Showing papers in "IEEE Transactions on Sustainable Energy in 2015"

Integrated Modeling and Assessment of the Operational Impact of Power-to-Gas (P2G) on Electrical and Gas Transmission Networks

Abstract: Power-to-gas (P2G) is the process whereby electricity is used to produce hydrogen or synthetic natural gas The electricity for the P2G process could, for instance, come from renewable energy which would otherwise be curtailed due to system or line constraints The existing natural gas network could then potentially be used as a means to store, transport, and reutilize this energy, thus preventing its waste While there are several ongoing discussions on P2G in different countries, these are generally not backed by quantitative studies on its potential network implications and benefits To bridge this gap, this paper introduces an original methodology to analyze different P2G processes and assess their operational impacts on both electricity and gas transmission networks This is carried out by using a novel integrated model specifically developed for the simulation of operational interdependences between the two networks considering P2G To demonstrate the several innovative features of the proposed model, technical, environmental, and economic operational aspects of P2G and its potential benefits are analyzed on the case of the Great Britains system, also providing insights into relief of gas and electrical transmission network constraints

Adaptive Droop Control Strategy for Load Sharing and Circulating Current Minimization in Low-Voltage Standalone DC Microgrid

Abstract: This paper addresses load current sharing and cir- culating current issues of parallel-connected dc-dc converters in low-voltage dc microgrid. Droop control is the popular technique for load current sharing in dc microgrid. The main drawbacks of the conventional droop method are poor current sharing and drop in dcgrid voltage due tothe droop action. Circulating current issue will also arise due to mismatch in the converters output voltages. In this work, a figure of merit called droop index (DI) is introduced in order to improve the performance of dc microgrid, which is a function of normalized current sharing difference and losses in the output side of the converters. This proposed adaptive droop con- trol method minimizes the circulating current and current sharing difference between the converters based on instantaneous virtual resistance Rdroop .U singRdroop shifting, the proposed method also eliminates the tradeoff between current sharing difference and voltage regulation. The detailed analysis and design procedure are explained for two dc-dc boost converters connected in paral- lel. The effectiveness of the proposed method is verified by detailed simulation and experimental studies.

Enhanced Energy Output From a PV System Under Partial Shaded Conditions Through Artificial Bee Colony

Abstract: For the maximum utilization of solar energy, photovoltaic (PV) power generation systems are operated at the maximum power point (MPP) under varying atmospheric conditions, and MPP tracking (MPPT) is generally achieved using several conventional methods. However, when partial shading occurs in a PV system, the resultant powervoltage (PV) curve exhibits multiple peaks and traditional methods that need not guarantee convergence to true MPP always. This paper proposes an artificial bee colony (ABC) algorithm for global MPP (GMPP) tracking under conditions of in-homogenous insolation. The formulation of the problem, application of the ABC algorithm, and the results are analyzed in this paper. The numerical simulations carried out on two different PV configurations under different shading patterns strongly suggest that the proposed method is far superior to existing MPPT alternatives. Experimental results are also provided to validate the new dispensation.

A New Sensorless Hybrid MPPT Algorithm Based on Fractional Short-Circuit Current Measurement and P&O MPPT

Abstract: This paper presents a new maximum power point tracking (MPPT) method for photovoltaic (PV) systems. The proposed method improves the working of the conventional perturb and observe (P&O) method in changing environmental conditions by using the fractional short-circuit current (FSCC) method. It takes the initial operating point of a PV system by using the short-circuit current method and later shifts to the conventional P&O technique. The advantage of having this two-stage algorithm is rapid tracking under changing environmental conditions. In addition, this scheme offers low-power oscillations around MPP and, therefore, more power harvesting compared with the common P&O method. The proposed MPPT decides intelligently about the moment of measuring short-circuit current and is, therefore, an irradiance sensorless scheme. The proposed method is validated with computer software simulation followed by a dSPACE DS1104-based experimental setup. A buck-boost dc-dc converter is used for simulation and experimental confirmation. Furthermore, the reliability of the proposed method is also calculated. The results show that the proposed MPPT technique works satisfactorily under given environmental scenarios.

