2,654 research outputs found

    A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization

    Get PDF
    In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy with a satisfactory trade-off between exploration and exploitation capabilities was added to the model predictive control. The proposed strategy was evaluated using a representative microgrid that includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage system. The achieved results demonstrate the validity of the proposed approach, outperforming a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost. In addition, the proposed approach also better manages the use of the energy storage system.Ministerio de Economía y Competitividad DPI2016-75294-C2-2-RUnión Europea (Programa Horizonte 2020) 76409

    Methods for Optimal Microgrid Management

    Get PDF
    Abstract During the last years, the number of distributed generators has grown significantly and it is expected to become higher in the future. Several new technologies are being de-veloped for this type of generation (including microturbines, photovoltaic plants, wind turbines and electrical storage systems) and have to be integrated in the electrical grid. In this framework, active loads (i.e., shiftable demands like electrical vehicles, intelligent buildings, etc.) and storage systems are crucial to make more flexible and smart the dis-tribution system. This thesis deals with the development and application of system engi-neering methods to solve real-world problems within the specific framework of microgrid control and management. The typical kind of problems that is considered when dealing with the manage-ment and control of Microgrids is generally related to optimal scheduling of the flows of energy among the various components in the systems, within a limited area. The general objective is to schedule the energy consumptions to maximize the expected system utility under energy consumption and energy generation constraints. Three different issues related to microgrid management will be considered in detail in this thesis: 1. The problem of Nowcasting and Forecasting of the photovoltaic power production (PV). This problem has been approached by means of several data-driven techniques. 2. The integration of stations to charge electric vehicles in the smart grids. The impact of this integration on the grid processes and on the demand satisfaction costs have been analysed. In particular, two different models have been developed for the optimal integration of microgrids with renewable sources, smart buildings, and the electrical vehicles (EVs), taking into account two different technologies. The first model is based on a discrete-time representation of the dynamics of the system, whereas the second one adopts a discrete-event representation. 3. The problem of the energy optimization for a set of interconnencted buildings. In ths connection, an architecture, structured as a two-level control scheme has been developed. More precisely, an upper decision maker solves an optimization problem to minimize its own costs and power losses, and provides references (as 3 regars the power flows) to local controllers, associated to buildings. Then, lower level (local) controllers, on the basis of a more detailed representation of each specific subsystem (the building associated to the controller), have the objective of managing local storage systems and devices in order to follow the reference values (provided by the upper level), to contain costs, and to achieve comfort requirements

    Optimal energy management of a grid-connected multiple energy carrier micro-grid

    Full text link
    © 2019 Elsevier Ltd This paper presents a novel modeling approach to optimize the electrical and thermal energy management of a multiple energy carrier micro-grid with the aim of minimizing the operation cost such that system constraints are satisfied. The proposed micro-grid includes a micro-turbine, a fuel cell, a rubbish burning power plant, a wind turbine generator system, a boiler, an anaerobic reactor-reformer system, an inverter, a rectifier, and some energy storage units. The model uses day-ahead forecasting (24 h) to estimate the electrical and thermal loads on a micro-grid network. A day-ahead forecast is also used to estimate electricity generation from wind turbines. Due to the uncertainty associated with day-ahead forecasts, a Monte Carlo simulation is used to estimate thermal loads, electrical loads, and wind power generation. Also, a real-time pricing demand response program is used to shift non-vital loads. The operating cost of the micro-grid is minimized through the particle swarm optimization algorithm. The simulation results demonstrate the proposed modeling framework is superior over conventional centralized optimal scheduling models widely used in the literature in terms of reducing operating cost and computational complexity. In addition, the results obtained by applying the proposed modeling framework are analyzed and validated through scenario testing

    Optimal operation of an energy hub considering the uncertainty associated with the power consumption of plug-in hybrid electric vehicles using information gap decision theory

    Full text link
    © 2019 Elsevier Ltd An energy hub is a multi-carrier energy system that is capable of coupling various energy networks. It increases the flexibility of energy management and creates opportunities to increase the efficiency and reliability of energy systems. When plug-in hybrid electric vehicles (PHEVs)are incorporated into the energy hub, batteries can act as an aggregated storage system, increasing the potential integration of variable renewable energy sources (RES)into power system networks. This paper presents a new model for the optimal operation of an energy hub that includes RES, PHEVs, fuel cell vehicles, a fuel cell, an electrolyzer, a hydrogen tank, a boiler, an inverter, a rectifier, and a heat storage system. A novel model is developed to estimate the uncertainty associated with the power consumption of PHEVs during trips using information gap decision theory (IGDT)under risk-averse and risk-seeking strategies. Simulation results demonstrate that the proposed method maximizes the objective function under the risk-neutral and risk-averse strategies, while minimizing the objective function under the risk-seeking strategy. Results from the modeling show that considering the uncertainty associated with the power consumption of PHEVs using IGDT enables the energy hub operator to make appropriate decisions when optimizing the operation of the energy hub against possible changes in power consumption of PHEVs

