Wind and hydro technologies represent an important part of the electricity generation sector. However, there have been few detailed studies that have investigated the synergies resulting from their combined operation. To address that, we formulate three optimization models where wind and reversible hydro technologies bid in a day-ahead market. The bidding strategies are divided into three categories: 1) separate wind and reversible hydro offers without a physical connection between them, 2) separate wind and reversible hydro offers with a physical connection to store by pumping the wind energy surplus, and 3) single wind and reversible hydro offers with a physical connection. Risk is considered in the models by means of the conditional value at risk (CVaR). A comparison of the models and relevant conclusions are drawn from an illustrative case study of the Iberian day-ahead electricity market.

The unpredictable and volatile nature of wind power is the main obstacle of this generation source in short-term trading. Owing to the ability of demand side to cover wind power imbalances, aggregated loads have been presented in the literature as a good complementary resource for the wind generation. To this end, this paper proposes a technique to obtain the best offering strategy for a hybrid power plant consisting of a wind power producer and a demand response provider in the power market. In addition, conditional value-at-risk is used to limit the risk on profit variability. Finally, a detailed analysis of a realistic case study based on a wind farm in Spain has illustrated that joint operation of wind power producers and demand response providers can increase the expected profit and reduce the potential risks.

The present evolution of fuel prices together with the reduction of premiums for renewable energies make it of vital importance to improve renewable production management. This paper proposes a model of a single renewable power producer to compete more efficiently against other generators. The single unit is composed of a wind power producer and a hydro-pump storage power producer. The synergies between both renewable producers make relevant the possibility of mitigating wind power uncertainty, and due to this, the imbalances of the wind power producer will be reduced. The reduction of wind imbalances can come from deviating part of the excess of wind generation through a physical connection toward the pumping system or by increasing hydro generation to mitigate the lack of wind generation. To evaluate the problem, stochastic mixed integer linear programming is proposed to address the problem of selling the energy from the single renewable unit through a bilateral contract and in the day-ahead market, as a new contribution to earlier studies. Furthermore, a balancing market is considered to penalize the imbalances. The decision is made to maximize the profit, considering risk-hedging through the Conditional Value at Risk. The model is tested and analyzed for a case study and relevant conclusions are presented.

The increased deployment of distributed generation calls for the coordination and interaction between the transmission and distribution levels. This requirement is particularly relevant for planning purposes when renewable-based generation is involved. Unfortunately, in current industry practice, transmission and distribution network planners solve their problems independent of each other, thereby leading to suboptimal solutions. Within this context, this paper addresses the integrated expansion planning problem of transmission and distribution systems where investments in network and generation assets are jointly considered. Several alternatives are available for the installation of lines as well as conventional and renewable-based generators at both system levels. Thus, the optimal expansion plan identifies the best alternative for the candidate assets under the uncertainty associated with demand and renewable-based power production. The proposed model is an instance of stochastic programming wherein uncertainty is characterized through a set of scenarios that explicitly capture the correlation between the uncertain parameters. The resulting stochastic program is driven by the minimization of the expected total cost, which comprises the costs related to investment decisions and system operation. The associated scenario-based deterministic equivalent is formulated as a mixed-integer linear program for which finite convergence to optimality is guaranteed. Numerical results show the effective performance of the proposed approach.

In deregulated electricity markets, producers offer their energy to the day-ahead market. As the subsidies for renewable producers are becoming lower and lower, they have to adapt to market prices. This paper models the energy trading in the day-ahead market for wind power producers. Different strategies are proposed for this purpose: 1) several wind farms offering their energy separately to the day-ahead market; 2) the same strategy as in 1) but compensating the imbalance among different wind farms; and 3) a joint offer involving several wind farms through an external agent in order to minimize the imbalances between the offer and the final power generation. The strategies are modeled with stochastic mixed integer linear programming and Conditional Value at Risk is used to consider the risk assessment. The expected profit including risk aversion is maximized for each wind power producer and for the set of wind power producers in the case of a joint offer. A comparison of the different cases is described in detail in a case study and relevant conclusions are provided.