Showing papers in "IEEE Transactions on Sustainable Energy in 2014"

A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output

Abstract: To improve real-time control performance and reduce possible negative impacts of photovoltaic (PV) systems, an accurate forecasting of PV output is required, which is an important function in the operation of an energy management system (EMS) for distributed energy resources. In this paper, a weather-based hybrid method for 1-day ahead hourly forecasting of PV power output is presented. The proposed approach comprises classification, training, and forecasting stages. In the classification stage, the self-organizing map (SOM) and learning vector quantization (LVQ) networks are used to classify the collected historical data of PV power output. The training stage employs the support vector regression (SVR) to train the input/output data sets for temperature, probability of precipitation, and solar irradiance of defined similar hours. In the forecasting stage, the fuzzy inference method is used to select an adequate trained model for accurate forecast, according to the weather information collected from Taiwan Central Weather Bureau (TCWB). The proposed approach is applied to a practical PV power generation system. Numerical results show that the proposed approach achieves better prediction accuracy than the simple SVR and traditional ANN methods.

Design and Analysis of Novel Control Strategy for Battery and Supercapacitor Storage System

Abstract: In this paper, a simple novel control strategy is designed and analyzed for a hybrid energy storage system (HESS). In the proposed method, batteries are used to balance the slow changing power surges, whereas supercapacitors (SC) are used to balance the fast changing power surges. The main advantage of the proposed control strategy is that, the slow response of battery system including dynamics of battery, controller, and converter operation, is overcome by diverting the power surges to the SC system. The proposed method inherits charge/discharge rate control to improve the life span and reduce the current stresses on battery. The proposed method features less computational burden as it uses simple control strategy. The detailed experimental results presented validate the proposed control strategy for sudden changes in photovoltaic (PV) generation and load demand.

Optimal PV Inverter Reactive Power Control and Real Power Curtailment to Improve Performance of Unbalanced Four-Wire LV Distribution Networks

Abstract: The rapid uptake of residential photovoltaic (PV) systems is causing serious power quality issues such as significant voltage fluctuation and unbalance that are restricting the ability of networks to accommodate further connections. Based on the latent reactive power capability and real power curtailment of single-phase inverters, this paper proposes a new comprehensive PV operational optimization strategy to improve the performance of significantly unbalanced three-phase four-wire low voltage (LV) distribution networks with high residential PV penetrations. A multiobjective optimal power flow (OPF) problem that can simultaneously improve voltage magnitude and balance profiles, while minimizing network losses and generation costs, is defined and then converted into an aggregated single-objective OPF problem using the weighted-sum method, which can be effectively solved by the global Sequential Quadratic Programming (SQP) approach with multiple starting points in MATLAB. Detailed simulations are performed and analyzed for various operating scenarios over 24 h on a real unbalanced four-wire LV distribution network in Perth Solar City trial, Australia. Finally, smart meter readings are used to justify the validity and accuracy of the proposed optimization model and considerations on the application of the proposed PV control strategy are also presented.

Real-Time Energy Management Algorithm for Plug-In Hybrid Electric Vehicle Charging Parks Involving Sustainable Energy

Abstract: In this paper, a real-time energy management algorithm (RTEMA) for a grid-connected charging park in an industrial/commercial workplace is developed. The charging park under study involves plug-in hybrid electric vehicles (PHEVs) with different sizes and battery ratings as well as a photovoltaic (PV) system. Statistical and forecasting models were developed as components in the developed RTEMA to model the various uncertainties involved such as the PV power, the PHEVs, arrival time, and the energy available in their batteries upon their arrival. The developed energy management algorithm aims at reducing the overall daily cost of charging the PHEVs, mitigating the impact of the charging park on the main grid, and contributing to shaving the peak of the load curve. Hence, the benefits of implementing this RTEMA is shared among the customers, the charging park considering all customers as a bulk of power connected to the grid, and the ac grid. This makes it applicable for various business models. The developed RTEMA utilizes a fuzzy controller to manage the random energy available in the PHEVs' batteries arriving at the charging park and their charging/discharging times, power sharing among individual PHEVs that is commonly known as vehicle-to-vehicle functionality, and vehicle-to-grid service between the charging park and the main ac grid. The developed RTEMA was simulated using the standard IEEE 69-bus system at different penetration and distribution levels. The obtained results verify the effectiveness and validity of the developed RTEMA.

