José Villar

Optimal Demand-Side Bidding Strategies in Electricity Spot Markets

This paper proposes a methodology for determining the optimal bidding strategy of a retailer who supplies electricity to end-users in the short-term electricity market. The aim is to minimize the cost of purchasing energy in the sequence of trading opportunities that provide the day-ahead and intraday markets. A genetic algorithm has been designed to optimize the parameters that define the best purchasing strategy. The proposed methodology has been tested using real data from the Spanish day-ahead and intraday markets over a period of two years with a significant cost reduction with respect to trading solely in the day-ahead market.

Connecting the Intraday Energy and Reserve Markets by an Optimal Redispatch

Electricity markets based on simple bids provide a very high degree of transparency and simplicity. However, simple bids fail to capture many well-known characteristics of generating units and, therefore, the responsibility for obtaining feasible schedules is transferred to market participants. The purpose of this paper is to help the generating utility to automatically analyze the last energy program cleared in the market and, in case this program is technically unfeasible, to provide an alternative schedule by re dispatching the generating units. This is achieved by formulating an optimization problem where the objective is to find the cheapest and feasible instantaneous power trajectory of each generator, trying to minimize the differences between its hourly average values and the last energy program. As the objectives of the utility can vary during the day, three different models are presented. Two of them are formulated as a joint energy and reserve dispatch in order to take into account possible commitments acquired in the ancillary services market of AGC regulation. In this sense, a novel approach for considering discontinuous ancillary regulation curves is proposed. Some numerical examples are included to illustrate the essential features of the models.

SGO: management information system for strategic bidding in electrical markets

This paper describes SGO, a management information system for bidding in deregulated electricity markets, developed for the Spanish case. SGO has a client-server architecture and consists of a set of integrated cooperative and flexible software tools for assisting the users during the whole bidding process: resources identification, bids generation, market performance characterisation, bidding strategy analysis and optimisation, generation of markets results reports and automatic performance monitoring for suggesting on-line corrective actions.

Distributed energy generation in smart cities

The smart city is a sustainable and efficient urban center that provides high quality of life to its inhabitants with an optimal management of its resources, where clean and cost effective energy generation is a key issue. Under this setting, distributed generation can provide an adequate tool to deal with energy reliability and to successfully implement renewable sources; nevertheless, selection and scaling of energy systems, considering location, is not a trivial task. Frequently, the stakeholders analyze only one or two “popular” generation systems, and then calculate the output and return of investment in a simplified and approximated approach. This practice could lead the stakeholder to an inadequate technology mix. To tackle this problem, this paper reviews and models most common energy sources for distributed generation in a smart city context. Then, a technical economic analysis is developed for 2 cases, a household and a district, considering not only renewable sources but also efficient non-renewable technologies. The results of the numerical analysis help to assess the more adequate generation systems for a given application, energetic demand, and geographical characteristics. A well-developed analysis is essential for a better understanding of the available technologies and their synergies; as a result, the investors can choose the appropriate solutions, maximizing overall benefits.

Experiences learned from the on-line internal monitoring of the behaviour of a transformer

Knowledge of the health of power transformers is important to prevent high costs caused by failures and to maintain the quality of the service. On-line monitoring and diagnosis seem to be the right way to reach this objective. In this paper a set of experiences related to the installation of vibration and temperature sensors inside a test transformer are described. Different types of sensors have been tested, and some of them discarded because of induced electromagnetic noise. Some of the main results and conclusions are shown in this paper. These experiences are included in a more general project called TRAFES, whose main objective is the continuous monitoring and diagnosis of large power transformers.

Hourly energy and reserve joint dispatch with a hydro-thermal technological based representation

Intermittent technologies are enhancing both the economic and technological value of ancillary services in the electric power system. Some of these services commonly denoted as reserves have been liberalized and are offered in the balancing markets in the European Union countries, or in the regulation markets in the USA. This paper presents a deterministic single-node centralized energy and secondary reserve dispatch that outputs hourly scheduled energies and reserves, and both commodities prices. In this model, units of each generation company are simplified into technologies and sub-technologies for faster performance, but still considering inter temporal constraints such as ramps and responding time for reserves, and unit commitment decisions such as start-up and shut-down costs. Detailed short-term hydro-thermal constraints (topology, efficiency, etc.) have been simplified by means of weekly constraints based on historical data (inflows, installed capacity, productions). The model has been validated by comparing its output prices and productions with the real ones occurred during 2010 in the Spanish market with satisfactory results. Furthermore, a study-case on high penetration of solar generation in the Spanish system reveals strong interactions between the energy and reserve markets and points out the importance of hydro technologies in the system.

