Junyong Liu

Robust Energy Management of Microgrid With Uncertain Renewable Generation and Load

A scenario-based robust energy management method accounting for the worst-case amount of renewable generation (RG) and load is developed in this paper. The economic and robust model is formulated to maximize the total exchange cost while getting the minimum social benefits cost at the same time. Uncertainty of RG and load is described as an uncertain set produced by interval prediction. Then, the Taguchis orthogonal array (OA) testing method is used to provide possible testing scenarios. A simple, but practical, search strategy based on OA is designed for solving the optimization problem. By optimizing the worst-case scenario, the energy management solution of the proposed model is robust against most of the possible realizations of the modeled uncertain set by Monte Carlo verification. Numerical cases on the typical microgrid system show the effectiveness of the model and solution strategy. In addition, the influence of exchange electricity price and other parameters are also discussed in the cases.

Parallel detrended fluctuation analysis for fast event detection on massive PMU data

Phasor measurement units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronized measurements. The devices high data reporting rates present major computational challenges in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents a novel parallel detrended fluctuation analysis (PDFA) approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain from the aspects of speedup, scalability, and accuracy. The speedup of the PDFA in computation is initially analyzed through Amdahls Law. A revision to the law is then proposed, suggesting enhancements to its capability to analyze the performance gain in computation when parallelizing data intensive applications in a cluster computing environment.

Efficacy comparison of the novel water-soluble propofol prodrug HX0969w and fospropofol in mice and rats

BACKGROUND HX0969w is a novel water-soluble prodrug designed to release propofol and gamma-hydroxybutyrate (GHB) and has a sedative-hypnotic effect. This study was performed to compare the efficacy of HX0969w with fospropofol in mice and rats. METHODS We performed hydrolysis studies in the plasma from mice and rats. The half-maximal effective doses (ED50) and half-maximal lethal doses (LD50) of fospropofol and HX0969w were determined. A pharmacodynamics comparison of these two compounds was also performed. Time to loss of righting reflex, time to return of righting reflex, recovery time, and adverse effects were recorded. RESULTS The hydrolysis studies demonstrated that HX0969w released propofol as expected. HX0969w ED50 values in mice and rats were 133.03 and 53.79 mg kg(-1), respectively, and LD50 values were 607.11 and 283.79 mg kg(-1), respectively. The calculated therapeutic index (TI), safety index (SI), and certain safety factor (CSF) of HX0969w were 4.56, 3.33, and 2.92 for mice, and 5.28, 3.94, and 3.49 for rats, respectively. The pharmacodynamic comparison studies suggest that HX0969w has a longer onset time and shorter duration than fospropofol. CONCLUSIONS Similar to fospropofol, HX0969w is an effective, water-soluble prodrug that is capable of inducing a sedative-hypnotic effect in mice and rats. Unlike fospropofol, HX0969w releases GHB instead of formaldehyde. Further studies regarding the efficacy and safety of HX0969w are necessary.

Big data analytics on PMU measurements

Phasor Measurement Units (PMUs) are being rapidly deployed in power grids due to their high sampling rates. PMUs offer a more current and accurate visibility of the power grids than traditional SCADA systems. However, the high sampling rates of PMUs bring in two major challenges that need to be addressed to fully benefit from these PMU measurements. On one hand, any transient events captured in the PMU measurements can negatively impact the performance of steady state analysis. On the other hand, processing the high volumes of PMU data in a timely manner poses another challenge in computation. This paper presents PDFA, a parallel detrended fluctuation analysis approach for fast detection of transient events on massive PMU measurements utilizing a computer cluster. The performance of PDFA is evaluated from the aspects of speedup, scalability and accuracy in comparison with the standalone DFA approach.

Optimal active distribution network planning:a review

Abstract The active distribution network is a new solution to the flexible utilization of distributed energy resources to suit the characteristics of the distribution network. Advanced “active” network management is to coordinate “generation, network, load” optimization and achieve the right balance between operational expenditure (OPEX) and capital expenditure (CAPEX). To demonstrate the advancement from introducing an active distribution network, the key features of distribution network planning mainly including traditional models is first introduced. Extensive literature in generic planning is then summarized and categorized in terms of objective functions, system modeling, solution algorithms, and tools. This is followed by an extended review and in-depth discussion of concepts and representative topic developments in optimal active distribution network planning. In contrast to traditional planning, it takes into account the effects from a range of active network interventions that are exercised at differing time scales while capturing the intrinsically stochastic behavior of renewable generation or demand response to produce strategic plans that are robust against a highly uncertain energy future. Finally, a multi-dimensional framework for optimal active distribution network planning is proposed to overcome the limitations of the current state of the art, and the challenges in each stage are also highlighted.

