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This article discusses the need for a multi-grade pricing strategy for emergency power supply services provided by mobile energy storage systems. The authors argue that the existing uniform pricing model fails to meet the differentiated demands of customers and undermines their enthusiasm for purchasing emergency supply services. They propose a hierarchical trading framework and a bi-level pricing optimization model based on a Stackelberg game to obtain tiered prices of emergency power supply and increase the revenue of mobile energy storage while reducing the cost for customers. The article provides insights and discussions on this pricing strategy, considering power supply capacity, duration, and response time as metrics for differentiating power supply levels. The document also suggests future research directions, such as integrating real-time scheduling of mobile energy storage into the pricing model. The research was supported by the Guangdong Basic and Applied Basic Research Foundation and the Scientific Research Startup Fund for Shenzhen High-Caliber Personnel. Additionally, there is a list of references cited in an article or research paper, which includes various sources related to the topic of energy storage systems, such as studies on optimal configuration and operation methods, pricing strategies, and the use of mobile energy storage in distribution networks. The document provides a range of perspectives and approaches to the subject, allowing library patrons to explore different aspects of energy storage for their research. [Extracted from the article]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the physical power information system undergoes continual advancement, mobile energy storage has become a pivotal component in the planning and orchestration of multi-component distribution networks. Furthermore, the evolution and enhancement of big data technologies have significantly contributed to enhancing the rationality and efficacy of various distribution network planning and layout approaches. At the same time, multi-distribution networks have also confronted numerous network attacks with increasing probability and severity. In this study, a Petri net is initially employed as a modeling technique to delineate the network attack flow within the distribution network. Subsequently, the data from prior network attacks are consolidated and scrutinized to evaluate the vulnerability of the cyber-physical system (CPS), thereby identifying the most critical network attack pattern for a multi-component distribution network. Following this, the defender-attacker-defender planning methodology is applied for scale modeling, incorporating rapidly evolving mobile energy storage into the prelayout, aiming to mitigate the detrimental impact of network attacks on the power grid. Ultimately, the column and constraint generation (C&CG) algorithm is utilized to simulate and validate the proposed planning strategy in a 33-node system with multiple control groups established to demonstrate the viability and merits of the proposed strategy. [ABSTRACT FROM AUTHOR]

The rapid growth of renewable energy is integrated into the distribution system. The creation of both new market mechanisms and investment models has critical effects on the economics and security of the distribution market. Mobile energy storage has been used to increase the resilience of distribution grids due to their advantages in mobility and flexibility, which offer electricity arbitrage options for merchant investments. This paper presents a bi-level optimization framework based on location marginal pricing settlement of mobile energy storage financial rights revenue in active distribution systems. In the developed framework, the upper-level problem determines the optimal capacity, routing, and dispatching of mobile energy storage to maximize revenue in the liberalized electricity market. The lower-level problem performs the joint optimization of energy and reserve market clearing as well as renewable energy capacity optimization based on the alternating current optimal power flow model. The non-convexity of the line flow and location marginal pricing is linearized via second-order cone relaxation and Karush-Kuhn-Tucker. The bi-level programming is reformatted as mathematical programming with equilibrium constraints. Further, the revenue risk due to renewable energy uncertainty is measured via conditional value-at-risk. Finally, the effectiveness of the optimization framework and solution method is verified using the modified IEEE-33 test system. The results show that mobile energy storage promotes renewable energy and reduces distribution network costs by 2.3%.

The increasing integration of renewable energy sources such as wind and solar into the distribution grid introduces new complexities and instabilities to traditional electrical grids. This study tackles these challenges by optimizing the configurations of Modular Mobile Battery Energy Storage (MMBES) in urban distribution grids, particularly focusing on capacity-limited areas. Our method investigates five core attributes of energy storage configurations and develops a model capable of adapting to the uncertainties presented by extreme scenarios. This approach not only enhances the adaptability of energy storage systems but also equips decision-makers with proactive and flexible tools for decision-making in complex environments. Empirical evidence from the study shows that modular mobile energy storage significantly improves distribution grid performance by effectively managing the challenges posed by renewable integration. Furthermore, the research confirms that optimizing decision-makers’ cognitive parameters to align with subjective preferences ensures economic viability and enhances grid resilience. This study offers a new perspective and methodology for configuring energy storage, contributing to more flexible and reliable grid operations amidst widespread renewable integration.

