A Decision Support System for Integrated Design of Hybrid Renewable Energy System

While large-scale wind farms and solar power stations have been used widely as supplement to the nuclear, fossil fuels, hydro and geothermal power generation, at smaller scales these resources are not reliable to be used independently and may result in load rejection or an over size design which is not cost effective. A possible solution to solve this issue is using them as part of a hybrid power system. Complexity in design and analysis of hybrid renewable energy systems (HRES) has attracted the attention of many researchers to find better solutions by using various optimisation methods. Majority of the reported researches on optimal sizing of HRES in the literature are either only considering one objective to the optimisation problem or if more than one objective is considered the effect of uncertainties are ignored. This dissertation work investigates deterministic and stochastic approach in design of HRES. In deterministic approach it shows how adding a battery bank to a grid connected HRES might result in more cost effective design depending on different grid electricity prices. This work also investigates the reliability of HRES designed by conventional deterministic design approach and shows the weakness of common reliability analysis. To perform the stochastic approach the renewable resources variation are modelled using time series analysis and statistical analysis of their available historical meteorological data and the results are compared in this work. Chance constrained programming (CCP) approach is used to design a standalone HRES and it is shown that the common CCP approach which solves the problem based on the assumption on the joint distribution of the uncertain variables limits the design space of problem. This work then proposes a new method to solve CCP to improve the size of design space. This dissertation comprises multi-objective optimisation method based on Non-dominated Sorting Genetic Algorithm (NSGA-II) with an innovative method to use CCP as a tool in estimating the expected value of the objective function instead of Monte-Carlo simulation to decrease the computational time

Multi-objective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming

The optimum design of Hybrid Renewable Energy Systems (HRES) depends on different economical, environmental and performance related criteria which are often conflicting objectives. The Non-dominated Sorting Genetic Algorithm (NSGA-II) provides a decision support mechanism in solving multi-objective problems and providing a set of non-dominated solutions where finding an absolute optimum solution is not possible. The present study uses NSGA-II algorithm in the design of a standalone HRES comprising wind turbine, PV panel and battery bank with the (economic) objective of minimum system total cost and (performance) objective of maximum reliability. To address the uncertainties in renewable resources (wind speed and solar irradiance), an innovative method is proposed which is based on Chance Constrained Programming (CCP). A case study is used to validate the proposed method, where the results obtained are compared with the conventional method of incorporating uncertainties using Monte Carlo simulation

A multivariable optimal energy management strategy for standalone DC microgrids

Due to substantial generation and demand fluctuations in standalone green microgrids, energy management strategies are becoming essential for the power sharing and voltage regulation purposes. The classical energy management strategies employ the maximum power point tracking (MPPT) algorithms and rely on batteries in case of possible excess or deficit of energy. However, in order to realize constant current-constant voltage (IU) charging regime and increase the life span of batteries, energy management strategies require being more flexible with the power curtailment feature. In this paper, a coordinated and multivariable energy management strategy is proposed that employs a wind turbine and a photovoltaic array of a standalone DC microgrid as controllable generators by adjusting the pitch angle and the switching duty cycles. The proposed strategy is developed as an online nonlinear model predictive control (NMPC) algorithm. Applying to a sample standalone dc microgrid, the developed controller realizes the IU regime for charging the battery bank. The variable load demands are also shared accurately between generators in proportion to their ratings. Moreover, the DC bus voltage is regulated within a predefined range, as a design parameter

Reliability criteria in optimal sizing of stand-alone hybrid wind-PV-battery bank system

The Hybrid Renewable Energy System (HRES) can be viable solution to bring electricity to isolated areas with no possibility of grid extension. Though the HRES performance is highly affected by climatic changes and interruptions in electricity supply may happen in the system. For that, the battery bank can be used as auxiliary source to reduce the sensibility of HRES to the climate changes and maintain a desired reliability through the whole time. However, a HRES designed with a predefined reliability may not be satisfactory to the owner since all the power shortages may occur in a close duration. This paper discusses optimal sizing of a HRES under a desired reliability using conventional reliability criterions and shows the weakness of current practice in delivering a satisfactory power generation system

Wind speed and solar irradiance variation simulation using ARMA models in design of hybrid wind-PV battery system

The financial support by Synchron Technology Ltd. through co-funding of this project is gratefully acknowledged.Peer reviewedPublisher PD

Chance constrained programming using non-Gaussian joint distribution function in design of standalone hybrid renewable energy systems

Performance of a HRES (hybrid renewable energy system) is highly affected by changes in renewable resources and therefore interruptions of electricity supply may happen in such systems. In this paper, a method to determine the optimal size of HRES components is proposed, considering uncertainties in renewable resources. The method is based on CCP (chance-constrained programming) to handle the uncertainties in power produced by renewable resources. The design variables are wind turbine rotor swept area, PV (photovoltaic) panel area and number of batteries. The common approach in solving problems with CCP is based on assuming the uncertainties to follow Gaussian distribution. The analysis presented in this paper shows that this assumption may result in a conservative solution rather than an optimum. The analysis is based on comparing the results of the common approach with those obtained by using the proposed method. The performance of the proposed method in design of HRES is validated by using the Monte Carlo simulation approach. To obtain accurate results in Monte Carlo simulation, the wind speed and solar irradiance variations are modelled with known distributions as well as using time series analysis; and the best fit models are selected as the random generators in Monte Carlo simulation

Optimal sizing of grid-connected hybrid wind-PV systems with battery bank storage

Conventionally a battery bank is used as the backup system in standalone Hybrid Renewable Energy Systems (HRES) while in grid-connected systems the grid performs as the backup during power shortage periods. For the latter, different prices of electricity during peak and off-peak hours raises a question about the cost effectiveness of using the grid as a backup. Adding a small storage system to maintain the shortage of electricity produced by renewable resources at peak hours may prove to be more cost effective backup. This paper focuses on the design of an optimised grid connected small-scale HRES, incorporating a battery bank to store electricity during off-peak periods and uses this storage to support the HRES during peak demands. This system is intended to be cost effective (taking into consideration the Feed-In-Tariff) and make building self sufficient with regard to energy use. The performance of the proposed design method is evaluated based on a case study for a typical household in UK