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With the development of renewable energy technologies such as photovoltaics and wind power, it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through the deployment of energy storage. To solve the problem of the interests of different subjects in the operation of the energy storage power stations (ESS) and the integrated energy multi-microgrid alliance (IEMA), this paper proposes the optimization operation method of the energy storage power station and the IEMA based on the Stackelberg game. In the upper layer, ESS optimizes charging and discharging decisions through a dynamic pricing mechanism. In the lower layer, IEMA optimizes the output of various energy conversion coupled devices within the IEMA, as well as energy interaction and demand response (DR), based on the energy interaction prices provided by ESS. The results demonstrate that the optimization strategy proposed in this paper not only effectively balances the benefits of the IEMA and ESS but also enhances energy consumption rates and reduces IEMA energy costs. [ABSTRACT FROM AUTHOR]

With the rapid development of hydrogen production by water electrolysis, the coupling between the electricity-hydrogen system has become closer, providing an effective way to consume surplus new energy generation. As a form of centralized management of distributed energy resources, virtual power plants can aggregate the integrated energy production and consumption segments in a certain region and participate in electricity market transactions as a single entity to enhance overall revenue. Based on this, this paper proposes an optimal scheduling model of an electricity-hydrogen coupling virtual power plant (EHC-VPP) considering hydrogen load response, relying on hydrogen to ammonia as a flexibly adjustable load-side resource in the EHC-VPP to enable the VPP to participate in the day-ahead energy market to maximize benefits. In addition, this paper also considers the impact of the carbon emission penalty to practice the green development concept of energy saving and emission reduction. To validate the economy of the proposed optimization scheduling method in this paper, the optimization scheduling results under three different operation scenarios are compared and analyzed. The results show that considering the hydrogen load response and fully exploiting the flexibility resources of the EHC-VPP can further reduce the system operating cost and improve the overall operating efficiency. [ABSTRACT FROM AUTHOR]

This paper reviews the integration of blockchain technology, smart microgrids, and the energy market, highlighting its potential to revolutionize the energy industry. The integration of blockchain technology into smart microgrids aims to address challenges related to energy efficiency, reliability, and sustainability. The paper provides an overview of blockchain technology, emphasizing its transparency, immutability, and decentralization characteristics. It explores the concept of smart microgrids, which enable efficient energy management and integration of renewable energy sources. The combination of blockchain and smart microgrids offers several benefits such as increased efficiency, reduced transaction costs, enhanced security, and improved grid reliability. One of the key advantages of this convergence is the ability to facilitate peer-to-peer energy trading. Blockchain technology allows for transparent and auditable energy transactions, enabling direct trading between energy producers and consumers. This empowers prosumers to actively participate in the energy market, promoting renewable energy adoption and democratizing energy access. However, some challenges need to be addressed, including scalability, interoperability, and regulatory frameworks. Ongoing initiatives, projects, and pilot studies are exploring the implementation of blockchain-enabled smart microgrids, and case studies provide real-world examples of successful deployments. In conclusion, the convergence of blockchain, smart microgrids, and the electrical energy market has the potential to transform the energy industry. Collaboration among stakeholders, including energy companies, technology providers, regulators, and consumers, is crucial to fully realize the benefits of this integration. By leveraging blockchain and smart microgrids, the energy industry can pave the way for a more efficient, sustainable, and decentralized energy future. [ABSTRACT FROM AUTHOR]

Microgrids are small power systems, often equipped with renewable energy sources, that are alternatives or supplementary to utility grids. Many studies have been conducted on the design and implementation of microgrids and their interconnects to utility grids, and investigations have been extended to the use of Internet of Things technology (IoT) to monitor and operate such power grids. However, the broad applications of the IoT technology itself also call for a green energy solution. This paper investigates how to power local IoT applications via an integration of a microgrid and the utility grid. Together, we call such a system an IoT microgrid. The goal of an IoT microgrid is to maintain the availability of IoT applications while saving energy costs, and this is achieved by sustaining IoT applications via local renewable energy from a microgrid and by mitigating the intermittent power supply using the utility grid. This paper characterizes the IoT microgrid and proposes a configurable cyber-physical testbed for its design and validation. The testbed incorporates the hardware-in-the-loop (HIL) approach, where real-time simulation is integrated with physical elements for quick prototyping of those components in an IoT microgrid. The paper concludes with an example implementation of the proposed testbed, which demonstrates its use for validating both an IoT microgrid and the IoT application it sustains. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to review the energy management of a community microgrid using adaptable renewable energy sources. Community microgrids have grown up as a viable strategy to successfully integrate renewable energy sources (RES) into local energy distribution networks in response to the growing worldwide need for sustainable and dependable energy solutions. This study presents an in-depth examination of the energy management tactics employed in community microgrids using adaptive RES, covering power generation, storage, and consumption. Energy communities are an innovative yet successful prosumer idea for the development of local energy systems. It is based on decentralized energy sources and the flexibility of electrical users in the community. Local energy communities serve as testing grounds for innovative energy practices such as cooperative microgrids, energy independence, and a variety of other exciting experiments as they seek the most efficient ways to interact both internally and with the external energy system. We discuss several energy management tactics utilized in community microgrids with flexible RES, Which include various renewable energy sources (wind, solar power, mechanical vibration energy) and storage devices. Various energy harvesting techniques have also been discussed in this paper. It also includes information on various power producing technology. Given the social, environmental, and economic benefits of a particular site for such a community, this paper proposes an integrated technique for constructing and efficiently managing community microgrids with an internal market. The report also discusses the obstacles that community microgrids confront and proposed methods for overcoming them. This paper analyzes future developments in community microgrids with adaptive RES. The study discusses potential developments in community microgrids with flexible energy trading systems. [ABSTRACT FROM AUTHOR]

The concept of the digital twin has been adopted as an important aspect in digital transformation of power systems. Although the notion of the digital twin is not new, its adoption into the energy sector has been recent and has targeted increased operational efficiency. This paper is focused on addressing an important gap in the research literature reviewing the state of the art in utilization of digital twin technology in microgrids, an important component of power systems. A microgrid is a local power network that acts as a dependable island within bigger regional and national electricity networks, providing power without interruption even when the main grid is down. Microgrids are essential components of smart cities that are both resilient and sustainable, providing smart cities the opportunity to develop sustainable energy delivery systems. Due to the complexity of design, development and maintenance of a microgrid, an efficient simulation model with ability to handle the complexity and spatio-temporal nature is important. The digital twin technologies have the potential to address the above-mentioned requirements, providing an exact virtual model of the physical entity of the power system. The paper reviews the application of digital twins in a microgrid at electrical points where the microgrid connects or disconnects from the main distribution grid, that is, points of common coupling. Furthermore, potential applications of the digital twin in microgrids for better control, security and resilient operation and challenges faced are also discussed. [ABSTRACT FROM AUTHOR]

