A Simple Operating Strategy of Small-Scale Battery Energy Storages for Energy Arbitrage under Dynamic Pricing Tariffs

Abstract

Abstract:Price arbitrage involves taking advantage of an electricity price difference, storing electricity during low-prices times, and selling it back to the grid during high-prices periods. This strategy can be exploited by customers in presence of dynamic pricing schemes, such as hourly electricity prices, where the customer electricity cost may vary at any hour of day, and power consumption can be managed in a more flexible and economical manner, taking advantage of the price differential. Instead of modifying their energy consumption, customers can install storage systems to reduce their electricity bill, shifting the energy consumption from on-peak to off-peak hours. This paper develops a detailed storage model linking together technical, economic and electricity market parameters. The proposed operating strategy aims to maximize the profit of the storage owner (electricity customer) under simplifying assumptions, by determining the optimal charge/discharge schedule. The model can be applied to several kinds of storages, although the simulations refer to three kinds of batteries: lead-acid, lithium-ion (Li-ion) and sodium-sulfur (NaS) batteries. Unlike literature reviews, often requiring an estimate of the end-user load profile, the proposed operation strategy is able to properly identify the battery-charging schedule, relying only on the hourly price profile, regardless of the specific facility's consumption, thanks to some simplifying assumptions in the sizing and the operation of the battery. This could be particularly useful when the customer load profile cannot be scheduled with sufficient reliability, because of the uncertainty inherent in load forecasting. The motivation behind this research is that storage devices can help to lower the average electricity prices, increasing flexibility and fostering the integration of renewable sources into the power system.

Transactive-Market-Based Operation of Distributed Electrical Energy Storage with Grid Constraints

“…The block allocation, as well as their sums, have to stay within the given limits in Equations (10), (14) and (11), (15). Equations (12) and (16) show reactive power demand and generation of load and DG units, respectively.…”

“…EES units have great potential to help form deeper penetrations of renewable energy into electricity grids and deliver efficient, low-cost, and fundamental grid services [13]. EES operation, however, is restricted by certain physical constraints depending on the unit's type and technology [14]. Some EES technologies have different depths of discharge (batteries are typically not discharged completely and depth of discharge refers to the extent to which they are discharged), charging/discharging and self-discharging efficiencies.…”

Energy Storage System Control Algorithm by Operating Target Power to Improve Energy Sustainability of Smart Home

“…E. Telaretti et al examined and evaluated the convenience of using battery energy storage systems to reduce customer electricity bill based on the parametric analysis of demand charges according to the battery types [26]. The state-of-the art of this paper also introduced various studies on energy storage system for user's economic benefit [27][28][29]. H. Kanchev et al introduced the central management of the microgrid and a local power management on the customers' side considering timing classification of control functions to plan lower energy costs for user and reduce greenhouse gas emissions.…”

Resource allocation problems in decentralized energy management

“…A popular steering signal is so-called time-of-use pricing, where the price of electricity varies over predetermined time intervals to incentivize customers to shift their consumption to times with abundant production (see, e.g. Telaretti et al 2016;You et al 2012). However, such (linear) pricing structures often suffer from drawbacks in that they often only shift the peak instead of lowering it or even cause new, higher peaks in case a large number of (automated) devices respond to the same price signal (Barbato and Capone 2014;McKenna and Keane 2014).…”