Percorrer por autor "Ghazvini, Mohammad Ali Fotouhi"
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- Dynamic electricity pricing for electric vehicles using stochastic programmingPublication . Soares, João; Ghazvini, Mohammad Ali Fotouhi; Borges, Nuno; Vale, ZitaElectric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Smart charging and vehicle-to-grid seem highly promising methods for EVs control. However, high capital costs remain a barrier to implementation. Meanwhile, incentive and price-based schemes that do not require high level of control can be implemented to influence the EVs’ demand. Having effective tools to deal with the increasing level of uncertainty is increasingly important for players, such as energy aggregators. This paper formulates a stochastic model for day-ahead energy resource scheduling, integrated with the dynamic electricity pricing for EVs, to address the challenges brought by the demand and renewable sources uncertainty. The two-stage stochastic programming approach is used to obtain the optimal electricity pricing for EVs. A realistic case study projected for 2030 is presented based on Zaragoza network. The results demonstrate that it is more effective than the deterministic model and that the optimal pricing is preferable. This study indicates that adequate DR schemes like the proposed one are promising to increase the customers’ satisfaction in addition to improve the profitability of the energy aggregation business.
- Dynamic electricity pricing for electric vehicles using stochastic programmingPublication . Soares, João; Ghazvini, Mohammad Ali Fotouhi; Borges, Nuno; Vale, ZitaElectric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Smart charging and vehicle-to-grid seem highly promising methods for EVs control. However, high capital costs remain a barrier to implementation. Meanwhile, incentive and price-based schemes that do not require high level of control can be implemented to influence the EVs' demand. Having effective tools to deal with the increasing level of uncertainty is increasingly important for players, such as energy aggregators. This paper formulates a stochastic model for day-ahead energy resource scheduling, integrated with the dynamic electricity pricing for EVs, to address the challenges brought by the demand and renewable sources uncertainty. The two-stage stochastic programming approach is used to obtain the optimal electricity pricing for EVs. A realistic case study projected for 2030 is presented based on Zaragoza network. The results demonstrate that it is more effective than the deterministic model and that the optimal pricing is preferable. This study indicates that adequate DR schemes like the proposed one are promising to increase the customers' satisfaction in addition to improve the profitability of the energy aggregation business.
- Multi-dimensional signaling method for population-based metaheuristics: Solving the large-scale scheduling problem in smart gridsPublication . Soares, João; Ghazvini, Mohammad Ali Fotouhi; Silva, Marco; Vale, ZitaThe dawn of smart grid is posing new challenges to grid operation. The introduction of Distributed Energy Resources (DER) requires tough planning and advanced tools to efficiently manage the system at reasonable costs. Virtual Power Players (VPP) are used as means of aggregating generation and demand, which enable smaller producers using different generation technologies to be more competitive. This paper discusses the problem of the centralized Energy Resource Management (ERM), including several types of resources, such as Demand Response (DR), Electric Vehicles (EV) and Energy Storage Systems (ESS) from the VPP's perspective to maximize profits. The complete formulation of this problem, which includes the network constraints, is represented with a complex large-scale mixed integer nonlinear problem. This paper focuses on deterministic and metaheuristics methods and proposes a new multi-dimensional signaling approach for population-based random search techniques. The new approach is tested with two networks with high penetration of DERs. The results show outstanding performance with the proposed multi-dimensional signaling and confirm that standard metaheuristics are prone to fail in solving these kind of problems.
- A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loadsPublication . Soares, João; Ghazvini, Mohammad Ali Fotouhi; Vale, Zita; Oliveira, P.B. de MouraIn this paper, a multi-objective framework is proposed for the daily operation of a Smart Grid (SG) with high penetration of sensitive loads. The Virtual Power Player (VPP) manages the day-ahead energy resource scheduling in the smart grid, considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G), while maintaining a highly reliable power for the sensitive loads. This work considers high penetration of sensitive loads, i.e. loads such as some industrial processes that require high power quality, high reliability and few interruptions. The weighted-sum approach is used with the distributed and parallel computing techniques to efficiently solve the multi-objective problem. A two-stage optimization method is proposed using a Particle Swarm Optimization (PSO) and a determin-istic technique based on Mixed-Integer Linear Programming (MILP). A realistic mathematical formulation considering the electric network constraints for the day-ahead scheduling model is described. The execu-tion time of the large-scale problem can be reduced by using a parallel and distributed computing plat-form. A Pareto front algorithm is applied to determine the set of non-dominated solutions. The maximization of the minimum available reserve is incorporated in the mathematical formulation in addi-tion to the cost minimization, to take into account the reliability requirements of sensitive and vulnerable loads. A case study with a 180-bus distribution network and a fleet of 1000 gridable Electric Vehicles (EVs) is used to illustrate the performance of the proposed method. The execution time to solve the opti-mization problem is reduced by using distributed computing.
- Scenario generation for electric vehicles' uncertain behavior in a smart city environmentPublication . Soares, João; Borges, Nuno; Ghazvini, Mohammad Ali Fotouhi; Vale, Zita; Oliveira, P.B. de MouraThis paper presents a framework and methods to estimate electric vehicles' possible states, regarding their demand, location and grid connection periods. The proposed methods use the Monte Carlo simulation to estimate the probability of occurrence for each state and a fuzzy logic probabilistic approach to characterize the uncertainty of electric vehicles' demand. Day-ahead and hour-ahead methodologies are proposed to support the smart grids' operational decisions. A numerical example is presented using an electric vehicles fleet in a smart city environment to obtain each electric vehicle possible states regarding their grid location.
- A stochastic model for energy resources management considering demand response in smart gridsPublication . Soares, João; Ghazvini, Mohammad Ali Fotouhi; Borges, Nuno; Vale, ZitaRenewable energy resources such as wind and solar are increasingly more important in distribution net-works and microgrids as their presence keeps flourishing. They help to reduce the carbon footprint ofpower systems, but on the other hand, the intermittency and variability of these resources pose seri-ous challenges to the operation of the grid. Meanwhile, more flexible loads, distributed generation, andenergy storage systems are being increasingly used. Moreover, electric vehicles impose an additionalstrain on the uncertainty level, due to their variable demand, departure time and physical location. Thispaper formulates a two-stage stochastic problem for energy resource scheduling to address the chal-lenge brought by the demand, renewable sources, electric vehicles, and market price uncertainty. Theproposed method aims to minimize the expected operational cost of the energy aggregator and is basedon stochastic programming. A realistic case study is presented using a real distribution network with201-bus from Zaragoza, Spain. The results demonstrate the effectiveness and efficiency of the stochasticmodel when compared with a deterministic formulation and suggest that demand response can play asignificant role in mitigating the uncertainty.
- Toward Retail Competition in the Portuguese Electricity MarketPublication . Ghazvini, Mohammad Ali Fotouhi; Ramos, Sérgio Filipe Carvalho; Soares, João; Vale, Zita; Castro, RuiPortugal has opened its’ retail electricity market for new entrants, while removing the burdens for competition among the supplier and customers’ switching between the electricity suppliers. The ultimate objective of this liberalization practice is to sharpen competition in the retail segment of the electricity market. In this paper, we assess the success of the liberalization process in the retail sector of the Portuguese electricity market by studying the customers’ behavior, changes in the retail rates and the market concentration. Finally, we provide some explanations for the high switching rates of the customers despite the increase in the retail rates. We also explain why the retail rates are not following the changes of the wholesale prices and the reason for the high market concentration in the retail segment.