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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]

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]

Highlights • Integrated volt-var optimization engine is proposed for distributed grid control. • The proposed method reduces up to 92% computational time. • The benefits and charging demands of electric vehicle owners can be guaranteed. • Costly diesel generator usage can be reduced by adopting more vehicle-to-grid var. • The computation time of optimization decreases even when more energy sources added. Abstract Electric Vehicles have been receiving increasing attention. As the number of electric vehicles increases, uncoordinated charging of electric vehicles can lead to voltage and frequency instability in microgrids. Various methods have been proposed for electrical vehicle coordination, where most of them focused on controlling active power. Vehicle-to-grid var has been recently included in volt-var optimization approaches, which aim at improving voltage stability using var sources. However, most of these approaches are based on computationally inefficient heuristic methods, which are not applicable to handle fast-changing vehicle-to-grid var. Furthermore, the uncertainties and charging demands of electric vehicles have not been considered thoroughly. In this paper, an integrated volt-var optimization engine is proposed for distributed electric vehicle charging coordination and fast vehicle-to-grid var dispatch, considering the uncertainties and charging demands of electric vehicles. The proposed method is based on a multi-agent system, which distributes complex optimization processes to enhance computational efficiency. Case studies show that the proposed distributed method reduces up to 92% computational time without economic losses, compared with the central coordination. It is also observed that the costly usage of diesel generators can be reduced by employing more vehicle-to-grid var due to their similar functionality in voltage regulation. Surprisingly, it is found that when utilizing the power support from electric vehicles and diesel generators, the computational time decreases even when more decision variables are added. [ABSTRACT FROM AUTHOR]

Highlights • An architecture of microgrid energy management system was established. • The architecture was designed for China in the context of Energy Internet. • Two-stage robust optimization model and algorithm were proposed. • Case study was conducted using a real case of Beijing Jinfeng Industrial Park. Abstract In order to address the practical challenges posed by the increasing penetration of distributed energy resources and electric vehicles, the evolution from traditional power systems to Energy Internet and the rapidly changing market and policy environments in China, this paper proposes an Energy-Internet-oriented architecture of microgrid energy management system for China, considering specific technical requirements, markets and policies in China. Following the proposed architecture, the robust operation of a multi-energy microgrid with renewable energy and load uncertainties is formulated as a two-stage robust optimization problem and the column and constraints generation algorithm is used to solve it. A real-life case study in China, Beijing Goldwind Industrial Park, is conducted to demonstrate the proposed architecture and method. Simulation results show that by exporting surplus generation to the external customers and bulk power grid, as an important feature of the market dimension of the proposed architecture, the revenue of the microgrid is improved significantly compared to the traditional business mode. Moreover, as important features of technology and regulation dimensions of the proposed architecture, it is shown that smart charging of electric vehicles is able to improve the revenue of the microgrid as well. Finally, the results also show that the proposed robust operation method achieves robustness at the cost of lower revenue in non-worst scenarios. [ABSTRACT FROM AUTHOR]

Highlights • An interactive mechanism is proposed for smart distribution networks with microgrids. • The interaction process is formulated as Stackelberg game under a market framework. • The uncertainty of microgrids is handled by two-stage robust method. • A collaborative strategy is established to promote the trading among microgrids. Abstract This paper proposes a two-level interactive mechanism to address the day-ahead scheduling optimization of smart distribution networks with multi-microgrids. Each microgrid realizes the economic dispatch in the lower level interactive model, where the uncertainties caused by renewable energy integration and loads as well as the coordination of controllable distributed generator, energy storage and demand response are handled through a two-stage robust model. In the upper level interactive model, the distribution network operator (DNO) guarantees the operational quality by considering the power flow constraints and decides the transaction price and quantity with each microgrid based on the solutions of the lower level problem, to minimize the operation cost. The interaction process is formulated as a Stackelberg game, in which the trading strategies of coupled microgrids, acted as followers, are influenced by price incentive mechanism of DNO and affect the decision of DNO in turn. A collaborative strategy promoting the internal power trading among microgrids is also presented to deal with the market power of DNO, such that the participating microgrids can benefit from the internal trading and further reduce the cost. Numerical cases on an improved IEEE 33-bus system show the effectiveness of the proposed model and solution strategy. [ABSTRACT FROM AUTHOR]

