Power system expansion specifically for distribution networks is gaining more importance due to the integration of distributed energy systems. Optimisation models are more used to tackle generation and transmission expansion problems (GTEP). This way, cloud computing, machine learning, big data, internet of things, simulation and optimisation are critical factors in the innovation of the power sector. In the recent past, a new inter-disciplinary subject focusing on energy and information system called energy informatics has emerged. This paper proposes a novel mathematical model to deal with GTEP regarding the collaboration and competition between all the nodes and actors in the power network. Additionally, this work proposes the usage of a parallel algorithm to solve the GTEP problem using the model efficiently. This way, to assist power network companies to make better strategic, tactical and operational decisions related to the investments, maintenance or evaluation of the power network a cloud-based Decision Support System (DSS) is proposed. Mainly, focused at: (i) integrate the data in the system, (ii) integrate the model, (iii) automate the resolution process, and finally present the results in an interactive way to the end-users. This work extends the advantages of optimisation and simulation models with the potential of parallel and cloud computing to automate and o↵er the knowledge and the analytics. The results show that the decision support system proposed helps decisions makers in real situations to do better planning by obtaining the competitive advantages of using the proposed model in a usable, flexible and straightforward way.