Assessment of energy consumption behaviour plays an important role in designing demand reduction programs by utility companies. Knowledge of appliance activities in a household aids in conducting the energy consumption behaviour assessment for a community load. Non-intrusive load monitoring (NILM) is a tool that can help in identifying the appliance activities. In this paper, a Modified Factorial Hidden Markov Model (MFHMM) based NILM framework is proposed, which models dependencies among appliance operating states and differential appliance operating states by considering differential in power consumption profiles over time. All the appliances are modelled as individual load models using the Hidden Markov Model (HMM). The appliance operating states are obtained with the application of an iterative k-means clustering algorithm. The aggregated power consumption profile is divided into segments using an optimization-based change-point detection (CPD) algorithm. The NILM problem is solved for each of the segments, and the obtained solution is corrected based on the voltage profile at the aggregated load point. The approach of segmentation and efficient identification of appliance operating states make the model less time complex. Simulations are carried out on publicly available datasets named AMPds, REDD, and UK-DALE. The efficacy of the proposed framework over existing frameworks is evident from the simulation results.

A practical loss allocation method should consider size, distance from source, and network usage of its' participants. The power flow pattern in distribution network has changed over a period due to increased integration of distributed generation (DG) and hence, the equivalent distance and usage of network have changed for the conventional distribution network. The regulators require a fair and robust model for loss allocation which should be able to discriminate between the contributions of multiple participants. The loss allocation techniques proposed earlier in the literature either require additional quantities over and above power flow solution or are specifically developed only for radial distribution networks. This paper presents a cooperative game approach which provides stable solution integrated with appropriate penalties or rewards to participants. The proposed method provides an analytical solution which is solved using Shapley value for radial as well as weakly meshed distribution network. The memory and computational burden with the Shapley value have been overcome by a final analytical formula that satisfies the axioms of fairness. The use of the method is straightforward without any assumptions and requires only network data and power flow solution. The results are illustrated using different scenarios of DG output levels and network topologies and compared with other methods to establish its effectiveness.

The distribution system operator performs optimal power flow calculations for efficient and economical scheduling of the distributed energy resources (DERs) and congestion management of the active distribution network. A linearized version of the ac optimal power flow (ACOPF) model is more suitable for practical implementation due to its simplicity and fast convergence. Thus, this article proposes a linearized ACOPF framework and locational marginal price (LMP) calculations for a generalized (i.e., radial and meshed) active balanced distribution system. The proposed linearized ACOPF for the distribution level market is formulated based on novel active and reactive power loss sensitivities by considering the effects of high $\boldsymbol{R/X}$ ratio and network constraints. The distribution LMPs are mathematically derived for active and reactive power generations and loads. Different reactive power loads are represented as a function of active power loads using the concept of power-factors. Thus, the load portfolio consists of nominal active power demand and nominal power-factor. Different LMPs are calculated for loads with different power-factors. DERs submit a separate offer for active and reactive power generations. Thus, separate LMPs are derived for DERs. The efficacy and efficiency of the proposed ACOPF framework are illustrated and compared on different radial and meshed distribution systems.

Probabilistic power system analysis tools like probabilistic power flow (PPF) help system operators in planning and security studies with multiple uncertainties. Sample-based probabilistic approaches like Monte-Carlo and Quasi-Monte Carlo (QMC) methods are preferred for solving PPF, owing to their non-intrusive nature and capability to use the full deterministic model. However, the drawbacks of inconsistent accuracy, variable convergence rate, and lack of a quality measure hinder QMC's wide applicability. To this end, we propose a nonparametric QMC framework to efficiently and accurately solve PPF with non-Gaussian and dependent uncertainties. The proposed framework introduces uniform experimental design (UD) sampling, which is scalable and improves the PPF's accuracy-efficiency balance. Leveraging on the copula viewpoint, the proposed method directly evaluates the desired correlation matrix in standard Gaussian space, reducing the computational burden. In addition, we introduce mixture discrepancy (MD) as a robust sample quality measure, which is helpful to practitioners in identifying the best QMC sample set for PPF without needing any tedious simulation. Results from detailed case studies on the modified 39-bus and 118-bus systems show that the proposed UD-based QMC improves the computational burden and provides accurate PPF results for fewer samples than the existing QMC methods.

Advanced stochastic programming-based operations planning (OP) requires wind power forecasts in the form of scenarios. The quality of the decisions made under uncertainty is sensitive to the quality of the infeed scenarios. Consequently, there is a need for accurate and efficient methods of scenario generation (SG). This article proposes a conditional joint wind power SG method for multiple wind farms. The proposed framework models the dependence among wind farms using vine copula and presents a novel analytical conditional sampling algorithm (CSA). Unlike the existing work, this CSA is inherently accurate, avoids the tedious manual conditional sample selection, and enables control over the number of conditional scenarios generated. Also, a uniform design-based variance reduction is introduced and integrated into the proposed CSA, which benefits the downstream OP applications with improved convergence and accuracy of the solutions. A detailed two-stage scenario evaluation procedure is carried out on a real-world dataset: univariate and multivariate quality metrics-based statistical evaluation in the first stage and an application-based evaluation in the second stage. Results suggest that the proposed SG method significantly improves the overall quality of the forecasted wind power scenarios and provides a better cost-risk balance in the application compared to the benchmarks.

Centralized grid operations are now facing data storage and computation complexities more than ever. These are further compounded by communication saturation and related issues. These have instigated the transition towards distributed computing paradigm. This paper proposes a novel agent-based distributed (node-node) state estimation that overcomes the complexities of centralized operations. The proposed methodology sketches distributed constraint satisfaction algorithm that enables an agent to estimate its local representative bus’s state only by local computation and asynchronous message exchange with connected neighboring agents. The case studies demonstrate the performance, efficacy and solution accuracy of the proposed methodology by comparing it with centralized and distributed algorithms.