121 results about "Probabilistic forecasting" patented technology

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention relates to the field of power system monitoring and fault prediction, in particular to a lightning arrester fault type prediction method and system based on an extremely cold environment working condition. Probabilistic prediction of future key indexes of the lightning arrester is realized, and physical state data is generated through real-time simulation of a digital twin platform; through weighted least square method fusion correction and fuzzy logic, an integrated decision model constructed by a dynamic Bayesian network, system output fault mode classification and early warning decision, the effects of early warning of fault risks in advance in an extremely cold environment and improvement of the safety of a power system are achieved.

The invention discloses a risk scheduling method for a water-wind-solar complementary system, and the method comprises the steps: collecting historical data and power grid topological parameters, accessing global and regional numerical weather forecast data, employing a coupling model, fusing meteorological grid data with a historical power station output sequence, and generating hourly reservoir incoming water amount and wind-solar power probability prediction results. According to the predicted time sequence and the generated scene, calculating the scene probability based on the generated scene; according to the prediction time sequence, using a quantification method to obtain a peak regulation risk quantification value; calculating power grid power flow distribution and critical clearing time according to the generation scene and the power grid topological parameters, and calculating a system stability margin; a multi-target optimization model is constructed according to a peak regulation risk quantized value after splitting and a system stability margin, the system stability margin is introduced as an optimization target, the overall stability of the system is improved, a meteorological-hydrological-output three-mode feature mapping method is provided, and meteorological feature extraction of a key grid region is enhanced through an attention mechanism.

A training data set which includes event sequences representing actions of respective users of an application, properties of the users, and dynamic attributes associated with events such as the elapsed time between successive events, is prepared. A machine learning model which provides probabilistic predictions of next events of input event sequences is trained using the training data set. A trained version of the model is stored.

The invention discloses an equipment prediction maintenance framework based on probability residual life, and belongs to the technical field of fault prediction and health management (PHM). According to the framework, through combining a Bayesian neural network and a reinforcement learning technology, probability prediction and dynamic maintenance decision optimization of the residual life of equipment are realized. The method specifically comprises the following steps: acquiring sensor data in equipment operation, and preprocessing to generate a training data set; constructing a Bayesian neural network (BNN), utilizing variation reasoning to approximate posteriori distribution, and outputting probability distribution of residual life through Monte Carlo sampling; based on a probability prediction result, constructing a reinforcement learning environment model, and defining a state space containing residual life distribution, a spare part state and a maintenance action and a reward function; a Double DQN algorithm is adopted to optimize a maintenance strategy, and intelligent decision-making of the optimal maintenance time and the optimal ordering time of equipment is dynamically realized through an epsilon-greedy algorithm, so that the maintenance cost and the fault risk are minimized. According to the method, probability residual life distribution and reinforcement learning decision are innovatively combined, the problem that a traditional point estimation model ignores uncertainty is solved, an intelligent maintenance framework is constructed through a reinforcement learning method, and dynamic optimization of maintenance decision is achieved. The NASA aero-engine data set verification shows that compared with a traditional method, the optimization effects of prediction errors, uncertainty quantification, the maintenance cost rate and the like are remarkable, and the reliability and economical efficiency of equipment are effectively balanced.

The invention provides a privacy protection multi-energy load day-ahead probability prediction method based on a diffusion model, and belongs to the field of multi-energy load prediction of an integrated energy system. Comprising the following steps: S1, acquiring respective energy consumption load data by a local main body, and preprocessing and normalizing the data; s2, dividing the energy consumption data into historical loads and labels, and respectively training two groups of self-encoders-self-decoders; s3, the local main body uploads the latent variables obtained after coding to the cloud; s4, grouping and integrating latent variables by the diffusion model of the cloud, and constructing a joint probability prediction model; and S5, during model reasoning, after a latent variable generated by the cloud is downloaded locally, a final predicted value is obtained through decoding by a self-decoder. According to the method, a prediction framework based on longitudinal federated learning is designed for a multi-energy load probability prediction scene related to a cross-energy form in an integrated energy system, and a diffusion model capable of predicting multi-energy load joint probability distribution is constructed by considering a coupling relationship among multi-energy loads.

