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Energy sharing has the potential to decrease operation costs by exploiting the complementarity of different entities. The further increase of microgrids’ generation capacity in the distribution network could trigger the emergence of MW-level microgrids aggregator (MGA) to participate in the regional energy sharing market (ESM) and sell excess energy. However, the uncertainty of renewable energy generation will cause the failure of energy sharing and an economic loss for the energy sharing market. Therefore, a novel two-sided market with the clearing price and risk management is proposed in this paper. In order to optimize the energy sharing strategy of MGAs, a network-constrained risk-averse two-stage stochastic game model based on DC optimal power flow (OPF) is established. The objective of the MGA is to maximize the revenue in the ESM while the subtransmission network operator (SNO) aims to achieve regional energy self-sufficiency. The sample average approximation (SAA) method is employed to approximate the stochastic Cournot-Nash equilibrium. A distributed market clearing algorithm based on the alternating direction method of multipliers (ADMM) and best response seeking is developed to solve the optimal bidding problem of MGAs and the optimal dispatch problem of the SNO. Furthermore, the existence of the SAA Nash equilibrium is also investigated. Numerical simulations prove that the proposed game model can effectively control the overbidding risk and promote regional energy self-sufficiency. [ABSTRACT FROM AUTHOR]

The rapid growth of microgrids with various distributed energy resources (DERs) brings new opportunities for local energy sharing in the microgrids. However, the uncertainties of renewable distributed generation and loads pose a great technical challenge for a microgrid operator (MGO). Thus, this paper proposes a transactive energy sharing (TES) approach for the MGO and DER aggregators to minimize the total social cost, considering network operating constraints. Accordingly, a two-settlement transactive energy (TE) market with an incentive energy pricing scheme is developed to encourage the participation in the energy sharing. Besides, the real-time energy transactions of the aggregators are considered in a day-ahead optimization stage to address their negative impacts on microgrid operation. To solve the proposed TES problem, an alternating direction method of multipliers (ADMM) is applied. The uncertainties in each ADMM local problem are further addressed by an adaptive robust optimization (ARO) method solved by a column-and-constraint generation (C&CG) algorithm. The updated dual and coupling variables at each ADMM iteration could interact with the C&CG algorithm, impairing ADMM convergence. To solve this issue, an alternating uncertainty-update procedure is developed. The simulation results verify the high efficiency and solution robustness of the proposed TES method. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESSs) have been widely deployed in microgrids to deal with uncertain output power of renewable distributed generation (DG) and improve renewable energy utilization efficiency. However, due to the short-term dispatch mode and BESS capacity limitation, current BESS dispatch decisions may not be efficient from a whole-day perspective, leading to an inadequate/excessive state of charge (SoC) for subsequent dispatch. To address this issue, an adaptive BESS dispatch method with SoC interval management is proposed for unbalanced three-phase microgrids, aiming to minimize the operating cost. In a day-ahead stage, an SoC interval is optimized considering the uncertainties of renewable DG, loads and market prices. During the day, the BESS is dispatched inside the SoC interval with a rolling horizon procedure to track uncertainty realization. Accordingly, a hybrid interval-robust optimization method is developed to solve the proposed scheduling problem under the uncertainties and procure the optimal SoC interval. Particularly, a decomposing and alternating solution algorithm is developed to identify the pessimistic case based on robust optimization while the SoC interval is determined on the basis of interval optimization. Simulation results verify high efficiency and solution robustness of the proposed hybrid interval-robust adaptive BESS scheduling method. [ABSTRACT FROM AUTHOR]

This article proposes an economic model predictive algorithm for optimal load frequency control, in which both the frequency regulation and economic load dispatch (ELD) are considered, in interconnected power systems. Two-layer hierarchical control can be achieved through one level by EMPC. An economic stage cost function, including ELD and frequency regulation, which can be written in general convex form, is optimized by the controller. The distributed way is utilized to realize the control of large-scale power systems. Each subsystem-based controller works cooperatively with neighboring subsystems to achieve system-wide control performance. Asymptotic stability of the system is guaranteed by the proper terminal cost function. The efficiency and advantages of the proposed method are manifested by the simulation. [ABSTRACT FROM AUTHOR]

