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

Abstract

The main purpose of this research paper is to investigate the long-term effects of the proposed demandside 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.

Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors

“…Traditionally, LF methods comprise regression models and time series methods 15,16 . These forecasting approaches have not achieved considerable improvement in prediction accuracy due to their deficient non‐linear fitting potential 17‐19 . It is specifically worth stating that the non‐parametric regression method raised the advancement of artificial intelligence (AI) approaches for LF 20 .…”

“…15,16 These forecasting approaches have not achieved considerable improvement in prediction accuracy due to their deficient non-linear fitting potential. [17][18][19] It is specifically worth stating that the non-parametric regression method raised the advancement of artificial intelligence (AI) approaches for LF. 20 Owing to the development of AI techniques, several new intelligent LF methods were extensively utilized because they can find the complex non-linear relationship between electricity demand and factors affecting electrical energy consumption.…”

A comparative study of time series analysis for forecasting energy demand in Nigeria

“…As electric power demand increases, energy consumption assessment becomes imperative which could be carried out either hourly, daily, weekly, monthly or yearly [6]. Accurate load forecasting is vital because an overestimation will lead to economic waste since huge investment is needed for the development of excessive power infrastructure, while underestimating may result in the degeneration and possible collapse of the existing infrastructure as underinvestment may ensue thereby leading to load shedding or consumer disconnection [7]. The categorization of load forecasting is usually based on usage measured in short, medium and long term.…”

Availability estimation of wind power forecasting and optimization of day-ahead unit commitment

“…Wind power generation is now a key component in renewable energy generation. In 2016, the installed capacity in China increased to 169 billion kW while the abandoned wind power increased to 49.7 billion kW, a 50% increase over the previous year, with 17% rate of curtailed power [7][8][9]. The main reason is that the output of wind power is obviously random and uncertain due to the climate and environment change.…”