Optimum control strategies for short term load forecasting in smart grids

Abstract Nonlinearity in load profile and variations in demand due to error margin in short term load forecasting cause power network overloading. The state of a power system is more severe when a fault occurs in the power system network that leads to overloading. Analyzing the effect due to these disturbances on power system network is an important feature of this work. This paper proposes a control algorithm that focuses on sophisticated fuzzy logic approach. Advanced fuzzy control takes overloading and variation in demand profile as input, which mitigate these disturbances by incorporating optimal power dispatch of renewable energy resources (RERs). To show the effectiveness and validity of the proposed model and fuzzy control design, 9 Bus test system of the transmission network is adopted. Not only normal mode but fault and overloading modes are used to verify the proposed approach. Competitiveness of the proposed control design in terms of reliability and optimal utilization of RERs are verified through simulation results.

[1]  Bo Zhao,et al.  Stochastic Optimal Operation of Microgrid Based on Chaotic Binary Particle Swarm Optimization , 2016, IEEE Transactions on Smart Grid.

[2]  B. Araabi,et al.  Short-Term Load Forecasting With a New Nonsymmetric Penalty Function , 2011, IEEE Transactions on Power Systems.

[3]  Mehmet Kurban,et al.  Hourly Forecasting of Long Term Electric Energy Demand Using a Novel Modeling Approach , 2009, 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC).

[4]  Xin Wang,et al.  A novel global harmony search algorithm for task assignment problem , 2010, J. Syst. Softw..

[5]  Yih-Der Lee,et al.  Multiscenario Underfrequency Load Shedding in a Microgrid Consisting of Intermittent Renewables , 2013, IEEE Transactions on Power Delivery.

[6]  Rahmat-Allah Hooshmand,et al.  A hybrid intelligent algorithm based short-term load forecasting approach , 2013 .

[7]  Bo Zhao,et al.  Optimal sizing, operating strategy and operational experience of a stand-alone microgrid on Dongfushan Island , 2014 .

[8]  Danladi Ali,et al.  Long-term load forecast modelling using a fuzzy logic approach , 2016 .

[9]  Omar Badran,et al.  A fuzzy inference model for short-term load forecasting , 2009 .

[10]  Gwo-Ching Liao,et al.  Solve environmental economic dispatch of Smart MicroGrid containing distributed generation system – Using chaotic quantum genetic algorithm , 2012 .

[11]  Panos M. Pardalos,et al.  An improved adaptive binary Harmony Search algorithm , 2013, Inf. Sci..

[12]  S. Surender Reddy,et al.  Short term electrical load forecasting using back propagation neural networks , 2014, 2014 North American Power Symposium (NAPS).

[13]  Jaime Lloret,et al.  Experimental Analysis of the Input Variables' Relevance to Forecast Next Day's Aggregated Electric Demand Using Neural Networks , 2013 .

[14]  Mehmet Çunkas,et al.  Short-term load forecasting using fuzzy logic and ANFIS , 2015, Neural Computing and Applications.

[15]  Dipti Srinivasan,et al.  A SOM-based hybrid linear-neural model for short-term load forecasting , 2011, Neurocomputing.

[16]  Majid Gandomkar,et al.  Microgrid dynamic responses enhancement using artificial neural network‐genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds , 2016 .

[17]  Rajesh Kumar,et al.  An Intelligent Tuned Harmony Search algorithm for optimisation , 2012, Inf. Sci..

[18]  Hongzhan Nie,et al.  Hybrid of ARIMA and SVMs for Short-Term Load Forecasting , 2012 .

[19]  Woo Joo Lee,et al.  A hybrid dynamic and fuzzy time series model for mid-term power load forecasting , 2015 .

[20]  Luu Ngoc An,et al.  Optimal energy management for grid connected microgrid by using dynamic programming method , 2015, 2015 IEEE Power & Energy Society General Meeting.

[21]  Jian Ye,et al.  A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting , 2008 .

[22]  Zhang Hong,et al.  The Application of the Pso Based BP Network in Short-Term Load Forecasting , 2012 .

[23]  Muhammad Tariq,et al.  Electricity Theft Detection and Localization in Grid-Tied Microgrids , 2018, IEEE Transactions on Smart Grid.

[24]  Surender Reddy Salkuti,et al.  Short-term electrical load forecasting using radial basis function neural networks considering weather factors , 2018 .

[25]  A. Selakov,et al.  Hybrid PSO-SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank , 2014, Appl. Soft Comput..