Under the current power trading model, especially in the context of the large-scale penetration of renewable energy and the rapid integration of renewable energy into the power system, reasonable medium- and long-term decomposition can reduce the fluctuation in the energy price when the integrated energy service provider (IESP) participates in the spot market. It helps to avoid the price risk of the spot market. Additionally, it promotes the optimization of the operation of the regional energy day-ahead scheduling. At the present stage, most of the medium- and long-term contract decomposition methods focus on the decomposition of a single power and take less consideration of the bidding space in the spot market. This limitation makes it challenging to achieve efficient interaction and interconnection among multi-energy resources and smooth integration between the medium- and long-term market and the spot market. To address these issues, this paper proposes an optimal monthly contract decomposition method for IESPs that takes into account the equilibrium of spot bidding. First, the linking process and rolling framework of multi-energy transactions between the medium- and long-term market and the spot market are designed. Second, an optimal decomposition model for monthly contracts is constructed, and a daily decomposition method for monthly medium- and long-term contracts that accounts for the spot bidding equilibrium is proposed. Then, the daily preliminary decomposition result of medium- and long-term multi-energy contracts is used as the boundary condition of the day-ahead scheduling model, and the coupling characteristics of the multi-energy networks of electricity, gas, and heat are taken into account, as well as the operational characteristics. Then, considering the coupling characteristics and operating characteristics of electricity, gas, and heat networks, the optimal scheduling model of a multi-energy network is constructed to minimize the sum of cumulative daily operating costs, and the monthly final contract decomposition value and daily spot bidding space are derived. Finally, examples are calculated to verify the validity of the decomposition model, and the examples show that the proposed method can reduce the variance in spot energy purchase by about 4.64%, and, at the same time, reduce the cost of contract decomposition by about USD 0.33 million. [ABSTRACT FROM AUTHOR]

Energy markets are crucial for integrating Distributed Energy Resources (DER) into modern power grids. However, this integration presents challenges due to the inherent variability and decentralized nature of DERs, as well as poorly adapted regulatory environments. This paper proposes a medium-term decision-making approach based on a comprehensive suite of computational tools for integrating DERs into Colombian energy markets. The proposed framework consists of modular tools that are aligned with the operation of a Commercial Virtual Power Plant (CVPP). The tools aim to optimize participation in bilateral contracts and short-term energy markets. They use forecasting, uncertainty management, and decision-making modules to create an optimal portfolio of DER assets. The suite's effectiveness and applicability are demonstrated and analyzed through its implementation with heterogeneous DER assets across various operational scenarios. [ABSTRACT FROM AUTHOR]

A grid blackout is an intractable problem with serious economic consequences in many developing countries. Although it has been proven that microgrids (MGs) are capable of solving this problem, the uncertainties regarding when and for how long blackouts occur lead to extreme difficulties in the design and operation of the related MGs. This paper addresses the optimal design problem of the MGs considering the uncertainties of the blackout starting time and duration utilizing the kernel density estimator method. Additionally, uncertainties in solar irradiance and ambient temperature are also considered. For that, chance-constrained optimization is employed to design residential and industrial PV-based MGs. The proposed approach aims to minimize the expected value of the levelized cost of energy ( L C O E ), where the restriction of the annual total loss of power supply ( T L P S ) is addressed as a chance constraint. The results show that blackout uncertainties have a considerable effect on calculating the size of the MG's components, especially the battery bank size. Additionally, it is proven that considering the uncertainties of the input parameters leads to an accurate estimation for the LCOE and increases the MG reliability level. [ABSTRACT FROM AUTHOR]

Currently, the global energy revolution in the direction of green and low-carbon technologies is flourishing. The large-scale integration of renewable energy into the grid has led to significant fluctuations in the net load of the power system. To meet the energy balance requirements of the power system, the pressure on conventional power generation units to adjust and regulate has increased. The efficient utilization of the regulation capability of controllable industrial loads and energy storage can achieve the similarity between renewable energy curves and load curves, thereby reducing the peak-to-valley difference and volatility of the net load. This approach also decreases the adjustment pressure on conventional generating units. Therefore, this paper proposes a two-stage optimization scheduling strategy considering the similarity between renewable energy and load, including energy storage and industrial load participation. The combination of the Euclidean distance, which measures the similarity between the magnitude of renewable energy–load curves, and the load tracking coefficient, which measures the similarity in curve shape, is used to measure the similarity between renewable energy and load profiles. This measurement method is introduced into the source-load-storage optimal scheduling to establish a two-stage optimization model. In the first stage, the model is set up to maximize the similarity between renewable energy and the load profile and minimize the cost of energy storage and industrial load regulation to obtain the desired load curve and new energy output curve. In the second stage, the model is set up to minimize the overall operation cost by considering the costs associated with abandoning the new energy sources and shedding loads to optimize the output of conventional generator sets. Through a case analysis, it is verified that the proposed scheduling strategy can achieve the tracking of the load curve to the new energy curve, reducing the peak-to-valley difference of the net load curve by 48.52% and the fluctuation by 67.54% compared to the original curve. These improvements effectively enhance the net load curve and reduce the difficulty in regulating conventional power generation units. Furthermore, the strategy achieves the full discard of renewable energy and reduces the system operating costs by 4.19%, effectively promoting the discard of renewable energy and reducing the system operating costs. [ABSTRACT FROM AUTHOR]

Considering the importance of the renewable energy sector in the distribution systems, energy operation, and management which are connected to the distribution network (DN) in the form of multiple microgrids (MMGs) is crucial in reducing cost and pollution. Hence, this paper aims to propose optimal energy management for MMGs in the DN. Different objective functions have been taken into account in this optimization, including network cost, pollution reduction, and distribution network power losses. To design the multi‐objective optimization problem, a fuzzy method has been adopted for simultaneous multi‐objective calculations. Furthermore, the effect of the placement of distributed generations (DGs) and microgrids (MGs) is considered to reduce the distribution network power losses. Information gap decision theory (IGDT) has formulated uncertainties about renewable sources and consumers. To solve this optimization problem, a new method of the modified Harris Hawk optimization (MHHO) algorithm has been implemented, compared with the original HHO and genetic algorithm (GA). Finally, the proposed method has been analysed under the IEEE 33‐bus distribution network for a 24‐hour time horizon, including three MGs considering different renewable energy sources (RESs). The simulation results have demonstrated the high performance of the allocated network with the MHHO algorithm compared to the other scenarios. [ABSTRACT FROM AUTHOR]

This paper presents both the techno-economic planning and a comprehensive sensitivity analysis of an off-grid fully renewable energy-based microgrid (MG) intended to be used as an electric vehicle (EV) charging station. Different possible plans are compared using technical, economic, and techno-economic characteristics for different numbers of wind turbines and solar panels, and both single and hybrid energy storage systems (ESSs) composed of new Li-ion, second-life Li-ion, and new lead–acid batteries. A modified cost of energy (MCOE) index including EVs' unmet energy penalties and present values of ESSs is proposed, which can combine both important technical and economic criteria together to enable a techno-economic decision to be made. Bi-objective and multi-objective decision-making are provided using the MCOE, total met load, and total costs in which different plans are introduced as the best plans from different aspects. The number of wind turbines and solar panels required for the case study is obtained with respect to the ESS capacity using weather data and assuming EV demand according to the EV population data, which can be generalized to other case studies according to the presented modelling. Through studies on hybrid-ESS-supported MGs, the impact of two different global energy management systems (EMSs) on techno-economic characteristics is investigated, including a power-sharing-based and a priority-based EMS. Single Li-ion battery ESSs in both forms, new and second-life, show the best plans according to the MCOE and total met load; however, the second-life Li-ion shows lower total costs. The hybrid ESSs of both the new and second-life Li-ion battery ESSs show the advantages of both the new and second-life types, i.e., deeper depths of discharge and cheaper plans. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to select the optimal model of a hybrid renewable-energy microgrid (MG) system for a village in India. The MG comprises solar photovoltaic (PV) modules, a wind turbine generator, a biomass generator, a battery bank, a diesel generator and an electric vehicle. The optimal model selection is based on technical, economic, environmental, social and reliability parameters. A novel spoonbill swarm optimization algorithm is proposed to select the best hybrid MG system. The optimization results are compared with particle swarm optimization, the genetic algorithm and the grasshopper optimization algorithm. The number or size of components of the optimized MG system is 215 PV modules, 92 kW of wind turbine generation, 25 kW of biomass generation, 267 batteries, 22 kW of electric vehicles and 30 kW of diesel generation. The optimized system was selected based on technical factors such as renewable dispersion (93.5%), the duty factor (5.85) and excess energy (15 975 kWh/year) as well as economic considerations including the net present cost (Rs. 34 686 622) and the cost of energy (9.3 Rs./kWh). Furthermore, environmental factors such as carbon emissions (396 348 kg/year) and atmospheric particulate matter (22.686 kg/year); social factors such as the human progress index (0.68411), the employment generation factor (0.0389) and local employment generation (15.64643); and reliability parameters including loss of power supply probability (0.01%) and availability index (99.99%) were considered during the selection process. The spoonbill swarm optimization algorithm has reduced the convergence time by 1.2 times and decreased the number of iterations by 0.83 times compared with other algorithms. The performance of the MG system is validated in the MATLAB® environment. The results show that the MG system is the optimal system considering technical, economic, environmental, social and reliability parameters. Additionally, the spoonbill swarm optimization algorithm is found to be more efficient than the other algorithms in terms of iteration time and convergence time. [ABSTRACT FROM AUTHOR]