As the physical power information system undergoes continual advancement, mobile energy storage has become a pivotal component in the planning and orchestration of multi-component distribution networks. Furthermore, the evolution and enhancement of big data technologies have significantly contributed to enhancing the rationality and efficacy of various distribution network planning and layout approaches. At the same time, multi-distribution networks have also confronted numerous network attacks with increasing probability and severity. In this study, a Petri net is initially employed as a modeling technique to delineate the network attack flow within the distribution network. Subsequently, the data from prior network attacks are consolidated and scrutinized to evaluate the vulnerability of the cyber-physical system (CPS), thereby identifying the most critical network attack pattern for a multi-component distribution network. Following this, the defender–attacker–defender planning methodology is applied for scale modeling, incorporating rapidly evolving mobile energy storage into the pre-layout, aiming to mitigate the detrimental impact of network attacks on the power grid. Ultimately, the column and constraint generation (C&CG) algorithm is utilized to simulate and validate the proposed planning strategy in a 33-node system with multiple control groups established to demonstrate the viability and merits of the proposed strategy.

Abstract Mobile energy storage (MES) has the flexibility to temporally and spatially shift energy, and the optimal configuration of MES shall significantly improve the active distribution network (ADN) operation economy and renewables consumption. In this study, an optimal planning model of MES is established for ADN with a goal of minimising the annual cost of a distribution system. Firstly, the annual cost of a distribution system is set up with consideration of the investment cost and operation cost of MES, wind and PV curtailment cost, network loss cost and the peak‐valley arbitrage income of MES. Then, the distributed photovoltaic and wind power access constraints, power conservation constraints of ADN, power generation constraint, system security constraint, energy coupling and displacement constraints of MES are further tailored to establish the MES planning model. Afterwards, the proposed model is solved by the second‐order cone relaxation combined with the large M algorithm. Finally, the simulation results of the modified IEEE 33‐bus distribution network validate the effectiveness of the proposed model.

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Mobile energy storage (MES) is a typical flexible resource, which can be used to provide an emergency power supply for the distribution system. However, it is inevitable to consider the complicated coupling relations of mobile energy storage, transportation network, and power grid, which can cause issues of complex modeling and low efficiency. To address that, this paper proposes a mobile energy storage dispatch model to minimize the load curtailment. The framework of rolling optimization is established to update the optimal strategy with the real-time factors, including grid, transportation, and MES. To increase solving efficiency, the model is extended to a mixed integer second-order cone programming model. Finally, the simulation results demonstrate the effectiveness of the proposed method in various scenarios.