The invention discloses a power system probability load prediction method, system and device, and a storage medium, relates to the technical field of hydrogen energy ship power system load prediction, and aims to solve the technical problems that multi-source data and physical information rules are not integrated and multi-scale chaos in an HPV complex operation environment is not considered in the prior art. The method specifically comprises the steps of collecting and preprocessing multi-source data; screening strong correlation data by using the maximum information coefficient, and reconstructing multi-source data into a unified spatial-temporal characteristic matrix; constructing an electrical and environmental parameter chaos model based on a Lorentz dynamic equation as a physical constraint term of a loss function; a BERT-PINN framework with an attention mechanism is established, and multi-step probability prediction is realized through two-stage training. According to the method, the data driving model and the physical information rule of the multi-source data are effectively integrated, the feature fusion problem of the multi-scale chaotic features is solved, and the model prediction performance is improved.

The invention discloses a long-span bridge typhoon effect probability prediction method based on physical-data cooperative driving, and the method comprises the steps: obtaining typhoon and typhoon effect monitoring data recorded by a structure health monitoring system, and calculating typhoon characteristic parameters and bridge vibration response parameters; then, taking typhoon characteristic parameters as model input and bridge vibration response parameters as model output, constructing a sample set, and dividing the sample set into a training set and a test set; a deep integration strategy of physical-data cooperative driving is further adopted to adjust a neural network architecture, and a typhoon effect probability prediction model providing response mean value and variance dynamic estimation is constructed; then, based on the divided data set and the established probability prediction model, carrying out model training and response prediction; and finally, carrying out model interpretation analysis by adopting an improved Shapril additive interpretation method. According to the method, the typhoon effect prediction accuracy and uncertainty quantification performance of the long-span bridge can be effectively improved, and the robustness and interpretability of the model are synchronously enhanced.

The invention belongs to the technical field of electric power, and provides a user load increase and probability boundary prediction method, which comprises the following steps of: firstly, realizing unified projection and deep fusion of multi-source heterogeneous data by adopting a topological adaptive space-time fusion mechanism; secondly, physical constraint comparative learning and hypergraph clustering are utilized to generate a load-sensitive topology mode according with a power grid rule; a CPT-Mama model is constructed by injecting physical rules, meteorological codes and lightweight NTK features, and spatial correction is performed by means of a graph attention network to improve the precision of load increment prediction, so that efficient prediction of the multi-modal load increment is realized; and finally, innovatively introducing a net rack spectral domain built-in operator, reconstructing a residual error and quantifying uncertainty, and accurately describing a probability boundary of load increase. According to the method, the medium and long term load prediction precision is improved, the crossing from point prediction to probability prediction is realized, and reliable technical support is provided for power grid dispatching, risk prevention and control and planning decision.

Computer-implemented methods optimising routes in a transport network for a geographical region are disclosed. Methods comprise obtaining an analysis of the geographical region; generating probabilistic predictions, checking whether at least a subset of the generated probabilistic predictions have previously been solved by a trained machine learning model; based on at least a first subset of the generated probabilistic predictions having previously been solved, retrieving the trained machine learning model previously used to solve the at least a first subset of the generated probabilistic predictions and executing the trained machine learning model to determine a first plurality of suggested routes for the transport network; based on at least a second subset of the generated probabilistic predictions not having previously been solved, training a new machine learning model to solve the at least a second subset of generated probabilistic predictions to determine a second plurality of suggested routes for the transport network.

