Renewable Distributed Resources Research Articles

Distributionally Robust Decentralized Scheduling Between the Transmission Market and Local Energy Hubs

The cross impacts between transmission and distribution systems have drawn extensive attention, where multi-energy carriers become increasingly dominant in local market operations. Local energy hubs, integrated with multi-energy carriers, e.g., electricity, gas, heat, present great potentials to provide adequate power and reserve support for the transmission system, which could mitigate issues such as boundary mismatches in the upstream market. However, energy hubs are typically equipped with abundant distributed renewable resources. Such local uncertainties can adversely affect the well-being of the coordinated multi-energy market hierarchy. This paper presents a novel multi-period scheduling framework that considers the coordination between the transmission network and distribution energy hubs. Each agent performs local scheduling operations capturing independent uncertainties via a distributionally robust formulation. We then apply a tailored accelerated augmented Lagrangian algorithm embedded with the column-and-constraint method to decentralize the overall operation with agents' privacy preserved. The fast and convergent feature of the algorithm ensures its scalability and reliability in real-world applications of the energy hub design with transmission coordination. Numerical experiments confirm the efficacy of the proposed method.

Reconfigurable Distribution Automation and Control Laboratory: Solar Microgrid Experiments

Renewable distributed resources, energy storage systems, and improved load control options are introducing new challenges in electric power distribution. It is necessary to integrate such smart grid concepts into formal power engineering curricula. To help meet this need, solar microgrid experiments are being developed at Drexel University. This work presents: first, an overview of the laboratory-scale microgrid equipment at Drexel; second, upgrades to the distribution energy management system in the Reconfigurable Distribution Automation and Control Laboratory; third, a detailed educational microgrid experiment; and fourth, corresponding pre- and postlaboratory assignments. In the experiment, students analyze a microgrid network in grid-powered, grid-interactive, and island modes with a renewable source that is not electrically (or physically) co-located with the utility supply. Students will observe power flow characteristics in each mode. In island mode, students will make network or load control decisions in order to satisfy the operating constraints, and then connect the renewable source to the node of their choice.

Simultaneous allocation of electric vehicles’ parking lots and distributed renewable resources in smart power distribution networks

Electric vehicles (EVs) and distributed renewable resources (DRRs) are introduced to achieve three of the most pivotal objectives of this century: using environmentally-friendly energy resources, reliable supply of the load demand, and sustainable development of power systems. To achieve the aforementioned goals, simultaneous utilization of DRRs and EVs should be implemented in a scheduled manner. In this paper, we propose a two-stage approach for allocation of EV parking lots and DRRs in power distribution network. Our method considers both the economical benefits of parking lot investor and the technical constraints of distribution network operator. First, the parking lot investor offers the candidate buses for installing the parking lot to the distribution network operator based on economic objectives. Then, the distribution network decision-making is obtained to reduce loss of system. The proposed framework not only improves the distribution network loss, but also ameliorates the availability of the parking lot from the economical point of view. In order to solve the formulated optimization problem, we utilize two optimization techniques. Genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are used for the distribution network loss minimization purpose. Besides, we model the EV parking lot by expanding single EV probabilistic model. The performance of the proposed method is evaluated by allocating DRRs and EV parking lots simultaneously on the IEEE standard distribution test system. This system is bus 2 of Roy Billinton Test System (RBTS).

A novel direct-current vector control technique for single-phase inverter with L, LC and LCL filters

Single-phase grid-connected converters (GCCs) are widely used to connect small-scale distributed renewable resources to the grid. However, unlike a three-phase system, design of the vector control for a single-phase GCC is more challenging. This paper presents a novel vector control design for a single-phase GCC based on a direct current vector control method. The design of the proposed vector controllers considered different filtering schemes, including L, LC and LCL filters. The behavior of the proposed direct current vector control is investigated through both simulation and hardware experimentation. Both demonstrate that the proposed control approach has strong ability to trace rapidly changing reference commands, tolerate system disturbances, and satisfy various control needs of single-phase GCCs under different filtering configurations.

Control of Single-Phase Grid-Connected Converters with LCL Filters Using Recurrent Neural Network and Conventional Control Methods

Single-phase grid-connected inverters are widely used to connect small-scale distributed renewable resources to the grid. However, unlike a three-phase system, control for a single-phase inverter is more challenging, especially when the inverter is used with an LCL filter. This paper proposes a novel recurrent neural network-based vector control method for a single-phase inverter with an LCL filter. The neural network is trained based on adaptive dynamic programming principle, and the objective of the training is to approximate optimal control. The Levenberg–Marquardt plus forward accumulation through time algorithm is developed for training the proposed recurrent neural network controller. The neural network vector control approach is compared with the conventional control methods, including the conventional PI-based vector control method and the PR-based control technique for single-phase inverters. Both the simulations and hardware experiments demonstrate the great advantages of the proposed neural network vector control over the conventional control methods. Compared with conventional control methods, the neural network control allows for low sampling rate and low switching frequency, while maintaining high performance in controlling a single-phase inverter. In addition, no specific damping policy is required to implement the proposed neural network vector control for an LCL -filter based single-phase inverter. The study shows that the neural network vector control is a robust control method, and can provide better control performance even when facing system parameter changes, while under this case, both the conventional PI-based vector control and the PR-based control failed to yield the acceptable results.

Challenges and R&D opportunities of smart distribution grids in China

In this paper, 5 investigations on the current states of power distribution grids in China have been introduced that cover the surveys of the practical distribution asset utilization, the simultaneity factor of feeder loading, the load compositions and characteristics, the urban reliability and the integration of distributed generations of renewable resources. Based on the survey results in the investigations, 12 Facts are clearly presented. It shows that the challenges to modernize the distribution grids in China are to improve the asset utilization rates, the electricity efficiencies and the reliability of distribution systems, as well as to integrate increasing amount of variable distributed renewable resources. Furthermore based on the challenges, 8 R&D opportunities have been identified.