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After end of decades of loadshedding, the power system of Nepal is moving towards the scenario of self-sufficiency. The Government of Nepal has formulated a policy to add 20 GW of hydropower in the next ten years, making a ten-fold increase compared to the current capacity. Therefore, there is a high need for the assessment of the power system stability of the current Nepal grid to mitigate any potential issues that can occur due to the addition of this huge generation. This paper conducts a comprehensive assessment of the power system stability namely voltage (static), angle (transient and small signal), and frequency stability. The proposed assessment methodology consists of three stages: the first stage constitutes the selection and modeling of the components (generator, load, line, excitation system, governor, renewable energy resources) of the power system of Nepal. The second stage then uses the stability assessment tools: time domain simulations for frequency and transient stability, modal (eigenvalue) analysis for small signal stability and voltage stability, and sensitivity analysis for identification of critical areas prone to instability. The result shows that the current grid of Nepal has a low margin of the small signal stability. The critical clearing time of the system is approximately 0.51s. The result also show that there are five potential voltage instability areas in Nepal but is robust in terms of the frequency stability. Additionally, the integration of photovoltaic generation to the Nepal grid has also been studied in this paper. The study shows that the margin of the low frequency oscillatory stability decreases with the penetration of photovoltaic generation of the power grid of Nepal.

The present paper proposes a new methodology to solve a problem in the literature in which a consumer becomes eligible for reactive power excess charging (RPEC) after installing a photovoltaic (PV) system, even if its reactive demand has not changed. This problem mainly affects this kind of consumer, which is known as PV prosumers, in countries where the power factor at the point of common coupling (PCC) is used as the main parameter in calculating reactive power excess charges instead of directly pricing the surplus reactive energy delivered from the distribution network. The classical approaches to reducing or eliminating RPEC are based on providing reactive power within the consumer’s installations from their PV inverters and their capacitor banks. However, the solutions presented require optimization studies since the costs of producing reactive energy can be even higher than the penalties involved. In this context, we developed a new hourly-based optimization model for the RPEC problem based on the variable neighborhood search meta-heuristic and the Fibonacci search method. In addition, we apply the proposed methodology to two medium voltage prosumers in Santo André, Brazil. The results showed the proposal’s effectiveness, mitigating RPEC and minimizing the prosumers’ electricity bills without installing new equipment to supply their reactive demand. The best results were found when reactive power was delivered by the capacitor bank and PV inverters simultaneously rather than applying these solutions separately. The proposed hourly-based correction method can help in day-ahead energy management, avoiding premature wear from switching the consumer’s reactive power compensation equipment switches. Also, the methodology proposal improved the power factor at the utility substation, which benefits the power distribution systems.

Ever-increasing dynamic surges in renewable-based electric power systems, notably wind and photovoltaic farms bring adverse impacts and challenges in terms of reliability and stability. The intermittency of renewable sources imposes significant deviations in frequency due to variations in demand. Wind power induces instability in the grid due to its vulnerable nature, and reduction in system inertia. To mitigate these dynamics issues, an optimal control technique based on flatness-based Active disturbance rejection control (FADRC) and utilizing an enhanced Beluga Whale optimization algorithm (EBWO) for a multi-area interconnected power system with photovoltaic generation. The proposed LFC model addresses the load perturbation and the deviation of tie-line power, with system uncertainties considered as lumped disturbances that are approximated by extended state observers. To achieve optimal performance, the Enhanced Beluga Whale optimization algorithm is adopted and integrated with the suggested controller to fine-tune the controller. To validate the formidable performance of the suggested scheme, different cases have been studied with the existing approaches. The simulation results reveal the supremacy and robustness of the dynamic response of the Flatness-based active disturbance rejection control as compared to other approaches under load variations and parameter uncertainty.

The goal of achieving decarbonized operation of power systems in response to climate change has led to an increase in the number of sources connected to electrical grids through inverters, of which photovoltaic systems are an example. The operating characteristics of these systems in steady-state or transient operation are different from those of synchronous machines, giving rise to issues related to power system stability. The vast majority of grid codes stipulate that fault ride-through operating requirements should be enabled during severe contingencies in the transmission network, prioritizing delivery of reactive power to the power system to support voltage stability. However, this control action may contribute to rapid collapse of the system voltage under adverse operating conditions, such as when the critical voltage in the power vs. voltage curve is near the reliable voltage operating bound. Supporting power system stability under these circumstances represents a new challenge for inverter control schemes. This paper adapts a recently published fault ride-through control scheme for PV inverters for use in a multimachine power system and shows that the scheme can successfully maintain transient and voltage stability even under adverse voltage operating conditions.