A probabilistic forecasting-based energy management method for photovoltaic energy storage systems uses a long short-term memory (LSTM) neural network to obtain photovoltaic probability forecasts. This method then samples multivariate distributions based on copula function models to generate forecast scenarios. Based on the forecast results, model predictive control is used to provide real-time optimization, aiming to maximize benefits and minimize costs while meeting load requirements and energy storage characteristics. This method quantifies forecast uncertainty, reduces the robustness optimization losses associated with traditional point forecasting, improves the safety and reliability of the energy storage system, and achieves efficient utilization of photovoltaic energy.

The invention discloses a photovoltaic power station frequency modulation capacity prediction method based on space-time correlation. The method comprises the following steps: firstly, screening main influence factors in numerical weather forecast data and photovoltaic power data by adopting a Pearson's correlation coefficient method; then constructing a space-time fusion diffusion model combining a training time weight perception mechanism and a multi-layer attention mechanism, improving prediction performance and training efficiency, and fully mining time sequence characteristics of photovoltaic power and spatial correlation of different environments; in the data generation stage, a back diffusion method of a denoising diffusion implicit model is adopted to generate a plurality of photovoltaic power curves of a day to be predicted; finally, probability intervals of photovoltaic power under different confidence coefficients are obtained through kernel density analysis, difference calculation is conducted on the probability intervals and a grid-connected limit value curve of a power grid, and probability prediction of the frequency modulation capacity is achieved. According to the method, the probability prediction precision and the operation efficiency can be considered, and a theoretical basis and a practical tool are provided for frequency modulation capacity declaration and consumption under a new energy power system.

The invention discloses a marine traffic flow probability prediction method based on multi-source AIS-meteorological data fusion, and belongs to the field of marine traffic safety management, and the method comprises the following steps: S1, obtaining a standardized multi-source data set; s2, fusing the dynamic statistical characteristics of the AIS ship and the meteorological environment characteristics through a ship motion transfer function, and constructing a space-time patch considering a short-term ship motion trend and a long-term sea condition trend; s3, based on the space-time patch, fusing patch Transform time sequence modeling and adaptive dynamic graph space correlation learning to obtain a space-time fusion feature; and S4, outputting the traffic flow state prediction value and the probability distribution of the traffic flow key indexes. The marine traffic flow probability prediction method based on multi-source AIS-meteorological data fusion breaks through the limitation that a traditional method is single in data dimension, insufficient in space-time correlation capture and lack of probability confidence, and high-precision probability prediction of the marine traffic flow under the complex sea condition is achieved.

The invention discloses an odor traceability management and control method based on multi-source data fusion and related equipment. The method comprises the following steps: acquiring weather forecast and monitoring data of a target area, preset emission intensity of a pollution source and real-time odor concentration; inputting the real-time monitoring data and the emission intensity into an odor complaint probability prediction model to obtain a theoretical risk value, and calculating a real-time deviation factor between the theoretical risk value and an actual risk value obtained based on the real-time concentration; obtaining a first complaint risk probability value of a future time period by using weather forecast data and emission intensity, and performing weighted correction on the first complaint risk probability value based on the real-time deviation factor to obtain a second complaint risk probability value; and when the second complaint risk probability value exceeds a risk threshold value, reversely calculating target emission intensity capable of enabling the risk to reach the standard according to the model and the real-time deviation factor, and generating a production working condition adjustment instruction according to the target emission intensity. According to the method, the problem that a static prediction model cannot accurately describe a complex and changeable environment, so that mistaken report and missing report of stink traceability management and control are frequent is solved.

The application discloses a flood forecasting method fusing a physical mechanism and a conditional denoising diffusion model, and comprises the following steps: obtaining multi-source hydrological data of a basin to be forecasted; driving a preset hydrological model to execute multi-stage sequential parameter optimization to obtain a basic forecasting sequence and an initial residual sequence; extracting multi-dimensional conditional variables based on the hydrological data and the initial residual sequence, and using a multi-dimensional evaluation criterion to perform dimension reduction screening to obtain a fusion conditional vector; inputting the fusion conditional vector as a guide condition into a pre-trained conditional denoising diffusion model, performing iterative denoising sampling through a multi-scale denoising operator to generate a forecasting residual probability sequence set; and coupling the basic forecasting sequence and the forecasting residual probability sequence set to obtain deterministic forecasting and probabilistic forecasting results of the basin to be forecasted. The application realizes a leap from single-point forecasting to probabilistic distribution deduction, can accurately obtain a flood deviation boundary under a changing hydrological environmental condition, and enhances the reliability of disaster prevention and mitigation decision-making.

