OPTIMIZING ENERGY CONTRACTS FOR BUSINESS ENTERPRISES AND COMPANIES
Wadim STRIELKOWSKI
PhD in Economics, Energy Policy Research Group, University of Cambridge, United Kingdom
PhD in Economics, Energy Policy Research Group, University of Cambridge, United Kingdom
Evgeny LISIN
PhD in Economics, Associate Professor of the Department of Economics in Power Engineering and Industry, National Research University “Moscow Power Engineering Institute”, Moscow, Russia
PhD in Economics, Associate Professor of the Department of Economics in Power Engineering and Industry, National Research University “Moscow Power Engineering Institute”, Moscow, Russia
TERRA ECONOMICUS, 2016, Vol. 14 (no. 2),
Soaring electricity prices constitute a serious obstacle for both small and large business enterprises, since the costs of electricity are projected into the market prices of their goods and services. This paper analyzes the current conditions and offers some measures for optimization by the means of the parametric model that describes the conditions of power purchase agreements (PPA) for different groups of consumers with different demand profiles. We use the case study of PPAs contracts in Moscow, the Russian capital, in order to model the optimal power supply for three hypothetical customers: a meat factory, a car wash, and an office unit. We employ a Heaviside step unit function in order to model the consumer demand and to calculate the gains for various types of consumers. Our results demonstrate that consumers with higher energy consumption are better off when moving to across tariff zones changing the value of the contract. Moreover, it appears that changes into the investment policy of the hypothetical Russian enterprise enable it to lower the costs of energy and thence to increase the competitiveness of its good and services offered on the market. Our results might also be important in the context of the recent focus on the renewable energy and low-carbon future.
Keywords: electricity markets; consumers; tariffs; parametric model; optimization; Russian Federation
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Publisher: Southern Federal University
Founder: Southern Federal University
ISSN: 2073-6606
Founder: Southern Federal University
ISSN: 2073-6606