Estimating the participation value of electricity demand-response programmes for a two-stage production system

ABSTRACT

Electricity demand-response programmes, such as the incentive-based and price-based programmes, have been used by utilities to induce customers to reduce their electricity consumption during peak periods. This study investigates the production decisions of a two-stage production system under these programmes in a situation where peak periods arrive randomly in the manufacturing cycle. Analytical results show that under demand-response programmes, the manufacturer, who aims at minimising the total operational cost, usually selects a lower production rate during peak periods and a higher one during non-peak periods. Notably, the uncertainty of the peak periods also has a significant influence on the manufacturer's production plans under these programmes. This paper further investigates the efficiency of different demand-response programmes in reducing the inventory holding cost and electricity cost. The results indicate that participating in the demand-response programmes does not always result in a higher inventory holding cost, which goes against the manufacturer's intuition about these programmes. In addition, this paper evaluates the manufacturers' preference for these demand-response programmes by comparing the operational cost savings generated from participating in the two programmes. It turns out that both the demand-response signals and the inventory holding cost substantially influence the manufacturer's willingness to participate in the programmes.

KEYWORDS:

• Incentive-based programme
• time-of-use pricing scheme
• uncertainty
• two-stage production system
• production planning

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 https://yearbook.enerdata.net/total-energy/world-consumption-statistics.html

2 https://data.worldbank.org/indicator/EG.USE.COMM.FO.ZS

3 https://data.worldbank.org/indicator/EN.ATM.CO2E.SF.ZS

4 https://databank.worldbank.org/reports.aspx?source=2&series=EG.FEC.RNEW.ZS

5 https://www.iea.org/news/global-energy-demand-rose-by-23-in-2018-its-fastest-pace-in-the-last-decade

6 https://www.iea.org/data-and-statistics/charts/world-electricity-final-consumption-by-sector-1974-2018

7 https://www.statista.com/statistics/1050448/industrial-electricity-prices-including-tax-germany/

Additional information

Funding

This study was supported by the National Natural Science Foundation of China under [grant number 71772191] and the Fundamental Research Funds for the Central Universities [grant number 2020jbkyzy03].

Notes on contributors

Yunrong Zhang

Yunrong Zhang is a senior research fellow of Sustainable Operations Management at Lanzhou University, China. Her research interests are in the areas of supply chain management, sustainable operations management and game theory. She has published papers in renowned international journals, such as the European Journal of Operational Research, the International Journal of Production Research, or the Chinese Journal of Management Science.

Christoph H. Glock

Christoph H. Glock is a Professor of Production and Supply Chain Management at Technical University of Darmstadt, Germany. His research interests are in the areas of inventory control, supply chain management, and production management. He has published in renowned international journals, e.g. in IISE Transactions, the European Journal of Operational Research, Omega, or Decision Sciences.

Zhixiang Chen

Zhixiang Chen is Professor of Production and Operation Management at Sun Yat-sen University, China. His research interests are in the areas of Management Science and Engineering, Production and Operation Management and Supply Chain Management. He has published papers in renowned journals, such as the European Journal of Operational Research, the International Journal of Production Research, the Chinese Journal of Management or the Chinese Journal of Management Science.