Smart Microgrid Research Articles

Intelligent Real-Time Utilization of Hybrid Energy Resources for Cost Optimization in Smart Microgrids

Intelligent Real-Time Utilization of Hybrid Energy Resources for Cost Optimization in Smart Microgrids

Interconnected Smart Transactive Microgrids—A Survey on Trading, Energy Management Systems, and Optimisation Approaches

The deployment of isolated microgrids has witnessed exponential growth globally, especially in the light of prevailing challenges faced by many larger power grids. However, these isolated microgrids remain separate entities, thus limiting their potential to significantly impact and improve the stability, efficiency, and reliability of the broader electrical power system. Thus, to address this gap, the concept of interconnected smart transactive microgrids (ISTMGs) has arisen, facilitating the interconnection of these isolated microgrids, each with its unique attributes aimed at enhancing the performance of the broader power grid system. Furthermore, ISTMGs are expected to create more robust and resilient energy networks that enable innovative and efficient mechanisms for energy trading and sharing between individual microgrids and the centralized power grid. This paradigm shift has sparked a surge in research aimed at developing effective ISTMG networks and mechanisms. Thus, in this paper, we present a review of the current state-of-the-art in ISTMGs with a focus on energy trading, energy management systems (EMS), and optimization techniques for effective energy management in ISTMGs. We discuss various types of trading, architectures, platforms, and stakeholders involved in ISTMGs. We proceed to elucidate the suitable applications of EMS within such ISTMG frameworks, emphasizing its utility in various domains. This includes an examination of optimization tools and methodologies for deploying EMS in ISTMGs. Subsequently, we conduct an analysis of current techniques and their constraints, and delineate prospects for future research to advance the establishment and utilization of ISTMGs.

Energy Flow Management in a Smart Microgrid Based on Photovoltaic Energy Supplying Multiple Loads

Energy Flow Management in a Smart Microgrid Based on Photovoltaic Energy Supplying Multiple Loads

Methodology for Energy Management in a Smart Microgrid Based on the Efficiency of Dispatchable Renewable Generation Sources and Distributed Storage Systems

This paper presents a methodology for energy management in a smart microgrid based on the efficiency of dispatchable generation sources and storage systems, with three different aims: elimination of power peaks; optimisation of the operation and performance of the microgrid; and reduction of energy consumption from the distribution network. The methodology is based on four steps: identification of elements of the microgrid, monitoring of the elements, characterization of the efficiency of the elements, and finally, microgrid energy management. A specific use case is shown at CEDER-CIEMAT (Centro para el Desarrollo de las Energías Renovables—Centro de Investi-gaciones Energéticas, Medioambientales y Tecnológicas), where consumption has been reduced during high tariff periods and power peaks have been eliminated, allowing an annual reduction of more than 25,000 kWh per year, which is equal to savings of more than 8500 €. It also allows the power contracted from the distribution company by CEDER (135 kW) not to be exceeded, which avoids penalties in the electricity bill.

Optimum Stochastic Allocation for Demand Response for Power Markets in Microgrids

This research incorporates an electricity market model based on a stochastic allocation of distributed resources and the analysis of an optimal demand response for a smart microgrid. This research develops a methodology that allows the application and comparison of various demand-response mechanisms and the analysis of the differences between them and the case of no-demand response, emphasizing economics, environmental care, demand curves, and other factors. By enabling more active participation by residential users of the smart microgrid, these demand-response methods help to flatten the demand curve and support the goals set by the electricity market model. Both conventional and non-conventional generators compete in the electricity market, with renewable energy sources preferred to encourage green generation. Conventional generators are required to supply electricity gradually, starting with the lowest pollution level. In addition, conventional generators are compensated for dispatch, system reliability, and availability. In addition, random variables are used in this study to predict initial load, solar radiation analysis, and biomass input before resources are optimized to meet demand.

Distributed control of energy storages for multi-time-step peak load shaving in a microgrid

Distributed energy storages (ESs) will be widely used in the future smart microgrids due to their continuous technology improvement and cost reduction. Meanwhile, the distributed control method also gains increasing attention for controlling distributed devices to protect user privacy and reduce computation burden compared with the centralized scheme. In this paper, a distributed control method of ESs is proposed for multi-time-step peak load shaving in a microgrid. Considering the ES efficiency is related to its power, an optimization is constructed to minimize the power loss during ES operations when performing peak load shaving function. By analyzing the net load curve and the ES available capacity, the multi-time-step peak load shaving problem is transformed into single-time-step ES operations at each time step. The Combine-Then-Adapt diffusion strategy is united with the consensus + innovation strategy to realize the peak load shaving in a fully distributed way. Case studies verify the efficiency of the proposed method.

