Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review

The parametric uncertainties inherent in the models of renewable and sustainable energy systems (RSESs) make the associated decision-making processes of integrated resource operation, planning, and designing profoundly complex. Accordingly, intelligent energy management strategies are recognised as an effective intervention to efficiently accommodate the variability inherent in various input data and integrate distributed demand-side flexibility resources. To identify the key methodological and content gaps in the area of stochastic dispatch and planning optimisation of RSESs in the presence of responsive loads, this paper systematically reviews and synthetically analyses 252 relevant peer-reviewed academic articles. The review reveals that academic studies have utilised a wide variety of methods for the joint quantification of uncertainties and procurement of demand response services, while optimally designing and scheduling RSESs. However, to minimise simulation-to-reality gaps, research is needed into more integrated energy optimisation models that simultaneously characterise a broader spectrum of problem-inherent uncertainties and make behaviourally-founded use of flexible demand-side resources. More specifically, the review finds that while the research in this area is rich in thematic scope, it is commonly associated with strong simplifying assumptions that disconnect the corresponding approaches from reality, and thereby obscure the real challenges of transferring simulations into the real world. Accordingly, based on the descriptive analyses conducted and knowledge gaps identified, the paper provides useful insights into myriad possibilities for new research to more effectively utilise the potential of responsive loads, whilst simultaneously characterising the most salient problem-inherent parametric sources of uncertainty, during the investment planning and operational phases of RSESs.