Dispatch Of Power System Research Articles

Wind‐driven water wave optimized economic load dispatch for the integration of renewable energy sources in micro‐grid system

The complication of economic load dispatch (ELD) of an electrical power system can be considered as an optimization problem with constraints to minimize the cost and emission concurrently. The optimization problems with various objectives gain significant momentum and priority with the advancements in renewable energy sources (RESs). Many techniques have been put forward to dispense this issue, but it still remains a challenging one. The existing optimization approaches are accompanied by a slow gathering rate with inferior computational complexity. To augment the ELD performance with renewable resources, in this research, the hybrid wind-driven water wave optimization (WDWWO) strategy is presented. The effectiveness of the proposed method is validated by the consideration of the IEEE 30-bus system. The proposed hybrid method is capable of solving convex and non-convex economic and emission dispatch issues. From the simulation results, it can be seen that, in the case of both PV and wind, the cost analysis of the developed WDWWO model effectuate 21% reduction when compared with wind-driven optimization (WDO), 23% reduction when compared with water wave optimization (WWO), 40% reduction when compared with modified shuffle leaf algorithm (MSFLA), and 42% reduction when compared with constrained multi-objective population external optimization technique (COMPEO) under 200 MW.

A step toward cleaner energy production: A water saving-based optimization approach for economic dispatch in modern power systems

This paper proposes a novel approach to discover an optimal solution of multi-area dynamic economic dispatch integrated with wind power and pumped hydroelectric storage. Minimizing of water consumption associated with power production in thermal power plants is yet to be addressed in economic dispatch problem. Thus, it is crucial to modernize the conventional power industry to save the water utilization in the process of generating electric energy. As a result, making a feasible power generation schedule by finding a trade-off between total generation cost and water consumption is the main focus of this paper. Toward this end, this study introduces an effective optimization approach for decreasing the contaminants ‎produced by the combustion-based power plants via reflecting the impacts of wind power and storage systems in the proposed problem. An efficient optimization algorithm considering the merits of whale optimization algorithm and wavelet mutation is also proposed to handle the complications of the trade-off between generation cost and water consumption. The proposed approach is applied to 40-unit 4-area IEEE system by 1) scrutinizing the performance of the hybrid framework from different standpoints, 2) achieving an improvement about 5.4% in the operation cost, and 3) promoting the decarbonized economy by implementing ‎renewable energy.

A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting

Short-term wind speed forecasting with high accuracy puts forward a positive influence on implementation of power system dispatch and wind energy utilization. Accordingly, to further promote the prediction accuracy and maintain the prediction stability, an innovation compositive architecture is developed incorporating three modules: data preprocessing, several individual predictors and Volterra multi-model fusion with enhanced multi-objective optimization algorithm. First and foremost, detection and correction of outliers are regarded as a preprocessing technique to process original wind speed sequences, after which empirical wavelet transform (EWT) is further adopted to adaptively decompose the corrected sequences into multiple subsequences. Then, individual predictors including several typical models and the proposed convolutional simplified gated recurrent unit (ConvSGRU), are applied to acquire prediction results by summating the predicted values of each component, which is constructed by PSR to obtain feature matrix as input of the above models. Subsequently, standard deviation (Std.) calculated between the actual values and predicted values of each model is severed as the basis for ensemble modeling, in which multi-model fusion based with smaller Std. on three models are adaptively selected to construct a multi-dimensional matrix as the input of Volterra. In this process, kernel identification parameters of Volterra can be acquired by multi-objective Harris hawks optimization (MOHHO) algorithm with mutation operator (HMOHHO), which can effectively guarantee accuracy and stability of the model simultaneously. Furthermore, four experiments with three datasets collected at different regions are revealed to ascertain forecasting capability of the proposed compositive architecture. The experimental results clarify that: (1) original wind speed sequences based on box-plot are employed to detect and correct, which can greatly decrease the influence of outliers on the sequences while preserving its mainstream trend; (2) the proposed deep network ConvSGRU can give assistance for the forecasting accuracy, thereby contributing to ameliorate performance of the entire compositive architecture; (3) multi-model fusion based on Volterra nonlinear fitting is conducive to further actively compensating for instability of individual models; (4) HMOHHO enables Volterra fitting to receive a series of constructive kernel identification parameters.

