Date of Award

18-6-2025

Document Type

Doctoral Thesis

Degree Name

Doctor of Philosophy

First Advisor

Dr. Michael D. Murphy

Second Advisor

Dr. John Upton

Abstract

Advancing the economic and environmental performance of dairy farms requires a comprehensive assessment of renewable energy systems (RES), energy storage technologies, and demand side management (DSM) techniques. This thesis aimed to address these needs by conducting a thorough assessment of RES, DSM techniques, and energy storage systems on dairy farms, through the development of a state-of-the-art simulation tool, trained and validated using modern energy data collected from a range of commercial dairy farms as part of this project. The research was conducted in three stages. Firstly, extensive data collection was undertaken across 26 commercial dairy farms employing herringbone and rotary milking parlours. These data were used to identify factors which influence energy consumption, and efficiency key performance indicators to inform the development of a state-of-the-art energy model called the Farm Electricity System Simulator (FESS). Secondly, FESS was developed using grey-box modelling techniques, with empirical data used for model training and validation, to create an accurate, scalable platform capable of assessing RES integration and DSM techniques on dairy farms. Finally, FESS was used to conduct an economic and environmental assessment of RES, including PV systems, battery energy storage (BES), a hot water diverter (HWD), and a novel thermal energy storage method referred to in this thesis as “deep-cooling”. Deep-cooling was defined as: the process of altering the set point temperature of the bulk tank throughout the day, depending on electricity prices, to reduce electricity related costs. As deep-cooling was a novel energy storage method, its optimum operation schedule was identified through the use of a genetic algorithm (GA). Analysis of the data showed that milk cooling was the largest direct energy consumer, accounting for 33% of electricity consumption over 12 months for the 16 herringbone dairy farms. Water heating was the second largest electricity consumer (31%) followed by the milking machine (16%). A moderate negative correlation (r = -0.57) between milking efficiency (cows/hour) and energy efficiency (kWh/cow), establishing milking efficiency as a factor which influences energy efficiency on dairy 2 farms. Thus, FESS included a sub-model which accounted for milking efficiency to help ensure accuracy. FESS achieved good predictive accuracy (R2 = 0.72 average across three dairy farms in 15-minute time steps) and its ability to assess RES integration and DSM techniques was demonstrated. The milking machine sub model in FESS achieved the highest R2 of all sub models on average across the three farms (R2 = 0.76). The water heating sub model achieved the second highest R2 of 0.69, while the milk cooling sub model achieved the lowest R2 of 0.67. Once FESS was validated it was used to conduct investment appraisals which showed the economic viability of PV systems, particularly when grant aid was provided. The use of traditional energy storage methods (HWD and BES) were investigated and their strengths and weaknesses were identified. Investing in a HWD was found to result in little to no financial benefit, depending on the electricity pricing structure and feed-in tariff selected. The HWD and PV investment was found to achieve the lowest additional profit of all investment scenarios across all farms and electricity tariffs. On average the HWD and PV investment achieved additional profit across all farms of €21,778 under a day/night tariff compared to €22,816 for PV alone. BES was found to reduce annual electricity costs when included with a PV system by €5,534 on average across all farms and electricity tariffs compared to €5,196 for PV alone. However, BES could result in a reduced return on investment, compared to investing in PV alone, due to its high capital cost. After assessing the performance of traditional energy storage methods on dairy farms of varying scale, a novel energy storage method (deep-cooling) was assessed in a more focused case study, where it was compared to a HWD and BES. Deep-cooling was found to be an effective energy storage method providing DSM opportunities and energy storage of excess PV generated electricity. Deep-cooling, as a stand-alone technology reduced dairy farm electricity costs by up to 19% in comparison to a baseline scenario without energy storage or RES. Furthermore, deep-cooling was the only energy storage method investigated in this thesis which resulted in reduced electricity consumption and related operational CO2 emissions. PV with deep-cooling under basic control was found to reduce annual operational CO2 emissions by 2.7 tonnes more than investing in PV alone. Investing in PV with a HWD or BES under basic control increased annual operational CO2 emissions by 0.03 tonnes and 0.44 tonnes respectively compared to investing in PV alone. Using a GA to optimise the set point control schedule for deep-cooling revealed that a hybrid approach provided the greatest reduction in dairy farm electricity costs. 3 This approach involves cooling milk with surplus PV generated electricity on days with high solar irradiation and using inexpensive night rate electricity on days with low solar irradiation. The identification of milking efficiency as a factor to be considered while assessing dairy farm energy efficiency provides future researchers with additional clarity regarding dairy farm energy consumption and will inform future research. The development of FESS provides decision support to dairy farmers, researchers, and policy makers. FESS facilitates the investigation of RES integration and DSM on dairy farms, which no previously published models were able to provide accurately. This functionality coupled with the scalability and modularity of FESS makes it the ideal energy simulation platform to investigate a near limitless spectrum of scenarios. This utility can be seen in the investment appraisal carried out in this thesis where the economic and environmental performance of PV, a HWD, and BES was undertaken. Moreover, the identification and demonstration of deep-cooling as a viable method of energy storage for dairy farms was achieved as a direct result of the development of FESS. It is anticipated that the research in this thesis will provide support to stakeholders in the dairy industry, including dairy farmers, policymakers, and researchers. The energy simulation methodologies developed in this thesis have already been deployed on the MTU website as a publicly available decision support tool. These contributions will facilitate data-driven decision making regarding the adoption of sustainable technologies and practices, promoting improved energy efficiency and reduced CO2 emissions in the dairy industry.

Access Level

info:eu-repo/semantics/openAccess

Download

Share

COinS