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Since microgrids can effectively integrate renewable energy, energy storage devices, and controllable loads, this advantage promotes the rapid development and application of microgrid technology. However, with the high proportion of renewable energy access, only considering how energy is optimally distributed in microgrids can no longer meet the actual demand. How to aggregate user-side controllable loads to form regulation resources has become a research hotspot, and the users, as a passive party in the load scheduling process, should also be an important consideration in their perception of the use of electricity. First, a control model for temperature-controlled loads and a time-shift model for time-shiftable loads are developed. Then, the comprehensive electricity satisfaction model of users is established, and the two-layer optimal scheduling model of microgrids considering users' satisfaction is proposed, with users as the upper layer and microgrids as the lower layer, and the two-layer model is transformed into a single-layer model according to the KKT condition for solving. Finally, the effect of the weighting factor for satisfaction on the economy is discussed through the analysis of examples, which verifies the effectiveness of the two-layer model. [ABSTRACT FROM AUTHOR]

The revolution of microgrids (MGs) in the modern world has impacted the power systems to deal with the loads with optimal use of renewable energy resources (RERs). In the models consisting of energy management systems (EMS) and generation sources, there will be power fluctuations between the source and the load under certain conditions due to the unpredictable behavior of RERs. Therefore, a reliable scheme has to be implemented between the source and the load to ensure optimal performance of direct current (DC)‐MGs. This article has presented a review of the EMS models introduced by various researchers to enable optimal power supply and efficient operation of the DC‐MGs. A thorough illustration of the schemes adopted to provide efficient operations under grid‐connected and islanded mode of operations are provided. The effects and advantages of centralized and decentralized EMSs are discussed. The role of a battery energy storage system (BESS) in microgrid scheduling is presented and compared with a few other energy storage systems. The scheduling strategies implemented for optimal EMS using different algorithms and techniques are elaborated. In addition, the effects of battery degradation and the formulated solutions to improve the degraded models with improved battery life are also provided with cycle life and calendar life. Then, the lithium ion and lead acid BESS effect on microgrid EMS is presented. Finally, the energy management methods for DC‐MGs end with challenges, research gaps, and future direction. [ABSTRACT FROM AUTHOR]

With the large increase of load users, the load side adjustable resources gradually increase, and the load side demand response (DR) resources are used to participate in microgrid scheduling to improve the level of new energy consumption. In order to give full play to the scheduling potential of DR resources and optimize the user side load management capability, a multi-time scale optimal scheduling strategy for microgrids that takes into account scheduling priorities was proposed. Firstly, according to the response characteristics of different DR resources, the price-based demand response(PDR) resources and incentive-based demand response(IDR) resources were subdivided into five types, and a DR model was constructed to match the scheduling period. Secondly, the multi-times cale optimal scheduling model of "day-intra-day 1 h-intra-day 15 min" was constructed, optimized scheduling of various adjustable resources in the microgrid, and established priority weights for real-time adjustable resources that have a direct impact on users' electricity consumption behavior. Finally, taking an actual microgrid as an example in Fujian, the simulation verifies the validity of the model. [ABSTRACT FROM AUTHOR]

The random output of renewable energy and the disorderly grid connection of electric vehicles (EV) will pose challenges to the safe and stable operation of the power system. In order to ensure the reliability and symmetry of the microgrid operation, this paper proposes a microgrid optimization scheduling strategy considering the access of EVs. Firstly, in order to reduce the impact of random access to EVs on power system operation, a schedulable model of an EV cluster is constructed based on the Minkowski sum. Then, based on the wavelet neural network (WNN), the renewable energy output is predicted to reduce the influence of its output fluctuation on the operation of the power system. Considering the operation constraints of each unit in the microgrid, the network active power loss and node voltage deviation are taken as the optimization objectives, and the established microgrid model is equivalently transformed via second-order cone relaxation to improve its solution efficiency. Based on network reconfiguration and flexible load participation in demand response, the economy and reliability of system operation are improved. Finally, the feasibility and effectiveness of the proposed method are verified based on the simulation examples. [ABSTRACT FROM AUTHOR]

Reinforcement Learning (RL) is a promising technique for scheduling and planning storage systems in microgrids, which are small-scale power networks that can operate independently or in coordination with the main grid. RL can enhance the utilization of local renewable energy sources and reduce the operational costs of microgrids. In this comprehensive study and review the state-of-the-art applications of RL for Microgrid Energy Management (MEM), focusing on battery storage systems is discussed. This work also identify the main challenges, limitations, and future directions in this domain. Furthermore, this article present a novel benchmark algorithm that compares the performance of RL with mixed-integer linear programming (MILP), a widely used optimization technique. This complete work provides a valuable insight into the current status and future prospects of RL for MEM. [ABSTRACT FROM AUTHOR]

