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• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

In low-inertia grids, distributed energy storage systems can provide fast frequency support to improve the frequency dynamics. However, the pre-determination of locational demands for distributed energy storage systems is difficult because the classical frequency dynamic equivalent response cannot capture the dynamic characteristics of the entire system. Additionally, optimal allocation of the distributed energy storage systems required for the different buses is challenging because of nonlinear constraints that account for these locational effects. This paper develops a method to evaluate locational frequency security. By simulating the worst-case G-1 contingency, the distribution network buses that violate the limit of the maximum rate of change of frequency are determined as the installation locations of distributed energy storage systems. We then propose a feasible region-based linearization method to replace the complex nonlinear constraints. Finally, we present a two-stage robust allocation model for distributed energy storage systems that is intended to maintain locational frequency security. The validity of the proposed method is verified through case studies performed on a modified IEEE RTS-24 bus system and a modified IEEE 118 bus system. The results show that the optimally allocated distributed energy storage systems effectively reduce the frequency safety risk that originally existed due to oscillation at the distribution network buses. • A locational frequency security evaluation method after G-1 contingencies is proposed. • Considering frequency oscillations, a feasible region-based linearization method is designed. • Distributed storage systems are optimally allocated for locational frequency security. [ABSTRACT FROM AUTHOR]

• A hierarchical approach is proposed considering quartile method for optimization. • Proposed CEEMDAN-DMD can regress the energy based on pre-set energy proportion. • Proposed hierarchical allocation strategy can be used for severe power volatility. The development of renewable energy dominated power system and the integration of large-scale wind-storage energy system are inevitable trends of the future society. This paper developed a new methodology of hierarchical power allocation strategy considering wind power fluctuation. A novel idea is presented that fully considers the fluctuation and energy proportion. Based on the Quartile Method (QM) and Hampel-Savitzky Golay (HSG) strategy, a layered-based method that considers the volatility interval to effectively overcome the limitations of a single strategy caused by severe volatility interval is proposed. Then, an improved power division method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Dynamic Mode Decomposition (DMD) is proposed where CEEMDAN is used to decompose the Intrinsic Mode Function (IMF) of the remaining fluctuations. Furthermore, according to the required setting of energy proportion, DMD strategy is used for power fitting of the hybrid energy storage system (HESS). Through experimental verification, the hierarchical strategy proposed in this paper can effectively reduce the probability of severe volatility interval or distortion interval and ensure the sequence in the ideal interval. Moreover, the proposed CEEMDAN-DMD strategy can realize the power division according to the energy proportion demand. In model verification, the volatility improvement ζλ 1 min = 85.4766%. Set the energy demand ≥ 80%, the fitting energy proportion accounts for 87.7533%, which outperforms the traditional power division method. In addition, it is demonstrated that the theoretical method proposed in this paper is reliable and promising. [ABSTRACT FROM AUTHOR]

• This paper proposes a weighted consistency algorithm based on DMPC and state constraints, which takes into account the power constraints of each BESU and can quickly realize the adaptive power allocation of multiple energy storage units with less communication times. • Based on the proposed consistency algorithm, this paper designs a grouping coordination control strategy for energy storage units, which can reduce the charge/discharge conversion times of BESU, and prolong the life of energy storage system and improve the energy conversion efficiency without double capacity configuration and without reducing the rated power of BESS. • Based on the proposed consistency algorithm, this paper designs a method to reduce the low-power (low-efficiency) operation ratio of BESU, so as to improve the operation efficiency of the energy storage system. This paper proposes a distributed packet consistency control strategy to solve the distributed coordinated control problem among multiple battery storage units. In order to ensure that the distributed power allocation results meet the power constraints of each energy storage unit and reduce the communication requirements of distributed control, this paper proposes a weighted consistency algorithm based on distributed model predictive control and state constraints. A grouping control strategy of energy storage units is proposed. The weight of energy storage unit is set according to SOC information to improve consistency of SOC of energy storage unit. Meanwhile, based on the proposed consistency algorithm, an inter-group coordination control strategy and an efficiency improvement strategy of energy storage units are developed to improve the regulation ability of energy storage system, extend the life of energy storage system and improve the energy conversion efficiency. Finally, the proposed algorithm and control strategy are simulated and analyzed by building a microgrid system with eight battery storage units in Matlab. The simulation results show that the proposed algorithm and control strategy have certain advantages in improving convergence speed, optimizing control effect, prolong energy storage life and improve the operation efficiency of the energy storage system. [ABSTRACT FROM AUTHOR]

