Thomas Reindl

Hybridizing genetic algorithm with differential evolution for solving the unit commitment scheduling problem

Abstract This paper proposes a hybrid of genetic algorithm (GA) and differential evolution (DE), termed hGADE, to solve one of the most important power system optimization problems known as the unit commitment (UC) scheduling. The UC problem is a nonlinear mixed-integer combinatorial high-dimensional and highly constrained optimization problem consisting of both binary UC variables and continuous power dispatch variables. Although GA is more capable of efficiently handling binary variables, the performance of DE is more remarkable in real parameter optimization. Thus, in the proposed algorithm hGADE, the binary UC variables are evolved using GA while the continuous power dispatch variables are evolved using DE. Two different variants of hGADE are presented by hybridizing GA with two classical variants of DE algorithm. Additionally, in this paper a problem specific heuristic initial population generation method and a replacement strategy based on preservation of infeasible solutions in the population are incorporated to enhance the search capability of the hybridized variants on the UC problem. The scalability of the proposed algorithm hGADE is demonstrated by testing on systems with generating units in the range of 10 up to 100 in one-day scheduling period and the simulation results demonstrate that hGADE algorithm can provide a system operator with remarkable cost savings as compared to the best approaches in the literature. Finally, an ensemble optimizer based on combination of hGADE variants is implemented to further amplify the performance of the presented algorithm.

Optimal Orientation and Tilt Angle for Maximizing in-Plane Solar Irradiation for PV Applications in Singapore

The performance of photovoltaic (PV) modules and systems is affected by the orientation and tilt angle, as these parameters determine the amount of solar radiation received by the surface of a PV module in a specific region. In this study, three sky models (Liu and Jordan, Klucher, and Perez et al .) are used to estimate the tilted irradiance, which would be received by a PV module at different orientations and tilt angles from the measured global horizontal irradiance (GHI) and diffuse horizontal irradiance (DHI) in Singapore (1.37°N, 103.75°E). Modeled results are compared with measured values from irradiance sensors facing 60° NE, tilted at 10°, 20°, 30°, 40°, and vertically tilted irradiance sensors facing north, south, east, and west in Singapore. Using the Perez model, it is found that a module facing east gives the maximum annual tilted irradiation for Singapores climatic conditions. These findings are further validated by one-year comprehensive monitoring of four PV systems (tilted at 10° facing north, south, east, and west) deployed in Singapore. The PV system tilted 10° facing east demonstrated the highest specific yield, with the performance ratio close to those of other orientations.

A genetic algorithm - differential evolution based hybrid framework

This research article proposes a hybrid evolutionary framework based on hybridization of genetic algorithm (GA) and differential evolution (DE) for solving a nonlinear, high-dimensional, highly constrained, mixed-integer optimization problem called the unit commitment (UC) problem. Although GA is more capable of efficiently handling binary variables, the performance of DE is better in real parameter optimization. Thus, in the proposed hybrid framework, termed hGADE, the binary variables are evolved using GA while the continuous variables are evolved using DE. To test the efficiency of the presented framework, GA is hybridized with 4 classical and 2 state-of-the-art self-adaptive DE variants. We also incorporate a heuristic initial population generation method and a replacement scheme based on preserving infeasible solutions in the population to enhance the performance of the hGADE variants. A systematic classification of the proposed hybrid optimizer is presented in accordance with a recently proposed taxonomy in the literature. Extensive case studies are presented on different test systems and the effectiveness of the heuristic initialization, the replacement scheme, and the hybrid strategy is verified through stringent simulated results. We perform exhaustive benchmarking against some of the best algorithms proposed in the literature for UC problem to demonstrate the efficiency of the hGADE variants. Furthermore, the proposed hGADE variants are statistically compared among themselves to determine the best hGADE variants. Additionally, GA and DE are hybridized within multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework and the effectiveness of hybridization is demonstrated on multi-objective UC problem as well. The proposed hybrid framework is generic and other discrete and/or real parameter operators can be easily incorporated within the framework for solving different mixed-integer optimization problems.

Performance Degradation of Various PV Module Technologies in Tropical Singapore

The performance of ten photovoltaic (PV) modules with nine different solar cell technologies (and one different module construction) is monitored in the tropical climate of Singapore. The types of modules included in this study are monocrystalline Si (glass-backsheet with frame and glass-glass without frame), heterojunction crystalline Si, monocrystalline Si back-contact, multicrystalline Si, double-junction “micromorph” Si, single-junction/double-junction amorphous Si, CdTe, and CIGS. Three years of outdoor monitoring data are used to extract degradation trends of the performance of the various modules. Statistical decomposition methods are used to extract trends for performance ratio (PR), short-circuit current (ISC), open-circuit voltage (VOC), and fill factor (FF). The degradation rates of the monocrystalline Si modules are found to be equal to or less than -0.8% per year, mainly contributed by the decrease in ISC. The multicrystalline Si module shows a slightly higher degradation rate of -1.0% per year. The amorphous Si, micromorph Si, and CdTe modules show degradation rates of around -2% per year. The CIGS module showed an exceptionally high degradation rate of -6% per year. The decrease in FF and VOC is found to be significant for all the thin-film modules but not for the crystalline silicon modules.

