Lower partial moment and information entropy-based dynamic electricity purchasing strategy

In the electricity market environment, load-serving entities (LSEs) will inevitably face risks in purchasing electricity because there are a plethora of uncertainties involved. To maximize profits and minimize risks, LSEs need to develop an optimal strategy to reasonably allocate the purchased electricity amount in different electricity markets such as the spot market, bilateral contract market, and options market. Because risks originate from uncertainties, an approach is presented to address the risk evaluation problem by the combined use of the lower partial moment and information entropy (LPME). The lower partial moment is used to measure the amount and probability of the loss, whereas the information entropy is used to represent the uncertainty of the loss. Electricity purchasing is a repeated procedure; therefore, the model presented represents a dynamic strategy. Under the chance-constrained programming framework, the developed optimization model minimizes the risk of the electricity purchasing portfolio in different markets because the actual profit of the LSE concerned is not less than the specified target under a required confidence level. Then, the particle swarm optimization (PSO) algorithm is employed to solve the optimization model. Finally, a sample example is used to illustrate the basic features of the developed model and method.

Long-term exploration and tours for energy constrained robots with online proprioceptive traversability estimation

This paper is concerned with how a localised and energy-constrained robot can maximise its time in the field by taking paths and tours that minimise its energy expenditure. A significant component of a robot's energy is expended on mobility and is a function of terrain traversability. We estimate traversability online from data sensed by the robot as it moves, and use this to generate maps, explore and ultimately converge on minimum energy tours of the environment. We provide results of detailed simulations and parameter studies that show the efficacy of this approach for a robot moving over terrain with unknown traversability as well as a number of a priori unknown hard obstacles.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Desalination with a solar-assisted heat pump : an economic optimization

The solar-assisted heat pump (SAHP) desalination, based on the Rankin cycle, operates in low temperature and utilizes both solar and ambient energy. An experimental SAHP desalination system has been constructed at the National University of Singapore, Singapore. The system consisted of two main sections: an SAHP and a water distillation section. Experiments were carried out under the different meteorological condition of Singapore and results showed that the system had a performance ratio close to 1.3. The heat pump has a coefficient of performance of about 8, with solar collector efficiencies of 80% and 60% for evaporator and liquid collectors, respectively. Economic analysis showed that at a production rate of 900 L/day and an evaporator collector area of around 70m2 will have a payback period of about 3.5 years.

Efficiency optimization for bidirectional IPT system

Compared with unidirectional inductive power transfer (UIPT) systems which are suitable for passive loads, bidirectional IPT (BIPT) systems can be used for active loads with power regenerative capability. There are numerous BIPT systems that have been proposed previously to achieve improved performance. However, typical BIPT systems are controlled through modulation of phase-shift of each converter while keeping the relative phase angle between voltages produced by two converters at ± 90 degrees. This paper presents theoretical analysis to show that there is a unique phase shift for each converter at which the inductive coils losses of the system is minimized for a given load. Simulated results of a BIPT system, compensated by CLCL resonant networks, are presented to demonstrate the applicability of the proposed concept and the validity of the mathematical model.

Clinically-based automatic implant fitting for shape optimization of fracture fixation plates

Complex bone contour and anatomical variations between individual bones complicate the process of deriving an implant shape that fits majority of the population. This thesis proposes an automatic fitting method for anatomically-precontoured plates based on clinical requirements, and investigated if 100% anatomical fit for a group of bone is achievable through manual bending of one plate shape. It was found that, for the plate used, 100% fit is impossible to achieve through manual bending alone. Rather, newly-developed shapes are also required to obtain anatomical fit in areas with more complex bone contour.

Activation of forkhead box O transcription factors by oncogenic BRAF promotes p21cip1-dependent senescence

Oncogene-induced senescence (OIS) is a potent tumor-suppressive mechanism that is thought to come at the cost of aging. The Forkhead box O (FOXO) transcription factors are regulators of life span and tumor suppression. However, whether and how FOXOs function in OIS have been unclear. Here, we show a role for FOXO4 in mediating senescence by the human BRAFV600E oncogene, which arises commonly in melanoma. BRAFV600E signaling through mitogen-activated protein kinase/extracellular signal-regulated kinase kinase resulted in increased reactive oxygen species levels and c-Jun NH 2 terminal kinase-mediated activation of FOXO4 via its phosphorylation on Thr223, Ser226, Thr447, and Thr451. BRAFV600E-induced FOXO4 phosphorylation resulted in p21cip1-mediated cell senescence independent of p16 ink4a or p27kip1. Importantly, melanocyte-specific activation of BRAFV600E in vivo resulted in the formation of skin nevi expressing Thr223/Ser226-phosphorylated FOXO4 and elevated p21cip1. Together, these findings support a model in which FOXOs mediate a trade-off between cancer and aging.

