599 research outputs found
An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes
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+
Demand response performance and uncertainty: A systematic literature review
The present review has been carried out, resorting to the PRISMA methodology, analyzing 218 published articles. A comprehensive analysis has been conducted regarding the consumer's role in the energy market. Moreover, the methods used to address demand response uncertainty and the strategies used to enhance performance and motivate participation have been reviewed. The authors find that participants will be willing to change their consumption pattern and behavior given that they have a complete awareness of the market environment, seeking the optimal decision. The authors also find that a contextual solution, giving the right signals according to the different behaviors and to the different types of participants in the DR event, can improve the performance of consumers' participation, providing a reliable response. DR is a mean of demand-side management, so both these concepts are addressed in the present paper. Finally, the pathways for future research are discussed.This article is a result of the project RETINA (NORTE-01-0145- FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team, and grants CEECIND/02887/2017 and SFRH/BD/144200/2019.info:eu-repo/semantics/publishedVersio
Quantification of operating reserves with high penetration of wind power considering extreme values
The high integration of wind energy in power systems requires operating reserves to ensure the reliability and security in the operation. The intermittency and volatility in wind power sets a challenge for day-ahead dispatching in order to schedule generation resources. Therefore,the quantification of operating reserves is addressed in this paper using extreme values through Monte-Carlo simulations. The uncertainty inwind power forecasting is captured by a generalized extreme value distribution to generate scenarios. The day-ahead dispatching model is formulated asa mixed-integer linear quadratic problem including ramping constraints. This approach is tested in the IEEE-118 bus test system including integration of wind power in the system. The results represent the range of values for operating reserves in day-ahead dispatchin
Optimal resilient allocation of mobile energy storages considering coordinated microgrids biddings
This paper presents an algorithm for optimal resilient allocation of Mobile Energy Storage Systems (MESSs) for an active distribution system considering the microgrids coordinated bidding process. The main contribution of this paper is that the impacts of coordinated biddings of microgrids on the allocation of MESSs in the day-ahead and real-time markets are investigated. The proposed optimization framework is another contribution of this paper that decomposes the optimization process into multiple procedures for the day-ahead and real-time optimization horizons. The active distribution system can transact active power, reactive power, spinning reserve, and regulating reserve with the microgrids in the day-ahead horizon. Further, the distribution system can transact active power, reactive power, and ramp services with microgrids on the real-time horizon. The self-healing index and coordinated gain index are introduced to assess the resiliency level and coordination gain of microgrids, respectively. The proposed algorithm was simulated for the 123-bus test system. The method reduced the average value of aggregated operating and interruption costs of the system by about 60.16% considering the coordinated bidding of microgrids for the worst-case external shock. The proposed algorithm successfully increased the self-healing index by about 49.88% for the test system.© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed
Uncertainty quantification in energy management procedures
Complex energy systems are made up of a number of components interacting together via different energy vectors. The assessment of their performance under dynamic working conditions, where user demand and energy prices vary over time, requires a simulation tool. Regardless of the accuracy of this procedure, the uncertainty in data, obtained both by measurements or by forecasting, is usually non-negligible and requires the study of the sensitivity of results versus input data. In this work, polynomial chaos expansion technique is used to evaluate the variation of cogeneration plant performance with respect to the uncertainty of energy prices and user requests. The procedure allows to obtain this information with a much lower computational cost than that of usual Monte-Carlo approaches. Furthermore, all the tools used in this paper, which were developed in Python, are published as free and open source software
Risk-Aware Stochastic Scheduling of Hybrid Integrated Energy Systems with 100% Renewables
Recently, ambitious endeavors have been carried out to facilitate the transition from traditional grids to hybrid interconnected energy networks in the form of grid modernization. Align to such efforts, this article aims at developing a novel framework for satisfying techno-economic-environmental goals in the grid modernization process. To this end, a detailed examination is conducted for the optimal exploitation of energy hubs (EHs) equipped with 100% renewables to pursue the environmental goal alongside intending technical and economic constraints. The energy conversion technology is adopted to enable the power-to-gas system for establishing multi-energy interactions among electricity and gas networks. Fully benefiting from renewable units has exposed the system to uncertain fluctuations that necessitate the modeling of uncertainties to achieve near-reality results. Hence, risk-averse and seeker strategies are developed based on robustness and opportunistic modes of the information gap decision theory (IGDT) method to deal with stochastic fluctuations of uncertain parameters. The integrated electricity and gas test system is considered to analyze the applicability of the proposed framework in modeling efficient multi-energy interactions. Given the obtained results, 43.68% more energy cost is reached for EHs when they adopted a robust strategy against uncertainties under the risk-averse strategy. Moreover, the proposed framework procured a rational decision-making model for balancing multi-energy in the hybrid energy grid with 100% renewables
Resilience assessment and planning in power distribution systems:Past and future considerations
Over the past decade, extreme weather events have significantly increased
worldwide, leading to widespread power outages and blackouts. As these threats
continue to challenge power distribution systems, the importance of mitigating
the impacts of extreme weather events has become paramount. Consequently,
resilience has become crucial for designing and operating power distribution
systems. This work comprehensively explores the current landscape of resilience
evaluation and metrics within the power distribution system domain, reviewing
existing methods and identifying key attributes that define effective
resilience metrics. The challenges encountered during the formulation,
development, and calculation of these metrics are also addressed. Additionally,
this review acknowledges the intricate interdependencies between power
distribution systems and critical infrastructures, including information and
communication technology, transportation, water distribution, and natural gas
networks. It is important to understand these interdependencies and their
impact on power distribution system resilience. Moreover, this work provides an
in-depth analysis of existing research on planning solutions to enhance
distribution system resilience and support power distribution system operators
and planners in developing effective mitigation strategies. These strategies
are crucial for minimizing the adverse impacts of extreme weather events and
fostering overall resilience within power distribution systems.Comment: 27 pages, 7 figures, submitted for review to Renewable and
Sustainable Energy Review
- …