Coalitional Game-Based Transactive Energy Management in Local Energy Communities

Abstract

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Economic MPC-Based Smart Home Scheduling With Comprehensive Load Types, Real-Time Tariffs, and Intermittent DERs

“…We denote the state variable of TCL k at time step t as () k yt, and the corresponding appliance can be scheduled to meet the users' requirements, namely, a desired temperature Tk and tolerable variation threshold k  . The temperature requirements can be modeled by (13). The sampling time of the measurement equipment servicing the TCL is ()…”

“…It should also be noted that the cost function minimizes the energy consumption of all the other time steps in the same time slot rather than that of the current step. , as suggested in (13). It should be mentioned that the cost function of the lowerlevel model optimizes the energy consumption rather than the energy cost, as does its output to the upper level.…”

“…Meanwhile, behind-the-meter household appliances and distributed energy resources (DERs) also fall under the category of smart home scheduling, which has been discussed extensively in studies such as [11] and [12]. At the community level, game theory is a common measure to address the interplay between different end-users and aggregators to consider their mutual benefits [13] by adopting new technologies such as multi-agents [14], clustering [15], and inference [16] from the artificial intelligence field. Treating the community as a whole, which is another perspective of community-level energy scheduling, falls into the area of distribution system operations.…”

Scaling Up Cooperative Game Theory-Based Energy Management Using Prosumer Clustering

“…However, as the number of prosumers in this cooperative energy management scheme increases, the nucleolus becomes computationally intractable, because 1) it requires solving convex optimization problems to calculate the values of all coalitions, the number of which is an exponential function of the number of prosumers [15]; 2) given the values of all coalitions, the number of iterative steps required to compute the nucleolus is also an exponential function of the number of prosumers [16]. A stabilizing payoff allocation in an analytical expression was proposed in [17], and a worst-case excess method was used in [18] to compute a partial nucleolus. However, both of these payoff allocations only address the second computational problem, leaving the coalition value calculations still intractable for large games.…”

“…However, both of these payoff allocations only address the second computational problem, leaving the coalition value calculations still intractable for large games. Among all the case studies for the cooperative energy management scheme in [11], [17], [18], the highest number of players shown is 15.…”