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As power systems around the world are rapidly evolving to achieve decarbonization objectives, it is crucial that power system planners and operators use appropriate models and tools to analyze and address the associated challenges. This paper provides a detailed overview of the properties of power market models in the context of the clean energy transition. We review common power market model methodologies, their readiness for low- and zero‑carbon grids, and new power market trends. Based on the review, we suggest model improvements and new designs to increase modeling capabilities for future grids. The paper highlights key modeling concepts related to power system flexibility, with a particular focus on hydropower and energy storage, as well as the representation of grid services, price formation, temporal structure, and the importance of uncertainty. We find that a changing resource mix, market restructuring, and growing price uncertainty require more precise modeling techniques to adequately capture the new technology constraints and the dynamics of future power markets. In particular, models must adequately represent resource opportunity costs, multi-horizon flexibility, and energy storage capabilities across the full range of grid services. Moreover, at the system level, it is increasingly important to consider sub-hourly time resolution, enhanced uncertainty representation, and introduce co-optimization for dual market clearing of energy and grid services. Likewise, models should capture interdependencies between multiple energy carriers and demand sectors. • Review methodologies and assumptions commonly used in power market models. • Identify model design features critical to analyzing the clean energy transition. • Survey current state-of-the-art in modeling low-carbon power markets. • Identify key model improvements needed for future deep decarbonization scenarios. • Highlight importance of tailoring tools for specific power market applications. [ABSTRACT FROM AUTHOR]

Contemporary cyber–physical systems have evolved into highly autonomous and distributed entities, enabled by cutting-edge control frameworks and advanced communication networks. This transformation has empowered these systems to efficiently accommodate inverter-based resources, harnessing the grid's dynamic potential. However, the increased integration of cyber and communication infrastructure has exposed these systems to vulnerabilities stemming from both cyber and physical disturbances. Power system errors in the physical layer of microgrids and malicious attacks on the communication layer pose substantial threats to grid stability and load supply. In response to these challenges, this paper presents an innovative unsupervised learning method for cyber–physical anomaly identification. Leveraging an autoencoder-based neural network, this approach addresses the intricate interplay between the physical and cyber aspects of power system failures and false data injection within the communication network. The method's efficacy is evaluated on an islanded inverter-based microgrid, representing a realistic and vulnerable testbed. The autoencoder error is introduced as a key performance metric, providing a robust and practical measure of the proposed detection technique's effectiveness. Simulation results demonstrate the promising utility of this method, positioning it as a valuable tool for enhancing the resilience of contemporary cyber–physical systems against cyber–physical anomalies. • Advanced control and network integration boosts autonomy in cyber–physical systems. • Systems efficiently host inverter resources, yet face cyber and physical threats. • Paper presents unsupervised learning for anomaly detection in microgrids. [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]

Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified. • Microgrid (MG) control as a cyber-physical system (CPS) against cyber-attacks (CAs) is one of the concerns of researchers. • CAs include deception attacks, denial of service (DoS) attacks and replay attacks. • Defense mechanisms against CAs consist of prevention, detection and isolation of CAs and resilience control of MG. • Future trends in the context include new methods of CAs prevention, detection and isolation, new resilient control methods. • Retrofitting existing equipment and/or accommodating advanced technologies into networks enhance resilience of MG. [ABSTRACT FROM AUTHOR]

Long-distance power support through High-voltage Direct Current (HVDC) has provided feasible solutions for power dispatch and control problems in multi-area power systems under high share of renewable energy. In this paper, an advanced multi-area intra-day dispatch strategy for power systems with high penetration of renewable energy considering power support capacity is established to increase the accommodation of wind power and enhance the stability of the power system. At first, a novel assessment method of regional active power support capacity is carried out in this paper, which can sufficiently quantify the regional power margin (RPM), the DC dispatchable power (DCDP), and the regional power support distance (RPSD) of a power system. Subsequently, a multi-area intra-day dispatch strategy based on power support capacity analysis is proposed, where constraints on conventional units, wind farms, transmission lines, and RPSD are developed. Finally, to enable the development of the dispatch model, a model predictive control (MPC) scheme is used to update the power support capacity indicators dynamically and improve model reliability. The effectiveness and superiority of the proposed strategy are verified by case studies. Numerical results show a 7.02% cost reduction and overload risk reduction by using the proposed strategy compared to conventional global optimization methods. • A novel assessment method of regional active power support capacity is proposed. • A multi-area dispatch strategy considering power support capacity is established. • A model predictive control scheme updates the power support capacity indicators. • Proposed strategy enhances the power system stability and reduces dispatch costs. [ABSTRACT FROM AUTHOR]

The trend towards reducing greenhouse gas emissions by dipping the use of traditional sources of energy in marine power grids, as well as the rapid expansion of renewable energy sources (RESs), were the driving forces behind the incorporation of RESs in maritime microgrid systems and enquiry of the ensuing prevalent control mechanism. The frequency stability of a Marine Microgrid System (MMGS) is a critical aspect that directly affects its reliable and efficient operation. This study describes a method for frequency stabilization in independent maritime microgrids consists of diverse renewable energy resources including wind turbine generators, sea wave energy/tidal power generation, solar generation, bio-diesel generator and energy storage systems. This paper aims to develop a new optimal cascaded order proportional integral based proportional derivative for marine load frequency control. Due to the fact that the performance of the controller is highly dependent on the parameters of the controller, optimizing these coefficients can have a significant impact on the output performance of the LFC control. In light of this, this paper presents the sewing training-based optimization (STBO), a new human-based metaheuristic algorithm to optimize the coefficient of the suggested controller. The essential stimulation of STBO is the teaching procedure of sewing to beginner tailors. By utilizing a population of candidate solutions, the algorithm iteratively refines the control parameters to find the optimal set that minimizes frequency deviations and enhances system stability. The responses of the cascaded PI-PD controller are linked to those of the PID and PI controllers in order to demonstrate its superiority. To evaluate the efficiency of STBO, it is compared to well-established recent techniques such as fitness dependent optimization, grey wolf optimization and Jellyfish search optimization. From the results, it is observed that our proposed approach improved the settling time by 25.35%, 45.89%, and 29.35%, reduced peak overshoot by 78.34%, 67.71%, and 78.23%, and similarly reduced undershoot by 81.56%, 56.22% and 76.56% as compared to FDO, GWO and JSO techniques respectively. Finally, the sensitivity analysis is performed under ±50% load variation and ± 40% power system parameters to prove the robustness of the proposed controller. • A diverse renewable energy resources including renewable energy resources and energy storage systems (ESS,s) incorporating with battery energy storage, capacitive energy storage, and flywheel energy storage system has been considered for marine microgrid. • A novel cascaded order PI-PD controller has been designed and implemented for the suggested distinct renewable and energy storage based maritime microgrid. • The coefficient of the proposed controller is optimized with a new human-based meta-heuristic algorithm known as sewing training-based optimization (STBO). • Multi objective functions including integral time absolute error (ITAE), integral time square error (ITSE) and integral square error (ISE) are formulated for the proposed power system. • Finally, sensitivity testing of the STBO adjusted PI-PD controller under uncertain parameters such as turbine gain variation (K CE), droop factor (R), and inertia constant (M) is performed to prove the robustness of the proposed frequency control technique. [ABSTRACT FROM AUTHOR]

This paper introduces a two-stage two-level optimization method for optimal day-ahead and real-time scheduling of multicarrier energy distribution systems and microgrids. The model considers the incentive-based and price-based demand response programs to encourage microgrids to transact electrical, heating, and cooling energy carriers with the energy distribution system, which is named hereafter as the energy system. Further, the model formulates the resilient operation of the energy system considering the energy transactions with the electrical, heating, and cooling markets. The main contribution of this paper is the integration of demand response procedures of microgrids in energy transactions with the energy system considering the switching of electrical switches and heating and cooling control valves. The optimization process is another contribution of this paper that is decomposed into two stages that consist of day-ahead and real-time horizons. The first stage is also decomposed into two levels that determine the optimal scheduling of the energy system and microgrids in day-ahead markets. The second stage is comprised of two levels that commit the energy system and microgrids resources. A resiliency index is proposed to assess the resiliency of the energy system in shock conditions. The proposed method was simulated for the 123-bus test system. Different types of microgrids, incentive-based and price-based demand response processes were considered. Simulation results confirmed that the proposed method can reduce the costs of residential, industrial, and commercial microgrids by about 4.47%, 3.88%, and 5.47% concerning only the real-time pricing process. Further, the model can increase the aggregated benefits of the energy system in the day-ahead and real-time markets by about 0.608 Million Monetary Units (MMUs) and 1.10 MMUs, respectively. • Optimal scheduling of a combined cool, heat, and power-based energy system. • Considering commitment scenarios of microgrids in multi-carrier energy markets. • Integrating resilient operational scheduling of multicarrier energy system. • Considering different demand response programs including incentive-based and price-based programs. [ABSTRACT FROM AUTHOR]

