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Abhijit R. Abhyankar

Bio: Abhijit R. Abhyankar is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Electric power system & Distributed generation. The author has an hindex of 18, co-authored 108 publications receiving 1608 citations. Previous affiliations of Abhijit R. Abhyankar include Indian Institute of Technology Bombay & Indian Institutes of Technology.


Papers
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Journal ArticleDOI
TL;DR: The application of Teaching Learning Based Optimization (TLBO) algorithm for optimal coordination of DOCR relays in a looped power system is discussed and the proposed algorithm TLBO gives optimal coordination margin between 0.3 and 0.8 s and no miscoordination between primary and backup pairs.

238 citations

Journal ArticleDOI
TL;DR: This paper proposes the evaluation of “best-fit” PFs by taking into account the minute-to-minute variability of solar, wind, and load demand, for a scheduling period, and results for two test systems have been obtained to verify the benefit.
Abstract: Real-time economic dispatch (RTED) is performed every 5–15 min with the static snapshot forecast data. During the period between two consecutive schedules, generators participate in managing power imbalance, based on participation factors (PFs) from previous economic dispatch (ED). In modern power systems with considerable renewable energy sources that have high variability, this conventional approach may not adequately accommodate the economic implication of the said variability. This paper proposes the evaluation of “best-fit” PFs by taking into account the minute-to-minute variability of solar, wind, and load demand, for a scheduling period. Since “best-fit” PFs are evaluated only once, i.e., at the start of scheduling interval, the dimensionality of optimization problem remains the same as that of conventional approach. The proposed approach is suggested for sequential and dynamic variants. Results for two test systems have been obtained to verify the benefit of the proposed approach.

212 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed an efficient approach for evolutionary algorithm based Optimal Power Flow (OPF), which uses the concept of incremental power flow model, based on sensitivities.

163 citations

Journal ArticleDOI
TL;DR: It is shown that the tracing problem can be formulated as a linear constrained optimization problem and a new paradigm is suggested that attempts to capture the best of the two methodologies by exploring multiplicity of the solution space of the traced problem, within the given constraints.
Abstract: Megawatt (MW) power flow tracing can assess the extent of network usage by the participants that can be effectively used for multiple objectives like transmission pricing, loss allocation, etc. MW power tracing, a post-facto analysis of power flow solution, is amenable to multiple solutions. This implies multiplicity of solution space of transmission cost and loss allocation problems. The conventional tracing methods enforce a "proportionate sharing rule" to calculate the shares. These shares are sensitive to quantity and distance as against the postage stamp method, which is immune to distance. Any of these methods will result in penalizing a set of constituents, which raises a fairness issue. This is evident from the experiences of developing countries like India. In this paper, a new paradigm is suggested that attempts to capture the best of the two methodologies by exploring multiplicity of the solution space of the tracing problem, within the given constraints. We show that the tracing problem can be formulated as a linear constrained optimization problem. We propose a tracing compliant modified postage stamp allocation method that computes a traceable solution that minimizes overall deviation from the postage stamp allocation. Results on actual data of central transmission utility of Western Regional Grid of India demonstrate the claims

134 citations

Journal ArticleDOI
TL;DR: This paper proposes an energy and spinning reserve market clearing (ESRMC) mechanism for wind-thermal power system, considering uncertainties in wind power and load forecasts, and Multiobjective Strength Pareto Evolutionary Algorithm 2+ (SPEA 2+) has been used to solve the problem.
Abstract: This paper proposes an energy and spinning reserve market clearing (ESRMC) mechanism for wind–thermal power system, considering uncertainties in wind power and load forecasts. Two different market models for the ESRMC are proposed. One model includes reserve offers from the conventional thermal generators, and the other includes reserve offers from both thermal generators and demand/consumers. The stochastic behavior of wind speed and wind power is represented by the Weibull probability density function (pdf), and that of the load is represented by a normal pdf. This paper considers two objectives: total cost minimization and the system-risk-level minimization. The first objective includes the cost of energy provided by thermal and wind generators, and the cost of reserves provided by thermal generators and loads. It also includes costs due to overestimation and underestimation of available wind power and load demand. The system risk level is considered as another objective as wind power is highly uncertain. Multiobjective Strength Pareto Evolutionary Algorithm 2+ (SPEA 2+) has been used to solve the ESRMC problem. The results of the IEEE 30 bus system demonstrate the utility of the proposed approach.

124 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, the authors present an energy fundiment analysis for power system stability, focusing on the reliability of the power system and its reliability in terms of power system performance and reliability.
Abstract: (1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

1,080 citations

Book ChapterDOI
01 Jan 2014
TL;DR: This chapter is devoted to a more detailed examination of game theory, and two game theoretic scenarios were examined: Simultaneous-move and multi-stage games.
Abstract: This chapter is devoted to a more detailed examination of game theory. Game theory is an important tool for analyzing strategic behavior, is concerned with how individuals make decisions when they recognize that their actions affect, and are affected by, the actions of other individuals or groups. Strategic behavior recognizes that the decision-making process is frequently mutually interdependent. Game theory is the study of the strategic behavior involving the interaction of two or more individuals, teams, or firms, usually referred to as players. Two game theoretic scenarios were examined in this chapter: Simultaneous-move and multi-stage games. In simultaneous-move games the players effectively move at the same time. A normal-form game summarizes the players, possible strategies and payoffs from alternative strategies in a simultaneous-move game. Simultaneous-move games may be either noncooperative or cooperative. In contrast to noncooperative games, players of cooperative games engage in collusive behavior. A Nash equilibrium, which is a solution to a problem in game theory, occurs when the players’ payoffs cannot be improved by changing strategies. Simultaneous-move games may be either one-shot or repeated games. One-shot games are played only once. Repeated games are games that are played more than once. Infinitely-repeated games are played over and over again without end. Finitely-repeated games are played a limited number of times. Finitely-repeated games have certain or uncertain ends.

814 citations

Journal ArticleDOI
TL;DR: In this paper, a novel Moth Swarm Algorithm (MSA) inspired by the orientation of moths towards moonlight was proposed to solve constrained optimal power flow (OPF) problem.

340 citations

Journal ArticleDOI
TL;DR: In this paper, an approach to solve optimal power flow combining stochastic wind and solar power with conventional thermal power generators in the system is proposed, where the objective function considers reserve cost for overestimation and penalty cost for underestimation of intermittent renewable sources.

286 citations

Journal ArticleDOI
TL;DR: The modified Sine-Cosine algorithm (MSCA) aims at reducing the computational time with a sufficient improvement in finding the optimal solution and feasibility, which is validated with solving the OPF problem for a number of benchmark test systems.

259 citations