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Optimal risk-based strategy of distributed generation-owning retailer in the day-ahead electricity market: Chance constraint optimization approach
This paper presents a risk-based decision-making framework for the Distributed Generation (DG)-owning retailer to determine the optimal participation level in the forward and the day-ahead electricity markets, as well as the optimal scheduling of DG units. The energy price in the day-ahead market is very volatile and varies every hour. Also, unpredicted failures of DG units may impose a great financial loss on the retailer. Therefore, the retailer has to evaluate the effects of uncertain parameters to hedge the financial loss. In this paper, the financial risk associated with the uncertain prices is evaluated using the chance constraint optimization method. The normal density function is applied to model the probabilistic realizations of uncertain prices, and scenarios are generated via the scenario tree technique. Moreover, the availability of generation units is modeled by the availability probability. The proposed risk-based framework allows the retailer to determine the optimal strategy at the given risk level. The objective-function of the presented model is based on maximizing the expected profit in a way that ensures the specific profit constraint will be satisfied at the operation period with a defined probability. The performance and efficiency of the presented decision-making framework are analyzed on the sample DG-owning retailer, and the optimal framework is simulated under different risk levels.
Optimal risk-based strategy of distributed generation-owning retailer in the day-ahead electricity market: Chance constraint optimization approach
This paper presents a risk-based decision-making framework for the Distributed Generation (DG)-owning retailer to determine the optimal participation level in the forward and the day-ahead electricity markets, as well as the optimal scheduling of DG units. The energy price in the day-ahead market is very volatile and varies every hour. Also, unpredicted failures of DG units may impose a great financial loss on the retailer. Therefore, the retailer has to evaluate the effects of uncertain parameters to hedge the financial loss. In this paper, the financial risk associated with the uncertain prices is evaluated using the chance constraint optimization method. The normal density function is applied to model the probabilistic realizations of uncertain prices, and scenarios are generated via the scenario tree technique. Moreover, the availability of generation units is modeled by the availability probability. The proposed risk-based framework allows the retailer to determine the optimal strategy at the given risk level. The objective-function of the presented model is based on maximizing the expected profit in a way that ensures the specific profit constraint will be satisfied at the operation period with a defined probability. The performance and efficiency of the presented decision-making framework are analyzed on the sample DG-owning retailer, and the optimal framework is simulated under different risk levels.
Optimal risk-based strategy of distributed generation-owning retailer in the day-ahead electricity market: Chance constraint optimization approach
Khojasteh, Meysam (author) / Jadid, Shahram (author)
2014-09-01
20 pages
Article (Journal)
Electronic Resource
English
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