As PV penetration in the power distribution network is growing on an unprecedented scale, probabilistic power flow analysis is becoming crucial to assess the health of network operation. State-of-the-art probabilistic analysis relies on Stochastic Response Surface Method and Gaussian Copula for including PV sources correlation. However, it has been observed how in the presence of complex statistical distributions of injected PV powers, such a standard approach can provide inaccurate evaluations of the output variable distributions. In this article, we carefully investigate the origins of such a drawback and propose a novel implementation flow that overcomes the problem. In order to check the correctness of the proposed methodology, the results obtained with the Stochastic Response Surface Method are compared with Monte Carlo simulations. Several investigations, such as voltage uncertainties, network health and probability of violating quality constraints are conducted on two test networks. [ABSTRACT FROM AUTHOR]

High penetrations of photovoltaic (PV) systems can cause severe voltage quality problems in distribution networks. This paper proposes a distributed control strategy based on the dynamic formation of coalitions to coordinate a large number of PV inverters for voltage regulation. In this strategy, a rule-based coalition formation scheme deals with the zonal voltage difference caused by the uneven integration of PV capacity. Under this scheme, PV inverters form into separate voltage regulation coalitions autonomously according to local, neighbor as well as coalition voltage magnitude and regulation capacity information. To coordinate control within each coalition, we develop a feedback-based leader-follower consensus algorithm which eliminates the voltage violations caused by the fast fluctuations of load and PV generation. This algorithm allocates the required reactive power contribution among the PV inverters according to their maximum available capacity to promote an effective and fair use of the overall voltage regulation capacity. Case studies based on realistic distribution networks and field-recorded data validate the effectiveness of the proposed control strategy. Moreover, comparison with a centralized network decomposition-based scheme shows the flexibility of coalition formation in organizing the distributed PV inverters. The robustness and generalizability of the proposed strategy are also demonstrated. [ABSTRACT FROM AUTHOR]

The stability of power systems can largely limit the power transfer capability limit of transmission lines. The PV generator control is different from synchronous generator control. The impact of PV generator dynamics on the power transfer limit constrained by stability is not well explored in the literature. This paper focuses on improving the stability constrained power transfer capability limit of transmission lines emanating from PV generators. The PV generator is provided with dc link voltage and reactive power control and is equipped with low voltage ride through capability, voltage and frequency support functionalities. In this paper, Lyapunov function analysis based adaptive tuning laws are proposed for PV control parameters to improve the power transfer capability limit (constrained by stability) of transmission lines connecting PV generators to grid. The tuning laws are proposed for Phase Locked Loop (PLL), outer and inner control loop parameters. The proposed tuning laws have inherent robustness towards faults, changes in solar irradiation, changes in grid topology, network impedances, generation and loads in the system. The effectiveness of the proposed method is validated on a Single PV-Synchronous Machine system, modified IEEE-39 and IEEE-118 bus system. The performance of the proposed method is investigated considering various disturbances such as symmetrical and asymmetrical faults on transmission line, switching of a transmission line and change in solar irradiation of PV. Comparison with existing method shows that the proposed tuning method can achieve higher power transfer capability limit considering stability. [ABSTRACT FROM AUTHOR]

The increasing number of megawatt-scale photovoltaic (PV) power plants and other large inverter-based power stations that are being added to the power system are leading to changes in the way the power grid is operated. In response to these changes, new grid code requirements establish that inverter-based power stations should not only remain connected to the grid during faulty conditions but, also provide dynamic support. This feature is referred in the literature to as momentary cessation operation. The few published studies about momentary cessation operation for PV power plants have not shed much light on the impact of these systems on the overall power system stability problem. As an attempt to address this issue, this paper proposes a control scheme for PV inverters that improves the transient stability of a synchronous generator connected to the grid. It is shown through the paper that the proposed control scheme makes the PV inverter’s dc link capacitors absorb some of the kinetic energy stored in the synchronous machine during momentary cessation. Besides that, the proposed solution is also able to improve voltage stability through the injection of reactive power. Experimental and simulation results are presented in order to demonstrate the effectiveness of the proposed control scheme.

A wireless self-powered temperature sensor node is presented. This sensor node is powered by a commercially available indoor photovoltaic energy harvester module. The sensor node uses Bluetooth® Low Energy as wireless connection. Both the hardware and firmware have been optimized to obtain a very low power consumption. An auto adaptive energy management routine has been implemented to extend the autonomy of the sensor node. The system consumption and photovoltaic generation have been characterized at different conditions, obtaining a tool to estimate the autonomy of the system. The system has also been tested in a climatic chamber to study the effect of the temperature, a key factor that affects the power consumption, and hence the autonomy of the sensor.

Solar photovoltaic (PV) generation is a promising energy cost-saving selection for depots that use electric vehicles (EVs) to deliver goods; however, the intermittency of power output renders it challenging to realize reliable and economical operations. This paper proposes a two-stage hierarchical operation management method of EVs for depots with on-site PV generation. In the day-ahead stage, considering real road networks and battery swapping mode, a coordinated scheduling model of delivery service and charging/discharging for EVs is constructed to minimize the overall energy cost and logistics delivery cost by using the forecasted PV power output. Moreover, an effective solution approach based on the natural aggregation algorithm and Floyd algorithm is developed for the model. In the actual operation stage, a model prediction control (MPC)-based rolling horizon operation strategy is proposed to correct EVs’ charging/discharging decisions with the realization of real-time PV power output, aiming at to minimize the deviation in the actual and day-ahead scheduled interactive power between the depot and upper grid. Finally, simulations are conducted to validate the proposed methods. [ABSTRACT FROM AUTHOR]

