Multi-objective evolutionary algorithm for the optimisation of stand-alone systems.
Applied in the optimisation of photovoltaic-wind-diesel-battery hybrid systems.
Minimise total net present cost and maximise human development index and job creation.
Obtain the optimal Pareto set in which no solution is better than another one.
In this paper we show a multi-objective evolutionary algorithm (MOEA) for the optimisation of stand-alone (off-grid) hybrid systems (photovoltaic-wind-diesel-battery) to minimise total net present cost (NPC) and maximise human development index (HDI) and job creation (JC). Optimisation of this kind of system is usually performed considering only the minimisation of cost (NPC or the levelised cost of energy), as well as the emissions and the unmet load in some cases. In this paper, for the first time, we consider the maximisation of HDI and JC as part of optimisation. HDI depends on the consumption of electricity, so the extra energy that can supply the hybrid system can improve the HDI index. JC is different for each technology, obtaining different values for each combination of components in the system. The three objectives are often opposed, so a Pareto-optimisation MOEA is a good option to obtain a set of possible solutions in which no solution is better than another one for all three objectives (optimal Pareto set). We provide an example in the optimisation of a hybrid system to supply electricity to a small community in the Sahrawi refugee camps of Tindouf.
- Renewable stand-alone systems;
- Net present cost;
- Human development index;
- Job creation;
- Multi-objective evolutionary algorithms
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