Showing papers by "Lin Jiang published in 2018"

Distributionally Robust Chance-Constrained Approximate AC-OPF With Wasserstein Metric

Abstract: Chance constrained optimal power flow (OPF) has been recognized as a promising framework to manage the risk from variable renewable energy (VRE). In the presence of VRE uncertainties, this paper discusses a distributionally robust chance constrained approximate ac-OPF. The power flow model employed in the proposed OPF formulation combines an exact ac power flow model at the nominal operation point and an approximate linear power flow model to reflect the system response under uncertainties. The ambiguity set employed in the distributionally robust formulation is the Wasserstein ball centered at the empirical distribution. The proposed OPF model minimizes the expectation of the quadratic cost function w.r.t. the worst-case probability distribution and guarantees the chance constraints satisfied for any distribution in the ambiguity set. The whole method is data-driven in the sense that the ambiguity set is constructed from historical data without any presumption on the type of the probability distribution, and more data leads to smaller ambiguity set and less conservative strategy. Moreover, special problem structures of the proposed problem formulation are exploited to develop an efficient and scalable solution approach. Case studies are carried out on the IEEE 14 and 118 bus systems to show the accuracy and necessity of the approximate ac model and the attractive features of the distributionally robust optimization approach compared with other methods to deal with uncertainties.

Minimum-Backflow-Power Scheme of DAB-Based Solid-State Transformer With Extended-Phase-Shift Control

Abstract: As key component for the flexible dc distributed power system, the dual active bridge (DAB)-converter-based solid-state transformer (SST) with high efficiency for a wide operating range is essential. However, with the traditional phase-shift control, high backflow power and current stress will significantly affect the conversion efficiency. In this paper, the backflow power characteristics in both sides of DAB-based SST converters are comprehensively analyzed. On this basis, complete transmission power, backflow power, and peak current mathematical models are established. Then, a minimum-backflow-power-based extended-phase-shift control strategy is proposed with the determination of optimal phase-shift pairs by using the Karush–Kuhn–Tucker function for various scenarios. The backflow power and current stress curves with different algorithms are compared. It shows the proposed control can improve the output power regulation flexibility, minimize the backflow power, and improve the efficiency in wide operating range. Finally, a DAB-based SST prototype was developed and the experimental results verified the effectiveness of the proposed control strategy.

Reactive Power Minimization in Bidirectional DC–DC Converters Using a Unified-Phasor-Based Particle Swarm Optimization

Abstract: This paper deals with an optimized three-level modulated phase shift control using particle swarm optimization (PSO) strategy based on the unified phasor analysis with an aim to improve the efficiency of the bidirectional dual active bridge (DAB) converter for the whole operation range. A unified mathematical model based on Fourier transform is built for the DAB converter. All possible operation states under the three-level modulated phase shift control are covered. Accurate complex mathematical expressions for the inductor current, the transmission power, and the reactive power are obtained. Both modulus and angle variables are illustrated with respect to the inner and outer phase shift angle with the phasor diagram. The proposed method is able to achieve the minimum reactive power under three-level modulated phase shift control by obtaining the optimal phase-shift angles directly. The cumbersome process of the optimal operation mode selection for different voltage conversion ratio and load conditions in conventional methods is overcome successfully, thus greatly simplifying the theoretical calculation and implementation difficulty. Simulation and experimental results in terms of the reactive power, soft-switching range, and efficiency are provided to verify the practical feasibility of the proposed method for the bidirectional DAB converters.

Modified Beta Algorithm for GMPPT and Partial Shading Detection in Photovoltaic Systems

Abstract: When the photovoltaic (PV) string is under the partial shading condition (PSC), the conventional maximum power point tracking (MPPT) techniques may fail to track the global maximum power point (GMPP). Although some global MPPT (GMPPT) techniques have been proposed, they may overlook the GMPP and fail to detect the PSC occurrence. Therefore, a novel GMPPT technique is proposed in this paper by modifying the conventional beta method. The proposed technique is more accurate than the previous techniques since it can guarantee that all the peaks in the $\beta$ range and never overlook the GMPP. Furthermore, the proposed technique can inherently detect the PSC occurrence without setting any additional threshold parameters or periodical interruption, which is simpler and more effective. In order to verify the advantages of the proposed technique, a prototype with buck-boost converter was constructed. For a fair comparison, two popular GMPPT techniques were also implemented and tested in the same prototype under various scenarios. The performance improvement with the proposed technique for different PSCs has been validated by both simulation and experimental results.

