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Research on Seasonal Power Balance Optimization of Power System Based on Intelligent Interactive Technology
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Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), the qualitative and quantitative comparisons have been conferred among these scientific rationales. The experimental results reveal that the ANN–LM with a single hidden layer performs relatively better in terms of performance indices compared to OE, ARX, ARMAX, SVM, ANN–PSO, KNN, ANN–LM with two hidden layers and bootstrap aggregation models.
For the utility to plan the resources accurately and balance the electricity supply and demand, accurate and timely forecasting is required. The proliferation of smart meters in the grids has resulted in an explosion of energy datasets. Processing such data is challenging and usually takes a longer time than the requirement of a short-term load forecast. The paper addresses this concern by utilizing parallel computing capabilities to minimize the execution time while maintaining highly accurate load forecasting models. In this paper, a thousand smart meter energy datasets are analyzed to perform day ahead, hourly short-term load forecast (STLF). The paper utilizes multi-processing to enhance the overall execution time of the forecasting models by submitting simultaneous jobs to all the processors available. The paper demonstrates the efficacy of the proposed approach through the choice of machine learning (ML) models, execution time, and scalability. The proposed approach is validated on real energy consumption data collected at distribution transformers’ level in Spanish Electrical Grid. Decision trees have outperformed the other models accomplishing a tradeoff between model accuracy and execution time. The methodology takes only 4 minutes to train 1,000 transformers for an hourly day-ahead forecast of (~24 million records) utilizing 32 processors.
As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential.
Short-term load forecasting of power systems is an important part of the daily dispatch of the power sector. The accuracy of short-term load forecasting directly affects the safety, reliability and economy of power system operation. Therefore, the research on short-term load forecasting methods has always been the focus of scholars at home and abroad. In recent years, artificial neural networks have been widely studied as an intelligent algorithm and applied to the field of short-term power load forecasting. The network structure and learning law of artificial neural network are introduced. The mathematical model of short-term load forecasting based on BP neural network and Elman neural network is established respectively. Based on the basic structure of the BP neural network, a subsequent layer is added to store the internal state, so that the system can adapt to the time-varying characteristics. In the neural network modeling process, the model is optimized from the aspects of network algorithm, excitation function and network structure to improve the convergence speed and prediction accuracy of the network. In the simulation process, the above two mathematical models use the same data samples for short-term power load forecasting. In the modeling process, in order to ensure the stability of the system, a small learning rate is set, and the selection range is between 0.01-0.8. During the modeling process, the error between different learning rates is compared. Finally, the learning rate of 0.43 is selected. By comparing the prediction error with the prediction accuracy, the prediction effect of the Elman neural network model is better than that of the BP neural network model.
For the future development of an integrated energy system (IES) with ultra-high penetration of renewable energy, a planning model for an electricity-hydrogen integrated energy system (EH-IES) is proposed with the considerations of hydrogen production and storage technologies. In this EH-IES, a reasonable power to heat and hydrogen (P2HH) model with startup/shutdown constraints and a novel model of seasonal hydrogen storage (SHS) are proposed for the first time. To cope with the challenges faced by EH-IES, we use a combination of stochastic and robust optimization approaches to address the generation-load uncertainties, making the planning results robust. The N-1 contingency of crucial devices is further considered to make the system reliable. Additionally, the effects of the annual ratios of different seasons on the optimal allocation and operation of SHS are investigated. The min-max-min model with binary variables is solved by a nested column-and-constraint generation algorithm. Finally, case studies verified the advantages and effectiveness of the proposed model and planning method.
Ocean wave energy is a developing industry that provides many attractive qualities for utilities to meet future energy demand. Therefore, it is important to investigate and understand the impact that ocean wave energy has on power system reliability. Reliability assessment techniques have been applied to systems with variable amounts of wind penetration, but as of yet, no study has explored the impacts that ocean wave energy may have on the reliability of power grids. The authors’ approach to this problem applies a sequential Monte Carlo technique coupled with a Well‐Being analysis approach to capture the seasonal variations associated with the ocean and then calculate key loss‐of‐load indices. The authors benchmark this method on the IEEE Reliability Test System 1996 and integrate synthesised ocean wave energy farms throughout the system. Their results suggest that wave energy results in a small decrease on the reliability of the power system, however, this change does not result in additional failure states as compared to the base system. Additionally, they are able to mitigate marginal system states with a relatively small increase in system capacity.
