Contract-Theoretic Demand Response Management in Smart Grid Systems

Abstract

The sheer growth of electricity demand and the rising number of electricity-hungry devices have highlighted and elevated the need of addressing the demand response management problem in residential smart grid systems. In this paper, a novel contract-theoretic demand response management (DRM) framework in residential smart grid systems is introduced based on the principles of labor economics. The residential households produce and consume electricity, acting as dynamic prosumers. Initially, the prosumers' personal electricity generation and consumption characteristics are captured by introducing the concept of prosumers' types. Then, the prosumers' and the electricity market's profit is depicted in representative utility functions. Based on the labor economics principles, Contract Theory is adopted to design the interactions among the electricity market, which offers personalized rewards to the prosumers in order to buy electricity at an announced price, and the prosumers, who offer their "effort" by paying for the purchased electricity. The contract-theoretic DRM problem is formulated as a maximization problem of the electricity market's utility, while jointly guaranteeing the optimal satisfaction of the prosumers, under the scenarios of complete and incomplete information from the electricity market's perspective regarding knowing or not the prosumers' types, respectively. The corresponding optimization problems are solved following a convex optimization approach and the optimal contracts, i.e., rewards and efforts, are determined. Detailed numerical results obtained via modeling and simulation, highlight the key operation features and superiority of the proposed framework.

A Review on Communication Aspects of Demand Response Management for Future 5G IoT- Based Smart Grids

“…If the price difference between hours or time periods is significant, customers adjust the schedule of their flexible loads in order to take advantage of lower price periods. Consequently, from the utility's point of view, significant peak shaving can be achieved [3], [23].…”

“…• Different time scales and stochastic reliablity with consideration of renewable energy [28] • Uncertainty in load, and the number of users [3], [28] • Number of appliances and users [2], [4], [29] • Networking aspects of DRM [5], [30] • Production cost [23] • Energy storage characteristics of appliances [31] • Real-time congestion management in distributed networks [32] • Operation duration of appliance [33] • Effect of cooperation/competition of utilities [4] • Outage cost [23], [30] • Utility cost [32], [30] • Costs paid for customer participation in demand response, and price elasticity [4] • Market model (e.g. electricity or capacity marketing) [34] • Customer behavioural characteristics [35] These parameters can affect the performance and efficiency of the grid which are studided widely in literature.…”

“…This offers the flexibility to run different DRM programmes. Literature in [3], [4], [5] explained contract based DRM in SGs with a focus on optimization approaches. DRM is closely related to concepts such as remote control and automatic communication, where 5G technologies play an important role.…”

Nash Equilibrium to Competitive Equilibrium Mechanisms Design: Subsidization and Punishment

“…Mathematical programs with equilibrium constraints [19] and equilibrium programs with equilibrium constraints [20] are widely used to address bi-level optimization market problems with single and multiple strategic generators. Novel approaches such as contact theory, which improves the profit of both the energy market and the participants [21], have also been developed. Due to the computational burden and challenges of nonconvexity in conventional game-theory approaches, machine learning approaches have been applied to address realistic energy markets with extensive complexity, as shown in [22].…”

Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors

“…Therefore, urban planners must adopt ambitious energy planning policies to ensure that future construction is carried out in a way that increases energy efficiency in buildings [8]. In this regard, energy consumption prediction and demand response management play an important role in analyzing each influencing factor that leads to energy preservation and reduces its impact on the environment [9]. Moreover, energy consumption prediction models can help in understanding the impact of energy retrofitting and energy supply programs because these models can be used to define energy requirements as a function of input parameters [10].…”