A Reliability-Cost–Benefits (RCB) Model for the Addition of DG to Independent Micro-Grid Networks
DOI:
https://doi.org/10.11113/jt.v69.2517Keywords:
Demand response, micro grid, renewable, reliability, economicAbstract
Interest in alternative energy sources has been increased due to improved public awareness about the high energy cost and the adverse environmental impact of the conventional energy sources. This has caused a transition to a new market-based power network which, among other characteristics, allows more flexible incentives based demand-response-programs (DRP). Reliability aspects have been more challenging in remotely operated Independent-Microgrid-Networks (IMGN) mainly due to weather dependence. Such stochastic behavior has influenced the market prices of electricity and has created real time challenges for trade. The success of such uncertain energy systems depends to a large extent on new technical and financial tools. This requires to consider together the reliability and the financial cost–benefit analysis for every stakeholder of the deregulated electricity market. A novel decision making strategy for relating the system's reliability with cost-benefit incentives under IMGN paradigm has been proposed with an application of economic and signaling-game-theory (SGT).A relationship has been formulated with the consideration of maximizing the profit expectation of each player in the presence of the independent multi-generation resources with different penetration levels. This proposed model can be used as a tool for task scheduling problems and demand side management under the real time pricing schemes. The basic idea is to deal with the uncertainties involved to improve the reliability of renewable power systems. A simulation methodology for reliability evaluation and cost assessment in IMGN has also been developed. Results are discussed in comparison with the Time-of-Use (ToU) price rates.
References
Zareen, N., Mustafa, M. W., Al Gizi, A. J. H. & Alsaedi, M. A. 2012. Worldwide Technological Revolutions and Its Challenges under Smart Grid Paradigm: A Comprehensive Study. Int. J. Sci. Eng. Res. 3(11).
Anbazhagan, S. & Kumarappan, N. 2012. Day-ahead Deregulated Electricity Market Price Classification Using Neural Network Input Featured by DCT. Int. J. Electr. Power Energy Syst. 37: 103–109.
Yang, W., Yu, R. & Rahardja, S. 2012. Benefits to Consumers Under RTP with a Statistical Demand Model. IEEE PES Innov. Smart Grid Technol. 1–6.
Azarpour, A., Suhaimi, S., Zahedi, G. & Bahadori, A. 2012. A Review on the Drawbacks of Renewable Energy as a Promising Energy Source of the Future. Arab. J. Sci. Eng. 38: 317–328.
Basu, A. K., Chowdhury, S. P., Chowdhury, S. & Ray, D. 2008. Reliability Study of a Micro-grid Power System. 43rd Int. Univ. Power Eng. Conf. 1–4.
Wu, C., Wen, F., & Lou, Y. 2008.The existed problems and possible solutions of micro-grid based on distributed generation. DRPT2008 Conference in Nanjing China 2008: 2763–2768.
Jiang, B. & Y. Fei. 2011. Dynamic Residential Demand Response and Distributed Generation Management in Smart Microgrid with Hierarchical Agents. Energy Procedia. 12: 76–90.
Karangelos, E. & Bouffard, F. 2012. Towards Full Integration of Demand-Side Resources in Joint Forward Energy/Reserve Electricity Markets. IEEE Trans. Power Syst. 27: 280–289.
Parvania, M. & Fotuhi-Firuzabad, M. 2012. Integrating Load Reduction Into Wholesale Energy Market With Application to Wind Power Integration. IEEE Syst. J. 6: 35–45.
Navid, N. & Rosenwald, G. 2012. Market Solutions for Managing Ramp Flexibility With High Penetration of Renewable Resource. IEEE Trans. Sustain. Energy. 3: 784–790.
Nikzad, M., Bashirvand, M., Mozafari, B. & Ranjbar, A. M. 2012. Prioritizing Demand Response Programs from Reliability Aspect. 11th Int. Conf. Environ. Electr. Eng., IEEE, 2012. 229–234.
