An Open-Source Tool for Automated Power Grid ...

An Open-Source Tool for Automated Power Grid Stress Level Prediction at Balancing Authorities Alan Berscheid, Yuri Makarov, Zhangshuan Hou, Ruisheng Diao, Yu Zhang, Nader Samaan, Yong Yuan, Huifen Zhou Pacific Northwest National Laboratory Richland, WA, USA {alan.berscheid, yuri.makarov, zhangshuan.hou, ruisheng.diao, yu.zhang, nader.samaan, yong.yuan, huifen.zhou}@pnnl.gov Abstract— The behavior of modern power systems is becoming more stochastic and dynamic, due to the increased penetration of variable generation, demand response, new power market structure, extreme weather conditions, contingencies, and unexpected events. It is critically important to predict potential system operational issues so that grid planners and operators can take preventive actions to mitigate the impact, e.g., lack of operational reserves. In this paper, an innovative software tool is presented to assist power grid operators in a balancing authority in predicting the grid stress level over the next operating day. It periodically collects necessary information from public domain such as weather forecasts, electricity demand, and automatically estimates the stress levels on a daily basis. Advanced Neural Network and regression tree algorithms are developed as the prediction engines to achieve this goal. The tool has been tested on a few key balancing authorities and successfully predicted the growing system peak load and increased stress levels under extreme heat waves. Keywords— automated tool, balancing authority, energy forecast, grid stress metric, neural network, open-source

I.

INTRODUCTION

electric power grid is exposed to various A modern external and internal risk factors that contribute to

stressed system conditions, e.g., extreme weather conditions (heat waves, ice storm, hurricane), extreme loads, generation inadequacies, contingencies, scheduled equipment outages, etc. Elevated stress levels typically imply higher risks of having system unreliability issues such as load loss, forced component outages, and partial or complete blackouts. Financial impacts of power supply interruption can be catastrophic. Even short blackouts can cause an annual estimated economic loss of between $104 and $164 billion across the United States [1]. In most parts of the country, peak energy demand and the lowest electricity reserve margins occur during summer heat wave events, which simultaneously increase demand for air conditioning, and reduce the supply of power by derating both natural gas-fired combustion turbines and combined cycle power plants. The heat also impacts performance of transmission lines, transformers, circuit breakers, and insulation to overheating conditions. Transformers may become unable to cool down sufficiently during night time. It negatively impacts energy transmission by inducing line sag [2]. Extreme heat events can also lead to high water temperatures, which further reduce the efficiency of thermal power plants and can even lead to power plants

-----------------------------------------------As part of its capability development, the U.S. Department of Homeland Security funded enhancement to perform contingency analysis for power grid. The project team thanks the National Protection and Programs Directorate, Office of Cyber and Infrastructure Analysis, for their continuing support of this work.

going offline due to environmental concerns. Thus, the power system components may fail more frequently and severely [2]. The likelihood of blackouts and cascading outages is heavily influenced by extreme weather [3],[5]. For example, a prolonged August heat wave in Texas produced two periods of very high wholesale prices in the Electric Reliability Council of Texas (ERCOT). Dayahead, on-peak wholesale power prices for August 2011 rose far above the range of prices seen during the previous five Augusts. On June 20th - 21st of 2017, CAISO issued a statewide Flex Alert calling for electricity conservation due to a heat wave. The same heat wave resulted in power outage for more than 40,000 PG&E customers. Forecasting demand is of critical importance to the secure and economic operation of bulk power grid. Over-forecasting electricity demand typically leads to unnecessary capital investment in building power plants and transmission lines or purchasing highly priced energy or reserves in real time. In contrast, severely under-forecasting demand can lead to emergency situations or even blackouts, if operators could not obtain sufficient generation capacity in a short time frame. The current practice to investigate the reliability impacts of extreme weather usually requires developing and running very complicated and time consuming power system models, such as production cost simulators [4], cascading events models [5], probabilistic composite reliability assessment, and power market simulators [7]. Results of such simulations can be hard to interpret. A significant difficulty is related to confidentiality of the system models and data availability. As a result, the existing analyses are limited to the “known” parts of the real power systems, where the results cannot be openly shared or are too generic, which do not have much in common with the real models. Power system operators make preparations for expected heat waves by addressing generator adequacy, reserves, contingency plans, and other important factors. But they are experiencing difficulties with accessing of the level of threat in their parts of the system or in the entire interconnection. To bridge this gap, a new, open-source software package was developed that can automatically download historical weather data, weather forecast, energy demand and other necessary information for automatically predicting system electricity demand and related grid stress levels for the major U.S. Balancing Authorities (BAs) at