Sectionalizing a distribution system with distributed energy resources (DERs) into multiple microgrids in the case of power outages has been recognized as a promising solution to restore critical loads. Nevertheless, most existing literatures focus on restoring loads by optimally dispatching distributed generators (DGs), where the controllability on the demand side is usually neglected. Moreover, unbalances in the restored microgrids should be limited in specified levels to prevent DGs from tripping to enhance the operational reliability. To address these issues, this paper proposes a novel method to restore a distribution system into multiple microgrids whilst taking the three-phase demand-side management (T-DSM) into account. The T-DSM exploits optimal phase positions of switchable loads to alleviate the unbalance issue from the demand side, which can be coordinated with network partition and DG dispatching to improve the controllability of microgrids during the restoration process. The problem is formulated as a mixed-integer second-order cone programming (MISOCP) which enables applying the off-the-shelve solvers. To improve computational performance, a multi-timescale strategy is developed to enhance its practicality. Simulation results based on the IEEE 34- and 123-bus feeders demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nested restoration decision system (NRDS), which aims to minimize unused capacity of distributed generation (DG) for service restoration due to contingency during islanding stage. The proposed algorithm is based on the concept of nested microgrid formation, where the network control schemes are framed on a layered structure. The power-sharing strategy between neighboring microgrids in the network is from outer to inner microgrid loads, where the outermost microgrid exchanges power with utility grid only. The first stage refers to pre-fault scenario, which finds a solution for networked microgrid distributed generation as the initial setting. Furthermore, the second stage calculates additional redistribution requirements based on respective microgrid's deficiency using a solution index matrix. The proposed control has been evaluated on a networked microgrid system, while performing islanding. [ABSTRACT FROM AUTHOR]

Microgrids can effectively integrate distributed generation (DG) to supply power to local loads. However, uncertainties from renewable DG and loads may lead to increased operating costs or operating constraint violations. To solve these issues, this paper proposes a two-stage coordination approach of price-based demand response (PBDR) and battery energy storage systems (BESSs) to minimize the total operating cost and enhance operational reliability. In the first stage, day-ahead PBDR is scheduled, aiming to shift loads to improve renewable energy utilization efficiency. Considering limited prediction horizon of uncertainties when dispatching BESSs, hourly state of charge (SoC) limits are also optimized over the whole-day horizon with consideration of BESS degradation cost in the day-ahead stage. Then in the second stage, the BESSs are dispatched hourly within the optimized SoC limits to track uncertainty realization and compensate the first-stage PBDR decisions. Furthermore, a two-stage interval optimization (TSIO) method is proposed to formulate the problem. Accordingly, a solution algorithm is developed to coordinately solve the two operation stages under the uncertainties. The proposed coordination approach is verified with uncertainty realization scenarios. The results indicate the high energy utilization efficiency and strong operational reliability of the proposed coordination approach. [ABSTRACT FROM AUTHOR]

Power grid resilience, reliability, and sustainability can be improved by decomposing the large-scale grids into the Networked Microgrids (NMGs). However, different MGs may have different roles and policies. Hence, in comparison with conventional networks, optimal energy management, as well as the grid reconfiguration of the NMGs is more completed and challenging. This article develops a three-layer cloud-fog computing architecture for energy management of reconfigurable NMGs considering dynamic thermal line rating (DLR) constraint. DLR can potentially affect the ampacity of feeders, especially in the islanding mode, when lines approach their maximum capacity. In order to avoid any feeder contingency in the off-grid (islanded) mode, the reconfiguration technique as well as the cloud fogs framework are employed to change the topology of the NMG network swiftly and release the line capacity. Finally, the proposed problem is formulated as a mixed-integer linear optimization problem considering DLR constraint. The proposed model is examined on a modified IEEE 69 bus test network. Results demonstrate the high performance and effectiveness of the developed model and also validate its reliability and economic assets. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the optimal scheduling of stochastic reconfigurable hybrid ac–dc microgrid (MG) in the presence of renewable energies and also considering dynamic line rating (DLR) constraint. DLR is a practical limitation that can potentially affect the ampacity of lines, particularly in the islanded mode when the lines reach their maximum capacity in lack of main generation source at the point of interconnection with the utility. In order to prevent overloading of the lines, the reconfiguration technique is developed to change the topology of the network by some prelocated switches. A linearization technique is adapted to address the nonlinearity of both nodal ac power flow and the DLR constraints. The unscented transform technique is utilized to model uncertainties including renewable energy generations, hourly load demands, and hourly market prices along with the DLR uncertainties such as solar radiation, wind speed, and ambient temperature. Finally, a sensitivity analysis is performed to see the effect of wind speed and solar radiation on the energy management of hybrid ac–dc MG. The performance of the proposed methodology is examined on a modified IEEE-33 bus test system, which demonstrates the high efficiency and importance of the proposed techniques in minimizing the hybrid ac–dc MG operation cost while all of the constraints of the network are satisfied. [ABSTRACT FROM AUTHOR]

