This article studies an energy transaction management of multienergy prosumers (MEPs) in integrated energy community (IEC). A novel MEPs coalitional game model with transferable utility is proposed to coordinate the electricity–heat transactions and enhance the local energy consumption among MEPs in IEC. The superadditivity of the proposed coalitional game is rigorously proven to ensure coalition incentives. A superadditivity-directed coalition formation algorithm is developed to achieve a stable and efficient coalition partition with significantly reduced computational burden. Furthermore, the nonemptiness of the core for the proposed coalitional game is rigorously proven, and a Shapley value-based payoff allocation mechanism is designed and proven to align with the core, ensuring both fairness and stability in the proposed coalitional game. Simulation results show that the proposed model and method achieve the highest payoff across all cases, ensure the fair and stable payoff allocation, achieve reductions of 99.2% in iterations and 98.98% in solving time, and confirm their feasibility for large-scale applications.

Mobile energy storage systems (MESSs) represent a proactive approach to load restoration of faulted microgrids. Existing studies mainly focus on minimizing the overall cost of the MESS fleet but fail to guarantee that self-interested MESSs are willing to follow the optimal social cost solution. Hence, this article allows MESSs to make independent decisions and formulates a nonconvex game. In particular, we incorporate the maximum tolerable service waiting time and construct a potential function to prove that the selfish actions of MESSs converge to a Nash equilibrium. We derive a small upper bound on the price of anarchy (PoA), demonstrating that granting MESSs the autonomy to make self-interested Nash decisions does not significantly increase the overall social cost. Moreover, we model subjective behaviors under uncertain grid power availability, accounting for both loss-sensitive and gain-seeking tendencies. Simulation results show that for different MESS fleet sizes and battery anxiety extents, Nash equilibria are always achieved, with all PoA values below the theoretical bound. Higher MESS numbers or battery anxiety extents improve load restoration performance. Under uncertain surplus energy, gain-seeking MESSs behave more aggressively and earn higher average profits.

Nowadays, wind energy is one of the cleanest and most economically affordable generating sources and there is an increasing trend towards doubly fed induction generator (DFIG)-based wind farms integrated with power grids through unified power flow controller (UPFC) devices for extracting large amounts of electrical power. The conventional protective relaying scheme used in transmission lines (TLs) is mostly affected by the nonlinear variation of output power due to the fluctuation of wind speed and power control mode operation of UPFC. Therefore, a novel approach of fast discrete S-transform assisted gradient boosting ensemble method for fault detection and classification scheme is proposed in this article to overcome the problem of such TL relaying scheme. The proposed method requires only grid-side current information to process the intelligent relaying scheme for this UPFC-compensated line connecting the DFIG-based wind farm. The performance of the proposed method is tested with different cases through the MATLAB/Simulink platform for different wind speeds. The proposed method has a low computational burden as it requires only three-phase current information. The performance of the proposed method is further tested on a larger network, the modified IEEE 39-bus New England system with a wind farm. Finally, the results of comparative analysis with some earlier established techniques justify the efficacy of the proposed method for fault detection and classification tasks. The proposed method has 100% accuracy for fault detection and 99.86% accuracy for fault classification.

Electric vehicles (EVs) have been gaining great popularity in recent years, but the lack of adequate infrastructure and the long battery charging time have hindered their further development. Therefore, a battery charging and swapping system (BCSS) can solve this problem by arranging battery charging and distributing the battery swapping system (BSS) to various locations while participating in the demand response of both power and transportation networks through time-of-use tariffs and congestion price. To optimally achieve the combined operation of BCSSs, this paper proposes a hybrid swapped battery charging and logistics dispatch model in the continuous-time domain. Specifically, the battery charging system will arrange the optimal battery charging strategy by a rectangle packing algorithm. Furthermore, the logistics system will set up a transportation dispatch model for the battery charging system to deliver the charged batteries from the battery charging system to the battery swapping system and then retrieve them. The combined model is involved in the demand side response considering the price. Simulation studies for different cases verify the effectiveness of the proposed model.

This article presents a dedicated microgrid planning and operation approach for distribution network (DN) support considering pumped-hydro storage (PHS). The term dedicated connotes the service responsibility of the microgrid rather than its operational independence. A nondedicated microgrid has a dual responsibility to serve the distribution system and commercial/industrial consumers. The payback period and lifetime benefit of a nondedicated microgrid partly rely on the time-of-use pricing mechanism, which drives the microgrid to maximize profit, and could compromise the overall operation of the DN. This article develops an approach for engaging dedicated microgrids having full obligation to serve the distribution system only. For effectively managing the operation of the microgrid without jeopardizing the operation of the DN, the approach considers a novel short-term operation capacity index, power purchase agreement, and levelized energy cost. Specific functionalities of PHSs are used to relax the microgrids sensitivity to operation penalty arising from output power deviation, and thus allowing the microgrid to increase profits. The results obtained showed mutual benefits for the system operator and microgrid owner since the DN operation cost was reduced and the microgrid owner increased its revenue.

Proactive security-constrained unit commitment (SCUC) is one of the effective resilience-oriented responses to keep the secure operation of the power system during natural disasters. The approaches proposed in the previous research works cannot guarantee high-quality response timely to various emergencies during typhoon disasters. This article proposes a proactive SCUC model solved by an approximate dynamic programming (ADP) algorithm to improve system resilience during typhoon disasters, while meeting the requirements of the computational time. Both the uncertainty of the typhoon motion path and the wind speed-dependent transmission line failure probability are considered in the scenario generation. The optimization process is formulated as a Markov decision process, and a piecewise linear function (PLF)-based ADP is employed for solving the model. With the exogenous information extracted from generated scenarios and recorded in the slopes of PLFs, the ADP algorithm can result in a near-optimal solution within a short computational time. Simulations are carried out using the modified IEEE RTS-79 system. The significant computational time saving and high-quality solution demonstrate the effectiveness of the proposed approach.

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 article, 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 microgrids (HMGs). Additionally, a novel method based on the sequential hypothesis testing 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. In addition, 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 like wind turbines. The results showed that a successful DIA on renewable units can severely affect the operation of electric grids and cause serious damage.

A nonlinear optimal (H-infinity) control method is developed for the Lotka-Volterra dynamical system. First, differential flatness properties are proven. The state-space description undergoes linearization, at each sampling instance, with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. Next, for the approximately linearized model of the system a stabilizing H-infinity feedback controller is designed. To compute the controller’s gains an algebraic Riccati equation has to be repetitively solved at each time-step of the control algorithm. Global stability properties are proven through Lyapunov analysis. Finally, the nonlinear optimal control method is compared against a flatness-based control approach implemented in successive loops.