Distributed energy resources have demonstrated their potential to mitigate the limitations of large, centralized generation systems. This is achieved through the geographical distribution of generation sources that capitalize on the potential of their respective environments to satisfy local demand. In a microgrid, the control problem is inherently distributed, rendering traditional control techniques inefficient due to the impracticality of central governance. Instead, coordination among its components is essential. The challenge involves enabling these components to operate under optimal conditions, such as charging batteries with surplus solar energy or deactivating controllable loads when market prices rise. Consequently, there is a pressing need for innovative distributed strategies like emergent control. Inspired by phenomena such as the environmentally responsive behavior of ants, emergent control involves decentralized coordination schemes. This paper introduces an emergent control strategy for microgrids, grounded in the response threshold model, to establish an autonomous distributed control approach. The results, utilizing our methodology, demonstrate seamless coordination among the diverse components of a microgrid. For instance, system resilience is evident in scenarios where, upon the failure of certain components, others commence operation. Moreover, in dynamic conditions, such as varying weather and economic factors, the microgrid adeptly adapts to meet demand fluctuations. Our emergent control scheme enhances response times, performance, and on/off delay times. In various test scenarios, Integrated Absolute Error (IAE) metrics of approximately 0.01% were achieved, indicating a negligible difference between supplied and demanded energy. Furthermore, our approach prioritizes the utilization of renewable sources, increasing their usage from 59.7% to 86.1%. This shift not only reduces reliance on the public grid but also leads to significant energy cost savings. [ABSTRACT FROM AUTHOR]

Introduction: Distributed power supply has increasingly taken over as the energy industry's primary development direction as a result of the advancement of new energy technology and energy connectivity technology. In order to build isolated island microgrids, such as villages, islands, and remote mountainous places, the distributed power supply design is frequently employed. Due to government subsidies and declining capital costs, the configured capacity of new energy resources like solar and wind energy has been substantially rising in recent years. However, the new energy sources might lead to a number of significant operational problems, including over-voltage and ongoing swings in the price of power. Additionally, the economic advantages availed by electricity consumers may be impacted by the change in electricity costs and the unpredictability of the output power of renewable energy sources. Methods: This paper proposes a novel framework for enhancing renewable energy management and reducing the investment constraint of energy storage. First, the energy storage incentive is determined through a bi-level game method. Then, the net incentive of each element is maximized by deploying a master-slave approach. Finally, a reward and punishment strategy is employed to optimize the energy storage in the cluster. Results: Simulation results show that the proposed framework has better performance under different operating conditions. Discussion: The energy storage operators and numerous energy storage users can implement master-slave game-based energy storage pricing and capacity optimization techniques to help each party make the best choices possible and realize the multi-subject interests of energy storage leasing supply and demand win-win conditions. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources (RES) such as wind and solar power is increasing rapidly to meet growing electricity demand. However, the intermittent nature of RES poses a challenge to grid stability. Energy storage (ES) technologies offer a solution by adding flexibility to the system. With the emergence of distributed energy resources (DERs) and the transition to prosumer-based electricity systems, energy management systems (EMSs) have become crucial to coordinate the operation of different devices and optimize system efficiency and functionality. This paper presents an EMS for a residential photovoltaic (PV) and battery system that addresses two different functionalities: energy cost minimization, and self-consumption maximization. The proposed EMS takes into account the operational requirements of the devices and their lower-level controllers. A genetic algorithm (GA) is used to solve the optimization problems, ensuring a desired State of Charge (SOC) at the end of the day based on the next day forecast, without discretizing the SOC transitions allowing a continuous search space. The importance of adhering to the manufacturer's operating specification to avoid premature battery degradation is highlighted, and a comparative analysis is performed with a simple tariff-driven solution, evaluating total cost, energy exchange, and peak power. Tests are carried out in a detailed model, where Power Electronics Converters (PECs) and their local controllers are considered together with the EMS. [ABSTRACT FROM AUTHOR]

This paper introduces a novel techno-economic feasibility analysis of energy management utilizing the Homer software v3.14.5 environment for an independent hybrid microgrid. This study focuses on a school with twelve classes, classifying the electrical components of the total load into three priority profiles: green, orange, and red. The developed approach involves implementing demand management for the hybrid microgrid through Bayesian inference, emphasizing goal-directed decision making within embodied or active inference. The Bayesian inference employs three parameters as inputs: the total production of the hybrid system, the load demand, and the state of charge of batteries to determine the supply for charge consumption. By framing decision making and action selection as variational Bayesian inference, the approach transforms the problem from selecting an optimal action to making optimal inferences about control. The results have led to the creation of a Bayesian inference approach for the new demand management strategy, applicable to load profiles resembling those of commercial and service institutions. Furthermore, Bayesian inference management has successfully reduced the total unmet load on secondary and tertiary priority charges to 1.9%, thereby decreasing the net present cost, initial cost, and energy cost by 37.93%, 41.43%, and 36.71%, respectively. This significant cost reduction has enabled a substantial decrease in investments for the same total energy consumption. [ABSTRACT FROM AUTHOR]

This research delves into the intricate landscape of energy scheduling and optimization within microgrid and residential contexts, addressing pivotal aspects such as real-time scheduling systems, challenges in dynamic pricing, and an array of optimization strategies. This paper introduces a cutting-edge scheduling algorithm, harnessing the power of artificial neural networks driven by Long Short-Term Memory Networks, and highlights its exceptional performance, boasting a significantly lower Mean Absolute Error of 5.32 compared to conventional models. This heightened predictive accuracy translates into tangible improvements in both energy efficiency and cost savings. This study underscores the delicate balance between user satisfaction, cost reduction, and efficient scheduling for sustainable energy consumption, showcasing a remarkable 38% enhancement in optimized schedules. Further granularity revealed substantial gains in energy efficiency and cost reduction across different scheduling intensities: 11.11% in light schedules, 20.09% in medium schedules, and an impressive 38.85% in heavy schedules. However, this research does not shy away from highlighting challenges related to data quality, computational demands, and generalizability. Future research trajectories encompass the development of adaptive models tailored to diverse data qualities, enhancements in scalability for and adaptability to various microgrid configurations, the integration of real-time data, the accommodation of user preferences, the exploration of energy storage and renewables, and an imperative focus on enhancing algorithm transparency. [ABSTRACT FROM AUTHOR]

