Modeling of wind generation fluctuations in a dispatch model

This paper examines a proposal for the introduction of incentive based demand curtailment in the New Zealand Electricity market (NZEM). Today NZEM participants do not submit demand bids (except in pre-dispatch schedule) to the markets for energy and regulation reserve. Professional literature identifies demand response (DR) in a broad variety of uses. Such uses range across peak load management, transmission congestion management, regulating reserve, market efficiency, transmission and distribution investment deferral, among other things. Despite the apparent benefits of these various options demand side participation has been very limited in today's markets throughout the world. Many US and Canadian markets have embraced demand response, but many markets are still reluctant to implement demand management products in real time markets. The New Zealand power system has a limited Advance Metering Infrastructure (AMI) and a low penetration of smart appliances. In the installed AMI much of the communication capability is limited to remote meter reading. Consequent perhaps on the limited TOU capability of the AMI, tariffs are fixed rather than TOU based. Some adhoc incentives have been given to persuade consumers to participate in conservation initiatives during periods of energy shortage. At the time of writing New Zealand does not use price based demand response methods. Our discussion is centered on a generic incentive based demand response. This paper reviews the status of the various DR implementations in the United States Independent System Operators (ISO's) as a basis of comparison for the NZEM. The paper investigates the effect of introducing incentive based demand side participation in the NZEM. Demand participation in the form of dispatchable energy bids are considered with the objective of investigating the LMP formulation changes necessary to accommodate DR in the energy, contingency and regulating reserve markets in the NZEM.

This paper presents wavelet-volatility analysis technique to measure the variation of wholesale electricity prices. The dynamic character of electricity price is mean-revering, with weekly variation, seasonal fluctuations, and instant jumps. The analysis decomposes historical price signals using wavelet transform in a set of constitutive series with different level/scale of details of the original price signals. A time-varying volatility model is then constructed based on these detailed signals using the GARCH(1,1) model. Results from the New Zealand Electricity Market (NZEM) for year 2004 are presented to illustrate the technique.

This paper reports upon the mathematical models and implementation of the Scheduling, Pricing and Dispatch (SPD) application for the New Zealand Electricity Market (NZEM). SPD analyzes bids for energy offers, reserve offers and energy demands, and recognizes explicitly the effects on bid clearing due to transmission congestion, network losses, reserve requirements, and ramp rate limits. Advanced LP solution methods are utilized to solve the large-scale constrained optimization problem. Results on a 67-bus test system and the NZEM are included.

This paper presents a validation of atmospheric reanalysis data sets for simulating onshore wind generation time series for large-scale energy system studies. The three reanalyses are the ERA5, the New European Wind Atlas (NEWA) and DTU’s previous generation European-level atmospheric reanalysis (EIWR). An optional scaling is applied to match the microscale mean wind speeds reported in the Global Wind Atlas version 2 (GWA2). This mean wind speed scaling is used to account for the effects of terrain on the wind speed distributions. The European wind power fleet for 2015–2018 is simulated, with commissioning of new wind power plants (WPPs) considered for each year. A generic wake model is implemented to include wake losses that are layout agnostic; the wake model captures the expected wake losses as function of wind speed given the technical characteristics of the WPP. We validate both point measurement wind speeds and generation time-series aggregated at the country-level. Wind measurements from 32 tall meteorological masts are used to validate the wind speed, while power production for four years from twelve European countries is used to validate the simulated country-level power production. Various metrics are used to rank the models according to the variables of interest: descriptive statistics, distributions, daily patterns, auto-correlation and spatial-correlation. We find that NEWA outperforms ERA5 and EIWR for the simulated wind speed, but, as expected, no model is able to fully describe the auto-correlation function of the wind speed at a single point. The mean wind speed scaling is found to be necessary to match the distribution of generation on country-level, with NEWA-GWA2 and ERA5-GWA2 showing highest accuracy and precision for simulating large-scale wind generation time-series.