The integration of the communications network and the Internet of Things in today’s critical infrastructures facilitates intelligent and online monitoring of these systems. However, although critical infrastructure’s digitalization brings tremendous advantages and opportunities for remote access and control, it significantly increases cyber-attack’s vulnerability. Therefore, efficient and proper detection and localization of cyber-attack are paramount for the critical infrastructure’s reliable and secure operation. This article proposes a deep learning-based cyber-attack detection and location identification system for critical infrastructures by constructing new representations and model the system behavior using multilayer autoencoders. The results show that the new representations capture the physical relationships among the measurements and have more discriminant power in distinguishing the location of the attack. Furthermore, the proposed method has outperformed conventional machine learning models under various cyber-attack scenarios using real-world data from the gas pipeline and water distribution supervisory control and data acquisition systems.

This article proposes a novel, disturbance observer-based decentralized frequency control method for interconnected power systems. The method employs extended Kalman filter (EKF) as an observer to estimate inaccessible dynamic states of the system, including the total disturbance as one of the state variables. An optimal decentralized disturbance observer based controller is suggested for multiarea power systems that compensates the estimated disturbance and further based on minimizing the joint error energies of state estimation error and state tracking error provides its value to the controller for regulating the frequency variation. The proposed method is mathematically designed to be robust against parametric and nonparametric uncertainties. The efficacy and accuracy of the proposed control method is verified considering different types of practical operation scenarios. The results confirm the brilliant and superiority of the proposed method in controlling the frequency in power systems with high renewable shares.

Nowadays, taking into account the intermittency of renewable energy resources such as wind farms and uncertainty of load demand seems helpful to obtain a more reliable strategy for the power systems. Nevertheless, information gap decision theory (IGDT) as a nonprobabilistic method has been employed in numerous papers to address the uncertain behavior of input parameters, the simultaneous optimal values of main objective functions (OF) such as cost and radius of uncertainty cannot be guaranteed. To overcome this issue, Fuzzy- IGDT is used, on the other hand, this approach reports the same value for the radius of uncertainty related to the uncertain resources. To cope with this problem, this article presents an algorithm considering uncertainties of multiple wind farms and load demand by acquiring different uncertain bands based on the decision-maker's preferences. Moreover, it is appropriate for uncertain resources with direct and inverse effects on the OF. In other words, it is more flexible than fuzzy-information gap decision theory. The precision of the proposed framework is evaluated by Monte Carlo simulation, meanwhile, its effectiveness and performances are proved. To guarantee global optimal results with reliable precision, the linearized approximation of the original mix integer nonlinear programming model has been accomplished in the GAMS platform. The performance of presented method is illustrated by utilizing IEEE 30 BUS and IEEE 62 bus systems.

Rapidly increasing distributed energy resources (DERs) bring more fluctuating output power to the distribution network and put forward a higher requirement on local regulation resources for maintaining the network's balance. Heating, ventilation, and air conditioning (HVAC) loads account for more than 40% of power consumption in modern cities and have huge regulation potential as flexible loads. However, HVACs equipped with inverter devices have rarely been studied for providing regulation services in the local electricity market (LEM), even though they have exceeded regular fixed-speed HVACs. To address this issue, this article proposes a real-time LEM and a distribution network's optimization framework to exploit the regulation potential of inverter-based HVACs considering multiple DERs. This LEM can avoid iterations in real time and significantly decrease the difficulty related to the participation of small end-users in urban distribution networks. Moreover, in this article, we propose a transactive capacity evaluation method to assist end-users in deciding their inverter-based HVACs regulation capacities in the real-time LEM, which considers buildings’ thermal features, users’ multiple comfort requirements, and dynamic ambient temperature. On this basis, a multilevel bidding strategy is developed for inverter-based HVACs to decrease energy cost, increase fluctuating DERs local utilization rate, and alleviate the distribution network's congestion. Finally, a realistic distribution network is utilized to verify the effectiveness of the proposed methods.

Feeder reconfiguration is an effective way to reduce power losses of distribution network. In this way, configuration of distribution system is changed in order to achieve possible minimum losses, while electricity demand of consumers has to be provided. Consumers’ power demand has an important role in feeder reconfiguration because any change in demand affects power losses directly. Whereas load demand has a variable and stochastic nature because of its dependence on consumption pattern and accuracy of forecasted load amounts. Accordingly, reconfiguration models should be robust enough against load uncertainty and variations. Thus, this article presents an efficient robust model for reconfiguration of distribution feeders under uncertain and variable loads. The proposed reconfiguration model is robust enough and efficient, in which its implementation is relatively simple. The results show higher efficiency and lower complexity of the proposed model compared to existing robust reconfiguration approaches.

Cloud computing has emerged as a new technological domain in the IT industry. Currently, several organizations working in various domains, such as healthcare, finance, manufacturing, smart grid, Internet of Things (IoT), and IT, are increasingly integrating cloud computing with their traditional applications. The key idea behind the usage of cloud computing in IoT is to increase efficiency without compromising the data quality. When it comes to collecting data of thousands or millions of servers, the cloud offers scalability and reduces the computational load on each sensor. The highly configured servers in the cloud are very useful in processing and analyzing the sensors’ data. The security of such a shared infrastructure is very crucial. It is the major barrier in the adoption of cloud-based services, followed by issues regarding compliance, privacy, and legal matters. The digitalization of critical infrastructures, such as smart grids (SGs), brings advantages and opportunities for remote access and control. It enables intelligent and online monitoring of these systems, which considerably enhances cyberattacks’ vulnerability. Cyberattacks are among the most important threats to SGs. Therefore, efficient control systems should be designed that can detect and isolate cyberattacks to keep the SG reliable and secure operation. This special section aims at providing a forum to discuss the most recent advances on security and privacy in cloud-assisted IoT and SG applications.

Due to the high penetration of wind power generation in power systems and electricity markets, wind power plants (WPPs) can, in some scenarios, influence the market prices and exercise market power in the day-ahead (DA) market. In order to evaluate the capability of WPPs to directly act as price-maker, this article proposes the strategic offering of a WPP in the DA market by using a bilevel stochastic optimization approach. The primary objective of the proposed model is to maximize the WPP's expected profit by strategically offering in DA market while minimizing the energy deviations in the regulating market. Moreover, the WPP can also sign bilateral contracts with customers to supply their required energy. In the subproblem, the system operator tends to minimize the sum of the total generation costs minus the sum of the total demand benefits. The effect of bilateral contracts on the strategic offering of WPP in the DA market and its impact on the transmission margin are also investigated. Results on real cases show that when the WPP enters into a bilateral contract, it should consider the effect of such contracts on the offering strategy to the DA market. The effects of bilateral contracts on the regulating market are also examined.

