Islanded microgrids as flexible, adaptive and sustainable smart cells of distribution power systems should be operated in accordance to both techno-economic purposes. Motivated by this need, the microgrid operators are in charge to elevate the active accommodation of both demand-side and supply-side distributed energy resources. To that end, in this paper, a new flexible frequency dependent energy management system is proposed through which distributed generators have time varying droop controllers with a gain-adaptive strategy. Besides to cope economically with uncertainty arise frequency excursions, a new, comfort-aware and versatile frequency dependent demand response program is mathematically formulated and conducted to the energy management system. It is aimed to co-optimize the microgrid energy resources such a way the day-ahead operational costs are managed subject to a secure frequency control portfolio. The presented model is solved using a two-stage stochastic programming and by a tractable efficient mixed integer linear programming approach. The simulation results are derived in 24-h scheduling time horizon and implemented on a typical test microgrid. The effectiveness of the proposed hourly gain assignment and frequency responsive load management program has been verified thoroughly by analyzing the results.

Tapered-waveguide quantum dot semiconductor optical amplifiers (TW-QDSOAs) have been modeled using numerical calculation of the rate and propagation equations in this article and the amplification characteristics and dynamics of pulse propagation in non-tapered QDSOA, linear, and exponential TW-QDSOA structures have been studied and compared in detail. It has been found that TW-QDSOAs apply less distortion to the amplified pulse and have greater optical gain than non-tapered QDSOA. In TW-QDSOAs, the amplified pulse becomes much less broadening and this amount is negligible, therefore the pulse bit rate can be increased. While the amplified pulse in a non-tapered QDSOA is much broadening than the TW-QDSOAs. The carrier density distribution and dependency of the amplifier gain to the output energy for both non-tapered and tapered structures have been studied. The carrier density and gain in non-tapered QDSOA decreases more than TW-QDSOAs, indicating that the tapered amplifiers are saturated at higher input energies. Our obtained results agree well with those previously obtained for conventional tapered amplifiers.

An idea towards stabilizing a grid with numerous distributed generators (DGs) and renewable energy sources (RESs) is to compensate system inertia, damping characteristic, and weak dynamics, virtually. This may be established by a primary power source (e.g., DG or energy storage) together with a flexible controlled power electronics converter. This setup will then operate to emulate desirable dynamics, by flexible shaping of its output active and reactive powers. This approach provides a promising solution to improve power grid stability and performance in the presence of high penetration of DGs/RESs. In this paper, the general idea of the flexible controlled power converter (CPC) is explained and its significant role in enhancing the renewable integrated power grids is emphasized.

This paper presents an inductor-less multilevel inverter (MLI) with a 13-level ac output. The proposed MLI consists of only 11 switches, 2 capacitors, and 2 input sources. Owing to the inductor-less symmetrical topology, the proposed MLI can reduce the component count in comparison of conventional MLIs. This leads to reduction of power loss and hardware cost. To clarify the effectiveness of the proposed MLI, the characteristic evaluation is given in detail. Some computer-based simulations in SPICE simulator environment as well as breadboard-based experimental tests are conducted concerning the proposed 13-level inverter. In the performed simulations, the power efficiency of the proposed MLI reaches about 85% and 88% at 1 kW in the case of a sinusoidal output and a triangular output, respectively. The proposed MLI improves power efficiency more than 2.5% comparing with the conventional K-type MLI when the output power is 1 kW. Furthermore, the experimental result proves the physical feasibility of the proposed topology by demonstrating the 13-level ac output.

This paper presents a novel light emitting diode (LED) sink driver with a high voltage gain for LED-based lighting applications. Unlike conventional LED sink drivers, the proposed driver has the nested switched-inductor/switched capacitor (SI/SC) topology consisting of an SI buck-boost converter block and a nesting-type SC cell. Owing to the nested SI/SC topology, the proposed driver offers not only a high voltage gain but also a flexible controllability of the LED currents and transformer-less structure. The effectiveness of the proposed driver is justified by some simulation results in SPICE software environment and laboratory experimental tests. In the performed simulations, the proposed driver in comparison of conventional drivers improved almost 6% power efficiency when the output power was 500 mW. Furthermore, in the performed experiments, the feasibility of the proposed topology is confirmed by demonstrating a high voltage gain and the output controllability.

Preserving the frequency stability of new important blocks in smart grids such as interconnected microgrids, is a serious challenge. Inertia can act as a significant stability/performance index for frequency regulation in the power grids. Renewable energy sources have low inertia and mostly need a special control mechanism to emulate an inertia and inject to the system. In this work, a frequency response model is introduced for AC interconnected microgrids based on the inertia concept. The absence of a suitable model for study on frequency and tie-line power control in the interconnected microgrids, limits research activities in this field. A clear classification has not been done considering the inherent dynamic behavior of all distributed generators types, which may help researchers to introduce more simple strategies to provide better dynamic performance. This paper addresses a new approach for study on inertia-based simple frequency response model for interconnected microgrids. The outcome can be useful for frequency and tie-line power control analysis and synthesis in interconnected microgrids.