Variable Perturbation Size Adaptive P&O MPPT Algorithm for Sudden Changes in Irradiance

Abstract: In this paper, a variable perturbation size adaptive perturb and observe (P&O) maximum power point tracking (MPPT) algorithm is proposed to track the maximum power under sudden changes in irradiance. The proposed method consists of three algorithms, namely current perturbation algorithm (CPA), adaptive control algorithm (ACA), and variable perturbation algorithm (VPA). CPA always tries to operate the photovoltaic (PV) panel at maximum power point (MPP). ACA sets the operating point closer to MPP, only if the operating limits are violated. These operating limits are expressed in terms of the operating current range of the PV panel and the sudden changes in irradiance. VPA dynamically reduces the perturbation size based on polarity of change in power. Two-stage variable size perturbation is proposed in this paper. The proposed algorithm is realized using a boost converter. The effectiveness of proposed algorithm in terms of dynamic performance and improved stability is validated by detailed simulation and experimental studies.

Optimal Dispatch of Photovoltaic Inverters in Residential Distribution Systems

Abstract: Low-voltage distribution feeders were designed to sustain unidirectional power flows to residential neighborhoods. The increased penetration of roof-top photovoltaic (PV) systems has highlighted pressing needs to address power quality and reliability concerns, especially when PV generation exceeds the household demand. A systematic method for determining the active- and reactive-power set points for PV inverters in residential systems is proposed in this paper, with the objective of optimizing the operation of the distribution feeder and ensuring voltage regulation. Binary PV-inverter selection variables and nonlinear power-flow relations render the optimal inverter dispatch problem nonconvex and NP-hard. Nevertheless, sparsity-promoting regularization approaches and semidefinite relaxation techniques are leveraged to obtain a computationally feasible convex reformulation. The merits of the proposed approach are demonstrated using real-world PV-generation and load-profile data for an illustrative low-voltage residential distribution system.

Decentralized Multi-Agent System-Based Cooperative Frequency Control for Autonomous Microgrids With Communication Constraints

Abstract: Based on power line carrier communication technology, a decentralized multi-agent system (DMAS)-based frequency control strategy is proposed and investigated in this study on an autonomous microgrid with communication constraints, where each agent can only communicate with its neighboring agents. Using the optimized average consensus algorithm, the global information (i.e., total active power deficiency of the microgrid) can be accurately shared in a decentralized way. Depending on the discovered global information, the cooperative frequency control strategy, which involves primary and secondary frequency control and multi-stage load shedding, is executed to achieve a cooperative frequency recovery. Simulation results indicate that the proposed frequency control approach can guarantee the consensus and coordination of the distributed agents and maintain the frequency stability of islanded microgrids even in emergency conditions.

Adaptive Control of Grid-Connected Inverters Based on Online Grid Impedance Measurements

Abstract: Stability of a grid-connected inverter depends on the ratio of the grid impedance to the inverter impedance. Since the grid impedance changes during normal power system conditions, this paper proposes a gain-scheduling adaptive control system that uses online grid impedance measurements. For grid impedance measurement, an impulse-response analysis method is programmed in the digital-signal processor (DSP) of the grid-connected inverter. For adaptation, a Routh-Hurwitz stability analysis approach is used to derive, analytically, the stable operation boundaries of the interconnected system. To simplify the analytical derivations, the grid impedance is assumed inductive at low frequencies and curve fitted to the online impedance measurements. Experimental measurements demonstrate the improvement in system stability, when the impedance identification and adaptive control algorithms are programmed together in the DSP of a three-phase inverter, which is connected to a grid with a variable feeder impedance.

