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In the context of renewable energy systems, microgrids (MG) are a solution to enhance the reliability of power systems. In the last few years, there has been a growing use of energy storage systems (ESSs), such as hydrogen and battery storage systems, because of their environmentally-friendly nature as power converter devices. However, their short lifespan represents a major challenge to their commercialization on a large scale. To address this issue, the control strategy proposed in this paper includes cost functions that consider the degradation of both hydrogen devices and batteries. Moreover, the proposed controller uses scenarios to reflect the stochastic nature of renewable energy resources (RESs) and load demand. The objective of this paper is to integrate a stochastic model predictive control (SMPC) strategy for an economical/environmental MG coupled with hydrogen and battery ESSs, which interacts with the main grid and external consumers. The system's participation in the electricity market is also managed. Numerical analyses are conducted using RESs profiles, and spot prices of solar panels and wind farms in Abu Dhabi, UAE, to demonstrate the effectiveness of the proposed controller in the presence of uncertainties. Based on the results, the developed control has been proven to effectively manage the integrated system by meeting overall constraints and energy demands, while also reducing the operational cost of hydrogen devices and extending battery lifetime. • Implementation of an SMPC strategy based on a scenario-based approach. • Reduction of operation and maintenance costs and integration of uncertainties. • Minimization of the overall operational costs and maximization the profits. • Reduction of complexity and computational time. • Implementation of a unique control architecture to manage a microgrid in different modes. [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]

Meeting global energy demands has been a major challenge due to the shortage of primary energy resources. Adding renewable energy resources to the grid has become increasingly popular. During peak hours of energy demand, the sun's irradiance is strong enough to generate a significant amount of energy. This paper will design a photovoltaic energy generating system that harnesses solar energy using the latest technological advancements available. A system like this one is an ideal solution to addressing rising energy demands and reducing carbon footprint. Renewable energy sources can minimize carbon emissions, operating costs, etc., but their intermittency and uncertainty make their reliability a serious concern. The objective of this paper is to improve the reliability of the power grid through the use of solar photovoltaic (PV), battery energy storage systems (BESS), and distributed generation systems (DG). [ABSTRACT FROM AUTHOR]

This paper presents a framework for the efficient design and evaluation of a standalone hybrid renewable energy system (HRES) to meet the energy requirements of a rural community in the north-eastern region of Nigeria. The proposed microgrid system incorporates solar photovoltaic, wind turbines, biomass gasifier, fuel cell, and Battery storage. The sizing of each component is determined through the utilization of real local meteorological data and the load demand over a year, employing the Levy flight-salp swarm algorithms (LF-SSA). The optimization objective is to minimize the annualized system cost (ASC) of the HRES, while also considering the reliability constraint of Loss of power supply probability (LPSP). The comparative outcomes demonstrate that the LF-SSA surpasses other examined algorithms, namely the salp swarm, genetic algorithm, and HOMER software by achieving substantial cost savings of $29033, $50796, $191771 respectively in relation to the proposed HRES. The result further shows that, the LF-SSA provides the lowest LCOE of $0.933162/kWh, in contrast to SSA at $0.947737/kWh, GA at $0.958660/kWh, and HOMER at $1.075351/kWh. Additionally, the results indicate that the implemented Energy Management System (EMS) has successfully facilitated the establishment of an environmentally friendly and cost-effective energy system. • An efficient energy storage system is essential for managing intermittent energy supply. • The fuel cell can generate DC power by combining hydrogen with oxygen. • The amount of power generated by a PV panel depends on temperature and solar radiation intensity. • Deterministic rule-based controllers operate based on pre-defined rules. • Energy Management System coordinate the flow of power between different components. [ABSTRACT FROM AUTHOR]

Optimal energy management of microgrids enables efficient integration of renewable energies by considering all system flexibilities. For systems with significant seasonal imbalance between energy production and demand, it may be necessary to integrate seasonal storage in order to achieve fully decarbonized operation. This paper develops a novel model predictive control strategy for a renewable microgrid with seasonal hydrogen storage. The strategy relies on data-based prediction of the energy production and consumption of an industrial power plant and finds optimized energy flows using a digital twin optimizer. To enable seasonal operation, incentives for long-term energy shifts are provided by assigning a cost value to the storage charge and adding it to the optimization target function. A hybrid control scheme based on rule-based heuristics compensates for imperfect predictions. With only 6% oversizing compared to the optimal system layout, the strategy manages to deliver enough energy to meet all demand while achieving balanced hydrogen production and consumption throughout the year. • PV with seasonal H 2 storage enables fully sustainable operation of industrial microgrids. • Model predictive control overcomes perfect foresight limitation in energy management. • H 2 production for long-term storage is incentivized by adding an extra cost term. • Hybrid predictive control enables a low cost system with nearly perfect yearly operation. [Display omitted] [ABSTRACT FROM AUTHOR]

The paper addresses challenges arising from the unpredictable and probabilistic nature of wind speed and solar irradiance, leading to variable power production in renewable sources. Power imbalance issues in grids result from the difficulty in accurately estimating these variables. The study introduces an improved data-driven uncertainty set, utilizing a neural network trained with extensive historical data from a multi-microgrid system. This set efficiently identifies strong coupling and extracts characteristic scenarios, ensuring high reliability with a minimal number of scenes. The proposed probabilistic model, implemented in the coordination of pumped storage, wind, and solar power plants, incorporates energy limits and storage characteristics. A four-mode IEEE model for storage units allows quick response to system needs. Simulating storage unit states every hour, considering system load, output power, and energy limitations, optimizes power dispatch based on battery capacity. Additionally, the paper addresses the uncertainty in electric power demand in microgrids, proposing an improved data-driven uncertainty set obtained from a neural network. By eliminating unrealistic worst-case scenarios, the approach accurately captures system characteristics, maintaining high reliability while significantly reducing convergence time. The proposed uncertainty set is applied in a novel two-stage robust optimization dispatch model, transforming the optimization problem into a mixed-integer linear programming problem, and solving it using a column and constraint generation algorithm. Numerical case studies verify the feasibility and superiority of the proposed approach in terms of economy and convergence performance compared to existing robust optimization methods. [ABSTRACT FROM AUTHOR]

The paper proposes a methodology for designing intelligent systems of autonomous distributed hybrid power complexes (ADHPC) ensuring a minimum level of energy consumption from the national external grid as well as diesel power plants used to deliver the power to regional households, and industrial consumers. It will be realized because of the effective use of generated energy from renewable energy sources in such ADHPC systems by considering their both normal and emergency operating modes. In doing so, ADHPC will not only help offload the existing power grid during peak demand, but also allow for significant process efficiencies in energy production from renewable energy sources (RES), including "green" hydrogen: for instance, the excess energy is accumulated during the period of its excess and is provided back to power consumers in the period of shortage, the electricity losses are reduced since effective management is applied for power flows circulating in the ADHPC systems. The methodology includes the following stages (subtasks): development of the structure of a distributed hybrid system for generating and transporting electricity from sources to consumers; development of a system for diagnostics of failures of generating units and wire breaks in sections of local power transportation networks and management based on the diagnostics results of power flows circulating in the ADHPC systems for both normal and emergency modes. Therefore, a balance of power capacities and minimization of their losses are ensured. [ABSTRACT FROM AUTHOR]

This paper considers off-grid microgrids (MGs) from the Philippine archipelago and analyses their energy generation in differents aspects. Seven different energy clusters are used, representing realistic configurations and renewable energy shares. A Robust Model Predictive Control (MPC) framework is used for the energy management and coordination task of these island MGs. The MPC is based on a min./max. optimization procedure, which takes into account the whole uncertainty set. The reliability of the MG operations are analysed with respect to the different clusters; this evaluation is conducted using μ -analysis, performed with respect to the baseline model and the uncertainty set. The demand-side compliance of the MG is also investigated, with respect to stochastic behaviours of the demands and of the renewable sources (wind and solar). Numerical simulation results are presented in order to demonstrate that reliable power outlets are produced despite variation in renewables and of the demands. This paper offers a thorough analysis of simple energy system coordinated via MPC, showing how this method can indeed be used for renewable MG management, offering robustness and ensuring reliability. • A generalized model is presented for Off-Grid Microgrids with multiple carriers. • The Philippine Off-grid islands are considered, with diesel, solar, and wind energy. • A Robust Model Predictive Control scheme is proposed to manage these Microgrids. • Demand compliance and operation reliability are assessed through robustness analysis. • Numerical simulation illustrate reliability under multiple scenarios. [ABSTRACT FROM AUTHOR]