An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data

Abstract: Due to the intermittency and randomness of solar photovoltaic (PV) power, it is difficult for system operators to dispatch PV power stations. In order to find a precise expectation for day-ahead PV power generation, conventional models have taken into consideration the temperature, humidity, and wind speed data for forecasting, but these predictions were always not accurate enough under extreme weather conditions. Aerosol index (AI), which indicates the particulate matter in the atmosphere, has been found to have strong linear correlation with solar radiation attenuation, and might have potential influence on the power generated by PV panels. A novel PV power forecasting model is proposed in this paper, considering AI data as an additional input parameter. Based on seasonal weather classification, the back propagation (BP) artificial neural network (ANN) approach is utilized to forecast the next 24-h PV power outputs. The estimated results of the proposed PV power forecasting model coincide well with measurement data, and the proposed model has shown the ability of improving prediction accuracy, compared with conventional methods using ANN.

Control of PMSG-Based Wind Turbines for System Inertial Response and Power Oscillation Damping

Abstract: This paper investigates an improved active power control method for variable speed wind turbine to enhance the inertial response and damping capability during transient events. The optimized power point tracking (OPPT) controller, which shifts the turbine operating point from the maximum power point tracking (MPPT) curve to the virtual inertia control (VIC) curves according to the frequency deviation, is proposed to release the “hidden” kinetic energy and provide dynamic frequency support to the grid. The effects of the VIC on power oscillation damping capability are theoretically evaluated. Compared to the conventional supplementary derivative regulator-based inertia control, the proposed control scheme can not only provide fast inertial response, but also increase the system damping capability during transient events. Thus, inertial response and power oscillation damping function can be obtained in a single controller by the proposed OPPT control. A prototype three-machine system containing two synchronous generators and a PMSG-based wind turbine with 31% of wind penetration is tested to validate the proposed control strategy on providing rapid inertial response and enhanced system damping.

Coordination of Interdependent Natural Gas and Electricity Infrastructures for Firming the Variability of Wind Energy in Stochastic Day-Ahead Scheduling

Abstract: In this paper, the coordination of constrained electricity and natural gas infrastructures is considered for firming the variability of wind energy in electric power systems. The stochastic security-constrained unit commitment is applied for minimizing the expected operation cost in the day-ahead scheduling of power grid. The low cost and sustainable wind energy could substitute natural gas-fired units, which are constrained by fuel availability and emission. Also, the flexibility and quick ramping capability of natural gas units could firm the variability of wind energy. The electricity and natural gas network constraints are considered in the proposed model (referred to as EGTran) and Benders decomposition is adopted to check the natural gas network feasibility. The autoregressive moving average (ARMA) time-series model is used to simulate wind speed forecast errors in multiple Monte Carlo scenarios. Illustrative examples demonstrate the effectiveness of EGTran for firming the variable wind energy by coordinating the constrained electricity and natural gas delivery systems.

Simulation and Hardware Implementation of New Maximum Power Point Tracking Technique for Partially Shaded PV System Using Hybrid DEPSO Method

Abstract: In photovoltaic (PV) power generation, partial shading is an unavoidable complication that significantly reduces the efficiency of the overall system Under this condition, the PV system produces a multiple-peak function in its output power characteristic Thus, a reliable technique is required to track the global maximum power point (GMPP) within an appropriate time This study aims to employ a hybrid evolutionary algorithm called the DEPSO technique, a combination of the differential evolutionary (DE) algorithm and particle swarm optimization (PSO), to detect the maximum power point under partial shading conditions The paper starts with a brief description about the behavior of PV systems under partial shading conditions Then, the DEPSO technique along with its implementation in maximum power point tracking (MPPT) is explained in detail Finally, Simulation and experimental results are presented to verify the performance of the proposed technique under different partial shading conditions Results prove the advantages of the proposed method, such as its reliability, system-independence, and accuracy in tracking the GMPP under partial shading conditions

