A. Movahednasab; M. Rashidinejad; A. Abdollahi
Volume 5, Issue 2 , Summer and Autumn 2017, , Pages 109-120
Abstract
By increasing the extraction of natural gas, its role in the restructured power systems is being expanded, due to its lower pollution. Iran has the second largest reserves of natural gas in the world and exports it to different countries. This paper represents long run analysis of natural gas export ...
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By increasing the extraction of natural gas, its role in the restructured power systems is being expanded, due to its lower pollution. Iran has the second largest reserves of natural gas in the world and exports it to different countries. This paper represents long run analysis of natural gas export from Iran to Turkey as a case study, considering direct transfer and exporting via the power market. In this regard, a system dynamics model is approached for long run analysis of the considered scenarios. The uncertainty of natural gas price is modeled by Markov Chain Monte Carlo (MCMC) for a long run period and four generation technologies including coal-fired, combined cycle gas turbine (CCGT), gas turbine (GT) and wind participate in the power market with a uniform price structure. The published data by energy information administration (EIA) about natural gas charges, costs of electricity generation and export of natural gas and electricity are applied in the simulated models. The results show that exporting the natural gas at real time price is profitable, while its conversion into electricity and exporting at market price is disadvantageous, even by expanding the renewable resources.
A. Tabandeh; A. Abdollahi; M. Rashidinejad
Abstract
Under the smart grid environment, demand response resources (DRRs) are introduced as a virtual power plant to enhance power system adequacy. DRRs often fail to reduce their load due to some external factors. In this paper, a reliability model of a DRR is constructed as multi-state conventional generation ...
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Under the smart grid environment, demand response resources (DRRs) are introduced as a virtual power plant to enhance power system adequacy. DRRs often fail to reduce their load due to some external factors. In this paper, a reliability model of a DRR is constructed as multi-state conventional generation units, where the probability, frequency of occurrence, and departure rate of each state can be acquired. DRRs as consequence of demand response program implementation can be applied to transmission congestion management. Therefore, this paper presents an optimal model of congestion management (CM) by means of multi-state DRRs, namely CM_DRR. In the proposed approach, in addition to DRRs, independent system operator relieves the existing transmission line congestions using the combination of generating unit rescheduling and involuntary load shedding. The hourly historical data associated with the Connecticut region in New England is employed to achieve the DRRs’ participation regime. Moreover, the impact of different capacities of DRRs on the congestion management cost and load shedding cost is evaluated. Results of applying the aforementioned model to the 24-bus Reliability Test System (RTS) indicate the efficiency of CM_DRR framework.
J. Shadmani; M. Rashidinejad; A. Abdollahi; I. Taheri
Abstract
In the restructured environment of electricity market, firstly the generating companies and the customers are looking for maximizing their profit and secondly independent system operator is looking for the stability of the power network and maximizing social welfare. In this paper, a one way auction ...
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In the restructured environment of electricity market, firstly the generating companies and the customers are looking for maximizing their profit and secondly independent system operator is looking for the stability of the power network and maximizing social welfare. In this paper, a one way auction in the electricity market for the generator companies is considered in both perfect and imperfect competition cases. A new model is provided to use the historical data of power market in the state of competition with imperfect information in which two probability functions were simultaneously used for the estimation of required information about each generator company. Nash equilibrium in the game theory is used to find the stability point in the biding strategy of generator companies. The effect of network conditions like limitation of transmission lines, network load, maximum generation of each generator company and the imperfect estimation of information about other competitors on the profit of generator companies and also on the market power of the generators in two mentioned competition methods were shown in the numerical simulation.
M. Hafezi_Nasab; M. Rashidinejad; A. Abdollahi; I. Taheri
Abstract
In this paper, the concept of Conjectural Variation (CV) is used to specify optimal generation decision for generation companies (Gencos). The conjecture of Genco is defined as its belief or expectation about the reaction of rivals to change of its output. Using CV method, each Genco has to learn and ...
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In this paper, the concept of Conjectural Variation (CV) is used to specify optimal generation decision for generation companies (Gencos). The conjecture of Genco is defined as its belief or expectation about the reaction of rivals to change of its output. Using CV method, each Genco has to learn and estimate strategic behaviors of other competitors from available historical market operation data. Therefore, accuracy of generation decision depends on the accuracy of estimating other competitors’ decision within CV context. In this paper, adjusted Lerner index is used to improve the accuracy of estimating CV parameter. In electricity market, the adjusted Lerner index can be directly computed using price, market shares, marginal cost and industry elasticity of demand. It must be noted that due to repeated power market, Gencos need to modify their behavior over time. In response to this need, dynamic learning is considered in case studies which improve results.