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty. • Proposed uncertainty quantification models for the source and load nodes of DHS. • Developed a novel credible interval calculation method for nodes' thermal power. • Quantified nodes' temperature dynamic response considering source-load uncertainty. [ABSTRACT FROM AUTHOR]

• A realistic PHS model is proposed for energy management applications. • Flow rate, water level, evaporation and precipitation affect model performance. • The proposed PHS model is validated with experiments in different operating points. • The error of the estimated stored water is reduced from 13.17% to 0.74%. This paper proposes a comprehensive pumped hydro storage model with applications in microgrids and smart grids. Existing models within current literature produce high error in calculating stored energy since some critical parameters are ignored. Thus, they are not suitable choices for energy management applications. Accordingly, the main objective of this study is to provide a more realistic model by estimating all the essential parameters in the system. First, all the losses due to the pump, pipes, and fittings are modelled. Next, a water balance approach is used to calculate the volume of water in the upper reservoir considering inflow, outflow, precipitation, and evaporation. Finally, the turbine power is calculated as a function of the water level in the reservoirs, considering the hydraulic losses of the turbine, pipes and fittings. The proposed model is validated using the experimental results of a physical system. The accuracy of the model is compared with other established models. The results demonstrate that the proposed model decreases the error of the estimated stored energy from 13.17% to 0.74%. Moreover, this study shows the capability of the model to simulate different configurations. The model provided in this paper assists researchers in the field and is of benefit to engineers in designing, sizing, and managing pumped hydro storage systems. [ABSTRACT FROM AUTHOR]

Building and microgrid designs with highly-distributed electrical storage have potential advantages over today's conventional topologies with centralized storage. This paper studies the capital cost benefits of several residential behind-the-meter distributed-storage topologies, including AC and DC versions of systems with load-packaged batteries and resilient sub-networks. The study begins by defining the block configuration of each topology. This work then develops a model for the cost of the power electronics necessary to interface with the storage elements. Finally, the analysis develops a model for the total cost of each storage topology, incorporating the installation and soft costs. The results suggest that while the cost of power electronics is lower in centralized topologies, the total cost is lower for distributed storage due to the avoided costs of installation and permitting. This paper also explores the benefits of load-packaged batteries for savings in electrical infrastructure. [ABSTRACT FROM AUTHOR]

This paper investigates the frequency performance problem of microgrids (MGs) integrated with renewable employing an energy storage system (ESS) equipped with virtual inertial control (VIC) support. To tackle the uncertainties related to the system operation, a two-layer multiple model predictive control (TLMMPC) method, consisting of nominal and ancillary MMPCs, is proposed to submit effective control signals to the ESS for improving system frequency performance. The ancillary MMPC generates the control commands for the VIC-based ESS utilizing the signals provided by the nominal MMPC and the frequency deviation signal of the actual system considering uncertainties and operating constraints. The control commands are generated to attain the minimum value of frequency response error with the least control endeavor while considering various operational and physical limitations. The TLMMPC method has the capability to work with different state of charge (SoC) levels to obtain the desired SoC and highest efficiency from the ESS and preserve the ESS's longevity. The dynamic performance of the proposed TLMMPC technique is investigated on an islanded MG and compared to model predictive control (MPC), fractional-order MPC, and tilt-integral-derivative controllers under different scenarios. The results validate that the proposed TLMMPC technique significantly improves the system frequency response from viewpoints of settling time, peak overshoot, and undershoot and obtains the most efficient ESS compared to the other methods. • This paper presents a TLMMPC method to control a VIC-based ESS in the LFC problem of an islanded MG. • The TLMMPC works with different SoC levels to obtain the highest efficiency from the ESS. • The performance of the TLMMPC method is compared to other methods under different scenarios. • The sensitivity analysis is done to study the TLMMPC's robustness against parameters' changes. [ABSTRACT FROM AUTHOR]

• A DAA-LFC method is proposed to balances the interests of different operators. • DAA-LFC method equates each area controller as an agent. • A DQMA-DMDPG achieves multi-task collaborative learning. • The proposed method reduces the frequency error and total generation cost. This paper presents a distributed area autonomy load frequency control (DAA-LFC) method capable of balancing the interests of different grid operators and achieving fast frequency recovery. The method treats each area controller in a multiarea microgrid as an agent. During offline training, the agents enter into gameplay with each other to obtain a global optimization policy. The agents involved in this method are capable of independent decision-making and need not communicate with each other during online operation. In addition, this paper presents a distributed quantum multiagent deep meta-deterministic policy gradient (DQMA-DMDPG) algorithm, which employs both large-scale learning and meta-learning to achieve collaborative multitask learning by setting reasonable exploration parameters under different tasks. A quantum method is used to set the exploration action noise as a set of superposition states to obtain richer samples. These innovations deliver better performance in terms of frequency deviation and total generation cost, thus satisfying the requirements of different grid operators. A simulation based on a four-area microgrid of the China Southern Grid (CSG) demonstrates that the proposed method can simultaneously reduce the frequency deviation and power generation costs and balance the interests of multiple operators. [ABSTRACT FROM AUTHOR]

Distributed control has been employed in autonomous microgrids (MGs) to attain secondary control goals. However, the reliance of MG's distributed secondary control on communication makes it vulnerable to degraded performance and the risk of instability due to communication delays. This paper enhances the flexibility of the MG control framework, with each distributed generator incorporating supplementary local feedback signals. This added flexibility strengthens the MG's ability to counteract disturbances caused by delays and enhances its dynamic performance. The introduction of extra feedback signals has a direct impact on the dynamic response of the MG, leaving its steady-state condition unaffected. In comparison to the methods found in existing literature, the increased flexibility relaxes the inherent trade-off between the objectives of improved transient response and the system's ability to mitigate disturbances caused by delays. To model the MG, a set of delay differential–algebraic equations (DDAEs) is used. The small-signal linearized model is derived and employed to evaluate the stability of the MG as well as fine-tune the control parameters. The effectiveness of this proposed control structure is demonstrated using the MATLAB/Simulink environment and a controller in the loop (CIL) setup, tested against both fixed universal and asynchronous delays, as well as time-varying delays. • A novel hierarchical distributed control to mitigate communication delays impact. • Enhancing the tolerance for a wide delay range without degrading convergence speed. • Additional feedback terms to the frequency control to enhance disturbance rejection. • Additional feedback terms to the voltage control to enhance disturbance rejection. • Verification via small-signal analysis and simulations in Simulink and CIL setup. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic energy storage systems (DPVES) offer a proactive means of harnessing green energy to drive the decarbonization efforts of China's manufacturing sector. Capacity planning for these systems in manufacturing enterprises requires additional consideration such as carbon price and load management. This paper proposed a triple-layer optimization model for DPVES capacity configuration in the manufacturing sector using a chemical fibre manufacturing enterprise for demonstration. Refined photovoltaic generation and energy storage lifetime models were used. Beyond the considerations of electricity prices and meteorological conditions, we further studied the influence of carbon price and user load management on system capacity configuration and associated economic feasibility. Firstly, without considering carbon, minimizing user costs requires maximizing PV capacity up to the area limit while adjusting the ES to its optimal capacity and power. The optimal DPVES annually reduces the grid electricity consumption and carbon emissions, resulting in a 12.73% annual cost reduction. When considering the costs of carbon emissions, the carbon reduction contributed by DPVES can reduce the annual costs, making the whole system more economically feasible. However, the presence of substantial carbon emissions costs diminishes the economic feasibility of the ES, leading to a reduction of 24.51% in the optimal capacity configuration. Finally, user load management can further reduce system costs because it replaces some of the functions of energy storage. This results in a decrease of over 39% in the optimal energy storage capacity and a further reduction in related costs. Additionally, we found that load management by enterprises is more effective during the low carbon price than the high carbon price, implying that companies should implement load management as soon as possible. • Established a triple-layer optimization model for capacity configuration of distributed photovoltaic energy storage systems • The annual cost can be reduced by about 12.73% through capacity and power configuration optimziation • High carbon prices may reduce the economic viability of the energy storage system, causing reduction in its optimal capacity • Load management can further reduce the energy storage capacity demand by 39%, bringing economic benefits especially in low carbon prices [ABSTRACT FROM AUTHOR]