An effective way to promote the consumption of renewable energy resource generation is by forming a joint power supply system between photovoltaic (PV) power plants widely distributed along a river basin and large-scale cascade hydropower plants, using shared transmission lines to transmit power to the power grid system. However, renewable energy resource generation has significant prediction uncertainty and can flood into the power grid, which is detrimental to its safe and stable operation. To solve the above issues, this paper proposes an improved interval optimization method to model the uncertainties of the inflow runoff of a cascade hydropower plant and the photovoltaic power output. On this basis, a medium- and long-term interval optimization scheduling model is established for a cascade hydro-PV complementary system based on extreme scenarios. This model does not need to obtain an accurate probability density distribution for renewable energy, and can realize the overall optimization of runoff-PV uncertainty scenarios within a certain range, taking into account the economy and robustness of system operation. In order to ensure the solution efficiency, the original model is transformed into a mixed integer linear programming (MILP) model by a variety of deterministic transformation methods and linearization techniques. The case study shows that: 1) Under the premise that the system power output is relatively stable, the power curtailment of the interval optimized scheduling scheme proposed in this paper is reduced by 47.5% compared with the robust scheduling scheme. The ability to cope with extreme scenarios is also significantly improved compared with the deterministic scheduling scheme. 2) There is a game relationship between the robustness and economy of the obtained scheduling scheme. When the fluctuation interval width of random variables increases from 20% to 30% and 40%, the corresponding total power generation decreases by 2.4% and 4.7%, respectively. 3) A large penalty factor is conducive to reducing the amount of abandoned hydropower, but in general, the penalty factor and ecological flow level have no obvious influence on the dispatching results, and the values of both can be reasonably determined according to the actual operation requirements. • An improved interval optimization method is proposed to deal with the uncertainty of inflow runoff and PV power output. • A MILP model for the medium- and long-term operation of a cascade hydro-PV complementary system based on extreme scenarios is established. • The resulting scheduling scheme achieves balance between economy and robustness. • The influence of penalty factor and ecological flow on the dispatching results is not obvious. [ABSTRACT FROM AUTHOR]

In recent years, the severe environmental degradation and high levels of fossil fuel consumption linked to conventional ship energy systems have drawn attention to the advancement of alternative ship energy systems. Consequently, ship energy systems based on the use of an electrical microgrid are coming to the fore as an increasingly popular alternative solution. However, managing the energy flows within a shipboard microgrid can be highly challenging due to the multiple energy sources (including renewable energy sources) and types of loads involved. Additionally, the integration of an energy storage system has been identified as an effective solution for improving the reliability of shipboard power systems, pointing out the important role of energy storage systems in maritime microgrids and their potential to enhance the energy management process. As such, effective energy management strategies are necessary to optimize performance and ensure that the vessel's energy demands are met while minimizing fuel consumption and reducing cost and environmental impacts. This paper presents a comprehensive review of such strategies and methods recently presented in the literature associated with energy management in shipboard microgrids integrating energy storage systems and examine the different techniques that can be utilized to achieve optimal system performance. Moreover, the paper also sheds light on the future challenges and perspectives imposed by the modern shipboard microgrids operation on the energy/power management of these systems. The aim of this paper is to provide a helpful guideline for researchers and engineers conducting research in the field of electrically powered ships. [Display omitted] • A comprehensive review of EMS for electrical and hybrid ship is presented. • A comprehensive review of ESS which can be used in such ships is presented. • Technologies of ESS and their suitability to ships are discussed and commented. • Future challenges and perspectives imposed by modern SMG operation are presented. [ABSTRACT FROM AUTHOR]

• Precise forecasts are vital for microgrid operation. • Optimal AI techniques affect microgrids. • AI methods comparison in microgrids. Forecasting renewable energy efficiency significantly impacts system management and operation because more precise forecasts mean reduced risk and improved stability and reliability of the network. There are several methods for forecasting and estimating energy production and demand. This paper discusses the significance of artificial neural network (ANN), machine learning (ML), and Deep Learning (DL) techniques in predicting renewable energy and load demand in various time horizons, including ultra-short-term, short-term, medium-term, and long-term. The purpose of this study is to comprehensively review the methodologies and applications that utilize the latest developments in ANN, ML, and DL for the purpose of forecasting in microgrids, with the aim of providing a systematic analysis. For this purpose, a comprehensive database from the Web of Science was selected to gather relevant research studies on the topic. This paper provides a comparison and evaluation of all three techniques for forecasting in microgrids using tables. The techniques mentioned here assist electrical engineers in becoming aware of the drawbacks and advantages of ANN, ML, and DL in both load demand and renewable energy forecasting in microgrids, enabling them to choose the best techniques for establishing a sustainable and resilient microgrid ecosystem. [ABSTRACT FROM AUTHOR]

• Providing an inclusive, up-to-date, and organized review of the published research. • Taxonomy of microgrid based on control structures, specifications and components. • Energy management summation and classification based on several significant factors. • Discussing some of the skilled methodologies and techniques developed or adopted. • Taxonomy of the different modeling and handling methods of the uncertainty. Energy is one of the essential foundations for the sustainable development of human society, so its management is necessary. Energy management system (EMS) can be explained as the procedure of optimizing, planning, controlling, monitoring, and saving energy to maximize operations and efficiency and minimize consumption. Microgrid (MG) requires EMS as an efficient and optimal tool owing to the stochastic nature of electrical loads and renewable sources. Moreover, energy management system is responsible for operation of a MG in reliable, secure and economical manner in either states of grid-connected or disconnected. Many literatures have recently focused on the expansion of advanced strategies of the MG energy management for establishing a self-sustained MG in both industrial and academic research. Thus, a comparative research is needed for having a 360° viewpoint of the energy management domain in MGs. In this regard, this research investigates a comparative and critical analysis of the developed strategies of the energy management for the MGs from different views and aspects from 2009 to 2022. The review strategy systematically adopted by the author includes: (i) Extracting research papers relevant to energy management in MGs; (ii) Filtering the significant papers to prepare a database of related research papers (iii) Classifying the used methods for EMS based on the technique, control strategies, and structure; (iv) Discussing potential directions for future studies. In a wider outlook, this research provides a systematic and updated review of energy management strategies for MGs developed by different researchers. The author hopes that academicians and practitioners can use the suggested framework as well as the offers presented for further studies on this significant yet sophisticated issue. [ABSTRACT FROM AUTHOR]

Economic, technological, and environmental causes are moving energy toward smart distribution networks. The change from fossil fuels to renewable energy sources is extremely environmentally friendly in both the building and transport sectors. Microgrids could be efficiently used in remote areas utilizing renewable energy resources for supplying different energy demands namely power, heat and cold. This paper presents a mixed-integer linear programming (MILP) model for optimizing planning and sizing decisions in microgrids connected to main grid. Planning decisions the amount of generation of each distributed generation (DG) technology and the amount of power transmission to other nodes. Also, sizing of different DGs including photovoltaic and combined heat and power facilities are performed by the proposed model. According to the results, the model is able to optimize sizing and planning decisions in grid-connected microgrids. [ABSTRACT FROM AUTHOR]

• The study makes the following main contributions: • 1) A mathematical layout is developed to include renewable resources for load scheduling in digital twin-based microgrids. • 2) Uncertainty modelling of PV production employing Beta PDF for load scheduling is utilized in digital twin environment. • 3) The load scheduling algorithm employing RL for minimizing the energy bills regarding a renewable resource is developed. Utility companies recognize the importance and necessity of demand response (DR) programs for reducing the increased production costs associated with rising energy demand. The advent of smart information and communication systems has made DR programs on the basis of cost a viable option to control load in smart microgrids. Small domestic consumers are rapidly using stochastic renewable energy resources such as photovoltaic (PV). The study examines an integrated layout for residential load scheduling or load commitment problems (LCP) with renewable energy resources no matter what kind of tariff is applied. Uncertainty-based decision-making problems are effectively solved using reinforcement learning (RL). The paper proposes an RL-enabled solution to the LCP in smart microgrids. An innovative aspect of the study is the development of an integrated layout containing an implementable solution that takes into account user satisfaction, stochastic renewable power, and tariffs. In simulation tests, the suggested layout is tested for its effectiveness and flexibility. An analysis of the algorithm's efficiency using a household user with schedule-able and non-schedulable devices, together with a PV resource, has been presented. [ABSTRACT FROM AUTHOR]

