Energy communities have gained significant interest in recent years as they enable active citizen participation in the energy transition. Most research in energy communities delves into strategies for enhancing sustainability, economic viability, and fairness. However, these strategies’ effectiveness largely depends on each energy community’s specific characteristics, including members types and available assets. This study focuses on understanding the impact of input parameters across different energy community typologies. It examines community size, the percentage of prosumers, and the diversity of members’ power profiles, analyzing 24,000 distinct configurations derived from an initial dataset of 92 load profiles. The study evaluates multiple setups for individual choices of solar photovoltaic systems and energy storage assets. The assessment applies a collective optimal management strategy to compare self-consumption and potential energy bill savings against a baseline where end-users operate individually. The same management strategy is applied consistently across various energy community typologies to demonstrate that the outcomes are primarily determined by the diversity of inputs (i.e., the energy community’s specific characteristics) rather than the energy management approach itself. The results indicate that energy communities with more than 20 members do not experience significant performance enhancements, regardless of operational choices. Additionally, the findings highlight that diversifying member types is more beneficial than oversizing the generation and storage asset capacity. Ultimately, the results exhibit that energy communities with a percentage of only consumers yield favorable outcomes for all members. Optimal configurations are identified when the composition comprises 75% of prosumers with heterogeneous load profiles.

The potential of Energy Communities (ECs) to foster local private investment in renewable energy production has been highlighted in various recent studies. Almost all these works assume that all investment decisions are taken at year 1, as well as static EC memberships throughout its lifetime. However, as part of a wider energy system, ECs may see their composition evolve with time as founding members may leave or other end-users may join. This uncertainty on the EC’s dynamic composition induces uncertainty on the real cost savings of its members. From this perspective, this work aims at quantifying the impact of newcomers on the profits from investments decided by founding members. To this end, an initial optimal sizing problem is solved before processing a Monte-Carlo analysis on the ECs composition’s evolution. Results collected on a test case composed of 92 end-users show that founding members can lose up to 25% of their expected savings by welcoming new members if no recourse actions are taken on top of the initial optimal investment.

Energy tariffs and incentives for renewable energy generation are key to encouraging residential users to engage in local energy production and form renewable energy communities (RECs). However, tariffs highly influence decisions and the benefits obtained by stakeholders. Typically, the retail tariff consists of fixed, time of use, inclined blocks, and/or dynamic fares; nevertheless, the more complex the tariff is, the less understandable it is for end-users and not necessarily the best tariff for all stakeholders. Therefore, this research analyzes the sensitivity of RECs' internal prices while implementing three grid purchasing tariffs (fix, time-of-use, and dynamic). The impact on both stakeholders (aggregator and retailer) is analyzed, as well as their interactions and the benefits for the end-users. Results indicate that, regardless of the internal pricing values, the community manager and the REC members receive optimal benefits when those internal tariffs are equal (e.g., purchasing equal to feeding in price).

This research discusses fairness in energy communities while investigating two types of organizations for operation and cost-sharing. On the one hand, centralized architectures consist of operating community assets in a coordinated manner with a central controller before a community manager shares the overall benefits between the users. Four sharing strategies are investigated and implemented in a monthly post-delivery phase. In contrast, in decentralized architectures, each user operates its assets independently. In such frameworks, the costs/benefits are usually shared among users through market-based mechanisms that rely on users' bids. This work then explores the Pool market and Peer-to-peer transactions to investigate the impact of different bidding from the users' perspective. Ultimately, all the proposed centralized and decentralized approaches (10 in total) are assessed based on economic performances at both users' and community levels. Specific attention is paid to fairness within the community, which is challenging. Three indexes derived from economy and game theory are then considered, along with metrics tailored for energy communities. Results from a seven-user community indicate that the pool market systematically returns considerable savings among decentralized frameworks compared to peer-to-peer markets. More importantly, centralized frameworks systematically yield the most significant bill reduction (16 %) and fairer cost allocation compared to decentralized frameworks.

Energy communities (ECs) aggregate users within proximity, which have diverse assets and consumption/generation power profiles. Such a variety of user arrangements significantly influences the benefits expected from the ECs. From a vast pool of EC configurations, this paper investigates their composition regarding users’ profiles impact on collective benefits. To that end, clustering is performed for i) different features to characterize an EC and for ii) the performance metrics of the EC once managed. The paper discusses how the community setup impacts its performance, which enables identifying the most relevant features. Hence, 1000 ECs are formed from 10 users. Additionally, two study cases are tested, one with 100% users with PV and battery (i.e., 100% prosumers) and the second with 50% prosumers. The results suggest that in terms of investment, the photovoltaic installed capacity is a more significant asset than storage capacity.

