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The transmission congestion issue from the high penetration of renewable energies places a premium on accurate dynamic line rating (DLR) as a short-term solution for the more efficient exploitation of the existing transmission infrastructure and the efficient and reliable operation of the power grids. Even though the DLR methods produce a worthy estimation of ampacity, they need the placement of measurement devices and communication networks along with the precise calibration of the estimators and the installation of sensors on the conductor surface. Herein, as a viable alternative, the DLR forecasting models with respect to historical meteorological data were developed using ensemble learning algorithms. Several cases were designed to explore the resiliency and accuracy of the proposed method for different forecasting horizons. The result of simulations proved that ensemble learning algorithms can be fruitfully used for the DLR forecasting, even in the presence of severe cyberattacks. The proposed method yielded an approximate capacity increase of 30\% for 400kV lines between Ghadamgah and Binalood wind farms, which is enough to relieve the congestion issue. Experiments revealed the generalizability and reliability of the forecasting models for the DLR at various points of the line without the deployment of measurement devices and communication infrastructures.

The secondary control layer of microgrids is often modelled as a multi‐agent distributed system, coordinated based on consensus protocols. Convergence time of consensus algorithm significantly affects transient stability of microgrids, due to changes in communication topology, switching of distributed generations (DGs), and uncertainty of intermittent energy sources. To minimise convergence time in consensus protocol, this work proposes a multilayer event‐based consensus control framework, which is resilient to communication delays and supports plug‐and‐play (P&P) addition or removal of DGs in DC microgrids of cellular energy systems. A novel bi‐layer optimisation algorithm minimises convergence time by selecting an optimal communication topology graph and then adjusts controllers' parameters. Average consensus is achieved among distributed controllers using an event‐based consensus protocol, considering non‐uniform delays between agents. A realisation method has also been introduced using the directional beamforming technique for topology assignment algorithm based on modern telecommunication technologies. Provided feasibility case study has been implemented on a real‐time hardware‐in‐the‐loop (HIL) experimental testbed, to validate the performance of the proposed framework for key purposes of voltage stabilisation and balanced power‐sharing in DC microgrids.

The electric power system is one of the most important critical infrastructures of a country. Recently, the number of natural and man‐made disasters is increased, which can impose extensive damages and costs to the power system. A resilient power system can withstand against, adapt to and recover from these disasters. Power system resilience is quantified by mathematical tools which are called “resilience metrics”. Currently, a lot of resilience metrics are proposed in the power system literature. In this paper, based on the extensive research in the critical infrastructure resilience literature which specifically concentrates on the “area‐based” resilience metrics, a new area‐based resilience metric is proposed which can measure the power system resilience considering the government policymaker criteria, which are rarely noticed before. The proposed and conventional area‐based resilience metrics are evaluated based on the real data from the 2012 Superstorm Sandy in the USA, which led to significant damage to the power distribution system. The simulation results show that the proposed area‐based resilience metric is very simple, can successfully address actual power system performance curves and is more meaningful and tangible than the conventional area‐based metrics for the government policymaker. The proposed area‐based resilience metric has also a general form and can be used for other critical infrastructures.

Proliferation of distributed energy resources (DERs) calls for a coordinated transmission and distribution (T&D) scheduling at the day‐ahead stage. The problem becomes more complicated dealing with the variability of stochastic parameters. Also, privacy and complexity are two barriers to the development of such coordinated platforms. This paper addresses these issues by introducing a hybrid centrally‐supported decentralized stochastic framework for the day‐ahead energy and reserve market with minimum complexity and the need for data‐sharing between system operators. The proposed model is able to calculate the bidirectional power exchange at the T&D interface and the separated costs, dispatches, and reserves of all market participants. The proposed model does not consider any priority for operators and increases the liquidity by facilitating participants’ access to the market platform. Also, the second‐order cone programming (SOCP) formulation is used for calculating the AC power flow of distribution grids, and the model is validated and compared with other implementation strategies. The proposed model is implemented on a modified IEEE 24‐bus test system, and results show that the model can schedule resources for supplying energy and reserves in both transmission and distribution levels in an acceptable computation time.

The role of electric vehicles (EVs) in energy systems will be crucial over the upcoming years due to their environmental-friendly nature and ability to mitigate/absorb excess power from renewable energy sources. Currently, a significant focus is given to EV smart charging (EVSC) solutions by researchers and industries around the globe to suitably meet the EVs' charging demand while overcoming their negative impacts on the power grid. Therefore, effective EVSC strategies and technologies are required to address such challenges. This review paper outlines the benefits and challenges of the EVSC procedure from different points of view. The role of EV aggregator in EVSC, charging methods and objectives, and required infrastructure for implementing EVSC are discussed. The study also deals with ancillary services provided by EVSC and EVs' load forecasting approaches. Moreover, the EVSC integrated energy systems, including homes, buildings, integrated energy systems, etc., are reviewed, followed by the smart green charging solutions to enhance the environmental benefit of EVs. The literature review shows the efficiency of EVSC in reducing charging costs by 30 %, grid operational costs by 10 %, and renewable curtailment by 40 %. The study gives key findings and recommendations which can be helpful for researchers and policymakers.