The exploitation and utilisation of solar energy is challenging because of both diurnal and seasonal variation. Seasonal thermal energy storage is a prominent solution to solve the problem of seasonal variation of solar production. This paper investigates both the optimal design and energy management of a renewable energy plant with seasonal thermal energy storage. As a case study, the thermal, cooling and electrical energy demands of a university campus during one year are considered. Three scenarios, characterized by a different available area (20,555 m2, area covered at present; 50,000 m2, potentially available area by exploiting the entire rooftop and parking area; unlimited area) for solar thermal collectors and photovoltaic panels are investigated. In the first two scenarios, the exploitation of solar energy allows a primary energy saving of 31% and 57%, respectively compared to the use of a conventional plant. The seasonal storage reaches an overall efficiency (thus considering both charging and discharging) up to 84%. Finally, the analysis of the third scenario shows that the feasibility of meeting the energy demands by only exploiting solar energy is technically challenging because a large area equal to about 100,293 m2 is needed for solar thermal collectors and photovoltaic panels. [ABSTRACT FROM AUTHOR]

Modular Gravity Energy Storage (M-GES) systems are emerging as a pivotal solution for large-scale renewable energy storage, essential for advancing green energy initiatives. This study introduces innovative capacity configuration strategies for M-GES plants, namely Equal Capacity Configuration (EC) and Double-Rate Capacity Configuration (DR), tailored to optimize energy storage efficiency and stability. Through comprehensive MATLAB/Simulink simulations, we demonstrate each configuration's distinct power characteristics and operational advantages. Our findings reveal that while the EC configuration offers enhanced power accuracy and stability, it requires a larger number of units. In contrast, the DR configuration, characterized by fewer but larger units, shows a trade-off with reduced power stability. This paper contributes significantly to the field by presenting a first-of-its-kind comparative analysis of these configurations, offering insights into their practical applications and scalability. The proposed strategies and findings lay a foundation for future research and development in gravity energy storage systems, marking a step forward in pursuing sustainable and reliable energy solutions. [Display omitted] • Two typical unit capacity configuration strategies for M-GES power plants are proposed. • The unit scheduling method of the M-GES power plant is proposed. • The coordination control method is provided for each proposed configuration strategy. • Equal capacity configuration has better power accuracy but requires more units. • Double rate configuration needs fewer but larger units and less power stability. [ABSTRACT FROM AUTHOR]

This paper addresses the critical challenge of sizing hybrid renewable energy systems over their lifespan while accounting for uncertainties in energy sources and load demands. By leveraging stochastic programming, we introduce a novel modeling technique that ensures robust optimization without compromising numerical tractability. Unlike conventional methods, which determine system parameters for the entire project duration upfront, our approach enables annual adjustments to the renewable components' sizing. This dynamic strategy alleviates early-stage under-utilization, aligning system capacity with evolving demand patterns. Central to our approach is the reformulation of the two-stage stochastic program as a quasi-optimal control. This reduces the number of optimization variables significantly, simplifying the solution process. Moreover, thousands of constraints are replaced with a system of differential equations, enhancing computational efficiency. By minimizing capital costs and dynamic operating expenses, we achieve an optimal system size. Through a real-world application in a rural area of South Africa, we demonstrate the effectiveness of our approach. The results showed a remarkable improvement in problem-solving efficiency, reduced under-utilization, and heightened adaptability to unpredictable weather conditions. This innovative framework represents a substantial leap forward in hybrid renewable energy systems design, offering a more agile and efficient solution for sustainable energy generation. [ABSTRACT FROM AUTHOR]

In recent years, permanent outages and lack of access to electricity have been significant problems in most cities of Iran. The increasing growth of fossil fuel consumption made it necessary to change the design of buildings. Designing a net-zero energy building (with natural ventilation and renewable energy systems) is a solution for reducing fossil fuels. In this paper, dynamic energy modeling of the building for the smart operation of the trapezoidal Trombe wall and underground air ducts is performed. In the first step, the Trombe Zones are considered for heating. Then the combination of the Trombe zones with underground air ducts for cooling is simulated. Then the system combination with a solar hot water system to supply hot water energy is evaluated. Finally, the economic analysis of the optimal hybrid system (with a photovoltaic system, wind turbine, battery, and inverter) for supplying the electricity of the building equipped with both passive energy strategies was evaluated by Homer software. The results show that by integrating the Trombe wall, the heating load is reduced by 44 %, and by integrating a coupled geothermal cooling system with the air duct and the Trombe wall, the cooling load is reduced by 39%. Also, the net present cost over the 25-year life of the optimized renewable system is reduced by about 12.5% due to both passive strategies. The average daily electricity energy demand of the base building is equal to 86.37 kWh and for the passive building, it is equal to 67.49 kWh. [ABSTRACT FROM AUTHOR]

Abstract Improvement of reliability is one of the important issues in the exploitation of microgrid (MG). In this regard, two methods of optimal use of batteries as well as interaction with the consumer in the framework of the use of various types of the demand response programs (DRPs) are the most efficient approaches to improve reliability. This paper presents approaches to select the technology, capacity and optimal rated power of the batteries used in an insular MG, which provides its power through the renewable energy sources (RESs) only. Because of the available uncertainties in power generation of RES, a scenario-based method is used. The purpose of using batteries in this paper is to improve reliability and increase profits from the sales of energy. By applying optimization of the incentive-based DRP in MG with optimization of incentive prices, their effect on the objective functions simultaneously and separately is investigated. The algorithm used for the desired optimization is exchange market algorithm (EMA). The results of optimization indicate the positive effect of the batteries and the incentive-based DRP on the reliability and profitability of the MG. Highlights • Proposing a framework for battery storage technology selection for microgrids. • Considering impact of different demand response programs. • Implementing a scenario based method for uncertainty modeling. • Using exchange market algorithm for solution of the problem. [ABSTRACT FROM AUTHOR]

Abstract As the smart grid paradigm is capable to encourage the active consumers for efficacious participation in increasing system efficiency, demand response programs (DRPs) have attracted much interest in the worldwide recently, especially in optimization of smart microgrids (MGs). Under this context, this paper proposes an integrated method relies on cleverly cooperation of time rate-based DRP and heterogeneous distributed energy resources (DERs) deployment with aim to reliability-oriented planning of multiple MGs. To do this, a novel two-stage decision making model is exploited in which at the first stage the MGs construction is formed by optimal dynamic planning of hybrid DERs simultaneously with section switch allocation considering a reliability criterion for MGs as loss of load expectation (LOLE) constraint. Subsequently, at the next stage the critical energy peak pricing-based program accomplishes in order to flatten the load profile as well as diminishing the investment costs of MGs. Besides, owing to the unpredictable nature pertaining to renewable power production, the uncertainty modeling is inevitable where in this paper, a novel pragmatic robust optimization approach has been employed to deal with intense uncertainty of the problem. Numerical results obtained from an illustrative case study elucidate how the proposed MGs planning and utilized DRP pairing significantly increases the expected profit of system and ameliorates the reliability of end-users. Highlights • An innovative two-stage framework is developed for DRP and MGs planning pairing. • A time-rate based DRP is employed to reduce and postpone the investment costs of MGs. • Dynamic model is used to design of multiple MGs considering reliability criterion. • A pragmatic robust optimization is proposed to cope with uncertainty of the problem. [ABSTRACT FROM AUTHOR]

