The purpose of this research is to find an alternative to the natural gas-fueled heating systems of dwellings for the neighborhood of Westenholte. Reasons for replacing this system include the high GHG emissions, resulting from the combustion product of natural gas (CO2), in addition to its greenhouse effect. As a solution to replace the GHG-intensive heating of dwellings, a district heating (DH) network is proposed. This heating system does not involve the vast amount of natural gas needed by the traditional system, leading to the desired reduction in CO2 emissions. However, constructing and using a DH network is considered a large investment, which might be higher in total cost in comparison to the traditional, CO2 emission intensive, heating system. Both the cost and CO2 emission of a new DH should be minimalized. Therefore, the following KPIs are selected for comparison of the DH network: CAPEX, OPEX, and LCOE to indicate the cost, as well as CO2 emission to indicate the difference in GHG emissions. The KPIs of the DH of Westenholte are then compared to a reference solution: a decentralized all-electric solution, which also stands largely independent of natural gas. These KPI of the DH of Westenholte are to be compared to a reference solution: a decentralized all-electric solution, which is also largely independent of natural gas. Two temperature regimes of the distribution temperature of the DH are calculated and optimized for the neighborhood of Westenholte. By using a lower temperature (50/30 °C) in comparison to the more conventional temperature (70/40 ° C), the KPIs could be improved. Lowering the distribution temperature could make the heat pumps more efficient, subsequently reducing the overall cost. Additionally, the DH can probably reduce the GHG emission and, at the same time, is cheaper by using multiple renewable heat sources. As the heat profile over time differs for all heat sources, using a combination of the energy sources is estimated to create a better match with the heat demand profile. This would reduce the required heat production, storage, and associated CO2 emission and cost. In this study, a DH network is divided into the different submodules – heat demand, supply, and coupling of demand and supply – and then modeled in Python. The heat demand submodule models the heat demand of the dwellings, including the domestic hot water (DHW). In the heat supply submodule, all the available thermal energy sources used are modeled: wastewater (TEWW), surface water (TESW), solar, and industrial waste (IWH). The coupling module then matches the demand and supply geographically, by modeling the distribution grid, and, in time, also by modeling the thermal energy storage. For the DH of Westenholte, the lowest LCOE of 0.15 € /kWh was found for a 70/40 °C DH combination of 25 percent IWH, 60 percent TEWW, and 15 percent TESW in addition to a peak supply, however, generating 34.67 ton/year CO2 emission. A DH network of 50/30 °C would provide the lowest CO2 emission, which would use 25 percent IWH and 75 percent solar thermal energy inducing a CO2 emission of 2.76 ton/year, nonetheless, requiring an LCOE of 0.40 e /kWh. In contrast to the all-electric reference scenario with an LCOE of 0.22 € /kWh and a CO2 emission of 49 ton/year, the LCOE-optimized scenario proved both cheaper and dissipating lower CO2 emissions.