This paper proposes a coordinated multilayer control strategy for the energy management (EM) of grid-connected ac microgrids. The strategy predicts the customer's load demand and the photovoltaics (PV) power generation for a day-ahead EM. It utilizes the PV power generations and the bidirectional energy transactions from electric vehicles and battery storage to provide a combined response for load support. The system also predicts any uncertainties in customers demand and power generations and implements day-ahead precautionary measures to tackle that uncertainty. Two different prediction strategies are used, autoregressive moving average and artificial neural networks, and their performances for a day-ahead EM are investigated. The reference power from the tertiary layer EM is sent to the local controllers for power regulation at the inverter level. Additionally, the varying power output reference obtained from a day-ahead EM is classified into the slow, medium, and fast variations. The performance of the local controller employed in the interfacing grid-connected three-phase inverter is tested during the above-mentioned power reference variations. The total harmonic distortion incorporating a moving window technique is calculated for the ac output current during each class of power variations over a day. [ABSTRACT FROM AUTHOR]

Microgrids have been recognised as one of the most promising but challenging research topics over the last decade. The optimal energy scheduling problem is regarded as the most essential aspect in the tertiary level control in microgrids. However, most existing centralised or distributed scheduling models only focus on the logical and dynamical feature of microgrids' operation or the non‐linear power flow constraint, which would overact the system performance without considering appropriate unit commitment requirements. Moreover, applying decomposition and iteration technics to complex scheduling problems would encounter convergence issues. To address this concern, this study presents a mixed integer linear reformulation to characterise the operation of different controllable devices and convex relaxation techniques to cope with non‐linear power flow constraints, leading to a mixed integer second‐order cone programming framework in a concordant yet computationally efficient pattern, capturing non‐linear, logical and dynamical properties of the optimal energy scheduling problem in microgrids. The effectiveness of the proposed framework is validated on the IEEE 33‐bus distribution network with both grid‐connected and islanded modes. [ABSTRACT FROM AUTHOR]

The prevalence of distributed energy resources encourages the concept of an electricity “Prosumer (Producer and Consumer)”. This paper proposes a distributed electricity trading system to facilitate the peer-to-peer electricity sharing among prosumers. The proposed system includes two layers. In the first layer, a multi-agent system is designed to support the prosumer network, and an agent coalition mechanism is proposed to enable the prosumers to form coalitions and negotiate electricity trading. In the second layer, a Blockchain based transaction settlement mechanism is proposed to enable the trusted and secure settlement of electricity trading transactions formed in the first layer. Simulations are conducted based on the java agent development environment to validate the proposed electricity trading process. [ABSTRACT FROM AUTHOR]

