A Price-elastic Approach for Optimal Scheduling of Small-scale Storage Devices in Smart Houses with Short-term and Long-term Constraints


Tanrioven K., DALDABAN F., Cebeci M. E. , Tor O. B. , Teimourzadeh S.

JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, vol.10, no.1, pp.163-169, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 10 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.35833/mpce.2019.000094
  • Journal Name: JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.163-169
  • Keywords: Storage device, depth-of-discharge, life span, smart house, MODEL, COST
  • Erciyes University Affiliated: Yes

Abstract

Consecutive charging and discharging of storage devices (SDs) might deem beneficial from the perspective of short-term operation. However, it highly impacts the life span of the embedded battery and render restrictions on energy storage capacity. We investigate short-term and long-term constraints of SDs through a three-stage price-elastic approach to the optimal operation of small-scale SDs in smart houses. The first stage deals with data and scenario characterization where the data for determining short-term and long-term operation constraints of SD are acquired. Proper number of scenarios are generated to represent uncertain parameters such as long-term demand forecasting, daily load profile, electricity price, and photovoltaic (PV) generation. The second stage optimizes the long-term operation of SD using the envisioned scenarios subject to the long-term operation constraints and the installment costs of SDs. The outputs of this stage are two indicators referred to as price elasticity and price offset coefficients, which are used as the inputs for the third stage. The third stage is responsible for decision-making on short-term operation of SDs. The outputs of the second stage along with short-term forecasting for daily electricity price, daily load and daily PV generation are acquired. Based on the acquired data, proper price elasticity and price offset are determined for optimal operation. Comprehensive simulations are performed for different demand forecasting and electricity prices. Simulation results confirm the effectiveness of the proposed approach.