The authors review the efforts made in the last five years to implement Demand Response (DR) programs, considering and studying several models and countries. As motivation, climate change has been a topic widely discussed in the last decades, namely in the power and energy sectors. Therefore, it is crucial to substitute non-renewable fuels with more environment-friendly solutions. Enabling Distributed Generation (DG), namely using renewable resources such as wind and solar, can be part of the solution to reduce the greenhouse effects. However, their unpredictable behavior might result in several problems for network management. Therefore, the consumer should become more flexible towards this new paradigm where the generation no longer follows the demand requests. With this, Demand Response (DR) concept is created as part of this solution. This paper studies the European Union and United States’ current status, with over 50 references.

Electric Vehicles (EV) are emerging into electricity grid, where the Vehicle-to-Grid (V2G) feature is a major flexibility opportunity for Demand Response (DR) programs. Optimized and fair management of EVs flexibility activation is then required. In the present paper, the authors propose a methodology to deal with the complex management of the Local Energy Communities (LEC) with such resources. The method allows the fair selection of DR and V2G participants. Focusing on the EVs, the authors compare two approaches: performance rate and clustering groups (considering extrinsic and intrinsic characteristics using modeling of preferences). The method was tested in five office buildings with a shared EV parking lot. Four different events were studied, and the results show that the performance rate might not be enough to have the best results from both the local manager and V2G participants. According to the numerical results, using the performance method the total reduction obtained was 60.20 kW confronting with the 105.13 kW reduced using the clustering method.

Distributed energy resources can improve the operation of power systems, improving economic and technical efficiency. Aggregation of small size resources, which exist in large number but with low individual capacity, is needed to make these resources’ use more efficient. In the present paper, a methodology for distributed resources management by an aggregator is proposed, which includes the resources scheduling, aggregation and remuneration. The aggregation, made using a k-means algorithm, is applied to different approaches concerning the definition of tariffs for the period of a week. Different consumer types are remunerated according to time-of-use tariffs existing in Portugal. Resources aggregation and remuneration profiles are obtained for over 20.000 consumers and 500 distributed generation units. The main goal of this paper is to understand how the aggregation phase, or the way that is performed, influences the final remuneration of the resources associated with Virtual Power Player (VPP). In order to fulfill the proposed objective, the authors carried out studies for different time frames (week days, week-end, whole week) and also analyzed the effect of the formation of the remuneration tariff by considering a mix of fixed and indexed tariff. The optimum number of clusters is calculated in order to determine the best number of DR programs to be implemented by the VPP.

Aggregation of small size consumers and Distributed Generation (DG) units have a considerable impact to catch the full flexibility potential, in the context of Demand Response programs. New incentive mechanisms are needed to remunerate consumers adequately and to recognize the ones that have more reliable participation. The authors propose an innovative approach to be used in the operation phase, to deal with the uncertainty to Demand Response events, where a certain target is requested for an energy community managed by the Aggregator. The innovative content deals with assigning and updating a Reliability Rate to each consumer according to the actual response in a reduction request. Three distinct methods have been implemented and compared. The initial rates assigned according to participation in the Demand Response events after one month of the enrolment period and the ones with higher reliability follow scheduling, performed using linear optimization. The results prove that using the proposed approach, the energy community manager finds the more reliable consumers in each period, and the reduction target achieved in DR events. A clustering algorithm is implemented to determine the final consumer rate for one month considering the centroid value.

The need for new business models to replace existing ones, soon obsolete, is a subject often discussed among researchers in the area. It is essential to find a practical solution that includes the concepts of demand response and distributed generation in the energy markets, these being the future of the electricity grid. It is believed that these resources can bring advantages to the operation of the system, namely increasing technical efficiency. However, one of the problems is the aggregation of small resources as a result of the associated uncertainties. The authors propose a business model with three main phases used in planning: optimal scheduling, aggregation, and remuneration. In this paper, a new phase was added, the classification, with the main purpose of assisting the aggregator of these small resources in operating situations. The focus is on the fair remuneration of participants in the management of the market, in addition to minimizing operating costs. After testing four different remuneration methods, it was proved that the method proposed by the authors obtained better results, proving the viability of the proposed model.