• proposing a MESS-assisted bulk power system parallel restoration framework. • Utilizing MESSs to transform non-black start (BS) units to MESS-assisted BS units. • Formulating a MESS mobility model in the transportation networks. • Developing an integrated restoration model considering optimal MESS dispatch. During bulk power system restoration, black-start (BS) units are used to crank non-BS units in such a way that the over system generation capability is maximized. However, traditional power system restoration methods rely on stationary BS units, whose number is limited due to high investment and geographic conditions, impeding a fast system restoration. To enhance restoration efficiency, this paper proposes an integrated power system parallel restoration method considering the support of mobile energy storage systems (MESSs), which can provide cranking power to non-BS units and transform them into MESS-assisted BS units. MESSs are routed and scheduled via transportation network, along with restoration planning in power network by integrating them into a mixed integrated linear programming model. Specifically, to describe the interactive behavior between MESSs and the power system, a mobility model for MESSs is proposed, which indicates the travel path, travel time and connection nodes. Then, an integrated optimization model involving BS unit location, zone partitioning, network reconfiguration and generator start-up is formulated. The proposed method was simulated in the modified IEEE 30-bus transmission system coupled with 11-node transportation system. The results show that, in contrast with a decoupled restoration method, the proposed method increases system generation capability by 6.3%. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to reach the optimal active and reactive power operation of multiple dispersed resources consisting of mobile energy storage system (MESS), demand response (DR) and photovoltaic (PV), for flexibility improvement of distribution network with uncertain PV and DR, minimization of power loss and operation cost whilst satisfaction of both power factor and voltage variation requirement. Especially, the flexibility aspect of distribution network is focused due to its significance for supporting economic operation without voltage rise issue during high PVs integration. Firstly, the active and reactive power operation spaces of MESS and PV inverter are discussed under power factor constraint. Then, the stochastic characteristics of PV generation and DR of microgrids are investigated using probability distribution. After that, the optimization framework coordination with dispersed MESS, PV inverter and DR to ensure operational flexibility of distribution network is proposed. Finally, the total cost minimization based flexibility improvement approach is presented by optimizing power loss, uncertain risk, operation cost of distribution network and MESS, satisfying operation constraints of both distribution network and dispersed resources. Simulation results conducted on the IEEE 69-bus system demonstrate the effectiveness of the proposed approach for PV accommodation, voltage quality improvement as well as peak load shaving.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the large-scale integration of renewable energy and changes in load characteristics, the power system is facing challenges of volatility and instability. Therefore, enhancing the safe and stable operation capability of the power system is an urgent problem that needs to be solved. Mobile energy storage can improve system flexibility, stability, and regional connectivity, and has the potential to serve as a supplement or even substitute for fixed energy storage in the future. However, there are few studies that comprehensively evaluate the operational performance and economy of fixed and mobile energy storage systems. To comprehensively evaluate the economic benefits of large-scale mobile energy storage systems, this paper constructs an overall horizontal cost model for energy storage systems that considers the power system and train transportation system. This model not only introduces the traditional concept of lifecycle cost of energy storage systems, but also considers the costs of traditional transmission lines and battery transportation. Secondly, to achieve simulation of large-scale mobile energy storage system planning and operation, this paper establishes a multi-region power planning and operation simulation (MPO) model and a battery transportation and logistics (BTL) model to accurately reflect the operation mode of fixed energy storage and mobile energy storage in the power system. Finally, taking the actual power grids and railway networks in Northeast and North China as case studies, this article provides an in-depth analysis of the technical, economic, and environmental performance of large fixed and mobile energy storage systems, and conducts a detailed comparison. The research results indicate that under high grid connection ratios (using 75% and 66% as examples), the overall cost of mobile energy storage systems continues to decrease to 1.42 CNY/kWh and 0.98 CNY/kWh. Compared to fixed energy storage at 5.45 CNY/kWh and 4.79 CNY/kWh, it has an absolute economic advantage and shows significant carbon reduction capabilities, reaching 241,800 to 1,394,600 tons. This discovery fully confirms the enormous potential and application value of mobile energy storage in high proportion renewable energy scenarios, providing strong technical support and economic analysis basis for the sustainable development of the power system. • The overall levelized cost model of large-scale mobile energy storage system is established. • Tech-economic performance of fixed and mobile energy storage system is compared. • The proposed method can improve system economics and renewable shares. [ABSTRACT FROM AUTHOR]

The increasing of distributed energy and flexible load incentivize market participants to participate in a more active market. A coordinated peer to peer (P2P) trading model with an aggregated alliance and reserve purchasing is proposed in this paper. Under such a coordinated trading model, the market participants form an alliance, where the agents perform aggregated alliance and purchase reserve from a mobile energy storage supplier. To reduce the risk of deviation penalty in P2P trading, the agents seek to maximize the welfare of the entire alliance in the P2P process, while considering product differentiation and deviation risks. The proposed trading model design comprises: (i) a coordinated P2P market design with the aggregated alliance and reserve purchasing, (ii) a two-stage P2P market-clearing model to maximize trading utility while reducing the risk of deviation. To solve this two-stage multivariable coupling problem in a distributed way, we propose a primal–dual based ADMM method. Through the case study, compared with the traditional P2P trading model, the proposed market model can reduce the deviation penalty and improve the comprehensive welfare, while the convergence can be effectively guaranteed.

