Published November 15, 2015 | Version v1
Journal article

The optimization model for multi-type customers assisting wind power consumptive considering uncertainty and demand response based on robust stochastic theory

  • 1. North China Electric Power University, Beijing 102206 (China)
  • 2. East Carolina University, Greenville, NC 27858 (United States)

Description

Highlights: • Our research focuses on demand response behaviors of multi-type customers. • A wind power simulation method is proposed based on the Brownian motion theory. • Demand response revenue functions are proposed for multi-type customers. • A robust stochastic optimization model is proposed for wind power consumptive. • Models are built to measure the impacts of demand response on wind power consumptive. - Abstract: In order to relieve the influence of wind power uncertainty on power system operation, demand response and robust stochastic theory are introduced to build a stochastic scheduling optimization model. Firstly, this paper presents a simulation method for wind power considering external environment based on Brownian motion theory. Secondly, price-based demand response and incentive-based demand response are introduced to build demand response model. Thirdly, the paper constructs the demand response revenue functions for electric vehicle customers, business customers, industry customers and residential customers. Furthermore, robust stochastic optimization theory is introduced to build a wind power consumption stochastic optimization model. Finally, simulation analysis is taken in the IEEE 36 nodes 10 units system connected with 650 MW wind farms. The results show the robust stochastic optimization theory is better to overcome wind power uncertainty. Demand response can improve system wind power consumption capability. Besides, price-based demand response could transform customers' load demand distribution, but its load curtailment capacity is not as obvious as incentive-based demand response. Since price-based demand response cannot transfer customer's load demand as the same as incentive-based demand response, the comprehensive optimization effect will reach best when incentive-based demand response and price-based demand response are both introduced.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.enconman.2015.08.079

Additional details

Identifiers

DOI
10.1016/j.enconman.2015.08.079;
PII
S0196-8904(15)00833-X;

Publishing Information

Journal Title
Energy Conversion and Management
Journal Volume
105
Journal Page Range
p. 1070-1081
ISSN
0196-8904
CODEN
ECMADL

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
48002805
Subject category
S17: WIND ENERGY;
Descriptors DEI
ALLOCATIONS; BROWNIAN MOVEMENT; BUSINESS; CAPACITY; COMPUTERIZED SIMULATION; ENERGY DEMAND; ENVIRONMENT; INDUSTRY; OPERATION; OPTIMIZATION; POWER SYSTEMS; PRICES; STOCHASTIC PROCESSES; WIND POWER; WIND TURBINE ARRAYS
Descriptors DEC
DEMAND; ENERGY SOURCES; ENERGY SYSTEMS; POWER; RENEWABLE ENERGY SOURCES; SIMULATION

Optional Information

Copyright
Copyright (c) 2015 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.