John K. Ward

Adaptive learning algorithms for vibration energy harvesting

By scavenging energy from their local environment, portable electronic devices such as MEMS devices, mobile phones, radios and wireless sensors can achieve greater run times with potentially lower weight. Vibration energy harvesting is one such approach where energy from parasitic vibrations can be converted into electrical energy through the use of piezoelectric and electromagnetic transducers. Parasitic vibrations come from a range of sources such as human movement, wind, seismic forces and traffic. Existing approaches to vibration energy harvesting typically utilize a rectifier circuit, which is tuned to the resonant frequency of the harvesting structure and the dominant frequency of vibration. We have developed a novel approach to vibration energy harvesting, including adaptation to non-periodic vibrations so as to extract the maximum amount of vibration energy available. Experimental results of an experimental apparatus using an off-the-shelf transducer (i.e.xa0speaker coil) show mechanical vibration to electrical energy conversion efficiencies of 27–34%.

Model-based feedback control of distributed air-conditioning loads for fast demand-side ancillary services

Load control (LC) of distributed populations of air conditioners (ACs) can provide effective demand-side ancillary services while reducing emissions and network operating costs. Pilot trials with ACs typically deploy model-free, open-loop strategies, which cannot deliver the full potential of LC as a network resource. Seeking more advanced strategies, much research in recent years has targeted the development of accurate models and LC approaches for this type of loads. Most existing approaches, however, are restricted to scenarios involving large numbers of ACs, which may not work in small populations, or require two-way communications with the controlled devices, which may come at high costs in widely distributed populations. This paper exploits a previously developed dynamic model for the aggregate demand of populations of ACs to design a simple controller readily implementable in such LC scenarios. The proposed feedback scheme broadcasts thermostat set-point offset changes to the ACs, and requires no direct communications from the devices to the central controller, using instead readings of total aggregate demand from a common power distribution connection point, which may include demand of uncontrolled loads. The scheme is validated on a numerical case study constructed by simulating a distributed population of ACs using real power and temperature data from a 70-house residential precinct, and is shown to deliver robust fast load following performance. The simulation results highlight the practical potential of the proposed model and feedback control scheme for analysing and shaping demand response of ACs using standard control techniques.

Model-Based Estimation of Energy Savings in Load Control Events for Thermostatically Controlled Loads

Load control (LC) of populations of air conditioners (ACs) is considered suitable to shift energy from on- to off-peak times, and track the intermittent power output of renewable generation. From a technical and economical point of view, it is paramount to quantify the amount of energy that can be saved by implementing these LC events. This paper proposes a new causal methodology to estimate such energy savings using a Kalman filter that includes a parametric second-order model of the aggregate demand of a population of ACs. The proposed methodology relies only on readings of aggregate electrical power at the feeder level and does not require historical load data, or a control group, and hence, it can be used where other methods reported in the literature are inapplicable. The proposed estimator is evaluated on a numerical case study that embeds simulated ACs in real power and temperature data from a 70-house residential precinct.

The Tiny Agent - Wireless Sensor Networks Controlling Energy Resources

CSIRO is using wireless sensor network technology to deploy “tiny agents”, working as autonomous controllers for individual pieces of electrical load/generation equipment in a distributed energy system. The tiny agent concept is a novel application of wireless sensor networks, providing the benefits of multi-agent systems science in a cheap, mobile, and highly distributable platform. However, the performance constraints inherent to wireless sensor networks mean the real-world realization of a tiny agent system is a significant challenge. This article details our work on tiny agents. We include a brief review of multiagent system benefits, and then discuss the challenges inherent to the tiny agent concept. We also detail our applications work in applying wireless sensor network technology to operate as tiny agents, with a focus on intelligent heating, ventilation and air- conditioning control.

Sizing and grid impact of PV battery systems - a comparative analysis for Australia and Germany

As the business case for home-scale PV battery systems emerges in Australia and Germany, the impact of different pricing schemes and grid integration approaches on the sizing and operation of such systems and on distribution grids has to be evaluated. This paper proposes an integrated approach which first derives optimally configured PV and battery systems using a mixed integer linear program and subsequently assesses their impact on grid planning aspects such as peak feed-in and peak load for an Australian and a German case study. The results show that small scale PV battery systems will be an economically viable option in the near future in Australia. A reduction in peak load is achievable in the presented example as well. In Germany, stand-alone PV systems will provide the economically more favorable option under current battery prices. For lower battery prices a high increase in installation rates can be expected. Depending on the control strategy, battery systems are able to significantly reduce PV peaks in Germany. For distribution grid operators estimations on the average peak reduction per installed PV battery are presented.