Independent operation of single microgrids (MGs) faces problems such as low self-consumption of local renewable energy, high operation cost and frequent power exchange with the grid. Interconnecting multiple MGs as a multi-microgrid (MMG) is an effective way to improve operational and economic performance. However, ensuring the optimal collaborative operation of a MMG is a challenging problem, especially under disturbances of intermittent renewable energy. In this paper, the economic and collaborative operation of MMGs is formulated as a unit commitment problem to describe the discrete characteristics of energy transaction combinations among MGs. A two-stage adaptive robust optimization based collaborative operation approach for a residential MMG is constructed to derive the scheduling scheme which minimizes the MMG operating cost under the worst realization of uncertain PV output. Transformed by its KKT optimality conditions, the reformulated model is efficiently solved by a column-and-constraint generation (C&CG) method. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy transactions in MMGs. The results show that the developed MMG operation approach is able to minimize the daily MMG operating cost while mitigating the disturbances of uncertainty in renewable energy sources. Compared to the non-interactive model, the proposed model can not only reduce the MMG operating cost but also mitigate the frequent energy interaction between the MMG and the grid. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed energy resources in the distribution grid is producing an ever-heightening interest in the use of the flexibility on offer by said distributed resources as an enhancement for the distribution grid operator. This paper proposes an optimization problem which enables satisfaction of distribution system operator requests on flexibility. This is a decision-making problem for a new aggregator type called Smart Energy Service Provider (SESP) to schedule flexible energy resources. This aggregator operates a local electricity market with high penetration of distributed energy resources. The optimization operation problem of SESP is formulated as an MILP problem and its performance has been tested by means of the simulation of test cases in a local market. The novel problem has also been validated in a microgrid laboratory with emulated loads and generation units. The performed tests produced positive results and proved the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

This paper proposes a bi–level formulation for a coupled microgrid power and reserve capacity planning problem, cast within the jurisdiction of a distribution system operator(DSO). The upper level problem of the proposed bi–level model represents a microgrid planner whose goal is to minimize its planning and operational cost, while the lower level problem represents a DSO whose primary duty is to ensure reliable power supply. The microgrid planner, pursues its interest by co–optimizing the design configuration and power output of individual distributed energy resources (DERs), while the DSO maximizes the capacity of flexible reserve resources. The proposed model is recast as a mathematical program with equilibrium constraints (MPEC) wherein the decision variables of the two problems are independently controlled. Application of the proposed approach to the energy infrastructure of a Canadian utility network is discussed. Results obtained through its application are compared to an alternative multi–objective planning model and the improved benefits are assigned to the corresponding stakeholders. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of optimally operating a set of grid-forming devices in an AC microgrid when a detailed network model is not available. The main aim of the approach is to maximize the power sharing of the controllable grid-forming devices and to maintain the frequency and the nodal voltages of the microgrid as close as possible to their corresponding references. The proposed control architecture is conformed by a local control layer in each grid-forming device that intends to emulate the performance of a synchronous machine and a centralized secondary controller composed of two complementary tools that coordinates the setpoints of the grid-forming devices: an online feedback optimization algorithm and an automatic generation control. The proposed method has been validated through simulations and hardware-in-the-loop tests, evidencing its good performance and robustness under different conditions. • A new control scheme to optimally operate a set of grid-forming Converter Interfaced Generators (CIGs) is proposed • A PLL-free primary control layer guarantees active power sharing and frequency support. • The secondary controller ensures reactive power sharing and optimal nodal voltages. • The secondary controller operates without a microgrid model, ensuring operational constraints. • The controller manages islanded low-voltage microgrids with 100% of renewable based CIGs. [ABSTRACT FROM AUTHOR]

