Resilience-oriented operation of microgrids in the presence of power-to-hydrogen systems

This study presents a novel framework for improving the resilience of microgrids based on the power-to-hydrogen concept and the ability of microgrids to operate independently (i.e., islanded mode). For this purpose, a model is being developed for the resilient operation of microgrids in which the compressed hydrogen produced by power-to-hydrogen systems can either be used to generate electricity through fuel cells or sold to other industries. The model is a bi-objective optimization problem, which minimizes the cost of operation and resilience by (i) reducing the active power exchange with the main grid, (ii) reducing the ohmic power losses, and (iii) increasing the amount of hydrogen stored in the tanks. A solution approach is also developed to deal with the complexity of the bi-objective model, combining a goal programming approach and Generalized Benders Decomposition, due to the mixed-integer nonlinear nature of the optimization problem. The results indicate that the resilience approach, although increasing the operation cost, does not lead to load shedding in the event of main grid failures. The study concludes that integrating distributed power-to-hydrogen systems results in significant benefits, including emission reductions of up to 20 % and cost savings of up to 30 %. Additionally, the integration of the decomposition method improves computational performance by 54 % compared to using commercial solvers within the GAMS software.© 2023 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

Smart integrated adaptive centralized controller for islanded microgrids under minimized load shedding

In this paper, a smart integrated adaptive centralized controller is proposed for monitoring and controlling integrated renewable energy sources (RESs), both for intentional and unintentional islanding modes of operation for microgrids, as well as, for a variable range of transient load shedding and fault scenarios corresponding to electrical power system outages. It is demonstrated that the proposed smart adaptive controller is capable of instructing fast frequency response by proper coordination of the dispatch of RESs units such as, mini-hydro, Photovoltaic (PV), Battery Energy Storage System (BESS) and standby diesel generators. In particular, the BESS used as power reserve, at the early stage of fault events can prevent detrimental and uncontrollable system frequency decline and the extent of load shedding. In summary, the performance of a centralized controller in terms of a fast frequency response recovery feature is validated for an actual microgrid distribution network of Malaysia. The demonstration of this intelligent control scheme highlights the advantage of utilizing the fast power recovery response of energy storage and standby generator, which fulfil the criteria for minimal load shedding from the main grid, during the unintentional microgrid islanding conditions

Performance Optimisation of Standalone and Grid Connected Microgrid Clusters

Remote areas usually supplied by isolated electricity systems known as microgrids which can operate in standalone and grid-connected mode. This research focus on reliable operation of microgrids with minimal fuel consumption and maximal renewables penetration, ensuring least voltage and frequency deviations. These problems can be solved by an optimisation-based technique. The objective function is formulated and solved with a Genetic Algorithm approach and performance of the proposal is evaluated by exhaustive numerical analyses in Matlab

Reliability Constrained Optimal Investment in a Microgrid with Renewable Energy, Storage, and Smart Resource Management

Environmental concerns have led to a rapid increase in renewable energy development and production as the global demand for electricity continues to increase. The intermittent and uncertain nature of electricity generation from renewable sources, such as wind and solar, however, create significant challenges in maintaining power system reliability at reasonable costs. Energy storage and smart-grid technologies are perceived to provide potential solutions to these challenges in modern power systems of different sizes. This work investigates the opportunity to incorporate energy storage in microgrids with renewable energy production, as well as applying smart microgrid management techniques to reduce the lifetime costs while maintaining an acceptable level of reliability. A microgrid consisting of a 5 home community with generation supplied by two propane generators to meet the “N-1” reliability criterion is used as the base case scenario. Actual load data of typical homes is obtained from the industry partner. An equivalent loss of load expectation criterion is used to benchmark the acceptable reliability level. A model is developed to calculate the lifetime operational cost of the base case scenario which is used to assess the benefit of the addition of renewable energy sources, energy storage, and smart microgrid management techniques. A MATLAB program is developed to assess the 20 year operational costs of various combinations of renewable energy sources and battery energy storage, which will be considered the lifetime of the system. The combination of generation and storage which yields the lowest lifetime operational cost is defined as the optimized microgrid, and is used as a basis to determine if additional savings are realized by the implementation of a microgrid operated by a Smart Microgrid Management System (SMMS). The conceptual layout of the proposed SMMS is presented along with identified methods of utilizing in-home thermal storage. The SMMS mechanism is discussed along with proposed functionality, potential methods of employment, and associated development and implementation costs. The microgrid operated by the SMMS is assessed, and its lifetime operational cost is presented and contrasted against the base case microgrid and the optimized microgrid. A power system reliability evaluation of the proposed microgrids are conducted using a probabilistic method to ensure that reliability is not sacrificed by the implementation of a cost-minimized microgrid. A sequential Monte Carlo simulation model is developed to assess the power system reliability of the various microgrid configuration cases. The functionality of this model is verified using an existing reliability assessment program. The results from the presented studies show that the implementation of renewable energy sources, energy storage, and smart microgrid management techniques are an effective way of reducing the operational cost of a remote microgrid while increasing its power system reliability.

