The transition toward decentralized energy systems requires intelligent, resilient, and user-centered infrastructures capable of supporting both autonomy and cooperation across buildings. This paper presents Caravels, which is a distributed system architecture designed to enable intelligent communities through container-based orchestration, focusing in two new features: personalized user modeling, and inter-building service and data sharing. Building upon a modular microservice deployment paradigm, Caravels integrates a graph-based user preference module for context-aware personalization and a data sharing mechanism that facilitates peer-to-peer cooperation. This paper presents a three-part case study evaluating the system across heterogeneous environments deployed in a community setting. Results demonstrate real-time adaptability through dynamic service deployment based on user-defined preferences, effective inter-building collaboration via shared IoT access and service consumption, and personalized automation through reinforcement learning embedded in graphs. The system maintains low-latency operation and minimal resource usage, validating its applicability to low-cost, edge-constrained environments. Caravels innovations enable distributed cooperation on edge, sharing data while preserving sovereignty, and context aware personalization at the building-level. • Organic intelligent framework for communities. • Distributed intelligent framework with central orchestration for dynamic operation. • A novel distributed container-based orchestration infrastructure for communities. • User- and Human-centric solution to enable the deployment of services in communities.

This paper presents the Reasoning-based, Explainable and Adaptive Contextual Tool for Smart Energy Management (REACTS). The tool enables the automatic management of energy resources in smart buildings, promoting transparent decision-making to foster user trust and support sustainability goals. It integrates Artificial Intelligence (AI) techniques, including machine learning models such as Multilayer Perceptrons, Random Forests, K-nearest Neighbors, and Support Vector Machines, to forecast energy usage and classify contextual states. Reinforcement Learning is employed to dynamically select and update forecasting models based on their historical performance. Semantic reasoning is used to represent domain knowledge through ontologies and rule-based inference, enabling context-aware decisions that adapt to user preferences and environmental conditions. Control actions are computed periodically using real-time sensor data enriched with semantic annotations. To ensure interpretability, REACTS employs Explainable AI methods, specifically SHapley Additive exPlanations, to generate feature-attribution-based justifications tailored to user profiles in both visual and textual formats. The system also incorporates green computing strategies, triggering model retraining only when performance degradation is detected, and scheduling updates during periods of lower energy or computational demand. REACTS is deployed in a real office building, operating continuously and demonstrating how contextual reasoning and explainability can enhance the reliability and sustainability of smart energy systems.

This work provides a comprehensive analysis of the optimization of a Virtual Power Plant (VPP), that consider the presence of energy storage systems and controllable loads, through the benchmarking of various solvers.It delves into the development of a Mixed Integer Linear Programming (MILP) algorithm aiming at optimizing energy management and exchange within a VPP, that takes into account the operation of shift electric appliances and battery storage systems among different houses.The proposed model aims to minimize the overall electricity cost while ensuring that the energy demand of the system is met, the battery state of charge is maintained within safe operating limits, and the shift electrical appliance is scheduled.Furthermore, the experimental comparisons, the study evaluates the performance of commercial and open-source solvers in handling the complex dynamics of energy demand and supply.The findings highlight the importance of solver selection in enhancing the management, scalability, and reliability of VPP optimization strategies, offering insights into the optimal combination of programming interfaces and solvers for efficient VPP operation.

Demand response programs, energy storage systems, electric vehicles, and local electricity markets are appropriate solutions to offset the uncertainty associated with the high penetration of distributed energy resources. It aims to enhance efficiency by adding such technologies to the energy resource management problem while also addressing current concerns using smart grid technologies and optimization methodologies. This paper presents an efficient intraday energy resource management starting from the day-ahead time horizon, which considers the uncertainty associated with load consumption, renewable generation, electric vehicles, electricity market prices, and the existence of extreme events in a 13-bus distribution network with high integration of renewables and electric vehicles. A risk analysis is implemented through conditional value-at-risk to address these extreme events. In the intraday model, we assume that an extreme event will occur to analyze the outcome of the developed solution. We analyze the solution’s impact departing from the day-ahead, considering different risk aversion levels. Multiple metaheuristics optimize the day-ahead problem, and the best-performing algorithm is used for the intraday problem. Results show that HyDE gives the best day-ahead solution compared to the other algorithms, achieving a reduction of around 37% in the cost of the worst scenarios. For the intraday model, considering risk aversion also reduces the impact of the extreme scenarios.

