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Multi-energy systems (MESs) make it possible to satisfy consumer's energy demand using multiple coupled energy infrastructures, thus increasing the reliability of the energy supply compared to separate energy systems (SESs). To accurately and efficiently assess and improve the reliability of MESs, this paper proposes a MES reliability and vulnerability assessment method using energy hub (EH) model. The energy conversion, transmission and storage in MESs are compactly and linearly described by EH model, making reliability and vulnerability assessment of MESs tractable. Indices for MES vulnerability assessment are proposed to find the key components for improving MES reliability. Multi-parametric linear programming (MPLP) with a self-adaptive critical region set is proposed to reduce the computational burden caused by iteratively solving LP problems for a large number of samples during the assessment process. The results of a case study show that the proposed reliability and vulnerability assessment method is able to effectively evaluate the energy supply reliability of different energy sectors in MES as well as find the critical component of an MES from reliability perspective to support its planning. The proposed algorithm, i.e., MPLP with a self-adaptive critical region set, can improve the computational efficiency by an order of magnitude compared to the traditional LP method. [ABSTRACT FROM AUTHOR]

With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impact-increment method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy. [ABSTRACT FROM AUTHOR]

To achieve high reliability, the urban distribution networks are mesh-constructed and radial-operated, in which the outage load can be restored to adjacent feeders via tie-lines after faults. Conventionally, iterative optimization-simulation methods and heuristics are adopted for distribution network planning, which cannot guarantee global optimality. Besides, existing reliability-constrained planning model cannot explicitly assess the reliability indices for mesh distribution networks, so the resulted plan scheme may be overly invested. In this paper, we propose a novel multistage expansion planning model for mesh distribution networks, in which reliability assessment is explicitly implemented as constraints. The different investment/reliability preferences for buses are also customized. Specifically, post-fault load restoration between feeders through tie-lines is modeled as a case of post-fault network reconfiguration. The planning model is then cast as an instance of mixed-integer linear programming and can be effectively solved by off-the-shelf solvers. We use a 54-node system to test the performance of proposed model. Simulation results show the effectiveness and flexibility of this methodology. [ABSTRACT FROM AUTHOR]

Requirements of economic and reliability criteria are often stressed simultaneously in operating and planning models for distribution systems. However, conventional reliability evaluation algorithms are hard to be integrated to these optimization models analytically. Recently, several optimization model-based reliability assessment methods have been proposed, which can be embedded into such models. Nevertheless, the detailed placement and actions of circuit breakers and switches are ignored or oversimplified in these methods, leading to an inconsistency with the real world situation. Thus, we propose a new optimization model-based reliability assessment method that fully considers detailed placement and actions of circuit breakers and switches in distribution networks. Based on fictitious fault flows, strategies for tripping circuit breakers to cut fault current, operating switches to isolate the fault and processing post-fault network reconfiguration are linearly modeled as constraints. Therefore, this method can be easily integrated into operating and planning models of distribution networks. Case studies on 54-node, 85-node, 137-node and 417-node distribution networks show the scalability and efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Power system reliability measures for residential distribution networks have gained a lot of attention in recent years. This is due to the introduction of smart grids, where the quality of the delivered power becomes a crucial issue. In this paper, a new methodology to include cold load pickup (CLPU) events in the reliability assessment of power distribution systems is proposed. The developed methodology presents a platform to include CLPU events in the reliability assessment assuming an optimal systematic procedure is used to restore the affected load points. The methodology is applied on a test system using a state-of-the-art search technique named lightning search algorithm (LSA). The optimal plan takes into account electrical code protection system settings, system operation limits, and stochastic behavior of the load. The reliability indices for a base case without considering CLPU is compared with the case that considers CLPU assuming an optimal restoration. Both cases are compared further under fixed-restoration plans with different time intervals. The results demonstrate the effectiveness of the proposed methodology to improve the distribution network reliability by considering the CLPU events. [ABSTRACT FROM AUTHOR]

Reliability assessment is of great importance to transmission expansion planning (TEP). In this paper, a novel incremental reliability assessment approach (IRAA) for TEP is proposed. The basic idea is to build an incremental reliability indices calculation model so as to quantitatively estimate the degree of reliability enhancement with high computational efficiency. First, contingencies are categorized into three scenarios according to the states of the added components of the planning schemes. Then, contribution of each scenario to the incremental reliability indices is analyzed in detail. It turns out that contingencies of only one scenario can affect the incremental reliability indices and, therefore, other contingencies can be eliminated. Based on this phenomenon, an IRAA is developed for TEP, which can be used to improve the performance of various traditional reliability assessment methods. Case studies are performed on IEEE RTS 24-bus and IEEE 300-bus test system. The results demonstrate that the IRAA has superior performance to traditional methods in terms of accuracy and efficiency. [ABSTRACT FROM PUBLISHER]