Wind and solar integration with plug-in electric vehicles smart charging strategies

Combining large penetration of Plug-in Electric Vehicles (PEVs) and generation from Renewable Energy Sources (RES) seems a promising solution for energy cost saving and emission reduction. Indeed, RES generation increases instability grid problems due to its intermittency and lack of correlation with final energy usage. But the energy storage of the PEVs connected to the grid, controlled with smart charging and generating strategies, can compensate these uncertainties: energy surplus at low demands and high RES production (strong wind or sunshine) can be returned from PEVs to the grid at larger demand periods, and even provide regulation services. This paper analyses the combined impact of PEVs and RES penetration in the current Spanish power system with a detailed hydro-thermal Unit Commitment (UC) model for energy and reserve. Different charging strategies (from plug-and-charge to V2G with regulation) and wind and solar power penetration are tested with a full year simulation with weekly water management. Simulations show how PEVs smart charging strategies adapt PEVs operation to the existing generation structure, contributing efficiently to higher RES penetration rates, decreasing emissions and system operation costs.

Impact of plug-in-electric vehicles penetration on electricity demand, prices and thermal generation dispatch

This paper proposes an optimization model based on a centralized dispatch to analyze the impact of plug-in electric vehicles (PEV) penetration on the Spanish electricity demand and price. Different charging strategies, from the plug-and-charge strategy to the smartest vehicle-to-grid (V2G) capabilities, have been simulated and compared with the non PEV scenario, under different penetration values. Traditional generation resources have been represented with technical aspects such as ramps and minimum loads. Hydraulic resources management is also partially supported but limited to weekly management. Distribution network constraints and how they could limit the contribution of PEV to the grid services have been neglected. The model considers both load and reserve requirements to evaluate the PEV contribution to energy production or consumption, but also to secondary reserve regulation. Interesting results comparing the different simulation scenarios are discussed in the case examples.

Cost-benefit analysis of plug-in electric vehicles penetration

It is expected that in the near future the number of Plug-in Electric Vehicles (PEV) could increase significantly due to their low pollution emissions, high fuel economy, and mitigation of security issues related to oil technical and economic management. Many works have dealt with the impact of PEV on the power and distribution grids, and on other particular aspects, but very few perform more general cost benefit analyses of their global impact. This paper does it in two steps. First, a hydro-thermal unit commitment for a full year simulation provides electricity and reserve prices for different charging strategies. Then, the model computes economic estimations for the costs of the charging infrastructure, specific PEV costs and main externalities (emissions, health benefits and energetic dependence). The model is intended to provide meaningful results on the global economic balance of PEV penetration, helping for example in feed-in tariffs, fuel taxes redesign or other regulatory analyses.

Evaluation and optimal scaling of distributed generation systems in a smart city

Distributed generation (DG) represents an important resource to address relevant energy issues, such as reliability and sustainability, in the current and future smart cities. It is expected that distributed generation will gain considerable presence in the following years; however, the selection and sizing of the generation and storage systems is commonly done without an adequate level of detail. This simplified or approximated approach usually results in a suboptimal technology mix with an inadequate type of system and/or scale, which could compromise the economic feasibility of the DG project. To tackle this problem, stakeholders should consider many factors, including geographical characteristics (sun, wind …), energy costs, local regulation, and energetic demand patterns, apart from analysing different technologies. Considering as an example location the city of Madrid, Spain, this paper proposes a linear programming model to evaluate the most common distributed generation technologies, with and without storage systems and under different electricity pricing scenarios. As a result, not only the optimal sizing, but also the optimal operation scheduling of the aforementioned systems are found. Then, an economic feasibility analysis is developed, comparing the different technologies and defining the best option for a given scenario. Furthermore, this study helps to find important milestones, such as battery prices, that could make distributed generation more attractive.