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There are numerous opportunities and challenges in integrating multiple energy sources, for example, electrical, heat, and electrified transportation. The operation of multi-energy sources needs to be coordinated and optimized to achieve maximum benefits and reliability. To address the electrical, thermal, and transportation electrification energy demands in a sustainable and environmentally friendly multi-energy microgrid, this paper presents a mixed integer linear optimization model that determines an optimized blend of energy sources (battery, combined heat and power units, thermal energy storage, gas boiler, and photovoltaic generators), size, and associated dispatch. The proposed energy management system seeks to minimize total annual expenses while simultaneously boosting system resilience during extended grid outages, based on an hourly electrical and thermal load profile. This approach has been tested in a hospital equipped with an EV charging station in Okinawa, Japan through several case studies. Following a M1/M2/c queuing model, the proposed grid-tied microgrid successfully integrates EVs into the system and assures continued and economic power supply even during grid failures in different weather conditions.
Distribution system planners and operators have increasingly exposed great attention to maximizing the penetration of renewable energy resources (RERs), and electric vehicles (EVs) toward modern microgrids. Accordingly, intensive operational and economic problems are expected in such microgrids. Specifically, the operators need to meet the increased demand for EVs and increase the dependence on RERs. The charging strategy for EVs and the RER penetration level may result in increased power loss, thermal loading, voltage deviation, and overall system cost. To address these concerns, this paper proposed an optimal planning approach for allocating EV charging stations with controllable charging and hybrid RERs within multi-microgrids, where the charging strategy in the proposed planning approach contributed to improving power quality and overall system cost, where the voltage deviation, energy not supplied, total cost have been reduced to 26.03%, 49.57%, and 70.45%, respectively. The simulation results are compared with different optimization techniques to verify the effectiveness of the proposed algorithm. The proposed simultaneous allocation approach of EV charging stations and RERs can reduce operating costs for RERs and conventional stations while increasing the charging stations' capacity.
The concept of transactive energy (TE) been adapted in the regulation of electricity market within the context of economic planning and control for grid reliability enhancement. The objective is to improve productivity and participation of the players in the market that is composed of distributed energy resources (DER). The main goal of implementing a market structure based on TE is to secure permission for the market players so that they could attain a higher payoff. In this study, an optimization-based algorithm in which an objective function premised on economic strategies, distribution limitations and the overall demand in the market structure is proposed. The objective function is solved for near global optima using four heuristically guided optimization algorithms. The proposed algorithm which ensures that none of the independent players has priority and/or advantage over others, emphasizes optimum use of electrical/thermal energy distribution resources, while maximizing profit for the owners of the home microgrids (H-MGs). Reduction in the market clearing price (MCP) for further participation and the response of the consumers’ responsive loads are also considered in the study. The feasibility of the proposed algorithm is validated in a coalition formation scenario among the existing H-MGs. Results show an increase in the profit attained, enhanced system reliability and a reduction in the electricity cost of the consumers.
The distribution networks can convincingly break down into small-scale self-controllable areas, namely microgrids to substitute microgrids arrangements for effectively coping with any perturbations. To achieve these targets, this paper examines a novel spatiotemporal algorithm to split the existing network into a set of self-healing microgrids. The main intention in the grid-tied state is to maximize the microgrids profit while equilibrating load and generation at the islanded state by sectionalizing on-fault area, executing resources rescheduling, network reconfiguration and load shedding when the main grid is interrupted. The proposed problem is formulated as an exact computationally efficient mixed integer linear programming problem relying on the column & constraint generation framework and an adjustable interval optimization is envisaged to make the microgrids less susceptible against renewables variability. Finally, the effectiveness of the proposed model is adequately assured by performing a realistic case study.
The dominance of distributed energy resources in microgrids and the associated weather dependency require flexible protection. They include devices capable of adapting their protective settings as a reaction to (potential) changes in system state. Communication technologies have a key role in this system since the reactions of the adaptive devices shall be coordinated. This coordination imposes strict requirements: communications must be available and ultra-reliable with bounded latency in the order of milliseconds. This paper reviews the state-of-the-art in the field and provides a thorough analysis of the main related communication technologies and optimization techniques. We also present our perspective on the future of communication deployments in microgrids, indicating the viability of 5G wireless systems and multi-connectivity to enable adaptive protection.
This paper presents a centralized model for operating multi-energy microgrids. The proposed model is based on a linearized optimal power flow (OPF) model for handling the network constraints in the distribution networks. It is assumed that each local microgrid is self-sustaining and can be operated independently from the other microgrids. However, the network access provides more flexibility to the multi-energy microgrid operators to supply their loads. The network-based electrical energy transactions are accepted in this study, while energy transformation from electricity to the other carriers is an asset to minimize the overall operating cost of the centralized multi-energy microgrid operation. The proposed model is tested and verified on the modified IEEE 33-bus test system.
Optimal power sharing between parallel inverters and the demand load in microgrids is challenging and particularly critical for power grids in islanding operation. This paper introduces a novel control approach for managing parallel distributed power sources in the presence of variable load in islanding regime. The proposed scheme is based on the modified sliding mode control (MSMC) which is combined with the optimal Riccati control method to achieve convergence at the slip level with higher accuracy. The mathematical principles of the network equations are derived and its stability is obtained using the Lyapunov function. The MSMC simulation results are discussed in relation to the conventional droop method, while the laboratory evaluation was carried out to characterize its dynamic and static response. The results show that the proposed scheme control is able to manage the distributed power generation for static and dynamic load scenarios, and as such, guarantying microgrid frequency stability.
The sharp increase in renewable energy generation and the number of electric vehicles enhance power systems’ modernization, decarbonization, and decentralization. As a result, microgrids (MGs) with renewable energy integration and charging facilities have attracted significant attention. Nonetheless, disregarding uncertainties in optimization models for MGs can lead to either risky or costly decisions. In addition, sustainable development and operation of MGs must enhance the system’s resiliency to guarantee functionality during abnormal situations. Therefore, this paper proposes a two-stage stochastic programming model to ensure the resilient operation of microgrids with charging facilities. At the same time, uncertainties associated with renewable generation, demand, and market price are addressed via scenarios. To enhance resiliency against unplanned islanding, a scenario for outages is defined so that preventive actions can be done in the first stage to robust the energy management of the microgrid.
In this paper, improved finite control set model predictive voltage control (FCS-MPVC) is proposed for the distributed energy resource (DER) in AC islanded microgrid (MG). Typically, AC MGs have two or more power electronic-based DERs, which have the ability to maintain a constant voltage at the point of common coupling (PCC) as well as perform power sharing among the DERs. Though linear controllers can achieve above-mentioned tasks, they have several restrictions such as slow transient response, poor disturbance rejection capability etc. The proposed control approach uses mathematical model of power converter to anticipate the voltage response for possible switching states in every sampling period. The proposed dual-objective cost function is designed to regulate the output voltage as well as load current under fault condition. Two-step horizon prediction technique reduces the switching frequency and computational burden of the designed algorithm. Performance of the proposed control technique is demonstrated through MATLAB/Simulink simulations for single distributed generator (DG) and AC MG under linear and non-linear loading conditions. The investigated work presents an excellent steady state performance, low computational overhead, better transient performance and robustness against parametric variations in contrast to classical controllers. Total harmonic distortion (THD) for linear and non-linear load is 0.89% and 1.4% respectively as illustrated in simulation results. Additionally, the three-phase symmetrical fault current has been successfully limited to the acceptable range.
