Short-term wind forecasting for energy resources scheduling

This paper proposes a wind power forecasting methodology based on two methods: direct wind power forecasting and wind speed forecasting in the first phase followed by wind power forecasting using turbines characteristics and the aforementioned wind speed forecast. The proposed forecasting methodology aims to support the operation in the scope of the intraday resources scheduling model, namely with a time horizon of 5 minutes. This intraday model supports distribution network operators in the short-term scheduling problem, in the smart grid context. A case study using a real database of 12 months recorded from a Portuguese wind power farm was used. The results show that the straightforward methodology can be applied in the intraday model with high wind speed and wind power accuracy. The wind power forecast direct method shows better performance than wind power forecast using turbine characteristics and wind speed forecast obtained in first phase.

A real time pricing approach to deal with excessive wind power situations

The introduction of wind power generation in several countries around the world, including in European countries, where energy policy directives have encouraged the use of renewables, led to several changes in market and power systems operation. The intensive integration of these sources has led to situations in which the demand is lower than the available renewable resources. In these situations a part of the available generation is wasted if not used for storage or to supply additional demand. This paper proposes a real time demand response methodology based on changing the electricity price for the consumers expecting an increase in the demand in the periods in which that demand is lower than the available renewable generation. The consumers response to the changes in electricity price is characterized by their price elasticity of demand considered distinct for each consumer type. The proposed methodology is applied to the Portuguese power system, in the context of the Iberian electricity market (MIBEL). The renewable-based producers are considered as special producers, with special tariffs, and so it is important to use the energy available as it will be paid anyway. In this context, consumers are entities actively participating in the operation of the market.

Real-time energy resources scheduling considering intensive wind penetration

The use of distributed energy resources, based on natural intermittent power sources, like wind generation, in power systems imposes the development of new adequate operation management and control methodologies. A short-term Energy Resource Management (ERM) methodology performed in two phases is proposed in this paper. The first one addresses the day-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. The ERM scheduling is a complex optimization problem due to the high quantity of variables and constraints. In this paper the main goal is to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixedinteger non-linear programming approach. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units and 1000 electric vehicles has been implemented in a simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Residential energy resources management by a SCADA system in case of excess wind generation

The end consumers in a smart grid context are seen as active players. The distributed generation resources applied in smart home system as a micro and small-scale systems can be wind generation, photovoltaic and combine heat and power facility. The paper addresses the management of domestic consumer resources, i.e. wind generation, solar photovoltaic, combined heat and power, electric vehicle with gridable capability and loads, in a SCADA system with intelligent methodology to support the user decision in real time. The main goal is to obtain the better management of excess wind generation that may arise in consumer’s distributed generation resources. The optimization methodology is performed in a SCADA House Intelligent Management context and the results are analyzed to validate the SCADA system.

Using demand response to deal with unexpected low wind power generation in the context of smart grid

Demand response is assumed an essential resource to fully achieve the smart grids operating benefits, namely in the context of competitive markets. Some advantages of Demand Response (DR) programs and of smart grids can only be achieved through the implementation of Real Time Pricing (RTP). The integration of the expected increasing amounts of distributed energy resources, as well as new players, requires new approaches for the changing operation of power systems. The methodology proposed aims the minimization of the operation costs in a smart grid operated by a virtual power player. It is especially useful when actual and day ahead wind forecast differ significantly. When facing lower wind power generation than expected, RTP is used in order to minimize the impacts of such wind availability change. The proposed model application is here illustrated using the scenario of a special wind availability reduction day in the Portuguese power system (8th February 2012).

Real-time energy resources scheduling considering short-term and very short-term wind forecast

This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process it is necessary the update of generation and consumption operation and of the storage and electric vehicles storage status. Besides the new operation condition, it is important more accurate forecast values of wind generation and of consumption using results of in short-term and very short-term methods. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented.

Virtual power players dealing with excessive wind power situations using real time pricing

The increasing importance given by environmental policies to the dissemination and use of wind power has led to its fast and large integration in power systems. In most cases, this integration has been done in an intensive way, causing several impacts and challenges in current and future power systems operation and planning. One of these challenges is dealing with the system conditions in which the available wind power is higher than the system demand. This is one of the possible applications of demand response, which is a very promising resource in the context of competitive environments that integrates even more amounts of distributed energy resources, as well as new players. The methodology proposed aims the maximization of the social welfare in a smart grid operated by a virtual power player that manages the available energy resources. When facing excessive wind power generation availability, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. The proposed method is especially useful when actual and day-ahead wind forecast differ significantly. The proposed method has been computationally implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20310 consumers and 548 distributed generators, some of them with must take contracts.

Adaptable control for electrical generation at irregural wind speeds

The main aims of this work are the development and the validation of one generic algorithm to provide the optimal control of small power wind generators. That means up to 40 kW and blades with fixed pitch angle. This algorithm allows the development of controllers to fetch the wind generators at the desired operational point in variable operating conditions. The problems posed by the variable wind intensity are solved using the proposed algorithm. This is done with no explicit measure of the wind velocity, and so no special equipment or anemometer is required to compute or measure the wind velocity.

