Strategic model for energy systems optimisation: Aspects of energy efficiency and risk management

Industries and companies recognize that increasing efficiency of energy use and/or implementing alternative methods of production and operation with energy conservation/saving technologies may increase profit. Due to deregulation of the energy sector and the setting of targets such as the 20/20/20 in the EU, operators are now more exposed to short-term market conditions. On the other hand, they have gained the opportunity to play a more active role in securing long-term supply, managing demand, and hedging against risk while improving existing buildings' infrastructures. In the presence of deregulation and market uncertainties, there is a dilemma to choose an efficient technological portfolio in the short-term while pursuing long-term goals. The solution of this problem involves the so-called two-stage dynamic stochastic optimisation models with a rolling horizon. In this paper, a two-stage stochastic model is proposed, where some decisions (first-stage decisions) regarding investments in new energy technologies have to be taken before uncertainties are resolved, and some others (second-stage decisions) will be taken once values for uncertain parameters become known, thereby providing a trade-off between long- and short-term decisions. Investment planning and operational optimization decisions concern demand and supply sides of different energy types (electricity, heat, etc.). The demand side is affected by old and new equipment and activities including such end uses as electricity only, heating, cooling, cooking, new types of windows and buildings, and energy-saving technologies, etc. New activities may change peak loads, whereas accumulators may considerably smooth energy demand-supply processes. The proposed stochastic model is capable of dealing with short- and long-term horizons. In particular, the model avoids unrealistic "end-of-the-world" effects of dynamic deterministic models. The model is illustrated with examples from simulated and real test sites