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• Metal hydride tanks were investigated experimentally. • Levelized cost of hydrogen was determined. • Alkaline and PEM- based generators were compared. • Avoided energy consumption comparing to compression was shown. • Universal methods for economic analysis were developed. This study presents methodology for the evaluation of appropriateness of a hydrogen generator for gas production in multiple distributed plants based on renewable energy sources. The general idea is to form hydrogen clusters integrated with storage and transportation. The paper focuses on the financial viability of the plants, presenting the results of economic evaluation together with sensitivity analysis for various economic factors. The analyzed case study proves that over a wide range of parameters alkaline electrolyzers show favorable economic characteristics, however, a PEM-based plant is more resilient to changes in the price of electricity, which is the main cost component in hydrogen generation. The study is enriched with an experimental investigation of low-pressure storage methods, based on porous metal hydride tanks. The effectiveness of the tanks (β) compared to pressurized hydrogen tanks, in the same volume and pressure is equal to β = 10.2. A solution is proposed whereby these can be used in a distributed hydrogen generation concept due to their safe and simple operation, without additional costly equipment, e.g., compressors. A method for evaluation of the avoided energy consumption as a function of the effectiveness of the tanks is developed. Avoided energy consumption resulting from implementing MH tanks equals 1.33 – 1.37 kWh per kilogram of hydrogen depending on the number of stages of a compressor. The methods proposed in this paper are universal and can be used for various green hydrogen facilities. [ABSTRACT FROM AUTHOR]

Achieving the goal of sustainable development is dependent on the widespread integration of renewable energy sources, energy storage systems (ESSs), and electric vehicles (EVs). However, a continuous increase in the penetration of such elements would bring more complexities to the distribution network. Accordingly, this paper presents a unified planning model comprising renewable energy-based distributed generation (DG), ESS, and electric vehicle charging stations (EVCSs). In this regard, a Latin Hypercube Sampling method is utilized to generate multi-scenario for describing the uncertainty of renewable energy and load demand. The stochastic EV charging behaviors are represented by various probability density functions (PDF). In addition, an exploitable capacity loss of ESS and EV batteries is calculated by the battery degradation model based on the depth of discharge (DOD). Furthermore, the battery degradation cost is incorporated into the objective of the planning model to identify the optimal decision for candidate assets. A piecewise linearization approach is introduced to convert the problem into a mix-integer linear programming (MILP) model. Numerical results demonstrate that the exploitable capacity loss of batteries plays a key role in asset planning and provides potential contributions to the optimal decisions of the distribution network. In the meantime, by considering battery degradation in the optimization model, the sustainability and lifetime of the battery can be preserved. • Uncertainties include the renewable energy, load demand, and behaviors of electric vehicles are considered. • Renewable energy-based DG, ESS, and EVCS are involved in the unified planning model. • The degradation model is proposed to analyze the exploitable capacity loss of ESS and EV battery. • The enhancement of investment decision while maintaining the lifetime of battery is achieved. [ABSTRACT FROM AUTHOR]

