This paper discusses the incorporation of the Hybrid Swarm Evolutionary programming (SEP) for optimal distributed generation (DG) in the distribution system. High load demand will result in unstable control power distribution due to power transmission loss. The compensation process can be implemented by installing a compensation device to prevent this from happening. It is required the optimal sizing and location for the devices to achieve the objective and this can be done by using an optimization technique. Thus, this project aims to develop a hybrid computational intelligence technique is called hybrid SEP for loss minimization. The proposed method embedded the element of Particle Swarm Optimization (PSO) into traditional Evolutionary programming (EP) to improve precision of traditional EP algorithm. The purpose of this study is to investigate the maximum benefits of DG integration to be gained. The proposed techniques are validated on IEEE-69 bus radial system with multiple units of DG. The results showed that the most effective type of DG inject to IEEE-69 bus radial system is with DG Type III, with 95% of active power loss reduction and the best on voltage profile improvement. Hybrid SEP shows superior to EP in term of loss minimization.

Power system these days appears to work at high-stress load, which could trigger voltage security problems. This is due to the fact that the system will operate under low voltage conditions, which could be possibly below the allowable voltage limit. The voltage collapse phenomenon can become one of the remarkable issues in the power systems which can lead to severe consequences of voltage instability. This paper proposes a method for managing the voltage stability risk using two methods which are Evolutionary programming (EP) and multiverse optimization (MVO). Consequently, EP and MVO were used to manage the risk in the power system due to load variations. The risk assessment is made in order to determine the risk of collapse for the system utilizing a pre-developed voltage stability index termed as Fast Voltage Stability Index (FVSI). It is used as the indicator of voltage stability conditions. Results obtained from the study revealed that the MVO technique is much more effective compared to EP.

One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized robust adaptive control method based on dynamic programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) controller. By applying a single line to ground fault in the network, the robustness of the designed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized robust adaptive control method based on dynamic programming for the stabilizing design of the power system and the complementary SVC controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

Utilization of combined heat and power (CHP) systems that simultaneously fulfill the electrical and heating energy needs is considered as an effective strategy for management of energy demand and consumption. Overall operational efficiency in excess of 70% is made possible, when these systems are utilized, and it is expected that needs for both the demand and supply sides are met even under the condition of unsubsidized energy resources. In Iran, where there are energy subsidies for fossil fuel and electricity, utilization of CHP systems requires proper modeling and selection of system components. Due to the large fluctuations in the electrical and heating load during various heating and cooling seasons, it is necessary to investigate the performance of a CHP system so that the optimal operating point is determined. The review of literature shows that the majority of these systems are studies without the means for thermal energy storage. In this study, the objective is the determination of optimal operational point for a CHP System with thermal energy storage tank utilizing Evolutionary programming methodology. The findings are compared with the case of a conventional alternative, where the electrical energy is purchased from local utility company and a heating energy is furnished by a conventional boiler. Based on the current utility costs, the results show that the operating cost of a CHP system is nearly 50% lower than that of the conventional alternative. Also, it is shown that the use of absorption chiller reduces dependency on purchased electricity and increases the efficiency of the CHP system.

Evolutionary altruism has been a serious problem for Evolutionary theory from its earliest times at mid-nineteen century. Group selection theory was its solution until 1960s.But at that time it was criticized so that it was no longer the solution to that problem. Since then theories put forward to solve the problem by individual and/or gene selection theories. Kin selection, reciprocal altruism, Evolutionary game theory, the selfish gene, and indirect reciprocity are the main theories of this sort. Altruism according to these theories is apparent not real. Multi-level selection theory is introduced since 1970s that defends "new" group selection theory. In addition cultural group selection theory has come into the field. Altruism according to these theories is real. I agree with the second type of theories that real altruism has been evolved. But perhaps more in agreement with the first type I see it in the "shadow" of Evolutionary egoism.