Power Smoothing of Large Solar PV Plant Using Hybrid Energy Storage

Abstract: This paper proposes a power smoothing strategy for a 1-MW grid-connected solar photovoltaic (PV) power plant. A hybrid energy storage system (HESS) composed of a vanadium redox battery and a supercapacitor bank is used to smooth the fluctuating output power of the PV plant. The power management of the HESS is purposely designed to reduce the required power rating of the SCB to only one-fifth of the VRB rating and to avoid the operation of the VRB at low power levels, thus increasing its overall efficiency. The PV plant including the HESS has been modeled using MATLAB/Simulink and PLECS software environment. The effectiveness of the proposed power control strategy is confirmed through extensive simulation results.

Combined Central and Local Active and Reactive Power Control of PV Inverters

Abstract: The increasing amount of photovoltaic (PV) generation results in a reverse power flow and a violation of the overvoltage limits in distribution networks. PV inverters can curtail active power or consume reactive power to avoid these excessive high voltages. Local controllers of active and reactive power that are based on measurements of the produced PV power have a fast response to the changing production levels of the PV installation. The performance of these local controllers depends on the tuning of the control parameters, which are grid and time dependent. In this paper, local control functions are defined as piecewise linear functions. The parameters of all the local control functions are regularly reoptimized. This results in an optimal use of reactive power and a minimum amount of curtailed active power, while respecting network limitations. The optimization of these parameters is formulated as a convex optimization problem, which can be solved sufficiently fast. The performance of the control is evaluated on an existing three-phase four-wire distribution grid and is compared with different local control methods.

Stochastic Performance Assessment and Sizing for a Hybrid Power System of Solar/Wind/Energy Storage

Abstract: This paper proposes a stochastic framework for optimal sizing and reliability analysis of a hybrid power system including the renewable resources and energy storage system. Uncertainties of wind power, photovoltaic (PV) power, and load are stochastically modeled using autoregressive moving average (ARMA). A pattern search-based optimization method is used in conjunction with a sequential Monte Carlo simulation (SMCS) to minimize the system cost and satisfy the reliability requirements. The SMCS simulates the chronological behavior of the system and calculates the reliability indices from a series of simulated experiments. Load shifting strategies are proposed to provide some flexibility and reduce the mismatch between the renewable generation and heating ventilation and air conditioning loads in a hybrid power system. Different percentages of load shifting and their potential impacts on the hybrid power system reliability/cost analysis are evaluated. Using a compromise-solution method, the best compromise between the reliability and cost is realized for the hybrid power system.

Operating Strategies for a GB Integrated Gas and Electricity Network Considering the Uncertainty in Wind Power Forecasts

Abstract: In many power systems, in particular in Great Britain (GB), significant wind generation is anticipated and gas-fired generation will continue to play an important role. Gas-fired generating units act as a link between the gas and electricity networks. The variability of wind power is, therefore, transferred to the gas network by influencing the gas demand for electricity generation. Operation of a GB integrated gas and electricity network considering the uncertainty in wind power forecast was investigated using three operational planning methods: deterministic, two-stage stochastic programming, and multistage stochastic programming. These methods were benchmarked against a perfect foresight model which has no uncertainty associated with the wind power forecast. In all the methods, thermal generators were controlled through an integrated unit commitment and economic dispatch algorithm that used mixed integer programming. The nonlinear characteristics of the gas network, including the gas flow along pipes and the operation of compressors, were taken into account and the resultant nonlinear problem was solved using successive linear programming. The operational strategies determined by the stochastic programming methods showed reductions of the operational costs compared to the solution of the deterministic method by almost 1%. Also, using the stochastic programming methods to schedule the thermal units was shown to make a better use of pumped storage plants to mitigate the variability and uncertainty of the net demand.

