Abstract
Economical and reliable provision of electricity has been one of the most significant research objectives since decades. With time, various economic load dispatch (ELD) techniques have emerged in power market. Apart from using these methods, changes in the use of conventional source of energy and incorporating non-conventional sources have emerged in recent years. Solar photovoltaic (PV) generation helps reducing emissions and dependency on fossil fuels. This chapter presents combined economic emission dispatch (CEED) of a hybrid thermal solar PV system. Artificial bee colony (ABC) algorithm is used as optimization tool for the scenario involving six thermal plants and thirteen solar plants. The effectiveness of this method is compared and validated with other methods available in recent literature.
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Abbreviations
- Min C:
-
Objective function
- \(\small F_{i} \left({P_{i}} \right)\) :
-
Fuel cost (in $/h) for i-th power generating unit
- \(\small E_{i } \left( {P_{i} } \right)\) :
-
Emission (in kg/h) for i-th power generating unit
- \(\small w\) :
-
Weight ratio
- \({\text{ppf}}\) :
-
Price penalty factor
- \(P_{i}\) :
-
Power generated by i-th source
- \(\small P_{L}\) :
-
Power loss
- \(P_{d}\) :
-
Power demand at that instant
- \(k_{r}\) :
-
Penalty cost factor underestimation
- \(\small P_{i\min } , P_{i\max }\) :
-
Minimum and maximum power limits for i-th thermal generating source, respectively
- \(\small u_{i } ,l_{i}\) :
-
Upper and lower bound of the solution space of objective function
- rand (0,1):
-
A random number \(\in\) (0,1)
- \(\small x_{k}\) :
-
Randomly selected food source
- \(\small \varphi_{mi}\) :
-
Random number \(\in\) (−1,1)
- \(P_{m}\) :
-
Probability function
- \(\small a_{i} ,b_{i} ,c_{i}\) :
-
Fuel cost coefficients 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 (MW)
- \(\small T_{\text{ref}}\) :
-
Reference temperature (25 °C in this case)
- \(T_{\text{amb}}\) :
-
Ambient temperature of solar plant
- \(\mu\) :
-
Temperature coefficient of solar plant (–0.47%)
- \(G_{i}\) :
-
Incident solar radiation \(( {\text{W/m}}^{ 2} )\) at i-th hour
- \(C_{j}\) :
-
Cost per unit for j-th solar plant
- \(\small {\text{Psch}}_{j}\) :
-
Scheduled power for j-th solar plant
- \(k_{p}\) :
-
Penalty cost factor for overestimation
- \(\small f_{m } \left( {x_{m} } \right)\) :
-
Objective function value of xm
- \(\small {\text{fit}}\left( {x_{m} } \right)\) :
-
Fitness of xm
- \(\small x_{m}\) :
-
Initial food sources
- \(\small v_{mi}\) :
-
Neighbor food source
- \(\small x_{mn}\) :
-
New solution
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Acknowledgements
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). Combined Economic Emission Dispatch of Hybrid Thermal PV System Using Artificial Bee Colony 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_5
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