Value mapping for sustainable business thinking

Pressures on business to operate sustainably are increasing. This requires companies to adopt a systemic approach that seeks to integrate consideration of the three dimensions of sustainability – social, environmental, and economic – in a manner that generates shared value creation for all stakeholders including the environment and society. This is referred to as sustainable business thinking. The business model concept offers a framework for system-level innovation for sustainability and provides the conceptual linkage with the activities of the firm such as design, production, supply chains, partnerships, and distribution channels. A value mapping tool has been presented in the literature to assist in sustainable business model innovation. This study explores the use of value mapping for broader sustainable business thinking, by reflection on its use in workshop settings. A range of new applications is identified which is expected to be of interest to business practitioners, policy makers, and academic researchers.This work was supported by Sustain Value, a European Commission’s 7th Framework Program [FP7/2007–2013]; and the EPSRC Center for Innovative Manufacturing in Industrial Sustainability [RG64858].This is the final published version. It first appeared at http://www.tandfonline.com/doi/full/10.1080/21681015.2014.1000399#.VWSB4S6fbe4

An Adaptive-Then-Combine Dynamic State Estimation Considering Renewable Generations in Smart Grids

© 1983-2012 IEEE. The penetration of renewable distributed energy resources, such as wind turbine, has been dramatically increased in distribution networks. Due to the intermittent property, the wind power generation patterns vary, which may risk distribution network operations. So, it is intrinsically necessary to monitor wind turbines in a distributed way. This paper presents an adaptive-Then-combine distributed dynamic approach for monitoring the grid under lossy communication links between the wind turbines and energy management system. First, the wind turbine is represented by a state-space linear model, with sensors deployed to obtain the system state information. Based on the mean squared error principle, an adaptive approach is proposed to estimate the local state information. The global estimation is designed by combining estimation results with weighting factors which are calculated by minimizing the estimation error covariance based on semidefinite programming. Finally, the convergence analysis indicates that the estimation error is gradually decreased, so the estimated state converges to the actual state. The efficacy of the developed approach is verified using the wind turbine and the IEEE 6-bus distribution system

Distributed State Estimation over Unreliable Communication Networks with an Application to Smart Grids

© 2017 IEEE. In contrast to the traditional centralized power system state estimation methods, this paper investigates the interconnected optimal filtering problem for distributed dynamic state estimation considering packet losses. Specifically, the power system incorporating microgrids is modeled as a state-space linear equation where sensors are deployed to obtain measurements. Basically, the sensing information is transmitted to the energy management system through a lossy communication network where measurements are lost. This can seriously deteriorate the system monitoring performance and even lose network stability. Second, as the system states are unavailable, so the estimation is essential to know the overall operating conditions of the electricity network. Availability of the system states provides designers with an accurate picture of the power network, so a suitable control strategy can be applied to avoid massive blackouts due to losing network stability. Particularly, the proposed estimator is based on the mean squared error between the actual state and its estimate. To obtain the distributed estimation, the optimal local and neighboring gains are computed to reach a consensus estimation after exchanging their information with the neighboring estimators. Then, the convergence of the developed algorithm is theoretically proved. Afterward, a distributed controller is designed based on the semidefinite programming approach. Simulation results demonstrate the accuracy of the developed approaches under the condition of missing measurements

Distributed condition monitoring of renewable microgrids using adaptive-then-combine algorithm

© 2016 IEEE. This paper explores the problem of distributed state estimation including packet losses for the environment-friendly renewable microgrid incorporating electricity generating circuits. The problem is becoming critical due to the global warming, increasing green house gas emissions, and practical infeasibility with computational burden of the large-scale centralized power system monitoring. To address the impending problem, a novel distributed microgrid state estimation algorithm is derived in the context of microgrids. Specifically, after modelling the microgrid, this paper proposes a local microgrid state estimation algorithm considering packet losses. Then a novel optimal weighting factor calculation method for the global state estimation is proposed. Particularly, it can automatically adjust the optimal weighting factors for different sensor measurements based on the observation quality, improving the estimation accuracy of the global estimation. Simulations show that the desired state estimation accuracy is achievable

Cyber attack protection and control in microgrids using channel code and semidefinite programming

© 2016 IEEE. The smart grid has been considered as a nextgeneration power system to modernize the traditional grid to improve its security, connectivity and sustainability. Unfortunately, the grid is susceptible to malicious cyber attacks, which can create serious technical, economical and control problems in power network operations. In contrast to the traditional cyber attack minimization techniques, this paper proposes a recursive systematic convolutional (RSC) code and Kalman filter based method in the context of microgrids. Specifically, the proposed RSC code is used to add redundancy in the microgrid states, and the log maximum a posterior is used to recover the state information which is affected by random noises and cyber attacks. Once the estimated states are obtained, a semidefinite programming based optimal feedback controller is proposed to regulate the system states. Test results show that the proposed approach can accurately mitigate the cyber attacks and properly estimate and control the system states

