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Abstract Accurate electric load forecasting at various time periods is considered a necessary challenge for electricity consumers and generators to maximize their economic efficiency in energy markets. Hence, the accuracy and effectiveness of existing electric load forecasting approaches depends on the data quality. Nowadays, with the implementation of modern power systems and Internet of Things technology, forecasting models are faced with a large volume of data, which puts the security and health of data at risk due to the use of numerous measuring devices and the threat of cyber‐attackers. In this study, a cyber‐resilient hybrid deep learning‐based model is developed that accurately forecasts electric load in short‐term and long‐term time horizons. The architecture of the proposed model systematically integrates stacked multilayer denoising autoencoder (SMDAE) and generative adversarial network (GAN) and is called SMDAE‐GAN. In the proposed framework, SMDAE layer is used to pre‐process and remove real fs and intentional anomalies in data, and GAN layer is also utilized to forecast electric load values. The effectiveness of the SMDAE‐GAN structure is studied using realistic electrical load data monitored in the distribution network of Tabriz, Iran, and meteorological data measured in weather station there. Compared with other conventional load forecasting approaches, the developed framework has the highest accuracy in both cases of using normal data with real‐world noise and damaged data under false data injection attacks.

Abstract The effective management of microgrids is important towards transition to sustainable energy paradigm. By optimizing the utilization of different energy sources, such as solar photovoltaic panels and energy storages, it improves the reliability of the grid and develops resiliency in dealing with of challenges of unexpected variations in demand. To this end, the proposed paper presents DeepEMS, a system developed to manage the energy of microgrids through the incorporation of diverse intelligent algorithms. DeepEMS provides dynamic microgrid management through the utilization of Bidirectional Long Short‐Term Memory (BiLSTM) networks, Sliding Linear Programming (SLP), and Random Forest (RF). By implementing these methodologies, DeepEMS can optimize energy consumption throughout the microgrid by dynamically identifying and coordinating the needs of various energy sources. DeepEMS achieves precise multimodal optimization and facilitates integration of storage systems, grid interactions, and renewable energy sources (RES), as demonstrated by simulations and data analytics. DeepEMS presented performance in control, resource allocation, management, and grid utilization. Furthermore, in a comparative analysis with alternative intelligent models including XGBoost, Light GBM, RF, and Decision Trees, DeepEMS consistently demonstrated higher performance as measured by several key performance indicators (KPIs).

Integrated energy systems are considered a practical solution to fulfill low-carbon energy systems. Accordingly, the concept of a virtual energy hub (VEH) and its capability to participate in different energy markets have attracted significant attention recently. In this regard, the self-scheduling problem of VEH, including a wide variety of uncertainties capable of participating in multiple energy markets, is addressed in this paper. To this end, a two-stage stochastic optimization has been implemented to solve the scheduling problem of a VEH equipped with renewable energy resources as well as conventional units as internal suppliers, different types of energy storage systems, hydrogen vehicles (HVs), and electric vehicles (EVs) in the intelligent parking lot (IPL). The studied VEH can participate in gas and hydrogen markets as well as day-ahead (DA) and real-time (RT) power and heat markets. The impact of flexible units, including energy storage systems and demand response programs, on the expected profit of the VEH is investigated accurately. Based on the obtained results employing a battery energy storage system (BESS), thermal energy storage system (TESS), hydrogen energy storage system (HESS), and cooling energy storage system (CESS) increases the profit of VEH by 0.88%, 0.62%, 1.5%, and 0.64%, respectively. Also, the profit of VEH can be increased by 1.02% and 0.25% by applying the electrical demand response program (EDRP) and thermal demand response program (TDRP), respectively. As risk management is critical for the participation of VEH in multiple energy markets, second-order-stochastic-dominance (SOSD) constraints are imposed on the scheduling problem instead of employing typical risk measures such as conditional value-at-risk (CVaR). Although the proposed risk-management method can shape optimal profit distribution based on the operators’ attitude toward risk, benchmark selection is the main obstacle to the mentioned approach. To this end, the CVaR-based benchmark selection method is applied to overcome the stated obstacle and guarantee the problem’s feasibility.