Aalami, H. A., Moghaddam, M. P. & Yousefi, G. R. 2010. Demand Response Modeling Considering Interruptible/Curtailable Loads and Capacity Market Programs. Appl. Energy. 87: 243–250.
Abido, M. A. & Al-Ali, N. A. 2012. Multi-Objective Optimal Power Flow Using Differential Evolution, Arab. J. Sci. Eng. 37: 991–1005.
Faria, P., Vale, Z., Soares, J. & Ferreira, J. 2011. Demand Response Management in Power Systems Using a Particle Swarm Optimization Approach. IEEE Intell. Syst. 1–9.
Faria, P. & Vale, Z. 2011. Demand Response in Electrical Energy Supply: An Optimal Real Time Pricing Approach. Energy. 36: 5374–5384.
Xiao, J., Chung ,J. Y. &, Li, J. 2010. Near Optimal Demand-Side Energy Management Under Real-time Demand-Response Pricing .IEEE Commun. Soc. CNSM 2010 Proceedings. 2010. 527–532.
Niknam, T., Azizipanah-Abarghooee, R. & Narimani, M. R. 2012. An Efficient Scenario-based Stochastic Programming Framework for Multi-objective Optimal Micro-grid Operation. Appl. Energy. 99: 455–470.
Chaouachi, A., Kamel ,R. M., Andoulsi, R. & Nagasaka, K. 2013. Multiobjective Intelligent Energy Management for a Microgrid. IEEE Trans. Ind. Electron. 60: 1688–1699.
Nguyen ,P. H., Kling ,W. L. & Kamphuis, I. G. 2010. Integration of Agent-based Functions to Facilitate Operation of Smart Distribution Networks. IEEE PES Innov. Smart Grid Technol. 2010. 1–5.
Yousefi ,S., Moghaddam, M. P. & Majd, V. J. 2011. Optimal Real Time Pricing in an Agent-based Retail Market Using A Comprehensive Demand Response Model. Energy. 36: 5716–5727.
Paulus, M. & Borggrefe, F. 2011. The Potential of Demand-side Management in Energy-intensive Industries for Electricity Markets in Germany. Appl. Energy. 88: 432–441.
Panapakidis, I. P. & Alexiadis, M. C. 2013. Load Profiling in the Deregulated Electricity Markets : A Review of the Applications. IEEE Int. Conf. Electro-Information Technol. EIT 2013.
Walawalkar, R., Blumsack, S. & S. Fernands. 2008. An Economic Welfare Analysis of Demand Response in the PJM Electricity Market. Energy Policy. 36: 3692–3702.
Ghosn, S. & Ranganathan, P. 2010. Agent-oriented Designs for a Self-healing Smart Grid. IEEE Conf. Smart Grid, 2010. 461–466.
Akinci, T. C. & Nogay, H. S. 2012. Wind Speed Correlation Between Neighboring Measuring Stations. Arab. J. Sci. Eng. 37: 1007–1019.
Akyuz, E., Demiral, D., Coskun, C. & Oktay, Z. 2012. Estimation of the Monthly Based Hourly Wind Speed Characteristics and the Generated Power Characteristics for Developing Bidding Strategies in an Actual Wind Farm: A Case Study. Arab. J. Sci. Eng. 38: 263–275.
Babar, M., Rizvi, A., Al-Ammar , E. A. & Malik, N. H. 2013. Analytical Model of Multi-junction Solar Cell. Arab. J. Sci. Eng 2013.
Shao, S., Pipattanasomporn, M. & Rahman, S. 2013. Development of Physical-based Demand Response-enabled Residential Load Models. IEEE Trans. Power Syst. 28: 607–614.
Shao, S., Pipattanasomporn, M. & Rahman, S. 2012. Grid Integration of Electric Vehicles and Demand Response With Customer Choice. IEEE Trans. Smart Grid. 3: 543–550.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