With the increasing number of integrated energy system (IES) construction, a multi-agent system group composed of multiple IESs would be formed in a certain region. Moreover, complex uncertainties gradually existed in the IES which affect the system optimal situation and optimization results. In order to take full use of each agent's advantages and improve the performance of the whole region under uncertainty, this paper focuses on the multiple energy types' interaction of multi-agent IESs within the region to realize the stable operation, economic and environmental improvement of each agent and the whole region. To this aim, a multi-objective multiple energy types' multi-agent interactive optimization model considering energy demand and renewable energy output uncertainties was built. The model was based on Nash bargaining cooperative game theory and took economy, flexibility and carbon emission as objectives. Moreover, energy interaction scheduling and its corresponding price can be optimized through the model. The results of non-cooperative operation mode without interaction and cooperative operation mode with interaction were compared to analyze the superiority of interaction from the cost, carbon emission and flexibility aspects. The overall regional carbon emission and the economic cost has been reduced by 13.84% and 9.94%, for each IES, the carbon emission and economic cost could reduce by up to 41.5% and 44.5%. Moreover, the flexibility of the IES was correspondingly increased by nearly 47%. These results denoted that after multi-agent interaction, not only the overall region's energy, economic and environmental performance are improved, but also that of each agent are significantly improved. In addition, different energy types among multiple agents could be effectively used and regional joint energy storage is realized, improving the region flexibility level and resilience. • A multi-objective integrated energy systems interaction model is established. • The interaction model considers multiple energy types and uncertainties. • The model can improve economy and flexibility of each system agent and the region. [ABSTRACT FROM AUTHOR]

• Peer-to-peer energy trading problem of multi-energy microgrids are investigated. • The concept of energy trading consistency is firstly proposed. • The off-design performance model of the energy conversion device is considered. • The decision-making problem is solved by multi-agent soft actor-critic approach. Multi-energy microgrid technology is an essential for addressing the diversification of energy demand and local consumption of renewable energy sources. Peer-to-peer energy trading has emerged as a promising paradigm for the design of a decentralized trading framework. Therefore, this paper investigated the external peer-to-peer energy trading problem and internal energy conversion problem of interconnected multi-energy microgrids. The concept of energy trading consistency to avoid unreasonable energy trading behavior is first proposed and an off-design performance model of the energy conversion device is considered to more accurately reflect the operating status of the device. The complex decision-making problem with significantly large high-dimensional data is formulated as a partially observable Markov decision process and solved using the proposed multi-agent deep reinforcement learning approach combining the centralized training decentralized execution framework and soft actor-critic algorithm. Finally, the effectiveness of the proposed method was verified through a case simulation. The simulation results showed that the proposed method can reduce the total cost compared with the rule-based method. [ABSTRACT FROM AUTHOR]

As an important modeling method for multi-energy systems, the energy hub (EH) plays an important role in the planning and operation research of multi-energy systems. In this regard, this paper proposes an optimal load dispatch model for the EH system by considering the coupling relationship of electric and thermal energy. The target of the proposed model is to reduce the total cost of the EH system, including resource cost, carbon emission cost, demand response (DR) cost, and system maintenance cost. An adjustable robust optimization method is presented to model the uncertainty of renewable energies (REs) and DR programs. Through duality theory, the proposed robust optimization problem is transformed into a mixed-integer linear programming problem and solved efficiently by a commercial solver. This paper carries out simulation experiments on three different cases with or without energy storage systems and DR programs. The simulation results show that the total cost of the EH system can be effectively reduced by installing energy storage systems and implementing DR programs. At the same time, the results also show that some economic benefits of the EH system need to be sacrificed to deal with the uncertainty of REs and DR programs. • A novel optimal load dispatch model of energy hub is proposed. • Both electrical and thermal demand response programs are taken into account. • The uncertainties of demand response and renewable energies are considered. • An adjustable robust optimization method is proposed to deal with the uncertainties. [ABSTRACT FROM AUTHOR]

• Proposed an energy sharing model of MMGs considering the multiple uncertainties and the cheating behaviors. • Using robust optimization to deal with uncertainties brought by grid tariffs and renewable energy. • Cheating behaviors in energy transaction are analyzed and a cheating equilibrium mechanism based on ITM is proposed. Multi-microgrid system (MMGs) has drawn extensive attention recently because of its high energy efficiency. However, MMGs' operational efficiency can be affected by market price fluctuations and intermittent renewable energy. This paper proposes an energy-sharing model based on the Nash bargaining game between multi-microgrids. The proposed model provides a robust energy trading schedule to deal with uncertainties brought by grid tariffs and renewable energy. To ensure the model is tractable, the original game problem is equivalently converted into a system benefit maximization subproblem and an additional profit distribution subproblem to get optimal energy sharing power and prices. In addition, microgrid has the motivation to cheat for maximizing its benefits which may lead to the breakdown of cooperation. Furthermore, cheating behaviors in energy transaction are analyzed; the energy sharing scheme based on cheating equilibrium is derived by proposing an intermediary transaction mode. Finally, the alternating direction method of multipliers (ADMM) is used to protect the players' privacies in a distributed way. Simulation results show that the proposed model can realize stable cooperation, effectively reduce operating costs and immunize against multiple uncertainties and cheating behaviors. [ABSTRACT FROM AUTHOR]

Seasonal pumped storage (SPS) is a sustainable and effective energy storage solution that can mitigate the seasonal fluctuations of renewable energy sources and provide flexibility to power systems. Despite its huge potentials, the operational mechanism of SPS, particularly for the multi-energy complementary operation, remains poorly understood. In this paper, we propose an optimal scheduling model of a regional power system with SPS and evaluate its potential contribution to energy efficiency, power system stability, and environmental impact. A case study in Qinghai, China, demonstrated that SPS effectively managed seasonal fluctuations of renewable energy sources, improved hydropower generation during the dry season, and stored excess power from renewable energy sources and the main power grid. Furthermore, our analysis revealed that SPS significantly improved energy efficiency compared to traditional pumped storage by increasing system energy efficiency by 11.21%, reducing wind and solar power by 72.97%, and lowering carbon emissions by 94.41%. Overall, SPS has immense potential in providing power system flexibility and enhancing the decarbonization of power systems. [Display omitted] [ABSTRACT FROM AUTHOR]

• Proposing optimal management of a local operator under the concept of P2P trading. • Assessing the performance of the local operator in different uncertainty attitudes. • Several sources of uncertainty considered such as load, price, and PV generation. • Risk-averse decision-maker tends to purchase from the grid to reduce potential risks. • Risk-seeking decision-maker targeting demand supply through the P2P trading scheme. Small-scale smart microgrid is prone to economic consideration of adequate transactive energy sharing and reliable certified power pool hub. Moreover, direct integration of efficient with renewable energy providers in presence of storage such as electric vehicle (EV) aggregation improves the performance of energy systems and guarantees secure trade among the consumers. However, unified modeling of the energy community framework faces the challenge of load management. In this paper, an optimal risk management procedure is proposed for renewable-based prosumers such as photovoltaic (PV), and EV to maximize the horizon of uncertainty parameter. The energy demand in peer-to-peer (P2P) household energy sharing is considered as the uncertain parameter. To this end, in order to study the behavior of a risk-averse and a risk-seeker decision-maker, an information-gap decision theory (IGDT) probability model is applied. In order to demonstrate the performance of the proposed approach, the model is implemented in a test microgrid and the simulation results are presented and discussed. [ABSTRACT FROM AUTHOR]

A hybrid renewable energy system (HRES) utilizes the coaction of diverse energy to enhance energy efficiency while improving economic benefit. Under the paradigm of the electricity market, the HRES decomposes bilateral contract (BC) signed in the medium to long-term market and bids in the spot market are a promising way to get more revenue while ensuring basic profits by exploiting the synergy of different markets. However, the scheme of BC decomposition and the uncertainties of renewable energy affect the revenue of HRES scheduling. This paper proposes an optimal scheduling model for HRES to perform the BC flexible decomposition while bidding in the day-ahead energy market to maximize total revenue. The HRES is a real system located in Southwest China, and its constituent units are a pumped storage, two hydropower, and two solar stations. The Wasserstein metric ambiguity set is adopted to portray the uncertainty of available solar power and the forecasted market price error. Then, the proposed model is formulated as a distributionally robust chance-constrained scheduling model. Finally, the conditional value-at-risk approximation and strong duality are employed to reformulate this model into a tractable problem. The numerical results show that the proposed model can increase the total profit of HRES by 2.26 % (i.e., 2.56 × 104 DKK) compared to the case of fixed BC decomposition and bidding. Moreover, the impact analysis related to the ambiguity set and risk tolerance of uncertainty on total revenue of the proposed model is also presented, which provides the measure for the HRES to trade-off the robustness and economy of the solution. • The dispatch of the hybrid renewable energy system in the power market is studied. • A distributionally robust chance-constrained model is proposed accordingly. • The bilateral contract flexible decomposition is more profitable for the system. • The model can balance the economy and conservativeness of the solution. [ABSTRACT FROM AUTHOR]