Pumped-storage hydropower plant (PSHP) is a type of valuable energy storage system and a flexible resource to the modern power system with increasing renewable energy integration. As independent market participants, a PSHP can participate in both the energy market and frequency regulation market to maximize its revenue and contribution to the secure and economic operation of the power system. In some PSHPs, both fixed-speed and variable-speed units are installed to improve the flexibility, especially when operating in the pumping mode. However, it's difficult to deal with the nonlinear relationships among power, flow, and water head in pumping and generating modes. This paper proposes iterative solution methods for scheduling the PSHP by considering the relationship between power and flow at different water heads for different types of units. The scheduling problem is established as a scenario-based optimization formulation by considering PSHP's participation in both the energy market and frequency regulation market. In each iteration, the optimal dispatch model is formulated as a Mixed Integer Linear Programming (MILP) problem. Case studies are performed and simulation results validate the effectiveness of the model and the iterative solution methods. [ABSTRACT FROM AUTHOR]

The increasing inclusion of electric vehicles (EVs) in distribution systems is a global trend due to their several advantages, such as increased autonomy and reduced price. However, this growth requires a high investment in electric vehicle charging stations (EVCSs) infrastructure to satisfy the demand. Thus, in this paper, an adequate planning of the EVCSs allocation and sizing is carried out to ensure better power quality indices, in addition to reducing costs related to EVCSs installation and EV users' recharging. Besides the optimal planning of EVCSs, this paper performs the optimal allocation and sizing of distributed energy resources (DERs) in order to mitigate the problem related to voltage levels and power losses. Additionally, a spatial distribution of EVs was performed for the 24 h, considering residential and commercial nodes of a distribution feeder test, from the closeness centrality of graph theory. The non-dominated sorting genetic algorithm II (NSGA-II) was applied to obtain the Pareto curve, which made it possible to minimize the objective functions for the IEEE 34-node test feeder. Even with the integration of EV loads, the optimal allocation and sizing of EVCSs and DERs promoted a reduction in voltage deviation and power losses of 11.576% and 56.683%, respectively, in addition to a low variation in the energy costs of 4.447%. [ABSTRACT FROM AUTHOR]

The article examines the creation of a Cost Economy Energy Savings Simulation Model (CEESS Model) as an economic scenario generator for energy-independent structures using the Monte Carlo method. The CEESS model is a continuous simulation model created on the ExtendSim simulation system platform. The problem is related to the constantly changing environmental parameters for the purpose of energy security for buildings as modern, energy-independent and self-sufficient systems. In terms of the implementation of the defined part of the research, a logistical approach was applied: system analysis, coordination, algorithm work, planning, efficiency. We define logistics as a system, principle, philosophy of management of flows. The numerous simulation experiments carried out show that the return on investment of the option with an initial investment of 5000 euros is in the range of 2422 to 4978 days, the return on investment of the option with an initial investment of 10000 euros is in the range of 4233 to 7902 days and the return on investment of the option with an initial investment of 15000 euros is in the range of 5691 to 10073 days. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the park integrated energy system (PIES) operation, and a robust bilevel optimal dispatch is proposed. Firstly, the robust uncertainty set is constructed based on the K-means++ algorithm to solve the uncertainty of renewable energy sources output in PIES. Then, the bi-level dispatch model is proposed, with the operator as the leader and consumers as the follower. The upper model establishes an electricity-heat-gas integrated energy network, and the lower model considers the demand response of consumers. Optimizing the pricing strategies of energy sources to determine the output of each energy conversion equipment and the demand response plan. Moreover, analyzing the decision-making process of the robust bi-level model and the solution method is given. Finally, case studies show that the proposed dispatch model can increase operator profits and reduce consumers' energy costs. The in-sample and out-of-sample simulations demonstrate that the proposed ellipsoid uncertainty set possesses high compactness, good robustness, and low conservatism. [ABSTRACT FROM AUTHOR]

The combination of demand response as demand side management together with energy management system has become essential to minimize energy cost, to maintain continuous supply of electricity, and to improve the safety of power system operation. This paper studies the optimal energy dispatch of connected microgrid units containing photovoltaic panels, wind turbine generators, diesel generators, and the main grid. The optimal set point of microgrid's units is determined to satisfy the required load demand for a day-ahead horizon time. As the demand response is an important way of demand side management, this paper proposes as the main contribution the implementation of demand response cost as one of the objective functions to be maximized to view its effect on load demand consumption, on MG energy production and on MG energy cost. The demand response is implemented by using an incentive based demand response program in the optimization model in addition to the fuel cost of diesel generators and the transfer cost of transferable power. The incentive payment offered by utilities is used to motivate consumers to change their energy consumption behavior and thus to reduce their power consumption and maintain the system reliability during on-peak periods. Thus the objective function is formulated to maximize microgrid operator's demand response benefit, and to minimize both the fuel cost of diesel generators, and the transfer cost of transferable power. For this purpose, the defined objective function is solved by a Hybrid Particle Swarm Optimization with Sine Cosine Acceleration Coefficients (H-PSO-SCAC) algorithm for an optimal energy management system of the connected microgrid. For the simulation tests, different algorithms are examined in order to validate the effectiveness of the H-PSO-SCAC algorithm. The impact of demand response program is analyzed on the load demand consumption, on the microgrid energy production and its influence on the optimized microgrid cost function. The results demonstrate that the implementation of demand response has changed the previous situation that costumers do not participate in the operation of the power system. And it enables microgrid to decrease load consumption, microgrid energy production, as well as energy cost. [ABSTRACT FROM AUTHOR]

In recent years, the energy form of microgrids is constantly enriching, while the decentralization requirements of microgrids are constantly developing. Considering the economic benefits of an integrated energy microgrid (IEM), this paper focuses on the distributed optimal dispatch of IEM based on a consensus algorithm. The microgrid structure and multi-agent system are combined organically to get the decentralized architecture of IEM. This paper takes the incremental cost rate of each unit in IEM as a consensus variable. Based on the consensus theory, iterative optimization is carried out to achieve the optimal economic operation and power supply-demand balance of IEM. The distributed optimal dispatch is realized, and the convergence of the algorithm is proved. The experiment is carried out with LabVIEW and MATLAB and verifies the effectiveness of the algorithm. The results show that the distributed optimal dispatch algorithm can effectively reduce the power generation cost of the integrated energy system. [ABSTRACT FROM AUTHOR]

Demand‐Side Management (DSM) Strategic plan of International Energy Agency states that the first option in all energy policies to achieve sustainable, reliable, and economic systems is DSM activities such as demand response programs (DRP). Thereby, these programs must also be considered in energy management of microgrids (MGs) and their operation and resiliency optimization. Energy management in MGs is carried out with different objectives such as reducing operation costs and enhancing the resilience response. In the first approach, the microgrid operator attempts to minimize the energy cost supplied by available resources in normal conditions. On the other hand, the goal of improving microgrid resilience response is to reduce the energy outages and load interruptions in emergencies such as occurrence of floods, earthquakes, and hurricanes. Naturally, in the second approach, the generation share of the microgrid resources itself in supplying the required demand is more; because the microgrid and main network connection will be lost in this approach. Since these two approaches may lead to different strategies in microgrid energy management, a compromise must be reached between them. In this paper, DRP, storage devices, renewable resources, such as wind and photovoltaic (PV) units, are modelled to manage energy in the MGs equipped with Combined Cooling, Heat, and Power to minimize the operation costs and enhance the resilience response. Three case studies are considered. The first case deals with the management of energy and structure in MGs without considering resilience objective function and responsive loads. In the second case, the resilience objective function is considered in the problem, and in the third study, responsive loads were also taken into account. The results show that the total objective function is reduced by considering the resilience objective function in case two. Also, the use of responsive loads in the third case also reduces more the total costs of MGs. [ABSTRACT FROM AUTHOR]