This paper introduces a two-stage two-level optimization method for optimal day-ahead and real-time scheduling of multicarrier energy distribution systems and microgrids. The model considers the incentive-based and price-based demand response programs to encourage microgrids to transact electrical, heating, and cooling energy carriers with the energy distribution system, which is named hereafter as the energy system. Further, the model formulates the resilient operation of the energy system considering the energy transactions with the electrical, heating, and cooling markets. The main contribution of this paper is the integration of demand response procedures of microgrids in energy transactions with the energy system considering the switching of electrical switches and heating and cooling control valves. The optimization process is another contribution of this paper that is decomposed into two stages that consist of day-ahead and real-time horizons. The first stage is also decomposed into two levels that determine the optimal scheduling of the energy system and microgrids in day-ahead markets. The second stage is comprised of two levels that commit the energy system and microgrids resources. A resiliency index is proposed to assess the resiliency of the energy system in shock conditions. The proposed method was simulated for the 123-bus test system. Different types of microgrids, incentive-based and price-based demand response processes were considered. Simulation results confirmed that the proposed method can reduce the costs of residential, industrial, and commercial microgrids by about 4.47%, 3.88%, and 5.47% concerning only the real-time pricing process. Further, the model can increase the aggregated benefits of the energy system in the day-ahead and real-time markets by about 0.608 Million Monetary Units (MMUs) and 1.10 MMUs, respectively.
Growing demand for energy carriers has led to an increased interest in developing and managing multiple energy carrier microgrids. Furthermore, the volatile nature of renewable resources as well as the uncertain electrical and thermal demands imposes significant challenges for the operation of microgrids. Motivated by this, the paper leverages a min max min robust framework for short-term operation of microgrids with natural gas network to capture the uncertainty of wind generation and electrical/thermal loads. The proposed model is linearized and solved using the column-and-constraint generation (C&CG) procedure that decomposes the framework into a master problem and a sub-problem. The master problem minimizes the unit commitment cost, while the sub-problem determines the dispatch cost associated with the worst realization of uncertainties via a max min objective function. Also, polyhedral uncertainty sets are defined with budget of uncertainty parameter that adjusts the trade-off between the operation cost and the degree of robustness. The effectiveness of the framework is assessed and discussed via a 21-node energy hub-based microgrid. It can be seen that the solution immunizes against all realizations of uncertainties, whereby increasing the budget of uncertainty and the forecast error, the system robustness is improved. Moreover, the dual variables of the sub-problem are converted to the primary variables in order to evaluate the unit commitment and energy dispatch results.
Energy storage system (ESS) has great importance in saving energy in new power systems. Optimum selection of these elements poses a new challenge to improve the energy management and prevent cost increases in the system. Also, renewable energy resources have been increasingly used in microgrids. The uncertainty and variation of renewable distributed generation (DG) affect the performance of power systems. In this paper, ESS implementations and photovoltaic (PV) power prediction are used to improve voltage/power profile of the system and reduce the total cost of the microgrid. The purpose of this paper is the optimal installation of ESSs in a microgrid to minimize the total cost where quantile nearest neighbour forecasting is utilized for PV output power prediction as an efficient approach. Gathering data of the last samples in time duration can be used for an effective prediction of PV output in this method, which can overcome PV uncertainty due to changes in solar irradiation and other parameters. Artificial neural networks combined with multi-layer perceptron and genetic algorithm are used for optimizing the size and location of ESSs in the system. Simulation results show that the proposed method improves the power profile as 14%, 21% and 28%, relatively to the scenarios of optimal ESS installation without PV prediction, using PV prediction but with no optimal ESS implementation and not using PV- no ESS implementation, respectively. Moreover, the accuracy of the proposed prediction method is more than the gradient-descent and RNN methods by about 12% and 5%, respectively, as shown in the simulation results. Also, the cost reduction of proposed method is enhanced as 24% and 31% relatively to the cases of optimal ESS installation without PV prediction and PV prediction without optimal ESS implementation, respectively.
This paper addresses an integrated framework for expansion planning of an Active Distribution Network (ADS) that supplies its downward Active MicroGrids (AMGs) and it participates in the upward wholesale market to sell its surplus electricity. The proposed novel model considers the impact of coordinated and uncoordinated bidding of AMGs and Demand Response Providers (DRPs) on the optimal expansion planning. The problem has six sources of uncertainty: upward electricity market prices, AMGs location and time of installation, AMGs power generation/consumption, ADS intermittent power generations, DRP biddings, and the ADS system contingencies. The model uses the Conditional Value at Risk (CVaR) criterion in order to handle the trading risks of ADS with the wholesale market. The proposed formulation integrates the most important deterministic and stochastic parameters of the risk-based expansion planning of ADS that is rare in the literature on this field. The introduced method uses a four-stage optimization algorithm that uses genetic algorithm, CPLEX and DICOPT solvers. The proposed method is applied to the 18-bus and 33-bus test systems to assess the proposed algorithm. The proposed method reduces the aggregated expansion planning costs for the 18-bus and 33-bus system about 44.04%, and 11.82% with respect to the uncoordinated bidding of AMGs/DRPs costs, respectively.
This paper presents a novel load shedding scheme with consideration of the active power ramping capability of Distributed Energy Resources (DERs) to address the challenges due to low inertia and diverse types of DERs in microgrids. In the paper, it is demonstrated that due to the small inertia in microgrids, even with sufficient reserve power, the frequency could rapidly drop to a low level and trigger the DERs under frequency protection (thus the total system collapse), if the reserve active power is not ramped up at a sufficient rate. The proposed load shedding scheme addresses this challenge by considering not only the DERs reserve, but also their speed in injecting active power to the system to determine the amount of load should be shed, so that critical frequency thresholds are not violated. The proposed load shedding scheme is tested using a realistic real time hardware-in-the-loop arrangement. The results show that the proposed scheme can correctly detect the cases when the DERs responses are too slow and trigger the required load shedding actions, thus effectively containing the frequency above the critical threshold.
This paper addresses the optimal sizing of renewable energy systems (RESs) in a microgrid (MG), where the MG participates in the electricity market. A novel method for reliability analysis is proposed in this study to deal with the high penetration of RESs. In this framework, the MG is considered as a price maker, having a two-direction relation with the electricity market. RESs, including photovoltaic (PV) panels, wind turbines (WTs), and fuel cells, are optimally sized based on the reliability index, and the results are evaluated before and after the MG involvement in the electricity market. The results show a 3.6% decrease in the total cost of the microgrid as a result of the transactions with the electricity market. Furthermore, the efficiency of the proposed approximate reliability method is verified, where the reliability of the MG is evaluated with less computational complexity and acceptable accuracy.