The development of emerging smart grid technologies has led to more and more penetration of renewable energy resources and electric energy storage in the residential sectors. Besides, owing to the significant evolution of power electronic devices, there is a rapid growth in penetration of DC loads and generations, such as PV and electric vehicles (EVs), into the buildings and homes as a building block of the future smart cities. This is despite the fact that the electricity infrastructure of the conventional buildings is designed based on AC electricity and as a result, there would be a lot of losses due to the frequent power conversion from AC to DC and vice versa. Besides, according to a significant amount of energy consumption in the residential sector, buildings have a prominent role to confront environmental problems and obtain sustainability. In such circumstances, and considering the energy outlook, rethinking the electrification structure of the built environment is necessary. This work is an effort in this regard and looks for a sustainable energy infrastructure for the cyber–physical homes of the future. Three disparate electrification architectures are analyzed. The proposed framework, which is formulated as a mixed-integer linear programming (MILP) problem, not only considers costs associated with investment and operation but also evaluates the reliability of each structure by considering the different ratios of DC loads. Moreover, the optimal size of renewable energy resources and the effect of EV demand response, and different prices of PV and battery are precisely investigated. The efficacy of the proposed approach is evaluated via numerical simulation.

This paper proposes a load control module based on the consumers' vulnerability indices to improve the ability of radial microgrid (MG) to deal with line contingencies. This consumer switching module (CSM) increases the operational flexibility and is suitable for the radial MGs, where, a sudden line outage restricts access to the energy resources and deviates the system frequency in the created island. To cope with this condition, CSM can instantly disconnect some of the loads and reconnects them in an appropriate time sequence, considering loads' vulnerabilities. To include the CSM in the short-term operation of MG, with the lowest complexity, a simple four-stage structure is used. At first, the capability of CSM is determined in different islands related to potential line events. Then, the approximate cost function of CSM is applied to the short-term MG operation which is hour-ahead scheduling (HAS) including both the normal and the contingency state operation. Eventually, the determined optimal CSM participation level in each island is allocated to the consumers based on their exact offered price to calculate the load switching sequence. To evaluate the performance of the proposed scheduling manner, the 13-bus and 123-bus IEEE test systems are precisely analyzed considering different simulation scenarios.

This study proposes an integrated framework for coordinated optimisation of the interdependent microgrid (MG) and electric vehicle (EV) fleet entities using the normalised normal constraint approach. By considering the active/reactive power management option of the bidirectional charger enabled EVs in the proposed model, the authors investigate the effectiveness of EV's integration in the presence of the techno-economical objective functions. This work concentrates on the trade-off analysis of two conflicting objectives, including the economic objective of the MG's operation cost minimisation and the technical objective of the MG's voltage deviation. Besides, they consider several uncertainty sources, e.g. wind, EV and solar panel (PV) power provision, as well as market price fluctuations in the proposed model affecting the aforementioned techno-economic trade-off solution. The proposed model is a stochastic multi-objective mixed-integer non-linear programming problem where the authors apply the designed integrated framework on a modified IEEE 18-bus test case in GAMS software. Through numerical results, they demonstrate MG optimal operation changes due to different MGO priorities and study the positive effects of EVs integrated energy management on the bi-objective operation.

As power systems are gradually evolving into more efficient and intelligent cyber-physical energy systems with the large-scale penetration of renewable energies and information technology, they become increasingly reliant on a more accurate forecasting. The accuracy and generalizability of the forecasting rest to a great extent upon the data quality, which is very susceptible to cyberattacks. False data injection (FDI) attacks constitute a class of cyberattacks that could maliciously alter a large portion of supposedly-protected data, which may not be easily detected by existing operational practices, thereby deteriorating the forecasting performance causing catastrophic consequences in the power system. This paper proposes a novel data-driven FDI attack detection mechanism to automatically detect the intrusions and thus enrich the reliability and resilience of the energy forecasting systems. The proposed mechanism is based on cross-validation and least-squares providing accurate detection with low computational cost and high scalability without utilizing the model and parameters of the system. Effectiveness of the proposed detector is corroborated through six representative tree-based wind power forecasting models including decision tree, bagging, random forest, boosting, gradient boosting, and XGboost. Experiments indicate that corrupted data is properly located and removed, whereby the accuracy and generalizability of the final forecasts is recovered.