The Internet of Things (IoT) interconnects various devices and services, of which recommendation services are an important component to help the development of IoT applications. Furthermore, without the aid of suitable online recommendation systems, Internet users will be overwhelmed by the tremendous amount of contents. Researchers have thus developed a large volume of recommendations. However, they are all flawed with high complexity, cold start issues, inability to generalize, etc. In recent years, some researchers had turned to variational inference (VI)-based recommendation systems, which can solve the above problems to some extent. However, these VI-based recommendations are merely hybrid methods of VI with the existing recommendation algorithms and are unable to be implemented well in real practices. Therefore, developing algorithms that can overcome these limitations of the existing online recommendation systems is essential for convenient and useful Internet searches. In this paper, we propose, develop, implement and test a more general, new and innovative Stein Variational Recommendation System algorithm (SVRS) to tackle the long plaguing recommendation problems. Based on Stein’s identity, the SVRS algorithm can compute the feature vector of existing users and items it had rated, and further predict the ratings for users that have not been engaged with certain content. SVRS provides more general insights into the forming of user ratings, can be easily extended to higher dimensions and has the merits of low complexity, easy scaling and generalizability. Experiments show that SVRS outperforms the other existing type of recommendation algorithms and it has higher accuracy in terms of mean absolute error (MAE) and root mean square error (RMSE).

Voltage-source inverter-fed Permanent Magnet Synchronous Machines are widely used in industry (for instance for the actuation of robotic and mechatronic systems, of cranes, in water pumping stations) as well as in transportation systems (for the traction of trains and electric vehicles). The present article proposes a nonlinear optimal control approach for voltage source inverter-fed Permanent Magnet Synchronous Machines (VSI-PMSMs). The nonlinear dynamic model of VSI-PMSMs undergoes approximate linearization around a temporary operating point which is recomputed at each iteration of the control method. This temporary operating point is defined by the present value of the voltage source inverter-fed PMSM state vector and by the last sampled value of the machine’s control inputs vector. The linearization relies on Taylor series expansion and on the calculation of the system’s Jacobian matrices. For the approximately linearized model of the voltage source inverter-fed PMSM an H-infinity feedback controller is designed. This controller stands for the solution of the nonlinear optimal control problem for the voltage source inverter-fed PMSM under model uncertainty and external perturbations. For the computation of the controller’s feedback gain an algebraic Riccati equation is iteratively solved at each time-step the control method. The global asymptotic stability properties of the control method are proven through Lyapunov analysis.

This article presents a k-means clustering and weighted k-nearest neighbor (k-NN) regression-based algorithm for the protection of transmission line. Three-phase current signals of both the terminals are synchronized and sampled with a sampling frequency of 3.84 kHz. Cumulative differential sum (CDS) is computed by subtracting the samples of current cycle from the previous cycle at both the terminals of transmission line. k-means clustering is applied on CDS to compute two centroids using moving window of width, equal to one cycle. Difference between the absolute values of centroids is computed at both the terminals and represented by the centroid difference (CD). The CD of both the terminals is added to compute the fault index. The computed fault index is used to detect and classify the types of faults. The location of the fault is estimated by the weighted k-NN regression method. Various case studies are performed to validate the robustness of the algorithm for different fault parameters such as fault impedance and fault location. The effect of noise is also considered to check the accuracy of the proposed algorithm in the noisy environment.

With the rapid evolution of the industrial Internet, cloud service has emerged as a next-generation industrial standard that has the potential to revolutionize and transform the enterprise industry. In recent years, numerous enterprises have acknowledged the benefits of cloud-based service models. However, the security issues are a major concern, such as stealthy malware attacks against virtual domains. In this article, we propose an introspection based security approach, called VMShield for securing virtual domains in a cloud based service platform, which is designed to detect malware in cloud infrastructure. VMShield performs virtual memory introspection from the hypervisor (trusted-domain) to collect the run-time behavior of processes, making it impossible for the malware to evade the security tool. The use of introspection makes the proposed approach a better choice over traditional static and dynamic state-of-the-art techniques which fail to detect stealthy attacks. The VMShield extracts the system call features using Bag of n-gram approach and selects important features using the meta-heuristic algorithm, binary particle swarm optimization. Random Forest (RF) classifier is used to classify the monitored programs into benign and malign processes, making it capable of detecting the variants of malware thus, an advantage over the typical signature-matching approach. The University of New Mexico (UNM) Dataset and Bare cloud Dataset (University of California) has been used for the demonstration and validation of VMShield. The results prove that VMShield achieves a higher attack detection rate and reduced storage compared to previously proposed techniques.

The custom power devices play important role for enhancing the power transfer capacity of transmission system. However, these devices introduce challenges of under reach or over reach, in the protection of transmission system. This article introduces a novel, protection algorithm based on wavelet-alienation-neural technique for STATCOM-compensated transmission system. For detecting and classifying faults, approximate coefficients are computed from the postfault quarter cycle current waveforms. Fault index, which is summation of alienation coefficients (computed by approximate coefficients) of both the buses, is computed and compared with the threshold magnitude for detecting and classifying the different faults. For the determination of fault location, artificial neural network is applied, with input as three-phase approximate coefficients, evaluated from the voltage and current signals over a time duration of a quarter cycle. Robustness of the developed scheme has been validated for various faults at different locations with varying fault impedances and angles of fault incidence.

This paper presents a new framework, utilizing information-gap decision theory (IGDT), for multiobjective robust security-constrained unit commitment of generating units in the presence of wind farms and gridable vehicles. Both the wind power and load demand uncertainties are considered, and modeled using a biobjective model. As the main advantage, the framework enables the system operator to take an appropriate operational decision with respect to the extremity of each uncertainty. The proposed problem is solved using a normal boundary intersection technique. Subsequently, VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR), a decision-making tool, is utilized to choose the best Pareto optimal solution. Finally, the IGDT-based framework presented in this paper is validated using a six-bus test system, the IEEE Reliability Test System with 24 buses, and the IEEE 118-bus system.

In this paper, a multiobjective optimization method for the placement of phasor measurement units (PMUs) is proposed. The proposed method simultaneously considers different objectives including the power system small signal stability, the probability of system observability, and the total cost of PMUs. In the proposed optimal PMUs placement (OPP) model, wide area measurement system (WAMS) uncertainties and network configuration changes are also considered, while the variable cost of PMUs, based on the number of PMU channels is also observed. The elitist nondominated sorting genetic algorithm (NSGA-II) is used to obtain the Pareto optimal solutions and the best compromise final solution is selected by the decision maker on the basis of his priorities. Some simulation results on different test systems in different scenarios are presented and discussed. They confirmed that the proposed multiobjective model provides effective information for small signal stability assessment with minimum PMU cost in the presence of uncertainties.