Data-Driven Affinely Adjustable Distributionally Robust Unit Commitment

Abstract: This paper proposes a data-driven affinely adjustable distributionally robust method for unit commitment considering uncertain load and renewable generation forecasting errors. The proposed formulation minimizes expected total operation costs, including the costs of generation, reserve, wind curtailment, and load shedding, while guaranteeing the system security. Without any presumption about the probability distribution of the uncertainties, the proposed method constructs an ambiguity set of distributions using historical data and immunizes the operation strategies against the worst case distribution in the ambiguity set. The more historical data is available, the smaller the ambiguity set is and the less conservative the solution is. The formulation is finally cast into a mixed integer linear programming whose scale remains unchanged as the amount of historical data increases. Numerical results and Monte Carlo simulations on the 118- and 1888-bus systems demonstrate the favorable features of the proposed method.

Energy Consumption Scheduling of HVAC Considering Weather Forecast Error Through the Distributionally Robust Approach

Abstract: In this paper, the distributionally robust optimization approach (DROA) is proposed to schedule the energy consumption of the heating, ventilation and air conditioning (HVAC) system with consideration of the weather forecast error. The maximum interval of the outdoor temperature is partitioned into subintervals, and the proposed DROA constructs the ambiguity set of the probability distribution of the outdoor temperature based on the probabilistic information of these subintervals of historical weather data. The actual energy consumption will be adjusted according to the forecast error and the scheduled consumption in real time. The energy consumption scheduling of HVAC through the proposed DROA is formulated as a nonlinear problem with distributionally robust chance constraints. These constraints are reformulated to be linear and then the problem is solved via linear programming. Compared with the method that takes into account the weather forecast error based on the mean and the variance of historical data, simulation results demonstrate that the proposed DROA effectively reduces the electricity cost with less computation time, and the electricity cost is reduced compared with the traditional robust method.

Blind Density-Peak-Based Modulation Format Identification for Elastic Optical Networks

Abstract: Optical modulation format identification is critical in the next generation of heterogeneous and reconfigurable optical networks. Here, we present a blind modulation format identification method by applying fast density-peak-based pattern recognition in the autonomous receiver of elastic optical networks. In this paper, we find that the different modulation format types show different energy level features which can be used as a metric to identify these modulation formats in two-dimensional Stokes plane. The proposed method does not require training symbols, and is insensitive to carrier phase noise, frequency offset as well as polarization mixing. The effectiveness is verified via numerical simulations and experiments with PDM-BPSK, PDM-QPSK, PDM-8PSK, PDM-16PSK, PDM-8QAM, and PDM-16QAM. The results show that high identification accuracy can be realized using our proposed method over wide optical signal-to-noise ratio ranges. Meanwhile, we also discuss the influence of the residual chromatic dispersion, polarization mode dispersion, and polarization dependent loss impairments to our proposed method. We believe that the simple and flexible identification method would certainly bring a great convenience to the future optical networks.

A Robust Optimization Approach for Demand Side Scheduling Considering Uncertainty of Manually Operated Appliances

Abstract: Manually operated appliances (MOAs) are manually operated based on users’ real-time demands and their energy consumption is uncertain to other schedulable appliances (SAs). This paper represents energy consumption scheduling of home appliances under the uncertainty of the MOAs as a robust optimization problem, as uncertainty distribution of MOAs is usually unknown and not easily estimated. Among all possible energy consumption cases of the MOAs, the robust approach takes into account the worst case to reduce electricity payment of all home appliances, based on the real-time electricity pricing scheme combined with inclining block rate. Intergeneration projection evolutionary algorithm, which is a nested heuristic algorithm with inner genetic algorithm and outer particle swarm optimization algorithm, is adopted to solve the robust optimization problem. Case studies are based on one day case, and one month case with various combinations of SAs and MOAs. Simulation results illustrate the effectiveness of the proposed approach in reduction of electricity payment compared with the approach without considering the uncertainty of MOAs, and the approach considering MOAs with fixed pattern.

ADMM-Based Multiperiod Optimal Power Flow Considering Plug-In Electric Vehicles Charging

Abstract: When plug-in electric vehicles (PEVs) participate in grid operation, the intertemporal feature of PEVs charging transforms the traditional optimal power flow (OPF) problem into multiperiod OPF (MOPF) problem. In the case that the population of PEVs is huge, the large number of variables and constraints renders the centralized solution technique unsuitable to solve the MOPF problem. Therefore, a distributed algorithm based on alternating direction method of multipliers is developed to decompose the MOPF into two update steps that are solved in an alternating and iterative style. To improve the solution efficiency, the second update step is transformed into a Euclidean projection problem by approximating the original objective with a surrogate function. Then, a projection algorithm is utilized to solve the approximate problem. Numerical results show that this reformulated model obtains suboptimal solutions with small relative error, but gains considerable speed-up. Furthermore, its scalability and effectiveness are tested in the 119-bus and 906-bus distribution networks.

