Search published articles


Economical Modeling for Managing the Power Transaction of EVs and Power Market in Smart Parking Lots
F. Askari, A. Khoshkholgh,
Volume 17, Issue 2 (6-2021)
Abstract
The battery of electric vehicles (EV) can be charged from the power grid or discharged back to it. Parking lots can aggregate hundreds of EVs which makes them a significant and flexible load/generation component in the grid. In a smart grid environment, the smart parking lot (SPL) can benefit from the situation of the simultaneous connection to the EVs and power grid. This paper proposes a new algorithm to maximize SPL profit from participation in the forward and spot markets. Monte-Carlo simulation is used to determine the participation of the SPL in the forward market. Then an economic model is proposed to optimize the charging or discharging time table of EVs at any hours of a day and SPL participation in the spot market in a way that maximum SPL profit and satisfaction of EV owners can be gained. The Genetic Algorithm (GA) is used to solve this optimization problem.

Multiple Electricity Markets Competitiveness Undergoing Symmetric and Asymmetric Renewables Development Policies
S. A. Mozdawar, A. Akbari Foroud, M. Amirahmadi,
Volume 18, Issue 1 (3-2022)
Abstract
This paper scrutinizes the impact of different renewable energy sources (RES) development policies on competitiveness within multiple electricity markets (MEMs). Also, the variation in market power indices by increasing the integration of the markets undergoing symmetric and asymmetric RES development policies is investigated. To do so, several stochastic mixed-integer non-linear programming objective functions are used in the agent-based simulation framework to model the power plants’ behavior and markets. The case study shows in the low RES penetrated markets, one can say the more integration level of the markets, the lower potential of exercising market power. The reciprocal judgment is true for a high RES penetrated market. Also, large asymmetry in RES development between markets within MEMs may bring about market power problem for a high RES penetrated market. Unlike the asymmetric RES development policies, adopting homogeneous policies in RES development within MEMs reduces the market power potential in all markets and this potential decreases with the increase in the integration of the markets.

An Information Gap Decision Theory Approach for Optimal Bidding of a Virtual Power Plant under System Uncertainties
A. Ghanuni, R. Sharifi, H. Feshki Farahani,
Volume 19, Issue 3 (9-2023)
Abstract
Operation scheduling of a Virtual Power Plant (VPP) includes several challenges for the system according to the uncertain parameters, and security requirements, which intensify the need for more efficient models for energy scheduling and power trading strategies. Making suitable decisions under uncertainties, related to Renewable Energy Resources (RES), loads, and market prices impose extra considerations for the problem to make a clearer insight for the system operators to participate in local markets. This paper proposes a new risk-based hybrid stochastic model to investigate the effects of wind turbine power fluctuations on profit function, energy scheduling, and market participating strategies. Also, an incentivized Demand Response Program (DRP) is used, to enhance the system’s efficiency. The results of the study indicate that the proposed model based on Information Gap Decision Theory (IGDT) approach makes a clearer environment for the decision-maker to be aware of the effects of risk-taking or a risk-averse strategy on financial profits. The results show that a 30% of robustness and opportunity consideration would change the profit function from -12.5% up to 14.5%, respectively. A modified IEEE 33 bus test system is used to simulate a technical VPP considering the voltage stability and thermal capacity of line requirements.

Page 1 from 1     
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.