Urban community energy systems design under uncertainty for specified levels of carbon dioxide emissions

This study examines how the optimal system design of an urban community energy system changes under the presence of uncertainties in energy prices and demands for a specified level of carbon dioxide emissions (CDE), which is measured as a percentage of CDE associated with the operation of standalone systems. In order to account for uncertainties and to reduce the computational times and retain accuracy, moment matching is used to discretize uncertain distributions. Diverse scenarios are constructed by random sampling of the vectors which contain discrete distributions of uncertain parameters. System design is carried out to minimize the annual total cost and to limit the average of the worst-case emissions with the 5% probability which corresponds to the conditional value at risk (CVaR) of emissions for the confidence level of 95%. The effects of the different values of CVaR on the design of the system are examined. It is shown how the system size changes due to uncertainty and as a function of the CDE target value. Design of an energy system for office buildings in Dalian, China, is presented. Since there is no significant amount of flat surfaces available in a dense urban core, photovoltaics and thermal solar are not considered as candidates for system components. It is shown that with the present-day technology, the lowest amount of CDE is 37% of emissions from standalone systems which use coal-based grid electricity. This indicates the necessity of a significant technological change to reduce CDE to be 10% of standalone systems.