Microgrid state estimation and control using Kalman filter and semidefinite programming technique

The design of environment-friendly microgrids at the smart distribution level requires a stable behaviour for multiple state operations. This paper develops a Kalman filter based optimal feedback control method for the microgrid state estimation and stabilization. First, the microgrid is modelled by a discrete-time state space equation. Then the cost-effective smart sensors are deployed in order to obtain the required system information. From the communication point of view, the recursive systematic convolution code is adopted to add the redundancy in the system. At the end, the soft output Viterbi decoder is used to recover the system information from the noisy measurements and transmission uncertainties. Thereafter, the Kalman filter is utilized to estimate the system states, which acts as a precursor for applying the control algorithm. Finally, this paper proposes an optimal feedback control method to stabilize the microgrid based on semidefinite programming. The performance of the proposed approach is demonstrated by extensive numerical simulations

Microgrid protection and control through reliable smart grid communication systems

© 2016 IEEE. Due to dramatically rising energy demand worldwide power system is often run near the operational and technical limits, where unexpected trivial disturbances can cause possibly massive blackouts. Cyber attacks on smart grid communication networks are one of the impending threats to cause large-scale cascading outage. In contrast to the traditional cyber attack protection techniques, this paper presents a recursive systematic convolutional code based defending technique from the signal processing perspective. This code introduces redundancy in the system for protecting the grid information. Furthermore, an optimal control law is designed to stabilize the power network. Specifically, the performance index for control is converted to a convex semidefinite programming problem. The proposed controller can work well for any initial values. The efficacy of the developed approach is verified through numerical simulations. Results show that the proposed strategy has stronger attack protection performance and the controller can stabilize the grid in a fairly short time. This approach provides a fundamental framework for the design of the smart grid energy management system and reliable communication infrastructure scheme with renewable integration applications

Modelling the Interconnected Synchronous Generators and its State Estimations

© 2018 IEEE. In contrast to the traditional centralized power system state estimation approaches, this paper investigates the optimal filtering problem for distributed dynamic systems. Particularly, the interconnected synchronous generators are modeled as a state-space linear equation where sensors are deployed to obtain measurements. As the synchronous generator states are unknown, the estimation is required to know the operating conditions of large-scale power networks. Availability of the system states gives the designer an accurate picture of power networks to avoid blackouts. Basically, the proposed algorithm is based on the minimization of the mean squared estimation error, and the optimal gain is determined by exchanging information with their neighboring estimators. Afterward, the convergence of the developed algorithm is proved so that it can be applied to real-time applications in modern smart grids. Simulation results demonstrate the efficacy of the developed algorithm

A specific case in the classification of woods by FTIR and chemometric: discrimination of Fagales from Malpighiales

Fourier transform infrared (FTIR) spectroscopic data was used to classify wood samples from nine species within the Fagales and Malpighiales using a range of multivariate statistical methods. Taxonomic classification of the family Fagaceae and Betulaceae from Angiosperm Phylogenetic System Classification (APG II System) was successfully performed using supervised pattern recognition techniques. A methodology for wood sample discrimination was developed using both sapwood and heartwood samples. Ten and eight biomarkers emerged from the dataset to discriminate order and family, respectively. In the species studied FTIR in combination with multivariate analysis highlighted significant chemical differences in hemicelluloses, cellulose and guaiacyl (lignin) and shows promise as a suitable approach for wood sample classification

Human Tyrosine Hydroxylase Natural Allelic Variation: Influence on Autonomic Function and Hypertension

The catecholamine biosynthetic pathway consists of several enzymatic steps in series, beginning with the amino acids phenylalanine and tyrosine, and eventuating in the catecholamines norepinephrine (noradrenaline) and epinephrine (adrenaline). Since the enzyme tyrosine hydroxylase (TH; tyrosine 3-mono-oxygenase; EC 1.14.16.2; chromosome 11p15.5) is generally considered to be rate-limiting in this pathway, probed as to whether common genetic variation at the TH gene occurred, and whether such variants contributed to inter-individual alterations in autonomic function, either biochemical or physiological. We began with sequencing a tetranucleotide (TCAT) repeat in the first intron, and found that the two most common versions, (TCAT)6 and (TCAT)10i, predicted heritable autonomic traits in twin pairs. We then conducted systematic polymorphism discovery across the ~8 kbp locus, and discovered numerous variants, principally non-coding. The proximal promoter block contained four common variants, and its haplotypes and SNPs (especially C-824T, rs10770141) predicted catecholamine secretion, environmental stress-induced BP increments, and hypertension. Finally, we found that two of the common promoter variants, C-824T (rs10770141) and A-581G (rs10770140), were functional in that they differentially affected transcriptional activity of the isolated promoter, disrupted recognition motifs for specific transcription factor binding, altered the promoter responses to the co-transfected (exogenous) factors, and bound the endogenous factors in the chromatin fraction of the nucleus. We concluded that common variation in the proximal TH promoter is functional, giving rise to changes in autonomic function and consequently cardiovascular risk