Global electric vehicles (EVs) fleet is expanding at a rapid pace. Considering the uncertain driving pattern of EVs, they are dynamic consumers of electricity and their integration can give rise to operational problems and jeopardize the security of the power system. Under such circumstances, the implementation of demand-side response (DSR) programs is more likely to be an effective solution for reducing the risks of load curtailment or security problems. This study proposes a voltage stability constrained DSR-coordinated planning model for increasing the penetration level of EVs in a distribution system consisting of photovoltaics (PVs), wind turbines (WTs) and responsive loads. The uncertainties of PV/WT generation, the driving pattern of EVs, and load demand are modeled by an improved form of information gap decision theory (IGDT), hereafter called weighted IGDT (WIGDT). Due to the fact that the proposed model is nonlinear and non-convex, a linearization technique is adopted and the proposed model is formulated as a mixed-integer linear programming (MILP), solved using the general algebraic modeling system (GAMS) software. The standard 33-bus distribution test system and a real-world smart distribution network, based in the Isle of Wight in the UK, are used to evaluate the performance of the model.

This paper proposes a two-objective linearized resilient architecture (LRA) model for distribution networks to achieve a strictly resilient network during natural disasters like earthquakes and floods. To obtain this goal, the proposed LRA framework is based on the planning of the energy storage system (ESS), hardening and tie lines, and backup distributed generation (DG). Therefore, the proposed model minimizes the sum of planning and expected operation costs in the first objective function, and the total load shedding and repair costs originates from earthquakes and floods in the second objective function. Also, it constraints to the network planning model, linearized equations of the system operation, and system reconfiguration formulation. Moreover, stochastic programming models the uncertain availability of the network equipment during the natural disaster condition, the load and electricity price. In the next step, the e-constraint-based Pareto optimization is used to achieve an equivalent single-objective LRA model and obtain the best compromise solution. Finally, the proposed strategy is applied to a standard test distribution network. Numerical simulation confirms the capability of the proposed method in obtaining a resilient distribution network during natural disasters.

Automatic reconfiguration is one of the key actions in self‐healing distribution networks. In these networks, after detecting and isolating the faulted portion, an automatic reconfiguration procedure is performed to restore the maximum possible affected loads without further interruptions during repair operations. This procedure becomes more complicated in the networks with integrated distributed generation units as they can bring security challenges for the reconfigured network after a fault event. To overcome these challenges, a stochastic framework is proposed here. In this framework, the reconfiguration procedure is conducted with a fast and reliable method which is based on the graph theory. Besides, the security challenges of utilizing distributed generations after an event are highlighted. Then, since a faulted network is more prone to subsequent faults, different actions of changing the distribution generations output power, preventing the insecure increment of short circuit capacity, and also considering the loadability improvement are proposed in the reconfiguration framework. Then in the final stage, the vulnerability of the distribution system to the uncertainties of load demand is resolved through a chance‐constrained programming‐based approach. To see the performance of the proposed stochastic framework, it is tested on a standard test system and the results prove its goodness and applicability for real distribution networks.

This study relies on a dynamic reliability-based model for distributed energy resources (DER) planning in electric energy distribution networks (EEDN) with the aim of maximizing the profit of EEDN companies by increasing income and reducing costs. Load uncertainty is considered in the proposed planning model and the robust optimization (RO) approach is employed to cope with the uncertainty. The developed methodology is illustrated using real-world voltage-dependent load models, including residential, commercial and industrial types. These load models are used in evaluating the reliability cost and energy selling for customers. The reliability cost is calculated based on the total unsupplied load after an outage. Furthermore, a new modified harmony search algorithm is proposed to solve the formulated robust dynamic DER planning problem. The solution of the proposed optimization model provides the size, location, and power factor of DER. Furthermore, the need for transformers or lines upgrades and the best year for DER installation are other decision variables determined by the model. The effectiveness and capability of the developed model have been demonstrated with the aid of a case study based on a typical EEDN. The obtained results indicate that installing DER in EEDNs can relieve congestion on feeders; therefore, it can mitigate or defer upgrade investment. Moreover, if carefully planned, other benefits of DER integration such as reliability improvement and energy loss reduction can be achieved.

Volatile impact of intermittent renewable energy sources (RESs) on the one hand and the uncertainties of loads and market prices, on the other hand, make the bidding strategy of microgrids (MGs) too risky and high-computational problem. To cope with these challenges, the bidding problem of MGs based on a three-stage hybrid stochastic/interval optimisation (HSIO) is devised in this study, which provides a trade-off between covering the volatilities by means of the MG potential flexibilities resources or by means of the energy provision from the real-time market (RTM). To tackle the uncertainties of the day-ahead market prices, the cost-effective stochastic programming (SP) is applied to maximise the profit of MG in the day-ahead stage of decision-making. In order to handle the volatilities of RESs production and uncertainties of RTM prices, a flexibility scheme based on the robust and low-computational interval optimisation (IO) approach is designed to minimise the balancing cost of MG in the real-time stages. Comprehensive numerical results are provided to compare the effectiveness, robustness, and computational complexity of the proposed model. Results show that the HSIO model takes advantage of the cost-effective solution from the SP model, and the robust solution with computational simplicity from the IO model.