An Improved MPPT Controller for Photovoltaic System Under Partial Shading Condition

Abstract: Maximum power point tracking (MPPT) is an integral part of a system of energy conversion using photovoltaic (PV) arrays. The power-voltage characteristic of PV arrays operating under partial shading conditions exhibits multiple local maximum power points (LMPPs). In this paper, a new method has been presented to track the global maximum power point (GMPP) of PV. Compared with the past proposed global MPPT techniques, the method proposed in this paper has the advantages of determining whether partial shading is present, calculating the number of peaks on P – V curves, and predicting the locations of GMPP and LMPP. The new method can quickly find GMPP, and avoid much energy loss due to blind scan. The experimental results verify that the proposed method guarantees convergence to the global MPP under partial shading conditions.

Wind Turbine Power Curve Modeling Using Advanced Parametric and Nonparametric Methods

Abstract: Wind turbine power curve modeling is an important tool in turbine performance monitoring and power forecasting. There are several statistical techniques to fit the empirical power curve of a wind turbine, which can be classified into parametric and nonparametric methods. In this paper, we study four of these methods to estimate the wind turbine power curve. Polynomial regression is studied as the benchmark parametric model, and issues associated with this technique are discussed. We then introduce the locally weighted polynomial regression method, and show its advantages over the polynomial regression. Also, the spline regression method is examined to achieve more flexibility for fitting the power curve. Finally, we develop a penalized spline regression model to address the issues of choosing the number and location of knots in the spline regression. The performance of the presented methods is evaluated using two simulated data sets as well as an actual operational power data of a wind farm in North America.

Long-Term Wind Speed Forecasting and General Pattern Recognition Using Neural Networks

Abstract: Long-term forecasting of wind speed has become a research hot spot in many different areas such as restructured electricity markets, energy management, and wind farm optimal design. However, wind energy with unstable and intermittent characteristics entails establishing accurate predicted data to avoid inefficient and less reliable results. The proposed study in this paper may provide a solution regarding the long-term wind speed forecast in order to solve the earlier-mentioned problems. For this purpose, two fundamentally different approaches, the statistical and the neural network-based approaches, have been developed to predict hourly wind speed data of the subsequent year. The novelty of this study is to forecast the general trend of the incoming year by designing a data fusion algorithm through several neural networks. A set of recent wind speed measurement samples from two meteorological stations in Malaysia, namely Kuala Terengganu and Mersing, are used to train and test the data set. The result obtained by the proposed method has given rather promising results in view of the very small mean absolute error (MAE).

Multiagent Genetic Algorithm: An Online Probabilistic View on Economic Dispatch of Energy Hubs Constrained by Wind Availability

Abstract: Multiple energy carriers (MECs) have been broadly engrossing power system planners and operators toward a modern standpoint in power system studies Energy hub, though playing an undeniable role as the intermediate in implementing the MECs, still needs to be put under examination in both modeling and operating concerns Since wind power continues to be one of the fastest-growing energy resources worldwide, its intrinsic challenges should be also treated as an element of crucial role in the vision of future energy networks In response, this paper aims to concentrate on the online economic dispatch (ED) of MECs for which it provides a probabilistic ED optimization model The presented model is treated via a robust optimization technique, ie, multiagent genetic algorithm (MAGA), whose outstanding feature is to find well the global optima of the ED problem ED once constrained by wind power availability, in the cases of wind power as one of the input energy carriers of the hub, faces an inevitable uncertainty that is also probabilistically overcome in the proposed model Efficiently approached via MAGA, the presented scheme is applied to test systems equipped with energy hubs and as expected, introduces its applicability and robustness in the ED problems

Sizing of Energy Storage and Diesel Generators in an Isolated Microgrid Using Discrete Fourier Transform (DFT)

Abstract: This paper proposes a method for coordinated sizing of energy storage (ES) and diesel generators in an isolated microgrid based on discrete Fourier transform (DFT). ES and diesel generators have different response characteristics and can complementarily compensate the generation-demand imbalance at different time scales. The DFT-based coordinated dispatch strategy allocates balance power between the two components through frequency-time domain transform. The proposed method ensures that ES and diesel generators work in their respective and most efficient, fit way. Diesel generators consecutively work at a high-level generation while ES compensates small and frequent power fluctuations. Then, the capacities of ES and diesel generators are determined based on the coordinated dispatch strategy. The proposed method can also be used in the planning of ES with other dispatchable sources such as micro-turbine or fuel-cell. Finally, the effectiveness of the proposed method and its advantages are demonstrated via a practical case study.