Data-Driven Distributionally Robust Energy-Reserve-Storage Dispatch

Abstract: This paper proposes distributionally robust energy-reserve-storage co-dispatch model and method to facilitate the integration of variable and uncertain renewable energy. The uncertainties of renewable generation forecasting errors are characterized through an ambiguity set, which is a set of probability distributions consistent with observed historical data. The proposed model minimizes the expected operation costs corresponding to the worst case distribution in the ambiguity set. Distributionally robust chance constraints are employed to guarantee reserve and transmission adequacy. The more historical data are available, the smaller the ambiguity set is and the less conservative the solution is. The formulation is finally cast into a mixed integer linear programming whose scale remains unchanged as the number of historical data increases. Inactive constraint identification and convex relaxation techniques are introduced to reduce the computational burden. Numerical results and Monte Carlo simulations on IEEE 118-bus systems demonstrate the effectiveness and efficiency of the proposed method.

Data-driven Affinely Adjustable Distributionally Robust Unit Commitment

Abstract: This paper proposes a data-driven affinely adjustable distributionally robust method for unit commitment considering uncertain load and renewable generation forecasting errors. The proposed formulation minimizes expected total operation costs, including the costs of generation, reserve, wind curtailment and load shedding, while guarantees the system security. Without any presumption about the probability distribution of the uncertainties, the proposed method constructs an ambiguity set of distributions using historical data and immunizes the operation strategies against the worst-case distribution in the ambiguity set. The more historical data is available, the smaller the ambiguity set is and the less conservative the solution is. The formulation is finally cast into a mixed integer linear programming whose scale remains unchanged as the amount of historical data increases. Numerical results and Monte Carlo simulations on the 118- and 1888-bus systems demonstrate the favorable features of the proposed method.

Passivity-based linear feedback control of permanent magnetic synchronous generator-based wind energy conversion system: design and analysis

Abstract: This study designs a passivity-based linear feedback control scheme of a permanent magnetic synchronous generator-based wind energy conversion system, which attempts to achieve a maximum power point tracking (MPPT) at generator-side voltage source converter (VSC) and enhance fault ride-through (FRT) capability at grid-side VSC simultaneously. A storage function is constructed based on the passivity theory, in which the actual role of each term is meticulously investigated while the beneficial ones are remained so as to significantly improve the transient responses. Then, an auxiliary input is employed in the form of linear feedback control to ensure a desired tracking error convergence. Moreover, the closed-loop system stability is thoroughly analysed, together with a detailed physical interpretation of the storage function. Three case studies are undertaken including step change of wind speed, stochastic wind speed variation, and FRT. Simulation results verify that the proposed approach can effectively achieve MPPT and dramatically improve the FRT capability under various operation conditions against that of vector control and feedback linearisation control.

Perturbation compensation-based non-linear adaptive control of ESS-DVR for the LVRT capability improvement of wind farms

Abstract: This study presents a non-linear adaptive control (NAC) for the energy storage system (ESS) embedded dynamic voltage restorer (DVR) in enhancing the low-voltage ride through (LVRT) capability of wind farms. The proposed NAC features a perturbation observer to estimate and then compensate the real perturbation of the whole system, including parameter uncertainties, measurement noise, and external disturbances such as different grid faults and intermittent wind power. It can achieve an adaptive and robust control without requiring accurate system model and full-state measurements. This control is then applied to the ESS embedded DVR (ESS-DVR) system, in which the ESS can store the blocked wind power for potential power fluctuation suppression. Simulation studies have verified that the proposed control for ESS-DVR can effectively enhance the LVRT capability of wind farms installed with different types of WTGs under different operating conditions. Moreover, its superiority has also been demonstrated by comparing with fixed gains-based conventional vector control and accurate system model-based feedback linearising control.

Interactive teaching–learning optimiser for parameter tuning of VSC-HVDC systems with offshore wind farm integration

Abstract: This study proposes a novel interactive teaching–learning optimiser (ITLO) for voltage source converter based high voltage direct current (VSC-HVDC) systems with offshore wind farm integration. Conventional vector control strategy is adopted, in which the parameters of eight proportional–integral loops are optimally tuned to achieve a reliable and satisfactory control performance under various operation conditions. Multiple classes are employed to realise a wider exploration compared with that of original teaching–learning based optimisation. Moreover, a small world network is incorporated for an interactive learning among the teachers or students from different classes, such that a deeper exploitation can be resulted in. Hence, ITLO can effectively avoid a local optimum due to the appropriate trade-off between explorations and exploitations. Three case studies are carried out, such as active and reactive power tracking, short-circuit fault at power grid, and offshore wind farm connection. Simulation results verify the effectiveness and advantages of ITLO over that of existing meta-heuristic optimisation algorithms.

