  We present our collaboration with the OCP Group, one of the world's largest
producers of phosphate and phosphate-based products, to reduce OCP's carbon
emissions significantly. We study the problem of decarbonizing OCP's
electricity supply by installing a mixture of solar panels and batteries to
minimize its time-discounted investment cost plus the cost of satisfying its
remaining demand via the national grid. OCP is currently designing its
renewable investment strategy, using insights gleaned from our optimization
model, and has pledged to invest 130 billion MAD (approx. 13 billion USD) in a
green initiative by 2027, a subset of which involves decarbonization. We
immunize our model against deviations between forecast and realized solar
generation output via a combination of robust and distributionally robust
optimization. To account for variability in daily solar generation, we propose
a data-driven robust optimization approach that prevents excessive conservatism
by averaging across uncertainty sets. To protect against variability in
seasonal weather patterns induced by climate change, we invoke distributionally
robust optimization techniques. Under a 10 billion MAD investment by OCP, the
proposed methodology reduces the carbon emissions which arise from OCP's energy
needs by more than 70% while generating a net present value (NPV) of 5 billion
MAD over twenty years. Moreover, a 20 billion MAD investment induces a 95%
reduction in carbon emissions and generates an NPV of around 2 billion MAD. To
fulfill the Paris climate agreement, rapidly decarbonizing the global economy
in a financially sustainable fashion is imperative. Accordingly, this work
develops a robust optimization methodology that enables OCP to decarbonize at a
profit by purchasing solar panels and batteries. Moreover, the methodology
could be applied to decarbonize other industrial consumers.