Energy management algorithms of microgrids have attracted growing attention for the efficient operation and coordination of active end-users. In developing these algorithms, leader–follower game approaches could provide effective strategies when the behavioral and operational characteristics of active end-users are integrated into the methodology. However, since these approaches require the exact implementation of the strategy determined by the leader, it is of great importance to deal with the uncertainty of renewable energy generation, charging/discharging energy of storage systems, and load consumption. Thus, possible negative effects of these uncertainties on the outcome can be minimized with an appropriate billing procedure. In this paper, a Stackelberg game approach is proposed for the energy sharing management of a microgrid including prosumers and plug-in electric vehicle (PEV) charging stations (CSs), and a proper billing scheme is designed to increase the robustness of the approach on real-time applications. In the hereby game-theoretic approach, the energy demands of PEV CSs are determined as variable demands considering the operational characteristics of CSs. The energy planning of CSs is based on determining the number of PEVs that each station can accept over the vehicle charging levels for a specified time interval. Also, by the necessary modifications identified for the presented approach, the microgrid can operate independently from the utility grid and satisfy the requirements of islanded mode for the time slots in which the microgrid exchanges energy with the grid based on the end-game results. The energy management strategy proposed in this study presents a comprehensive solution capable of optimizing energy sharing in microgrids while taking into account end-users' operational characteristics and network requirements. Furthermore, the designed billing scheme ensures that the accuracy of the proposed game approach remains unaffected by the uncertainties, thus preserving the microgrid's profit. • Energy sharing management problem of a microgrid is modeled as a Stackelberg game. • Loads of PEV charging stations are determined by an integer programming problem. • The microgrid can operate independently from the utility grid under islanded mode. • A billing scheme is proposed to minimize the negative effects caused by uncertainties. [ABSTRACT FROM AUTHOR]

• Accordingly, a flexible traction power supply system (FTPSS) composed of a TT and multi-port power hub and its coordinated control strategy are proposed for VU compensation and energy conservation in this paper. The main contributions can be summarized as follows: • Different from previous research, a power hub is installed in the TPSS to achieve power flow control among the utility grid, traction network, and DC grid. The FTPPS with the power hub can enable VU compensation for the utility grid, which also offers a friendly interface with distributed PV and ESS for on-site access. • A three-port VU compensation method based on active power flow control and reactive power injection is presented, which divides the system demand compensation capacity and reduces the performance requirements for power electronics, compared with traditional compensation methods. • A coordinated control strategy with a hierarchical structure is designed for FTPSS. The control strategy enables real-time power management to enhance energy utilization and mitigate VU in the system layer and coordinates multiple converters to realize the multi-function operation of the system in the converter layer. To mitigate voltage unbalance (VU) and eliminate the neutral sections while reducing the energy consumption of railways, a flexible traction power supply system (FTPSS) with the power hub is proposed. Different from conventional schemes, the proposed system provides a friendly interface to realize the on-site access of distributed photovoltaic (PV) generation along the railway line and concentrated integration of energy storage systems. The power flow is centrally managed by the power hub to reduce energy consumption and the proposed three-port VU compensation method is coupled with the power hub to suppress negative sequence current caused by trains. Considering the volatility of PV generation and traction loads, four operation modes including grid-connected, valley filling, VU compensation, and peak clipping mode, are proposed to dynamically regulate the operation of FTPSS. Moreover, a coordinated control strategy with a hierarchical structure is developed to enable the operation modes. The strategy is implemented with a global controller and multiple local controllers, which can realize real-time power management and flexible transition among different modes. Finally, different simulation and experimental case studies based on the field data are carried out to comprehensively verify the feasibility and effectiveness of the proposed FTPSS and control strategy. [ABSTRACT FROM AUTHOR]

