Agustín A. Sánchez de la Nieta

Optimal coordinated wind-hydro bidding strategies in day-ahead markets

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.

Modeling the Impact of a Wind Power Producer as a Price-Maker

Wind energy is present in many countries throughout the world. The main types of wind sales in electricity markets are via regulated tariffs or pool-based markets. Production companies choose cost-effective options for selling wind energy, and some markets, like the Irish electricity market, use regulated tariffs to remunerate wind production. This paper aims to provide some answers to explain what effect wind offers may have in an electricity market if wind power producers participated in the day-ahead market without receiving any premium or aid. A price-maker optimization model is used to detect its effect on prices. The model encompasses energy offers by other technologies using residual demand curves and detailed modeling of wind imbalances. It is observed that wind acting as price-maker reduces electricity prices and the imbalance penalties help the system operator to reduce imbalances. A realistic case study using data from the Irish electricity market illustrates the methodology used comparing the effect of imbalance penalties in the models in terms of profit and total imbalance of the system.

ECOTOOL: A general MATLAB Forecasting Toolbox with Applications to Electricity Markets

Electricity markets are composed of different agents that make their offers to sell and/or buy energy. These agents need forecasting tools to have an accurate prediction of the prices that they will face either in the day-ahead or long-term time spans. This work presents the ECOnometrics TOOLbox (ECOTOOL), a new MATLAB forecasting toolbox that embodies several tools for identification, validation and forecasting models based on time series analysis, among them, ARIMA, Exponential Smoothing, Unobserved Components, ARX, ARMAX, Transfer Function, Dynamic Regression and Distributed Lag models. The toolbox is presented in all its potentiality and several real case studies, both on the short and medium term, are shown to illustrate its applicability.

Risk-Constrained Offering Strategy for Aggregated Hybrid Power Plant Including Wind Power Producer and Demand Response Provider

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.

Optimal Bidding of a Group of Wind Farms in Day-Ahead Markets Through an External Agent

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.

Optimal Single Wind Hydro-Pump Storage Bidding in Day-Ahead Markets Including Bilateral Contracts

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.

Impacts of Stochastic Wind Power and Storage Participation on Economic Dispatch in Distribution Systems

Evaluating the impact related to stochastic wind generation and generic storage on economic dispatch in distribution system operation is an important issue in power systems. This paper presents the analysis of the impacts of high wind power and storage participation on a distribution system over a period of 24 h using grid reconfiguration for electrical distribution system (EDS) radial operation. In order to meet this objective, a stochastic mixed integer linear programming (SMILP) is proposed, where the balance between load and generation has to be satisfied minimizing the expected cost during the operation period. The model also considers distributed generation (DG) represented by wind scenarios and conventional generation, bus loads represented through a typical demand profile, and generic storage. A case study provides results for a weakly meshed distribution network with 70 buses, describing in a comprehensive manner the effects of stochastic wind scenarios and storage location on distribution network parameters, voltage, substation behavior as well as power losses, and the expected cost of the system.

Optimal Wind Reversible Hydro Offering Strategies for Midterm Planning

A coordinated strategy between wind and reversible hydro units for the midterm planning that reduces the imbalance of wind power and improves system efficiency is proposed. A stochastic mixed integer linear model is used, which maximizes the joint profit of wind and hydro units, where conditional value at risk (CVaR) is used for model risk. The offering strategies studied are 1)separate wind and hydro pumping offer, where the units work separately without a physical connection and 2)a single wind and hydro pumping offer with a physical connection between them to store wind energy for future use. The effects of a coordinated wind-hydro strategy for midterm planning are analyzed, considering CVaR and the future water value. The future water value in the reservoirs is analyzed hourly for a period of 1 week and 2 months, in two realistic case studies.

Optimal generic energy storage system offering in day-ahead electricity markets

This paper models the offers and bids of a generic storage system in an electricity market through stochastic mixed integer linear programming. The objective function aims at maximizing the profit from buying or selling energy for a general storage system. Some parameters such as storage system efficiency, losses of the energy stored and marginal costs are parameterized to evaluate the offers and bids. Market prices are forecasted for 24 hours using AR, MA and ARIMA time series models. The problem is tested for a case study, analyzing the behaviour of the offers and bids. Also, the results obtained are studied and relevant conclusions are presented.

Evaluation of load-following reserves for power systems with significant RES penetration considering risk management

In this study a novel two-stage stochastic programming based day-ahead joint energy and reserve scheduling model is developed. Demand-side as a reserve resource is explicitly modeled through responsive load aggregations, as well as large industrial consumers that directly participate in the scheduling procedure. Furthermore, a risk-hedging measure is introduced, namely the Conditional Value-at-Risk (CVaR), to analyze the behavior of energy and reserve scheduling by both the generation and the demand-side for a risk-averse ISO. The proposed methodology is tested on the practical non-interconnected insular power system of Crete, Greece, which is characterized by a significant penetration of Renewable Energy Sources (RES).