This paper addresses a framework for expansion planning of an active distribution network (ADS) that supplies its downward active microgrids (AMGs) and it participates in the upward wholesale market to sell its surplus electricity. The proposed novel model considers the impact of coordinated and uncoordinated bidding of AMGs and demand response providers (DRPs) on the optimal expansion planning. The problem has six sources of uncertainty: upward electricity market prices, AMGs location and time of installation, AMGs power generation/consumption, ADS intermittent power generations, DRP biddings, and the ADS system contingencies. The model uses the conditional value at risk (CVaR) criterion in order to handle the trading risks of ADS with the wholesale market. The proposed formulation integrates the deterministic and stochastic parameters of the risk‐based expansion planning of ADS that is rare in the literature on this field. The introduced method uses a four‐stage optimisation algorithm that uses genetic algorithm, CPLEX and DICOPT solvers. The proposed method is applied to the 18‐bus and 33‐bus test systems to assess the proposed algorithm. The proposed method reduces the aggregated expansion planning costs for the 18‐bus and 33‐bus system about 44.04% and 11.82% with respect to the uncoordinated bidding of AMGs/DRPs costs, respectively.
Environmental concerns have led to increased penetration of renewable energy sources into the power grid. Many researches have considered localized and small-scale renewable energy sources supplying microgrids-small-scale localized distribution networks-as a backup to the main grid. Further, the aging of traditional transmission networks has led some researchers to propose islanded operations of the microgrid; the grid is isolated from the main grid and operates independently. However, islanded operations face many challenges such as power quality, voltage regulation, network stability, and protection. Moreover, renewable energy sources are unreliable and intermittent. Recent developments in power electronics have made it possible to develop competitive and reliable low-voltage DC (LVDC) distribution networks. Further, advances in information and communications technology (ICT) have led to smart grids in which various devices in the network can communicate with each other and/or a control center. In this paper, we consider an islanded LVDC smart microgrid that uses renewable energy sources. An energy management system (EMS) that ensures efficient energy and power balancing and voltage regulation is proposed for the network. The DC network utilizes solar panels for electricity production and lead-acid batteries as energy storage to support the production. The EMS uses the master/slave method with robust communication infrastructure to control the production, storage, and loads. The logical basis for the EMS operations has been established by proposing functionalities for the network components and defining operation modes that encompass all situations. During loss-of-power-supply periods, load prioritizations and disconnections are used to maintain power supply to at least some loads. The successful performance of the proposed EMS to maintain energy balance in the network has been demonstrated by simulations.
Microgrids might enable environmental and economic improvements to the electric grid. The introduction of renewable energy sources to the power supply has opened new doors for a cleaner power system and novel grid structure changes such as microgrids. Microgrids are local power networks that can operate in grid-connected or islanded mode. One of the main challenges of microgrids is reliable and accurate protection. Intermittent generation and multiple modes of operation might change fault current behaviour drastically. Protection schemes must always function regardless of network topology, mode of operation, or generation level. Simulation case studies are helpful to test microgrid protection schemes for different microgrid states. In this thesis work, a microgrid is modelled to the needed extent for protection studies using PSCAD software. Protection use cases are simulated with PSCAD to demonstrate protection considerations for microgrids operating in grid-connected and islanded modes.
This paper presents a new bi-level multi-objective model to valorize the microgrid (MG) flexibility based on flexible power management system. It considers the presence of renewable and flexibility resources including demand response program (DRP), energy storage system and integrated unit of electric spring with electric vehicles (EVs) parking (IUEE). The proposed bi-level model in the upper-level maximizes expected flexibility resources profit subject to flexibility constraints. Also, in the lower level, minimizing MG energy cost and voltage deviation function based on the Pareto optimization technique is considered as the objective functions; it is bounded by the linearized AC optimal power flow constraints, renewable and flexibility resources limits, and the MG flexibility restrictions. In the following, the proposed bi-level model using Karush–Kuhn–Tucker (KKT) technique is converted to a single-level counterpart, and the fuzzy decision-making method is employed to achieve the best compromise solution. Further, hybrid stochastic-robust programming models uncertain parameters of the proposed model, so that stochastic programming models uncertainties associated with demand, energy price, and the maximum renewables active generation. Also, to capture the flexible potential capabilities of the IUEE, robust optimization models the EVs’ parameters uncertainty. Finally, numerical results confirm the proposed model could jointly improve operation, economic and flexibility conditions of the MG and turned it to a flexi-optimized-renewable MG.
The operation of microgrids is a complex task because it involves several stakeholders and controlling a large number of different active and intelligent resources or devices. Management functions, such as frequency control or islanding, are defined in the microgrid concept, but depending on the application, some functions may not be needed. In order to analyze the required functions for network operation and visualize the interactions between the actors operating a particular microgrid, a comprehensive use case analysis is needed. This paper presents the use case modelling method applied for microgrid management from an abstract or concept level to a more practical level. By utilizing case studies, the potential entities can be detected where the development or improvement of practical solutions is necessary. The use case analysis has been conducted from top-down until test use cases by real-time simulation models. Test use cases are applied to a real distribution network model, Sundom Smart Grid, with measurement data and newly developed controllers.. The functional analysis provides valuable results when studying several microgrid functions operating in parallel and affecting each other. For example, as shown in this paper, ancillary services provided by an active customer may mean that both the active power and reactive power from customer premises are controlled at the same time by different stakeholders.
The hybrid AC/DC microgrids have become considerably popular as they are reliable, accessible and robust. They are utilized for solving environmental, economic, operational and power-related political issues. Having this increased necessity taken into consideration, this paper performs a comprehensive review of the fundamentals of hybrid AC/DC microgrids and describes their components. Mathematical models and valid comparisons among different renewable energy sources’ generations are discussed. Subsequently, various operational zones, control and optimization methods, power flow calculations in the presence of uncertainties related to renewable energy resources are reviewed.
Increasing amount of distributed energy resources necessitates more flexibility a t t he distribution network level. One option to attain this flexibility i s b y aggregation o f these resources within microgrids and further supervisory control of the latter in active network management. Among other reasons preventing their realization, these flexibility services I ack standardized information and communication technology solution. This study assesses the required communication, information, and functional competences for such services and describes them by means of a use case modeling on smart grid architecture model planes. Specifically, t he paper focuses o nan information exchange built on the basis of web application programming interface called Smart API. The results of the study present a smart grid architecture that would enable real-time control of microgrid resources in active network management through flexibility market services.