Power pooling has emerged as a regional strategy for accelerating generation capacity expansion in West Africa with the aim of leveraging vast domestic energy resources and promoting investment in regional power infrastructure. As part of their climate action pledges, most West African countries have committed to increasing the shares of variable renewable energy (VRE), particularly solar photovoltaic and wind power, in their generation mix. It, however, appears that approaches to utility-scale capacity expansion planning tend to overlook the inherent temporal intermittency and spatial variability of VRE-based generation. Moreover, despite influencing the techno-economic rationale for grid expansion and off-grid electrification, as well as the trade-offs between high renewable supply areas and grid expansion, the spatial distribution of demand has been overlooked in planning approaches, leading to conservative and weak prospects for the contribution of utility-scale VRE. Such inconsistencies with the region-wide potentials and policy ambitions highlight that it is paramount for West Africa to design its power pooling such that spatial and temporal fluctuations of VRE supply are duly considered in capacity expansion planning while taking advantage of complementarities between VRE supply and national electricity demand profiles. To address this, the present paper applies a long-term generation capacity expansion model, OptGen, soft-linked to a least-cost operation module, the SDDP tool, which is enhanced by geospatial electrification analysis using the OpeN Source Spatial Electrification Tool (OnSSET), to a subset of four member countries of the West African Power Pool – Burkina Faso, Côte d'Ivoire, Ghana, and Mali – for the 2023–2040 time horizon. The results highlight that current frameworks lead to missed opportunities for bridging the supply-demand gap in all countries, not only in terms of VRE generation capacity, but also of cross-border power trade. • Long-term capacity expansion planning and dispatch models are soft-linked. • The spatial and hourly distributions of demand and renewables supply are considered. • The 10% limit on renewables share used in the regional master plan is not optimal. • The current system operation is not conducive to optimal cross-border trade. • The relaxed transmission scenario yields higher dispatch factors for renewables. [ABSTRACT FROM AUTHOR]

To make full use of the flexible adjustment capability of DC tie-lines in multiarea AC/DC systems and to coordinate the generation resources and load demand of multiarea AC/DC systems, this paper presents an intraday two-stage hierarchical optimal scheduling model for multiarea AC/DC systems based on analytical target cascading (ATC). To avoid repeated adjustment and overadjustment of DC tie-lines after wind power integration, a two-stage rolling coordinated scheduling model for the area subsystem based on model predictive control (MPC) is proposed. The two-stage rolling coordinated scheduling method takes into consideration the influence of the predicted value in the future finite time domain and the latest measured DC tie-line power on the current scheduling state. Based on the ATC and the area decomposition criterion of the AC/DC grid, an optimal scheduling model for the upper-level system is proposed that takes into consideration the DC tie-line adjustment constraints and the area coupling constraints. The optimal scheduling model for the upper-level system formulates the two-stage DC tie-line plan for the multiarea AC/DC system, and the two-stage rolling coordinated scheduling model of the area subsystem solves the subproblems of the generation plan for each area subsystem in a parallel manner considering the area coupling constraints. The proposed method can achieve intraday two-stage decoupling scheduling of multiarea AC/DC systems and promote the cross-area consumption of large-scale wind power through flexible adjustment of the DC tie line. This approach also reduces the communication burden between the area subsystems and ensures the efficiency of the solution algorithm. • Based on analytical target cascading technology (ATC), we integrate the hierarchical control structure of decomposition-coordination into the model predictive control (MPC) method to achieve decentralized dispatch for multiarea AC/DC systems. • A two-stage rolling coordinated scheduling model for the area subsystem considering the area coupling constraints based on the MPC is proposed. • Based on the ATC and the area decomposition criterion of the AC/DC grid, an optimal scheduling model for the upper-level system is proposed. • Flexible adjustment of the DC tie-line and decoupling scheduling of multiarea AC/DC systems are achieved. • The hierarchical optimal scheduling model reduces the communication burden between the subsystems and ensures the efficiency of the solution algorithm. [ABSTRACT FROM AUTHOR]

Conventional microgrid optimization schemes fall short in achieving global optimality for both sizing and scheduling aspects. In response to the demand for simultaneous optimization, this paper presents a novel inner-outer layer framework that includes an outer layer dedicated to sizing optimization and an inner layer focused on Energy Management System (EMS) operational optimization. This approach has a distinctive mechanism of two-layer interaction, where the outer layer focuses on the size assignment and renewal, while the inner layer is constrained by the size assignment and provides feedback on the corresponding operational cost to the outer layer. It enhances the capability of global optimization since the optimal sizes are searched within a fixed space. To establish the range of initial generation values, the framework commences with a pre-optimization phase. Based on the convexity nature of optimization problems, a combined solution methodology involving Rolling Horizon Optimization (RHO) and Particle Swarm Optimization (PSO) is implemented. The case study demonstrates that the algorithm exhibits a good convergence within 30 generations, reaching a global satisfactory solution with a population size of 50. Furthermore, our analysis of optimal sizes across diverse scenarios fills the research gap in understanding the relationship between optimal sizes and operational scenarios. These results underscore that the impact of the time-of-day tariff strategy is significant, and it is risky to determine the optimal sizes by rule-based strategies. Finally, the comparison to other co-optimization methods shows that the proposed framework can reduce the annual total cost by up to 23.1%, which highlights its greater capability of global optimization. [Display omitted] • A novel inner-outer layer framework for microgrid co-optimization is introduced. • An integrated solution methodology is employed. • A comprehensive analysis of optimal sizes across diverse scenarios is conducted. • A comparative analysis demonstrates its superior global optimization capability. [ABSTRACT FROM AUTHOR]

The increasing proportion of renewable energy introduces both long-term and short-term uncertainty to power systems, which restricts the implementation of energy management systems (EMSs) with high dependency on accurate prediction techniques. A hierarchical online EMS (HEMS) is proposed in this paper to economically operate the Hybrid hydrogen–electricity Storage System (HSS) in a residential microgrid (RMG). The HEMS dispatches an electrolyzer-fuel cell-based hydrogen energy storage (ES) unit for seasonal energy shifting and an on-site battery stack for daily energy allocation against the uncertainty from the renewable energy source (RES) and demand side. The online decision-making of the proposed HEMS is realized through two parallel fuzzy logic (FL)-based controllers which are decoupled by different operating frequencies. An original local energy estimation model (LEEM) is specifically designed for the decision process of FL controllers to comprehensively evaluate the system status and quantify the electricity price expectation for the HEMS. The proposed HEMS is independent of RES prediction or load forecasting, and gives the optimal operation for HSS in separated resolutions: the hydrogen ES unit is dispatched hourly and the battery is operated every minute. The performance of the proposed method is verified by numerical experiments fed by real-world datasets. The superiority of the HEMS in expense-saving manner is validated through comparison with PSO-based day-ahead optimization methods, fuzzy logic EMS, and rule-based online EMS. • EMS performance degrades without considering energy storage dynamics. • A hierarchical EMS for hybrid storage system redistributes renewable energy. • A local energy estimation model establishes the EMS intelligent awareness. • Fuzzy logic-based controllers realize system real-time operation. • Frequency-decoupling controllers benefit energy storage units' cooperation. [ABSTRACT FROM AUTHOR]