In dc microgrids (dc MGs), the dc link capacitor is very small to provide the inherent inertial. As a result, large voltage deviations occur during load changes or uncertain fluctuations from the fluctuated power resources. This leads to degradation of voltage quality. To overcome the low inertia problem, this paper proposes a fast-responding energy storage system such as supercapacitor can mimic inertial responses through some specified control algorithm. A bidirectional dc–dc converter is used for interfacing supercapacitor energy storage to a dc MG. The proposed control scheme is composed of a virtual capacitor and a virtual conductance. It is implemented in the inner loop controls, i.e. current loop control to be fast enough emulating inertia and damping concept. In order to study the stability of dc MG, a comprehensive small-signal model is derived and then, an acceptable range of inertia response parameters is determined by using the system’s root locus analysis. Performance of the proposed control structure is demonstrated through numerical simulations.

Increasing the share of the renewable energy sources (RESs) in supplying the electrical demand of the energy systems facilitates achieving the sustainable development goals. The main challenge in this regard is the uncertain behavior of the RESs in the real-time (RT) operation. To address this issue, the RESs can participate in the RT energy market through the distributed energy resource aggregator (DERA). The participation of the DERA as a price-maker player in the RT market is addressed in this paper. For this purpose, a bi-level optimization approach is employed in which the problem of the DERA and the RT market are modeled as the upper-level and the lower-level problems, respectively. Moreover, the uncertainties of the RESs are modeled in the DERA’s problem using a risk-based two-stage stochastic model. In this model, the risk level of the DERA is managed using the Conditional Value at Risk (CVaR). The proposed non-linear bi-level model is transformed into a linear single-level one using the Karush-Kuhn-Tucker conditions and the dual theory. The proposed model is applied on the IEEE 6-bus and 24-bus test systems. The results show that the social welfare of the RT market increases in the presence of the DERA through supplying the demand of the market with RESs. Moreover, the effect of the RESs’ uncertainties on the DERA’s decisions and the RT energy market results is managed using the risk-level of the DERA.

he operation problem of the Smart Stand-Alone Microgrids (SSAMGs) considering the economic and the technical indices is investigated in many studies. The aim of the most studies is to meet the demand of the system with the minimum cost and high reliability considering the technical constraints of the system. In this paper, maximizing the RES penetration and improving the voltage profile of the system are modeled besides the minimization of the operation cost through a multi-objective optimization model. Moreover, the demand side management (DSM) strategies consisting of the demand shifting (DS) and the demand curtailing (DC) are employed in the decision-making problem of the SSAMGs to improve its economic and technical indices. To analysis the performance of the system, the battery bank (BB) lifetime and the peak-to-average ratio (PAR) indices are used. The resulted model is solved by the augmented epsilon-constraint approach in General Algebraic Modeling System (GAMS) optimization software environment. The best solution is selected using the fuzzy and the weight sum approaches. To investigate the effectiveness of the model and its solution methodology, it is applied on the IEEE 33-bus through defining four case studies. The results show the better performance of the objective functions in the presence of DSM strategies. Moreover, the PAR and the BB lifetime indices are improved when the DSM strategies are used in the SSAMGs to meet the demand.

Implementation of distributed energy resources (DERs) has led to a decrement in the cost of supplying demand in distribution networks. Integration of DERs in the forms of micro-grids (MGs) is a solution to enhance the operation of these resources in the low voltage networks. To meet the demand by MG operator, both technical and economic characteristics as well as the prices offered by retailers are considered to schedule DERs optimally. In these networks, the profit of retailers is maximized by power trading with MGs and optimally purchasing the energy from wholesale markets. Due to the existence of several retailers and MGs in active distribution networks (ADNs), hierarchical decision-making frameworks are needed to model their operation problem. For this purpose, a bi-level optimization technique is proposed in this paper to model the operation problem of retailers and MGs as decision-making variables in distribution networks in the upper and lower levels, respectively. To solve the proposed model, multi-objective particle swarm optimization (MOPSO) algorithm is used. The proposed model and its solution method are applied to a hypothetical distribution network with several retailers and MGs to validate the theories and discussions. Numerical results show that the maximum capacity of DG and the amount of demand have an important effect on this decision and the prices of purchased power from wholesale markets determine the amount of retailers’ offers to MGs.

Formulating a novel mixed integer linear programing problem, this paper introduces an optimal Internet-of-Things-based Energy Management (EM) framework for general distribution networks in Smart Cities (SCs), in the presence of shiftable loads. The system’s decisions are optimally shared between its two main designed layers; a “core cloud” and the “edge clouds”. The EM of a Microgrid (MG), covered by an edge cloud, is directly done by its operator and the Distribution System Operator (DSO) is responsible for optimising the EM of the core cloud. Changing the load consumption pattern, based on market energy prices, for the edge clouds and their peak load hours, the framework results in decreasing the total operation cost of the edge clouds. Using the optimal trading power of the MGs aggregators as the input parameters of the core cloud optimisation problem, the DSO optimises the network’s total operation cost addressing the optimal scheduling of the energy storages. The energy storages are charged in low energy prices through the purchasing power from the market and discharged in high energy prices to meet the demand of the network and to satisfy the energy required by the edge clouds. As a result, the shiftable loads and the energy storages are used by the DSO and the MGs to meet the energy balance with the minimum cost.

