Data-Driven Multi-Energy Investment and Management Under Earthquakes

Abstract

Seismic events can severely damage both electricity and natural gas systems, causing devastating consequences. Ensuring the secure and reliable operation of the integrated energy system (IES) is of high importance to avoid potential damage to the infrastructure and reduce economic losses. This paper proposes a new optimal two-stage optimization to enhance the reliability of IES planning and operation against seismic attacks. In the first stage, hardening investment on the IES is conducted, featuring preventive measures for seismic attacks. The second stage minimizes the expected operation cost of emergency response. The random seismic attack is modelled as uncertainty, which is realized after the first stage. An explicit damage assessment model is developed to define the budget set of the uncertain seismic activity. Based on the survivability of transmission lines and gas pipelines of IES, an optimal system investment plan is developed. The problem is formulated as a two-stage distributionally robust optimization (DRO) model, which is tested on an integrated IEEE 30-bus system and 20-node gas network. Case studies demonstrate that the twostage DRO outperforms robust optimization and a single-stage optimization model in terms of minimizing the investment cost and expected economic loss. This work can help system operators to make economical hardening and operation strategies to improve the reliability of IES under seismic attacks, thus managing a more robust and secure energy system.

Two-Stage Co-Optimization for Utility-Social Systems With Social-Aware P2P Trading

“…To address this limitation, the use of distributionally robust optimization (DRO) is appealing. It represents an effective uncertain programming method for energy system operation problems [40][41][42][43], rather than relying on a voluminous data set or sacrificing computational effectiveness. For example, Liu et al [40] design a real-time economic dispatch for power transmission systems by considering frequency regulations and using DRO to cope with inaccurate renewable energy forecasts.…”

“…The training-based ambiguity set can be controlled by altering the number of training scenarios. Zhao et al [42] propose a DRO-based hardening plan to address the unreliable planning in catastrophic natural disasters. Ryu et al [44] design an optimal distributionally robust AC power flow model that considers the uncertain electric field caused by geomagnetic disturbances.…”