Recent severe power outages caused by extreme weather hazards have highlighted the importance and urgency of improving the resilience of electric distribution grids. Microgrids with various types of distributed generators (DGs) have the potential to enhance the electricity supply continuity and thus facilitate resilient distribution grids under natural disasters. In this paper, a novel load restoration optimization model is proposed to coordinate topology reconfiguration and microgrid formation while satisfying a variety of operational constraints. The proposed method exploits benefits of operational flexibility provided by grid modernization to enable more critical load pickup. Specifically, a mixed-integer second order cone programming is employed to reduce the computational complexity of the proposed optimization with optimality guaranteed. Finally, the effectiveness of the proposed method has been verified on an IEEE 33-bus test case and a modified 615-bus test system. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive strategy for the scheduling of unbalanced microgrids is proposed. The microgrids are characterized by the presence of a variety of distributed resources, such as data centers, electric vehicles, and distributed generation systems. The operation strategy is based on the solution of a multi-objective optimization problem, whose objective functions are related to specific services and requirements of the microgrid, such as cost minimization, power quality improvements, and energy savings. Particular attention is paid to the unbalances presence in the microgrid with the aim to outline how the proposed strategy is able to significantly reduce them taking contemporaneously into account other important economic and technical objectives. An analysis of the objectives and the different operational perspectives also is proposed, which allows us to exploit the effectiveness of the multi-objective approach. The results of numerical applications are presented to show the effects of the considered strategy in terms of achievable advantages. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new morphological filter is used for power quality disturbances in DC microgrid. • A deep stacked autoencoder is used for extracting unsupervised features. • Kernel random vector functional link network is used for disturbance classification. • A new hybrid optimization technique is used for optimization of kernel parameters. • Hardware in the loop approach is used to validate the classification performance. In this paper, an adaptive multiscale enhanced average combination morphological filter is proposed to analyze voltage signals captured at the point of common coupling of the photovoltaic integrated direct current microgrid for diagnosing several power quality disturbances. This method performs multiscale operation on the power quality disturbance signals from which the optimized structuring element scales are chosen by using maximum value of sparse envelope kurtosis index. Further, statistical features are extracted by using optimized scales. The extracted features are used as inputs to a deep stacked extreme leaning machine autoencoder to retrieve the most distinguishable unsupervised features and these features are processed through kernel random vector functional link network based supervised classifier to classify the power quality disturbances. Further, to improve the classification performance of the proposed technique, a meta heuristic chaotic particle swarm optimization integrated with sine cosine optimization algorithm is used. This optimization algorithm optimizes the kernel parameters by minimizing the mean absolute error. The efficacy of the proposed technique is tested in terms of classification accuracy and confusion matrix and is verified by multifunction high-speed national instrument device for monitoring of power quality disturbances in the MATLAB/Simulink platform. The simulations and hardware results demonstrate that the proposed technique exhibits remarkable accuracy, faster training time and low computational complexity as compared with other existing methods. [ABSTRACT FROM AUTHOR]

• Proposing an energy management strategy for a renewable-based microgrid. • Minimum total operating cost is achieved successfully. • The bus voltage has been stabilized employing a flat controller. • Comprehensive analysis and statistics have been provided. This research proposes an effective energy management strategy (EMS) for the economic operation under standalone and grid-connected operating modes of integrated solar renewables microgrid. The proposed microgrid is composed of a photovoltaic generator (PV), a fuel cell system (FC), and a battery storage system. The random nature of the renewable and the load power imposed some stability problems and economic problems, including operating costs. The suggested technique was based on the bald eagle search optimization algorithm (BES), which was designed for a one-day scheduling horizon. The key objectives of this paper were to satisfy the load power with the lowest operating costs under a stable direct current (DC) bus voltage, enhance the overall system efficiency and protect the battery from deep discharge and overcharge. To demonstrate the effectiveness of the proposed strategy, the obtained results were compared with other optimizers, including particle swarm optimization (PSO), salp swarm algorithm (SSA), artificial eco-system optimizer (AEO), COOT optimizer, and political optimizer (PO). The comparison confirmed the superiority of the proposed strategy in terms of minimum operating energy cost (0.1577c€/kW), high efficiency (87.395%) and final SoC (33.268%). [ABSTRACT FROM AUTHOR]

• Novel method for transition optimization to reliable and resilient microgrids. • Efficient Column-and-Constraint-Generation solution boosts scalability. • Comprehensive modeling of internal and external faults minimizes total costs. • Storage is cost-optimal for resilient microgrids with renewable generation. • Optimal design solutions are robust in the range [12,24] h of islanded operation. Reliability and resilience are the main drivers for the transition of distribution networks from passive systems to active microgrids; as such, quantifying the potential benefits of microgrids in the design phase can support the transition of passive distribution networks into microgrids. To support this transition, this paper presents a mathematical optimization model which integrates techno-economic, resilience and reliability objectives. Storage and distributed generation are optionally installed to complement renewable generation, enabling the microgrid to supply priority demands during stochastic islanding events with uncertain duration. Islanding due to external events is combined with a detailed model of internal faults for comprehensive quantification and optimization of microgrid resilience and reliability. Minimizing the interruption costs yields optimal capacities and placements of distributed energy resources and new lines for reconfiguration. The proposed method produces microgrid designs with up to 95% reliability and resilience gain and moderate cost increase in two benchmark distribution networks using data from the United States Department of Energy. The developed methodology is scalable to large networks owing to the tailored Column-and-Constraint-Generation approach, reducing the computational time by 92% compared to state-of-the-art Benders decomposition for a 123 bus network. [ABSTRACT FROM AUTHOR]