Wireless sensor networks (WSNs) are the backbone of the Internet of Things in smart cities, delivering the real-time insights that keep urban services adaptive and resilient. However, positioning those sensors within a dynamic urban environment is a holistic, multi-objective challenge that must consider spatial coverage, urban infrastructure and reliability, without sacrificing energy efficiency, sensing coverage, and connectivity. In order to address this issue and enhance sensors coverage in different smart city scenarios, a quality-aware optimization framework driven by the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is introduced. The method is aimed to optimize coverage, sensing and network-connectivity quality for heterogeneous WSNs that mix scalar and visual sensor nodes. NSGA-II hyper-parameters are tuned through grid search, and the resulting layouts are benchmarked in both ideal and randomly distributed deployments. The proposed methodology consistently yields high-quality, cost-effective topologies that may strengthen smart city monitoring in diverse environments.

This study addresses the challenges of dependability in Wireless Sensor Networks by proposing a Machine Learning-based approach using Convolutional Neural Networks for network planning for smart cities. Simulated scenarios were used to train the model, which predicts sensor placement and communication configurations to optimize coverage and availability. Results show significant improvements, including an average of 10.7% increase in dependability index and a rise in area coverage from 59% to 73% in 7-node networks, while reducing path failure rates by 27.6%. The method proves effective for enhancing WSN performance and adaptability in safety-critical applications.

Network reconfiguration (NR) is a highly complex combinatorial problem with discrete and nonlinear characteristics. With the expansion of the distribution network (DN), solving the NR problem faces the challenge of high dimensions. In this article, we propose a structural decomposition approach (SDA), where the NR problem is suitably allocated to three processes: partition, reconfiguration of equivalent networks, and merging. According to the loop in the original DN, the loop-oriented network partition model is proposed to divide the original DN into multiple equivalent networks, including a loop region and a compressed region. The reconfiguration model is built for the equivalent networks, and the solutions for the loop region and compressed region can be obtained. Then the merging model with the correction method is proposed to merge all reconfiguration solutions of equivalent networks, deal with the inconsistent solutions of different equivalent networks, and obtain the optimal reconfiguration solution of the original DN. Numerical case studies were conducted on the IEEE 33-bus and 119-bus DNs. Compared with other heuristic algorithms for solving NR problem, SDA reduces computation time by 70% while ensuring the optimality of NR strategies. These results demonstrate the effectiveness and exceptional performance of the proposed method.

In power systems, taking economic actions in accordance with the N-k criterion despite the failure of the components is becoming important in today’s smart grid. In this context, developing strategies against load shedding during unexpected events plays a critical role in ensuring energy sustainability. In this article, a loop-in-loop model that includes a rolling horizon-based distributed coordination is established among the transmission system operator (TSO), distribution system operator (DSO), and an industrial parking region operator (IPRO) connected to the transmission network (TN) to address unexpected TN contingencies. The model minimizes the total operational costs for each operator while also minimizing load shedding in the distribution network during contingency situations by leveraging the dc fast discharge capacity of heavy-duty electric trucks (HDETs) with a high-capacity battery located in the industrial parking region. In addition, a contingency system operator (CSO) is introduced to manage contingency and optimally reward the discharge of HDETs through a pricing mechanism. Consequently, in the rolling horizon-based real-time contingency management, the proposed approach reduces the economic losses for the TSO and the amount of shedding load for the DSO compared with uncoordinated operations. Numerical results demonstrate that the proposed strategy achieves up to 33.18% reduction in load shedding and 28.18% cost savings compared with the baseline scheme, confirming its effectiveness under contingency scenarios.