An Adaptive Protection Scheme based on Fault Location for Smart Micro-Grids

The traditional distribution systems are radial and have unidirectional power flow. The overcurrent protection system of these networks is negatively affected by Distributed Generation (DG) units. The time and current coordination of protection devices of these systems is the most important concern in the case of high DGs penetration. Actually, DGs connection and disconnection change the short circuit level of feeders and disturb the coordination of protection devices. So, new protection schemes should be proposed which must not to be affected by DGs and should work properly in both islanded and grid connected operation modes of the micro-grid. In this paper, an adaptive protection scheme, based on fault location, is proposed for smart micro-grids. The functionality of each protection device is adaptively selected based on detected network topology and fault type. The proposed solution is simulated in the SimPower software. The results show the efficiency of the proposed protection scheme which cannot be affected by DGs.

Cooperative Control Strategy of Multifunctional Inverters For Power Quality Enhancement in Smart Microgrid

This paper deals with a cooperative strategy, based on the conservative power theory decomposition, which can be applied to control the inverters used to connect the distributed energy resources in a three-phase microgrid. In addition to inject/absorb active power at their point of connection, these inverters are operated according to their available power capability so as to enhance the power quality at the point of common coupling to the main network. Simulation results are shown considering, as an example, a three-phase three-wire microgrid equipped with two inverters, together with unbalanced and nonlinear loads. Among the different analyzed situations, the possibility to supply reactive power at the point of common coupling is shown with a view to provide voltage support, which is an asset in the framework of smart distribution networks.

A new approach for reliability assessment of the smart microgrids using k-shortest path algorithms

The reliability of cyber-physical microgrids (MGs) is crucial in the development of smart grids. The reliability of MGs can be affected by cyber network failures, which have a significant impact on the physical components. Since MGs have interdependent cyber and physical elements, a smart MG can be viewed as a system of n dependent two-mode components. This paper proposes an approach to finding the k most likely configurations of the system. The method involves three phases. Firstly, the multi-mode model is obtained for physical components, considering the operation and topology of the cyber network. Then, the problem is transformed to finding the k most likely state of a system consisting of n independent multi-mode components. In the second phase, a transformation using new transformed metrics is applied. This results in the problem being converted to finding the k shortest path, which can be solved using efficient algorithms. Finally, the states are evaluated using a DC load flow, and reliability indices such as loss of load probability (LOLP) and expected energy not supplied (EENS) are calculated. Moreover, we have incorporated the dynamic thermal rating (DTR) system into our proposed model, addressing the safe enhancement of system component ratings. The results indicate that the most probable states of the system are related to the failure of distribution generators. The most severe events occur due to failure in the cyber network, and cyber network malfunction has a higher effect on EENS compared to LOLP. Additionally, we observe a significant enhancement in reliability indices when considering the DTR system over the static thermal rating (STR) system. This approach is efficient in reliability calculation using fewer system states.

An Energy Management Strategy for an Electrified Railway Smart Microgrid System Based on Integrated Empirical Mode Decomposition

The integration of a renewable energy and hybrid energy storage system (HESS) into electrified railways to build an electric railway smart microgrid system (ERSMS) is beneficial for reducing fossil fuel consumption and minimizing energy waste. However, the fluctuations of renewable energy generation and traction load challenge the effectiveness of the energy management for such a complex system. In this work, an energy management strategy is proposed which firstly decomposes the renewable energy into low-frequency and high-frequency components by an integrated empirical mode decomposition (IEMD). Then, a two-stage energy distribution approach is utilized to appropriately distribute the energy flow in the ERSMS. Finally, the feasibility and effectiveness of the proposed solution are validated through case study.

Intelligent energy management system of a smart microgrid using multiagent systems

Intelligent energy management system of a smart microgrid using multiagent systems

Optimal Day Ahead Active and Reactive Power Management in Residential Buildings using Particle Swarm Optimization

This paper introduces an innovative Active and Reactive Residential Building Energy Management (AR-RBEM) system, seamlessly integrated with a smart microgrid and the Electrical Power Grid (EPG) supplier. The proposed AR-RBEM computes optimal active and reactive power setpoints for each energy source over the next 24 hours based on predicted PV power generation and load consumption. The proposed strategy aims to achieve two primary objectives: minimize overall energy expenses, considering both active and reactive power consumption, and account for Energy Storage System (ESS) degradation costs. Additionally, it seeks to reduce the Peak-to-Average Ratio (PAR) and carbon emissions from the supplier’s perspective. To tackle this multi-objective problem, a metaheuristic algorithm which is Particle Swarm Optimization (PSO), is employed. To assess the effectiveness of the proposed approach, a comparative analyse is conducted, emphasizing the significance of concurrently managing active and reactive power compared to a system exclusively managing active power (A-RBEM). Both strategies are evaluated using Time of Use Tarriff (TOU) and real world data. The comparative analysis underscores the advantages of the proposed AR-RBEM, showcasing benefits in terms of cost reduction for both active and reactive power of 59,6%, a substantial 25.3% reduction in carbon emissions compared to the alternative strategy.

