In today’s era of socioeconomic and environmental concerns, conventional energy sources are incapable of meeting increasing energy demands which leads to the significance of Microgrid (MG) systems incorporating renewable energy sources. An ideally designed microgrid capable of operating in standalone and grid connected mode is considered with photovoltaic panels, wind turbines, fuel cell, battery and Microturbines (MT) as sources. The two noteworthy disadvantages of microgrid are very expensive power supply and intermittency of power generation. Besides ensuring a steady supply, the power delivered should be of moderate cost. So, this work focuses on reducing the cost associated with its operation and emission by simultaneously analyzing the impact of uncertainty. The uncertainties can be caused due to different DGs present in MG and the load varations. The variations of uncertainty are included as outage cost. The MG operating under islanded and grid connected modes are analyzed for different scenarios to obtain optimal value of customer outage cost and operating cost. So a heuristic optimization technique which can handle the multi-objective problem is considered. The optimal solution for different operational scenarios of grid connected and islanded mode are obtained using Multi-Objective Particle Swarm Optimization (MOPSO) algorithm.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Motevasel M, Seifi AR (2014) Expert energy management of a micro-grid considering wind energy uncertainty. Energy Convers Manag: 58–72
Wang C-S, et al (2011) Multi-scenario, multi-objective optimization of grid-parallel Microgrid. Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 4th International Conference on. IEEE
Mohammadi, Hosseinian SH, Gharehpetian GB (2012) Optimization of hybrid solar energy sources/wind turbine systems integrated to utility grids as microgrid (MG) under pool/bilateral/hybrid electricity market using PSO. Elsevier-Solar Energy, p 112–125
Wu H, Liu X, Ding M (2014) Dynamic economic dispatch of a micro grid: Mathematical models and solution algorithm. Elsevier-Electric Power and Energy Systems, p 336–346
Mizani S, Yazdani A (2009) Optimal design and operation of a grid-connected microgrid. Electrical Power & Energy Conference (EPEC), IEEE
Zemīte L, Gerhards J (2009) Evaluation of distribution network customer outage costs. Power & Electrical Engineering
Hassanzadeh Fard H, Bahreyni SA, Dashti R, Shayanfa HA (2016) Evaluation of reliability parameters in micro-grid. Itanian Journal of Electrical Engineering
Gyuk I et al (2003) A framework and review of customer outage costs: integration and analysis of electric utility outage cost surveys. Ernest Orlando Lawrence Berkeley National Laboratory
Billinton R, Wangdee W (2003). Estimating customer outage costs due to a specific failure event. IEE Proceedings-Generation, Transmission and Distribution, p 668–672
Ninan J et al (2018) Microgrid cost optimization: a case study on Abu Dhabi. Conference: 8th International Conference on Intelligent Systems, Modelling and Simulation.IEEE, p 120–125
Alvehag K, Söder L (2012) Comparison of cost models for estimating customer interruption costs. Probabilistic Methods Applied to Power Systems (PMAPS)
Suchitra D, Jegatheesan R, Deepika TJ (2016) Optimal design of hybrid power generation system and its integration in the distribution network. Elsevier- Electrical Power and Energy Systems, p 136–149
Diaf S, Notton G, Belhamel M, Haddadi M, Louche A (2008) Design and techno-economical optimization for hybrid PV/wind system under various meteorological conditions. Elsevier- Applied Energy, p 968–987
Moghaddama AA, Seifia A, Niknamb T (2011) Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source. Elsevier- Energy, p 6490–6507
Hina Fathima A, Palanisamy K (2015) Optimization in microgrid with hybrid energy systems – A review. Elsevier- Renewable and Sustainable Energy Reviews, p 431–446
Partha K, Chanda CK (2013) Placement of wind and solar based DGs in distribution system for power loss minimization and voltage stability improvement. International Journal of Electrical Power Energy System, Elsevier, p 795–809
Shi W, Nali, Chu C-c, Gadh R (2017) Real time energy management in microgrids. IEEE transaction
Raquel CR, Naval Jr PC (2005) An effective use of crowding distance in multi objective particle swarm optimization. In: GECCO, p 257–64
Wang L, Singh C (2009) Multi-criteria design of hybrid power generation systems based on a modified particle swarm optimization algorithm. IEEE Trans Energy Convers, p 63–72
Kalyanmoy D (2001) Multi-objective optimization using evolutionary algorithms. John Wiley & Sons Ltd Publications, Hoboken
Reichl J, Schmidthale M, Schneider F (2013) Power outage cost evaluation: reasoning, methods and an application. Journal of Scientific Research & Reports 2(1):249–276
Zhou W, Wu J et al (2018) Optimal and elastic energy trading for green microgrids: a two-layer game approach. Mobile Networks and Applications
Abdisa LT et al (2018) Power outages, economic cost and firm performance:evidence from Ethiopia. Utilities Policy, Elsevier, p 111–120
Hanna R et al (2017) Evaluating business models for microgrids: interation of technology and policy. Energy Policy, Elsevier, p 47–61
Papathanassiou S, Hatziargyrious N, Strunz K (2005) A benchmark low voltage Microgrid network. Proc. CIGRE Symposium Power systems with dispersed generation: technologies, impacts on development, operation and performances
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Rajarajeswari, R., Suchitra, D., Vijayakumar, K. et al. Analyzing Customer Outage Cost in a Microgrid. Mobile Netw Appl 24, 1821–1834 (2019). https://doi.org/10.1007/s11036-019-01381-w
- Renewable energy resources