Details

Autor(en) / Beteiligte

• Elsir, Mohamed
• Al-Awami, Ali T.
• Antar, Mohamed A.
• Oikonomou, Konstantinos
• Parvania, Masood

Titel

Risk-Based Operation Coordination of Water Desalination and Renewable-Rich Power Systems

Ist Teil von

• IEEE transactions on power systems, 2023-03, Vol.38 (2), p.1162-1175

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2023

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Handling the variability and uncertainty associated with integrating large capacities of renewable energy sources (RES) into the power grid is a challenge that is increasingly influencing the power systems operation. At the same time, the growing need for desalinated water in arid areas increases the importance of suitable energy sources for sustainable operation of water desalination plants. However, as power and water system operators have traditionally operated their systems in isolation, there is a lack of understanding of the interdependence and interactions between these two systems. This paper addresses this gap by proposing a risk-based two-stage stochastic co-optimization framework that coordinates the operation of a renewable-rich power system with the operation of grid-connected reverse-osmosis water desalination plants (RO-WDP) to minimize their combined operational costs while increasing the utilization of RES. From the power system operation standpoint, the RO-WDPs are considered as controllable demand, and the proposed model integrates the energy flexibility of RO-WDPs in the day-ahead power system operation. The proposed model considers the operational constraints of both power and water desalination systems, thus co-optimizing their operation without compromising the reliable supply of power and water to end-users, while taking into account the uncertainty of the demands and RES. Simulation results demonstrate the benefits of the proposed coordination on enhancing the power system efficiency, facilitating RES integration, and minimizing the combined operational costs of both systems while minimizing their operating risk using conditional value at risk.