Avoided Costs-Based Comparison of Consumer-Scale Energy Storage Control Approaches

The aim of the paper is to obtain new knowledge for the improvement of planning and control of manufacturing processes in energy-intensive enterprises, considering variations in electricity market price and energy storage possibilities. Particularly, optimisation of the planning and control of consumer-scale battery energy storage system connected behind the meter is considered. To solve the optimisation problem, price forecasting procedures, models of the objects under study, control, optimisation, and verification algorithms were chosen. The objective function of the optimisation problem is formulated in the form of avoided costs, defined as the difference between the costs of consumed electricity with and without an energy storage system. Deterministic and stochastic approaches were used and compared.

[1]  Ka Lok Man,et al.  Electricity Price Forecasting for Nord Pool Data , 2018, 2018 International Conference on Platform Technology and Service (PlatCon).

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

[3]  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).

[4]  Andreas Jossen,et al.  Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids , 2017 .

[5]  Antonio Violi,et al.  Dynamic pricing of electricity in retail markets , 2009, 4OR.

[6]  Tomaz Dentinho,et al.  Electrical Energy Storage Systems Feasibility; the Case of Terceira Island , 2017 .

[7]  Warren B. Powell,et al.  Clearing the Jungle of Stochastic Optimization , 2014 .

[8]  Lubov Petrichenko,et al.  Estimating the Economic Impacts of Net Metering Schemes for Residential PV Systems with Profiling of Power Demand, Generation, and Market Prices , 2018, Energies.

[9]  Warren B. Powell,et al.  Tutorial on Stochastic Optimization in Energy—Part II: An Energy Storage Illustration , 2016, IEEE Transactions on Power Systems.

[10]  Javier Reneses,et al.  Cost–benefit analysis of battery storage in medium-voltage distribution networks , 2016 .

[11]  Hrvoje Pandzic,et al.  Energy storage operation in the day-ahead electricity market , 2015, 2015 12th International Conference on the European Energy Market (EEM).

[12]  Florentina Paraschiv,et al.  Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks , 2016 .

[13]  E. Telaretti,et al.  A Simple Operating Strategy of Small-Scale Battery Energy Storages for Energy Arbitrage under Dynamic Pricing Tariffs , 2015 .

[14]  S. Amrouche,et al.  Overview of energy storage in renewable energy systems , 2015, 2015 3rd International Renewable and Sustainable Energy Conference (IRSEC).

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

[16]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[17]  Warren B. Powell,et al.  Tutorial on Stochastic Optimization in Energy—Part I: Modeling and Policies , 2016, IEEE Transactions on Power Systems.

[18]  Guido Carpinelli,et al.  A New Hybrid Approach Using the Simultaneous Perturbation Stochastic Approximation Method for the Optimal Allocation of Electrical Energy Storage Systems , 2018, Energies.

[19]  V. Mendes,et al.  Short-term electricity prices forecasting in a competitive market: A neural network approach , 2007 .

[20]  Bart De Schutter,et al.  Forecasting day-ahead electricity prices in Europe: the importance of considering market integration , 2017, ArXiv.

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

[22]  Antans Sauhats,et al.  Cost-Benefit Analysis of Li-Ion Batteries in a Distribution Network , 2018, 2018 15th International Conference on the European Energy Market (EEM).

[23]  Raymond H. Byrne,et al.  Maximizing the cost-savings for time-of-use and net-metering customers using behind-the-meter energy storage systems , 2017, 2017 North American Power Symposium (NAPS).

[24]  J. Partanen,et al.  Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks , 2013 .