To provide a congestion probability prediction device capable of facilitating power generation control of power generation equipment with non-firm type connection while especially considering the uncertainties.SOLUTION: A disclosed congestion probability prediction device for predicting the congestion of a power system includes: a weather forecast data input unit that acquires weather forecast data regarding the weather a predetermined number of days after the current date; a power system data input unit that acquires demand data regarding electricity demand a specified number of days after the current date and that acquires system data regarding the power system after the predetermined number of days after the current date; a system congestion probability prediction unit that predicts congestion points and congestion probability data of the power system the specified number of days after the current date.SELECTED DRAWING: Figure 1

The invention discloses a prediction method, and particularly relates to a ship weather risk and congestion probability prediction method based on multi-source data, which comprises a data preprocessing module, a data rebalancing module, a model training module and a weather and congestion risk prediction module, and is characterized in that the data preprocessing module is connected with the data rebalancing module; the data rebalancing module is connected with the model training module, and the model training module is connected with the weather and congestion risk prediction module. Compared with a traditional data processing method, the multi-source data processing method has the advantages that the multi-source data is comprehensively preprocessed, including data cleaning, normalization and denoising, and the quality and consistency of the data are effectively improved. Therefore, the model is more stable in performance under different weather and congestion conditions, so that the accuracy and reliability of risk prediction are improved.

The application discloses a photovoltaic ultra-short-term probability prediction method based on data decomposition and reconstruction, solves the problems of low prediction accuracy, weak generalization ability, poor prediction interval calibration, insufficient stability across data sets and low calculation efficiency of existing methods, and cannot meet the 5-minute ultra-short-term photovoltaic probability prediction engineering requirements. The method first collects and pre-processes photovoltaic power and meteorological data, uses the ACEEMDAN algorithm to decompose the photovoltaic power into fixed high-frequency and low-frequency dual components through four stages and regularizes; then the corresponding features of the three types of data are extracted through CNN, iTransformer and BiLSTM respectively, and the unified feature representation is obtained after the multi-head attention mechanism fusion, and then the EQN network is input; finally, the improved EQN loss function containing the width penalty term is used to train the model to convergence, and the probability prediction result is output. The method performs excellently in the deterministic and probability evaluation indexes, has strong stability across data sets, and can be directly applied to power grid dispatching and photovoltaic power generation system operation management.

A method and system for probabilistic solar generation forecasting under rapidly changing weather conditions integrate copula theory with an extreme gradient tree boosting (XGBoost) classifier to enhance forecast accuracy. Historical weather data is partitioned into meteorological clusters, and bivariate copulas analyze spatiotemporal correlations to select optimal features. Multivariable Vine and Gaussian copulas model variable dependencies, with an XGBoost classifier dynamically selecting the optimal copula based on real-time weather conditions. Synthetic weather data, generated using the selected copula, captures uncertainties and is applied to a trained XGBoost regression tree to produce probabilistic forecasts. The method achieves up to 60% higher accuracy than conventional models under non-sunny conditions, leveraging Gaussian Kernel Density Estimation and Huber loss for robustness. The system supports real-time grid operations, offering reliable solar power predictions for diverse weather scenarios, validated with real-world data from multiple global locations.