A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods

Abstract: Wind speed forecasting is challenging due to its intermittent nature. The wind speed time series (TS) has nonlinear and nonstationary characteristics and not normally distributed, which make it difficult to be predicted by statistical or computational intelligent methods. Empirical mode decomposition (EMD) and its improved versions are powerful tools to decompose a complex TS into a collection of simpler ones. The improved versions discussed in this paper include ensemble EMD (EEMD), complementary EEMD (CEEMD), and complete EEMD with adaptive noise (CEEMDAN). The EMD and its improved versions are hybridized with two computational intelligence-based predictors: support vector regression (SVR) and artificial neural network (ANN). The EMD-based hybrid forecasting methods are evaluated with 12 wind speed TS. The performances of the hybrid methods are compared and discussed. It shows that EMD and its improved versions enhance the performance of SVR significantly but marginally on ANN, and among the EMD-based hybrid methods, the proposed CEEMDAN-SVR is the best method. Possible future works are also recommended for wind speed forecasting.

Robust Optimal Power Management System for a Hybrid AC/DC Micro-Grid

Abstract: Hybrid ac/dc micro-grid is a new concept decoupling dc sources with dc loads and ac sources with ac loads, while power is exchanged between both sides using a bidirectional converter/inverter. This necessitates a supervisory control system to split power between its different resources, which has sparked attention on the development of power management systems (PMSs). In this paper, a robust optimal PMS (ROPMS) is developed for a hybrid ac/dc micro-grid, where the power flow in the micro-grid is supervised based on solving an optimization problem. Satisfying demanded power with maximum utilization of renewable resources, minimum usage of fuel-based generator, extending batteries lifetime, and limited utilization of the main power converter between the ac and dc micro-grids are important factors that are considered in this approach. Uncertainties in the resources output power and generation forecast errors, along with static and dynamic constraints of the resources, are taken into account. Furthermore, since uncertainties in the resources output power may result in fluctuations in the dc bus voltage, a two-level controller is used to regulate charge/discharge power of the battery banks. Effectiveness of the proposed supervisory system is evaluated through extensive simulation runs based on dynamical models of the power resources.

Investigating the Impacts of Wind Generation Participation in Interconnection Frequency Response

Abstract: The electrical frequency of an interconnection must be maintained very close to its nominal level at all times. Excessive frequency deviations can lead to load shedding, instability, machine damage, and even blackouts. There is rising concern in the power industry in recent years about the declining amount of inertia and primary frequency response (PFR) in many interconnections. This decline may continue due to increasing penetrations of inverter-coupled generation and the planned retirements of conventional thermal plants. Inverter-coupled variable wind generation is capable of contributing to PFR and inertia; however, wind generation PFR and inertia responses differ from those of conventional generators, and it is not entirely understood how this will affect the system at different wind power penetration levels. The simulation work presented in this paper evaluates the impact of the wind generation provision of these active power control strategies on a large, synchronous interconnection. All simulations were conducted on the U.S. Western Interconnection with different levels of wind power penetration levels. The ability of wind power plants to provide PFRand a combination of synthetic inertial response and PFRsignificantly improved the frequency response performance of the system. The simulation results provide insight to designing and operating wind generation active power controls to facilitate adequate frequency response performance of an interconnection.

Coordinated Operational Planning for Wind Farm With Battery Energy Storage System

Abstract: This paper proposes a coordinated operational dispatch scheme for a wind farm with a battery energy storage system (BESS). The main advantages of the proposed dispatch scheme are that it can reduce the impacts of wind power forecast errors while prolonging the lifetime of BESS. The scheme starts from the planning stage, where a BESS capacity determination method is proposed to compute the optimal power capacity and energy capacity of BESS based on historical wind power data; and then, at the operation stage, a flexible short-term BESS-wind farm dispatch scheme is proposed based on the forecasted wind power generation scenarios. Three case studies are provided to validate the performance of the proposed method. The results show that the proposed scheme can largely improve the wind farm dispatchability.