Renewing the Sustainable Energy Curriculum – Providing Internationally Relevant Skills for a Carbon Constrained Economy : Final Report 2014

The aims of the project were to scope and develop sustainable energy curriculum frameworks for Australian higher education Institutions that meet the needs of Australian and international student graduates and employers, both now and into the near future. The focus was on student centred learning and outcomes and to support graduates with the knowledge, skills and generic attributes required to work in the rapidly expanding sustainable energy industry in Australia and globally. The outputs of the project are designed to be relevant to specialist Sustainable Engineering and Energy Studies programs, as well as conventional engineering, science and humanities and social science programs that have a sustainable energy focus or major.

A model predictive approach for community battery energy storage system optimization

This paper presents an efficient algorithm for optimizing the operation of battery storage in a low voltage distribution network with a high penetration of PV generation. A predictive control solution is presented that uses wavelet neural networks to predict the load and PV generation at hourly intervals for twelve hours into the future. The load and generation forecast, and the previous twelve hours of load and generation history, is used to assemble load profile. A diurnal charging profile can be compactly represented by a vector of Fourier coefficients allowing a direct search optimization algorithm to be applied. The optimal profile is updated hourly allowing the state of charge profile to respond to changing forecasts in load.

Lifecycle optimization and tradeoff analysis among competing objectives for a road corridor

While the philosophical motivation behind Civil Infrastructure Management Systems is to achieve optimal level of service at a minimum cost, the allocation of scarce resources among competing alternatives is still a matter of debate. It appears to be widely accepted that results from tradeoff analysis can be measured by the degree of accomplishment of the objectives. Road management systems not only deal with different asset types but also with conflicting objectives. This paper presents a case study of lifecycle optimization with tradeoff analysis for a road corridor in New Brunswick. Objectives of the study included condition of bridge and roads and road safety. A road safety index was created based on potential for improvement. Road condition was based on roughness, rutting and cracking. Initial results show lack of sustainability in bridge performance. Therefore, bridges where broken by components: deck, superstructure and substructure. Visual inspections, in addition to construction age of each bridge, were combined to generate a surrogate apparent age. Two life cycle analysis were conducted; one aimed to minimize overall cost while achieving sustainable results and another one purely for optimization. -used to identify required levels of budget. Such analyses were used to identify the minimum required budget and to demonstrate that with the same amount of money it was possible to achieve better levels of performance. Dominance and performance driven criteria were combined to identify and select an optimal result. It was found that achievement of optimally sustained results is conditioned by the availability of treatments for all asset classes at across their life spans. For the case study a disaggregated bridge condition index was introduced to the original algorithm to attempt to achieve sustainability in all bridges components, however lack of early stage treatments for substructures produce declining trends for such a component.

An efficiency optimization scheme for bidirectional inductive power transfer systems

Unidirectional inductive power transfer (UIPT) systems allow loads to consume power while bidirectional IPT (BIPT) systems are more suitable for loads requiring two way power flow such as vehicle to grid (V2G) applications with electric vehicles (EVs). Many attempts have been made to improve the performance of BIPT systems. In a typical BIPT system, the output power is control using the pickup converter phase shift angle (PSA) while the primary converter regulates the input current. This paper proposes an optimized phase shift modulation strategy to minimize the coil losses of a series – series (SS) compensated BIPT system. In addition, a comprehensive study on the impact of power converters on the overall efficiency of the system is also presented. A closed loop controller is proposed to optimize the overall efficiency of the BIPT system. Theoretical results are presented in comparison to both simulations and measurements of a 0.5 kW prototype to show the benefits of the proposed concept. Results convincingly demonstrate the applicability of the proposed system offering high efficiency over a wide range of output power.