Day-ahead optimal dispatching of multi-source power system

In this paper, the day-ahead optimal dispatching model of power system that is combined by wind-photovoltaic-hydropower-thermal-pumped storage is established. Firstly, according to the characteristics of the short-term hydropower system dispatching problem, a new mathematical model of the cascade hydropower group system with pumped storage power stations is proposed. The coordinated optimized dispatching model of the hydropower group system formed by the cascading of pumped storage power plants and conventional hydropower in the power system is studied. Secondly, the opportunity constraint programming model of forecast error reserve is used to deal with the output uncertainty of wind power and photovoltaic. The randomness and intermittency of renewable energy on the stability of the power system are overcame by the combination of wind-photovoltaic-pumped storage. Thirdly, the model for the joint optimal dispatch of short-term wind, photovoltaic, hydropower and thermal power systems with pumped storage is developed with system economics as the goal. Fourthly, the operation volatility coefficient of thermal power units is proposed to study the impact of renewable energy on the operation of thermal power. Finally, an example system is used to verify the correctness of the proposed dispatching optimal scheduling model, and the results prove that the daily dispatch optimization model proposed in this paper can increase the economic efficiency of the power system by 5%, reduce the start-stop times of thermal power units by 36.55%, and reduce the fluctuation coefficient of the unit by 2.8.

Medium-long term load forecasting method considering industry correlation for power management

Medium-long term load forecasting is recognized as the basis of economic dispatch in power systems, and high-precision load forecasting results can provide effective support for power management. Therefore, a medium-long term load forecasting method based on the deep learning algorithm is proposed with industry correlation considered. First, the missing monthly load data is supplemented based on the iterative interpolation method, and min–max normalization method is used for data preprocessing. On this basis, the Pearson correlation coefficient is adopted for quantifying the industry correlation and selecting the strongly related industries for load forecasting. Then, a machine learning model long-short term memory (LSTM) is utilized to forecast the medium-long term industry load with the historical data of related industries. Finally, the feasibility and accuracy of the proposed method are illustrated by the case studies on 2 major industries and 30 related industries of Jiangsu Province, China. The load forecasting error shown in the simulation results of the method proposed is kept within 5%, which is much better than other traditional methods.

Research and Application of Power System Data Anomaly Identification Based on Time Series and Deep Learning

Abnormal data in the power system will reduce the accuracy of system state estimation and affect the safe operation of the power dispatch system. This paper proposes a data anomaly identification model based on time series and neural network, which establishes time series for various measuring points of the control master station, creates a time series group of associated measuring points based on the network topology, and extracts the sample characteristics of the time series. The neural network model is used to realize the intelligent identification of normal data and abnormal data. The neural network recognition results are compared with normal distribution and DBSCAN density clustering methods to verify the abnormal recognition performance of the neural network. Using a provincial power grid dispatch center operating data set as a training and testing sample, it verifies the advancement of the proposed method in the comprehensive performance of anomaly detection recall rate and precision rate and its feasibility in actual system application.

A clock synchronization method in power dispatching based on BeiDou navigation system

This paper introduces the problems of poor signal condition of a single time source in time synchronization or error in time source switching in the power system. It adopts the two-clock mode timing scheme of BeiDou 2 synchronous satellite and GPS synchronous satellite to ensure the stable output of the main clock within a certain accuracy. The system takes full advantage of the field programmable gate array (FPGA) hardware method, aligns the internal clock lock signal with the input second pulse phase, and proposes an improved method for the problem of large clock synchronization deviation based on the network, which proves the effectiveness and superiority of the system to accurately output the pulse and provide reliable time synchronization for the power dispatching system.