Simultaneous Reconfiguration, Optimal Placement of DSTATCOM, and Photovoltaic Array in a Distribution System Based on Fuzzy-ACO Approach

Abstract: In this paper, a combination of a fuzzy multiobjective approach and ant colony optimization (ACO) as a metaheuristic algorithm is used to solve the simultaneous reconfiguration and optimal allocation (size and location) of photovoltaic (PV) arrays as a distributed generation (DG) and distribution static compensator (DSTATCOM) as a distribution flexible ac transmission system (DFACT) device in a distribution system. The purpose of this research includes loss reduction, voltage profile (VP) improvement, and increase in the feeder load balancing (LB). The proposed method is validated using the IEEE 33-bus test system and a Tai-Power 11.4-kV distribution system as a real distribution network. The results proved that simultaneous reconfiguration and optimal allocation of PV array and DSTATCOM unit leads to significantly reduced losses, improved VP, and increased LB. Obtained results have been compared with the base value and found that simultaneous placement of PV and DSTATCOM along with reconfiguration is more beneficial than separate single-objective optimization. Also, the proposed fuzzy-ACO approach is more accurate as compared to ACO and other intelligent techniques like fuzzy-genetic algorithm (GA) and fuzzy-particle swarm optimization (PSO).

Distributed Generation Allocation on Radial Distribution Networks Under Uncertainties of Load and Generation Using Genetic Algorithm

Abstract: This paper presents a distribution generation (DG) allocation strategy for radial distribution networks under uncertainties of load and generation using adaptive genetic algorithm (GA). The uncertainties of load and generation are modeled using fuzzy-based approach. The optimal locations for DG integration and the optimal amount of generation for these locations are determined by minimizing the network power loss and maximum node voltage deviation. Since GA is a metaheuristic algorithm, the results of multiple runs are taken and the statistical variations for locations and generations for DG units are shown. The locations and sizes for DG units obtained with fuzzy-based approach are found to be different than those obtained with deterministic approach. The results obtained with fuzzy-based approach are found to be comparatively efficient in working with future load growth. The proposed approach is demonstrated on the IEEE 33-node test network and a 52-node Indian practical distribution network.

Impacts of High Penetration of DFIG Wind Turbines on Rotor Angle Stability of Power Systems

Abstract: With the integration of wind power into power systems continues to increase, the impact of high penetration of wind power on power system stability becomes a very important issue. This paper investigates the impact of doubly fed induction generator (DFIG) control and operation on rotor angle stability. Acontrol strategy for both the rotor-side converter (RSC) and grid-side converter (GSC) of the DFIG is proposed to mitigate DFIGs impacts on the system stability. DFIG-GSC is utilized to be controlled as static synchronous compensator (STATCOM) to provide reactive power support during grid faults. In addition, a power system stabilizer (PSS) is implemented in the reactive power control loop of DFIG-RSC. The proposed approaches are validated on a realistic Western System Coordinating Council (WSCC) power system under both small and large disturbances. The simulation results show the effectiveness and robustness of both DFIG-GSC control strategy and PSS to enhance rotor angle stability of power system.

Flexibility Envelopes for Power System Operational Planning

Abstract: Modern power systems are undergoing a transitional phase, increasingly incorporating renewable energy sources (RES) to harness their economic and environmental benefits. The main challenge with this transitional phase is the management of the increased variability and uncertainty in the power balance. Legacy operation and planning practices are gradually seen as becoming inadequate or ill-adapted in addressing this challenge. One particular gap in the state of the art, which is of great importance, is estimating the operational flexibility potential of individual power system assets and their aggregation at the system level. System operators need to evaluate and plan ahead flexibility adequacy for their power systems in order to ensure feasible and economical operation under high RES penetration. Likewise, asset owners need to integrate the notion of asset flexibility as part of their investment and operations decisions. To this end, we propose the concept of the flexibility envelope to describe the flexibility potential dynamics of a power system and its individual resources in the operational planning time-frame. We demonstrate that the resulting envelope dynamics can be a starting point for flexibility adequacy planning in systems with highly variable generation.

Online Fault Detection in PV Systems

Abstract: This paper presents the development of a practical fault detection approach in photovoltaic (PV) systems, intended for online implementation. The approach was developed and validated using field measurements from a Canadian PV system. It has a fairly low degree of complexity, but achieves a high fault detection rate and is able to successfully cope with abnormalities present in real-life measurements. The fault detection is based on the comparison between the measured and model prediction results of the ac power production. The model estimates the ac power production using solar irradiance and PV panel temperature measurements. Prior to model development, a data analysis procedure was used to identify values not representative of a normal PV system operation. The original 10-min measurements were averaged over 1h, and both datasets were used for modeling. In order to better represent the PV system performance at different sunlight levels, models for different irradiance ranges were developed. The results reveal that the models based on hourly averages are more accurate than the models using 10-min measurements, and the models for different irradiance intervals lead to a fault detection rate greater than 90%. The PV system performance ratio (PR) was used to keep track of the system's long-term performance.