Power quality (PQ) is a vital issue in the present power systems integrated with large renewable energy sources since more power electronics devices are incorporated in the system. This article proposes a novel method for assessing PQ associated with wind energy integration. This method is effective to recognize PQ issues in power systems with high penetration of wind energy with a low computational burden. Furthermore, it detects different operational issues in the distribution network. Stockwell transform (S-transform) is utilized to decompose the voltage signal and calculate the S-matrix. To assess the PQ, a plot is developed from this matrix. The features of this matrix such as mean, standard deviation, and maximum deviation are further utilized for detecting the operational issues such as wind speed variation, islanding, synchronization, and outage of the wind generation by using clustering with fuzzy C-means. A modified IEEE 13-bus test system is utilized to validate the proposed method, which is also supported by hardware and real-time digital simulator results. The quality of power is graded with the help of a proposed PQ index under various operational events with different levels of wind energy penetration. The proposed method is effective for the identification and grading of different operational events in terms of PQ and recognizing a wide range of PQ issues with a high share of wind energy. The performance of the proposed scheme is established by comparing its results with other approaches.

In the context of microgrid, renewable energy variations are still a major concern for operators, especially in industrial applications in which microgrids are typically located in remote areas and are operated autonomously. Information gap decision theory (IGDT) is a nonprobabilistic method utilized to appraise various levels of risk without the availability of statistical data, such as probability density functions of uncertain parameters. Despite such a rewarding feature, the IGDT in its current form is unable to obtain time-varying robustness bands, meaning that it does not take into consideration the system risk imposed by renewable energy injections at each individual time interval in a short-term operation horizon. To overcome this issue, this article presents a modified version of the IGDT named weighted IGDT (W-IGDT), yielding risk-based time-varying robustness bands rather than time-independent ones. This article also proposes a W-IGDT-based energy management system (EMS) based on a linked unit commitment-optimal power flow (UC-OPF) framework, which simultaneously incorporates the generating units on/off status as well as power flow limits into the optimization procedure. In order to illustrate the performance of the proposed EMS, a CIGRE microgrid benchmark is utilized, and the results indicate the effectiveness of the W-IGDT-based EMS in terms of optimal operation and addressing the intermittency of renewable energy sources.

Fault diagnosis in energy conversion systems is performed with the use of neural networks and statistical decision making. An energy conversion system comprising a solar power unit, a DC-DC converter and a DC motor is considered and the related condition monitoring problem is solved. A neural network is used to model the dynamics of this energy conversion system after processing its input and output measurements, being accumulated at different operating conditions. The considered neural model is trained with the use of first-order gradient algorithms and consists of a hidden layer of Gauss-Hermite polynomial activation functions and of an output layer with linear weights. The neural network and the resulting model represents the fault-free functioning of the energy conversion system. At a next stage, the measurements of the real output of the energy conversion system are compared against the estimated outputs which are provided by the neural model. This provides, the residuals sequence. It holds that the sum of the squares of the residuals' vectors, multiplied with the inverse of the associated covariance matrix, stands for a stochastic variable (statistical test) which follows the χ2distribution. One can have a precise and almost infallible decision making tool about the appearance of faults in the energy conversion system, by selecting the 96% or the 98% confidence intervals of this distribution. When the upper or lower bound of the confidence interval are persistently exceeded one can conclude that the system has been subject to a fault. Finally, fault isolation can be also accomplished, by applying the statistical test into subspaces of the energy conversion system's state-space model.

Industrial and commercial electricity customers have significant potential in providing flexibility for power systems through diverse demand response (DR) programs. However, the industrial and commercial potential of DR is not yet completely understood, especially regarding the emerging and advanced technologies associated with the smart grid. Advances in smart meter technology that allow monitoring and controlling responsive loads in real time will also be key enablers of DR potential. It can be more complex to implement DR for industrial loads if compared to residential loads mainly due to the reliability management that is more vital for industrial plants. Hence, this paper aims at providing a comprehensive review of the most recent advances on industrial and commercial DR. On this basis, this survey first presents the potential and technologies of DR in industrial and commercial sectors. Then, the existing models of DR in the mentioned sectors are presented. The presence of industrial and commercial DR in electricity markets is also investigated. Finally, the main positive and beneficial aspects, as well as challenges and barriers of industrial and commercial DR, are investigated.

Optimal generation companies' participation in different electricity markets can be modeled as a competitive game. Each company tries to behave optimally considering other players' actions, in the game. Generally, the player's profit is formulated based on expected utility method with objective behavior completely. However, the subjectivity of players causes the differences between expected and real actions. This paper proposes a game of generation companies' participation in adjusting the optimal bidding strategies for the day ahead energy market. Moreover, the subjectivity of players and how it affects their strategy determination have been modeled by applying the prospect theory on the game. In addition, this paper presents the full formulation of mixed strategy noncooperative game of GENCOs in both conditions of objective and subjective behavior of players. Finally, simulation results have been provided to show how subjectivity causes players' avoidance from the strategies expected to have higher losses than others.

Electric vehicles (EVs) have many advantages over vehicles with an internal-combustion engine (ICE), and they have the capability to dominate the vehicles' market in the next years. In order to be charged, EVs require public charging stations that can be two-sided when vehicle-to-grid (V2G) services are indispensable to participate in an ancillary service market and in this case the capacity of the charging station should be estimated. Previous researches in the field of EVs used the queuing theory to model the arrival and departure times of EVs for estimating the V2G capacity. However, they did not consider the constraints of the queuing system. In this paper, a new queuing model is proposed to estimate the day-ahead capacity of a charging station, including balking, reneging, and retrial inside a finite-source queue system and phase-type services. A day-ahead service scheduling method is introduced for charging stations, which includes all constraints in the queuing model. Furthermore, control and management of the electric power at the charging station are carried out by using a nonpreemptive priority queue considering ac Level II and dc Level III chargers. Simulations are performed in three scenarios by using random input data of six types of EVs and the obtained results demonstrate that the proposed approach is able to obtain a realistic and precise estimation of the power capacity of a charging station.

Control of the milling process of mining products (ore milling) is a non-trivial problem due to being related with a strongly nonlinear and multivariable state-space model. To provide an efficient solution to this problem, in this article a nonlinear optimal (H-infinity) control method is developed. In the considered nonlinear optimal control method, the dynamic model of the mining products' mill undergoes first approximate linearization with the use of Taylor series expansion and with the computation of the associated Jacobian matrices. The linearization point (temporary equilibrium) is recomputed at each time step of the control method and comprises the present value of the system's state vector and the last value of the control inputs' vector that was exerted on it. For the linearized description of the mill's functioning the optimal control problem is solved by applying an H-infinity controller. The feedback gain is computed again at each iteration of the control algorithm through the solution of an algebraic Riccati equation. The stability of the control scheme is confirmed through Lyapunov analysis. First, it is shown that the control method satisfies the H-infinity tracking performance, and this signifies elevated robustness against model uncertainty and external perturbations. Next, under moderate conditions, it is proven that the control loop is globally asymptotically stable.

