Hybrid Renewable Energy Systems: History
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Subjects: Energy & Fuels
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Md Mijanur Rahman

According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. 

  • artificial neural network (ANN)
  • backpropagation algorithm
  • energy prediction
  • hybrid renewable energy system (HRES)
  • machine learning

1. Machine Learning in HRES

Machine learning (ML) techniques have been employed for efficient energy management in generation and demand sectors. ML methods can be utilized either in a stand-alone or grid-connected renewable resources, depend on the requirements and the characteristics of the nature of obstacles. Figure 1 presents the sectors where machine learning methods can be used for power prediction, demand forecasting, management of renewable energy systems as well as enhancing the system performance. The major usages of ML methods in HRES are briefly described in the following subsections:

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Figure 1. Usages of advanced technologies in hybrid-renewable-energy system (HRES).

(i)

Predicting the Output Generation of Renewable Energy. Forecasting energy generation is a vital issue for renewable energy sources and machine learning as a tool of forecasting energy production play an important role in this context [1][2]. Solar or wind energy can be predicted using historical data. The accuracy of prediction is challenging due to the nature of dependency of the source of these energies to the environment condition [3]. This study presents different neural network techniques that are used to predict the output of the renewable energies.

(ii)

Specifying the Geographical Location, Configuration, and Sizing of Renewable Power Plants. Optimal sizing of renewable power plants is a challenging task in HRES. The location of the energy plant and other parameters depend on different factors, such as weather, territory, availability, and expenses. Also, unlike fossil fuel sources, the operation of renewable energy plants needs space. Hence, it is essential to specify size and analyze the location in case of e.g., weather data, humidity, temperature, wind speed, irradiation etc. [4]. Machine learning techniques have the ability to assist these decision making steps [5].

(iii)

Managing the Overall Operation of RE Integrated Smart Grid. Smart grid (SG) is a new generation of power plants that optimizes all sectors of the grid from generation to distribution, and storing energy [6][7]. With the speedy expansion of the power-grid and continuous advancement of making it intelligent, more effective, and efficient operation are expected by stakeholders to manage the grid. The intelligent techniques as well as the combination of AI, IoT, and ICT tools are needed to satisfy the efficient management of the smart grid [8][9] and provide the solutions for problems facing by power grids, such as demand-supply balancing, fault detection, operation of the grid, and management, data management of grid and control, and so on [10].

(iv)

Forecasting the Energy Demand. Energy demand prediction ensures the reliability of supply, and the demand-supply chain has to be perfectly balanced [11]. Since different stakeholders with diverse characteristics are existed in HRES, the energy demand forecasting is a difficult task. ML methods are able to sort the accurate estimation of power consumption and demand [12] as well as renewable energy production and supply [13].

(v)

Developing Renewable Energy Materials. Machine learning is expanding its capability to renovate materials discovery. It can be used to support other energy-related fields, such as solar cells, batteries, catalysis, and crystal discovery [14]. Thus, ML approaches can be used for developing renewable energy materials. ML is also used in another promising and exciting area, such as inverse design where the properties of the material are given to the ML model, and it finds the materials from those properties [15].

2. RE Forecasting Approaches

Energy forecasting is a process of estimating the energy generation from different sources. The growing perception of advanced technology has made energy forecasting a popular task in the today’s power system. There are two different approaches for energy forecasting. The first approach is called the top-down approach, where the prediction is done at the highest level. The second approach is called the bottom-up or build-up approach, where prediction is made from lower-level and predictions are collected to higher levels of the forecasting hierarchy [16][17]. In a hybrid system, a bottom-up approach is more meaningful and suitable for seeking the individual value of the forecast ed components [18]. An overview of a bottom-up method for predicting the energy generation of each source is illustrated in Figure 2. This section reviews the energy predictions of some common RE sources, such as wind, solar, hydro, biomass energies.

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Figure 2. Bottom-up or build-up approach for energy generation forecasting.

2.1. Solar Energy Prediction

Solar energy is a genuinely sustainable source of energy that directly uses the sun‘s energy either in the form of electricity (Photovoltaic) or heat (photo-thermal) [19][20]. Photovoltaic energy is non-contaminating, doesn’t make greenhouse gas like other forms of energy sources such as fuel-based energies. One of the meteorological parameters which is so difficult to predict the sun irradiance because of its dependency on diverse astronomical, geographical, and climatic parameters such as air pressure, ambient temperature, humidity, wind speed/direction, and sunshine period [21][22]. Due to the uncertainty nature of these parameters, the machine learning approach is used most frequently to predict global irradiance on a monthly, daily, or hourly basis [23].

2.2. Wind Power Prediction

The primary application of wind energy is to use the kinetic energy of the air and convert it to electricity on a large scale either onshore (on land) or offshore (on oceans, seas, etc.) [24]. The renewable and sustainable wind energy also depends on solar irradiance, wind speed as well as other ambiance conditions. For the intermittency and uncertainty of wind power [25], forecasting is needed to use this energy. Like solar energy, wind power prediction can be classified as hourly (immediate-short-term), daily (short-term), and monthly (long-term) prediction, according to time-horizons [26]. The techniques of prediction of wind energy are varying from physical (deterministic) approach to statistical or machine learning approach based on historical and time-series data analysis [27]. Accurately prediction of the wind power is challenging due to the intermittency of the speed of the wind over time. To enhance the forecasting accuracy in the long/short-term, various dynamic ANN-based techniques, such as CNN, RNN (with multi-variable, such as wind direction/speed, ambient temperature, solar irradiance, environment humidity, and air pressure [28]) have been proposed [29].

2.3. Hydro Power Prediction

Hydropower is a source of energy that harnesses from the kinetic energy of water, to generate electricity. Hydropower generators normally build in the river’s pathway. Hydropower energy has many advantages over the majority of the other sources of energy, such as a high level of reliability, high performance, low maintenance cost, and the capability of adjusting according to the load changes [30]. Hydropower depends on the volume of the water that passes through the turbine and the size of the turbine itself. For instance, in the rainy season hydropower turbines can produce more power due to the plentifulness of water, while the bigger turbines may produce more electricity. In other seasons due to lack of water, smaller turbines may be more useful. Thus the optimization and prediction of the size of hydropower turbines is essential here. However, the relationship between the turbine size and flowrate of water are non-linear and very complex in nature. The optimization can be done by artificial intelligence and machine learning methods, such as “support-vector-machine” (SVM), “genetic-algorithm” (GA) [31], and “artificial-neural-network” (ANN) [32]. Like other predictions, the hydro power prediction is a dynamic process that requires constantly updating information about weather measurements and previous energy production to ensure proper regulation of the system.

2.4. Biomass Energy Prediction

Biomass energy is another source of renewable energy that is harnessed from biological sources. Biomass can be considered as a part of the carbon cycle from the atmosphere into plants, from plants to soil, and finally from soil to the atmosphere during plant decay. Bio-energy can be utilized in many aspects, such as transport in the form of bio-diesel, generating electricity, and heating. Most of the relevant biomass is produced in village agricultural fields, wastelands, and forests. The process of biomass prediction is precise in nature, and different methodologies are used for biomass energy estimation. Literature shows that the accuracy of the image-derived-based prediction of the biomass energy is approximately high even by utilizing the linear regression models. Although, it is a big challenge to employ these methods in experiments in different situations such as environmental variables and conditions or a lack of data-sets for estimation [33].

 

This entry is adapted from the peer-reviewed paper 10.3390/su13042393

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