Intrusion Detection Method of Electric Power Information Network in Cloud Computing Environment

With the widespread application of power grid systems, the information security problems faced by power grids have become more obvious. Various internal and external intrusion attacks that occur frequently have become an important issue affecting the normal operation of power generation and operations. The purpose of this paper is to study the intrusion detection method of electric power information(PI) network in the cloud computing environment. With the help of the cloud platform’s ability to process big data, and based on the analysis of the PI network structure, a DBN optimized BP network algorithm is proposed, and the optimized BP neural network is used as a runtime classification program. Experimental results show that MR-DBN-BP has a detection rate of 96.7% for intrusion detection of PI networks, which can effectively detect intrusions and effectively protect the power dispatch system network.

Optimization of Automatic Generation Control and Economic Load Dispatch for Two Area Six Unit Interconnected Power System

This paper proposes an integrated Economic Load Dispatch (ELD) and Automatic Generation Control (AGC) for interconnected power systems. Based on their participation factor determined from the economic load dispatch calculation, each unit shares the total change in the same control region. In this study, two control areas are considered. Three thermal units are located in each control area. An integral controller (I) is only used for the AGC mechanism's secondary controller and is used for the primary controller for the ELD mechanism. An Improved Grey Wolf Optimizer (IGWO) technique is used to evaluate the optimum parameters of the integral controllers for primary and secondary controllers. An integral time square error (ITSE) has been used as the objective function to tune the suitability of the proposed controller gains. The simulation results demonstrate that the integrated AGC with ELD has the superiority in reducing the overshoot and fast steady state compared with AGC only.

Forecast electricity demand in commercial building with machine learning models to enable demand response programs

Electricity load forecasting is an important part of power system dispatching. Accurately forecasting electricity load have great impact on a number of departments in power systems. Compared to electricity load simulation (white-box model), electricity load forecasting (black-box model) does not require expertise in building construction. The development cycle of the electricity load forecasting model is much shorter than the design cycle of the electricity load simulation. Recent developments in machine learning have lead to the creation of models with strong fitting and accuracy to deal with nonlinear characteristics. Based on the real load dataset, this paper evaluates and compares the two mainstream short-term load forecasting techniques. Before the experiment, this paper first enumerates the common methods of short-term load forecasting and explains the principles of Long Short-term Memory Networks (LSTMs) and Support Vector Machines (SVM) used in this paper. Secondly, based on the characteristics of the electricity load dataset, data pre-processing and feature selection takes place. This paper describes the results of a controlled experiment to study the importance of feature selection. The LSTMs model and SVM model are applied to one-hour ahead load forecasting and one-day ahead peak and valley load forecasting. The predictive accuracy of these models are calculated based on the error between the actual and predicted loads, and the runtime of the model is recorded. The results show that the LSTMs model have a higher prediction accuracy when the load data is sufficient. However, the overall performance of the SVM model is better when the load data used to train the model is insufficient and the time cost is prioritized.

A regional pretraining-classification-selection forecasting system for wind power point forecasting and interval forecasting

Wind power forecasting is extremely crucial for power market transactions and power system operation. Although a lot of researches have concentrated on wind power forecasting, the forecasting performances were confined and the forecasting models were only suitable for providing good performance at a few sites of investigation. To bridge these gaps, a novel regional pretraining-classification-selection wind power forecasting system is proposed in this paper based on four modules-pretraining module, classification module, point forecasting module, and interval forecasting module, which effectively improves forecasting performance and extends the applicability to different data characteristics. 10-min wind power data obtained from 20 datasets are used to verify the forecasting ability of the proposed forecasting system. The experimental analyses and discussions reveal that the proposed forecasting system is accurate and reliable for achieving high-quality wind power point and interval forecasting results. Thus, it could provide useful references for wind producers and managers in power system dispatch and operation.