The intermittent nature of wind power presents special challenges for utility system operators when performing system economic dispatch, unit commitment, and deciding on system energy reserve capacity. Also, participation of wind power in future electricity markets requires more systematic modeling of wind power. It is expected that the installed energy capacities from wind sources in the United States will increase by up to 20% by the year 2020. New York Independent System Operator (NYISO), General Electric (GE), and Automatic Weather Stations Inc., (AWS) conducted a project for the future of wind energy integration in the United States. They stated that NY State has 101 potential wind energy sites and it should be able to integrate wind generation up to at least 10% of system peak load without further expansion (GE report 2005). In order to integrate wind power systematically, it is necessary to solve the technical challenges as well as policy regulation designs. Some of these polices have been updated to allow increased intermittent renewable energy by settling imbalances in generation rulemakings and portfolio standards, where the most commonly used one at this time is the production tax credit portfolios. Due to intermittent nature of wind power, forecasting methods become a powerful tool and of great importance to many power system applications that include uncertainties in generation outputs. The recent work has discussed several methods to develop wind power forecasting algorithms to anticipate the degrees of uncertainty and variability of wind generation. (C. Lindsay & Judith, 2008) use an auto-regressive moving average model to estimate the next ten-minute ahead production level for a hypothetical wind farm and investigate the possibility of pairing wind output with responsive demand to reduce the variability in the net wind output. In (Kittipong M. et al., 2007), the authors develop an Artificial Neural Network (ANN) model to forecast wind generation power with 10-min resolution. Current and previous wind speed and wind power generation are used as input parameters to the network where the output from the ANN is the wind generation power. (M. S. Miranda & R. W. Dunn, 2006) predicted one-hour-ahead of wind speed using both an auto-regressive model and Bayesian approach. (D. Hawkins & M. Rothleder, 2006), discuss operational concerns with increased amount of wind energy in the Day-aheadand Hourahead-Market for CAISO in California. They emphasize the importance of forecasting accuracy for unit commitment and ancillary services and the implications of load following or supplemental energy dispatch to rebalance the system every five minutes. In (Alberto F. at el., 2005), the authors propose a probabilistic method to estimate the forecasting error for

Dispatch results depend on the forecast wind power in an electric power grid. High levels of uncertainty in wind power lead to large forecast errors (FEs). Wind power FE creates an imbalance between power load demand and supply, which poses risks to the power grid. To minimise the risk caused by uncertainty in wind power, FE was analysed. The actual and forecast power of wind generations were studied to determine which factors had strong relationships with FE. After being analysed by principle component analysis, these factors were used to build an assessment model to estimate FE. According to the output of the assessment model, risk factor (RF) was defined to evaluate the risk‐level associated with wind power. Moreover, based on a grid including wind generation and fossil fuel‐fired power plants, a dispatch model was built to account for the cost of fossils fuels and RF. This dispatch model was studied to find strategies for decreasing the impact of wind power on the grid. The results from their research will increase the safety and reliability of power grids with uncertain levels of wind power and promote the widespread use of wind power.

This article presents results from models developed to improve wind speed forecasting based on techniques of echo state networks, multi-layer perceptron (MLP) and neuro-fuzzy system (ANFIS). Wind generation is a direct function of wind speed and, in contrast with conventional generation systems, is not easily dispatchable. Wind forecasting is crucial both for wind farm operators and utility operators. Accurate forecasting allows operators to achieve favorable trading performances on the electricity markets. The further in advance an operator can make a reliable estimate about how much electricity he will produce the better. However, the use of wind to generate electricity has some drawbacks, such as uncertainties in generation and some difficulty in planning and operation of the power system. Models presented in this paper investigated the contribution of using climate variables such as humidity, temperature and radiation, in order to improve the performance of wind speed forecasting. The models were adjusted to forecast the wind speed with steps up to four hours ahead. The inclusion of climate variables have enabled significant gains in wind speed forecasting results. The gain of some models developed, in relation to the reference models, were approximately 50% for forecasts in a period of four hours in advance.