As the proliferation of electric vehicles (EVs) continues to accelerate, the inherent attributes of EVs warrant meticulous consideration in the realm of energy dispatch. In order to evaluate the ability of EVs as mobile energy storage, this paper presents an energy management framework for the microgrids' online dispatch, which accounts for the spatio-temporal energy transmission of EVs between microgrids. The energy management framework contains two iterative processes: optimizing charging price and guiding charging dispatch. To sufficiently capture the uncertainties of the renewable energy and load demand, chance-constrained optimization is utilized to determine the charging price by reasonable power allocation. To achieve a continuous and efficient control policy, a normalized advantage function-deep Q learning network (NAF-DQN) is developed for EV dispatch under V2G technology. The above two processes as a coupled optimization problem are solved alternately until convergence. Numerical cases considering energy transmission of EVs between microgrids are studied to demonstrate the superiority of the proposed dispatch framework. The simulation results indicate improved computational efficiency and higher-quality solution. • A novel energy management framework for the microgrids' online dispatch is proposed. • The proposed method considers uncertainties introduced by PV, WT, and load. • A normalized advantage function-deep Q learning network is developed for EV dispatch. • The simulation results indicate improved computational efficiency and higher-quality solution. [ABSTRACT FROM AUTHOR]

With the progress of decarbonization, renewable-powered microgrids are attracting wide attention. To cope with the fluctuation of renewable power at different timescales, both long-term and short-term energy storage devices are required. This paper studies the operation of renewable-dominated isolated microgrids integrated with hybrid seasonal-battery storage. A data-driven scheduling-correction framework is proposed. By leveraging the historical data of renewable power and load, the scheduling module generates ex-post optimal state-of-charge (SoC) sequences of the seasonal energy storage ahead of the operating year. In each period of real-time operation, the correction module performs two steps: the first step is to update the reference SoC for the seasonal storage based on the ex-post optimal SoC sequences and the newly observed data; the second step is to solve a bi-objective rolling-horizon optimization problem which minimizes the instant operating cost while steering the SoC of seasonal storage to its reference value. An appealing feature of the proposed method is that the long-term renewable power forecasts are not required. A microgrid system is devised to verify the proposed framework. Numerical tests show that the scheduling-correction framework outperforms existing rolling horizon approaches in compromising economy, power supply reliability, and renewable energy utilization. • A data-driven scheduling-correction framework is proposed to provide real-time actions with a global vision, making better use of the seasonal hydrogen storage. In the scheduling module, ex-post optimal SoC sequences of the seasonal storage are generated using historical data. In the correction module, the real-time operation relies on a bi-objective rolling-horizon optimization problem which minimizes the operating cost while following the SoC reference of the seasonal storage, which is sequentially updated along with time. • The kernel regression technique is established to sequentially update the SoC reference of the seasonal hydrogen storage. This technique calibrates a conditional probability distribution based on the newly observed data; the resulting distribution (weight coefficients) interprets how likely the current year is similar to each of the past years. The reference is updated in each period according to the weighted sum of the ex-post optimal SoC sequences. • Systematic tests are conducted to compare the cost, load shedding, and renewable energy curtailment achieved by different methods. Numerical results show that the scheduling-correction framework can fully exploit the seasonal storage. The proposed method outperforms existing rolling horizon approaches in compromising economy, power supply reliability, and renewable energy utilization. [ABSTRACT FROM AUTHOR]

• A multiple criteria analysis method is proposed to compare renewable energy and traditional solutions for off-grid island power supply. • Life cycle cost is used to build the economic model. • The fuzzy analytic hierarchy process is used to quantify the risk level. • A simulation platform is built to obtain the energy utilization. • Applied to the actual project to verify the feasibility of the method. Renewable energy microgrids are a new option for powering remote islands. To explore the feasibility of constructing island microgrid in China, based on the failed Dongfushan Island Demonstration Microgrid Project caused by equipment failure, low economic benefits, low residents' satisfaction, and poor power supply stability, this paper compares two power supply methods, microgrid and submarine cable. A systematic multiple criteria analysis method is established to compare these two methods, including economic analysis, risk assessment, energy spilled and energy shortage. Net present value and the life-cycle cost method are used to establish the economic cost model. Fuzzy analytic hierarchy process is used to establish the risk assessment model. Additionally, the simulation using PVsyst software is used to calculate the energy spilled and energy shortage. Furthermore, future trends are predicted with data to provide suggestions for decision-makers. Multiple criteria analysis results showed that energy spilled and shortages are inevitable for the use of microgrid on seasonally tourist islands like Dongfushan Island, with energy spilled and shortages rate as high as 50.11% and 37.57% in the worst case. The microgrid risk level is relatively low risk, higher than the lowest risk level for submarine cables. At full capacity, the cost of microgrid (34.03 million yuan) is comparable to submarine cables (30.31 million yuan), making the latter more suitable for power supply due to its better reliability. [Display omitted] [ABSTRACT FROM AUTHOR]

The application prospects of shared energy storage services have gained widespread recognition due to the increasing use of renewable energy sources. However, the decision-making process for connecting different renewable energy generators and determining the appropriate size of the shared energy storage capacity becomes a complex and interrelated problem when considering the multi-site spatio-temporal characteristics. This paper presents an optimal planning and operation architecture for multi-site renewable energy generators that share an energy storage system on the generation side. Furthermore, an economic-environmental model is proposed to minimize the costs associated with the energy system infrastructure while maintaining a high penetration rate of renewable energy. The centralized multi-objective model allows renewable energy generators to make cost-optimal planning decisions for connecting to the shared energy storage station, while also optimizing the size of the storage capacity to maximize renewable energy generation and minimize costs. The Non-dominated Sorting Genetic Algorithm-II is employed in a centralized manner to solve the multi-objective nonlinear model. Numerical experiments are conducted to demonstrate the economic and environmental benefits of the proposed system. Therefore, this method is highly recommended for implementation on the generation side. • Design a centralized renewable energy connecting and shared energy storage sizing framework. • Exploit multi-site renewables with spatio-temporal complementarity on the power generation side. • Propose an economic-environmental model for renewable energy connecting and energy storage sizing. • Shared storage service is an effective approach toward a grid with high penetration of renewable energy. [Display omitted] [ABSTRACT FROM AUTHOR]

• Joint dispatching of autonomous mobility-on-demand system and power system. • Multi-spatiotemporal scale dispatching models in power-traffic coordination. • Dispatching performance validated on a large-scale real-world case. • 15% reduction of total carbon emissions with vehicle-to-grid (V2G) considered. • Sensitivity analysis on fleet size, renewable energy profile, power system capacity. Electrified autonomous mobility-on-demand (E-AMoD) systems will play a crucial role in future cities, which couple the carbon emissions generated by the transportation system and the power system. This paper proposes a joint dispatching framework to facilitate city decarbonization by coordinating the fleet operator and power system operator. Given the substantial differences in spatial and temporal dispatching scales between the two systems, this framework effectively integrates micro vehicle-level service dispatching with macro fleet-level charging scheduling. Firstly, a novel bipartite matching model efficiently solves the short-term passenger serving and rebalancing dispatching at the vehicle level. The service dispatching results are then aggregated as spatial-temporal transition flows across different areas and time intervals. Incorporating these mobility constraints, the long-term charging-discharging scheduling at the fleet level is captured as an expanded network flow model and integrated with the power system dispatching model, which aims at renewable energy utilization and carbon emission mitigation. Finally, the fleet-level scheduling results are disaggregated to the serving-rebalancing-charging sequences of each vehicle, for practical implementation. To validate the proposed framework, numerical experiments are conducted on a large-scale real-world case. Multiple charging schedule scenarios, including heuristic charging, smart charging, and smart charging-discharging, are investigated under both centralized and distributed supplied power systems. (1) The framework proves to be an effective solution for addressing the differences in dispatching spatiotemporal scales, calculation efficiencies, and privacy concerns between power and traffic systems. (2) Through power-traffic coordination, the E-AMoD fleet demonstrates its ability to alleviate 8% of average renewable energy curtailment and reduce nearly 15% of the total emissions in a power system installed with renewable distributed generators. (3) Furthermore, the extent of emission mitigation effects is influenced by critical factors such as fleet size, renewable energy profile, and power system capacity. Overall, the E-AMoD system show great potential as mobile storage devices to provide spatial-temporal flexibility to the power system and contribute to city decarbonization. [ABSTRACT FROM AUTHOR]