Utilities are challenged to develop economically competitive plans for expanding power transfer capabilities and increasing network resilience under uncertainties. To overcome these challenges, utilities frequently operate their underground cable systems with flexible ratings under planned or emergency network contingencies. At present, the increased risk associated with such an emergency loading operation of cables and networks is not quantified. To address this gap, the paper presents a novel advancement in existing methodology for evaluating power network reliability, which accounts for the increased risk of failures and ageing that underground cables experience during emergencies. The proposed framework integrates a double sequential Monte Carlo simulation loop with a detailed cable system model incorporating important plant design properties. This integration captures the risk of cable ageing at emergency events within a multiyear network analysis. The methodology is demonstrated on the modified IEEE 14-bus network with long- and short-term emergency ratings under both steady-state and transient electro-thermal implementations. Crucially, the methodology can inform utilities about the emergency loading risks for network performance, including adequacy, resiliency, flexibility, and cable ageing. [ABSTRACT FROM AUTHOR]

Reliability assessment is of crucial importance for power systems planning and operation. However, traditional reliability assessment methods mainly analyze electricity outages observed from the system side, regardless of whether end consumers are in need of electricity during such outage events. That is, they may not truly reflect actual impacts of various outages on consumers. To overcome this deficiency, a more precise consumer-centric spatiotemporal reliability assessment model is proposed for smart grid. Several new metrics are proposed to accurately evaluate the reliability level sensed by consumers, by considering actual interruption times of outages, consumers’ power consumption features, and load demand. Based on these new metrics, a method is proposed to quantitatively assess consumer-centric reliability compensation in the context of future power market. Furthermore, reliability and economy sensitive components that are key to improve consumer-centric reliability of systems are defined, detected, and analyzed, and a concise reward mechanism is established. The IEEE-RBTS distribution system is studied to illustrate the effectiveness and efficiency of the proposed models, metrics, and methods. [ABSTRACT FROM PUBLISHER]

This paper formulates a block-layer method for the reliability assessment of distribution systems. The characteristics of the method include: 1) identifying the impact of distribution component reliability on the system and load points using a block-layer structured assessment; 2) incorporating time-dependent failure parameters; and 3) taking account of the topological, seasonal, and meteorological features of the distribution systems under analysis. The proposed method first identifies the three critical parts of the distribution system: main supply, feeder, and secondary substation. It can also include reserve connections and distributed generation. Second, the method frames these parts into the layered structure, each corresponding to the zone of total load curtailment. To verify this reliability technique, this paper simulates distribution feeders assembled in a number of topologies and compares them with the state sampling technique. Data provided by the local utility company are processed to model the equipment and load for the base year. The results show that the proposed method can provide a wide range of partial and system indices. These values assist in the identification of parts of the distribution system and scenarios with low reliability and to determine possible remedial actions. [ABSTRACT FROM AUTHOR]

Meeting the reliability requirements in wind integrated power systems is a challenging problem due to the volatility of wind power generation. Wide implementation of demand response (DR) programs is an effective solution for this problem. This paper studies the impacts of DR programs on short-term reliability assessment of wind integrated power systems. A new algorithm including the effects of time and components initial states is presented for short-term reliability evaluation. This algorithm involves a multi-segment optimal power flow (OPF) approach to model the lead-time of DR and reserve resources. Taking into account the uncertainties associated with DR programs, a new model for these programs is also proposed. In addition, a reliability modeling is developed for wind energy conversion systems to be employed in short-term studies. The results show that DR programs can eliminate the negative impacts of wind energy volatility on power system reliability. It is also shown that the actual operating conditions, such as the uncertainties in demand-side participation and elasticity estimation, and the lead-time of remedial resources, may significantly affect the efficiency of DR programs. Therefore, it seems that these issues should also be included in the reliability-based planning of DR resources. [ABSTRACT FROM AUTHOR]

Utilities continually investigate ways to optimize the utilization of their existing plant and improve their network's flexibility and resiliency to future uncertainties. With respect to overhead lines (OHL), smart grid solutions aim to increase adequacy through the implementation of probabilistic thermal rating (PTR), dynamic thermal rating (DTR) or novel high temperature low sag (HTLS) conductor technologies. At present, the risks related to these particular solutions, both for the OHL plant and the power network, are not quantified. This paper presents a novel methodology for power network reliability evaluation which integrates a network level sequential Monte Carlo algorithm with a detailed modeling of OHL. This integration facilitates a holistic evaluation of power network reliability as it considers the properties of OHL design technologies and their associated ageing risks. As a result, the network performance (adequacy) and plant risks (ageing) introduced by increased OHL capacities through PTR, DTR, and HTLS solutions can be objectively quantified. An application of the methodology is demonstrated using the IEEE-RTS 96 network which is assessed considering PTR-based OHL capacities. [ABSTRACT FROM AUTHOR]

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems. [ABSTRACT FROM AUTHOR]