    An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes

    Get PDF
    In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. Despite the positive features associated to this paradigm, new problems arise such as coordination and uncertainty. In this framework, microgrids constitute an effective solution to deal with the coordination and operation of these distributed energy resources. This paper proposes a Genetic Algorithm (GA) to address the combined problem of Unit Commitment (UC) and Economic Dispatch (ED). With this end, a model of a microgrid is introduced together with all the control variables and physical constraints. To optimally operate the microgrid, three operation modes are introduced. The first two attend to optimize economical and environmental factors, while the last operation mode considers the errors induced by the uncertainties in the demand forecasting. Therefore, it achieves a robust design that guarantees the power supply for different confidence levels. Finally, the algorithm was applied to an example scenario to illustrate its performance. The achieved simulation results demonstrate the validity of the proposed approach.Ministerio de Ciencia, Innovación y Universidades TEC2016-80242-PMinisterio de Economía y Competitividad PCIN-2015-043Universidad de Sevilla Programa propio de I+D+

    A Widespread Review of Smart Grids Towards Smart Cities

    Get PDF
    © 2019 by the authorsNowadays, the importance of energy management and optimization by means of smart devices has arisen as an important issue. On the other hand, the intelligent application of smart devices stands as a key element in establishing smart cities, which have been suggested as the solution to complicated future urbanization difficulties in coming years. Considering the scarcity of traditional fossil fuels in the near future, besides their ecological problems the new smart grids have demonstrated the potential to merge the non-renewable and renewable energy resources into each other leading to the reduction of environmental problems and optimizing operating costs. The current paper clarifies the importance of smart grids in launching smart cities by reviewing the advancement of micro/nano grids, applications of renewable energies, energy-storage technologies, smart water grids in smart cities. Additionally a review of the major European smart city projects has been carried out. These will offer a wider vision for researchers in the operation, monitoring, control and audit of smart-grid systems.publishedVersio

    Optimal scheduling of PEV charging/discharging in microgrids with combined objectives

    Get PDF
    While renewable power generation and vehicle electrification are promising solutions to reduce greenhouse gas emissions, it faces great challenges to effectively integrate them in a power grid. The weather-dependent power generation of renewable energy sources, such as Photovoltaic (PV) arrays, could introduce significant intermittency to a power grid. Meanwhile, uncontrolled PEV charging may cause load surge in a power grid. This paper studies the optimization of PEV charging/discharging scheduling to reduce customer cost and improve grid performance. Optimization algorithms are developed for three cases: 1) minimize cost, 2) minimize power deviation from a pre-defined power profile, and 3) combine objective functions in 1) and 2). A Microgrid with PV arrays, bi-directional PEV charging stations, and a commercial building is used in this study. The bi-directional power from/to PEVs provides the opportunity of using PEVs to reduce the intermittency of PV power generation and the peak load of the Microgrid. Simulation has been performed for all three cases and the simulation results show that the presented optimization algorithms can meet defined objectives

    Performance of Smart Homes for participating in Electricity Markets

    Get PDF
    Devido ao crescente consumo de energia proveniente de residências, o comportamento dos consumidores de Smart Homes vem sendo estudado nos últimos anos, com o objetivo de otimizar a eficiência energética e o consumo de energia. Além disso, é necessário otimizar o consumo de energia da casa para minimizar custos e reduzir as emissões de gases. Atualmente, o Mercado de Energia Elétrica tem se mostrado muito mais competitivo devido ao surgimento de fontes renováveis ​​de energia e à participação ativa do consumidor no mercado, utilizando programas de demand response. O objetivo deste projeto é desenvolver e melhorar um código-fonte para permitir a gestão da demanda de uma casa inteligente, incluindo geração de energia renovável, veículo elétrico e outros aparelhos inteligentes e dispositivos/electrodomésticos elétricos. Além disso, a redução do custo esperado do consumo de energia e o aumento do conforto do consumidor são considerados como metas do projeto.Due to the rising energy consumption of residential consumers, smart home consumers' behaviour is being studied in the last years to achieve optimal energy efficiency and power consumption. Also, there is a need to optimize house energy consumption to minimize costs and reduce gas emissions. Nowadays, Electricity Market has been much more competitive due to the rising of renewable energy sources and consumer's active participation in the market, using demand response programs. This project aims to develop and improve a source code to allow demand management of a Smart Home, including renewable energy generation, electric vehicle and other smart appliances and electrical devices. Furthermore, the reduction of the expected cost of energy consumption and the rise of consumer's comfort are considered as goals for the project
    corecore