Aiming at a large number of decentralized resources such as rural biomass, rooftop photovoltaic, and decentralized wind power, a novel rural virtual power plant (VPP) has been conceptualized. The VPP is integrated by biomass waste conversion system (BWC), carbon to energy (C2P), demand response aggregator (DRA), and distributed renewable energy (DRE), namely, BDCD-VPP. Then, this paper proposes a data-driven two-stage robust optimization dispatching model for BDCD-VPP considering carbon-green certificates equivalent conversion mechanism. This model aims to address the uncertainty of wind power plants (WPP) and photovoltaic (PV) power generation. Strong duality theorem (SDT) and C&CG algorithm are applied to construct this model. Thirdly, the Aumann-Shapley method (A-S) was enhanced by incorporating risk factor, cost factor, and carbon reduction factor. This refinement results in the development of a synergistic scheduling revenue allocation method for electricity‑carbon-green certificates. Finally, the Lankao rural energy revolution pilot program in China is selected as the case study, the results showed: (1) The BDCD-VPP aggregates distributed energy sources such as the rural WPP and the PV to realize electricity‑carbon-electricity cycle effect. The BDCD-VPP generates a green certificate revenue of 47.17¥/MWh and exhibited carbon emissions of 0.37 t/MWh. The proposed Carbon-Green Certificates Equivalent Conversion Mechanism increases BDCD-VPP benefit ratio by 3.52%. (2) The proposed two-stage robust optimization dispatching model enhances the ability of BDCD-VPP to adapt to uncertainty. Compared to the day-ahead stage, biomass power generation (BPG) and waste power generation (WPG) increase upward peaking power by 24.05% and 9.99% in intra-day stage. Flue gas treatment system (FG-TS), gas-power plant carbon capture (GPPCC) and power to gas (P2G) increase downward peaking power by 65.15%, 70.99% and 25.94%. (3) Utilizing the proposed benefit allocation methodology, BPG and WPG need to concede 38.25¥/MWh and 65.02¥/MWh for carbon emissions associated with electricity. WPP and PV need to concede 64.25¥/MWh and 33.71¥/MWh for power generation uncertainty. GPPCC, P2G, DRA, and small hydropower station (SHS) obtain revenues of 24.48 ¥/MWh, 55.07 ¥/MWh, 70.38 ¥/MWh, and 51.30 ¥/MWh. Compared to the A-S, the proposed benefit allocation methodology exhibits 6.92% improvement in satisfaction. Overall, the proposed data-driven two-stage robust optimization dispatching model and benefit allocation strategy facilitates the optimal aggregation and utilization of rural distributed energy resources, considering the interests of various stakeholders. This is conducive to achieving a clean and low-carbon transformation of the overall energy structure. • Proposed a novel rural virtual power plant considering biomass waste conversion and carbon to power. • Designed carbon-green certificate equivalence conversion mechanism. • Proposed a data-driven scenarios generation and reduction strategy for uncertainty factors. • Construct a two-stage robust optimization dispatching model and strong duality C&CG algorithm. • Construct an improved Aumann-Shapley-based benefit allocation strategy [ABSTRACT FROM AUTHOR]

This paper presents a refined quantum Harrow Hassidim Lloyd (HHL) algorithm for microgrid control. The first novelty of the developed method is that a mapping shift function enables the original HHL algorithm to handle general linear equations with non-singular and indefinite matrix. Second, a method of Matrix Extension for Amplifying Sampling Probabilities of Intended Solution (ME-ASPI) is proposed to design the reformulated linear algebraic equations, allowing for improved sampling efficiency of the quantum tomography in the refined HHL algorithm. Then, we applied the method to solve the model predictive control (MPC) problem in nonlinear dynamical microgrids. Specifically, with the ME-ASPI method, the refined HHL algorithm can effectively obtain the intended partial optimal control inputs for MPC. The optimization of quadratic programming problem in each time step of MPC is transformed into a linear system problem, which is addressed by the proposed quantum solver through using only partial information, with the time complexity improved from O (N 2. 37286) classically to O (N 2 log N × p log p) in quantum. Numerical examples have validated the effectiveness of the refined HHL algorithm with the proposed mapping function and the ME-ASPI method. By leveraging quantum properties, the proposed method provides a hybrid quantum–classical framework for microgrid control. This generic method can also potentially tackle many other challenges in analyzing and controlling general complex engineered systems. • Mapping shift function for the implementation of Harrow-Hassidim-Lloyd algorithm. • Enhanced Sampling for control input of interest in Model Predictive Control. • A bound for an important parameter in Harrow-Hassidim-Lloyd algorithm. [ABSTRACT FROM AUTHOR]

Reducing the dependence on traditional energy sources and shifting towards the utilization of renewable energy sources (RES) of energy in the maritime sector is imperative for reducing greenhouse gas emissions. Inherently, RES sources like solar and wind are intermittent and variable, resulting in inconsistent power availability and hence leading to energy supply fluctuations and potential shortages. In this respect, an efficient control strategy to maintain system stability and address intermittency effectively is essential. This work considers a hybrid marine microgrid with various energy sources like photovoltaics (PV), wind energy conversion system (WECS), marine biodiesel generator, Archimedes wave power generation, solid oxide fuel cell, and batteries. A 2-degree of freedom (2DOF) structure is designed and implemented with the tilt-integral-derivative filter (TIDN) to address frequency variations. Furthermore, an Archimedes optimization algorithm (AOA) is used to optimize the 2DOF-TIDN controller. The stability of the proposed microgrid system is assessed under various combinations of RES availabilities, including real-time data from WECS and PV. The AOA-based 2DOF-TIDN performance is compared to the following algorithms: genetic, Jaya, bat, grasshopper optimization, particle swarm optimization, and moth flame optimization. Simulation results obtained show that the AOA-based 2DOF-TIDN control strategy achieves shorter settling times in mitigating the changes of marine microgrid systems under different dynamic conditions as compared to the other algorithms. Finally, the controller being proposed in this paper was tested for robustness with parameter deviations of +25%, −20%, and − 40% from the nominal values, and proved to be the proposed 2DOF-TIDN controller parameters demonstrate significant robustness in effectively managing the uncertainties and parametric variations. • Maritime Microgrid system is designed for investigations with RE & storage units. • PV, Wind, MBG, AWPG, SOFC, and battery storage units are considered. • 2DOF-TIDN controller is developed to mitigate the changes in the system. • The AOA method is proposed for optimal gains of the 2DOF-TIDN under uncertainties. • Sensitivity analysis, Time delay analysis, and stability analysis are performed [ABSTRACT FROM AUTHOR]

Controlling flexible hybrid microgrids (MGs) is difficult due to the system's complexity, which includes multiple energy sources, storage devices, and loads. Although adding new components to the MG system through the plug-and-play (PnP) feature enables operating of the system in different modes, it adds to the system's complexity, hence necessitates careful control system design. The most challenging aspect of designing the control system is ensuring that it can control the MG optimally in its various modes of operation. Previous methods based on logical control allow for synthesizing a controller capable of controlling the MG in its various operational modes. However, the resultant controller does not optimally operate the MG. Classical model predictive control allows optimal control of the MG only in specific operating modes. On the other hand, switched model predictive control (S-MPC) can optimally control the MG in its various modes. However, the design of S-MPC is complex, particularly for MGs with many operating modes or complex switching logic. Multiple factors contribute to the complexity, including model development, mode detection, and switching logic. This paper presents a hybrid method based on ɛ -variables and classical MPC for constructing the S-MPC for flexible hybrid MG with PnP capabilities. Our results show that the proposed controller synthesis approach provides an effective solution for optimally controlling flexible hybrid MGs with PnP capabilities as the proposed method enables: (i) an increase in the amount of energy export to the utility grid by 50.77% and (ii) a significant decrease in the amount of energy import from the grid by 46.7%. [Display omitted] • Managing complex control systems for plug-and-play flexible hybrid MGs. • Addresses complexities in mode detection, switching logic, and model development. • Use ɛ -variables and MPC to create S-MPC for plug-and-play-ready MGs. • Increase in exported energy (+50.77%) and a decrease in imported energy (−46.9%). [ABSTRACT FROM AUTHOR]

• Dynamic event-triggered robust secondary frequency controller is proposed. • It provides a good balance between dynamic performance and communication burden. • The controller design does not depend on the full-order state matrices of the plant. • Communication intervals are enlarged by the dynamic event-triggered mechanism. • Simulation on detailed models verifies the effectiveness of Dynamic event-triggered robust secondary frequency controller. The permeability of renewable energy sources rapidly increases in the microgrid for economic and environmental concerns. However, the uncertainty of renewable energy may lead to frequency fluctuation or even instability in the microgrid. This paper proposes a dynamic event-triggered robust secondary frequency control scheme for the islanded microgrid to handle the uncertainty of the renewable energy source. In addition to the enhancement of frequency stability, the communication burden is also considered in this work. The proposed dynamic event-triggered communication scheme provides a good balance between dynamic performance and communication burden. Based on the discontinuous Lyapunov function, the stability analysis helps the optimal gain selecting. Given the designed controller, the derived theorems in this paper can help to examine whether the communication system fulfills the requirements from the controllers. To validate the proposed method, case studies are carried out based on two typical microgrids. The simulation results are compared with conventional frequency controllers. It shows that the proposed controller outperforms other types of controllers in term of the frequency response performance and communication burden. [ABSTRACT FROM AUTHOR]

Highlights • A near-optimal energy cost optimization algorithm, named ECO-Trade, is proposed. • The resulting trading strategy minimizes the total cost of all households. • Results show that unrestrained peer-to-peer trading may increase the energy cost. • Any potentially unfair cost distribution is addressed by ensuring Pareto optimality. Abstract This paper evaluates the impact of Peer-to-Peer (P2P) energy trading among the smart homes in a microgrid. Recent trends show that the households are gradually adopting renewables (e.g., photovoltaics) and energy storage (e.g., electric vehicles) in their premises. This research addresses the energy cost optimization problem in the smart homes which are connected together for energy sharing. The contributions of this paper is threefold. First, we propose a near-optimal algorithm, named Energy Cost Optimization via Trade (ECO-Trade), which coordinates P2P energy trading among the smart homes with a Demand Side Management (DSM) system. Our results show that, for real datasets, 99% of the solutions generated by the ECO-Trade algorithm are optimal solutions. Second, P2P energy trading in the microgrid potentially results in an unfair cost distribution among the participating households. We address this unfair cost distribution problem by enforcing Pareto optimality, ensuring that no households will be worse off to improve the cost of others. Finally, we evaluate the impact of renewables and storage penetration rate in the microgrid. Our results show that cost savings do not always increase linearly with an increase in the renewables and storage penetration rate. Rather they decrease gradually after a saturation point. [ABSTRACT FROM AUTHOR]