The power demand uncertainties and intrinsic intermittent characteristics of wind and photovoltaic (PV) distributed energy resources (DERs) make the conventional load flow methods inefficient in active distribution networks (ADNs) and microgrids. Some statistical tools such as Monte Carlo simulation (MCS) are always a reliable solution. However, statistical tools are time-consuming and rather useless in large power systems. In this paper, a new method is proposed for robust probabilistic load flow (PLF) in microgrids and ADNs, including renewable energy resources (RERs), based on singular value decomposition (SVD) unscented Kalman filtering. The probability density functions (PDFs) and cumulative distribution functions (CDFs) for some of the ADN variables are compared with the other reported PLF methods for different test systems and the results validate the robustness, efficiency and accuracy of the proposed method.

Permanent Magnet Linear Synchronous Motors are of wide use in industry in applications where actuation through rotational motors and a gears-based transmission system can be costly and prone to failures. In this article, a nonlinear optimal (H-infinity) control method is proposed for Permanent Magnet Linear Synchronous Motors (PMLSM). The dynamic model of the Permanent Magnet Linear Synchronous Motor undergoes approximate linearization around a temporary operating point (equilibrium) which is recomputed at each iteration of the control method. The linearization procedure is based on first-order Taylor-series expansion and on the computation of the Jacobian matrices of the motor’s model. For the approximately linearized model of the motor an H-infinity feedback controller is designed. This controller stands for the solution of the motor’s optimal control problem under model uncertainty and external disturbances. The computation of the controller’s feedback gain requires the solution of an algebraic Riccati equation, which is performed again at each time-step of the control algorithm. The stability properties of the control scheme are proven trough Lyapunov analysis. First, it is confirmed that the controller satisfies the H-infinity tracking performance criterion which ascertains its robustness. Moreover, it is proven that the control loop is globally asymptotically stable. Finally, to implement sensorless control of the motor the H-infinity Kalman Filter is used as a robust state estimator.

The article proposes a method for diagnosing faults and cyberattacks in electric power generation units that consist of a wind-turbine and of an asynchronous (DFIG) generator. The method relies on a differential flatness theory-based implementation of the nonlinear Kalman Filter, known as Derivative-free nonlinear Kalman Filter. The estimated outputs provided by the Kalman filter are subtracted from the real outputs measured from the power unit, thus generating the residuals sequence. It is proven that the sum of the squares of the residuals vectors, weighted by the inverse of the residuals covariance matrix, stands for a stochastic variable that follows the χ2distribution. By exploiting the statistical properties of the χ2distribution one can define confidence intervals which allow for deciding at a high certainty level about the appearance of a fault or cyberattack in the wind-power system.

Recent research works have demonstrated that providing ancillary services for future microgrids is a challenging task due to the lack of sufficient spinning reserves and high cost of storage devices. Therefore, an increasing attention has been given to demand response (DR) as an emerging source to provide the required reserve, especially in emergency operation of the system. This paper proposes a decentralized multi-agent based DR strategy to control the domestic demands during the emergency operation of the microgrid (MG). According to the proposed multi-agent based DR strategy, the domestic loads are grouped based on a predefined priority and are assigned to specific load agents. To implement the information sharing process among the load agents, the consensus strategy is used. Communications among the load agents as a challenging issue of multi-agent systems (MAS) is considered and the effect of communication time delay is investigated. Simulation studies have been carried out on the CIGRE benchmark microgrid with various microsources and domestic loads, showing the effectiveness of the proposed decentralized control scheme.

The eleven papers in this special section focus on the industrial and commercial potential of demand response (DR). Customers from this non-residential market base have great potential in providing flexibility for power systems through diverse demand response (DR) programs. Intelligent energy management can be carried out with DR in industrial and commercial facilities, especially if onsite control, information, and communication technologies are available, enabling also the inherent automation capabilities of heating, ventilation, and air conditioning systems. In the dawn of the Smart Grid era, with increasing distributed generation and the conversion of traditionally passive consumers to newly active energy players in the market, DR is being effectively considered for outage management and network reinforcement deferral.

An optimal placement of protective devices could increase the reliability and quality level of a distribution network. An innovative mixed-integer nonlinear programming model is proposed in this paper to find the type, optimal siting, and number of protective devices to be accurately installed in distribution networks. The customer outage and protective devices costs are considered to derive a value-based reliability equation. To ensure the effectiveness of the proposed formulation economic and technical constraints is considered. Further, this paper aims at aiding decision-makers in providing appropriate protective device allocation by minimizing the expected interruption cost index. Case studies are employed to demonstrate the reliability optimization of a test network and a typical real-size network in which the several cost constraints and protection schemes are assumed to extract the results. Accuracy and effectiveness of the proposed method are assessed and sensitivities analysis is carried out.

This paper presents an adaptive power management and control for a self-reliant dc microgrid powered by photovoltaic (PV) array and fuel cell stack that is escorted with the supercapacitor- and electrolyzer-based hybrid energy storage device. The prime operational impediments associated with this dc microgrid are the unpredictable variations in the PV generation and load demand, fuel starvation phenomenon of the fuel cell, the time constant associated with the electrolyzer operation, and state-of-charge limitation of the supercapacitor. In this paper, the above-mentioned issues are addressed by employing an adaptive dynamic power management strategy (ADPMS) that supervises the overall power flow in the system. The ADPMS enables the co-existence of high power density and high energy density storage devices to deliver the power flow required by the loads. The expeditious support extended by the supercapacitor helps in swift regulation of the dc-link voltage during unforeseen transient load/source power variations. The electrolyzer-fuel cell combination employed in this system can supplement a battery bank and can equip high energy density and self-reliance to the system. The set-reset flip-flop based fixed frequency current controller is simple and effective in accurately tracking the reference currents established by the ADPMS. The proposed ADPMS along with the control is validated for transient unforeseen load profiles and renewable generation changes via simulation studies and its performance is also validated through an experimental analysis on a laboratory-scale dc microgrid testbed.

Home energy management (HEM) systems enable residential consumers to participate in demand response programs (DRPs) more actively. However, HEM systems confront some practical difficulties due to the uncertainty related to renewable energies as well as the uncertainty of consumers' behavior. Moreover, the consumers aim for the highest level of comfort and satisfaction in operating their electrical appliances. In addition, technical limits of the appliances must be considered. Furthermore, DR providers aim at keeping the participation of consumers in DRPs and minimize the “response fatigue” phenomenon in the long-term period. In this paper, a stochastic model of an HEM system is proposed by considering uncertainties of electric vehicles availability and small-scale renewable energy generation. The model optimizes the customer's cost in different DRPs, while guarantees the inhabitants' satisfaction by introducing a response fatigue index. Different case studies indicate that the implementation of the proposed stochastic HEM system can considerably decrease both the customers' cost and response fatigue.