In a previous paper, we have introduced a simulation and optimization framework for the integration of renewable generators into an electrical distribution network. The framework searches for the optimal size and location of the distributed renewable generation units (DG). Uncertainties in renewable resources availability, components failure and repair events, loads and grid power supply are incorporated. A Monte Carlo simulation–optimal power flow (MCS–OPF) computational model is used to generate scenarios of the uncertain variables and evaluate the network electric performance with respect to the expected value of the global cost ( ECG ). The framework is quite general and complete, but at the expenses of large computational times for the analysis of real systems. In this respect, the work of the present paper addresses the issue and introduces a purposely tailored, original technique for reducing the computational efforts of the analysis. The originality of the proposed approach lies in the development of a new search engine for performing the minimization of the ECG , which embeds hierarchical clustering analysis (HCA) within a differential evolution (DE) search scheme to identify groups of similar individuals in the DE population and, then, ECG is calculated for selected representative individuals of the groups only, thus reducing the number of objective function evaluations. For exemplification, the framework is applied to a distribution network derived from the IEEE 13 nodes test feeder. The results show that the newly proposed hierarchical clustering differential evolution (HCDE) MCS–OPF framework is effective in finding optimal DG-integrated network configurations with reduced computational efforts. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Coordinated control of three- and arbitrarily connected single-phase energy sources. • Accurate power flow regulation at the microgrid's point-of-common-coupling. • Unbalance compensation by single- and three-phase inverter-interfaced resources. • Active and reactive power sharing proportional to local power availabilities. • Compliance with constraints and limits of distributed energy resources. Abstract This paper proposes the third generation of the Power-Based Control, that is, an approach to effectively coordinate the operation of single- and three-phase inverter-interfaced distributed energy resources that can be arbitrarily connected among the phases of three-phase four-wire low-voltage microgrids. The aim of the approach is to precisely regulate the power exchanged with the main grid by fairly exploiting the available distributed resources while respecting their own local constraints. This allows to achieve demand-response, unbalance compensation, and improved voltage profiles, which is valuable for limiting stress conditions to the distribution infrastructure. The technique is based on a master/slave microgrid architecture where the distributed inverters act as slave units driven by a centralized master controller. This latter employs the Power-Based Control in order to steer the contribution of the inverters on the basis of the microgrid power status. In particular, active, reactive, and unbalance power terms are processed by the master controller and the corresponding microgrid's power needs shared among the distributed energy resources, to achieve the compensation target at the point-of-common-coupling with the main grid. The strategy proposed herein drives the three-phase inverters to operate balanced, which avoids unwanted voltage fluctuations at the DC-side of the inverters; instead, single-phase inverters, which can be connected arbitrarily among the phases of the distribution grid, are exploited to compensate any residual unbalances (i.e., negative- and zero-components). The control technique is analyzed and demonstrated by computer simulations considering a real urban meshed distribution network. Steady-state and dynamic results and performances are reported and discussed considering typical demand and generation profiles. [ABSTRACT FROM AUTHOR]

Microgrids are considered one of the most promising solutions to integrate renewable distributed generation into the electric power system. During the last decade, the microgrid concept has been studied and developed and nowadays it is becoming a reality. Hence, in the coming years a transformation of the current electric power system to a multi-microgrid power system can be expected. In this direction, the study of multi-microgrids is currently being explored. Accordingly, this paper examines the possible multi-microgrid architectures to form a grid of microgrids. For this purpose, the microgrid as a single entity and its possible interactions with external grids is first defined. Then, the possible multi-microgrid architectures are defined in terms of layout, line technology and interface technology. Finally, a comparison between the different architectures is performed in terms of cost, scalability, protection, reliability, stability, communications and business models. This analysis is expected to be of great utility for grid planners and policy makers, who can select the most adequate architecture in function of their necessities. [ABSTRACT FROM AUTHOR]

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

The large-scale application of renewable energy is promising to solve the energy crisis over the world. Renewable energy is generally integrated into distribution networks or microgrids through inverters. Inverter-interfaced distributed generators (IIDGs) have the advantage of utilizing renewable energy effectively and flexibly. However, the wide interconnection of IIDGs causes transient stability to the public grid. This paper presents the transient stability mechanism of the grid-connected IIDGs using droop control. The transient stability mechanism is obtained by combining the transient stability condition and the comprehensive dynamic trace. The transient stability condition is proposed by the transient model and IIDGs’ operation features. The comprehensive dynamic trace is formulated by the active and reactive power curves during transient events. The mechanism is able to reflect the relations between transient stability and droop factors and reveal the transient behaviors of IIDGs. The transient stability judgment is correspondingly developed. A series of simulations demonstrate the correctness and effectiveness of the transient stability mechanism. The transient stability mechanism better expands and develops the utilization of renewable energy. [ABSTRACT FROM AUTHOR]

• Non-simulation-based analytical reliability evaluation of renewable energy integrated distribution systems. • New linear expressions for reliability assessment of radial distribution systems. • Application of the proposed reliability model on the 37-node to 1080-node practical radial test systems. One of the most critical goals in the operation and planning of distribution networks is the creation of networks with sufficient reliability. Existing models are often simulation-based or try to introduce topology-independent algebraic reliability measures with simplifications or extensive computations. This paper presents new and efficient topology-variable-based linear expressions that can evaluate the reliability indices of practical radial distribution networks. Furthermore, the model is extended to consider the inclusion of renewable distributed generation (DG) units to restore part of restorable loads in the islanded mode of operation. Also, the stochastic nature of renewable generation, as well as load demand, is considered. Therefore, the proposed model can be readily used in various optimization models to operate and plan distribution networks with reliability concerns. The application of the proposed method to several small- to large-scale test cases ranging from standard 37-node up to practical 1080-node benchmarks shows the proposed model's accuracy and computational effectiveness compared to the state-of-the-art conventional simulation-based topology-variable-based approaches. The impact of the system's islanded operation on the reliability indices is also evaluated on the modified DG-enhanced 37-node test system. [ABSTRACT FROM AUTHOR]