• Designed a new control model using Finite Control Set Model Predictive-based Virtual Inductance Droop Control. • Incorporated the virtual inductance decision to the droop control to improve the system voltage profile. • Used finite control set model predictive with a virtual inductance droop control to enhance the control performance and stability. • Implement the proposed control to a four-leg inverter to achieve optimal utilization of renewable energy. • The proposed control technique is tested under various transient and steady-state load conditions of balanced, unbalanced, and non-linear loads. As global energy demand hikes up, the search is to provide adequate penetration of green energy to the power grid to solve this threat of energy crisis and preserve the globe from pollution. Thus, this paper proposes an inverter control algorithm in the form of a finite control set model predictive-based virtual inductance droop voltage control strategy for a hybrid microgrid structure, which will provide a more effective platform for high penetration and efficient utilization of green energy. The new control algorithm as tabled in this paper considers the following aspects: DC voltage control, enhancement of reactive power issues with conventional control, fast dynamic response to AC load voltage regulation under balanced, unbalanced, and unbalanced non-linear loads, and power quality delivery in an autonomous hybrid microgrid system. To verify the performance of the proposed control method, simulation models are built under various operating modes of load variations and source unavailability. When analyzed, the findings show the control method's great efficacy to achieve desirable efficiency of green energy utilization and its greater accommodation into the microgrid. Additionally, it alleviates pressure on conventional generation and the utility system, avoiding the need for expensive grid improvements. [ABSTRACT FROM AUTHOR]

Wind as a renewable energy source is characterized by rapid growth of installed power in many countries, including Poland, where its share in the electricity production has grown over 25 times in the last decade, now reaching about 10%. Since renewable energy has priority in access to the grid, it forces the dispatchable coal-fired power plants to adjust their load. This causes additional consumption of primary energy due to more frequent shut-downs/start-ups and due to efficiency penalty when operating below nominal parameters. In this paper, actual data on the operation of Polish power plants in several years are analysed. The analysis, together with simulations of performance of the power system without the presence of wind energy, focuses on the influence of wind power plants on the utility ones. The results of simulations are additionally processed using the concept of thermo-ecological cost to compare the "operational" impact of wind farms on the power system with their "investment" environmental burden. A conclusion is that current share of wind energy at the level of 10% is enough to have an adverse effect on the coal power plants, but depending on the structure of the power system it may actually increase its overall efficiency. • Introduction of wind power to the grid disrupts the operation of other power plants. • Average efficiency of individual utility power plants decreases. • The number of start-up/shut-down procedures greatly increases. • Depending on the structure of the system, overall efficiency may actually increase. • Thermo-ecological cost of compensation for wind energy is non-negligible. [ABSTRACT FROM AUTHOR]

• A novel distributed event-triggered control system for DC microgrids with the fractional-order local controllers, for voltage stabilization and balancing of ESSs has been proposed in this paper. The controllers communicate in an event-based manner over a sparse communication graph to achieve an average consensus of the bus voltages and energy levels of ESSs. Furthermore, the FOPI controllers are tuned in the primary layer that further increases the performance of the droop controller for accurate power-sharing. The paper's contributions can therefore be outlined as: • A distributed event-triggered secondary control system for DC microgrids to achieve voltage stabilization and balanced ESS levels. • FOPI local droop controllers for accurate power-sharing and higher disturbance rejection caused by the intermittent sources. Recently, direct current (DC) microgrids have become prominent due to their less complicated architecture and better integration of renewable energy sources (RES). Energy storage systems (ESSs) and RES, such as photovoltaic (PV) panels and fuel cells (FCs), are internally DC. Hence, it is easier for them to be integrated into DC microgrids compared to AC systems. However, DC microgrids have low inertia, and voltage stability is harder to achieve due to the inexistence of reactive power compensators. Therefore, this paper suggests a novel distributed control system for DC microgrids to achieve voltage stabilization and accurate energy balancing of ESSs based on event-triggered average consensus protocol and fractional-order proportional-integral (FOPI) local controllers. The fractional-order controller has many advantages over the integer-order classical controller, such as achieving better performance, flexibility, and a higher degree of freedom in the controller design. In this regard, the FOPI local controllers provide accurate load sharing for the droop mechanism. The distributed secondary layer controller compensates for the voltage offset, meanwhile balancing the energy level of ESSs. In the conventional distributed control strategies, the communication between the agents is periodically at discrete sampling time, resulting in network traffic increase and excessive energy consumption in the devices. Therefore, in this paper, the average consensus protocol is event-based, saving considerable network capacity for other microgrid communication needs. The effectiveness of the proposed control strategy is confirmed by the simulation of a 380V DC microgrid in MATLAB/Simulink that shows the achievement of the control objectives. Simulation results show the proper voltage stabilization and accurate energy balancing of ESSs in both islanded and grid-connected modes and a significant reduction in the transmitted information between agents. [ABSTRACT FROM AUTHOR]

Sustainable and clean multi-energy systems have been hybrid coordinated in microgrid (MG) to fulfill regional multi-energy users economically, but shortages and surpluses still exist. The growth of win-win energy sharing between MGs attracts attention to smart information center (SIC) concept in MGs' community, which facilitates sustainable solutions across three key objectives: lower carbon emission, sufficient utilization, and long-term cooperation. This paper designs a two-stage decision framework for day-ahead operations of SIC, where the first stage minimizes the total cost (i.e. operational cost and carbon emission cost) by the proposed multi-energy intra-scheduling and inter-sharing model (MIIM) and the second stage implements the smart cost-saving redistribution model (SCRM) to stimulate MGs in long-term joining of energy sharing. The extended alternating direction multipliers method algorithm (E-ADMM) is developed to protect the privacy of MGs' data while approximating the optimal solution of MIIM. The case study proves that a) demand response and energy sharing in MIIM result in cost savings of 2.01% for carbon emissions and 7.25% for total operations; b) renewable energy has been fully utilized and the total multi-energy loss has been avoided nearly 92.22% in MIIM; c) the asymmetric Nash bargaining approach fits the SCRM with incentive cooperation, whereby those who contribute more gain a higher share; d) the optimal gap of E-ADMM is less than 0.2% by iterated insensitive decision result from each MG, which computes in polynomial time when the scale of MG increases; e) the two-stage framework performs better with more heterogeneity of MGs. Furthermore, we provide valuable insights for guidance and models with worldwide generalization potential. [ABSTRACT FROM AUTHOR]

In order to ensure the stable energy supply for isolated island areas scattered around the globe, a reliable and flexible energy supply system, and corresponding efficiency and intelligent energy management strategies are indispensable. In this paper, power to gas(P2G) is introduced to promote the absorption of distributed renewable energy, and facilitate the deeply coupling of electric–gas networks in multi-energy complementary energy system. Besides, considering the inherent characteristics and actual coupling situation of each module, six possible cooperative game and non-cooperative game schemes are proposed, under the fixed time-sharing electricity price mechanism and dynamic incentive electricity price mechanism, to compare the differences in energy utilization efficiency, pollution emission level and total revenue. The final study results show that under the two price mechanisms, the synergetic response of fuel cell and gas turbine makes the curtailment rate of distributed renewable energy decreased by 5.68% and 3.76% respectively, and the output share of DG decreased by 5.77% and 7.36% respectively. It has realized efficient utilization of energy and emission reduction of pollutants. In terms of revenue, the cooperative game mode with dynamic electricity price mechanism increases the total revenue of the system by 25.02%. • A electric–gas deeply coupled comprehensive energy model is established. • Four comparative game modes are proposed and six sets of energy scheduling schemes are developed. • Optimal scheduling strategy of and island microgrid under dual electricity price mechanisms. • Users have a better electricity experience. • The absorption rate of distributed renewable energy is improved and emissions of polluting gases is reduced. [ABSTRACT FROM AUTHOR]