This paper presents a multi-stage day-ahead and real-time optimization algorithm for scheduling of system’s energy resources in the normal and external shock operational conditions. The main contribution of this paper is that the model considers the non-utility electricity generation facilities capacity withholding opportunities in the optimal scheduling of system resources. The real-time simulation of external shock impacts is another contribution of this paper that the algorithm simulates the sectionalizing of the system into multi-microgrids to increase the resiliency of the system. The optimization process is categorized into two stages that compromise normal and contingent operational conditions. Further, the normal operational scheduling problem is decomposed into three steps. At the first step, the optimal day-ahead scheduling of system resources and the switching of normally opened switches are determined. At the second step, the optimal real-time market scheduling is performed and the switching of normally closed switches is optimized. At the third step, different extreme shock scenarios are simulated in the real-time horizon and the effectiveness of sectionalizing the system into multi-micro grids are assessed. Finally, at the contingent operational conditions, the optimal topology of the system and scheduling of energy resources are determined. The proposed method was successfully assessed for the 33-bus and 123-bus test systems. The algorithm were reduced the expected cost of the worst-case contingencies for the 33-bus and 123-bus systems by about 97.89% and 88.11%, respectively. Further, the average and maximum values of the 123-bus system capacity-withholding index for real-time conditions reduced by about 67.40% and 71.05%, respectively.
Microgrids are a group of localized electrical resources mainly using renewable resources as a main source of power, which can operate independently or in collaboration with utility grid. When connection of a microgrid is concerned, switching from an islanding to grid-connected mode is always a difficult task for a microgrid mainly due to transients and mismatching in synchronization. Hierarchical control structure of a microgrid eradicates this issue by separating the control structure in multiple levels. This thesis explains different levels of hierarchical control strategies, which constitute primary control, secondary and tertiary control. The primary control is based on droop control including output virtual impedance, secondary control performs restoration of voltage and frequency performed by primary and tertiary control maintain the power flow between the micro grid and external utility. In first step, this thesis covers the technical overview of traditional control methods of power converters and then the latter part consists of detailed description of all three levels of hierarchical control with synchronization and power flow analysis. Various types of primary controls, like with and without communication, and improvements to droop control are discussed and compared. In the end, concepts explained in previous chapters, are done in practice and simulated results are discussed.
A voltage source inverter (VSI) is the key component of grid-tied AC Microgrid (MG) which requires a fast response, and stable, robust controllers to ensure efficient operation. In this paper, a fuzzy logic controller (FLC)-based direct power control (DPC) method for photovoltaic (PV) VSI was proposed, which was modelled by modulating MG’s point of common coupling (PCC) voltage. This paper also introduces a modified grid synchronization method through the direct power calculation of PCC voltage and current, instead of using a conventional phase-locked loop (PLL) system. FLC is used to minimize the errors between the calculated and reference powers to generate the required control signals for the VSI through sinusoidal pulse width modulation (SPWM). The proposed FLC-based DPC (FLDPC) method has shown better tracking performance with less computational time, compared with the conventional MG power control methods, due to the elimination of PLL and the use of a single power control loop. In addition, due to the use of FLC, the proposed FLDPC exhibited negligible steady-state oscillations in the output power of MG’s PV-VSI. The proposed FLDPC method performance was validated by conducting real-time simulations through real time digital simulator (RTDS). The results have demonstrated that the proposed FLDPC method has a better reference power tracking time of 0.03 s along with reduction in power ripples and less current total harmonic distortion (THD) of 1.59%.
In this paper, a robust power management system (RPMS) for a DC microgrid is proposed. A novel neural network-based scheme is proposed for the PV cells’ generation prediction using ultraviolet (UV) index, temperature, and cloud coverage which gives a considerable improvement in the prediction error in comparison with the existing works. Moreover, another neural network predicts the demand for the microgrid. The proposed RPMS will make decisions under the uncertainties of these prediction errors such that the system stays robust and works near the optimal operating point. Besides, three different possible scenarios for operation of the microgrid are considered which represents all real operating conditions. Then, three corresponding optimization problems are introduced for theses scenarios. Moreover, without loss of generality, load buses are clustered in one critical load bus and three sheddable load buses. The RPMS keeps the critical load bus voltage in a standard range while feeding the maximum possible sheddable buses with 0.9 p.u. voltage or disconnecting them sequentially. The numerical simulation results show the feasibility and effectiveness of the proposed strategy.
The increasing number of airports pursuing sustainability goals through electrification faces challenges when the existing electrical network cannot meet demands. In an effort to reduce the carbon footprint, airports need to take into account the environmental impact of the entire electrification process. It is, therefore, crucial to consider how electricity is generated. To minimize emissions and reduce the carbon footprint of airport operations, it's important to use renewable energy sources that are compatible with the airport's specific regulations. It might be challenging to meet the demand for electricity at all times when renewable energy sources (RES) like solar or wind power don't supply energy consistently or predictably. Batteries are frequently combined with RES to improve the system's overall efficiency in order to solve this problem. Properly forecasting future load patterns is crucial in determining the appropriate size and capacity of a renewable energy system that combines photovoltaic solar panels with battery energy storage systems. The present study investigates the feasibility of electrifying an airport using a microgrid solution that includes photovoltaic (PV) solar panels and battery energy storage systems (BESS). The study examines the energy flow of the airport under five different scenarios, which include the existing load, the addition of electric aircraft, the addition of E-bus load, the addition of electric ground handling equipment (GHE) loads, and the addition of electric vehicles. To accomplish this, we provide modeling and simulation to incorporate electrified loads that will be present in the future. Moreover, a pre-built simulator based on cost minimization is used to optimize the sizing of the PV and BESS components of the microgrid solution. Energy flow analysis, which gauges the amount of energy received and given to the network, is the foundation for the economic analysis of the microgrid solution. On the basis of this study, the annual cost of electricity purchase is then determined and compared.
This research investigates a new coordination strategy for both isolated single-area and interconnected multi-area microgrids (MGs) using a modified virtual rotor-based derivative technique supported with Jaya optimizer based on balloon effect modulation (BE). Accordingly, the main concept of BE is to assist the classic Jaya to be more sensitive and trackable in the event of disturbances, as well as to provide optimum integral gain value on the secondary frequency controller adaptively for both suggested MGs. The proposed modified virtual rotor mechanism is consisting of virtual inertia and virtual damping that are added as a tertiary controller within proposed MGs considering full participation of the inverter-based energy storage systems. The proposed virtual rotor mechanism is consisting of virtual inertia and virtual damping that are added as a tertiary controller within proposed MGs to emulate the reduction in system inertia and the enhanced damping properties. Several nonlinearities were proposed in this work such as a dead band of governor, generation rate constraints, and communication time-delay are considered within the dynamic model of the suggested MGs. In addition, the proposed design of multi-area MGs takes the interval time-varying communication delays into account for stability conditions. In this study, A comparative study using unimodal (i.e., Sphere) and multimodal (i.e., Rastrigin) benchmark test functions are conducted to validate the proposed direct adaptive Jaya-based BE. Furthermore, Wilcoxon’s rank-signed non-parametric statistical test using a pairwise comparison was performed at a 5 % risk level to judge whether the proposed algorithm output varies from those of the other algorithms in a statistically significant manner. Thence, the superiority and effectiveness of the proposed method have also been verified against a variety of other metaheuristics optimization techniques, including classic electro-search, particle swarm, multi-objective seagull, and Jaya optimizers. In addition, an operative performance is assessed against the conventional integral controller, coefficient diagram method, and classic Jaya with/without virtual inertia. The final findings emphasize the superiority of the proposed direct adaptive Jaya-based BE supported by a modified virtual rotor and state better performance and stability compared to existing controllers.