This study proposes a time-based security (TBS) evaluation mechanism for radial microgrids (MGs) short-term operation. In the proposed framework, system security is modelled using time-based sensitivity analysis in the load points. Occurring branch contingencies in a radial MG creates some islands with oscillating frequencies. The settling time of frequency oscillation, as the successful islanding time, and the island's capability to continue the load supply are considered in TBS assessment. A system frequency response model and the stored energy of storages are used to calculate the two mentioned factors. Depending on the loads' sensitivities to the successful islanding time and the service continuity duration, they are exposed to extreme damage. In the case of branches' sudden outages, it may be impossible to operate the MG without any extreme damage of loads, due to the capacity shortage in the island, dynamic behaviours of the MG and inadequate stored energy of storages. Under these conditions, the load shedding unit participates considering the customer damage function. To evaluate the performance of the proposed security assessment mechanism, the 123-bus distribution test system is precisely analysed.

Recently, the provision of the reserve from energy storage systems (ESSs) is introduced as a source for ancillary services to address the uncertainties of renewable power generations. The performance of ESSs is analysed while they are applied as a provider of regulation reserves. It has been revealed that previous stochastic models neglect the impact of corrective dispatches, related to the provision of regulation reserves, on the energy level stored in the ESSs, which can lead to large deviations. This study coordinates the stored energy of ESSs to be feasible regarding the dispatches in the base schedule and rescheduling within scenarios. Also, the wind speed fluctuations are considered as the source of uncertainty, and scenarios of wind energy are generated using the Weibull distribution function. The IEEE 24‐Bus standard test system is applied for the examination of the proposed model. The results show that the proposed model can manage the performance of ESSs in rescheduling within scenarios, while the coordinated reserve provision of ESSs can remove the concerns about insufficient stored energy of ESSs.

Over the last decades, environmental concerns and the global tendency to reduce the use of fossil fuels and replacing them with renewable energy sources (RESs) to face the increasing rate of greenhouse gas (GHG) emissions have increased. Buildings consume a significant amount of energy and therefore, they are responsible for a noticeable part of the total GHG emission. Thus, when we talk about decarbonization of the energy systems, buildings are an important sector of the energy system that needs to be considered. Using RESs, smart technologies, and information and communication technologies along with the improvement in energy efficiency, are a number of endeavors to increase the role of building on the way toward decarbonization. In the new environment, the buildings are not passive players of the energy systems and they are able to take an active role and participate in the energy-efficient operation. While they are able to manage their resources and serve the local energy requirements of the residents in the best possible manner, they can participate in the energy and balancing markets and support the network operators as a service provider. In this paper, we present a comprehensive review of active buildings' concept, challenges and outlook to pave the way for the researchers from academia and industry who want to start working in this area.

This paper proposes to replace controlled islanding, which is a defense mechanism against cascading failures, by tree partitioning whereby some of the tie-lines connecting the clusters are still connected in such a way that the cluster-level graph forms a tree. Tree-partitioning prevents line failures from spreading between clusters, similarly as for islanding, but keeps the clusters connected. That results in three main advantages. Power transfers between the clusters can still take place, helping to balance each cluster and limiting any necessary load shedding. Fewer lines are cut, which reduces the shock to the system. There is no need to re-synchronize the clusters after the emergency. This paper offers a simple graph-theoretic justification for tree-partitioning, rather than one based on the spectral analysis of network Laplacian proposed in the literature. It also proposes a two-stage methodology, which utilizes spectral clustering, for splitting a network into tree-connected clusters. Test results performed on the 118 node IEEE test network have confirmed the usefulness of the methodology.

In this paper, a bi-level Stackelberg-based model between an electricity retailer and consumers is presented, in which the upper-level consists of a price-maker retailer (PMR) modeled as the leader who seeks to maximize its own profit by adopting optimal pricing strategies for a pool-based electricity market. At the same time, PMR reduces its risks by encouraging consumers to actively participate in demand response (DR) programs. The lower-level of the model consists of 4 followers, three of them represent customer groups with distinct reactions to DR programs, and their objective function is defined as minimizing the cost of purchased electricity while preserving the welfare level. The fourth follower is the electricity pool, which is responsible for implementation of market mechanism and determination of market clearing price (MCP) with the aim of increasing the consumers’ welfare. In the proposed framework, the reaction of consumers to prices and DR programs are also organized and studied in form of several scenarios. The outputs of the proposed model will be enhancing the retailer’s profit and determination of the effect of DR programs on power consumption during peak hours as well as consumers’ welfare.

The incorporation of the reconfiguration into the expansion planning of smart distribution networks is addressed in this paper, in which the potential of distributed energy resources and demand response (DR) are modeled. The system of systems (SoS) architecture is employed to model the strategy of a distribution company (DISCO), a private investor (PI), and a DR provider (DRP). The SoS is an efficient modeling architecture to model the behavior of independent and autonomous systems with distinct objective functions who are able to share some data and work together. The aim of the DISCO is to upgrade the system with the optimal cost and reliability, whereas the PI and DRP want to maximize their profit. The DISCO should try to persuade the PI to install DGs (Distributed generations) by offering the guaranteed purchasing prices. Furthermore, the DRP is a market player who can negotiate with the DISCO to sign a contract to sell the purchased DR capacities from the customers. The uncertainties of the DISCO problem is handled by using the chance-constraint method, but the PI and DRP use the conditional value at risk method to model their uncertainties. Finally, to solve the proposed model, the multiobjective optimization algorithm is employed.

