It is essential to improve the resiliency of power distribution systems (PDS) given the increase in extreme weather events, number of malicious threats, and consumers' need for higher reliability. ...This paper provides a formal approach to evaluate the operational resiliency of PDS and quantify the resiliency of a system using a code-based metric. A combination of steady state and dynamic simulation tools is used to determine the resiliency metric. Dynamic simulation tools help analyzing the impact of short-term events, which might affect operational resiliency in the long term. A dynamic optimization algorithm for changing operating criteria to increase the sustainability of the most critical loads has been proposed. The proposed theoretical approach is validated using a simple PDS model and simulation results demonstrate the ability to quantify the resiliency using the proposed code-based metric. The time-dependent quantification of resiliency has been demonstrated on a test system of two connected Consortium of Electric Reliability Technology Solutions (CERTS) microgrids.
Existence of distributed generators (DGs) makes it possible to split distribution systems into multi‐microgrids (MGs) in emergency conditions. In this study, after optimal allocation of distributed ...generation sources with a multi‐objective function, the test system is optimally sub‐divided into some self‐adequate MGs with the least not supplied active and reactive power during large‐scale events. By using the proposed method, the network will be more resilient against large‐scale events such as critical lines outages or big generation outage under natural events with the optimal voltage regulation for each MG. The multi‐objective problem is solved by the fuzzy satisfying approach and Pareto optimality methods. The proposed method in this study is based on the exchange market algorithm and tested on two systems, IEEE 33‐ and 69‐bus distribution systems. Also, under normal network conditions, operation of distribution networks is evaluated using different objective functions like micro‐virtual networks with minimum power exchange and loss minimisation, to determine which function causes the network to have the best status in terms of reliability and resiliency. Obtained results confirm the efficiency of this method for partitioning distribution systems to increase network robustness before a fault occurrence and also improve reliability and resiliency after a fault occurrence.
Power outages cost billions of dollars every year and jeopardise the lives of hospital patients. Traditionally, power distribution system takes a long time to recover after a major blackout, due to ...its top‐down operation strategy. New technologies in modern distribution systems bring opportunities and challenges to distribution system restoration. As fast response energy resources, plug‐in hybrid electric vehicles (PHEVs) can accelerate the load pickup by compensating the imbalance between available generation and distribution system load. This study provides a bottom‐up restoration strategy to use PHEVs for reliable load pickup and faster restoration process. The optimisation problem of finding load pickup sequence to maximise restored energy is formulated as a mixed integer linear programming (MILP) problem. Moreover, the coordination between transmission and distribution restoration is developed to efficiently restore the entire system back to normal operating conditions. Simulation results on one 100‐feeder test system demonstrate the efficiency of MILP‐based restoration strategy and the benefit from PHEVs to restore more energy in given restoration time. The proposed restoration strategy has great potential to facilitate system operators to achieve efficient system restoration plans. It also provides incentives to deploy a large amount of PHEVs to improve system resiliency.
Distribution system operators face a challenging environment marked by increased decentralization, digitalization, and the decarbonization of transport and heating sectors. In particular, the ...integration of large numbers of electric vehicles (EVs) will pose challenges for distribution grid operation and planning. However, EVs also open the opportunity to offer flexibility services to different actors in the electricity system using smart charging and vehicle-to-grid (V2G) technology. This work reviewed the scientific literature and key European demonstrator projects on the proactive integration of EVs into distribution grids. The main technical, economic, regulatory, and user-related aspects were analyzed and the associated barriers identified. There is a broad scientific literature on the technical feasibility of EV flexibility provision and coordination schemes, which has as well been proved in demonstrator projects, even though the required technologies for V2G (bidirectional chargers, communication protocols) are not yet widespread. On the other hand, main barriers are economic and institutional, largely due to a lack of regulatory frameworks to value flexibility at distribution level and thus uncertainty on the value of these flexibility services. In particular, this work analyzed four possible value frameworks (grid codes, connection agreements, tariffs and market platforms) to use flexibility at the distribution level, and their implementations with EV fleets in demonstrator projects.
•Holistic review of scientific literature and EV demonstrator projects.•Identification of flexibility services that EVs can provide throughout the value chain.•Identification of technical, economic and regulatory barriers for active EV integration.•Analysis of possible frameworks to use EV-driven flexibility at distribution level.
This paper proposes a two-stage energy management scheme (EMS) for AC-DC hybrid smart distribution systems (DSs). The proposed EMS is formulated as a multi-objective optimization problem to minimize ...the DS operation costs and energy losses. The proposed EMS is achieved in two stages. In the first stage, a network reconfiguration algorithm determines the optimal day-ahead reconfiguration schedule for a hybrid DS. In the second stage, a real-time optimal power flow algorithm determines the real-time operational schedule of the energy resources. This paper also introduces a new linearized power flow model for AC-DC hybrid DSs. This new model facilitates the formulation of the first-stage algorithm as a mixed-integer linear programming problem and the formulation of the second-stage algorithm as a linear programming problem. The proposed two-stage EMS was tested on a case study of a hybrid DS that included different types of loads and distributed generators. The results demonstrate the efficacy of the proposed EMS: the optimal day-ahead reconfiguration schedule was successfully obtained in the first stage, and the proper and optimal real-time operation was achieved in the second stage.
•The interplay of life-cycle resilience and time dependent reliability is introduced.•A deep reinforcement learning framework for grid hardening decisions is proposed.•A novel risk-based ranking is ...developed and integrated into reinforcement learning.•A large-scale power distribution system consisting of over 7000 poles is studied.•Resilience is improved by over 30% for a 100-year horizon.