Highlights • Environmental control is enormously costly for the U.S. military, especially in remote locations. • Environmental control units (ECUs) are currently powered by generators and are unmanaged. • We propose an optimization-based approach to coordinate multiple ECUs and generators. • By operating generators at higher loads, we achieve significant fuel savings (28% for the configuration considered). Abstract This paper examines efficiencies achieved by optimal scheduling of power generation equipment and electrical loads in a hybrid smart micro-grid under conditions where load activity may be delayed or advanced with negligible impact to system performance. We model military expeditionary energy systems to analyze the performance of existing unmanaged components and estimate potential savings obtained by coordinated management of battlefield heating and cooling systems. We employ rolling horizon optimization models to examine performance under varying degrees of uncertainty about future load demand and renewable production. We propose a novel mechanism to reduce power production costs through optimal prescriptive scheduling of loads, reducing peak demand and generator peak-to-average power ratios and facilitating a persistent shift to higher fuel efficiencies. In contrast to existing methods that employ either supply-side or demand-side management, we propose intelligently coordinating both sides to achieve greater efficiency. Using sensitivity analysis, this paper quantitatively demonstrates how grid composition, temperature band tolerance, and energy storage capabilities contribute to fuel efficiency under this approach. [ABSTRACT FROM AUTHOR]

Highlights • Realize IT load demand response controllability with data center resource scheduling. • Design server cluster workload scheduling method with time shift mechanism. • Design data center UPS energy storage dynamic response mechanism. • Holistic dynamic response method smoothens data center tie-line power fluctuation. Abstract With the rapid development of cloud computing, artificial intelligence technologies and big data applications, data centers have become widely deployed. High density IT equipment in data centers consumes a lot of electrical power, and makes data center a hungry monster of energy consumption. To solve this problem, renewable energy is increasingly integrated into data center power provisioning systems. Compared to the traditional power supply methods, renewable energy has its unique characteristics, such as intermittency and randomness. When renewable energy supplies power to the data center industrial park, this kind of power supply not only has negative effects on the normal operation of precision equipment, such as CPU/GPU chips and hard disk, in data center, but it would also impact the stability of the utility power grids operation. To solve this problem, this paper presents a novel tie-line power fluctuation smoothing algorithm with consideration of data center's holistic demand response. The contributions of this paper are: (1) overcoming the limitations of treating IT load as uncontrollable workload in the traditional demand response research, we design a data center resource scheduling model to realize IT load demand response controllability; (2) two novel mechanisms are proposed: (i) the server cluster workload scheduling method with time shift mechanism, and (ii) the data center UPS (Uninterruptible Power Supply) energy storage dynamic response mechanism. (3) Combining these two mechanisms as holistic demand response of data center, we present a tie-line power fluctuation smoothing algorithm to improve power supply reliability, which is beneficial to both the high density and precision IT equipment in the data center and the utility power grid. In the experiments, the results show that the new algorithm can effectively regulate the tie-line power fluctuations under different server cluster utilization ranges and scenarios of large-scale penetration of distributed renewable energy scenarios. The new algorithm is hence able to contribute beneficially to the reliability and stability of intelligent industrial park micro-grid and utility power grids. [ABSTRACT FROM AUTHOR]

Highlights • Stochastic day-ahead microgrid management algorithm considering uncertainties in PV, load and temperature. • Quantification of the effectiveness of demand side management by implementing thermal models and thermal comfort constraints. • Comparison of comfort constraint violations and annual operational costs of a stochastically managed microgrid. • Comparison of deterministic and stochastic day-ahead management strategies. Abstract Microgrid operations are challenging due to variability in loads and renewable energy generation. Advanced tools capable of taking uncertainty into account are essential to maximize microgrid benefits when operating microgrid owned DERs. This paper proposes a novel optimization model for day-ahead economic dispatch of flexible resources within a microgrid environment, considering uncertainty of PV and loads.This model is conceived to support the microgrid supervisory control layer, providing a security-constrained day-ahead strategy to operate three types of microgrid flexible resources: PV, electric storage and controllable loads. The work presented in this paper introduces a novelty in microgrid operations by presenting a stochastic version of the day ahead scheduling of microgrid DERs to deal with uncertainties associated with PV, load and temperature while considering microgrid network limits and end-user comfort as optimization constraints. An annual analysis quantifies the benefits of to the microgrid-owner of a stochastic formulation over a deterministic one both in terms of ensuring end-user comfort and decreasing operation costs. [ABSTRACT FROM AUTHOR]

Highlights • Conduct comprehensive literature review of U.S. microgrid development in the recent decade. • Discuss U.S. progress on microgrid policies, demonstration projects, control methods, and software tools. • Summarize key successful experience of U.S. microgrid development. Abstract Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool’s core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries. [ABSTRACT FROM AUTHOR]

Highlights • Weather information is a fundamental input for microgrid scheduling. • A wide variety of weather information has been incorporated into prior microgrid studies. • The diversity of procedures to implement weather data in microgrid energy management systems are analyzed. • Discussion and recommendations for future works are conducted. Abstract Meteorological conditions determine the renewable energy generation and, to a lesser extent, the load of microgrids. Weather forecasts are thus necessary to establish optimal plans according to the operational objectives and priorities of each microgid. Weather forecast errors are also responsible for deviations from these plans, thereby being an important source of uncertainty in the scheduling process. Despite this, weather information plays a secondary role in most of microgrid studies. This paper provides a general overview of the use of meteorological data in microgrids, focusing on the implementation of weather forecasts in microgrid energy management systems. Data sources, methodologies, uncertainty approaches and results from a selection of papers with complete information about the forecast context are analysed in detail. Additionally, similarities and differences regarding other energy forecast applications apart from microgrids are discussed. Finally, on the basis of the above, a list of recommendations for future implementations of weather forecasts in microgrid energy management systems is presented. [ABSTRACT FROM AUTHOR]

This paper proposes a convex non-linear cost saving model for optimal economic dispatch in a microgrid. The model incorporates energy storage degradation cost and intermittent renewable generation. Cell degradation cost being a non-linear model, its incorporation in an objective function alters the convexity of the optimization problem and stochastic algorithms are required for its solution. This paper builds on the scope for usage of macroscopically semi-empirical models for degradation cost in economic dispatch problems and proves that these cost models derived from the existing semi-empirical capacity fade equations for LiFePO 4 cells are convex under some operating conditions. The proposed non-linear model was tested on two data sets of varying size which portray different trends of seasonality. The results show that the model reflects the trends of seasonality existing in the data sets and it minimizes the total fuel cost globally when compared to conventional systems of economic dispatch. The results thus indicate that the model achieves a more accurate estimate of fuel cost in the system and can be effectively utilized for cost analysis in power system applications. [ABSTRACT FROM AUTHOR]

In the current paper, an improved control strategy designed for synchronizing and transferring autonomous microgrids (MGs) to the grid is presented. The proposed approach is based on an MG leading inverter (MGLI) supplied by a supercapacitor energy storage system, which takes over the MG only during a transitory load and distributes it to the available MG supporting inverters (MGSIs). This paper presents the control systems for both the proposed MGLI and the associated MGSIs, the focus being on the specific issues related to MG synchronization and grid transfer. By means of the proposed solution, the operation of the MG and its transition between autonomous and grid-connected modes require merely the adaptation of the MGLI control system, while the MGSIs operation remain unchanged during all MG states. The comprehensive experimental results, which were carried out on a laboratory-scale MG, have shown that the system is kept stable and with minimum disturbance to the local MG voltage and frequency during the analysed events, namely the scheduled MG transfer to the grid, power-flow control during grid-connection operation, and disconnection from the grid, respectively. [ABSTRACT FROM AUTHOR]

Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper proposes an advanced ML algorithm, called Recurrent Trend Predictive Neural Network based Forecast Embedded Scheduling (rTPNN-FES), to provide efficient residential demand control. rTPNN-FES is a novel neural network architecture that simultaneously forecasts renewable energy generation and schedules household appliances. By its embedded structure, rTPNN-FES eliminates the utilization of separate algorithms for forecasting and scheduling and generates a schedule that is robust against forecasting errors. This paper also evaluates the performance of the proposed algorithm for an IoT-enabled smart home. The evaluation results reveal that rTPNN-FES provides near-optimal scheduling 37.5 times faster than the optimization while outperforming state-of-the-art forecasting techniques. • A novel rTPNN-based Forecast Embedded Scheduling (rTPNN-FES) is presented. • rTPNN-FES simultaneously forecasts and schedules household loads. • Significant improvement is achieved in forecasting compared to the state-of-the-art. • Near optimal scheduling is achieved with much lower computation time. [ABSTRACT FROM AUTHOR]

Meshed microgrids have been used in a plethora of specialised applications that demand increased system resilience, from data centres to the international space station. When resilience maximisation is the desideratum, topology design is the fundamental factor determining the overall system performance. Very few published papers on this problem are found in literature and they take into consideration partial available topologies, or they fail to quantify resilience or assess microgrid stability. To address these gaps on microgrid topology planning (MTP), this paper proposes a holistic optimal topology design framework, comprised of six stages: (a) graph generator to extract all possible connected, non-isomorphic networks for a given number of nodes, (b) optimal asset positioning upon each generated graph using mixed-integer linear programming, (c) small-signal stability assessment and (d) post-outage flow matrix calculation for each microgrid to quantify repercussions from fault/damage of all interconnecting lines, (e) quantification of resilience metrics originally applied to distribution systems to assess vulnerability and criticality of each line, along with classic graph-theoretical indicators and finally, (f) aggregation of said metrics to rank the produced microgrid topologies. The proposed methodology is evaluated through detailed discrete simulations to assess its efficacy and the dynamic stability of the optimal microgrid topology. [Display omitted] • A novel 5-step resilient topology design for isolated DC microgrids is presented. • Assets are optimally sized and positioned on buses with the focus on resilience. • Small-signal stability check is included to identify inherent topology instability. • Microgrid resilience is quantified by metrics deriving from distribution grids. • The superior performance of the optimal topology is proven under worst-case outages. [ABSTRACT FROM AUTHOR]

This paper deals with development of a versatile and compact control strategy for voltage source converters in grid-connected and islanded microgrids using synchronous current converters technology. The key feature is its new integrated high-order controller/synchronizer with applicability to both operational modes without strategy rearrangement. Using high-order controllers, on the other hand, results in rather complex analysis and design process, therefore this paper aims at providing a general and simple theme for the design and parameter selection. The controller also provides adaptive and automated current-based grid synchronization. Moreover, the controller realizes a power-source current-controlled microgrid with minimum control loops, as compared to widely adopted voltage controlled microgrids in the literature, with advantages such as fault-ride-through and inherent droop-less power sharing capabilities. Adaptive current-based synchronization and smooth switching to islanding mode provides high flexibility, reliability and only-plug operation capability. Extensive simulation and experimental results are presented to demonstrate performance of the proposed control and management strategy. [ABSTRACT FROM AUTHOR]

Along with the technical feasibility of microgrids, the importance of supervisory communications, computing, and control (CCC) systems that are necessary to operate a microgrid in a stable and economically feasible manner have been firmly established in the literature. While several investigators have proposed and demonstrated algorithms and applications of CCC, the functional entities and responsibility centers for realizing CCC within microgrids in the context of multiple independent customers and distribution system entities have not been situated and/or studied. In this context, the term emergent microgrid may be used to define an electrical network that consists of local generation, loads, and storage. Such a grid has the potential to be clustered as a microgrid, but does not have all the CCC features to become a full-fledged microgrid. In this paper, a simple electric utility platform (SEUP) that is an end-to-end solution with all the hardware/software interfaces and components necessary for augmenting emergent microgrids with CCC into full-fledged microgrids is presented. SEUP abstracts the details of sensing and CCC for the microgrid developers, installers, operators and end-users to provide a seamless system. SEUP is distinctive from other CCC platforms in that it is entirely based on publicly available infrastructure resources (wireless cellular networks, internet and cloud computing) without any intellectual property restrictions. The paper introduces the functional roles, main architecture, components and security features of SEUP along with the results from a sample application case of a laboratory-scale power system. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewables, microgrid integration has been considered as one of the most promising approaches to enhance the utilizations of various types of energy resources in smart distribution systems. In the near future, with the higher levels of intermittent renewables are envisaged, the distribution network operators will face significant challenges to the control and operation of distribution networks dispersedly. Consequently, the development of effective and motivating ancillary service schemes in a decentralized way for executing real-time control and reducing calculation and communication burden is still in its infancy and needs to be researched. To address this issue, this paper explores an optimal voltage regulation method with the participants of multi-microgrids based on multiple agent systems. Without an arbitration agent, peer agents in the multiple agent system calculate voltage sensitivities by local and neighbourhood measurements only. In this paper, a bi-level game model is proposed for voltage control process. In the upper-level, the distribution network operator searches the reasonable incentive mechanism based on the Stackelberg game. In the lower-level, microgrids make voltage control strategies autonomously based on a static game among microgrids. In the proposed method, microgrids participate in voltage control in distribution networks as ancillary service providers while maximizing their own profits. Meanwhile, the distribution network operator reduces the infrastructure reinforcement and avoids unnecessary renewable energy curtailment. Finally, the feasibility and effectiveness of the proposed method has been demonstrated on a modified IEEE 33-bus system. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy with stochastic characteristics and continuous refinement of carbon emission policies put forward higher requirements for the construction of new power systems. This paper proposes a risk-averse stochastic capacity planning and peer-to-peer (P2P) trading collaborative optimization method for multi-energy microgrids (MEMGs) considering carbon emission limitations. First, a cooperative operation model for MEMGs considering the capacity planning of distributed generation units, uncertainty from renewable energy generations, carbon emission limitations and P2P electricity trading among MEMGs is formulated. Second, a risk-averse stochastic programming method is applied to avoid the potential risk losses caused by randomness and intermittence of renewable energy. Third, an asymmetric Nash bargaining approach is adopted to ensure the fair allocation of benefits and maintain the willingness of individual MEMGs to participate in cooperation. Then, to protect the privacy of individual MEMGs belonging to different stakeholders, the alternating direction method of multipliers is used to solve the two subproblems in a distributed manner. Meanwhile, to further alleviate the computation burdens, a diagonal quadratic approximation method is applied to linearize the quadratic penalty term in the augmented Lagrangian function and realize the parallel solution of all optimization subproblems. Moreover, the influence of different carbon emission targets on the optimal resource combination strategy of the system is investigated by introducing carbon emission factors. Simulations on different models, strategies, and distributed algorithms are conducted to verify the effectiveness and superiority of the proposed method. • Developed a risk-averse stochastic collaborative optimization model for MEMGs. • Proposed an ANB method to ensure the fairness of benefits allocation. • Investigated the relationship between CRR and optimization strategy for MEMGs. • Designed a parallel distributed algorithm to protect privacy and save computation time. [ABSTRACT FROM AUTHOR]

The reversible solid oxide cell (rSOC) is a promising technology for advancing energy decarbonization by enabling bidirectional conversion between electricity and hydrogen in a single device. However, previous studies have not fully explored the operational flexibility of rSOCs due to inadequate consideration of heat recovery potentials and dynamics of operating mode transitions. To address this research gap, this paper presents a model-based optimal operation method for managing multi-energy transactions in rSOC-based microgrids, aiming to minimize operation costs. The method incorporates detailed operational models of the rSOC, including a lumped thermal model to account for heat recovery capability and modeling of various operating modes and their transitions. Additionally, a linearization process is introduced to address nonlinear and implicit operational constraints, resulting in a computationally efficient mixed-integer linear programming (MILP) formulation for the operation model. Comparative case studies are conducted using modified energy portfolios of a Danish energy island. The results demonstrate that the proposed method effectively captures operating mode transitions within the rSOC and enhances its profitability via waste heat recovery. Notably, the rSOC model contributes to enhanced operational flexibility through heat recovery behaviors and a wider temperature range, resulting in substantial economic savings for the microgrid. • Dynamic transition modeling of operating modes in rSOCs. • Waste heat recovery analysis from rSOCs for district heating systems. • Computationally efficient economic operation model for rSOC-integrated microgrids. • Unleashing rSOC flexibility through heat recovery and extended temperature range. • Enhanced cost-effectiveness of microgrids through heat recovery benefits of rSOCs. [ABSTRACT FROM AUTHOR]