Following recent advances in network infrastructure, cloud computing, and embedded systems, fascinating work is underway exploring the utility of demand response in increasing grid stability while permitting high penetration of intermittent renewable distributed generation resources. Although works have demonstrated diverse theoretical advantages of demand response programs, little real-world data are available and utilities generally remain reticent in moving forward with large-scale implementation due to risks inherent to any modification of the power system. Deciding that the next pertinent step in bringing demand response from theory to technology is developing an Internet-of-things hardware-in-the-loop simulation power system integrated device capable of empirically testing the theoretical mechanisms, this work presents an architecture testbed for providing demand response (telemetric monitoring and actuation of loads), which is real node in a power system simulation where virtual node's parameters derive real node data. We test a demand response algorithm, which provides frequency regulation services.

A nonlinear optimal H-infinity control approach is proposed for bioreactors aiming at improved biofuels production. The dynamic model of the bioprocess taking place in the bioreactor undergoes approximate linearization round temporary equilibria which are recomputed at each iteration of the control method. The linearization makes use of Taylor series expansion and of the computation of the system's Jacobian matrices. For the approximately linearized model of the bioprocess an H-infinity feedback controller is designed. The feedback gain of the controller is found from the repetitive solution of an algebraic Riccati equation, taking place at each iteration of the control method. The stability of the proposed control scheme is evaluated through Lyapunov analysis. First, it is demonstrated that the control system satisfies the H-infinity tracking performance criterion, which signifies robustness against modelling uncertainty and external perturbations. Moreover, under moderate conditions it is proven that the control loop is globally asymptotically stable. The proposed control method solves finally the nonlinear optimal control problem for bioreactors in a computational efficient and of proven convergence manner.

A novel adaptive neurofuzzy H-infinity control approach to feedback control and stabilization of the nonlinear dynamical model of bioreactors used in biofuels production is developed. The form and the parameters of the differential equations that constitute the dynamic model of the bioreactor are considered to be unknown, while there is only knowledge about the order of the system. The model of the controlled system undergoes approximate linearization round a temporary equilibrium which is recomputed at each iteration of the control algorithm. The linearization procedure makes use of Taylor series expansion and the computation of Jacobian matrices. For the approximately linearized model of the bioreactor it is possible to design a stabilizing H-infinity feedback controller, provided that knowledge about the matrices of the linearized state-space description is available. Neurofuzy networks are used to estimate the unknown dynamics of the system and its Jacobians. The computation of the feedback controller's gain comes from the solution of an algebraic Riccati equation taking place at each iteration of the control method, and this allows the implementation of the H-infinity feedback controller. The learning rate of the neurofuzzy approximators is chosen from the requirement the first derivative of the system's Lyapunov function to be always a negative one, thus assuring the stability of the control loop. The global asymptotic stability and the robustness properties of the control method are proven through Lyapunov stability analysis.

This study proposes an eco-friendly navigation algorithm, eRouting, to save energy and reduce CO2emission. The important research issue of traffic information industry, eco-friendly navigation, has been widely studied. As an improvement, in this paper, combining real-time traffic information and a representative factor-based energy/emission model, a calculated route is dynamically adjusted during the travel of a vehicle. eRouting is a centralized algorithm. It profits from the following aspects to achieve improved performance: a representative factor-based energy/emission model, a real-time traffic information-based dynamic adjustment, and an objective function to control the optimization direction to optimize the final energy consumption of vehicle's travel. As a peculiarity of this paper, the design of the representative factor-based model mines the impact of road-level parameters on energy consumption and CO2emission. Such mining is helpful to improve the pertinence of a model by formulating the key influence factors into the model. Experimental results are presented to prove the validity of eRouting. In addition, by contrast experiments, eRouting shows improved performance compared with an eco-friendly navigation algorithm and three traditional navigation algorithms.

The PDE model of the commodities price dynamics is shown to be equivalent to a multi-asset Black-Scholes PDE. Actually it is a diffusion process evolving in a 2D assets space, where the first asset is the commodity's spot price and the second asset is the convenience yield. By applying semi-discretization and a finite differences scheme this multi-asset PDE is transformed into a state-space model consisting of ordinary nonlinear differential equations. For the local subsystems, into which the commodities PDE is decomposed, it becomes possible to apply boundary-based feedback control. The controller design proceeds by showing that the state-space model of the commodities PDE stands for a differentially flat system. Next, for each subsystem which is related to a nonlinear ODE, a virtual control input is computed, that can invert the subsystem's dynamics and can eliminate the subsystem's tracking error. From the last row of the state-space description, the control input (boundary condition) that is actually applied to the multi-factor commodities' PDE system is found. This control input contains recursively all virtual control inputs which were computed for the individual ODE subsystems associated with the previous rows of the state-space equation. Thus, by tracing the rows of the state-space model backwards, at each iteration of the control algorithm, one can finally obtain the control input that should be applied to the commodities PDE system so as to assure that all its state variables will converge to the desirable setpoints.

The Stochastic nature of wind power can cause insufficiency of supply in electrical systems. Applying an energy storage system can alleviate the impact of wind power forecast error on power systems performance and increase system tolerance against deficiency of supply. This paper attempts to investigate a new unit commitment (UC) problem based on the cost-benefit analysis and here-and-now (HN) approach for optimal sizing of battery banks (BBs) imicrogrids (MGs) with wind power systems. To solve this problem, particle swarm optimization is used to minimize the total cost and maximize the total benefit. In this paper, 12 scenarios have been considered in the presence of BBs and without them in 2 operating modes: 1) stand-alone mode and 2) grid-connected mode. Using the HN approach, the uncertainty of wind power is applied as a constraint in these operating modes. The mathematical formulations related to the HN approach in MGs and its combination in a UC problem are presented in detail for optimal sizing of BBs. Simulation results show that the best sizes of BBs and the scheduling of distributed generations would be entirely different when the accessibility of wind power is taken into consideration by applying HN approach to the proposed probabilistic UC problem.

A new method of adaptive control for active power filters is developed in this article. By proving that the active power filter is a differentially flat system, its transformation to the linear canonical (Brunovsky) form becomes possible. In this new description the control input of the active power filter comprises unknown nonlinear terms which are identified by neurofuzzy networks and through an adaptation / learning procedure. These estimated parts of the system's dynamics are used in an indirect adaptive control scheme, which finally makes the outputs of the active power filter converge to the desirable setpoints. The learning rate in the aforementioned adaptation procedure is given a value which assures that a suitably chosen Lyapunov function will remain negative definite. Under the proposed control method, the closed loop of the active power filter is shown to satisfy the H-infinity tracking criterion, which implies a maximum capability for rejection of external perturbations as well as of modelling errors. flatness-based adaptive fuzzy control based on differential flatness theory is a completely model-free control method. When designing the controller, there is no need for prior knowledge of the system's parameters and state-space equations.

