The energy planning problem has been vastly addressed during the last few decades, for different reasons (e.g. oil crises, energy geopolitics, environment threats, etc.). A literature review on energy planning studies allows the identification of two model types in what pertains to the detail level in modelling the system: top-down models; and bottom-up models.
The top-down models are macroeconomic tools, while bottom-up models are engineering tools that dive into a detailed characterization of the energy system. Most top-down models are fairly oriented to provide a global picture of the energy system, being fed by macroeconomic input data. On their hand, bottom-up engineering approaches are developed to address a specific technical field or energy vector. Taking into account these models’ characteristics and the upcoming challenges of the energy systems, one observed lack of an integrated offer to deal with the following topics:
Energy planning with electricity storage (grid-scale batteries, hydrogen, vehicle-to-grid, etc.);
Multicriteria comparison of different alternatives for energy planning, for different scenarios;
Technical, environmental and economic impact from the implementation, in the power system, of new operation strategies, such as electricity storage and EVs V2G;
Modelling strategies for demand-side energy efficiency, in terms of energy/emissions savings and associated costs;
Reliability of the power systems (e.g. adequacy of the generation capacity);
Operation costs of the power system, from a zoom-in perspective (e.g. unit commitment and economic dispatch);
Environmental and economic impact from the adoption of new mobility strategies, such as electric mobility, hydrogen vehicles, biofuels and behavioral change;
Uncertainty modelling.
In view of this market gap, Wiimer developed a decision-support model for planning Power Generation & Storage, through multicriteria evaluation of strategies for renewable energy sources integration, electricity storage, sustainable mobility and the adoption of energy efficiency measures. This model consists in a process of systematic building of energy planning alternatives, namely the integration of renewable energy sources, strategies for sustainable mobility (ICV-based, electric mobility or hydrogen vehicles), electricity storage options (pumped hydro, grid-scale batteries, power-to-gas, hydrogen, compressed air, etc.) and adoption of energy efficiency measures.
The proposed methodology includes the consideration of uncertainty in the prices, by defining scenarios and implementing the regret concept, through a multicriteria and multi-scenario perspective. For evaluating the alternatives, a detailed modelling of meaningful criteria is provided, i.e.: environmental impact; economic and financial costs; and adequacy of the generation system.
One significant contribution of this model is related with the proven mathematical formulation for computing the selected attributes (associated to the criteria), especially the equations describing the economic and financial costs of the storage facilities and electric mobility. The proposed energy planning tool benefited from the development of an algorithm for unit commitment and economic dispatch, including renewable energy sources, electricity storage, and EVs charging and V2G. This innovative algorithm allows a granular hourly analysis of the power system, including related operation costs, carbon emissions and clearing prices.
This formulation was subjected to scientific peer review and produced a PhD thesis and several papers.