Robust Day-Ahead Dispatch for Integrated Power-Heat-Gas Microgrid considering Wind Power Uncertainty

Abstract

Wind power generation has been widely deployed in the modern power system due to the issues of energy crisis and environment pollution. Meanwhile, the microgrid is gradually regarded as a feasible way to connect and accommodate the distributed wind power generations. Recently, more research studies also focus on incorporating various energy systems, for example, heat and gas into the microgrid in terms of satisfying different types of load demands. However, the uncertainty of wind power significantly impacts the economy of the integrated power-heat-gas microgrid. To deal with this issue, this paper presents a two-stage robust model to achieve the optimal day-ahead economic dispatch strategy considering the worst-case wind power scenarios. The first stage makes the initial day-ahead dispatch decision before the observation of uncertain wind power. The additional adjustment action is made in the second stage once the wind power uncertainty is observed. Based on the duality theory and Big-M approach, the original second-stage problem can be dualized and linearized. Therefore, the column-and-constraint generation algorithm can be further implemented to achieve the optimal day-ahead economic dispatch strategy for the integrated power-heat-gas microgrid. The experimental results indicate the effectiveness of the presented approach for achieving operation cost reduction and promoting wind power utilization. The robustness and the economy of the two-stage robust model can be balanced, of which the performances significantly outperform those of the single-stage robust model and the deterministic model.

Low-Carbon Robust Predictive Dispatch Strategy of Photovoltaic Microgrids in Industrial Parks

“…Therefore, in order to adapt to the dynamic characteristics of various devices in the microgrid, various types of scheduling strategies have been designed in various literature studies to cope with different uncertain information (Yang and Su, 2021). Robust optimization (RO) strategy ensures the stable operation of the microgrid in a complex operating environment by finding the worst scenario for the scheduling plan (Liu et al, 2020;Choi et al, 2019). In Liu et al (2020), a two-stage robust model is proposed for an integrated power-heat-gas microgrid to achieve the optimal day-ahead economic scheduling considering the uncertainty of wind power scenarios.…”

“…Robust optimization (RO) strategy ensures the stable operation of the microgrid in a complex operating environment by finding the worst scenario for the scheduling plan (Liu et al, 2020;Choi et al, 2019). In Liu et al (2020), a two-stage robust model is proposed for an integrated power-heat-gas microgrid to achieve the optimal day-ahead economic scheduling considering the uncertainty of wind power scenarios. In Li et al (2021a), a data-driven set-based robust optimization (DSRO) model considering the uncertainties of wind power and multiple demand response programs (DRPs) has been proposed, and a combined cooling, heating, and power (CCHP) microgrid with the power-to-gas (P2G) device is used to verify its feasibility.…”

A Quantitative Risk-Averse Model for Optimal Management of Multi-Source Standalone Microgrid with Demand Response and Pumped Hydro Storage

“…However, the authors in [5] stated that hydro storage is more cost-competitive than battery storage, and presents practical potential and technically feasible opportunities for power supply in remote areas. Thus, coordinating the traditional controllable hydropower with the uncontrollable wind and solar power to form a hybrid multi-source microgrid is a promising solution for promoting renewable energy penetration [6,7].…”

Robust Stochastic Dynamic Optimal Power Flow Model of Electricity-Gas Integrated Energy System considering Wind Power Uncertainty

“…How to deal with the uncertainty of wind power output is one of the important difficulties in the collaborative optimization of electricity-gas IES. Stochastic optimization (SO) [13] and robust optimization (RO) [14,15] are two effective methods. e SO method is described by probability distribution with a heavy computational burden.…”