• A novel smart soft open point (sSOP) is proposed to interconnect between railway network with distribution grid. • The sSOP provides additional flexibility and controllability for both networks. • An integrated simulation platform of rail and grid has been developed to evaluate the proposed sSOP. • The aggregated braking efficiency of the railway network is improved. • The local consumption of the RES in the LV grid is increased. • The energy consumption from the LV grid supplying point is reduced. This paper presents a novel smart soft open point (sSOP) that interconnects railway electrification system to the local low voltage (LV) distribution grid to provide additional flexibility and controllability for both networks. The proposed sSOP builds on the concept of soft open points, adding an energy storage system and embedding a rail and grid (R + G) energy management system to enable the energy transfer between the two networks at different average power levels. The smartness of the proposed soft open point is realised in the integration of an energy storage system to act as an energy buffer between these two networks of different dynamics. The paper demonstrates that the proposed sSOP improves the braking efficiency of trains and increase the local use of renewable energy through employing an integrated simulation platform between a railway and smart grid simulators that takes into account the trains' timetables as well as the daily variation of the grid loads and renewable energy generation. The results show that an appropriate design and control of the sSOP enables the recuperation of most of the braking energy available from the railway and that energy is enough to mainly supply the local power distribution grid, thereby effectively implementing the concept of smart grids. [ABSTRACT FROM AUTHOR]

• Coordinating conventional Volt-VAr devices with distributed energy resources. • Incorporating demand response program into the Volt-VAr control problem. • Utilizing ε-constraint approach to generate Pareto optimal solutions. • Using IGDT to develop a robust decision-making approach. • Developing a mixed-integer linear programing framework. Distributed energy resources (DERs), such as renewable energy sources (RESs) and energy storage systems (ESSs), are inevitable parts of future power grids. However, the high penetration of RESs may cause voltage violation issues in distribution levels. To this end, the application of the Volt-VAr management (VVM) scheme is highlighted. This paper aims to maintain voltage levels within standard ranges with optimal coordination of different resources at minimum operational costs and voltage deviation. In more detail, this article develops a new VVM in distribution networks to effectively coordinate novel smart grid technologies such as PV inverters, time of use demand response program (TOU-DRP), energy storage systems (ESSs) with traditional devices, including on-line tap changer transformer (OLTC) and switchable capacitor banks (SCBs). Because the coordinated VVM is a bi-objective model that minimizes both operating cost and voltage deviation, it is proposed to use the ε-constraint technique to identify optimal Pareto solutions. To handle the uncertainty related to RESs and load demands, a robust technique based on information gap decision theory (IGDT) is also utilized. The formulation is configured as a mixed-integer linear program (MILP) and solved by CPLEX. It was implemented on the 118-bus distribution network under various conditions. The simulation results clearly show that the coordination of DERs and traditional devices has a significant impact on maintaining voltage levels within permissible ranges while minimizing operational costs. A higher degree of uncertainty radius can also be obtained if DRP participates in the VVM scheme. [ABSTRACT FROM AUTHOR]

In DC electric railways, energy storage systems (ESSs) have been addressed to assist in the energy efficiency improvement, which is achieved by exploiting the captured excess braking energy of decelerating trains in order to reduce the traction energy demand of the accelerating trains. Conventionally, stationary ESSs are assumed to have access to the substations and grid, which is being a significant shortcoming since the ESS in many locations such as the London Underground has neither access to the traction substations nor external sources. Since ESSs cannot have infinite capacity and have practical restrictions on the state of charge (SOC), much of the regenerative energies during braking are being lost, which reduces the benefits of ESSs in enhancing energy efficiency of electric railways. In this paper, boosting the energy efficiency of the system is achieved by proposing a new adaptive control method based on fuzzy logic control (FLC) to dynamically control a stationary ESS connected to a DC track system. Therefore, the power flow between the track and the ESS with respect to the storage capacity is managed without affecting the benefits yielded by the system. The proposed control method is simulated and tested experimentally on a real prototype including a supercapacitor ESS. Three traffic scenarios are applied to evaluate the feasibility and the performance of the proposed controller with respect to the undefined traffic scenarios matching the case in the London Underground. The proposed control method applied to three traffic scenarios have achieved energy savings of 39.2–41.4%. The experimental results are compared against the simulation results, showing a percentage error ranging from 1.8 to 3.6%. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

