• Energy Research

<p>This paper presents cuckoo search algorithm (CSA) for solving non-convex economic load dispatch (ELD) problems of fossil fuel fired generators considering transmission losses and valve point loading effect. CSA is a new meta-heuristic optimisation technique inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species. The strength of the proposed meta-heuristic optimization technique CSA has been tested and validated on the standard IEEE 14-bus, 26-bus and 30-bus system with several heuristic load patterns. The results have indicated that the proposed approach is able to obtain significant economic load dispatch solutions than those of Firefly Algorithm (FFA) and other soft computing techniques reported in the literature.</p>

AbstractThe use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating a large number of PHEVs with advanced control and storage capabilities can enhance the flexibility of the distribution grid. This study proposes an innovative energy management strategy (EMS) using an Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids with renewable energy sources (RESs) and PHEVs. The goal is to optimize multi-objective scheduling for a microgrid with wind turbines, micro-turbines, fuel cells, solar photovoltaic systems, and batteries to balance power and store excess energy. The aim is to minimize microgrid operating costs while considering environmental impacts. The optimization problem is framed as a multi-objective problem with nonlinear constraints, using fuzzy logic to aid decision-making. In the first scenario, the microgrid is optimized with all RESs installed within predetermined boundaries, in addition to grid connection. In the second scenario, the microgrid operates with a wind turbine at rated power. The third case study involves integrating plug-in hybrid electric vehicles (PHEVs) into the microgrid in three charging modes: coordinated, smart, and uncoordinated, utilizing standard and rated RES power. The SaCryStAl algorithm showed superior performance in operation cost, emissions, and execution time compared to traditional CryStAl and other recent optimization methods. The proposed SaCryStAl algorithm achieved optimal solutions in the first scenario for cost and emissions at 177.29 €ct and 469.92 kg, respectively, within a reasonable time frame. In the second scenario, it yielded optimal cost and emissions values of 112.02 €ct and 196.15 kg, respectively. Lastly, in the third scenario, the SaCryStAl algorithm achieves optimal cost values of 319.9301 €ct, 160.9827 €ct and 128.2815 €ct for uncoordinated charging, coordinated charging and smart charging modes respectively. Optimization results reveal that the proposed SaCryStAl outperformed other evolutionary optimization algorithms, such as differential evolution, CryStAl, Grey Wolf Optimizer, particle swarm optimization, and genetic algorithm, as confirmed through test cases.

The current research study proposes a multi-objective optimal power flow (OPF) solution using a modified Interior Search Algorithm in which Levy Flight feature with two different strategies is incorporated to accelerate the convergence speed and to enhance solution quality. In traditional OPF problems, the thermal generation units alone are accounted, whereas the security challenges faced by the network are mostly ignored. In other terms, the emission needs to be significantly reduced in terms of environmental sustainability aspects. So, the electrical grid must be infused with power generated from different renewable energy sources. Consequently, the current research article proposes an approach in order to accomplish OPF through a combination of stochastic wind and solar power coupled with traditional thermal power generators in the system. The authors leveraged modified IEEE 30-bus system, IEEE 118-bus system and real-time electrical network 62-bus Indian Utility System in order to validate the Levy Interior Search Algorithm proposed in the study by incorporating renewable energy sources. During implementation, the researchers considered different factors such as network security limitations, for instance transmission line capacity, bus voltage limits and restricted operation zones for thermal units. The simulation results obtained using the proposed LISA Strategy-II algorithm are compared with the results obtained using LISA Strategy-I, ISA and other optimization algorithms reported in the literature. The results achieved from the implementation infer that the proposed method has inherently good convergence characteristic and affords better exploration of the Pareto front.

This paper presents Interior search algorithm for solving economic load dispatch problem of a microgrid. ISA is a aesthetic seeking algorithm which is inspired from the interior design and decoration. The efficacy of ISA has been validated on a simple microgrid test system which comprises wind generators, fuel cells, diesel generators. Simulation results reveal that ISA appears to be a robust unconventional technique for elucidating the ELD problems in microgrid when compared with the other optimization techniques taking into consideration the superiority of the solution attained.

This paper presents a new algorithm called Firefly Algorithm to solve the well-known power system Economic load dispatch problem. Firefly Algorithm is a novel nature-inspired algorithm inspired by social behavior of fireflies and the phenomenon of bioluminescent communication between them. Our proposed algorithm utilized fireflies food searching mechanism to optimize economic load dispatch problem in power system. This makes the algorithm more convergence and less complexity. It is also appropriate for high-speed real-time application due to fast and less execution time. In order to demonstrate the effectiveness of the proposed algorithm has been tested with the standard 6-bus, IEEE-14 bus and IEEE-30 bus system with several heuristic load patterns. The numerical result reveals that the proposed algorithm can provide appreciably better solutions within reasonable time.

Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm's efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.

Electric power quality, which is a current interest to several power utilities all over the world, is often severely affected by harmonics and transient disturbances. There is no unique model which can assess the power quality problem and to identify and classify them properly. Existing automatic recognition methods need improvement in terms of their versatility, reliability, and accuracy. The FUZZY LOGIC based tools have been applied for the PQ classification. This paper addresses Power quality problem classification by wavelet and fuzzy expert system. Major Key issues and challenges related to these advanced techniques in automatic classification of PQ problems are highlighted. New intelligent system technologies using DSP, expert systems, AI and machine learning provide some unique advantages in intelligent classification of PQ distortions