In this paper, the day-ahead scheduling problem of a smart microgrid (SMG) is modeled as a multi-objective function consisting of: i) minimizing the operation cost and the emission pollution in generation side ii) minimizing the load curtailment cost with the strategic conversion of curtailable loads (CLs) and iii) coordinating of shiftable loads (SLs) and the output power of wind turbines (WTs). The second and the third objectives present a new approach of Demand Side Management (DSM) strategies to improve the customer’s satisfaction (CS) and the WT penetration (WTP) using stimulation of customers to use their loads regarding the demand profile of the system. Also, the output power of WTs is considered as the stochastic model, and the participation of the SLs based on the availability of WTs output power are scheduled. To confirm the proposed approach, all objective functions are optimized by the ε-constraint approach in the GAMS optimization software and the best solution of the non-dominated Pareto solutions is selected using the decision-making method. To investigate the effectiveness of the proposed model, it is applied on a 24-node microgrid through four case studies.

In order to stabilize the operation of power systems, it’s essential to model the uncertainties threatening the normal performance of these systems. In this study, a renewable based grid-photovoltaic (PV)-boiler-battery-fuel cell hybrid energy system (RBHES) has been scheduled according to the uncertainty modeling of upstream net price and employment of demand response program (DRP). The main reason of implementing DRP is to motivate electricity consumers to modify their energy usage pattern in a way that economic goals of RBHES are obtained. In this paper, interval optimization technique has been used to model the uncertainty of upstream net price and to prepare a stabilized condition for safe operation of RBHES. Converting the single-objective-uncertainty-based model into a deterministic multi-objective model with average and deviation costs, the interval optimization technique models uncertainty and guarantees the optimal performance of RBHES under the minimum impact of upstream net price uncertainty. To solve the aforementioned multi-objective model, the weighted sum technique and the fuzzy approach are used. An RBHES has been investigated as a case study and the simulation results showing positive effects of employed techniques are presented for comparison. In cases with and without DRP, the average costs of RBHES in the interval method in comparison with the deterministic method have increased 1.61 % and 2.06 %, respectively. This is while the deviation costs of RBHES has reduced up to 13.61 % and 15.28 %, respectively. Due to the successful implementation of the DRP, the average cost and the deviation cost of the RBHES have lessened for 5.89 % and 11.08 %, respectively, in comparison with the case lacking the DRP.

In this paper, the decision making problem of microgrids (MGs) in simultaneous participation in the energy and reserve markets under uncertainty is investigated using a two-level framework. In the first level, the uncertainties of wind speed, solar radiation, probability of calling reserve, energy and reserve market prices, and demand are modeled using their probability distribution functions regarding which the operation problem of MG operator (MGO) is formulated as a two-stage stochastic optimization approach. The expected total cost and the amount of the provided reserve by the MGO for the reserve market are the output decisions of this problem which are considered as the parameters in the second level problem. The uncertainty of the accepted reserve by the market regarding the required reserve of the system and the behavior of the market players are modeled using the information gap decision theory (IGDT) approach as the second level problem. Therefore, the risk of the MGO is controlled using the conditional value at risk (CVaR) and IGDT risk-aversion parameters. Applying the proposed model on the 15-bus modified MG and 40-bus real test system shows the optimal decisions of the MGO in both markets to manage its uncertainties. Moreover, the sensitivity analysis is done to investigate the behavior of the MGO with changing the risk aversion parameters.

Optimal energy consumption is one of the sustainable development issues in many countries to improve the economic and environmental indices in the energy sector. This paper presents a tri-objective optimal performance of a smart hybrid energy system (SHES) in the presence of customer’s participation to optimally reshape the demand profile in the day-ahead energy market. Minimizing the operation costs and the emission pollution as well as maximizing the customer satisfaction level are considered as the objectives of this problem. The three types of demand response (DR) programs consisting of 1) demand curtailment, 2) demand shifting and 3) onsite generation program are considered for optimal scheduling of the electrical and the thermal energy consumption by the customers. The demand curtailment program is considered as the reserve for SHES and the Plug Electric Vehicles (PEVs) are taken into account as the onsite generation program. The uncertainties of energy and reserve prices are modeled using lognormal distribution function. The shuffled frog leaping algorithm (SFLA) is employed to solve the problem from which the non-dominated solutions are generated. Then, the best solution of the non-dominated solutions is selected by the hybrid approach of fuzzy method and the weight sum. To validate the mentioned approach, five case studies are investigated and the results demonstrate optimal scheduling of SHES with acceptable levels of operation costs, emission pollution and customer satisfaction.