Electric Vehicle Charging in an Office Building Microgrid With Distributed Energy Resources

Abstract: This paper discusses the charging of plug-in hybrid electric vehicles (PHEVs) in an existing office building microgrid equipped with a photovoltaic (PV) system and a combined heat and power (CHP) unit. Different charging strategies and charging power ratings for workplace charging are examined for their grid impact and their impact on the self-consumption of the locally generated electricity. The grid impact can be significantly reduced by using strategies that require limited future knowledge of the EV mobility behavior and limited communication infrastructure. These strategies allow a high number of EVs to be charged at an office building, even with a limited number of charging spots, due to the large standstill times.

A Comprehensive Study of the Impacts of PHEVs on Residential Distribution Networks

Abstract: This paper presents the development of a probabilistic benchmark for assessing the impacts of the uncontrolled charging of plug-in hybrid electric vehicles (PHEVs) on residential distribution networks. Unlike the previous research, which adopted several assumptions and approximations, this paper analyzes the available load and transportation data to extract probability distribution functions describing different uncertainties characterizing the charging process. Monte Carlo simulation is utilized to handle these uncertainties and to predict the anticipated impacts of PHEVs on a representative test network. Finally, conclusions are drawn to assist utilities in integrating PHEVs into their networks.

Emission Reduction and Economical Optimization of an Urban Microgrid Operation Including Dispatched PV-Based Active Generators

Abstract: In order to take full advantage of distributed generators, an evolution of the classical power system organization and management is also necessary. An aggregator of a residential urban electrical network can be considered by the distribution system operator as a stakeholder, which is able to control a cluster of local generators and loads with technical constraints for the connection with the remaining distribution grid and commercial contracts with outer electrical producers. This paper is focused on the design of the microgrid central energy management system which relies on a day-ahead operational planning and an online adjustment procedure during the operation. A dynamic programming-based algorithm is derived to solve the unit commitment problem with a multiobjective function in order to reduce the economic cost and CO 2 equivalent emissions. The proposed energy management system is implemented into a supervisory control and data acquisition (SCADA) and tested by using a hardware-in-the-loop simulation of the urban network. Economic and environmental gains are evaluated.

Renewable Electricity Futures for the United States

Abstract: This paper highlights the key results from the Renewable Electricity (RE) Futures Study. It is a detailed consideration of renewable electricity in the United States. The paper focuses on technical issues related to the operability of the U.S. electricity grid and provides initial answers to important questions about the integration of high penetrations of renewable electricity technologies from a national perspective. The results indicate that the future U.S. electricity system that is largely powered by renewable sources is possible and the further work is warranted to investigate this clean generation pathway. The central conclusion of the analysis is that renewable electricity generation from technologies that are commercially available today, in combination with a more flexible electric system, is more than adequate to supply 80% of the total U.S. electricity generation in 2050 while meeting electricity demand on an hourly basis in every region of the United States.

A Statistical Description of the Error on Wind Power Forecasts for Probabilistic Reserve Sizing

Abstract: As the share of wind power in the electricity system rises, the limited predictability of wind power generation becomes increasingly critical for operating a power system reliably. In most operational and economic models, the wind power forecast error (WPFE) is often assumed to follow a Gaussian or the so-called $\betab$ -distribution. However, these distributions might not be suited to fully describe the skewed and heavy-tailed character of WPFE data. In this paper, the Levy $\alphab$ -stable distribution is proposed as an improved description of the WPFE. The method presented allows us to quantify the probability of a certain error, given a certain wind power forecast. Based on recent historical wind power data, the feasibility of the Levy $\alphab$ -stable distribution as a WPFE description is demonstrated. The added value of this improved statistical model of the WPFE is illustrated in a state-of-the-art probabilistic reserve sizing method. Results show that this new statistical description of the WPFE can hold important information for short-term economic and operational (reliability) studies for power systems with a significant wind power penetration.