• determine the optimal capacity, type, number and location of renewable energy resource • Optimal location of the switch for clustering the traditional DN into a set of interconnected MGs • Considering multiple function contains; operation cost, reliability, network structure and environmental factors together • Considering uncertainty to generate and reduce scenario-based modelling. In this paper, a scenario-based strategy for optimizing multiple microgrids has been proposed by considering the demand and renewable energy sources (RES) unit unpredictability. The approach seeks to determine the appropriate capability, category, number, and position of renewable and controllable distributed RES, and the ideal switch site for clustering the traditional distributed network (DN) into a collection of interconnected micro-grids (MGs) with an economical and trustworthy structure. The suggested solution attempts to minimize all plans, counting asset and process expenses, system loss, air pollution, and the cost of microgrid s' unsupplied energy. The Monte Carlo method was adopted to generate and reduce the scenarios, and the Newton-Raphson method was used for power flow. To optimize the system, we used the improved shark smell optimization algorithm in the simulation. Obtained results of economic and reliability assessment demonstrate that by dividing the traditional DNs into a set of multiple MGs, the reductions in reliability costs and technical costs are provided due to the reduction in losses and the improvement of the voltage profile, thereby reducing the total system costs. Also, obtained numerical analyzes show 0.34%, 0.14% and 0.12% improvement, in comparison to genetic algorithms, particle community and artificial bee colony, respectively. [ABSTRACT FROM AUTHOR]

• A rolling optimization strategy is proposed for microgrid operation optimization. • Flexible load and hybrid energy storage are applied jointly to power regulation. • A priority-based power fluctuations smoothing method is first proposed. • Energy trigger mechanism is designed to keep the optimal state of supercapacitor. The rise of microgrids provides an effective solution to the problem of local consumption of renewable energy sources. However, the power fluctuations are the crucial issue for the widespread adoption of the grid-connected microgrid with renewable energy sources. We aim to economically and locally solve the problem of grid-connected power fluctuations of microgrid. In this paper, a novel rolling optimization strategy considering grid-connected power fluctuations smoothing for microgrids is provided. Firstly, the mathematical model of the microgrid is described, which contains the grid-connected power limits and supercapacitor-battery hybrid energy storage system. Then, a priority-based smoothing method of power fluctuations is proposed for the first time. As a flexible load, the heating/cooling load is used as virtual energy storage to participate in power regulation. Finally, the rolling optimization strategy integrated with the energy trigger mechanism is designed to achieve the operation optimization. The objectives of this paper are to help the microgrids improve grid-connection friendliness and minimize the daily operating costs that include fluctuation penalties. Simulation results on different scenarios for a grid-connected microgrid show that the novel rolling optimization strategy has better performance in any scenario. Compared with the traditional strategy, the total operating costs of the proposed strategy can save 5.67% for a given scenario. We can conclude that the proposed rolling optimization strategy can effectively reduce cost payment and meet the requirement of grid-connected power fluctuations smoothing. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Energy demand side management is optimized to improve the system performance. • A game theoretic approach for distributed energy management is developed. • Energy supply constraints and storage facilities are evaluated. • Blockchain technology is applied for efficient and trustable micro-grid operation. • Decentralized energy supply and demand management is implemented in practice. Abstract The management of energy supply and demand is becoming more challenging, especially in regions where the demand continues to grow rapidly and more intermittent renewable supply sources are added to the energy infrastructure. In this context, Demand Side Management (DSM) can be employed to align supply and demand and improve reliability of the system under supply constraints and stretch the capacity limits of the existing grid infrastructure. A game theoretic approach for a DSM model incorporating storage components is suggested in this paper especially for environments with energy supply constraints. The proposed model is able to not only reduce the Peak-to-Average ratio to benefit the electrical grid, but can also smoothen the dips in the load profile caused by supply constraints. In a case study to demonstrate the value of the model presented, a variety of residential demand types are evaluated within a micro-grid to maximize the payoff utility of both individual users and the whole system. Moreover, emerging blockchain technologies are introduced to guarantee the seamless and secure implementation of the proposed scheme, illustrating how a decentralized demand side management approach could be implemented in practice to facilitate peer-to-peer transactions following optimized demand profiles. [ABSTRACT FROM AUTHOR]

• In order to adapt to the uncertainty of RES, the fluctuation coefficient(k) is introduced. • Setting C D C to measure the ability of EVs to suppress fluctuations in RES. • E A M and E S M control EVs to enter the CS and formulate a charging strategy, respectively. When both the renewable energy sources (RES) generation and utilization occur simultaneously, energy storage costs can be reduced, and voltage oscillation and system instability caused by RES grid connection can be reduced. Therefore, this paper constructs a microgrid model that includes EVs, defines the charge and discharge capacity (CDC) of EVs, and uses the flexibility and dispatchability of EVs to overcome the intermittency and volatility of RES. By using the Monte Carlo method, this paper establish an EV driving model and proposes a two-stage process, that is, the first-stage is EV admission mechanism (E A M) and the second-stage is EV scheduling mechanism (E S M), to adapt to the uncertainty of RES, and maximize energy utilization and EV users satisfaction (Charging success rate). Through comparison with natural charging (NC) strategy, it is shown that the proposed two-stage process effectively overcomes the intermittency and volatility of RES and enables the stable operation of EVs in charging stations(CSs) supported by microgrids. [ABSTRACT FROM AUTHOR]

• We proposed an adaptive (two-stage) robust energy management for an isolated microgrid considering renewables uncertainty. • Minimization of the expected total operating costs of the microgrid is the objective function. • Reactive power costs from diesel generators are considered and are co-optimized with active power costs • The reactive power capability of the inverter-interfaced distributed energy resources is considered. • Detailed modeling of the diesel generators and inverter interfaced distributed energy resources by the consideration of their capability curves is provided. • The results obtained validate the effectiveness of the proposed solution. This paper presents an adaptive robust Energy Management (EM) model for an isolated Microgrid (MG) to decide for the day-ahead optimal dispatch to effectively manage the MG power sources considering uncertainties in Renewable Energy Sources (RESs). The aim of this model is to minimize the operation costs, reactive power costs, spinning reserve and load shedding. Usually, fuel consumption costs of diesel generators are considered to be dependent on active power generation only. However, neglecting the related reactive power costs might result in increased operation costs and deviations from optimal dispatches. Hence, this paper optimizes the costs related to both active and reactive powers of diesel generators. Furthermore, simultaneous active/reactive power dispatch can lead to accurate operation decisions compared to separate dispatch for active or reactive power alone. In addition, this study considers the reactive power capability of inverter-interfaced Distributed Energy Resources (DERs) so that reactive powers can be supplied from inverter-interfaced DERs. Moreover, detailed models for the different resources are presented, especially for diesel generators and inverter interfaced DERs where actual capability curves are used instead of the widely used box constraints. The problem is formulated as nonlinear programming problem using GAMS program and solved by the CONOPT solver. [ABSTRACT FROM AUTHOR]

Green smart cities that rely completely on sustainable renewable energy sources are being constructed to attract investors, diversify countries' economies, and move away from dependence on fossil fuels. The main renewable energy sources used in such cities today include solar, wind, and—to a lesser extent—biomass. However, these sources are stochastic in nature, and the amount of energy they produce is not entirely controllable. This results in difficulty maintaining the balance between generation and demand in the power grid. Recent technological advances in smart grids, microgrids, virtual power plants, energy hubs, and energy interconnection technologies offer a wide range of plausible solutions to this problem. Such advances motivated the development of a complementary technology known as the energy warehouse (EW). An EW is a controlled and managed heterogeneous massive modular energy storage and wheeling system. It provides links between microgrids and bulk power systems (BPSs). After describing the structure of an EW and its energy management system and illustrating example EW operating scenarios, this paper uses the concept of bankruptcy to explain the business side of an EW. For the first time, this principle is used to address cases where the EW falls short of its obligations to the connected microgrids and BPSs. This treatment of EW contributes to a better understanding of its operation and opens further opportunities for contributions. Furthermore, our study confirms that a properly sized and located EW promises significant technical and economic benefits. These including load leveling and energy time-shift, as well as the potential to eliminate the typical transmission infrastructure, thereby reducing losses, mitigating congestion, and increasing efficiency. [ABSTRACT FROM AUTHOR]

