• Abstract and Figures
• Public Full-text

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 con- struction of new of-grid-connected wind power plants in diferent areas. The proposed model is based on nine features including construction cost, side costs (cost of replacement, maintenance, and repairs), pollution, electricity generation, proft, renewability level, green economy, rate of return on investment, and consumption. First, the inputs of the mathemati- cal 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 diferent 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 sufcient capability in determination of optimal wind power price. Keywords Wind energy · Mathematical optimization model · Pricing · Wind power plant · Kermanshah List of symbols Parameters Q i Indices i Number of applicant in area uikl Utility of level l of feature k in area i i = 1,2,…I Areas (obtained by joint analysis methods; k = 1, 2, … , K Features of wind power plant expressed in price per kilowatt of wind lk = 1, 2, … , Lk Levels of feature k power) m = 1, 2, … , M New wind power plants uin Utility of the electricity generated by n = 1, 2, … , N Existing (rival) wind power plants rival wind power plant n for area i Variables Pn Price of the electricity generated by rival xmkl A 0–1 variable; 1 if level l of feature k wind power plant n fix is allocated to wind power plant m; 0 C Fixed production cost otherwise ckl Production cost associated with level yim A 0–1 variable; 1 if wind power plant m l of feature k (obtained from software is allocated to area i; otherwise simulation) Pm Price of the electricity generated by Qm Size of applicant for wind power plant m wind power plant m (per kilowatt) PRim Probability of the electricity generated by wind power plant m being bought by area i Uim Utility of the electricity generated by * Ali Mostafaeipour wind power plant m for area i ($/KW) [email protected] var Cm Variable production cost of wind power 1 m Industrial Engineering Department, Yazd University, Yazd, plant ($/KW) Iran Vol.:(0123456789)1 3 International Journal of Energy and Environmental Engineering Introduction There is an extensive literature on the electricity pricing in diferent parts of the world, and more specifcally on pric- Recent years have witnessed a steady increase in the share ing of wind power. Levitt et al. [18] studied the pricing of of renewable energies in the world’s energy portfolio; an electricity produced from coastal wind farms using the data increase that has contributed not only to reduction of green- gathered from 35 projects in Europe, China and the United house gas emissions but also to diversifcation and security States of America, and computed the breakeven price of of energy supplies and growth of business and employment this type of electricity. Simao et al. [19] studied the pricing in renewable energy industry [1, 2]. Despite the high poten- of wind power to ensure integration in a European com- tials of a variety of renewable energies in Iran, inadequate petitive electricity market. Rubin and Babcock [20] assessed pricing and access to relatively cheap oil and gas resources the impact of wind power capacity developments and wind have impeded the progress of renewable energies in this power pricing methods on the performance of unregulated country [3]. In the past 15 years, guaranteed purchase of electricity markets. Heydarian-Forushani and Golshan [21] renewable electricity has been as common support measure used a fexible pricing schedule and TOU (time of use) pric- in many countries. This practice is relatively new in Iran ing scheme to introduce fexibility to wind power pricing. and investment in this sector under current situation is not Oskouei and Yazdankhah [22] presented a scenario-based cost-efective [4–6]. It has been shown that pricing is one of stochastic optimal operation for iteration-based optimiza- the key factors of promotion and success of renewable ener- tion of wind, solar, and pumped-storage electricity prices. gies. Renewable energy pricing is identical to fossil energy Gao et al. [23] performed a metric-based analysis on the pricing except that it also takes account of environmental wind power prices to promote the use of wind energy by impacts of fossil fuel [7]. identifying the trends of change in wind power prices. Katz In general, electricity pricing is a function of three groups et al. [24] analyzed the efect of residential electricity expen- of factors: organizational factors, customer factors, and ditures on the changes in electricity price using a partial market factors. There are two approaches to electricity pric- equilibrium model. Amirnekooei et al. [25] used the inte- ing. In the traditional approach, some researchers believe rior point algorithm for pricing of natural gas and electricity in Marginal Cost Pricing while others believe in monopoly based on technical–economic constraints. Oseni and Pollitt of production side. The second approach is based on the [26] conducted a business-model study on various aspects of use of modern smart methods that allow the generators to energy prices based on 50-year records of residential power introduce time-varying electricity tarifs [8, 9]. It is impor- pricing. Gersema and Wozabal [27] introduced an equilib- tant to note that there are six major methods of electricity rium pricing model for wind power features. Pircalabu et al. pricing, including: fat rate, United Nations’ method, Long [28] used the copula model to study the association of size of Run Marginal Cost (LRMC, the cost imposed on the system wind power generation with the costs that afect wind power per kW increase in consumption), social welfare optimiza- pricing in Danish power market. tion subject to market balance constraints, market clearing Brenna et al. [29] investigated diferent possible compari- price (the intersection of supply and applicant functions), son methods for electricity production for Ethiopia. They and cost-based pricing [10–14]. Price is a numerical quantity used diferent variations for this study, also used HOMER representing the value of a commodity relative to others, so software. It was concluded that the optimum result was by pricing of a product may serve as an incentive for both inves- implementing a hybrid system model including Photovoltaic tors and consumers [15]. In addition, there is a close associa- (PV)–Wind–Hydro–Battery. Brenna et al. [30] developed a tion between price, consumption, and sectorial development. model for evaluating solutions to reduce wind curtailment in This association also applies to wind energy, so proper wind South and North parts of Italy. Their program was capable power pricing leads to a stable power generation sector and of simulating the electricity dispatching mechanism. The better motivation and organization of small and large gen- new software which was developed by them performed well erators participating in renewable power generation eforts enough with wind curtailment reductions of the order of [16]. Nevertheless, renewable electricity purchase prices 73.4 and 79.1%, respectively, for two diferent cases. need to be higher than conventional electricity prices of the Despite the breadth of research conducted by diferent same market so that producers remain interested in further researchers on the pricing of renewable electricity in difer- investment in renewable electricity generation [17]. In Iran’s ent parts of the world, the authors found no mathematical current electricity market, however, per kilowatt price of model that could compute the optimal price of wind power wind electricity is being computed without any incentive to with respect to changes of wind power generation capacity in motivate private investment, so the pricing method actually the area. Meanwhile, the mentioned problems in Iran’s wind impedes the development of this sector. power pricing method make the purchase prices extremely unrealistic (about half of world’s average), discourage pri- vate investment in this sector, and thereby impedes the 1 3 International Journal of Energy and Environmental Engineering realization of vast wind power potentials of this country. a moderate and mountainous climate. It rains most in winter Thus, modifcation of wind power pricing may increase and is moderately warm in summer. The annual rainfall is investment, improve productivity, and increase the efciency 500 mm. The average temperature in the hottest months is of this sector. above 22 °C [31, 33]. In the present research, three areas Therefore, this study aims to accommodate the need of including Taze-Abad Salas, Gilan-Gharb and Sumar have Iran’s wind power sector for a wind power pricing model that been chosen. Figure 1 shows map of Iran including Kerman- could update the price according to changes in area’s wind shah province and the case studies. power generation capacity and supply-applicant levels.

Load more

  10

Copy Link