Adjustable Robust Real-Time Power Dispatch With Large-Scale Wind Power Integration

Abstract: In this paper, we propose a robust real-time wind power dispatch framework for coordinating wind farms, automatic generation control (AGC) units, and nonAGC units, which enables wind farms to operate flexibly using maximum power-point tracking strategies. Robust real-time dispatch is formulated as an adjustable robust optimization model incorporating an affinely adjustable controlling strategy compatible with AGC systems. The proposed model can be equivalently transformed to a nonlinear programming problem with linear constraints via duality. The proposed model can also be approximately reduced to a quadratic programming with the objective function simplified. Monte Carlo simulations are carried out to compare the performance of the proposed method against the conventional real-time dispatch scheme. The results show the proposed scheme is robust and reliable.

Load Balancing With EV Chargers and PV Inverters in Unbalanced Distribution Grids

Abstract: Balanced three-phase four-wire distribution grids can host significantly more distributed generation and electric vehicles. Three-phase photovoltaic (PV) inverters and electric vehicle (EV) chargers can be adapted to transfer power from highly loaded to less loaded phases, without overloading the inverter or charger. Grid conditions will be improved due to a more balanced operation of the network and more PV panels and EVs can be connected before the limits of the network are reached. A classic coordinated charging strategy for EVs is adapted in this paper. It is shown that the charging of EVs can be improved when power can be transferred from one phase to another. Using PV inverters with a balancing inverter, the power injected in each phase will become a controllable variable as the total amount of produced power does not necessarily need to be equally divided across the three phases. The improvements made by using EV chargers and PV inverters that can balance the network are investigated. Several load flow simulations with realistic data show a positive effect on the system losses, the grid voltage, and voltage unbalance. Finally, a local controller is proposed to control the balancing between the phases when a real-time communication channel is not available.

Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting

Abstract: It is important to forecast the wind speed for managing operations in wind power plants. However, wind speed prediction is extremely complex and difficult due to the volatility and deviation of the wind. As existing forecasting methods directly model the raw wind speed data, it is difficult for them to provide higher inference accuracy. Differently, this paper presents a sophisticated deep-learning technique for short-term and long-term wind speed forecast, i.e., the predictive deep Boltzmann machine (PDBM) and corresponding learning algorithm. The proposed deep model forecasts wind speed by analyzing the higher level features abstracted from lower level features of the wind speed data. These automatically learnt features are very informative and appropriate for the prediction. The proposed PDBM is a deep stochastic model that can represent the wind speed very well, and is inspired by two aspects. 1)The stochastic model is suitable to capture the probabilistic characteristics of wind speed. 2)Recent developments in neural networks with deep architectures show that deep generative models have competitive capability to approximate nonlinear and nonsmooth functions. The evaluation of the proposed PDBM model is depicted by both hour-ahead and day-ahead prediction experiments based on real wind speed datasets. The prediction accuracy of the PDBM model outperforms existing methods by more than 10%.

Multitime-Scale Data-Driven Spatio-Temporal Forecast of Photovoltaic Generation

Abstract: The increasing penetration of stochastic photovoltaic (PV) generation in electric power systems poses significant challenges to system operators. To ensure reliable operation of power systems, accurate forecasting of PV power production is essential. In this paper, we propose a novel multitime-scale data-driven forecast model to improve the accuracy of short-term PV power production. This model leverages both spatial and temporal correlations among neighboring solar sites, and is shown to have improved performance compared to the conventional persistence (PSS) model. The tradeoff between computation cost and improved forecast quality is studied using real datasets from PV sites in California and Colorado.