With recent advances in information and communication technology (ICT), the bleeding edge concept of digital twin (DT) has enticed the attention of many researchers to revolutionize the entire modern industries. DT concept refers to a digital representation of a physical entity that is able to reflect its physical behavior by applying platforms and bidirectional interaction of data in real-time. The remarkable deployment of the internet of things in the power grid has led to reliable access to information that improves its performance and equips it with a powerful tool for real-time data management and analysis. This paper aims to trace the continuous investigation and propose practical ideas in originating and developing DT technology, according to various application domains of power systems, and also describes the proposed solutions to deal with the challenges associated with DT. Indeed, with the development of modern cities, different energy layers such as transportation systems, smart grids, and microgrids have emerged facing various issues that challenge the multi-dimensional energy management system. For example, in transportation systems, traffic is a major problem that requires real-time management, planning, and analysis. In power grids, remote data transfer within the grid and also various analyzes needing real data are just some of the current challenges in the field. These problems can be cracked by providing and analyzing a real twin framework in each section. All in all, this paper aims to survey different applications of DT in the development of the various aspects of energy management within a city including transportation systems, power grids, and microgrids. Besides, the security of DT technology based on ML is discussed. It also provides a complete view for the readers to be able to develop and deploy a DT technology for various power system applications.
The optimal management of distributed energy resources (DERs) and renewable-based generation in multi-energy systems (MESs) is crucial as it is expected that these entities will be the backbone of future energy systems. To optimally manage these numerous and diverse entities, an aggregator is required. This paper proposes the self-scheduling of a DER aggregator through a hybrid Info-gap Decision Theory (IGDT)-stochastic approach in an MES. In this approach, there are several renewable energy resources such as wind and photovoltaic (PV) units as well as multiple DERs, including combined heat and power (CHP) units, and auxiliary boilers (ABs). The approach also considers an EV parking lot and thermal energy storage systems (TESs). Moreover, two demand response (DR) programs from both price-based and incentive-based categories are employed in the microgrid to provide flexibility for the participants. The uncertainty in the generation is addressed through stochastic programming. At the same time, the uncertainty posed by the energy market prices is managed through the application of the IGDT method. A major goal of this model is to choose the risk measure based on the nature and characteristics of the uncertain parameters in the MES. Additionally, the behavior of the risk-averse and risk-seeking decision-makers is also studied. In the first stage, the sole-stochastic results are presented and then, the hybrid stochastic-IGDT results for both risk-averse and risk-seeker decision-makers are discussed. The proposed problem is simulated on the modified IEEE 15-bus system to demonstrate the effectiveness and usefulness of the technique.
Datacentres are becoming a sizable part of the energy system and are one of the biggest consumers of the energy grid. The so-called Green Datacentre is capable of not only consuming but also producing power, thus becoming an important kind of prosumers in the electric grid. Green datacentres consist of a microgrid with a backup uninterrupted power supply and renewable generation, e.g., using photovoltaic panels. As such, datacentres could realistically be important participants in demand / response applications. However, this requires reconsidering their currently rigid control and automation systems and the use of simulation models for online estimation of the control actions impact. This paper presents such a microgrid simulation model modelled after a real edge datacentre. A case study consumption scenario is presented for the purpose of validating the developed microgrid model against data traces collected from the green edge datacentre. Both simulation and real-time validation tests are performed to validate the accuracy of the datacentre model. Then the model is connected to the automation environment to be used for the online impact estimation and virtual commissioning purposes.
In this chapter, the focus of the study is on the MGs equipped with EMS. There have been introduced several approaches to the energy management of MGs. However, in most of them, economic aspects, i.e., cost reduction, are the top priority desire of the problem from the MG stakeholders’ point of view. This could be done in different ways. On the one hand, reducing the total costs of the MGs by maximum utilization of self-production facilities (PV panels, wind turbines, etc.) as well as changing the energy consumption over time from peak hours to off-peak hours during the day. On the other hand, exploiting MGs’ flexibility so as to help the upstream grid in critical moments for monetary profits in return.
This study presents a novel framework for improving the resilience of microgrids based on the power-to-hydrogen concept and the ability of microgrids to operate independently (i.e., islanded mode). For this purpose, a model is being developed for the resilient operation of microgrids in which the compressed hydrogen produced by power-to-hydrogen systems can either be used to generate electricity through fuel cells or sold to other industries. The model is a bi-objective optimization problem, which minimizes the cost of operation and resilience by (i) reducing the active power exchange with the main grid, (ii) reducing the ohmic power losses, and (iii) increasing the amount of hydrogen stored in the tanks. A solution approach is also developed to deal with the complexity of the bi-objective model, combining a goal programming approach and Generalized Benders Decomposition, due to the mixed-integer nonlinear nature of the optimization problem. The results indicate that the resilience approach, although increasing the operation cost, does not lead to load shedding in the event of main grid failures. The study concludes that integrating distributed power-to-hydrogen systems results in significant benefits, including emission reductions of up to 20 % and cost savings of up to 30 %. Additionally, the integration of the decomposition method improves computational performance by 54 % compared to using commercial solvers within the GAMS software.
This paper presents an algorithm for optimal resilient allocation of Mobile Energy Storage Systems (MESSs) for an active distribution system considering the microgrids coordinated bidding process. The main contribution of this paper is that the impacts of coordinated biddings of microgrids on the allocation of MESSs in the day-ahead and real-time markets are investigated. The proposed optimization framework is another contribution of this paper that decomposes the optimization process into multiple procedures for the day-ahead and real-time optimization horizons. The active distribution system can transact active power, reactive power, spinning reserve, and regulating reserve with the microgrids in the day-ahead horizon. Further, the distribution system can transact active power, reactive power, and ramp services with microgrids on the real-time horizon. The self-healing index and coordinated gain index are introduced to assess the resiliency level and coordination gain of microgrids, respectively. The proposed algorithm was simulated for the 123-bus test system. The method reduced the average value of aggregated operating and interruption costs of the system by about 60.16% considering the coordinated bidding of microgrids for the worst-case external shock. The proposed algorithm successfully increased the self-healing index by about 49.88% for the test system.
This paper presents a new framework for the scheduling of microgrids and distribution feeder reconfiguration (DFR), taking into consideration the uncertainties due to the load demand, market price, and renewable power generation. The model is implemented on the modified IEEE 118-bus test system, including microgrids and smart homes. The problem has been formulated as a two-stage model, which at the first stage, the day-ahead self-scheduling of each microgrid is carried out as a two-objective optimization problem. The two objectives include the minimization of the total operating cost and maximization of the consumer's comfort index. Then, the solution, obtained from the first stage is delivered to the distribution system operator (DSO). Then, at the second stage, the DSO determines the optimal configuration of the system with the aim of minimizing operating costs of the main grid and the penalty of deviating from microgrid scheduling. Note that the penalty is due to the difference in power exchange requested by the microgrids from the power exchange finalized by the DSO. The presented two-stage optimization problem is modeled in a mixed-integer linear programing (MILP) framework with four case studies, and solved in GAMS by using the GURUBI solver. The simulation results show that in the cases the DSO is able to reconfigure the system, the deviation from the optimal scheduling of microgrids would be considerably lower than the cases with fixed system configuration.
