Ground source heat pump systems (GSHP) for residential building heating, cooling, and hot water are highly energy efficient but capital intensive when sized for peak demands. The use of supplemental ...sources of energy with GSHP systems enables improved life-cycle economics through the reduction in the size and cost of the GSHP components. This paper investigates the life-cycle economics of hybrid solar-assisted ground source heat pump systems (SAGSHP) using simulations validated from field data. The economics and optimal sizing of SAGSHP systems for heating dominant climates in four locations in Australia and ten locations elsewhere are evaluated in order to explore the suitability and relative merits of SAGSHP systems in a range of heating dominant climates. In locations having high or moderate levels of solar irradiation, high electricity prices, and high or moderate gas prices, SAGSHP systems are shown to have the lowest life cycle cost amongst alternatives, with predicted savings of up to 30%.
•A comprehensive investigation of the design and performance of hybrid GHSPs.•A comparison of hybrid GSHPs and conventional systems on cost and CO2 emissions.•Effects of local climatic and economic conditions are evaluated for 14 global cities.•Hybrid GSHPs have shown to be the most economical system for 10 out of 14 locations.•Local energy price is a key factor that influences the feasibility of hybrid GSHPs.
This paper describes the methods used by Team Cassandra, a joint effort between IBM Research Australia and the University of Melbourne, in the GEFCom2017 load forecasting competition. An important ...first phase in the forecasting effort involved a deep exploration of the underlying dataset. Several data visualisation techniques were applied to help us better understand the nature and size of gaps, outliers, the relationships between different entities in the dataset, and the relevance of custom date ranges. Improved, cleaned data were then used to train multiple probabilistic forecasting models. These included a number of standard and well-known approaches, as well as a neural-network based quantile forecast model that was developed specifically for this dataset. Finally, model selection and forecast combination were used to choose a custom forecasting model for every entity in the dataset.
Microgrid technology is poised to transform the electricity industry. In the context of commercial/domestic buildings and data centers, where most loads are native direct current, DC microgrids are ...in fact a natural choice. Voltage stability and current/power‐sharing between sources within a DC microgrid have been studied extensively in recent years. DC voltage droop control is known to have its drawbacks in that current or power‐sharing is relatively poor. To eliminate this drawback, some have proposed to add a communication‐based consensus control in addition to the primary voltage droop control loop. The current sharing performance is improved, however, the voltage deviation inherent in droop control requires a further, slower control to achieve voltage quality control. To overcome this complication, and reduction in response time, a low latency communication‐based control technique that achieves proportional current sharing without significant voltage deviations is proposed in this work. The stability of the proposed control technique is compared to state‐of‐the‐art using eigenvalue and transient analyses. The negative impact of communication delays on proposed control is discussed in detail.
There has been an increased interest in reducing the cost and environmental impact of building heating, ventilation, and cooling systems by using hybrid renewable energy systems. Among them, heat ...pumps which combine geothermal and solar thermal energy have gained attention due to their high efficiency and reliability. However, such systems can have high install costs. It is therefore important to design them in an economically optimal way, and to evaluate and compare them to conventional solutions over the full system life cycle. This paper presents a detailed study into the optimal system operation and design of a Solar-Assisted Ground Source Heat Pump system. The design variables include solar collector area, borehole depth and volume of the thermal storage tank. The operation and design methodology is demonstrated using data gathered from a real system in Melbourne, Australia. For this system and location, the outcome is that an optimally designed Ground Source Heat Pump system should cover approximately 90% of the total heating demand, with the remainder covered by conventional sources. The approach can be applied in the same way to other systems and other geographies.
The recent emergence of distributed generation, smart meters, and electric vehicles means that much attention is now being given to network modelling and analysis at the distribution, rather than ...transmission, level. Many optimisation studies, both regarding technical and economic questions, aim to satisfy the constraints posed by grid infrastructure. We explore in detail one of these network constraints, minimum required voltage, at the distribution level and demonstrate that the physical locations of individual loads in the network play a significant role in determining whether voltages throughout the network remain within required limits or not. Our simulations use real distribution network data and are run on models of two real neighbourhoods. We show that the addition of a single load at a weak point of the network can have the same impact as considerably greater numbers of loads at stronger locations of the network. This has important implications for applications such as electric vehicle charging, and suggests that spatial distribution of loads should be taken into account when analysing network stability.