hourly resolution for up to several days ahead, based on the developed Grid Stress Metric (GSM) and weather forecasts. To achieve this goal, a few innovative algorithms and tools were developed, including (1) an automatic data harvesting tool based on the high performance database, HIVE, that collects weather information and electricity demand information on a daily basis; (2) new metrics to quantify power grid stress levels at balancing authorities; (3) machine learning based prediction engines, and (4) sensitivity-based algorithms to quantify stress prediction. The developed grid stress prediction tool can collect and archive historical data, including observations as well as post-processed and extracted features. It creates an open framework that can quantify the grid stress levels based on combination of multiple sources of information in the public domain with advanced heuristic solutions.

and red GSM. This helps to simplify the methodology, avoid using complicated measures like expected unserved energy, and avoid using vendor-owned software and confidential datasets. Two main factors that can cause system failures are considered: air temperature and system load. The categories are determined by using experimental probability distributions of these parameters based on historical information collected for a BA and broken into percentiles as shown in Fig. 2: • Never Observed: air temperature and/or system load never observed before. • Extreme: events outside the 99% percentile • Alert: Events outside the 95% percentile.

The remainder of this paper is as follows: Section II provides the architecture of the grid stress prediction tool and the design of the key function modules; Section III shows the tool capabilities with more details; Section IV provides the details of software package development; Section V presents a case study using Arizona Public Service datasets is demonstrated; and Section VI outlines conclusions and identifies potential future work. II. DESIGN OF THE GRID STRESS PREDICTION TOOL A. Architecture Design The architecture of the grid stress tool is depicted in Fig.1, highlighting a few key function modules. In Stage 1, the tool collects public information regarding system load, load forecast, historical weather, and weather forecast data in Stage 1; while in Stage 2, the information collected can be extended to power grid component outage, load loss, frequency data, which are typically private information. In the model training module, metrics are defined as prediction objectives while training neural networks and/or regression tree based models for prediction during the next 24 hours or longer. The tool is launched automatically on a daily basis and the grid stress levels will be displayed for the major balancing authorities in the U.S.

Fig. 2. An example of stress categories for BA load.

The following color convention and confidence levels are used, which is also shown in Fig. 3. Code Red (high stress) • Never observed load or weather information – (High confidence) • Two extremes (High confidence) • One extreme (Moderate confidence) • Two alerts (Low confidence). Code Yellow – elevated stress (alert) • Any alert level reached (Moderate confidence) Code Green – low stress • All is within the normal range (Moderate)

Model Training Stage 1: 1. Actual load data 2. Load forecast data 3. Actual weather information 4. Predicted weather information 5. Wind/solar/hydro actuals and forecasts (optional)

Prediction functions: ƒ Electricity demand ƒ Grid stress level ƒ Loss of load probability (Stage 2)

Temperature Performance measure No

Converged ? Yes Display

95%

Store information

Stage 2: 1. Component outage information 2. Load loss information 3. Frequency data

Review mode

100% Calculated GSM for the past (Past experience series)

Fig.1. Architecture of the grid stress prediction tool.

B. Grid Stress Metric (GSM) The GSM is calculated based on the historical distributions of forecasted system parameters and their combinations. We selected a heuristic approach to quantifying the grid stress using color codes: green, yellow,

Load

Fig. 3. Percentiles and color convention.

C. Available Datasets in Public Domain In order to improve prediction accuracy and comprehensively test the developed methodologies for quantifying grid stress, it is necessary to collect sufficient historical data that covers a wide region and sufficiently long time window. The required data to perform the desired

