Abstract
In the last few years, several investigations have been carried out in the field of optimal sizing of energy storage systems (ESSs) at both the transmission and distribution levels. Nevertheless, most of these works make important assumptions about key factors affecting ESS profitability such as efficiency and life cycles and especially about the specific costs of the ESS, without considering the uncertainty involved. In this context, this work aims to answer the question: what should be the costs of different ESS technologies in order to make a profit when considering peak shaving applications? The paper presents a comprehensive sensitivity analysis of the interaction between the profitability of an ESS project and some key parameters influencing the project performance. The proposed approach determines the break-even points for different ESSs considering a wide range of life cycles, efficiencies, energy prices, and power prices. To do this, an optimization algorithm for the sizing of ESSs is proposed from a distribution company perspective. From the results, it is possible to conclude that, depending on the values of round trip efficiency, life cycles, and power price, there are four battery energy storage systems (BESS) technologies that are already profitable when only peak shaving applications are considered: lead acid, NaS, ZnBr, and vanadium redox.
Highlights
In power systems, the load profile during the day is characterized by short periods of time when significant amounts of power are required, the so called “peak load times” of the system
Ir is the interest rate, g1 is the annual growth of the energy price, g2 is the annual growth of the peak power price paid by the distribution network (DN), and T is the battery energy storage systems (BESS) lifetime in years
This paper has investigated the interaction between the optimal sizing of BESSs and key factors
Summary
The load profile during the day is characterized by short periods of time when significant amounts of power are required, the so called “peak load times” of the system. Depending on the load composition (industrial, residential, and/or commercial) and the season of the year, peak load time periods may occur at different hours during the day. Generation units with low efficiencies have to be started to meet this peak demand. These peak demands have a direct impact on network planning [1]. Distribution network (DN) customers are charged according to their total energy consumption and according to their highest power demand. The exact pricing structure may vary slightly from country to country, but the basis is essentially the same everywhere [1] These demand charges usually represent an important portion of the total electricity payments of DN customers. DN customers are extremely interested in lowering these charges without lowering their energy consumption
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