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This paper presents a novel robust current droop controller (RCDC) using a single droop loop. This scheme is unified supporting dual mode of operation for micro-grids (MGs), including grid connected mode (GCM) and islanded mode (ISM) while ensuring seamless transition between the two modes with proportional power sharing maintained. The proposed controller is further incorporated with an improved maximum power point tracking (MPPT) technique presented for the parallel operation of single-stage inverters fed by multi-string PV array topology. In addition, an improved phase-locked-loop-less (PLL-less) method is presented supporting self-synchronization strategy of the parallel operation of PV-inverters with the main grid while maintaining the full capabilities of the unified control architecture. This obviates the usage of conventional PLLs, which are widely used with active synchronization techniques. The performance of the proposed control scheme is validated using real time simulations (RTS) developed by dSPACE MicroLabBox.

This paper introduces a new framework for optimum design and operation of hybrid renewable energy plants (HREP) augmented with battery energy storage systems (BESS). A new renewable energy management system (REMS) is developed comprising three components: 1) Enhanced joint forecasting of wind and solar outputs based on deep neural networks and also multiplicative weights update (MWU); 2) an advanced optimization model for sizing the HREP-BESS components and the policy of BESS operation; and 3) Augmenting the rolling hourly dispatch for HREP-BESS with a novel dynamic ramping limit and a criterion for reduction of deviations from the hour-ahead dispatch schedule. The proposed REMS tool enables maintaining the inter-hourly ramping of the HREP-BESS output within a threshold. In this context, a novel dynamic ramp limit is proposed to minimize the energy curtailment during operation and maximize energy sales to the power grid. The advantage of the proposed REMS tool over the classical renewable energy systems operation scheme is the mitigation of the volatility of renewable energy sources (RES) by suppressing extreme ramping events with minimum curtailment. Moreover, the costs and revenues of the HREP-BESS design and operation are assessed over a 25 years period. The design problem is solved for different scenarios, and the optimal solution always encloses a hybrid mix of renewables where the share of the PV plant can reach up to 37.1% of the total plant size. With the proposed REMS, the curtailment of RES never exceeds 12.9% even when the HREP is operated without a reserve margin. For the selected design, the optimum BESS capacity is 12.9% of the HREP capacity. The number of hours which observe a ramping violation event is 2.4% of the season's length (2184 hours). 99% of all ramping events fall within the defined ramping limits. The use of the MWU method increases the total profit by 2.53% compared with adopting the average forecast.

A routine task faced by Microgrid (MG) operators is to optimally allocate incoming power demand requests while accounting for the underlying power distribution network and the associated constraints. Typically, this has been formulated as an offline optimization problem for day-ahead scheduling, assuming perfect forecasting of the demands. In practice, however, these loads are often requested in an ad-hoc manner and the control decisions are to be computed without any foresight into future inputs. With this in view, the present work contributes to the modeling and algorithmic foundations of real-time load scheduling problem in a demand response (DR) program. We model the problem within an AC Optimal Power Flow (OPF) framework and design an efficient online algorithm that outputs scheduling decisions provided with information on past and present inputs solely. Furthermore, a rigorous theoretical bound on the competitive ratio of the algorithm is derived. Practicality of the proposed approach is corroborated through numerical simulations on two benchmark MG systems against a representative greedy algorithm.

In this article, a novel step-up transformerless inverter topology is presented for the grid-connected solar PV system. The proposed topology has the features of a dc–dc step-up converter and two-level inverter. First, it offers the transformerless grid connection of solar PV system with negligible leakage current and higher efficiency, and second, it provides inherent high gain step-up so that low-voltage solar PV modules can be directly connected. In addition, the proposed topology has low-voltage stress, less power losses, and requires fewer independent gate drive signals compared to the other existing transformerless inverter configurations with voltage boosting capability. The design, modes of operation, analysis, and developed PWM strategy, for the proposed inverter topology, are presented in detail. Furthermore, the system performance is validated experimentally through a 400 VA laboratory prototype. The experimental investigation demonstrates that the proposed topology results in transformerless voltage boosting while achieving improved system efficiency.

