Abstract
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.
Highlights
Given the increased expectations of customers, in both quality and quantity [1], the limited energy resources, and the lengthy and expensive process of exploiting new resources, the reliability of the grid has been put in danger and there is a need to develop new methods to increase the grid efficiency
We propose a complementarity algorithm to solve the real-time pricing of smart grid
The price-response based demand response (DR) programs, especially, can effectively promote user enthusiasm to participate in the market, and it is one of the most cost-effective elements as regards energy cost reduction for residential and small industrial buildings [2, 3]
Summary
Given the increased expectations of customers, in both quality and quantity [1], the limited energy resources, and the lengthy and expensive process of exploiting new resources, the reliability of the grid has been put in danger and there is a need to develop new methods to increase the grid efficiency. Samadi et al used the Lagrangian multiplier to determine the basic price of electricity and designed a distributed algorithm for real-time pricing in smart grid. We propose an algorithm for the real-time pricing of smart grid that can provide enhanced computation speed and accuracy over the dual optimisation method. We consider the Karush–Kuhn–Tucker (KKT) condition in the social welfare maximisation model and use a system of nonsmooth equations to solve the real-time pricing of smart grid. (1) Based on social welfare maximisation model, this paper establishes the real-time pricing model of smart grid by KKT condition. Users can shift their usage and save electricity loads to reduce electricity costs while satisfying their requirements according to the prices provided by energy provider in each time slot, and the energy provider can adjust prices and electricity production levels according to users’ demand and expectations.
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