Distributed, small-scale solar photovoltaic (PV) systems are being installed at a rapidly increasing rate. This can cause major impacts on distribution networks and energy markets. As a result, there ...is a significant need for improved forecasting of the power generation of these systems at different time resolutions and horizons. However, the performance of forecasting models depends on the resolution and horizon. Forecast combinations (ensembles), that combine the forecasts of multiple models into a single forecast may be robust in such cases. Therefore, in this paper, we provide comparisons and insights into the performance of five state-of-the-art forecast models and existing forecast combinations at multiple resolutions and horizons. We propose a forecast combination approach based on particle swarm optimization (PSO) that will enable a forecaster to produce accurate forecasts for the task at hand by weighting the forecasts produced by individual models. Furthermore, we compare the performance of the proposed combination approach with existing forecast combination approaches. A comprehensive evaluation is conducted using a real-world residential PV power data set measured at 25 houses located in three locations in the United States. The results across four different resolutions and four different horizons show that the PSO-based forecast combination approach outperforms the use of any individual forecast model and other forecast combination counterparts, with an average Mean Absolute Scaled Error reduction by 3.81% compared to the best performing individual model. Our approach enables a solar forecaster to produce accurate forecasts for their application regardless of the forecast resolution or horizon.
•Five solar forecasting models were analysed across multiple resolutions and horizons.•No model is consistently the best, and a forecast combination is necessary.•A forecast combination approach based on particle swarm optimization is proposed.•This approach can be adapted to any resolution or horizon.•In an extensive evaluation, it outperforms individual models and other combinations.
Energy storage systems have the potential to deliver value in multiple ways, and these must be traded off against one another. An operational strategy that aims to maximize the returned value of such ...a system can often be significantly improved with the use of forecasting-of demand, generation, and pricing-but consideration of battery degradation is important too. This paper proposes a stochastic dynamic programming approach to optimally operate an energy storage system across a receding horizon. The method operates an energy storage asset to deliver maximal lifetime value, by using available forecasts and by applying a multi-factor battery degradation model that takes into account operational impacts on system degradation. Applying the method to a dataset of a residential Australian customer base demonstrates that an optimally operated system returns a lifetime value which is 160% more, on average, than that of the same system operated using a set-point-based method applied in many settings today.
The increasing uptake of electric vehicles suggests that vehicle charging will have a significant impact on the electricity grid. Finding ways to shift this charging to off-peak periods has been ...recognized as a key challenge for integration of electric vehicles into the electricity grid on a large scale. In this paper, electric vehicle charging is formulated as a receding horizon optimization problem that takes into account the present and anticipated constraints of the distribution network over a finite charging horizon. The constraint set includes transformer and line limitations, phase unbalance, and voltage stability within the network. By using a linear approximation of voltage drop within the network, the problem solution may be computed repeatedly in near real time, and thereby take into account the dynamic nature of changing demand and vehicle arrival and departure. It is shown that this linear approximation of the network constraints is quick to compute, while still ensuring that network constraints are respected. The approach is demonstrated on a validated model of a real network via simulations that use real vehicle travel profiles and real demand data. Using the optimal charging method, high percentages of vehicle uptake can be sustained in existing networks without requiring any further network upgrades, leading to more efficient use of existing assets and savings for the consumer.
The stability and power sharing properties of droop-controlled inverter-based microgrids are adversely affected by model uncertainty, and inverter parameter drifts. Even when frequency stability may ...be guaranteed, power sharing remains sensitive to clock/frequency drifts. A novel coordinating control law is proposed to overcome these issues. It guarantees both stability and power sharing in the presence of parameter uncertainty, including frequency drift. The new control law uses sparse inter-node communications. Conditions to achieve (local) stability with power sharing are presented. It is also shown that our power sharing control is robust to reasonable clock drifts and very small droop coefficients. Simulation and experimental results illustrate the performance of the proposed control law under clock drift scenarios.
There has been an increased interest in cost and energy efficiency for heating, ventilation, and air conditioning systems for buildings since these are responsible for between 25% and 40% of total ...building energy demand. Solar assisted ground source heat pump systems which combine solar and geothermal energy are gaining attention due to their higher efficiency and greater functional diversity when compared with conventional systems. This paper presents a mixed integer linear programming approach to minimize the operational cost of a solar assisted ground source heat pump system, considering time-of-use electricity price (peak, off peak). Two types of system configurations are investigated in order to examine the effect of thermal storage in the system. Two different objectives are explored: minimizing electricity consumption and operational cost. The results indicate that the system having integrated thermal storage leads to improved peak shaving, which reduces the need for expensive peak electricity production for the grid, and has a reduction of operating cost by 7.8% when it is optimized for minimal cost.
•Model Predictive Control is proposed for the intermittent operation of a solar assisted ground source heat pump system.•Time-of-use electricity price is considered to reduce the electricity consumption of the system during peak hours.•The effect of adding a thermal storage on performance of a solar assisted heat pump system is investigated.
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