Improved slime mould algorithm for optimal hybrid power system scheduling

This paper proposes an improved slime mould algorithm (ISMA) to minimize the total electricity generation cost of thermal power plants (TPs) in newly developed hybrid power systems with pumped storage hydropower plants (PSHPs), cascaded hydropower plants (CHPs), photovoltaic power plants (PVPs), conventional hydropower plants, and TPs. ISMA is a better version of the original slime mould algorithm (OSMA) by cancelling OSMA’s shortcomings and applying new methods to update new control variables. ISMA replaces two equations of OSMA to update new solutions. In the first equation, ISMA search around each old solution by adding one more increased interval, while OSMA multiplies each old solution by a vector with terms having smaller values than one. In the second equation, OSMA searches around the best solution by adding one more increased interval, but ISMA has a more flexible search by expanding search zones with two additional increased intervals or keeping narrow search zones with one additional increased interval. In addition to OSMA, ISMA is also compared to equilibrium optimizer (EO), jellyfish algorithm (JS), and northern goshawk optimization algorithm (NGO). After solving a test system, ISMA reaches a smaller cost than NGO, JS, EO, and OSMA by 2.43%, 0.68%, 2.83%, and 1.52%, respectively. Then, ISMA is applied for two cases: considering and neglecting the water storage function of PSHPs. Thanks to the water storage function of PSHPs, the total cost of the system is smaller than another case without the water storage function of PSHPs by $196,578.6, corresponding to 8.4%. Clearly, the contribution of the PSHPs to the economic effectiveness of hybrid power systems is huge, and the proposed ISMA is a very suitable optimization tool for finding operation solutions for newly developed hybrid power systems. Hence, the proposed ISMA should be tried for other hybrid power systems with the PSHPs and other power sources, as well as more practical conditions. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.