For the microgrid system under the "fish-light complementarity", a multi-objective optimization energy scheduling method is proposed to balance the "impossible triangle" of energy. Firstly, based on the background of "fish-light complementarity", a light-storage-load microgrid system model is established, and multidimensional dynamic objective functions that include economic, environmental, and comprehensive aspects are proposed. Secondly, considering the shortcomings of the sparrow search algorithm, such as insufficient diversity and easy trapping in local optima, an improved sparrow search algorithm （SA-ISSA） based on simulated annealing algorithm and hybrid strategy is proposed for microgrid energy optimization scheduling. Finally, a case study was conducted on the "fish-light complementarity" factory breeding base in Puyue Town, Chongming, Shanghai, where the SA-ISSA algorithm is applied to solve the microgrid model and obtain the optimal energy scheduling scheme. The experimental results demonstrate that the SA-ISSA algorithm performs better and has higher application value in solving the microgrid energy optimization scheduling problem, providing reference for energy optimization scheduling strategies in microgrid systems under the "fish-light complementarity". [ABSTRACT FROM AUTHOR]

Microgrids are an effective solution to decentralize electrical grids and improve usage of distributed energy resources (DERs). Within a microgrid there are multiple active players and it can be computationally expensive to consider all their interactions. An optimal scheduler ensures that the needs within the microgrid are met without wasting electricity. With higher requirements for electric vehicle charging stations (EVCSs), schedulers are essential to ensure EV charging demands are met while being profitable and flattening peak load on the main power grid (MPG). This paper introduces two novel microgrid models, combining energy generated by a DER, the possibility of storage with an energy storage system (ESS), a load entity in the form of an EVCS and electricity trading with the MPG. The model incorporates all important environment parameters created by these players in an intelligent way that keeps the action space relatively small and thus avoiding the problems associated with a high computational complexity. These models are proven to successfully shift the load from the MPG, while still providing high customer satisfaction and throughput, in a profitable way, despite costs incurred by the DER. Instead of relying on models, real data is used, ensuring that the model is robust. Additional real world stress tests are carried out with respect to electricity costs, wind energy generation, and charging rates. Reinforcement learning is implemented to find the optimal scheduler by maximizing overall profits. In all cases considered a self-sustaining system is established, that is a more profitable and reliable EVCS.

As a large number of flexible elements such as distributed power and flexible load are connected to microgrids, the economic improvement of microgrids has become an important topic through rational energy distribution. In this paper, an economic scheduling model considering the load demand for a microgrid system under the mechanism of a peak–valley tariff is proposed. A mathematical model of the microgrid components is proposed to determine the exchange power between the microgrid and main network. Meanwhile, an improved War Strategy Optimization (WSO) algorithm is proposed to investigate three scenarios: (i) without batteries, (ii) with batteries and (iii) with batteries and demand response. Additionally, demand response optimization is carried out with the Particle Swarm Optimization (PSO) algorithm and the improved WSO is compared with four other algorithms in scenario (iii). The comparison shows that the improved WSO algorithm has a better optimization performance in solving the proposed scheduling model. [ABSTRACT FROM AUTHOR]

The disorderly access of large-scale electric vehicles (EVs) will have adverse effects on the microgrid, such as increasing the peak-valley difference, decreasing the power quality, and increasing the difficulty in microgrid operation optimization and control. To this end, the author proposed an EV to microgrid (V2M) interaction scheduling strategy using cluster optimization to balance power demand and supply in the microgrid. First, in the lower-level vehicle-to aggregator (V2A) stage, the EVs in each period are dynamically divided into regular and regulated clusters according to their battery, time, and charging/discharging conversion time constraints, with the regular cluster carrying out disorderly charging and the regulated cluster containing charging and discharging clusters. Then, in the upper-level aggregator-to-microgrid (A2M) stage, the dispatchable load of the control cluster is optimized at the control center to minimize the total load variance during the study period, using the cluster division and cluster load information as constraints. Finally, the power allocation algorithm is used to realize the spatial and temporal distributions of the EV cluster charging demand and discharging capacity for the scheduling strategy of V2A and A2M interactions. The proposed method can ensure that the EVs can cut the peak and fill the valley of the microgrid while meeting the travel demand. [ABSTRACT FROM AUTHOR]

Optimal energy management has become a challenging task to accomplish in today's advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids under grid-connected, as well as isolated modes. The authors presented a new optimization algorithm, i.e., Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) in the current study to elucidate the optimal operation in microgrids that is loaded with sustainable, as well as unsustainable energy sources. With the integration of non-Renewable Energy Sources (RES) with microgrids, environmental pollution is reduced. The current study proposes this hybrid algorithm to avoid stagnation and achieve premature convergence. Having been strategized as a bi-objective optimization problem, the ultimate aim of this model's optimal operation is to cut the costs incurred upon operations and reduce the emission of pollutants in a 24-h scheduling period. In the current study, the authors considered a Micro Turbine (MT) followed by a Wind Turbine (WT), a battery unit and a Fuel Cell (FC) as storage devices. The microgrid was assumed under the grid-connected mode. The authors validated the proposed algorithm upon three different scenarios to establish the former's efficiency and efficacy. In addition to these, the optimization results attained from the proposed technique were also compared with that of the results from techniques implemented earlier. According to the outcomes, it can be inferred that the presented OGGWO approach outperformed other methods in terms of cost mitigation and pollution reduction. [ABSTRACT FROM AUTHOR]