Hybrid energy system is an appropriate solution to meet the local electrical and thermal loads using different energy resources. The operator of hybrid energy system decides on optimal scheduling of its resources and trading power with the main grid in an optimal way. The price of trading power with the main grid as an uncertain parameter has an important impact of the decisions of the hybrid energy system’s operator. The novelty of this paper is to apply interval optimization approach for obtaining a risk-constrained operation of photovoltaic/fuel cell/battery hybrid energy system considering the uncertainty of energy price, which is a powerful uncertainty modeling approach. Using the interval optimization approach the objective function of the problem is transformed into a multi-objective optimization one in which there are two conflicting objectives consist of the average and the deviation costs of operation problem of hybrid energy system. This problem is minimized using ε-constraint and fuzzy decision making approaches. Moreover, demand response program has been used to help the operator of hybrid energy system to reduce its total operation cost. The total increase of operation cost with demand response program is 5.52% less in comparison with the case no demand response program is implemented while cost deviation with demand response program is 13.28% less in comparison with no demand response program case.

In Active Distribution Networks (ADNs), Distribution Company (Disco) follows two main strategies of dispatching of Distributed Energy Resources (DERs) and trading energy with wholesale energy markets, including Day-Ahead (DA) and Real-Time (RT) markets, to meet the demand. An attempt is made in this paper to model the strategic behavior of the Disco, in the wholesale DA and RT energy markets, through a bi-level optimization approach While the objective of the upper-level problem is to minimize the expected cost of the Disco, the lower-level problem (with two optimization problems) formulates to simultaneously maximize the social-welfare of the DA market and minimize the cost of the RT market. Furthermore, uncertain behavior of renewable energy sources as well as demand is tackled into the problem formulation. To this end, Disco decision-making represents as a risk-based two-stage stochastic problem where the Disco’s risk aversion is modeled using conditional value at risk (CVaR) method. Generally stated, the proposed model is a non-linear bi-level problem which may be transformed into a non-linear but single-level problem through Karush–Kuhn–Tucker (KKT) conditions and dual theory. Detailed numerical results on a 6-bus and RTS 24-bus power systems are used to demonstrate efficiency of the proposed model. Moreover, sensitivity analysis is carried out to investigate the effect of risk-aversion parameter on the decision making of the Disco and the offers/bids in both the DA and RT markets.

Uncertainty, a familiar concept for power system operators has been set to be one of important topics in the industry of electricity systems. This circumstance is mainly caused by uncertain behavior of some parameters like price. Since the forecasting techniques are usually unable to guarantee a fixed and accurate value of such parameters therefore uncertainty modeling becomes essential. This work has applied an interval based optimization model for optimal performances of intelligent parking lot (IPL) of electric vehicles (EVs) within severe uncertainty of upper grid price under demand response program (DRP). In fact, DRP is used to enable IPL reduce its daily operation cost by shifting some parts of load demand from peak time intervals to off-peak time intervals. It should be mentioned that interval approach does not solve single objective problem and instead of that it generates a multi-objective optimization problem within which average and deviation costs are minimized as the bi-objective model. To do this, weighted sum and fuzzy approached are applied to solve the bi-objective problem. A sample system containing IPL, local dispatchable generation (LDG) units, non-renewable and renewable generation systems is studied under uncertainty of upper grid price through mentioned techniques and the results proving efficiency of employed techniques are investigated for comparison. According to the compared results, under DRP, average cost of IPL is reduced up to 4.37% while deviation cost representing uncertainty impact is also decreased up to 10.93%.

High penetration of renewable energy sources (RESs) and electrical energy storage (EES) systems in electrical distribution grids has changed the energy balance of distribution system operators (DSO). For this purpose, the energy scheduling problem of a Residential smart electrical distribution grid (RSEDG) with RESs and demand side management (DSM) is modeled as a tri-objective model consisting of: (1) minimization the operation cost, and emission pollutions in generation side; (2) minimization the loss of load expectation (LOLE) in demand side, and (3) minimization the deviation between the demand curve and output power of RESs. The third objective function is used as a DSM strategy to model the joint scheduling of RESs and the deferrable loads (DLs) where the DLs can be shifted regarding the output power of RESs to improve the load factor (LF). The uncertain behavior of the RESs are modeled using the stochastic optimization approach. The proposed model is solved using the epsilon-constraint method. Since the proposed approach has three objective functions; different Pareto solutions are obtained and the best solution is determined by the decision-making method. To investigate the effectiveness of the proposed method, it is applied on the 83-bus distribution grid and its results are compared for four cases studies.

