Optimal Scheduling of demand response resources and distributed energy resources for peak management

Document Type : Original Article

Authors

Department of Electrical Engineering, Faculty and Research Institute of Passive Defense and Engineering, Imam Hossein University, Tehran, Iran

10.22059/ses.2022.90569

Abstract

With the warming of the weather conditions and significant load growth in the distribution sector, peak management in power systems has become an essential issue in hot seasons, and demand response programs are one of the most suitable common methods for managing peak. In this article, the authors propose a new approach to network peak management in critical conditions by the distribution company. This approach is the optimal scheduling of distributed energy sources and various demand response resources to reduce the network's peak consumption during critical peak hours and with the lowest cost. Demand response resources in this article include the three critical peak pricing, incentive-based load curtailment, and emergency load response, which the distribution company implements. The problem is optimized using a robust complex number linear programming and considering the power uncertainty of renewable resources. Providing a peak price 14% larger than the peak price of the normal tariff can bring a suitable load impact from the critical peak program customers in the network. The participation of customers in the load curtailment program in the beginning and end hours of peak management is equal to 30% and 20% of the customer's baseline. Considering the risk related to solar production, power contribution in this program increases by 117 and 74 kilowatts for the same hours. Adopting a risk-averse strategy increases the cost by 745 $ for the distribution company and the reason is the increase in curtailment of 120 kW through the emergency demand response program.

Keywords

References

  • Pavani P, Bak-Jensen B, Pillai J. Impact of demand side management in active distribution networks. 2017 IEEE Power & Energy Society General Meeting. 2017.
  • O׳Connell N, Pinson P, Madsen H, O׳Malley M. Benefits and challenges of electrical demand response: A critical review. Renewable and Sustainable Energy Reviews. 2014;39:686-699.
  • Jordehi A. Optimisation of demand response in electric power systems, a review. Renewable and Sustainable Energy Reviews. 2019;103:308-319.
  • Gamil M, Senjyu T, Takahashi H, Hemeida A, Krishna N, Lotfy M. Optimal multi-objective sizing of a residential microgrid in Egypt with different ToU demand response percentages. Sustainable Cities and Society. 2021;75:103293.
  • Moghimi F, Barforoushi T. A short-term decision-making model for a price-maker distribution company in wholesale and retail electricity markets considering demand response and real-time pricing. International Journal of Electrical Power & Energy Systems. 2020;117:105701.
  • Albadi M, El-Saadany E. A summary of demand response in electricity markets. Electric Power Systems Research. 2008;78(11):1989-1996.
  • Alasseri R, Tripathi A, Joji Rao T, Sreekanth K. A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs. Renewable and Sustainable Energy Reviews. 2017;77:617-635.
  • Li Y, Gao W, Ruan Y, Ushifusa Y. Demand response of customers in Kitakyushu smart community project to critical peak pricing of electricity. Energy and Buildings. 2018;168:251-260.
  • Cappers P, Spurlock C, Todd A, Jin L. Are vulnerable customers any different than their peers when exposed to critical peak pricing: Evidence from the U.S. Energy Policy. 2018;123:421-432.
  • Aslam S, Iqbal Z, Javaid N, Khan Z, Aurangzeb K, Haider S. Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes. Energies. 2017;10(12):2065.
  • Javaid N, Ahmed A, Iqbal S, Ashraf M. Day Ahead Real Time Pricing and Critical Peak Pricing Based Power Scheduling for Smart Homes with Different Duty Cycles. Energies. 2018;11(6):1464.
  • Monfared H, Ghasemi A, Loni A, Marzband M. A hybrid price-based demand response program for the residential micro-grid. Energy. 2019;185:274-285.
  • Gazijahani F, Salehi J. Reliability constrained two-stage optimization of multiple renewable-based microgrids incorporating critical energy peak pricing demand response program using robust optimization approach. Energy. 2018;161:999-1015.
  • Monfared H, Ghasemi A, Loni A, Marzband M. A hybrid price-based demand response program for the residential micro-grid. Energy. 2019;185:274-285.
  • Yu M, Hong S. Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach. Applied Energy. 2017;203:267-279.

