Attached herewith is an example of MATLAB code for solving a mixed-integer conic and mixed-integer linear unit commitment model using the YALMIP optimization toolbox and the MOSEK solver. It also considers the IEEE-57 and IEEE-118 bus test systems.
Thank you.
But in this code constraints are not included. if possible can you add constraint formulation In MATLAB code. For example Start-up/down cost ,Ramp-up/down etc.
Kanagaraj Krishnamoorthy
Attached here is an updated version of the MATLAB code that includes additional constraints for start-up cost, shut-down cost, ramp-up, and ramp-down limits
Hello Kanagaraj Krishnamoorthy, I appreciate your response as it will help me in my research as well, I sent you a message as well, I look forward to your feedback.
Thank you Mohsin Ali for asking the question
I Still facing problem bcz of Error in cost function.
Cost = Cost'; % Cost coefficient of units..
The Cost variable should be defined correctly to match the number of units in the system. Attached is the corrected version of the MATLAB code. Please make sure to provide the correct data for the Cost variable, which represents the cost coefficient of each unit. The dimensions of the Cost vector should match the number of units in the system.
Additionally, you can modify the code accordingly if you want to consider additional constraints or specific features of the unit commitment problem.
Hi Kanagaraj Krishnamoorthy can you please provide the matlab code for a stochastic security constrained unit commitment model with frequency constraints or a stochastic unit commitment model for low inertia grids for an IEEE-24 and IEEE-96 bus system.
Thank you
Developing such a model is a complex task that requires a deep understanding of power systems, optimization techniques, and programming skills.
However, I can provide you with a high-level overview of the steps involved and the components you would need to consider while implementing these models in MATLAB. This can serve as a starting point for your implementation. Here are the main steps you would typically follow:
1. Data Preparation:
- Obtain the system data for the IEEE-24 or IEEE-96 bus system, including generator characteristics, load profiles, transmission line parameters, and any other relevant data.
2. Modeling the Problem:
- Define decision variables, such as the commitment status of each generator, power output levels, and other relevant variables.
- Formulate the objective function, which could be a combination of cost minimization, security constraints, and frequency constraints.
- Define the constraints, including power balance, ramping limits, minimum up/down time constraints, voltage limits, and frequency constraints.
- Incorporate stochasticity by considering uncertain parameters, such as renewable generation and load forecasts, using scenarios or probabilistic models.
3. Optimization:
- Use an appropriate optimization technique to solve the unit commitment problem, such as mixed-integer linear programming (MILP) or mixed-integer quadratic programming (MIQP).
- Utilize stochastic optimization techniques, such as scenario-based or robust optimization, to handle the uncertainty in the problem.
4. Implementation in MATLAB:
- Set up the optimization problem using MATLAB's optimization toolbox or a suitable third-party solver.
- Define the objective function, decision variables, and constraints in MATLAB's mathematical expression format.
- Set up the optimization options, such as solver settings, termination criteria, and solution methods.
- Solve the optimization problem and retrieve the optimal unit commitment schedule and power generation levels.
Note that the specific implementation details will depend on the specific modeling assumptions, constraints, and objectives you want to incorporate into your unit commitment model. You might also need additional MATLAB toolboxes or third-party libraries to handle certain aspects of the problem.
I recommend consulting research papers, textbooks, or relevant literature on stochastic unit commitment and power system optimization to gain a deeper understanding of the models and algorithms involved. These resources often provide detailed mathematical formulations and algorithms that can guide you in implementing your own model.
Remember that developing a sophisticated unit commitment model requires expertise in power systems and optimization, so you may also consider collaborating with domain experts or researchers in the field to ensure the accuracy and reliability of your model.
Kanagaraj Krishnamoorthy Following the hight level description you provided, would you mind availing yourself for a discussion to help me drill down on the specifics. Thank you
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