In this paper, two variants of the magnetless dual rotor wound field excited flux switching generator (DRWFEFSG) are comparatively analyzed for single rotor wind turbine (termed as machine I (MI)) and counter rotating wind turbine applications (termed as machine II (MII)) for household direct current (DC) microgrid system. First, design concept and integration to wind turbine system are discussed. Secondly, comprehensively performance analysis i.e., static analysis, dynamic load/speed analysis and electromagnetic performance analysis are executed. Finite element analysis (FEA) based static analysis evident that MI offers 7.6% higher torque in inner rotor whereas MII exhibits 18% higher torque in outer rotor. Dynamic respond unveils that in comparison to MII, MI suffers from the issue of voltage regulation. Additionally, electromagnetic analysis reveals that both MI and MII exhibits excellent response under variable field Magneto-motive force (MMF). Comparative analysis unveils that MII requires lower field MMF to achieve same response to counterpart MI. Quantitative analysis divulge efficiency of 88.49% and power factor of 0.93 in MI whereas MII at rated operating point demonstrates 85.9% efficiency and 0.96 power factor. Finally, effectiveness of the study and validity of DRWFEFSG for wind turbine applications are tested with hardware prototypes of both MI and MII that fairly matched with FEA results. • Generator design and comparative analysis for household DC microgrid system. • Generator design for co-rotating and counter rotating wind turbine application. • Generator for rooftop wind turbine integrated with wind turbine. • Improve power wind turbines generators. • Analysis and comparative study of generator design for wind power generation. [ABSTRACT FROM AUTHOR]

Future energy system will feature in a high-share of renewable energies (REs), which poses huge challenges to obtain full utilization of renewable power generation. To solve the problem, this paper presents a joint-operation two-stage mixed integer linear programming model to coordinate the power system and train transportation system by optimizing the logistics of mobile batteries and real-time charge/discharge in cities. The full/empty batteries are transported through the train transportation system between the load side and the renewable energy station, which improves renewable energy penetration, economics, and mobilities. The first-stage model deals with the preliminary logistic optimization of batteries including routes, time and capacities of transportation, and the second-stage model optimizes real-time schedule of charge/discharge of batteries. The aim of the proposed model is to minimize the transportation cost, maximize the utilization rate of renewable energy in an energy system and smooth the daily load curve. The proposed model considers technical constraints such as railway transportation capacity, load demand satisfaction and renewable energy consumption in the power system. The optimal logistics plan and real-time charging and discharging plan can be obtained for both full and empty battery transportation. The validity of the method is verified with real data from Northeast China and Northern China, including railway routes, renewable energy output, and load profiles. The results show that the renewable energy penetration rate increases from 84.4% to 95.3% in Northeast China and from 42.7% to 78.2% in Northern China under the 2050 scenario with the proposed method. The peak-to-valley difference in the daily load curve decreases by 84% and 100% in Northeast China and Northern China, respectively. Meanwhile, the case study shows that the costs of battery transportation decrease from 0.398 CNY/kWh and 0.377 CNY/kWh to 0.252 CNY/kWh and 0.254 CNY/kWh when energy density of battery increases from 0.170 kWh/kg to 0.250 kWh/kg. This indicates that mobile energy storage has great economics. Moreover, renewable energy penetration rate is increased and peak-to-valley difference of daily load curve is decreased with no additional transmission lines. The model moves renewable energy from renewable energy power plants to end users. [Display omitted] • MESS aims to solve uneven spatio-temporal distribution of renewable energy and load. • A framework for joint operation of power system and transportation system is proposed. • The model is validated based on real data from Northeast and North China. [ABSTRACT FROM AUTHOR]

Energy storage system is an important component of the microgrid for peak shaving, and vanadium redox flow battery is suitable for small-scale microgrid owing to its high flexibility, fast response and long service time. Therefore, a microgrid based on vanadium redox flow battery is studied for rural applications in this paper, in which biomass gasification and solid oxide fuel cell are integrated as power generation units to provide stable electricity. A predictive control method is presented to improve the efficiency of flow battery and the economic feasibility of this system is evaluated. The mathematical model is validated with the experimental data of published literature. The results indicate that controlling the electrolyte flow rate according to the current and the state of battery can reduce the energy loss of flow battery. The optimal electrolyte flow rate is determined by predictive control method, which can significantly improve the performance of flow battery compared with previous method. The overall efficiency of battery for peak shaving is achieved by 84% and the pay back period of this microgrid system is 7.33 year. [Display omitted] • A stable and high-efficiency power generation system is proposed for microgrid. • Predictive control method is presented to improve the efficiency of flow battery. • The efficiency of flow battery in a complete charge-discharge cycle is 84%. • The payback time of this microgrid is 7.33 year based on time sharing tariffs. [ABSTRACT FROM AUTHOR]

Multi-energy microgrids (mMGs) are gaining rapid popularity due to the incorporation of multiple types of energy sources. Given the importance of mMGs in future energy networks, resilient, accurate economic, and environmental assessments of mMGs, as well as their interconnection, have become immense challenges. To deal with this problem, this paper presents a resilient optimization method for optimal sizing and operation of renewable-based mMGs to meet electricity and heating demand. The primary goals of this research are to reduce the system's overall energy cost, ensure continuous power supply during power outages, and reduce environmental emission rates in mMGs enriched by the combined heat and power (CHP) unit, photovoltaic (PV), boiler unit, battery, thermal energy storage (TES), and geothermal heat pump (GHP) technologies. Game theory concepts, such as nucleolus and Shapley value , are leveraged to allocate costs between interconnected mMGs running under a coalitional paradigm, resulting in a lower optimized cost. Further, a techno-economic analysis is performed to investigate the performance of the proposed system over the business as usual (BaU) case. The results affirm the lucrativeness of the proposed model and the substantial reduction in life cycle cost, utility cost, and emission while remaining outage resilient. • All the proposed microgrid's power supply units are extensively modeled. • The optimal sizing and dispatch strategy of each source of multi-energy microgrid is performed with the purpose of minimizing cost. • The environmental impact of purchasing electricity from the grid and using on-site CHP fuel is greatly modeled. • Participating microgrids employ a cost-based interconnection strategy which further reduces their overall operational cost. • Developed and tested a microgrid resilience index for prolonged grid outages under various loads. [ABSTRACT FROM AUTHOR]

This work investigates the design optimization of aggregated energy systems (multi-energy systems, microgrids, energy districts, etc.) with (N-1) -reliability requirements. The problem is formulated as a two-stage stochastic Mixed Integer Linear Program which optimizes design (first stage variables) and operation variables (second stage variables) simultaneously considering a set of typical and extreme days. The analysis proposes and compares different approaches to include the (N-1) reliability requirement in the optimization problem. Moreover, the paper proposes two effective decomposition algorithms to solve the large-scale Mixed Integer Linear Program suitable for design problems with and without (N-1) reliability requirements. Depending on the instance, such decomposition algorithms allow reducing the computational time by one or more orders of magnitude (from days to a few hours, in the worst cases tested in this work). The proposed methodology is tested to design the aggregated energy system for a real case study considering both a grid-connected and off-grid installation. Results indicate that the actual reliability of the design solutions depends by the profiles of energy demand and renewable production considered in the failure scenarios included in the design problem. Including N-1 reliability requirements causes an increase in the total annual cost in the range 15–20%, due to the increase in capital costs. • N-1 reliability is included in MILP for the optimal design of Aggregated Energy Systems. • Different approaches to include reliability constraints are proposed and compared. • Two decomposition algorithms allow reducing the computational time considerably. • Including N-1 reliability results in a 15–20% Total Annual Cost increase. [ABSTRACT FROM AUTHOR]

This paper addresses the escalating need for low-carbon solutions and discusses the integrated control system for the electric-hydrogen DC microgrids. A multi-time scale control mechanism for the DC microgrid is proposed, which aims to enhance its adaptability and scalability. The proposed multi-time control scheme comprises two control layers. The system-level control layer utilizes optimization to address economic issues and carbon trading, aiming to minimize operational costs and carbon emissions in the microgrid. The local-level control layer incorporates an advanced nonlinear control method based on the exponential reaching law-powered terminal sliding mode control (ERL-TSMC) for accurate tracking capabilities at the local-level and to handle dynamic control challenges in real-time. Furthermore, an event-triggered control mechanism is introduced to reduce considerable control efforts while guaranteeing system robustness under various disturbances. Both simulations and hardware experiments demonstrate that the proposed control method guarantees efficient low-carbon operation, achieving a significantly lower average carbon intensity of 0.19 kg CO 2 /kWh. Additionally, it exhibits robust performance at the local-level, with a steady-state error of only 0.03%. • A novel multi-time scale control strategy for electric-hydrogen DC microgrid is proposed. • System-level energy management that optimizes costs and carbon emissions is discussed. • Local-level event-triggered exponential reaching law based terminal sliding mode control is proposed. • The proposed control structure promises rigid DC bus voltage and low-carbon operation. • Real-time processor-in-the-loop (PIL) tests are performed to verify the simulation results. [ABSTRACT FROM AUTHOR]

