ABSTRACT
In this chapter, we present stochastic methodologies for energy-efficient technology investment planning, which can provide robust decisions against inherent uncertainties for optimal energy production and storage capacity expansion and operation policies involving stochastic renewable energy sources. The approach for robust decision support relies on a new two-stage, dynamic stochastic optimization model with moving random time horizons. This allows us to analyze and model systemic impacts of potential extreme events and structural changes emerging from policy interventions and stakeholders’ dialogues, which may occur during the decision- making process. The stopping time moments induce endogenous risk aversion in strategic decisions in a form of dynamic VaR-type systemic risk measures dependent on the system’s structure. The model allows the representation of all relevant energy subsystem components (e.g., traditional and renewable) and their interactions, dealing with both strategic and operational decisions and planning. Energy storage is represented and modeled in a rather general way. For example, the excess electricity can be used for hydrogen and fertilizer production. Unlike the static nature of deterministic models, the proposed stochastic model delivers solutions that are responsive to revealed information about systemic uncertainties and risks such as stochastic supply, demand, prices, weather variability, technological change, in order to adjust local or regional energy structure and management policies in a cost-effective and risk hedging manner. Integration of the operational and strategic models under the umbrella of the two-stage stochastic optimization provides an effective way to make real-time decisions consistent with the long-term strategic goals of energy system planners to guarantee secure energy provision in all uncertainty scenarios.