[26]  José Ramón Cancelo,et al.  Forecasting the electricity load from one day to one week ahead for the Spanish system operator , 2008 .

[27]  Luis Neves,et al.  Short‐term load forecasting based on support vector regression and load profiling , 2014 .

[28]  Li-Chih Ying,et al.  Using adaptive network based fuzzy inference system to forecast regional electricity loads , 2008 .

[29]  Jose I. Bilbao,et al.  A review and analysis of regression and machine learning models on commercial building electricity load forecasting , 2017 .

[30]  M. Çunkaş,et al.  Turkey's Electricity Consumption Forecasting Using Genetic Programming , 2011 .

[31]  H. Vincent Poor,et al.  Load flow balancing and transient stability analysis in renewable integrated power grids , 2019, International Journal of Electrical Power & Energy Systems.

[32]  Zili Li,et al.  Forecasting day-ahead electricity load using a multiple equation time series approach , 2016, Eur. J. Oper. Res..

[33]  Leandro Fleck Fadel Miguel,et al.  Damage detection under ambient vibration by harmony search algorithm , 2012, Expert Syst. Appl..

[34]  Ali Akbar Abdoos,et al.  Short term load forecasting using a hybrid intelligent method , 2015, Knowl. Based Syst..

[35]  Jaime Lloret,et al.  A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings , 2014, IEEE Communications Surveys & Tutorials.

[36]  Kenneth A. Loparo,et al.  An Energy Scheduling Algorithm Supporting Power Quality Management in Commercial Building Microgrids , 2016, IEEE Transactions on Smart Grid.

[37]  Devendra K. Chaturvedi,et al.  Short term load forecast using fuzzy logic and wavelet transform integrated generalized neural network , 2015 .

[38]  Wei-Chiang Hong,et al.  Application of chaotic ant swarm optimization in electric load forecasting , 2010 .

[39]  Chul-Hwan Kim,et al.  An Application of Harmony Search Algorithm for Operational Cost Minimization of MicroGrid System , 2009 .

[40]  Jingrui Zhang,et al.  A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints , 2016 .

[41]  Muammer Gökbulut,et al.  Tachogenerator DC Motor Speed Control with PID and Fuzzy Logic , 2011 .

[42]  Abdul Hanan Abdullah,et al.  Optimization of plate-fin heat exchangers by an improved harmony search algorithm , 2013 .

[43]  Rob J Hyndman,et al.  Short-Term Load Forecasting Based on a Semi-Parametric Additive Model , 2012, IEEE Transactions on Power Systems.

[44]  S. Surender Reddy,et al.  Short-Term Load Forecasting Using Artificial Neural Networks and Wavelet Transform , 2016 .

[45]  Sanjay Bahadoorsingh,et al.  Performance of exponential smoothing, a neural network and a hybrid algorithm to the short term load forecasting of batch and continuous loads , 2017, 2017 IEEE Manchester PowerTech.

[46]  Javad Sadeh,et al.  Parameter-free fault location for transmission lines based on optimisation , 2015 .

[47]  Tao Chen,et al.  Back propagation neural network with adaptive differential evolution algorithm for time series forecasting , 2015, Expert Syst. Appl..

[48]  S. Surender Reddy,et al.  Bat algorithm-based back propagation approach for short-term load forecasting considering weather factors , 2018 .

[49]  Pierluigi Siano,et al.  Optimal allocation of wind turbines in microgrids by using genetic algorithm , 2013, J. Ambient Intell. Humaniz. Comput..

[50]  Adem Alpaslan Altun,et al.  Long Term Electricity Demand Forecasting in Turkey Using Artificial Neural Networks , 2010 .

[51]  R. Larson,et al.  The Energy Box: Locally Automated Optimal Control of Residential Electricity Usage , 2009 .

[52]  Hasan Hüseyin Çevik,et al.  Forecasting hourly electricity demand using a hybrid method , 2017, 2017 International Conference on Consumer Electronics and Devices (ICCED).

[53]  Yasunori Mitani,et al.  Optimization of a battery energy storage system using particle swarm optimization for stand-alone microgrids , 2016 .

[54]  Alagan Anpalagan,et al.  Improved short-term load forecasting using bagged neural networks , 2015 .

[55]  Leijiao Ge,et al.  Improved Interval Optimization Method Based on Differential Evolution for Microgrid Economic Dispatch , 2015 .