Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment

Evolutionary hybrid approaches for generation scheduling in power systems

Unit commitment (UC) is one of the most important optimization tasks of the daily generation scheduling in power system. However, getting the global optimal solution to meet all the constraints is a comparatively tough problem. The binary coding and stochastic operators in traditional genetic algorithm (GA) are not suitable for solving large-scale UC problem. In this paper, the hybrid intelligent messy genetic algorithm (HIMGA) is proposed to solve the problem due to the characteristics of the daily generation scheduling in power system. The proposed algorithm, using operation status of unit as the genotype and combining heuristic self-adapted intelligent operators, is not only very simple but it also reduces the scale of the UC problems, improves the diversity of evolution population, enhances the searching efficiency and develops the convergence of the algorithm. The simulation results prove the correctness and validity of the proposed method.

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

The authors propose a rigorous method to treat multiarea generation scheduling with tie line limits. An expert system was used for obtaining the initial solution. As the generation scheduling problem involves unit commitment and economic dispatch, the method adopts an iterative procedure to deal with these two phases. The hourly load demand and the area power generation will cause the tie flows to change. To maintain the operation security in every area, the spinning reserve should comply with the area power generation rather than its load demand. After economic dispatch, it is necessary to adjust the unit commitment in each area for preserving the spinning reserve requirements. Heuristics were used to modify the generation unit combinations. The objective is to find an economic generation schedule for a multiarea system. The interchange transactions among areas represent the transportation problem, embedded within the nonlinear optimization process. The equivalent system concept is adopted, and the transmission losses are included in this study. A four-area system with each area consisting of 26 units was used to test the efficiency of the proposed algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

As an important component of generation scheduling in power system, no matter planning or operation of the Power System, Power load forecasting could be of great significance. In This article, the prediction model integrated the technology of equal dimension and new information and gray prediction model was applied to power load forecasting, and the process of modeling and application were presented through the demonstration, which displayed that the model can provide effective decision support and suit to power load forecasting.

Generation Scheduling problem by Intelligent Genetic Algorithm

An accurate medium term load forecasting is significant for power generation scheduling, economic and reliable operation in power system. Most of classical approach for medium term load forecasting only consider total daily load demand. This approach may not provide accurate results since the load demand is fluctuated in a day. In this paper, a hybrid Ant-Lion Optimizer Least-square Support Vector Machine (ALO-LSSVM) is proposed to forecast 24-hour load demand for the next year. Ant-Lion Optimizer (ALO) is utilized to optimize the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimization is to minimize the Mean Absolute Percentage Error (MAPE). The performance of ALO-LSSVM technique was compared with those obtained from LS-SVM technique through a 10-fold cross-validation procedure. The historical hourly load data are analyzed and appropriate features are selected for the model. There are 24 inputs and 24 outputs vectors for this model which represents 24-hour load demand for whole year. The results revealed that the high accuracy of prediction could be achieved using ALO-LSSVM.

This paper introduces a unit commitment method based on artificial neural network (ANN) and fuzzy dynamic programming (FDP). Comparison among two ANN methods and FDP method is discussed. The experimental results indicate that the proposed ANN algorithm can significantly reduce the execution time in unit commitment based on binary codes, and the application of Gray code can reduce the dimensions of neural network and its training time consequently. The fuzzy approach to unit commitment achieves a reasonable operation cost and optimum state for constrained power systems.

The global transition towards sustainable and resilient energy systems has emphasized the need for efficient utilization of renewable energy sources (RESs) and rapid electrification of transportation. However, smart grids must address the intermittency of solar and wind power while accommodating the growing demand from electric vehicles (EVs). Hence, in this paper, a data-driven energy management system (EMS) is proposed that combines multivariable forecasting, generation scheduling, and EV charging coordination in a dual-level decentralized framework to increase the efficiency, reliability, and scalability of modern power grids. First, short-term forecasts of solar irradiance, wind speed, and load demand are addressed via five machine learning models ranging from nonlinear to ensemble models. Accordingly, a unified CatBoost-based platform for forecasting these three variables is selected because of its better performance and accuracy. These forecasts are subsequently utilized in a mixed-integer linear programming (MILP) framework for optimal generation scheduling in the considered network, fulfilling load demand at reduced electricity and emission costs while maintaining grid stability. Finally, a priority-based scheme is proposed for charging/discharging coordination of the aggregated EVs, minimizing demand variability while fulfilling vehicles’ charging needs and maintaining their batteries’ lifetime. The superiority of the proposed method lies in integrating a multivariable forecasting pipeline, linear MILP generation scheduling, and battery-health-aware V2G coordination in a unified decoupled framework, unlike many recent frontier works that treat these capabilities independently. Simulation results, under different scenarios, confirm that the proposed intelligent EMS can significantly reduce operational fluctuations, satisfy load and EV demands, optimize RES utilization, and support system cost-effectiveness, sustainability, and resilience.

