Abstract
Continuous and sustainable electricity is one of the major concerns in this modern world. This has led to the implementation of microgrid (MG) in order to establish an independent, efficient and cost-effective power supply system. The generation in MG can be conventional or non-conventional but due to increasing power demand, high fuel prices, scarcity of fossil fuels and degrading environment, there is a growing demand of using renewable energy sources (RS) for power generation. Solar PV units play an indispensable part in producing clean energy and coping with this modern-day power demand challenges. Grey wolf optimization (GWO), which is a metaheuristic technique inspired by the hierarchical hunting mechanism of grey wolves, is used in this chapter for solving a multi-objective problem in a dynamic environment of a microgrid. Dynamic dispatch is a more practical way which aims to provide an optimum solution in a scheduling horizon over twenty-four hours a day. A hybrid system comprising six conventional thermal plants and a solar farm containing thirteen solar PV units are discussed in this chapter. The performance and effectiveness of GWO are compared and validated with other two well-proven methods ABC and DE.
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- \(\small a_{i} ,b_{i} ,c_{i}\) :
-
Fuel cost coefficients of i-th generating unit
- \(\small P_{i}\) :
-
Output power in MW of i-th generating unit
- \(\small \alpha_{i} , \beta_{i} , \gamma_{i }\) :
-
Emission coefficients of i-th generating unit
- \(\small P_{\text{rated}}\) :
-
Rated output of a solar plant
- T ref :
-
Reference temperature taken (25 °C in this case)
- T amb :
-
Ambient temperature of solar plant
- µ :
-
Temperature coefficient of solar plant (–0.50% in this case)
- S t :
-
Incident solar radiation (W/m2) at t-th hour
- \(P_{L}\) :
-
Power loss
- \(\small{{\mathrm{UR}}_{i} ,\,{\mathrm{DR}}_{i}}\) :
-
Up rate and down rate of ith generating unit, respectively
- \(A,C\) :
-
Coefficient vectors
- \({\mathcal{X}}\left( t \right)\) :
-
Position vector of the prey
- \(\small {\mathcal{X}}\) :
-
Position vector of a grey wolf
- r1, r2:
-
Random vectors \(\in\)[0, 1]
- \({\mathcal{X}}_{1} ,{\mathcal{X}}_{2} ,{\mathcal{X}}_{3}\) :
-
Best position of alpha \(\left( \alpha \right)\), beta \(\left( \beta \right)\) and delta \(\left( \delta \right)\), respectively
- \({\mathcal{X}}\left( {t + 1} \right)\) :
-
Final position
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Acknowledgement
The authors acknowledge financial support provided by AICTE-RPS project File No. 8-36/RIFD/RPS/POLICY-1/2016-17 dated 2.9.2017 and TEQIP III. The authors also thank the Director and management of M.I.T.S. Gwalior, India, for providing facilities for carrying out this work.
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Dubey, S.M., Dubey, H.M., Pandit, M. (2020). Dynamic Scheduling of Energy Resources in Microgrid Using Grey Wolf Optimization. In: Pandit, M., Dubey, H., Bansal, J. (eds) Nature Inspired Optimization for Electrical Power System. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-4004-2_6
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DOI: https://doi.org/10.1007/978-981-15-4004-2_6
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