This article evaluates the performance of different metaheuristics (evolutionary algorithms) solving a cost mini-mization problem in demand response contract markets. The problem considers a contract market in which a distribution system operator (DSO) requests flexibility from aggregators with DR capabilities. We include a network validation approach in the evaluation of solutions, i.e., the DSO determines losses and voltage limit violations depending on the location of aggregators in the network. The validation of the network increases the complexity of the objective function since new network constraints are included in the formulation. Therefore, we advocate the use of metaheuristic optimization and a simulation procedure to overcome this issue. We compare different evolutionary algorithms, including the well-known differential evolution and other two more recent algorithms, the vortex search and the hybrid-adaptive differential evolution with decay function. Results demonstrate the effectiveness of these approaches in solving the proposed complex model under a realistic case study.

Energy community markets have emerged to promote prosumers' active participation and empowerment in the electrical power system. These initiatives allow prosumers to transact electricity locally without an intermediary such as an aggregator. However, it is necessary to implement optimization methods that determine the best transactions within the energy community, obtaining the best solution under these models. Particle Swarm Optimization (PSO) fits this type of problem well because it allows reaching results in short optimization times. Furthermore, applying this metaheuristic to the problem is easy compared to other available optimization tools. In this work, we provide a sensitivity analysis of the impact of different parameters of PSO in solving an energy community market problem. As a result, the combination of parameters that lead to the best results is obtained, demonstrating the effectiveness of PSO solving different case studies.

The ever-increasing number of electric vehicles (EVs) circulating on the roads and renewable energy production to achieve carbon footprint reduction targets has brought many challenges to the electrical grid. The increasing integration of distributed energy resources (DER) in the grid is causing severe operational challenges, such as congestion and overloading for the grid. Active management of distribution network using the smart grid (SG) technologies and artificial intelligence (AI) techniques can support the grid's operation under such situations. Implementing evolutionary computational algorithms has become possible using SG technologies. This paper proposes an optimal day-ahead resource scheduling to minimize multiple aggregators' operational costs in a SG, considering a high DER penetration. The optimization is achieved considering three metaheuristics (DE, HyDE-DF, CUMDANCauchy++). Results show that CUMDANCauchy++ and HyDE-DF present the best overall results in comparison to the standard DE.

In this article, a cost optimization problem in local energy markets is analyzed considering fixed-term flexibility contracts between the DSO and aggregators. The DSO procures flexibility while aggregators of different types (e.g., conventional demand response or thermo-load aggregators) offer the service. We solve the proposed model using evolutionary algorithms based on the well-known differential evolution (DE). First, a parameter-tuning analysis is done to assess the impact of the DE parameters on the quality of solutions to the problem. Later, after finding the best set of parameters for the "tuned" DE strategies, we compare their performance with other self-adaptive parameter algorithms, namely the HyDE, HyDE-DF, and vortex search algorithms. Results show that with the identification of the best set of parameters to be used for each strategy, the tuned DE versions lead to better results than the other tested EAs. Overall, the algorithms are able to find near-optimal solutions to the problem and can be considered an alternative solver for more complex instances of the model.

As renewable energy sources penetration is increasing in the power distribution network, an energy aggregator can provide a highly flexible generation and demand as required by the smart grid paradigm. However, this energy aggregator entity needs adequate decision support tools to overcome the complex challenges and deal with a number of energy resources. So, the energy resource management is crucial for the aggregator, to increase the profits, reduce the operation costs, reduce the carbon footprint and also to improve the system stability. Thus, this paper proposes a model for a large-scale energy resource scheduling problem of aggregators in a smart grid. Also, it is compared the performance of five evolutionary algorithms to solve this kind of problem. A realistic case study is performed using a real distribution network in Zaragoza, Spain. The results show that load flexibility can contribute to the profitability improvement of the aggregators' entities.

