Nilufar Neyestani

Modeling the PEV traffic pattern in an urban environment with parking lots and charging stations

In this paper, a mixed-integer linear programing (MILP) model for the traffic behavior of plug-in electric vehicles (PEVs) in an urban environment is proposed. It is assumed that any environment can be categorized into different zones based on their urban functions (e.g. industrial, residential, and commercial). Therefore, the interaction of PEVs that travel between these zones has to be modeled. Besides, it is assumed that in each zone a parking lot (PL) and individual charging stations exist to provide the required state of charge (SOC) for PEVs during their daily travel. As a result, the amount of power that these PEVs consume (rather in PL or charging stations) and the amount of SOC that PEVs carry with them should be precisely computed. The proposed MILP model is applied on two zones urban area with residential and industrial districts and the numerical results prove the proficiency of the model.

Aggregation of Distributed Energy Resources Under the Concept of Multienergy Players in Local Energy Systems

In recent years, in addition to the traditional aspects concerning efficiency and profitability, the energy sector is facing new challenges given by environmental issues, security of supply, and the increasing role of the local demand. Therefore, the researchers have developed new decision-making frameworks enabling higher local integration of distributed energy resources (DER). In this context, new energy players appeared in the retail markets, increasing the level of competition on the demand side. In this paper, a multienergy player (MEP) is defined, which behaves as a DER aggregator between the wholesale energy market and a number of local energy systems (LES). The MEP and the LES have to find a long-term equilibrium in the multienergy retail market, in which they are interrelated through the price signals. To achieve this goal, in this paper the decision-making conflict between the market players is represented through a bilevel model, in which the decision variables of the MEP at the upper level are parameters for the decision-making problem at the lower level (for the individual LES). The problem is transformed into a mathematical program with equilibrium constraints by implementing duality theory, which is solved with the CPLEX 12 solver. The numerical results show the different MEP behavior in various conditions that impact on the total flexibility of the energy system.

Comparison of various operational statuses of PIEV aggregators with home-charged EVs and parking lots

Emerging technologies arising in modern power systems have propelled the presence of new agents to manipulate these facilities. Participation of plug-in electric vehicles (PIEVs) in the electricity market is one of the main issues in this environment. PIEV participation in market place can affect the agents strategy. Therefore, this paper investigates two states of power system where individual aggregators participate in the power market on behalf of home-charged PIEVs and parking lots (PLs) separately, as well as the coordinated version of the problem. Several scenarios are developed for deriving specific characteristics of PIEVs in home levels and in PLs. An optimization model is built and solved using mixed integer linear programming. The results are produced to suggest the optimum procedure for an aggregator to whether take the authority of home-charging PIEVs and PLs individually or coordinately.

Modeling Energy Demand Dependency in Smart Multi-Energy Systems

Smart local energy networks provide an opportunity for more penetration of distributed energy resources. However, these resources cause an extra dependency in both time and carrier domains that should be considered through a comprehensive model. Hence, this paper introduces a new concept for internal and external dependencies in Smart Multi-Energy Systems (SMES). Internal dependencies are caused by converters and storages existing in operation centers and modeled by coupling matrix. On the other hand, external dependencies are defined as the behavior of multi-energy demand in shifting among carriers or time periods. In this paper, system dependency is modeled based on energy hub approach through adding virtual ports and making new coupling matrix. Being achieved by SMESs, the dependencies release demand-side flexibility and subsequently enhance system efficiency. Moreover, a test SMES that includes several elements and multi-energy demand in output is applied to show the effectiveness of the model.

Effects of PEV Traffic Flows on the Operation of Parking Lots and Charging Stations

The introduction of plug-in electric vehicles (PEVs) in the electrical system is bringing various challenges. The main issue is incorporating the PEV owner’s preferences in the models. One of the main attributes representing the preference of the owners is their travel purposes, impacting on the traffic flow pattern. The PEVs’ traffic pattern defines the required charging schedule of the PEVs, and consequently, characterizes the operation of the charging facilities such as PEV parking lots (PLs). The deployment of resources such as PEV PL requires a detailed modeling of the factors affecting their operation. In this regard, this paper aims to model the power flow of the PEVs based on their traffic flow. Different travel types and purposes are considered for the PEVs traffic modeling. Two types of charging infrastructure (i.e., PLs and individual charging stations) are considered. The study is performed on a distribution network categorized based on the consumption patterns of the zones.

