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SGO

Smart Grids

Optimization

OP

CHAIN-

SGO

OPTIMIZING THE VALUE CHAIN

OPCHAIN-SGO corresponde a un conjunto de modelos matemáticos orientados a soportar de las decisiones de los agentes que participan en las smart grids. Está integrado por los siguientes modelos:

 OPCHAIN-SGO-DRO (Demand Response Optimization) optimización de la gestión de la energía eléctrica en conjuntos de edificios; como universidades, urbanizaciones, centros comerciales, ...

 OPCHAIN-SGO-EEO (Energy Efficiency Optimization) optimización de la gestión de la energía eléctrica y emisión de gases en sistemas industriales intensivos en el consumo de energía.

 OPCHAIN-SGO-FRES (Forecast of Renewable Energy Sources): predicción de corto/mediano

plazo de la disponibilidad de fuentes energía renovables, como: el viento, la radiación solar y los recursos hídricos.

 OPCHAIN-SGO-OLM (Optimization of Load Management): optimización de la gestión de la carga eléctrica que consiste en planificar y el consumo o ajustarlo a un valor objetivo, permitiendo por ejemplo agregar los consumos de varias sedes (empresas multisite).

OP

CHAIN-

SGO

Smart Grids Optimization

OPCHAIN-SGO-FRES (Forecast of Renewable Energy Sources): predicción de corto/mediano plazo de la disponibilidad de fuentes energía renovables, como: el viento, la radiación solar y los recursos hídricos.

 Situación:

 Las nuevas fuentes de energía renovable tienen un comportamiento marcadamente aleatorio y es necesario su proyección a futuro para que los agentes generadores puedan asumir compromisos de energía firme y/o de despacho de acuerdo a la reglamentación de la región donde operen.

 Objetivos de la optimización:

 Identificar modelos óptimos de “forecast” de la disponibilidad de las fuentes renovables en el corto/mediano/largo plazo.

 Decisiones:

 Apoyo indirecto a las decisiones del operador de las centrales de energías renovables.

 Parámetros:

 Series historias de las disponibilidad de las fuentes de energía renovables.

 Cliente:

 Distribuidores - Comercializadores

OP

CHAIN-

SGO-FRES

Smart Grids Optimization

FUENTES DE ENERGÍA RENOVABLES

ENERGY STORAGE

GTE

GHI

GES

TES

GSH

EÓLICA

SOLAR

HES

FILO DE AGUA

ESI

FORECAST FUENTES RENOVABLES DE ENERGÍA

(EÓLICA - SOLAR)

SOLAR FORECASTING METHODOLOGIES

Broadly solar power forecasting methods are classified into three

categories:



PHYSICAL is based on the numerical weather prediction (NWP),

cloud observations by satellite or Total Sky Imager (TSI) or

atmosphere by using physical data such as temperature, pressure,

humidity and cloud cover



STATISTICAL

PHYSICAL METHODS

CLOUD IMAGERY AND SATELLITE BASED MODELS

The satellite and cloud imagery based model is a physical forecasting model that analyzes clouds. The satellite imagery deals with the cloudiness with high spatial resolution. The high spatial resolution satellite has the potential to derive the required information on cloud motion. The cloud motion helps in locating the position of cloud and hence solar irradiance can be forecasted. The parameters which have the most influence on solar irradiance at the surface are cloud covers and cloud optical depth. The processing of satellite and cloud imageries are done to characterize clouds and detect their variability and then forecast the GHI up to 6 hours ahead. This model works by determining the cloud structures during earlier recorded time steps. The structure of the clouds and their positions helps in predicting solar irradiance.

NUMERICAL WEATHER PREDICTION MODELS

(NWP)

Numerical Weather Prediction Models are based on dynamical equations that predict the evolution of the atmosphere up to several days ahead from initial conditions. The NWP models that underlie all others are global models covering the whole Earth. The model equations and inputs are discretized on a three‐dimensional grid extending

vertically from the surface of the Earth. Since global models are computationally and otherwise intensive, there are only 14 of these currently in operation worldwide (Traunmüller and Steinmaurer, 2010).

