Residential load forecasting under a demand response program based on economic incentives

Summary In China, the continuous increase of peak load has posed a significant challenge to the safety and stability of the power grid. By reasonable control of the air‐conditioning (AC) load, peak load could be reduced, and balance could be achieved between supply and demand at lower cost without affecting customers' comfort. In this paper, a baseline model of the aggregated AC load is introduced and simplified to describe the relationship between temperature setpoint and AC power. The relationship between the electricity price and the temperature setpoint of AC is described based on the consumer psychology theory. Then, a new demand response project called progressive time‐differentiated peak pricing for the AC load is designed. Finally, case study proves the feasibility of the optimal pricing mechanism proposed in this paper. The influences of different price shapes and different compositions of consumers on simulation results are analyzed, providing a theoretical basis and reference data for the practical implementation of progressive time‐differentiated peak pricing.

Section: Modeling Methodsologiesmentioning

confidence: 99%

Section: Equivalent Thermal Parameter Model Of Ac and Baseline Forementioning

confidence: 99%

Progressive time-differentiated peak pricing (PTPP) for aggregated air-conditioning load in demand response programs

Shen

Zhou

et al. 2018

“…The advent of smart grids brings new opportunities and challenges for programs of price‐based demand response . The use of microgeneration and the availability of electricity tariffs, in real time, allow consumers to adapt their level of consumption or generation according to market conditions.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks and fuzzy logic to long-term load forecast considering the price elasticity of electricity demand

Miranda

Abaide

Sperandio

et al. 2018

Summary Over the past few decades, the behavior of electricity consumption has been changing, especially because of improvements in the distributed generation segment and technological innovations presented by smart grids. The use of microgeneration and the availability of electricity pricing in real time allow consumers to control their consumption, or generation, according to market conditions. This new dynamic tends to increasingly change the price elasticity of electricity demand, by indicating the need to readjust load forecasting models. In this market environment, in addition to providing robust estimates for the planning and operation of electric power systems, load forecasting models have become fundamental in the context of demand management. Thus, this paper proposes to develop an artificial neural network and fuzzy logic for load forecasting to perform an efficiency analysis. This system is able to provide estimates of the elasticity of electricity demand behavior with more satisfactory results. To do so, improvements in the neural network with multilayer perceptron are proposed. In this case, the adaptation of parameters to correlate variations in consumption with the changes in electricity tariffs was developed. The addition of this new structure produced better results compared with the conventional neural network. Computer tests were conducted using historical data from the ISO New England Inc and PJM Interconnection. Price elasticity estimates of electricity demand showed a sharp increase of demand in relation to the elasticity behavior.

“…In literature, there are different methods for forecasting the future energy . In Li et al a method has been proposed for short‐term load forecasting based on wavelet transform and partial least squares regression.…”

Section: Introductionmentioning

confidence: 99%

A new combined method for future energy forecasting in electrical networks

Baesmat

Shiri

2018

Summary Growing the industry and population in a region leads to the growth of required amount of electrical energy. Electrical companies should provide high quality energy according to the demand of customers. Load is an effective parameter of any system, and the network programmers should consider the annual load growth of the system and predict the required energy for each region by investigating previously recorded data of consumed electrical energy and stochastic analysis. In this paper, a new combined method for long‐term energy forecasting is proposed. This method, which combines the land‐consumption method and curve fitting based on generalization method, in addition to having simple calculations, takes into account the saturation. Moreover, loads from detailed formal program provided by the relevant institutes have been used, which results in better coordination between all organizations in charge of energy predictions and development of the countries. A suitable filtering method is also employed for input data to improve the method accuracy. To show the effectiveness of the proposed method, the results of different methods have been compared with those of the proposed method as well as real data. Then, real data of former 11 years of consumed energy gathered from Shiraz Electrical Distribution Company subscribers are employed and the energy for future 11 years is forecasted.