جستجوی مقالات مرتبط با کلیدواژه « resiliency » در نشریات گروه « برق »

After extreme natural events like earthquakes, storms, and floods, the electrical distribution network may be isolated from the upstream grid. In such a situation, the electrical power of the upstream grid is lost and the available microgrids (MGs) are the only power sources. Since the power outputs of MGs are restricted, restoring all de-energized loads is impossible and restoring critical loads (CLs) becomes the most important concern of the distribution network operators. Therefore, in this paper, a new hour-by-hour restoration strategy for CLs restoration after a blackout is proposed. The desired objective functions include restored energy and switching operations. The proposed restoration strategy presents a restoration plan for each hour of the outage period. The efficiency of the proposed strategy is carried out on an IEEE 123-bus distribution network and the simulation results confirm the superiority of the proposed hour-by-hour restoration strategy over the other conventional restoration methods.

Due to the growing use of Plug-in Electric vehicles in transportation networks, scheduling the charge/discharge of electric vehicles in parking lots can have great impacts on the distribution network's resiliency. This paper presents a Bi-level optimization model to improve the resiliency of the distribution network that takes into account the interaction between the distribution network islanding problem and the charge/discharge scheduling of electric vehicles in available parking lots. In the upper-level problem, regarding the electrical loads and managing the charge/discharge of electric vehicles in electrified parking lots, the island boundaries are determined with the aim of maximize the load restoration. By knowing the islands' boundaries and restored parking lots from the upper-level problem, the changes in destination parking lots and travels characteristics are determined in lower-level problem to minimize the shortest path between the out-of-service destinations and in-service parking lots. The proposed model has been implemented and validated through applying several concurrent faults to the 118-bus active distribution network in presence of electric vehicles in the 25-node traffic network. A combination of mathematical programming and the evolutionary algorithm is applied to reach the final solution. The case studies' results confirm the efficiency of the proposed model for improving the resiliency of distribution networks with managing the charge/discharge of electric vehicle fleets in restored parking lots.

Resilience characteristics in power systems refer to the system's ability to withstand against severe disturbances with a low probability of occurrence. As extreme dust storms in the south and southwest in recent years have caused heavy damage to the IRAN's electricity industry, in this paper, a bi-level planning model is proposed to simultaneous hardening of distribution lines and substations against this phenomenon. In the first and second levels of the proposed model, the investment costs of distribution system hardening and total expected operating costs are minimized subject to financial and operational constraints, respectively. The planning results in different case studies have shown that the simultaneous hardening planning of substations and distribution lines can, in addition to reducing operating costs in the emergency conditions, play a significant role in reducing investment costs. The proposed model is implemented on a large-scale power distribution system in Khuzestan province, and the simulation results confirm the efficiency of the proposed scheme at different budget levels.

Wind power generation is making an increasingly significant contribution to global electricity production. The high penetration of wind power poses many operational and control challenges that affects the reliability and stability of power systems. In this Paper, the reported technical challenges caused by the grid integration of wind energy conversion system (WECS) and the proposed solutions methodologies represents. The wind-generating system components and architecture are investigated at the beginning of this article for analysis and stability studies purposes, then are addressed various technical challenges; each challenge is discussed individually, focusing on the bulk integration of wind energy into the power systems. Some solutions, including grids code, energy storage technologies, appropriate control strategies, and other methodologies employed to mitigate the effects of the integration, are also included. This review is ready-reckoner of essential topics for further research of wind energy and available technologies in this field. This review provides ready-reckoner of essential topics for grid integration of wind energy and available technologies in direction of overcome the related difficulties.

In this research, an optimal energy management model of a residential microgrid with the aim of resiliency and flexibility improvement is presented. Accordingly, a residential microgrid consisting of a solar power plant with energy storage system is considered. Each of houses in the microgrid has an electric vehicle. The residential microgrid is connected to the main grid and has been equipped with smart grid infrastructures. The outage of the main grid causes load shedding that consequently reduces the resiliency. Keeping the security of supply during these distortions is necessary. Therefore, in this paper, a new model of microgrid operation based on solar power plant and energy storage system is proposed. The efficiency of the proposed model is evaluated through numerical calculation of resiliency using a new index proposed in this paper. In addition, the proposed model improves the system flexibility. The amount of this improvement is evaluated using practical and accurate indicators including self-consumption and storage efficiency. The results show that the proposed model improves the resiliency in off-peak and peak hours by 4.7% and 9.75% respectively. Moreover, two indexes of flexibility, including self-consumption and storage efficiency, improve by 9.1% and 2.2% respectively.

