Multi-Objective Optimization of a Solar-Assisted Combined Cooling, Heating and Power Generation System Using the Greywolf Optimizer

Abstract

Energy demand and consumption have, in recent times, witnessed a rapid proliferation influenced by technological developments, increased population and economic growth. This has fuelled research trends in the domain of energy management employing tri-generation systems such as combined cooling, heating and power (CCHP) systems. Furthermore, the incorporation of renewable energy, especially solar energy, to complement the thermal input of fossil fuels has facilitated the effectiveness and sustainability of CCHP systems. This study proposes a new approach to improve the overall efficiency of CCHP systems and to compute optimal design parameters in order to assist decision makers to identify the best geometrical configuration. A multi-objective optimization formulation of a solar-assisted CCHP system was adopted to maximize the net power and exergy efficiency and to minimize the CO2 emission using the greywolf optimization technique. In addition, the effects of the decision variables on the objective functions were analysed. The proposed optimization approach yielded 100 set of Pareto optimal solutions which would serve as options for the decision maker when making a selection to choose from when seeking to improve the performance of a solar-assisted CCHP system. It also yielded higher exergy efficiency and lower CO2 emission values when compared with a similar study. The results obtained indicate that a system with high net power output does not necessarily translate to a highly efficient system. Additionally, minimal CO2 emissions were recorded for a system with low compression ratio, low combustion chamber inlet temperature and high inlet turbine temperature. This study demonstrates that the proposed approach is potentially suitable for the optimization of a solar-assisted CCHP system.

Improved Load Frequency Control in Power Systems Hosting Wind Turbines by an Augmented Fractional Order PID Controller Optimized by the Powerful Owl Search Algorithm

“…The use of wind turbines in power systems is growing due to the increasing demand for sustainable and environmentally friendly electrical energy [1][2][3]. Wind turbines have several advantages as a stable source of electricity in power systems, such as: (1) abundant energy source: for electricity generation, wind turbines require an abundant wind energy source that is usually available day and night; (2) reduced environmental pollution: using wind turbines as a clean energy source reduces environmental pollution; (3) cost reduction in electricity production: the cost of electricity production with wind turbines is lower than that of fossil fuels.…”

“…Figure 9. (a)The FR of the power system is based on the proposed controller, Scenario(1). (b) The FR of the power system using controller 1, Scenario (1).…”

“…(b) The FR of the power system using controller 1, Scenario (1). (c) The FR of the power system using controller 2, Scenario (1), (d) The FR of the power system using controller 3, Scenario(1). (e) The FR of the power system using LFC is based on controller 4, Scenario (1).…”