The increasing structural and operational interdependencies between power distribution networks (PDNs) and district heating networks (DHNs) have intensified the need for integrated and resilient contingency management strategies. This necessitates the development of advanced analytical frameworks for probabilisticN-1 andN-ksecurity assessments that capture the temporal and spatial coupling of these networks throughout all phases of resilient operations. Owing to this necessity, this article proposes a novel contingency multicriteria assessment framework to quantify the interdependencies between the resilience of PDNs and DHNs, incorporating various phases of contingency chains. The methodology utilizes the simultaneous evaluation of criteria and alternatives (SECA) approach to systematically rank critical contingencies, thereby revealing the most severe potential cascading failures that threaten energy security. The following stage entails the presentation of a pioneering spatiotemporal cascading failure analytical model, which is developed to coordinate the withstand-and-recover phases across interdependent PDNs and DHNs. This model is supported by tailored operational resilience key performance indicators (KPIs) that trace power-induced heating service degradation across diverse backup configurations, including line-pack storage, energy storage systems (ESSs), and integrated demand–response programs (DRPs). As another unattainable novelty, the developed decision-making framework integrates static and dynamic resilience analyses to provide an all-encompassing comprehension of the integrated PDNs and DHNs’ resilience. The proposed framework is validated through co-simulation of a modified IEEE 33-bus PDN and a 32-node DHN, employing analytical and empirical methodologies. The results indicate that the strategic implementation of backup flexibility resources, when synchronized with DHN service provision, can enhance DHN service continuity by up to 29%, while concurrently reducing the full recovery time by nearly 41%. Dynamic resilience analyses are further conducted in DIgSILENT PowerFactory to evaluate the real-time transient response under top-ranked contingency scenarios.

In recent literature, the value of electric vehicles (EVs) for the resilience enhancement of urban microgrids has been shown. Furthermore, on a larger scale, there has been a growing recognition of the potential of EV cooperation in enhancing the overall resilience of smart cities. To this end, the city can be partitioned into a set of blocks, each encompassing buildings. Within each block, EV traveling time can be ignored. As a step forward, this study presents a Preventive Energy Management (PEM) strategy along with a rescheduling procedure by cooperation of EVs, local distributed energy resources (DERs), and buildings in different city blocks. Based on the available information related to the amount of curtailed loads, two cases are modeled and studied. In the proposed PEM strategy, EV owners’ opt-in preferences such as arrival and departure times, and the city block in which they are willing to give energy services are modeled. As a more realistic consideration, the proposed model does not consider the buildings’ load as a lumped load, instead the PEM strategy is designed to consider each of the buildings separately. The resulting optimization model is flexible enough to enable EVs to switch from one building to another to provide energy in different time slots. By applying disjunctive-constraint-based transformation, the model is recast as a Mixed Integer Linear Programming (MILP) that could be efficiently solved by commercial optimization solvers. The proposed approach is applied to a benchmark and the results are analyzed. According to the results, using EVs in the PEM strategy has been proven to be effective and the importance of the length of the period of service and opt-in preferences for optimal scheduling are highlighted.

Uncertainty quantification (UQ) for predictions generated by neural networks (NNs) is of vital importance in safety-critical applications. An ideal model is supposed to generate low uncertainty for correct predictions and high uncertainty for incorrect predictions. The main focus of state-of-the-art training algorithms is to optimize the NN parameters to improve the accuracy-related metrics. Training based on uncertainty metrics has been fully ignored or overlooked in the literature. This article introduces a novel uncertainty-aware training algorithm for classification tasks. A novel predictive uncertainty estimate-based objective function is defined and optimized using the stochastic gradient descent method. This new multiobjective loss function covers both accuracy and uncertainty accuracy (UA) simultaneously during training. The performance of the proposed training framework is compared from different aspects with other UQ techniques for different benchmarks. The obtained results demonstrate the effectiveness of the proposed framework for developing the NN models capable of generating reliable uncertainty estimates.