A mobile energy storage system (MESS) is a localizable transportable storage system that provides various utility services. These services include load leveling, load shifting, losses minimization, and energy arbitrage. A MESS is also controlled for voltage regulation in weak grids. The MESS mobility enables a single storage unit to achieve the tasks of multiple stationary units at different locations. The MESS is connected to the grid at specific substations (or buses) known as MESS stations. This paper proposes an optimization algorithm for sizing and allocation of a MESS for multi-services in a power distribution system. The design accounts for load variation, renewable resources intermittency, and market price fluctuations. A realistic dynamic model for the MESS is adopted to consider the capacity and lifetime constraints. A detailed network power flow model is utilized to include voltage constraints, feeders, and transformers ampacity in the problem formulation. By considering all these constraints, the resulting sizing problem is a mixed-integer nonlinear problem. This paper presents the problem formulation and proposes a solution using a hybrid optimization technique. The adopted technique is based on the particle swarm algorithm and mixed-integer convex programming. A case study is conducted on a real 41-bus radial feeder to validate the proposed sizing technique, and investigate the MESS profitability to the system operator.

For remote structures in military applications located in high mountains and outlying islands, the configuration of isolated microgrids constructed in these locations is highly important. To denote oxygen generation/seawater desalination devices and military battery packs in practical applications, shiftable load models and mobile energy storage models are used. A multiobjective optimization model, including the annual system cost, demand shortage rate, and the ratio of diesel energy supply, is constructed for configuration design. This model is solved using the improved preference-incentive coevolution algorithm (PICEA-ng) presented in this paper. Based on practical data and numerical simulation, the utilization strategy in terms of lost time and output power for mobile energy storage, stationary energy storage, diesel generators, and shiftable loads are generated and validated. Finally, the operational performance of the shiftable load and mobile energy storage over the entire microgrid system is analyzed and discussed.

Multi-microgrids have gained interest in academics and industry in recent years. Multi-microgrid (MG) allows the integration of different distributed energy resources (DERs), including intermittent renewables and controllable local generators, and provides a more flexible, reliable, and efficient power grid. This research formulates and proposes a solution for finding optimal location and operation of mobile energy storage (MES) in multi-MG power distribution systems (PDS) with different resources during extreme events to maximize system resiliency. For this purpose, a multi-stage event-based system resiliency index is defined and the impact of the Internet of things (IoT) application in MES operation in multi-MG systems is investigated. Moreover, the demand and price uncertainty impact on multi-MG operational performance indices is presented. This research uses a popular PG & E 69-bus multi-MG power distribution network for simulation and case studies. A new hybrid PSO-TS optimization algorithm is constructed for the simulations to better understand the contributions of MES units and different DERs and IoT on the operational aspects of a multi-MG system. The results obtained from the simulations illustrate that optimal operation of MES and other energy resources, along with the corresponding energy sharing strategies, significantly improves the distribution system operational performance.

Mobile energy storage systems (MESSs) provide promising solutions to enhance distribution system resilience in terms of mobility and flexibility. This paper proposes a rolling integrated service restoration strategy to minimize the total system cost by coordinating the scheduling of MESS fleets, resource dispatching of microgrids, and network reconfiguration of distribution systems. The integrated strategy takes into account damage and repair to both the roads in transportation networks and the branches in distribution systems. The uncertainties in load consumption and the status of roads and branches are modeled as scenario trees using Monte Carlo simulation method. The operation strategy of MESSs is modeled by a stochastic multi-layer time-space network technique. A rolling optimization framework is adopted to dynamically update system damage, and the coordinated scheduling at each time interval over the prediction horizon is formulated as a two-stage stochastic mixed-integer linear program with temporal–spatial and operation constraints. The proposed model is verified on two integrated test systems, one is with Sioux Falls transportation network and four 33-bus distribution systems, and the other is the Singapore transportation network-based test system connecting six 33-bus distribution systems. The results demonstrate the effectiveness of MESS mobility to enhance distribution system resilience due to the coordination of mobile and stationary resources. [ABSTRACT FROM AUTHOR]