 

  • Lu X, Ge X, Li K, Wang F, Shen H, Tao P et al. Optimal Bidding Strategy of Demand Response Aggregator Based On Customers’ Responsiveness Behaviors Modeling Under Different Incentives. IEEE Transactions on Industry Applications. 2021;57(4):3329-3340.
  • Liu D, Sun Y, Li B, Xiangying X, Yudong L. Differentiated Incentive Strategy for Demand Response in Electric Market Considering the Difference in User Response Flexibility. IEEE Access. 2020;8:17080-17092.
  • Zalzar S, Bompard E. An Incentive-Based Settlement Mechanism for Participation of Flexible Demands in Day-ahead Markets. 2019 International Conference on Smart Energy Systems and Technologies (SEST). 2019.
  • Shahryari E, Shayeghi H, Mohammadi-ivatloo B, Moradzadeh M. An improved incentive-based demand response program in day-ahead and intra-day electricity markets. Energy. 2018;155:205-214.
  • Alasseri R, Rao T, Sreekanth K. Institution of incentive-based demand response programs and prospective policy assessments for a subsidized electricity market. Renewable and Sustainable Energy Reviews. 2020;117:109490.
  • Wang Z, Li H, Deng N, Cheng K, Lu B, Zhang B et al. How to effectively implement an incentive-based residential electricity demand response policy? Experience from large-scale trials and matching questionnaires. Energy Policy. 2020;141:111450.
  • Astriani Y, Shafiullah G, Shahnia F. Incentive determination of a demand response program for microgrids. Applied Energy. 2021;292:116624.
  • Lu R, Hong S. Incentive-based demand response for smart grid with reinforcement learning and deep neural network. Applied Energy. 2019;236:937-949.
  • Luo Z, Hong S, Ding Y. A data mining-driven incentive-based demand response scheme for a virtual power plant. Applied Energy. 2019;239:549-559.
  • Wen L, Zhou K, Li J, Wang S. Modified deep learning and reinforcement learning for an incentive-based demand response model. Energy. 2020;205:118019.
  • Kong X, Kong D, Yao J, Bai L, Xiao J. Online pricing of demand response based on long short-term memory and reinforcement learning. Applied Energy. 2020;271:114945.
  • Ju P, Jiang T, Chung C, Gong Y, Zhou H. Incorporating demand response in two-stage frequency emergency control. International Journal of Electrical Power & Energy Systems. 2021;131:107122.
  • Sahebi MM, Duki EA, Kia M, Soroudi A, Ehsan M. Simultaneous emergency demand response programming and unit commitment programming in comparison with interruptible load contracts. IET generation, transmission & distribution. 2012;6(7):605-11.
  • Cui H, Xia W, Yang S, Wang X. Real-time emergency demand response strategy for optimal load dispatch of heat and power micro-grids. International Journal of Electrical Power & Energy Systems. 2020;121:106127.
  • Monfared HJ, Ghasemi A, Loni A, Marzband M. A hybrid price-based demand response program for the residential micro-grid. Energy. 2019;185:274-85.
  • Kamarulazam F, Bashir S, Subramaniam R, Kasi R, Ashok Sharma SS. Review of Peak Shaving Features of the Power Box. Energy Technology. 2022:2101055.
  • Silva BN, Khan M, Han K. Futuristic sustainable energy management in smart environments: A review of peak load shaving and demand response strategies, challenges, and opportunities. Sustainability. 2020;12(14):5561.
  • Tang H, Liu C, Cao Y, Lv K, Zhang Q. Hierarchical scheduling learning optimisation of two-area active distribution system considering peak shaving demand of power grid. Discrete Event Dynamic Systems. 2021;31(3):439-68.
  • de la Nieta AA, Ilieva I, Gibescu M, Bremdal B, Simonsen S, Gramme E. Optimal midterm peak shaving cost in an electricity management system using behind customers’ smart meter configuration. Applied Energy. 2021;283:116282.
  • Alboaouh KA, Mohagheghi S. Impact of rooftop photovoltaics on the distribution system. Journal of Renewable Energy. 2020;2020.
  • Fathi, A., Salehi, M., Komarizadeh, A., Choubineh, K., Golkar, S., Ghahremani, L. Evaluating a Dual-Axis Solar Tracker’s Performance on Cloudy and Partly Cloudy Days. Journal of sustainable Energy Systems, 2021; 1(1): 71-81. [Persian]
  • Yuan H, Li F, Wei Y, Zhu J. Novel linearized power flow and linearized OPF models for active distribution networks with application in distribution LMP. IEEE Transactions on Smart Grid. 2016;9(1):438-48.
  • Safdarian A, Fotuhi-Firuzabad M, Lehtonen M. Integration of price-based demand response in DisCos' short-term decision model. IEEE Transactions on Smart Grid. 2014;5(5):2235-45.
  • Nojavan S, Akbari-Dibavar A, Farahmand-Zahed A, Zare K. Risk-constrained scheduling of a CHP-based microgrid including hydrogen energy storage using robust optimization approach. International Journal of Hydrogen Energy. 2020;45(56):32269-84.
  • Vita V. Development of a decision-making algorithm for the optimum size and placement of distributed generation units in distribution networks. Energies. 2017;10(9):1433.

 

 

Journal of sustainable Energy Systems
Volume 1, Issue 2
April 2022
Pages 131-146

Files

Share

How to cite

Statistics