In the evolution of the power systems, a particular case is the presence of a number of microgrids (MGs) operated with mutual interconnection, but without connection to the main distribution system. The interconnected MGs form a structure in which the overall system operation and resource scheduling can be determined by considering centralized or decentralized approaches. This article introduces local energy and reserve markets (LERMs) in which the MG managers (MGMs) can meet their required energy and reserve with optimal scheduling of their resources, besides competing with the other MGs. To model such decision-making framework for MGMs, a bilevel optimization approach is developed in which the MGMs’ problem is modeled as the upper level problem and the LERMs clearing problem is modeled as the lower level problem. This model is transformed into a mathematical programming with equilibrium constraints (MPEC) using the primal-dual transformation. Then, the resulting MPEC for each MG is replaced with its Karush–Kuhn–Tucker conditions, obtaining an equilibrium problem with equilibrium constraints (EPEC) model. The nonlinear terms of the model are linearized through different approaches. Finally, the EPEC model is transformed into a mixed-integer linear problem considering the objective function of all MGMs. The model is applied to a test system with three interconnected MGs. Moreover, the sensitivity of the results to the probability of calling reserve is investigated.
This article presents a new passive islanding detection technique in MGs that uses locally measured voltage signals at the PoC of DERs. The proposed method distinguishes islanding events from normal/non-islanding conditions by utilizing superimposed harmonic spectra extracted through a full-cycle discrete Fourier transform. Our solution utilizes a machine-learning-based one-class classifier to define and adjust thresholds for full harmonic spectra. Unlike other methods, our approach does not require data synchronization or communication infrastructure, nor does it suffer from common errors that often arise in current transformers. Moreover, our design is compatible with distributed and decentralized control strategies, as it relies solely on local voltage measurements at the PoC. Another advantage of this method is its low sampling frequency requirement, in the range of 1 kHz, making it cost-effective and implementable in most existing systems. In a comprehensive evaluation of a typical MG test system that included synchronous and inverter-based DERs, the proposed scheme demonstrated exceptional performance. Specifically, the scheme was able to detect 99.06% of different islanding events within the training range, with a detection time of just 10 to 21 ms. Additionally, the scheme remained 100% stable during various normal conditions, short-circuit faults, load changes, voltage changes, capacitor switching, and frequency changes.
Ilmaston lämpeneminen pakottaa energiasektorin siirtymään pois fossiilisista polttoaineista kohti uusiutuvia energialähteitä. Invertteripohjaisten uusiutuvien energiavarojen lisääntyminen vähentää sähköverkkoisin kytkeytyvän inertian määrää, mikä vaikuttaa niiden kykyyn sietää muutoksia taajuudessa. Tällä muutoksella on merkittävämpi vaikutus mikroverkkoihin kuin pe-rinteisiin sähköverkkoihin jo ennestään pienemmän inertian vuoksi. Tässä diplomityössä tutki-taan inertian pienenemisestä johtuvaa muutosta, joka auttaa optimoimaan taajuuden säädön saarekekäytössä oleville mikroverkoille, jossa on korkea IBG-penetraatio. Se tehdään tutkimalla taajuuskäyttäytymistä kolmessa eri mikroverkossa. Nämä mikroverkot eroavat toisistaan tahti-generaattoripohjaisen ja invertteripohjaisen tuotannon osuuden perusteella. Mallien luomiseen ja eri skenaarioiden simulointiin käytetään PSCAD-nimistä simulointiohjelmistoa. Mallit on kehi-tetty perustuen IEEE 14 -solmupisteenjärjestelmään, jota tässä tapauksessa pidetään yleisenä mikroverkkona. Tuotannon puolella tarkastellaan kolmea eriä tuotanto rakennetta. Nämä mallit eroavata toisistaan tahtigeneraattoripohjaisen tuotannon ja invertteripohjaisen tuotannon osuuden mukaan. Kaikissa simulaatiotapauksissa mikroverkkoon on liitettynä akkukäyttöinen energiavarasto. Taajuuskäyttäytymistä tarkkaillaan luomalla neljä erilaista tapahtumaa mikro-verkkoon. Nämä tapahtumat luodaan uudelleen neljässä eri olosuhteissa, mukaan lukien ihan-teellinen tilanne. Tässä diplomityössä saatujen tulosten perusteella invertteripohjaiset tuotan-toyksiköt voivat tukea mikroverkon taajuutta lähes samalla tasolla kuin tahtigeneraattorit. Ne ovat kuitenkin erittäin arvaamattomia ja vaativat lisäksi vaihtoehtoisia tuotantoyksiköitä, jos uu-siutuvaa energiaa ei ole saatavilla. Tämän diplomityön avulla luodaan myös Wärtsilälle modu-laarinen työkalu, jonka avulla voidaan analysoida taajuuskäyttäytymistä mahdollisissa tulevissa ja olemassa olevissa mikroverkoissa. Tämä tutkimus kuitenkin osoittaa, että tämä PSCAD malli ei sovellu yrityskäyttöön tarpeettoman pitkien simulointiaikojen vuoksi.
Maintaining a continuous balance between energy production and consumption is essential for the reliable operation of the electricity system. The importance of managing power balance has been emphasized as the share of weather-dependent renewable energy production has increased and the share of fossil-based control power has decreased. As local consumer level production increases, so does the need for intelligent energy management. Energy management systems enable the fusion of local renewable energy sources and storages into a small intelligent and independent power grid; a microgrid. Microgrids are able to interact in real time with the operation of the entire energy system, taking into account energy market prices, weather conditions and locally forecasted energy needs and production volumes. In this thesis microgrids and energy management were studied based on the latest literature to form the starting point for an integration of a microgrid energy management system in a building management system. The thesis literature review comprehensively examined the structure of the Finnish energy system and the management of its balance, both locally and at the system level. In addition, the work focused on the areas that enable energy management in microgrids, such as the determination of the flexibility potential and the modeling and optimization of the energy system. The integration requirements were evaluated based on the use case examination and the literature reviewed. Implementation approaches were compared based on the reviewed literature. The communication between the two systems was tested with performance tests in a test environment. Communication protocols BACnet IP and Modbus TCP were selected for the performance tests, as they are widely supported in building management systems. The performance of these protocols in a automation server was measured with performance indicators specified from the automation server’s perspective. The suitability of these protocols was evaluated based on the test results. In the use case performance tests, Modbus TCP met the integration requirements with significantly lower automation server resource requirements. Based on the results, it can be concluded that the communication between these systems is straightforward to implement in a similar use case. The same implementation principles can be applied between different building automation systems as well as energy management systems. In terms of energy management, the most complex area is the energy modeling of the system and the construction of the optimization methods used to fulfill the system goals. A great deal of research has been done in recent history of optimization approaches in different microgrid use cases. Based on the literature reviewed in the work, significant benefits have been achieved with energy management and optimization. These benefits are realized locally and reflect positively to the entire energy system as the overall stability improves.