Nowadays, multilevel inverters (MLIs) are gaining huge popularity for high power transformerless PV applications. Among the traditional MLIs, the cascaded H-bridge (CHB) MLI accommodates lower voltage rated input dc sources, which reduce the voltage stress across the devices. However, the CHB MLI requires multiple PV sources as separate dc-link voltage sources, which create more paths for leakage currents. Therefore, it is a challenging task to deal with the leakage currents in the case of CHB MLIs. In this article, a novel three-phase transformerless inverter topology for grid-connected solar PV application is introduced. This proposed that the inverter topology has six switches per phase, and it has the combined advantages of dc-bypass and ac-bypass circuit configurations. A new modulation strategy is developed for the proposed topology; it is based on a sine triangle pulsewidth modulation technique combined with the dedicated logic functions. The gate pulses for all switches are provided by using these dedicated logic functions. The switching pulses obtained from the dedicated logic functions control all the inverter switches so that the variation of common-mode voltage (CMV) is constant during the inverter operation. This results in reduced leakage current throughout the operation of the inverter. The theoretical analysis, simulations, and experimental results are presented to validate the proposed topology concept for a 3-kVA grid-connected system. Both the simulation and experimental results show that the proposed solution can well attenuate the leakage current; all the results are presented in the article.

This article introduces a new time-domain fault location algorithm for two-terminal parallel transmission lines connected to large scale wind farms. The proposed algorithm employs only a half-cycle data window of synchronized current samples at both line terminals to avoid inaccurate estimation of current phasors due to the generated sub- and inter-harmonics currents by the wind farms. The proposed algorithm does not need any transformation method to decouple the double-circuit transmission line. Moreover, it takes into consideration the effect of the line asymmetry and the potential couplings between the six phases. The fault location equation is deduced by equalizing the differential components of the calculated instantaneous voltages at the fault point, and then the fault distance is estimated directly without any iterative algorithm. The two-terminal parallel transmission line is emulated by PSCAD/EMTDC platform utilizing the frequency-dependent phase model, and the required calculations for fault location are conducted by MATLAB software. The proposed algorithm is tested for several fault resistances and fault locations, and all fault types, including cross-circuit faults. In addition, the effect of measurement, synchronization, and line parameters errors on the fault location accuracy is investigated. The obtained results confirm acceptable accuracy of the proposed fault location algorithm.

This letter proposes a hybrid Beta-kernel density estimation (KDE) model for solar irradiance probability density estimation. The model combines parametric and nonparametric approaches to avoid KDE boundary bias and obtain a more reliable statistical model of solar irradiance. The performance of the hybrid model is assessed via comparisons with the Beta distribution and two KDE models that employ different bandwidth selection methods. The assessment is carried out using the Kolmogorov–Smirnov goodness-of-fit test, and four measures of error: root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean bias error (MBE). Results confirm the accuracy of the hybrid model for solar irradiance modeling with percentage improvements over the Beta distribution of up to 13.8% (RMSE), 11.7% (MAE), 19.3% (MAPE), and 72.5% (MBE). The K–S test results show that the proposed Beta-KDE hybrid is the only model for which the null hypothesis is not rejected for any of the eight datasets considered in this study.

Solar energy is one of the most important and widely utilized renewable energy resource. Despite the many attractive features of solar-based renewables, the wide scale integration of solar generation into electric power systems presents significant challenges to network planners and operators, mainly due to the intermittent nature of solar energy. Most published work in this area is, however, focused on short-term forecasting studies used for unit commitment and energy market decisions with very little attention paid to solar irradiance probability density estimation needed for network planning and design studies. In this paper, a reliable nonparametric model of solar irradiance probability density is proposed based on the application of local linear regression in tandem with a root transformation method, introduced here for the first time. The performance of the proposed estimator is assessed via comparisons with the parametric Beta distribution (conventionally employed to model solar irradiance probability density) and two nonparametric kernel density estimation models; using the Kolmogorov–Smirnov (K–S) goodness-of-fit test, coefficient of determination ( $R^2$ ), and a number of error metrics. Results confirm the suitability and accuracy of the proposed method for solar irradiance probability density estimation.