Multiagent-Based Hybrid Energy Management System for Microgrids

Abstract: Energy management systems for microgrids (EMS-MG) play an important role in ensuring their stable and economic operation. This paper presents a multiagent-based hybrid EMS-MG (HEMS-MG) with both centralized and decentralized energy control functionalities. Based on this framework, three-level hierarchical energy management strategies are presented, in which a cooperation method with contract net protocol and multifactor evaluation mechanisms are applied. A coordinated energy management framework is realized by the combination of autonomous control of local distributed energy resources at the local level with coordinated energy control at the central level of the microgrid. A novel simulation platform for the HEMS-MG is designed in terms of the client–server frame and implemented under the C++ Builder environment. To prove the effectiveness and benefits of the proposed control system, an example of generation cooperation control of a laboratory microgrid is provided. The simulation results show that the proposed control system is an effective way to manage and optimize microgrid operation.

Optimization of Battery–Supercapacitor Hybrid Energy Storage Station in Wind/Solar Generation System

Abstract: In capacity optimization of hybrid energy storage station (HESS) in wind/solar generation system, how to make full use of wind and solar energy by effectively reducing the investment and operation costs based on the load demand through allocating suitable capacity of HESS is an optimization problem. The optimization objective is to minimize one-time investment and operation costs in the whole life cycle, the constraints are utilization rate, and reliability of power supply. In this paper, mathematical models of wind/solar generation systems, battery, and supercapacitor are built, the objective optimization function of HESS is proposed, and various constraints are considered. To solve the optimization problem, improved simulated annealing particle swarm optimization algorithm is proposed by introducing the simulated annealing idea into particle swarm algorithm. The new algorithm enhance the ability to escape from local optimum and improve the diversity of particle swarm, then help to avoid prematurity and enhance the global searching ability of the algorithm. With the example system, the optimization results show that the convergence of new algorithm is faster than the traditional particle swarm optimization algorithm and its cost optimization is better, which demonstrated the correctness and validity of the proposed models and algorithms. This method can provide a reference for the capacity optimization of HESS in wind/solar generation system.

Optimal Allocation of CHP-Based Distributed Generation on Urban Energy Distribution Networks

Abstract: The continuing penetration of combined heat and power (CHP)-based distributed generation (DG) has made urban electricity, water, and natural gas distribution networks increasingly interconnected. This paper analyzes the optimal network capacity and distribution of the CHP-based DG based on urban energy distribution networks by introducing an integrated system dispatch model. The electricity, water, and gas network models were designed and developed individually. The CHP-based DG model was developed to couple these energy distribution systems. The results indicate the optimal allocation of CHP-based DG by analyzing multiple factors and mutual impacts on operational performances of the CHP-based DG units and energy distribution networks. The designed typical gas system is capable of supplying sufficient natural gas for DG normal operations, whereas the present water system cannot support the complete recovery of the exhaust heat from large CHP-based DG penetration.

A Demand Response and Battery Storage Coordination Algorithm for Providing Microgrid Tie-Line Smoothing Services

Abstract: This paper presents a demand response (DR) and battery storage coordination algorithm for providing microgrid tie-line smoothing services. A modified coordinating control strategy is implemented through two-way communication networks to manage distributed heat pumps in a microgrid for smoothing the tie-line (connect the microgrid to the main grid) power fluctuations. A total of 1000 residential electric heat pumps and a battery storage system are modeled to demonstrate the effectiveness and robustness of the proposed algorithm. The impact of outdoor temperature changes and customer room temperature preferences is considered in the simulation. The results show that coordinating with DR programs can significantly reduce the size of conventional energy storage systems for large-scale integration of renewable generation resources in microgrids and improve the power quality.