A data-mining based methodology for wind forecasting

In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.

A new wind farm in Santiago Island, cape verde - simulation and economic studies

Ensuring sustainable development conditions is presently world widely recognized as a critically important goal. This makes the use of electricity generation technologies based on renewable energy sources very relevant. Developing countries depend on an adequate availability of electrical energy to assure economic progress and are usually characterized by a high increase in electricity consumption. This makes sustainable development a huge challenge but it can also be taken as an opportunity, especially for countries which do not have fossil resources. This paper presents a study concerning the expansion of an already existent wind farm, located in Praia, the capital of Cape Verde Republic. The paper includes results from simulation studies that have been undertaken using PSCAD software and some economic considerations.

Dispachability improvement of wind generation by the virtual power producers

Nowadays, there is a growing environmental concern about were the energy that we use comes from, bringing the att ention on renewable energies. However, the use and trade of renewable e nergies in the market seem to be complicated because of the lack of guara ntees of generation, mainly in the wind farms. The lack of guarantees is usually addressed by using a reserve generation. The aggregation of DG p lants gives place to a new concept: the Virtual Power Producer (VPP). VPPs can reinforce the importance of wind generation technologies, making them valuable in electricity markets. This paper presents some resul ts obtained with a simulation tool (ViProd) developed to support VPPs in the analysis of their operation and management methods and of their strat egies effects.

Wind energy technology

Electricity is regarded as one of the indispensable means to growth of any country’s economy. This source of power is the heartbeat of everything from the huge metropolitans, industries, worldwide computer networks and our global communication systems down to our homes. Electrical energy is the lifeline for any economic and societal development of a region or country. It is central to develop countries for maintaining acquired life styles and essential to developing countries for industrialisation and escaping poverty.

Neural networks for condition monitoring of wind turbines gearbox

Wind energy is considered a hope in future as a clean and sustainable energy, as can be seen by the growing number of wind farms installed all over the world. With the huge proliferation of wind farms, as an alternative to the traditional fossil power generation, the economic issues dictate the necessity of monitoring systems to optimize the availability and profits. The relatively high cost of operation and maintenance associated to wind power is a major issue. Wind turbines are most of the time located in remote areas or offshore and these factors increase the referred operation and maintenance costs. Good maintenance strategies are needed to increase the health management of wind turbines. The objective of this paper is to show the application of neural networks to analyze all the wind turbine information to identify possible future failures, based on previous information of the turbine.

The importance of control monitoring systems in wind parks maintenance

The objective of every wind energy producer is to reduce operational costs associated to the production as a way to increase profits. One other issue that must be looked carefully is the equipment maintenance. Increase the availability of wind turbines by reducing the downtime associated to failures is a good strategy to achieve the main goal of increase profits. As a way to help in the definition of the best maintenance strategies, condition monitoring systems (CMS) have an important role to play. Informatics tools to make the condition monitoring of the wind turbines were developed and are now being installed as a way to help producers reducing the operational costs. There are a lot of developed systems to do the monitoring of a wind turbine or the whole wind park, in this paper will be made an overview of the most important systems.

A sensitivity study of the WRF model in wind simulation for an area of high wind energy

The performance of the Weather Research and Forecast (WRF) model in wind simulation was evaluated under different numerical and physical options for an area of Portugal, located in complex terrain and characterized by its significant wind energy resource. The grid nudging and integration time of the simulations were the tested numerical options. Since the goal is to simulate the near-surface wind, the physical parameterization schemes regarding the boundary layer were the ones under evaluation. Also, the influences of the local terrain complexity and simulation domain resolution on the model results were also studied. Data from three wind measuring stations located within the chosen area were compared with the model results, in terms of Root Mean Square Error, Standard Deviation Error and Bias. Wind speed histograms, occurrences and energy wind roses were also used for model evaluation. Globally, the model accurately reproduced the local wind regime, despite a significant underestimation of the wind speed. The wind direction is reasonably simulated by the model especially in wind regimes where there is a clear dominant sector, but in the presence of low wind speeds the characterization of the wind direction (observed and simulated) is very subjective and led to higher deviations between simulations and observations. Within the tested options, results show that the use of grid nudging in simulations that should not exceed an integration time of 2 days is the best numerical configuration, and the parameterization set composed by the physical schemes MM5–Yonsei University–Noah are the most suitable for this site. Results were poorer in sites with higher terrain complexity, mainly due to limitations of the terrain data supplied to the model. The increase of the simulation domain resolution alone is not enough to significantly improve the model performance. Results suggest that error minimization in the wind simulation can be achieved by testing and choosing a suitable numerical and physical configuration for the region of interest together with the use of high resolution terrain data, if available.