Battery swapping station (BSS) is an emerging form of energy storage that can be integrated with microgrid (MG) for economical operation of the system. To manage the scheduling between MG and BSSs, this paper proposes an optimal scheduling model for promoting the participation of BSSs in regulating the MG economic operation. The proposed grid-connected MG consists of wind turbine, photovoltaic (PV) modules, and BSSs. After the BSSs are connected to MG, the batteries in the BSSs can offer auxiliary service to grid through the battery to grid (B2G) operation. Battery swap requests, electricity prices, and MG load influence the decision making in operation scheduling of MG and BSSs. Case studies prove the effectiveness of proposed model by minimizing the MG net costs and maximizing the profits of the BSS with B2G technique and charging strategy that reduces daily charging cost. [ABSTRACT FROM AUTHOR]

Due to the growth of electricity consumption in residential and commercial buildings, there is a growing burden on utilities in urban areas. Sustainable buildings are being developed by integrating renewable energy generation to make an eco-friendly environment. An isolated building-integrated (IBI) DC microgrid fed by a hybrid solar PV (photo voltaic) generator-thermo electric generator (TEG) is one viable option for obtaining self-sustainable energy for the building. The battery is an important component in such IBI DC microgrid fed by the hybrid solar PV generator-TEG for providing reliable power to the loads. However, the operation and planning of the IBI DC microgrid are challenging, and therefore it needs a suitable load scheduling strategy. This paper proposes the optimal load scheduling strategy (OLSS) under demand response to schedule the household loads in the building. The proposed OLSS aims to balance two conflictive objectives, user comfort and peak-to-average ratio (PAR), using a binary particle swarm optimization algorithm (BPSO). The proposed OLSS optimizes user comfort and PAR and reduces peak demand and battery usage. It also helps in the optimum utilization of power generated from hybrid solar PV generator-TEG by shifting the loads during sun hours. Reducing battery usage reduces the conversion loss and extends the battery life. To validate the proposed OLSS, the investigations are carried out using the data obtained across different seasons. The results show that the proposed OLSS schedules the household loads with the desired user comfort and reduces PAR by 22 % in future buildings. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, microgrids are emerging as an invaluable framework for the integration of renewable energy sources and demand response programs. In such systems, energy storage facilities are also frequently deployed to properly manage surplus energy from renewable sources on pursuing more efficient management of the system. Hybrid storage systems in which various storage facilities are combined may result in a more effective solution than only considering one storage technology. This way, the good features of the different technologies may be jointly exploited while their drawbacks are minimized. Due to the large‐scale integration of renewable energies in this kind of grid, coping with uncertainties becomes a critical issue. Moreover, the operation of microgrids frequently deals with other kinds of uncertainties related to energy pricing from the upscale grid (in the case of grid‐connected mode) or local demand. This way, proper modeling of uncertainties is essential for adequately operating these systems. This paper contributes to this pool by developing a novel interval‐based formulation, for optimal scheduling of microgrids considering battery and pumped‐hydro storage systems. To achieve this goal, the optimal scheduling of a microgrid with pumped‐hydro and battery energy storage considering demand response is modeled, firstly. Then, the new interval‐based formulation is used to cope with the uncertainties. Finally, the suggested model is verified using simulations in various cases, and the results confirm the effectiveness of the novel interval‐based formulation for the optimal scheduling of microgrid with pumped‐hydro and battery energy storage under uncertainty. [ABSTRACT FROM AUTHOR]

As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an economical and energy-efficient power management scheme is required for the power provision of EVs while considering the local load demand of the MG. For these purposes, this study presents the power management scheme of interdependent MG and EV fleets aided by a novel EV charging/discharging scheduling algorithm. In this algorithm, the maximum amount of discharging power from parked EVs is determined based on the difference between local load demand and photovoltaic (PV) power production to alleviate imbalances occurred between them. For the power management of the MG with charging/discharging scheduling of parked EVs in the PV-based charging station, multi-objective optimization is performed to minimize the operating cost and grid dependency. In addition, the proposed scheme maximizes the utilization of EV charging/discharging while satisfying the charging requirements of parked EVs. Moreover, a more economical and energy-efficient PV-based charging station is established using the future trends of local load demand and PV power production predicted by a gated recurrent unit (GRU) network. With the proposed EV charging/discharging scheduling algorithm, the operating cost of PV-based charging station is decreased by 167.71% and 28.85% compared with the EV charging scheduling algorithm and the conventional EV charging/discharging scheduling algorithm, respectively. It is obvious that the economical and energy-efficient operation of PV-based charging station can be accomplished by applying the power management scheme with the proposed EV charging/discharging scheduling strategy. [ABSTRACT FROM AUTHOR]

Microgrids incorporate an increasing number of distributed energy resources (DERs), which induce a higher variability and faster dispatch capabilities in power systems. This paper proposes a two-layer real-time scheduling model for microgrids, based on approximate future cost function (AFCF), where the future cost represents the opportunity cost for the microgrid operation in subsequent periods. At the upper layer, the look-ahead rolling scheduling is adopted to optimize microgrid operations, in which the future cost function (FCF) in deterministic and stochastic scenarios is approximated by a piecewise linear function. At the lower layer, a real-time parameter updating strategy based on real-time data is proposed. In this case, the real-time scheduling readjusts the look-ahead schedule using the immediate cost in the current period and the future cost calculated by the updated AFCF. The proposed two-layer real-time scheduling model uses an offline optimization, in which most of the computation tasks are completed at the upper layer, and applies a real-time optimization, in which the time-consuming problem is avoided at the lower layer. The effectiveness of the proposed two-layer real-time scheduling model of microgrids is validated by using a grid-connected microgrid system. For comparison, other existing real-time scheduling methods are also implemented in the same microgrid system. [ABSTRACT FROM AUTHOR]