In this paper, a hybrid machine learning model is applied to evaluate the relationship between random initial states and the power system’s vulnerability to cascading outages. A cascading outage simulator (CS), which uses off-line AC power flows, is proposed for generating training data. The initial states are randomly selected and the CS model is deployed for each initial state, where power system generation and loads are adjusted dynamically and power flows are redistributed to quantify the vulnerability metric. Furthermore, the proposed hybrid machine learning model deploys a combined Support Vector Machine (SVM) classification and Gradient Boosting Regression (GBR) to improve the learning precision. The classification model is trained by SVM, which divides the data into two categories with and without load shedding. Then, GBR is adopted only for the data with load shedding to determine the relationship between input power outage states and the vulnerability metric. The proposed vulnerability analysis approach is applied to several test systems and the results are analyzed. Note to Practitioners—The power system vulnerability can be quantified by cascading outage simulations. However, there are two challenges: i) there are a huge number of possible initial states and we cannot enumerate all these initial states for the cascading outage simulation. Neither can we precisely quantify the bus vulnerability. ii) The cascading outage simulation may be time-consuming for large-scale power systems, which is challenging for the online application. To address the above challenges, we expect to design a machine learning technique to predict the power system vulnerability, which can train the model in an offline way and then use it for the online application. Firstly, since there is not enough operation data from practical power systems, we develop a cascading outage simulator, using off-line AC power flows, for generating synthetic training data. Secondly, we observe that the training precision by directly applying the regression model may be very poor because the output of the machine learning model may take on an uneven distribution concerning input parameters. Thus, we propose a hybrid machine learning model with a combined classification and regression method, where the classification model is employed to remove the data without the load shedding, and the regression model then determines the relationship between input power outage states and the vulnerability metric. The proposed model and method have been tested on several systems including a practical large-scale Polish power system to show the effectiveness.

Accurate and practical load modeling plays a critical role in the power system studies including stability, control, and protection. Recently, wide-area measurement systems (WAMSs) are utilized to model the static and dynamic behavior of the load consumption pattern in real-time, simultaneously. In this article, a WAMS-based load modeling method is established based on a multi-residual deep learning structure. To do so, a comprehensive and efficient load model founded on combination of impedance-current-power and induction motor (IM) is constructed at the first step. Then, a deep learning-based framework is developed to understand the time-varying and complex behavior of the composite load model (CLM). To do so, a residual convolutional neural network (ResCNN) is developed to capture the spatial features of the load at different location of the large-scale power system. Then, gated recurrent unit (GRU) is used to fully understand the temporal features from highly variant time-domain signals. It is essential to provide a balance between fast and slow variant parameters. Thus, the designed structure is implemented in a parallel manner to fulfill the balance and moreover, weighted fusion method is used to estimate the parameters, as well. Consequently, an error-based loss function is reformulated to improve the training process as well as robustness in the noisy conditions. The numerical experiments on IEEE 68-bus and Iranian 95-bus systems verify the effectiveness and robustness of the proposed load modeling approach. Furthermore, a comparative study with some relevant methods demonstrates the superiority of the proposed structure. The obtained results in the worst-case scenario show error lower than 0.055% considering noisy condition and at least 50% improvement comparing the several state-of-art methods.

The papers in this special section focus on demand response applications in cloud computing technologie.s The use of distributed energy resources for self-generation and self-consumption along with Information and Communications Technologies and the Internet of Things is rapidly increasing the ability of the consumers and prosumers to actively engage with the electric energy system. Sustained consumer and prosumer engagement in demand response programs has been identified as a key factor in future electric energy systems, especially with a high penetration of renewable energy sources. This engagement has allowed demand-side resources to play a larger role in energy and reserve markets, whether by generating, storing or participating in demand response programs through increased flexibility, towards the consumer-driven energy transition. However, in real life, there is still a long way to go until demand response solutions take off and become entirely integrated into the daily life of the consumers, thus utilizing their full potential. Stronger engagement of consumers and prosumers is needed, as well as more flexibility services for system operation, benefiting Smart Grid developments.

