Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods

Review of Optimization Techniques for Sizing Renewable Energy Systems

The growing evidence of the global warning phenomena and the rapid depletion of fossil fuels have drawn the world attention to the exploitation of renewable energy sources (RES). However standalone RES have been proven to be very expensive and unreliable in nature owing to the stochastic nature of the energy sources. Hybrid energy system is an excellent solution for electrification of areas where the grid extension is difficult and not economical. One of the main attribute of hybridising is to be able to optimally size each RES including storages with the aim of minimizing operation costs while efficiently and reliably responding to load demand. Hybrid RES emerges as a trend born out of the need to fully utilize and solve problems associated with the reliability of RES. This paper present a review of techniques used in recent optimal sizing of hybrid RES. It discusses several methodologies and criteria for optimization of hybrid RES. The recent trend in optimization in the field of hybrid RES shows that bio-inspired techniques may provide good optimization of system without extensive long weather data

Monte-carlo based robust analytical method for optimal sizing and reliability of hybrid renewable energy system

The need for a more reliable power from the utility grid and ever-increasing concerns on Greenhouse Gas (GHG) emission effect has globally promoting Renewable Energy Sources (RES). RES is increasingly being adopted in complementing traditional fossil fuels in the energy power supplies. Hybrid Renewable Energy (HRE) systems incorporating wind and solar sources offers lower costs, higher reliability, reduced investment risks, fuel diversification etc. However, wind speed and solar radiation are characterized by their limitations of inherent intermittency and variability. These limitations have led to the concept of optimal sizing and reliability assessments to maintain a balance between generated power and the system loads. Nonetheless, RES reliability assessment studies are site-specific, but existing studies are inexhaustive given the capacity availability and reliability requirements of various sites as well as their performance evaluations. This thesis presents the optimal sizing and reliability assessment of a hybrid solar and wind energy systems for a selected location. Weibull statistical method and air temperature amplitude based statistical models are adopted for wind and solar energy potential assessments of the selected site. The Weibull parameters were estimated using standard deviation method for wind energy potential assessment. Moreover, the air temperature based models of Hargreaves and Samani; Allen; Samani; and Bristow-Campbell models were used for solar energy potential assessment. Simulation of the uncertainty in the wind speed and its probability distribution is performed by using Auto-Regressive Moving Average (ARMA) model to improve wind speed normal distribution. In this approach, the best normal distribution for the simulated wind speed for the reliability analysis is chosen. To improve the performance of the Photovoltaic (PV) module, a single diode six parameter model is developed. First, the P-V and I-V curves were used to generate the required constraints. These constraints were then used to obtain the solution vector of the six parameters using MATLAB and System Advisor Model (SAM). Also, the system’s capacity availability and reliability was assessed using Monte Carlo (MC) simulation. Finally, the result of the MC reliability assessment is later served as Loss of Power Supply Probability (LPSP) constraints to Artificial Bee Colony (ABC) algorithm for the system’s optimal sizing and enhanced reliability assessment. Results from the study show that both wind and solar energy potential of the selected site is high and can generate power at utility level. The ARMA simulated wind speed shows an improvement of 21.8% in standard deviation over the measured wind speed. The adoption of the negative components in the ARMA model transformation resulted in least error of 23.34% in the final wind simulation. Results obtained based on the six parameter solution vector gives improved performance of the PV module. Using the developed MC technique, capacity availability of 100% and LPSP of zero is achieved. The developed ABC algorithm resulted in system reliability improvement of 98.92% when the MC results are constraint into the ABC for the optimal sizing. Various results were validated at appropriate sections and finally, the optimal sizing results of PV/battery RES power system is found to give the best reliability. Such a system has great reliability and can be implemented in facilities requiring constant power supplies such as critical infrastructure

Optimal energy control of a grid connected solar-wind based electric power plant.

Doctor of Philosophy in Electrical Engineering. University of KwaZulu-Natal, Howard College 2016.In the present context of urge energy demand, renewable energy is considered as an alternative source of clean energy. In view of the increase in the price of fossil fuel due to its rarity and emissions, more integration of renewable sources is needed for better economic management of the grid. This research work has been done in two parts. The first part deals with the daily energy consumption variations for the low demand season and high demand season on weekdays and weekends. The intention is to correlate the corresponding fuel cost and estimate the operational efficiency of the hybrid system, which comprises the PV, PW, DG, battery system, for a period of 24 hours taken as control horizon. The latest published research literature has shown that a good deal of work has been done using a fixed load and uniform daily operational cost. The economic dispatch strategy, fuel cost, energy flows and energy sales are analysed in this study. The results show that a renewable energy system, which combines the PV/PW/diesel/battery models, achieves more fuel saving during both the high demand and low demand seasons than a model where the diesel generator satisfies the load on its own. The fuel cost during the low demand and high demand seasons for weekdays and weekends shows considerable fluctuations, which should not be neglected if accurate operational costs are to be obtained. The model shows the achievement of a more practical estimate of fuel costs, which reflects the fluctuation of power consumption behaviour for any given model. In the last part of the thesis model predictive control (MPC) is introduced in the management and control of power flow. The highlight in this thesis is the management of the energy flow from the hydro pump, wind, photovoltaic system and turbine when the system is subject to severe disturbances. The results demonstrated in the thesis prove the advantages of the approach and its robustness against uncertainties and external disturbances. When analysed with the open loop control system, MPC is more robust because of its stability of the system when external disturbances occur in the system. This thesis presents a practical solution to energy sale, control, optimization and management