This paper presents a modular grid-connected single-phase system based on series-connected current-source module integrated converters (MICs). The modular configuration improves the reliability, redundancy and scalability of photovoltaic (PV) distributed generators. In this system, each PV panel is connected to a dc/ac inverter to permit individual Maximum Power Point Tracking (MPPT) operation for each panel. Thus, the harvested power from the PV system will increase significantly. There are four different inverter topologies suitable to be used as MICs with different performances in terms of filtering elements size, power losses, efficiency, output voltage range, and high frequency transformers' size. For the MPPT control, the oscillating even order harmonic components should be eliminated from the inverter's input side otherwise the maximum power cannot be extracted. The proposed modulation scheme in this paper will ease the control of inverter's input and output sides. Therefore, the 2nd order harmonic in the input current can be eliminated without adding new active semiconductor switches. A repetitive controller coupled with proportional-resonant controllers are employed to achieve accurate tracking for grid side as well as input side currents. Comparisons and performance evaluations for the proposed MICs are presented and validated with 1 kVA prototype controlled by TMS320F29335 DSP.

The optimal planning for distributed generations (DGs) associated with photovoltaics (PVs) in the utility-owned distribution system is crucial for increasing high penetration of renewables while against practical system operation constraints. Such PV-DG planning is categorized as a complicated mixed-integer nonlinear programming (MINLP) problem and is extremely difficult to solve by using conventional methods. In recent years, several bio-inspired metaheuristic algorithms have been proposed to tackle various complicated real-parameter optimization problems. This paper proposes a two-stage approach including a new bio-inspired algorithm, Coyote Optimization Algorithm (COA), to solve the large-scale MINLP PV-DG sizing problem considering different load levels. The objective function terms under consideration include the total system power loss and voltage regulator tap changes at different load levels while against limits of rms bus voltages, tap changes, and PV-DG constraints at each candidate bus. The proposed method is tested using the IEEE 123-bus unbalanced benchmark system and an actual utility distribution network. Results obtained are then compared with those obtained by a classic MINLP solver-based and four other bio-inspired methods. Moreover, results also show that the proposed method leads to lower loss, a minimum number of regulator tap changes, and higher PV penetration capacity among the compared methods and is suitable for solving the large-scale PV-DG planning problem in distribution systems.

The proliferation of Photovoltaic (PV) generation has brought several changes in the transient stability analysis of power systems. The rotor angle dynamics based stability analysis used for synchronous generators may not be applicable for PV generators. In the existing literature, the transient stability of a grid connected PV generator with dc link voltage and reactive power control (Vdc and Q control) is analyzed from the coupled nonlinear functions corresponding to PV control loops, Phase Locked Loop (PLL) and network dynamics. The existing method may not be suitable for online transient stability assessment because it is computationally heavy. This paper proposes an online transient stability criterion for a PV generator (with Vdc and Q control) connected to grid modelled as infinite bus. The PV generator is considered to have Low Voltage Ride Through (LVRT) capability, but does not provide voltage support. In this study, the relation between the ac and dc side dynamics is explored. A stability criterion is proposed to assess the transient stability by monitoring the swing in the energy of the dc link capacitor. The proposed criterion requires the dc link voltage trajectory only till the first swing of the dc link voltage. As a result, the proposed assessment criterion is computationally less demanding and less prone to measurement errors in comparison to existing methods. Thus, the proposed criterion is suitable for online transient stability assessment. The effectiveness of the proposed criterion is verified for single-stage as well as two-stage converter configurations of PV generator. The proposed criterion is also verified on a modified IEEE 39 bus system. Time-domain simulations are carried out to validate the proposed criterion. The proposed approach is compared with an existing transient stability criterion for Vdc and Q control based grid connected PV generator. The advantages of the proposed method as compared to the existing method are discussed.

The problem of optimal placement and sizing (OPS) of renewable distributed generation (RDG) is followed by numerous technical, economical, geographical, and ecological constraints. In this paper, it is investigated from two viewpoints, namely the simultaneous minimization of total energy loss of a distribution network and the maximization of profit for RDG owner. The stochastic nature of RDG such as the wind turbine and photovoltaic generation is accounted using suitable probabilistic models. To solve this problem, a hybrid metaheuristic algorithm is proposed, which is a combination of the phasor particle swarm optimization and the gravitational search algorithm. The proposed algorithm is tested on an IEEE 69-bus system for several cases in two scenarios. The results obtained by the hybrid algorithm shows that it provides high-quality solution for all cases considered and has better performances for solving the OPS problem compared to other metaheuristic population-based techniques.

Utility engineers, professionals from regulatory agencies, manufacturers and researchers have discussed if high-frequency distortions produced by rooftop photovoltaic generators are a general power quality concern for distribution systems. In this context, this paper presents the results of an extensive research to investigate the emission and propagation characteristics of such high-frequency distortions. Analytical and theoretical analyses, computer simulations, laboratory experiments and field measurements revealed that high-frequency distortions emitted by rooftop photovoltaic inverters are produced with a voltage source characteristic. As a result, the equivalent grid impedance limits and attenuates significantly the high-frequency distortions injection and propagation into the distribution systems. Moreover, the front-end, low-pass filters found in many home appliances also contribute with such attenuation by sinking part of the high-frequency distortions. In summary, the results indicate that these distortions are unlikely to become a generalized power quality concern, so that the front-end filters commonly available for such devices, when properly designed, are enough to deal with this issue. [ABSTRACT FROM AUTHOR]

