2,047 research outputs found

    Spatially explicit model for anaerobic co‐digestion facilities location and pre‐dimensioning considering spatial distribution of resource supply and biogas yield in northwest Portugal

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    The high volumes of animal manure and sewage sludge, as a consequence of the development of intensive and specialized cattle dairy farms in peri-urban areas, pose challenges to local environmental quality and demands for systems innovation. Besides these negative impacts, energy recovery from biogas produced in anaerobic co-digestion processes should contribute to local sustainable development. This research considers technical data obtained from the optimization of biomethanization processes using sewage sludge and cattle manure liquid fraction, aiming to develop a spatially explicit model including multicriteria evaluation and an analytical hierarchy process to locate biogas production facilities, allocate energy resources and consider biogas unit pre-dimensioning analysis. According to the biophysical conditions and socioeconomic dynamics of the study area (Vila do Conde, Northwest Portugal), a spatially explicit model using multicriteria and multiobjective techniques allowed the definition of suitable locations, as well as the allocation of resources and support pre-dimensioning of biogas facilities. A p-median model allowed us to allocate resources and pre-dimensioning biogas facilities according to distance and accessibility elements. The results indicate: (i) the location of areas with adequate environmental conditions and socioeconomic suitability advantages to install biogas production facilities, and (ii) the ability to compare the options of centralized or distributed location alternatives and associated pre-dimensioning.This research was funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq Brazil), grant number GDE 201469/2014‐6.info:eu-repo/semantics/publishedVersio

    On green routing and scheduling problem

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    The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

    Multi-Criteria Performance Evaluation and Control in Power and Energy Systems

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    The role of intuition and human preferences are often overlooked in autonomous control of power and energy systems. However, the growing operational diversity of many systems such as microgrids, electric/hybrid-electric vehicles and maritime vessels has created a need for more flexible control and optimization methods. In order to develop such flexible control methods, the role of human decision makers and their desired performance metrics must be studied in power and energy systems. This dissertation investigates the concept of multi-criteria decision making as a gateway to integrate human decision makers and their opinions into complex mathematical control laws. There are two major steps this research takes to algorithmically integrate human preferences into control environments: MetaMetric (MM) performance benchmark: considering the interrelations of mathematical and psychological convergence, and the potential conflict of opinion between the control designer and end-user, a novel holistic performance benchmark, denoted as MM, is developed to evaluate control performance in real-time. MM uses sensor measurements and implicit human opinions to construct a unique criterion that benchmarks the system\u27s performance characteristics. MM decision support system (DSS): the concept of MM is incorporated into multi-objective evolutionary optimization algorithms as their DSS. The DSS\u27s role is to guide and sort the optimization decisions such that they reflect the best outcome desired by the human decision-maker and mathematical considerations. A diverse set of case studies including a ship power system, a terrestrial power system, and a vehicular traction system are used to validate the approaches proposed in this work. Additionally, the MM DSS is designed in a modular way such that it is not specific to any underlying evolutionary optimization algorithm

    Optimal integration and management of solar generation and battery storage system in distribution systems under uncertain environment

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    The simultaneous placement of solar photovoltaics (SPVs) and battery energy storage systems (BESSs) in distribution systems is a highly complex combinatorial optimization problem. It not only involves siting and sizing but is also embedded with charging and discharging dispatches of BESSs under dynamically varying system states with intermittency of SPVs and operational constraints. This makes the simultaneous allocation a nested problem, where the operational part acts as a constraint for the planning part and adds complexity to the problem. This paper presents a bi-layer optimization strategy to optimally place SPVs and BESSs in the distribution system. A simple and effective operating BESS strategy model is developed to mitigate reverse power flow, enhance load deviation index and absorb variability of load and power generation which are essential features for the faithful exploitation of available renewable energy sources (RESs). In the proposed optimization strategy, the inner layer optimizes the energy management of BESSs for the sizing and siting as suggested by the outer layer. Since the inner layer optimizes each system state separately, the problem search space of GA is significantly reduced. The application results on a benchmark 33-bus test distribution system highlight the importance of the proposed method

