(513c) A Community-Level Energy Planning Model for the Dynamic Optimization of Waste-to-Gas and Waste-to-Power Anaerobic Digestion Supply Chains

Society has growing concerns over the long-term environmental impacts of climate change and the accumulation of organic waste in local communities. Community energy planning is increasingly focused on the use of novel technologies to provide sustainable and cost-effective heat and power. Furthermore, changes in energy demand patterns support the development of dynamic optimization models of energy dispatch. This study focuses on the use of anaerobic digestion (AD) technology for converting human, agricultural, and industrial waste into renewable natural gas (RNG) and power. We develop a dynamic mixed integer linear programming (MILP) in Calliope to optimize the production and dispatch of RNG and power.

Anaerobic Digestion (AD) is a microbiological process that is used in processing organic, biodegradable wastes and side streams into methane-rich biogas and nutrient-rich digestate. Previous techno-economic (TEA) and lifecycle (LCA) analyses indicate that AD can be a commercially competitive and sustainable technology for communities to generate clean energy. In this study, we evaluate the use of AD in Ames, Iowa to convert human and animal waste, biomass, and wastewater into RNG and power. RNG and power are consumed locally to provide industrial, commercial, and residential heat and power demand. The AD process generates by-product digestate that is returned to Iowa farms. The model covers simulates 1 year of energy demand in hourly and monthly increments.

The MILP identified 13 locations in the vicinity of Ames, Iowa capable of producing power or RNG. The electricity price ranged between $0.065 - $0.18/kWh and the RNG price is estimated at $0.026/kWh ($7.62/mmbtu). Electricity production varied between 30 and 50 million kWh per month. Gas production varied between 15 and 40 million kWh per month. All the electricity demand in Ames, Iowa could be supplied from local waste resources. Summer gas demand can be supplied locally. However, the gas supply will need to be supplemented with gas from the external grid to meet winter demand. Technological improvements and public incentives could increase the adoption of AD in community energy planning systems. Further research will evaluate the impacts of variabilities in energy supply and demand to address grid disruptions.