A mathematical model for simultaneous optimization of renewable electricity price and construction of new wind power plants (case study: Kermanshah)

International Journal of Energy and Environmental Engineering https://doi.org/10.1007/s40095-017-0254-4 ORIGINAL RESEARCH A mathematical model for simultaneous optimization of renewable electricity price and construction of new wind power plants (case study: Kermanshah) Mojtaba Qolipour1 · Ali Mostafaeipour1 · Mostafa Rezaei1 Received: 20 July 2017 / Accepted: 11 November 2017 © The Author(s) 2017. This article is an open access publication Abstract This study aimed to provide a mathematical model for the determination of optimal wind power price in the case of construction of new off-grid-connected wind power plants in different areas. The proposed model is based on nine features including construction cost, side costs (cost of replacement, maintenance, and repairs), pollution, electricity generation, profit, renewability level, green economy, rate of return on investment, and consumption. First, the inputs of the mathematical model were obtained by technical–economic feasibility evaluation of the study areas in the software Homer using the 10-year wind speed data (2006–2016). The optimal wind power prices were then determined in three different modes by solving the mathematical model with MATLAB. The modes considered in optimization were the construction of 1, 2, and 3 wind power plants in the study areas. Simulation of construction of wind power plants in each mode was conducted in the software Homer. The results showed that the optimal wind power price resulting from construction of 1, 2, and 3 are 0.159, 0.151, and 0.140 $ per kilowatt, respectively. The proposed mathematical model was found to have sufficient capability in determination of optimal wind power price. Keywords Wind energy · Mathematical optimization model · Pricing · Wind power plant · Kermanshah List of symbols Indices i = 1,2,…I Areas k = 1, 2, … , K Features of wind power plant lk = 1, 2, … , Lk Levels of feature k m = 1, 2, … , M New wind power plants n = 1, 2, … , N Existing (rival) wind power plants Variables xmkl A 0–1 variable; 1 if level l of feature k is allocated to wind power plant m; 0 otherwise yim A 0–1 variable; 1 if wind power plant m is allocated to area i; otherwise Pm Price of the electricity generated by wind power plant m (per kilowatt) * Ali Mostafaeipour 1 Industrial Engineering Department, Yazd University, Yazd, Iran Parameters Qi Number of applicant in area i uikl Utility of level l of feature k in area i (obtained by joint analysis methods; expressed in price per kilowatt of wind power) uin Utility of the electricity generated by rival wind power plant n for area i Pn Price of the electricity generated by rival wind power plant n Cfix Fixed production cost ckl Production cost associated with level l of feature k (obtained from software simulation) Qm Size of applicant for wind power plant m PRim Probability of the electricity generated by wind power plant m being bought by area i Uim Utility of the electricity generated by wind power plant m for area i ($/KW) Cmvar Variable production cost of wind power plant m ($/KW) 13 Vol.:(0123456789) International Journal of Energy and Environmental Engineering Introduction Recent years have witnessed a steady increase in the share of renewable energies in the world’s energy portfolio; an increase that has contributed not only to reduction of greenhouse gas emissions but also to diversification and security of energy supplies and growth of business and employment in renewable energy industry [1, 2]. Despite the high potentials of a variety of renewable energies in Iran, inadequate pricing and access to relatively cheap oil and gas resources have impeded the progress of renewable energies in this country [3]. In the past 15 years, guaranteed purchase of renewable electricity has been as common support measure in many countries. This practice is relatively new in Iran and investment in this sector under current situation is not cost-effective [4–6]. It has been shown that pricing is one of the key factors of promotion and success of renewable energies. Renewable energy pricing is identical to fossil energy pricing except that it also takes account of environmental impacts of fossil fuel [7]. In general, electricity pricing is a function of three groups of factors: organizational factors, customer factors, and market factors. There are two approaches to electricity pricing. In the traditional approach, some researchers believe in Marginal Cost Pricing while others believe in monopoly of production side. The second approach is based on the use of modern smart methods that allow the generators to introduce time-varying electricity tariffs [8, 9]. It is important to note that there are six major methods of electricity pricing, including: flat rate, United Nations’ method, Long Run Marginal Cost (LRMC, the cost imposed on the system per kW increase in consumption), social welfare optimization subject to market balance constraints, market clearing price (the intersection of supply and applicant functions), and cost-based pricing [10–14]. Price is a numerical quantity representing the value of a commodity relative to others, so pricing of a product may serve as an incentive for both investors and consumers [15]. In addition, there is a close association between price, consumption, and sectorial development. This association also applies to wind energy, so proper wind power pricing leads to a stable power generation sector and better motivation and organization of small and large generators participating in renewable power generation efforts [16]. Nevertheless, renewable electricity purchase prices need to be higher than conventional electricity prices of the same market so that producers remain interested in further investment in renewable electricity generation [17]. In Iran’s current electricity market, however, per kilowatt price of wind electricity is being computed without any incentive to motivate private investment, so the pricing method actually impedes the development of this sector. 13 There is an extensive literature on the electricity pricing in different parts of the world, and more specifically on pricing of wind power. Levitt et al. [18] studied the pricing of electricity produced from coastal wind farms using the data gathered from 35 projects in Europe, China and the United States of America, and computed the breakeven price of this type of electricity. Simao et al. [19] studied the pricing of wind power to ensure integration in a European competitive electricity market. Rubin and Babcock [20] assessed the impact of wind power capacity developments and wind power pricing methods on the performance of unregulated electricity markets. Heydarian-Forushani and Golshan [21] used a flexible pricing schedule and TOU (time of use) pricing scheme to introduce flexibility to wind power pricing. Oskouei and Yazdankhah [22] presented a scenario-based stochastic optimal operation for iteration-based opti (...truncated)