This document discusses the challenges and opportunities within the energy industry's industry chain, supply chain, and value chain. It emphasizes the importance of digital transformation, renewable energy sources, and sustainable practices in achieving a low-carbon future. The document includes research papers that cover topics such as carbon emissions, resource management, game theory, market integration, technological innovation, and environmental governance. Overall, the document provides valuable insights and recommendations for policymakers, industry stakeholders, and researchers interested in advancing the global energy transition. [Extracted from the article]

With the continuous promotion of the energy revolution, the market-oriented reform of electricity has become the first priority in the energy field, and small-scale energy storage devices on the user side have received more and more attention. However, the disorderly management mode of user-side energy storage not only causes a waste of resources, but also brings hidden dangers to the safe operation of the power grid, such as stability, scheduling and operation, power quality and other problems. To address this issue, this paper proposes a user-side shared energy storage pricing strategy based on Nash game. Firstly, an optimal operation model is established for each participant of energy storage operators, users and grid. Secondly, a cooperative game model is established based on Nash equilibrium theory for the participants under the constraints. Finally, the optimal pricing scheme is solved by simulation analysis of the model, and the feasibility of the proposed pricing mechanism is verified. The results show that the optimal pricing scheme can achieve the purpose of peak shaving and resource saving. At the same time, it can also realize the win–win situation for all parties. [ABSTRACT FROM AUTHOR]

An expressway microgrid can make full use of renewable resources near the road area and enable joint carbon reduction in both transportation and energy sectors. It is important to research the optimal construction mode and capacity configuration method of expressway microgrid considering the carbon trading and carbon offset mechanism. This paper establishes a design model for an expressway microgrid considering the operating features of each component in the microgrid under two patterns of grid-connected/islanded and two types of AC/DC. The goal of the proposed model is to minimize the annualized comprehensive cost, which includes the annualized investment cost, operational cost, and carbon trading cost. The model designates the optimal construction mode of an expressway microgrid, i.e., grid-connected or islanded, AC or DC. As a mixed integer nonlinear programming (MINLP) problem, the proposed model can be solved in a commercial solver conveniently, such as GUROBI and CPLEX. The validity and practicality of the proposed model have been demonstrated through case studies in several different application scenarios, which also demonstrate the necessity of considering carbon trading mechanisms in the design model. [ABSTRACT FROM AUTHOR]

In a modern and technological world that has a great demand for energy, a versatile energy market, and a renewed electric infrastructure capable of expanding the electric power system under the premise of universal access to electricity, that seeks to minimize the effects of climate change, and that requires an improvement in its reliability, security, and resilience, microgrids are born as one of the systems that have the potential to supply each of these requirements in order to guarantee an adequate decarbonization, decentralization, digitalization, diversification, and democratization of the future grid. However, the integration of microgrids into the electric power system will generate impacts that are currently under study. This paper identifies and analyzes the technical impacts in the electric power system due to the implementation of microgrids, based on what has been recognized in the literature, so that those who have purposes of installation, creation, innovation, and research of microgrids, such as grid operators, technology providers, companies, and researchers, can establish criteria and indicators through which the feasibility of projects involving microgrids can be determined. The concept, importance, and characteristics of microgrids are given, along with a technical justification of the impacts. In addition, technical impacts on some study cases of real microgrids around the globe are identified. Finally, an analysis of the identified technical impacts is offered, and conclusions are drawn. [ABSTRACT FROM AUTHOR]

With further penetration of low-carbon energy conversion, microgrids (MGs) have become a necessary tool for expanding the consumption of renewable energies. In this paper, an optimal operation model for a microgrid-based multi-agent system is proposed. The goal is to save the total energy cost, which is expressed as a sum of locally observable convex functions. Therefore, improving the operational efficiency of microgrids is the key to promoting renewable energy development. This paper develops a three-layer multi-agent system model considering energy storage system and power thermal load demand response to solve the energy management problem of microgrids. In order to investigate the effect of energy storage system and demand response in microgrids, this paper designs three simulation cases, namely infrastructure case, energy storage case and demand response case. In order to prove the effectiveness of the proposed method, this paper uses the proposed method to solve three cases and compare the result with other meta-heuristic algorithms. The comparison results show that: (1) the multi-agent system model can realize the joint optimization of "resource, network, load and storage". (2) The introduction of energy storage and demand response system in microgrids can stabilize the output. Renewable energy units promote the use of renewable energy and reduce the overall operating cost of microgrids. Moreover, the results clearly demonstrate that the proposed algorithm has far better performance than other optimization methods. Also, the analysis obtained from the results shows that the cost is reduced by 1.82%. PV and WT output increased by 14.54% and 2.42%. In addition, their standard deviation decreases after ESS participation. The proposed approach is very effective through a simulation case study, which shows high potential for applications. [ABSTRACT FROM AUTHOR]

As photovoltaics (PV), also known as solar electricity, has been growing over the years, the energy markets have been gradually moving toward decentralization. However, recent media accusations suggest that decentralized renewable energy is slowly becoming unpopular because of the hidden fees being charged to owners of installed PV systems. In response, this paper investigates the potential for alternative approaches to incentivize owners using peer-to-peer (P2P) sharing. This study provides an analytical comparison between the use of the P2P mechanism, the net-metering mechanism, and a combination of these in the commercial sector. Through the use of a simulation, this case study presents the possible outcomes of the implementation of these models in a microgrid. Using technical and economic indexes the comparison was made by looking at the following indexes: peak power, energy balance, economic benefit, and transaction index. Based on a microgrid of 28 commercial buildings, readings of consumption were taken at intervals of one hour, and a Python model was made to find PV size and compare trading mechanisms. It was found that the combination of P2P and net-metering had the best overall performance, followed by net-metering itself, with the best season being all for both, and summer for net-metering by itself. This shows that a P2P model implemented in a microgrid helps create more energy balance, although the combination would achieve the highest performance. This study can be used by policymakers for proposing renewable energy policies and regulations that are more beneficial to all prosumers and consumers. [ABSTRACT FROM AUTHOR]

The interest for microgrids has increased in the last decades, bringing important conditions such as energy efficiency, reduction of production pollution, reliability of the system. Microgrid as a key of Smart Grid plays a vital role in power losses reduction, voltage profile improvement, mitigating the pollutant emission, enhance the reliability and quality of power system. In this paper the techno-economic and environmental analysis of Karabuk university Microgrid are considered. The Microgrid of Karabuk university campus is simulated and analyzed by HOMER (Hybrid Optimization Models for Energy Resources) software for optimization, sensitivity, demand response and pollutant emissions. The results of the techno-economic and environmental analysis suggest the integration of new distributed generation for 25-years of service time. In the proposed scenario, legalized cost of energy is $0.284 with renewable fraction of 14.8%, net present cost and operating cost decrease to 11.28% and 21.21%, respectively. It has showed that the proposed hybrid microgrid system contributes to the clean university campus concept and provides the lowest cost of electricity with the best payback time. [ABSTRACT FROM AUTHOR]