This paper aims to give an insight on the motivation of end-users within an energy community to encourage other users to join by sponsoring them.The proposed community organization is divided in two stages: first one for energy management and second one for costs allocation in an energy community (i.e. the way the overall bill is distributed among the members).In particular, two billing allocation approaches are proposed and account for end-user's preferences and their willingness to pay.Those strategies are based on an approach designed to set individual tariffs while preserving the properties of traditional allocation methods.This work gives perspective on different end-user's preferences and facilitates the understanding of energy communities farther than merely financial enterprises.

Demand response (DR) is an ancillary service that provides frequency support to the power grid. However, since its controller considers frequency deviations, the compensation provided by controllable loads such as thermostatically controllable loads (TCLs), may generate undesired harmonics into the power grid. This feature could be included into the controller of the TCLs by filtering the high harmonics. Hilbert-Huang Transform (HHT) uses an empirical decomposition method (EMD) from which the signal is separated into several intrinsic mode functions (IMFs), that can detect the high harmonics to be filtered. This paper proposes a frequency analysis of the power provided by TCLs, using HHT. Moreover, the study case considers variability of renewable generation in a microgrid. This was validated by using real demand and generation data from UK national system and simultaneously, temperature data for the same region and time frame, and implemented in Simulink/MATLAB.

The need for flexible networks is an emerging challenge for power system operators (SO). The use of additional support, such as demand response (DR), must be quantified in order to offer a reliable service, given that this information is vital for demand aggregators. Thermostatically controlled loads (TCLs) are one of the most promising options among DR solutions; due to TCLs' thermal characteristics their power may be increased or reduced accounting as ancillary services. However, TCLs tend to synchronize their behavior, which may affect their capacity to provide flexibility. This paper proposes a method for quantifying TCLs' power flexibility, taking into account different scenarios, types of controllers and loads. Two control methods are compared, and a modified control algorithm is applied to the controllers under analysis to avoid TCL synchronization. The analysis was validated by simultaneously using real demand data from the UK National Grid and temperature data for the same region and time frame.

Demand flexibility is the capacity of demand-side loads to change their consumption at any instant. It makes electricity more affordable by helping customers to use less power when prices are high. On the other hand, demand flexibility can also help to increase the reliability of the power grid when is highly stressed, by reducing demand for power or for integrating renewable generation. Thermostatic controlled loads (TCLs) are one of the most promising options among demand response (DR) solutions however, conventional methods for controlling single TCLs are not easily extensible to aggregated TCLs since it may cause them to synchronize. In addition, the ambient temperature may significantly influence the power flexibility offered by the TCLs. In this paper, a metric of flexibility is applied along with a modified control algorithm to de-synchronize the TCLs with the aim of fairly comparing the different control approaches applied to aggregated TCLs. Furthermore, a sensitivity analysis, considering variations of temperature in several periods of time, is performed over the TCLs power flexibility. The results were validated in Simulink/MATLAB using real demand and generation data from UK national system and simultaneously, temperature data for the same region and time frame.

The proportional-resonant control (PR-control) is a simple and efficient control, which has the ability to eliminate error in linear time varying signals. This type of control is typically used in applications such as the integration of solar panels, energy storage and electric vehicles among others. Despite having non-linear dynamics, the analysis of the PR-control is usually performed using linear systems theory. This paper proposes a non-linear stability analysis based on Lyapunov theory which formalizes the main stability results on these controls that are standard in industrial applications. The proposed approach is general and independent of the structure of the phase-locked loop which is considered as a vanishing perturbation. This approach allows a better understanding of the control from a non-linear perspective. In addition, it gives an exact criteria for tuning the parameters of the controller. Numerical simulation results in a wide range of scenarios complement the analysis and validate them.

Time and frequency localizations are of crucial importance in the analysis of nonlinear and non-stationary processes, especially in systems with high level of complexity where detection of information/events, estimation of parameters and classification of signals in classes is necessary to take decisions. The Hilbert Huang Transform (HHT) offers an adaptive approach to analyze no-linear and non-stationary processes. This paper exposes the MIT approach and its new methodologies for improvement of the analysis, such as the masking process. Two examples are given to show the techniques, first a synthetic signal, representing a typical behavior of an electrical signal immersed in a power electronic environment and second a brain signal to extend the acknowledgment to a biological process. Finally a mode mixing separation technique is presented.

This paper presents a wide area control for power systems based on Linear Matrix Inequalities (LMI). The methodology is based on a linearized model of the multimachine system considering the second order model of the machine. The proposed methodology improves the transient response of the power system considering wide area measurement based on phasor measurement units. Stability is ensured because Lyapunov-based constrains are included in the method. The LMI is solved using CVX, a program for convex optimizations that includes semidefinite programming models. Tests on two power systems considering different load scenarios were performed. The capacity of the methodology to generate a robust control was demonstrated. Finally restrictions associated with the overshoot on the control variable for stricter initial conditions within a permanent ellipsoid are included.