A distributed game-theoretic demand response with multi-class appliance control in smart grid

Abstract

We propose an event-triggered game-theoretic strategy for managing the power grids demand side, capable of responding to changes in consumer preferences or the price parameters coming from the wholesale market. The relationship between the retailer and the residential consumers is modeled as one-leader, N-follower Stackelberg game. We provide a detailed characterization of the household appliances to reflect the reality and improve the efficiency of the demand response (DR). Moreover, to consider all the appliances' essentials, the consumer's objective function is formulated as a mixed integer non-linear program (MINLP), which, unlike conventional procedures, is solved via an integrated method. The proposed method consists of a day-ahead stage, in which the DR problem is solved for the next scheduling horizon, and a real-time stage which runs repeatedly to tackle the change in the parameters and adapt to the new condition. For any change in the grid, the consumers use the estimated optimal parameters (given by the original objective function) and develop another Stackelberg game based solution to maximize the satisfaction level. Given the appliances of multi-class nature, the proposed method is shown to be very tractable for ancillary services and reducing the mismatch between the renewable power generation and the load demand.

A Robust Scalable Demand-Side Management Based on Diffusion-ADMM Strategy for Smart Grid

“…To tackle the diversity and taking the exact characteristics of all the appliances, some researches involved several classes of domestic appliances including deferrable, curtailable, thermal, and critical [11]. Others formulated multi-residential DSM problems in the smart grids with multi-class appliances models, such as [12]- [14]. An energy management method was introduced in [15] to optimally control the energy supply and the temperature settings of distributed heating and ventilation systems for residential buildings.…”

A GBDT‐SOA approach for the system modelling of optimal energy management in grid‐connected micro‐grid system

“…Few methods are anticipated to maximize the power system self-healing ability together with system reliability, improve economic including cost minimization, decrease energy consumption and maximized customer participation in the direction of decreasing gas emissions, renewable energy system persistent employ on conventional grid also demand side management. 20,21 The rationale behind the research of these problem solutions in terms of data driven strategies at energy management system modeling is owing to the fact that rare cases only the EMS can evaluate local micro-grid energy optimal schedule flows in real time. For this purpose, the linear programming (LP), mixed-integer linear programming (MILP), greedy algorithms, or dynamic programming (DP) based deterministic algorithm are commonly reported in the literature.…”

“…Normally, goal of smart grid is to use communication also calculation approach. Few methods are anticipated to maximize the power system self‐healing ability together with system reliability, improve economic including cost minimization, decrease energy consumption and maximized customer participation in the direction of decreasing gas emissions, renewable energy system persistent employ on conventional grid also demand side management 20,21 …”

Evolutionary Analysis for Residential Consumer Participating in Demand Response Considering Irrational Behavior

“…Authors in [16] formulated an energy consumption scheduling program with game theory, where players are residential users and their strategies are the daily schedules of household appliances. Authors in [17] proposed an event-triggered game-theoretic strategy for managing the power grid's demand side, capable of responding to changes in consumer preferences or the price parameters coming from the wholesale market. Reference [18] adopted a dynamic non-cooperative repeated game with Pareto-efficient pure strategies as the decentralized approach to optimize the energy consumption and energy trading amounts for the next day.…”