This article proposes a two-layer optimal scheduling strategy to handle the overvoltage problem in high photovoltaic (PV) power-penetrated distribution networks. The voltage regulators can be classified as the power utility and PV owners, which are referred to as stakeholders. The proposed scheduling strategy includes autonomous optimization layer and coordination optimization layer. In the autonomous optimization layer, a min-max robust game model and a mixed-integer second-order cone programming-based model are respectively proposed to minimize the operating costs of PV stakeholders and the power utility stakeholder. A parallel optimization is employed to solve the two models in the autonomous optimization layer. In the coordination optimization layer, a noncooperative game-based model is presented to coordinate scheduling solutions of each stakeholder. Finally, an actual 10 kV, 106-bus feeder in Zhejiang Province, China, and a modified IEEE 123-bus distribution system are employed to verify the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract A small-signal model of photovoltaic (PV) generation connected to weak AC grid is established based on a detailed model of the structure and connection of a PV generation system. An eigenvalue analysis is then employed to study the stability of PV generation for different grid strengths and control parameters in a phase-locked loop (PLL) controller in the voltage source converter. The transfer function of the power control loop in the dq rotation frame is developed to reveal the influence mechanism of PLL gains on the small-signal stability of PV generation. The results can be summarized as follows: ① oscillation phenomena at a frequency of about 5 Hz may occur when the grid strength is low; ② the tuning control parameters of the PLL have a noticeable effect on the damping characteristics of the system, and larger proportional gain can improve the system damping;③ within a frequency range of 4-5 Hz, the PLL controller has positive feedback on the power loop of PV generation. A virtual inductance control strategy is proposed to improve the operational stability of PV generation. Finally, a simulation model of PV generation connected to weak AC grid is built in PSCAD/EMTDC and the simulation results are used to validate the analysis.

Abstract This paper presents a model predictive control (MPC) for off-board plug-in electric vehicle (PEV) chargers with photovoltaic (PV) integration using two-level four-leg inverter topology. The PEV charger is controlled by a unified controller that incorporates direct power and current MPC to dynamically control decoupled active-reactive power flow in a smart grid environment as well as to control PEV battery charging and discharging reliably. PV power generation with maximum power tracking is seamlessly integrated with the power flow control to provide additional power generation. Fast dynamic response and good steady-state performance under all power flow modes and various environmental conditions are evaluated and analyzed. From the results obtained, the charger demonstrates less than 1.5% total harmonic distortion as well as low active and reactive power ripple of less than 7% and 8% respectively on the grid for all power flow modes. The PEV battery also experiences a low charging and discharging current ripple of less than 2.5%. Therefore, the results indicate the successful implementation of the proposed charger and its control for PV integrated off-board PEV chargers.

In active distribution networks, high penetration of distributed photovoltaic power generation may cause voltage fluctuation and violation issues. To conquer the challenges, this paper firstly proposes a load-weighted voltage deviation index (LVDI) to quantify network voltage deviation. Secondly, this paper proposes a multi-objective adaptive voltage/VAR control (VVC) framework which coordinates multiple devices in multiple timescales to minimize voltage deviation and power loss simultaneously. Then, a multi-objective adaptive robust optimization method is proposed to obtain robust Pareto solutions under uncertainties. Accordingly, solution algorithms based on different multi-objective programming algorithms and a column-and-constraint generation algorithm are developed and systematically compared. The proposed method is verified through comprehensive tests on the IEEE 123-bus system and simulation results demonstrate high effectiveness of the LVDI, high efficiency of the solution algorithms and full operating robustness of the proposed VVC method against any uncertainty realization. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a joint real-time load scheduling and energy storage management at a grid-connected solar powered electric vehicle. Without any a priori knowledge, we consider a finite time approach with arbitrary dynamics of system inputs. Our aim is to minimize an average aggregated system cost through joint optimization of electric vehicle's energy procurement price, load scheduling delays, photovoltaic sufficiency in terms of locally generated renewable energy mix, and battery degradation. Through subsequent modification and reformulation of the joint optimization problem, we utilize the concept of one-slot look-ahead queue stability to solve the problem by employing the Lyapunov optimization technique. We show that the joint optimization problem is separable into sub-problems, which are sequentially solved with asymptotic optimality and a bounded performance guarantee. Simulations are carried in different scenarios and under varying weather conditions. Results show that our proposed algorithm can achieve a daily electric vehicle's photovoltaic sufficiency up to 50.50%, a monthly bill reduction up to 72.61%, and a yearly reduced CO $_2$ emission level up to 6.06 kg, while meeting electric vehicle user's energy and delay requirements. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) inverters can provide fast and flexible reactive power support for voltage regulation and power loss reduction in distribution networks. Conventionally, central and local voltage/VAR control (VVC) strategies are separately determined, lacking a cross-hierarchy coordination. This paper proposes a novel hierarchically-coordinated VVC (HC-VVC) method where central hierarchy dispatches the inverter reactive power output to minimize network power loss and local hierarchy responds to real-time voltage deviation through a linear droop controller. The proposed method simultaneously optimizes inverter reactive power output setpoints for the central dispatch and droop functions for the local control so that the two control hierarchies are optimally coordinated under stochastic PV power generation and load variations. The coordination model is solved by a scenario-based stochastic optimization approach with probabilistic uncertainty modeling where real-time variations of the uncertainties are fully addressed. Simulation results show that, compared with existing methods, the proposed HC-VVC method is overall superior in minimizing power loss and voltage deviation. [ABSTRACT FROM AUTHOR]

