|Title||Towards the Optimal Development of Low-Carbon Community Energy Systems|
|Publication Type||Conference Proceedings|
|Year of Publication||2016|
|Authors||Ping Liu, Wei Feng, Chris Marnay, Spencer M Dutton, Ming Jin, Lingwei Zheng, Nan Zhou|
|Conference Name||2016 ACEEE Summer Study on Energy Efficiency in Buildings|
|Conference Location||Pacific Grove, CA|
More and more communities and cities are moving toward low-carbon and sustainable development using smart grid concepts. In China, the development of numerous newly planned communities presents an opportunity to incorporate sustainable energy systems. What the opportunity requires are the scientific tools necessary to deploy the energy technologies that are most suited to a particular site. This paper focuses on optimizing the planning and operation of low-carbon district energy systems that incorporate solar photovoltaics (PV), thermal energy storage, and combined heat and power plants. We developed an optimization tool that is modeled as a multi-objective, mixed integer linear programming problem regarding meeting a community's heating, cooling, and electricity needs. One objective in solving the problem is to minimize the installation and operating costs for low-carbon energy technologies, which in turn will minimize the cost attributed to carbon emissions associated with the community's energy production. We also consider the potential for profit from selling surplus electricity and heating energy outside of the community. The distribution network is modeled to account for losses through the pipelines that deliver thermal energy. The model determines the optimal capacity and operation of each low-carbon option within the optimization time horizon. The model is being tested on an eco-city project in China.This model developed for this study could be used as a district energy system planning tool for developers, urban planners, practitioners, or energy mangers seeking to meet a community's energy needs. Alternatively, the optimization tool could be used to identify the optimal operation of established thermal energy systems.