Ruosi Zhang
About
My research project
Optimization of integrated energy systems in aquacultureAquaculture is a fast-growing industry and is becoming an important source of protein, with this growth expected to continue. Recirculated aquaculture systems (RAS) are the most common and critical technology that can reuse the water in the system and be recirculated. RAS control the indoor environment to make harvesting times predictable and maximize the yield. The quality and temperature control of water makes the energy consumption of RAS large, so energy management is an essential part of system design. It is therefore important to establish an economic and sustainable electricity-heat integrated energy system for aquaculture.
Renewable energy has low carbon emissions, sustainability benefits, and low generation costs. However, the nature of renewable generation is stochastic, and inaccuracy of output forecast brings a significant challenge to system operations, which brings risks to the system and markets. To determine the operation schedule, such as the unit on or off status and generators production level, the unit commitment (UC) optimization problem is solved a day ahead to ensure that power generators meet the forecasted load at a minimum cost of system operation. AC optimal power flow is a nonconvex and complex problem and so is computationally expensive and cannot guarantee global solutions. Developed algorithms need to ensure fast computation to global optimality for real-time applications. In addition, these models do not typically explicitly consider the uncertainty in the model and forecasts.
Optimization under uncertainty in large-scale power systems operations with renewable energy has recently become a topic of significant interest. This project will develop a data-driven algorithm, to solve the model predictive control problem of optimal power flow and unit commitment problem for a RAS, in order to minimize the costs associated with these complex systems.
Supervisors
Aquaculture is a fast-growing industry and is becoming an important source of protein, with this growth expected to continue. Recirculated aquaculture systems (RAS) are the most common and critical technology that can reuse the water in the system and be recirculated. RAS control the indoor environment to make harvesting times predictable and maximize the yield. The quality and temperature control of water makes the energy consumption of RAS large, so energy management is an essential part of system design. It is therefore important to establish an economic and sustainable electricity-heat integrated energy system for aquaculture.
Renewable energy has low carbon emissions, sustainability benefits, and low generation costs. However, the nature of renewable generation is stochastic, and inaccuracy of output forecast brings a significant challenge to system operations, which brings risks to the system and markets. To determine the operation schedule, such as the unit on or off status and generators production level, the unit commitment (UC) optimization problem is solved a day ahead to ensure that power generators meet the forecasted load at a minimum cost of system operation. AC optimal power flow is a nonconvex and complex problem and so is computationally expensive and cannot guarantee global solutions. Developed algorithms need to ensure fast computation to global optimality for real-time applications. In addition, these models do not typically explicitly consider the uncertainty in the model and forecasts.
Optimization under uncertainty in large-scale power systems operations with renewable energy has recently become a topic of significant interest. This project will develop a data-driven algorithm, to solve the model predictive control problem of optimal power flow and unit commitment problem for a RAS, in order to minimize the costs associated with these complex systems.