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.

Virtual Power Plants (VPPs) allow the aggregated management of diverse power technologies in systems of different size, complexity and connectivity. The main objectives of a VPP are to ensure the correct integration of these technologies into power systems, to optimize the power production and load demand, and to meet the technical and commercial requirements, considering the stakeholders’ needs. This paper provides a literature review of the most influential factors as regards the management of VPPs. This literature review includes aspects like the evolutionary frequency of the concepts/definitions most used, the VPP operation, planning and interaction with the systems, programming methods, math techniques and software tools used to solve the issues proposed and the international VPP projects as reference for the systems development. Therefore, this work aims to provide the reader the information and concepts that can support for a better understanding of the role of VPPs in power systems, the approach to sustainability and the interaction with the power market as the good way to bring new economic opportunities to small producers, consumers and prosumers of renewable energy.

A model to obtain the optimal allocation and timing of renewable distributed generation under uncertainty is proposed as part of distribution expansion planning. The problem is formulated using a stochastic two-stage multiperiod mixed-integer linear programming (MILP) model, where investment decisions are done in the first stage and scenario-dependent operation variables are solved in the second stage. The model aims to minimize renewable distributed generation (photovoltaic and wind) investment costs, substation expansion investment cost, operation and maintenance costs, energy losses cost, and the cost of the power purchased from the transmission system. Active and reactive power flow equations are linearized and constraints include voltage limits, substation and feeders capacities, renewable generation limits, and investment constraints. The model is tested on a 34-bus system and conclusions are duly drawn.

This paper proposes a bilevel programming approach to determine the optimal contract price of dispatchable distributed generation (DG) units in distribution systems. Two different agents are considered in the model, namely, the distribution company (DisCo) and the owner of the DG. The former seeks the minimization of the payments incurred in attending the forecasted demand, while the latter seeks the maximization of his profit. To meet the expected demand, the DisCo has the option to purchase energy from any DG unit within its network and directly from the wholesale electricity market. A traditional distribution utility model with no competition among DG units is considered. The proposed model positions the DG owner in the outer optimization level and the DisCo in the inner one. This last optimization problem is substituted by its Karush-Kuhn-Tucker optimality conditions, turning the bilevel programming problem into an equivalent single-level nonlinear programming problem which is solved using commercially available software. Tests are performed in a modified IEEE 34-bus distribution network.

A mixed integer nonlinear programming (MINLP) model for scheduling of the short-term integrated operation of a series of price-taker hydroelectric plants (H-GENCO) along a cascaded reservoir system in a pool-based electricity market is presented. The objective of the H-GENCO can be either to maximize profit, taking into account technical efficiency, or to maximize technical efficiency, maintaining a profit level. In both cases, the efficiency can be accurately obtained using the “Hill diagram” supplied by turbine manufacturers. A multiple nonlinear regression analysis of the unit's technical efficiency is estimated as a quadratic function of net head and water discharge. Several case studies of realistic dimensions are described, where results indicate that a profit-based MINLP produces better results compared to an MILP model, on the other hand, higher efficiencies and water savings are obtained in the efficiency-based model.