Masoud Rashidinejad

Bio: Masoud Rashidinejad is an academic researcher from Shahid Bahonar University of Kerman. The author has contributed to research in topics: Electric power system & Demand response. The author has an hindex of 25, co-authored 170 publications receiving 2292 citations. Previous affiliations of Masoud Rashidinejad include Brunel University London & Chamber of commerce.

Investigation of Economic and Environmental-Driven Demand Response Measures Incorporating UC

Abstract: In recent years, load management (LM) programs are introduced as an impressive option in all energy policy decisions. Under deregulation, the scope of LM programs has considerably been expanded to include demand response programs (DRPs). Basically, DRPs are divided into two main categories namely, incentive-based programs (IBPs) and time-based rate (TBR) programs. In this paper, an economic model of responsive loads is derived based upon price elasticity of demand and customers' benefit function. In order to investigate the economic-driven and environmental-driven measures of demand response programs, a new linearized formulation of cost-emission based unit commitment problem associated with DRPs (UCDR) is presented. Here, UCDR is modeled as a mixed-integer programming (MIP) problem. The proposed model is applied to determine loads provided by DRPs and schedule commitment status of generating units. Moreover, the optimum value of incentive as a crucial issue for implementing DRPs is derived. Several analyses are conducted to investigate the impact of some important factors such as elasticity on the UCDR problem. The strategy success index (SSI) is employed to prioritize DRPs from the independent system operator (ISO) perspective. The conventional 10-unit test system is used to demonstrate effectiveness of the proposed methodology.

Probabilistic Optimal Power Flow in Correlated Hybrid Wind–Photovoltaic Power Systems

Abstract: As a matter of course, the unprecedented ascending penetration of distributed energy resources (DERs), mainly harvesting renewable energies (REs), is concomitant with environmentally friendly concerns. This type of energy resources are innately uncertain and bring about more uncertainties in the power system, consequently, necessitates probabilistic analyses of the system performance. Moreover, the uncertain parameters may have a considerable level of correlation to each other, in addition to their uncertainties. The two point estimation method (2PEM) is recognized as an appropriate probabilistic method in small scale or even medium scale problems. This paper develops a new methodology for probabilistic optimal power flow (P-OPF) studies for such problems by modifying the 2PEM. The original 2PEM cannot handle correlated uncertain variables but the proposed method has been equipped with this ability. In order to justify the impressiveness of the method, two case studies namely the Wood & Woollenberg 6-bus and the Mathpower 30-bus test systems are examined using the proposed method, then, the obtained results are compared against the Monte Carlo simulation (MCS) results. Comparison of the results justifies the effectiveness of the method in the respected area with regards to both accuracy and execution time criteria.

On possibilistic and probabilistic uncertainty assessment of power flow problem: A review and a new approach

Abstract: As energy resource planning associated with environmental consideration are getting more and more challenging all around the world, the penetration of distributed energy resources (DERs) mainly those harvesting renewable energies (REs) ascend with an unprecedented rate. This fact causes new uncertainties to the power system context; ergo, the uncertainty analysis of the system performance seems necessary. In general, uncertainties in any engineering system study can be represented probabilistically or possibilistically. When sufficient historical data of the system variables is not available, a probability density function (PDF) might not be defined, while they must be represented in another manner i.e. possibilistically. When some of system uncertain variables are probabilistic and some are possibilistic, neither the conventional pure probabilistic nor pure possibilistic methods can be implemented. Hence, a combined solution methodology is needed. This paper proposes a new analytical probabilistic- possibilistic tool for the power flow uncertainty assessment. The proposed methodology is based upon the evidence theory and joint propagation of possibilistic and probabilistic uncertainties. This possibilistic–probabilistic formulation is solved in an uncertain power flow (UPF) study problem.

A comprehensive sequential review study through the generation expansion planning

Abstract: The generation expansion planning (GEP) problem involves the determination of types, location and construction time of new generation technologies which should be added to the existing system in order to meet the growing demand over a planning horizon It is a vital issue due to electricity-dependent aspect of everyday life as well as most production and service systems in society Over the past few decades, the GEP has experienced multifarious changes resulting in emerging a multitude of new models and solving methods In this context, many studies have been carried out to achieve optimal expansion strategies with respect to the different energy-related issues, such as electricity market, uncertainties, low carbon economy requirements, distributed generation, renewable sources, energy policies, demand-side programs, and so on In pursuance of finding out further research ideas in the field of GEP and identify less-addressed areas, a need for a comprehensive review of accomplished studies has been felt By reviewing almost all aspects that have been considered in the GEP as yet, the present paper provides a detailed classification of a large number of done GEP studies Reviewing results provide useful insights into the future of GEP research activities

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

Abstract: The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.