Fully electrified ships, which is known as the “all-electric ships (AESs)”, have the potentials to bring great economic /environmental benefits. To further improve the energy efficiency of AESs, PV generations are gradually integrated, which introduces uncertainties to the AES operation. However, current researches mostly focus on sizing problem whereas rarely concern the operation. In this perspective, a data-driven robust coordination of generation and demand-side is proposed to properly address the onboard PV generation uncertainties as well as reducing the fuel cost of AESs, which consists of an extreme learning machine (ELM) based PV uncertainty forecasting method and a two-stage operating framework, where the first stage for the worst PV generation case and the second stage targets at the uncertainty realization. A 4-DG AES is implemented into the case study and the simulation results show that the ELM-based method can well characterize the PV uncertainties, and the two-stage operating framework can well accommodate the onboard PV uncertainties. Further analysis also demonstrates the proposed method has enough flexibility when facing working condition variations. [ABSTRACT FROM AUTHOR]

In Brazil, in the lasts five years there has been a significant growth of photovoltaic generation installed in the consumer side of me-ters, the so-called prosumers. However, most of these installations occur in an individual way, as self-consumption, with net-metering billing. The possibility of forming a community grouping several consumer units is little used. This paper analyzes the eco-nomic benefit obtained by prosumers acting individually and by prosumers grouped in a community when investing in photovolta-ic generation. A mixed integer linear programming model was used to select photovoltaic generation systems, considering the Brazilian net-metering billing, in order to minimize the annualized total cost to meet the demand of the prosumers. Tests were con-ducted for prosumers connected individually and in communities, for two tariff modalities, conventional and time of use. The results show that there is an increase in the economic benefit obtained by some of the individually connected prosumers when they install photovoltaic generation. However, when the option is to form a community the economic benefit can be 120% higher. [ABSTRACT FROM AUTHOR]

In the last years, the manufacturing and installation costs of photovoltaic generators (PV) are gradually shrinking. This reduction in costs incentives the use of PV by the end user to satisfy its own demand and sell the surplus energy to the power distribution network. This paper evaluates the competitiveness to integrate small-scale PV in the city of Quito-Ecuador. These PV will be used to self-consumption for residential and commercial users affiliated to "Empresa Eléctrica Quito", which is the entity that manages the power requirements in Quito city. The levelized cost of electricity incurred by building and operating a PV is calculated and compared with the price used to purchase energy from the electrical distribution network. The concept of grid parity is analyzed by this comparison, including electricity tariffs and costs for the year 2018. OpenDSS software is used to estimate the energy supplied from PV in function of temperature and irradiation data gathered in Quito city. Finally, annual economic benefits obtained by the users are determined. Results show that there is not grid parity in 2018 yet, but the parity status could be reached by economic incentive schemes as Net Billing. [ABSTRACT FROM AUTHOR]

With the increasing penetration of distributed photovoltaic generation and energy storage systems in the demand side of the power system, new demand side model structures are necessary in order to better describe the dynamic performance of the power system. In this paper, a composite demand side model structure with load, distributed photovoltaic generation, and energy storage system together with a model parameter identification method are proposed to improve the traditional load model identification. The structure of the demand side model is proposed first and is further simplified so as to be identified at a high voltage level bus. The model parameter identifiability analysis is conducted based on the sensitivity method. The ambient signal data and disturbance data based model parameter identification method is proposed for the new demand side model structure using the differential evolution optimization method. The case study results for the WSCC 9 bus system show the effectiveness of the proposed model structure. Then, the case study results in a simplified 500-kV network of the Guangdong Power Grid show the effectiveness of the parameter identification method. [ABSTRACT FROM AUTHOR]

The uncoordinated charging of several plug-in electric vehicles in a parking garage can potentially overload distribution transformer reducing its lifespan. Attempting to mitigate this issue, this paper proposes a smart charging method to minimize electricity consumption costs and avoid transformer overloading by considering a charging station integrated with photovoltaic generation and battery energy storage system. The optimal coordination among all these elements is investigated assessing transformer hottest-spot temperature and loss-of-life, considering a case study with time-of-use rate and meteorological data from Texas, USA. In addition, an economic analysis is developed to evaluate the viability of the project, with a sensitivity study considering different charging fees and variation in the daily number of vehicles parked in the garage. The results show the proposed approach is feasible, yields tangible financial benefits and can preserve distribution transformer life. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation is characterized by significant variability. Accurate PV forecasts are a prerequisite to securely and economically operating electricity networks, especially in the case of large-scale penetration. In this paper, we propose a probabilistic spatio-temporal model for the PV power production that exploits production information from neighboring plants. The model provides the complete future probability density function of PV production for very short-term horizons (0–6 h). The method is based on quantile regression and a $L_1$ penalization technique for automatic selection of the input variables. The proposed modeling chain is simple, making the model fast and scalable to direct on-line application. The performance of the proposed approach is evaluated using a real-world test case, with a high number of geographically distributed PV installations and by comparison with state-of-the-art probabilistic methods. [ABSTRACT FROM AUTHOR]

