Independent System Operator New England Research Articles

Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution.

Precise short-term load forecasting (STLF) plays a crucial role in the smooth operation of power systems, future capacity planning, unit commitment, and demand response. However, due to its non-stationary and its dependency on multiple cyclic and non-cyclic calendric features and non-linear highly correlated metrological features, an accurate load forecasting with already existing techniques is challenging. To overcome this challenge, a novel hybrid technique based on long short-term memory (LSTM) and a modified split-convolution (SC) neural network (LSTM-SC) is proposed for single-step and multi-step STLF. The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features. The model is evaluated by the Pakistan National Grid load dataset recorded by the National Transmission and Dispatch Company (NTDC). The load data is pre-processed and multiple other correlated features are incorporated into the data for performance enhancement. For generalization capability, the performance of LSTM-SC is evaluated on publicly available datasets of American Electric Power (AEP) and Independent System Operator New England (ISO-NE). The effect of temperature, a highly correlated input feature, on load forecasting is investigated either by removing the temperature or adding a Gaussian random noise into it. The performance evaluation in terms of RMSE, MAE, and MAPE of the proposed model on the NTDC dataset are 500.98, 372.62, and 3.72% for multi-step while 322.90, 244.22, and 2.38% for single-step load forecasting. The result shows that the proposed method has less forecasting error, strong generalization capability, and satisfactory performance on multi-horizon.

Hyperparameter optimization of support vector machine using adaptive differential evolution for electricity load forecasting

Peak load forecasting plays an integral part in the planning and operating of energy plants for the utility companies and policymakers to devise reliable and stable power infrastructure. However, the electricity load profile is considered a complex signal due to the non-linear and stochastic behavior of the consumer. Therefore, a rigid forecasting model with assertive stochastic and non-linear behavior capturing abilities is required to estimate the demand capacity accurately. To handle these uncertainties, this paper proposed a hybrid model that integrates the multivariate empirical modal decomposition (MEMD) and adaptive differential evolution (ADE) algorithm with a support vector machine (SVM). MEMD allows the decomposition of multivariate data to deteriorate over time to effectively extract the unique information from exogenous variables over different time frequencies to ensure high computational efficiency. The ADE algorithm obtains and tunes the SVM model’s appropriate parameters to effectively avoid trapping into local optimum and returns accurate forecasting results. Consequently, the proposed MEMD-ADE-SVM forecasting model simultaneously achieves good accuracy (93.145%), stability, and convergence rate, respectively. A historical load dataset from the independent system operator (ISO) New England (ISO-NE) energy sector is analyzed to verify the MEMD-ADE-SVM hybrid model. The results show that the developed MEMD-ADE-SVM model outperforms the benchmark frameworks such as; SVR-based model by hybridizing variational mode decomposition, the chaotic mapping mechanism, and the grey wolf optimizer (VMD-SVR-CGWO), SVM based on data preprocessing and whale optimization algorithm (DCP-SVM-WO), intelligent optimized SVR model based on variational mode decomposition and Fast Fourier transform (VMD-FFT-IOSVR), SVR model based on multivariate empirical mode decomposition and particle swarm optimization (EMD-SVR-PSO), and MEMD-ADE-LSTM for day-ahead and week-ahead electricity peak load forecasting in terms of accuracy, stability, and convergence rate.

Energy-Net: A Deep Learning Approach for Smart Energy Management in IoT-Based Smart Cities

Although intelligent load forecasting is essential for optimal energy management (EM) in smart cities, there is a lack of current research exploring EM in well-regulated Internet-of-Things (IoT) networks. This article develops a new deep learning (DL) model for efficient forecasting of short-term energy consumption while maintaining effective communication between energy providers and users. The proposed Energy-Net stack comprises multiple stacked spatiotemporal modules, where each module consists of a temporal transformer (TT) submodule and a spatial transformer (ST) submodule. The TT models the temporal relationships in load data; and the ST submodule extracts hidden spatial information by integrating convolutional layers and includes an improved self-attention mechanism. The experimental evaluation on IHPEC and independent system operator New England (ISO-NE) data set demonstrates the superiority of Energy-Net over recent cutting-edge DL models with root mean-square error (RMSE) of 0.354 and 0.535, respectively. The computational complexity of Energy-Net is appropriate for dependable resource-constrained IoT devices (i.e., fog nodes or edge nodes) linked to a joint IoT-cloud server that interacts with connected smart grids to handle EM tasks.

Shareholders v. ratepayers in New England

This paper reviews claims by the Independent System Operator-New England and regional transmission owners that billions of dollars invested in transmission infrastructure over the last two decades are justified by more competitive markets, higher reliability, and lower emissions. Absent evidence for these claims, the paper concludes that the Federal Energy Regulatory Commission has instead opted to transfer economic rent from ratepayers to shareholders of transmission companies.

A Hybrid Approach for Energy Consumption Forecasting With a New Feature Engineering and Optimization Framework in Smart Grid

Electric energy consumption forecasting enables distribution system operators to perform efficient energy management by flexibly engaging energy consumers under the intelligent demand-response program in the smart grid (SG). With this motivation, in this paper, a fast and accurate hybrid electrical energy forecasting (FA-HELF) framework is developed. The proposed framework integrates two modules with support vector machine (SVM) based forecaster. These modules are data pre-processing and feature engineering, and modified enhanced differential evolution (mEDE) based optimizer. First, feature selection algorithms like random forests and relief-F are combined to devise a hybrid feature selection algorithm to alleviate redundancy. Secondly, for feature extraction, a radial basis Kernel-based principal component analysis algorithm is employed to eliminate the dimensionality reduction problem. Finally, to conduct accurate and fast electrical energy consumption forecasting, the mEDE based optimizer is integrated with the SVM based forecaster. The resulting FA-HELF framework is tested on publicly available independent system operator New England (ISO-NE) control area hourly load data. The results demonstrate that the FA-HELF framework is robust and shows significant improvements when compared to other benchmark frameworks in terms of accuracy and convergence speed.