Wireless Sensor Networks (WSNs) is the base for many critical applications where a large number of nodes are deployed in the monitored field. Those nodes suffer from limited processing capabilities as well as energy. They are also required to live for a long time; sensors send their data to the sink node through multi-hop routing. The sink node is responsible for making the appropriate decision based on the received data. This shows how critical the information fusion process at the sink node. Such fusion is affected by the quality of the collected information from the deployed sensors. In addition, information handling in the WSN is also affected by environmental conditions, sensors energy, and data reliability, which play important roles in the eminence of information fusion. This paper introduces an efficient environment-aware fusion-based reliable routing algorithm named E3AF. The algorithm takes different parameters into consideration, including environmental metrics, energy consumption balance among sensor nodes, and network and data reliability. The algorithm models the environment to allow the routing algorithm to avoid going through areas marked dangerous. The paper formulates the problem into an optimization problem, which is structured as an Integer Linear programming (ILP) to help grasp and obtain the optimum solution. Furthermore, the paper presents swarm intelligence as a heuristic algorithm when the optimal solution is not possible. In comparison to EFMRP, some of the used equations were corrected, and more realistic conditions and constraints were added. With extensive experiments, the proposed algorithm is evaluated and proved its efficiency.

The emerging interest in deployment of plug-in electric vehicles (PEVs) in distribution networks represents a great challenge to both system planners and owners of PEV-parking lots. The owners of PEV-parking lots might be interested in maximizing their profit via installing charging units to supply the PEV demand. However, with stringent rules of network upgrades, installing these charging units would be very challenging. Network constraints could be relaxed via controlling the net demand through integrating distributed generation (DG) and/or storage units. This paper presents an optimization model for determining the optimal mix of solar-based DG and storage units, as well as the optimal charging prices for PEVs. The main objective is to maximize the benefit of the PEV-parking lot's owner without violating system constraints. Two cases are considered in this paper: uncoordinated and coordinated PEV demand. A novel mathematical model is further developed whereby the behavior of vehicles' drivers, in response to different charging prices, is considered in generating the energy consumption of PEVs.

Smart grids have been emerging nowadays as an initiative to operate modern distribution systems in a more economic and efficient way. Energy storage systems (ESSs) are one of the promising technologies that can achieve the goals of smart grids via facilitating the connection of renewable sources, improving system reliability, and controlling the net demand through peak load shaving, etc. In this paper, a comprehensive planning framework is introduced for ascertaining the most cost effective siting and sizing of ESSs that maximize their benefits in distribution networks. A probabilistic approach is further adopted that includes the consideration of the stochastic nature of system components. Such approach allows determining the optimal operation of ESS at each load state. Moreover, contingency planning decisions, in the form of load points to be shed during contingencies, are identified through the approach proposed.

An optimized communication and control infrastructure, based on the microgrid building block concept, for active distribution systems is essential to facilitate powerful control framework under the smart grid paradigm. In this paper, a novel methodology for designing a communication and control infrastructure is presented. The new design takes into account both communication system and distribution system-related aspects. The proposed design facilitates systematic and optimized clustering of the distribution system into a set of virtual microgrids with optimized communication requirements while considering the power quality aspects, characteristics of distributed generation units, distributed energy storage units, and distributed reactive sources. The new design facilitates robust infrastructure for smart distribution systems operation and control, e.g., self-healing control and optimized system-level operation, by using virtual microgrids as building blocks in future distribution systems. The motivations, conceptual design, problem formulation and solution algorithms are presented in this paper. The well-known PG&E 69-bus distribution system is selected as a test case and through several sensitivity studies, the effect of optimization coefficients on the design, and the robustness of the algorithm are investigated. Finally, the algorithm is tested on the IEEE 123-bus distribution system.

The recent deployment of distributed generation has led to a revolution in the use of distribution systems and the emergence of “smart grid” concepts. Smart grids are intended primarily as a means of facilitating the integration of renewable energy sources and of achieving greater system reliability and efficiency. Energy storage systems (ESSs) offer a number of benefits that can help utilities move toward those goals. One of those benefits is the capacity to improve the utilization of network infrastructure by means of proper load management. This paper proposes a methodology for allocating ESSs in distribution systems in order to defer system upgrades, minimize system losses, and take advantage of the arbitrage benefit. The cost and arbitrage benefit of energy storage installation are optimized with respect to system upgrade and energy losses costs. The primary goal of this research is to determine the optimal size and location of storage units to be installed, in addition to their optimal operation, so that total system costs are minimized, while system benefits are maximized. In this paper, a probabilistic load model is adopted instead of utilizing time-series based models, which provide an optimal solution that is valid only for the time-series pattern that is applied.

