Wind Energy Resource Prediction and Optimal Storage Sizing to Guarantee Dispatchability: A Case Study in the Kenyan Power Grid

Abstract

Kenya is experiencing a fast increase in grid-connected intermittent renewable energy sources (RESs) to meet its increased power demand, and at the same time be able to fulfill its Paris Agreement obligations of abating greenhouse gas emissions. For instance, Kenya has 102 MW of grid-tied solar power and 410 MW of grid-tied wind power. However, these sources are very intermittent with low predictability. Thus, after their installation and integration into the grid, they impose a new challenge for the secure, reliable, and economic operation of the system. To mitigate these and to ensure proper planning of the system operations, accurate and faster prediction of the generation output of the wind energy resources and optimal design and sizing of storage for the large-scale wind energy integration into the grid are of paramount importance. Artificial intelligence (AI) and metaheuristic techniques have proven to be efficient and robust in offering solutions to complex nonlinear prediction and optimization problems. Therefore, this study aims to utilize backpropagation neural network (BPNN) algorithm to conduct hourly prediction of the generation output of Lake Turkana Wind Power Plant (LTWPP), a 310 MW plant connected to the Kenyan power grid, and optimally size its battery energy storage system (BESS) using genetic algorithm (GA) to guarantee its dispatchability. The historical weather data, namely wind speed, ambient temperature, relative humidity, wind direction, and generation output from LTWPP, are employed in the training, testing, and validation of the neural network. LTWPP and BESS are modelled in MATLAB R2016a software. Thereafter, the developed BPNN and GA algorithms are applied to the modelled systems to predict the wind output and optimize the storage system, respectively. BESS optimization with neural prediction reduces the BESS capacity and investment costs by 59.82%, while the overall dispatchability of LTWPP is increased from 73.36% to 90.14%, hence enabling the farm to meet its allowable loss of power supply probability (LPSP) index of 0.1 while guaranteeing its dispatchability.

Enhancing Active Power Dispatchability of WTG-BESS Through Integrated Control Considering Wind Forecast Error

“…This strategy efficiently tracks the dispatching power demand using the hybrid wind/battery storage system's output, improving the wind farm's dispatchability while additionally decreasing battery switching times and prolonging battery life. [10] discusses the use of artificial intelligence and metaheuristic techniques to predict wind energy generation and optimize BESS sizing. The optimized BESS improves the dispatchability of the Lake Turkana Wind Power Plant (LTWPP) in Kenya.…”

Assessment of Offshore Wind Resources, Based on Improved Particle Swarm Optimization

“…The adaptive PSO can combine the advantages of different training algorithms, to evaluate the wind resources, has stronger learning capabilities, global search capabilities, and can avoid the shortcomings of the BPNN from falling into the local optimum [30]. The wind resource assessment method, based on the hybrid NN not only comprehensively considers the geographical location information around the wind farm, but also improves the accuracy of wind resource assessment [31]. Through using the PSO algorithm to optimize the BPNN, this can accurately and effectively identify the type of wind turbine gearbox fault [32].…”