The distribution network operation problem (DNOP) is an optimization problem in which the objective function is the total operation cost of the distribution company (Disco), to be minimized considering the technical constraints of the network. In the presence of distributed energy resources (DERs) and microgrids (MGs), new decision makers, including MG and DER operators or managing entities, are emerging and are changing the decision-making framework for distribution systems. To describe the cooperation and competition between the Disco, MG and DER operators, different frameworks and models have been proposed in the literature. Moreover, different computational techniques and metaheuristic algorithms have been used to solve the optimal operation problems. Hence, this paper considers DNOP as one of the timely problems under study and of major interest for future research, presenting a comprehensive review on the decision-making frameworks referring to DNOP in the presence of DERs and MGs, as a new contribution to earlier studies. The focus is set on the comparison among different frameworks characterized by increasingly higher level of participation of the DER managers to the distribution system operation, offering a complementary view with respect to available reviews on similar topics based on technical aspects of the DER connection and integration in MGs and distribution networks, which is noteworthy.

Islanded microgrids as flexible, adaptive and sustainable smart cells of distribution power systems should be operated in accordance to both techno-economic purposes. Motivated by this need, the microgrid operators are in charge to elevate the active accommodation of both demand-side and supply-side distributed energy resources. To that end, in this paper, a new flexible frequency dependent energy management system is proposed through which distributed generators have time varying droop controllers with a gain-adaptive strategy. Besides to cope economically with uncertainty arise frequency excursions, a new, comfort-aware and versatile frequency dependent demand response program is mathematically formulated and conducted to the energy management system. It is aimed to co-optimize the microgrid energy resources such a way the day-ahead operational costs are managed subject to a secure frequency control portfolio. The presented model is solved using a two-stage stochastic programming and by a tractable efficient mixed integer linear programming approach. The simulation results are derived in 24-h scheduling time horizon and implemented on a typical test microgrid. The effectiveness of the proposed hourly gain assignment and frequency responsive load management program has been verified thoroughly by analyzing the results.

In the light of the smart grid paradigm, power systems have recently brought about higher levels of adaptability and flexibility. The associated functions in energy management system blocks need to adopt robust strategies to provide greater levels of control and protective service. Under-frequency load shedding (UFLS) schemes can be regarded as a critical function, through which the system integrity and sustainability indices are protected reliably. Conventional UFLS schemes usually suffer from low adaptability and optimal setting in response to changes in the operational characteristics of the power system. This paper seeks to propose an hourly framework for the optimal design of an adaptive UFLS function based on advanced metering infrastructure (AMI) in smart grids. In this portfolio, the target is tailored to fit the main UFLS settings based on the system contingencies and associated operational uncertainties. The simulations are executed on the IEEE 39-bus test system, implemented on a 24-hour scheduling time horizon. The effectiveness of the proposed adaptive model is evaluated with a thorough analysis of the derived numerical results.

With the widespread development of microgrids (MGs) in future smart distribution networks, a number of neighboring MGs can be connected and form a multi-microgrid (MMG) cluster. In this regard, the energy management of a MMG is challenging due to more complex components and higher degrees of uncertainty in a small region of power system. Likewise, in the islanded MMG (IMMG) clusters, due to the low-inertia and high intermittent energy delivery of renewable resources, the frequency security should be considered in the energy management. To address this issue, this paper proposes an energy management system (EMS) in which hierarchical control structure of IMMG clusters is precisely modeled. The proposed EMS aims to minimize total operation cost of IMMG cluster while sufficient primary and secondary reserves are scheduled to preserve frequency security in a predefined range. Besides, the proposed EMS provides optimal strategies for MGs to exchange energy and reserves during scheduling horizon. To consider operational uncertainties, the proposed EMS is formulated as a two-stage stochastic mixed-integer linear programming problem that guaranties the global optimal solution. The obtained results verify that through the proposed EMS, total operation cost of the IMMG cluster is minimized while the frequency can be cost-effectively preserved within a pre-defined secure range.

This paper proposes a day-ahead self-healing scheduling approach in isolated networked microgrid (NMG) systems. The proposed approach is based on a two-level flexible energy management system (EMS). The upper-level EMS is responsible for optimal scheduling of the normal-operated MGs, while the lower-level help the MGs for operating on-fault in self-healing and islanded modes. When a fault occurs in an MG, it divides the on-fault MG into two zones. The grid-connected zone receives power support from the other normal-operated MGs. Furthermore, a secure optimization problem is presented to operate the separated zone in an islanded operation. The proposed approach is implemented on a test system with five NMGs. To cope with the operational uncertainties, a stochastic programming approach is applied. The simulation is run over a 24-hour scheduling time horizon. The results of the simulations in the normal, self-healing, and islanded operation modes for normal-operated and on-faulted MGs in cases without and with DRP are presented. The results reveal the effectiveness of the proposed stochastic EMS model in enhancing the performance of the network and decreasing the operational costs of the MGs.This paper proposes a day-ahead self-healing scheduling approach in isolated networked microgrid (NMG) systems. The proposed approach is based on a two-level flexible energy management system (EMS). The upper-level EMS is responsible for optimal scheduling of the normal-operated MGs, while the lower-level help the MGs for operating on-fault in self-healing and islanded modes. When a fault occurs in an MG, it divides the on-fault MG into two zones. The grid-connected zone receives power support from the other normal-operated MGs. Furthermore, a secure optimization problem is presented to operate the separated zone in an islanded operation. The proposed approach is implemented on a test system with five NMGs. To cope with the operational uncertainties, a stochastic programming approach is applied. The simulation is run over a 24-hour scheduling time horizon. The results of the simulations in the normal, self-healing, and islanded operation modes for normal-operated and on-faulted MGs in cases without and with DRP are presented. The results reveal the effectiveness of the proposed stochastic EMS model in enhancing the performance of the network and decreasing the operational costs of the MGs.

