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• The state-of-the-art research advances in power system restoration with large renewable penetration are reviewed from different system levels, i.e., transmission system, distribution system, and coordinated transmission and distribution system. • The methods of handling renewable power uncertainty during system restoration are summarized. • The laboratory validations and practical applications of the renewable power system restoration strategies are reviewed. • Promising research directions for renewable system restoration are provided, including uncertainty handling, artificial intelligence techniques, transient stability issues, cyber-physical interlinking, and multi-energy coupling. The catastrophic blackout events and ever-increasing penetration of renewable power generation highlight an advanced restoration strategy to effectively and reliably employ renewable power generation to contribute to renewable power system restoration. This paper reviews the research advances of power system restoration involving large renewable penetration, and the methods of handling renewable power uncertainty during restoration are also summarized. First, the transmission system restoration processes are divided into three sequential stages: a) black-start, b) network reconfiguration and c) load restoration, and the research advances of these three restoration stages involving large renewable generation are presented. Then, the distribution system restoration assisted by multiple flexible resources, such as renewable distributed generators, remotely-controlled switches, energy storage systems, and soft open points, are reviewed with emerging techniques, including micro-grids, multi-agent systems, repair crews, and mobile power sources. Furthermore, with the growing activeness and flexibility of the distribution system, the coordinated transmission and distribution system restoration and their information interactions are also discussed. Finally, for practical applications, the laboratory validations of restoration strategies using realistic power grid data, real-world restoration strategies, decision support systems, and field tests of black-start procedures are introduced to complete this literature review. Effectively deploying renewable power sources can significantly improve the power system restoration efficiency, and their inherent uncertainty should be carefully handled. Moreover, promising research trends are provided to improve the power system restoration with large renewable penetration. [ABSTRACT FROM AUTHOR]

The fluctuation and intermittence of renewable energies are raising concerns about the economical scheduling and the security operation of power system. The shift from traditional fossil energies to renewable energies causes the shortage in rotational inertia and affects the frequency stability of power system. One of the promising solutions is to construct a certain number of energy storage facilities with virtual inertia in suitable places for improving stability, which simulates the characteristics of traditional generators through specific controls over the converters of energy storages. While transient issues about the optimal allocation of energy storages have not been well addressed. From the perspective of system inertia support, the system state-space model is firstly established in this paper. Then the guidance of allocating energy storages optimally is provided via ℋ 2 -norm calculation and projected gradient derivation. Moreover, for the purpose of evaluating system disturbances more precisely, an appropriate method for equivalenting the disturbance strength of nodes is proposed, the advantages of which are later examined in comparative simulations. The contribution of this paper is verified through the time-domain simulations of the New England 39-bus system together with the illustration and comparative analysis of the optimal results. • Construct the guidance of allocating energy storages optimally for inertia support. • Propose a method for equivalenting node disturbance and its transmission mechanism. • The proposed method can improve both transient performance and allocation costs. [ABSTRACT FROM AUTHOR]

Highlights • A model of district heating network is established in detail. • Utilization of the energy storage characteristics of DHN to reduce wind curtailment. • We establish a thermal and electrical coordination stochastic optimal dispatch model. • We use Benders decomposition algorithm to solve the model. • The energy storage characteristics of the DHN is quantitatively analyzed. Abstract The strong coupling between heating supply and power generation in heat and power (CHP) units limits their consumption of wind power and lacks the necessary flexibility. Based on the theory of heat transfer, this paper considers the temperature loss and energy storage characteristics on the transmission side to establish a stochastic optimal dispatch model of considering thermal and electrical coordination. Utilization of the energy storage characteristics of the district heating network (DHN) to reduce the wind power curtailment and accommodate variable wind power. Benders decomposition algorithm is used to solve the model and improve the efficiency of the solution. Finally, the effectiveness of the proposed model and algorithm was verified by a thermal/electrical system example, and the energy storage characteristics of the DHN were quantitatively analyzed. [ABSTRACT FROM AUTHOR]

Highlights • A work package of energy storage system for grid frequency regulation is proposed. • The package includes grid network modeling, ESS sizing, and control algorithms. • The proposal shows ESS is able to maintain frequency under photovoltaic systems. • The required cyclical operation of ESS for frequency regulation remains a concern. • The modularity design of the test system allows various permutations to be tested. Abstract Grid frequency deviation occurs due to the power mismatch and is corrected by a series of control actions carried out by the generators. At present, the correction of the frequency deviation is limited by the ramp rates of generators, which depend on the inertia of their rotating masses. Under the increasing penetration of intermittent renewable energy sources, the task of frequency regulation becomes more demanding in terms of response times due to the frequent power mismatches and reduction of system inertia. An energy storage system (ESS) can be an effective means of regulating the frequency due to its general fast response characteristics. A comprehensive work package is developed in MATLAB/Simulink and Matpower to study how ESS can handle the rapid changes of frequency continuously within a specified window of its state of charge (SOC). This paper includes an IEEE 24-Bus grid network modeling, the sizing of ESS and also proposes ESS controllers with offset control algorithms as the sustainable solution for frequency regulation. As such, the modularity design of the test system allows various types of ESS or loads to be tested. The locations for ESS to be placed on the network are also identified for the best frequency regulation response without causing any grid voltage violations. Meanwhile, the scenarios of high penetration of photovoltaic systems and undersized ESS are also investigated. The results indicate that ESS is able to maintain the grid frequency under the intermittency of photovoltaic systems continuously. [ABSTRACT FROM AUTHOR]