Location of parking lots for plug-in electric vehicles considering traffic model and market participation

This paper addresses the location of parking lots (PLs) to be used for plug-in electric vehicles (PEVs) by using a probabilistic traffic model and taking into account the PL participation in electricity markets. The PLs are used both for grid-to-vehicle and vehicle-to-grid. The system includes private or public charging stations only used for PEV charging. The traffic model considers the partitioning of the territory into areas. The case study is based on traffic and market data referring to Italy.

Modeling the cross impact of multi-energy player's price equilibrium in retail and wholesale markets

Integration of emerging energy resources in distribution level reveals new opportunities for decision makers to coordinate various energy vectors under the concept of multi-energy system (MES). In this paper, the behavior of a multi-energy player (MEP) who can trade more than one energy carrier to enhance operational flexibility of MES has been investigated. MEP participates in retail and wholesale energy markets to maximize its profit. The strategic behavior of MEP in these two markets is modeled as two synchronized bi-level problems. The problem is linearized and solved through CPLEX 12 solver. Numerical results show the behavior of MEP as a prosumer in the electricity market to make a smother demand and price profile. Moreover, the results reveal a mutual effect of local and wholesale equilibrium prices by increasing the market share of MEP.

Pricing G2V/V2G modes through characterizing the PEVs traffic behavior

In this paper, a mixed-integer linear programing (MILP) model for characterizing the traffic behavior of plug-in electric vehicles (PEVs) in an urban environment is proposed. The paper proposes the procedure to mathematically model the uncertain behavior of the PEVS. As the PEVs traffic pattern affects the potential of PEV parking lot (PL) in terms of available capacity and state of charge, the characterized PEVs are employed to provide the proper pricing schemes for the PEVs in PL. The results discuss the mutual effect of PEVs behavior on the profit of PL, as well as the effects of PLs tariffs on motivating PEVs to provide more flexibility for the PL. The results show that with a proper pricing scheme both parties (i.e., PEV owners and PL) can earn higher profits, while the charging requirements are met based on the preferences of the owners.

Assessing the effectiveness of decision making frameworks in local energy systems

This paper investigates the effectiveness of using different decision-making frameworks in local energy systems (LES) through the assessment of the long-term equilibrium of energy players. For this purpose, the energy system is modelled through two levels of multi-energy player (MEP) and LES, coupled by energy price signals. The conflict between the decision-making of these two levels of players is modelled through a bi-level approach. A mathematical problem with equilibrium constraint is formulated by applying the duality theory, resorting to a linear representation of the constraints. The solution is found by using the CPLEX12 solver. The numerical results show the characteristics of the MEP behaviour in different energy aggregation modes for the LES, with centralised management or uniform pricing. The MEP may find benefits from possible synergies among the LES due to availability of energy carriers with complementary characteristics.

Oligopolistic behavior of wind power producer in electricity markets including Demand Response Resources

This paper proposes an oligopolistic model for a wind power producer (WPP) with a market power to compete with other Gencos and take part in day-ahead, intraday and balancing markets. In order to model the mentioned oligopoly markets from WPPs viewpoint, a bi-level optimization framework is proposed based on multi-agent system and incomplete information game theory. In this context, the WPP participates in the intraday market where demand response resources are incorporated, to update its day-ahead offers. The problem uncertainties, i.e., wind power and market prices, are considered using a multi-stage stochastic programming approach. Because of these uncertainties, a well-known risk measurement, CVaR, is considered for problem optimization. Several numerical studies are accomplished and various aspects of the problem are analyzed. According to the obtained results, the proposed WPP model reveals that the prices of day-ahead and balancing markets could be increased due to the market power of WPP.