# On the Determination of Meshed Distribution Networks Operational Points after Reinforcement
## 1 Introduction
Building upon this direction, bibliography proposes electric vehicle charging regulation that is organized on the transmission system or to the level of the distribution network based on a range of decision factors [16] that have to do with operational conditions or production from intermitted renewables. Having mentioned the above, electric vehicles are to be charged in a way to provide always the necessary load [17]. This approach would contribute in achieving grid operational optimization as it is also projected in this study. This optimization would be based on the incoming intermittent energy from renewables connected to the distribution grid and the voltage profile of the line under investigation. Except from the electric vehicles, the already widely developed connection of distributed generation [18] affects the operation of the grid. It creates bidirectional power flows [19] and changes network topology. As expected, network planning with distributed generation research applies similar to meshed network planning optimization methods [20; 21]. Meshed planning in conjunction to the connection of distributed generation could potentially enhance network's operation [22]. It has to be mentioned that in our work all components are assumed as functioning; however, in practice there could be malicious and faulty units [23]. Several optimization methods are being used for distribution network analysis, including Tabu search (TS), Simulated annealing (SA), Genetic algorithm (GA), Evolutionary strategy (ES), Artificial immune system (AIS), Ant colony optimization (ACO), Ant colony system (ACS), Particle swarm optimization (PSO) and Hybrid TS/GA (Memetic) [24]. This research is based on the Monte Carlo method [25], which is implemented to a plethora of applications [26], including network planning. On power systems, it is widely used to simulate probabilistic phenomena [27] in general, and power flows for steady state simulations [28]. Active distribution [29] and expansion [30] planning under uncertainty remains an issue of paramount importance. The dynamic programming method is a viable alternative [31]. Monte Carlo is frequently the method of choice to this direction [32]. It requires, even for today's standards, high computational power; hence, for the simulations of this work, Okeanos cloud computing [33] is used, which is a supercomputing facility, organized on virtual machines available to the research community. All the proposed methods for distribution network planning seek to optimize a value directly related to grids' operation. This could be total installation and operational cost, minimum power or copper losses or voltage deviation. The proposed solutions up to today offer a deterministic approach towards solving the given electric vehicle connection capacity problem. However, they lack the capability to enhance user's decision making capability offering additional information of system robustness, behavior and performance after reinforcement. This could be a useful tool for engineers who engage in network planning, operation and large users of the system. In this paper, a calculation procedure is developed in an effort to upgrade the operation of distribution grids, providing benefits in terms of their optimal point of operation that can be used for demand management purposes. It can be complimentary to any other optimization method for distribution grid planning. Moreover, it aims to demonstrate that distribution medium voltage network is better if operated as meshed and to provide solutions in terms of network planning. Its main contribution is the application of a novel method, based on Monte Carlo and objective functions to evaluate decisions for network reinforcement. This research is organized in three sections. Section 2. 'System under examination and problem formulation' describes the network under investigation and the procedure that it was followed to tackle the research question. Section 3, 'Results and Discussion' provides the outcome of our analysis and its commenting.