To assure power quality and stability of the electricity network it is important to perform control and synchronization of the inverters which are used in the connection of distributed DC power generation units to the grid. It is proven that the model of the inverters, is a differentially flat one. By exploiting differential flatness properties it is shown that the multiple inverters model can be transformed into a set of local inverter models which are decoupled and linearized. For each local inverter the design of a state feedback controller becomes possible, e.g. using pole placement methods. Such a controller processes measurements not only coming from the individual inverter but also coming from other inverters which are connected to the grid. Moreover, to estimate the non-measurable state variables of each local inverter, the Derivative-free nonlinear Kalman Filter is used. This consists of the Kalman Filter recursion applied to the local linearized model of the inverter and of an inverse transformation that is based on differential flatness theory, which enables to compute estimates of the state variables of the initial nonlinear model of the inverter. Furthermore, by redesigning the aforementioned filter as a disturbance observer it becomes also possible to estimate and compensate for disturbance terms that affect each local inverter.

The paper proposes a systematic method for forecasting default probabilities for financial firms with particular interest in electric power corporations. According to credit risk theory a company's proximity to default is determined by the distance of its assets' value from its debts. The assets' value depends primarily on the company's market (option) value through a complex nonlinear relation. Therefore, by forecasting with accuracy the enterprize's option value it becomes also possible to estimate the future value of the enterprize's asset value and the associated probability of default. This paper proposes a systematic method for forecasting the probability to default for companies (option / asset value forecasting methods) using a new nonlinear Kalman Filtering method under the name Derivative-free nonlinear Kalman Filter. The company's option value is considered to be described by the Black-Scholes nonlinear partial differential equation. Using differential flatness theory the partial differential equation is transformed into an equivalent state-space model in the so-called canonical form. Using the latter model and by redesigning the Derivative-free nonlinear Kalman Filter as a m-step ahead predictor, estimates are obtained of the company's future option values. Thus, by forecasting the company's market (option) values, it becomes also possible to forecast the associated asset value and volatility and finally to estimate the company's future default risk.

The paper proposes a new method for the control of nonlinear dynamical systems which is based on differential flatness theory. The method assumes that the system is already found or can be transformed to the so-called triangular form. The controller design proceeds by showing that each row of the statespace model of the nonlinear system stands for a differentially flat system, where the flat output is chosen to be the associated state variable. Next, for each subsystem which is linked with a row of the state-space model a virtual control input is computed, that can invert the subsystem's dynamics and can eliminate the subsystem's tracking error. From the last row of the state-space description, the control input that is actually applied to the nonlinear system is found. This control input contains recursively all virtual control inputs which were computed for the individual subsystems associated with the previous rows of the state-space equation. Thus, by tracing the rows of the state-space model backwards, at each iteration of the control algorithm, one can finally obtain the control input that should be applied to the nonlinear system so as to assure that all its state vector elements will converge to the desirable setpoints. The proposed flatness-based control method can solve efficiently several nonlinear control problems. Indicative evaluation results are presented in the manuscript in the form of simulation experiments. These confirm also the potential application of the proposed control method to electric power generators and to renewable power generation systems.

Optimal design of switching converters for the integration and optimal exploitation of renewable energy sources (RES) represents a crucial issue often debated in the recent power electronics literature. The design problem required to carry out a multiobjective optimization characterized by simultaneous conflicting objectives, such as efficiency, reliability, and price, where the best compromise solution should be found by the decision maker among Pareto-optimal solutions. In this paper, a novel design method for distributed maximum power point tracking (DMPPT) synchronous boost converter is proposed. The method is based on nondominated sorting genetic algorithm with the aim to obtain the best synchronous rectification (SR) boost topology while considering different targets such as converter efficiency and reliability maximization, as well as converter price minimization. New weighted indices are also proposed for a more realistic characterization of the devices.

This paper proposes an innovative exhaustive search method for the optimal placement of wind turbines (WTs) in electrical distribution systems taking into account wind speed and load demand uncertainty, and the variability of electrical energy prices within a distribution network operator (DNO) acquisition market environment. The method combines Monte Carlo simulation (MCS) and market-based optimal power flow (OPF) to maximize the net present value (NPV) related to the investment made by WTs’ developers over a planning horizon. In particular, the MCS data feed the market-based OPF problem with inter-temporal constraints in order to find the most convenient WTs allocation and priority on the network, based on distribution-locational marginal prices (D-LMPs) in a competitive electricity market. The effectiveness of the proposed method is demonstrated with an 84-bus 11.4-kV radial distribution system.

For the better integration of distributed and renewable generation, new investments in innovative distribution networks, such as active distribution networks and smart grids (SGs), are required. Regulators should decide what incentives are desirable to create a flexible economic regulatory framework that can drive distribution system operators to invest in innovation and in active distribution networks. An innovative method that allows regulators assessing the impact of incentives related to investments in innovation in an electrical distribution network is presented in this paper. The method is flexible enough to allow different stakeholders like regulators, distribution companies, and developers to evaluate the long-term economic effects of their decisions. The effectiveness of the proposed method, based on nondominated sorting genetic algorithm and on multi-period optimal power flow, is verified on a 69-bus network.

The nine papers in this special section cover a wide range of interesting smart grid topics related to the integration of distributed energy resources and grid components as well as to virtual power plants.

This research work introduces a new method for feedback control of nonlinear dynamical systems and considers as application example the problem of trajectory tracking for autonomous robotic vehicles. The control method consists of a repetitive solution of an H-infinity control problem for the mobile robot, that makes use of a locally linearized model of the robot and takes place at each iteration of the control algorithm. The vehicle's model is locally linearized round its current position through the computation of the associated Jacobian matrices. Using the linearized model of the vehicle an H-infinity feedback control law is computed. The known robustness features of H-infinity control enable to compensate for the errors of the approximative linearization, as well as to eliminate the effects of external perturbations. The efficiency of the proposed control scheme is shown analytically and is confirmed through simulation experiments. The method can be applied to a wide class of nonlinear dynamical systems.

This research work introduces a new control method for feedback control of nonlinear power electronics systems with application example the problem of three-phase voltage source converters. The control method consists of a repetitive solution of an H-infinity control problem for the voltage source converter, that makes use of a locally linearized model of the converter and takes place at each iteration of the control algorithm. The converter's model is locally linearized round its current operating point through the computation of the associated Jacobian matrices. Using the linearized model of the converter an H-infinity feedback control law is computed. The known robustness features of H-infinity control enable to compensate for the errors of the approximative linearization, as well as to eliminate the effects of external perturbations. The performance of the proposed control scheme is validated analytically and is confirmed through simulation experiments.

