## 2 Materials and Methods  
There has been a steady increase in the use of DR aggregators to maximize benefits to DSOs, independent service operators (ISOs), and end users in recent years. In [10], a system of aggregator remuneration was proposed using two clustering algorithms: hierarchical and fuzzy c-means clustering. In this paper, the virtual case study considered 2592 operator scenarios involving 548 distributed generators, and 20,310 consumers. The aim was to maximize gains to both distributed generation (DG)  
Figure 1: Role of the aggregator in connecting a distribution service operator (DSO) and end users in a cluster. operation and consumers based on both real-time pricing (RTP) and incentive-based DR programs, while simultaneously minimizing DG operating costs. In [11], a customer incentive pricing (CIP) program was proposed using a heuristic optimization approach. The simulation system comprised 5555 residential consumer households with 56,642 schedulable appliances using an RTP strategy. Simulation was done over a 24 h period and optimization was done to maximize financial benefits to both the aggregator and the end user. In [12], a DR aggregator was proposed for wholesale energy markets as a mixed-integer linear programming model using a stochastic bi-level mathematical program. The model was formulated for both RTP and day-ahead (DA) markets considering load curtailment. The aim was to investigate the financial benefits to the aggregator by deployment of DR contracts; results show positive gains to DR aggregators, and consequently to end users. In [13], a tri-level framework was proposed, consisting of an independent system operator (ISO) at the top layer, demand response aggregators at the middle layer, and consumers at the bottom layer. The framework was modelled as two bi-level optimization models detailing the interaction between the ISO and the aggregator, and also between the consumers and the aggregator. The proposed model was tested on the IEEE 9-bus and IEEE 118-bus test systems. The objective of the upper layer model was profit maximization for the DR aggregator, while that of the lower layer was profit maximization for both the ISO and consumers. The satisfaction of participating DR consumers was the focus of the research in [14]. The proposed model determined financial rewards to be offered to consumers while maximizing aggregator profit. Results showed that financial rewards to consumers for participating in DR programs reduced as they aspired to increase their level of satisfaction. In [15], the potential of DSM to optimally balance energy supply in a fully renewable European power system was investigated. An optimal mix of wind and solar photovoltaic (PV) was considered as backup for the conventional grid. The DSM was implemented as an alternative to the renewable energy mix; it was reported from simulation results that DSM utilization reduced the need for backup energy on the continent by one-third of the current capacity. In [16], the author reviewed business models for energy efficiency and demand response providers. These models were based on the different segments of the electricity market, namely system operation, generation, transmission, and distribution, as well as retail and load. The results of this research revealed three key findings among others: (1) There are more feasible business models related to the retailer segment of the energy market compared to generation and transmission segments (2) DR has more feasible business models compared to energy efficiency (3) Most of the analysed business models are reinforced by renewable energy penetration. In [17], a decision-making model for a demand aggregator as a load-serving entity (LSE) was proposed. The LSE maximized profit in an uncertain day-ahead market by optimal scheduling of interruptible loads as well as efficient charging and discharging of plug-in electric vehicles (PEVs). The optimization approach was Karush-Kuhn-Tucker (KKT) with duality theory.