The paper presents a control system for the management of operation of the low-voltage (LV) networks with a large number of renewable energy sources (RES) connected in prosumer's installations. The system is aimed to reduce power congestion and regulate voltage in individual phases of the network nodes. The applied strategy assumes using controllable energy sources and energy storage systems (ESS) located in prosumers' installations and performing local energy management, on the basis of ancillary services. The ESSs and RESs reactive and active powers are used as control measures with the reactive power as a priority. The system strategy and control algorithms for the voltage and power regulation are described in the paper. Then, the authors present the results of simulations which were performed using the model of a real LV network with prosumer installations. Simulation results proved that the system is effective and the use of prosumers' ESS capacities for regulation purposes is minimized. Finally, the developed system was verified experimentally at the Laboratory of Distributed Generation at Lodz University of Technology, using the Real Time Digital Simulator and the Hardware-in-the-Loop method. Conclusions found are that the proposed system can successfully solve operational problems in LV networks with high penetration of RES without the need of network reinforcement or use additional regulatory devices. The strategy based on the concept of ancillary services encourages prosumers to actively influence the network operation for benefits of prosumers, consumers and the network operator. [Display omitted] • An effective tool for DSO to manage LV networks with distributed generation. • A distributed control system for LV voltage networks with renewable energy sources. • Ancillary services provided by prosumers for power quality improvement. • Concept of strategy for controlling energy storages in prosumer installations. • The Hardware-in-the-Loop method for the verification of the network control system. [ABSTRACT FROM AUTHOR]

In this paper, an ontology-driven multi-agent based energy management system (EMS) is proposed for monitoring and optimal control of an integrated homes/buildings and microgrid system with various renewable energy resources (RESs) and controllable loads. Different agents ranging from simple-reflex to complex learning agents are designed and implemented to cooperate with each other to reach an optimal operating strategy for the mentioned integrated energy system (IES) while meeting the system’s objectives and related constraints. The optimization process for the EMS is defined as a coordinated distributed generation (DG) and demand response (DR) management problem within the studied environment and is solved by the proposed agent-based approach utilizing cooperation and communication among decision agents. To verify the effectiveness and applicability of the proposed multi-agent based EMS, several case studies are carried out and corresponding results are presented. [ABSTRACT FROM AUTHOR]

Hybrid systems are implemented to improve the efficiency of individual generation technologies by complementing each other. Intermittence is a challenge to overcome especially for renewable energy sources for electric generation, as in the case of wind power. This paper proposes a hybrid system as an approach for reducing and overcoming the volatility of wind power, by implementing storage technology, forecasts and predictive control. The proposed hybrid system, which is suitable for the distributed generation level, consists of a wind generator, an electrolyzer, hydrogen storage and a polymer electrolyte membrane fuel cell, which are embedded in one complete system with the wind power. This study uses historic wind speed data from Mexico; the forecasts are obtained using the recursive least square algorithm with a forgetting factor. The proposed approach provides probabilistic information for short-term wind power generation and electric generation as the outcome of the hybrid system. A method for a semi-dispatchable electric generation based on time series analysis is presented, and the implementation of wind power and polymer electrolyte membrane fuel cell models controlled by a model predictive control approach is developed. [ABSTRACT FROM AUTHOR]

Chongming’s current energy system is highly import-dependent and the distributed energy system with high renewable energy penetration should be taken into consideration. In this paper, a simulation model based on EnergyPLAN software with 2014 as the reference year is developed to represent Chongming’s present energy structure. Four future scenarios which propose different further energy constructions have been observed. Results show that reductions in primary energy consumption, electricity import, annual operating costs as well as CO 2 emissions are realizable when switching to an energy system with a high share of renewable sources combining natural gas CHP (Combined Heating and Power). After preliminary discussions, the feasibility of different renewable sources penetration are analyzed through calculating the excess electricity production (CEEP). The estimated optimal combination is generated when the PV fraction is 20% while wind energy fraction is 80%. Moreover, the analyses have taken into account that several flexible techniques are needed in order to balance supply and demand and to ensure the grid voltage and frequency stabilities. As a result, through the recommended three techniques, the excess production can be avoided as far as a renewable energy input of up to nearly 60%. [ABSTRACT FROM AUTHOR]

