid stability and fairness. Customer Profiles: Analyze the profiles of customers connected to PV plants to understand their energy needs and preferences. Tailoring curtailment decisions based on customer profiles can enhance fairness by considering individual requirements and priorities. Environmental Impact: Evaluate the environmental impact of curtailment decisions, such as the reduction in carbon emissions or the promotion of renewable energy integration. Balancing fairness with environmental considerations can lead to more sustainable and equitable curtailment practices. Economic Considerations: Incorporate economic factors, such as the cost implications of curtailment for both consumers and utilities. By optimizing curtailment decisions to minimize economic losses and maximize benefits, fairness can be enhanced from a financial perspective. By considering these additional factors alongside past curtailment actions, the fairness in PV curtailment decisions can be further enhanced, leading to more equitable and efficient distribution of curtailed PV generation.