According to statistical reports, thermal power plants have long played a critical role in supplying electricity using fossil fuels. However, due to the high investment and operation costs of these power plants and their destructive effects on the environment, renewable energy sources (RESs) in power networks have been considered an effective alternative to traditional power plants. Optimal scheduling of microgrid in smart grids has a significant impact on reducing energy consumption, environmental pollutants and end-user's energy prices. In this paper, the influence of plug-in hybrid electric vehicles (PHEVs) charging on microgrids' optimal operation is evaluated. To assess the behaviour of PHEVs, three various charging patterns consisting of uncontrolled, controlled and smart charging methods are considered. Uncertainties due to forecast error in the PHEVs, loads, prices and renewable source output power are also considered in microgrid modelling energy management. To deal with the optimisation scheduling of microgrid considering uncertainty, a modified harmony search (MHS) algorithm is used. The suggested scheme is validated using simulations and without the PHEV charging effects compared with the conventional methods. [ABSTRACT FROM AUTHOR]

With the deepening integration and interaction of the prosumer's "source‐load‐storage," for a microgrid (MG) with prosumers, the comprehensive demand‐side response (CDSR) of the prosumer's multiresources is conducive to improving the system's new energy consumption，economic benefits, and prosumer's comprehensive electricity consumption satisfaction (CECS) of multiresources usage. Consequently, an optimal scheduling strategy for the MG with prosumers is proposed in this article, considering the CDSR and the CECS of the prosumer. In addition, a master–slave game model with the MG as the leader and the prosumer the follower is established, to stimulate the activity of prosumers participating in the scheduling by bargaining the price of the MG. Taking the maximum operation benefit as the optimization objective, the MG formulates the electricity price incentive policy to encourage prosumers to participate in the MG's peak adjustment dispatching; responding to the MG's price policy, the prosumer develops the scheduling strategy to minimize its total cost and maximize the CECS. After multiple rounds of iterative optimization, the Stackelberg equilibrium is obtained, while two parties are unable to obtain greater benefits by adjusting their own strategies. The simulation results show that the prosumer with the CDSR and the CECS may obtain the best scheduling performance, and the interests of the whole system are promoted with the proposed strategy. [ABSTRACT FROM AUTHOR]

This article reports an artificial neural network (ANN)-based binary backtracking search algorithm (BBSA) for optimal scheduling controller applied on IEEE 14-bus system for controlling microgrids (MGs) formed virtual power plant (VPP) toward sustainable renewable energy sources (RESs) integration. The model of VPP was simulated and validated based on actual parameters and load data reported in Perlis, Malaysia. BBSA optimization algorithm offers the best binary fitness function to find the best cell. It creates the optimum scheduling using the actual data for wind speed, solar radiation, fuel conditions, battery charging/discharging, and specific hour demand. The developed ANN-based BBSA search for the optimal ANN parameters architecture, e.g., (the number of neurons and learning rate) that enhanced the ANN controller to predict the optimal schedules to regulate power-sharing via prioritizing the utilization of RES in place of the national grid purchases. The results of the optimal on/off status prediction of the 25 DGs showed that the ANN-BBSA gives a mean absolute error (MAE) of 6.2 × 10−3 with a unity correlation coefficient. The results showed a significant reduction in the cost and emission by 41.88% and 40.7%, respectively. Thus, the developed algorithms reduced the energy cost while delivered reliable power toward grid decarbonization. [ABSTRACT FROM AUTHOR]

This paper develops a stochastic framework for the energy management of a microgrid to minimize the energy cost from the grid. It considers the uncertainties in solar photovoltaic (PV) generation, load demand, and electricity price. Furthermore, the opportunity of flexible load demand, i.e., the effect of demand response (DR), on the test system is studied. The uncertainties are modeled by using Monte Carlo simulations and the generated scenarios are reduced to improve the computational tractability. In general, microgrid scheduling is implemented by using substation (source node) price as a reference, but that reference price is not the same at all nodes. Therefore, this paper develops the nodal price based energy management in a microgrid to improve the scheduling accuracy. The stochastic energy management framework is formulated as a mixed integer non-linear programming (MINLP). Four case studies are simulated for a modified 15-node radial distribution network integrated with solar PV and battery energy storage system (BESS) to validate the effectiveness of the energy management framework for a microgrid with nodal pricing.

