An Improved Artificial Ecosystem Algorithm for Economic Dispatch with Combined Heat and Power Units

Abstract

The most effective use of numerous Combined Heat and Power Units (CHPUs) is a challenging issue that requires strong approaches to handle the Economic Dispatch (ED) with CHPUs. It aims at minimizing the fuel costs by managing the Power-Only Units (POUs), CHPUs, and Heat-Only Units (HOUs). The transmission losses are also integrated, which increases the non-convexity of the ED problem. This paper proposes a Modified Artificial Ecosystem Algorithm (MAEA) motivated by three energy transfer processes in an ecosystem: production, consumption, and decomposition. The MAEA incorporates a Fitness Distance Balance Model (FDBM) with the basic AEA to improve the quality of the solution in non-linear and multivariate optimization environments. The FDBM is a selection approach meant to find individuals which will provide the most to the searching pathways within a population as part of a reliable and productive approach. Consequently, the diversity and intensification processes are carried out in a balanced manner. The basic AEA and the proposed MAEA are performed, in a comparative manner considering the 7-unit and 48-unit test systems. According to numerical data, the proposed MAEA shows a robustness improvement of 97.31% and 96.63% for the 7-unit system and 46.03% and 60.57% for the 48-unit system, with and without the power losses, respectively. On the side of convergence, based on the average statistics, the proposed MAEA shows a considerable improvement of 47% and 43% of the total number of iterations for the 7-unit system and 13% and 20% of the total number of iterations for the 48-unit system, with and without the power losses, respectively. Thus, the suggested MAEA provides significant improvements in the robustness and convergence properties. The proposed MAEA also provides superior performance compared with different reported results, which indicates a promising solution methodology based on the proposed MAEA.

An Advanced Bio-Inspired Mantis Search Algorithm for Characterization of PV Panel and Global Optimization of Its Model Parameters

“…We recommend the variations in input parameters, noise in the data, or uncertainties in the PV module characteristic are incorporated in future work. The efficient application of the MSA in this study can also be extended to several other power system engineering problems, such as economic dispatch, combined heat and power optimisation [61][62][63], and the integration of renewable sources [64], etc.…”

A Novel Mantis Search Algorithm for Economic Dispatch in Combined Heat and Power Systems

“…Additionally, to illustrate the superiority of the proposed MSA, a comparative study is conducted between the proposed MSA and other reported optimizers as manifested in Table 3. The proposed MSA, in this table, is compared with Differential Evolution (DE) [48], CPSO [49], Improved Artificial Ecosystem Algorithm (IAEA) [50], time varying acceleration coefficients based PSO (TVAC-PSO) [17], WVO-PSO [37], ECSA [25], CSO [51], improved Mühlenbein mutation based real coded genetic algorithm (RCGA-IMM) [20], Manta Ray Foraging (MRF) optimizer [52], CSO&PPS [53], teaching learning based optimization (TLBO) [49], Bee Colony Optimization (BCO) [54], LCA [55], AIS [56], IGA-NCM [21], and TVAC-particle swarm optimization (PSO) [18]. The proposed MSA achieves the highest performance by using the suggested technique for this problem, as demonstrated in the table, and achieves the lowest generation cost over different optimizers.…”

RIS-Assisted Integrated Sensing and Communication Systems: Joint Reflection and Beamforming Design

“…Numerous real-world engineering optimisation issues have been addressed using the AEO technique. These include choosing the best electrical representation for PV cells [49], planning the path of unmanned combat aerial vehicles (UCAVs) [50], modelling groundwater levels [51], solving the economic dispatch problem with combined heat and power (CHP) units [52], optimizing the allocation of distributed generation and capacitors in power networks [53], and estimating energy costs [54]. This paper introduces an EAEO as a framework for optimizing beamforming in reconfigurable intelligent surfaces integrated with sensing and communication systems.…”