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.2015.08.079Additional 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.