A microgrid can be formed by the integration of different components such as loads, renewable/conventional units, and energy storage systems in a local area. Microgrids with the advantages of being flexible, environmentally friendly, and self-sufficient can improve the power system performance metrics such as resiliency and reliability. However, design and implementation of microgrids are always faced with different challenges considering the uncertainties associated with loads and renewable energy resources (RERs), sudden load variations, energy management of several energy resources, etc. Therefore, it is required to employ such rapid and accurate methods, as artificial intelligence (AI) techniques, to address these challenges and improve the MG's efficiency, stability, security, and reliability. Utilization of AI helps to develop systems as intelligent as humans to learn, decide, and solve problems. This paper presents a review on different applications of AI-based techniques in microgrids such as energy management, load and generation forecasting, protection, power electronics control, and cyber security. Different AI tasks such as regression and classification in microgrids are discussed using methods including machine learning, artificial neural networks, fuzzy logic, support vector machines, etc. The advantages, limitation, and future trends of AI applications in microgrids are discussed.
The microgrid (MG) is a complicated cyber-physical system that operates based on interactions between physical processes and computational components, which make it vulnerable to varied cyber-attacks. In this paper, the impact of data integrity attack (DIA) has been considered, as one of the most dangerous cyber threats to MGs, on the steady-state operation of hybrid MGs (HMGs). Additionally, a novel method based on sequential hypothesis testing (SHT) approach, is proposed to detect DIA on the renewable energy sources’ metering infrastructure and improve the data security within the HMGs. The proposed method generates a binary sample, which is used to compute a test statistic that is further used against two thresholds to decide among three alternatives. The performance of the suggested method is examined using an IEEE standard test system. The results illustrated the acceptable performance of the proposed methodology in detection of DIAs. Also, to evaluate the effect of DIA on the operation of the HMGs, DIAs with different severities are launched on the measured power generation of renewable energy resources (RESs) like wind turbine (WT). The results of this part showed that a successful DIA on renewable units can severely affect the operation of electric grids and cause serious damages.
A significant challenge for designing a coordinated and effective protection architecture of a microgrid (MG) is the aim of an efficient, reliable, and fast protection scheme for both the grid-connected and islanded modes of operation. To this end, bidirectional power flow, varying short-circuit power, low voltage ride-through (LVRT) capability, and the plug-and-play characteristics of distributed generation units (DGUs), which are key issues in a MG system must be considered; otherwise, a mal-operation of protection devices (PDs) may occur. In this sense, a conventional protection system with a single threshold/setting may not be able to fully protect an MG system. To tackle this challenge, this work presents a comprehensive coordinated adaptive protection scheme for AC MGs that can tune their protection setting according to the system states and the operation mode, and is able to switch the PDs’ setting. In the first step of the proposed adaptive algorithm, an offline setting will be adopted for selective and sensitive fault detection, isolation, and coordination among proposed protective modules. As any change in the system is detected by the proposed algorithm in the online step, a new set of setting for proposed modules will be performed to adapt the settings accordingly. In this way, a new set of settings are adapted to maintain a fast and reliable operation, which covers selective, sensitive, and adaptive requirements. The pickup current (Ip) and time multiple settings (TMS) of directional over-current relays (DOCR), as well as coordinated time delays for the proposed protection scheme for both of the grid-connected and islanded modes of operation, are calculated offline. Then, an online adaptive protection scheme is proposed to detect different fault types in different locations. The simulation results show that the proposed method provides a coordinated reliable solution, which can detect and isolate fault conditions in a fast, selective and coordinated adaptive pattern.
The ameliorations in high-precision phasor measurement units (μPMUs) and synchrophasor units have accommodated the distribution grid with peculiar visibility. Therefore, investigating the challenges of uncertainty consideration on precise fault detection in microgrids has become a new research milestone. This paper presents an effective data-driven stochastic method that justifies the adoption of only two μPMUs that are communicating under an IoT-based umbrella to detect and allocate irregularities in a microgrid. The proposed method has the ability to operate under a variety of case studies and scenarios including but not limited to the capacitor bank switching, distributed energy resources (DERs) diversity and high impedance fault occurrence, whilst considering the uncertainty in load, without installing individual sensors. Furthermore, a two-point estimate approach is utilized to model the uncertainties of the problem. Not only does the proposed stochastic framework benefit from the voltage magnitude measurement, but it also utilizes its angle in event allocation, which manifests better performance compared to ordinary voltage and current sensors. The simulation results on the proposed microgrid indicate the high accuracy and a sound success is obtained under a variety of case studies. The results show the high accuracy and applicable aspect of the proposed data-driven approach for fault allocation using a few μPMUs in the IoT context.
Detta diplomarbete är gjort åt ett teknologiföretag beläget i Vasa, Finland, vilket fokuserar på utfasning av fossila bränslen (eng. decarbonisation) i mikronätverk genom optimering inom olika fokusområden. Exempel på dessa är minskning av utsläpp och bränsleförbrukning, ökning av nätverksstabilitet och tillgänglighet av elproduktionsanläggningar samt optimering av driftskostnader. Målet med diplomarbetet är att utreda inom vilka områden det kan uppstå utmaningar när man i ett tidigt skede kartlägger möjligheterna för utfasning av fossila bränslen genom optimering i industriella mikronätverk. Det här är gjort genom en kvalitativ studie baserad på semistrukturerade intervjuer med sakkunniga med olika expertisområden inom företaget i fråga. Intervjuerna är sedan analyserade och jämförda med relevant litteratur inom ämnet. Resultatet av studien är ett klassificeringssystem indelat i tre olika huvudområden: generering, nätverk & kontroll och last. Dessa är vidare uppdelade i underområden med egna teman i vilka olika kategorier är listade. Därtill i arbetet ges även förslag på användningsområden för detta klassificeringssystem, exempelvis i kundsamtal eller som hjälpmedel för experter.
Volt-VAr optimization (VVO) is an important issue for distribution network operators in active distribution networks with a number of AC and DC microgrids. This paper introduces a coordinated decentralized framework with application to Volt-VAr optimization in distribution networks with multiple AC and DC microgrids in the presence of distributed energy resources (DERs). To this end, a non-cooperative coordination model is proposed so that micro-grids operators (MGOs) and distribution system operator (DSO) could minimize their own active power losses, separately. In fact, the DSO and MGOs interact together to achieve an equilibrium that satisfies their expected active power loss. In order to solve the proposed coordinated non-cooperative problem, a novel decentralized optimization approach is suggested so that the master problem is decomposed into two sub-problems from DSO and MGOs point of views. These two sub-problems are linked to each other through sharing boundary information. On this basis, the communicational and computational loads are decreased while information secrecy is guaranteed. The modified IEEE 69-bus distribution network considering a number of AC and DC MGs has been chosen with the aim of conducting several numerical analyses.