This paper presents a decentralized scheduling algorithm for electric vehicles charging. The charging control model follows the architecture of a Multi-Agent System (MAS). The MAS consists of an Electric Vehicle (EV)/Distributed Generation (DG) aggregator agent and “Responsive” or “Unresponsive” EV agents. The EV/DG aggregator agent is responsible to maximize the aggregator’s profit by designing the appropriate virtual pricing policy according to accurate power demand and generation forecasts. “Responsive” EV agents are the ones that respond rationally to the virtual pricing signals, whereas “Unresponsive” EV agents define their charging schedule regardless the virtual cost. The performance of the control model is experimentally demonstrated through different case studies at the micro-grid laboratory of the National Technical University of Athens (NTUA) using Real Time Digital Simulator. The results highlighted the adaptive behaviour of “Responsive” EV agents and proved their ability to charge preferentially from renewable energy sources. [ABSTRACT FROM AUTHOR]

As various forms of renewable distributed generators (DGs) embedded in microgrids (MGs) often exhibit unstable characteristics, matching the generation to the demand whilst optimally utilizing the DGs is a non-trivial task. This paper investigates the optimal coordinated operation of multiple autonomous MGs and reveals the potential technical benefit. The proposed solution identifies the optimal network topologies and allocates the critical loads (CLs) to appropriate DGs based on the minimum spanning tree (MST) algorithm with power loss and reliability considerations. The non-critical loads (NLs) are determined to be supplied by the MGs based on the Linear Matrix Inequality (LMI) approach, which effectively improves the global utilization efficiency of DGs. Through the event-driven resource reallocation across multiple cooperative MGs, the dynamic balance between the power generation and demand can be attempted. The proposed approach is verified by using the IEEE 33-bus network model and its performance and scalability are further assessed through a large-scale IEEE 300-bus network scenario. The numerical results confirm that the suggested cooperative control of multiple MGs can effectively promote the capability of secure power supply to CLs, and simultaneously improves the global utilization efficiency of DGs significantly, even without any energy storage in the network. [ABSTRACT FROM AUTHOR]

• The DCGAN model is adopted to characterize the renewable power generation. • An improved k-medoids algorithm is used for reduction of generated scenarios. • An optimal grid-connected microgrid capacity configuration model is proposed. • A case study is carried out to validate the proposed capacity planning solution. Microgrid is considered an efficient paradigm for managing the massive number of distributed renewable generation and storage facilities. The optimal microgrid capacity planning is a non-trivial task due to the impact of randomness and uncertainties of renewable generation sources, and the adopted energy management strategies. In this paper, an optimal capacity planning model for the grid-connected microgrid is developed fully considering the renewable generation uncertainties through efficient scenario generation and reduction based on the deep convolutional generative adversarial network (DCGAN) and improved k -medoids clustering algorithm, as well as the microgrid energy management strategy. The proposed solution optimizes the capacity planning for the maximization of renewable energy utilization efficiency, and minimizes the economic cost and carbon emissions. The proposed solution is assessed using a case study of a microgrid (MG) project in northern China through a comparative study and the numerical results confirm the cost-effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

• A comprehensive review of recent advancements in the development of ADSs and MGs. • The regulatory requirements for the evolution of passive DNs to ADSs are detailed. • The paper provides some novel factors for transition and formation ADSs to MGs. • The survey assists DSOs to find requirements to participate in day-ahead markets. Restructuring of power systems along with the integration of renewable energy resources in electricity networks have transformed traditional distribution networks (DNs) into new active distribution systems (ADSs). In addition, rapid advancement of technology has enabled the bulk utilization of power generation units and energy storage (ES) systems in distribution networks. The next step in this trend is to decentralize ADSs to microgrids (MGs). This paper aims to present a review on the recent advancements in the development of ADSs and MGs. In this respect, the regulatory requirements and economic concepts, by which the traditional passive DNs are evolved into ADSs, are categorized and illustrated first. Then, the state-of-the-art of ADS formation is detailed based on the novel standpoint of grid operation factors which are involved in deregulated electricity markets at the distribution level. After presenting highlighted projects of MGs across the world, a similar review approach has been adopted to explain the formation of MGs which play a vital role in the decentralization of ADSs. This survey can provide both policy makers and distribution system planners with new perspectives to establish or participate in day-ahead wholesale markets. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents three alternative analytical expressions, including two new expressions, to determine the optimum sizes and operating strategy of distributed generation (DG) units considering power loss minimization and a methodology to identify the best location. These expressions can be easily adapted to consider the renewable DG units (i.e., biomass, wind and photovoltaic) for minimizing energy losses by considering time-varying demand and possible operating conditions of DG units. The results obtained on a 69-bus distribution test system demonstrate that the proposed approaches can be adequate to determine the location, size and power factor of DG unit for minimizing energy losses. The alternative expressions can be utilized depending on availability of required data and these produce similar outcomes. [Copyright &y& Elsevier]