This paper proposes two novel strategies for determining the bilateral trading preferences of households participating in a fully Peer-to-Peer (P2P) local energy market. The first strategy matches between surplus power supply and demand of participants, while the second is based on the distance between them in the network. The impact of bilateral trading preferences on the price and amount of energy traded is assessed for the two strategies. A decentralized fully P2P energy trading market is developed to generate the results in a day-ahead setting. After that, a permissioned blockchain-smart contract platform is used for the implementation of the decentralized P2P trading market on a digital platform. Actual data from a residential neighborhood in the Netherlands, with different varieties of distributed energy resources, is used for the simulations. Results show that in the two strategies, the energy procurement cost and grid interaction of all participants in P2P trading are reduced compared to a baseline scenario. The total amount of P2P energy traded is found to be higher when the trading preferences are based on distance, which could also be considered as a proxy for energy efficiency in the network by encouraging P2P trading among nearby households. However, the P2P trading prices in this strategy are found to be lower. Further, a comparison is made between two scenarios: with and without electric heating in households. Although the electrification of heating reduces the total amount of P2P energy trading, its impact on the trading prices is found to be limited.

The use of data from residential smart meters can help in the management and control of distribution grids. This provides significant benefits to electricity retailers as well as distribution system operators but raises important questions related to the privacy of consumers' information. In this article, an innovative differential privacy (DP) compliant algorithm is developed to ensure that the data from consumer's smart meters are protected. The effects of this novel algorithm on the operation of the distribution grid are thoroughly investigated not only from a consumer's electricity bill point of view but also from a power systems point of view. This method allows for an empirical investigation into the losses, power quality issues, and extra costs that such a privacy-preserving mechanism may introduce to the system. In addition, severalcost allocation mechanisms based on the cooperative game theory are used to ensure that the extra costs are divided among the participants in a fair, efficient, and equitable manner. Overall, the comprehensive results show that the approach provides privacy preservation in line with the consumer's preferences and does not lead to significant cost or loss increases for the energy retailer. In addition, the novel algorithm is computationally efficient and performs very well with a large number of consumers, thus demonstrating its scalability.

The microgrid operation is addressed in this article based on a multicarrier energy hub. Natural gas, electricity, heating, cooling, hydrogen, carbon dioxide, and renewable energies are considered as the energy carriers. The designed microgrid optimizes and utilizes a wide range of resources at the same time including renewables, electrical storage, hybrid storage, heating-cooling storage, electric vehicles (EVs) charging station, power to gas unit, combined cooling-heating-power, and carbon capture-storage. The purpose is to reduce the environmental pollutions and operating costs. The resilience and flexibility of the energy hub is also improved. Vehicle to grid and fully-partial charge models are incorporated for EVs to improve the system resilience and supplying the critical loads following events. Different events are modeled to evaluate the system resilience. The model is expressed as a stochastic mixed integer linear programming problem. Both active and reactive powers are modeled. The microgrid is simulated under four different cases. The results show that the multitype energy storages reduce the annual cost of energy while the integrated charging station can decrease the load shedding.

The concept of smart cities has emerged as an ongoing research in recent years. In this case, there is a proven association between the smart cities and the smart devices, which have caused the power systems to become more flexible, controllable and detectable. Along with these promising results, many disputes have been generated over the cyber-attacks as unpredictable destructive threats, if not properly repelled, which could seriously endanger the power system. With this in mind, this paper explores a novel stochastic virtual assignment (SVA) method based on a directed acyclic graph (DAG) approach, where the essential data of the system sections are broadcasted decentralized through the data blocks, as a worthwhile step to deal with the cyber attacks’ risk. To do so, an additional security layer is added to the data blocks aiming to enhance the security of the data against the long lasting data sampling by virtually assigning the hash addresses (HAs) to the data blocks, which are randomly changed based on a stochastic process. The basic network architecture is based on a Provchain structure as a new framework to constantly monitor data operation. Two pivotal strategies also represented to deal with the energy and time needed for the HAs generation process, which have improved the proposed method. In this paper, the proposed security framework is implemented in a smart city environment to provide a secure energy transaction platform. Results show the authenticity of this model and demonstrate the effectiveness of the SVA method in decreasing the successful probability of cyber threat, increasing the time needed for the cyber attacker to decrypt and manipulate the data block.