Distributed generators including photovoltaic (PV) panels have been integrated dramatically in active distribution networks (ADNs). Due to the strong volatility and uncertainty, the high penetration of PV generation immensely exacerbates the conditions of voltage violation in ADNs. However, the emerging flexible interconnection technology based on soft open points (SOPs) provides increased controllability and flexibility to the system operation. For fully exploiting the regulation ability of SOPs to address the problems caused by PV, this paper proposes a robust optimization method to achieve the robust optimal operation of SOPs in ADNs. A two-stage adjustable robust optimization model is built to tackle the uncertainties of PV outputs, in which robust operation strategies of SOPs are generated to eliminate the voltage violations and reduce the power losses of ADNs. A column-and-constraint generation algorithm is developed to solve the proposed robust optimization model, which are formulated as second-order cone program to facilitate the accuracy and computation efficiency. Case studies on the modified IEEE 33-node system and comparisons with the deterministic optimization approach are conducted to verify the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper studies inherent equalization of lithium-ion batteries based on leakage current. Through constant potential load tests, the leakage current proves to vary exponentially with applied voltage and cell temperature. A hopping model is applied to the solid-electrolyte interface to explain the above-mentioned property. Stability of cell voltages is theoretically proved, which guarantees that stored charges gather near individual cell capacities at a steady state. Simulations agree well with measurements. A photovoltaic system is operated for 11 months during which cell voltages are balanced without external circuit. As far as leakage currents are close, balance of cell voltages is kept. [ABSTRACT FROM AUTHOR]

This paper proposes a novel three-stage robust inverter-based voltage/var control (TRI-VVC) approach for high photovoltaic (PV)-penetrated distribution networks. The approach aims at coordinating three different control stages from centralized to local VVC to reduce energy loss and mitigate voltage deviation. In the first stage, capacitor banks and an on-load tap changer are scheduled hourly in a rolling horizon. In the second stage, PV inverters are dispatched in a short time-window. In the third stage, the inverters respond to real-time voltage violation through local droop controllers. A new PV inverter model for voltage control is developed to support both the centralized var dispatch and the local var droop control. To address uncertain PV output and load demand, a robust optimization (RO) model is proposed to optimize the first two stages while taking into account the droop voltage control support from the third stage. A linearized distribution power flow model with power loss is developed and applied in the RO. The simulation results show high efficiency and robustness of the proposed TRI-VVC strategy. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for the sizing of an off-grid photovoltaic DC microgrid with emphasis on cost reduction and design simplification. The sizing algorithms used to design and build each of the subsystems of the installation are also presented and validated by the SimPowerSystem tool. The methodology has been tested on a real building, the hall of residence from the University of Deusto, and its novelty lies in the capacity of supplying electricity to a building, which average daily consumption is around 41.10 kWh, located in one of the regions of Spain with low solar radiation. Experiments provide satisfactory results by reducing both the necessary number of panels around 46% and the energy storage down to 50% in comparison with the sizing methods established in the literature. Our proposed methodology includes highly adaptable algorithms that enable a rapid design and posterior adjustment of a new installation for newer buildings. [ABSTRACT FROM AUTHOR]

Distributed renewable energy, particularly photovoltaics (PV), has expanded rapidly over the past decade. Distributed PV is located behind the meter and is, thus, invisible to the retailers and the distribution system operator. This invisible generation, thus, injects additional uncertainty in the net load and makes it harder to forecast. This paper proposes a data-driven probabilistic net load forecasting method specifically designed to handle a high penetration of behind-the-meter (BtM) PV. The capacity of BtM PV is first estimated using a maximal information coefficient based correlation analysis and a grid search. The net load profile is then decomposed into three parts (PV output, actual load, and residual) which are forecast individually. Correlation analysis based on copula theory is conducted on the distributions and dependencies of the forecasting errors to generate a probabilistic net load forecast. Case studies based on ISO New England data demonstrate that the proposed method outperforms other approaches, particularly when the penetration of BtM PV is high. [ABSTRACT FROM PUBLISHER]

In this paper, a new approach for photovoltaic (PV) arrays modeling and maximum power point estimation (MPPE) in real operating conditions is presented. The PV array polynomial model is developed from its single-diode equivalent circuit, and the parameters of the proposed model are estimated by measuring the voltage and current of three points around the maximum power point (MPP). The maximum power point voltage (MPPV) is then calculated from the proposed model, and the PV array voltage is fixed at MPPV. Therefore, unlike the maximum power point tracking (MPPT) algorithms, there is no oscillation around the MPP. Using the PV array polynomial model with only three measurements around the operating point, results in less calculation burden, increases the estimation speed and decreases the I–V curve estimation process loss. In order to validate the proposed MPPE algorithm, we have compared it with the most commonly used perturb and observe (P&O) MPPT algorithm, a zero oscillation adaptive P&O technique, and a fast MPPT method by simulation and experimental results. [ABSTRACT FROM AUTHOR]