This paper presents a robust framework for optimally coordinating integrated electricity and gas networks incorporating energy hubs and active loads. The energy hubs contain different sources, converters, and storage units to supply a variety of consumers. The designed system internally exchanges energy and gas using interface equipment and it simultaneously can participate in the day-ahead markets. This study also investigates the influence of various components such as renewable resources and energy storage systems on reactive power management. According to the high penetration of renewable energies and their significant impact on the network operation, a robust method consisting of information gap decision theory and fuzzy approach is applied to handle the uncertainty of wind and solar units. In addition, the role of flexible loads and prosumers is investigated by a time-based strategy, and eventually, a sensitivity analysis is implemented to evaluate the behavior of critical parameters. The presented model is a non-linear type that is linearized utilizing appropriate procedures to guarantee the problem convergence. The results show that the robust optimization with the maximum confidence level increases pollution and cost functions by 14.73% and 49.68%, respectively. In return, active loads decrease the mentioned values by 7.67% and 16.04%, respectively which validates their notable effects. The reactive power control of renewable resources and storage systems reduces the reactive power transferring through tie-lines and consequently improves technical specifications.

Abstract Due to the strong dependence between electricity generation and heating supply and the need for optimal use of renewable energies to supply power and heat loads, it is essential to provide an integrated energy management structure for electricity and heating systems. This paper studies the techno‐economic and environmental aspects of a combined electricity distribution and district heating system by considering multi‐type flexible energy resources and the environmental and practical constraints of both networks. The integrated energy system (IES) is equipped with wind power plants, combined heat and power plants, gas boilers, heat pumps, multi‐energy storage systems, electric vehicle parking lots, and multi‐energy demand response, as well as the IES operator can take part in the energy market to supply thermal and electrical demands. Besides, a robust strategy is adopted to control the power price uncertainty in the day‐ahead scheduling of IES, where a robustness parameter is considered to adjust the risk level of scheduling. The numerical results show that technical and environmental constraints can significantly increase the operation cost of IES by 11.8% and 3.6%, respectively. In addition, multi‐type flexible resources can improve the operational flexibility of IES by 16.1%.

This paper investigates the impacts of conservation voltage reduction (CVR) on electricity prices, the local market, and technical issues in distribution networks. An increase in electricity demand is one of the key challenges for developing sustainable societies. An increase in electric consumption puts immense pressure on electricity providers, which forces them to apply for load reduction programs during peak-demand time intervals. The CVR is one of the popular methods for load reduction, but how it would impact the pricing process and electricity market at the distribution level needs further investigation. The proposed methodology includes a power tracing and loss allocation-based pricing method. Since the distribution networks are going to be confronted by penetration of distributed energy resources (DER), prosumers, and microgrids, it is important to have a comprehensive methodology. This paper deploys a bi-level optimization algorithm to consider the financial benefits of all participating agents. In addition to CVR, the demand response (DR) programs are considered to shift and curtail flexible loads by the distribution system operator (DSO) and prosumers, respectively. The price sensitivity of prosumers toward change in the network’s voltage for better planning is calculated. The operation costs/profits of DSO/prosumers decrease/increase during CVR and DR programs by 4.63% / 3%, respectively.

Abstract As the technology of multi-energy carbon-free systems is strikingly developed, renewable-based multi-vector energy integration has become a prevalent trend in the decarbonization procedure of multi-carrier energy networks (MCENs). This paper proposes a fair transactive energy model for structuring an innovative local multi-energy trading market to allow multi-carrier multi-microgrids (MCMGs) with 100% renewable energy sources (RESs) in Chicago for free energy exchange aiming to balance energy in the renewable-dominant environment. Indeed, the main goal of the proposed model is to facilitate the modernization of future MCENs that are targeted to be equipped with 100% RESs and require a holistic model engaged with innovative technologies for the realization. To this end, the transactive energy architecture is designed for techno-environmental-economic assessing hybrid MCMGs to increase their flexibility in unbroken energy serving, decreasing their dependency on the main grid, and improving their economic benefits by considering their contribution level in energy interactions. To effectively model uncertainties of MCENs with 100% RESs, the novel hybrid technique is proposed that considers various stochastic changes of uncertain parameters to achieve confident results. The results highlighted the capability of the proposed model in effectively utilizing fully produced clean energy as well as continuously multi-energy serving of MCMGs in the presence of 100% RESs. Moreover, MCMGs reached techno-environmental-economic benefits by operating under the proposed transactive energy-based model, in which the technical, environmental, and economic goals are respectively realized by considering all constraints of MCENs, producing 100% clean energy by RESs, and reducing the total energy cost from $1,274,742.55 in the based model to $1,159,235.89 in the proposed one.

