Hybrid Honey Bees Mating Optimization Algorithm for Identifying the Near-Optimal Solution in Web Service Composition

This paper addresses the problem of optimality in semantic Web service composition by proposing a hybrid nature-inspired method for selecting the optimal or near-optimal solution in semantic Web Service Composition. The method hybridizes the Honey-Bees Mating Optimization algorithm with components inspired from genetic algorithms, reinforcement learning, and tabu search. To prove the necessity of hybridization, we have analyzed comparatively the experimental results provided by our hybrid selection algorithm versus the ones obtained with the classical Honey Bees Mating Optimization algorithm and with the genetic-inspired algorithm of Canfora et al

A Deep Q-Learning based Smart Scheduling of EVs for Demand Response in Smart Grids

Economic and policy factors are driving the continuous increase in the adoption and usage of electrical vehicles (EVs). However, despite being a cleaner alternative to combustion engine vehicles, EVs have negative impacts on the lifespan of microgrid equipment and energy balance due to increased power demand and the timing of their usage. In our view grid management should leverage on EVs scheduling flexibility to support local network balancing through active participation in demand response programs. In this paper, we propose a model-free solution, leveraging Deep Q-Learning to schedule the charging and discharging activities of EVs within a microgrid to align with a target energy profile provided by the distribution system operator. We adapted the Bellman Equation to assess the value of a state based on specific rewards for EV scheduling actions and used a neural network to estimate Q-values for available actions and the epsilon-greedy algorithm to balance exploitation and exploration to meet the target energy profile. The results are promising showing that the proposed solution can effectively schedule the EVs charging and discharging actions to align with the target profile with a Person coefficient of 0.99, handling effective EVs scheduling situations that involve dynamicity given by the e-mobility features, relying only on data with no knowledge of EVs and microgrid dynamics.Comment: Submitted to journa

Exploring the Selection of the Optimal Web Service Composition through Ant Colony Optimization

This paper presents an ant-inspired method for selecting the optimal or a near optimal solution in semantic Web service composition. The proposed method adapts and enhances the Ant Colony Optimization meta-heuristic and considers as selection criteria the QoS attributes of the services involved in the composition as well as the semantic similarity between them. To improve the performance of the proposed selection method a 1-OPT heuristic is defined which expands the search space in a controlled way so as to avoid the stagnation on local optimal solutions. The ant-inspired selection method has been evaluated on a set of scenarios having different complexities and comparatively analyzed with a cuckoo-inspired and a bee-inspired selection method

Alfalfa Powder: Healthy Food Supplement for Sustainable Consumption

An upward trend for plant dietary supplements has been reported lately in the Romanian market, suggesting that they could become a component of a sustainable food diet for consumers. The aim of this study was (1) to explore consumers’ perceptions about alfalfa powder (a plant dietary supplement), to identify their needs and expectations regarding the use of this product and to define the consumer profile; (2) to outline the significant factors of alfalfa sustainable consumption. For this purpose, the evaluation was performed using a questionnaire on a sample of consumers from two important Transylvanian cities (Deva and Cluj-Napoca). The results of the study indicate that the product is consumed by youth, adults and elderly people with upper-class education and high incomes. Emphasis was placed on identifying the main benefits perceived by the use of alfalfa powder. Hence, these are directly related to immunity (10%), detoxification (15%) and healthy dietary supplements (32%). Furthermore, since sustainability is a key factor for increasing quality of life, evidence emerged revealing alfalfa sustainable consumption. Consequently, this study shows that a more sustainable consumption of alfalfa can be stimulated through successful strategies for consumer education through label information including traceability data

Social Factors in P2P Energy Trading Using Hedonic Games

Within energy communities, the prosumers can engage in peer-to-peer trading, fostering local collaborations and increasing awareness about energy usage and flexible consumption. However, even under these favorable technological and organizational mechanisms, prosumer engagement levels remain low, requiring trading mechanisms that are aligned with their social values and expectations. In this paper, we introduce an innovative hedonic game coordination and cooperation model for peer-to-peer (P2P) energy trading among prosumers which considers the social relationships within an energy community to create energy coalitions and facilitate energy transactions among them. A heuristic is defined to optimize the prosumers’ coalitions, considering their social and price preferences while balancing the energy demand and supply within the community. The proposed hedonic game model was integrated into a state-of-the-art blockchain-based P2P energy flexibility market and evaluated its performance within an energy community of prosumers. The evaluation results demonstrate the effectiveness of considering social factors when creating coalitions, resulting in a 5% increase in the total amount of energy transacted in a market session compared to other game theory-based solutions and a degree of satisfaction varying between 91-96%. The results underscore the importance of the social dimensions of P2P energy trading, the positive social dynamics within the energy community leading to an even greater increase in energy transacted around 30%, potentially generating daily savings between ${\$}$ 34 and ${\$}$ 47 and additional carbon savings of approximately 15.4 kgCO2eq

Deep Q-Learning-Based Smart Scheduling of EVs for Demand Response in Smart Grids

Economic and policy factors are driving the continuous increase in the adoption and usage of electrical vehicles (EVs). However, despite being a cleaner alternative to combustion engine vehicles, EVs have negative impacts on the lifespan of microgrid equipment and energy balance due to increased power demands and the timing of their usage. In our view, grid management should leverage on EV scheduling flexibility to support local network balancing through active participation in demand response programs. In this paper, we propose a model-free solution, leveraging deep Q-learning to schedule the charging and discharging activities of EVs within a microgrid to align with a target energy profile provided by the distribution system operator. We adapted the Bellman equation to assess the value of a state based on specific rewards for EV scheduling actions and used a neural network to estimate Q-values for available actions and the epsilon-greedy algorithm to balance exploitation and exploration to meet the target energy profile. The results are promising, showing the effectiveness of the proposed solution in scheduling the charging and discharging actions for a fleet of 30 EVs to align with the target energy profile in demand response programs, achieving a Pearson coefficient of 0.99. This solution also demonstrates a high degree of adaptability in effectively managing scheduling situations for EVs that involve dynamicity, influenced by various state-of-charge distributions and e-mobility features. Adaptability is achieved solely through learning from data without requiring prior knowledge, configurations, or fine-tuning