Microgrids expansion problems with battery energy storage (BES) have gained a great attention in recent years. To ensure reliable, resilient, and cost effective operation of microgrids, the installed BES must be optimally sized. However, critical factors that have a great impact on the accuracy and practicality of the BES sizing results are normally overlooked. These factors include the wide range of characteristics for different technologies, the distributed deployment, the impact of depth of discharge and the number of charging/discharging cycles on the BES degradation, and the coordination of microgrid operation modes. Thus, this paper proposes a comprehensive BES sizing model for microgrid applications which takes these critical factors into account when solving the microgrid expansion problem and accordingly returns the optimal BES size, technology, number, and maximum depth of discharge. The microgrid expansion problem is formulated using mixed integer linear programming. The nonlinear relationship between BES depth of discharge and lifecycle is linearized using piece wise linearization technique and implemented to model BES degradation. The conducted numerical simulation shows that the proposed model is able to determine the optimal BES size, technology, number, and maximum depth of discharge and further enhances the accuracy and practicality of the BES sizing solution.

In this paper, a novel energy management framework for energy Internet with many energy bodies is presented, which features multicoupling of different energy forms, diversified energy roles, and peer-to-peer energy supply/demand, etc. The energy body as an integrated energy unit, which may have various functionalities and play multiple roles at the same time, is formulated for the system model development. Forecasting errors, confidence intervals, and penalty factor are also taken into account to model renewable energy resources to provide tradeoff between optimality and possibility. Furthermore, a novel distributed-consensus alternating direction method of multipliers (ADMM) algorithm, which contains a dynamic average consensus algorithm and distributed ADMM algorithm, is presented to solve the optimal energy management problem of energy Internet. The proposed algorithm can effectively handle the problems of power-heat-gas-coupling, global constraint limits, and nonlinear objective function. With this effort, not only the optimal energy market clearing price but also the optimal energy outputs/demands can be obtained through only local communication and computation. Simulation results are presented to illustrate the effectiveness of the proposed distributed algorithm.

With an increasing penetration of wind power in the modern electrical grid, the increasing replacement of large conventional synchronous generators by wind power plants will potentially result in deteriorated frequency regulation performance due to the reduced system inertia and primary frequency response. A series of challenging issues arise from the aspects of power system planning, operation, control and protection. Therefore, it is valuable to develop variable speed wind turbines (VSWTs) equipped with frequency regulation capabilities that allow them to effectively participate in addressing severe frequency contingencies. This paper provides a comprehensive survey on frequency regulation methods for VSWTs. It fully describes the concepts, principles and control strategies of prevailing frequency controls of VSWTs, including future development trends. It concludes with a performance comparison of frequency regulation by the four main types of wind power plants.

This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.

A novel inertial control method based on the torque limit control (TLC) is proposed in this study for the purpose of maximizing the temporary inertial response of permanent magnet synchronous generator-wind turbine generator (PMSG-WTG) over a wide range of variable wind speed conditions. To eliminate the secondary frequency drop issue during the rotor speed restoration, a small-scale battery energy storage system (BESS) is utilized by adopting the coordinated control strategy between BESS and PMSG-WTG. For the sake of minimizing the excessive use of BESS energy, this control strategy can smoothly discontinue the participation of BESS once the system frequency restores to the specified value. The simulation results conclude that the overall system frequency regulation performance can be significantly improved through such coordination control of BESS, PMSG-WTG, and conventional generators for the enhanced inertial response under different wind power penetration levels as well as variable wind speed conditions. Furthermore, the potential impact of TLC on the mechanical structures of wind turbine throughout the inertial response is investigated by using the CART2-PMSG integrated model.

This paper introduces a new consensus-based control scheme for distributed power systems to solve the distributed economic dispatch (DED) problem. With the development of renewable energy, smart grid, and microgrid, more and more distributed generators will be connected to power systems. Thus the distributed control schemes become more and more important for power systems to control and manage the distributed equipment. Therefore, some consensus-based methods are published previously to solve the DED problem. But the challenge to apply the consensus-based method is to meet the power balance constraint without the centralized facility, since the value of power mismatch is hard to be obtained for a distributed control system. To solve the problem, this paper proposes the method to combine the frequency control methods with the consensus protocol from graph theory to develop the DED control method, so that the real power between the load and the generation can be balanced during the economic dispatch. With this method, a totally distributed optimal control scheme is obtained. The PI frequency controller and neural network frequency controller are employed to develop the DED control method. Then the communication failure problem is considered in this paper. The robust DED control method is proposed to improve the robustness of this distributed control method, allowing the system to stay at the optimal dispatching even if the communication is failed. In addition, the model of transmission line losses is incorporated in the DED problem to reduce the line losses. Finally, several case studies are given in this paper to verify the proposed method.