The active role of distribution system operators (DSOs) coordinated with the transmission system operator (TSO) is highlighted by increasing the competition of new parties in energy markets. Besides, combined heat and power (CHP) units are the primary heat supplier in district heating systems, and their electricity production is strongly coupled with heat productions. This paper evaluates an integrated model for scheduling electricity and heat with considering TSO–DSO cooperation to increase the operating efficiency in the day-ahead horizon. Also, this paper considers the cooperation of the electrical TSO, electrical DSOs, and district heating systems’ operators. The proposed model facilitates energy transactions between systems by considering intermediary variables. The share of each market party is calculated using intermediary variables and locational marginal prices of electrical and heating systems, and the values are compared to the cases with the isolated operating of energy systems. Also, the model considers thermal energy storage systems (TESs) and the heat transaction capability between neighbor systems, and the feasible convex region is used for the operation of CHP units. The DC power flow equations are used at the transmission level, while the AC power flow is used for distribution grids. The AC power flow equations are relaxed into the second-order cone programming (SOCP) formulation, which results in a mixed-integer second-order cone programming (MISOCP) problem. The proposed model is applied to the modified IEEE 24-bus test system, which contains electrical and heating systems at the distribution level. The result shows that the proposed model successfully reduces the operational costs and energy prices compared to the isolated scheduling of energy systems. Also, the model facilitates energy trading between market parties.

This study recommends a stochastic optimization model for the security constrained unit commitment (SCUC), which incorporates the optimal transmission switching (OTS) for managing the uncertainty of wind power generation and equipment failures, i.e. unit/line outages. Also, this study presents a technique in stochastic SCUC model with the OTS action using the AC optimal power flow (AC OPF). The AC OPF provides a more accurate picture of power flow in the power system compared to the DC optimal power flow that is usually considered in the literature for the stochastic SCUC models and the OTS action. While the stochastic SCUC model with the OTS action based on AC OPF is a mixed-integer non-linear programming model, this study transforms it into a mixed-integer linear programming (MILP) model. The MILP approach uses a piecewise linear model of AC OPF, which allows the reactive power and voltage to be considered directly in power flow model. The proposed stochastic SCUC problem is evaluated on the 6 bus, IEEE 118-bus and 662-bus test systems in pre- and post-OTS action. Obtained results demonstrate the effectiveness of the proposed model.

This paper develops a robust bundled active and reactive power management of EV integrated smart distribution networks. To model the problem, at first, the deterministic formulation of the problem is expressed as a non-linear programing (NLP), which minimizes the difference between the energy cost and the revenue of EVs’ (parking lot’s) reactive power exchange with the network as the objective function, subject to the AC power flow equations, system operation limits and EVs’ characteristics as the problem constraints. Then, while the NLP optimization reveals local optima, the NLP model is converted into a linear programming (LP) model using linearized AC power flow equations. The system uncertainties including active and reactive loads, electrical energy and reactive power prices as well as EVs’ charging/discharging schedules are modeled in the proposed linear model. Accordingly, the robust model is implemented and it considers one scenario, namely the most-conservative scenario of the objective function in the main problem. To decrease the calculation time, Benders decomposition (BD) approach is used to speed up the total processing time. The proposed robust linear architecture is tested on three distribution test networks to demonstrate its efficiency and performance. The results show that the NLP model can be substituted with the high-speed LP model. Moreover, the computation speed is improved by using the BD method. In addition, the capacity of the injected power of EVs is reduced in the most-conservative scenario in comparison with the deterministic model’s scenario, while the consumed power of loads and EVs have been increased in this scenario. The proposed robust architecture against uncertainties is shown to yield a more robust solutions at the expense of higher operation cost.

Over the last decade, it has been considerable attempts to replace thermal power plants by renewable energy sources (RESs), mainly to reduce harmful gaseous emissions. Ubiquitous nature of these sources in the emerging smart grid, demands a dominated RES power system for a long-term horizon. This study proposed a model for 100% RES-based system which is tractable and flexible enough to be used for any mixture of generation unit types with any level of uncertainty needless of the correlation between their random and unpredictable behaviours. To overcome the complex nature of RESs, an efficient stochastic multiobjective mixed-integer linear programming framework is proposed. The efficacy of the proposed model is evaluated via numerical simulation.