This paper presents a maintenance strategy for overhead lines based on condition monitoring and system reliability assessment. A quantitative relationship between the condition monitoring data and failure rate of overhead lines is established. The system reliability is evaluated using monitoring data; the reliability benefit due to the maintenance of each line is quantified; and the maintenance ranking order is created using the benefit/cost ratio index. The proposed strategy can provide the largest reliability improvement at the lowest maintenance cost and within budget. Case studies were performed on a 182-bus, 5474-MW transmission system located in southwest China. [ABSTRACT FROM PUBLISHER]

This paper proposes a series of new metrics for the reliability and economic assessment of microgrids in distribution system. These metrics include reliability parameters for a microgrid in the islanded mode, indices indicating distributed generation (DG) and load characteristics in the microgrid, microgrid economic indices, and customer based microgrid reliability indices. A two-step Monte Carlo simulation (MCS) method is proposed to assess the reliability and economics of a microgrid with intermittent DGs as well as the reliability of distribution system with microgrids. An application in IEEE-RBTS shows the effectiveness of the reliability and economic assessment technique with the proposed metrics, which can provide scientific and comparative information for the design and operation of microgrids. [ABSTRACT FROM PUBLISHER]

This paper presents a new algorithm to estimate the optimal importance sampling (IS) probability distribution in generating capacity reliability (GCR) problems. The proposed approach results from a combination of the cross-entropy (CE) concepts with the standard analytical GCR assessment. A mathematical analysis of the CE equations is carried out to demonstrate that the optimal change of measure or distortion can be obtained by simply dividing the annualized GCR indices for two different configurations of the generating system. Under these hypotheses, a straightforward algorithm based on fast Fourier transform is proposed to systematically obtain the optimal distorted unavailabilities for all generating units in the system. The accuracy and computational performance of the proposed approach are compared with the standard CE optimization process using different generating systems. The IEEE-RTS 79, IEEE-RTS 96, and two configurations of the Brazilian South-Southeastern system are all used for this purpose. [ABSTRACT FROM PUBLISHER]

Miscellaneous sorts of renewable energies, despite of their positive impacts on the power system operation cost and environmental concerns, could jeopardize the system reliability due to introducing significant degrees of intermittency and uncertainty. This challenge could be overcome by composing various sources of renewable energies with complimentary natures. This paper devises an explicit mathematical framework to compromise the contribution of wind and solar energies. The optimization problem is to maximize the system reliability subject to a fixed monetary investment associated with both wind and solar. The problem formulation is based on mixed-integer programming (MIP) format in which the optimality of the solution is guaranteed or can be traded with the execution time. The decision variables are the number of wind turbines and solar modules to be deployed. In addition, uncertainties associated with the hourly load, wind speed, and solar radiation are captured by scenarios based on the probability density functions (PDF). These scenarios are generated by the Monte Carlo simulation method. They are then decreased to a tractable set using a scenario reduction technique. Effectiveness of the proposed methodology is demonstrated through numerical evaluations using real field data. [ABSTRACT FROM PUBLISHER]

This paper presents a sequential Monte Carlo simulation algorithm that can simultaneously assess composite system adequacy and detect wind power curtailment events. A simple procedure at the end of the state evaluation stage is proposed to categorize wind power curtailment events according to their cause. Furthermore, the dual variables of the DC optimal power flow procedure are used to identify which transmission circuits are restricting the use of the total wind power available. In the first set of experiments, the composite system adequacy is assessed, incorporating different generation technologies. This is conducted to clarify the usual comparisons made between wind and thermal technologies which, in fact, depend on the performance measure selected. A second set of experiments considering several wind penetration scenarios is also performed to determine the operational rules or system components responsible for the largest amount of wind energy curtailed. The experiments are carried out on configurations of the IEEE-RTS 79 power system. [ABSTRACT FROM PUBLISHER]

This paper proposes a new methodology to evaluate system generating capacity reliability indices considering time-dependent power sources and loads. Based on sequential Monte Carlo simulation (MCS) and the cross-entropy (CE) method, the basic idea is to find an optimal distortion for the equipment transition rates using the CE concepts. A chronological simulation is then carried out using these newly found optimal reference parameters. This process suitably modifies the chronological evolution of the system in order to improve its statistical efficiency and convergence properties. As a result, the computational efforts of the sequential simulation can be greatly reduced while retaining many of its main advantages. Comparisons with the chronological, pseudo-chronological, and quasi-sequential MCS are carried out using the IEEE-RTS-96 (Reliability Test System) and modifications of this system that include renewable sources. [ABSTRACT FROM PUBLISHER]

This letter presents a method to build weather-related fuzzy models of outage rate, repair time, and unavailability of overhead power lines. [ABSTRACT FROM AUTHOR]

This letter presents a method to incorporate a combined fuzzy and probabilistic load model in power system reliability assessment. [ABSTRACT FROM AUTHOR]