This paper proposes linear operation of a photovoltaic array which is directly connected to lithium-ion batteries. The array voltage is fixed at the total battery voltage. When the amount of insolation is given, the array power decreases as the cell temperature rises. The maximum power point (MPP) voltage at the highest temperature of a year is selected as the array voltage. This voltage determines the optimal number of the batteries. The array power is guaranteed to be equal to the MPP power or greater through the year. Some simulation and experiment were executed to verify the effectiveness of the method. The utilization efficiency reaches 96.5%. The loss of power proved to be small compared with the maximum power point tracking, which can be economically recovered by using PV modules of larger capacity. A simple switch can substitute for a dc–dc converter. [ABSTRACT FROM PUBLISHER]

The Significant growth of wind and photovoltaic generation experienced in many countries around the world will soon challenge the ability of transmission system operators (TSOs) to guarantee the security of supply. To cope with such future low-carbon electricity systems, it is imperative to increase the portfolio of flexibility sources in a cost-effective and sustainable manner. This is likely to require exploring solutions beyond the use of traditional players connected at higher voltages, such as fast-acting generation plants and large customers. [ABSTRACT FROM PUBLISHER]

Optimally dispatching photovoltaic (PV) inverters is an efficient way to avoid overvoltage in active distribution networks, which may occur in the case of the PV generation surplus load demand. Typically, the dispatching optimization objective is to identify critical PV inverters that have the most significant impact on the network voltage level. Following, it ensures the optimal set-points of both active power and reactive power for the selected inverters, guaranteeing the entire system operating constraints (e.g., the network voltage magnitude) within reasonable ranges. However, the intermittent nature of solar PV energy may affect the selection of the critical PV inverters and also the final optimal objective value. In order to address this issue, a two-stage robust centralized-optimal dispatch model is proposed in this paper to achieve a robust PV inverter dispatch solution considering the PV output uncertainties. In addition, the conic relaxation-based branch flow formulation and the column-and-constraint generation algorithm are employed to deal with the proposed robust optimization model. Case studies on a 33-bus distribution network and comparisons with the deterministic optimization approach have demonstrated the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper proposes a modified cascaded neutral-point-clamped grid-connected inverter (MCNPC-GCI) to achieve a wide operational range and high European efficiency in the photovoltaic generation system. Two bidirectional thyristors are added into the standard single-phase cascaded neutral-point-clamped (NPC) inverter to, respectively, connect the positive and negative poles of the two NPC inverters to achieve the transformation between the cascaded NPC inverter and the equivalent single NPC inverter. The losses and grid current total harmonics distortions of the two topology modes are derived to determine their optimal operational ranges and an approach of online smoothly switching the two topology modes is proposed. Detailed experimental results of the proposed MCNPC-GCI are shown to verify its accuracy and feasibility. [ABSTRACT FROM AUTHOR]

The output power of photovoltaic generation (PVG) varies with solar irradiance and temperature nonlinearly. Therefore, the maximum power point tracking (MPPT) is very important to operate PVG to harvest the maximum solar energy in real time. The perturbation and observation (P&O) method is one of the most commonly used MPPT methods. However, P&O MPPT suffers a tradeoff in selecting the perturbation step sizes of the control variables. This paper proposes a parameter-estimation-based MPPT for PVG. Based on the measured voltages and currents and the characteristic output function of PVG, the proposed MPPT uses parameter estimation to calculate the solar irradiance and temperature directly. The operating voltage of maximum power point can then be found out immediately. The proposed MPPT can effectively reduce the maximum power tracking time of conventional MPPT methods and increase the harvested energy during MPPT. Simulation and experimental results comparing the proposed MPPT to a P&O MPPT demonstrate the performance achieved by the proposed MPPT. [ABSTRACT FROM PUBLISHER]

This paper proposes a probabilistic power flow analysis technique based on the stochastic response surface method. The probability distributions and statistics of power flow responses can be accurately and efficiently estimated by the proposed method without using series expansions such as the Gram-Charlier, Cornish-Fisher, or Edgeworth series. The stochastic continuous input variables following normal distributions such as loads or non-normal distributions such as photovoltaic generation and wind power and their multiple correlations can be easily modeled. The correctness, effectiveness and adaptability of the proposed method are demonstrated by comparing the probabilistic power flow analysis results of the IEEE 14-bus and 57-bus standard test systems obtained from the proposed method, the point estimate method, and the Monte Carlo simulation method. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy sources with nonlinear characteristics, such as wind and photovoltaic power plants, demands a clear procedure for harmonic current estimation on point of coupling of these sources. The summation law is widely used to estimate the total harmonic current in a coupling point between the network and a distributed generation. This is done for each harmonic order and the analysis is performed by mean harmonic currents provide by an individual wind turbines. In this work, the summation law will be analyzed by a wind park case study. [ABSTRACT FROM PUBLISHER]