Modeling of Grid-Connected DFIG-Based Wind Turbines for DC-Link Voltage Stability Analysis

Abstract: The electromagnetic stability issues of the grid-connected doubly fed induction generator (DFIG) system are usually overlooked This paper presents a reduced order small-signal model that can be used to analyze the stability of DFIGs dc-link voltage control system, especially under weak ac grid conditions This model neglects DFIG flux and fast current control dynamics However, the effects of operating points, grid strengths and control loops interactions on system dynamic performance are taken into account An eigenvalue comparison shows the proposed model holds dominant oscillation mode featured by the detailed model and is suitable for stability analysis of dc-link voltage control system of DFIG Influence coefficients reflecting control loops interactions are also presented Application studies of the proposed model show it is suitable for illustrating the effect of grid strength on dynamic performance of the DFIGs dc-link voltage control system Meanwhile, phase-locked loop (PLL) and rotor-side converter (RSC) active power control (APC)/reactive power controls (RPC) effect on system stability are also explored

Reduced-Order Small-Signal Model of Microgrid Systems

Abstract: The objective of this study was to develop a reduced-order small-signal model of a microgrid system capable of operating in both the grid-tied and the islanded conditions. The nonlinear equations of the proposed system were derived in the $dq$ reference frame and then linearized around stable operating points to construct a small-signal model. The high-order state matrix was then reduced using the singular perturbation technique. The dynamic equations were divided into two groups based on the small-signal model parameters $\varepsilon$ . The slow states, which dominated the systems dynamics, were preserved, whereas the fast states were eliminated. Step responses of the model were compared to the experimental results from a hardware test to assess their accuracy and similarity to the full-order system. The proposed reduced-order model was applied to a modified IEEE-37 bus grid-tied microgrid system to evaluate systems dynamic response in grid-tied mode, islanded mode, and transition from grid-tied to islanded mode.

Stochastic Reconfiguration and Optimal Coordination of V2G Plug-in Electric Vehicles Considering Correlated Wind Power Generation

Abstract: This paper investigates the optimal operation of distribution feeder reconfiguration (DFR) strategy in the smart grids with high penetration of plug-in electric vehicles (PEVs) and correlated wind power generation. The increased utilization of PEVs in the system with stochastic volatile behavior along with the high penetration of renewable power sources such as wind turbines (WTs) can create new challenges in the system that will affect the DFR strategy greatly. In order to reach the most efficiency from the PEVs, the idea of vehicle-to-grid (V2G) is employed in this paper to make a bidirectional power flow (either charging/discharging or idle mode) strategy when providing the main charging needs of PEVs. In this regard, we suggest a new stochastic framework based on unscented transformation (UT) to model the uncertainties of the PEVs behaviors when considering the correlated power generation of WTs. The feasibility and satisfying performance of the proposed framework are examined on the IEEE 69-bus test system.

Optimal Sizing of a Vanadium Redox Battery System for Microgrid Systems

Abstract: The vanadium redox battery (VRB) has proven to be a reliable and highly efficient energy storage system (ESS) for microgrid applications. However, one challenge in designing a microgrid system is specifying the size of the ESS. This selection is made more complex due to the independent power and energy ratings inherent in VRB systems. Sizing a VRB for both required power output and energy storage capacity requires an in-depth analysis to produce both optimal scheduling capabilities and minimum capital costs. This paper presents an analytical method to determine the optimal ratings of VRB energy storage based on an optimal scheduling analysis and cost-benefit analysis for microgrid applications. A dynamic programming (DP) algorithm is used to solve the optimal scheduling problem considering the efficiency and operating characteristics of the VRBs. The proposed method has been applied to determine the optimal VRB power and energy ratings for both isolated and grid-connected microgrids, which contain PV arrays and fossil-fuel-based generation. We first consider the case in which a grid-tie is not available and diesel generation is the backup source of power. The method is then extended to consider the case in which a utility grid tie is available.

Mitigating Voltage Problem in Distribution System With Distributed Solar Generation Using Electric Vehicles

Abstract: Distributed solar generation has the potential to reach high penetration levels in distribution systems. However, its integration reshapes distribution system power flows and causes rapid-fluctuations in system statuses. The facts challenge major voltage management approaches, nowadays, such as using online load tap changing (OLTC) transformers, voltage regulators (VRs), or shunt capacitors. In this paper, we explore the capability of using vehicle-to-grid (V2G) electric vehicles (EVs) to join distribution system voltage management, and to collaborate with OLTCs to mitigate the voltage problems caused by distribution solar generations. A two-stage control method is proposed for this purpose. The first stage controls the making of rolling schedules for EV charging and OLTC tap positions, while the second controls the EVs to resist the solar generation fluctuation to maintain voltage profiles. A case system with simultaneous overvoltage/undervoltage risks is designed to test the effectiveness of the proposed method. The results demonstrate that both the over/undervoltage risks are mitigated.

