Search

The paper presents reliability-based operation (RBO) of an energy hub consisting of electric vehicles (EVs) and a combined cooling, heating, power (CCHP) system in the electricity, natural gas, and district heating networks. The proposed strategy aims to minimize the total expected operating and reliability costs of the mentioned energy networks as objective functions. Furthermore, the problem is subject to optimal power flow (OPF) equations, reliability constraints, and the hub energy model including constraints of EVs parking lot and CCHP. This strategy has a mixed-integer nonlinear programming (MINLP) model; hence, mixed-integer linear programming (MILP) will be utilized to achieve a unique optimal solution in less computational time for the proposed scheme. Moreover, the scheme includes uncertainties of the load, energy price, the demand of EVs, and equipment availability of the given systems, which are modeled using scenario-based stochastic programming (SBSP). Finally, the approach is implemented on a standard test system in the GAMS software environment. Then, according to the numerical results, it is observed that the mentioned outline can achieve optimal operation conditions and high reliability in energy systems if the EVs and CCHPs are optimally managed in the energy hub form.

Abstract Inertial navigation is an edge computing-based method for determining the position and orientation of a moving vehicle that operates according to Newton’s laws of motion on which all the computations are performed at the edge level without need to other far resources. One of the most crucial struggles in Global Positioning System (GPS) and Inertial Navigation System (INS) fusion algorithms is that the accuracy of the algorithm is reduced during GPS interruptions. In this paper, a low-cost method for GPS/INS fusion and error compensation of the GPS/INS fusion algorithm during GPS interruption is proposed. To further enhance the reliability and performance of the GPS/INS fusion algorithm, a Robust Kalman Filter (RKF) is used to compensate the influence of gross error from INS observations. When GPS data is interrupted, Kalman filter observations will not be updated, and the accuracy of the position will continuously decrease over time. To bridge GPS data interruption, an artificial neural network-based fusion method is proposed to provide missing position information. A well-trained neural network is used to predict and compensate the interrupted position signal error. Finally, to evaluate the effectiveness of the proposed method, an outdoor test using a custom-designed hardware, GPS, and INS sensors is employed. The results indicate that the accuracy of the positioning has improved by 67% in each axis during an interruption. The proposed algorithm can enhance the accuracy of the GPS/INS integrated system in the required navigation performance.