Due to large-scale penetration of Renewable Energy Resources, many different changes have occurred in power system. For example, changes made in customers consumption pattern due to the conversion of passive loads to active and responsive loads, development and increase of microgrids all over the world, and conversion of traditional distribution networks to smart distribution networks. Therefore it is necessary to develop a decentralized scheduling. Nowadays, central scheduling of distribution networks does not meet the customer’s requirements for different reasons, Such as long run time of scheduling program, vulnerability against cyber-attacks, not considering customer’s privacy and most importantly presence of microgrids with independent generation from distribution networks.In this thesis, decentralized scheduling of distribution networks and microgrids is modeled using Analytical Target Cascading (ATC) algorithm. Each distribution network and its microgrids has different types of loads, such as shiftable, curtailable and fix loads, programmable distributed generations, and energy storage systems. First, centralized scheduling of a distribution network and its microgrids is modeled with an optimization. After that, centralized optimization is decomposed into several optimizations including distribution network’s optimization and an optimization for each microgrid. Power price of transmission bus, which the distribution network is connected to, is modeled by price conjecture curve for each hour. First, it is assumed that only one distribution network is connected to the transmission bus. Decentralized optimizations are solved iteratively using exchange of limited information between distribution network and it’s microgrids. After that, it is assumed that two distribution networks are connected to the transmission bus. In this case, data are exchanged in two stages. Internal information exchange between each distribution network and microgrids, and information exchange between distribution networks. In this thesis, both DC and AC power flow for radial distribution networks are implemented. In DC power flow model, only active power information is exchanged between each distribution network and microgrids. In AC power flow model, in addition to the exchange of active power information, reactive power information and common bus voltage between distribution network and microgrids are exchanged. The proposed method is applied to a 39-bus test distribution network and a 78-bus test network, which consists of two distribution networks and they are both connected to the same transmission bus. Each distribution network contains 4 microgrids. In the end, the results are discussed. In order to validate the proposed decentralized scheduling approach, decentralized problem results are compared with those of centralized problem. |
