Strategic Offering for a Price-Maker Wind Power Producer in Oligopoly Markets Considering Demand Response Exchange

Shafie‐khah, Miadreza; Heydarian‐Forushani, Ehsan; Golshan, Mohamad Esmail Hamedani; Moghaddam, Mohsen Parsa; Sheikh‐El‐Eslami, Mohammad Kazem

doi:10.1109/tii.2015.2472339

Cited by 56 publications

(27 citation statements)

References 37 publications

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“…The constraints from Equations (10) to (15) are related to energy storage. Equations (10) and (11) are the upper limit of charging and discharging energy, where…”

Section: Monthly Robust Stochastic Optimizationmentioning

confidence: 99%

“…To date, a number of studies have been done on power exchange strategies. In [9,10], a demand response exchange model was both applied to managing the variability of renewable energy sources. In [11], an energy exchange strategy between electric vehicle loads and wind generation utilities was built to mitigate wind energy and electric vehicle imbalance threats.…”

Section: Introductionmentioning

confidence: 99%

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A Power Exchange Strategy for Multiple Areas with Hydro Power and Flexible Loads

et al. 2019

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Areas with hydro power may purchase extra power from the outside power market during dry seasons, which will cause a deviation between the actual and expected power purchase amount due to the inaccurate judgment of the market situation. Because of the uncertainty of price fluctuations, the risk of purchasing power in the real-time market to eliminate this deviation is very high. This paper proposes an innovative trade mode, where the power exchange strategy between multiple areas is adopted through forming an alliance, i.e., one area can use the controllable elements within others, and constructing a monthly and post day-ahead two phase optimization model. The objective function of the monthly stochastic robust optimization considers the power purchase cost to determine the controllable elements dispatch dates for every area in the alliance. Thus, areas can make reasonable dispatch schedules for controllable elements to avoid the resource waste that means more controllable elements are prepared before post day-ahead optimization but less are used after post day-ahead optimization. While the post day-ahead optimization model determines the internal controllable elements dispatch and power exchange amount after the day-ahead market clearing process, users’ satisfaction and dispatch schedule changes for energy storage device are also considered. In order to solve the proposed two phase model, the dual principle and linearization methods are used to convert them into mixed-integer linear programming problems that can be effectively solved by the Cplex solver. The study case verifies the power deviation cost decreases with the power exchange strategy and the important role of energy storage devices.

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“…The constraints from Equations (10) to (15) are related to energy storage. Equations (10) and (11) are the upper limit of charging and discharging energy, where…”

Section: Monthly Robust Stochastic Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Power Exchange Strategy for Multiple Areas with Hydro Power and Flexible Loads

et al. 2019

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“…The prices with regard to the expected delivered up/down reserve through production plants are formulated in the next term. The scenarios of wind power are generated based on a roulette wheel mechanism presented in [18]. The objective function is subjected to following constraints.…”

Section: The Proposed Modelmentioning

confidence: 99%

The Impacts of Demand Response on the Efficiency of Energy Markets in the Presence of Wind Farms

Hajibandeh

Shafie‐khah

Talari

et al. 2017

IFIP Advances in Information and Communication Technology

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In this paper, an optimal scheduling of thermal and wind power plants is presented by using a stochastic programming approach to cover the uncertainties of the forecasted generation of wind farms. Uncertainties related to wind forecast error, consequently wind generation outage power and also system load demand are modeled through scenario generation. Then, with regard to day-ahead and real-time energy markets and taking into account the relevant constraints, the thermal unit commitment problem is solved considering wind energy injection into the system. Besides, in order to assess impacts of Demand Response (DR) on the problem, a load reduction demand response model has been applied in the base model. In this approach, self and cross elasticity is used for modeling the customers' behavior modeling. The results indicate that the DR Programs (DRPs) improves the market efficiency especially in peak hours when the thermal Gencos become critical suppliers and the combination of DRPs and wind farm can be so efficient.

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“…There has been increasing interest in DRX-based DR because of the flexibility in modeling the objective function, and the ability to maximize the social welfare of the DR provider and other market entities. There has been considerable research in forming market clearing mechanisms for DRX with various objective functions [27][28][29]. In this work, we implement the DRX for minimizing electric utility payments.…”

Section: Introductionmentioning

confidence: 99%

Benefits of a Demand Response Exchange Participating in Existing Bulk-Power Markets

Durvasulu

Hansen

2018

Energies

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In most U.S. market sponsored demand response (DR) programs, revenue earned from energy markets has been relatively low compared to DR used for capacity markets and ancillary services. This paper presents an aggregated DR model participating in the bulk-power market as a service through a pool-based entity called demand response exchange (DRX). Using the DRX structure, DR providers can participate in energy markets as a service to benefit bulk-power market entities. The benefits and challenges to each market entity using DR-as-a-service are presented in an extended review. The DRX model in this study is a market entity that operates with the day-ahead market to select DR offers that minimize electric utility payments. A case study was performed using the proposed DRX model on the IEEE 24-bus system, augmented to represent actual bulk-power market prices to study factors that influence utility payments under the DRX-market paradigm. Two high-price days of the PJM market were simulated, and it was shown for a single day on the augmented test case that spending $69,955 for DR-as-a-service results in a reduction of utility payments of $864,199. The day-ahead generator supply curve, network congestion, and DR curtailment were found to be the most influencing factors that impact the benefit of using DR-as-a-service.

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Strategic Offering for a Price-Maker Wind Power Producer in Oligopoly Markets Considering Demand Response Exchange

Cited by 56 publications

References 37 publications

A Power Exchange Strategy for Multiple Areas with Hydro Power and Flexible Loads

A Power Exchange Strategy for Multiple Areas with Hydro Power and Flexible Loads

The Impacts of Demand Response on the Efficiency of Energy Markets in the Presence of Wind Farms

Benefits of a Demand Response Exchange Participating in Existing Bulk-Power Markets

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