This paper presents an evaluation framework for Techno-Economic-Environmental (TEE) performance of the Integrated Gas and Electricity Distribution Networks (IGEDNs). The proposed framework is based on a coupled gas and electric load flow model, facilitating the consideration of all the parameters affecting the operation of IGEDNs, such as different gas mixtures, gas temperature, pipeline characteristics and the electricity network topology. This framework can assess the impact of different storage configurations, different levels of Renewable Energy Sources (RESs) and different levels of energy demand on the amount of imported energy from the upstream networks, operational costs and emissions of the IGEDNs. The evaluation framework can perform the TEE operational analysis of future scenarios of IGEDNs through various coupling components and storage devices including Single-Vector Storage (SVS) as well as Vector-Coupling Storage (VCS) devices to provide a basis for well-informed design choices for meeting the Greenhouse Gas (GHG) reduction targets. The TEE evaluation framework is tested on a real-world case study from a rural area in Scotland, and analysed from different aspects, to show the effectiveness of the model for analysis of the interactions of gas and electricity distribution networks. The results reveal that integrated operation of the gas and electricity networks improves all the considered technical, economic and environmental parameters.

This paper coordinates the reconfiguration of distribution systems with the expansion problem while the potential of demand response (DR) programs and distributed generation (DG) units are modeled in the active distribution expansion planning. The concept of system of systems (SoS) is proposed to model the expansion of DGs that are owned by private investors. SoS is an efficient system consisting of some autonomous and heterogeneous systems with distinct objective functions. According to the concept of SoS, a decision-making paradigm is developed to determine the location, size, and time of DG investment made by a commercial agent, as well as the price of generated power. From the distribution company (DISCO) viewpoint, the proposed model is a multi-objective (MO) optimization problem. The first objective function is the net present value of the total investment and operation costs related to the network. The second objective function is a reliability index, i.e. the expected energy not-supplied (EENS). The uncertainty of load growth in future years is handled by using a scenario-based approach. The introduced problem is solved by using multi-objective particle swarm optimization (MOPSO) algorithm empowered with an innovative three-layer procedure that is provided to better manage the space of the decision variables. The first layer is based on PSO particles while the second and third layers are based on a sensitivity analysis. Finally, a standard 33-bus distribution system is utilized to obtain the simulation results that show the performance and advantages of the proposed method.

When a primary substation reaches its capacity limit reinforcement is required, usually via additional circuits. Load transfer constitutes an alternative solution to this problem, as it can provide substantial capacity support at little, or even zero, capital expenditure. This paper provides a probabilistic method which quantifies the capacity value of load transfer using the Effective Load Carrying Capability methodology within a Sequential Monte Carlo Simulation framework. Load transfer is mathematically formulated as a mixed-integer second-order cone programming problem, which can be efficiently solved using commercial solvers. The proposed methodology is applied to a realistically sized distribution network considering three different redundancy levels, namely N-1, N-0.75, and N-0.5. The results show a maximum capacity value of 25% and 37% of the base case demand for manual and remote control load transfer, respectively, for the N-0.5 case with 4.21 MWh/year. The results also show that the capacity value of load transfer is significantly higher if the initial level of reliability of the network is lower, indicating that the network operator is prepared to accept a higher level of risk.

One of the most important aspects of the development of Electric Vehicles (EVs) is the optimal sizing and allocation of charging stations. Due to the interactions between the electricity and transportation systems, the key features of these systems (such as traffic network characteristics, charging demands and power system constraints) should be taken into account for the optimal planning. This paper addressed the optimal sizing and allocation of the fast-charging stations in a distribution network. The traffic flow of EVs is modeled using the User Equilibrium-based Traffic Assignment Model (UETAM). Moreover, a stochastic framework is developed based on the Queuing Theory (QT) to model the load levels (EVs’ charging demand). The objective function of the problem is to minimize the annual investment cost, as well as the energy losses that are optimized through chance-constrained programming. The probabilistic aspects of the proposed problem are modeled by using the point estimation method and Gram-Charlier expansion. Furthermore, the probabilistic dominance criteria are employed in order to compare the uncertain alternatives. Finally, the simulation results are provided for both the distribution and traffic systems to illustrate the performance of the proposed problem.

This paper proposes a comprehensive framework for generation and transmission planning of renewable dominated power systems, which is formulated as a stochastic multiobjective problem. In this regard, a Normalized Normal Constraint (NNC) solution approach is proposed to solve the introduced stochastic multiobjective generation and transmission planning (GTP) problem. The NNC is utilized in this paper as a relation between different objective functions with different dimensions to find the optimal weighting factors of these objectives. The NNC is applied for solving the GTP problem with objective functions including the investment and operation costs along with the transmission losses, while considering the cost of unserved energy, as well as the uncertainty of load and Renewable Energy Resources (RERs). A fuzzy-based decision making framework is utilized to select the best solution among the optimal non-dominated solution points. A scenario-based approach is used to model the uncertainties. The Garver 6-bus and IEEE 118-bus test systems are utilized to perform the numerical analysis. The simulation results validate the performance and importance of the proposed model, as well as the effectiveness of the NNC to find the evenly distributed Pareto solutions of the multiobjective problems.