An Improved Particle Swarm Optimization for Reconfiguration of Distribution Network

To deal with distribution network reconfiguration, a hierarchical structure poly-particle swarm optimization (HSPPSO) approach using the hierarchical structure concept of control theory is presented. In the bottom layer, parallel optimization calculation is performed on poly-particle swarms. In the top layer, each particle swam in the bottom layer is treated as a particle. The best position found by each particle swarm in the bottom layer is regarded as the best position of single particle of the top layer particle swarm. The result of optimization on the top layer particle swarm is fed back to the bottom layer. Considering the features of distribution network, the probability of producing feasible solutions is improved by modifying the rule of position updating. The test of proposed method on two typical IEEE testing systems shows that HSPPSO performance is better than particle swarm optimization (PSO) both on convergence rate and accuracy.

Pharmacokinetics of intravenous emulsified isoflurane in beagle dogs

BACKGROUND We previously demonstrated that i.v. emulsified isoflurane induces general anaesthesia in animals. In this study, we compared the pharmacokinetics of emulsified isoflurane given as i.v. bolus and as infusion in beagle dogs. METHODS Sixteen beagle dogs were assigned randomly to a bolus group comprising three subgroups and an infusion group. The three bolus subgroups received 120, 150, or 180 mg kg(-1) of isoflurane and the infusion group received isoflurane at 12 mg kg(-1) min(-1) for 150 min. Isoflurane concentrations were determined by gas chromatography. The parameters involved in the pharmacokinetic model were calculated using the DAS ver1.0 software. RESULTS A two-compartment model best described the data in both bolus and infusion groups. The half-lives of distribution [t(1/2α): 1.77 (0.57) min] and elimination [t(1/2β): 17.66 (5.56) min] in the bolus group were shorter than those in the infusion group [14.12 (4.04) min, 58.21 (11.39) min, P<0.01]. The apparent volume of the central compartment [V(1), 0.377 (0.138) litre kg(-1)] in the bolus group was less than that in the infusion group [0.809 (0.077) litre kg(-1), P<0.01]. The total body clearance [Cl, 0.043 (0.032) litre kg(-1) min(-1)] in the bolus group was greater than that in the infusion group [0.028 (0.008) litre kg(-1) min(-1)]. CONCLUSIONS A two-compartment model adequately describes the pharmacokinetics of emulsified isoflurane for both bolus and infusion. The resulting kinetic parameters differ mainly because of the increasing blood/gas partition coefficient and the sustained nature of the isoflurane partial pressure during infusion.

Market evolution modeling for electric vehicles based on system dynamics and multi-agents

The deployment of electrical charging service network and power distribution network depend largely on the precisely electric vehicle prediction. System dynamics and agent based modeling are adopted in this paper to build the system model of the electric vehicle prediction through the systematic analysis of the various factors which affecting the development of electric vehicles. Case simulation is conducted to analysis the evolution of market share of electric vehicles. The key factors which affect the development of EVs in different stages, demonstration promotion, rapid growth and steady development, is evaluated through sensitivity analysis. It is found that the electric vehicles will present the fast growth in next decades, and it is mainly affected by government policies at initial period, then the development of technology and infrastructure construction will become the important factors in future.

Robust Coordinated Optimization of Active and Reactive Power in Active Distribution Systems

Active power dispatch and reactive power optimization problems are usually handled separately in active distribution systems, aiming at minimizing the total generation cost or transmission losses. However, the separate optimization cannot achieve a global optimum scheme in distribution system operations. Moreover, the significant relationship between the active power and reactive power may pose great challenges to distribution system operations due to the uncertain nature of load demands and intermittent renewable energy resources. In this paper, using the branch flow model-based relaxed optimal power flow, we formulate a robust coordinated optimization problem for active and reactive powers as a mixed integer second-order cone (SOC) programming problem. Furthermore, in order to address the uncertainties, a two-stage robust optimization model is proposed to coordinate the on load tap changer ratios, reactive power compensators, and charge–discharge power of energy storage system to find a robust optimal solution. Then the column-and-constraint generation algorithm is applied to solve the proposed robust two-stage optimization model. In the relaxed optimal power flow, a stricter cut is added to speed up the computation process of the SOC relaxation in order to guarantee the exactness for representative cases, such as those with high penetration of distributed energy resources. Numerical results based on the 33-bus and 69-bus systems verify the effectiveness of the proposed method.

Substation cloud computing for secondary auxiliary equipment

This paper introduces the design and development of a cloud computing platform for substation secondary auxiliary equipment functions, the Substation Cloud in short, which including fault detection particularly for neutral indirectly earth systems, wide area fault location, harmonic monitoring and state monitoring of power equipments and so on. In the system, a specially designed interfacing unit at each substation receiving data from merge units is responsible for sending data from the substation to the cloud platform developed through communication network, those data including static, dynamic, transient measurements and states of circuit breakers etc. The cloud platform receives the data from substations, extracts valuable information from and allocates them to various specially designed computation algorithms in the platform to perform advanced functions in order to identify the faulted line, the accurate fault location and the contents of harmonics etc.