The distribution networks can convincingly break down into small-scale self-controllable areas, namely microgrids (μG), to substitute μGs arrangements for effectively coping with perturbations. This flexible structure not only could potentially possess the strength to recover quickly, but also ensures the supply of vital loads and preserves functionalities under any contingency. To achieve these targets, this article examines a novel spatiotemporal algorithm to split the existing network into a set of self-healing μGs. In this endeavor, after designing the μGs by determining a mix of heterogeneous generation resources and allocating remotely controlled switches, the μGs operational scheduling is decomposed into interconnected and islanded modes. The main intention in the grid-tied state is to maximize the μGs profit while equilibrating load and generation at the islanded state by sectionalizing on-fault area, executing resources rescheduling, network reconfiguration and load shedding when the main grid is interrupted. The proposed problem is formulated as an exact computationally efficient mixed integer linear programming problem relying on the column & constraint generation framework and an adjustable interval optimization is envisaged to make the μGs less susceptible against renewables variability. Finally, the effectiveness of the proposed model is adequately assured by performing a realistic case study.

This article presents a contingency-based stochastic security-constrained unit commitment to address the integration of wind power producers to the joint energy and reserve markets. The model considers ancillary services as a solution to cope with the uncertainties of the problem. In this regard, a comprehensive model is considered that maintains the profit of supplementary services. The contingency ranking is a popular method for reducing the computation burden of the unit commitment problem, but performing the contingency analysis changes the high-impact events in previous ranking methods. This article employs an intelligent contingency ranking technique to address the above issue and to find the actual top-ranked outages based on the final solution. The proposed algorithm simultaneously clears the energy and reserve based on the mechanism of the day-ahead market. The main idea of this article is to develop a framework for considering the most effective outages in the presence of the uncertainty of wind power without a heavy computation burden. Also, energy storage systems are considered to evaluate the impact of the scheduling of storage under uncertainties. Also, an accelerated Benders decomposition technique is applied to solve the problem. Numerical results on a six-bus and the IEEE 118-bus test systems show the effectiveness of the proposed approach. Furthermore, it shows that utilizing both wind farms and storage devices will reduce the total operational cost of the system, while the intelligent contingency ranking analysis and enough reserves ensure the security of power supply.

The problem of electricity load forecasting has emerged as an essential topic for power systems and electricity markets seeking to minimize costs. However, this topic has a high level of complexity. Over the past few years, convolutional neural networks (CNNs) have been used to solve several complex deep learning challenges, making substantial progress in some fields and contributing to state of the art performances. Nevertheless, CNN architecture design remains a challenging problem. Moreover, designing an optimal architecture for CNNs leads to improve their performance in the prediction process. This article proposes an effective approach for the electricity load forecasting problem using a deep neuroevolution algorithm to automatically design the CNN structures using a novel modified evolutionary algorithm called enhanced grey wolf optimizer (EGWO). The architecture of CNNs and its hyperparameters are optimized by the novel discrete EGWO algorithm for enhancing its load forecasting accuracy. The proposed method is evaluated on real time data obtained from datasets of Australian Energy Market Operator in the year 2018. The simulation results demonstrated that the proposed method outperforms other compared forecasting algorithms based on different evaluation metrics.

This article proposes a unified solution to address the energy issues in net-zero energy building (ZEB), as a new contribution to earlier studies. The multicarrier energy system, including hydro-wind-solar-hydrogen-methane-carbon dioxide-thermal energies is integrated and modeled in ZEB. The electrical sector is supplied by hydro-wind-solar, combined heat and power (CHP), and pumped hydro storage (PHS). The thermal sector is supplied by CHP, thermal boiler, and electric heating. The hydrogen storage system and Methanation process operate as the interface energy carriers between the electrical and thermal sectors. The carbon dioxide (CO2) of the ZEB is captured and fed into the Methanation process. The purpose is minimizing the released CO2to the atmosphere while all the electrical-thermal load demands are successfully supplied considering events and disruptions. The model improves simultaneously the energy resilience and minimizes the environmental pollutions. The results demonstrate that the developed model reduces the CO2pollution by about 33 451 kg per year. The model is a resilient energy system that can handle all failures of components. The model can efficiently handle 26% increment in the electrical loads and 110% increment in the thermal loads.