Economic generation scheduling (EGS) is a non-convex optimization problem for allocating optimal generation among the committed units that can meet given real-world practical limits such as ramp rate limits, prohibited operating zones, valve loading effects, multi-fuel options, spinning reserve and transmission system losses at the minimum fuel cost. Moreover, considering environmental issues results in an environmental/economic generation scheduling (EEGS) problem that is a multiobjective optimization model with two non-commensurable and contradictory objectives. In this paper, a novel method has been presented in order to minimize production cost and emission of the steam power plants in short term periods. The obtained results showed that the proposed method can be used in short-term decision making of steam power plants which will be absolutely effective in long-term emission target oriented strategies. A framework is proposed for solving single objective EGS and multiobjective EEGS problems considering the aforementioned constraints. The problem is solved by a new meta-heuristic optimization called Ray Optimization (RO) to determine the optimal power generation. The performance of the proposed algorithm is investigated by applying it to solve diverse test systems having non-convex solution spaces. Numerical results have been comprehensively compared with some of the most recently published research works in the area in order to validate the results and confirm the potential of the proposed approach. The obtained results show the application of the proposed framework and effectiveness of the solutions.

With the evolution of smart grids, penetration of distributed energy resources (DERs) in the distribution networks has become ever-increasing problem. To improve network reliability, the complexity of the two important aspects of adequacy and security must be well assessed. There is a trade-off between adequacy of DERs, and the distribution network security, i.e. improving the adequacy can reduce the security. In this study, enhancement of the distribution network adequacy and security is proposed. In this regard, capacity of simultaneous reconfiguration and DERs sizing are utilised to improve the adequacy and security of an active distribution network. In the reconfiguration process, graph theory concept is adopted to implement a fast reconfiguration method. Since DERs are active, a combined bus and line security index is used to overcome security concerns of their existence. The IEEE 33-bus distribution network as a widely used standard test system in reconfiguration studies, and a practical 83-bus distribution network of Taiwan Power Company (TPC) which is a part of a real distribution network, are used to test the performance of the proposed method. The simulation results demonstrate the performance of the proposed framework.

This paper proposes a coordinated distribution system reconfiguration and planning model to deal with the problem of active distribution expansion planning. DR (Demand response) programs are modeled as virtual distributed resources to cover the effect of uncertain parameters. A bi-level optimization procedure is developed to solve the proposed model. At the first level, an optimization problem is solved using PSO (particle swarm optimization) algorithm to determine the system expansion and reconfiguration plans. Next, the second level minimization problem is developed based on the sensitivity analysis. The DR programs are taken into account in the second level problem to encounter with the problem uncertainties. Therefore, the proposed model incorporates the problem of DSR (distribution system reconfiguration) with system expansion problem, while the presence of DR is considered to enhance the effectiveness of the problem. The IEEE 33-bus standard test system is utilized to investigate the performance of the proposed model. The simulation results approve the advantages of the proposed model and its economical profits for distribution network owners.

Before restructuring in the electricity industry, the primary decision-makers of the electricity market were deemed to be power generation and transmission companies, market regulation boards, and power industry regulators. In this traditional structure, consumers were interested in receiving electricity at flat rates while paying no attention to the problems of this industry. This attitude was the source of many problems, sometimes leading to collapse of power systems and widespread blackouts. Restructuring of the electricity industry however provided a multitude of solutions to these problems. The most important solution can be demand response (DR) programs. This paper proposes an economic DR model for residential consumers in liberalized electricity markets to change their consumption pattern from times of high energy prices to other times to maximize their utility functions. This economic model is developed based on constant elasticity of substitution (CES) utility function known as one of the most popular utility functions in microeconomics. Simulation results indicate that the proposed model is adaptable to any group of residential consumers with any disposition toward participation in DR programs and can be adjusted for any time period according to the preference given by the residential consumer.

This paper presents a two-stage formwork for the coordinated distributed battery energy storage systems (DBESSs) planning and the flexible energy management (FEM) in a smart distribution network (SDN) in the presence of electric vehicles’ (EVs’) parking lot and variable renewable energy sources (VRESs). In the first stage, from distribution system operator's (DSO's) viewpoint, the linear DBESSs planning problem minimizes the difference between summation of its investment, degradation and charging costs and its revenue due to injecting power into the network at the discharging mode, where this problem subjects to the linear SDN optimal power flow equations and VRES, DBESS and EVs’ parking lot constraints. Noted that the bounded uncertainty–based robust model (BURM) is used to model the uncertainty of load, charging/discharging price, VRES power and EV parameters according to the uncertainty levels. In addition, the FEM strategy is applied to the SDN to obtain the suitable flexibility, security and operational indices based on the second stage problem formulation. This strategy minimizes the difference between energy cost paid to the upstream network and flexibility benefit from DSO and flexibility operator (FO) viewpoints while it considers linear AC optimal power flow and renewable and flexible sources equations as problem constraints. Moreover, in order to achieve the robust flexibility capability of EVs’ parking lot in the SDN, the uncertainty of the FEM strategy is modelled in the proposed hybrid stochastic/robust optimization. Hence, the scenario-based stochastic programming (SBSP) is used for the uncertain parameters of load, energy price and VRES power, but, BURM models the EVs uncertainty. Finally, the proposed two-stage formwork is simulated on the 19-bus LV CIGRE benchmark grid using GAMS software to investigate the capability and efficiency of the model. According to numerical results, the proposed strategy calculates the optimal location and size for DBESSs depending on the energy price, consumers and EVs demand and VRESs size in the SDN, and thus, it can obtain flexible, secure and efficient operation indices in this network based on the lowest possible cost for the investment of DBESSs.

