What the study found
The study found that an improved sparrow search algorithm (SSA) was used to optimize the capacity configuration of photovoltaic and energy storage systems for enterprise parks. In the case study, the proposed algorithm achieved a maximum economic benefit improvement of 7.32% over conventional intelligent algorithms and also improved power supply reliability.
Why the authors say this matters
The authors say the work matters because enterprise parks face high electricity costs, large peak-valley price differences, and limited use of renewable energy. The study suggests that adding price-based demand response and cycle life constraints to capacity planning can help address these issues.
What the researchers tested
The researchers built a multi-objective optimization model that aimed to minimize equivalent annualized comprehensive cost and energy imbalance rate. They then improved the traditional SSA with chaotic mapping, adaptive inertia weight, Harris Hawks encircling, and predation strategies, and tested it on real load data from an enterprise park in Zhuzhou City.
What worked and what didn't
The improved SSA was reported to improve convergence speed and accuracy in high-dimensional problems compared with the traditional algorithm. In the case study, it produced a maximum economic benefit improvement of 7.32% over conventional intelligent algorithms and further enhanced power supply reliability.
What to keep in mind
The abstract does not describe detailed limitations, and the results are presented from a single case study using real load data from one enterprise park. The comparison is stated against conventional intelligent algorithms, but the abstract does not provide the full set of comparison methods or detailed numerical results.
Key points
- The study optimized photovoltaic and energy storage capacity for enterprise parks using an improved sparrow search algorithm.
- The model combined price-based demand response and cycle life constraints.
- The case study reported a maximum economic benefit improvement of 7.32% over conventional intelligent algorithms.
- The abstract says the improved algorithm increased convergence speed and accuracy in high-dimensional problems.
- The study also reports improved power supply reliability.
Disclosure
- Research title:
- Improved algorithm optimized photovoltaic-storage capacity
- Authors:
- Luting Zhang, Wei Zhao, Jinhui Zeng, Jie Liu
- Institutions:
- Hunan University of Technology
- Publication date:
- 2026-02-02
- OpenAlex record:
- View
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