Power systems are vulnerable to extreme contingencies (like an outage of a major generating substation or transmission line) that can cause significant generation and load loss and can lead to further cascading outages of other transmission facilities and generators in the system. Power systems operators take time-ahead actions (real time to day ahead) actions based on forecasted data as well as telemetry. However, there is a probability of extreme swings in wind generation that can push the power system that is already close to system boundaries into unreliable states. Increasing penetration on wind generation and uncertainties in short term forecast can move the system into operational states that can violate N-l reliability criteria. While the power system planning accounts for worst case analyses to mitigate such emergencies, it is necessary to understand the spatial and temporal correlation of load buses with intermittent wind generation. The ranking of wind generators based on stochastic correlation to unreliability margins of load buses provides an insight of system vulnerabilities that can be corrected by appropriate emergency controls. A Monte-Carlo simulation of wind uncertainty forecast is performed to identify the probability distributions of unreliability margins. Ranking of wind generators is performed based on stochastic correlation analysis of the wind power generated and the load losses at the buses. This helps in not only identifying weak buses in the power system but also in identifying the vulnerable wind locations to plan for system reinforcements. Power system modeling and hybrid simulation to calculate unreliability margins are performed using nCAT and demonstrated on the 32-bus Nordic test system.

We demonstrate levels of skill for forecasts of seasonal-mean wind speed and solar irradiance in Europe, using seasonal forecast systems available from the Copernicus Climate Change Service (C3S). While skill is patchy, there is potential for the development of climate services for the energy sector. Following previous studies, we show that, where there is skill, a simple linear regression-based method using the hindcast and forecast ensemble means provides a straightforward approach for producing calibrated probabilistic seasonal forecasts. This method extends naturally to using a larger-scale feature of the climate, such as the North Atlantic Oscillation, as the climate model predictor, and we show that this provides opportunities to improve the skill in some cases.We further demonstrate that, on seasonal-average and regional (e.g. national) average scales, wind and solar power generation are highly correlated with single climate variables (wind speed and irradiance). The detailed non-linear transformations from meteorological quantities to energy quantities, which are essential for detailed simulation of power system operations, are usually not necessary when forecasting gross wind or solar generation potential at seasonal-mean regional-mean scales.Together, our results demonstrate that where there is skill in seasonal forecasts of wind speed and irradiance, or a correlated larger-scale climate predictor, skilful forecasts of seasonal mean wind and solar power generation can be made based on the climate variable alone, without requiring complex transformations. This greatly simplifies the process of developing a useful seasonal climate service.

Abstract: Permanent magnet synchronous generators in wind generation systems are equipped with two sets of converters connected back to back. For maximum power transfer under variable wind speed conditions, the generator side as well as grid side converter reference settings have to be optimized. In this article, the generator side converter speed settings are determined through an adaptive neuro-fuzzy algorithm for extracting maximum power from wind. The algorithm contains two series neuro-fuzzy networks. Wind power and corresponding generator speeds against wind speed are trained by the first network. The second network trains wind speed against optimum generator rotor speeds. An optimum relation based (ORB) tracking algorithm is used to determine the grid side converter (inverter) DC voltage reference settings. Converter DC power and current ratios are compared with pre-computed optimum values. Following any change in the system, the desired DC voltage settings are determined through a simple perturb and observe technique. The MPPT algorithms were tested on the wind generation system model which includes the detailed dynamics of the various components. Studies show that with incorporation of the proposed algorithms, delivery of power to the grid is achievable with maximum possible value of power coefficient. The MPPT techniques using neuro-fuzzy and ORB techniques do not require measurement of wind speed or any system parameter. Keywords Renewable energy, Wind generator, MPPT, ANFIS, PMSG, Optimum rotor speed

In this paper, a computational framework for integrating wind power uncertainty and carbon tax in economic dispatch (ED) model is developed. The probability of stochastic wind power based on nonlinear wind power curve and Weibull distribution is included in the model. In order to solve the revised dispatch strategy, quantum-inspired particle swarm optimization (QPSO) is also adopted, which shows stronger search ability and quicker convergence speed. The dispatch model is tested on a modified IEEE benchmark system involving six thermal units and two wind farms using the real wind speed data obtained from two meteorological stations in Australia.

