# Methods for Aggregation and Remuneration of Distributed Energy Resources
## 1 Introduction
### Related Literature  
With the aim of aggregating resources, the way to create groups can consider several approaches and distinct features, namely, on which basis the resources are grouped and the number of groups [14]. These issues are part of the activities of an aggregator; thus, decision support techniques or tools capable of helping the aggregator in these matters are needed. A usual way of creating these kinds of tools is using clustering methods in order to find the similarities/patterns in large amounts of data, and then join the related resources [15, 16, 17, 18]. Another major feature of the aggregator is the remuneration of resources, i.e., if the consumers and distributed generators are a part of the aggregator's schedule, these will require remuneration for the service they are providing [19, 20, 21]. In this way, besides the scheduling and aggregation, decision support regarding the strategies implemented for the remuneration of resources (e.g., the resources can be paid individually, by type, according to the group, etc.) is needed. In [22], the authors present work regarding the scheduling of resources in a microgrid, considering flexible loads of several types (load curtailment and shifting). Also, the forecast of electricity prices is made in order to minimize the microgrid operation costs. In [23], the authors refer to aggregators as a player oriented for the integration of demand response in energy markets, by presenting a model for the frequency regulation with the use of this type of resource. This is currently one of the main activities in European markets; several demand response programs presented by the system operator are related to frequency regulation. In [23], the authors perform a scheduling of resources, and compute their remuneration, proposing a business model for distributed resources in the energy markets. In fact, other methods can be found in the literature concerning the aggregation and remuneration of resources, namely, the ones based on Locational Marginal Pricing (LMP). The authors in [24] proposed a methodology for the distributed generation remuneration with LMP, in radial distribution systems. This method was applied to a 34-bus network. In [25], a similar study, with concern for demand response, was applied to a 12-bus system with real-time pricing. These works, [24,25], have focused on the use of LMPs for the network cost and optimal resource allocation in a planning phase in order to optimize the use of the grid. With focus on congestion management and pricing, the authors in [26] considered both active and reactive power in a distribution system addressing also the voltage support. In [27], according to the calculated congestion, demand response is scheduled, also considering the interactions between the aggregator and distribution system operator. In this way, signals are given to the consumers/producers in the day-ahead in order to efficiently manage the congestion situations. In many works, like in [28], the use of LMP is mainly driven by the marginal cost of non-renewable resources. These works are very relevant in the field, considering the assumptions of the main objective of the work. In the present paper, the authors assume that distributed generators and demand response resources are available randomly in the network, so we are not aiming at deciding the optimal allocation of the resources. Also, we are not focusing on congestion management and voltage support; in fact, our main concern is the remuneration of active power/real energy. Additionally, for a fair remuneration of the resources, the authors in the present paper assume that all the consumers in the same tariff are paid the same price, disregarding the location in the network. The power-flow implemented in the proposed methodology is needed in order to ensure that there is no overuse of line capacity and violations in voltages.