Modeling smart electrical microgrid with demand response and storage systems for optimal operation in critical conditions

This study examines the issue in standard and operational scenarios of microgrids that arise during critical conditions. Initially, the ideal energy storage size and discharge depth are identified for optimal microgrid planning under operating conditions. Subsequently, by utilizing the energy storage system and load response, the microgrid’s vulnerability is reduced and the cost of load shedding is minimized when in critical conditions, leading to the microgrid being disconnected from the main network and operating in island mode. This model aims to analyze how the system performance is influenced by the presence of storage systems and load response programs in a numerical scenario, particularly during severe weather events. In addition, the study examines the role of advanced control algorithms and communication systems in optimizing the operation of the microgrid. By implementing smart grid technologies, the microgrid can better manage its energy resources, anticipate fluctuations in demand, and respond quickly to changing conditions. This proactive approach helps to ensure the stability and reliability of the microgrid, even in the face of unforeseen challenges. Overall, this research contributes valuable insights into the challenges and opportunities facing microgrids in both normal and emergency situations. By identifying the most effective energy storage solutions, load response strategies, renewable energy integration methods, and advanced control systems, the study aims to enhance the resilience, efficiency, and sustainability of microgrid systems in the future. The results of the proposed approach in critical operational mode represent the optimal condition of the electrical microgrid with minimum cost and load shedding considering storage systems and load response programs.

Energy management of the residential smart microgrid with optimal planning of the energy resources and demand side

The study specifically aimed to model the optimal operation of these appliances based on their usage patterns, rather than relying on the capacity of demand flexibility in demand response (DR) and energy pricing. The modeling operation of the appliances is done using two-layer energy optimization. In this optimization, energy consumption by appliances is reshaped via DR and load shifting in first-layer optimization. Then, minimizing the consumption costs and consumers’ discomfort in the second layer is formulated with consideration of the optimized consumption from the first layer. The lp-metric method is employed to solve the proposed optimization in the GAMS software. Finally, the efficiency of the two-layer optimization is confirmed using testing proposed case studies in the numerical simulation.

Energy Management System in Smart Micro-Grid

This paper focuses on discussing an energy management system (EMS) for a smart microgrid integrating multiple renewable sources. The task of the EMS is to efficiently balance power generation and consumption by controlling various energy sources, including photovoltaic systems, energy storage units, engine generator set and the utility grid. An EMS optimizes power flow between the microgrid components and keeps the micro-grid stable, by using different control strategies. In this microgrid, the PV system serves as the primary energy source, while the other sources of electrical energy act as backups. The energy management system prioritizes supplying load demand from either the PV system or the other sources, with load shedding during peak hours if the supplied energy is insufficient. The approaches based on StateMachine and StateFlow are discussed in this paper for enhancing the energy management system's performance.

Two-Stage Approach for Demand Side Management in Multi-Smart Grids

In the face of escalating electricity demand driven by technological advancements and a growing population, efficient management becomes imperative. This study explores demand-side management (DSM) within smart grids (SGs) to minimize customer bills, alleviate peak loads, and mitigate losses. The research employs diverse strategies and compares their effectiveness, focusing on optimal load consumption across smart microgrids. Key contributions include a DSM strategy for cost reduction, efficient peak load reduction, and active power loss minimization in smart microgrids. The proposed approach is validated through simulations, with results contributing to real-world SG implementation. The study highlights a 23.4% and 31.6% cost saving for residential and commercial customers, respectively, and emphasizes the significance of load management in enhancing operational efficiency and cost-effectiveness within the energy hub framework. The research provides valuable insights for developing advanced consumption management strategies, ensuring energy efficiency and sustainability in future power systems.

Optimal Placement of Battery Swapping Stations for Power Quality Improvement: A Novel Multi Techno-Economic Objective Function Approach

In recent years, battery swapping stations have become increasingly popular in smart energy networks. Its advantages include reducing the time required for recharging energy, balancing the grid’s load, and extending the battery’s lifespan. Despite efforts focused on the placement and operation of battery swapping stations (BSSs), there is still a lack of a comprehensive and systematic examination that covers all aspects of both the economic and technical aspects of the power network. This encompasses considerations such as power quality and reliability, particularly in terms of where these stations should be located. This paper introduces a novel framework for strategically positioning BSS within smart microgrids that integrate distributed energy resources (DERs). It takes into account various technical factors (such as reliability and power quality) and economic factors (like the cost of generation and operation), which have been overlooked in the previous research. To achieve this goal, a unique hybrid optimization strategy is developed, incorporating a combination of epsilon-constraint and lexicographic (DECL) optimization methods. This approach tackles a multi-objective challenge, treating factors like the number, locations, and sizes of BSS as independent variables, while operational costs and technical power quality metrics are considered dependent variables. To validate this approach, it is tested on standard benchmark distribution power networks such as IEEE 33, 69, and 118 bus systems. The simulation results, showcasing the strengths and capabilities of this innovative strategy, are compared to the findings of previous research studies.