Huanglongmycin A-C, Cytotoxic Polyketides Biosynthesized by a Putative Type II Polyketide Synthase From Streptomyces sp. CB09001.

Abstract: Three natural products of nonaketide biosynthetic origin, probably biosynthesized from nine molecules of malonyl-CoA, have been isolated. Herein we described the isolation and structure elucidation of huanglongmycin (HLM) A-C and identification of the putative hlm biosynthetic gene cluster from Streptomyces sp. CB09001, isolated from a karstic cave in Xiangxi, China. Albeit previously isolated, HLM A was reported for the first time to exhibit moderate cytotoxicity against A549 lung cancer cell line (IC50 = 13.8 ± 1.5 μM) and weak antibacterial activity against gram-negative clinical isolates. A putative biosynthetic pathway for HLM A, featuring a nonaketide-specific type II polyketide synthase, was proposed. It would be consistent with the isolation of HLM B and C, which are two new natural products and likely shunt metabolites during HLM A biosynthesis.

A Fast Sensitivity-Based Preventive Control Selection Method for Online Voltage Stability Assessment

Abstract: Modern power system suffers voltage instability more frequently due to the increasing load. Sensitivity analysis based preventive control is widely recognized as an effective method for preventing voltage instability. However, the well-known load margin based sensitivity methods are not suitable for real-time application since they must solve the left eigenvector of zero eigenvalue of the Jacobi matrix at the critical point and require the system-wide information. Therefore, this paper proposes a fast sensitivity-based method for selecting the most effective preventive controls. The sensitivity analysis uses a local voltage stability index, i.e., load impedance modulus margin, which is derived from the local measurements (nodal voltage and current), and the sensitivity ranking can be obtained more accurately and faster than traditional methods based on load margin. Such computational advantages make it suitable for online application. The effectiveness of the proposed method is successfully validated by both small and large-scale power systems.

Chromatic Dispersion, Nonlinear Parameter, and Modulation Format Monitoring Based on Godard's Error for Coherent Optical Transmission Systems

Abstract: This paper considers Godard's error as signal quality metric to monitor chromatic dispersion (CD), nonlinear parameter and modulation format in the DSP module of the coherent receivers. We first review a CD monitoring based on Godard's error that can be able to accurately monitor arbitrarily large dispersion values in uncompensated transmission links in combination with frequency domain equalizer, then extend the previous nonlinear parameter monitoring method based on Godard's error by blindly obtaining the optimized value γξp to significantly improve the adaptive capability, and present a simple and effective modulation format monitoring based on Godard's error. Meanwhile, the effectiveness has been experimentally verified in 128-Gb/s PDM-QPSK, 192-Gb/s PDM-8QAM, and 256-Gb/s PDM-16QAM systems.

Design and implementation of perturbation observer-based robust passivity-based control for VSC-MTDC systems considering offshore wind power integration

Abstract: Voltage source converter-based multi-terminal high-voltage direct current (VSC-MTDC) systems are starting to be commissioned. However, concentrated integration of large-scale wind power demands stronger robustness against power fluctuation and system disturbances to increase the reliability of the whole system. This study proposes a perturbation observer-based robust passivity-based control (PORPC) for VSC-MTDC systems connected to an offshore wind farm to meet the demands. The aggregated effect of system nonlinearities, parameter uncertainties, unmodelled dynamics and external disturbances includes grid faults and time-varying wind power output is estimated by a linear perturbation observer and fully compensated by a passive controller, thus no accurate VSC-MTDC system model is required. PORPC attempts to regulate DC voltage and reactive power at the rectifier side, as well as active power and reactive power at the inverters side connected to an offshore wind farm. Besides, a DC-link voltage droop controller is introduced so as to provide immediate response to the grid unbalance situation. Moreover, a noticeable robustness against parameter uncertainties can be achieved as no accurate system model is needed. Case studies are carried out to compare the performance of PORPC to other typical approaches. Finally, a hardware-in-the-loop test is undertaken via dSPACE which validate its implementation feasibility.

Residue Theorem based soft sliding mode control for wind power generation systems

Abstract: This paper proposes a residue theorem based soft sliding mode control strategy for a permanent magnet synchronous generator (PMSG) based wind power generation system (WPGS), to achieve the maximum energy conversion and improved in the system dynamic performance. The main idea is to set a soft dynamic boundary for the controlled variables around a reference point. Thus the controlled variables would lie on a point inside the boundary. The convergence of the operating point is ensured by following the Forward Euler method. The proposed control has been verified via simulation and experiments, compared with conventional sliding mode control (SMC) and proportional integral (PI) control.