For a successful implementation of Demand Response (DR) in the real markets, the uncertainty from the players' response must be reduced. From the community manager's perspective, the proposed methodology focus...
For a successful implementation of Demand Response (DR) in the real markets, the uncertainty from the players' response must be reduced. From the community manager's perspective, the proposed methodology focuses on selecting trustworthy participants according to their availability for the triggered DR event. As innovation from previous works, the authors focus now on the consumer perspective. The present paper introduces a tool developed for performance enhancement by displaying the expected rewards according to consumer participation. In other words, the tool performs a sensitivity analysis to study all the possibilities and achieve the player's goal. For example, the tool might suggest the optimal one according to the frequency and type of DR programs triggered, but the participation is voluntary and depends on player availability. The case study results discuss the perspective of four players with distinct objectives.
Production and maintenance scheduling for total cost-effective and machine longevity manufacturing is a key aspect in dealing with the ever-increasing energy prices, competitiveness, added maintenance costs, and envir...
Production and maintenance scheduling for total cost-effective and machine longevity manufacturing is a key aspect in dealing with the ever-increasing energy prices, competitiveness, added maintenance costs, and environmental pressures that the manufacturing sector faces nowadays. This paper addresses these issues by proposing a novel intelligent production scheduling system for joint optimization of production and maintenance for overall cost minimization and machine longevity improvement. To achieve this, it is proposed a Genetic Algorithm (GA) for production and maintenance scheduling of flexible job shop manufacturing environments. The proposed GA takes into account volatile market energy prices, Renewable Energy Resources (RERs), surplus energy selling, maintenance activities, and constraints imposed on the production plan. A case study from the literature is used as a baseline scenario to validate the proposed scheduler. It uses real-production data and considers three unique machines with 275 tasks to be scheduled among them. Using the baseline scenario, it was possible to demonstrate the robustness of the proposed scheduler in reducing total costs, by taking advantage of the volatility of energy prices, as well as utilizing RERs to cover energy expenses or for selling excess energy. Furthermore, it highlights the ability to reduce the overload of single machines. Accordingly, it was possible to achieve cost reductions of up to 11.0% and improvements of 24.4% in machine longevity when compared to the baseline scenario.
This article analysis, through simulation, the impact that local transactions and the penetration of EVs have on the distribution network and the costs and incomes of local market participants. An auction-based compet...
This article analysis, through simulation, the impact that local transactions and the penetration of EVs have on the distribution network and the costs and incomes of local market participants. An auction-based competitive market and mathematical model are provided to simulate end-user transactions with EVs on the low-voltage grid. The proposed framework is validated in a scenario considering 55 end-users (some with PV generation and EVs) and six combined heat and power generators trading energy in the IEEE European Low Voltage Test Feeder system. Furthermore, a distribution system operator is considered by defining network constraints (voltage limits and lines overloading) that ensure the correct operation of the distribution grid.
The integration of renewable energy sources into the power grid has led to the need for efficient energy management systems that consider the presence of energy storage systems and controllable loads. In this paper, t...
The integration of renewable energy sources into the power grid has led to the need for efficient energy management systems that consider the presence of energy storage systems and controllable loads. In this paper, the authors propose a mixed-integer linear programming optimization model for energy management that takes into account the operation of shift electric appliances and battery storage systems. 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 electric appliance met with the user requirements. The authors consider the operational constraints of shifting electric appliances, which can be scheduled to operate during periods of low electricity prices. Additionally, they account for the energy storage system, which can be charged during periods of excess renewable energy production and discharged during periods of high demand. Four different scenarios are tested to prove the value of the model.
While energy prices are seen as the major drive for competitiveness in the manufacturing field, intelligent maintenance scheduling is also one of the most widespread and significant issues plaguing the manufacturing i...
While energy prices are seen as the major drive for competitiveness in the manufacturing field, intelligent maintenance scheduling is also one of the most widespread and significant issues plaguing the manufacturing industry. Maintenance activities can cost between 15% and 70% of the price of goods sold, thus it is essential to optimize both energy and maintenance costs. The purpose of this paper is to propose a production line optimization system that focuses on reducing the total costs (i.e., energy and maintenance costs) while also taking into account dynamic pricing, Renewable Energy Resource (RER) usage, and constraints applied in the production plan. In the proposed system, tasks and maintenance activities are scheduled by using a Genetic Algorithm. To validate the proposed system, a baseline scenario that uses real production data is considered. The obtained results show that the system is able to comply with imposed maintenance hours while also minimizing costs by shifting tasks to higher RER generation and lower energy price times, while the opposite is done to maintenance activities.