Local energy markets (LM) are attracting significant interest due to their potential of balancing generation and consumption and supporting the adoption of distributed renewable sources at the distribution level. Besides, LMs aim at increasing the participation of small end-users in energy transactions, setting the stage for transactive energy systems. In this work, we explore the use of ant colony optimization (ACO) for learning bidding strategies under a bi-level optimization framework that arises when trading energy in an LM. We performed an empirical analysis of the impact of ACO parameters have in the learning process and the obtained profits of agents. After that, we analyze and compare ACO performance against an evolutionary algorithm under a realistic case study with nine agents trading energy in the day-ahead LM. Results suggest that ACO can be efficient for strategic learning of agents, providing solutions in which all agents can improve their profits. Overall, it is shown the advantages that an LM can bring to market participants, thereby increasing the tolerable penetration of renewable resources and facilitating the energy transition.

This paper presents an application of Evolutionary Computation (EC) to the benchmark of the safety isolating transformer problem. The benchmark adopts multidisciplinary optimization strategies, namely the multidisciplinary feasible (MDF) and the individual discipline feasible (IDF) formulations. The benchmark meets the requirements of engineers and scientists working with machine design problem, such as in the first part of the design process that is the choice of structure and materials. The EC methods employed in this paper are based on Evolutionary Algorithms (EAs), namely two variants of Differential Evolution (DE), two variants of Hybrid Adaptive DE (HyDE) and the Vortex Search (VS). The results showed in this paper suggest that EA methods are competitive with the classical optimization method, the sequential quadratic programming (SQP). Among the developed EAs, HyDE-DF is able to obtain better values than SQP on a significant battery of trials.

This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computation, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations.

This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computing, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations. AiD-EM demonstration is enabled by its connection to MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Power systems are showing a dynamic evolution in the last few years, caused in part by the adoption of smart grid technologies. The integration of new elements that represent a source of uncertainty, such as renewables generation, electric vehicles, variable loads and electricity markets, poses a higher degree of complexity causing that traditional mathematical formulations struggle in finding efficient solutions to problems in the smart grid context. In some situations, where traditional approaches fail, computational intelligence has demonstrated being a very powerful tool for solving optimization problems. In this paper, we analyze the application of Differential Evolution (DE) to address an energy resource management problem under uncertain environments. We perform a systematic parameter tuning to determine the best set of parameters of four state-of-the-art DE strategies. Having knowledge of the sensitivity of DE to the parameter selection, self-adaptive parameter control DE algorithms are also implemented, showing that competitive results can be achieved without the application of parameter tuning methodologies. Finally, a new hybrid-adaptive DE algorithm, HyDE, which uses a new “DE/target - to - perturbed_best/1” strategy and an adaptive control parameter mechanism, is proposed to solve the problem. Results show that DE strategies with fixed parameters, despite very sensitive to the setting, can find better solutions than some adaptive DE versions. Overall, our HyDE algorithm excelled all the other tested algorithms, proving its effectiveness solving a smart grid application under uncertainty.

Smart Grid technologies enable the intelligent integration and management of distributed energy resources. Also, the advanced communication and control capabilities in smart grids facilitate the active participation of aggregators at different levels in the available electricity markets. The portfolio optimization problem consists in finding the optimal bid allocation in the different available markets. In this scenario, the aggregator should be able to provide a solution within a timeframe. Therefore, the application of metaheuristic approaches is justified, since they have proven to be an effective tool to provide near-optimal solutions in acceptable execution times. Among the vast variety of metaheuristics available in the literature, Differential Evolution (DE) is arguably one of the most popular and successful evolutionary algorithms due to its simplicity and effectiveness. In this paper, the use of DE is analyzed for solving the portfolio optimization problem in electricity markets. Moreover, the performance of DE is compared with another powerful metaheuristic, the Particle Swarm optimization (PSO), showing that despite both algorithms provide good results for the problem, DE overcomes PSO in terms of quality of the solutions.