A routine task faced by Microgrid (MG) operators is to optimally allocate incoming power demand requests while accounting for the underlying power distribution network and the associated constraints. Typically, this has been formulated as an offline optimization problem for day-ahead scheduling, assuming perfect forecasting of the demands. In practice, however, these loads are often requested in an ad-hoc manner and the control decisions are to be computed without any foresight into future inputs. With this in view, the present work contributes to the modeling and algorithmic foundations of real-time load scheduling problem in a demand response (DR) program. We model the problem within an AC Optimal Power Flow (OPF) framework and design an efficient online algorithm that outputs scheduling decisions provided with information on past and present inputs solely. Furthermore, a rigorous theoretical bound on the competitive ratio of the algorithm is derived. Practicality of the proposed approach is corroborated through numerical simulations on two benchmark MG systems against a representative greedy algorithm. [ABSTRACT FROM AUTHOR]

Summary: Microgrids (MGs) can operate in two modes: grid‐connected and islanded. Islanding can be scheduled or unintentional. MG should be able to maintain its stability in case of unintentional islanding. Therefore, it is necessary to provide the required spinning reserve for stable islanding. In this paper, a two‐stage scheduling strategy is proposed to determine the required reserve for stable islanding by considering uncertainties of load and renewable generations. The proposed strategy uses advantages of the load as a reserve provider to compensate the deficiency of DGs and batteries in providing the required reserve. A novel objective function is proposed to determine the optimal amount of demand which considered as reserve. The objective function minimizes the sum of reserve preparation costs, expected cost of calling load reserve, and expected cost of emergency load curtailment. Another significant feature of the proposed model is its modularity that enables the model to be integrated with any other scheduling model. The effectiveness of the proposed model is evaluated by mathematical analysis and numerical simulations. [ABSTRACT FROM AUTHOR]

To meet the diverse operational requirements of microgrids with multi-uncertainties, a tri-level two-stage mixed-integer robust optimization model is proposed to solve the optimal scheduling problem with source-load power and unit N-1 uncertainties. In the first stage, the optimal goal is to obtain robust scheduling plans that can be applied effectively in satisfying the operation and adjustment constraints, as well as to find the minimal operating cost in the basic scenario. Considering the system may concern a specific operating factor (e.g., load satisfaction rate) under a N-1 contingency, the second stage of the model minimizes the total energy of load shedding and renewable energy abandonment in the worst scenario. As the proposed robust model is a multi-level coupled mixed-integer optimization problem, which is difficult to address by either the duality method or the Karush–Kuhn–Tucker (KKT) method, a novel nested reformulation-and-decomposition (R&D) algorithm is proposed to handle this complex problem. Numerical simulations and tests have verified the effectiveness and superiority of the proposed robust model and the solution algorithm. Furthermore, the robust optimal schemes satisfy the engineering application more reasonably. [ABSTRACT FROM AUTHOR]

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New Information and Communications Technologies (ICT), such as the Internet of Things (IoT), are enabling the evolution of energy grids towards a sophisticated power network called Smart Grid (SG). In the context of SGs, a microgrid is a self-sustained network that can operate in both grid-connected or stand-along modes. The long-term scheduling of the operation of distributed generators (DG) and renewable energy resources (RES) in microgrids is a problem that requires tough planning and the use of advanced tools to be efficiently addressed. This paper discusses different ICT technologies that can enable microgrid communication for control and management of distributed energy resources (DER). Based on such ICT, we propose a novel agent-based model to address the long-term scheduling of DER in microgrids as a distributed constraint optimization problem (DCOP). However, finding the optimal solution for a DCOP is known to be an NP-Hard problem, making difficult to guarantee optimal solutions even for short optimization periods. Hence, for the long-term scheduling of DER, we propose to split the problem into small time windows that can be effectively solved sequentially by off-the-shelf DCOP algorithms. A particular, but general enough case study is used to compare different DCOP algorithms under the proposed model. Results show that DCOP algorithms can find optimal and near-optimal solutions depending on the window size and the scenario considered. [ABSTRACT FROM AUTHOR]

Microgrid can operate in both grid-connected and islanded modes. Islanding can be scheduled or unscheduled. In the event of unscheduled islanding, the microgrid should be able to maintain its sustainability. Therefore, it is necessary to determine and supply the amount of required spinning reserve for stable islanding. In this paper, a two-stage strategy for optimal scheduling of the microgrid is proposed while uncertainties of renewable generations and demand are considered. In the first stage, co-optimization of power and reserve is carried out with the presence of distributed generations and batteries. Therefore, in the second stage, the demand is also used as reserve to provide required reserve for stable islanding. In order to determine the optimal amount of reserve of demand, a novel objective function is proposed. The objective function minimizes the sum of the standby reserve cost, expected cost of called reserve, and expected cost of emergency curtailment. One of the salient features of the proposed model is its modularity. That is, it can be added easily to any scheduling model in which the demand is not considered as reserve. Mathematical analysis and numerical simulations show the accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Highlights • Matheuristic for emergency response in electric distribution system with islanding. • Cost impact analysis of islanded customers in a time-function. • MILP model of the emergency order scheduling problem with islanding operation. • Validation on the IEEE 33-bus system, modified with the insertion of DGs. Abstract Since power supply may suffer interruptions, caused by climate events or even due to equipment and human failures, these interruptions are associated with procedures commonly referred as emergency orders. These emergency orders must be attended by repair crews in an attempt to reestablish the power supply all over the system. Assuming a set of these emergency orders, the network operation center (NOC) must find a scheduling so that they are all attended by the repair crews and the power supply reestablished as soon as possible, while considering the minimization of the total service cost. Distributed generation (DG) may be one alternative for furnishing temporally power supply, such as photovoltaic and wind power generation sources. In such approach, the islanding operation confers such a complexity when considering how much time should be needed to supply the affected loads and which ones may be even selected. This study proposes a novel matheuristic for emergency response to the contingency planning problem related to electric distribution operations, considering unforeseen islanding of DG. From using a mixed integer linear programming (MILP) model, based on the emergency order scheduling problem (EOSP) with islanding operation formulations, the optimization criteria involves the minimization of the cost of non-supplied energy and the penalties related to high important customers, while respecting electrical constraints. Computational results on the IEEE 33-bus system, modified with the insertion of DGs, show the effectiveness of the proposed solution on reducing the total attendance cost when compared to a traditional scheduling solution without considering the islanding operation. [ABSTRACT FROM AUTHOR]

