• Energy Research

In this paper, an optimal power flow problem is formulated in order to minimize the total operation cost by considering real-time pricing in DC microgrids. Each generation resource in the system, including the utility grid, is modeled in terms of operation cost, which combines the cost-efficiency of the system with the demand response requirements of the utility. By considering the primary (local) control of the grid-forming converters of a microgrid, optimal parameters can be directly applied to the control of this level, thus achieving higher control accuracy and faster response. The optimization problem is solved in a heuristic way by using genetic algorithms. In order to test the proposed algorithm, a six-bus droop-controlled DC microgrid is used as a case-study. The obtained simulation results show that under variable renewable generation, load, and electricity prices, the proposed method can successfully dispatch the resources in the microgrid with lower total operation costs.

In this paper, an economic dispatch problem for total operation cost minimization in DC microgrids is formulated. An operating cost is associated with each generator in themicrogrid, including the utility grid, combining the cost-efficiency of the system with demand response requirements of the utility. The power flow model is included in the optimization problem, thus the transmission losses can be considered for generation dispatch. By considering the primary (local) control of the grid-forming converters of a microgrid, optimal parameters can be directly applied to this control level, thus achieving higher control accuracy and faster response. The optimization problem issolved in a heuristic method. In order to test the proposed algorithm, a six-bus droop-controlled DC microgrid is used in the case studies. Simulation results show that under variable renewable energy generation, load consumption and electricity prices, the proposed method can successfully reduce the operating cost by dispatching economically the resources in the microgrid.

Recently, microgrids are attracting increasing research interest as promising technologies to integrate renewable energy resources into the distribution system. Although many works have been done on droop control applied to microgrids, they mainly focus on achieving proportional power sharing based on the power rating of the power converters. With various primary source for the distributed generator (DG), factors that are closely related to the operation cost, such as fuel cost of the generators and losses should be taken into account in order to improve the efficiency of the whole system. In this paper, a multiagent-based distributed method is proposed to minimize the operation cost in AC microgrids. In the microgrid, each DG is acting as an agent which regulates the power individually using a novel power regulation method based on frequency scheduling. An optimal power command is obtained through carefully designed consensus algorithm by using sparse communication links only among neighbouring agents. Experimental results for different cases verified that the proposed control strategy can effectively reduce the operation cost.

With the increasing penetration of Internet of Things devices and distributed energy resources in the next-generation distribution network, the efficient energy management for system operation are facing new challenges. One reason is that the large-scale resources cannot be all connected to the supervisory control and data acquisition system, which have limited storage and computation capabilities. In order to adapt to the new energy management requirements of next-generation distribution networks, a state-of-the-art energy management method called cloud-fog hierarchical architecture is proposed in this work. Based on this architecture, we established a utility and revenue model for various stakeholders, including normal customers, prosumers, and distribution system operators. Furthermore, by embedding an artificial intelligence module in the proposed architecture, energy management could be implemented automatically. In this work, neural network are used at fog computing layers to achieve regression prediction of energy usage behavior and power source output. Moreover, based on the maximizing utility objective function, the amount of energy consumption of customers and prosumers in the distribution network was optimized with a genetic algorithm at cloud layer. The proposed methods were tested with a set of normal customers and prosumers in a general distribution network, and the results, including the captured usage patterns of the customers and revenues of various stakeholders, verify the effectiveness of the proposed method. This work provides an effective reference for the development of real-time energy management systems for the next-generation distribution network.

Increasing power electronics controlled distributed generation resources and active loads provide a future distributed system with more controllability as well as challenges to synthesize heterogeneous devices. From system engineering's perspective, control and operation of the future distribution system need more insight into the system architecture of the grid. In this paper, in light of the start-of-the-art control strategies for microgrids which rely on power electronics systems, a grid architecture model for future distribution system is proposed based on microgrid clusters. Both the physical and cyber structures for this architecture are described. Two illustrative examples are presented to explain different control methods that can be adopted in this model to harmonize different devices. This architecture can be used to guide the system design for the smart distribution system.

Due to higher power quality, lower conversion loss, and more DC loads, there has been an increasing awareness on DC microgrid. Previous emphasis has been on equal power sharing among different units in the DC microgrid, whileoverlooking the coordination of the energy storage units to maintain the State-of-Charge balance. In this paper, a new droop method based on voltage scheduling for State-of-Charge balance is proposed to keep the SoC balance for the energy storage units. The proposed method has the advantage of avoiding the stability problem existed in traditional methods based on droop gainscheduling. Simulation experiment is taken in Matlab on a DC microgrid with two distributed energy storage units. The simulation results show that the proposed method has successfully achieved SoC balance during the load changes whilemaintaining the DC bus voltage within the allowable range.