One of the emergent prospects for active distribution networks (DN) is to establish new roles to the distribution company (DISCO). The DISCO can act as an aggregator of the resources existing in the DN, also when parts of the network are structured and managed as microgrids (MGs). The new roles of the DISCO may open the participation of the DISCO as a player trading energy in the wholesale markets, as well as in local energy markets. In this paper, the decision making aspects involving the DISCO are addressed by proposing a bilevel optimization approach in which the DISCO problem is modeled as the upper-level problem and the MGs problems and day-ahead wholesale market clearing process are modeled as the lower-level problems. To include the uncertainty of renewable energy sources, a risk-based two-stage stochastic problem is formulated, in which the DISCO's risk aversion is modeled by using the conditional value at risk. The resulting nonlinear bilevel model is transformed into a linear single-level one by applying the Karush-Kuhn-Tucker conditions and the duality theory. The effectiveness of the model is shown in the application to the IEEE 33-bus DN connected to the IEEE RTS 24-bus power system.

As a result of Demand Response (DR) programs implementation in the industrial sector varying electricity prices based on Time-of-Use (ToU) rates are becoming more common replacing traditional flate-rates per unit of energy consumption. On the other hand increased automation of industrial facilities is gaining interest due to their reliability flexibility and robustness. However it is necessary to determine a suitable task schedule in order to ensure their cost-efficiency and maximize profits. In this study a Market-Based approach is considered to solve the Multi-Agent Task Allocation (MATA) problem for a group of homogeneous agents and tasks. While most previous studies model the problem considering flate-rates for electricity consumption the main contribution of this study is accounting for the implementation of a DR program with varying ToU rates. The effects of optimizing the task allocation process on the costs incurred are investigated and compared to the effects of random assignment. Four different case studies are analyzed considering different-sized maps and number of tasks. The results show the computational efficiency of the proposed algorithm and its ability to massively decrease the electrical charging costs.

This paper focuses on the long-term planning of power systems considering the impacts of Electrical Energy Storage Devices (ESSD) as well as Demand Response Programs (DRPs). The proposed model incorporates a two-stage optimization strategy in order to reduce the computational burden of the nonlinear problem. The upper-level of optimization model includes investment decision variables (long-term planning) while in the lower-level, the optimal operation of the model for short-term horizon has been addressed. In the operational stage, the optimal scheduling of power system in the presence of suggested ESSD size and location from the upper level is evaluated. Moreover, the Time-of-Use (ToU) Demand Response (DR) pricing scheme has been applied in the operational stage to evaluate its capability to reduce the total operating costs. The simulation results on the standard 6-bus test system validates the applicability of the proposed two-stage optimization model and illustrates that the optimal sizing and location of ESSDs along with DRP implementation could effectively reduce the total systems costs and improve the power system load factor.

In this paper, we propose an active fault tolerant control (FTC) to regulate the active and reactive output powers of a voltage source converter (VSC) in the case of actuator failure. The active fault tolerant controller of the VSC which connects a distributed energy resource to the distribution power grid is achieved through the fault diagnostic and controller reconfiguration units. The diagnostic unit reveals the actuator failure by comparing the known inputs and measured outputs of VSC with those of the faultless model of the system and testing their consistency. In the case of actuator failure, the reconfiguration unit adapts the controller to the faulty system which enables the VSC to track the desired active and reactive output powers. The reconfiguration unit is designed using the virtual actuator which does not interfere with the regular controller of the VSC. The effectiveness of the proposed active FTC is evaluated by the numerical simulation of a VSC connected to the AC distribution grid.

