• Energy Research

Nowadays, the electric power system and natural gas network are becoming increasingly coupled and interdependent. A harmonized integration of natural gas and electricity network with bi-directional energy conversion is expected to accommodate high penetration levels of renewables in terms of system flexibility. This work focuses on the steady-state analysis of the integrated natural gas and electric power system with bi-directional energy conversion. A unified energy flow formulation is developed to describe the nodal balance and branch flow in both systems and it is solved with the Newton–Raphson method. Both the unification of units and the per-unit system are proposed to simplify the system description and to enhance the computation efficiency. The applicability of the proposed method is demonstrated by analyzing an IEEE-9 test system integrated with a 7-node natural gas network. Later, time series of wind power and power load are used to investigate the mitigation effect of the integrated energy system. At last, the effect of wind power and power demand on the output of Power to Gas (P2G) and gas-fired power generation (GPG) has also been investigated.

This paper provides a two-stage stochastic programming approach for joint operating multi-energy systems under uncertainty. Simulation is carried out in a test system to demonstrate the feasibility and efficiency of the proposed approach. The test energy system includes a gas subsystem with a gas source and a gas storage facility, and electricity subsystem that includes a coal-fired power plant, a combined heat and power unit and a power-to-gas facility, and a conventional district heating subsystem.

Frequency control over networks is done using the frequency droop control technique which has the simplicity advantage although it allows that, in certain situations, frequency control is not very efficient. Artificial intelligence techniques have been increasingly used, so it is justified to explore their viability in electrical networks. The present work analyzes the use of Artificial Intelligence in networks to improve the frequency droop control. In order to realize this, a deep reinforcement learning (DRL)-based agent is proposed to tune the controller parameters for voltage source converter (VSC) in this paper. The DRL-based agent is trained by numerous hybrid grid operation conditions to lean the optimal control policy, which make it achieve a good adaptability to variety of operation conditions. For the purpose of demonstrating this method, a time-domain simulation model of hybrid power system is built with MATLAB/Simulink to act as test system. The simulation results verify the effectiveness of the proposed method.

The potential of offshore wind energy has been commonly recognized and explored globally. Many countries have implemented and planned offshore wind farms to meet their increasing electricity demands and public environmental appeals, especially in Europe. With relatively less space limitation, an offshore wind farm could have a capacity rating to hundreds of MWs or even GWs that is large enough to compete with conventional power plants. Thus the impacts of a large offshore wind farm on power system operation and security should be thoroughly studied and understood. This paper investigates the impacts of integrating a large-scale offshore wind farm into the transmission system of a power grid through VSC-HVDC connection. The concerns are focused on steady-state voltage stability, dynamic voltage stability and transient angle stability. Simulation results based on an exemplary power system are presented and analyzed.

The potential of renewable energy should be fully exploited in the transportation sector to achieve a cleaner production. Therefore, this paper proposes an on-grid hybrid hydrogen refueling and battery swapping station powered by wind energy. This novel concept can promote the development of low-carbon emission vehicles including hydrogen-based vehicle and battery electric vehicle. During the daily operation of the station, the multiple uncertainties may lead to a higher operational cost. To address this problem, a hybrid stochastic/distributionally robust optimization method is proposed to handle different uncertainties for the energy management problem. The first type of uncertainties can be depicted by a certain distribution, i.e. electricity price and wind power, which is processed by a stochastic optimization method. The second type of uncertainties is associated with human behaviors and is difficult to find its probability distribution, i.e. the hydrogen demand of hydrogen-based vehicles, so the second type is processed by a distributionally robust optimization method. The overall objective is to minimize the total operational cost of the station, which also considers the battery swapping station overstock punishment. Because a reasonable battery swapping scheduling can reduce the waiting time of users and operational cost of the station. The results indicate that the proposed method can effectively address the conservatism of solutions as its total operational cost is 4.4% lower than that of the hybrid stochastic/robust optimization method under a high confidence level.

Due to the increasing penetration level of tidal power in power system, the tidal turbines can provide less frequency support than conventional generators due to their small rotor mass. This makes the power system with low inertia and cause frequency problem. This paper presents a simulation model of a tidal power farm based on a MW-level variable speed tidal turbine with doubly-fed induction generator (DFIG) developed in the simulation tool of Matlab/Simulink. According to the reserve capacity required for primary frequency control, a de-loading control method is proposed in this paper to resolve the issue of primary frequency control via tidal power plant. Based on the analysis method of the frequency control characteristics of DFIGs, it is proposed by improved variable pitch control method. The control strategy, which is based on pitch control system of tidal turbine, is proposed in order to participate into primary frequency regulation of power system. Simulation results show that the proposed control strategy is effective means the tidal turbines with DFIG generators could providing frequency support for power system when they are working under the de-loading condition in this paper.

Significant penetration of distributed generation in many distribution systems has opened an option of operating distribution systems in island mode for economical and technical reasons. However, balancing frequency of the islanded system is still an issue to be solved, especially when the demand exceeds the generation in the power island. This paper presents a strategy to shed an optimal number of loads in the island to stabilize the frequency. The load shedding strategy is based on frequency information, rate of change of frequency, customers' willingness to pay, and loads histories. Different scenarios have been simulated with results showing that the proposed method is effective in shedding the optimal number of loads while stabilizing frequency. Udgivelsesdato: April