Abstarct: Nowadays cogeneration systems are widely being used as an alternative for various applications such as residential, commercial and industrial because of its high potential of energy saving and independence from electrical network grid. This energy saving is highly dependent on operation of power generation unit and other components of the system and variation of energy demands of especial consumer. In this study a Combined Cooling, Heating and Power system has been used for hourly techno-economic optimization of energy system respect to conventional separate production system in a sample residential apartment in desert region of Iran (Mashhad) adopting different algorithms and strategies according to dynamic and time varying cooling, space heating, hot water and power demands. Important parameters has been investigated in optimization of cogeneration system, such as cost of energy, primary energy consumption, overall efficiency of the system, energy cost difference respect to conventional separate production system, energy cost difference ratio, payback ratio and etc. A proposed optimization algorithm (ProOptAlgorithm) has been proposed for optimization of the cogeneration system focusing on different possible modes of operation in power generation unit baxsed on fuel to electric energy conversion relationship and other components of the system and energy flow in different components of the system in each time step of optimization process which minimize the cost of energy in each step. In addition, optimization of cogeneration system has been performed by usage of Genetic Algorithm (GA) and implementation of network flow model and definition of two different objective functions with the aim of minimizing the energy cost difference between cogeneration system and separate production system. Strategy of operation on the basis of following electric energy demand of the apartment (FEL) has been studied. Strategy of maximum power output of power generation unit and selling back (export) the surplus electricity to the network grid (MaxSEG) was used to for techno-economic examination of the cogeneration system. Strategy of maximum power output of power generation unit and storing the surplus electricity in battery block storage model (MaxSBBS) was studied to find the most appropriate battery block in terms of capacity regard to the two practical feasibility conditions of the battery block storage model. As mentioned before, techno-economic optimization of cogeneration systems requires simultaneous consideration of various parameters that they have not similar trend and increase or decrease in one parameter lead to different behaviors in other parameters. Results show that from aspect of dependency and interaction with electric grid, proposed optimization algorithm, FEL and MaxSBBS strategies approximately operate quite independence from electric grid and there was almost no interaction with electric grid. And in MaxSEG strategy the amount of 51043.2 kWh surplus electricity per year was sold back to electric grid. Only MaxSEG and MaxSBBS strategies had the capability of storing heating energy because of continuous operation of power generation unit in maximum power output. MaxSEG strategy has the highest amount of primary energy consumption and thereafter there is GA and after them with a great difference, proposed optimization algorithm and FEL strategy and MaxSBBS strategy have the highest primary energy consumption, respectively. So the MaxSBBS has the least consumption. MaxSEG strategy has the highest cost of energy and then there is GA. After them, there are proposed algorithm and FEL strategy and MAxSBBS has the least amount of cost of energy. And correspond with parameter of cost of energy, energy cost difference ratio in MaxSEG is negative (least amount) and in MaxSBBS has highest amount (3.69%) in a year. MaxSBBS strategy has the highest efficiency (66.05%) and thereafter there are MaxSEG strategy (65.84%), FEL strategy (64.7%), proposed algorithm (64.57%) and GA (61.3%) respectively. In terms of payback ratio, FEL strategy and proposed algorithm have the best payback ratio respectively.