Incentive payment design for interruptible load management of power systems

2011 ◽  
Author(s):  
Tang Yongwei ◽  
Zhang Shaohua ◽  
Liu Yanyan
Keyword(s):  
Power Systems ◽  
Load Management ◽  
Incentive Payment ◽  
Interruptible Load

Application of Intelligent Algorithms to Interruptible Load Dispatch Model

2012 ◽  
Vol 239-240 ◽  
pp. 1487-1492
Author(s):  
Quan Sha ◽  
Shao Hua Zhang
Keyword(s):  
Power Systems ◽  
Optimization Problem ◽  
Solution Method ◽  
Power Markets ◽  
Load Management ◽  
Binary Particle Swarm Optimization ◽  
Intelligent Algorithms ◽  
Intelligent Optimization ◽  
Interruptible Load ◽  
Intelligent Optimization Algorithms

Interruptible load management plays an important role in maintaining the reliability and security of power systems and reducing price spike of power markets, thus has been widely used in power systems. Interruptible loads dispatch problem with multi-period and multi-interruptible-load is usually a multi-objective, combinatorial optimization problem, and its solution method needs to be carefully investigated. Application of intelligent optimization algorithms to the multi-period interruptible load dispatch problem is discussed in this paper. Multi objectives, such as minimizing the interruption payments and interruption frequency, can be included in the proposed interruptible load dispatch model. The characteristics of different interruptible loads are also considered. Numerical simulations using discrete binary particle swarm optimization (BPSO) and the genetic algorithm (GA) show that the BPSO can obtain better solutions than that of the GA. Thus the BPSO presents better potentials in solving the interruptible load dispatch problem.

A jump-driven Markovian electric load model

1990 ◽  
Vol 22 (3) ◽  
pp. 564-586 ◽  
Author(s):  
Roland Malhamé
Keyword(s):  
Power Systems ◽  
Load Management ◽  
Water Heating ◽  
State Behavior ◽  
Physically Based ◽  
The Individual ◽  
Discrete Part ◽  
Heating Loads ◽  
Transient And Steady State

Electric water heating loads, in power systems, can be adequately modeled by Markov processes comprising a mix of continuous and discrete states. A physically-based characterization of the dynamic behavior of large aggregates of electric water heating loads is obtained by deriving the forward Kolmogorov equations associated with the individual hybrid-state processes. In addition, by focusing on the discrete part of the state, a Markov renewal viewpoint of the processes is developed. Both viewpoints are used to analyze and predict the transient and steady-state behavior of these loads, of great importance in load management applications.

Optimum load management strategy for wind/diesel/battery hybrid power systems

2012 ◽  
Vol 44 ◽  
pp. 288-295 ◽  
Author(s):  
Juan M. Lujano-Rojas ◽  
Cláudio Monteiro ◽  
Rodolfo Dufo-López ◽  
José L. Bernal-Agustín
Keyword(s):  
Power Systems ◽  
Management Strategy ◽  
Load Management ◽  
Hybrid Power Systems ◽  
Optimum Load ◽  
Hybrid Power

Interruptible Load Risk Analysis Based on Portfolio Theory

2013 ◽  
Vol 732-733 ◽  
pp. 1427-1431
Author(s):  
Jin Feng Wang ◽  
Rong Zhu ◽  
Yan Jiang
Keyword(s):  
Power Supply ◽  
Value At Risk ◽  
Portfolio Theory ◽  
Conditional Value At Risk ◽  
Load Management ◽  
Interruptible Load ◽  
Proposed Model ◽  
Stochastic Load ◽  
Risk And Benefit ◽  
Load Risk

Due to the uncertain price and stochastic load, power supply companies will face the trade-off between profits and risks when exercising Interruptible Load Management (ILM). Customers of different type are looked as sub-markets with different risk and benefit. A model is established for Interruptible Load (IL) in the framework of portfolio theory, with the object of maximizing the expected profits and risks of conditional value at risk (CVaR). Genetic Algorithm (GA), is adopted to solve the model. Finally, a numerical example is served for demonstrating the market property of high profits accompanied by high risks and the feasibility of the proposed model, thus a reference is provided to a power supply company to manage risks.

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