The microgrid inertia as a result of tiny structure and barely tolerance variations, is fairly low. Thus, the maintenance of voltage stability and frequency specifically in islanded mode is extremely demanding. Even if these products have efficient control system, they can’t retain microgrid stability due to the low speed of response in primary sources of energy and communication delays of the links between outer unit and control system in distributed generation. Introducing a structure of fuzzy control arranged with neural network to balance between generation part and consumption part in micro grid is the main purpose of this paper. Using the fuzzy logic, this controller enables flexible operation of microgrids in both network and islanded modes. In the proposed control system, a trainable neural network in different operating conditions is responsible for fine tuning of the fuzzy logic system. Because of the sensitivity of the loads in the microgrid the proposed structure is designed to interact with the storage source in order to increase the response speed to the imbalance between production and consumption. This might prevent excessive voltage and frequency deviation, especially in the severe situations. With this controller, fluctuations in the production of renewable resources quickly compensated without a negative impact on resilience and instability of the microgrid, especially while disconnecting from the main network

Smart grids all over the world aim at providing reliable and resilient power to customers. During major contingencies of large-scale natural disasters, Distributed Generations (DGs) play a key role in delivering a resilient and reliable supply of loads. Major natural disasters such as floods and hurricanes often cause lengthy interruptions in electricity distribution systems and degrade the level of service to end-users. The utilities have mainly focused on restoring the distribution system since the power grid is susceptible to natural disasters. A resilient systemchr('39')s primary purpose is to allow the restoration of out-of-service loads as soon as possible after an extreme event. The resilience of a power system can be defined as “the ability of the system to prepare and plan for absorbing the damage and adapting/recovering in order to prevent the impacts of similar events in the future”. Therefore, the resilience of a power system is briefly attributed to three aspects of prevention, survivability, and recovery.  Improvements in any or all of these features can enhance the overall resilience of the power system. This paper presents a self-healing restoration algorithm for power distribution systems exposed to extreme natural disasters. Indeed, an improved restoration algorithm in distribution systems in the presence of renewable and dispatchable DGs is proposed for enhancing the survivability of out-of-service loads due to extreme events, like natural disasters. The algorithm can analyze the effects of multiple faults, which arise due to a low-probability, high-impact event like a natural disaster. In the presented method, an optimal strategy is introduced to restore maximum loads with minimum switching operations and maximum load restoration under fault conditions. In order to consider uncertain parameters, a stochastic scenario-based approach is considered and the expected values as objectives are minimized to consider the effect of all scenarios. In the proposed method, the Genetic Algorithm (GA) is utilized as a powerful algorithm in optimization and how to implement and solve the proposed model by the GA is introduced. An evaluation of the proposed approach is conducted through a typical case study. A modified IEEE 33-node system is considered for this reason. The simulated results indicate that in the presence of microgrids and an automated switching-based distribution system, the systemchr('39')s resilience is improved significantly. However, the present study did not address microgridschr('39') dynamic response. In the event of extreme natural disasters, utilities can use the proposed algorithm to improve the recovery of out-of-service loads.

Recently, the frequency and severity of natural and man-made disasters (extreme events), which have a high-impact low-frequency (HILF) property, are increased. These disasters can lead to extensive outages, damages, and costs in electric power systems. A power system must be built with “resilience” against disasters, which means its ability to withstand disasters efficiently while ensuring the least possible interruption in the supply of electricity, sustaining critical social services, and enabling a quick recovery and restoration to the normal operation state. Quantifying the power system resilience is a complicated and controversial problem. However, this is necessary for the evaluation and comparison of different resilience enhancement strategies. The resilience metrics are mathematical tools to measure the resilience level of a power system, which are normally employed for resilience cost-benefit in the planning and operation domains. Numerous resilience metrics have been presented in the power system literature. However, there is a lack of a comprehensive conceptual framework regarding the different types of resilience metrics in electric power systems, and existing frameworks have essential shortcomings. In this paper, after introducing and criticizing the existing frameworks, a conceptual framework is suggested to classify different types of resilience metrics in the power system literature. In this conceptual framework, power system resilience metrics are divided into “non-performance-based” and “performance-based” groups. The “performance-based” resilience metrics are also divided into “performance” and “consequence (outcome)” groups. The “performance” resilience metrics consist of five groups including “power”, “duration”, “frequency”, “probability” and “curve”. The “consequence (outcome)” resilience metrics consist of four groups including “economic”, “social”, “geographic” and “safety and health”. In addition, both of the “performance” and “consequence (outcome)” groups have a distinct group naming “general”. In order to verify and validate the comprehensiveness and inclusivity of the proposed conceptual framework, two actions are accomplished. Firstly, the existing power system resilience metrics are allocated to the framework’s groups. Secondly, the proposed conceptual framework is compared with the existing frameworks. These actions show that the proposed conceptual framework can cover and classify different types of power system resilience metrics in the literature, is more comprehensive comparing the existing frameworks, and lacks the essential shortcoming of those frameworks. Thus, the proposed conceptual framework is comprehensive and useful. The proposed conceptual framework can be used by academic and industrial researchers. Academic researchers can concentrate on groups that need further research to propose new resilience metrics, whereas industrial researchers can choose the appropriate resilience metric according to their needs.