Due to amount of today's electricity consumption, one of the most important tasks of the energy operators is to be able to predict the consumption and be ready to control the energy generation based on the estimated consumption for the future. In this way, having a trustable forecast of the electricity consumption is essential to control the consumption and maintain the balance in energy distribution networks. This study presents a day ahead forecasting approach based on a genetic fuzzy system for fuzzy rule learning based on the MOGUL methodology (GFS.FR.MOGUL). The proposed approach is used to forecast the electricity consumption of an office building in the following 24 hours. The goal of this work is to present a more reliable profile of the electricity consumption comparing to previous works. Therefore, this paper also includes the comparison of the results of day ahead forecasting using GFS.FR.MOGUL method against other fuzzy rule based methods, as well as a set of Artificial Neural Network(ANN) approaches. This comparison shows that using the GFS.FR.MOGUL forecasting method for day-ahead electricity consumption forecasting is able to estimate a more trustable value than the other approaches.

This paper at first presents an aggregation model including optimization tools for optimal resource scheduling and aggregating, and then, it proposes a real implemented SCADA system in an office building for decision support techniques and participating in demand response events. The aggregator model controls and manages the consumption and generation of customers by establishing contract with them. The SCADA based office building presented in this paper is considered as a customer of proposed aggregation model. In the case study, a distribution network with 21 buses, including 20 consumers and 26 distributed generations, is proposed for the aggregator network, and optimal resource scheduling of aggregator, and performance of implemented SCADA system for the office building, will be surveyed. The scientific contribution of this paper is to address from an optimization-based aggregator model to a SCADA based customer.

Home Energy Management System creates the scopes to small household electrical appliances users to participate in the demand response programs. Among several load controllable electrical household appliances water heater is more suitable. Integration of water heater is considered to manage the demand response events that can contribute to smart grid technology. This paper represents a thermodynamic load model for a water heater, which is considered as to be controlled through direct load control for demand response program. The daily electricity consumption and temperature profile of the heater is also considered, the direct load control method is activated to the heater as soon as the energy consumption reaches to 1 kW, with the effects the device is turned off for next one hour. In results, it gained a significant reduction in the electricity consumption for the users without making any discomfort as temperature does not reduce to disruption level. Real time electricity pricing is also compared which implied financial benefit to the consumers. The result exhibit that the method applied to this heater can contribute and participate in the demand response events.

This paper presents an optimization-based home energy management system, by taking advantages of renewable resources and energy storage system for optimally managing the energy consumption and generation of the house. The surplus of renewable generation will be stored in energy storage system or will be injected into the main grid. An optimization algorithm is developed for this system in order to minimize the electricity bill of the house considering electricity tariffs. Four home appliances are considered to be controlled by this system for reducing the consumption in critical periods. The outcomes of optimization problem are the optimal scheduling of the resources including renewable generation, energy storage system, consumption reduction, and power transactions with the grid. In the case study, the developed model will be employed in three different scenarios, which considers simple electricity prices and time-of- use tariffs in order to test and validate the performance of the developed model.

The new power system paradigm demands a more active end-consumer participation in smart grids environment. To achieve this participation, new demand side management solutions should be developed and analyzed. Moreover, the massive dis- semination of internet of things devices inside buildings are a reality in nowadays. This paper proposes a multi-agent system for microgrid representation that integrates internet of thing devices to boost the energy management in today's buildings. The paper will present the proposed multi-agent system as well as an environmental awareness smart plug. The case study in this paper will present the data acquisition from a real building using a combination of market internet of things smart plugs, the proposed environmental awareness smart plug and a load emulator.

This paper proposes a Building Energy Management System (BEMS) as part of an organization-based Multi-Agent system that models the Smart Home Electricity System (MASHES). The proposed BEMS consists of an Energy Management System (EMS) and a Prediction Engine (PE). The considered Smart Home Electricity System (SHES) consists of different agents, each with different tasks in the system. In this context, smart homes are able to connect to the power grid to sell/buy electrical energy to/from the Local Electricity Market (LEM), and manage electrical energy inside of the smart home. Moreover, a Modified Stochastic Predicted Bands (MSPB) interval optimization method is used to model the uncertainty in the Building Energy Management (BEM) problem. A demand response program (DRP) based on time of use (TOU) rate is also used. The performance of the proposed BEMS is evaluated using a JADE implementation of the proposed organization-based MASHES.