Networked microgrids that integrate the hydrogen fueling stations (HFSs) with the on-site renewable energy sources (RES), power-to-hydrogen (P2H) facilities, and hydrogen storage could help decarbonize the energy and transportation sectors. In this paper, to support the hydrogen-based networked microgrids planning subject to multiple uncertainties (e.g., RES generation, electric loads, and the refueling demands of hydrogen vehicles), we propose a two-stage stochastic formulation with mixed integer conic program (MICP) recourse decisions. Our formulation involves the holistic investment and operation modeling to optimally site and configure the microgrids with HFSs. The MICP problems appearing in the second-stage capture the nonlinear power flow of networked microgrids system with binary decisions on storage charging/discharging status and energy transactions (including the trading of electricity, hydrogen, and carbon credits to recover the capital expenditures). To handle the computational challenges associated with the stochastic program with MICP recourse, an augmented Benders decomposition algorithm (ABD) is developed. Numerical studies on 33- and 47-bus exemplary networks demonstrate the economics viability of electricity-hydrogen coordination on microgrids level, as well as the benefits of stochastic modeling. Also, our augmented algorithm significantly outperforms existing methods, e.g., the progressive hedging algorithm (PHA) and the direct use of a professional MIP solver, which has largely improved the solution quality and reduced the computation time by orders of magnitude. Note to Practitioners—This paper proposes an optimal planning model for electricity-hydrogen microgrids with the renewable hydrogen production, storage, and refueling infrastructures. Our planning model is extended under a two-stage stochastic framework to address the multi-energy-sector uncertainties, e.g., RES generation, electric loads, and the refueling demands of hydrogen vehicles. The first-stage problem is to optimize the siting and sizing plan of microgrids. Then, in the second-stage problem, the coordinated scheduling of electricity and hydrogen supply systems is modeled as second-order conic programs (SOCPs) to accurately capture the power flow representation under stochastic scenarios. Also, the logical constraints with binary variables are introduced to describe the energy transactions and storage operations, which results in an MICP recourse structure. Note that the stochastic MICP formulation could be very challenging to compute even with a moderate number of scenarios. One challenge certainly comes from integer variables that cause the problem nonconvex. Another challenge follows from the fact that the strong duality of SOCPs might not hold in general. To mitigate those two challenges, we prove that the continuous relaxation of our recourse problem has strong duality, and make use of that continuous relaxation and other enhancements to design an augmented decomposition algorithm. As revealed by our numerical tests, the proposed decomposition method outperforms PHA in both the solution quality and computational efficiency. Comparing to the PHA, our ABD method often achieves tighter bounds with trivial optimality gaps. Also, it could reduce the computation time by orders of magnitude. With the help of advanced analytical tool, the proposed planning framework can be readily implemented in real-world applications. [ABSTRACT FROM AUTHOR]

The multi-microgrid structure is emerging as one of the most promising concept for future distribution systems to provide resilience and independence energy operation with the energy exchange of other entities. In the distribution system, all microgrid owners and other stakeholders are benefited by sharing the locally generated energy with the adjacent microgrid entities with the help of energy trading process. The peer-to-peer (P2P) energy trading is one of the best suitable energy-trading method for multi-microgrid systems because of the absence of third-party entity. This paper proposes a hierarchical P2P energy trading model with the incorporation of an energy management scheme for multi-microgrid systems to provide efficient and effective results in the energy trading market. The proposed model is applied to a typical multi-microgrid community system to show the economic benefits of various microgrid stakeholders. The different case studies have been performed in the result analysis to show the impact of energy management scheme on the P2P energy-trading model. From the simulation results, it is evident that the implementation of energy management scheme reduces the operation cost of all microgrids together in the community area by 28.38% in scenario 1, 38.86% in scenario 2, and 39.21% in scenario 3 respectively. The overall result analysis shows that the proposed hierarchical P2P model with the energy management integration can provide the better cost reduction when compared to other scenarios, and also the total load demand is reduced by 29.51%, the renewable energy utilization is improved by 22.39% within the multi-microgrid community system. [ABSTRACT FROM AUTHOR]

The rising use of electric mobility has weakened the relationship between wholesale energy prices and price-responsive demand such as charging Electric vehicles (EV). This adversely affects network flows. The situation can further worsen, causing congestion in the distribution network and a rise in energy prices. In this paper, an attempt is made to resolve this issue through Distributed generators (DGs) placed in a grid-connected microgrid. The authors propose a methodology based on "Distribution Locational Marginal pricing (DLMP)" and the use of "Hybrid optimal Firefly Particle Swarm Optimization" with "TOPSIS" (HFPSO-TOPSIS) approach for optimal DG sizing in this methodology. The methodology consists of two stages. The first stage is locating DGs on the bus, and the second stage is sizing DGs and their participation in providing ancillary service of congestion management through a grid-connected microgrid. The method is tested on a "modified IEEE 33 bus radial active distribution system". Two different system conditions in which congestion can occur are analyzed and congestion is successfully removed by this proposed method. Power Loss reduction of more than 75% is achieved in each case after the implementation of the proposed methodology. Minimization of voltage deviation and maximization of voltage stability is achieved. The generation cost is minimized by almost 70%. The results obtained show better performance of HFPSO-TOPSIS as compared with other existing techniques. The results obtained exhibit that the methodology is generic and can be implemented for any congestion condition and can successfully remove congestion with improvement in system performance bringing social welfare. [ABSTRACT FROM AUTHOR]

Providing electricity access to remote rural areas plays a crucial role in the socio-economic development of the rural community. The use of renewable energy sources (RES) for electrification of remote rural areas has been increased in the past two decades. The integrated renewable energy system (IRES), which embeds two or more RES, is paid great attention to satisfy the energy needs of the rural areas. In the present study, the IRES model is developed using solar, micro-hydro, biomass, biogas, and wind energy sources to meet the electricity demand of the seven un-electrified clusters of hamlets of Limkheda taluka in Dahod district of Gujarat state in India. The proposed IRES model is optimized for the lowest total net present cost (TNPC) and cost of energy (COE) of the system using artificial bee colony (ABC), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) algorithms. The optimal operational strategy has been developed to minimize the operating cost of the IRES and the optimum sizing of the system. An optimal power scheduling method with a linear programming problem (LPP) approach has been carried out in the MATLAB environment. The proposed power scheduling method effectively reduces the TNPC and COE of the IRES. Finally, the results recorded in the paper confirm that the proposed configuration together with the TLBO method offers the lowest TNPC of INR 9.5951 million and TAC of INR 1.1274 million at the estimated COE of INR 5.68 per kWh, which is comparable with respect to standardized ABC and GWO. [ABSTRACT FROM AUTHOR]

Electric cars have a lot of promise in future energy markets as a manageable load. A popular vehicle-to-grid control interface, which enables the aggregation of the charging mechanism for energy management in the distribution grid, is one of the most significant road blocks to realize this opportunity. Understanding the ecology of electric transportation and integrating it in local communities to alleviate the energy shortage at peak hours is very complicated. In this research paper, recent standardization initiatives aimed at overcoming obstacles such as the integration of electric cars into smart grids are discussed. A charge control scheme focused on vehicle-to-grid connectivity is implemented. It is observed that the rise of environmentally sustainable energy sources, such as photovoltaic (PV) and wind energy, is straining the power network and their infrequent power generation is causing problems in power system operation, regulation and planning. The introduction of electric vehicles (EVs) into the electricity grid has been proposed to overcome grid load variations. Finally, the article discusses the incorporation of renewable energy sources and latest potential solutions involving electric vehicles. [ABSTRACT FROM AUTHOR]

