A reliability‐based optimal μ‐PMU (micro‐phasor measurement unit) placement scheme is suggested for efficient observability enhancement of smart distribution grids at steady‐state and contingencies conditions. This article introduces a unique method for the μ‐PMU allocation in reconfigurable smart distribution grids in which communication system requirements and zero injection nodes (ZINs) are considered. The original objective function and limitations are proposed aiming at minimizing the capital cost, including communication links and installation costs of μ‐PMU, optical power ground wire cost, power losses cost, and reliability cost as well as obtaining the maximum number of measurement redundancy constrained to full system observability in the presence of ZINs and tie switches. The suggested method is formulated as a mixed‐integer linear programming problem applied to find optimal μ‐PMU locations considering the cost of communication infrastructure and co‐optimize the system switching plan simultaneously. In this regard, CPLEX—a high‐performance mathematical solver—is used to solve the proposed mixed‐integer linear optimization problem to reach the global optimality. The simulations are performed on 33, 69, and 85‐bus radial distribution networks, and comprehensive simulation studies show the effectiveness of the suggested method.

With the growth of distributed energy resources, development of storage systems technology and an increase in the application of electric vehicles (EV), the energy management of these kinds of sources needs additional research. The task of the distribution network operator is to optimize the microgrid operation at the lowest operating cost. This paper suggests a novel concept for parallel scheduling of EVs and battery storage while taking demand response programs (DRP) into account, with the goal of minimizing total operating costs. The influence of cooperation of DRP and optimal scheduling of EVs and energy storages on the operating costs, power transaction with upstream grid, hourly DERs, and systems technical features such as voltage profile and power loss has been studied. Numerical results obtained from an illustrative case study elucidate that the proposed model reduce 5.62 percent from total power loss and improve the minimum voltage from 0.91 to 0.94 p.u.

Measuring the furan concentration in transformer oil is one of the most common methods of monitoring and evaluating insulation paper and estimating its degree of polymerization. Here, methods for measuring furan concentration, especially ultraviolet-visible spectroscopy method, are reviewed on the old transformers oil. The results show that the excessive turbidity of old oils and noise at low wavelengths are the main disadvantages of this method, which reduces the measurements accuracy. Following solutions are proposed to overcome these disadvantages: a) Diluting insulation oil with hexane to reduce absorption intensity, b) using average absorption at a given wavelength to reduce noise impact. These solutions are applied to the oil samples of 10 old distribution transformer. It is observed that the correlation between 2-FAL concentration and average absorption intensity in 190-600 nm wavelength range is equal to R² = 0.98. A mathematical equation for calculating 2-FAL concentration based on average absorption is presented. It has been shown that to reduce the test time, a 190-400 nm range is the optimal measurement wavelength range. The results show that the average difference between the values of degree of polymerization in the proposed method and the direct measurement method is equal to 77.5.

Preserving the frequency stability of multiple microgrid clusters is a serious challenge. This work presents a dynamic model of multiple microgrid clusters with different types of distributed energy resources (DERs) and energy storage systems (ESSs) that was used to examine the load frequency control (LFC) of microgrids. The classical proportional integral derivative (PID) controllers were designed to tune the frequency of microgrids. Furthermore, an imperialist competitive algorithm (ICA) was proposed to investigate the frequency deviations of microgrids by considering renewable energy resources (RERs) and their load uncertainties. The simulation results confirmed the performance of the optimized PID controllers under different disturbances. Furthermore, the frequency control of the microgrids was evaluated by applying regional demand response programs (RDRPs). The simulation results showed that applying the RDRPs caused the damping of frequency fluctuations.

This paper presents a new tri-level optimization approach to financial interaction the local electricity market (LEM) with prosumers and the wholesale electricity market (WEM) based on transactive energy (TE) concepts under uncertainty. In the first level of the problem, prosumers are modeled as separate microgrids, which use smart vehicle charging strategies with high penetration of renewable energy sources (RESs) to minimize expected operation costs by submitting offers/bids to the LEM. The LEM also seeks to minimize its expected operating costs at the second level problem by considering the physical constraints of the electricity distribution network (EDN) and by using demand response programs (DRPs). At the third level problem, the WEM, considering the physical constraints of the transmission network (TN) and the high penetration of wind farms, seeks to market-clearing for maximized social welfare. The equilibrium of the proposed tri-level problem is proved by applying a new hybrid Karush–Kuhn–Tucker (KKT) & two-stage iterative-based method. A moment-based distributionally robust optimization (DRO) algorithm has been developed to provide a robust solution against the uncertain behavior of RESs in prosumers and wind farms in the WEM based on the TE concepts. The case studies are simulated by three networked microgrids, a standard 33-bus EDN test system and a standard 6-bus transmission test system. The results of the case studies show that the proposed TE-based framework can effectively coordinate the optimal scheduling of all three main levels, namely prosumers, LEM and WEM, to reduce expected operating costs significantly.