This application discloses a multi-objective power grid dispatching decision-making method and system, relating to the field of power grid dispatching technology. The method includes: constructing a normal dispatching objective based on minimum load fluctuation; constructing an abnormal weather dispatching objective based on minimum dispatching quantity and minimum dispatching loss; constructing a device abnormality dispatching objective based on minimum switching loss; constructing a multi-objective optimization function by combining the normal dispatching objective, the abnormal weather dispatching objective, and the device abnormality dispatching objective with initial priority weights; obtaining probabilistic prediction values ​​based on regional historical load data, regional real-time load data, regional historical weather data, regional real-time weather data, regional historical device maintenance data, and regional real-time device data; obtaining the dispatching weights for each dispatching objective based on the probabilistic prediction values; and updating the multi-objective optimization function with the dispatching weights to update the multi-objective power grid dispatching decision. The beneficial effects of this application are: balancing scenario switching flexibility and power dispatching accuracy when facing complex real-world scenarios.

A method for forecasting wind power output of a target wind farm. The method includes normalizing, wind power output data for each wind farm of a group of wind farms, based, at least in part, on a respective installed capacity; transforming, the normalized power output data to yield transformed normalized wind power output data. Fitting, by the temporal module, each temporal model of at least one temporal model to model input data for each wind farm. The model input data corresponds to normalized wind power output data or transformed normalized wind power output data. The method further includes fitting, by a spatial module, a DVINE copula model for the group of wind farms, based, at least in part, on at least one residual value. Each residual value is determined based, at least in part on a selected fitted temporal model for each wind farm in the group.

State-of-the-art machine-learning models for forecasting fail to address the non- stationarity and uncertainty in data, rely on assumptions about data distribution, and / or produce unusable probability distributions. Accordingly, a machine-learning architecture for end-to- end probabilistic forecasting is disclosed to address these and other problems. In particular, the machine-learning model may utilize a persistence module that outputs a seed forecast value of a target variable, a neural-network stack that predicts incremental forecast value(s) of the target variable (e.g., using back-casting), and an aggregator that aggregates the seed forecast value and the incremental forecast value(s) to produce an aggregate forecast value of the target variable. In an embodiment, this aggregate forecast value may be input to an incremental quantile module that comprises a second neural-network stack to predict the forecast value of the target variable for each of a plurality of quantiles, and which aggregates these forecast values into a probability distribution.

The invention discloses a Bayesian model averaging-based power prediction model evaluation method and a related device, and belongs to the technical field of power system prediction. The method comprises the following steps: constructing a candidate model set comprising a plurality of power prediction models; secondly, historical data are used for training all the models, the posterior model probability of each model is calculated based on the Bayesian theorem, the posterior model probability serves as the scientific weight of the model, and the goodness of fit and complexity of the model are considered in the weight at the same time; and finally, for a new prediction input, performing weighted average on the prediction distribution of each candidate model by taking the posterior probability as the weight to generate comprehensive probability prediction distribution. According to the method, the advantages and disadvantages of each candidate model are scientifically evaluated through the posterior probability, and a comprehensive and probabilistic prediction result is finally generated, so that the robustness and reliability of prediction are improved, and richer decision information is provided for power grid dispatching.

The invention discloses a day-ahead electricity price prediction method and device based on real-time market demand prediction. The method comprises the following steps: acquiring a market game expected index and traditional market data in a historical transaction period; constructing a fusion feature matrix based on the market game expected indexes in the historical transaction period and the traditional market data; matching the fusion feature matrix with preset historical day-ahead electricity price data to generate a training data set; training the integrated probability prediction model according to the training data set to obtain a target integrated probability prediction model; acquiring a market game expected index and traditional market data of a to-be-predicted period to construct a to-be-predicted feature matrix; inputting the to-be-predicted feature matrix into a target integrated probability prediction model to obtain a prediction processing result; and according to the prediction processing result, generating a day-ahead electricity price single-point prediction value and a day-ahead electricity price prediction interval. By implementing the scheme, more comprehensive and reliable price signals and risk quantification information can be provided for the operation optimization decision of the power system.

