Optimal Time-of-Use Management with Power Factor Correction Using Behind-the-Meter Energy Storage Systems

In this work, we provide an economic analysis of using behind-the-meter (BTM) energy storage systems (ESS) for time-of-use (TOU) bill management together with power factor correction. A nonlinear optimization problem is formulated to find the optimal ESS’s charge/discharge operating scheme that minimizes the energy and demand charges while correcting the power factor of the utility customers. The energy storage’s state of charge (SOC) and inverter’s power factor (PF) are considered in the constraints of the optimization. The problem is then transformed to a Linear Programming (LP) problem and formulated using Pyomo optimization modeling language. Case studies are conducted for a waste water treatment plant (WWTP) in New Mexico.

[1]  Raymond H. Byrne,et al.  Potential revenue from electrical energy storage in ERCOT: The impact of location and recent trends , 2015, 2015 IEEE Power & Energy Society General Meeting.

[2]  J. Neubauer,et al.  Deployment of Behind-The-Meter Energy Storage for Demand Charge Reduction , 2015 .

[3]  Raymond H. Byrne,et al.  Energy Management and Optimization Methods for Grid Energy Storage Systems , 2018, IEEE Access.

[4]  David L. Woodruff,et al.  Pyomo — Optimization Modeling in Python , 2012, Springer Optimization and Its Applications.

[5]  Andrew Thomas,et al.  Evaluation of technical and financial benefits of battery-based energy storage systems in distribution networks , 2016 .

[6]  Raymond H. Byrne,et al.  Estimating potential revenue from electrical energy storage in PJM , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[7]  Sandia Report,et al.  Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide A Study for the DOE Energy Storage Systems Program , 2010 .

[8]  Patrick Balducci,et al.  Economic analysis and optimal sizing for behind-the-meter battery storage , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[9]  Lukas G. Swan,et al.  Battery storage system for residential electricity peak demand shaving , 2012 .

[10]  Changhong Zhao,et al.  Profit-Maximizing Planning and Control of Battery Energy Storage Systems for Primary Frequency Control , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[11]  R. Ahuja.  Minimax linear programming problem , 1985 .

[12]  Raja Ayyanar,et al.  Design and Strategy for the Deployment of Energy Storage Systems in a Distribution Feeder With Penetration of Renewable Resources , 2015, IEEE Transactions on Sustainable Energy.

[13]  Hadi Khani,et al.  Transmission Congestion Relief Using Privately Owned Large-Scale Energy Storage Systems in a Competitive Electricity Market , 2016, IEEE Transactions on Power Systems.

[14]  Armando Fresquez,et al.  Performance of utility interconnected photovoltaic inverters operating beyond typical modes of operation , 2013, 2013 IEEE 39th Photovoltaic Specialists Conference (PVSC).

[15]  R. Byrne.  Estimating the Maximum Potential Revenue for Grid Connected Electricity Storage: Arbitrage and Regulation , 2012 .

[16]  Raymond H. Byrne,et al.  Maximizing revenue from electrical energy storage in MISO energy & frequency regulation markets , 2017, 2017 IEEE Power & Energy Society General Meeting.

[17]  A. Oudalov,et al.  Sizing and Optimal Operation of Battery Energy Storage System for Peak Shaving Application , 2007, 2007 IEEE Lausanne Power Tech.