Yateendra Mishra

Bio: Yateendra Mishra is an academic researcher from Queensland University of Technology. The author has contributed to research in topics: Electric power system & Photovoltaic system. The author has an hindex of 24, co-authored 165 publications receiving 2851 citations. Previous affiliations of Yateendra Mishra include University of Tennessee & Southeast University.

A review and evaluation of the state-of-the-art in PV solar power forecasting:Techniques and optimization

Abstract: Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlational analyses reveal that solar irradiance is most correlated with Photovoltaic output, and so, weather classification and cloud motion study are crucial. Moreover, the best data cleansing processes: normalization and wavelet transforms, and augmentation using generative adversarial network are recommended for network training and forecasting. Furthermore, optimization of inputs and network parameters, using genetic algorithm and particle swarm optimization, is emphasized. Next, established performance evaluation metrics MAE, RMSE and MAPE are discussed, with suggestions for including economic utility metrics. Subsequently, modelling approaches are critiqued, objectively compared and categorized into physical, statistical, artificial intelligence, ensemble and hybrid approaches. It is determined that ensembles of artificial neural networks are best for forecasting short term photovoltaic power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty. Additionally, convolutional neural network is found to excel in eliciting a model's deep underlying non-linear input-output relationships. The conclusions drawn impart fresh insights in photovoltaic power forecast initiatives, especially in the use of hybrid artificial neural networks and evolutionary algorithms.

Coordinated Control of Grid-Connected Photovoltaic Reactive Power and Battery Energy Storage Systems to Improve the Voltage Profile of a Residential Distribution Feeder

Abstract: Increasing penetration of photovoltaic (PV), as well as increasing peak load demand, has resulted in poor voltage profile for some residential distribution networks. This paper proposes coordinated use of PV and battery energy storage (BES) to address voltage rise and/or dip problems. The reactive capability of PV inverter combined with droop-based BES system is evaluated for rural and urban scenarios (having different \mbi R/X ratios). Results show that reactive compensation from PV inverters alone is sufficient to maintain acceptable voltage profile in an urban scenario (low-resistance feeder), whereas coordinated PV and BES support is required for the rural scenario (high-resistance feeder). Constant, as well as variable, droop-based BES schemes are analyzed. The required BES sizing and associated cost to maintain the acceptable voltage profile under both schemes are presented. Uncertainties in PV generation and load are considered, with probabilistic estimation of PV generation and randomness in load modeled to characterize the effective utilization of BES. Actual PV generation data and distribution system network data are used to verify the efficacy of the proposed method.

A Decentralized Bilateral Energy Trading System for Peer-to-Peer Electricity Markets

Abstract: Increase in the deployment of distributed energy resources (DERs) has triggered a new trend to redesign electricity markets as consumer-centric markets relying on peer-to-peer (P2P) approaches. In the P2P markets, players can directly negotiate under bilateral energy trading to match demand and supply. The trading scheme should be designed adequately to incentivise players to participate in the trading process actively. This article proposes a decentralized P2P energy trading scheme for electricity markets with high penetration of DERs. A novel algorithm using primal-dual gradient method is described to clear the market in a fully decentralized manner without interaction of any central entity. Also, to incorporate technical constraints in the energy trading, line flow constraints are modeled in the bilateral energy trading to avoid overloaded or congested lines in the system. This market structure respects market players’ preferences by allowing bilateral energy trading with product differentiation. The performance of the proposed method is evaluated using simulation studies, and it is found that market players can trade energy to maximize their welfare without violating line flow constraints. Also, compared with other similar methods for P2P trading, the proposed approach needs lower data exchange and has a faster convergence.

Demand Response for Residential Appliances via Customer Reward Scheme

Abstract: This paper proposes a reward based demand response algorithm for residential customers to shave network peaks. Customer survey information is used to calculate various criteria indices reflecting their priority and flexibility. Criteria indices and sensitivity based house ranking is used for appropriate load selection in the feeder for demand response. Customer Rewards (CR) are paid based on load shift and voltage improvement due to load adjustment. The proposed algorithm can be deployed in residential distribution networks using a two-level hierarchical control scheme. Realistic residential load model consisting of non-controllable and controllable appliances is considered in this study. The effectiveness of the proposed demand response scheme on the annual load growth of the feeder is also investigated. Simulation results show that reduced peak demand, improved network voltage performance, and customer satisfaction can be achieved.

Small-Signal Stability Analysis of a DFIG-Based Wind Power System Under Different Modes of Operation

Abstract: This paper focuses on the super/subsynchronous operation of the doubly fed induction generator (DFIG) system. The impact of a damping controller on the different modes of operation for the DFIG-based wind generation system is investigated. The coordinated tuning of the damping controller to enhance the damping of the oscillatory modes using bacteria foraging technique is presented. The results from eigenvalue analysis are presented to elucidate the effectiveness of the tuned damping controller in the DFIG system. The robustness issue of the damping controller is also investigated.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Energy function analysis for power system stability

Abstract: (1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

An Overview of Recent Research and Emerging PV Converter Technology

Abstract: Photovoltaic (PV) energy has grown at an average annual rate of 60% in the last five years, surpassing one third of the cumulative wind energy installed capacity, and is quickly becoming an important part of the energy mix in some regions and power systems. This has been driven by a reduction in the cost of PV modules. This growth has also triggered the evolution of classic PV power converters from conventional singlephase grid-tied inverters to more complex topologies to increase efficiency, power extraction from the modules, and reliability without impacting the cost. This article presents an overview of the existing PV energy conversion systems, addressing the system configuration of different PV plants and the PV converter topologies that have found practical applications for grid-connected systems. In addition, the recent research and emerging PV converter technology are discussed, highlighting their possible advantages compared with the present technology. Solar PV energy conversion systems have had a huge growth from an accumulative total power equal to approximately 1.2 GW in 1992 to 136 GW in 2013 (36 GW during 2013) [1]. This phenomenon has been possible because of several factors all working together to push the PV energy to cope with one important position today (and potentially a fundamental position in the near future). Among these factors are the cost reduction and increase in efficiency of the PV modules, the search for alternative clean energy sources (not based on fossil fuels), increased environmental awareness, and favorable political regulations from local governments (establishing feed-in tariffs designed to accelerate investment in renewable energy technologies). It has become usual to see PV systems installed on the roofs of houses or PV farms next to the roads in the countryside. Grid-connected PV systems account for more than 99% of the PV installed capacity compared to

Grid-Connected Photovoltaic Systems: An Overview of Recent Research and Emerging PV Converter Technology

Abstract: Photovoltaic (PV) energy has grown at an average annual rate of 60% in the last five years, surpassing one third of the cumulative wind energy installed capacity, and is quickly becoming an important part of the energy mix in some regions and power systems. This has been driven by a reduction in the cost of PV modules. This growth has also triggered the evolution of classic PV power converters from conventional single-phase grid-tied inverters to more complex topologies to increase efficiency, power extraction from the modules, and reliability without impacting the cost. This article presents an overview of the existing PV energy conversion systems, addressing the system configuration of different PV plants and the PV converter topologies that have found practical applications for grid-connected systems. In addition, the recent research and emerging PV converter technology are discussed, highlighting their possible advantages compared with the present technology.

Online Short-term Solar Power Forecasting

Abstract: This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.