Renewable energy resources with uncertain output are being connected to the power grid, which puts great pressure on the grid frequency regulation (FR). Virtual power plants (VPPs), which aggregate numerous flexible resources, have significant potential for participation in FR services. However, the FR capability of VPPs is often unreliable due to uncertain resource potential and unreliable communications, which limits their FR applications. Therefore, a cyber-physical system (CPS) planning method considering multidimensional uncertainties for hierarchical VPPs to provide reliable FR capacity is proposed in this paper. Firstly, a fast and reliable hierarchical architecture for VPPs is presented. Then, the reliable capacity model of VPPs for service planning is established within this architecture. The minimum probability scenario method based on the Pauta criterion and the failure mode and effects analysis (FMEA) method are used to quantify the impact of information-physical uncertainty in the model. Based on this, a CPS planning model for VPPs with the objective of minimizing investment and the constraints of normal capacity, reliable capacity and delay under various scenarios is constructed. By linearizing the model, the optimal solution for coordinating flexible resource pool composition, edge terminal allocation and communication can be solved. Numerical simulations on the improved IEEE 33-node and 123-node systems demonstrate the effectiveness of the proposed method. • Establish a CPS planning model to more economically increase the reliable capacity of VPPs for frequency regulation. • Enhance real-time performance and reliability of VPP FR by designing hierarchical architecture and communication. • Assess the impacts of CPS information-physical uncertainty on the FR capability of VPPs based on an integrated approach. • Validate the proposed approach in the modified IEEE 33-node and 123-node systems by comparing several schemes. [ABSTRACT FROM AUTHOR]

The rapid development of microgrids (MGs) with various prosumers promotes the accommodation of renewable distributed generation (RDG) and provides platforms for local energy sharing among prosumers. However, the operational uncertainties pose enormous challenges to the day-ahead bidding of MGs in the wholesale electricity market and there is an urgent need for a local market to facilitate the local energy sharing. Thus, this paper proposes a risk-averse day-ahead bidding strategy for MGs with full consideration of the multiple uncertainties originating from the wholesale electricity market, RDG and loads. Based on the transactive energy (TE) sharing concept, the local market is formulated as a Stackelberg game (SG) to effectively capture the strategic interaction among the MG and prosumers, where a distributed iterative algorithm with a bisection approach that only requires exchanging TE-related information is adopted to achieve the SG equilibrium without compromising privacy concerns. To handle the uncertainties of RDG and loads, the power balances are formulated as chance constraints and a data-driven quantile forecasting method is developed for achieving the computational tractability of chance constraints without any prior knowledge or probability distribution assumptions. Furthermore, a risk criterion of the conditional value-at-risk is incorporated in the day-ahead bidding model of MGs for risk aversion towards uncertainties of the wholesale electricity market. The effectiveness of the proposed solution is extensively demonstrated through numerical simulation. • A risk-averse day-ahead bidding strategy of TE sharing MGs is presented. • A SG-based local TE market with privacy protection is developed. • The wholesale market uncertainties are addressed in a risk-averse manner via CVaR. • The power balance chance constraints are built to handle RDG and load uncertainties. • A data-driven quantile forecasting method is proposed to solve chance constraints. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are widely recognized as promising solutions to reduce carbon emissions from the transportation sector. The rapid proliferation of EVs intensifies the interdependency between the power distribution system (PDS) and transportation system (TS). This paper proposes a distributed low-carbon economic dispatch scheme for the integrated power and transportation system (IPTS). Carbon emissions from the TS are quantified by a carbon dioxide calculation model, and the emissions from the PDS are determined by the electricity generation of fossil-fueled generators and the carbon reduction of carbon capture and storage (CCS) devices. An integrated model reformulation method is employed to transform the original non-convex model into a tractable one. Then a distributed solution method based on the spectral penalty parameter based adaptive alternating direction method of multipliers (SPPA-ADMM) is developed to protect the privacy of the PDS and TS. The reformulated model is solved iteratively, and the penalty parameter of the SPPA-ADMM is adjusted adaptively to improve the convergence performance of the method. Numerical results on three test systems demonstrate the effectiveness of the proposed scheme in achieving low-carbon economic dispatch of IPTS. Moreover, case studies validate the superiority of the SPPA-ADMM based distributed solution method in computation performance. • A low-carbon economic dispatch scheme for integrated power and transportation system is proposed. • An integrated model reformulation method is designed to transform the non-convex model into a tractable one. • The SPPA-ADMM based distributed solution method is used to protect the privacy and improve computation efficiency. • Effects of the proliferation of EVs on costs and carbon emissions are discussed in sensitivity analysis. [ABSTRACT FROM AUTHOR]

Sustainable cyber–physical power systems (CPPSs) significantly reduce carbon emissions due to climate change. However, when the data exchange in CPPSs suffers from hybrid attacks, the distributed state estimation and optimal power flow (OPF) analysis will inevitably be compromised, leading to inadequate or faulty scheduling of clean energy and thermal power generations and further affecting the total carbon emissions and economic cost. To address these problems, this paper proposes a novel consensus-based distributed security state estimation (DSSE) method for CPPSs, which is used to analyze the impact of hybrid attacks on carbon emissions and economic cost. Firstly, the incomplete and non-authentic data features caused by hybrid attacks are described, and their influence on distributed state estimation model is analyzed. A new residual-based attack detection method is then constructed in each subregion, where secure and non-secure sets are employed to describe whether the subregion is attacked and the compensation mechanism is designed for the non-secure set. Secondly, considering data compensation, distributed state estimation model is reconstructed, and a distributed security state estimation method under hybrid attacks is proposed while its convergence condition is derived. Thirdly, the impacts of hybrid attacks on carbon emissions and economic costs are analyzed based on the proposed DSSE method. Finally, experimental results confirm the validity of the theoretic analysis. [Display omitted] • The incomplete and non-authentic features of the data caused by hybrid attacks are investigated. • A novel residual-based attack detection method is proposed to determine secure and non-secure sets. • A distributed security state estimation method with a compensation mechanism is proposed. • Carbon emissions and economic costs can be reduced by mitigating the attack impact on state estimation. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy sources and the consequent integration of such distributed energy generation systems into the grid have significantly increased the interaction between power sources, which can result in low frequency oscillations. Different control techniques are proposed in literature to suppress low frequency oscillations, but the first step to attenuate such oscillations is to characterize them in terms of their dominant modes. This paper combines different results from literature to propose a unified two-step methodology for the mode characterization of low frequency oscillations based on the signals acquired by wide area measurement systems. Since the measured signals typically contain noise, the first stage of the proposed method uses the basis pursuit denoising method combined with a Tunable Q-factor wavelet transform to increase the signal-to-noise ratio and improve the identification results. In a second stage, an improved version of the matrix pencil algorithm, as proposed in this paper, is used to identify the parameters of low frequency oscillation dominant modes. Both simulation and experimental results show that the proposed method has better characterization accuracy than traditional methods, especially when Gaussian noise is considered in the measurements. In addition, the processing time has proven to be reasonable for online identification and characterization of low frequency oscillations. [ABSTRACT FROM AUTHOR]

• Cloud-based technologies adoption in real-time microgrid management. • Boosting the microgrid data forecasting by machine-learning techniques. • Impact of the internet of things usage in data acquisition for a real-life energy management system. Under a hierarchical structure perspective, the integration of tools like the internet of things, cloud computing, and machine learning into a microgrid real-time energy management system involves superior capabilities like an autonomous and scalable design, massive storage capabilities, real-time information analysis and processing, and security issues control, to mention a few. This paper evaluates most of them using a cloud-based real-time energy management system integrated into a real-life hardware-in-the-loop testbed. The proposed cloud-based system is tested by solving an economic power dispatch problem including the equalization of the multiple battery-based energy storage systems interacting within a multi-microgrid environment. The test assessment combined reviewing and running microgrid models, incorporating these models into a real-life power-hardware-in-the-loop unit, linking the testbed to a cloud server, and merging the energy management system with on-demand computing tools, primarily machine learning and the internet of things. As established by the experimental evidence, this paper cites the benefits of combining machine learning techniques and internet of things tools with a scalable and autonomous real-life cloud-based energy management system architecture to improve the framework's functionality, enhance the energy forecasting for generation and usage, and cut down the price paid to the service provider. [ABSTRACT FROM AUTHOR]