This study presents a multi‐objective problem of the optimal power management of micro‐grids, which is based on modified Tribe‐PSO. Minimising total cost and emission over a 24 h time horizon are the objective functions in this problem. For the participation of consumers in the management of micro‐grids, demand response program is used. The scheduling approach is tested on the IEEE standard distribution network with 33 buses and is compared in two modes: grid‐connected and stand‐alone. Simulation results show that the presented power and demand scheduling provide a lower operation cost and more efficient utilisation of the renewable energy sources and the demand response programs. [ABSTRACT FROM AUTHOR]

In order to achieve better economic and environmental benefits of microgrids (MGs) under multiple uncertainties in renewable energy resources and loads, a novel energy production scheduling method is proposed based on robust multi-objective optimization with minimax criterion. Firstly, a mixed integer minimax multi-objective formulation is developed to capture uncertainties as well as minimize economic and environmental objectives. Secondly, the primal problem is decomposed into a bi-level optimization problem, which attempts to seek robust scheduling scheme set under the worst-case realization of uncertainties in a multi-objective framework. Finally, a hierarchical meta-heuristic solution strategy, including multi-objective cross entropy algorithm and δ+ indicator, is designed to solve the reconstructed problem. Numerical results demonstrate that the proposed scheduling method can effectively attenuate the disturbance of uncertainties as well as reduce energy costs and emissions, as compared with single-objective robust optimization and multi-objective optimization scheduling approaches. This study could offer useful insights which help decision-makers balance robustness and comprehensive benefits in the operation of MGs. [ABSTRACT FROM AUTHOR]

Renewable energy-based multi-energy supply microgrids are expected to play an important role in smart cities. How to schedule such microgrids in grid-connected mode and dispatch power among sources inside the microgrids is a problem. Moreover, as electric vehicles are becoming more and more common, the charging of large numbers of vehicles is also a challenge for the utility grid. In this paper, we build a temporal-spatial electric vehicle charging demand model, which includes three parts: trip plans, duration of stay, and search for the shortest path based on the Dijkstra algorithm. Then, we build a grid-connected gas/electricity/heat microgrid and present a coordinated scheduling method for this microgrid. A day-ahead scheduling method is used to decide the role of the microgrid (i.e., operate as a load or as a generator from the point of view of the utility), a real-time rolling-horizon dispatching algorithm is used to respond to the forecasting errors and at the same time implement the real-time actual power exchange between the microgrid and the main grid. The problem is formulated as a mixed integer linear programming problem. The temporal-spatial electric vehicle charging demands model is simulated based on a 81-node transportation network, while the energy supply network is a combined IEEE-30, gas-20 and heat-14 network. The simulation results show the effectiveness of this coordinated scheduling method. [ABSTRACT FROM AUTHOR]

In this paper, a novel multilayer control structure for microgrids is proposed. A scheduling layer comprehends the minimization of the microgrid operating costs together with the CO2 emissions produced and provides a sequence of power references for the next 24 h. Subsequently, within the executive layer, an off line ac power flow calculation will be performed to obtain the initial values of the voltage magnitudes of the different microgrid buses. The adjustment layer, which is the scope of this paper, includes a control strategy to maintain the voltage in the network. The purpose of this third layer is to keep the voltage within a pre-specified tolerance band by adjusting the power provided by the microgrid distributed energy resources (DER). Depending on the voltage deviation, the location of the DER units in the network and their distance from the voltage deviation, an appropriate dynamic gain will be provided to the relevant DER units. The renewed settings are then fed back to the first layer, which performs a new optimization and redistributes the adjusted reference set points among the DER units. The performance and effectiveness of the proposed hierarchical multilayer control structure were evaluated and demonstrated by several case studies. [ABSTRACT FROM AUTHOR]

This work presents a Mixed Integer Linear Programming (MILP) approach based on a combination of a rolling horizon and stochastic programming formulation. The objective of the proposed formulation is the optimal management of the supply and demand of energy and heat in microgrids under uncertainty, in order to minimise the operational cost. Delays in the starting time of energy demands are allowed within a predefined time windows to tackle flexible demand profiles. This approach uses a scenario-based stochastic programming formulation. These scenarios consider uncertainty in the wind speed forecast, the processing time of the energy tasks and the overall heat demand, to take into account all possible scenarios related to the generation and demand of energy and heat. Nevertheless, embracing all external scenarios associated with wind speed prediction makes their consideration computationally intractable. Thus, updating input information (e.g., wind speed forecast) is required to guarantee good quality and practical solutions. Hence, the two-stage stochastic MILP formulation is introduced into a rolling horizon approach that periodically updates input information. [ABSTRACT FROM AUTHOR]

