Microgrid formation is a potential solution in postdisaster electric grid recovery efforts. Recent works propose a distribution level microgrid formation model that applies to radial distribution systems characterized by directed power flows. However, with high renewable penetration levels in future power systems, the flows are expected to be undirected even in distribution grids. In this paper, we develop a model to deal with the restoration process of future power systems, embedding some of the characteristics these systems are likely to have. More specifically, our formulation can deal with radial and meshed topologies, and it requires little preprocessing of the input data. Additionally, we extend the model to allow for possible mobile and fixed distributed generation technologies and distributed energy resources, and explicitly include demand responsive loads with a minimum satisfiability constraint. Thus, this extended formulation can be used as an operation and a short term planning tool for the DG scenario-based location problem. We compare our extended formulation results to those obtained for the radial cases found in the literature. Also, we apply the proposed approach to other instances of the problem based on the IEEE 37-bus, 30-bus, and 118-bus systems.

Microgrid formation is a potential solution in post-disaster electric grid recovery efforts. Recent works propose distribution level microgrid formation models using mixed-integer linear programming techniques. However, these models can only be solved for small and medium size power systems due to their computational intractability. In this article we introduce a heuristic approach that allows to approximately solve the post-disturbance microgrid formation problem for medium to large, more realistic, instances. Furthermore, the proposed approach allows to approximately solve the pre-disturbance microgrid formation problem (a stochastic version of the post-disturbance problem), in which the aim is to allocate extra generation capacity to the network to immunize it, as best as possible, against an uncertain cascading failure. Our results are illustrated by solving versions of the problem on ten test cases with up to 3012 buses.

Flexible ramping products (FRPs) have been implemented by several independent system operators (ISOs) to procure adequate flexible resources. Currently, system operators estimate the system level ramping requirements ignoring the spatiotemporal correlations among various uncertainty sources. This leads to overestimates/underestimates of ramping requirements. In addition, the explicit FRPs model considers only the generation ramping limitation. Other security constraints, such as the transmission limits, are not considered, which leads to deliverability issues with FRPs. To deal with these shortcomings of current FRPs models, this paper proposes a deliverable FRPs based on a distributionally-robust chance constrained multi-interval optimal power flow considering the spatiotemporal correlation of wind power and demand uncertainties endogenously. Furthermore, an asymmetrical affine policy (AAP) is proposed to leverage generation flexibility and mitigate the uncertainty in different directions. The pricing mechanism of the FRPs is proposed using a novel uncertainty-contained locational marginal price (U_LMP) which is derived from the proposed AAP-DRCC-MIOPF model. New components representing the price of FRPs are added into the traditional LMP formulation. Finally, the PJM 5-bus, IEEE 39-bus and IEEE 118-bus systems case studies validate the proposed FRPS approach. The payment of uncertain demand and wind power on FRPs are analyzed.

A critical step in stochastic optimization models of power system analysis is to select a set of appropriate scenarios and significant numbers of scenario generation methods exist in the literature. This paper develops a clustering based scenario generation method, which aims to improve the performance of existing scenario generation techniques by grouping a set of correlated wind sites into clusters according to their cross-correlations. Copula based models are utilized to model spatiotemporal correlations and the Gibbs sampling is then used to generate scenarios for day-ahead markets. Our results show that the generated scenarios based on clustered wind sites outperform existing approaches in terms of reliability and sharpness and can reduce the total computational time for scenario generation and reduction significantly. The clustering-based framework can therefore provide a better support for real-world market simulations with high wind penetration.

The characterization of wind speed distribution and the optimal assessment of wind energy potential are critical factors in selecting a suitable site for wind power plants (WPP). The Weibull distribution law has been used extensively to analyze the wind characteristics of candidate WPP sites, and to estimate the available and deliverable energy. This paper presents a comparative study of five wind energy resource assessment methods as they applied to the context of wind sites in West Sub-Saharan Africa. We investigated three numerical approaches, namely, the adaptive neuro-fuzzy inference system (ANFIS), the multilayer perceptron method (MLP), and support vector regression (SVR), to derive the distribution law of wind speeds and to optimally quantify the corresponding wind energy potential. Next, we compared these three approaches to two well-known Weibull distribution law-based methods: the empirical method of Justus (EMJ) and the maximum likelihood method (MLM). Case study results indicated that the neural network-based methods, ANFIS and MLP, yielded the most accurate distribution fits and wind energy potential estimates, and consequently, are the most recommended methods for the wind sites in Togo and Benin. The orders of magnitude of the root mean squared error (RMSE) in estimating the recoverable energy using ANFIS were, respectively, 10-4 and 10-5 for Lomé and Cotonou, while MLP achieved an RMSE order of magnitude of 10-3 for both sites.

This paper introduces a novel electricity retail pricing scheme we designate generalized minimax (GenMinimax). GenMinimax is characterized by three rate zones with the narrowest in the middle, called threshold band, constituting the attraction zone where the lowest rate is charged. The scheme is designed so that the threshold band envelops a negotiated reference consumption profile. We consider a cooperative group of customers pooling together their flexible loads and participating in the energy market via an aggregator. The aggregator computes an optimal daily profile taking into account the gross daily demand from the consumer group and the energy market's expected conditions and opportunities, and assigns prices and rate zones accordingly. The consumer group reacts by deferring and curtailing the flexible loads in order to minimize their daily cost consisting of the energy bill, the utility cost, and the curtailment reward. We model the consumers' response and interaction with the aggregator as a two-stage sequential optimization problem. We perform sensitivity analysis over the most significant parameter combinations through two scenarios and five cases. We compare the performance of our scheme to that of time-of-use (TOU) and real time pricing (RTP). Using a 40-home aggregate energy profile, we show that consumers can match a test supply profile with 5% maximum error and 2% average error while TOU and RTP can lead, respectively, to 163% and 97% maximum error, and 37% and 25% average error.