Abstract: This paper presents the analysis and comparison of the main active techniques for islanding detection used in grid-connected microinverters for power processing of renewable energy sources. These techniques can be classified into two classes: techniques introducing positive feedback in the control of the inverter and techniques based on harmonics injection. Accurate PSIMTM simulations have been carried out in order to perform a comparative analysis of the techniques under study and to establish their advantages and disadvantages according to IEEE standards. [ABSTRACT FROM AUTHOR]

• An interactive energy monitoring for smart roof-top PV via non-intrusive scheme. • Unified photovoltaic signature model addressing multiple uncertainties. • Systematic energy disaggregation formulation by revised dictionary learning. • Robust sparse coding-based solution via supplementary event disaggregation. • Verifications on both simulation platform and practical field measurement scenario. Quantities of advanced technologies have been utilized to realize the carbon neutrality, among which the smart implementations at energy user side are highlighted due to the extensive coverage. Supported by multiple energy management technologies, the integration of distributed renewable energy is prominent, but the compatibility problem of diverse technologies still exists especially considering the newly-developed features. In this paper, an interactive energy monitoring via non-intrusive way is allocated to analyze the roof-top photovoltaic generation and individual appliance consumption from integral energy measurements, and a complete solution is proposed to tackle the distributed generation intelligence with smart voltage maintenance. At first stage, aiming at the domestic reactive power management of photovoltaic inverters, the framework and strategy of coordination between smart meter and intelligent photovoltaic generation are proposed, and then the unified energy signature formulation of photovoltaic generation is presented. Afterwards, the energy disaggregation model is formularized by modified dictionary learning scheme with hybrid validation strategy. Lastly, a sparse coding and auxiliary optimization-based solution is proposed to solve the problem, achieving the robustness under real time architecture. Both the specific low voltage network simulator platform and the practical field measurement scenarios are utilized to verify the effectiveness of the proposed study, and results demonstrate that the proposed study provides an efficient monitoring solution to the intelligent photovoltaic generation, which helps to move toward the smart energy user implementation. [ABSTRACT FROM AUTHOR]

• Developed Heuristic-Hopfield network power flow method for voltage stability study. • Global optima is obtained by adapting dynamics of network using heuristic approach. • A composite severity index is proposed for contingency ranking. • Convergence of solution for power network is assured by Lyapunov stability notion. • Enhanced stability with distributed generation is studied under severe contingency. The increasing penetration of alternative energy sources into the integrated energy systems often influences the voltage stability (VS) of the entire system. However, developing a technique that comprehensively analyze the energy flow in this environment is a major challenge. This paper presents a novel heuristic-based recurrent type Hopfield Neural Network (h-HNN) planning tool for VS assessment of power system; towards reducing the computational cost of conventional power flow (PF) method. The proposed approach is a Jacobian-less, energy function-based approach, which was formulated using power residuals of the system. The dynamics of neural networks were governed by the differential equations of energy function, which would be minimized by the heuristic particle swarm optimization-gravitational search algorithm to deduce the unknown parameters of voltage magnitude and phase angle. The proposed technique was coded in MATLAB and its effectiveness was tested on IEEE 14-, 30-, and 57- buses, as well as a 1354-bus test system. The obtained results were compared with well-known PF techniques, and the robustness was demonstrated for ill-conditioned network. A composite severity index was proposed to rank the critical contingency of the energy network. Then, the VS assessment was performed in IEEE 14-bus system under severe contingency conditions and improvement of VS is observed under the penetration of distributed energy sources (DES). During the case of DES placement, (i) the voltage profile of the system is maintained within the acceptable range of 0.95 to 1.05 pu and (ii) the VS of the system evaluated using stability indices are enhanced by an amount of 16.54 % to 88.16 %. The application results indicate that the proposed method is useful for electric energy utilities to assess the state of the system under monitoring process. [ABSTRACT FROM AUTHOR]