Renewable energy sources (RESs) in the power system have led to the continuation of electricity supply to the consumer and to the sustainable development of the society. Due to the increased penetration of RESs, the uncertainties of electrical energy systems have been intensified, and smart power system operators are thus faced with growing levels of frequency security risks. For preservation of frequency in a power system, a multi-stage automated Under-Frequency Load Shedding (UFLS) program can be utilized to shed the minimum amount of load. In the light of the smart grid paradigm, Demand Response (DR) can be considered as an appropriate strategy to face the adverse effects of the uncertainties of the RESs and increase the balance between generation and consumption. Consequently, it is regarded as a necessity in a smart grid environment to design an innovative UFLS scheme that includes the uncertainties of the RESs and DR programs. This paper addressed a new optimal UFLS intended to minimize load shedding, after accurate tracking of frequency deviations. To resolve the problem of setting UFLS system parameters stochastically, a Mixed-Integer Linear Programming (MILP) formulation is used in an optimization framework. The proposed model is implemented on the IEEE 39-bus test system.

Renewable energies and electric vehicles are introduced as promising solutions to save energy costs and reduce environmental impacts in microgrid systems. However, the uncertainty of such resources would necessitate the development of advanced management models for optimal operation of microgrids. To address this issue, this paper proposes a new model for energy and reserve management of microgrids in the presence of electric vehicles. To effectively cope with uncertainties, a robust optimization methodology is proposed and applied to handle the uncertain parameters. Furthermore, the optimization problem is transferred into a mixed-integer linear programming model to ensure achieving near-global and tractable solutions. The proposed model aims to coordinate energy serving entities a way that the microgrid social welfare is optimized while at the same time driving requirements of the electric vehicle owners satisfied reliably. The methodology is implemented to a microgrid and solved over a day-ahead scheduling time horizon. The trends of techno-economic-environmental indices confronting to the increasing level of uncertainty control parameter are evaluated thoroughly in four case-studies. A robust multi-objective model is developed to trade-off between social welfare and emission. The numerical results are verified through a Monte-Carlo Simulation strategy to demonstrate the impressiveness of the proposed methodology.

This paper proposes a novel energy management system (EMS) for an isolated structure of networked microgrids (NMGs). The interconnected microgrids consist of the cyber-physical connections for information and power exchanges. A bi-level EMS is presented in which the outer-level EMS is aimed to exchange the required information and power between the interconnected microgrids, and the inner-level EMS is intended for energy scheduling of each on-fault microgrid in case of separation from other microgrids. This paper focuses on the operation of interconnected microgrids. A step-wise demand response program (DRP) is also considered in the energy management to attain the cost-effective operation. Furthermore, a new pricing model based on microgrid marginal pricing (MGMP) is introduced for the power exchanges between the interconnected microgrids. To cope with the uncertainties of the renewable energy sources and loads, some scenarios are generated using the scenario-based analysis. Also, a backward scenario reduction method is used to reduce the number of the scenarios. Besides, a mixed integer linear programming (MILP) is applied to the stochastic optimization problem of the NMGs. The proposed model is implemented on a test system with five NMGs. The simulation is run over a 24-hour scheduling time horizon. Both cases without and with demand response program (DRP) are compared in the numerical results. The results of the simulation demonstrate that using the proposed DRP in the energy management increases the performance of the generation units and decreases the total operational cost of the proposed NMGs. Also, the voltages of the buses converge to their rated values.

The ever-increasing penetration of Renewable Energy Sources (RESs) into the power system has faced system operators with higher risks subject to a growing level of the associated uncertainties. To preserve the system frequency security, an under-frequency load shedding (UFLS) scheme can usually be utilized as a final remedial action, which is aimed at removing the excessive load. UFLS can be managed in a multi-stage portfolio based on the priority and sensitivity of the loads under control to cope optimally with the occurring power imbalances. Design of an optimal, robust UFLS scheme is a vital procedure. To that end, the present paper proposes a new UFLS protective system which is conducted to shed the minimum optimal load after precise detection of the frequency excursions. The problem is transferred into a Mixed-Integer Linear Programming (MILP) based optimization framework, and it is to be solved in several stochastic scenarios for setting the UFLS system parameters. The analysis of the UFLS setting results, which are extracted through implementation of the model on the IEEE 39-bus test system, demonstrates the effectiveness of the proposed MILP-based methodology in dealing with the severe uncertainties resulting from RES output variations and load fluctuations.

