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This work investigates the interaction between power producers with conventional and wind generation portfolios participating in a network-constrained pool-based market. A stochastic bi-level problem is introduced to model the strategic behavior of each single producer. The upper-level problem maximizes the producers’ expected profits and the lower-level problem optimizes the jointly cleared energy and balancing market under economic dispatch. Market participants’ offers are modeled using linear stepwise curves, and the stochastic wind power generation is realized through a set of plausible wind scenarios. The bi-level problem is recast into a single-level mathematical problem with equilibrium constraints with primal-dual formulation using the Karush-Kuhn-Tacker first order optimality conditions and the strong duality theorem. The joint solution of all strategic producers’ problems constitutes an equilibrium problem with equilibrium constraints. The latter is reduced into an equivalent mixed integer linear program by using disjunctive constraints. Different objective functions are applied to the final program to define the range of market equilibria, and a single-iterate diagonalization process is used to justify those equilibria that are meaningful. The model addresses several cases considering different types of market competition, transmission line congestions, and different levels of wind power penetration and volatility.

The power sector faces a rapid transformation worldwide from a dominant fossil-fueled towards a low carbon electricity generation mix. Renewable energy technologies (RES) are steadily becoming a greater part of the global energy mix, in particular in regions that have put in place policies and measures to promote their utilization. This paper presents an optimization-based approach to address the generation expansion planning (GEP) problem of a large-scale, central power system in a highly uncertain and volatile electricity industry environment. A multi-regional, multi-period linear mixed-integer linear programming (MILP) model is presented, combining optimization techniques with a Monte Carlo (MCA) method and demand response concepts. The optimization goal concerns the minimization of the total discounted cost by determining optimal power capacity additions per time interval and region, and the power generation mix per technology and time period. The model is evaluated on the Greek power system (GPS), taking also into consideration the scheduled interconnection of the mainland power system with those of selected autonomous islands (Cyclades and Crete), and aims at providing full insight into the composition of the long-term energy roadmap at a national level.

This Special issue deals with the Energy Systems Engineering as a part Computer Aided Process Engineering. In this editorial introduction, the editors are highlighting the individual articles presented and discussed in this issue. Main areas of this issue can be summarised as follows: Process Integration including Total Sites, exergy, distributed energy production and consumption, energy production and generation control and energy related environmental impact.

This work presents a systematic approach for modelling, optimization and control of metal hydride beds used for hydrogen storage. A detailed 2-D mathematical model is developed and validated against experimental and theoretical literature results. Based on recent advances in dynamic optimization, the objective is then to find the optimal process design (e.g. cooling systems design) and operating strategy (e.g. cooling fluid profile over time) so as to minimize the storing time, while satisfying a number of operating constraints. Such constraints account for pressure drop limitations, cooling fluid availability, maximum tank temperature, etc. Optimization results indicate that almost 60% improvement of the storage time can be achieved, over the case where the system is not optimized, for a minimum storage capacity of 99% of the total bed capacity. Trade-offs between various objectives, alternative design options and optimal cooling control policies are systematically revealed illustrating the potential offered by modern optimization techniques.