A Reserve-Based Method for Mitigating the Impact of Renewable Energy

The fundamental operating paradigm of today\u27s power systems is undergoing a significant shift. This is partially motivated by the increased desire for incorporating variable renewable energy resources into generation portfolios. While these generating technologies offer clean energy at zero marginal cost, i.e. no fuel costs, they also offer unique operating challenges for system operators. Perhaps the biggest operating challenge these resources introduce is accommodating their intermittent fuel source availability. For this reason, these generators increase the system-wide variability and uncertainty. As a result, system operators are revisiting traditional operating strategies to more efficiently incorporate these generation resources to maximize the benefit they provide while minimizing the challenges they introduce. One way system operators have accounted for system variability and uncertainty is through the use of operating reserves. Operating reserves can be simplified as excess capacity kept online during real time operations to help accommodate unforeseen fluctuations in demand. With new generation resources, a new class of operating reserves has emerged that is generally known as flexibility, or ramping, reserves. This new reserve class is meant to better position systems to mitigate severe ramping in the net load profile. The best way to define this new requirement is still under investigation. Typical requirement definitions focus on the additional uncertainty introduced by variable generation and there is room for improvement regarding explicit consideration for the variability they introduce. An exogenous reserve modification method is introduced in this report that can improve system reliability with minimal impacts on total system wide production costs. Another potential solution to this problem is to formulate the problem as a stochastic programming problem. The unit commitment and economic dispatch problems are typically formulated as deterministic problems due to fast solution times and the solutions being sufficient for operations. Improvements in technical computing hardware have reignited interest in stochastic modeling. The variability of wind and solar naturally lends itself to stochastic modeling. The use of explicit reserve requirements in stochastic models is an area of interest for power system researchers. This report introduces a new reserve modification implementation based on previous results to be used in a stochastic modeling framework. With technological improvements in distributed generation technologies, microgrids are currently being researched and implemented. Microgrids are small power systems that have the ability to serve their demand with their own generation resources and may have a connection to a larger power system. As battery technologies improve, they are becoming a more viable option in these distributed power systems and research is necessary to determine the most efficient way to utilize them. This report will investigate several unique operating strategies for batteries in small power systems and analyze their benefits. These new operating strategies will help reduce operating costs and improve system reliability

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

Peer-to-Peer Energy Trading for Networked Microgrids

Considering the limitations of the existing centralized power infrastructure, research interests have been directed to decentralized smart power systems constructed as networks of interconnected microgrids. Therefore, it has become critical to develop secure and efficient energy trading mechanisms among networked microgrids for reliability and economic mutual benefits. Furthermore, integrating blockchain technologies into the energy sector has gained significant interest among researchers and industry professionals. Considering these trends, the work in this thesis focuses on developing Peer-to-Peer (P2P) energy trading models to facilitate transactions among microgrids in a multiagent network. Price negotiation mechanisms are proposed for both islanded and grid-connected microgrid networks. To enable a trusted settlement of electricity trading transactions, a two-stage blockchain-based settlement consensus protocol is also developed. Simulation results have shown that the model has successfully facilitated energy trading for networked microgrids

Generation Scheduling in Microgrids under Uncertainties in Power Generation

Recently, the concept of Microgrids (MG) has been introduced in the distribution network. Microgrids are defined as small power systems that consist of various distributed micro generators that are capable of supplying a significant portion of the local demand. Microgrids can operate in grid-connected mode, in which they are connected to the upstream grid, or in isolated mode, where they are disconnected from the upstream grid and the local generators are the only source of power supply. In order to maximize the benefits of the resources available in a microgrid, an optimal scheduling of the power generation is required. Renewable resources have an intermittent nature that causes uncertainties in the system. These added uncertainties must be taken into consideration when solving the generation scheduling problem in order to obtain reliable solutions. This research studies the scheduling of power generation in a microgrid that has a group of dispatchable and non-dispatchable generators. The operation of a microgrid during grid-connected mode and isolated mode is analyzed under variable demand profiles. Two mixed integer linear programming (MILP) models for the day-ahead unit commitment problem in a microgrid are proposed. Each model corresponds to one mode of operation. Uncertainty handling techniques are integrated in both models. The models are solved using the General Algebraic Modeling System (GAMS). A number of study cases are examined to study the operation of the microgrid and to evaluate the effects of uncertainties and spinning reserve requirement on the microgrid’s expenses