F.-Javier Heredia

Optimal Energy Management for a Residential Microgrid Including a Vehicle-to-Grid System

An optimization model is proposed to manage a residential microgrid including a charging spot with a vehicle-to-grid system and renewable energy sources. In order to achieve a realistic and convenient management, we take into account: (1) the household load split into three different profiles depending on the characteristics of the elements considered; (2) a realistic approach to owner behavior by introducing the novel concept of range anxiety; (3) the vehicle battery management considering the mobility profile of the owner and (4) different domestic renewable energy sources. We consider the microgrid operated in grid-connected mode. The model is executed one-day-ahead and generates a schedule for all components of the microgrid. The results obtained show daily costs in the range of 2.82 C = to 3.33 C =; the proximity of these values to the actual energy costs for Spanish households validate the modeling. The experimental results of applying the designed managing strategies show daily costs savings of nearly 10%.

Optimal Bidding Strategies for Thermal and Generic Programming Units in the Day-Ahead Electricity Market

This study has developed a stochastic programming model that integrates the day-ahead optimal bidding problem with the most recent regulation rules of the Iberian Electricity Market (MIBEL) for bilateral contracts (BC), with a special consideration for the new mechanism to balance the competition of the production market, namely virtual power plant (VPP) auctions. The model allows a price-taking generation company (GenCo) to decide on the unit commitment of the thermal units, the economic dispatch of the BCs between the thermal units and the generic programming unit (GPU), and the optimal sale/purchase bids for all units (thermal and generic), by observing the MIBEL regulation. The uncertainty of the spot prices has been represented through scenario sets built from the most recent real data using scenario reduction techniques. The model has been solved using real data from a Spanish generation company and spot prices, and the results have been reported and analyzed.

A stochastic programming model for the thermal optimal day-ahead bid problem with physical futures contracts

The reorganization of the electricity industry in Spain completed a new step with the start-up of the Derivatives Market. One main characteristic of MIBELs Derivatives Market is the existence of physical futures contracts; they imply the obligation to physically settle the energy. The market regulation establishes the mechanism for including those physical futures in the day-ahead bidding of the generation companies. The goal of this work is to optimize coordination between physical futures contracts and the day-ahead bidding which follow this regulation. We propose a stochastic quadratic mixed-integer programming model which maximizes the expected profits, taking into account futures contracts settlement. The model gives the simultaneous optimization for the Day-Ahead Market bidding strategy and power planning production (unit commitment) for the thermal units of a price-taker generation company. The uncertainty of the Day-Ahead Market price is included in the stochastic model through a set of scenarios. Implementation details and some first computational experiences for small real cases are presented.

A stochastic programming model for the optimal electricity market bid problem with bilateral contracts for thermal and combined cycle units

This paper develops a stochastic programming model that integrates the most recent regulation rules of the Spanish peninsular system for bilateral contracts in the day-ahead optimal bid problem. Our model allows a price-taker generation company to decide the unit commitment of the thermal and combined cycle programming units, the economic dispatch of the bilateral contract between all the programming units and the optimal sale bid by observing the Spanish peninsular regulation. The model was solved using real data of a typical generation company and a set of scenarios for the Spanish market price. The results are reported and analyzed.

Efficient solution of optimal multimarket electricity bid models

Short-term electricity market is made up of a sequence of markets, that is, it is a multimarket enviroment. In the case of the Iberian Energy Market the sequence of major short-term electricity markets are the day-ahead market, the ancillary service market or secondary reserve market (henceforth reserve market), and a set of six intraday markets. Generation Companies (GenCos) that participate in the electricity market could increase their benefits by jointly optimizing their participation in this sequence of electricity markets. This work proposes a stochastic programming model that gives the GenCo the optimal bidding strategy for the day-ahead market (DAM), which considers the benefits and costs of participating in the subsequent markets and which includes both physical futures contracts and bilateral contracts.

Optimal Day-Ahead bidding in the MIBEL's multimarket energy production system

A Generation Company (GenCo) can participate in the Iberian Electricity Market (MIBEL) through different mechanisms and pools: the bilateral contracts, the physical derivatives products at the Derivatives Market, the bids to the Day-Ahead Market, the Intraday Markets or the Ancillary Services Markets. From the short-term generation planning point of view, the most important problem to solve is the bidding strategy for the Day-Ahead Market (DAM) given that the 85% of the physical energy traded in Spain is negotiated in it, but this participation cannot be tackled independently of other subsequent markets.

Optimal electricity market bidding strategies considering emission allowances

There are many factors that influence the day-ahead market bidding strategies of a GenCo in the current energy market framework. In this work we study the influence of both the allowances and emission reduction plan and the incorporation of the derivatives medium-term commitments in the optimal generation bidding strategy to the day-ahead electricity market. Two different technologies have been considered: the coal thermal units, high-emission technology, and the combined cycle gas turbine units, low-emission technology. The operational characteristics of both kinds of units are modeled in detail. We deal with this problem in the framework of the Iberian Electricity Market and the Spanish National Emissions and Allocation Plans. The economic implications for a GenCo of including the environmental restrictions of these National Plans are analyzed.

Economic analysis of battery electric storage systems operating in electricity markets

Battery electric storage systems (BESS) in the range of 1-10 MWh is a key technology allowing a more efficient operation of small electricity market producer. The aim of this work is to assess the economic viability of Li-ion based BESS systems for small electricity producers. The results of the ex-post economic analysis performed with real data from the Iberian Electricity Market shows the economic viability of a Li-ion based BESS thanks to the optimal operation in day-ahead and ancillary electricity markets.

Stochastic optimal generation bid to electricity markets with emissions risk constraints

There are many factors that influence the day-ahead market bidding strategies of a generation company (GenCo) within the framework of the current energy market. Environmental policy issues are giving rise to emission limitation that are becoming more and more important for fossil-fueled power plants, and these must be considered in their management. This work investigates the influence of the emissions reduction plan and the incorporation of the medium-term derivative commitments in the optimal generation bidding strategy for the day-ahead electricity market. Two different technologies have been considered: the high-emission technology of thermal coal units and the low-emission technology of combined cycle gas turbine units. The Iberian Electricity Market (MIBEL) and the Spanish National Emissions Reduction Plan (NERP) defines the environmental framework for dealing with the day-ahead market bidding strategies. To address emission limitations, we have extended some of the standard risk management methodologies developed for financial markets, such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), thus leading to the new concept of Conditional Emission at Risk (CEaR). This study offers electricity generation utilities a mathematical model for determining the units optimal generation bid to the wholesale electricity market such that it maximizes the long-term profits of the utility while allowing it to abide by the Iberian Electricity Market rules as well as the environmental restrictions set by the Spanish National Emissions Reduction Plan. We analyze the economic implications for a GenCo that includes the environmental restrictions of this National Plan as well as the NERPs effects on the expected profits and the optimal generation bid.

Solving electric market quadratic problems by Branch and Fix Coordination methods

The electric market regulation in Spain (MIBEL) establishes the rules for bilateral and futures contracts in the day-ahead optimal bid problem. Our model allows a price-taker generation company to decide the unit commitment of the thermal units, the economic dispatch of the bilateral and futures contracts between the thermal units and the optimal sale bids for the thermal units observing the MIBEL regulation. The uncertainty of the spot prices is represented through scenario sets. We solve this model on the framework of the Branch and Fix Coordination metodology as a quadratic two-stage stochastic problem. In order to gain computational efficiency, we use scenario clusters and propose to use perspective cuts. Numerical results are reported.