Design and Optimization of a Containerized Small-Scale Polygeneration System for Emergency Camps : A Case Study in Nepal Kathmandu

Sammanfattning

As a result of humanitarian crises and natural calamities the number of displaced people has increased significantly in recent years and emergency camps have become more common. In emergency camps, there is a critical need for essential resources such as drinking water, heat, energy for cooking and electricity. Current systems utilized to meet these needs often rely on diesel for electricity generation and wood or coal for cooking and heating. This leads to several issues, conflicting with the sustainable development goals (SDGs), for instance air pollution, greenhouse gas emissions, local deforestation, and high operating costs. To address these needs in an ecologically, technically and economically sustainable way, this thesis investigates and proposes a polygeneration system module able to fit in an ISO 40 feetstandard shipping container. The technologies investigated to be a part of the system are solar PV panels, small wind turbines, energy storage, water purification unit, and a biogas genset.Through these technologies the polygeneration system investigated is designed to provide drinking water, electricity, heat, and energy for cooking. The system was modeled and optimized using HOMER Pro software and a Python-based multi-objective optimization model. The modeling was based on a hypothetical emergency camp located in the Kathmandu valley, Nepal, which served as the foundation for the case study. Based on literature review and data collection, suitable demand profiles were created for electricity, heat, and cooking energy. The polygeneration system was evaluated based on three key performance indicators (KPIs) related to environmental impact as well as technical and economic performance. Specifically, these were measured as carbon dioxide emissions in kilograms per year [tCO₂/yr], unmet demand in percentage [%], and levelized cost of energy in dollars per kilowatt-hour [$/kWh]. For the results Python programming and HOMER outputs are presented. For the Python programming outputs a Pareto front is presented, from which an optimal configuration isselected. The final optimized system configuration had installed capacities of 82 kW solar PV, 89 kWh battery storage, 209 kW biogas genset and 87 kW converter. The finalized system hadyearly emissions of 255 tonnes CO2, an energy deficit of 0.6% and the levelized cost of energyand electricity were 0.23 $/kWh and 0.47$/kWhe, respectively. Energy deficit was defined as total unmet kWh energy divided by total energy demand. Also levelized cost of energy considers heat and energy for cooking aspects as well. In HOMER, the corresponding numbers were 284.6 tonnes of CO2 emissions, 0% energy deficit and a levelized cost of electricity of iv0.50 $/kWhe. Also, six scenarios, called Tier 3 (warm, temperate, cold) and Tier 2 (warm, temperate, cold) are presented highlighting how the system KPIs changes for different electricity and thermal satisfaction conditions. A sensitivity analysis is also presented in the results evaluating biogas genset CAPEX and OPEX as parameters