In the event of a severe incident with a high impact and low probability of occurrence, distribution networks may be separated from upstream networks and several feeders may be disconnected simultaneously within the distribution networks. In such circumstances, to maximize the resilience of the distribution networks and to prevent long-term global outages, they are reconfigured and islanded to allow restoring the loads as much as possible. On the other hand, because of the increasing penetration of electric vehicles in todaychr('39')s advanced societies for environmental reasons and fossil fuel prices, charging and discharging management of plug-in electric vehicles (PEVs) into parking lots can have a significant impact on improving the resilience of distribution networks. Technically, electric vehicles can be connected to the electrical grid in two modes of charging or grid-to-vehicle (G2V) mode as an electrical load and discharging or vehicle-to-grid (V2G) mode as energy storage units. Therefore, this paper proposes a mixed integer linear stochastic programming model to improve the resilience of distribution networks equipped with distributed generation (DG) resources. The proposed model optimizes the distribution network reconfiguration with the aim of maximizing load restoration criteria by using optimal charging and discharging planning of PEVs in parking lots. In the proposed model, the master and slave DGs in each island and the charging and discharge planning of PEVs are optimally determined considering the movement of PEVs between parking lots and the formation and boundaries of the islands. To present a realistic model, the uncertainty of the generation capacity of renewable wind resources is also taken into account. The proposed model has been implemented and validated by applying several concurrent faults on a 118-bus distribution network in GAMS software. The results show the positive impact of PEVs parking lots on improving the resilience and increasing the load recovery of the distribution network.

The energy hub is defined as a model for the integrated study of different energy infrastructures connected to electricity and natural gas networks and meet the different demands of consumers through different converters and storage devices. Security of consumer demand during the gas network outage is an important issue that needs to be studied because of the most important components of the hub run on gas fuel. In this paper, a framework for a hub assessment in gas network outage conditions is presented. Also in order to improve the resiliency from renewable sources and electricity to gas (P2G) technology have been used. The effectiveness of the proposed framework has been investigated through the simulation of several case studies. Finally, the effect of the participation of renewable resources and P2G in gas network outage conditions is calculated by a numerical index. The results show that the rate of resiliency improves with only renewable resources and the participation of renewable resources and P2G simultaneously 6.78% and 20.12% respectively.

Power systems have been traditionally designed and operated based on the main principles of reliability, i.e. security and adequacy. These principles can properly deal with the known failures in the power infrastructure components. However, recently occurred High Impact Low Probability (HILP) threats have raised the concerns about the classical reliability-oriented view. Hence, resilient distribution networks against various threats and enhancement of energy security in critical situations are the most significant challenges that must be taken into consideration. Distribution network plays vital role in power systems because of its connection point to the end users and due to its wide range, security of electricity supply is strongly dependent on it. Hence, a lot of efforts have been done on distribution networks scheduling, planning and operation. So, optimal resilient planning of conventional medium voltage distribution network is aimed at mitigating vulnerability to natural disasters. In this paper, a new method is suggested to make a relation between network component fragility curves, component geographical location and natural disasters spatial risk index. To achieve this goal, the resiliency concept and effective solutions for resiliency increment, elicitation of vulnerability atlas of distribution networks and natural disaster risk based modelling have been discussed and assessed comprehensively in this paper. The simulation of proposed method is applied on a large scale distribution network. The obtained results validate the effectiveness of proposed methodology.

Charging demand of electric vehicles in fast charging stations can have a significant impact on the process of distribution network reconfiguration and islanding. In this paper, the problem of dynamic islanding and reconfiguration of distribution network taking into account the interaction between the electrical and traffic networks is presented in the form of a two-stage model. In the first step, considering the tendency of electric vehicle to charge at nearby fast charging stations, a linear model has been proposed to determine the charging demand of the stations in the traffic network. In the second step, a scenario-based mixed integer linear model is proposed with the aim of maximizing the resiliency index of the distribution network equipped with dispersed production resources using dynamic islanding and reconfiguration plans. The resiliency index consists of two objectives, including maximizing network load recovery and maximizing the power supply of fast charging stations. In order to provide a realistic model, the uncertainty in predicting the productive capacity of renewable wind resources has been considered. The proposed model has been implemented on the 118 bus network by applying several simultaneous faults and the presence of electric vehicles in the 25-node traffic network in the GAMS software. The simulation results illustrate the effectiveness of the proposed model in evaluating the resiliency of the active distribution network in the presence of electric vehicles.

Readiness of power systems to cope with emergencies, especially those of sabotage origins, can improve the resiliency and minimize the restoration cost and time. In the current paper, in a tri-level robust decision making framework, system operator (SO) schedules the power generation for a base case in the upper level while a rational attacker tries to disrupt the most sensitive substation and consequently the connected generation units and transmission lines which leads to maximum load shedding in the middle level. Meanwhile, the SO once again tries to minimize the load shedding by redispatching the generation fleet in the lower level. In addition, in power systems with high wind penetration rates the SO should also consider the impacts of wind generation uncertainty for a more realistic robust scheduling. In contrast with two-stage robust methods with full recourse which immunize solutions against worst economic scenario (over-conservative solutions), the proposed column and constraint generation (CCG)-based model tries to find the base case solution while ensuring that in case of malicious attacks or wind realizations the solution can be adaptively adjusted with least load shedding. The performance of the proposed model is examined in 118-bus IEEE test system.