Recent studies on power flow analysis of islanded microgrids have become increasingly important and different solutions have been proposed. However, they are often limited to only ac or only dc microgrids and hence, the power flow must be solved separately. This paper proposes a straightforward and efficient method to solve power flows of hybrid ac/dc microgrids simultaneously, based on the well-established Newton–Raphson approach. It considers the lack of slack bus during islanded operation, the coupling of ac frequency and dc bus voltage, as well as the droop control of distributed generators. To achieve power sharing between the ac and dc subgrids, this method incorporates the interlinking converter droop constants in the equation. The algorithm was tested on modified hybrid microgrids involving multiple ac–dc subgrids. The results are compared against results from time-domain simulations to validate the algorithm’s accuracy. The proposed technique can be easily implemented to aid the design and planning process of hybrid microgrids. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) has been rapidly integrated into microgrids. However, uncertain power generation of intermittent DG such as wind turbines brings challenges to the DG planning problem. This paper proposes a novel probability-weighted robust optimization (PRO) method to allocate DG units including microturbines and wind turbines in microgrids, aiming to maximize the total profit over a long-term planning horizon. First, probability-weighted uncertainty sets are proposed to model uncertainties including wind turbine output and load demand during a long-term planning horizon which can fully cover the uncertainty spectrum and accurately represent the uncertainty probability distribution. Then, the PRO method optimizes DG sizing and locating under the worst uncertainty cases considering their occurrence probabilities. Therefore, the planning decision is robust against any possible uncertainty realization. Besides, a modified column-and-constraint generation algorithm is developed to solve the PRO problem. Simulation results show that the DG planning obtained by the proposed method can achieve full operating robustness. [ABSTRACT FROM AUTHOR]

Microgrid is an effective means to integrate distributed generation (DG) resource. However, uncertain renewable DG such as wind turbine and photovoltaic outputs and load demands can introduce tremendous difficulties for energy management in microgrids. To mitigate such difficulties, price-based demand response (PBDR) can adjust the loads to adapt to the renewables. On the other hand, dispatchable DG such as microturbines can coordinate with the PBDR to further manage the power balance and achieve economic benefits. In this paper, a two-stage robust microgrid coordination strategy is proposed: a PBDR is scheduled a day ahead and microturbine outputs are modified hourly. A two-stage robust optimization model is proposed to address the coordination problem with guaranteed robustness against the uncertainties of renewable DG and load demands. Simulation results show PBDR and multiple DG units can coordinate effectively to accommodate the renewable and demand uncertainties while maximizing the microgrid benefits. [ABSTRACT FROM AUTHOR]

A novel scheme for fast detection of islanding events in a microgrid is proposed in this paper. The scheme consists of a passive islanding detection process using an adaptive ensemble classifier and measurements of three-phase voltage at the distributed generation (DG) terminals. Initial features inputs to the classifier are obtained using the phase-space method. They are then used to train an ensemble classifier which comprises a set of randomized neural networks called extreme learning machine (ELM). The optimal parameter settings for each ELM are identified through evolutionary computation. An adaptive decision mechanism is designed to progressively adjust the decision time of the events classification, where its decision speed can be significantly improved over existing methods. Extensive tests are conducted on a radial distribution system with two identical DG units and the IEEE 33-bus with four DG units, considering various scenarios of islanding and non-islanding conditions, as well as the different types of DG. The simulation results show that the proposed scheme can make accurate decisions at appropriately earlier times, which achieves a good balance between efficiency and accuracy in detecting the islanding events. [ABSTRACT FROM PUBLISHER]

To help the integration of various distributed energy resources (DERs), this paper proposes a transactive approach to the optimal scheduling for prosumers in coupled energy systems. DERs are coordinately operated in the form of a virtual power plant (VPP), which actively participates in the day-ahead and real-time electricity market, as well as the wholesale gas market. In the day-ahead electricity and wholesale gas markets, a VPP aims to maximize expected profits by determining the unit commitments and hourly scheduling of DERs. In the real-time balancing market, a VPP adjusts DER schedules to minimize imbalance costs. In this paper, we address the energy conversions between electric power and gas loads and investigate the interacting operations of gas and power systems. Our simulation results show that hierarchical, coordinated power and gas scheduling can identify more accurate operation plans for coupled transactive energy systems. [ABSTRACT FROM AUTHOR]