Wind power is currently the fastest growing power generation sector in the world-worldwide growth in wind power generation has been at 40% a year for the last 10 years. With this rapid growth of generation, new challenges are being introduced to power markets. Wind power is an intermittent source. For example, at a wind site in Tasmania, Australia, the average change in mean wind speed vectors over a 2.5- minute period is about 44.4% and the average change in maximum wind speeds over the same period is 55.0%. This demonstrates that the wind speed and its direction can change rapidly by a large amount over a very short period of time. Great variability in wind farm generation has both technical and commercial implications for the efficient planning and operation of power systems. Large wind farms tend to use large wind turbines. Large turbines have a significant rotational inertia when considering a short time frame. As a result, wind patterns shorter than 10 seconds usually have a negligible effect on the turbine output, and it is a standard practice in wind generation to neglect short-term gusts. However, trends over several minutes are of vital importance. In order to accommodate the increase in wind power generation, we need to change the way that wind generation is scheduled. Both utilities and electricity market operators will be required to forecast the wind generation to improve system performance. Wind generation will have to be bid into the market like any other generation. In order to handle these requirements, power system operators will need to have better wind forecasting models and tools. Forecasting is a vital part of business planning in today’s competitive environment, regardless of the field in which you work. To be ready for a situation before it occurs gives a distinct advantage over the competition. Wind prediction is complex due to the wind’s high degree of volatility and deviation. This means that standard time length definitions for power engineering do not strictly apply. The phrase shortterm in the case of wind forecasting, normally means periods from minutes out to half an hour while longterm forecasting means periods from several hours out to a few days. This difference between the two forecasting time periods is important when it comes to creating a prediction system. Three main classes of different techniques have been used for wind power forecasting. These are numerical weather prediction (NWP) methods, statistical methods, and methods based on applications of artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFISs). TheNWP methods are dominant for forecasts over 10 hours ahead. However, statistical and ANN-based methods appear to be more accurate over shorter periods (minutes to a few hours). They are also much simpler than the NWP methods.

Spacial coordination between data centers and power system considering uncertainties of both source and load sides

In order to study the good cooling characteristics of evaporative cooling technology in wind power generation technology, the fully immersed evaporative cooling semi-direct-drive permanent magnet wind generators (ECSDDPMWGs) are taken as the research object to study the dynamic thermal characteristics of the windings in stator radial channel under variable wind speed in this paper. Firstly, the lump parameter thermal network (LPTN) model of evaporative cooling stator radial channels is established and the dynamic temperature response of the windings is analyzed based on the LPTN model and verified by the temperature rise experiment of the prototype. Then the output power of the wind generators under variable wind speed is analyzed based on the Weibull distribution of the wind speed. Finally, the dynamic thermal of the windings in stator radial channels is analyzed based on the LPTN model and variable wind speed. The results show that the LPTN model can effectively analyze the variable thermal of windings in the stator radial channel, which can provide a theoretical reference for the dynamic thermal prediction of windings in ECSDDPMWGs. In addition, it can be seen from the analysis that the evaporative cooling technology has a good adaptive performance to the variable load wind turbine applications.

The intermittency of wind generation and the potential need for adequate transmission expansion are the major concerns in wind generation integration to power system. One solution being considered is to build on-site energy storage with the wind farms. The idea of building such a composite system is not only to minimize the real-time variation of the composite system output, but also to optimize the transmission upgrades needed for delivery of the wind generation. A novel probabilistic reliability assessment method is proposed in this paper for determining the adequate size of on-site energy storage and the transmission upgrades needed in connecting wind generation with the power system. The practical applications of the proposed model are illustrated using the IEEE Reliability Test System (IEEE-RTS).