Due to growing global awareness of climate change, increasing penetration of distributed solar generation is to be expected worldwide. When the grid integration of these intermittent renewable technologies reaches a high penetration level, technical adaptation, such as generation back-up capacity or distribution network capacity, is required. In this sense, electrical energy storage systems (ESS) offsets variations in renewable electricity production and plays a vital role in integrating these variable generation resources into the grid. In turn, ESS can be used to store excess electricity generated during off-peak demand periods for discharge at peak hours that is known as load peak-shaving, thus saving the system's power for when it is most needed. This paper presents a comprehensive methodology for valuing investments of ESS in distribution networks with high penetration of photovoltaic distributed generation. Specifically, it is focused on the integral assessment of ESS as a flexible option for investment deferral into the expansion planning of the power grid, considering both economic and technical constraints and mainly using the ESS for load peak-shaving. The proposed methodology is tested on a typical Latin American distribution network, particularly from the San Juan Province in Argentina. For this, two expansion planning valuation are performed: one, taking into account traditional expansion alternatives and, the other, considering ESS as flexible expansion options. Results show that the greatest contribution of ESS lies in the flexibility it gives to distribution expansion planning, mainly by deferring network reinforcements. [ABSTRACT FROM PUBLISHER]

Photovoltaic (PV) power generation is being decentralized and introduced on various scales in electric power systems that are expanding over wider areas. To understand the effect of PV power generation on these electric power systems (in areas ranging from power generation operation and control to voltage management of the distribution system), areal solar irradiance must be estimated for a target area, particularly, for the position and surface area that correspond to the PV power generation equipment location. By applying spatiotemporal kriging and covariance analysis to observed solar irradiance time series (obtained from sparsely distributed solar irradiance meters), we developed a technique that determines areal solar irradiance for an arbitrary location and surface area to estimate its surficial distribution. The weather conditions under which the spatiotemporal kriging technique can be applied were clarified using solar irradiance and meteorological observations during a one-year period at 25 observation points in the Kansai area. [ABSTRACT FROM AUTHOR]

This paper proposes a modified cascaded multilevel grid-connected inverter (MCM-GCI) suitable for photovoltaic grid-connected generation system, which considers wide operation range, low grid current total harmonics distortion (THD), and high European efficiency. In the proposed topology, a bidirectional power switch is added to the standard single-phase cascaded multilevel inverter (CMI) to implement the transformation between CMI mode and H-bridge inverter (HBI) mode. An online topology transformation approach of the MCM-GCI is proposed to guarantee the topology modes transformed safely and smoothly. The proposed MCM-GCI operates in CMI mode when the PV arrays’ output voltage and power are low, and transforms into HBI mode when the PV arrays’ output voltage and power are high. Experimental results of five-level MCM-GCI are represented to validate the feasibility of the proposed topology and it is also compared with other three classic grid-connected inverters to highlight its advantages. Furthermore, more structures based on hybrid CMI, which possess the topology transformation ability, are shown to further improve the generation range and obtain a higher efficiency of the system. [ABSTRACT FROM PUBLISHER]

Impact of increasing penetration of photovoltaic (PV) generation is expected in Japan in the near future; thus a new voltage control scheme is required in order to improve the operation of distribution networks. We propose a new voltage control scheme for distribution networks based on a multi-agent system. The large voltage fluctuations caused by PV generations are effectively regulated by adjusting the conventional types of tap-changing controllers. We achieved an optimal control performance by developing a new optimal control law applicable to decentralized autonomous control. The autonomy is accomplished by using a multi-agent system. The proposed method is applicable to general distribution network and highly efficient for popular radial networks under certain approximations. The effectiveness of the proposed scheme is demonstrated through numerical simulations of radial 6.6-kV test system with successful results. [ABSTRACT FROM AUTHOR]

In photovoltaic (PV) grid-connected generation system, the key focus is how to expand the generation range of the PV array and enhance the total efficiency of the system. This paper originally derived expressions of the total loss and grid current total harmonics distortions of cascaded inverter and H-bridge inverter under the conditions of variable output voltage and power of the PV array. It is proved that, compared with the H-bridge inverter, the operation range of the cascaded inverter is wider, whereas the total loss is larger. Furthermore, a novel online variable topology-type grid-connected inverter is proposed. A bidirectional power switch is introduced into the conventional cascaded inverter to connect the negative terminals of the PV arrays. When the output voltages of the PV arrays are lower, the proposed inverter works under cascaded inverter mode to obtain wider generation range. When the output voltages of the PV arrays are higher, the proposed inverter is transformed into equivalent H-bridge inverter mode to reduce the total loss. Thus, wider generation range and higher efficiency are both implemented. Specific demonstration of the proposed inverter based on five-level cascaded inverter is presented. The detailed experimental results verify the accuracy and feasibility of the theoretical analysis and the proposed inverter topology. [ABSTRACT FROM PUBLISHER]

This paper proposes the new energy management method based on the photovoltaic (PV) hybrid power conditioning system of 4 kW with an energy storage device (ESD). The use of the ESD such as a lithium-ion battery improves the energy efficiency of the overall system depending on time and weather conditions. In addition, the proposed system provides the new function for energy management in real time without weather forecasting while it considers the power generations from the PV system, residential load variations, and electricity price. In other words, the proposed energy management system (EMS) can reduce the electricity price by charging the energy of nighttime electric power. In addition, it can suppress the grid voltage variations caused by the large amount of PV generations during daytime with respect to relatively less load demand. The performances of the proposed EMS system based on the 4-kW PV HPCS with a lithium-ion battery are evaluated by time-domain simulation. Thereafter, the experimental prototype is implemented in hardware to verify the usefulness of the proposed system. [ABSTRACT FROM PUBLISHER]