Quasi-oppositional chemical reaction optimization for combined economic emission dispatch in power system considering wind power uncertainties

Hazra, Sunanda; Roy, Provas Kumar

doi:10.1016/j.ref.2019.10.005

Cited by 31 publications

(17 citation statements)

References 47 publications

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“…From optimal scheduling of thermal units, the probabilistic prediction of WP output is of high significance. Therefore, scientific research studies and technological developments, although they seek to resolve this kind of problem, are in the process of finding the best solutions [5,6]. The economic dispatch (ED) problem is among the most investigated issues.…”

Section: Research Backgroundmentioning

confidence: 99%

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Chaotic sine–cosine algorithm for chance‐constrained economic emission dispatch problem including wind energy

Guesmi

Farah

Ismail

et al. 2020

IET Renewable Power Generation

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One of the most effective approaches to reduce carbon emissions is the integration of renewable energy sources into electrical power networks. Currently, wind turbines are the fastest growing among all renewable sources. With the integration of wind farms into electrical grids, the economic emission dispatch (EED) problem is becoming more complicated due to the stochastic availability of wind energy. In this study, a new approach is proposed to solve the EED problem incorporating wind farms. The problem is formulated as a chance-constrained problem to deal with the stochastic characteristic of wind power. A novel chaotic sine-cosine algorithm (CSCA) is proposed to provide the optimal generation schedule to minimise simultaneously the generation cost and emission. Some weakness has been encountered in exploitation and exploration capabilities in standard sine cosine algorithm (SCA). Hence, the chaos is integrated into the original SCA to improve its performance. In addition, a new mutation strategy is added to the SCA. In this study, the new algorithm is based on three mutually exclusive equations. The new technique is applied on the 69-bus ten-unit and 40-unit test systems with and without wind energy. The results performed by CSCA are compared with those generated by other recent techniques.

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Section: Research Backgroundmentioning

confidence: 99%

“…In the literature, several approaches were suggested to describe the impact of the wind turbine output on power system operation [5][6][7]. In [8,9], sampling methods based on the probability distribution of the random power output of the WP are used to develop an optimal power flow framework.…”

Section: Related Workmentioning

confidence: 99%

Chaotic sine–cosine algorithm for chance‐constrained economic emission dispatch problem including wind energy

Guesmi

Farah

Ismail

et al. 2020

IET Renewable Power Generation

View full text Add to dashboard Cite

show abstract

“…Several constraints may be considered in the resolution of the DEED problem, such as generator ramp-rate limits (RRL), prohibited operating zones (POZ) and valve-point loading effects (VPLE). Unfortunately, POZ constraints, due to the vibrations in the shaft bearing, cause discontinuities in the objective functions [3]. Moreover, VPLE constraints make fuel cost function with ripples [2,3].…”

Section: Research Backgroundmentioning

confidence: 99%

“…Unfortunately, POZ constraints, due to the vibrations in the shaft bearing, cause discontinuities in the objective functions [3]. Moreover, VPLE constraints make fuel cost function with ripples [2,3].…”

Section: Research Backgroundmentioning

confidence: 99%

“…In [19], the overestimation and underestimation costs of WP availability are combined with the total fuel cost of thermal generators and then a hybrid PSO and artificial physics optimization (APO) algorithm is used for minimization of the total cost. An improved version of the chemical reaction optimization method is proposed in [3] for the SEED problem incorporating penalty costs associated with wind energy availability. In [20], a scenario-based stochastic framework was established for describing the stochastic DEED problem considering WP.…”

Section: Related Workmentioning

confidence: 99%

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An Elitist Multi-Objective Particle Swarm Optimization Algorithm for Sustainable Dynamic Economic Emission Dispatch Integrating Wind Farms

2020

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In recent years, wind energy has been widely used as an alternative energy source as it is a clean energy with a low running cost. However, the high penetration of wind power (WP) in power networks has created major challenges due to their intermittency. In this study, an elitist multi-objective evolutionary algorithm called non-dominated sorting particle swarm optimization (NSPSO) is proposed to solve the dynamic economic emission dispatch (DEED) problem with WP. The proposed optimization technique referred to as NSPSO uses the non-dominated sorting principle to rank the non-dominated solutions. A crowding distance calculation is added at the end of all iterations of the algorithm. In this study, WP is represented by a chance-constraint which describes the probability that the power balance cannot be met. The uncertainty of WP is described by the Weibull distribution function. In this study, the chance constraint DEED problem is converted into a deterministic problem. Then, the NSPSO is applied to simultaneously minimize the total generation cost and emission of harmful gases. To proof the performance of the proposed method, the ten-unit and forty-unit systems—including wind farms—are used. Simulation results obtained by the NSPSO method are compared with other optimization techniques that were presented recently in the literature. Moreover, the impact of the penetration ratio of WP is investigated.

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Optimal dispatch using moth‐flame optimization for hydro‐thermal‐wind scheduling problem

Hazra

Roy

2020

Int Trans Electr Energ Syst

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A proficient algorithm, based on the moth‐flame optimization (MFO), is founded for solving economic and emission dispatch for hydro‐thermal‐wind (HTW) scheduling problem. The renewable wind power associated with hydropower‐integrated thermal power plant is a non‐linear, non‐convex optimization problem due to water discharge rate, hydraulic continuity constraint, reservoir storage limits, variable wind speed, scheduling time linkage, water transport delay, power balance constraints, as well as operation limits of renewable wind units. A renewable wind power‐oriented multi‐objective hydro‐thermal scheduling has significant value to trim down the power generation cost and emission. The usefulness of the projected method is verified on two case studies. In the most recent literature, the statistical outcomes of the applied MFO algorithm are compared with others evolutionary optimization. It is also observed that the proposed MFO method is skillful for developing hopeful outcomes and for significantly reducing the computation time.

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Quasi-oppositional chemical reaction optimization for combined economic emission dispatch in power system considering wind power uncertainties

Cited by 31 publications

References 47 publications

Chaotic sine–cosine algorithm for chance‐constrained economic emission dispatch problem including wind energy

Chaotic sine–cosine algorithm for chance‐constrained economic emission dispatch problem including wind energy

An Elitist Multi-Objective Particle Swarm Optimization Algorithm for Sustainable Dynamic Economic Emission Dispatch Integrating Wind Farms

Optimal dispatch using moth‐flame optimization for hydro‐thermal‐wind scheduling problem

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