studies include demand, demand forecast [8], and the corresponding weather information over a time period [9][10]. The decision variables (features) are obtained and extracted from the data sources, including Load (MW), Temperature (deg F), Dewpoint Temperature (deg F), Heat Index (deg F), Surface Wind, Sky Cover (%), Precipitation Potential (%), and Relative Humidity (%). In addition to the weather attributes, the power grid load/demand is also dependent on factors such as hour of the day, day of the week, working day/weekend day, previous week same hour load, previous day same hour load, and previous 24 hour average load. Meanwhile, a site-specific time delay of power grid demand response to temperature variation is considered, which can be seen visually or by crosscorrelation analysis. III. TOOL DEVELOPMENT – CORE ALGORITHMS AND CODE MANAGEMENT There are three core algorithms in the software package: (1) Data harvesting and analysis -- The data harvesting tool involves the PNNL cloudera Hadoop 5.9 HIVE database system. The tool enables automatic downloading of power demand/load and weather data once every morning, and provides a web interface for the users to access and visualize the downloaded data files. (2) Historical data processing -- The historical data processing and analysis includes algorithms for data cleaning (e.g., time stamp correction, outlier removal), historical weather forecast error analysis, cross-correlation and lag analysis, and so on. These analyses provide necessary information for training and validating the prediction model tool, and guidance on which and how predictors are to be included in the final model. (3) Timer-launched grid stress prediction using deep learning (i.e., neural network modeling). Accurate forecasts are critical for real-time operations and short-term planning for power utilities and independent system operators. The grid stress prediction tool has multiple algorithms, such as artificial neural network (ANN), regression tree (RT), and multivariate regression splines (MARS) [11-13]. The ANN model is used for demonstration in this paper, which is based on a collection of connected units called artificial neurons, (analogous to axons in a biological brain). Typically, neurons are organized in various layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first (input), to the last (output) layer, possibly after traversing the layers multiple times. The ANN code is written in MATLAB, by taking the advantage of the available ANN library packages. The input nodes here can include any information that are available prior to the prediction time period, including weather forecast of temperature and dew point temperature, hour of the day, day of the week, working day/weekend day, previous week same hour load, previous day same hour load, previous 24 hour average load, and so on. The prediction model needs to be validated and finalized before running in an operational mode. Validation is conducted by dividing the historical data into the training and testing subsets. The day-ahead forecast of demand is compared to the actuals and the forecast errors can be analyzed using various statistical measures and plots. Here we use plots of the observed and forecasted time series, the

forecast errors, as well as histograms/boxplots of the errors with respect to hour of day, day of week, weekend vs weekday, summer vs all season, etc. The details will be explained in the case study session below. IV. TOOL DEMONSTRATION AND CASE STUDY A. Study Area Dataset Weather attributes and demand data were downloaded by the data harvesting tool for the recent two years 20152017. Our focus areas are those located in the southern United States, which are more frequently subject to heat wave impacts. The corresponding BAs include APS (Arizona Power Service), TEP (Tucson Electric Power), SRP (Salt River Project), ERCOT (Electric Reliability Council of Texas), and Florida Power (FPC/FPL). The weather stations are then identified near the major cities within each BA. The stations IDs are used by the data harvesting tool to download the corresponding data (including historical weather observations and historical forecasts). Weather and power demand data, downloaded from the National Oceanic and Atmospheric Administration (NOAA) and the Energy Information Administration (EIA) websites, need to be preprocessed to remove outliers, correct the time stamps to exact hours, and make adjustment to the corrected time zones (e.g., by using the eastern daylight saving time EDT as the standard). B. Statistical Summaries This study investigates the influences of the weather conditions such as temperature, dew point, humidity, and wind speed on electricity demand by using two-year hourly data, which involves univariate variable summary statistical analysis, correlation coefficient test, and visualization plots. Weather condition changes affects electricity demand, especially during the high temperature days. Time lag phenomenon has also been identified, for instance, the demand response has a delay to temperature variation. As an example, Fig.4 shows the weather/demand analysis at APS and a weather station (KPHX) in Phoenix, AZ. The distribution of each variable, the Pearson correlation coefficient result and the feature plots of two variables are provided. The histogram of temperature shows that it is a little bimodal although its distribution is symmetric. The histogram of dew point is a little skewed to the left but close to normal. The histogram of wind speed, humidity, and electricity demand of APS are left skewed. There is a strong positive relationship between electricity demand and temperature with a correlation of 0.74. Dew point has a medium linear correlation relationship with electricity demand with a correlation coefficient value of 0.40. Wind speed also has a linear but weak relationship with electricity demand, and the correlation is 0.22. The humidity has a negative relationship with electricity demand and the correlation is -0.29. Humidity is crossdependent on temperature and dew point, with a strong negative correlation with temperature and strong positive correlation with dew point. Wind speed only has a weak correlation with temperature. Given the analysis, the primary factors that should be included in the predictive model are the temperature and dew point temperature. The scatter plot shows that when the temperature is above 80oF, there is a strong positive linear relationship between temperature and power demand, and negative when the temperature is below 60oF. Between 60oF and

80oF, the relationship is weak. This suggests a strong nonlinear relationship that is season and hour specific, explaining why hour of day, day of week factors are added to the list of predictors. These findings are further illustrated in Fig. 5, where APS demand and temperature are grouped to the same cluster.

hours was observed. The lag can be automatically evaluated and integrated in the predictive model in the framework.

Fig. 6. Residual Normal QQ Plot of KPHX’s high temperature model.

