Long-term forecasting of annual peak load considering effects of demand-side programs

The main purpose of this research paper is to investigate the long-term effects of the proposed demand-side program, and its impact on annual peak load forecasting important for strategic network planning. The program comprises a particular set of demand-side measures aimed at reducing the annual peak load. The paper also presents the program simulations for the case study of the Electricity Distribution Company of Belgrade (EDB). According to the methodology used, the first step is to determine the available controllable load of the distribution utility/area under consideration. The controllable load is presumed constant over the analyzed time horizon, and the smart grid (SG) infrastructure available. The saturation of positive effects during intense program application is also taken into account. Technical and economic input data are taken from the real projects. The conducted calculations indicate that demand-side programs can bring about the same results as the energy storage in the grids with a strong impact of distributed generation from variable renewable sources (V-RES). In conclusion, the proposed demand-side program is a good alternative to building new power facilities, which can postpone investment costs for a considerable period of time.

[1]  Kankar Bhattacharya,et al.  Optimal Operation of Residential Energy Hubs in Smart Grids , 2012, IEEE Transactions on Smart Grid.

[2]  Nikola Rajakovic,et al.  POWER TRANSFORMER MONITORING AND AMR SYSTEM SUPPORT FOR COMBINED OPERATION OF DISTRIBUTED RES AND DEMAND SIDE MANAGEMENT , 2014 .

[3]  Colin Fitzpatrick,et al.  Demand side management of a domestic dishwasher: Wind energy gains, financial savings and peak-time load reduction , 2013 .

[4]  Paul McNamara,et al.  Hierarchical Demand Response for Peak Minimization Using Dantzig–Wolfe Decomposition , 2015, IEEE Transactions on Smart Grid.

[5]  Qun Zhou,et al.  Impact of demand response contracts on load forecasting in a smart grid environment , 2012, PES 2012.

[6]  Zuo-Jun Shen,et al.  Thermostats for the Smart Grid: Models, Benchmarks, and Insights , 2012 .

[7]  S.M. Maksimovich,et al.  The Peak Load Forecasting Afterwards Its Intensive Reduction , 2009, IEEE Transactions on Power Delivery.

[8]  Prashant J. Shenoy,et al.  SmartCap: Flattening peak electricity demand in smart homes , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications.

[9]  N. L. Rajakovic,et al.  Determination of the amount of cost-effective DSM/DR module , 2014 .

[10]  Nikola Rajaković,et al.  Demand response capacity estimation in various supply areas , 2015 .

[11]  Jacob Beal,et al.  Precise Mass-Market Energy Demand Management Through Stochastic Distributed Computing , 2013, IEEE Transactions on Smart Grid.

[12]  Saifur Rahman,et al.  An energy management model to study energy and peak power savings from PV and storage in demand responsive buildings , 2016 .

[13]  Clark W Gellings,et al.  The Smart Grid: Enabling Energy Efficiency and Demand Response , 2020 .

[14]  Vincent W. S. Wong,et al.  Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design , 2012, IEEE Transactions on Smart Grid.

[15]  P. Ferrao,et al.  The impact of demand side management strategies in the penetration of renewable electricity , 2012 .

[16]  Wayes Tushar,et al.  Customer Engagement Plans for Peak Load Reduction in Residential Smart Grids , 2015, IEEE Transactions on Smart Grid.

[17]  Marco L. Della Vedova,et al.  Electric load management approaches for peak load reduction: A systematic literature review and state of the art , 2016 .