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.

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Online Short-term Solar Power Forecasting

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling. A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO. On the other hand, basic PSO is more appropriate to process static, simple optimization problem. Modification PSO is developed for solving the basic PSO problem. The observation and review 46 related studies in the period between 2002 and 2010 focusing on function of PSO, advantages and disadvantages of PSO, the basic variant of PSO, Modification of PSO and applications that have implemented using PSO. The application can show which one the modified or variant PSO that haven’t been made and which one the modified or variant PSO that will be developed.

/pdf/particle-swarm-optimization-technique-system-and-challenges-1t22wy4m1r.pdf

Particle Swarm Optimization: Technique, System and Challenges

https://vbn.aau.dk/ws/files/206009396/Correction.pdf

Practical operation strategies for pumped hydroelectric energy storage (PHES) utilising electricity price arbitrage

Phasor measurement units (PMUs) are rapidly being deployed in electric power networks across the globe. Wide-area measurement system (WAMS), which builds upon PMUs and fast communication links, is consequently emerging as an advanced monitoring and control infrastructure. Rapid adaptation of such devices and technologies has led the researchers to investigate multitude of challenges and pursue opportunities in synchrophasor measurement technology, PMU structural design, PMU placement, miscellaneous applications of PMU from local perspectives, and various WAMS functionalities from the system perspective. Relevant research articles appeared in the IEEE and IET publications from 1983 through 2014 are rigorously surveyed in this paper to represent a panorama of research progress lines. This bibliography will aid academic researchers and practicing engineers in adopting appropriate topics and will stimulate utilities toward development and implementation of software packages.

Synchrophasor Measurement Technology in Power Systems: Panorama and State-of-the-Art

Experimental validation of a real time energy management system for microgrids in islanded mode using a local day-ahead electricity market and MINLP

In real time electricity markets, the objective of generation companies while bidding is to maximize their profit. The strategies for learning optimal bidding have been formulated through game theoretical approaches and stochastic optimization problems. Similar studies in reactive power markets have not been reported so far because the network voltage operating conditions have an increased impact on reactive power markets than on active power markets. Contrary to active power markets, the bids of rivals are not directly related to fuel costs in reactive power markets. Hence, the assumption of a suitable probability distribution function is unrealistic, making the strategies adopted in active power markets unsuitable for learning optimal bids in reactive power market mechanisms. Therefore, a bidding strategy is to be learnt from market observations and experience in imperfect oligopolistic competition-based markets. In this paper, a pioneer work on learning optimal bidding strategies from observation and experience in a three-stage reactive power market is reported.

Neural Fitted Q Iteration based Optimal Bidding Strategy in Real Time Reactive Power Market_1.

This paper investigates the impact on operating time delay and relay maloperation i.e. selectivity, when high sampling rate is used in numerical distance protection (NDP). Simulation is carried out in an on-line way, for different source to line reach impedance ratio (SIR), fault type, fault locations and for different loading conditions. The voltage and current signals from capacitive voltage transformers and current transformers respectively are fed to the numerical distance relay (NDR). Sampling rate of 12, 16, 24, 32 and 64 times the fundamental frequency (50 Hz) are chosen for this investigation, since these are typically used in conventional distance relays.

Investigating performance of numerical distance relay with higher sampling rate

Zone 1 reach setting of a distance relay depend upon many factors, such as, dc offset in current signals, loading effect, fault resistance in the presence of in-feed from remote end and also capacitive voltage transformer (CVT) transients. All these factors are taken care of except for CVT transients, which is dealt either by reducing zone 1 reach or by providing a fixed time delay in today's intelligent electronic devices (IED). This paper proposes a new and simple logic to prevent numerical distance relay mal-operation by zone 1 element due to CVT transients without sacrificing zone 1 reach. This is achieved by providing adaptive time delay by actually monitoring the CVT transients in time domain. The proposed logic is validated in real time by incorporating the proposed logic along with positive sequence polarized distance relay model in field programmable gate array (FPGA).

Smart trip logic for Smart Grids to block distance relay maloperation — Implementation and validation

This paper presents a novel Optimization based corrective strategy based on graph theory to mitigate overloads during contingencies. Generator rescheduling and/or load shedding is performed locally, to restore the system from abnormal to normal operating state. The directed acyclic graph (DAG) technique is used to identify the participating generators and load buses for corrective control action with respect to a contingency. Particle swarm optimization (PSO) is used to compute the optimal adjustments of generation and loads with security constraints such as line flows and bus voltage limits. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 14- and 30-bus systems. The result shows that the proposed approach is computationally fast, reliable and efficient, in restoring the system to normal state after a contingency with minimal control actions. Copyright © 2009 John Wiley & Sons, Ltd.

Optimization based graph theoretic approach for corrective control strategies to mitigate overloads

This paper develops optimal bidding strategies for a pumped storage plant in competitive electricity market. In a restructured environment, when compared to a simple hydro electric generator, the profit of the pumped storage plant is maximized by operating it as a generator when market clearing price is high and as a pump when price is low. Based on fore- casted hourly market clearing price curves, a multistage looping algorithm to maximize the profit of a pumped storage plant is developed considering both the spinning and non-spinning reserve bids and meeting the technical operating constraints. The proposed model is adaptive for the nonlinear three-dimensional relationship between the power produced, the energy stored, and the head of the associated reservoir. Different operating cycles for the storage plant are considered and simulation results are reported and analyzed. I. INTRODUCTION

K. Shanti Swarup

Papers

Neural Fitted Q Iteration based Optimal Bidding Strategy in Real Time Reactive Power Market_1.

Investigating performance of numerical distance relay with higher sampling rate

Smart trip logic for Smart Grids to block distance relay maloperation — Implementation and validation

Optimization based graph theoretic approach for corrective control strategies to mitigate overloads

Optimal Bidding Strategies for Pumped Storage Plant in Pool-Based Electricity Market