Economic Dispatch of BESS and Renewable Generators in DC Microgrids Using Voltage-Dependent Load Models

This paper addresses the optimal dispatch problem for battery energy storage systems (BESSs) in direct current (DC) mode for an operational period of 24 h. The problem is represented by a nonlinear programming (NLP) model that was formulated using an exponential voltage-dependent load model, which is the main contribution of this paper. An artificial neural network was employed for the short-term prediction of available renewable energy from wind and photovoltaic sources. The NLP model was solved by using the general algebraic modeling system (GAMS) to implement a 30-node test feeder composed of four renewable generators and three batteries. Simulation results demonstrate that the cost reduction for a daily operation is drastically affected by the operating conditions of the BESS, as well as the type of load model used.

[1]  C. K. Das,et al.  Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm , 2018, Applied Energy.

[2]  Jianhui Wang,et al.  Optimal Operation Mode Selection for a DC Microgrid , 2016, IEEE Transactions on Smart Grid.

[3]  A. Koch,et al.  Composite forecasting approach, application for next-day electricity price forecasting , 2017 .

[4]  Zhinong Wei,et al.  Representing ZIP loads in convex relaxations of optimal power flow problems , 2019, International Journal of Electrical Power & Energy Systems.

[5]  Dianguo Xu,et al.  Optimal Sizing of Distributed Generations in DC Microgrids With Comprehensive Consideration of System Operation Modes and Operation Targets , 2018, IEEE Access.

[6]  Alejandro Garces,et al.  Distribution Systems Operation Considering Energy Storage Devices and Distributed Generation , 2017, IEEE Latin America Transactions.

[7]  S. H. Lui,et al.  Pure and Applied Mathematics: A Wiley Series of Texts, Monographs and Tracts , 2011 .

[8]  Ahmet Onen,et al.  Assessment of Battery Storage Technologies for a Turkish Power Network , 2019, Sustainability.

[9]  Subhransu Ranjan Samantaray,et al.  An active islanding detection scheme for inverter-based DG with frequency dependent ZIP–Exponential static load model , 2016 .

[10]  Matti Lehtonen,et al.  Optimal location-allocation of storage devices and renewable-based DG in distribution systems , 2019, Electric Power Systems Research.

[11]  Xiaojuan Han,et al.  Day-ahead forecasting of photovoltaic output power with similar cloud space fusion based on incomplete historical data mining , 2017 .

[12]  Vigna K. Ramachandaramurthy,et al.  Review on the optimal placement, sizing and control of an energy storage system in the distribution network , 2019, Journal of Energy Storage.

[13]  Thokozani Majozi,et al.  GAMS supported optimization and predictability study of a multi-objective adsorption process with conflicting regions of optimal operating conditions , 2016, Comput. Chem. Eng..

[14]  Dexuan Zou,et al.  Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy , 2019, Applied Energy.

[15]  Mehdi Rahmani-andebili,et al.  Stochastic, adaptive, and dynamic control of energy storage systems integrated with renewable energy sources for power loss minimization , 2017 .

[16]  Oscar Danilo Montoya Giraldo Solving a Classical Optimization Problem Using GAMS Optimizer Package: Economic Dispatch Problem Implementation , 2017 .

[17]  Z. Dong,et al.  Optimal Allocation of Energy Storage System for Risk Mitigation of DISCOs With High Renewable Penetrations , 2014, IEEE Transactions on Power Systems.

[18]  Josep M. Guerrero,et al.  Energy Management System for an Islanded Microgrid With Convex Relaxation , 2019, IEEE Transactions on Industry Applications.

[19]  Behnam Mohammadi-Ivatloo,et al.  GAMS based approach for optimal design and sizing of a pressure retarded osmosis power plant in Bahmanshir river of Iran , 2015 .

[20]  Didier Giraldo-Buitrago,et al.  Control Global del Péndulo de Furuta Empleando Redes Neuronales Artificiales y Realimentación de Variables de Estado Global Control of the Furuta Pendulum Using Artificial Neural Networks and Feedback of State Variables , 2013 .

[21]  Josep M. Guerrero,et al.  A coordinated control of hybrid ac/dc microgrids with PV-wind-battery under variable generation and load conditions , 2019, International Journal of Electrical Power & Energy Systems.

[22]  Yunming Ye,et al.  Sentiment analysis through critic learning for optimizing convolutional neural networks with rules , 2019, Neurocomputing.

[23]  Eenjun Hwang,et al.  Recurrent inception convolution neural network for multi short-term load forecasting , 2019, Energy and Buildings.

[24]  Hossein Shahsavari,et al.  A cost-efficient application of different battery energy storage technologies in microgrids considering load uncertainty , 2019, Journal of Energy Storage.

[25]  Nick Papanikolaou,et al.  Wireless Power Transfer for Distributed Energy Sources Exploitation in DC Microgrids , 2019, IEEE Transactions on Sustainable Energy.

[26]  Sanjib Ganguly,et al.  Multi-objective planning for the allocation of PV-BESS integrated open UPQC for peak load shaving of radial distribution networks , 2019, Journal of Energy Storage.

[27]  Josep M. Guerrero,et al.  Energy Storage Systems for Shipboard Microgrids—A Review , 2018, Energies.

[28]  Xiaofeng Yin,et al.  Optimal battery sizing of smart home via convex programming , 2017 .

[29]  Luis Fontan,et al.  Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control , 2018, Renewable Energy.

[30]  Enrique Castillo,et al.  Building and Solving Mathematical Programming Models in Engineering and Science , 2001 .

[31]  Hoay Beng Gooi,et al.  Solar radiation forecast based on fuzzy logic and neural networks , 2013 .

[32]  Navid Ghaffarzadeh,et al.  Optimal sizing of battery energy storage systems in off-grid micro grids using convex optimization , 2019, Journal of Energy Storage.

[33]  Chul-Hwan Kim,et al.  Optimal sizing and allocation of battery energy storage systems with wind and solar power DGs in a distribution network for voltage regulation considering the lifespan of batteries , 2017 .

[34]  David J. Hill,et al.  Multi-Agent Optimal Allocation of Energy Storage Systems in Distribution Systems , 2017, IEEE Transactions on Sustainable Energy.

[35]  B. Sivaneasan,et al.  Solar Forecasting using ANN with Fuzzy Logic Pre-processing , 2017 .

[36]  L. F. Grisales-Norena,et al.  Economic dispatch of energy storage systems in dc microgrids employing a semidefinite programming model , 2019, Journal of Energy Storage.