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:: Search published articles ::
Showing 5 results for Energy Market

Mostafa Vahedipour Dahraie, , ,
Volume 7, Issue 1 (9-2018)
Abstract

By increasing the number of electric vehicles (EVs) in the power system, it is necessary to manage their interaction with the grid such that to minimize the costs of the owners as well as to maximize the profit of their serving entities. Therefore, in this paper, a stochastic bi-level decision making problem for the participation of EV aggregator in a competitive environment is presented, with considering various sources of uncertainty. The sources of uncertainty for the participation of aggregators in the electricity market include day-ahead (DA) and balancing market prices as well as rivals' suggested prices and charging and discharging EVs demand that are modeled with time series. In the proposed bi-level program, the goal of the first level is to maximize the aggregator's profit in interaction with the network, and the goal of the second level is to minimize the payments of the owners. Since the objective function of the first and second levels are in contradiction with each other, with using KKT optimization conditions and the duality theory, the proposed two-level problem has become a linear single-level problem. Finally, the proposed program is implemented in typical test system and the results show that by using this model for the aggregator's decision making, it can offer proper charge and discharge price signals to the EV owners to attract the them in a competitive market and also to maximize its profit.
Saleh Sadeghi Gougheri, Hamidreza Jahangir, Masoud Aliakbar Golkar,
Volume 9, Issue 2 (6-2020)
Abstract

In recent years, the penetration of distributed energy resources has been increased dramatically in the power system, however, according to their small capacity, we need to aggregate these resources in an incorporated unit and examine their participation in the energy and ancillary services market. This goal can be achieved using virtual power plants (VPPs) concept. This paper describes the optimal bidding strategy of a VPP in the energy and spinning reserve markets. In order to improve the performance of the VPP, dispatchable distributed generation (DGs) sources, wind turbines (WTs), thermal and electrical energy storages, combined heat and power (CHP) unit and electric vehicles (EVs) have been considered in the VPP structure. The optimization task has also been addressed in the form of a MILP problem with considering the network and unit constraints and smart EVs’ charging. Finally, to evaluate the effectiveness of the proposed method, simulations are performed on a 21-bus VPP. The simulation results show that the VPP profit will increase by 23% by participating in the spinning reserve market.

 
Atena Tazikeh Lemeski, Reza Ebrahimi, Alireza Zakariazadeh,
Volume 10, Issue 4 (1-2022)
Abstract

In recent years, the issue of air pollution caused by greenhouse gas emissions and rising energy prices have contributed to developing and increasing the number of electric vehicles. Despite the many advantages of these vehicles, their increasing number and consequently their simultaneous charging in the distribution network can have destructive effects such as increased peak load, increased losses, unauthorized voltage drop, etc. On the other hand, managing the charging of vehicles by aggregators and using them as flexible loads and, if there is vehicle-to-grid (V2G) capability, as distributed generation units distributed across the distribution network can bring many financial and technical opportunities for the network. Accordingly, managing and planning the charging and discharging of these vehicles from the view point of network operators, aggregators, or vehicle owners in a centralized and decentralized manner are among the interesting topics that many articles have dealt with so far. This paper presents, a new solution for self-scheduling the charging and discharging of the private aggregator of electric vehicles to increase their profitability in the distribution network. Given the private ownership of the aggregator, it is obvious that the only factor influencing planning is cost reduction or profit enhancement, so its effect is unknown and/or negative on network indicators such as losses and voltage profiles. To solve this problem, a Time of Use (TOU) pricing model has been proposed by the Distribution Network Operator (DSO), so the aggregator plans to charge and discharge vehicles so that it can improve indicators such as losses and voltage profiles of the network in addition to be profitable. Density functions might have been used to include the uncertainty of vehicle drivers' behavior and to model the possible parameters related to him/her. Finally, the proposed approach is applied to a 33-bus test network by a genetic optimization algorithm using a private aggregator. The simulation results show that, in addition to maximizing the aggregator gain, the proposed method smoothes the network load curve, which reduces losses and improves voltage profile. It seems that in the probabilistic environment of vehicle behavior, the combination of TOU in private aggregator planning, which has led to an increase in their profits and at the same time in terms of the use of improved technical indicators, has not been studied yet.
 
Ramin Dehghani, Dr ‪asghar Akbari Foroud,
Volume 11, Issue 4 (11-2022)
Abstract

Following deregulation in the electricity grids, power systems has faced new challenges in terms of diversification of generation units and demands types, which requires a more comprehensive management framework. For this purpose, several new players were introduced to resolve the challenges between generation and demand side. Among others, retailers are the one that play a crucial role by creating a link between electricity market operators and the consumers, seeking maximize profits and reduction of the costs of their customers. Electric vehicles (EVs), meanwhile, are among the bilateral consumers which retailers are able to both provide energy for as well as see as an energy sources for sales in Day-Ahead (DA) energy and reserve markets. Nevertheless, Retailers face several uncertainties regarding the physical characteristics of electric vehicles, the behavior of their owners, in addition to the uncertainties inherent in energy and reserve markets faced by any player. In order to optimal participation of retailers in those markets as well as to meeting the needs of electric vehicles, a two-stage optimization framework is presented in this paper. Vehicle clustering is also utilized to model all uncertainties simultaneously.
Thus the main contributions of this paper can be summarized as follows:
  1. A new method for classifying electric vehicles based on battery characteristics (such as battery capacity, charge and discharge rate, etc.) and owners' behaviors (availability at parking stations, arrival and departure times, initial charge state, etc.) is proposed. This clustering helps reduce the computational load by avoiding duplicate calculations.
  2. A novel model is presented for retailers to the participate in the reserve market using the capabilities of electric vehicles. Therefore, in this paper, retailers participate in the energy and reserve markets simultaneously using the potential of electric vehicles.
  3. A two-stage stochastic linear model has been introduced to consider most of the uncertainties with respect to the aggregation of the potential of EVs by the retailer to plan their participation in different electricity markets.
  4. Using the proposed optimization framework and vehicle classification, all uncertainties related to the initial charge of the EVs’ batteries, type and capacity of batteries, the expected final state of charge, the times of arrivals and departures of vehicles to / from parking lots, EVs’ battery charging and discharging rates, EVs’ battery efficiency, reserve market call status, as well as uncertainties related to DA energy and spinning reserve prices, and the number of EVs in parking lots are modeled simultaneously.
Finally, the model has been implemented in GAMS considering the option of the retailer participation as a seller in the energy and spinning reserve markets. It has been shown that if the retailer has the mentioned choice, (s)he can benefit from selling in both markets even if sell energy at a low price to electric vehicles in parking lots.

Mehdi Sarlak, Abouzar Samimi, Mehdi Nikzad, Amir Hossein Salemi,
Volume 12, Issue 2 (8-2023)
Abstract

In this paper, the optimal operation of micro-grid in the presence of thermal block, distributed generation, storages and demand response to achieve the optimal scheduling of active, reactive and thermal power of these elements in the day-ahead energy and reactive power markets is presented. The thermal block includes combined heat and power system, boiler, and thermal demand response. This scheme minimizes the difference between total operation cost of micro-grid and sources, and total revenue obtained from the mentioned markets. It subjects to AC power flow equations, network operation limits, and operation model of these elements. Moreover, the scheme includes uncertainties of energy price, load and renewable power, where unscented transformation method models these uncertainty parameters. Finally, by implement of the proposed scheme on the 119-bus radial micro-grid, the obtained simulation results confirm the capability of the scheme in the improving of economic and operation situation of micro-grid.
 



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نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران Iranian Electric Industry Journal of Quality and Productivity
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