Optimal Planning and Investment Return Analysis of Grid-Side Energy Storage System Addressing Multi-Dimensional Grid Security Requirements

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper introduces an integrated framework for planning and sizing energy storage within the electrical grid. This approach combines multidimensional grid security indicators with a hybrid weighting and ranking methodology balancing subjective and objective criteria, and is further supported by an economic and financial assessment to evaluate feasibility and impact. To enhance the clarity and practical relevance of the work, the following recommendations are proposed:

• Improve the visual quality and readability of the figures, ensuring that graphical elements effectively communicate key insights.
• Streamline the presentation of results for the 220 kV substations in QZ, China, by adopting a more concise and comparative format. This could include a compact summary table highlighting the top-ranked substations, with normalized indicators and expected constraint-specific impacts, accompanied by a simple bar chart showing composite scores.
• Add one or two brief “before-and-after” scenarios to illustrate the technical and economic benefits of the proposed method. These examples would help demonstrate the robustness and practical value of the framework without requiring major methodological changes.

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The article proposes an optimal planning and investment return framework for grid-side energy storage systems (GSESS), integrating technical, economic, and security dimensions. It combines AHP–entropy weighting with TOPSIS to rank substations, identifies severe grid scenarios using improved K-means clustering, and formulates an optimization model ensuring multi-dimensional grid security. A case study on 15 substations in Quzhou City confirms its effectiveness: a 100 MW/200 MWh LFP system achieves a 32.28% IRR and a 3.24-year payback period, proving strong feasibility and profitability.

However, the study could be strengthened by addressing the following points:

1. Have you validated the proposed framework on grids of different voltage levels or regions beyond QZ to ensure scalability?
2. Can the scenario clustering and capacity planning models adapt to evolving load and renewable generation patterns over multi-year horizons?
3. How robust is the planning outcome to measurement errors or incomplete operational data, which are common in large grid systems?
4. Have you considered stochastic modeling of electricity price fluctuations or future policy adjustments in the investment return analysis?
5. Why was the proposed model not compared with other multi-objective optimization techniques such as NSGA-II, PSO, or reinforcement learning frameworks?
6. What is the computational cost of solving the mixed-integer nonlinear optimization (using Gurobi) for large-scale grids? Could real-time or near-real-time applications be feasible?
7. How do carbon pricing mechanisms or renewable curtailment policies affect the investment return evaluation?
8. Can the contribution of each evaluation indicator (e.g., voltage stability vs. renewable accommodation) be visualized to guide policy or engineering decisions?

Author Response

Please see the attachment.

Author Response File: Author Response.docx