In this paper, price-controlled energy management is investigated in a bi-level optimization framework, that is, energy scheduling problem of smart homes (SHs) and generation scheduling and unit commitment (UC) problems of a generation company (GENCO). SHs as the responsive customers (respect to the energy management) include a variety of sources such as photovoltaic (PV) panels, diesel generator, and battery as an energy storage. In addition, SHs are able to transact electricity with the GENCO through the power system. In this paper, the goal of GENCO is to design an optimal energy management scheme (optimal price of electricity) to maximize its daily profit. Herein, each SH reacts to the energy management scheme and reschedules its energy resources to minimize its daily operation cost. In this paper, a scenario-based stochastic optimization approach is applied in the energy scheduling problem of an SH to address the variability and uncertainty issues of the PV panels. Also, a combination of genetic algorithm (GA) and linear programming is applied as the optimization tool for the energy scheduling problem of an SH. Moreover, lambda-iteration economic dispatch and GA techniques are applied to solve the generation scheduling and UC problems of the GENCO, respectively. The numerical study demonstrates that in order to reach the maximum profit of GENCO, the energy management must be optimally designed and implemented; otherwise, the energy management scheme may result in detriment. Moreover, it is shown that each SH is able to get benefit from the energy management scheme and minimize its daily operation cost.

The work presented in this thesis concerns the optimisation of the hour-to-hour generation schedule (over a scheduling interval of 24 hours) of the generating stations in an all-hydro electric - system. The day-to-day system storage policy is assumed known from the long-term schedule. System load demand is first treated as deterministic. Later, attempts are made to treat it as stochastic. Throughout the text, system transmission losses are represented by means of the B-coefficient loss formula. Chapter 1 presents brief discussions on various topics which may play - a part in the optimization process. In particular, the concept of frequency control and the distinction between long-term and daily scheduling are outlined. The optimization objective is then formulated and some of the known methods of obtaining the solution are briefly discussed. In chapter 2, the optimization problem is first formulated . as that of minimizing the instantaneous total power drawn from the system subject to satisfying a . predetermined time-varying load demand.This is analogous to the minimization of the instantaneous total cost rate in thermal systems. Station characteristic curves are approximated by straight line segments and head variations are assumed negligible. The optimising - conditions are derived using the Lagrange multiplier technique. A small system consisting of 4 stations is studied, where optimisation is achieved by means of an analogue computer. By introducing weighting factors in the objective function, the storage policy can be satisfied. (Analysis carried out in chapter 3 later reveals that this is the correct measure which must be taken to meet the storage policy). Problems do arise, however, when - there are multi-unit stations present in the system. Due to the non-monotonic nature . of the incremental Output curves' of such stations, a. number of solutions would satisfy the optimising conditions. The optimum solution can only be established after testing each possible solution, a prohibitive task for large systems. Approximating the incremental output curves of such stations may create undesirable errors. Another weakness of the method is that it cannot readily take into account the effects of head variations. The application of Pontryagin's Maximum Principle is attempted in chapter 3. It is shown that, for systems with fixed‚ÄövÑvÆ head stations, the optimising conditions are exactly identical to that derived using the Lagrange multiplier technique with weightings being introduced in the objective function. Unfortunately, those problems associated with the presence of multi‚ÄövÑvÆunit stations are still unsolved. Under ideal system conditions, it is shown that optimum generation schedule would result if all stations; except the frequency control station, were step loaded. Step loading mode is defined as an operating mode where the instantaneous station discharge rate is only allowed to take one of two discrete values with stepwise transitions. The analysis indicates that the switching instants do not play any part, except they must be so chosen that the output of the frequency control station is kept within its limits. When step loading technique is applied to non‚ÄövÑvÆideal systems, the optimisation. becomes that of searching for the optimum switching instants of the step loaded stations. This is discussed in chapter 4. For practical reasons, only two step loading modes are assumed admissible. A system consisting of 18 stations is studied. Experimental results indicate that the optimum step loading modes can be established within - a reasonably short computation time. One striking feature of the step loading technique is that it can handle the optimal scheduling problem where the load demand is treated as being stochastic. The effects of head variations can be estimated fairly accurately. The application of the principle of dynamic programming is discussed in chapter 5. Due to the multi-dimensional nature of the problem, this principle must be combined with the relaxation principle to minimise the computation time and computer storage requirements. A good initial guess for the optimum schedule may be obtained using step loading technique. The step loading modes are then modified by applying the combination of dynamic programming and relaxaxtion principles. Finally, chapter 6 outlines the general conclusions drawn from the work presented in the preceeding chapters. The possibility. of applying Pontryagin's Maximum Principle to obtain the long-term storage policy is also studied. Related future research areas are suggested. To the author's best knowledge, the materials presented in chapters 3, 4 and 5 are original. Chapter 2 is based on the work of Chandler Jr. and Gabrielle, where the mathematical error found in their text has been corrected and the weighting procedure, among other materials has been included.