This work analyses the economic and technical impact of local reactive power provision in grid-connected microgrids with distributed energy resources. Costs of reactive power provision by photovoltaic systems and battery energy storage systems are explicitly formulated and an objective function incorporating the costs is proposed. The advantage of the proposed objective function is validated by comparing it with other objective functions frequently employed in the literature. From various case studies, the extent of economic and technical benefits of local reactive power provision for the microgrid is established. Subsequently, the technical and economic competitiveness of reactive power provision using inverter-based distributed energy resources are compared against those using switched capacitors. Extensive sensitivity analyses are performed to determine the scenarios in which one technology is more competitive than the other. Inverter efficiency has been identified as the most important parameter for reactive power provision from distributed energy resources while electricity price is the most crucial factor for switched capacitors’ competitiveness in producing reactive power. [ABSTRACT FROM AUTHOR]

This work is focused on the optimal management of electricity and heat generation and demand in microgrids. The objective of the proposed mathematical model is to adjust energy and heat availability profiles resulting from the use of renewable energy sources and flexible energy and heat demands. The optimisation of the resulting short-term problem is addressed through a Mixed-Integer Linear Programming (MILP) mathematical model to minimise the operational cost of the microgrid. Delays in the energy demands are allowed to tackle flexible demand profiles, under penalties in the objective function. An additional characteristic was the consideration of non-constant profiles in the considered tasks. Also, this model takes into account eventual interruptions in the tasks, applying penalties in the economic objective function. The main decisions to be made includes the schedule of tasks, as well as energy and heat generation levels, purchases from and exportation to the power grid, and storage levels. [ABSTRACT FROM AUTHOR]

With increasing concerns about worldwide environmental conditions and rapid development of renewable energy technologies, microgrids have been regarded as a promising solution to reduce the burden of infrastructure-based power systems. However, due to the intrinsically intermittent features of existing renewable energy, along with random residential behavior patterns, unpredictable plugged-in or unplugged actions of electric vehicles (EVs) and the time-varying price of electricity, it is challenging for microgrid operators to efficiently perform load scheduling and energy management. In this paper, we propose an online algorithm to conduct cost-aware scheduling of EV loads and energy supplies for microgrids. We formulate this problem into a stochastic optimization problem with the objective of minimizing the time-average cost of a microgrid, including the purchase cost of electricity from the main grid, the cost of charging and discharging batteries, renewable harvesting costs, and life-cycle greenhouse-gas emission costs. To solve this problem, the key idea is to exploit the dynamics of the price of electricity to conduct battery charging and discharging operations, renewable energy harvesting, and schedule EV loads properly. Our method is based on the Lyapunov optimization technique, which has low computational complexity and only requires limited prediction of price information. The theoretical analysis of our algorithm confirms that the proposed strategy can achieve optimality with explicit bound. By conducting extensive real-data driven simulations, we demonstrate that our proposed algorithm can achieve much lower cost and be more eco-friendly than other alternative solutions. [ABSTRACT FROM PUBLISHER]

Due to different viewpoints, procedures, limitations, and objectives, the scheduling problem of distributed energy resources (DERs) is a very important issue in power systems. This problem can be solved by considering different frameworks. Microgrids and Virtual Power Plants (VPPs) are two famous and suitable concepts by which this problem is solved within their frameworks. Each of these two solutions has its own special significance and may be employed for different purposes. Therefore, it is necessary to assess and review papers and literature in this field. In this paper, the scheduling problem of DERs is studied from various aspects such as modeling techniques, solving methods, reliability, emission, uncertainty, stability, demand response (DR), and multi-objective standpoint in the microgrid and VPP frameworks. This review enables researchers with different points of view to look for possible applications in the area of microgrid and VPP scheduling. [ABSTRACT FROM AUTHOR]

• A multi-time scale optimal scheduling framework is proposed for microgrid scheduling to deal with the uncertainty of source and load. • A two-stage distributionally robust model is constructed to improve the robustness of the day-ahead scheduling plan. • The distributed model prediction control method is used to maintain the smooth power of contact lines. • The proposed multi-time scale scheduling method can achieve robust and economic operation of the microgrid. With the increase of renewable energy penetration in microgrid and the stochasticity of customer load, microgrid faces new difficulties in maintaining the smooth power of contact lines and system economy when achieving optimal scheduling. In order to balance the accuracy, economy and robustness of microgrid scheduling better, a multi-time scale optimal scheduling strategy for microgrids considering the uncertainty of source and load is proposed. In the day-ahead scheduling stage, a two-stage distributionally robust optimal scheduling model is established with the objective of minimizing the comprehensive day-ahead scheduling cost of the microgrid, and the optimal day-ahead scheduling solution is found under the probability distribution of the worst scenario. In the intra-day scheduling stage, based on the prediction value of source and load with higher accuracy, a distributed model predictive control method is used to build an intra-day rolling optimization and real-time adjustment optimal scheduling model, which ensures the effective implementation of the day-ahead plan and fully suppresses power fluctuation of the contact line. The simulation results show that the proposed multi-time scale optimal scheduling method can not only maintain the smooth power of contact lines, but also achieve robust and economic operation of the microgrid. [ABSTRACT FROM AUTHOR]

