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This paper empirically explores the organizational processes at the onset of disruptions and the factors that determine different configurations of responses. It examines how processes of response, both before and in the aftermath of a disruption, support the building and development of organizational resilience. Using case study data from three U.K. based organizations, this paper makes the following three contributions. First, it identifies the common elements involved in decision making at the onset of a disruption, and explains the iterative stages and processes that led to the development of resilience. It explains the criticality and relationships between the elements of detection, activation, and response. Second, this paper explains why responses vary from one situation to another, by identifying two dimensions that determine the configurations of organizational resilience, namely Preparedness and Adaption. Third, this paper presents the resilience configurations matrix that gives rise to and establishes four distinct types of organizational configurations, which are Process Based, Resourceful, At High Risk, and Resilience Focused. This paper concludes by discussing the implications for theory and practice of resilience. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-resolution clustering (MRC) method that for the first time classifies end customers directly from massive, volatile and uncertain smart metering data. It will firstly extract spectral features of load profiles by multi-resolution analysis (MRA), and then cluster and classify these features in the spectral-domain instead of time-domain. The key advantage is that the proposed method will allow a dynamic load profiling to be flexibly re-constructed from each spectral level. MRC addresses three key limitations in time-series based load profiling: i) large sample size: sample size is reduced by a novel two-stage clustering, which firstly clusters each customers' massive daily profiles into several Gaussian mixture models (GMMs) and then clusters all GMMs; ii) volatility: it avoids the interferences between different load features (e.g. magnitude, overall trend, spikes) by decomposing them onto different resolution levels, and then clustering separately; iii) uncertainties: as the GMM can give a probabilistic cluster membership instead of a deterministic one, an additive classification model based on the posterior probability is proposed to reflect the uncertainty between days. The proposed method is implemented on 6369 smart metered customers from Ireland, and compared with the load profiles used by the U.K. industry and traditional K-means clustering. The results show that the developed MRC outperformed the traditional methods in its ability in profiling load for big, volatile and uncertain smart metering data. [ABSTRACT FROM PUBLISHER]

Location of the static VAR compensator (SVC) and other types of shunt compensation devices is important for the enhancement of the voltage stability for practical power systems. With the theory of the normal forms of diffeomorphism, this paper proposes a new method to solve this problem. The proposed method makes use of the nonlinear participation factors, in which the nonlinearity of power systems can be taken into consideration. As a result, the most suitable location where the SVC should be used in power system can be determined, even for the cases in which the system is characterized with strong nonlinearity. In order to show the effectiveness of the proposed method, the New England 39-bus power system with SVC is used as an example. Calculation results show that with the SVC located at the place where the proposed method determined, the voltage stability is considerably enhanced. The steady-state voltage stability index and the time domain simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

If transmission charges are to reflect costs, they should be affected by the location of demand and generation. This paper describes the investment cost-related pricing (ICRP) methodology used to calculate transmission charges in Great Britain (GB), which is based on the marginal investment cost of additional demand or generation, using a dc load flow transport model. We apply this existing method to calculate charges for the Supergen FutureNet scenarios for 2020. This study highlights the sensitivities in charges for use of the transmission system arising from plausible demand and generation developments. The changes in tariffs will present financial challenges for system users in some areas. The objective of the work presented is to illustrate the sensitivity of the charges produced by this methodology to changes in demand, generation, and network topology rather than compare alternative pricing approaches. The conclusion drawn is that the ICRP system pricing method may be suitable in future years but only with some important issues investigated and resolved. [ABSTRACT FROM AUTHOR]

Numerous methods have been proposed for forecasting load for normal days. Modeling of anomalous load, however, has often been ignored in the research literature. Occurring on special days, such as public holidays, anomalous load conditions pose considerable modeling challenges due to their infrequent occurrence and significant deviation from normal load. To overcome these limitations, we adopt a rule-based approach, which allows incorporation of prior expert knowledge of load profiles into the statistical model. We use triple seasonal Holt-Winters-Taylor (HWT) exponential smoothing, triple seasonal autoregressive moving average (ARMA), artificial neural networks (ANNs), and triple seasonal intraweek singular value decomposition (SVD) based exponential smoothing. These methods have been shown to be competitive for modeling load for normal days. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model load for special days, when used in conjunction with a rule-based approach. The proposed rule-based method is able to model normal and anomalous load in a unified framework. Using nine years of half-hourly load for Great Britain, we evaluate point forecasts, for lead times from one half-hour up to a day ahead. A combination of two rule-based methods generated the most accurate forecasts. [ABSTRACT FROM PUBLISHER]

This paper discusses an alternative to the ex post pricing model currently under development within ISO New England's (ISO NE) ancillary service market. Since ISO NE's real-time ancillary service market co-optimizes both energy and reserve products, ex post pricing is needed for both energy and reserve. A co-optimization approach for ex post pricing requires defining complex pricing rules and implementing heuristics to ensure consistent binding constraints with the ex ante dispatch. In order to avoid complexity and uncertainty of required heuristics, a new ex post pricing schema is proposed to calculate the ex post energy and reserve prices in a decoupled way while recognizing the coupling effect of energy and reserve. In the proposed approach, ex post energy prices are first computed by incorporating the ex ante marginal opportunity cost of reserve in the energy offer; the ex post reserve prices are then calculated by considering the ex post marginal opportunity cost of energy. This approach simplifies the implementation of ex post pricing rules in the ISO NE market. Numerical examples are presented to further demonstrate the validity of this approach. [ABSTRACT FROM AUTHOR]

Due to decarbonisation and decentralisation of energy sectors, the rise of distributed generation (DG) will modify generation and demand patterns at grid supply points (GSPs), where the interface between distribution and transmission systems takes place. With the increasing penetration of such devices, methodologies able to evaluate its contribution to both local distribution network and transmission system become crucial. This article proposes an analytical method to evaluate the DG locational incremental contribution (LIC) to the interface with the transmission grid. Accordingly, the original model of the U.K. engineering recommendation P2 is enhanced by studying the impacts from high-voltage distribution networks (i.e., ≤ 132 kV) under normal and contingent conditions. This approach enables a more accurate network security assessment, especially when considering contingencies such as distribution system faults. To illustrate the proposed method, the original P2 model is compared against different enhanced approaches on three basic distribution networks, followed by a case study and a sensitivity analysis on a revised IEEE 14-bus GSP system. The proposed LIC method produces results that assess a wider range of conditions including DG penetration, concentration, and system reliability. Furthermore, it provides an increased DG visibility for transmission planning and operation. [ABSTRACT FROM AUTHOR]