Learning the Optimal Strategy of Power System Operation With Varying Renewable Generations

Optimal dispatch of modern power systems often entails efficiently solving large-scale optimization problems, especially when generators have to respond to the fast fluctuation of renewable generation. This paper develops a method to learn the optimal strategy from a mixed-integer quadratic program with time-varying parameters, which can model many power system operation problems such as unit commitment and optimal power flow. Different from existing machine learning methods that learn a map from the parameter to the optimal action, the proposed method learns the map from the parameter to the optimal integer solution and the optimal basis, forming a discrete pattern. Such a framework naturally gives rise to a classification problem: the parameter set is partitioned into polyhedral regions; in each region, the optimal 0-1 variable and the set of active constraints remain unchanged, and the optimal continuous variables are affine functions in the parameter. The outcome of classification is compared with analytical results derived from multi-parametric programming theory, showing interesting connections between traditional mathematical programming theory and the interpretability of the learning-based method. Tests on a small-scale problem demonstrate the partition of the parameter set learned from data meets the theoretical outcome. More tests on the IEEE 57-bus system and a real-world 1881-bus system validate the performance of the proposed method with a high-dimensional parameter for which the analytical method is intractable.

Economic Dispatch of Power System Considering Wind Power Integration

Economic Dispatch of Power System Considering Wind Power Integration

Урахування режимів експлуатації двокорпусних енергоблоків ТЕС у моделі математичного програмування оптимального завантаження електростанцій енергосистеми

The mathematical formulas are presented which are used as additional constraints of the mathematical MILP model for optimal dispatch of power system's generation and allow adequately describe the operation of twin-boiler thermal power units both in single-boiler mode and two-boiler mode. These operation modes are widely used by the coal-fired thermal power twin-boiler units connected to the Integrated Power System of Ukraine and due to actual wider operation range allow to increase power system flexibility that is important under the big share of intermittent renewables, namely wind and solar generations. The results of calculations demonstrate that the use of additional proposed constraints allows finding more flexible operation modes of thermal power units resulting in lower curtailment levels for intermittent renewables, lower electricity generation by fossil fuel thermal power units. Notwithstanding that the operation of a twin-boiler coal-fired thermal unit in a single-boiler mode has a higher specific fuel consumption compared with a two-boiler mode the daily coal consumption is lower and that leads to the actual lower environmental impact from the fossil fuel power plants. The time of calculations using proposed MILP for optimal dispatch of power system's generation for one day (24 modeling periods or hours) is about 240 seconds and is long enough which is explained by the increase of the number of integers variables and by the dimension of the constrain matrix (the number of the rows about two times bigger the number of the columns). Those factors essentially complicate the computations with the optimization algorithm used by GLPK solver. Such essential time for the calculations requires more powerful computational equipment for the future generation mix projections task because the time for the calculations for one year could take about 24 hours. Nevertheless, for the one-day generation optimal load dispatch task the proposed model is absolutely suitable. Keywords: Integrated Power System, Load profile, thermal power plant, twin-boiler thermal unit, linear programming model, generation dispatch

Research on Power Optimization Operation Based on Computer Analysis of Energy Saving Dispatching and Demand Side Management

At present, the energy-saving application of power system has become an important means to ensure the sustainable development of social economy. However, there are still many deficiencies and problems in energy-saving dispatching and demand side management of power system, which urgently needs innovation and improvement. Based on this, this paper first analyzes the development status of energy-saving power operation mechanism, then studies the power operation optimization based on computer-based energy-saving dispatching, and finally gives the power operation optimization strategy based on power demand side management.

Multi-layer distributed multi-objective consensus algorithm for multi-objective economic dispatch of large-scale multi-area interconnected power systems

Traditional multi-objective economic dispatch problems all apply centralized multi-objective optimization methods. However, with the rapid development of smart grids, issues such as system calculation speed, robustness, and information privacy have been increasingly vitally. Although the distributed multi-objective optimization method can solve the problems of system robustness and privacy when the scale of the interconnected power system expands and the number of agents increases, each agent independently optimizes its sub-problems and then exchanges information to complete the entire system optimization. The pressure of system communication calculation leads to reduce the overall calculation speed greatly. In response to the above problems, this paper proposes a multi-layer distributed multi-objective consensus algorithm. This method first calculates the optimal power generation of each area of each layer through the network topology and then calculates the power of each unit in each area in parallel according to the calculated optimal power generation. The analysis of the three system simulation results of IEEE118-bus, IEEE2154-bus, and Xinjiang Turpan 117-bus shows that the proposed method can solve the problems of multi-objective and information privacy, and realize the rapid solution of economic dispatch in large-scale multi-area interconnected power systems.