Renewable energy generation is inherently unpredictable and may contain unacceptably significant variations. In particular, high solar photovoltaic (PV) penetration makes it difficult for utility companies to coordinate production and consumption on the grid. PV generation with an energy storage element flattens the duck curve and eliminates the PV curtailment during the mid-day hours. This paper proposes a novel improved Gamma (Γ -) type Y-source inverter for rooftop photovoltaic (PV)-grid applications with battery storage ability. The proposed inverter can maintain high voltage gain even under partial shading conditions, where the PV voltage at the maximum power point is far away from the open circuit PV voltage. The proposed inverter allows plant owners to charge the battery from PV source during low-peak loading hours and can reduce power import during high-peak loading hours (or) export the excess power to the distribution grid, which increases the return on investment. The operation of the proposed inverter is analysed in three modes (a) stand-alone operational mode without battery, (b) stand-alone operational mode with battery, and (c) stand-alone operational mode with PV and battery. A scaled-down laboratory prototype of 70 W is created to test the functionality of the proposed inverter and validate the simulation results. Experiments show that the proposed Improved Gamma type YSI can generate a boosted ac voltage using independent PV and battery sources, as well as their combination. • A novel improved Gamma (Γ -) type Y-source inverter is proposed for rooftop PV-grid applications with battery storage ability. • The proposed inverter can maintain high voltage gain even under partial shading conditions. • The proposed system flattens the duck curve energy demand and stores excess energy during the over-generation period, avoiding the curtailment operation. • The proposed system aids in the tracking of MPP power from PV modules when partial shading is present. • Experiments show that the proposed inverter can generate a boosted AC voltage using independent PV and battery sources, as well as their combination. [ABSTRACT FROM AUTHOR]

• Decision makers' subjective perceptions are considered in the configuration. • MMBES can earn extra benefit in disaster scenarios. • Credit capacity of MMBES is used to calculate the years of delaying expansion. • Case study verifies the feasibility of the MMBES optimal configuration method. The comprehensive utilization of energy storage and the resilience of power grid in disaster scenarios are critical research objects in distribution network. However, the traditional literatures were mainly focused on the fixed energy storage devices. Meanwhile, conventional energy storage planning did not consider its utility in disaster scenarios. In this paper, a prospect theory-based optimal configuration of modular mobile battery energy storage (MMBES) is proposed to tackle the shortcomings. To better leverage the capabilities of MMBES, operation and coordination under normal, fault and disaster scenarios are studied, and five attributes of the energy storage configuration are extracted by analyzing various beneficial types. Then, a prospect theory-based optimal configuration model is established to maximize the comprehensive prospect value with the consideration of uncertain disaster scenarios. Sub-models of network loss reduction, reliability improvement, delay of network expansion, outage cost in disaster scenarios, and installation cost are well integrated. Finally, the established non-linear optimization model is solved by utilizing the differential evolution approach. Case studies demonstrate that the flexible application of MMBES can significantly improve the comprehensive benefits and that the optimal configuration can reflect the preference of decision makers. [ABSTRACT FROM AUTHOR]

• A P2P energy trading structure in a prosumer community with self-build PV and ESS. • An energy-trading model with utility maximum model and hierarchical pricing model. • ADMM with predictor–corrector mechanism is introduced to solve decentralized problems. With the continuous expansion of distributed energy resources (DERs) in the demand side, the distributed peer-to-peer (P2P) energy-trading market in prosumer community receives extensive research interests. A proper transaction structure and pricing model are inevitable. This paper proposes a bilateral energy-trading structure considering the energy trading agent (ETA) with self-built photovoltaic (PV) and energy storage system (ESS), to provide more sufficient energy transactions in community. A bilateral energy-trading model is also developed to make better use of local resources, including utility maximum model and hierarchical dynamic pricing model for the self-build energy-owed ETA and prosumers. Considering the personalized flexibilities of energy consumption and market contributions, the proposed pricing model focuses on the share of demand and supply market and demand response (DR) discount factor to make differentiated pricing decisions and promote market competitiveness. To solve the model, the distributed alternating direction method of multiplier (ADMM) with the predictor–corrector mechanism is realized. Finally, an example simulation verifies the effectiveness and advantages, that the proposed model can decrease the total cost by 5.11% and increase the ETA's income by more than 9.38% compared to other designed cases. [ABSTRACT FROM AUTHOR]