With the rapid development and wide application of distributed generation technology and new energy trading methods, the integrated energy system has developed rapidly in Europe in recent years and has become the focus of new strategic competition and cooperation among countries. As a key technology and decision-making approach for operation, optimization, and control of integrated energy systems, power consumption prediction faces new challenges. The user-side power demand and load characteristics change due to the influence of distributed energy. At the same time, in the open retail market of electricity sales, the forecast of electricity consumption faces the power demand of small-scale users, which is more easily disturbed by random factors than by a traditional load forecast. Therefore, this study proposes a model based on X12 and Seasonal and Trend decomposition using Loess (STL) decomposition of monthly electricity consumption forecasting methods. The first use of the STL model according to the properties of electricity each month is its power consumption time series decomposition individuation. It influences the factorization of monthly electricity consumption into season, trend, and random components. Then, the change in the characteristics of the three components over time is considered. Finally, the appropriate model is selected to predict the components in the reconfiguration of the monthly electricity consumption forecast. A forecasting program is developed based on R language and MATLAB, and a case study is conducted on the power consumption data of a university campus containing distributed energy. Results show that the proposed method is reasonable and effective.
Hydroelectric power generation, water supplies for municipal, agricultural, manufacturing, and service industry uses including technology-sector requirements, dam safety, flood control, recreational uses, and ecological and legal constraints, all place simultaneous, competing demands on the heavily stressed water management infrastructure of the mostly arid American West. Optimally managing these resources depends on predicting water availability. We built a probabilistic nonlinear regression water supply forecast (WSF) technique for the US Department of Agriculture, which runs the largest stand-alone WSF system in the US West. Design criteria included improved accuracy over the existing system; uncertainty estimates that seamlessly handle complex (heteroscedastic, non-Gaussian) prediction errors; integration of physical hydrometeorological process knowledge and domain-specific expert experience; ability to accommodate nonlinearity, model selection uncertainty and equifinality, and predictor multicollinearity and high dimensionality; and relatively easy, low-cost implementation. Some methods satisfied some of these requirements but none met all, leading us to develop a novel, interdisciplinary, and pragmatic prediction metasystem through a carefully considered synthesis of well-established, off-the-shelf components and approaches, spanning supervised and unsupervised machine learning, nonparametric statistical modeling, ensemble learning, and evolutionary optimization, focusing on maintaining but radically updating the principal components regression framework widely used for WSF. Testing this integrated multi-method prediction engine demonstrated its value for river forecasting; USDA adoption is a landmark for transitioning machine learning from research into practice in this field. Its ability to handle all the foregoing design criteria and requirements, which are not unique to WSF, suggests potential for extension to complex probabilistic prediction problems in other fields.
The optimal operation planning (OOP) of electrical distribution systems (EDS) is very sensible to the quality of the short-term load forecasts. Assuming aggregated demands in EDS as univariate non-stationary seasonal time series, and based on historical measurements gathered by smart meters, this paper presents a parsimonious short-term load forecasting method to estimate the expected outcomes of future demands, and the standard deviations of forecast errors. The chosen short-term load forecasting method is an adaptation of the multiplicative autoregressive integrated moving average (ARIMA) models. Seasonal ARIMA models are parsimonious forecasting techniques because they require very few parameters and low computational resources to provide an adequate representation of stochastic time series. Two approaches are used in this paper to estimate the parameters that constitute the proposed multiplicative ARIMA model: a frequentist and a Bayesian approach. Advantages and disadvantages of both methods are compared by simulating a centralized self-healing scheme of a real EDS that uses the forecasts to deploy a robust restoration plan. Results shown that the proposed seasonal ARIMA model is a fast, precise, straightforward and adaptable load forecasting method, suitable for OOP of highly supervised EDS.
Short-term load forecasting (STLF) is very important for an efficient operation of the power system because the exact and stable load forecasting brings good results to the power system. This manuscript presents the application of two new models in STLF i.e. Cross multi-models and second decision mechanism and Residential load forecasting in smart grid using deep neural network models. In the cross multi-model and second decision mechanism method, the horizontal and longitudinal load characteristics are useful for the construction of the model with the calculation of the total load. The dataset for this model is considered from Maine in New England, Singapore, and New South Wales of Australia. While, In the residential load forecasting method, the Spatio-temporal correlation technique is used for the construction of the iterative ResBlock and deep neural network which helps to give the characteristics of residential load with the use of a publicly available Redd dataset. The performances of the proposed models are calculated by the Root Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error. From the simulation results, it is concluded that the performance of cross multi-model and second decision mechanism is good as compare to the residential load forecasting.
Medium-term load forecasting based on cointegration-Granger causality test and seasonal decomposition
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