This paper proposes a robust optimization approach for optimal operation of microgrids. The uncertain output variation of renewable energy sources (RESs) is addressed by collaboratively scheduling of energy storage (ES) and direct load control (DLC) through a two-stage complementary framework: an hour-ahead charging/discharging of ES and a quarter-hour-ahead activation of DLC. The objective is to maximize the total profit of the microgrid considering operation and maintenance costs of ES units, wind turbines and photovoltaics, and transaction with main grid and customer loads. Assuming the power output of RES randomly varies within a bounded uncertainty set, the problem is modeled to a two-stage robust optimization model and solved by a column-and-constraint generation algorithm. Compared with conventional operation methods, the ES and DLC are coordinated in different time-scales, and RES uncertainties are fully addressed during operation decision-making, ensuring the solutions to be optimal and robust for any realization of uncertainty. The proposed methodology is verified on the IEEE 33-bus distribution system through a wide range of different tests. [ABSTRACT FROM AUTHOR]

The intermittency of renewable energy poses challenges on the reliable and economical operations of microgrids. This paper considers a grid-connected microgrid model which consists of a logistics distribution system, where electric vehicles (EVs) depart from the depot, deliver the goods to multiple demand loads, and then, return to the depot. Based on this, this paper studies the coordinated dispatch strategies of EVs to smooth renewable energy and load fluctuations of the microgrid while ensuring the quality of logistics services. A microgrid operation model is proposed to optimize the driving routes, fast-charging time, and regular-charging/discharging strategies of EVs. Specifically, the objective is to minimize the overall operation cost of the microgrid while satisfying the requirements of the logistics distribution tasks. A self-adaptively imperialist competitive algorithm is proposed to solve the model. The simulation results demonstrate the effectiveness of the proposed model. [ABSTRACT FROM PUBLISHER]

In this letter, a distributed economic model predictive control (EMPC) strategy is proposed for economic load dispatch and load frequency control of interconnected power systems. A general economic cost function is used in the EMPC to calculate the control sequence directly optimizes the economic performance of the system without using a hierarchical control structure, thus achieving both the real-time optimization and transient optimization. A cooperation-based distributed EMPC algorithm is proposed and the asymptotic stability is proven. Simulation results reveal the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Steel production is one of the most energy-intensive industries on demand side. Highly distributed energy resource-penetrated multi-energy microgrids (MEMGs) with combined heat and power (CHP) units can supply both electricity and heat while the by-product coal gases during manufacturing can be reused for onsite power supply. However, there is a lack of coordination between steel production and MEMG operation, and the steelmaking process is not fully modelled. Thus, this paper proposes a three-stage coordinated operation method for steel plant-based MEMGs, aiming to minimize the total operating cost. In this method, the steel production is scheduled weekly-ahead to meet the production demand considering carbon emission reduction. Then, the CHP commitment and day-ahead energy transaction are optimized in a day-ahead stage, while the dispatchable device output and intraday energy transaction are determined hourly-ahead based on uncertainty realizations. Accordingly, the steel production is modelled as continuous and discontinuous processes in parallel or series. To tackle the uncertainty of renewable generation, a scenario-based stochastic optimization method is utilized. Moreover, different carbon prices are applied to investigate their effects on steel production. The results show that the proposed method can decrease the operating cost by 14.33% and 1.45% compared with the other two conventional methods. [Display omitted] • Three-stage steel plant MEMG low-carbon operation coordinated with process optimization. • Generalized continuous/discontinuous process modelling for steel production. • Uncertain intervals of renewable generation tackled with SO and SAA in different stage. [ABSTRACT FROM AUTHOR]

• An algorithm for power flow analysis considering high renewable penetration. • Regionalization of distribution system into conventional & renewable dominant regions. • Load flow investigations for both type of regions. • Minimization of power losses and less computational burden are major benefits. To harmonize the operation of renewable and conventional generation, an approach of regionalized microgrid is proposed. Regionalization has been adopted for islanded microgrid in which the distribution system has been sectionalized into conventional generation-based regions (CGRs) operating with droop control and renewable generation-based regions (RGRs) operating with constant frequency de-coupled PQ control method. Back-to-back converters have been installed between the regions for bi-directional power exchange. In this paper, a novel load flow algorithm has been proposed for a regionalized microgrid in an islanded mode that aims to solve the power flow problem for both types of the regions. The algorithm considers the uncertainty of renewable distributed generators (RDGs) and loads. The approach focuses on the frequency regulation in both types of regions. Case studies have been carried out for IEEE 15, 33 and 69-bus distribution systems by converting them into regionalized microgrids (RMG). Results have demonstrated the strength of proposed load flow approach with a fast convergence speed. [Display omitted] [ABSTRACT FROM AUTHOR]