• A day-ahead, intraday, multi-time scale economic scheduling method based on lightly robust optimization and model predictive control (MPC) is also proposed. An optimized microgrid scheduling model is established considering demand responses, forecast errors, and the effects of uncertainties in different scheduling stages. A day-ahead, intraday, multi-time scale economic scheduling method based on light robust optimization and model predictive control (MPC) is also proposed. In the day-ahead, long-time-scale scheduling stage, light robustness optimization is used to cope with low-frequency components in prediction errors and uncertainties, and mitigates the deviations between the day-ahead scheduling plan and the actual economic scheduling outcomes under source and load forecast errors and uncertainties. At the intraday, short-time-scale stages, the MPC tracks the day-ahead light robustness economic scheduling plan, considering the high frequency components of prediction errors and uncertainties, so as to achieve robust open-loop control, better tracking results, and better economy. Analytical results demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

• The proposed study focuses on exploiting the potential of IoT technology in mission-critical energy management systems; • IoT-orchestration is enabled for nanogrid energy management architecture that focuses on minimizing the use of non-renewables and maximizing the use of renewables; • The orchestration module automatically generates the energy tasks and assigns them to the corresponding physical devices, which leads to enhanced efficiency and robustness in comparison with conventional energy management architectures; • The study also proposes a time-aware task scheduling algorithm wherein the task's surplus time is optimized to determine the best execution order; • Compared to conventional scheduling techniques, the proposed algorithm improved energy tasks' latency, response time, and round trip time (RTT). Internet of Things (IoT) has brought an immense revolution in diverse fields related to mission-critical systems including healthcare and navigation systems. We argue that the potential of IoT has not been fully exploited in the energy sector. There is a dire need to shift the traditional paradigm of mission-critical electric power architectures to IoT-enabled fully orchestrated architectures to enhance the overall performance. In this study, we present a novel IoT task orchestration architecture for efficient energy management of a nanogrid system that focuses on minimizing the use of non-renewable energy resources and maximizing the use of renewable energy resources. Since network orchestration deals with automating the interaction between multiple components involved to execute a particular service, therefore, scheduling the relevant processes within strict deadlines becomes the core pillar of the system's performance. The mission-critical systems with urgent task execution often suffer from missing task deadlines issues. To overcome this issue, we present a task scheduling algorithm that incorporates the optimized surplus time, and efficiently executes the energy management-related tasks contemplating to their types. The study utilizes sensors to obtain data from physical IoT devices, including photovoltaic (PV), Energy Storage System (ESS), and diesel generator (DG). The performance of the proposed model is evaluated using data set of nanogrid houses. The outcomes revealed that IoT-task orchestration has played a pivotal role in efficient energy management for nanogrid missioncritical systems. Furthermore, the comparison showed that the task starvation rate is reduced to 16% and 12% when compared with state-of-the-art scheduling algorithms. [ABSTRACT FROM AUTHOR]

With increasing hydrogen usage, hydrogen subsystem should be considered in the multi-energy chain and a model is required that assumes current energy infrastructures, while preserving independent energy subsystems. In this study, a stochastic agent-based model is introduced for the coordinated scheduling of multi-vector microgrids considering interactions between electricity, hydrogen, and gas agents. Power to hydrogen (P2H) through electrolysis, hydrogen to power (H2P) through fuel cells, hydrogen to gas (H2G) through methanation, and gas to power (G2P) through distributed generation (DG) units are modeled to present the interactions among energy agents. The interactions in terms of shared variables and coupling constraints are described using augmented Lagrangian relaxation (ALR) and alternating direction method of multipliers (ADMM) to obtain three correlated optimization problems, preserving the privacy of energy sectors with minimum data exchange. An iterative process is accomplished among energy sectors to reach a consensus. Uncertainties in the wind turbine (WT) and photovoltaic (PV) power output, hydrogen vehicles (HVs), demands, and prices are captured using a stochastic method. To evaluate the proposed method, case studies are conducted using a multi-energy microgrid. The results verify that the microgrid is well scheduled and the interactions are accurately modeled, representing the effectiveness of the proposed method. • An electricity-hydrogen-gas microgrid is developed. • Different facilities including P2H, H2P, H2G, G2P and HSS are considered. • A multi-agent model is proposed to preserve the privacy of energy parties. • Independent optimization problems are solved cooperatively to reach a consensus. • Interactions among electricity, hydrogen, and gas subsystems are modeled. [ABSTRACT FROM AUTHOR]

Using the example of a two-layer energy management system for remote microgrids, a novel scheduling algorithm is proposed that considers the battery lifetime and is expected to reduce the operational cost of the microgrid. This method combines the objectives of minimizing fuel consumption and battery degradation costs into a single weighted goal. [ABSTRACT FROM AUTHOR]

This work focuses on the development of optimization-based scheduling strategies for the coordination of microgrids. The main novelty of this work is the simultaneous management of energy production and energy demand within a reactive scheduling approach to deal with the presence of uncertainty associated to production and consumption. Delays in the nominal energy demands are allowed under associated penalty costs to tackle flexible and fluctuating demand profiles. In this study, the basic microgrid structure consists of renewable energy systems (photovoltaic panels, wind turbines) and energy storage units. Consequently, a Mixed Integer Linear Programming (MILP) formulation is presented and used within a rolling horizon scheme that periodically updates input data information. [ABSTRACT FROM AUTHOR]