Fairness-Aware Game Theoretic Approach for Demand Response in Microgrids

Abstract

Demand response programs are implemented by the utilities to manage the energy consumption at consumer side. Demands are managed in response to supply conditions as opposed to the traditional grid. The existing programs focus mainly on achieving system objectives such as minimizing peaks and reducing the cost of power generation. Consequently, users' electrical bills will be reduced but the question of fairness has barely been discussed in the literature. In this paper, the issue of fairness within demand response programs is addressed. In this context, a fair pricing model based on the contribution of each user toward attaining the aggregated system cost is proposed. In addition, the proposed system considers a more realistic scenario that consists of multiple energy sources within a microgrid as opposed to existing ones that use only one shared energy source. We first employ the concept of Shapley value in the pricing model to evaluate the fairness of existing works. Then, we use an approximation of this value to drive our proposed demand response program. Finally, we evaluate the use of the approximate Shapley within our proposed demand response algorithm and compare it to existing works.

Personalized real time pricing for efficient and fair demand response in energy cooperatives and highly competitive flexibility markets

“…The general idea described above has been approached in different ways in the literature, including ex-post [10,11] & ex-ante pricing methods [12][13][14][15][16][17][18][19][20][21][22][23][24]. Many pricing mechanisms [2,[12][13][14][15] opt for system efficiency (KPI1), but at a risk of either running a deficit or extracting a large surplus from the users as explained in [4] and are not compatible with the emerging environments described.…”

“…Finally, a third class of studies [21][22][23][24], opts for enhancing the system's fairness (KPI3). In particular, the authors in [21] propose a pricing model based on the principle that each user should be billed according to her contribution to the system's cost.…”

Mechanism Design for Fair and Efficient DSO Flexibility Markets

“…In order to prevent such phenomena, the objective of the market can be defined so as to maximize fairness. In [27] and [28], fairness is defined using the Shapley value, whereas in [29] a compensation mechanism for flexibility activation is proposed as a fair way to compensate aggregators. In [30] and [31] fairness is assessed on the basis of equally distributing electricity costs among users, based on their level of effect on the community's electricity cost.…”

Fair and efficient energy consumption scheduling algorithm using tabu search for future smart grids

“…To enhance the billing fairness between customers, the work in [18][19][20][21] proposes other billing mechanisms based on the flexibility that the customer provides to attain the goals of the program. The billing mechanisms in [15,[18][19][20][21] ignore the impact of customers violations after the consumption schedules are assigned, which results in unfair billing. This is mainly because the billing model penalises all customers participating in the program if any customer violates the assigned operation schedule.…”

“…In this work, a fair and efficient group ADSM program for the scheduling of a day-ahead energy consumption in the residential sector, is proposed. As an alternative to the billing mechanisms in [15,[18][19][20][21], an energy consumption billing mechanism that considers the impact of consumption violations on the program performance is proposed to ensure a higher level of fairness. Furthermore, practical aspects and realistic assumptions of the modelling of the existing smart appliances are taken into consideration.…”