An attempt is made in this paper to propose an analytical approach to mathematically assess the impact of MGs penetration level on power system frequency stability. Firstly, experimental-based model of constitute DGs of the University of Kurdistan-MG (UOK-MG) in islanded mode may be derived. Afterwards, realistic model of the MG in grid connected mode may be derived to realize penetrated system for future studies. Finally, some simple algebraic equations are derived to predict frequency dynamics of interest in penetrated system. The method interprets the frequency dynamic behavior of the penetrated system based on the conventional system in terms of frequency nadir, Rate of Change of Frequency (RoCoF), steady state deviation and a rolling window. Simulation results demonstrate high efficiency of the proposed approach for dynamical modeling and stability analysis of penetrated power systems.

Silicon-on-insulator (SOI) evanescent field whispering gallery mode (WGM) ring (disk) sensors are promising components for biological sensing applications. We study the optimum design parameters that result in the highest sensitivity (S) and figure of merit (FOM) simultaneously, for single and dual coupled SOI WGM ring (disk) based sensors. These devices are respectively consisting of one and two silicon ring (disk) resonator coupled to a straight waveguide. Periodic behavior of coupling parameters in biological solutions, are modelled by analytical conformal transformation method and coupled mode theory. These methods are verified by experimental results. We demonstrate optimum design parameters at twelve different configurations of single SOI ring (disk) based sensors. Based on the results, the calculated quality factor of second TE WGM resonance at these resonators in water solutions is Q_t ≥ 25000. This gives S and FOM parameters to be 193 (134) nm/RIU and 3112 (2161) /RIU, for single ring (disk) resonator sensor. We also obtain an ultra-improved intrinsic limit of detection (ILOD=7.32 × 10⁻⁸ RIU) biosensor based on two coupled ring resonators. The ILOD value, shows that our structure performs two and three orders of magnitude better than respectively two and three SOI cascade ring resonators based sensors, reported lately.

In this paper, an online adaptive optimal controller is firstly designed to optimize the performance of an automatic voltage regulator (AVR). Towards this end, an optimal quadratic tracking problem is defined based on the error between the synchronous generator’s terminal voltage and its desired value. Then, this optimal control problem is solved using an adaptive dynamic programming (ADP) method, called the policy iteration technique. Using this technique, the optimal performance is achieved without knowledge of the AVR parameters. To eliminate the steady state error between the AVR output and its desired value, a modification is made in the original policy iteration technique base on the integral action control and another adaptive optimal controller is designed. In addition, an approach is proposed to represent a large-scale power system through several Single-Machine Infinite-Bus (SMIB) systems. The equivalent SMIB system then may be linearized through modal analysis to be employed by the proposed control scheme. Simulation results show that the designed adaptive optimal controllers are so effective and can be used in practical power systems.

This study addresses the design procedure of an optimized fuzzy fine-tuning (OFFT) approach as an intelligent coordinator for gate controlled series capacitors (GCSC) and automatic generation control (AGC) in hybrid multi-area power system. To do so, a detailed mathematical formulation for the participation of GCSC in tie-line power flow exchange is presented. The proposed OFFT approach is intended for valid adjustment of proportional–integral controller gains in GCSC structure and integral gain of secondary control loop in the AGC structure. Unlike the conventional classic controllers with constant gains that are generally designed for fixed operating conditions, the outlined approach demonstrates robust performance in load disturbances with adapting the gains of classic controllers. The parameters are adjusted in an online manner via the fuzzy logic method in which the sine cosine algorithm subjoined to optimize the fuzzy logic. To prove the scalability of the proposed approach, the design has also been implemented on a hybrid interconnected two-area power system with nonlinearity effect of governor dead band and generation rate constraint. Success of the proposed OFFT approach is established in three scenarios by comparing the dynamic performance of concerned power system with several optimization algorithms including artificial bee colony algorithm, genetic algorithm, improved particle swarm optimization algorithm, ant colony optimization algorithm and sine cosine algorithm.

Many researches have investigated the optimal energy scheduling to meet the demand regarding the sustainable development issues and the economic and environmental indices. The energy hub system (EHS) is introduced as an appropriate framework in which the demands are supplied by multi carrier energy (MCE) such as electricity, natural gas, and thermal energy. In this paper, the optimal scheduling problem of an EHS is modeled as a tri-objective optimization problem in which the operation cost, the emission pollution, and the deviation of the electrical load profile from its desired value is minimized. In the proposed model, the third objective function as a Demand-side Management (DSM) strategy is considered for the optimal shifting of the electrical deferrable loads (EDLs) based on the day-ahead electrical energy prices. Moreover, the reserve scheduling is proposed by the interruptible loads (ILs) of the thermal and the electrical demand. The proposed model is solved using the augmented ε-constraint method in the General Algebraic Modeling System (GAMS) optimization software environment. The generated solutions by the augmented ε-constraint method have diverse non-dominated solutions in which the best solution is selected by the fuzzy approach. In order to validate the proposed approach, three Case studies are investigated and their results are compared with each other. The operation cost and the emission pollution of the EHS in the first case is 402130.12 $ and 36162.26 kg. With the participation of EDLs in the second case, the operation cost and the emission reduces 0.4% and 3.02%, respectively. Finally, with the simultaneous participation of EDLs and ILs in the third case, the operation cost and the emission pollution reduces 0.38% and 2.53% in comparison with the second case.