Fig. 4. Correlation coefficient value and histogram and feature plot of electricity demand and weather attributes.

In general, based on Pearson correlation coefficient results, temperature and dew point temperature have high positive relevance to electricity demand, and there is a general time delay of electricity demand to respond to changes in temperature. Such relationships are particularly strong when the temperature is relatively high. Additional analyses show that demand in weekends is generally lower than weekday demand, but the relationships of temperature vs demand is even stronger during weekends. C. Neural Network Model Prediction ANN model was trained and used for day-ahead prediction using the two-year hourly weather attributes and demand data. As a demonstration, the ANN-predicted demand and actuals for a testing period 6/17/201706/30/2017 are shown in Fig.7. The period was chosen particularly because there were several significant heat wave events occurred and reported. The training period is the time before any day-ahead prediction point.

Fig. 5. Heatmap of correlation coefficients among weather attributes and power demand for APS/KPHX.

A multivariate linear model is chosen to evaluate the relative contributions of the weather attributes to electricity demand, as follows: Electricity demand=β0+β1*Humidity+ β2*Temperature + β3*Dew Point+ β4*Wind Speed+ εi,. The R2 is 0.61, which means 61% of the variation in electricity demand of APS can be explained by the variation in weather conditions such as temperature, dew point, wind speed, and humidity. A high temperature model is then adopted, where only the obs. with high temperature are selected and 80oF of temperature is considered as a threshold. As mentioned earlier, time lag phenomenon exists in electricity demand and weather conditions model. 1-, 2-, 3-, and 4 hour time lags are evaluated, with a 1-hour lag, the model’s R2 is 0.88, which means 88% electricity demand can be explained by weather conditions. Residual normal quantile-quantile (QQ) plot shows that almost all spots are on the straight line and only several spots are apart from it - see Fig.6. At other BAs, a different lag of 2-3

The comparison between actual load and the forecasted load in APS using the ANN model is given in the upper panel in Fig.7. It clearly shows the good performance of the demand forecast engine, given the small deviations between the two time series. The goodness of forecast can be measured by mean absolute error (MAE) and mean absolute percent error (MAPE). The MAPE is 1% for the testing period, with a symmetric distribution (see Fig.8). With a breakdown to hour of the day (see Fig.9), it seems that the predictions have smaller errors and uncertainty ranges in the early morning (before sunrise) in APS. In the late afternoon, the errors are slightly higher, still a satisfactory performance for day-ahead predictions. D. Model Validation with Weather Forecast Errors The comparison results in the previous section show satisfactory prediction capability using the ANN approach, but in order to use the model for future (e.g., 24-hour ahead) predictions, one should be careful because the weather forecast itself has errors, which will propagate to electricity demand forecast errors. In order to evaluate the impact of such forecast errors and to further validate this model, we extracted and analyzed the historical weather forecast errors. The average historical weather forecast errors were about 2~3oF, roughly 3% for the study period. The testing results show that with 3% noises added to the historical weather data, the MAPE stays the same as 1% at

APS, which is very satisfactory. It is worth to mention that the historical weather forecast errors were not additive in propagation to electricity demand forecast errors.

E. Grid Stress Quantification The predicted electricity demand can be converted to grid stress metrics (GSMs) as explained in the methodology section. For example, Fig. 10 shows the color coded grid stress with respect to hour of the day, on 6/21/2017 with time of the day in EDT. The GSMs can be calculated based on the historical distributions of forecasted system parameters (i.e., electricity demand) and their combinations (e.g., temperature). V. CONCLUSIONS AND FUTURE WORK

Fig.7. Observed APS demand and ANN day-ahead predictions during the period 6/17/2017-06/30/2017. The percentiles are for the 2-year period.

A new tool to evaluate the level of power system failure risk due to abnormal load and weather extremes (e.g., air temperature) has been developed and implemented. The tool is based on statistical analyses of historical data on and a heuristic procedure used to evaluate a qualitative level of risk. The key advantage of our approach is based on simplicity of its use, reliance on publicly available information, and independence from vendor-based software tools. The future work will include incorporation of additional factors in the analysis such as thunderstorms, extreme winds, and brush fires. Ideally, the authors plan to verify the stress color convention against the actual system failure information and release the tool to public. References [1] [2]

[3]

Fig.8. ANN demand forecast error distributions at APS. [4] [5]

[6]

[7]

Fig. 9. ANN forecast errors with respect to hour of the day at APS.

[8]

[9]

[10] [11] [12] [13]

Fig.10. GSMs with respect to hour of the day on June 21, 2017 at APS.

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