The specific objectives of this project phase are stated as follows:
1) To develop a comprehensive model of the proposed facility to simulate its operation over one year. The model entails the simulation of sub-operations including the supply of water from an RO system, production of hydrogen through water electrolysis using PEM technology, the supply of energy from the Hawa Akkar WT plant modules, and the associated battery storage, water storage, and hydrogen storage systems.
2) To develop an ordinal optimization (OO) procedure that will use the simulation model to search for an optimum technology mix and components sizes. OO allows a quick investigation of the search space using a simple model to identify the promising alternative designs and then select the best among these as the set of “good enough” solutions and examine them using an accurate model as done in many disciplines
Challenges
1. Development of the Dynamic Models of Subsystems:
We will first develop subsystems dynamic models that are sensitive to environmental and other input variables. In the development of the subsystems models, we will rely on well-established industry-grade models to develop a kind of look-up table models that can cater for input variation on some parameters to see how it will fit in the overall integration and still give meaningful results.
2. Simulation Model Integration
The developed subsystems models will be integrated into a simulation model in which we will specify the different subsystem sizes to assess how the proposed system will operate under the given environmental conditions of the proposed site. An economic and environmental assessment will be deduced from such system simulation.
3. Developing Structure of Ordinal Optimization (OO) Model
Develop the program structure of the OO model and identify what variables need to be optimized. These developed subsystem models will be used to run hundred of thousands of simulations for proposed system designs sampled from the design search space. These simulations use a “simple model” that catches the environmental constraints from typical days that represent yearly variations. We will then rank these simulated designs in ascending order of economic or environmental cost and select the top-S design among these. Ordinal optimization theory specifies the size of the top-S designs known as the set of “good enough” designs. We will then simulate the operation of the top-S designs using an accurate model each design over a full year to determine a few truly good enough designs which should include the optimum with a high alignment probability.
Methods
Electrical Science
Mathematics and Statistics
Economics and Finance
Computer Science
Physics
Electro-Chemistry
Academic Majors of Interest
Electrical Engineering
Mechanical Engineering
Chemical Engineering
Econmics and Finance
Preferred Skills
Renewable Energy Systems Modelling
Economics and Finance Analysis
Computer Programming
Energy Storage Modelling
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