These days, a new concept called intelligent parking lot (IPL) has been extensively paid consideration to be used in power system industry. Using charge/discharge of electric vehicles (EV), IPLs attempt to exchange power with the upstream grid. In addition to IPL, studied model involves non-renewable and renewable units such as wind turbine, photovoltaic (PV) system, local dispatchable generator (LDG) like micro-turbine and hydrogen storage system (HSS) which are used all together to satisfy energy demand. In this work, optimal scheduling of an IPL has been studied under time-of-use (TOU) rate of demand response program (DRP) in which price of upstream gird is set to be uncertain which uncertainty is modeled via interval optimization technique. This technique transforms uncertainty based model into a deterministic multi-objective model with deviation and average costs as the inconsistency objective functions. Then, applying ε-constraint technique and fuzzy approach, mentioned multi-objective problem is solved. Obtained Pareto results as well as selected trade-off results in various case studies have been compared to prove efficiency of employed techniques. Obtained results revealed that due to positive influence of DRP, increase of average cost of IPL has been reduced up to 2.46% while deviation cost of IPL has been decreased up to 12.49%.

Formulating a novel mixed integer linear programing problem, this paper introduces an optimal Internet-of-Things-based Energy Management (EM) framework for general distribution networks in Smart Cities (SCs), in the presence of shiftable loads. The system’s decisions are optimally shared between its two main designed layers; a “core cloud” and the “edge clouds”. The EM of a Microgrid (MG), covered by an edge cloud, is directly done by its operator and the Distribution System Operator (DSO) is responsible for optimising the EM of the core cloud. Changing the load consumption pattern, based on market energy prices, for the edge clouds and their peak load hours, the framework results in decreasing the total operation cost of the edge clouds. Using the optimal trading power of the MGs aggregators as the input parameters of the core cloud optimisation problem, the DSO optimises the network’s total operation cost addressing the optimal scheduling of the energy storages. The energy storages are charged in low energy prices through the purchasing power from the market and discharged in high energy prices to meet the demand of the network and to satisfy the energy required by the edge clouds. As a result, the shiftable loads and the energy storages are used by the DSO and the MGs to meet the energy balance with the minimum cost.

Uncertainties of renewable power generation, load, and electricity price imposes serious challenges in optimal scheduling of microgrids (MGs). To cope with this issue, advanced models should be developed to optimize scheduling problem of MGs under uncertainties. However, the optimality and robustness of solutions as well as the computational burden are still a matter of concern when applied to scheduling of a practical MG. To provide a model which is adjustably robust and computationally tractable and also financial effective at the same time, this paper proposes a novel uncertainty handling framework based on stochastic p-robust optimization method for day-ahead scheduling of MGs including renewable and non-renewable distributed generations, combined heat and power systems, energy-storage systems, as well as demand response providers. The objective is to find optimal scheduling of MG in a way that the expected profit is maximized while the regret in each scenario is bounded. The effectiveness of the proposed model is tested on a MG test system thorough different case studies. Simulation results demonstrate that the robustness of the scheduling results is enhanced significantly without substantial decrease in the expected profit of MG.

Microgrid (MG), as a cluster of Distributed Generations (DGs) and local loads, may be characterized with zero or small inertia constant. Reduction of system rotational inertia, in response to increasing MGs penetration level, renders frequency dynamics faster. This paper presents an attempt to propose a new analytical approach to mathematically assess the impact of MGs penetration level on power system frequency stability. In this way, an experimental-based model is employed to make the simulation results realistic. The method interprets the frequency dynamic behavior of the penetrated system based on the conventional system in terms of frequency nadir, steady state deviation and a 15-second rolling window.

Maximum Power Point Tracking (MPPT) has an important role in studying and implementing photovoltaic systems. Different solutions have been recently proposed in numerous papers. Predictive control is simple in both method and implementation aspects. The main purpose of this control is to improve the maximum power point tracking using predictive error in Perturb and Observe method step. The current is predicted using the system model and the optimal operating point is determined via a certain cost function. By using the predictive equations, the additional sensors are removed. In MPC, the system equations must be ideally formulated. It means that if the system equations are written according to the real, actual conditions, the formulations become very complicated and take up a large amount of microprocessor time and memory. Another disadvantage of MPC is that the actual values of the system elements either are not available or can change over time. In this article, a Cuk converter is analyzed using the predictive control and Fuzzy Model Predictive Control (FMPC) method to achieve MPPT in photovoltaic systems. The Cuk converter has fewer active elements compared to ultra-step-up boost converter and the extra sensors are eliminated. It is shown that the predictive control method in ideal system has a better performance in both faster dynamic and steady state response under rapidly changing atmospheric conditions. The Predictive Control Method is compared with Perturb and Observe and Fuzzy Model Predictive Control via simulation results using Matlab and experimental results.