Refine your search
Collections
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Pasupuleti, Jagadeesh
- Self-Scheduling of Wind Power Generation with Direct Load Control Demand Response as a Virtual Power Plant
Abstract Views :404 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical Power Engineering, Universiti Tenaga Nasional, 43000 Kajang, Selangor, MY
1 Department of Electrical Power Engineering, Universiti Tenaga Nasional, 43000 Kajang, Selangor, MY
Source
Indian Journal of Science and Technology, Vol 6, No 11 (2013), Pagination: 5443–5449Abstract
A virtual power plant, VPP, defined as a collection of dispersed generator units, storages and controllable load systems aggregated as a unique power plant. VPPs were categorized as Commercial virtual power plant, CVPP and Technical virtual power plant, TVPP. CVPP was related to the market and expected to obtain a maximum benefit from the generation and demand portfolio. The TVPP takes into consideration the operation of the grid and expected to solve possible contingencies. Two technologies that could be offered as a CVPP were wind power producer, WPP, and Flexible consumption especially direct load control, DLC, demand response program. This work focused on the economic operation of CVPP consisting of WPP and DLC in a competitive electricity market and the WPP intermittent compensation. The researcher in this paper used self-scheduling, SS, method to derive maximum expected profit from the Energy Markets. The problem of self-scheduling of the CVPP was formulated and solved by GAMS software. The case study and numerical results were presented in this paper.Keywords
Virtual Power Plant (VPP), Distributed Energy Resource (DER), Wind Power Producer (WPP), Direct Load Control (DLC), Self-Scheduling (SS)Full Text
References
- Alishahi E, Moghaddam M P et al. (2012). A system dynamics approach for investigating impacts of incentive mechanisms on wind power investment, Renewable Energy, vol 37(1), 310–317.
- Moura P S, De Almeida A T et al. (2010). The role of demand-side management in the grid integration of wind power, Applied Energy, vol 87(8), 2581–2588.
- Braun M (2009). Virtual power plants in real applications, Sixth Research Framework Programme of the European Union for the FENIX project (SES6–518272), Available from: http://www.iwes.fraunhofer.de
- Corera J (2009). Flexible electricity networks to integrate the expected energy evolution, FENIX project, Bilbao, Spain.
- Pudjianto D, Ramsay C et al. (2007). Virtual power plant and system integration of distributed energy resources, Renewable Power Generation, IET, vol 1(1), 10–16.
- Chambers A, Schnoor B et al. (2001). Distributed generation: a non-technical guide, PennWell Books, Oklahoma, USA, Chapter 1, 1–19.
- IEA Demand Side Management Programme Report (2009). Integration of demand side management, distributed generation, renewable energy sources and energy storages, Available from: http://www.ieadsm.org
- ISO Technical Report (2009). Alternate Route: Electrifying the Transportation Sector, New York, USA.
- Moghaddam M P, Baboli E T et al. (2010). Flexible load following the wind power generation, 2010 IEEE Internat-ional Energy Conference and Exhibition, 802–807.
- Usaola J, and Angarita J (2007). Bidding wind energy under uncertainty, International Conference on Clean Electrical Power, ICCEP’07, IEEE, 754–759.
- Ackermann T. (2005). Wind power in power systems, John Wiley, Chichester, UK, vol 140, Chapter 4, 47–72.
- Galloway S, Bell G et al. (2006). Managing the risk of trading wind energy in a competitive market, IEE Proceedings-Generation, Transmission and Distribution, vol 153(1), 106–114.
- Federal Energy Regulatory Commission (FERC) (2006). Assessment of demand response and advanced metering, Department of Energy, Washington, DC, USA, Available from: http://www.ferc.gov/legal/staff-reports/demand-response.pdf.
- Kirschen D (2003). Demand-side view of electricity markets, IEEE Transactions on Power Systems, vol 18(2), 520–527.
- Aalami H A, Moghaddam M P et al. (2010). Modeling and prioritizing demand response programs in power markets, Electric Power Systems Research, vol 80(4), 426–435.
- Garcia-Gonzalez J, de la Muela R R et al. (2008). Stochastic joint optimization of wind generation and pumped-storage units in an electricity market, IEEE Transactions on Power Systems, vol 23(2), 460–468.
- StoventoWindfarm (2013). Available from: http://www.sotaventogalicia.com
- OMEL (2013). Market operator of the electricity market of Mainland Spain, Available from: http://www.omel.es
- Aalami H, Yousefi G R et al. (2008). A MADM-based support system for DR programs, Universities Power Engineering Conference, 43rd International Universities Power Engineering Conference, UPEC 2008, 1–7.
- De Jonghe C, Hobbs B F et al. (2012). Optimal generation mix with short-term demand response and wind penetration, IEEE Transactions on Power Systems, vol 27(2), 830–839.
- Kirschen D, Strbac G et al. (2004). Fundamentals of power system economics, John Wiley & Sons, London, UK.
- Stevens M J M, and Smulders P T (1979). The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes, Wind Engineering, vol 3(2), 132–145, Chapter 2, 16–17.
- Pallabazzer R (2004). Previsional estimation of the energy output of windgenerators, Renewable Energy, vol 29(3), 413–420.
- Coordination of PSS and PID Controller for Power System Stability Enhancement – Overview
Abstract Views :245 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical Power Engineering, UNITEN, MY
1 Department of Electrical Power Engineering, UNITEN, MY
Source
Indian Journal of Science and Technology, Vol 8, No 2 (2015), Pagination: 142–151Abstract
In power systems, Low Frequency Oscillations (LFO) in the range of 0.1 - 2.5 Hz have been solved through Power System Stabilizer (PSS). Proportional Integral Derivative (PID) controller is the simplest and effective solution to the most of control engineering applications today. Based on advantage, the PID controller combined with PSS to enhance the stability in power system. In practice most of the PID controller and parameters of PSS are tuned manually and fixed for certain operating conditions. In general power systems are non linear, conventional methods had lack of robustness. Therefore it is necessary to take advantage in simplifying the problem and implementation by utilizing most efficient optimization methods. From this view, many optimization methods and algorithms have been employed to tune the PID gains and PSS parameters. This paper broadly reviews the optimization methods and algorithms such as Conventional methods, Soft Computing, Genetic Algorithm (GA), Evolutionary Programming (EP), Differential Evolution (DE) and Swarm Intelligence methods were available for tuning the PID gains and PSS parameters successfully. Research showed the design of controllers based on conventional methods; soft computing and population based algorithms suffer from limitations. However, swarm intelligence techniques proved to be able to overcome these limitations. Swarm intelligence based coordinated controller (PID+PSS), will effectively enhance the small signal stability and transient stability in power system. An effort is made in this paper to present a broad analysis of tuning the PID gains and PSS parameters by various researchers.Keywords
Algorithms, Optimization Methods, PID Controller, Power System Stabilizer.Full Text