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

Hajibandeh, Neda; Shafie‐khah, Miadreza; Talari, Saber; Catalão, João P.S.

doi:10.1007/978-3-319-56077-9_28

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…Structure of the distribution and transmission networks, the input data, and location of DERs in the DN are shown in Fig. 3 and Tables 4-8 [28][29][30]. While TNL #3, locates at bus 5, is replaced with DN, bus #1 is considered as the reference bus.…”

Section: Input Datamentioning

confidence: 99%

Risk management model for simultaneous participation of a distribution company in Day-ahead and Real-time markets

Golpîra

Sheikhahmadi

Bahramara

et al. 2020

Sustainable Energy, Grids and Networks

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Section: Input Datamentioning

confidence: 99%

Risk management model for simultaneous participation of a distribution company in Day-ahead and Real-time markets

Golpîra

Sheikhahmadi

Bahramara

et al. 2020

Sustainable Energy, Grids and Networks

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“…It is noticed that a number of studies have modeled the DR uncertainty in order to have a more precise DR estimation [25][26][27][28][29][30]. For instance, the unknown price-elastic demand curve has been modeled in the reliability unit commitment problem to be run by the independent system operator (ISO) using the robust optimization method in [25].…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

“…The uncertainty in the realization of DR provided by DR providers in the day-ahead electricity market clearing process has been investigated in [26,27]. The authors in [28][29][30] have modeled the fail in customer's behavior by proposing a two-state reliability model for DR resources including the availability and unavailability of DR resources.…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

Multi-Objective Market Clearing Model with an Autonomous Demand Response Scheme

et al. 2019

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Demand response (DR) is known as a key solution in modern power systems and electricity markets for mitigating wind power uncertainties. However, effective incorporation of DR into power system operation scheduling needs knowledge of the price–elastic demand curve that relies on several factors such as estimation of a customer’s elasticity as well as their participation level in DR programs. To overcome this challenge, this paper proposes a novel autonomous DR scheme without prediction of the price–elastic demand curve so that the DR providers apply their selected load profiles ranked in the high priority to the independent system operator (ISO). The energy and reserve markets clearing procedures have been run by using a multi-objective decision-making framework. In fact, its objective function includes the operation cost and the customer’s disutility based on the final individual load profile for each DR provider. A two-stage stochastic model is implemented to solve this scheduling problem, which is a mixed-integer linear programming approach. The presented approach is tested on a modified IEEE 24-bus system. The performance of the proposed model is successfully evaluated from economic, technical and wind power integration aspects from the ISO viewpoint.

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Modeling a Hybrid Microgrid Using Probabilistic Reconfiguration under System Uncertainties

et al. 2017

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Abstract:A novel method for a day-ahead optimal operation of a hybrid microgrid system including fuel cells, photovoltaic arrays, a microturbine, and battery energy storage in order to fulfill the required load demand is presented in this paper. In the proposed system, the microgrid has access to the main utility grid in order to exchange power when required. Available municipal waste is utilized to produce the hydrogen required for running the fuel cells, and natural gas will be used as the backup source. In the proposed method, an energy scheduling is introduced to optimize the generating unit power outputs for the next day, as well as the power flow with the main grid, in order to minimize the operational costs and produced greenhouse gases emissions. The nature of renewable energies and electric power consumption is both intermittent and unpredictable, and the uncertainty related to the PV array power generation and power consumption has been considered in the next-day energy scheduling. In order to model uncertainties, some scenarios are produced according to Monte Carlo (MC) simulations, and microgrid optimal energy scheduling is analyzed under the generated scenarios. In addition, various scenarios created by MC simulations are applied in order to solve unit commitment (UC) problems. The microgrid's day-ahead operation and emission costs are considered as the objective functions, and the particle swarm optimization algorithm is employed to solve the optimization problem. Overall, the proposed model is capable of minimizing the system costs, as well as the unfavorable influence of uncertainties on the microgrid's profit, by generating different scenarios.

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

Cited by 3 publications

References 22 publications

Risk management model for simultaneous participation of a distribution company in Day-ahead and Real-time markets

Risk management model for simultaneous participation of a distribution company in Day-ahead and Real-time markets

Multi-Objective Market Clearing Model with an Autonomous Demand Response Scheme

Modeling a Hybrid Microgrid Using Probabilistic Reconfiguration under System Uncertainties

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