• An energy management system (EMS) with a hierarchical three-level distributed control approach is proposed for a photovoltaic/wind turbine/diesel generator with energy storage in a hybrid DC microgrid (MG). • The EMS with three different operating modes is performed to share power between in the DC MG sources optimally. • The proposed EMS for DC MG will manage the power flow in an optimum, autonomous and stable manner. • The MG system has the feature of plug-and-play flexibility and could be applied to power generation for rural and industrial areas. This paper presents a developed energy management system (EMS) with a hierarchical three-level distributed control approach proposed for a photovoltaic/wind turbine/diesel generator with energy storage in an islanded hybrid DC microgrid (MG). In the hierarchical three-level control structure, the power to be transferred by the distributed energy sources (DER) is provided by controlling the converters in the primary control layer. The secondary control layer is used to achieve DC bus voltage restoration and increase current-sharing accuracy simultaneously. In the tertiary control layer, it is aimed to minimize the production cost by performing optimum load-sharing. For the operation of this control structure, a strategy is proposed to optimally manage the energy demand of the hybrid system autonomously under variable load conditions. The EMS with three different operating modes (Mode-I, Mode-II, Mode-III) is proposed to share power between DC MG sources optimally. In Mode-I, renewable energy sources (RES) are enabled to operate with MPPT operation. Energy consumption minimization strategy (ECMS)-based linear programming (LP), genetic algorithm (GA) and pattern search (PS) algorithms are used to minimize the fuel consumption and carbon emissions of the diesel generator (DG) in Mode-II operating Mode. In Mode-III operating mode, DG is not needed, and the power flow between DERs is optimized by using the fuzzy logic controller (FLC) and state machine control strategy (SMCS). The amount of fuel consumed, the cost of fuel consumption and ESS's SOC level are used as the EMS's performance criteria for the MG. The minimum fuel consumption of the DG is calculated as 2.328 L for the operation of LP-based ECMS, and the fuel cost, in this case, is found to be $3.26. Considering the charging of the ESS, the highest SOC level is achieved with 64.87% for the PS-based ECMS-SMCS combination. By considering fuel minimization and SOC level together, it can be seen that the best result is achieved with the LP-based ECMS-SMCS combination. [ABSTRACT FROM AUTHOR]

• A framework of multi-energy system integrating with a liquid air energy storage system was proposed. • The LAES system could interact with the converter components of the combine cooling, heating and power system, such as combined heating and power, auxiliary boiler and chillers, to realize the complementary utilization of power, thermal energy and cold energy. • The information gap decision theory method was used to study power dispatching strategies of the LAES integrated systems, including risk-neutral, risk-averse and risk-taker strategy. In this paper, a framework of multi-energy system (MES) integrating with a liquid air energy storage (LAES) system was proposed. LAES, where liquid air works as an energy storage media, is a powerful and eco-friendly technology for storing renewable energy resources and reducing grid curtailment. Considering the characteristics of LAES (i.e. cold and heat circulation), the incorporation of LAES system into the Combined Cooling, Heating and Power system can achieve integrated use of energy and effectively save energy. Moreover, the prices of electricity will affect the overall cost of the MES. In other words, the decision-makers of the MES need to consider the uncertainty of electricity prices when making power dispatching decisions. To model the uncertainty of electricity prices, the information gap decision theory method was used to study power dispatching strategies of the MES. Three different strategies were proposed, including risk-neutral, risk-averse and risk-taker. In addition, demand response algorithms were used to study load transfer strategies. The results show that the demand responses of the three strategies are effective in terms of load transfer and cost saving. The total operation cost in the risk-neutral strategy with demand response can be 6.82% less than that without demand response; In the risk-taker strategy with demand response, the allowable grid electricity price is reduced by 25.24% when the opportunity cost drops by $8,000, and 23.32% without demand response. With additional robustness cost, the acceptable price change ratio using demand response is 21.91% in the risk-averse strategy, and 20.04% without demand response. [ABSTRACT FROM AUTHOR]