    Energy Optimization for Distributed Energy Resources Scheduling with Enhancements in Voltage Stability Margin

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    The need for developing new methodologies in order to improve power system stability has increased due to the recent growth of distributed energy resources. In this paper, the inclusion of a voltage stability index in distributed energy resources scheduling is proposed. Two techniques were used to evaluate the resultingmultiobjective optimization problem: the sum-weighted Pareto front and an adapted goal programmingmethodology.With this newmethodology, the systemoperators can consider both the costs and voltage stability. Priority can be assigned to one objective function according to the operating scenario. Additionally, it is possible to evaluate the impact of the distributed generation and the electric vehicles in the management of voltage stability in the future electric networks.One detailed case study considering a distribution network with high penetration of distributed energy resources is presented to analyse the proposed methodology. Additionally, the methodology is tested in a real distribution network.info:eu-repo/semantics/publishedVersio

    Distribution energy storage investment prioritization with a real coded multi-objective genetic algorithm

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    Energy Storage Systems (ESSs) are progressively becoming an essential requisite for the upcoming Smart Distribution Systems thanks to the flexibility they introduce in the network operation. A rapid improvement in ESS technology efficiency has been seen, but not yet sufficient to drastically reduce the high investments associated. Thus, optimal planning and management of these devices are crucial to identify specific configurations that can justify ESSs installation. This consideration has motivated a strong interest of the researchers in this field that, however, have separately solved the optimal ESS location and the optimal ESS schedule. In the paper, a novel multi-objective approach is presented, based on the Non-dominated Sorted Genetic Algorithm - II integrated with a real codification that allows joining in a single optimization all the main features of an optimal ESS implementation project: siting, sizing and scheduling. The methodology has been tested on a real-size rural distribution network

    Development of an environmental and economic optimization model for distributed generation energy systems

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    The reduction of pollutant emissions is one of the current main targets fixed by the most important international authorities. The reduction of the energy needs in the residential-tertiary sector can help achieving this goal, as it represents one of the dominant energy consuming sectors in industrialized societies. However the adoption of an energy system still depend on technical and economical evaluations, while environmental considerations are not taken into consideration yet. For this reason, a development of a tool for the selection of an energy system which allows the reduction of the overall costs containing in the meanwhile the pollutant emissions could help reaching the environmental targets. The thesis proposes a methodology for the Multiobjective optimization of a Dis- tributed Generation Energy System. Such a system is normally constituted by several users connected to each other and to a central unit through a District Heating Network. Furthermore, each unit can be equipped with an internal production unit for the production of its energy needs. Therefore, the determination of the optimal energy system requires the simultaneous optimization of the synthesis, design and operation of the whole energy system. The total annual cost for owning, operating and maintaining the whole system is considered as economic objective function, while the total annual operation CO2 emissions is considered as environmental objective function. An optimization MILP model for the optimization of tertiary sector Distributed Generation Energy Systems is developed and is applied to a real case study, made up of nine tertiary sector users located in a small town city center situated in the North-East of Italy. A preliminary energy audit allowed the determination of the users\u2019 energy needs. The energy system is optimized for different configurations in order to understand how different components affect the optimal solution

    Virtual power plant models and electricity markets - A review

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    In recent years, the integration of distributed generation in power systems has been accompanied by new facility operations strategies. Thus, it has become increasingly important to enhance management capabilities regarding the aggregation of distributed electricity production and demand through different types of virtual power plants (VPPs). It is also important to exploit their ability to participate in electricity markets to maximize operating profits. This review article focuses on the classification and in-depth analysis of recent studies that propose VPP models including interactions with different types of energy markets. This classification is formulated according to the most important aspects to be considered for these VPPs. These include the formulation of the model, techniques for solving mathematical problems, participation in different types of markets, and the applicability of the proposed models to real case studies. From the analysis of the studies, it is concluded that the most recent models tend to be more complete and realistic in addition to featuring greater diversity in the types of electricity markets in which VPPs participate. The aim of this review is to identify the most profitable VPP scheme to be applied in each regulatory environment. It also highlights the challenges remaining in this field of study
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