This paper proposes a simple, passive, voltage-balancing scheme for induction generators directly interfaced with single-phase utility grids. The proposed scheme employs two capacitors for balancing the stator voltage and is insensitive to the machine’s slip. The paper presents the mathematical analysis and an algorithm for the calculation of the two capacitances. Further, the effectiveness of the proposed scheme is demonstrated through the simulation studies in the PSCAD/EMTDC software environment. [ABSTRACT FROM AUTHOR]

The planning and design of an electric power system, including high-voltage direct-current transmission, is a complex optimization problem. The optimization must integrate and model the engineering requirements and limitations of the generation, while simultaneously balancing the system electric load at all times. The problem is made more difficult with the introduction of variable generators, such as wind and solar photovoltaics. In the present paper, we introduce two comprehensive linear programming techniques to solve these problems. Linear programming is intentionally chosen to keep the problems tractable in terms of time and computational resources. The first is an optimization that minimizes the deviation from the electric load requirements. The procedure includes variable generators, conventional generators, transmission, and storage, along with their most salient engineering requirements. In addition, the optimization includes some basic electric power system requirements. The second optimization is one that minimizes the overall system costs per annum while taking into consideration all the aspects of the first optimization. We discuss the benefits and disadvantages of the proposed approaches. We show that the cost optimization, although computationally more expensive, is superior in terms of optimizing a real-world electric power system. The present paper shows that linear programming techniques can represent an electrical power system from a high-level without undue complication brought on by moving to mixed integer or nonlinear programming. In addition, the optimizations can be implemented in the future in planning tools. [ABSTRACT FROM AUTHOR]

As numerous generation side assets, such as the generating units and the plant equipments, in the Smart Grid system have already aged, well planned maintenance and operational scheduling is needed to maintain their expected longevity. Forced outages and short-notice/temporary maintenance of thermal units should also be considered as likely events. Backup energy sources are required to replace the generating units during the maintenance period. Using conventional storage devices for this purpose is feasible, but costly. Electric vehicles and plug-in hybrid electric vehicles, with vehicle-to-grid capability, referred to as “gridable vehicles”, are a useful choice as storage devices for the same. Second life batteries, disassembled from gridable vehicles after passing their automotive life, are another candidate for this purpose from the economic perspective. A system model that aggregates gridable vehicles and second life batteries together in an intelligent way to provide such backup energy can reduce significant storage costs. Such a system model is presented in this paper for a Smart Grid environment. Our simulation results suggest that using gridable vehicles and second life batteries together can save up to 70% of conventional storage energy costs and recover capital costs for the second life batteries in only 1.5 years. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV) with vehicle-to-grid capability, referred to as "gridable vehicles" (GVs), have become a useful choice for storage devices in the smart grid environment. The success of a smart grid system with GVs heavily depends on the market penetration of GVs and their participation rate in the vehicle-to-grid (V2G) program. Battery lifetime and revenue earning potential are influential factors for GV owners considering whether to participate in the V2G program. Second use of GV batteries, after their automotive life, can generate some revenue for the owners to recover a part of their battery purchase cost. However, this is dependent on the remaining capacity of the battery and the capacity degradation rate both in the automotive and second life. In this paper, capacity degradation and the remaining energy of a GV battery at different operating cycles have been quantified in both their automotive and second lives. Cost of battery energy both in automotive and second life have also been modelled that informs the owners of the revenue potentials, especially from the second life use. Finally, an economic load dispatch model with the inclusion of second life revenue has been developed to establish that using GV batteries in this way would earn extra revenue thus contributing to the initial buying price and encouraging more GV participation in the smart grid. Simulation results show that the proposed model can contribute up to 19.56% of the initial battery purchase cost, while still ensuring economic load dispatch. [ABSTRACT FROM AUTHOR]

Highlights: [•] A comprehensive survey on the VSG literature, VSG topologies and achievements are presented. [•] The fundamentals and concept of VSG as a solution towards stability improvement of future grids are addressed. [•] The paper addresses a new VSG-based frequency control scheme. [•] We focused on the poetical of VSGs in the grid frequency regulation task. [•] The main relevant technical challenges, key issues, and further research needs are also emphasized. [ABSTRACT FROM AUTHOR]

