• Energy Research

Abstract In recent years, rapidly increasing electric power demand and organizational concerns regarding social and environmental issues have resulted in significant attention being given to microgrids. However, sustainable microgrids that simultaneously address economic benefits and environmental and social issues have not been broadly explored by researches. This study addresses the sustainable microgrid design problem by leveraging blockchain technology to provide the real time-based demand response programs. Three sustainable objectives (economy, environment, and society) are formulated by a multi-objective mixed integer-linear programming model. A robust fuzzy multi-objective optimization approach is proposed to determine the optimal number, location, and capacity of renewable distributed generation units as well as the equilibrium supply and dynamic pricing decisions under uncertain demand, capacity, and economic, environmental, and social parameters. The proposed model and solution approach are then applied to a case study in Vietnam. We find that blockchain technology-based sustainable microgrid can result in a 1.68% and 2.61% increase of profitability and consumer satisfaction, respectively, and a 0.97% reduction of environmental impacts.

The global climate change leads to more extreme meteorological conditions such as icing weather, which have caused great losses to power systems. Comprehensive simulation tools are required to enhance the capability of power system risk assessment under extreme weather conditions. A hybrid numerical simulation scheme integrating icing weather events with power system dynamics is proposed to extend power system numerical simulation. A technique is developed to efficiently simulate the interaction of slow dynamics of weather events and fast dynamics of power systems. An extended package for PSS/E enabling hybrid simulation of icing event and power system disturbance is developed, based on which a hybrid simulation platform is established. Numerical studies show that the functionality of power system simulation is greatly extended by taking into account the icing weather events.

The N-1 security principle is a key principle for distribution operation. However, it is not taken into account in modern distribution network reconfiguration models, which cannot satisfy security demands. And in order to solve this fundamental problem, this paper proposes a new reconfiguration method for distribution network considering N-1 security constraints. Firstly, distribution system security region are introduced to analyze real-time security as well as sub-concepts of security boundary and security distance are analyzed. Secondly, distribution feeder reconfiguration model is introduced to minimize the net loss considering N-1 security operation constraints. Thirdly, the model is solved by the improved genetic algorithm. Finally, the model of this paper is compared to the existing model and the results show that the reconfiguration strategy can satisfy the N-1 security principle and is better than modern models considering security of power systems, which prove that the proposed model and solving method are correct and effective.

Abstract This paper proposes a distributed coordinated active and reactive power control scheme for wind farms based on the model predictive control (MPC) along with the consensus-based distributed information synchronization and estimation, which can optimally dispatch the active power of wind turbines (WTs) and regulate the voltages within the wind farm. For the active power control, the pitch angle and generator torque of WTs are optimally controlled to alleviate fatigue loads of WTs while tracking the power reference of the wind farm required by system operators. For the reactive power/voltage control, the reactive power outputs of WTs are controlled to mitigate the voltage deviations and simultaneously optimize reactive power sharing. Considering the high R / X ratio of the wind farm collector systems, the impact of active power variations on voltages is taken into account to improve the voltage regulation. The proposed scheme is center-free and only requires a sparse communication network. Each WT only exchanges information with its immediate neighbors and the local optimal control problems are solved in parallel, implying good scalability and flexibility for large-scale wind farms. The predictive model of a WT is derived and then the MPC problem is formulated. A wind farm with ten WTs was used to verify the proposed control scheme.

Solar energy plays an important role in the global energy framework for future. Comparing with conventional generation systems using fossil fuels, the cost structure of photovoltaic (PV) systems is different: the capital cost is higher while the operation cost is negligible. Reliabilities of the PV system can also influence the cost for producing electricity. Investors, planners and regulators require deep insight into the return and cost of a PV project. A reliability based economical assessment of large-scale PV systems has been conducted utilizing Universal Generating Function (UGF) techniques. The reliability models of solar panel arrays, PV inverters and energy production units (EPUs) are represented as the corresponding UGFs. The expected energy production models for different PV system configurations have also been developed. The expected unit cost of electricity has been calculated to provide informative metrics for making optimal decisions. The proposed method has been applied to determine the PV system configuration which provides electricity for a water purification process.

This paper develops a novel model free-predictive flux vector control method (MF-PFVC) for high-performance stator flux and torque monitoring of the multi-module three-phase PMSM (N ∗ 3-Phase PMSM) drive with parameters mismatch. The proposed MF-PFVC method is based on a convenient combination of direct predictive torque control (PTC) and model free predictive control techniques. First, the voltage source inverter (VSI) vector control of N ∗ 3-Phase PMSM is determined by the three-segment stator windings structure. Then, the parameter mismatch sensitivity of the N ∗ 3-Phase PMSM drive system is analyzed based on the conventional PTC. Furthermore, a novel MF-PFVC method with multivariable unknown disturbance integral terminal sliding mode observer (SMO) is proposed for N ∗ 3-Phase PMSM drive, which can effectively enhance robustness against the parameters mismatch. Finally, compared with the conventional PTC, the simulation and experimental results of MF-PFVC method illustrated improved stator flux control performance concerning lower stator flux ripple as well as reduced torque ripple while it can eliminate the influence of parameter mismatch.