Reducing energy use and operational cost of air conditioning systems with multi-objective evolutionary algorithms

Air conditioning is responsible for around 60% of energy use in commercial buildings and is rapidly increasing in the residential sector. Although each system is individually small, the proliferation of air conditioning and the correlation of energy use with temperature is driving peak demand and the need for electricity distribution network upgrades. Energy retailers are now looking for ways to reduce this aggregate peak demand, leading to a tradeoff between peak demand, energy cost and the thermal comfort of building occupants. This paper presents a multi-objective evolutionary algorithm (MOEA) to quantify trade-offs amongst these three competing goals. We study a scenario with 8 air conditioners (ACs) and compare our findings against the case of having all ACs working independently, irrespective of global goals. The results show that, with statistically significant certainty, any run of the MOEA outperforms any scenario where the ACs function independently to keep a given level of comfort on a typical hot day.

A stability vulnerability in the interaction between Volt-VAR and Volt-Watt response functions for smart inverters

The strong uptake of PV systems, both within Australia and internationally, and particularly for small-scale systems within residential distribution networks, has raised concerns over potential impacts such as over-voltages. Responding to these potential issues, distribution network operators are beginning to impose restrictions on PV installations, including limiting system size, ramp rates, and exporting and managing reactive power. Additionally, inverter standards (such as AS/NZS 4777) are being updated with revised power quality functionality such as Volt-VAR and Volt-Watt control functions. This paper considers a specific case of the interaction between Volt-VAR and Volt-Watt inverter functions and demonstrates, both analytically and via a simulation example, that this interaction can lead to voltage instability if not adequately designed.

An analytical characterisation of cold-load pickup oscillations in thermostatically controlled loads

Large groups of thermostatically controlled loads can be controlled to achieve the necessary balance between generation and demand in power networks. When a significant portion of a population of thermostatically controlled loads is forced to change their on-off state simultaneously, the aggregate power demand of such population presents large, underdamped oscillations, a well-known phenomenon referred to by power utilities as “cold-load pickup”. Characterising these oscillations and, in general, the aggregate dynamics of the population facilitates mathematical analysis and control design. In this paper we present a stochastic model for the power response and derive simple expressions for the period and envelope of the oscillations.

Environmentally active buildings: the controls challenge

The widespread deployment of air-conditioning systems has added significant flexibility to building design and form. This flexibility has not, however, been without its costs. In Australia, commercial buildings account for over 25% of national electricity use and the associated greenhouse gas emissions. Naturally ventilated and mixed mode buildings, controlled for adaptive thermal comfort and integrated with renewable energy generation, provide a pathway towards net zero-energy buildings. This vision is not without complexity – requiring occupant comfort, building dynamics and weather and usage patterns to be integrated into an appropriate control strategy. In this article we outline approaches and factors that challenge the controls design of such buildings, including some of our experiences with commercial buildings in Australia. We suggest that substantial emissions reductions can only be achieved through closely coupling controls to local climate and adaptive occupant comfort – not just responding to conditions alone, but implementing adaptive, predictive and ‘occupant aware’ controls towards achieving a low-energy future.

Irradiance forecasting for the photovoltaic systems

Global warming has emerged as a key environmental issue, with one of the most exciting approaches to greenhouse gas reductions being the use renewable energy. Photovoltaic (PV) generation of electricity is an important renewable energy source, especially at small scale, such as in homes. To increase the value of small-scale renewable generators, one efficient way is to aggregate and control their output and group them in zones. One challenge of doing this is the precise forecasting of each PV output. This paper introduces our developed irradiance forecasting algorithm based on the data from a sky camera. A SVM regression technique is used to generate regress curves between future irradiance and other available information. A series of experimental results are presented to evaluate and demonstrate our forecasting accuracy.