Short-term Load Forecasting by Using Improved GEP and Abnormal Load Recognition

Load forecasting in short term is very important to economic dispatch and safety assessment of power system. Although existing load forecasting in short-term algorithms have reached required forecast accuracy, most of the forecasting models are black boxes and cannot be constructed to display mathematical models. At the same time, because of the abnormal load caused by the failure of the load data collection device, time synchronization, and malicious tampering, the accuracy of the existing load forecasting models is greatly reduced. To address these problems, this article proposes a Short-Term Load Forecasting algorithm by using Improved Gene Expression Programming and Abnormal Load Recognition (STLF-IGEP_ALR). First, the Recognition algorithm of Abnormal Load based on Probability Distribution and Cross Validation is proposed. By analyzing the probability distribution of rows and columns in load data, and using the probability distribution of rows and columns for cross-validation, misjudgment of normal load in abnormal load data can be better solved. Second, by designing strategies for adaptive generation of population parameters, individual evolution of populations and dynamic adjustment of genetic operation probability, an Improved Gene Expression Programming based on Evolutionary Parameter Optimization is proposed. Finally, the experimental results on two real load datasets and one open load dataset show that compared with the existing abnormal data detection algorithms, the algorithm proposed in this article have higher advantages in missing detection rate, false detection rate and precision rate, and STLF-IGEP_ALR is superior to other short-term load forecasting algorithms in terms of the convergence speed, MAE, MAPE, RSME, and R 2 .

Optimal Dispatching of Power System by Introducing Concentrating Solar Power Station to Promote Large-scale Wind Power and Photovoltaic Accommodation

Background: With respect to the problem of wind power and photovoltaic (PV) gridconnected accommodation, a power system optimization dispatching method is proposed that introduces a concentrating solar power (CSP) station to promote wind power and PV accommodation, considering the dispatch-ability of the thermal storage system (TSS) and the accommodation capacity of the electric heating device (EHD) in the CSP station. Methods: The method analyzes an internal simplified model of a CSP station and a short-timescale identification method of wind and PV power ramping events based on a CSP-Wind-PV system structure. Furthermore, the operating characteristics and constraints of various units are considered with the goal of the lowest comprehensive system cost to establish a CSP-Wind-PV optimization model for dispatching power system. Results: The method is simulated and verified on the IEEE-RTS24 node system by using the MATLAB optimization toolbox. Conclusion: The results demonstrate that the participation of a CSP station can decrease the number of power ramping events and reduce the curtailment of wind and PV power while ensuring the economical operation of the power system.

A novel quantum inspired hybrid metaheuristic for dispatch of power system including solar photovoltaic generation

ABSTRACT This manuscript proposes a novel metaheuristic, which is an amalgamation of the quantum concept and the gravitational search particle swarm optimization technique. The inclusion of quantum concepts in the algorithm enhances its capability, as in quantum space there is no restriction on the movement of particle and the solution can be obtained with smaller population and faster convergence. An adaptive contraction expansion factor is also introduced which ensures better exploration of the algorithm. The technique is applied to solve the combined economic emission dispatch of a hybrid solar thermal unit operating in New Delhi, India for full and reduced solar radiation. To verify the effectiveness of the proposed technique, it is also applied to solve the combined economic emission dispatch of a 10 unit, 2000 MW system. It is observed that the proposed approach provides a fuel cost saving of up to 1300$ when compared with other referred techniques.

Optimal Active and Reactive Power Dispatch and Excitation System Controller Design Using Genetic , Algorithm.(Dept.E)

Power system economical operation consists of two aspects: active power regulation and reactive power dispatch. This multiobjective optimization problem is solved in this p ap er as two subproblems: P- and Q-problems. The solution is applied to an EEE 14-bus system using Genetic Algorithm (GA). Additionally, An exciter system lead-lag controller and feedback compensator parameters are also optimally selected using GA.