Demand Response for Residential Electric Vehicles With Random Usage Patterns in Smart Grids

Abstract: Electric vehicles (EVs) are expected to become widespread in future years. Thus, it is foreseen that EVs will become the new high-electricity-consuming appliances in the households. The characteristics of the extra power load that they impose on the distribution grid follow the patterns of people's random usage behaviors. In this paper, we seek to provide answers to the following question: assigning real-world randomness to the EVs' availability in the households and their charging requirements, how can EVs' demand response (DR) help to minimize the peak power demand and, in general, shape the aggregated demand profile of the system? We present a general demand-shaping problem applicable for limit order bids to a day-ahead (DA) energy market. We propose an algorithm for distributed DR of the EVs to shape the daily demand profile or to minimize the peak demand. Additionally, we put these problems in a game framework. Extensive simulations show that, for certain practical distributions of EVs' usage, it is possible to accommodate EVs for all the users in the system and yet achieve the same peak demand as when there is no EV in the system without any changes in the users' commuting behaviors.

Stochastic Capacity Expansion Planning of Remote Microgrids With Wind Farms and Energy Storage

Abstract: A majority of remote power systems are going to be supplied by diesel-renewable resources such as wind and photovoltaic energy in the future. However, the unpredictable nature of wind generation increases the concern about the reliable operation of these isolated microgrids. Using energy storage systems (ESSs) is recently accepted as an efficient solution to the volatility and intermittency of renewable energy sources. In this paper, a stochastic programming based on the Monte Carlo approach is introduced for optimal planning of remote systems. So far, most literatures have focused exclusively on the energy storage initial sizing. However, capacity expansion of ESS through the time span can result in significant cost saving and will be illustrated in this paper. Factors such as reliability criteria together with the investment and the operation costs are taken into account in the proposed methodology. This method utilizes practical operational constraints of ESS including efficiency and life cycle. Considering life cycle constraint reinforces the proposed method to completely investigate the difference between ESS technologies. The results of case study demonstrate that the proposed capacity expansion algorithm could lead to about 10% more profit over the traditional energy storage sizing.

Single- and Two-Stage Inverter-Based Grid-Connected Photovoltaic Power Plants With Ride-Through Capability Under Grid Faults

Abstract: Grid-connected distributed generation sources interfaced with voltage source inverters (VSIs) need to be disconnected from the grid under: 1)excessive dc-link voltage; 2)excessive ac currents; and 3)loss of grid-voltage synchronization. In this paper, the control of single- and two-stage grid-connected VSIs in photovoltaic (PV) power plants is developed to address the issue of inverter disconnecting under various grid faults. Inverter control incorporates reactive power support in the case of voltage sags based on the grid codes (GCs) requirements to ride-through the faults and support the grid voltages. A case study of a 1-MW system simulated in MATLAB/Simulink software is used to illustrate the proposed control. Problems that may occur during grid faults along with associated remedies are discussed. The results presented illustrate the capability of the system to ride-through different types of grid faults.

A Short-Term Wind Power Forecasting Approach With Adjustment of Numerical Weather Prediction Input by Data Mining

Abstract: This paper proposes a novel short-term wind power forecasting approach by mining the bad data of numerical weather prediction (NWP). Today's short-term wind power forecast (WPF) highly depends on the NWP, which contributes the most in the WPF error. This paper first introduces a bad data analyzer to fully study the relationship between the WPF error with several new extracted features from the raw NWP. Second, a hierarchical structure is proposed, which is composed of a K -means clustering-based bad data detection module and a neural network (NN)-based forecasting module. In the NN module, the WPF is fully adjusted based on the output of the bad data analyzer. Simulations are performed comparing with two other different methods. It proves that the proposed approach can improve the short-term wind power forecasting by effectively identifying and adjusting the errors from NWP.