• An establishment method for iLES-MDP knowledge base is proposed. • A dynamic updating approach is introduced to expand reference solutions in knowledge base. • A multi-objective optimization framework is designed to find feasible trade-off dispatch schemes. • Feasible Pareto solutions can be obtained efficiently for iLES-MDPs. Integrated local energy system (iLES) can take full advantage of local energy resources and diversify energy supplies. However, the complexity and flexibility of multiple-input multiple-output system structure makes it difficult for iLES to guarantee energy supply quality, which needs be solved by making reliable dispatching schemes. In this paper, the optimization model of typical iLES multi-objective dispatch problem (MDP) is built and the common problems are analyzed. Aiming at adapting to the changes of the iLES-MDPs model during the practical operation process, a dynamic knowledge base-based multi-objective optimization method is proposed, by which a knowledge base is established for iLES-MDPs taking account of the characteristic of system before optimization process. Besides, considering the frequent variations of system parameters, iLES structure, energy input and load demands, an updating approach is introduced to dynamically adjust the knowledge base. Thereafter, a multi-objective optimization framework is designed to find the feasible Pareto solutions for the iLES-MDPs, by utilizing the reference solutions in knowledge base during the evolutionary procedure. Finally, the proposed method is applied to a series of iLES-MDPs, which involve renewable energy sources, combined cooling heating and power system, energy storage devices and different operation modes. The simulation results verify that the method can well adapt to the challenges of all types of iLES-MDPs. Moreover, it can obtain feasible Pareto solutions efficiently as well as reduce the performance requirement of optimization algorithms. [ABSTRACT FROM AUTHOR]

• A robust optimization model is proposed to handle economic dispatch problems with uncertainties. • An improved distributed approach with self-adaptive step-size and a polynomial filter is proposed. • It has faster convergence performances and robustness to random communication failures. Power systems are confronted with prodigious challenges of scheduling and operation incurred by the high penetration of distributed renewable energy with intermittency and uncertainty. Therefore, this paper proposes an improved distributed robust optimization approach with self-adaptive step-sizes based on the line search method and a polynomial filter, to minimize the overall costs of flexible resources including conventional generators, energy storage systems, renewable energy curtailments, deferrable loads and tie-line power exchanges, while considering various constraints, such as supply-demand power balance, line congestion constraints and power output limits. Numerical case studies conducted in a modified IEEE 14-bus system and a modified IEEE 118-bus system demonstrate the reliability, robustness and extensibility of the proposed approach. In addition, the effectiveness and accuracy of the proposed distributed robust optimization approach are validated through comparisons with the traditional centralized gradient method and the convergence performance is better in contrast to other distributed optimization algorithm. [ABSTRACT FROM AUTHOR]

• An optimal configuration model of multiport SSTs integrated AC/DC hybrid system is proposed. • A general multiport SST model is established for optimal planning of SST integrated system. • An optimal strategy of multiport SSTs in parallel operation is proposed. • A coordinate strategy of AC and DC cross-zone distributed energy storage is proposed. With the rapid growth in DC energy-using equipment and distributed DC power, traditional AC distribution networks increasingly have problems, such as high loss and poor flexibility. This paper studies an AC/DC hybrid system integrated with multiport solid-state transformers (SSTs) and distributed renewable energy and proposes an optimal configuration model for the AC/DC hybrid system based on a coordinate operation strategy of SSTs and energy storage. First, the composition and topology of the AC/DC hybrid system are proposed. Then, a realistic efficiency model of a multiport SST is established. Based on the minimum operating loss of the SST, a multiport SST dual-machine parallel optimal operation strategy is established. A multi-energy storage system collaborative optimization operation strategy is established based on the electricity price, grid-connected mode and synergistic relationship among each energy storage system. Finally, based on the proposed AC/DC hybrid system model, the optimization objective of the system life cycle net present cost (NPC), constraints and GA algorithm are set. The proposed method is verified by industrial scenarios. The simulation results show that the proposed model can improve the economic and efficiency of the SST integrated AC/DC hybrid system. [ABSTRACT FROM AUTHOR]

• An MG PPF model is established considering operation modes of MGs, unbalanced three phases and non-normally distributed random variables. • The Borgonovo index is calculated by SPCE method. • The proposed GSA method identifies critical uncertainties that significantly affect MG power flow. With the development of renewable-based distributed generation (RDG), there are increasing uncertainties in the operation of microgrids (MGs), and stochastic evaluation methods are attracting more attention nowadays. In this paper, a probabilistic power flow (PPF) analysis method is proposed to evaluate the influence of uncertainties on the power flow of MGs. First, the MG PPF model is established considering different operation modes of MGs and uncertainties of RDG and load demands. Then, the Borgonovo method, which is a density-based global sensitivity analysis (GSA) method, is used to evaluate the importance of input variables in PPF calculation. To improve the computational efficiency of GSA, the sparse polynomial chaos expansion (SPCE) is used to establish the surrogate model of MG PPF, and the Borgonovo index is calculated based on the surrogate model. Finally, the procedure of applying GSA to MG power flow is established. The proposed method is tested using 33-node and 123-node MGs, and is compared with other methods to validate its effectiveness. Simulation results indicate that the proposed method identifies critical uncertainties that affect MG power flow. Based on the rankings of input variables, the influences of critical uncertainties are diminished with energy storage systems. [ABSTRACT FROM AUTHOR]