The use of distributed generation and demand-response (DR) programs is needed for improving business models, namely concerning the remuneration of these resources in the context of smart grids. In this paper, a methodology is proposed in which a virtual power player aggregates several small-sized resources, including consumers participating in DR programs. The global operation costs resulting from the resource scheduling are minimized. After scheduling the resources in several operation scenarios, clustering tools are applied in order to obtain distinct resources' groups. The remuneration structure that better fits the aggregator goals is then determined. Two clustering algorithms are compared: 1) hierarchical; and 2) fuzzy c-means clustering. The remuneration of small resources and consumers that are aggregated is made considering the maximum tariff in each group. The implemented case study considers 2592 operation scenarios based on a real Portuguese distribution network with 548 distributed generators and 20 310 consumers.

The increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources' particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players' participation in electric power negotiations while improving energy efficiency. The opportunity for players' participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator-MIBEL.

Demand response can play a very relevant role in future power systems in which distributed generation can help to assure service continuity in some fault situations. This paper deals with the demand response concept and discusses its use in the context of competitive electricity markets and intensive use of distributed generation. The paper presents DemSi, a demand response simulator that allows studying demand response actions and schemes using a realistic network simulation based on PSCAD. Demand response opportunities are used in an optimized way considering flexible contracts between consumers and suppliers. A case study evidences the advantages of using flexible contracts and optimizing the available generation when there is a lack of supply.

A supervisory control and data acquisition (SCADA) system is an integrated platform that incorporates several components and it has been applied in the field of power systems and several engineering applications to monitor, operate and control a lot of processes. In the future electrical networks, SCADA systems are essential for an intelligent management of resources like distributed generation and demand response, implemented in the smart grid context. This paper presents a SCADA system for a typical residential house. The application is implemented on MOVICON™11 software. The main objective is to manage the residential consumption, reducing or curtailing loads to keep the power consumption in or below a specified setpoint, imposed by the costumer and the generation availability.

This paper presents a methodology to address reactive power compensation using Evolutionary Particle Swarm Optimization (EPSO) technique programmed in the MATLAB environment. The main objective is to find the best operation point minimizing power losses with reactive power compensation, subjected to all operational constraints, namely full AC power flow equations, active and reactive power generation constraints. The methodology has been tested with the IEEE 14 bus test system demonstrating the ability and effectiveness of the proposed approach to handle the reactive power compensation problem.

Presents some important issues that must drive the knowledge representation in the area of intelligent tutoring systems (ITSs). A framework for representation of the domain knowledge used by an ITS in control centre operator training is presented. The requirements that such a structure must obey and how it is manipulated by the ITS are discussed. This paper also discusses the problems that have conditioned the success of this kind of system in the industrial environment, namely the learning material preparation process. A set of tools was developed in order to automate this process as much as possible and thus minimise this obstacle.

The use of information technologies within distribution utilities is increasing. However, a problem is emerging: traditional technologies cannot cope with large distribution automation systems based on heterogeneous sub-systems. To overcome this problem, we identify the need for systems integration, high-level co-operation protocols and powerful and flexible, yet robust, models of interaction. We describe a system architecture for distribution automation composed of object-oriented agents developed in different environments, interconnected using CORBA (Common Object Request Broker Architecture).

Knowledge management is one of the most important goals of any organization. Therefore, several automatic tools are used for that purpose, e.g. Knowledge Based Systems (KBS), Experts Systems, Data Mining Applications and Computer Aided Decision Systems. The validation and verification (V&V) process is fundamental in order to ensure the quality of used knowledge. The usage of automatic verification tools can be a reliable, inexpensive and reusable way to overcome the constant growth of the Knowledge Bases, the shortening of development times and the costs of Validation, specially field tests. This paper addresses the verification of Knowledge Based Systems, focussing on VERITAS, a verification tool initially developed to verify a KBS used to assist operators of Portuguese Transmission Control Centers in incident analysis and power restoration VERITAS performs knowledge base structural analysis allowing the detection of knowledge anomalies.