# Optimal Operation of CCHP Smart Distribution Grid with Integration of Renewable Energy
###### Abstract
Reference [17] presented the optimization approach of CCHP with the integration of electric vehicles (EVs) as an electric source. The result reveals that the integration of EVs increases the benefits compared to EVs. The proposed optimization algorithm of the CCHP system is either to minimize the operating cost or emission of greenhouse gases (GHGs) in the trading-off approach due to conflict between two objective functions. The simulation results showed the effectiveness of the proposed approach to improve the system efficiency and reduction of greenhouse gases. The author in reference [18] demonstrated a multi-objective optimal operation to reduce the total expense of the operation. However, in [17; 18] the proposed analysis is modelled as a multi-objective model, and the compromise Pareto solution set of nondominated solutions is obtained. However, this method is a tradeoff method; there is not any single optimal solution, and it is not suitable for online operation. Furthermore, the demand response is ignored in this approach. The exchange in energy with the utility grid is overridden in this model. Reference [19] proposed an optimal planning of CCHP with the integration of electric vehicles as new energy sources to reduce the cost of operation. Different scenarios have been conducted depending on the variable of consumer consumption during different seasons. The proposed model overrides the emission cost and demand side in their approach, and many constraints and unit commitments are not considered in the proposed approach. References [20; 21; 22; 23] suggested optimal planning to reduce the cost of fuel consumption under uncertainty of renewable energy generation and load by employing a two-stage approach. The first stage is the day-ahead economic schedule, and the second stage is a correction stage of sustainable energy resource generation and load prediction. All references do not take into consideration the emission cost and demand side response. This paper takes into consideration the demand response mechanism, energy storage devices, and exchange in energy with the utility grid with the flexibility of operation of CCHPsystem devices by transforming energy from one form to another to mitigate the fluctuation in the renewable energy resources and loads variability. In addition, the robustness of the system is improved without excessive expense. Authors in [24; 25; 26] presented an optimization approach to minimize the operating cost of CCHP based on an energy hub. However, these papers ignored the emission cost and demand side management from their formulation of the optimization problem and many realistic constraints. Additionally, the unit commitment technique is not considered in their model. The optimal planning of both the generation and demand sides is an important technique to tackle the impacts of the intermittence of renewable energy generation. Accordingly, many researchers proposed the optimization approach of smart grid with integration of demand side management [27; 28; 29; 30; 31; 32]. However, these studies proposed the optimization problem with demand-side management of single carrier microgrids without considering heating and cooling systems. Few researchers suggested an optimal operation of CCHP with demand response [12; 33]. However, reference [33] ignored the environmental cost, UC strategy, renewable energy, gas boiler, and chillers models in their proposed model. Furthermore, the demand response is not considered a decision variable in the optimization problem. Reference [12] overrides the environmental cost, startup and shutdown cost of generators, UC strategy, ramp rate constraints, and constraints relevant to environmental damage. In this paper, a novel UC optimal operation of CCHP with the integration of demand response and renewable energy sources is proposed to minimize both the total running and environmental costs, where the DR program is considered as a decision variable in the optimization problem.