Algorithm and Design Tool Development of Sizing a Stand-Alone Pv/Wt/Electrolyzer/Hydrogen/Fuelcell Power Generation System for Electricity Supply Under Weatherrelated Uncertainty Considerations and Thermo-Fluid Effects
Today, reducing the greenhouse gas emissions and decentralizing the electrical power plantshas become a worldwide target, which, in turns, helps in avoiding causing any damage to the environment and increasing and easing the accessibility to the network, respectively, especially at remote areas. One of the most promising ways for achieving this target is the utilization of green and renewable energy resources (RER) such as solar energy (SE) and wind energy (WE). However, in nature, SE and WE suffer from the intermittence and variability (IV) (also called volatility), which have made the task of forecasting the power outputs from and designing/sizing a RE-based power plant (REPP) very challenging and have made the reliability to become a big source of concern for the decisions makers. Nonetheless, researchers have been attempting to develop different approaches for the purpose of utilizing these RER and mitigating this natural issue as much as possible, simultaneously. One of the most promising and appreciated mitigator for the IV effect is by hybridizing the power plants of SE and WE (H-REPP), with not or equal penetration levels, by integrating the H-REPP with an energy storage system (ESS) (e.g., compressed air, pumped hydro (PHS), regenerative fuel cell (RFC), etc.), and by using a method that can forecast the historical weather data (called WYGM from now onward) for the sake of obtaining any statistical data that can map the potential at any site of interests. Moreover, since it is impractical to request unlimited surface area and energy storage capacity (ESC) for harvesting and storage SE/WE, respectively, but to be reasonable via developing sizing algorithms, instead; this has been introduced in the literature under the topic of sizing REPP. However, usually, in the literature, these three tasks (modeling H-REPP, formulating WYGMs, and developing sizing algorithms) are separately studied/adopted, which should not be the case due to the strong connections among these tasks; additionally, the literature is lacking from modeling and sizing algorithms for H-REPP 17 where the above-mentioned complexity is addressed. Therefore, in this work, the core goal is to estimate the least required size of a H-REPP that can operate for a year-round, according to different weather conditions, and consists of: solar photovoltaic power generation system (SPVPGS) (thus, the concentrated solar panel (CSP) are excluded) and wind turbine power generation system (WTPGS) for direct power supply and RFC for indirect power supply and/or hydrogen production (called green hydrogen production system). This core goal has been divided into a set of objectives as follows: 1) building an advanced modeling of SPVPGS and WTGPS, 2) designing a novel WYGM that efficiently involve the IV effect and fulfills the requirements of such H-REPP at any particular site of interest and along with a new spatial-temporal weighting (STW) approach, 3) developing an algorithm for sizing such kind of H-REPP along with a RFC as function of the supply-demand period, 4). Optimizing the penetration levels of SE-WE according to the estimated potential of the location; also, improve the possible sub-optimal results by decreasing the size and the updating step. Additionally, this conducted research aims at: studying the effect of the initial conditions for the sake of selecting the “best day of start energy harvest and storage (BDHS), proposing a new method of determining the most-frequent data point for a data set that has a strongly varied standard deviation from time to time, considering the need for updating the surface roughness with wind direction inside the simulation of WTPGS, and implementing the entire work into a software. These objectives have been planned to be achieved in four phases: I. simple SPVPGS-RFC and WYGM, II. advanced SPVPGS-WTPGS and simple RFC and semi-advanced WYGM, III. advanced SPVPGS-WTPGS-RFC (and other forms of ESS as possible) and advanced WYGM. IV. releasing of the software (called renewable energy power system sizing software (REPPS)). The obtained results have showed the following. First, the 18 hybridization of multiple resources (i.e., PV-WT) have reduced the required size of power plant, which has proven that such solution can minimize the intermittency effect and then, eventually, increase the reliability. Second, investigating and considering more than one statical type of weather years (i.e., average, most-frequent, worst, and best) has led to a major and significant finding, but not limited to, which is that Considering only one type is not recommended due to the huge variation on the harvested energy among all the types. Moreover, the novel developed generation methodology for weather year data set, in general, and the most-frequent data point selection method, in particular, have showed a very promising solution along with an acceptable range of errors. Also, it has been validated and showed that its performance is capable of predicting the limits of the highest and lowest possible of energy harvesting and supply. Additionally, the effect of different miscellaneous factors (i.e., tracking systems, round-trip efficiency of different energy storage systems, model-related uncertainty, etc.) have been investigated. Among the major and unique results that have been attained using the developed algorithm (REPPS) and weather year data set generator (ABTMY(SITY)) is the estimated range of uncertainty for the four modes; and, based on the validation, the tested day for a year-round operation and based on a real-new data set, the required area and energy storage capacity have both fallen within the estimated range. Future work can be conducted to study the effect of the environment on the performance of the RFC, consider the cooling and shading effects on the performance of the SPVPGS, apply the STW to the developed WYGM, and optimize the levels of penetration by the SE and WE at the studied site and selected conditions.
Big Data, Green Hydrogen Production, Renewable Energy Software, Solar PV, Uncertainty, Wind Energy
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