Abstract
This chapter focuses on designing an optimal decentralised energy network for an isolated rural neighbourhood grid in Tsumkwe, Namibia. The idea of covering all energy requirements with a single renewable energy type is usually unrealistic; therefore, a hybrid energy system will be considered. A hybrid energy system is one that considers various energy sources and technologies for supply to demand profiles. Renewable and non-renewable energy technologies, as well as energy storage systems, are technologies available for selection to meet the neighbourhood’s energy demand. Hybrid systems reduce the intermittency associated with single stand-alone renewable energy technologies. The integration of supply chain networks within the local energy network, to cater for the variability in the availability of certain of these renewable energy sources (e.g. liquid fuels and biofeedstocks), will also be considered. Due to logistical difficulty and cost implications associated with bringing fuel (such as diesel) from distant suppliers to remote locations, it is important for rural communities to optimise the use of locally available biomass resources and have effective planning of outsourced fuel in their supply chain. Mapping the varying demand and supply of energy in a cost-optimal way across different temporal discretisations (hourly and seasonal) and accounting for supply chain considerations are the core of this work. It is, however, important to note the complexity that comes with the optimisation of such systems with several variables, constraints and objectives. To solve this complex system of power networks in rural communities isolated from the main grid, a multi-period mixed-integer linear programming (MILP) approach is proposed. This study includes the formulation of an algebraic mathematical model, numeric optimisation of the model and analysis of model outputs. The case study developed an optimal network for five houses with a total of 432 kWh/year of electricity supplied to the neighbourhood houses. The technologies selected by the model depend on demand profile, available energy resources, price of feedstock, energy conversion technology and component cost to determine the optimal technology mix. A total annualised cost of N$ 51,000 was obtained for the network with a 69.3% and 30.7% energy supply from the supply chain and the PV unit, respectively.
Published Version
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