This paper proposes a demand response-based energy management model for energy communities, considering the respective members’ data privacy. Through forecasting and clustering algorithms, this model can identify dem...
This paper proposes a demand response-based energy management model for energy communities, considering the respective members’ data privacy. Through forecasting and clustering algorithms, this model can identify demand response opportunities for the next day, rank and select the participants for the event and monitor and evaluate the respective event. The paper’s novelty lies in increasing the energy community members’ active participation by allowing them to submit one or more proposals to participate in a demand response event. In this way, a member can, on the one hand, have more freedom to choose how to participate and, on the other hand, have more opportunities to contribute to the DR event with their energy flexibility. This model was tested with real data from an energy community with 50 buildings, which can provide flexibility through reductions and shifting. The results showed that the model’s efficiency could increase by considering the multiple proposals per member. Moreover, it was verified that with just one event, it is possible to reduce the CO 2 emissions and the energy cost by 50% and 73%, respectively, as well increasing the energy community sustainability by 10%.
Automatic energy management systems allow users’ active participation in flexibility management while assuring their energy demands. We propose a transparent framework for automated energy management to increase trus...
Automatic energy management systems allow users’ active participation in flexibility management while assuring their energy demands. We propose a transparent framework for automated energy management to increase trust and improve the learning process, combining machine learning, experts’ knowledge, and semantic reasoning. A practical example of thermal comfort shows the advantages of the framework.
The EU is encouraging the creation of local energy communities (LECs) for electricity trading, promoting local balance and a self-sustained community while reducing electricity bills. Local electricity markets (LEMs) ...
The EU is encouraging the creation of local energy communities (LECs) for electricity trading, promoting local balance and a self-sustained community while reducing electricity bills. Local electricity markets (LEMs) ease the electricity trading of distributed energy resources while incentivizing the integration of renewable energy sources into the grid. However, presently, LEMs have low liquidity, and the demand is significantly higher than the supply. One possible solution to address this issue is participation in the wholesale market, assessing lower prices, and providing additional savings. This work proposes a multilevel electricity trading framework for LECs’ participation. The simulation framework comprises different LEM models for electricity trading at different levels, culminating in wholesale market participation through a LEC aggregator. Results show the benefits of LECs’ participation in the multilevel trading platform with significant savings.
In local energy markets, Demand Response (DR) concept plays an essential role in balancing the generation and demand at a local level. Consumers and prosumers, assisted by aggregators, participate in DR events by resp...
In local energy markets, Demand Response (DR) concept plays an essential role in balancing the generation and demand at a local level. Consumers and prosumers, assisted by aggregators, participate in DR events by responding to signals to adjust their energy consumption patterns. Aggregators act as intermediaries of small consumers, coordinating their participation. However, their response is uncertain due to their volatile behavior. Previous work by the authors designed a trustworthy rate to deal with uncertainty but did not consider any competition between players. This study approach allows competition between power and energy players. The idea is to give freedom of choice to the DR participants using a tool developed by the authors. In the end, it is expected to create an aggregator portfolio that aligns more closely with its objectives, particularly when it comes to maximizing profits.
The use of multi-agent systems enables the modelling of complex and decentralized solutions, giving the ability to have agents representing different entities and assets in a social environment where they can interact...
The use of multi-agent systems enables the modelling of complex and decentralized solutions, giving the ability to have agents representing different entities and assets in a social environment where they can interact and pursue their individual goals. However, multi-agent systems are usually data-driven solutions in which interactions are performed based on data sharing and environmental feedback. Therefore, the integration of multi-agent systems with federated learning, a knowledge-driven approach, allows agents to share knowledge among them in a collaborative and cooperative approach. This integration can be well seen in decentralized solutions where similar entities can benefit from collaborative and cooperative environments. This is the case in industrial environments and in smart grid environments, namely for the improvement of learning models. This paper proposes a methodology composed of a multi-agent system where the agents are empowered by federated learning. The proposed methodology was tested and validated using a genetic programming model with MNIST dataset in terms of feasibility and performance.
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