Low voltage ride-through (LVRT) is a grid code that enables distributed energy resources (DERs) to stay connected under voltage sags. However, a DER is exposed to being tripped if it does not meet the LVRT requirement. For a renewable-penetrated distribution network (RPDN), the lost amount of DER capacity over a voltage sag is the so-called LVRT response of an RPDN. Besides the behavior of DERs during the transient undervoltage condition, their trip may have a negative impact on transmission system security. This paper addresses a general mathematical model for analyzing the DERs behavior during the transient condition. The proposed model is implemented to obtain the RPDNs LVRT response to different voltage sags. This LVRT response is defined as such that concerns the uncertainty in the output power of renewable resources. Furthermore, the expected ride-through capability index is introduced to capture the generation availability due to meeting the LVRT requirements.

Abstract A two‐stage stochastic programming scheme is proposed in order to evaluate the offering strategy of a distributed energy resource aggregator in both wholesale and local electricity markets and appropriately cope with uncertainties associated with its decision‐making problem. In this regard, the aggregator combines a broad range of virtual and real distributed energy resources to simultaneously participate in the local electricity market as a price‐maker or strategic player and the wholesale electricity market as a price‐taker or non‐strategic player. To model the studied aggregator as a strategic entity in the local market, a bilevel programming approach is exploited in this work. Accordingly, at the upper level of the raised problem, the aggregator tends to promote its expected profit through taking part in the wholesale and local electricity markets, while at the lower level, the considered local market is cleared in a way to maximise the social welfare. In the end, the effectiveness of the proposed framework for the simultaneous participation of the distributed energy resource aggregator in these two markets has been explored utilising a case study.

Abstract Distributed energy resources are being progressively deployed by industry. The penetration of distributed generation in low voltage (LV) networks can position traditional consumers as market participants. With the improvements in communication networks and the introduction of new energy markets, these prosumers are incentivized to sell their excess production to other industries by participating in peer‐to‐peer (P2P) energy markets. This market paves the way for developing new technologies such as a shared battery energy storage system (SBESS). In this paper, a central storage unit rents its capacity for the prosumers to reduce the overall peak‐load of the microgrid. Each user requests the required SBESS capacity and calculates the best charging and discharging times to reduce their cost. To this end, this paper organized a P2P energy trading paradigm with the presence of SBESS. The optimization problem was simulated and solved using the alternating direction method of multipliers (ADMM) algorithm. Results demonstrate how combining features of P2P energy trading and SBESS can save up to 29% for the industrial town.

Today, due to the high penetration of renewable energy resources and restructuring of power systems, photovoltaic power plants (PVPPs) and wind power plants (WPPs) as renewable power plants (RPPs) can participate in the electricity markets. However, the intermittent power generation of RPPs may be challenging for the owners of these power plants. In order to mitigate the unpredictable and intermittent power generation of RPPs, energy storage systems like compressed air energy storage (CAES) can be an appropriate solution. In this paper, the optimal day-ahead and look-ahead strategic offering and bidding of integrated RPPs and CAES in the electricity market are investigated. Also, a stochastic-robust approach is proposed for modeling renewable generation and electricity price uncertainty. The proposed mixed-integer linear program (MILP) is formulated in GAMS software under the CPLEX solver. Three case studies are investigated to validate the proposed method. According to numerical results, in the optimistic strategy, the coordinator of RPPs and CAES has more opportunities to participate in the electricity market. But in the pessimistic strategy, due to low electricity market (EM) prices, the coordinator has no more tendency to participate in the electricity market compared to the optimistic strategy.