A Data-Driven Architecture for Smart Renewable Energy Microgrids in Non-Interconnected Zones: A Colombian Case Study

Implementing smart microgrids for Non-Interconnected Zones (NIZs) has become an alternative solution to provide electrical energy by taking advantage of the resources available through the generation of renewable energy within these isolated areas. Within this context, in this study, the challenges related to microgrids and data analysis are presented, and different relevant data architectures described in the literature are compared. This paper focuses on the design of a data architecture for a smart microgrid for NIZs whose microgrid contains two 260 W solar panels, a 480 W inverter, and two 260 Ah batteries. Regarding the Colombian context, this paper describes the limitations (connectivity, isolation, appropriation of technologies) and opportunities (low demand, access to natural resources, state interest) from which the functional and non-functional requirements for the architecture are established. Finally, a data architecture is proposed and implemented in a NIZ in Colombia, and this paper also includes a description of the architecture, its characteristics, its associated opportunities and challenges, and discussions regarding its implementation.

Preface

Preface It is our great pleasure to have you at the 2023 3rd International Conference on Smart Grid and Energy Internet (SGEI 2023), which took place in Chengdu, China from September 8th to 10th, 2023 (virtual conference). SGEI 2023 was successfully held and received unanimous praise from various experts and scholars all over the world.SGEI 2023 existed as an international forum for academic communications between experts and scholars in the fields of Smart Grid and Energy Internet. It promoted the research and developmental activities in related fields and scientific information interchange among all the participants, establishing research connections for them to find global partners for potential collaboration.After months of well preparation and hard work, the Proceedings of SGEI 2023 covering a bunch of papers, having been checked through rigorous review and processes to meet the requirements of publication, are smoothly published. These papers feature but are not limited to the following topics: Smart Power Distribution and Microgrid, Energy Strategy and Energy Internet, Energy and Distribution Management Systems, Power System Communication and Control, etc.We had about 80 participations both as speakers and participants, including researchers, industrial experts, teachers, students, and other practitioners. During the keynote speech part, each keynote speaker was given 30–40 minutes for keynote speech. Among them, Professor Ismail Musirin from Universiti Teknologi MARA, Malaysia performed a keynote speech. Most of his researches dealt with the algorithms and techniques development in solving problems in Power System involving Voltage Stability, Economic Power Dispatch, Transient Stability, Distributed Generation, Renewable Energy Optimization, Power System Optimization and Application of Artificial Intelligence in Power System. He has got 22 years of research experience in the field of Power System Engineering and has received numerous research grants from the Ministry of Higher Education, Malaysia (MOHE) and Ministry of Science, Technology and Innovation, Malaysia (MOSTI). The insightful speeches of each keynote speakers triggered interesting discussions and a good number of informal talks between all the participants.We would like to express our gratitude to the organizers, keynote speakers and all the participants of SGEI 2023, for their fruitful work and contribution to the success of the Conference. We are thankful to all the reviewers for providing constructive criticisms, stimulated comments and suggestions to the authors. Particularly, our special thanks go to Journal of Physics: Conference Series, for the endeavor of all its colleagues in publishing this paper volume.May this event be a spark that ignites many more new inventions in the fields related to smart grid and energy internet!The Committee of SGEI 2023List of Committee Member is available in this Pdf.

Practical prototype for energy management system in smart microgrid considering uncertainties and energy theft

The conventional electrical grid faces significant issues, which this paper aims to address one of most of them using a proposed prototype of a smart microgrid energy management system. In addition to relying too heavily on fossil fuels, electricity theft is another great issue. The proposed energy management system can simultaneously detect electricity theft and implement demand response tactics by employing time-of-use pricing principles and comparing real electricity consumption with grid data. The system uses the Al-Biruni earth radius (BER) optimization algorithm to make smart choices about how to distribute the load, intending to reduce energy consumption and costs without sacrificing comfort. As a bonus, it considers limitations imposed by battery charging/discharging and decentralized power generation. Incorporating sensors and SCADA-based monitoring, the system provides accurate measurement and management of energy usage through load monitoring and control. An intuitive mobile app also helps consumers connect, allowing for more active participation and better control over energy use. Extensive field testing of the prototype shows that by moving loads from peak period to another off-peak period, electricity expenditures can be reduced by up to 48.45%. The energy theft value was calculated to be 1199 W, proving that the system's theft detection model was effective.