scholarly journals Real-Time Pricing for Demand Response in Smart Grid Based on Alternating Direction Method of Multipliers

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Hongbo Zhu ◽  
Yan Gao ◽  
Yong Hou
Keyword(s):  
Smart Grid ◽  
Real Time ◽  
Supply And Demand ◽  
Iteration Process ◽  
Optimization Method ◽  
Alternating Direction Method ◽  
Lagrangian Multiplier ◽  
Method Of Multipliers ◽  
Alternating Direction ◽  
Real Time Pricing

The real-time pricing (RTP) scheme is an ideal method to adjust the power balance between supply and demand in smart grid systems. This scheme has a profound impact on users’ behavior, system operation, and overall grid management in the electricity industry. In this research, we conduct an extended discussion of a RTP optimization model and give a theoretical analysis of the existence and uniqueness of the Lagrangian multiplier. A distributed optimization method based on the alternating direction method of multipliers (ADMM) algorithm with Gaussian back substitution (GBS) is proposed in this study. On the one hand, the proposed algorithm takes abundant advantage of the separability among variables in the model. On the other hand, the proposed algorithm can not only speed up the convergence rate to enhance the efficiency of computing, but also overcome the deficiency of the distributed dual subgradient algorithm, the possibility of nonconvergence in the iteration process. In addition, we give the theoretical proof of the convergence of the proposed algorithm. Furthermore, the interdependent relationship between variables has been discussed in depth during numerical simulations in the study. Compared with the dual subgradient method, the simulation results validate that the proposed algorithm has a higher convergence speed and better implementation effect.

scholarly journals Real-Time Pricing Strategy Based on the Stability of Smart Grid for Green Internet of Things

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Huwei Chen ◽  
Hui Hui ◽  
Zhou Su ◽  
Dongfeng Fang ◽  
Yilong Hui
Keyword(s):  
Internet Of Things ◽  
Smart Grid ◽  
Real Time ◽  
Power Supply ◽  
Stackelberg Game ◽  
Supply And Demand ◽  
Pricing Strategy ◽  
Maximal Profit ◽  
Real Time Pricing ◽  
The Stability

The ever increasing demand of energy efficiency and the strong awareness of environment have led to the enhanced interests in green Internet of things (IoTs). How to efficiently deliver power, especially, with the smart grid based on the stability of network becomes a challenge for green IoTs. Therefore, in this paper we present a novel real-time pricing strategy based on the network stability in the green IoTs enabled smart grid. Firstly, the outage is analyzed by considering the imbalance of power supply and demand as well as the load uncertainty. Secondly, the problem of power supply with multiple-retailers is formulated as a Stackelberg game, where the optimal price can be obtained with the maximal profit for retailers and users. Thirdly, the stability of price is analyzed under the constraints. In addition, simulation results show the efficiency of the proposed strategy.

scholarly journals Fast Model Predictive Control Based on Adaptive Alternating Direction Method of Multipliers

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Yu Li ◽  
Qiming Zou ◽  
Xiaoru Ji ◽  
Chanyuan Zhang ◽  
Ke Lu
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Large Scale ◽  
Alternating Direction Method ◽  
Control Input ◽  
Method Of Multipliers ◽  
Time Step ◽  
Prediction Horizon ◽  
Alternating Direction

Model Predictive Control (MPC) can effectively handle control problem with disturbances, multicontrol variables, and complex constraints and is widely used in various control systems. In MPC, the control input at each time step is obtained by solving an online optimization problem, which will cause a time delay in real time on embedded computers with limited computational resources. In this paper, we utilize adaptive Alternating Direction Method of Multipliers (a-ADMM) to accelerate the solution of MPC. This method adaptively adjusts penalty parameter to balance the value of primal residual and dual residual. The performance of this approach is profiled via the control of a quadcopter with 12 states and 4 controls and prediction horizon ranging from 10 to 40. The simulation results demonstrate that the MPC based on a-ADMM has a significant improvement in real-time and convergence performance and thus is more suitable for solving large-scale optimal control problems.

scholarly journals Distributed Real-Time Pricing Method Incorporating Load Uncertainty Based on Nonsmooth Equations for Smart Grid

2019 ◽  
Vol 2019 ◽  
pp. 1-18
Author(s):  
Hongjie Wang ◽  
Yan Gao
Keyword(s):  
Smart Grid ◽  
Real Time ◽  
Distributed Algorithm ◽  
Energy Supply ◽  
Supply And Demand ◽  
Nonsmooth Equations ◽  
Lagrangian Multipliers ◽  
Load Uncertainty ◽  
The Real ◽  
Real Time Pricing

The real-time pricing mechanism of smart grid based on demand response is an effective means to adjust the balance between energy supply and demand, whose implementation will impact the user's electricity consumption behaviour, the operation, and management in the future power systems. In this paper, we propose a complementarity algorithm to solve the real-time pricing of smart grid. The Karush–Kuhn–Tucker condition is considered in the social welfare maximisation model incorporating load uncertainty to transforming the model into a system of nonsmooth equations with Lagrangian multipliers, i.e., the shadow prices. The shadow price is used to determine the basic price of electricity. The system of nonsmooth equations is a complementarity problem, which enables us to study the existence and uniqueness of the equilibrium price and to design an online distributed algorithm to achieve the equilibrium between energy supply and demand. The proposed method is implemented in a simulation system composed of an energy provider and 100 users. Simulations results show that the proposed algorithm can motivate the users’ enthusiasm to participate in the demand side management and shift the peak loading. Furthermore, the proposed algorithm can improve the supply shortage. When compared with an online distributed algorithm based on the dual optimisation method, the proposed algorithm has a significantly lower running time and more accurate Lagrangian multipliers.

Alternating Direction Method of Multipliers as Simple Heuristic for Topology Optimization of a Truss With Uniformed Member Cross Sections

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Yoshihiro Kanno
Keyword(s):  
Global Optimization ◽  
Topology Optimization ◽  
Computational Cost ◽  
Optimization Method ◽  
Alternating Direction Method ◽  
Mixed Integer ◽  
Computational Time ◽  
Method Of Multipliers ◽  
Global Optimization Method ◽  
Alternating Direction

This paper presents a simple and effective heuristic for topology optimization of a truss under the constraint that all the members of the truss have the common cross-sectional area. The proposed method consists of multiple restarts of the alternating direction method of multipliers (ADMM) with random initial points. It is shown that each iteration of the ADMM can be carried out very easily. In the numerical experiments, the efficiency of the proposed heuristic is compared with the existing global optimization method based on the mixed-integer second-order cone programming (MISOCP). It is shown that even for large-scale problem instances that the global optimization method cannot solve within practically acceptable computational cost, the proposed method can often find a feasible solution having a fairly good objective value within moderate computational time.

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