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In this research, we proposed and investigated the physical layer system called the full-duplex (FD) power beacon-assisted (PB) energy harvesting (EH) relaying cooperative network. The system model has one PB node, one destination (D), one source (S), and one relay (R) node. We investigated the system performance in terms of outage probability (OP) and system throughput (ST) with the power-splitting (PS) protocol in both delay-tolerant (DTT) and delay-limited (DLT) transmission modes. Moreover, the optimal power splitting (PS) factor in both DDT and DLT modes is proposed and derived. Finally, the mathematical closed-form expressions of the OP and ST are derived by using the Monte Carlo simulation with the help of MATLAB software. From the results, it can be observed that the analytical values and simulation values are the same in the effect of the main system parameters.

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This paper proposes a cuckoo search algorithm (CSA) using different distributions for solving the short-term hydrothermal scheduling (ST-HTS) problem with reservoir storage constraint on hydropower plants. The CSA method is a new meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species for solving optimization problems. The advantages of the CSA method are few control parameters and effective for optimization problems with complicated constraints. In the proposed CSA, three distributions have been used including Lévy distribution, Gaussian distribution and Cauchy distribution. The proposed method has been tested on two test systems and the obtained results have been compared to that from other methods available in the literature. The result comparisons have indicated that the proposed method is a very favorable method for solving the short term hydrothermal scheduling problems with reservoir constraint.

The paper proposes a modified Bat algorithm (MBA) for searching optimal solutions of Economic dispatch of combined heat and power generation (CHPGED) with the heat and power generation from three different types of units consisting of pure power generation units, pure heat generation units and cogeneration units. The CHPGED problem becomes complicated and big challenge to optimization tools since it considers both heat and power generation from cogeneration units. Thus, we apply MBA method with the purpose of enhancing high quality solution search ability as well as search speed of conventional Bat algorithm (BA). This proposed approach is established based on three modifications on BA. The first is the adaptive frequency adjustment, the second is the optimal range of updated velocity, and the third is the retained condition of a good solution with objective to ameliorate the search performance of traditional BA. The effectiveness of the proposed approach is evaluated by testing on 7, 24, and 48 units systems and IEEE 14-bus system and comparing results with BA together with other existing methods. As a result, it can conclude that the proposed MBA method is a favorable meta-heuristic algorithm for solving CHPGED problem.Energies 2018, 11, 3113 2 of 27 is a major challenge for the optimization tools for finding optimal solutions satisfying all constraints exactly and reducing total fuel cost effectively.Over several decades, the authors in [2][3][4][5][6][7] have introduced different methods to solve the CHPGED. The Newton [2] and Lagrange relaxation (LR) [3] methods are two of those methods that were first applied to solve the CHPGED issue. However, they have a common main disadvantage of being limited when dealing with a large-scale system. In order to overcome this disadvantage, the authors in [4] have proposed the combined method between the augmented Lagrange and Hopfield network (ALHN). As a result, a very good solution with a short computation time is obtained. In order to reduce the number of iterations and the loop time by speeding up the computation, the authors of [5] have presented the novel direct search (NDS) method based on a successive refinement search technique. The Newton method and other methods such as ALHN and NDS can solve nonlinear constrained optimization problems but Lagrange relaxation (LR) cannot deal with the issue. Thus, ref.[6] has proposed the combination of sequential quadratic programming (SQP) and LR, called SQP-LR method. In the method, SQP could solve nonlinear constraints successfully while LR could find optimal solution satisfying all remaining constraints. Unlike SQP-LR, meta-heuristic algorithms can solve non-linear problems simply and successfully although they do not need to use SQP method. LR with surrogate sub-gradient technique (LR-SST) [7] has been developed by using the main search function of LR and the updating function of SST. LR has also been used for the same purpose as the methods in [3,6] while SST has been used to calculate the values of Lagrange multipliers...