Vaihtovirtaan (AC) perustuvien mikroverkkojen käyttöönottoon liittyy monia taloudellisia ja teknisiä haasteita. Merkittävimmät tekniset haasteet koskevat järjestelmän ohjausta ja suojausta. AC-mikroverkkojen toiminta sekä muuhun verkkoon kytkeytyneenä että itsenäisenä saarekkeena, hajautettujen energiaresurssien (DER) kytkeminen ja irrottaminen sekä niiden rajallinen kyky syöttää vikavirtaa edellyttävät mukautuvia ohjaus- ja suojausjärjestelmiä. Eri ohjaus- ja suojalaitteiden välillä on oltava tietoliikenneyhteydet adaptiivisten suojausjärjestelmien toteuttamiseksi. Suojalaitteiden tulee pystyä käsittelemään ennakoimattomia viiveitä ja virheitä tietoliikenteessä. DER-yksiköiden säädön tulee toimia normaalin verkkoa seuraavan tilan lisäksi myös verkon muodostavassa tilassa toimittaessa saarekekäytössä. Lisäksi tarvitaan strategia energian varastointiin kuormituksen ja tuotantovaihteluiden tasoittamiseksi saareketilanteessa. Verkkoonliitynnän määräykset ovat yleensä saatavilla verkkoon kytketylle toimintatavalle, joten saarekekäytölle on kehitettävä uusia ohjeita. Tarvetta olisi myös kehittää suunnattu suojausjärjestelmä, joka toimisi erilaisilla mikroverkoilla ja eri toimintatiloissa. Edellä mainitut tekijät huomioon ottaen tässä väitöskirjassa ehdotetaan, analysoidaan ja validoidaan verkkovaihtosuuntaajiin perustuvia energiaresursseja sisältävien AC-mikroverkkojen tietoliikennepohjaisia adaptiivisia suojausratkaisuja sekä ei-reaaliaikaisilla simuloinneilla että reaaliaikaisilla hardware-in-the-loop (HIL) -simuloinneilla. Työssä on tarkasteltu Ethernet-pohjaisia viestintäprotokollia, ja IEC 61850 -standardin mukainen GOOSE-protokolla on valittu käytettäväksi adaptiivisissa suojausjärjestelmissä. Simulointitulosten perusteella työssä ehdotetaan käytettäväksi ylivirtasuojareleiden vakioaikaan perustuvaa koordinointia saareketilassa. Saareketilan osalta on tutkittu DER-yksiköiden ja akkuenergiavarastojen (BESS) tuottaman vikavirran lisäämistä niin, että adaptiivinen ylivirtasuojaus voitaisiin välttää ja varmistettaisiin nopea sulakesuojauksen toiminta. Lisäksi työssä on ehdotettu ja arvioitu virran suuruusvertailuun ja symmetristen komponenttien käyttöön perustuvia suunnattuja suojausjärjestelmiä. Väitöskirjassa ehdotetaan myös menetelmiä vaihekatkosten havaitsemiseksi sekä uusia rajakäyriä vaihtosuuntaajapohjaisten DER-yksiköiden jännitekuopan (LVRT) ja jännitteen nousun (HVRT) sietoisuudelle.
Modern households usually have independent energy sources such as wind generators, photovoltaic (PV) panels, and similar green energy production equipment. Experts predict that soon, there will be an increasing number of such prosumers who both produce and consume energy. This process alleviates and reduces the load on large national electricity networks and also contributes to overall energy security. In this paper, a simulation model of a household, which employs a wind generator as its independent source of electricity, is developed. It is expected that this approach will be easily replicated for more complex configurations. The other components of the single prosumer microgrid that will be assessed are the non-shiftable electricity consumption equipment, which is used mainly in households and deployed separately for water heater, with a separate battery to meet the needs of these non-shiftable consumers. The 5-min data intervals for the year of simulation have been used. The characteristics of energy flowaccording to production and consumption schedules and the capacity of storage equipment have been modelled and simulated. Results disclose that wind turbine production size and buffer battery have a crucial impact on the demand cover factor.
This paper presents a new framework for island formation prior to windstorms, which considers tree-caused failures of distribution networks. In the proposed framework, both direct and indirect effects of windstorms on distribution lines are quantified. Thus, a novel discrete Markov chain model is proposed for representing the failure modes of trees in each time interval of windstorm duration. This model categorizes 'healthy', 'uprooted', 'stem breakage', and 'branch breakage' states of a tree. In addition, a new line-tree interaction model is presented for calculating tree-caused failure probability of overhead lines. The results of the proposed Markov model are taken as inputs by the developed line-tree interaction model. In these models, the different characteristics of windstorms are taken into account. Tree vulnerability to windstorms is characterized by different factors such as their species, height, and critical wind speeds. Windstorm duration is sectionalized into multiple time intervals, and the proposed models are applied to trees and distribution system components in each interval. Moreover, the interdependency between the intervals is captured by the Markov model. The results of the models are used by an optimization model, thereby dividing a distribution system into multiple islands before storm onset. Subsequently, the framework is extended as a two-stage stochastic optimization problem to address the uncertainties of loads. In addition, this framework considers the allocation of mobile emergency resources. The proposed models are implemented on the IEEE 33- and 123-bus test systems, as well as a practical distribution feeder, and their effectiveness is demonstrated through several case studies.
Optimal economic scheduling of microgrids with photovoltaic (PV) and wind generation has gained increased attention during recent years. Integration of renewable energy resources in microgrids requires increasingly active control and management of energy storages and demand response (DR). In this paper, a risk-based stochastic optimal energy management model is developed for microgrid with renewables, energy storage and load control by time-of-use-based DR programs. Microgrid includes PV system, wind system, micro-turbine, fuel cell, electric vehicle (EV), and energy storage. Information-gap decision theory (IGDT) is employed to address the uncertainty of loads and to provide the operating strategies for the microgrid controllable energy resources. This proposed model has been solved as a mixed-integer non-linear programming (MINLP) in General Algebraic Modeling System (GAMS) software and simulation results in different conditions are studied and discussed. Three different risk management strategies have been studied such as risk-averse, risk-neutral and risk-seeker mode. The simulation results indicate that the impacts of risk-averseness or risk-seeker of the decision maker affect the system operation. For instance, the results showed the DR program's role in risk-averse and risk-taking strategies, impacting consumption and costs. The proposed model ensures the risk-averse decision-maker that if the uncertain parameter deviates within the optimum robustness region, the final cost will not exceed the critical cost. On the other hand, the risk-seeking decision-maker can reach lower final costs by accepting the risks if the uncertain parameter deviates favorably within the opportunity region. Decision-makers can manage risks by adjusting consumption. Thus, considering the cost of risk management is crucial, as it increases with robust or opportunistic approaches.