When we want to integrate Distributed Generation units like PV and Wind in a radial distribution system, it is very important to find Optimal location and Optimal capacity of DGs in order to reduce the system total losses and improve the voltage profile. In this regard, I have two questions initially.
1. Which we have to find first? Whether we have to find the optimal location first? or we have to find the optimal capacity first?
2. How to find the optimal location? Whether the optimal location will find through the algorithm? or by using an index like the Voltage Stability Index, Loss factor Index, etc.
Rajesh Kumar Samala : This is an interesting question. Here are my suggestions:
1. In a radial distribution system, it is straightforward to aggregate the total demand at any location on the feeder (by aggregating/rolling up loads+ losses), hence finding the total capacity at any node in a radial system is straightforward as well. Hence, it is ok to start with optimization of location, by using rough estimates of capacity initially, and then as a subsequent step, the capacity can be optimized.
2. I think in the location optimization problem, one should consider all the factors that you mentioned above, voltage stability, loss factor, including whether your DG system/microgrid would be stand alone or grid connected and whether it would have storage or no storage.
In my opinion, you need to define goal function or cost function: do you want to minimize losses, maximize reliability, improve voltage profile (limits) placing DG (PV or other types) in the appropriate busbars of the feeder. There are many optimization algorithms for that task PSO, GA, ANN, Tabu search algorithm, which can be found in some software as the separate modules.
To make more sense, in my view, you should simultaneously find optimal locations and capacities associated with DG resources. In fact, you can mathematically model your problem and implement it in a modeling environment like GAMS. Given the objective of your problem (i.e., total cost, total loss, and so on), you can easily find the location and capacity of DGs through adding capacity constraints and binary decision variables, respectively.
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