Multi-Objective Demand Response Model Considering the Probabilistic Characteristic of Price Elastic Load

Abstract

Demand response (DR) programs provide an effective approach for dealing with the challenge of wind power output fluctuations. Given that uncertain DR, such as price elastic load (PEL), plays an important role, the uncertainty of demand response behavior must be studied. In this paper, a multi-objective stochastic optimization problem of PEL is proposed on the basis of the analysis of the relationship between price elasticity and probabilistic characteristic, which is about stochastic demand models for consumer loads. The analysis aims to improve the capability of accommodating wind output uncertainty. In our approach, the relationship between the amount of demand response and interaction efficiency is developed by actively participating in power grid interaction. The probabilistic representation and uncertainty range of the PEL demand response amount are formulated differently compared with those of previous research. Based on the aforementioned findings, a stochastic optimization model with the combined uncertainties from the wind power output and the demand response scenario is proposed. The proposed model analyzes the demand response behavior of PEL by maximizing the electricity consumption satisfaction and interaction benefit satisfaction of PEL. Finally, a case simulation on the provincial power grid with a 151-bus system verifies the effectiveness and feasibility of the proposed mechanism and models.

Smart Distribution Systems

“…Connect/disconnect functions of smart meters provide distribution operators with more remote control capability to reduce dispatching field crews [14]. (5) Demand response: demand response is aimed to reduce the peak load [40], which avoids utilities from purchasing electricity at a high cost and delays the construction of new power substations. According to the U.S. Federal Energy Regulatory Commission (FERC), an estimation of 41,000 MW power is reduced through existing demand response programs in 2008 [41].…”

Fuzzy Stochastic Unit Commitment Model with Wind Power and Demand Response under Conditional Value-At-Risk Assessment

“…Zhao et al [22] proposed a multistage, robust optimization model considering the combined uncertainties of wind power and PEL on the basis of the interval programming and robust programming. Yang et al [23] elaborated the probabilistic DR behaviors under an imprecise price elasticity demand curve.…”

Electric Power Systems Research

“…Power system stability; (2) Power system reliability; (3) Flexible alternating current transmission systems (FACTS) applied to power systems; (4) Application of optimization methods to power systems; (5) Architectures and models of smart grids; (6) Power market; (7) Control, operation, and planning of distributed generation resources; (8) Smart home with energy management systems; (9) Microgrids and active distribution networks; (10) Virtual power plants and demand response.…”

“…The distribution system category covers many topics, such as voltage control, network reconfiguration, microgrids, power quality, smart distribution, and multi-agents [6,10,11,14,20,22]. The end-users category covers demand response, frequency control, and critical peak pricing [7,15,18,19]. The power market category deals with problems of contract design and load serving entities [9,18].…”