Reducing power system costs with thermal energy storage

Thermal energy storage (TES) have been shown to be locally beneficial, helping building managers reduce their electricity bills. Due to increasing interest in TES, it is important for utilities and policy-makers alike to consider the economic implications of increasing TES penetration levels on to the power system. The aim of this paper is to show that TES can also bring significant benefits, and that these benefits are maximized when loads are properly controlled. This paper studies the effect of a heuristic optimal TES load allocation strategy on the New York Independent System Operator (NYISO) system’s load factor, peak-to-valley ratio, ramping, and operation costs. These results are also compared to different control methods in order to justify the need for such a model and also to justify the results. We first determine the total amount of cooling load that can be shifted in New York State through the use of TES technology by using data from various government agencies. Using a coefficient of performance (COP) model for the chiller to account for efficiency changes throughout the day, the flexible cooling demand for the system is estimated. A method to optimally allocate flexible cooling loads is then used with the goal of reducing the power system cost, while providing the necessary cooling load to keep buildings at comfortable temperature levels throughout the state. Power system cost is determined by using a wholesale energy cost model that was developed using NYISO market and load data for both the day-ahead and real-time wholesale markets. By flattening out the system load, increasing the electrical system’s load factor, and reducing system ramping, TES can reduce steady-state and ramping costs, thus reducing the overall power system’s operation costs.

[1]  Fabio Polonara,et al.  State of the art of thermal storage for demand-side management , 2012 .

[2]  K. M. Zhang,et al.  Using vehicle-to-grid technology for frequency regulation and peak-load reduction , 2011 .

[3]  Moncef Krarti,et al.  Optimal control of building storage systems using both ice storage and thermal mass – Part I: Simulation environment , 2012 .

[4]  J. Eto,et al.  Understanding the cost of power interruptions to U.S. electricity consumers , 2004 .

[5]  Moncef Krarti,et al.  Development of a Predictive Optimal Controller for Thermal Energy Storage Systems , 1997 .

[6]  Saifur Rahman,et al.  Impacts of ice storage on electrical energy consumptions in office buildings , 2012 .

[7]  Gregor P. Henze,et al.  An Overview of Optimal Control for Central Cooling Plants with Ice Thermal Energy Storage , 2003 .

[8]  Ibrahim Dincer,et al.  On thermal energy storage systems and applications in buildings , 2002 .

[9]  K. F. Valentine,et al.  Electric vehicle charging and wind power integration: Coupled or decoupled electricity market resources? , 2012, 2012 IEEE Power and Energy Society General Meeting.

[10]  Moncef Krarti,et al.  Guidelines for improved performance of ice storage systems , 2003 .

[11]  William G. Temple,et al.  Intelligent electric vehicle charging: Rethinking the valley-fill , 2011 .

[12]  Jiangjiang Wang,et al.  Using the fuzzy multi-criteria model to select the optimal cool storage system for air conditioning , 2008 .

[13]  Gregor P. Henze Impact of real-time pricing rate uncertainty on the annual performance of cool storage systems , 2003 .

[14]  Timothy D. Mount,et al.  DEVELOPING A SMART GRID THAT CUSTOMERS CAN AFFORD , 2015 .

[15]  Makoto Tanaka Real-time pricing with ramping costs: A new approach to managing a steep change in electricity demand , 2006 .

[16]  Adnan A.W. Al-Qalamchi,et al.  Performance of ice storage system utilizing a combined partial and full storage strategy , 2007 .

[17]  Douglas Hilleman,et al.  A New Paradigm: Cycling Operations at Nuclear Power Plants in the United States , 2013 .

[18]  Sih-Li Chen,et al.  Optimization of an ice-storage air conditioning system using dynamic programming method , 2005 .

[19]  Sri.S.R Sadugol Effect of System Load Factor on Transmission & Distribution Losses , 2012 .

[20]  Wen-Shing Lee,et al.  Optimization for ice-storage air-conditioning system using particle swarm algorithm , 2009 .