Enhanced Multiobjective Evolutionary Algorithm Based on Decomposition for Solving the Unit Commitment Problem

In this paper, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is proposed to solve the unit commitment (UC) problem as a multiobjective optimization problem (MOP) considering minimizing cost and emission as the multiple objectives. Since UC problem is a mixed-integer optimization problem, a hybrid strategy is integrated within the framework of MOEA/D such that genetic algorithm (GA) evolves the binary variables, while differential evolution (DE) evolves the continuous variables. Further, a novel nonuniform weight-vector distribution (NUWD) strategy is proposed and an ensemble algorithm based on combination of MOEA/D with uniform weight-vector distribution (UWD) and NUWD strategy is implemented to enhance the performance of the presented algorithm. Extensive case studies are presented on different test systems and the effectiveness of the hybrid strategy, the NUWD strategy, and the ensemble algorithm is verified through stringent simulated results. Further, exhaustive benchmarking against the algorithm proposed in the literature is presented to demonstrate the superiority of the proposed algorithm.

Effect of Solar Spectrum on the Performance of Various Thin-Film PV Module Technologies in Tropical Singapore

The spectral influence on the performance of four different thin-film photovoltaic (PV) modules (single-junction amorphous Si, CdTe, CIGS, and double-junction “micromorph” Si) is studied. Two methods are used to quantify the effective irradiance intensity for the investigated four modules. The first is based on spectral mismatch factors calculated from the measured outdoor spectrum and the spectral responses of the PV modules. The second is based on the measured short-circuit currents of the modules. The effective irradiance ratio (EIR) of a PV module technology, for a given time period, is defined as the ratio between the cumulative effective irradiance intensity received by the module and the cumulative irradiance intensity measured by a c-Si reference cell. We find that the average photon energy of the spectrum in Singapore is higher than that of the AM1.5G reference spectrum, indicating that the spectrum is “blue-rich.” Compared with the AM1.5G spectrum, this blue-rich spectral irradiance results in an annual EIR of 1.07 and 1.03 for the single-junction a-Si module and the CdTe module, respectively. An EIR larger than 1 indicates energetic irradiance gain. CIGS is not significantly affected by the blue-rich spectrum, while micromorph Si shows an annual EIR of less than 1.

Economic and Environmental Generation and Voyage Scheduling of All-Electric Ships

An all-electric ship (AES) uses integrated power generators and an energy storage system (ESS) to match its propulsion and service loads, thus forming an isolated microgrid. Existing work has hitherto set the minimisation of the AES operational cost as the single optimisation objective, with the reduction of the greenhouse gas (GHG) emission merely treated as a constraint of the optimisation. Moreover, the potential of the ESS as part of the scheduling has not been fully exploited. The work presented in this paper emphasises the environmental concerns and makes GHG mitigation a separate objective, thus expanding the optimisation into multi-objective. In achieving both objectives, the ESS-integrated joint generation - voyage scheduling of the AES is proposed: the generation scheduling is combined with the load management (propulsion load - cruising speed) and the ESS dispatch to optimally operate the diesel generators. The optimisation is formulated using optimal control, and is solved using non-dominated sorting genetic algorithm II (NSGA-II). Extensive simulations demonstrate that optimising the cruising speed (voyage) jointly with the generation scheduling results in the reduction of both operational cost and GHG emission, compared to the fixed-voyage generation scheduling. Integrating the ESS dispatch into the generation scheduling further enhances the benefits.

Seasonal variation of PV module performance in tropical regions

PV systems need to be designed in line with the local conditions to ensure optimized performance. This paper analyzes the PV module performance in tropical regions, specifically in Singapore. Three PV modules technologies were investigated: monocrystalline Si, single junction amorphous Si and micromorph Si thin-film modules. Long-term monitoring results reveal that PV modules exhibit evident enhancement from middle of November to middle of January, when is the rainy season in Singapore. Correlation analysis shows that temperature is the main factor for the seasonal performance variation. Monocrystalline Si shows the highest correlation with temperature and irradiance. a-Si single junction thin-film modules are less dependent on temperature, while micromorph Si shows the least correlation with the two variables. The influence of spectral variation (air mass) is discussed as well.

Spatial Load Forecasting With Communication Failure Using Time-Forward Kriging

Short-term and very short-term load forecasting are essential for power grid management operations such as automatic generation control, unit commitment scheduling, and transmission loss estimation. Most existing forecasting techniques require that the load data be available up to the current time step. In recent years, cyber attacks increasingly threaten the secure operation of power grids. One potential cyber threat is communication failure which will affect load forecasting. When communication failure happens and actual load data are not available, forecasting accuracy suffers. To overcome this problem, we propose a time-forward kriging based approach to forecast load with and without communication failure. This technique has the ability to forecast the load by utilizing load information from neighboring regions. The proposed method has been tested using NYISO and PJM load data with 5-min and 1-h intervals, respectively. Our results show that the proposed method is capable of forecasting load under communication failure with acceptable accuracy and improved accuracy when compared with other forecasting techniques.

A Fast and Scalable Protection Scheme for Distribution Networks With Distributed Generation

The increasing penetration of distributed generators (DGs) in modern day power grids results in varying fault current levels and network scenarios which may affect the conventional overcurrent protection relays. This necessitates a protection scheme with efficient fault signal estimations and smart decision-making capabilities in case of unexpected events. In this paper, a novel, fast, and adaptive relay mechanism has been proposed for complete protection of radial distribution systems with DG penetration. A fast recursive discrete Fourier transform (FRDFT) algorithm is used here for efficient fundamental tracking of varying power system signals. The numerical relay design using the FRDFT algorithm is embedded with a fuzzy-logic decision-making module for obtaining optimal protection settings in case of changing system conditions. The proposed adaptive scheme is tested on a standard IEEE 34-bus distribution system equipped with DGs by simulating various case studies. Simulation results verify that the adaptive relay is able to capture the changing system scenarios and select the protection settings accordingly.