• This paper presents a review on the state-of-the-art electric vehicle charging technologies i.e., fast, super-fast, and ultra-super-fast charging stations that are under development. These have the potential to cause power quality issues such as charging transients, rapid voltage fluctuations, and harmonics in the power grids. • EVs can participate as mobile storage to provide vehicle-to-grid (V2G) support and ancillary services. There are still some barriers to the wide implementation of V2G systems. This paper has highlighted these issues and their impacts to power grids. • This paper investigates the impacts of random and fluctuating EV fast-charging loads on the electric power grids, mainly considering the random connection of EVs to the power grids through DC fast-charging stations as the principal source of fluctuating EV loads. • The charging stations are modeled for three different commercial DC fast charger connections (CHAdeMO, SAE CCS, and Chargepoint Express 20), with separate CC-CV charging modes of the DC fast chargers incorporated. To quantify the impacts, case studies are conducted on the simulation platform for two different scenarios. • The impact on the electric power grid is evaluated in terms of voltage drops, maximum power demand, current, and voltage total harmonic distortion (THD) experienced by the corresponding transformer connected to the charging stations. • The results have revealed that random and fluctuating EV fast-charging loads pose much more intense impacts on the power systems than the normal EV slow charging loads. The era of the electrified transportation system is fast approaching. Although the socioeconomic and environmental benefits of electric vehicles (EVs) have contributed to their large-scale utilization, it has also created a huge load demand on the existing power grids throughout the world. Moreover, fast, super-fast, and ultra-super-fast charging stations are under development, some of which are now in the markets. These have the potential to cause power quality issues such as charging transients, rapid voltage fluctuations, and harmonics in the power grids. Moreover, EVs can participate as mobile storage to provide vehicle-to-grid (V2G) support and ancillary services. There are still some barriers to the wide implementation of V2G systems. This paper addresses these issues and provides a review of the state-of-the-art EV technologies and their impacts on power grids. This paper also investigates the impacts of random and fluctuating EV fast-charging loads on the electric power grids, mainly considering the random connection of EVs to the power grids through DC fast-charging stations as the principal source of fluctuating EV loads. A practical electrical grid of Wollongong, New South Wales, Australia has been considered in this work to separately analyze the impacts of constant current (CC) and constant voltage (CV) charging modes upon the grid. Furthermore, design and modeling of three different commercial DC fast charger connections (CHAdeMO, SAE CCS, and ChargePoint Express 200), with separate CC-CV charging modes of the DC fast chargers have been incorporated. To quantify the impacts, two separate scenarios were examined using a simulation platform, with case studies conducted to determine the impacts on the power grid. The first scenario involved three fast charging stations, while the second scenario featured ten stations that were able to charge six and twenty electric vehicles respectively, with various load combinations considered. Each of these scenarios was analyzed under different conditions to evaluate their impact on the grid, using factors such as voltage drops, maximum power demand, current, and voltage total harmonic distortion (THD) for the transformer that was connected to the charging stations. The study results indicated that the power systems were affected more significantly by random and fluctuating EV fast-charging loads, compared to normal EV slow-charging loads. [ABSTRACT FROM AUTHOR]

Natural disasters can lead to large-scale power outages, affecting critical infrastructure and causing social and economic damages. These events are exacerbated by climate change, which increases their frequency and magnitude. Improving power grid resilience can help mitigate the damages caused by these events. Mobile energy storage systems, classified as truck-mounted or towable battery storage systems, have recently been considered to enhance distribution grid resilience by providing localized support to critical loads during an outage. Compared to stationary batteries and other energy storage systems, their mobility provides operational flexibility to support geographically dispersed loads across an outage area. This paper provides a comprehensive and critical review of academic literature on mobile energy storage for power system resilience enhancement. As mobile energy storage is often coupled with mobile emergency generators or electric buses, those technologies are also considered in the review. Allocation of these resources for power grid resilience enhancement requires modeling of both the transportation system constraints and the power grid operational constraints. These aspects are discussed, along with a discussion on the cost–benefit analysis of mobile energy resources. The paper concludes by presenting research gaps, associated challenges, and potential future directions to address these challenges.

Electrochemical energy storage (ES) units (e.g., batteries) have been field-validated as an efficient back-up resource that enhances resilience of distribution systems. However, using these units for resilience is insufficient to justify their installation economically and, therefore, these units are often installed in locations where they yield the greatest economic value during normal operations. Motivated by the recent progress in mobile ES technologies, i.e., ES units can be moved using public transportation routes, this paper proposes using this spatial flexibility to bridge the gap between the economically optimal locations during normal operations and the locations where extra back-up capacity is necessary during disasters. We propose a two-stage optimization model that optimizes investments in mobile ES units in the first stage and can re-route the installed mobile ES units in the second stage to form dynamic microgrids (MGs) and to avoid the expected load shedding caused by disasters. Since the proposed model cannot be solved efficiently with off-the-shelf solvers, even for relatively small instances, we apply the progressive hedging algorithm. The proposed model and algorithm are tested on a 15-bus radial distribution test system. [ABSTRACT FROM AUTHOR]

