Optimization
Mehrnoosh Khaji; Maghsoud Amiri; Mohammad Taghi Taghavifard
Abstract
The present study aimed to develop a model for determining an optimal bidding strategy for electricity producers, including the recommended selling price and the amount of electricity to be offered for participation in both the competitive electricity market and the energy exchange market. Hourly bids ...
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The present study aimed to develop a model for determining an optimal bidding strategy for electricity producers, including the recommended selling price and the amount of electricity to be offered for participation in both the competitive electricity market and the energy exchange market. Hourly bids are suggested for the electricity market, while a monthly package, comprising peak load, medium load, low load, and base load, is proposed for the exchange market. By modeling a self-scheduling problem, the study aimed to develop optimal power production plans that maximize net profit over a one-month period. The research approach involved mathematical modeling using mixed-integer non-linear programming, which was performed in Lingo software and then validated in terms of effectiveness through an application to the case of a thermal power station. Relying on fuzzy necessity, credibility, and possibility, the research presented a robust model against the uncertainty of price with an adjustable level of robustness. Sensitivity analysis and the simulation approach were used to validate the performance of the model, demonstrating that the optimal response from the robust model, compared to the deterministic model, can maintain its efficiency in the face of fluctuations in the parameter of price uncertainty. Furthermore, the findings indicated that offering a base load package on the energy exchange market can yield a higher net profit value for the producer. Finally, the fuzzy interest rate and decision-making based on fuzzy goals were also examined.1. IntroductionIn recent years, researchers have directed their attention toward robust optimization in markets with uniform pricing systems. However, the application of robust methods in pay-as-bid systems remains unexplored. Therefore, a notable research gap exists, specifically in the robust optimization of pay-as-bid systems in the Iranian electricity market. Moreover, with the establishment of the energy exchange market in Iran, the simultaneous bidding, in both the energy exchange market and the day-ahead electricity market, has surfaced as a significant gap in existing research literature. In this respect, the present study contributes to relevant research by addressing existing gaps while considering the specific needs of the Iranian electricity market. The study tried to model the self-scheduling problem of an electricity producer to determine an optimal and robust strategy. Employing fuzzy theory to address the uncertainty of the market clearing price parameter, the model can protect the producer from electricity price uncertainty in the market, as well as foster a more secure environment for participation in competitive electricity markets.2. Materials and MethodsAs an applied and developmental research, the present study aimed to develop robust optimization models for bidding in the electricity market. This descriptive–analytical study examined, described, and explained uncertainty in decision-making, employing a fuzzy approach to tackle uncertainty. The research involved the mathematical modeling of the problem of determining the bidding strategy for electricity producers, presented as mixed-integer programming. First, the variables and parameters of the modeling process were introduced, followed by presenting the problem formulation. Subsequently, the implementation and its procedural steps were performed in the Lingo software to validate the effectiveness of the proposed model by applying it to the case of a thermal power station. 3.Results and DiscussionThe research proposed a model designed to address bidding challenges encountered by a price-taker electricity producer. The model centers on optimizing simultaneous monthly bidding in both the day-ahead electricity market and the energy exchange market. The objective is to optimally allocate the producer’s capacity between these two markets to maximize profit. To handle the uncertainty of electricity prices, a robust method is employed, necessitating estimates of the next day’s market price and the energy exchange price for the upcoming month. The proposed model underwent testing across various modes, including base load, off-peak load, medium load, and peak load packages. The results revealed that the producer’s profit is maximized when offering the base load package to the energy exchange market, followed by the medium load package. Peak and off-peak packages ranked third with equal values. Therefore, it is recommended for producers seeking participation in the energy exchange market to consider offering a base load package.4. ConclusionThe present research employed a robust fuzzy technique to deal with the volatility of electricity market prices, allowing decision-makers to make firm decisions with an adjustable level of robustness. The results of the proposed method indicated that the possibility criterion adopts an optimistic stance towards the settlement price, thus suggesting prices at higher levels than the necessity and credibility criteria. This criterion is suitable only when market signals indicate a potential price increase. In contrast, the necessity criterion adopts a cautious approach, showing robustness even at low confidence levels. This approach is well-suited for risk-averse decision-makers and scenarios where market signals point towards a potential price reduction.
Macroeconomics
Ehsan Habibpour Moghaddam; Seyed Mahdi Barakchian; Masoud Nili
Abstract
Since the beginning of the 2010s, the investment in Iran has experienced a continuous and severe fall and the level of the total real investment at the end of 2018 has approximately reached its 2002 level. In this paper, we show that the fluctuation of the investment (in machinery) up to the beginning ...
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Since the beginning of the 2010s, the investment in Iran has experienced a continuous and severe fall and the level of the total real investment at the end of 2018 has approximately reached its 2002 level. In this paper, we show that the fluctuation of the investment (in machinery) up to the beginning of 2010s can be explained by the use of a regression model which includes macroeconomic variables as well as measures of instability in macro environment. However, this model is not able to predict the investment drop during the 2010s and it seems that other factors play a crucial role in the severe fall of the investment in this decade. We will introduce “Political Conflicts” and “Economic Policy Uncertainty” as two indices which are constructed by applying the text analysis method to the press and digital media from 2002 to 2019. The trend of these two indices show a high degree of uncertainty during the recent decade. We will show that the “Political Conflicts” index can explain the investment drop in the 2010s.
Mehdi Moradpour; Mohsen Ebrahimi; Vahid Abbasion
Volume 11, Issue 35 , July 2008, , Pages 159-176
Abstract
This paper studies the effect of real exchange rate uncertainty on private investment in Iran. We estimate the effect using a model for private investment in Iran and the time series data for the period 1977-2004. We use the GARCH method to construct an estimate of the real exchange rate uncertainty, ...
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This paper studies the effect of real exchange rate uncertainty on private investment in Iran. We estimate the effect using a model for private investment in Iran and the time series data for the period 1977-2004. We use the GARCH method to construct an estimate of the real exchange rate uncertainty, and estimate the model by OLS method. The results show that the effect of uncertainty of real exchange rate on investment by private sector is significantly negative.