The paper expresses the problem of flexibility pricing in energy hubs (EHs) that are in connection with electricity, heat, and gas networks considering of uncertain energy generation sources. Scheme includes a bilevel formulation. Its upper-level states for modeling of the flexibility services are provided by various resources within the EH. The problem considers maximization of the expected profit of these resources in the flexibility market. The problem constraints include the flexibility model of flexible resources such as storage devices, responsive loads, and controllable distributed generations (DGs). The flexibility model of resources relies on their active and heat power. The lower-level problem calculates energy and flexibility prices and formulates the flexible operation of energy resources considering EHs. Here, constraints include optimal power flow equations in the energy networks; operation model of EHs with power sources, storage devices, and different responsive loads; and flexibility limits of EHs. Also, a linear approximation model is adopted in the suggested design using conventional linearization techniques. Next, the Karush–Kuhn–Tucker (KKT) method is used to derive a single-level model for the problem. The scheme adopts scenario-based stochastic programming (SBSP) so that uncertainties of renewable power, energy price, load, and energy consumption of mobile storage devices are properly modeled. Finally, the results validate the suggested design's potential in modifying and enhancing the operation, flexibility, and economic situation of energy networks and EHs. [ABSTRACT FROM AUTHOR]

Renewable energy systems have mushroomed in the form of microgrids and Virtual Power Plants (VPP). In Australia itself, there are over three million Distributed Energy Resources (DERs). Integrating these new energy ecosystems into current systems is becoming a horrendous task as multi-dimensional energy flows and new energy business models are evolving. The stakeholders in the energy sector are focused on reducing costs prematurely while integration standards are still evolving, and interoperability of Internet of things (IoT) with legacy systems has outstanding security concerns. In this paper, the changing energy landscape is examined, and cybersecurity issues associated with the operation of VPPs and renewable energy-based microgrids are highlighted. The security and operational scenarios of new ecosystems are outlined, with an emphasis on DER interoperability, security, and integration. The design and development of these evolving standards into these new energy ecosystems is detailed based on observations from experiments on real-world DER installations. This paper is an extension of work originally reported in proceedings of the 31st Australasian Universities Power Engineering Conference [1]. [ABSTRACT FROM AUTHOR]

The demand for renewable sources-based micro-grid systems is increasing all over the world to address the United Nation's (UN) sustainable development goal 7 (SDG7) "affordable and clean energy". However, without proper viability analysis, these micro-grid systems might lead to economic losses to both customers and investors. Therefore, this paper aims to explore the feasibility and sustainability of a hybrid micro-grid system based on available renewable resources in remote hill tracts region of Bangladesh. Nine different scenarios are analyzed here, and a combination of solar, hydro, biogas, and diesel generator systems are found to be the best feasible solution in regard to the least cost of electricity and emission. The optimized result shows that with a renewable fraction of 0.995, the unit levelized cost of energy of the micro-grid system is $0.182 and it emits 54 and 117 times less CO2 compared to grid-based and diesel-based systems. Further, the fuel share of the system being 0.5% and greenhouse gas per energy being 0.06425 kg/KWh, validate the system as highly sustainable and eco-friendly. With the ability to fulfill load demands without interrupting supply, and reducing the emissions of greenhouse gases, the designed microgrid can provide sustainable energy solutions to any hill-tracts of Bangladesh. [ABSTRACT FROM AUTHOR]

Renewable energy penetration increases Smart Grid (SG) instability. A power balance between consumption and production can mitigate this instability. For this, intelligent and optimizing techniques can be used to properly combine and manage storage devices like Electric Vehicle Batteries (EVBs) with Demand-Side Management (DSM) strategies. The EVB helps distribution networks with auxiliary services, backup power, reliability, demand response, peak shaving, lower renewable power production's climate unpredictability, etc. In this paper, a new energy management system based on Artificial Neural Networks (ANNs) is developed to maximize the performance of islanded SG-connected EVBs. The proposed ANN controller can operate at specified periods based on the demand curve and EVB charge level to implement a peak load shaving (PLS) DSM strategy. The intelligent controller's inputs include the time of day and the EVB's State of Charge (SOC). After the controller detects a peak demand, it alerts the EVB to start delivering power. This decrease in peak demand enhances the load factor and benefits both SG investors and end users. In this study, the adopted SG includes five parallel Distribution Generators (DGs) powered by renewable resources, which are three solar Photovoltaics (PVs) and two Wind Turbines (WTs). Sharing power among these DGs ensures the SG's stability and efficiency. To fulfill demand problem-free, this study dynamically alters the power flow toward equity in power sharing using virtual impedance-based adaptive primary control level. This study proposes a decentralized robust hierarchical secondary control system employing Genetic Algorithm (GA)-optimized Proportional-Integral (PI) controller parameters with fine-grained online tuning using ANNs to restore frequency and voltage deviations. The proposed system is evidenced to be effective through MATLAB simulations and real-time data analysis on the ThingSpeak platform using internet energy technology. Our presented model not only benefits users by enhancing their utility but also reduces energy costs with robust implementation of a control structure by restoring any frequency and voltage deviations by distributing power equally among DGs regardless of demand condition variations. [ABSTRACT FROM AUTHOR]

Recent advances in electric grid technology have led to sustainable, modern, decentralized, bidirectional microgrids (MGs). The MGs can support energy storage, renewable energy sources (RESs), power electronics converters, and energy management systems. The MG system is less costly and creates less CO2 than traditional power systems, which have significant operational and fuel expenses. In this paper, the proposed hybrid MG adopts renewable energies, including solar photovoltaic (PV), wind turbines (WT), biomass gasifiers (biogasifier), batteries' storage energies, and a backup diesel generator. The energy management system of the adopted MG resources is intended to satisfy the load demand of Basra, a city in southern Iraq, considering the city's real climate and demand data. For optimal sizing of the proposed MG components, a meta-heuristic optimization algorithm (Hybrid Grey Wolf with Cuckoo Search Optimization (GWCSO)) is applied. The simulation results are compared with those achieved using Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), and Antlion Optimization (ALO) to evaluate the optimal sizing results with minimum costs. Since the adopted GWCSO has the lowest deviation, it is more robust than the other algorithms, and their optimal number of component units, annual cost, and Levelized Cost Of Energy (LCOE) are superior to the other ones. According to the optimal annual analysis, LCOE is 0.1192 and the overall system will cost about USD 2.6918 billion. [ABSTRACT FROM AUTHOR]

The advent of energy communities will revolutionize the energy market. However, exploiting their full potential requires innovations in the structure and management of low-voltage grids. End users shall be aggregated within microgrids, where their physical interaction is possible and coordinated operation of power sources and energy storage systems can be achieved. Moreover, meshed network topologies will enable multiple paths for the power flow. The combination of smart control and meshed networks can dramatically improve microgrid performance in terms of power quality, efficiency, and resilience to transients and faults. Ubiquitous control of the power flow becomes possible, as well as active fault clearing and isolation of subgrids without tripping circuit breakers. This paper proposes a control approach that pursues such goals without requiring modification of control and communication hardware implemented in commercial inverters. Instead, a revision of control firmware, integrated with local measurements, allows retrofitting existing plants to improve microgrid operation. Further improvements may derive from the installation of community power sources and energy storage systems, which can extend microgrid operation to pursue demand response and islanding. The potential of the proposed control methods is demonstrated by simulation considering a standard microgrid under different operating conditions. [ABSTRACT FROM AUTHOR]

