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- Distributed Constrained Optimization Towards Effective Agent-Based Microgrid Energy Resource ManagementPublication . Lezama, Fernando; Munoz de Cote, Enrique; Farinelli, Alessandro; Soares, João; Pinto, Tiago; Vale, ZitaThe current energy scenario requires actions towards the reduction of energy consumption and the use of renewable resources. In this context, a microgrid is a self-sustained network that can operate connected to the smart grid or in isolation. The long-term scheduling of on/off cycles of devices is a critical problem that has been commonly addressed by centralized approaches. In this work, we propose a novel agent-based method to solve the long-term scheduling problem as a distributed constraint optimization problem (DCOP) by modelling future system configurations rather than reacting to changes. Moreover, with respect to approaches based on decentralised reinforcement learning, we can directly encode system-wide hard constraints (such as for example the Kirchhoff law) which are not easy to represent in a factored representation of the problem. We compare different multi-agent DCOP algorithms showing that the proposed method can find optimal/near-optimal solutions for a specific case study.
- A Short Review on Data Mining Techniques for Electricity Customers CharacterizationPublication . Cembranel, Samuel S.; Lezama, Fernando; Soares, João; Filipe Ramos, Sérgio; Gomes, Antonio; Vale, ZitaAn important tool to manage electrical systems is the knowledge of customers' consumption patterns. Data Mining (DM) emerges as an important tool for extracting information about energy consumption in databases and identifying consumption patterns. This paper presents a short review on DM, with a focus on the characterization of electricity customers supported on knowledge discovery in database (KDD) process. The study includes several steps: first, few concepts of the KDD process are presented; following, a short review of clustering algorithms is presented including partitional, hierarchical, fuzzy, evolutionary methods, and Self-Organizing Maps; finally, the main concepts and methods for load classification, based on load shape indices are presented. The main objective of this work is to present a short review of DM techniques applied to identify typical load profiles in electrical systems and new customers' classification.
- Differential evolution strategies for large-scale energy resource management in smart gridsPublication . Lezama, Fernando; Sucar, Luis Enrique; de Cote, Enrique Munoz; Soares, João; Vale, ZitaSmart Grid (SG) technologies are leading the modifications of power grids worldwide. The Energy Resource Management (ERM) in SGs is a highly complex problem that needs to be efficiently addressed to maximize incomes while minimizing operational costs. Due to the nature of the problem, which includes mixed-integer variables and non-linear constraints, Evolutionary Algorithms (EA) are considered a good tool to find optimal and near-optimal solutions to large-scale problems. In this paper, we analyze the application of Differential Evolution (DE) to solve the large-scale ERM problem in SGs through extensive experimentation on a case study using a 33-Bus power network with high penetration of Distributed Energy Resources (DER) and Electric Vehicles (EVs), as well as advanced features such as energy stock exchanges and Demand Response (DR) programs. We analyze the impact of DE parameter seing on four state-of-the art DE strategies. Moreover, DE strategies are compared with other well-known EAs and a deterministic approach based on MINLP. Results suggest that, even when DE strategies are very sensitive to the seing of their parameters, they can find beer solutions than other EAs, and near-optimal solutions in acceptable times compared with a MINLP approach.
- Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy MarketsPublication . Dabhi, Dharmesh; Pandya, Kartik; Soares, João; Lezama, Fernando; Vale, ZitaThe increased penetration of renewables in power distribution networks has motivated significant interest in local energy systems. One of the main goals of local energy markets is to promote the participation of small consumers in energy transactions. Such transactions in local energy markets can be modeled as a bi-level optimization problem in which players (e.g., consumers, prosumers, or producers) at the upper level try to maximize their profits, whereas a market mechanism at the lower level maximizes the energy transacted. However, the strategic bidding in local energy markets is a complex NP-hard problem, due to its inherently nonlinear and discontinued characteristics. Thus, this article proposes the application of a hybridized Cross Entropy Covariance Matrix Adaptation Evolution Strategy (CE-CMAES) to tackle such a complex bi-level problem. The proposed CE-CMAES uses cross entropy for global exploration of search space and covariance matrix adaptation evolution strategy for local exploitation. The CE-CMAES prevents premature convergence while efficiently exploring the search space, thanks to its adaptive step-size mechanism. The performance of the algorithm is tested through simulation in a practical distribution system with renewable energy penetration. The comparative analysis shows that CE-CMAES achieves superior results concerning overall cost, mean fitness, and Ranking Index (i.e., a metric used in the competition for evaluation) compared with state-of-the-art algorithms. Wilcoxon Signed-Rank Statistical test is also applied, demonstrating that CE-CMAES results are statistically different and superior from the other tested algorithms.