The intention of this paper is to provide an overview of using agent and service-oriented technologies in intelligent energy systems. It focuses mainly on ongoing research and development activities related to smart grids. Key challenges as a result of the massive deployment of distributed energy resources are discussed, such as aggregation, supply-demand balancing, electricity markets, as well as fault handling and diagnostics. Concepts and technologies like multiagent systems or service-oriented architectures are able to deal with future requirements supporting a flexible, intelligent, and active power grid management. This work monitors major achievements in the field and provides a brief overview of large-scale smart grid projects using agent and service-oriented principles. In addition, future trends in the digitalization of power grids are discussed covering the deployment of resource constrained devices and appropriate communication protocols. The employment of ontologies ensuring semantic interoperability as well as the improvement of security issues related to smart grids is also discussed.

Cyber physical systems (CPSs) are the systematic combination of physical processes and information and communication technology (ICT). They constitute the next generation of networked, embedded systems that explicitly consider the physical parts during their design and operations. CPS applied to the energy system leads to the possibility of more sophisticated controls, the interworking of different energy types, cooperative loads, smart factories, the interaction of markets and infrastructure, smart integration of renewable energy sources, automated and grid-friendly buildings, more knowledge about the system due to sensor networks and analytics, multiagent systems, usage of smart storage, information technology (IT) security challenges, and many other aspects of what is sometimes called smart grids.

The paper studies differential flatness properties and an input-output linearization procedure for doubly-fed induction generators (DFIGs). By defining flat outputs which are associated with the rotor's angle and the magnetic flux of the stator an equivalent DFIG description in the Brunovksy (canonical) form is obtained. For the linearized canonical model of the generator a feedback controller is designed. Moreover, a comparison of the differential flatness theory-based control method against Lie algebra-based control is provided. At a second stage, a novel Kalman Filtering method (derivative-free nonlinear Kalman Filtering) is introduced. The proposed Kalman Filter is redesigned as disturbance observer for estimating additive input disturbances to the DFIG model. These estimated disturbance terms are finally used by a feedback controller that enables the generator's state variables to track desirable setpoints. The efficiency of the proposed state estimation-based control scheme is tested through simulation experiments.

The paper is concerned with proving differential flatness of the three-phase voltage source converter (VSC) model and its resulting description in the Brunovksy (canonical) form. For the linearized canonical model of the converter a feedback controller is designed. At a second stage, a novel Kalman Filtering method (derivative-free nonlinear Kalman Filtering) is introduced. The proposed Kalman Filter is redesigned as disturbance observer for estimating perturbations in the VSC model. These estimated disturbance terms are finally used by a feedback controller that enables the DC output voltage to track desirable setpoints. The efficiency of the proposed state estimation-based control scheme is tested through simulation experiments.

The existing passive distribution networks, characterized by unidirectional power flows and a partial and centralized control, limit optimal management of Renewable Energy Sources (RES), therefore restraining their exploitation. A deep revision of the planning and management methodologies for distribution electrical networks is therefore required due to the increasing penetration of RES in distribution electrical networks In order to overcome the integration problems of Distributed Generation (DG) and RES, existing electrical distribution networks will, therefore, evolve from passive to active networks and smart grids, managed through systems based on Information and Communication Technology (ICT). In this paper a hybrid optimization method able to maximize the Net Present Value related to the investment made by Wind Turbines developers in an active distribution network and smart grids is proposed. The method, that combines Genetic Algorithms with a multi-period optimal power flow, is validated on a 69-bus 11 kV radial distribution network.

This article discusses recent trends in Smart Grid technology. Three selected aspects are (1) distributed information technology (IT), used for smart metering and multi-agent based controls, (2) big-data, resulting out of metering, sensors and other IT-enabled sources of information, and (3) an intelligent demand side, where demand response serves as contribution to grid services. New elements in the grid, most notably large numbers of fluctuating renewable energy sources, and new functionality like markets make it necessary to introduce methods and technologies from other domains that already faced such changes.

Sensorless control for induction motors using Unscented Kalman Filtering is studied. The complete 6-th order dynamic model of the induction motor is analyzed and a nonlinear controller based on differential flatness theory is developed. The Unscented Kalman Filter is proposed to estimate the state vector of the nonlinear electric motor using a limited number of sensors, such as the ones measuring stator currents. Next, control of the induction motor is implemented through feedback of the estimated state vector. The efficiency of the Unscented Kalman Filter-based control scheme, is tested through simulation experiments.

A method for optimal switches placement in distribution systems with distributed generation is presented in this paper. According to both technical and economical issues, the method allows minimizing the unsupplied loads in case of permanent faults, while limiting the number of installed switches. The problem is formulated as a mixed integer non linear programming problem (MINLP) and the solution is obtained by a new metaheuristic algorithm, i.e., the Alliance Algorithm. The method is based on self-microgrids forming and allows improving the continuity of the service as confirmed by simulation results on both an IEEE standard and a real test network.

This paper proposes an Energy Management System for the optimal operation of Smart Grids and Microgrids, using Fully Connected Neuron Networks combined with Optimal Power Flow. An adaptive training algorithm based on Genetic Algorithms, Fuzzy Clustering and Neuron-by-Neuron Algorithms is used for generating new clusters and new neural networks. The proposed approach, integrating Demand Side Management and Active Management Schemes, allows significant enhancements in energy saving, customers' active participation in the open market and exploitation of renewable energy resources. The effectiveness of the proposed Energy Management System and adaptive training algorithm is verified on a 23-bus 11 kV microgrid.

The paper proposes a new fuzzy controller for variable speed wind turbines (WTs) in order to compensate the variations at the point of common coupling (PCC) by controlling the reactive power generated by WTs. A protection system is used to disconnect the WTs from the grid when the controller is unable to compensate the voltage variations. Simulations carried out on a real 37-bus Italian weak distribution network demonstrated that the controller allows compensating voltage variations during voltage sags.

From a technologic point of view, the problem of fuzzy control deals with the real implementation of a controller on a specific hardware. Today, the market of micro-controller offers different solutions able to implement a fuzzy controller varying from application domains to programming language support. Considering the integration issue, made easier from the cheap network infrastructure, there is the need to empower practical approaches suitable to support various and different components ruled by advanced (fuzzy) control strategies. In this work we first present a general Web-based architecture that supports a high integration of heterogeneous and increasingly complex control systems, and then we focus on a Takagi-Sugeno-Kang (TSK) fuzzy model able to reproduce the thermal behaviour of mineral-oil-filled power transformers for implementing a protective overload system. The TSK fuzzy model, working on the load current waveform and on the top oil temperature (TOT), gives an accurate global prediction of the hot-spot temperature (HST) pattern. In order to validate the usefulness of the approach suggested herein, some data cases, derived from various laboratory applications, are presented to measure the accuracy and robustness of the proposed fuzzy model.