Abstract The energy hub (EH) concept is an efficient way to integrate various energy carriers. In addition, demand response programmes (DRPs) are complementary to improving an EH's efficiency and increase energy system flexibility. The hydrogen storage system, as a green energy carrier, has an essential role in balancing supply and demand precisely, similar to other storage systems. A hybrid robust‐stochastic approach is applied herein to address fluctuations in wind power generation, multiple demands, and electricity market price in a hydrogen‐based smart micro‐energy hub (SMEH) with multi‐energy storage systems. The proposed hybrid approach enables the operator to manage the existing uncertainties with more flexibility. Also, flexible electrical and thermal demands under an integrated demand response programme (IDRP) are implemented in the proposed SMEH. The optimal scheduling of the hydrogen‐based SMEH problem considering wind power generation and electricity market price fluctuations, as well as IDRP, is modelled via a mixed‐integer linear programming problem. Finally, the validity and applicability of the proposed model are verified through simulation and numerical results.

Abstract The combined heat and power (CHP) plant is one of the emerging technologies of gas‐fired units, which plays an important role in reducing environmental pollutants and delivering high energy efficiency. Moreover, the hydrogen energy storage (HES) system with extra power storage from wind turbine via power to hydrogen technology allows the injection of stored energy into the power grid by reverse hydrogen to power services, offsetting in this way the uncertainty of wind power. Consequently, simultaneous usage of CHP and HES units not only makes the maximum use of wind power distribution but also increases flexibility and reduces the operating costs of the entire network. Therefore, this paper proposes an interval optimization technique for managing the uncertainty of wind power generation in the integrated electricity and natural gas (NG) networks considering CHP–HES. Moreover, to enhance the flexibility of the NG network, a linearized Taylor series‐based model is proposed for modelling linepack of gas pipelines in the proposed scheduling framework that is formulated mixed‐integer linear programming and solved using the Cplex solver. The obtained results indicate that the simultaneous use of CHP–HES in the day‐ahead scheduling reduces the operating cost and increases the flexibility of the whole network.

Integrating microgrids within distribution systems can significantly improve the power system's reliability while reducing operating costs. However, due to the unintentional disaster conditions, sometimes distribution systems and microgrids cannot support each other, and the microgrids are forced to work in the islanded mode. Accordingly, we developed an optimal resilient scheduling scheme that guarantees networked microgrids (NMGs) reliable operation in the normal and islanding modes. To achieve this aim, the problem is decomposed into day-ahead normal operation (grid-connected) and real-time islanded examination by benders decomposition algorithm perspective. The specified scheme of NMGs in the normal operation will be scrutinized in the real-time islanded mode. According to the benders decomposition theory, the scheduling of NMGs would be revised in the next iteration if the current schedule is not feasible for possible real-time islanding conditions. The status of thermal units, charging, and discharging of energy storage systems respecting their other constraints are changed depending on the type and severity of mismatches between generation and demand. Three different interconnection topologies are tested for assessing the performance of the proposed method and the impact of transaction energy between NMGs on that. Numerical simulations illustrate the advantages of the proposed scheme and explain its merits.

Integrating Distributed Energy Resources (DERs) and Micro-Grid (MG) into a system evolved the traditional power system. In spite of their significant advantages, MGs may result in volatility and uncertainty in the power systems. For reliable operation of the grid, energy trading among MGs should be optimized to maintain a fair trading price, maximize participants’ profit, and satisfy network constraints. In this paper, the optimal power trading among multiple reconfigurable MGs is formulated as a Mixed-Integer Nonlinear Programming (MINLP) considering all energy resources and their dynamic prices. In spite of the other methods in the literature, the proposed method minimizes the total cost (increase sales and decrease purchases) and transmission loss considering all energy resources in the MGs. In order to flatten the load profile, a time-based load profile is considered for the demand response program. The performance of the proposed model is evaluated on an IEEE 6-bus network as well as a modified IEEE 33-bus test system. The results verify that the proposed method, (i) determines the best configuration among MGs with a switching reduction of about 30%, (ii) optimizes the power generation of energy resources with 12% reduction in energy production, and (iii) optimizes the power trading costs with a 10% reduction in costs compared with the basic model without DR and trade that is introduced as $Scen.1$ in this paper.

