# Optimal Scheduling of an Isolated Microgrid with Battery Storage Considering Load and Renewable Generation Uncertainties

arXiv: https://arxiv.org/abs/1808.05691 · HTML: https://mlsanity.com/paper/1808.05691/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2018.

## Built on by (most cited first, 7 of 7)

- [Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-based Multi-period Forecasting](https://mlsanity.com/paper/2108.06764/) (2021)
- [Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach](https://mlsanity.com/paper/2212.13472/) (2022)
- [Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm](https://mlsanity.com/paper/2304.01223/) (2023)
- [Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2305.00127/) (2023)
- [Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms](https://mlsanity.com/paper/2211.00221/) (2022)
- [The State of the Art in transformer fault diagnosis with artificial intelligence and Dissolved Gas Analysis: A Review of the Literature](https://mlsanity.com/paper/2304.11880/) (2023)
- [Research on short-term load forecasting model based on VMD and IPSO-ELM](https://mlsanity.com/paper/2410.05300/) (2024)

## Abstract (arXiv)

By modeling the uncertainty of spinning reserves provided by energy storage with probabilistic constraints, a new optimal scheduling mode is proposed for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming. The model is transformed into a readily solvable mixed integer linear programming (MILP) formulation in GAMS via a proposed discretized step transformation (DST) approach and finally solved by applying the CPLEX solver. By properly setting the confidence levels of the spinning reserve probability constraints, the MG operation can be achieved a trade-off between reliability and economy. The test results on the modified ORNL DECC lab MG test system reveal that the proposal significantly exceeds the commonly used hybrid intelligent algorithm with much better and more stable optimization results and significantly reduced calculation times.