In this paper, a risk-constrained optimal scheduling framework is proposed for an economic and reliable operation of microgrids. The framework is developed based on a scenario-based optimization technique, to schedule the microgrid operation both in normal and islanding modes. The prevailing uncertainties of islanding duration as well as prediction errors of loads, market prices and renewable power generation are addressed in the scheduling problem. The effect of participation of customers in demand response (DR) programs is investigated on economic-reliable operating solutions. Also, the uncertainties associated with wind power, loads and electricity prices as well as the uncertainties of islanding duration events of the microgrid are modeled, properly. The optimal scheduling carried out through a unit commitment algorithm and an AC power flow procedure by considering system's objectives and constraints. Moreover, to adequately handle the uncertainties of the problem, conditional value-at-risk (CVaR) metric is incorporated into the optimization model to evaluate the profit risk associated with operator's decisions in different conditions. With the proposed model, the impacts of DR actions, in terms of economy and reliability, are investigated with a 400 V microgrid system.

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.

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.

Developments of the smart grid infrastructure can facilitate the upsurge of Demand Response (DR) share in power system resources. This paper models the effects of Demand Response Programs (DRPs) on the behavior of the electricity market in the Day-Ahead (DA) session. Decision makers look for the best DR tariff to employ it as a tool to obtain a flexible and sustainable energy market. Employing the most effective DRP is of crucial importance. An optimized DR model and the optimum rates for each DRP are found to meet the decision makers’ requirements. optimizing the nodal tariff and incentive values of different DRPs are proposed in the electricity market. In such environment, market interactions are considered by means of a security constrained unit commitment problem. Both types of Price-Based Demand Response (PBDR) and Incentive-Based Demand Response (IBDR) are modeled. The numerical results presented indicate the effectiveness of the proposed model.

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.

The recent interest in the smart grid vision and the technological advancement in the communication and control infrastructure enable several smart applications at different levels of the power grid structure, while specific importance is given to the demand side. As a result, changes in load patterns due to demand response (DR) activities at end-user premises, such as smart households, constitute a vital point to take into account both in system planning and operation phases. In this study, the impact of price-based DR strategies on smart household load pattern variations is assessed. The household load datasets are acquired using model of a smart household performing optimal appliance scheduling considering an hourly varying price tariff scheme. Then, an approach based on artificial neural networks (ANN) and wavelet transform (WT) is employed for the forecasting of the response of residential loads to different price signals. From the literature perspective, the contribution of this study is the consideration of the DR effect on load pattern forecasting, being a useful tool for market participants such as aggregators in pool-based market structures, or for load serving entities to investigate potential change requirements in existing DR strategies, and effectively plan new ones.

In this paper, a detailed home energy management system structure is developed to determine the optimal day-ahead appliance scheduling of a smart household under hourly pricing and peak power-limiting (hard and soft power limitation)-based demand response strategies. All types of controllable assets have been explicitly modeled, including thermostatically controllable (air conditioners and water heaters) and nonthermostatically controllable (washing machines and dishwashers) appliances, together with electric vehicles (EVs). Furthermore, an energy storage system (ESS) and distributed generation at the end-user premises are taken into account. Bidirectional energy flow is also considered through advanced options for EV and ESS operation. Finally, a realistic test-case is presented with a sufficiently reduced time granularity being thoroughly discussed to investigate the effectiveness of the model. Stringent simulation results are provided using data gathered from real appliances and real measurements.

This paper proposes an offering strategy for a wind power producer (WPP) that participates in both day-ahead (DA) and balancing oligopoly markets as a price maker. Penetration of demand response (DR) resources into smart grids is modeled by intraday demand response exchange (IDRX) architecture. A bilevel optimization framework is proposed based on multiagent system and incomplete information game theory. Modeling the WPPs in high penetration of wind power as price makers can reflect the capability of this market player to directly affect the market prices. Simulation results indicate that the price-taker model of WPP is not accurate for WPPs that have significant market shares. By comparing the results obtained from modeling the WPPs as price makers with the ones as price takers, it can be concluded that WPPs have the market power not only to increase the prices of both DA and balancing markets, but also to reduce the amount of DR through IDRX market mechanism.