- Optimal expansion planning considering storage investment and seasonal effect of demand and renewable generationPublication . Canizes, Bruno; Soares, João; Lezama, Fernando; Silva, Cátia; Vale, Zita; Corchado, Juan ManuelA new era of cleaner distributed generators, like wind and solar, dispersed along the distribution network are gaining great importance and contributing to the environment and political goals. However, the variability and intermittency of those generators pose new complexities and challenges to the network planning. This research paper proposes an innovative stochastic methodology to deal with the expansion planning of large distribution networks in a smart grid context with high penetration of distributed renewable energy sources and considering the seasonal impact. Also, new power lines locations and types, the size and the location of energy storage systems are considered in the optimization. A distribution network with 180 buses located in Portugal considering high distributed generators penetration is used to illustrate the application of the proposed methodology. The results demonstrate the advantage of the stochastic model when compared with a deterministic formulation, avoiding the need for larger investments in new lines and energy storage systems.
- A residential energy management system with offline population-based optimizationPublication . Soares, João; Lezama, Fernando; Ramos, Sérgio; Vale, Zita; Lopes, AndreExpectable improvements in battery technology and lower prices will certainly contribute to increase the interest in residential energy storage systems in the near future. The installment of photovoltaic panels and the use of energy storage systems will help to reduce power losses in distribution and transmission power grid and increase network availability, and consequently, to reduce the dependency on the use of fossil fuels. The paper presents a light implementation of residential energy management system that integrates photovoltaic generation, an energy storage system and an electric vehicle. The goal of the system is to reduce the costs of electric consumer energy bill. The effectiveness of the system is verified through its application in several scenarios for the Portuguese context. An offline population-based algorithm, namely differential evolution method is used to adjust the objective function for the online control of the energy devices in the residential house.
- Day-Ahead Stochastic Scheduling Model Considering Market Transactions in Smart GridsPublication . Soares, João; Lezama, Fernando; Canizes, Bruno; Fotouhi Ghazvini, Mohammad Ali; Vale, Zita; Pinto, TiagoThe integration of renewable generation and electric vehicles (EVs) into smart grids poses an additional challenge to the stochastic energy resource management problem due to the uncertainty related to weather forecast and EVs user-behavior. Moreover, when electricity markets are considered, market price variations cannot be disregarded. In this paper, a two-stage stochastic programming approach to schedule the day-ahead operation of energy resources in smart grids under uncertainty is presented. A realistic case study is performed using a large-scale scenario with nearly 4 million variables with the goal to minimize expected operation cost of energy aggregators. Three scenarios are analyzed to understand the effect of market transactions and external suppliers on the aggregator model. The results suggest that the market transactions can reduce expected cost, while the external supplier offers risk-free price. In addition, the performance metric shows the superiority of the stochastic approach over an equivalent deterministic model
- A Sensitivity Analysis of PSO Parameters Solving the P2P Electricity Market ProblemPublication . Vieira, Miguel; Faia, Ricardo; Lezama, Fernando; Canizes, Bruno; Vale, ZitaEnergy 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.
- Electric Vehicles’ User Charging Behaviour Simulator for a Smart CityPublication . Canizes, Bruno; Soares, João; Costa, Ângelo; Pinto, Tiago; Lezama, Fernando; Novais, Paulo; Vale, ZitaThe increase of variable renewable energy generation has brought several new challenges to power and energy systems. Solutions based on storage systems and consumption flexibility are being proposed to balance the variability from generation sources that depend directly on environmental conditions. The widespread use of electric vehicles is seen as a resource that includes both distributed storage capabilities and the potential for consumption (charging) flexibility. However, to take advantage of the full potential of electric vehicles’ flexibility, it is essential that proper incentives are provided and that the management is performed with the variation of generation. This paper presents a research study on the impact of the variation of the electricity prices on the behavior of electric vehicle’s users. This study compared the benefits when using the variable and fixed charging prices. The variable prices are determined based on the calculation of distribution locational marginal pricing, which are recalculated and adapted continuously accordingly to the users’ trips and behavior. A travel simulation tool was developed for simulating real environments taking into account the behavior of real users. Results show that variable-rate of electricity prices demonstrate to be more advantageous to the users, enabling them to reduce charging costs while contributing to the required flexibility for the system.
- A Specialized Long-Term Distribution System Expansion Planning Method With the Integration of Distributed Energy ResourcesPublication . De Lima, Tayenne D.; Franco, John F.; Lezama, Fernando; Soares, JoãoThe electrical distribution system (EDS) has undergone major changes in the last decade due to the increasing integration of distributed generation (DG), particularly renewable energy DG. Since renewable energy resources have uncertain generation, energy storage systems (ESSs) in the EDS can reduce the impact of those uncertainties. Besides, electric vehicles (EVs) have been increasing in recent years leveraged by environmental concerns, bringing new challenges to the operation and planning of the EDS. In this context, new approaches for the distribution system expansion planning (DSEP) problem should consider the distributed energy resources (DG units, ESSs, and EVs) and address environmental impacts. This paper proposes a mixed-integer linear programming model for the DSEP problem considering DG units, ESSs, and EV charging stations, thus incorporating the environmental impact and uncertainties associated with demand (conventional and EVs) and renewable generation. In contrast to other approaches, the proposed model includes the simultaneous optimization of investments in substations, circuits, and distributed energy resources, including environmental aspects (CO 2 emissions). The optimization method was developed in the modeling language AMPL and solved via CPLEX. Tests carried out with a 24-node system illustrate its effectiveness as a valuable tool that can assist EDS planners in the integration of distributed energy resources.