Model runs are typically initiated two to four times per day, for example at 0, 6, 12 and 18 UTC. Their initial conditions are derived from satellite, radar, radiosonde and ground station measurements that are processed and interpolated to the 3D grid. In order to limit computational requirements, the resolution of global NWP models is relatively coarse, with grid spacings of the order of 40 km to 90 km (Traunmüller and Steinmaurer, 2010). Mesoscale or limited area models are NWP models that cover a limited geographical area with higher resolution, and that attempt to account for local terrain and weather phenomena in more detail than global models. Initial conditions for these models are extracted from the global models. The best day‐ahead solar and PV forecasts combine

NWP forecasts with post‐processing of these forecasts to improve them or to generate

forecasts that are not included in the direct model outputs of the NWP, such as PV forecasts or direct normal irradiance forecasts.

NUMERICAL WHEATHER PREDICTION GLOBAL FORECAST SYSTEM (GFS)

One of the most well-known global NWP models is the Global Forecast System (GFS). The GFS model is run by NOAA (National Oceanic and Atmospheric Administration) every six hours and produces forecasts up to 384 hours (16 days) in advance on a 28km x 28km grid for the global domain [74]. The GFS loop time steps are 6 hours out to 180 hours (7.5 days), then change to 12-hour time steps out to 384 hours (16 days). In addition to the 28km x 28km horizontal discretization, the GFS models 64 vertical layers of the atmosphere. The RTM of the GFS accepts as inputs: predicted values of a fully three dimensional aerosol concentration field, predicted values of a two dimensional (horizontal) H2O, O2 and O3 concentration field as well as a constant two dimensional (horizontal) CO2 field. The GFS model also calculates wavelength specific attenuation of both upwelling and downwelling diffuse irradiances through a sophisticated scattering/absorbing scheme [75]. It should be noted that the radiant flux attenuation is dependent on H2O phase, temperature and particle size which makes the GFS sensitive to temperature errors.

CLOUD IMAGERY

The second broad category of physical solar power prediction approaches uses cloud images to predict cloud movement, and the associated impacts on plane of array irradiance and back of module temperature into the future at a particular geographic site. This approach reduces forecast error over NWP forecasts in the 0 – 6 hour-ahead (intra-day) timeframe. Higher spatial resolution (10-100 meters) and shorter sampling rates (30 seconds) of cloud images are achieved using total sky imaging (TSI) devices relative to satellite cloud images (1 km and 15 minutes respectively).

CLOUD IMAGERY

TSI-based forecasting is used to predict solar plant power output in real time (now-cast) up to 10 - 30 minutes ahead (intra-hour) through advanced image processing and cloud tracking techniques. Due to the limited view capabilities, TSI devices are unable to detect clouds that will impact a site beyond the 30 minute time horizon. TSI devices take an overhead image of the surrounding sky (Figure 6), which provides the images that are then processed to generate a forecast. In general this approach assumes the opacity, direction, and velocity of movement of the clouds in the future is consistent with the initial conditions observed through the TSI device. Irradiance is predicted based on current cloud shadows and then the cloud shadows are relocated forward in time based on cloud velocity and direction to generate the forecast (Pellend et. al, 2013). One total sky imager, depending on cloud height, can deliver an image for 5 - 10 square miles under cloudy sky conditions and 15 square miles in totally clear sky conditions. Thus, one imager can be used for a multi-MW solar farm, but several TSI devices would be needed for a multi-hundred MW farm. Solar power predictions using a TSI-based system are the best technique to predict short term ramps for individual solar power plants. However, the need for managing ramps on a large grid system decreases with geographic distribution (discussed in greater detail below). Thus, total sky imagers may have limited applicability to island regions, small balancing authorities, and perhaps to independent solar power producers that must meet certain ramp rate parameters.

METEOROLOGICAL DATABASES

(NWS, NDFD, ETC.)

"the computer-based

mathematical modeling is the

greatest invention of all times"

Herbert Simon

Premio Nobel en Economía (1978)

"for his pioneering research into the decision-making process within