Optimal stochastic scheduling of plug-in electric vehicles as mobile energy storage systems for resilience enhancement of multi-agent multi-energy networked microgrids

Secure energy providing is a critical feature of sustainable societies, where this subject requires reliable operation of generation, transmission and distribution infrastructures. In order to achieve the mentioned goal, the influence of uncertainties should be analyzed using appropriate methods, criteria and tools. The high‐impact low‐probability (HILP) events are special uncertainties that can affect the safe operation and lead to irreparable damages. This paper presents a comprehensive review of resilience in electrical energy systems consisting of fundamental concepts and definitions, challenges, enhancements and future proceedings. In this regard, a resilience evaluation framework is designed first in which all the necessary actions for a resilience study are defined. In the next step, various HILP events are introduced and then the resilience concept is defined accurately and related curves are investigated. Afterwards, the resilience assessment indices and quantitative measures are described and their details and applications are precisely explained. As well, the resilience enhancement strategies are discussed from short‐term and long‐term perspectives and eventually, resilience‐based objective functions and optimization approaches are classified in the last part. It is noteworthy that the main research gaps are also clarified according to the reviewed papers and further suggestions are expressed to cope with the declared problems.

Section: Mobile Power Sourcesmentioning

confidence: 99%

Electrical energy systems resilience: A comprehensive review on definitions, challenges, enhancements and future proceedings

Amini

Zadeh

Rostami

et al. 2023

“…Over the past decades, significant revolutions have occurred in renewable energy systems to reduce electricity costs and increase profits [1]. Photovoltaic [2], wind farms [3], electric vehicles [4], hydrogen [5], and many other renewable energies have been widely applied in integrated energy systems. Integrated energy system is internally coupled with a variety of energy supply, storage, and conversion devices, and it can realize the efficient utilization of energy (especially renewable energy) through the multi-energy complementation and interaction between supply and demand.…”

Section: Motivationsmentioning

confidence: 99%

An optimization capacity design method of household integrated energy system based on multi‐objective egret swarm optimization

Liu

et al. 2023

The construction of a household integrated energy system will reduce greenhouse gas emissions and promote sustainable development. Firstly, a household energy system is proposed, which consists of a photovoltaic, wind turbine, electrolysis cell, hydrogen storage tank, and hydrogen‐fired gas turbine. The proposed system is modelled as a bi‐objective optimization problem in which the minimum daily system economic cost, and the minimum loss of energy supply probability. Secondly, a novel multi‐objective egret swarm optimization algorithm with strong search capability and fast convergence is proposed. Thirdly, a household load corresponding to a typical day in spring is chosen as the study case. The optimization results show that the daily system economic cost with the optimal number of devices is 97.48 RMB, and the loss of energy supply probability is 8.33% at the lowest. Finally, to validate the efficiency of the proposed method, the proposed method is compared with NSGA‐II (a widely used multi‐objective evolutionary algorithm). The comparison indicates that the proposed method has a better diversity due to the random searchability. As a consequence, the proposed method can be used in the optimization capacity design of the integrated energy system.

“…Constraints ( 27) and ( 28) illustrate charging and discharging limits and equality constraint ( 29) is the energy level of BESS proportional to the amount of charging and discharging, and also Equation ( 30) limits the deviation of energy level in BESS [37][38][39]. In order to make a connection between the normal condition and the hours after the event occurrence, constraint (31) is considered that indicates the initial energy of the BESS at the beginning of the preparation period is equal to the stored energy of BESS in the normal situation, at the occurrence event time of scenario 𝜔 e (T 𝜔 e occ ). The inequality constraint (32) limits the final energy of the batteries at the end of the preparation period (T PP end ).…”

Section: Constraintsmentioning

confidence: 99%

“…The work finally concludes that the presence of solar panels and battery units can have a significant impact on resiliency enhancement in response to storm events. Reference [31] also as a scheduling work, presents an optimal framework for enhancing resilience in the case of emergency, using plug‐in electric vehicles. That framework optimizes sending electric vehicles as mobile power storage, from grid‐connected microgrids into islanded microgrids.…”

Section: Introductionmentioning

confidence: 99%

Resilience‐oriented planning and management of battery storage systems in distribution networks

Gilasi

Hosseini

Ranjbar

2023

The occurrence of unforeseen incidents is one of the major causes of extensive blackouts and financial damages to electrical networks. The utilization of distributed energy resources (DERs) as a local energy supplier can potentially reduce the destructive effects of unforeseen events along with their benefits for the normal operation of the network. In this paper, a new framework is proposed for the optimal siting and sizing of solar photovoltaic distributed generations (PVDGs) and battery energy storage systems (BESSs) in the distribution network to increase resiliency against the earthquake event considering the advantages of these resources in both normal and event conditions. Furthermore, an optimal energy pattern for BESSs has been provided that, in addition to being used for the normal condition, also prepares them for transferring to the emergency state. The objective function is formulated as a mixed‐integer linear programming (MILP) problem aiming at minimizing both the normal and emergency costs. Eventually, the numerical results of implementing the proposed model on the IEEE‐33 bus system are presented to illustrate its effectiveness and accuracy. These results show by using PVDG installation, the resilience can be improved by 14% while total installation and operating costs are reduced by 1.7%. It is possible, however, to decrease the load curtailment up to 55.46% by involving BESSs along with PVDGs, despite an 8.57% increase in costs.