Optimal Decomposition and Reconstruction of Discrete Wavelet Transformation for Short-Term Load Forecasting

To achieve high accuracy in prediction, a load forecasting algorithm must model various consumer behaviors in response to weather conditions or special events. Different triggers will have various effects on different customers and lead to difficulties in constructing an adequate prediction model due to non-stationary and uncertain characteristics in load variations. This paper proposes an open-ended model of short-term load forecasting (STLF) which has general prediction ability to capture the non-linear relationship between the load demand and the exogenous inputs. The prediction method uses the whale optimization algorithm, discrete wavelet transform, and multiple linear regression model (WOA-DWT-MLR model) to predict both system load and aggregated load of power consumers. WOA is used to optimize the best combination of detail and approximation signals from DWT to construct an optimal MLR model. The proposed model is validated with both the system-side data set and the end-user data set for Independent System Operator-New England (ISO-NE) and smart meter load data, respectively, based on Mean Absolute Percentage Error (MAPE) criterion. The results demonstrate that the proposed method achieves lower prediction error than existing methods and can have consistent prediction of non-stationary load conditions that exist in both test systems. The proposed method is, thus, beneficial to use in the energy management system.

Research on the joint fluctuation laws between locational marginal price and renewables based on complex networks: A case study in Independent System Operator New England

AbstractThe growing share of renewable energy has an increasingly significant impact on the electricity market. Locational marginal price (LMP), as a sensitive price signal, has a potential but considerable correlation with renewable energy generation. In this context, this paper focuses on the joint fluctuation laws between LMP and renewables consumed to generate electricity. At first, LMP and renewables consumption data from Independent System Operator New England (ISO‐NE) are collected and preprocessed. Then, through highly comparative time‐series analysis (hctsa), it is found that discrete symbolization features have effective description ability. Therefore, a coarse‐grained method is conducted to convert the real matrix data into a symbol vector based on numerical distribution analysis. Next, joint fluctuation complex networks are constructed according to the symbol vector. From a global perspective, the joint fluctuation network follows an exponential distribution and exhibits hierarchical modularity. From a local perspective, the joint fluctuation network follows a power‐law distribution. The research results of this paper provide a new perspective to understand the evolution of the electricity market with large‐scale renewable energy and have reference value for identifying the valuable joint fluctuation patterns and improving the LMP forecast results.

Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System

A novel method for short-term load forecasting (STLF) is proposed in this paper. The method utilizes both long and short data sequences which are fed to a wavenet based model that employs dilated causal residual convolutional neural network (CNN) and long short-term memory (LSTM) layer respectively to hourly forecast future load demand. This model is aimed to support the demand response program in hybrid energy systems, especially systems using renewable and fossil sources. In order to prove the generality of our model, two different datasets are used which are the ENTSO-E (European Network of Transmission System Operators for Electricity) dataset and ISO-NE (Independent System Operator New England) dataset. Moreover, two different ways of model testing are conducted. The first is testing with the dataset having identical distribution with validation data, while the second is testing with data having unknown distribution. The result shows that our proposed model outperforms other deep learning-based model in terms of root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In detail, our model achieves RMSE, MAE, and MAPE equal to 203.23, 142.23, and 2.02 for the ENTSO-E testing dataset 1 and 292.07, 196.95 and 3.1 for ENTSO-E dataset 2. Meanwhile, in the ISO-NE dataset, the RMSE, MAE, and MAPE equal to 85.12, 58.96, and 0.4 for ISO-NE testing dataset 1 and 85.31, 62.23, and 0.46 for ISO-NE dataset 2.

The value of day-ahead solar power forecasting improvement

The value of day-ahead solar power forecasting improvements was analyzed by simulating the operation of the Independent System Operator – New England (ISO-NE) power system under a range of scenarios with varying solar power penetrations and solar power forecasting improvements. The results showed how the integration of solar power decreased operational electricity generation costs, by decreasing fuel and variable operation and maintenance costs, while decreasing start and shutdown costs of fossil fueled conventional generators. Solar power forecasting improvements changed the impacts that the uncertainty of solar power has on bulk power system operations; electricity generation from the fast start and lower efficiency power plants, ramping of all generators, start and shutdown costs, and solar power curtailment were all reduced. These impacts led to a reduction in overall operational electricity generation costs in the system that translates into an annual economic value for improving solar power forecasting.

Application of Gaussian Process to Locational Marginal Pricing Forecasting

In this paper, a new method is proposed for Locational Marginal Pricing (LMP) forecasting in Smart Grid. The marginal cost is required to supply electricity to incremental loads in case where a certain node increases power demands in a balanced power system. LMP plays an important role to maintain economic efficiency in power markets in a way that electricity flows from a low-cost area to high-cost one and the transmission network congestion is alleviated. The power market players are interested in maximizing the profits and minimizing the risks through selling and buying electricity. As a result, it is of importance to obtain accurate information on electricity pricing forecasting in advance so that their desire is reflected. This paper presents the Gaussian Process (GP) technique that comes from the extension of Support Vector Machine (SVM) in a way that hierarchical Bayesian estimation is introduced to express the model parameter as the stochastic variables. The advantage is that the model accuracy of GP is better than others. The proposed method is successfully applied to real data of ISONE(Independent System Operator New England) in USA.