Integrated energy carriers in the framework of energy hub system (EHS) have an undeniable role in reducing operating costs and increasing energy efficiency as well as the system's reliability. Nowadays, power‐to‐gas (P2G), as a novel technology, is a great choice to intensify the interdependency between electricity and natural gas networks. The proposed strategy of this study is divided into two parts: (i) a conditional value‐at‐risk‐based stochastic model is presented to determine the optimal day‐ahead scheduling of the EHS with the coordinated operating of P2G storage and tri‐state compressed air energy storage (CAES) system. The main objective of the proposed strategy is to indicate the positive impact of P2G storage and tri‐state CAES on lessening the system uncertainties including electricity market price, power generation of the wind turbine, and even electrical, gas, and thermal demands. (ii) A demand response program focusing on day‐ahead load shifting is applied to the multiple electrical loads according to the load's activity schedule. The proposed strategy is successfully applied to an illustrative example and is solved by general algebraic modeling system software. The obtained results validate the proposed strategy by demonstrating the considerable diminution in the operating cost of the EHS by almost 4.5%.

Distribution expansion planning (DEP) is a mixed integer nonlinear programming (MINLP) problem and contains various options to be considered where the nonlinearity makes it difficult to solve. Over the years, different heuristic and classical optimization methods have been introduced to solve this problem. In a few classical optimization methods, this problem has been linearized. In this contribution, an e-constraint based multi-objective method was proposed for mixed integer linear programming (MILP) based DEP model considering network loss. Operational network loss was linearized and incorporated in the model, then the operational loss cost was obtained by modeling the production cost. In order to model the production cost, the well-known supply price curve was considered, where the price of energy for each loading condition was obtained based on a linear model. Using the e-constraint method, the Pareto optimal front was formed, which provides the decision maker with a range of solutions showing relationship among conflicting objectives. Then, a Fuzzy satisfying method was utilized to obtain the best compromised solution. Simulations were performed on an 18-node primary distribution network. Results showed the effectiveness of the proposed method for the MILP-based multi-objective DEP models.

Low voltage ride through (LVRT) is one of the indispensable issues of recent decade in the context of grid codes. LVRT stands for the ability of a generation facility to stay connected during the voltage dip. Despite the numerous discussions in recent works, but they mostly concentrate on the LVRT-based control of distributed energy resources (DERs) integrated into a microgrid and its improvement. However, what has been hidden and not addressed any more yet is an index to measure the LVRT capability of a DER-penetrated distribution network (DPDN) under different voltage sags. This takes precedence when we want to evaluate the LVRT capability of DPDNs with consideration of various LVRT categories of DERs mandated in IEEE 1547 standard. This paper introduces a general framework for LVRT assessment of a DPDN by solving a system of differential algebraic equations (DAEs). Then expected LVRT capability of a DPDN is evaluated by a proposed LVRT index through the implementation of Monte Carlo simulation technique.

This paper proposes a two-stage robust-stochastic framework to evaluate the effect of the battery-based energy storage transport (BEST) system in a day-ahead market-clearing model. The model integrates the energy market-clearing process with a train routing problem, where a time-space network is used to describe the limitations of the rail transport network (RTN). Likewise, a price-sensitive shiftable (PSS) demand bidding approach is applied to increase the flexibility of the power grid operation and reduce carbon emissions in the system. The main objective of the proposed model is to determine the optimal hourly location, charge/discharge scheduling of the BEST system, power dispatch of thermal units, flexible loads scheduling as well as finding the locational marginal price (LMP) considering the daily carbon emission limit of thermal units. The proposed two-stage framework allows the market operator to differentiate between the risk level of all existing uncertainties and achieve a more flexible decision-making model. The operator can modify the conservatism degree of the market-clearing using a non-probabilistic method based on info-gap decision theory (IGDT), to reduce the effect of wind power fluctuations in real-time. In contrast, a risk-neutral-based stochastic technique is used to meet power demand uncertainty. The results of the proposed mixed-integer linear programming (MILP) problem, confirm the potential of BEST and PSS demand in decreasing the LMP, line congestion, carbon emission, and daily operation cost.