• A multi-period distributionally robust resilient enhancement model is proposed for transmission and distribution coordinated systems and a modified three-level analytical target cascading algorithm is applied to solve it. • Mobile energy storage is applied and shows benefits in improving load restoration after natural disasters. • An unmanned aerial vehicle dispatch model after earthquakes is established to restore the communication system, and the overlay control Lloyd algorithm is applied to solve this model. Extreme natural disasters can seriously disrupt power systems. The increased damage intensity of natural disasters also leads to synchronous failures in communication systems. Mobile energy storage and unmanned aerial vehicles have high economy and flexibility, so they can provide various services including power support and temporary information transmission when disasters occur and disable the whole system. To address the unavailability of the communication system and improve the collaborative restoration of transmission and distribution systems, this paper proposes a multi-period distributionally robust resilience enhancement model (MPDRRM) for the transmission and distribution coordinated system, and a large quantity of mobile energy storage and unmanned aerial vehicles are rented before natural disasters and dispatched to support critical loads and maintain power balance during restoration. The MPDRRM is divided into three kinds of sub-problems: i) a communication system sub-problem, ii) a transmission system sub-problem, iii) several distribution system sub-problems, and solved by a modified three-level analytical target cascading algorithm. Case studies demonstrate the benefits of mobile energy storage and unmanned aerial vehicles in improving load restoration and increasing the resilience of a TDCS against natural disasters. [ABSTRACT FROM AUTHOR]

This paper presents a proactive operation scheme for improving distribution system resiliency against natural hazards, specifically windstorms. In this context, important attributes associated with the windstorm consisting the distance from the windstorm route, the wind speed, the distance from tall trees and buildings, and cable type are used in a deep neural network (DNN) engine to identify the vulnerable branches and predict their failure during the windstorm. The DNN predictive model is integrated in the proposed scheme. Afterwards, a power flow-based optimization engine is employed to proactively enhance the grid resiliency. Grid resiliency is measured by the inevitable action of load shedding. For minimum load shedding, the optimization engine reconfigures the network topology, optimizes the droop parameter settings, and allocates mobile energy storage systems (ESSs) before the arrival of the windstorm. This optimization engine is integrated in the proposed scheme. To validate its performance, the proposed proactive scheme is tested on a 33-bus test system with a mix of diesel units (DUs), wind turbines (WTs), and photovoltaic units (PVs). The simulation results demonstrate that without the proposed learning mechanism, the load shedding can reach up to 36% for the system under study, while the learning-based scheme can reduce the load shedding to 13%. The proposed learning-based proactive operation scheme would substantially improve the distribution system resiliency during windstorms. • A proactive scheme is given for distribution systems against extreme weather events. • Addition of a learning feature to the proactive grid optimization against windstorms. • Employing a DNN-based learning method with improved accuracy over existing methods. • Consideration of event-driven dynamic features and key static factors. • Application of multiple resources for proactive scheduling of distribution systems. [ABSTRACT FROM AUTHOR]

Multi-microgrids have gained interest in academics and industry in recent years. Multi-microgrid (MG) allows the integration of different distributed energy resources (DERs), including intermittent renewables and controllable local generators, and provides a more flexible, reliable, and efficient power grid. This research formulates and proposes a solution for finding optimal location and operation of mobile energy storage (MES) in multi-MG power distribution systems (PDS) with different resources during extreme events to maximize system resiliency. For this purpose, a multi-stage event-based system resiliency index is defined and the impact of the Internet of things (IoT) application in MES operation in multi-MG systems is investigated. Moreover, the demand and price uncertainty impact on multi-MG operational performance indices is presented. This research uses a popular PG & E 69-bus multi-MG power distribution network for simulation and case studies. A new hybrid PSO-TS optimization algorithm is constructed for the simulations to better understand the contributions of MES units and different DERs and IoT on the operational aspects of a multi-MG system. The results obtained from the simulations illustrate that optimal operation of MES and other energy resources, along with the corresponding energy sharing strategies, significantly improves the distribution system operational performance. [ABSTRACT FROM AUTHOR]

