Microgrid Energy Management Strategy with Battery Energy Storage System and Approximate Dynamic Programming

The growth in distributed renewable power systems provides opportunities to construct more microgrids. With the help of battery energy storage systems (BESS) in the microgrids, the variable and intermittent renewable energy can be smoothed and utilized locally without risking the main electrical grid. Furthermore, the energy costs in microgrids can be reduced significantly with proper control of BESS based on predicted data of renewable power and electricity price. In this paper, we propose an energy management strategy for microgrids with renewable power systems integrated with BESS. A deregulated energy market environment is considered, which allows energy trading from private participates to the main electrical grids. The objective is to maximize the profit gained by selling the excessive renewable energy generated and minimize the cost to meet the load demand in the microgrid, and an approximate dynamic programming algorithm is proposed to solve the issue. The operational cost from battery lifetime consumption is considered, and the rolling horizon approach is utilized for long-term simulation. Both short-term and long-term forecasting are used in the algorithm and updated in every control stage. The algorithm is tested with practical wind power, load demand, and electricity price data.

[1]  David J. Hill,et al.  Optimal Short-term Power Dispatch Scheduling for a Wind Farm with Battery Energy Storage System , 2015 .

[2]  N.D. Hatziargyriou,et al.  An Advanced Statistical Method for Wind Power Forecasting , 2007, IEEE Transactions on Power Systems.

[3]  A. Keeli,et al.  Optimal use of second life battery for peak load management and improving the life of the battery , 2012, 2012 IEEE International Electric Vehicle Conference.

[4]  S. Drouilhet,et al.  A Battery Life Prediction Method for Hybrid Power Applications Preprint , 1997 .

[5]  Remus Teodorescu,et al.  Accelerated lifetime testing methodology for lifetime estimation of Lithium-ion batteries used in augmented wind power plants , 2013, 2013 IEEE Energy Conversion Congress and Exposition.

[6]  A. V. Savkin,et al.  A Method for Short-Term Wind Power Prediction With Multiple Observation Points , 2012, IEEE Transactions on Power Systems.

[7]  Bri-Mathias Hodge,et al.  Wind power forecasting error distributions over multiple timescales , 2011, 2011 IEEE Power and Energy Society General Meeting.

[8]  Pukar Mahat,et al.  A micro-grid battery storage management , 2013, 2013 IEEE Power & Energy Society General Meeting.

[9]  Andrey V. Savkin,et al.  Minimization and control of battery energy storage for wind power smoothing: Aggregated, distributed and semi-distributed storage , 2014 .

[10]  Warren B. Powell,et al.  What you should know about approximate dynamic programming , 2009, Naval Research Logistics (NRL).

[11]  Sotiris Papantoniou,et al.  Development of optimization algorithms for the Leaf Community microgrid , 2015 .

[12]  Alistair J. Davidson,et al.  Lead batteries for utility energy storage: A review , 2018 .

[13]  Andrey V. Savkin,et al.  A market-oriented wind power dispatch strategy using adaptive price thresholds and battery energy storage: A market-oriented wind power dispatch strategy using adaptive price thresholds and battery energy storage , 2018 .

[14]  Heinz Wenzl,et al.  Comparison of different approaches for lifetime prediction of electrochemical systems—Using lead-acid batteries as example , 2008 .

[15]  Andrey V. Savkin,et al.  Minimizing the energy cost for microgrids integrated with renewable energy resources and conventional generation using controlled battery energy storage , 2016 .

[16]  Andrey V. Savkin,et al.  A Constrained Monotonic Charging/Discharging Strategy for Optimal Capacity of Battery Energy Storage Supporting Wind Farms , 2016, IEEE Transactions on Sustainable Energy.

[17]  Dong Yue,et al.  Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization , 2017 .

[18]  Warren B. Powell,et al.  Approximate Dynamic Programming - Solving the Curses of Dimensionality , 2007 .

[19]  V. G. Agelidis,et al.  Improving Wind Farm Dispatch in the Australian Electricity Market With Battery Energy Storage Using Model Predictive Control , 2013, IEEE Transactions on Sustainable Energy.

[20]  S. Ali Pourmousavi,et al.  A framework for real-time power management of a grid-tied microgrid to extend battery lifetime and reduce cost of energy , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[21]  Anuj Puri.  Optimally smoothing output of PV farms , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[22]  Andrey V. Savkin,et al.  On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting , 2018 .

[23]  Andrey V. Savkin,et al.  A model predictive control approach to the problem of wind power smoothing with controlled battery storage , 2010 .