Power distribution systems are continually challenged by extreme climatic events. The reliance of the energy sector on overhead infrastructures for electricity distribution has necessitated a paradigm shift in grid management toward resilience enhancement. Grid hardening strategies are among effective methods for improving resilience. Limited budget and resources, however, demand for optimal planning for hardening strategies. This paper develops a planning framework based on Deep Reinforcement Learning (DRL) to enhance the long-term resilience of distribution systems using hardening strategies. The resilience maximization problem is formulated as a Markov decision process and solved via integration of a novel ranking strategy, neural networks, and reinforcement learning. As opposed to targeting resilience against a single future hazard – a common approach in existing methods – the proposed framework quantifies life-cycle resilience considering the possibility of multiple stochastic events over a system’s life. This development is facilitated by a temporal reliability model that captures the compounding effects of gradual deterioration and hazard effects for stochastic hurricane occurrences. The framework is applied to a large-scale power distribution system with over 7000 poles. Results are compared to an optimal strategy by a mixed-integer nonlinear programming model solved using Branch and Bound (BB), as well as the strength-based strategy by U.S. National Electric Safety Code (NESC). Results indicate that the proposed framework significantly enhances the long-term resilience of the system compared to the NESC strategy by over 30% for a 100-year planning horizon. Furthermore, the DRL-based approach yields optimal solutions for problems that are computationally intractable for the BB algorithm.
This paper proposes an efficient algorithmic approach that overcomes the critical challenges in real-time unbalanced distribution system state estimation, topology error processing, and outage ...identification simultaneously. These challenges include (1) limited locations of measurement devices and unsynchronized measurement data as well as missing and bad data, (2) complicated mixed-phase switch actions and mutual impedances and shunt admittances, and (3) the nonlinear nature of unbalanced distribution system power flow with distributed energy resources (DERs). A single snap-shot mixed-integer quadratic programming (MIQP) optimization framework is proposed to cope with these challenges, which simultaneously identifies real-time network topology, estimates system state, and detects the outages via analytical constraints. An AC optimal power flow (ACOPF) approach is proposed to accurately model unbalanced distribution systems. In the proposed MIQP formulation, nonlinearities due to the complicated mixed-phase switch operations and the ACOPF approach are linearized. The effectiveness of the proposed approach is verified on an actual distribution feeder in Arizona. The results illustrate that the proposed model is robust, accurate, and computationally efficient for implementation in a distribution management system (DMS).
AbstractWater loss reduction is important in sustainable water resource management. As one of the main water loss control methods, early detection of hydraulic accidents in district metering areas ...(DMAs) has emerged as a research focus. This study presents a data-driven method for burst detection which consists of three stages: prediction, classification and correction. A prediction stage is used to improve accuracy of flow prediction, a classification stage utilizes multiple thresholds to make the method robust to time variation, and an outlier feedback correction stage allows consecutive detection of outliers. The proposed method was capable of triggering burst alarms with 99.80% detection accuracy (DA), 85.71% true-positive rate (TPR), and 0.14% false-positive rate (FPR) in simulated experiments, and 99.77% DA, 94.82% TPR and 0.21% FPR in synthetic experiments over a 10-min detection time in a real-life DMA. The identifiable minimum burst rate was as low as 2.79% of average DMA inflow. The proposed method outperformed the single threshold-based method, window size–based method, and clustering-based method. It provides a sensitive and effective solution for burst detection in water distribution systems.
It is imperative to distribution system operators to provide quantitative as well as qualitative power demand and satisfy consumers’ satisfaction. So, it is important to address one of the most ...promising combinatorial optimization problems for the optimal integration of power distribution network reconfiguration (PDNR) with distributed generations (DGs). In this regard, this paper proposes an improved equilibrium optimization algorithm (IEOA) combined with a proposed recycling strategy for configuring the power distribution networks with optimal allocation of multiple distributed generators. The recycling strategy is augmented to explore the solution space more effectively during iterations. The effectiveness of the proposed algorithm is checked on 23 standard benchmark functions. Simultaneous integration of PDNR and DG are carried out considering the 33 and 69-bus distribution test systems at three different load levels and its superiority is established. Verification of the proposed technique on large scale distribution system with a variety of control variables is introduced on a 137-bus large scale distribution system. These simulations lead to enhanced distribution system performance, quality and reliability. While, the integration represents a challenge for complexity and disability to achieve optimal solutions of the considered problem especially for multi-objective framework. To solve this challenge, a multi-objective function is developed considering total active power loss and overall voltage enhancement with respecting the system limitations. The proposed algorithm is contrasted with harmony search, genetic, refined genetic, fireworks, and firefly optimization algorithms. The obtained results confirm the effectiveness and robustness of the proposed technique compared with the competitive algorithms.
Display omitted
•A newly proposed meta-heuristic IEOA is introduced in this work.•The proposed IEOA is applied on 23 standard benchmark functions.•The proposed technique verification is compared with other previous techniques.•The suggested IEOA is validated on three distribution systems.•The findings of simultaneous PDNR/DG allocation show the IFOA effectiveness.
In the more electric aircraft context, dc distribution systems have a time-varying structure due to the flexible distributed loads and complex operation conditions. This feature poses challenges for ...system stability and increases the difficulty of the stability analysis. Besides, the risk of instability may be increased under constant power load condition due to the negative incremental impedance characteristic. To this end, this article proposes an improved interconnection and damping assignment passivity-based control scheme. Particularly, an adaptive interconnection matrix is developed to establish the internal links in port-controlled Hamiltonian models and to generate the unique control law. The damping assignment technique is addressed to tune the dynamic characteristic. In order to meet the load requirements of different voltage levels, the design procedures were given for determining the control law in both boost converter and buck converter cases. The simulation and experimental results are performed to demonstrate the validity of the proposed control approach.
PDF
