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This paper gives a novel hybrid optimization method to find optimal sitting and operation of an autonomous MG at the same time. The operation is optimized via finding the optimal droop gain parameters of DGs. The optimization problem is formulated as a multi-objective problem where the objectives are applied to minimize the fuel consumption of DGs and to improve the voltage profile and stability of MG subject to operational and security constraints. A hybrid algorithm, named HS-GA, is developed to solve the paper optimization problem. A new formulation of power flow is derived to run the proposed algorithm where the steady state frequency of system, reference frequency, reference voltage and droop coefficients of DGs, based on a droop controller, are considered as optimization variables. The performance of the paper approach is compared with other optimization and non-optimization methods in MG with 33and 69 buses using MATLAB. The performance of the proposed method is compared with a method that the parameters of DGs are pre-determined without conducting any optimization process. The results show, which optimized droop parameters improves the operation of the MG. [ABSTRACT FROM AUTHOR]

To meet the 2050 CO 2 targets, the shipping industry which is responsible for about 3% of global CO 2 emissions needs to be optimized in several aspects. Obviously, alternative fuels constitute the main measure in this respect. However, relatively high fuel prices in combination with increasing political and economic pressure may raise the need for more efficient ship operation. Ship route optimization can make an indispensable contribution to achieving this goal. In this sense, this paper applies an innovative approach for route optimization using Reinforcement Learning (RL). For this purpose, a generic ship model is first developed using Artificial Neural Networks (ANNs) to predict the fuel consumption of the ship. Moreover, various RL methods, namely Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO) are applied. The application of RL enables continuous action space and simultaneous optimization of ship speed and heading. DDPG demonstrates the best results as an off-policy and policy gradient method which allows a continuous action space. For example, in the fuel consumption minimization scenario without time limitation, this method can achieve savings of 6.64%. For DQN as a method with discrete action space, this value is 1.07%. [Display omitted] • ANN models provide an accurate and realistic representation of the ship's fuel consumption. • Reinforcement learning enables continuous action space for route optimization. • DDPG proves to be the best option for ship route estimation with RL. • Compared to the reference conditions, fuel savings of up to 6.64% are achieved. • The advantages of continuous action space over discrete action space are confirmed. [ABSTRACT FROM AUTHOR]

Identifying location and operation of DGs are vital in effective performance of autonomous MGs. This paper gives a novel optimization method for finding the optimal operation and location of DGs. The optimal operation is obtained via setting DGs economic droop parameters which operate based on droop control. A multi-objective function is developed to provide a new problem formulation for finding the optimal operation of DG units. The objectives include the fuel consumption cost, voltage stability index and the total voltage variation of MG. A hybrid optimization algorithm is proposed to solve the problem by combing the harmony algorithm (HS) and mutation and crossover operators of the genetic algorithm (GA). To find the best solution of non-dominated results a fuzzy method is employed. The performance of the paper approach is compared with other optimization and non-optimization methods in MG with 33 and 69 buses using MATLAB. The results support a good performance for the paper approach. [ABSTRACT FROM AUTHOR]