This paper presents a robust optimization problem of the flexible bidirectional power management of a smart distribution network and harmonic compensation of nonlinear loads using electric vehicles (EVs) equipped with bidirectional chargers. The base deterministic model of the proposed problem is as mixed-integer nonlinear programming (MINLP), having the objective function to minimize the economic and technical indices subject to harmonic load flow equations, EVs constraints, system operation and harmonic indices limits. This model is converted to a mixed-integer linear programming (MILP) model in the next step. In the proposed MILP model, the active, reactive and apparent loads, electrical energy, reactive power and harmonic current prices, as well as EVs characteristics, are considered uncertain parameters. Accordingly, two alternative robust optimization approaches have been implemented for the conditions of having both the probability distribution function or the bounded uncertainty in the proposed MILP problem model. The proposed model is tested on distribution networks to demonstrate its efficiency and performance. The results show that the MINLP model can be substituted by the proposed high-speed MILP model. In addition, the capacity of the injecting power of EVs is reduced in the worst-case scenario with respect to the scenario that is used in the deterministic model, while the consumed power of loads and EVs and energy price increases in this scenario. Finally, the payment of EV owners is reduced by considering EVs power and harmonic control.

Economic load dispatch is the method of determining the most efficient, low-cost and reliable operation of a power system by dispatching the available electricity generation resources to supply the load on the system. Environmental concerns that arise due to the operation of fossil fuel fired electric generators, change the classical problem into multiobjective emission/economic dispatch (MEED) which is formulated as a constrained nonlinear multiobjective mathematical programming (MMP). The proposed MEED formulation includes emission minimization objective, AC load flow constraints and security constraints of the power system which usually are found simultaneously in real-world power systems. The proposed model has a more accurate evaluation of transmission losses obtained from the load flow equations. The MMP approach based on ɛ-constraint algorithm has been proposed for generating Pareto-optimal solutions of power systems MEED problem. Moreover, fuzzy decision making process is employed to extract one of the Pareto-optimal solutions as the best compromise nondominated solution. The proposed approach is simulated on the IEEE 30-bus six-generator test system and obtained results have been comprehensively compared with some of the most recently published research in the area (from the both aspects of precision and execution tome) which confirms the potential and effectiveness of the proposed approach.

Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formulating their risk management strategies. They need to know future electricity prices as their profitability depends on them. This research project classifies and compares different techniques of electricity price forecasting in the literature and selects artificial neural networks (ANN) as a suitable method for price forecasting. To perform this task, market knowledge should be used to optimize the selection of input data for an electricity price forecasting tool. Then sensitivity analysis is used in this research to aid in the selection of the optimum inputs of the ANN and fuzzy c-mean (FCM) algorithm is used for daily load pattern clustering. Finally, ANN with a modified Levenberg–Marquardt (LM) learning algorithm are implemented for forecasting prices in Pennsylvania–New Jersey–Maryland (PJM) market. The forecasting results were compared with the previous works and showed that the results are reasonable and accurate.

Market participants in the new competitive electricity markets find price forecasting so valuable in developing their bidding strategies. In this paper, the Bayesian neural network (BNN) approach is proposed for day-ahead prediction of locational marginal prices (LMPs) in the pool based energy markets, in which to select the optimum inputs of the BNN, the correlation coefficient technique is used. LMP has a volatile and time dependent behavior. Hence, prediction of such a complex signal is a challenging task requiring a qualified forecasting tool, which not only fits well to the training data, but also can predict the stochastic behavior of unseen part of the signal. The proposed Bayesian approach to predict day-ahead electricity prices presents noteworthy advantages over the classical neural networks (NN) methods which include the avoidance of network overfitting, an indication of the degree of uncertainty in the predictions, automatic selection of an appropriate scale for network weights and, accordingly, selection of the optimal forecasting model. Examining the proposed forecast strategy on the different periods of the Pennsylvania-New Jersey-Maryland (PJM) market, it is demonstrated that the proposed method can provide more accurate results than the other price forecasting techniques, such as, ARIMA time series, wavelet-ARIMA, classical NN, and also a similar day method. Copyright © 2008 John Wiley & Sons, Ltd.