This paper presents a new solution to the problem of transmission cost allocation to its users. The proposed technique utilizes modified Z-bus theory, equivalent current injection and impedance of the generators and loads, and is developed by the equal-sharing as well as the orthogonal projection principle. The procedure is performed in three steps. First, the modified Z-bus theory is used to calculate the contribution of the users into the network bus voltages as well as the branch currents. Then, the equal sharing principle is confirmed by the game theory solutions and is subsequently applied to identify the users' contributions into the branch power flows. After that, the orthogonal projections of the contributions are calculated and the concept of effective contributions is suggested. The proposed methodology provides the percentage shares of the users on the network complex variables, which help to better assess the contributions. A 2-bus and the IEEE 30-bus test system are used to validate the proposed technique. Finally, the proposed methodology is applied to the polish 2383-bus system to emphasize its applicability to large practical systems.

Nowadays, utilities aim to find methods for improving the reliability of distribution systems and satisfying the customers by providing the continuity of power supply. Different methodologies exist for utilities to improve the reliability of network. In this paper, demand response (DR) programs and smart charging/discharging of plug-in electric vehicles (PEVs) are investigated for improving the reliability of radial distribution systems adopting particle swarm optimization (PSO) algorithm. Such analysis is accomplished due to the positive effects of both DR and PEVs for dealing with emerging challenges of the world such as fossil fuel reserves reduction, urban air pollution and greenhouse gas emissions. Additionally, the prioritization of DR and PEVs is presented for improving the reliability and analyzing the characteristics of distribution networks. The reliability analysis is performed in terms of loss of load expectation (LOLE) and expected energy not served (EENS) indexes, where the characteristics contain load profile, load peak, voltage profile and energy loss. Numerical simulations are accomplished to assess the effectiveness and practicality of the proposed scheme.

This paper proposes a new method to allocate the transmission fixed costs among the network participants in a pool-based electricity market. The allocation process relies on the circuit laws, utilizes the modified impedance matrix and is performed in two individual steps for the generators and loads. To determine the partial branch power flows due to the participants, the equal sharing principle is used and validated by the Shapley and Aumann-Shapley values as two preferred game-theoretic solutions. The proposed approach is also applied to determine the generators' contributions into the loads, and a new concept, named circuit-theory-based equivalent bilateral exchange (EBE), is introduced. Using the proposed method, fairly stable tariffs are provided for the participants. Cross-subsidies are reduced and a fair competition is made by the proposed method due to the counter-flows being alleviated compared with the well-known Z-bus method. Numerical results are reported and discussed to validate the proposed cost allocation method. Comparative analysis reveals that the method satisfies all conditions desired in a fair and efficient cost allocation method. Finally, the developed technique has been implemented successfully on the 2383-bus Polish power system to emphasize that the method is applicable to very large systems.

In this paper, fractional order proportional-integral-derivative (FOPID) and tilted-integral-derivative (TID) controllers are applied in the design of load frequency controller (LFC). Tuneable parameters are adjusted via an improved particle swarm optimization algorithm which is powered by chaotic parameter and crossover operator to find a globally optimal solution. The FOMCON toolbox is employed within MATLAB/Simulink for implementing fractional order modeling and control. A two-area diverse-unit power system is examined considering the physical constraints of generation rate constraint and governor dead band. Comparative evaluations demonstrate that the FOPID-based LFC achieves the greatest dynamic performance under different load disturbance patterns. Sensitivity analysis is carried out to demonstrate the robustness of the proposed LFCs under wide variations in the system loading condition and parameter. Keywords: Fractional order proportional-integral-derivative (FOPID), Tilted-integral-derivative (TID), Load frequency controller (LFC), Diverse-unit power system, FOMCON toolbox

This paper presents heuristic modified group search optimization (MGSO) algorithm for solving multi-objective combined economic power and emission dispatch problem. Some modifications are applied on the conventional GSO method to improve its convergence characteristics. Simulations are carried out on IEEE 30 bus standard system in order to validate effectiveness of the proposed method. Simulation analyses are implemented for mentioned system with and without loss considering pure power, pure emission and combined economic power and emission dispatch. Numerical results indicate that, the proposed method has better performance and convergence time, in comparison with some recently published methods. Keywords: Multi-objective optimization, Environmental dispatch, Group search optimization, Combined power and emission dispatch

