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 ...
Read More
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.
Optimization
Hamed Azizi Ganzagh; Ahmad Jafari Samimi; Zahra Mila Elmi; Amir Mansour Tehranchian
Abstract
Inflation forecasting is one of the most important issues for the economies of countries, As the existing literature suggests, hybrid models will bring better prediction accuracy due to attention to both linear and non-linear dimensions. Furthermore, the use of ARDL model can include lags of other variables ...
Read More
Inflation forecasting is one of the most important issues for the economies of countries, As the existing literature suggests, hybrid models will bring better prediction accuracy due to attention to both linear and non-linear dimensions. Furthermore, the use of ARDL model can include lags of other variables in tandem with having linear features. It should also be noted that LSTM models have a forgetting gate due to their non-linear estimation characteristics, and they can incorporate data with very distant lags in the model. Therefore, the combination of these two models can significantly improve the prediction accuracy. Accordingly, attempts have been made in the current study to compare ARDL, NARX, LSTM and ARDL-D-LSTM models with one another and to introduce a suitable model for predicting Iran's monthly inflation rate in the short-term and long-term time horizon. After estimating the monthly inflation rate of Iran in the period of 4/21/2005 to 8/22/2018 and testing the model on the data for the period of 9/22/2018 to 12/21/ 2020 it was found that the NARX model and the ARDL-D-LSTM hybrid model performed well respectively for short-term time horizon and the long-term horizon according to the RMSE criteria.
