As is illustrated in many literature, optimization decomposition technologies such as Benders Decomposition or ADMM have been used in OPF , UC or ED models to keep the privacy of different agents and simplify the calculation of the whole model. However, the iterative relation of problem serialization is quite different from the parallel structure of real situation. The information exchanged between the main and sub problems may also reveal the user's privacy. My question is how to measure the privacy of exchanged information? Is there a suitable algorithm which can solve the problem perfectly?
You can keep the privacy of everything in the microgrid through the blockchain framework. If you need to keep privacy, you can use the elliptic curve to generate a private key and public key; this public key will be used publicly. If you need more detail, I need to know what you are doing exactly.
Haissam Badih Thank you for your kind answer. The anonymity of blockchain does have the ability to protect the market participants. But my question is focus on the use of optimization decomposition algorithms. Each microgrid is optimized independently and combined into the whole distributed optimization architecture like 10.1109/TSG.2019.2959242 .
Yuanxing, You can hook the microgrid into blockchain technology. And you can use the optimization decomposition algorithms as concensus. Each microgrid will be independently and in a distributed way.
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