In this paper, fractional order proportional-integral-differential (FOPID) controller is employed in the design of thyristor controlled series capacitor (TCSC)-based damping controller in coordination with the secondary integral controller as automatic generation control (AGC) loop. In doing so, the contribution of the TCSC in tie-line power exchange is extracted mathematically for small load disturbance. Adjustable parameters of the proposed FOPID-based TCSC damping controller and the AGC loop are optimized concurrently via an improved particle swarm optimization (IPSO) algorithm which is reinforced by chaotic parameter and crossover operator to obtain a globally optimal solution. The powerful FOMCON toolbox is used along with MATLAB for handling fractional order modeling and control. An interconnected multi-source power system is simulated regarding the physical constraints of generation rate constraint (GRC) nonlinearity and governor dead band (GDB) effect. Simulation results using FOMCON toolbox demonstrate that the proposed FOPID-based TCSC damping controller achieves the greatest dynamic performance under different load perturbation patterns in comparison with phase lead-lag and classical PID-based TCSC damping controllers, all in coordination with the integral AGC. Moreover, sensitivity analyses are performed to show the robustness of the proposed controller under various uncertainty scenarios.

The primary goals of employing series flexible ac transmission system (FACTS) in automatic generation control (AGC) studies of interconnected power systems are mitigating area frequency and tie-line power oscillations. This paper compares dynamic performance of thyristor controlled series capacitor (TCSC) as damping controller with thyristor controlled phase shifter (TCPS) and static synchronous series compensator (SSSC) which are series FACTS damping controllers. Commonly used lead-lag controllers are used in structure of damping controllers. The effect of TCSC in tie-line power exchange is modeled mathematically based on the Taylor series expansion for small-signal load disturbance. The performance of the proposed TCSC controller in coordination with integral AGC is compared with cases of TCPS–AGC and SSSC–AGC. An improved particle swarm optimization (IPSO) algorithm and integral of time multiplied squared error (ITSE) performance index are used to design the damping controllers. A two-area power system having generations from reheat thermal, hydro, and gas units in each area is evaluated regarding nonlinearity effects of generation rate constraint (GRC) and governor dead band (GDB). The simulations results in MATLAB/SIMULINK environment show that the proposed TCSC–AGC yields superior performance than others in damping of area frequencies and tie-line oscillations. Furthermore, sensitivity analyses are performed to show greater robustness of TCSC–AGC.

The cascaded multilevel inverter (CMI) is one type of common inverter in industrial applications. This type of inverter can be synthesized either as a symmetric configuration with several identical H-bridge (HB) cells or as an asymmetric configuration with non-identical HB cells. In photovoltaic (PV) applications with the CMI, the PV modules can be used to replace the isolated dc sources; however, this brings inter-module leakage currents. To tackle the issue, the single-source CMI is preferred. Furthermore, in a grid-tied PV system, the main constraint is the capacitive leakage current. This problem can be addressed by providing a common ground, which is shared by PV modules and the ac grid. This paper thus proposes a topology that fulfills the mentioned requirements and thus, CMI is a promising inverter with wide-ranging industrial uses, such as PV applications. The proposed CMI topology also features high boosting capability, fault current limiting, and a transformerless configuration. To demonstrate the capabilities of this CMI, simulations and experimental results are provided.

Recently, with the penetration of numerous Distributed Energy Resources (DER) in Smart Distribution Networks (SDN), Local Transactive Markets have emerged. Exchanging energy between all participants of local markets results in the satisfaction of producers and consumers. Based on these issues, this study provides a novel framework for the participation of SDN-independent entities in wholesale and local electricity markets simultaneously. In this regard, the considered system’s players, namely Distribution System Operator (DSO) and DER Aggregator (AG), take part within local as well as wholesale markets in two-day ahead and real-time stages. Moreover, to deal with the inherent conflict between the existing players’ interests, a Stackelberg game-based technique is proposed. In the raised competition, the leader, DSO, attempts to minimize its operating costs, while the follower, DER AG, tends to maximize its profit. Therefore, actors’ actions choices within both markets are made non-cooperatively. On the other hand, to handle the uncertain nature of stochastic parameters in the depicted problem, Monte Carlo Simulation (MCS), together with a fast backward/forward scenario reduction approach, is exploited. Ultimately, to evaluate the efficiency of the proposed scheme, two different case studies, with and without considering the competitive environment, are implemented on a modified IEEE-33 bus SDN.