• Study the vision of engaging networked data centers into distributed energy markets. • Propose a decision-making model to optimize the bidding decisions and energy resource operations of networked data centers in multiple geo-distributed local energy markets. • Conduct comprehensive numerical simulations to validate the benefit the cloud service provider can obtain from local energy markets. Active distribution network Internet Data Centers (IDCs) are large energy consumers in modern distribution networks. The integration of renewable energy and energy storage systems transforms IDCs into proactive energy prosumers, and enables them to participate in the energy trading in regional local energy markets. This paper proposes an energy management framework for a Cloud Service Provider (CSP) that manages multiple geographically distributed IDCs. The framework provides decision-making support for the CSP to optimally schedule the IDCs' cyber-energy resources (computing requests, on-site battery energy storage systems, and on-site renewable energy sources). In this way, the proposed framework facilitates the CSP to proactively participate in the local energy markets situated in the regions that host the IDCs, with the objective to minimize the CSP's total cost over a finite operation horizon. We use an effective evolutionary computation algorithm to solve the proposed energy management model and design extensive numerical simulations to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• New security-constrained OPF model for a microgrid considering tie-line switching. • New Benders decomposition based solution algorithm to solve the OPF problem. • Stochastic optimization method with probabilistic modeling to address uncertainty. With the rapid development of microgrid, its tie-line switching from grid-connected to islanded mode is a topic worth discussing for considering both main grid resilience and microgrid security. In this paper, a stochastic security-constrained optimal power flow (OPF) method is proposed to deal with these conditions under high uncertainties. Firstly, a linear load flow model and a backward forward sweep algorithm are applied to present microgrid power flow with reduced computing burdens. Secondly, with consideration of tie-line switching from grid-connected to islanded operation mode, a security-constrained OPF problem for a microgrid is proposed to minimize operating cost and by optimizing microturbine setpoints and load shedding coefficient. To promise stable islanded operation after disconnection from the main grid, a Benders decomposition method is developed to decouple the OPF problem into a grid-connected master problem and an islanded sub-problem and then solve them iteratively with Benders cuts to guarantee microgrid security after tie-line switching. Last, a stochastic optimization method with probabilistic modelling is adopted to address the uncertainty issue caused by renewable energy sources and loads. The proposed stochastic security-constrained OPF method has been verified with high computing efficiency and robust security via comprehensive numerical simulations. [ABSTRACT FROM AUTHOR]

• Consensus-based control of distributed generators to minimize frequency deviation. • Consensus-based control of interlinking converters to align AC subgrid frequencies. • Modifications on Backward/Forward Sweep-based three-phase AC-DC power flow analysis. • Accurate, scalable and generic method for networked islanded hybrid microgrids. This paper proposes the incorporation of distributed and decentralized control models of generators and converters into the power flow analysis of islanded hybrid microgrids (IHMG). The absence of a slack bus in an islanded microgrid presents challenges to its frequency and voltage regulation as well as power flow calculation. While droop mechanism as the primary control has been well incorporated into load flow analysis, distributed secondary control has only recently been developed for islanded microgrids. This paper proposes a distributed secondary control for a) generators in an AC subgrid of an IHMG and b) interlinking converters in networked IHMGs. Both the distributed and droop control are incorporated into a three-phase power flow calculation based on a modified Backward Forward Sweep method. The algorithm has been tested on several case studies, and the power flow results have been verified against a time-domain simulation. The proposed method proves to be accurate and useful for the planning and design of three-phase networked IHMGs. [ABSTRACT FROM AUTHOR]