Optimization methods for a hybrid microgrid system that integrated renewable energy sources (RES) and supplies reliable power to remote areas, were considered in order to overcome the intermittent nature of RESs. The hybrid AC/DC microgrid system was constructed with a solar photovoltaic system, wind turbine, battery storage, converter, and diesel generator. There is a steady increase in the utilization of hybrid renewable energy sources with hybrid AC/DC microgrids; consequently, it is necessary to solve optimization techniques. Therefore, the present study proposed utilizing multi-objective optimization methods using evolutionary algorithms. In this context, a few papers were reviewed regarding multi-objective optimization to determine the capacity and optimal design of a hybrid AC/DC microgrid with RESs. Here, the optimal system consisted of the minimum cost of energy, minimum net present cost, low operating cost, low carbon emissions and a high renewable fraction. These were determined by using multi-objective optimization (MOO) algorithms. The sizing optimization of the hybrid AC/DC microgrid was based on the multi-objective grey wolf optimizer (MOGWO) and multi-objective particle swarm optimization (MOPSO). Similarly, multi-objective optimization with different evolutionary algorithms (MOGA, MOGOA etc.) reduces energy cost and net present cost, and increases the reliability of islanded hybrid microgrid systems. [ABSTRACT FROM AUTHOR]

The microgrid concept has evolved from the humble origins of simple remote electrification applications in rural environments to complex architectures. Microgrids are key enablers to the integration of higher penetrations of renewables in the energy sector (including electricity, heating, cooling, transport and industry). In addition to the local energy sources, energy storage systems and loads, the modern microgrid encompasses sophisticated energy and power management systems, peer-to-peer energy markets and digital technologies to support this energy transition. The microgrid concept has recently been applied to all energy sectors, in order to develop solutions that address pressing issues related to climate change and the decarbonization of these important sectors. This paper initially reviews novel applications in which the microgrid concept is being applied, from a detailed analysis of recent literature. This consists of a comprehensive analysis of the state of the art in shipboard microgrids, port microgrids, aircraft microgrids, airport microgrids and space microgrids. Future research directions are then presented, based on the authors' perspectives on pushing the boundaries of microgrids further. [ABSTRACT FROM AUTHOR]

The European Union's energy and climate policies are geared on reducing carbon dioxide emissions and advancing sustainable energy, focusing on a faster propagation of renewable energy sources to decarbonize the energy sector. The management of locally produced energy, which can be implemented by a microgrid capable of either being linked to the main grid or operating independently, is equally crucial. Additionally, it seems that electricity storage is the only practical way to manage energy effectively within a microgrid. Energy storage is hence one of the main technological parameters upon which future energy management has to be based. Especially during crisis periods (such as the COVID-19 pandemic or the ongoing energy crisis), storage is a valuable tool to optimize energy management, particularly from renewables, in order to successfully cover demand fluctuation, hence achieving resilience, while at the same time reducing overall energy costs. The purpose of the paper is to analyze and present, in brief, the state-of-the-art of the energy storage systems that are available on the market and discuss the upcoming technological improvements of the storage systems and, in particular, of batteries. The analysis will focus on the storage systems that can be used within a stand-alone community such as a microgrid, but not limited to it. In the analysis, short- and long-term storage options are discussed, as well as varying storage capacities of the different technologies. The analysis is based on contemporary optimization tools and methods used for standalone communities. Understanding the state-of-the-art of energy storage technology is crucial in order to achieve optimum solutions and will form the base for any further research. [ABSTRACT FROM AUTHOR]

The electricity production from Renewable Energy (RE) in isolated locations requires long-term energy storage systems. To that end, Hybrid Energy Storage Systems (HESS), through a combination of hydrogen and batteries, can benefit from the different advantages of both technologies. This paper presents a hybrid Power-to-Power (PtP) Optimal Power Dispatch (OPD) model for isolated systems with no electric grid access. Currently, the electricity supply in such cases is usually based on a mix of RE as the primary energy source sustained by a diesel genset acting as a backup generator. In this context, the model delivers the hourly energy flows between renewable production sources, energy storage devices and the electrical load, which minimises costs and Green House Gases (GHG) emissions. For validation purposes, the model was tested through its application to a case study in an isolated area in the Canary Islands, Spain. The results show that the algorithm calculates the hourly OPD successfully for a given plant sizing, considering the defined operational states of the different assets. These operational constraints showed a decrease in the PtP round-trip efficiency of 5.4% and a reduction of the hydrogen production of 9.7%. Finally, the techno-economic analysis of the results proves that the combination of hydrogen and batteries with RE production is a feasible alternative to phasing out fossil fuels for the selected case study – reducing the diesel generator usage down to 1.2% of the yearly energy supply. • Novel multi-state optimal power dispatch model for power-to-power energy systems. • Multi-year techno-economic evaluation of an off-grid case study. • Phasing out fossil fuels with renewable energy-based hybrid energy storage systems. • Hydrogen technology contributes to a lower levelized cost of energy. • High initial investment cost was a significant entry barrier to deploying hydrogen. [ABSTRACT FROM AUTHOR]

With the rapid development of microgrid in the electric power industry, the microgrid electric energy transaction has begun to be marketized, and the research on the microgrid electric energy trusted transaction has important theoretical research value and social value. The existing blockchain-based microgrid electric energy trusted transaction models mostly focus on energy management and scheduling control between microgrids when conducting electric energy transactions, and do not fully consider the bidding problems in the market-based transaction of microgrid electric energy, resulting in trading strategies are difficult to adapt to new market changes. In response to this problem, this paper proposes a reliable transaction approach for microgrid electric energy based on a continuous two-way auction mechanism. The proposed strategy accounts for the volatility of electricity prices in the microgrid trading market and employs the continuous two-way auction mechanism to evaluate the microgrid electricity trading tactics. In the microgrid electric energy transaction, the self-adaptive learning theory is applied to adjust the quotations of both parties, so that both parties can make reasonable quotations according to the market environment. By simulating experimental data, the findings indicate that the continuous two-way auction mechanism transaction strategy enables both parties to modify their quotations based on market transaction information, thereby displaying a high degree of flexibility in microgrid electricity's market-oriented trading. [ABSTRACT FROM AUTHOR]

This paper presents an efficient model for optimal planning of electrical energy management in a smart residential microgrid (SRMG) by considering energy interactions among smart homes and with the purpose of SRMG's resilience improvement. A two-state linearized mathematical programming framework is given for the development of such notions. The first state proclaims a regular, disruption-free SRMG state, whereas the second state declares a disturbed SRMG state. In the initial state, two steps are taken into account. In the first stage, the minimum energy cost for each smart home, and in the second stage, minimum load profile deviation for SRMG is considered. In the second state energy interactions among in-home energy management systems to enhance the resilience is deliberated. The main objective of these two states is to advance SRMG's resiliency by considering modified SRMG's load profile and minimized smart homes' daily energy cost. Based on the obtained numerical results, energy interactions among smart homes have diminished smart home's energy cost about 3.34% in normal state, modified SRMG's load profile deviation about 3.62% in normal state, and improved SRMG's resilience about 32% in disrupted state. [ABSTRACT FROM AUTHOR]