An Explicit PV String Model Based on the Lambert $W$ Function and Simplified MPP Expressions for Operation Under Partial Shading

Abstract: In this paper, a reformulation of the widely used one-diode model of the photovoltaic (PV) cell is introduced, employing the Lambert W function. This leads to an efficient PV string model, where the terminal voltage is expressed as an explicit function of the current, resulting in significantly reduced calculation times and improved robustness of simulation. The model is experimentally validated and then used for studying the operation of PV strings under partial shading conditions. Various shading patterns are investigated to outline the effect on the string I-V and P-V characteristics. Simplified formulae are then derived to calculate the maximum power points of a PV string operating under any number of irradiance levels, without resorting to detailed modeling and simulation. Both the explicit model and the simplified expressions are intended for application in shading loss and energy yield calculations.

A Robust Wind Power Optimization Method for Look-Ahead Power Dispatch

Abstract: The main purpose of the look-ahead dispatch is to manage operational uncertainties over the next several hours, with benefits for the exploitation of renewable energy resources. This paper describes a robust optimization (RO) model for wind power look-ahead dispatch. The model calculates allowable interval solutions for wind power generation and provides optimal economic solutions for conventional power generation to mitigate the uncertainty inherent to wind power. By introducing the interval values as control targets for wind farms, the method can reduce the curtailment of wind power and the frequency of regulation. Interval wind power look-ahead dispatch is a two-layer RO problem, which can be transformed into a quadratic programming problem using strong duality theory, allowing for a more straightforward solution. The results of numerical simulations using the IEEE RTS system, as well as field tests using a 1.8-GW transmission system, are reported.

V2G Parking Lot With PV Rooftop for Capacity Enhancement of a Distribution System

Abstract: Vehicle-to-grid (V2G) units are gaining prominence and may dominate the auto-market in the near future. The V2G batteries require corporate parking lots for charging and discharging operations. The electric power capacity of an existing parking lot can be increased by the installation of photovoltaic (PV) rooftops. This paper describes mathematical models for estimating the electric power capacity of a V2G parking lot (VPL) system with PV canopy. The electric vehicle (EV) demand/supply model was formulated as a queuing theory problem, exhibiting stochastic characteristics. New formulae were developed to address the impacts of battery charger efficiency on the amount of power demand during battery charging, and also how the latter is effected by inverter efficiency during discharging. Mathematical models for grid gain factor were developed. The proposed models were tested using Tesla Roadster EV and Nissan leaf EV. Promising simulation results are gained leading to a conclusion that vehicle parking lots with PV facilities can potentially relieve and enhance the grid capacity. Results show that 60% gain factor is possible. The effect of weather uncertainties and energy market price were studied. The study could be useful in battery-charger control studies.

Short-Term Operation Scheduling in Renewable-Powered Microgrids: A Duality-Based Approach

Abstract: This paper considers the operation scheduling problem in renewable-powered microgrids, which is used to determine the least-cost unit commitment (UC) and the associated dispatch, while meeting load, environmental, and system operating requirements. The intermittency nature of the renewable energy sources, as well as microgrid's capacity to operate either in parallel with, or autonomously of, the traditional power grid, pose new challenges to this classic optimization task. A probability-based concept, probability of self-sufficiency (PSS), is introduced to indicate the probability that the microgrid is capable of meeting local demand in a self-sufficient manner. Furthermore, to the best of our knowledge, we make the first attempt in approaching the mixed-integer UC problem from a convex optimization perspective, which leads to an analytical closed-form characterization of the optimal commitment and dispatch solutions. The simulation results show that 1) the proposed method achieves an efficient performance that incurs no loss of optimality with lower complexity than existing algorithms; 2) an energy storage system (ESS) with suitable capacity contributes to the self-sufficiency target of a microgrid, and the stored energy varies less remarkably as the microgrid tends to operate more independently; 3) the proposed method provides guidelines in deciding the ESS size to achieve a desired PSS.