Hydrothermal scheduling is an important aspect of power system planning that focuses on optimizing the operation of hydro and thermal power plants. It involves a complex nonlinear objective function as well as a mix of linear, nonlinear, and dynamic network flow constraints, as opposed to economic load dispatch. The primary goal is to reduce the total fuel cost of thermal plants while maintaining power balance and adhering to constraints such as power generation limits, water discharge limits, and water volume limits.
Therefore, its goal is to determine the optimal power generation from hydro and thermal units to meet electricity demand. Because of the distinct characteristics and cost structures of these two types of power plants, coordination is essential.
What are the Types of Hydrothermal Scheduling Problems?
The goal of dealing with hydrothermal scheduling issues is to optimize electricity generation and lower power costs. In general, the integrated management of the hydro-thermal system is divided into two categories: long-term problems and short-term problems. The long-term hydrothermal scheduling problem entails planning over a year, whereas the short-term hydrothermal scheduling problem focuses on optimizing operations over shorter timeframes ranging from an hour to a week.
Also See: What is Thermal Energy Storage?
What are the Methods of Hydrothermal Scheduling?
Various methods are used to solve hydrothermal scheduling problems, which involve optimizing the coordination of hydro and thermal power plants. These methods include dynamic programming, network flow, linear programming, nonlinear programming, mathematical decomposition, expert systems, and artificial neural networks.
The three most common solution methods are λ-γ iteration, gradient method, and dynamic programming. Each method has the following drawbacks:
Drawbacks
1. λ-γ Iteration
- Problems with coordination equations, resulting in plant generations that exceed capacity.
- Negative generation for certain plants.
- Changes in constraints necessitated adjustments.
2. Gradient Method
- Inefficiency with larger system sizes.
3. Dynamic Programming with Successive Approximation
- The requirement is to specify an initial feasible schedule for each reservoir.
- A lack of adaptability in dealing with coupling constraints.
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