A computer-implemented method for producing probabilistic forecasts of extreme values. The method comprises obtaining input data comprising a plurality of signals of interest and a plurality of covariates associated therewith, each covariate of the plurality of covariates having an associated data type. The method further comprises performing a first forecast based on the input data. Performing the first forecast comprises: obtaining one or more trained machine learning models, each trained machine learning model of the one or more trained machine learning models having been trained to map one or more covariates of a respective data type to one or more surrogate covariates; mapping, using the one or more trained machine learning models and the input data, the plurality of covariates to one or more surrogate covariates, the one or more surrogate covariates corresponding to a compressed representation of the input data; fitting a statistical model of extremes to the plurality of signals of interest and the one or more surrogate covariates thereby generating a fitted statistical model of extremes, the statistical model of extremes being defined according to a predetermined distribution having a plurality of parameters; and obtaining a probabilistic forecast of future extreme values based on the fitted statistical model of extremes for one or more future time steps. The method further comprises causing control of a controllable system based at least in part on the probabilistic forecast of future extremes.

This invention relates to the field of power system electricity price forecasting technology, and discloses a joint probabilistic forecasting method for nodal electricity prices, comprising: constructing a dual-state monitoring system to simultaneously track the physical state of the power grid and market liquidity indicators, generating physical constraint state vectors and market liquidity state vectors; designing critical early warning indicators by nonlinearly combining the degree of physical constraint and liquidity level; introducing a state-space mapping algorithm to divide the physical liquidity state space into stable, metastable, and critical regions, constructing a refined representation of the system state; based on a directed causal graph propagation mechanism model, tracking how changes in physical constraints affect market participant behavior and thus market liquidity; applying conditional extreme value theory to model extreme price distributions for identified critical states; and by establishing a coupled model of physical constraints and market liquidity, this invention effectively solves the technical problem that existing methods cannot capture the complex interaction between the two.

The invention belongs to the technical field of post-earthquake building falling object accumulation range probability prediction models and urban road network blocking vulnerability estimation, and discloses a post-earthquake building ruin distribution range probability prediction model and an urban road network blocking vulnerability estimation method. The building falling object accumulation range prediction model proposed based on the Bayesian theory considers multiple damage states of the building under the earthquake action, including building complete collapse and non-structural member damage; posterior probability distribution of unknown parameters of a prediction model can be obtained based on the Bayesian theory, and accordingly cognitive uncertainty from the model parameters is quantified in post-earthquake road blocking vulnerability evaluation. The method not only helps to improve the accuracy of post-earthquake road congestion risk assessment, but also helps to reasonably plan a post-disaster rescue evacuation path.

The invention discloses a new energy output probability prediction method and device and electronic equipment, which are applied to a probability prediction model and comprise a feature extraction module, a feature fusion module, a probability prediction module and a result output module. The method comprises the following steps: acquiring day multi-source data; extracting meteorological element features of the weather forecast data in a first preset time period through a feature extraction module, extracting image features of the remote sensing image data, and extracting time sequence features of the power data; fusing the meteorological element features, the image features and the time sequence features through a feature fusion module to obtain target fusion feature data; processing the target fusion feature data with the time sequence through a probability prediction module to obtain probability distribution of new energy power generation power; and analyzing the probability distribution through a result output module, and outputting a prediction result. According to the method, the complete probability distribution of the future output potential can be directly output, and a more comprehensive decision basis is provided for market participants.

The application provides a new energy power system power imbalance quantitative evaluation method, device and equipment, by considering the random volatility of uncertain power output and load demand and the mutual influence therebetween, a joint probability prediction model for different prediction time scales of a future to-be-evaluated period is constructed, and by using the joint probability prediction model and the working state information of the uncertain power and load in the time dimension provided by the operation scheduling plan, a probability distribution of the net difference of the uncertain power in the to-be-evaluated period is generated, the net difference of the deterministic power and load is considered, and the power imbalance distribution of the whole power system is accurately predicted, so as to provide accurate data support for the scheduling and operation of the power system.