The recent deployment of distributed generation has led to a revolution in the use of distribution systems and the emergence of smart-grid concepts. Smart grids are intended primarily as a means of facilitating the integration of renewable energy sources and of achieving greater system reliability and efficiency. Energy storage systems offer a number of benefits that can help utilities move toward those goals. One of those benefits is the capacity to improve system reliability through successful islanding operation. This paper proposes a methodology for the cost-effective improvement of system reliability through the allocation of distributed storage units in distribution systems. The costs of energy storage installation are optimized with respect to reliability value expressed as customers' willingness to pay in order to avoid power interruptions. The primary goal of this research was thus to determine the optimal combination of storage units to be installed and the loads to be shed so that all possible contingencies can be effectively addressed. A probabilistic approach is therefore adopted that includes consideration of the stochastic nature of system components.

Integration of large-scale energy storage systems (ESSs) is desirable nowadays to achieve higher reliability and efficiency for smart grids. Controlling ESS operation usually depends on electricity market prices so as to charge when the price is low and discharge when the price is high. On the other hand, the market-clearing price itself is determined based on the net demand, i.e., including energy storage output, at every hour. Therefore, it is crucial to develop a mathematical model to determine the optimal ESS operation as well as the market-clearing prices. The problem is formulated as a mixed complementarity problem (MCP) that allows the representation of special (incentive) prices, which cannot be represented in a single optimization model. The proposed model is useful for power system operators to determine the optimal storage dispatch simultaneously with the market-clearing price in addition to the conventional generation dispatch. The impact of energy storage size and location on market price, total generation cost, energy storage arbitrage benefit, and total consumer payment is further investigated in this paper. The latter analysis provides some guidelines for power system planners to identify the optimal size and location for installing large-scale ESSs.

Recently, the concept of microgrids (clusters of distributed generation, energy storage units, and reactive power sources serving a cluster of distributed loads in grid-connected and isolated grid modes) has gained a lot of interest under the smart grid vision. However, there is a strong need to develop systematic procedure for optimal construction of microgrids. This paper presents systematic and optimized approaches for clustering of the distribution system into a set of virtual microgrids with optimized self-adequacy. The probabilistic characteristics of distributed generation (DG) units are also considered by defining two new probabilistic indices representing real and reactive power of the lines. Next, the advantages of installing both distributed energy storage resources (DESRs) and distributed reactive sources (DRSs) are investigated to improve the self-adequacy of the constructed micro-grids. The new strategy facilitates robust infrastructure for smart distribution systems operational control functions, such as self-healing, by using virtual microgrids as building blocks in future distribution systems. The problem formulation and solution algorithms are presented in this paper. The well-known PG&E 69-bus distribution system is selected as a test case and through several sensitivity studies, the effect of the total DESRs or DRSs capacities on the design and the robustness of the algorithm are investigated.

The objective of this paper is to provide an assessment on voltage profiles in residential neighborhoods in the presence of photovoltaic (PV) systems. The network was modeled in PSCAD using common feeder characteristics that Canadian system planners use in suburban residential regions. A simulation study was performed to investigate potential voltage rise issues in the network up to 11.25% total PV penetration in the feeder and LV transformer capacity penetration up to 75%. Results indicate that the PV penetration level should not adversely impact the voltage on the grid when the distributed PV resources do not exceed 2.5 kW per household on average on a typical distribution grid. Moreover, the role of feeder impedance, feeder length, and the transformer short circuit resistance in the determination of the voltage rise is quantified.

Overvoltages in low voltage (LV) feeders with high penetration of photovoltaics (PV) are usually prevented by limiting the feeder's PV capacity to very conservative values, even if the critical periods rarely occur. This paper discusses the use of droop-based active power curtailment techniques for overvoltage prevention in radial LV feeders as a means for increasing the installed PV capacity and energy yield. Two schemes are proposed and tested in a typical 240-V/75-kVA Canadian suburban distribution feeder with 12 houses with roof-top PV systems. In the first scheme, all PV inverters have the same droop coefficients. In the second, the droop coefficients are different so as to share the total active power curtailed among all PV inverters/houses. Simulation results demonstrate the effectiveness of the proposed schemes and that the option of sharing the power curtailment among all customers comes at the cost of an overall higher amount of power curtailed.