Natural disasters can lead to large-scale power outages, affecting critical infrastructure and causing social and economic damages. These events are exacerbated by climate change, which increases their frequency and magnitude. Improving power grid resilience can help mitigate the damages caused by these events. Mobile energy storage systems, classified as truck-mounted or towable battery storage systems, have recently been considered to enhance distribution grid resilience by providing localized support to critical loads during an outage. Compared to stationary batteries and other energy storage systems, their mobility provides operational flexibility to support geographically dispersed loads across an outage area. This paper provides a comprehensive and critical review of academic literature on mobile energy storage for power system resilience enhancement. As mobile energy storage is often coupled with mobile emergency generators or electric buses, those technologies are also considered in the review. Allocation of these resources for power grid resilience enhancement requires modeling of both the transportation system constraints and the power grid operational constraints. These aspects are discussed, along with a discussion on the cost–benefit analysis of mobile energy resources. The paper concludes by presenting research gaps, associated challenges, and potential future directions to address these challenges. [ABSTRACT FROM AUTHOR]

The global share of renewable energy sources (RES) in total generation capacity reached 34.7% in 2019 and has been continuously increasing. Power system flexibility addressing the uncertainty and variability of RES has become a major concern in energy transition. This paper proposes to apply mobile energy storage (MES) from independent MES owners as a flexibility-enhancement ancillary service in the day-ahead electricity market. First, we have proposed a market-based methodology to incent MES owners to provide flexibility service in power systems by simultaneously releasing Locational Marginal price (LMP) and Uncertainty Marginal Price (UMP). The LMP and UMP were obtained by solving robust unit commitment and security-constrained economic dispatch, representing the need for flexibility resources. Second, the optimal operational strategy of MESs was established to respond to LMP and UMP and maximize their profits by providing energy and reserve flexibility subjects to the reserve and traffic constraints. Third, a game-theoretic model was developed to determine market equilibrium by minimizing the operational costs of the power system and maximizing the profits of MES owners. To avoid homogenous behaviors of MESs in solving this model, a sequential dispatching algorithm was developed such that each MES of one MES owner has a unique price signal. An objective function update strategy was accordingly developed to take the interaction among MESs into consideration and ensure a maximum total profit for MES owners. Finally, case studies with IEEE 6-bus system, IEEE 118-bus system, and MATPOWER 300-bus system were provided to validate the proposed methods, demonstrating that MESs increase power system flexibility and their own profits while decreasing the energy and reserve payment of consumers. • Mobile energy storage has temporal, spatial, and power/energy flexibility. • We provide a market-based methodology to incent mobile energy storage to provide flexibility. • Efficient algorithm is developed to obtain the market equilibrium. • Flexibility provided by mobile energy storages reduces the operational cost of power systems. • Mobile energy storage owners obtain revenue by providing flexibility service. [ABSTRACT FROM AUTHOR]

• Mobile energy storage and thermal energy storage are developed into a unified analytic model. • A novel robust method is proposed to obtain more accurate "worst scenario". • A three-stage iteration algorithm is presented to accelerate the solution. • Multi-storage energy management system can improve the voltage level and operational economy of the distribution network. The randomness, volatility and anti-peaking characteristic from distributed renewable energy generation rise great challenges for the safe and economic operation of the distribution networks (DNs). To address this problem, this paper proposes a novel multi-energy-storage energy management system (EMS) to co-optimize the electricity-driven mobile energy storage (MES) and inverter air-conditioning (AC)-based thermal energy storage (TES). To facilitate the energy management of the DN, the MES that considers the delay factors and the TES that regulates reactive power have been developed into a unified analytic model capable of charging and discharging. In addition, considering the impact of the forecasting uncertainties and the risk-aversion of the dispatcher, a novel robust optimization method is proposed to obtain more accurate "worst scenario". The dispatching model is then converted into a mixed integer second-order cone programming problem (MI-SOCP) and a mixed integer linear programming problem (MILP), and linearized techniques and an iteration method are used to efficiently solve these problems. Simulation studies on a 41-node DN in Ontario indicate that the operational cost and power loss of the DN can be reduced by no less than 1% and 8% using the proposed EMS, respectively, while a safer voltage level with a voltage deviation of 5% can be obtained. The results confirm the effectiveness of the MES and TES for peak shaving, valley filling and voltage supporting. [ABSTRACT FROM AUTHOR]