Strategic CBDR bidding considering FTR and wind power

Fang, Xiuzhong; Li, Fangxing; Hu, Qinran; Wei, Yimin

doi:10.1049/iet-gtd.2015.1305

Cited by 25 publications

(6 citation statements)

References 40 publications

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“…For Case 1, because the wind ramps up with the same load ramp direction, the ramp-up product price is still 0. For Case 2, the wind ramp-up price is $13.75/MW, as shown in Equation (28). Therefore, the clearing price for the ramp-up product increases to $43.75/MW, which recovers the opportunity cost of curtailing wind to provide the rampup product.…”

Section: Ramp Price For Wind Ramping Productsmentioning

confidence: 99%

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Strategic Offering for Wind Power Producers Considering Energy and Flexible Ramping Products

Fang

Krishnan

2018

Energies

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The increasing deployments of renewable generation methods, such as wind, affects the flexibility of electric power system operating due to their inherent variability and uncertainty. To mitigate this, power systems need flexible resources. This paper investigates the potential for wind power to provide flexible ramping products in the real-time market, an additional value stream to the energy it provides. The proposed model for wind power's strategic offering is formulated as a bi-level optimization problem with wind profit maximization at the upper level and the independent system operator's economic dispatch-considering both the energy balance and the flexible ramping requirement to counter uncertainty-at the lower level. This bi-level model is converted to a mathematical program with equilibrium constraints (MPEC) by recasting the lower level problem with its Karush-Kuhn-Tucker optimality conditions. Then, through strong duality theory and the big-M method, the MPEC model is converted to a mixed-integer linear programming model. The opportunity cost and the price for wind power-providing ramping products are analyzed. Numerical examples based on a 5-bus network are presented to verify the proposed model and concept.

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Section: Ramp Price For Wind Ramping Productsmentioning

confidence: 99%

“…ε are the auxiliary binary variables [27][28][29][30]. The forecast uncertainty of other power plants offers can be modeled through a set of the probabilistic scenarios.…”

Section: Mixed-integer Linear Programmingmentioning

confidence: 99%

Strategic Offering for Wind Power Producers Considering Energy and Flexible Ramping Products

Fang

Krishnan

2018

Energies

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“…Equations (9)- (12) are the detailed formulations of the chargedischarge cost, the SR cost provided by MTs, the fuel cost of MTs, and the SR cost, respectively, provided by ESS, where T and t denote an entire scheduling cycle and a time period (in hours), respectively. In (9), ω rt_price, t is the TOU price during period t, P t CH and P t DC are the ESS charge-discharge powers, respectively.…”

Section: Img Operation Costmentioning

confidence: 99%

“…MT represents the MT output power during the period t. In (12), ω rc_price denotes the SR price of the ESS, and P Ress, t is the reserve capacity provided by ESS.…”

Section: Img Operation Costmentioning

confidence: 99%

Incorporating energy storage and user experience in isolated microgrid dispatch using a multi‐objective model

Yang

Zhao

et al. 2019

IET Renewable Power Generation

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In order to coordinate multiple different scheduling objectives from the perspectives of economy, environment and users, a practical multi-objective dynamic optimal dispatch model incorporating energy storage and user experience is proposed for isolated microgrids. In this model, besides Microturbine units, energy storage is employed to provide spinning reserve services for microgirds; and furthermore, from the perspective of demand side management, a consumer satisfaction indicator is developed to measure the quality of user experience. A two-step solution methodology incorporating multi-objective optimization (MOO) and decision analysis is put forward to address this model. First, a powerful heuristic optimization algorithm, called the θ-dominance based evolutionary algorithm, is used to find a well-distributed set of Pareto-optimal solutions of the problem. And thereby, the best compromise solutions (BCSs) are identified from the entire solutions with the use of decision analysis by integrating fuzzy C-means clustering and grey relation projection. The simulation results on the modified Oak Ridge National Laboratory Distributed Energy Control and Communication lab microgrid test system demonstrate the effectiveness of the proposed approach.

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“…PTP obligation is a financial transmission right (FTR) product used in electricity markets such as in Electric Reliability Council of Texas (ERCOT). A few papers have investigated bidding strategies with consideration of FTR [17], [18]. Joint bidding strategies in the FTR and energy markets are investigated in [19].…”

Section: Introductionmentioning

confidence: 99%