In the last decade, the significant level of carbon emission in the atmosphere has increased the temperature of the Earth by 50% compared to the pre-industrial period. In recent years, considering renewable energy sources integrated with different energy systems such as cooling, heating, and power has become widespread around the globe as the ambition is to produce sustainable and clean energy. However, as the conventional methods for energy generation are highly reliable, their usage for supplying energy demands is inevitable. Therefore, in such renewable-based generation systems, the issues arising from uncertainties are the main challenges to the high penetration of these technologies. In this paper, the aim is to appraise such challenges in a multi-energy combined heat and power (CHP)-based microgrid integrated with power-to-gas (P-2-G) technology. In the proposed system, the downside risk constraint (DRC) approach as an effective risk management method is considered for risk assessment. The proposed model is a mixed integer linear program that is modeled as a multi-objective risk-based problem, for which the epsilon constraints method is used to achieve the optimal solutions for the objectives. According to the results, using P-2-G as an environmentally friendly tool can reduce approximately 55% of the emission cost. Further, the operating cost is increased nearly 13% to obtain a zero-risk condition for optimal management. [Display omitted] • Risk-constrained emission reduction and flexibility improvement in the fossil-fueled CHP-based multi-vector system. • A stochastic-based risk-aware multi-objective optimization framework with iterative solution mechanism. • Risk analysis with downside risk constraint (DRC) approach for the multi-vector uncertainties. [ABSTRACT FROM AUTHOR]

Renewable energy resources are currently being deployed on a large scale to meet the requirements of increased energy demand, mitigate the environmental pollutants, and achieve socio-economic benefits for sustainable development. The integration of such distributed energy sources into utility grid paves the way for microgrids. The microgrid concept is introduced to have a self-sustained system consisting of distributed energy resources that can operate in an islanded mode during grid failures. In microgrid, an energy management system is essential for optimal use of these distributed energy resources in intelligent, secure, reliable, and coordinated ways. Therefore, this review paper presents a comparative and critical analysis on decision making strategies and their solution methods for microgrid energy management systems. To manage the volatility and intermittency of renewable energy resources and load demand, various uncertainty quantification methods are summarized. A comparative analysis on communication technologies is also discussed for cost-effective implementation of microgrid energy management systems. Finally, insights into future directions and real world applications are provided. [ABSTRACT FROM AUTHOR]

Renewable energy sources have been increasingly deployed as distributed generators in remote areas. Meanwhile, fluctuating power generation from renewable energy sources, together with variable power demand, poses challenges in stable and reliable power supply. In this paper, a microgrid with solar photovoltaic (PV) and battery energy storage (BES) is studied. A state of charge (SOC)-oriented charging scheme is developed to control the BES to smooth the PV output. Most importantly, a sophisticated control algorithm, consisting of a model predictive voltage control (MPVC) and a model predictive power control (MPPC), is proposed for the interlinking converter. It enables stable voltage in islanded mode. Also, in grid-connected mode, flexible reactive power can be injected into the main grid for grid support according to the voltage variation level. Finally, by considering the intermittent nature of the PV and the load profile, an energy management system (EMS) is designed to ensure power balance within the system. Case studies are provided to demonstrate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Systematically organizing and analyzing the energy consumption structure of the world can reveal the organic connections between countries. Furthermore, such tasks can provide a global reference system for each country, enabling each one to adjust and optimize its energy consumption structure. Most previous studies of the global energy consumption structure overlooked the associations between countries and the evolutionary trends associated with the energy consumption structures of countries. This paper analyzes the evolution of the global energy consumption structure using an evolution tree model. The visual structure of this model provides a novel perspective for understanding and analyzing the underlying trends. First, 144 countries and regions are categorized into four different types using the k-means clustering algorithm. Countries and regions that belong to the same type generally follow similar evolutionary paths. Moreover, type IV countries, which are mainly developed countries, have the most diverse energy consumption structures. By contrast, the energy consumption structures of other types of countries and regions can be improved. Countries can be located in the global energy consumption structure of the evolution tree, and these locations can provide a basis for improving the energy consumption structure of a country based on similar countries that are more diverse. [ABSTRACT FROM AUTHOR]

Due to the unique structure and special characteristics of electric distribution networks, along with the increase in the number and severity of the natural disasters in recent years, presenting a proper framework and procedure for evaluating the resiliency of these systems is more essential. In addition, it is obligatory to model the different capabilities and features of the smart grids components in order to provide a better knowledge about their expected performance in such circumstances. In response to these challenges, this paper addresses the concept of resiliency and its dimensions in distribution networks. A new model based on mixed-integer linear programming is proposed to properly model and evaluate the resiliency of smart distribution systems. In the proposed model, optimal formation of dynamic microgrids (MGs), their service areas, and the optimal management of different technologies such as energy storage (ES) units, demand side management programs and distributed generations (DGs) units are investigated. In addition, employing a two-stage framework based on stochastic programming, the impact of increasing penetration level of the renewable energy resources and their related uncertainties on system resiliency is examined. The efficiency and applicability of the proposed integrated model is verified by performing multiple simulations on the modified 118-bus test system and a real distribution network. [ABSTRACT FROM AUTHOR]

• Improving the performance of diesel generators gives economic and environmental benefits for hybrid microgrids planning. • Better interaction among diesel generators and renewable energy for rural electrification can be achieved using cost optimisation tools. • Including biofuel blends in a cost optimisation tool allows for assessing locally produced fuels for diesel substitution. • PM 2.5 and NO X emissions influence the biofuel selection to be used within hybrid microgrids. • Cost-benefit analysis helps to determine the best microgrid system configuration, considering financial and environmental aspects. Diesel generators (diesel gensets) are widely used within microgrid (MG) and off-grid systems for rural electrification, particularly in developing countries. The sizing and selection techniques during the MG planning stage are a key for maximising cost-effectiveness and minimising environmental impacts. This becomes more important for hybrid mini-grid systems when photovoltaic (PV) electricity generation and other renewable energies are included in the system as special attention is needed to limit the genset's output power, to keep it within the recommended operating range. This paper presents a cost optimisation model, centred on the generatoŕs performance, within diesel/PV/battery MGs, for minimising the MG's operating costs and environmental impact. The model considers fuel consumption equations adapted for castor oil-diesel (COD) blends and two major pollutant emissions (NOx and PM 2.5), which are not considered in other optimisation models. The optimisation was implemented for high, medium, and low electricity demand scenarios, with eight possible system configurations, for the Lindi Region of Tanzania as it belongs to one of the five countries with the lowest electricity access in sub-Saharan Africa (SSA). An economic assessment was done to compare the Levelized Cost of Energy (LCOE) of the system configurations. The impact of the fuel price and pollutant emission costs on the fuel selection was investigated using sensitivity analysis. The results confirmed that for specific electricity demand each scenario requires a unique set of diesel generators and the selection is affected by the PV share and the battery energy storage (BES) units included. The best LCOE for the high, medium, and low electricity demand scenarios were 0.43£/kWh, 0.42£/kWh, and 0.45£/kWh, respectively. The sensitivity analysis revealed that the pollutant emission costs have a significant impact on LCOE for the different fuel choices whereas the variation of fuel prices has a minimal effect unless the diesel price increased by 100%. [ABSTRACT FROM AUTHOR]

• Investigation on the INVELOX and its positive effect on the power system's future. • Implementation of TOU programs by considering INVELOX to show its efficiency. • The microgrid, nanogrid, and main grid are connected to form proposed multi-grids. • Considering total microgrid – nanogrid cost, and pollution as the triple functions. • Modeled as a MIP and optimized using AEC and LP metric approaches simultaneously. This paper investigates the energy management of the proposed multilateral structure (nanogrid-microgrid-main grid) by considering invelox turbines as a novel option for use at this level. Furthermore, the demand response program (DRP), especially the time of use (TOU), is considered to evaluate such a turbine's operation in the demand management issues. The authors have demonstrated the potential of these turbines in renewable base grids and expressed their influences on the future of renewable energy exploitation. The proposed multi-grid invelox wind turbine (MG-IWT) consists of various renewable and non-renewable resources like CHP, boiler, photovoltaic, etc., and different loads to show the completeness of the system. The triple objective function of the MG-IWT includes total cost, contamination, and the nanogrid's cost. The problem is modeled as mixed integer programming (MIP) and solved in GAMS software by using a cplex solver. The augmented epsilon constraint (AEC) approach and LP metric method simultaneously optimize the problem using GAMS and assess the responses. Results show the invelox's effectiveness in the multilateral structure in which the microgrid and nanogrid's costs are decreased from $ 961.8 and $ 12.2 to $ 789.2 and $ 5.8 by considering the invelox and demand response program, respectively. [ABSTRACT FROM AUTHOR]