Uttarakhand state of India was formed in the year 2000 and simultaneously power sector was unbundled from state electricity board to power generation, transmission, and distribution utilities. Previous years Tariff Orders clearly indicate that Uttarakhand is becoming energy surplus state to energy deficit state from its inception. Despite repeated guidelines from state power regulator, state power utilities need to use more smart technologies and accurate short-term electrical load forecasting in consideration with weather and other parameters for predicting system load with a leading time of one hour to 24 hours, which is necessary for adequate scheduling and operation of power systems. It will also help for working of their electrical infrastructure efficiently, securely, and economically. This paper describes 24-hour-ahead load prediction whose results will give day ahead load forecast for the future day. Artificial Neural Networks is used for creating such algorithm. The ANN is a tool that duplicates the idea of the person’s brain. The ANN is designed and skilled to receive past load and climate information like temperature, humidity, wind speed, precipitation, pressure, and irradiance as input and after calculating correlation between load and meteorological parameters and load and days to get optimized inputs which produce load forecast as its output. ANN provides predicted load with minimum error and Mean Absolute Percent Error (MAPE) is calculated. Considering this work to use such type of short-term scheduling, short term power purchase process and its related suggestions may help state to be profit making organization and make state energy surplus again.

This study is on hot spot temperature (HST) prediction models, including loss of life (LoL) of transformers, and uses the Asaba transmission substation as a case study. One year loading profile data was collected from the substation while Artificial Neural Network (ANN) and Bagging Regression (BR) algorithms were modeled using this data on MATLAB and Python to predict the HST for Day-Ahead (24-Hours), Week-Ahead (120-Hours), and Weekend (48-Hours). The results show the nonlinear relation between Load and HST, as an increase in load values from 305.8A – 328.9A resulted in an increase in HST from 52.23oC – 54.88oC. Model performance was compared using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Using MATLAB indicated ANN had MAPE values of 2.51, 2.74, and 2.48 with RMSE values of 1.83, 1.71, and 1.85, respectively, for Day-Ahead, Week-Ahead, and Weekend prediction. In contrast, BR had MAPE values of 5.93, 5.83, and 5.77 with RMSE values of 4.30,4.86, and 4,43, respectively, for the same prediction period. Using Python indicated ANN had MAPE values of 6.67, 7.29, and 6.96 with RMSE values of 3.34, 4.27, and 1.95, respectively, for Day-Ahead, Week-Ahead, and Weekend prediction, while BR had MAPE values of 6.16, 6.07, and 6.5 with RMSE values of 5.7, 5.85 and 4.5 respectively for same prediction period. MATLAB software gave better results than Python, which had a higher percentage error for the ANN and BR models, likely due to the model's ability to better capture consistent thermal responses during sustained high loads. LoL for a 24-hour load cycle gave a cumulative age hour of 0.029959 hours, revealing how the HST significantly impacts the transformer’s insulation and life expectancy. It is recommended that different modeling techniques be explored for prediction analysis for medium-term transformer HST prediction if better results are desired. Also, operators should incorporate monitoring systems like Arduino to shut down the network and protect the transformer anytime the temperature exceeds set limits. This study presents the following contribution to knowledge: Data bank of a 60 MVA transformer can be obtained; Correlation between HST and Load demand has been established.

Optimal Short-Term Scheduling for Cascaded Hydroelectric Power Systems considering Variations in Electricity Prices

Previous studies developed models to predict final construction cost (FCC) values based on many inputs, which makes them difficult to use. However, relying on models with relatively few inputs will reduce the accuracy of the prediction results. This paper aims to develop an artificial neural network (ANN) model to predict the FCC based on contract cost (CC), contract duration, and project sector at an early stage of a project. The data collected and used for the ANN model was 135 Saudi Arabian construction projects. The Zavadskas and Turskis logarithmic approaches, and the Pasini method were utilized to overcome the limited data. Then, the ANN model was developed through two stages. The purpose of the first stage was to enhance the data by identifying the abnormal data using absolute percentage errors (APE). The enhanced data was used to develop the ANN in the second stage. The finding showed that the ANN model provided an average MAPE (mean absolute percentage error) of 18.7%. The MAPE of the ANN model is decreased to 8.7% on average by deleting data with an APE higher than 35%. The model allows stakeholders to evaluate the financial importance of potential risks and develop appropriate risk management strategies.